bugfix

Minor changes for installation on windows

README.md

@@ -1,9 +1,9 @@
-# V2TRADING - Advanced Algorithmic Trading Platform
+**README - V2TRADING - Advanced Algorithmic Trading Platform**
 
-## Overview
+**Overview**
-Custom-built algorithmic trading platform for research, backtesting and live trading. Trading engine capable of processing tick data, providing custom aggregation, managing trades, and supporting backtesting in a highly accurate and efficient manner.
+Custom-built algorithmic trading platform for research, backtesting and automated trading. Trading engine capable of processing tick data, managing trades, and supporting backtesting in a highly accurate and efficient manner.
 
-## Key Features
+**Key Features**
 - **Trading Engine**: At the core of the platform is a trading engine that processes tick data in real time. This engine is responsible for aggregating data and managing the execution of trades, ensuring precision and speed in trade placement and execution.
 
 - **High-Fidelity Backtesting Environment**: ability to backtest strategies with 1:1 precision - meaning a tick-by-tick backtesting. This level of precision in backtesting, down to millisecond accuracy, mirrors live trading environments and is vital for developing and testing high-frequency trading strategies.
@@ -51,78 +51,3 @@ This repository represents a sophisticated and evolving tool for algorithmic tra
 </p>
 
 
-# Installation Instructions
-This document outlines the steps for installing and setting up the necessary environment for the application. These instructions are applicable for both Windows and Linux operating systems. Please follow the steps carefully to ensure a smooth setup.
-
-## Prerequisites
-Before beginning the installation process, ensure the following prerequisites are met:
-
-- TA-Lib Library:
-  - Windows: Download and build the TA-Lib library. Install Visual Studio Community with the Visual C++ feature. Navigate to `C:\ta-lib\c\make\cdr\win32\msvc` in the command prompt and build the library using the available makefile.
-  - Linux: Install TA-Lib using your distribution's package manager or compile from source following the instructions available on the TA-Lib GitHub repository.
- 
-- Alpaca Paper Trading Account: Create an account at [Alpaca Markets](https://alpaca.markets/) and generate `API_KEY` and `SECRET_KEY` for your paper trading account.
-
-## Installation Steps
-**Clone the Repository:** Clone the remote repository to your local machine.
-   `git clone git@github.com:drew2323/v2trading.git <name_of_local_folder>`
-   
-**Install Python:** Ensure Python 3.10.11 is installed on your system.
-
-**Create a Virtual Environment:** Set up a Python virtual environment.
-   `python -m venv <path_to_venv_folder>`
-   
-**Activate Virtual Environment:**
-   - Windows: `source ./<venv_folder>/Scripts/activate`
-   - Linux: `source ./<venv_folder>/bin/activate`
-
-**Install Dependencies:** Install the program requirements.
-   pip install -r requirements.txt
-   Note: It's permissible to comment out references to `keras` and `tensorflow` modules, as well as the `ml-room` repository in `requirements.txt`.
-
-**Environment Variables:** In `run.sh`, modify the `VIRTUAL_ENV_DIR` and `PYTHON_TO_USE` variables as necessary.
-
-**Data Directory:** Navigate to `DATA_DIR` and create folders: `aggcache`, `tradecache`, and `models`.
-
-**Media and Static Folders:** Create `media` and `static` folders one level above the repository directory. Also create `.env` file there.
-
-**Database Setup:** Create the `v2trading.db` file using SQL commands from `v2trading_create_db.sql`.
-```
-    import sqlite3
-    with open("v2trading_create_db.sql", "r") as f:
-        sql_statements = f.read()
-    conn = sqlite3.connect('v2trading.db')
-    cursor = conn.cursor()
-    cursor.executescript(sql_statements)
-    conn.commit()
-    conn.close()
-```
-Ensure the `config_table` is not empty by making an initial entry.
-
-**Start the Application:** Run `main.py` in VSCode to start the application.
-
-**Accessing the Application:** If the uvicorn server runs successfully at `http://0.0.0.0:8000`, access the application at `http://localhost:8000/static/`.
-
-**Database Configuration:** Add dynamic button and JS configurations to the `config_table` in `v2trading.db` via the "Config" section on the main page.
-Please replace placeholders (e.g., `<name_of_local_folder>`, `<path_to_venv_folder>`) with your actual paths and details. Follow these instructions to ensure the application is set up correctly and ready for use.
-
-## Environmental variables
-Trading platform can support N different accounts. Their API keys are stored as environmental variables in .env file located in the root directory.
-Account for trading api is selected when each strategy is run. However for realtime websocket data), always ACCOUNT1 is used for all strategies. The data point selection (iex vs sip) is set by LIVE_DATA_FEED environment variable.  
-
-.env file should contain:
-
-```
-ACCOUNT1_LIVE_API_KEY=<ACCOUNT1_LIVE_API_KEY>
-ACCOUNT1_LIVE_SECRET_KEY=<ACCOUNT1_LIVE_SECRET_KEY>
-ACCOUNT1_LIVE_FEED=sip
-ACCOUNT1_PAPER_API_KEY=<ACCOUNT1_PAPER_API_KEY>
-ACCOUNT1_PAPER_SECRET_KEY=<ACCOUNT1_PAPER_SECRET_KEY>
-ACCOUNT1_PAPER_FEED=sip
-ACCOUNT2_PAPER_API_KEY=<ACCOUNT2_PAPER_API_KEY>
-ACCOUNT2_PAPER_SECRET_KEY=ACCOUNT2_PAPER_SECRET_KEY<>
-ACCOUNT2_PAPER_FEED=iex
-WEB_API_KEY=<pass-for-webapi>
-```
-
-

requirements_new.txt

@@ -1,34 +1,21 @@
 absl-py==2.0.0
 alpaca==1.0.0
-alpaca-py==0.18.1
+alpaca-py==0.7.1
 altair==4.2.2
-annotated-types==0.6.0
 anyio==3.6.2
 appdirs==1.4.4
-appnope==0.1.3
-APScheduler==3.10.4
-argon2-cffi==23.1.0
-argon2-cffi-bindings==21.2.0
-arrow==1.3.0
 asttokens==2.2.1
 astunparse==1.6.3
-async-lru==2.0.4
 attrs==22.2.0
-Babel==2.15.0
-beautifulsoup4==4.12.3
 better-exceptions==0.3.3
 bleach==6.0.0
 blinker==1.5
-bottle==0.12.25
 cachetools==5.3.0
-CD==1.1.0
 certifi==2022.12.7
-cffi==1.16.0
 chardet==5.1.0
 charset-normalizer==3.0.1
 click==8.1.3
 colorama==0.4.6
-comm==0.1.4
 contourpy==1.0.7
 cycler==0.11.0
 dash==2.9.1
@@ -36,189 +23,90 @@ dash-bootstrap-components==1.4.1
 dash-core-components==2.0.0
 dash-html-components==2.0.0
 dash-table==5.0.0
-dateparser==1.1.8
-debugpy==1.8.1
 decorator==5.1.1
-defusedxml==0.7.1
-dill==0.3.7
-dm-tree==0.1.8
 entrypoints==0.4
-exceptiongroup==1.1.3
 executing==1.2.0
-fastapi==0.109.2
+fastapi==0.95.0
-fastjsonschema==2.19.1
-filelock==3.13.1
 Flask==2.2.3
 flatbuffers==23.5.26
 fonttools==4.39.0
-fpdf2==2.7.6
-fqdn==1.5.1
 gast==0.4.0
 gitdb==4.0.10
 GitPython==3.1.31
 google-auth==2.23.0
 google-auth-oauthlib==1.0.0
 google-pasta==0.2.0
-greenlet==3.0.3
 grpcio==1.58.0
 h11==0.14.0
-h5py==3.10.0
+h5py==3.9.0
-html2text==2024.2.26
-httpcore==1.0.5
-httpx==0.27.0
-humanize==4.9.0
 icecream==2.1.3
 idna==3.4
-imageio==2.31.6
 importlib-metadata==6.1.0
-ipykernel==6.29.4
-ipython==8.17.2
-ipywidgets==8.1.1
-isoduration==20.11.0
-itables==2.0.1
 itsdangerous==2.1.2
-jax==0.4.23
-jaxlib==0.4.23
-jedi==0.19.1
 Jinja2==3.1.2
 joblib==1.3.2
-json5==0.9.25
+jsonschema==4.17.3
-jsonpointer==2.4
+keras==2.13.1
-jsonschema==4.22.0
-jsonschema-specifications==2023.12.1
-jupyter-events==0.10.0
-jupyter-lsp==2.2.5
-jupyter_client==8.6.1
-jupyter_core==5.7.2
-jupyter_server==2.14.0
-jupyter_server_terminals==0.5.3
-jupyterlab==4.1.8
-jupyterlab-widgets==3.0.9
-jupyterlab_pygments==0.3.0
-jupyterlab_server==2.27.1
-kaleido==0.2.1
-keras==3.0.2
-keras-core==0.1.7
-keras-nightly==3.0.3.dev2024010203
-keras-nlp-nightly==0.7.0.dev2024010203
-keras-tcn @ git+https://github.com/drew2323/keras-tcn.git@4bddb17a02cb2f31c9fe2e8f616b357b1ddb0e11
 kiwisolver==1.4.4
 libclang==16.0.6
-lightweight-charts @ git+https://github.com/drew2323/lightweight-charts-python@10fd42f785182edfbf6b46a19a4ef66e85985a23
-llvmlite==0.39.1
 Markdown==3.4.3
 markdown-it-py==2.2.0
 MarkupSafe==2.1.2
-matplotlib==3.8.2
+matplotlib==3.7.1
-matplotlib-inline==0.1.6
 mdurl==0.1.2
-mistune==3.0.2
-ml-dtypes==0.3.1
-mlroom @ git+https://github.com/drew2323/mlroom.git@692900e274c4e0542d945d231645c270fc508437
-mplfinance==0.12.10b0
 msgpack==1.0.4
-mypy-extensions==1.0.0
-namex==0.0.7
-nbclient==0.10.0
-nbconvert==7.16.4
-nbformat==5.10.4
-nest-asyncio==1.6.0
 newtulipy==0.4.6
-notebook_shim==0.2.4
+numpy==1.24.2
-numba==0.56.4
-numpy==1.23.5
 oauthlib==3.2.2
 opt-einsum==3.3.0
-orjson==3.9.10
-overrides==7.7.0
 packaging==23.0
-pandas==2.2.1
+pandas==1.5.3
-pandocfilters==1.5.1
 param==1.13.0
-parso==0.8.3
-patsy==0.5.6
-pexpect==4.8.0
 Pillow==9.4.0
-platformdirs==4.2.0
+plotly==5.13.1
-plotly==5.22.0
-prometheus_client==0.20.0
-prompt-toolkit==3.0.39
 proto-plus==1.22.2
 protobuf==3.20.3
-proxy-tools==0.1.0
-psutil==5.9.8
-ptyprocess==0.7.0
-pure-eval==0.2.2
 pyarrow==11.0.0
 pyasn1==0.4.8
 pyasn1-modules==0.2.8
-pycparser==2.22
 pyct==0.5.0
-pydantic==2.6.4
+pydantic==1.10.5
-pydantic_core==2.16.3
 pydeck==0.8.0
 Pygments==2.14.0
 pyinstrument==4.5.3
 Pympler==1.0.1
-pyobjc-core==10.3
-pyobjc-framework-Cocoa==10.3
-pyobjc-framework-Security==10.3
-pyobjc-framework-WebKit==10.3
 pyparsing==3.0.9
 pyrsistent==0.19.3
 pysos==1.3.0
 python-dateutil==2.8.2
 python-dotenv==1.0.0
-python-json-logger==2.0.7
-python-multipart==0.0.6
 pytz==2022.7.1
 pytz-deprecation-shim==0.1.0.post0
 pyviz-comms==2.2.1
-PyWavelets==1.5.0
-pywebview==5.1
 PyYAML==6.0
-pyzmq==25.1.2
-referencing==0.35.1
-regex==2023.10.3
 requests==2.31.0
 requests-oauthlib==1.3.1
-rfc3339-validator==0.1.4
-rfc3986-validator==0.1.1
 rich==13.3.1
-rpds-py==0.18.0
 rsa==4.9
-schedule==1.2.1
+scikit-learn==1.3.1
-scikit-learn==1.3.2
 scipy==1.11.2
 seaborn==0.12.2
 semver==2.13.0
-Send2Trash==1.8.3
 six==1.16.0
 smmap==5.0.0
 sniffio==1.3.0
-soupsieve==2.5
-SQLAlchemy==2.0.27
 sseclient-py==1.7.2
-stack-data==0.6.3
+starlette==0.26.1
-starlette==0.36.3
-statsmodels==0.14.1
 streamlit==1.20.0
 structlog==23.1.0
-TA-Lib==0.4.28
-tb-nightly==2.16.0a20240102
 tenacity==8.2.2
-tensorboard==2.15.1
+tensorboard==2.13.0
 tensorboard-data-server==0.7.1
-tensorflow-addons==0.23.0
+tensorflow==2.13.0
-tensorflow-estimator==2.15.0
+tensorflow-estimator==2.13.0
 tensorflow-io-gcs-filesystem==0.34.0
 termcolor==2.3.0
-terminado==0.18.1
-tf-estimator-nightly==2.14.0.dev2023080308
-tf-nightly==2.16.0.dev20240101
-tf_keras-nightly==2.16.0.dev2023123010
 threadpoolctl==3.2.0
-tinycss2==1.3.0
 tinydb==4.7.1
 tinydb-serialization==2.1.0
 tinyflux==0.4.0
@@ -227,24 +115,15 @@ tomli==2.0.1
 toolz==0.12.0
 tornado==6.2
 tqdm==4.65.0
-traitlets==5.13.0
+typing_extensions==4.5.0
-typeguard==2.13.3
-types-python-dateutil==2.9.0.20240316
-typing_extensions==4.9.0
 tzdata==2023.2
 tzlocal==4.3
-uri-template==1.3.0
 urllib3==1.26.14
 uvicorn==0.21.1
--e git+https://github.com/drew2323/v2trading.git@1f85b271dba2b9baf2c61b591a08849e9d684374#egg=v2realbot
+#-e git+https://github.com/drew2323/v2trading.git@940348412f67ecd551ef8d0aaedf84452abf1320#egg=v2realbot
 validators==0.20.0
-vectorbtpro @ file:///Users/davidbrazda/Downloads/vectorbt.pro-2024.2.22
-wcwidth==0.2.9
-webcolors==1.13
 webencodings==0.5.1
-websocket-client==1.7.0
+websockets==10.4
-websockets==11.0.3
 Werkzeug==2.2.3
-widgetsnbextension==4.0.9
+wrapt==1.15.0
-wrapt==1.14.1
 zipp==3.15.0

requirements_newest.txt

@@ -1,243 +0,0 @@
-absl-py
-alpaca
-alpaca-py
-altair
-annotated-types
-anyio
-appdirs
-appnope
-APScheduler
-argon2-cffi
-argon2-cffi-bindings
-arrow
-asttokens
-astunparse
-async-lru
-attrs
-Babel
-beautifulsoup4
-better-exceptions
-bleach
-blinker
-bottle
-cachetools
-CD
-certifi
-cffi
-chardet
-charset-normalizer
-click
-colorama
-comm
-contourpy
-cycler
-dash
-dash-bootstrap-components
-dash-core-components
-dash-html-components
-dash-table
-dateparser
-debugpy
-decorator
-defusedxml
-dill
-dm-tree
-entrypoints
-exceptiongroup
-executing
-fastapi
-fastjsonschema
-filelock
-Flask
-flatbuffers
-fonttools
-fpdf2
-fqdn
-gast
-gitdb
-GitPython
-google-auth
-google-auth-oauthlib
-google-pasta
-greenlet
-grpcio
-h11
-h5py
-html2text
-httpcore
-httpx
-humanize
-icecream
-idna
-imageio
-importlib-metadata
-ipykernel
-ipython
-ipywidgets
-isoduration
-itables
-itsdangerous
-jax
-jaxlib
-jedi
-Jinja2
-joblib
-json5
-jsonpointer
-jsonschema
-jsonschema-specifications
-jupyter-events
-jupyter-lsp
-jupyter_client
-jupyter_core
-jupyter_server
-jupyter_server_terminals
-jupyterlab
-jupyterlab-widgets
-jupyterlab_pygments
-jupyterlab_server
-kaleido
-keras
-keras-core
-keras-nightly
-keras-nlp-nightly
-keras-tcn @ git+https://github.com/drew2323/keras-tcn.git
-kiwisolver
-libclang
-lightweight-charts @ git+https://github.com/drew2323/lightweight-charts-python.git
-llvmlite
-Markdown
-markdown-it-py
-MarkupSafe
-matplotlib
-matplotlib-inline
-mdurl
-mistune
-ml-dtypes
-mlroom @ git+https://github.com/drew2323/mlroom.git
-mplfinance
-msgpack
-mypy-extensions
-namex
-nbclient
-nbconvert
-nbformat
-nest-asyncio
-newtulipy
-notebook_shim
-numba
-numpy
-oauthlib
-opt-einsum
-orjson
-overrides
-packaging
-pandas
-pandocfilters
-param
-parso
-patsy
-pexpect
-Pillow
-platformdirs
-plotly
-prometheus_client
-prompt-toolkit
-proto-plus
-protobuf
-proxy-tools
-psutil
-ptyprocess
-pure-eval
-pyarrow
-pyasn1
-pyasn1-modules
-pycparser
-pyct
-pydantic
-pydantic_core
-pydeck
-Pygments
-pyinstrument
-pyparsing
-pyrsistent
-pysos
-python-dateutil
-python-dotenv
-python-json-logger
-python-multipart
-pytz
-pytz-deprecation-shim
-pyviz-comms
-PyWavelets
-pywebview
-PyYAML
-pyzmq
-referencing
-regex
-requests
-requests-oauthlib
-rfc3339-validator
-rfc3986-validator
-rich
-rpds-py
-rsa
-schedule
-scikit-learn
-scipy
-seaborn
-semver
-Send2Trash
-six
-smmap
-sniffio
-soupsieve
-SQLAlchemy
-sseclient-py
-stack-data
-starlette
-statsmodels
-streamlit
-structlog
-TA-Lib
-tb-nightly
-tenacity
-tensorboard
-tensorboard-data-server
-tensorflow-addons
-tensorflow-estimator
-tensorflow-io-gcs-filesystem
-termcolor
-terminado
-tf-estimator-nightly
-tf-nightly
-tf_keras-nightly
-threadpoolctl
-tinycss2
-tinydb
-tinydb-serialization
-tinyflux
-toml
-tomli
-toolz
-tornado
-tqdm
-traitlets
-typeguard
-types-python-dateutil
-typing_extensions
-tzdata
-tzlocal
-uri-template
-urllib3
-uvicorn
-validators
-wcwidth
-webcolors
-webencodings
-websocket-client
-websockets
-Werkzeug
-widgetsnbextension
-wrapt
-zipp

research/get_trades_at_once.ipynb

@@ -1,410 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Loading trades and vectorized aggregation\n",
-    "Describes how to fetch trades (remote/cached) and use new vectorized aggregation to aggregate bars of given type (time, volume, dollar) and resolution\n",
-    "\n",
-    "`fetch_trades_parallel` enables to fetch trades of given symbol and interval, also can filter conditions and minimum size. return `trades_df`\n",
-    "`aggregate_trades` acceptss `trades_df` and ressolution and type of bars (VOLUME, TIME, DOLLAR) and return aggregated ohlcv dataframe `ohlcv_df`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Activating profile profile1\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "Activating profile profile1\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "trades_df-BAC-2024-01-11T09:30:00-2024-01-12T16:00:00.parquet\n",
-      "trades_df-SPY-2024-01-01T09:30:00-2024-05-14T16:00:00.parquet\n",
-      "ohlcv_df-BAC-2024-01-11T09:30:00-2024-01-12T16:00:00.parquet\n",
-      "ohlcv_df-SPY-2024-01-01T09:30:00-2024-05-14T16:00:00.parquet\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from numba import jit\n",
-    "from alpaca.data.historical import StockHistoricalDataClient\n",
-    "from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, DATA_DIR\n",
-    "from alpaca.data.requests import StockTradesRequest\n",
-    "from v2realbot.enums.enums import BarType\n",
-    "import time\n",
-    "from datetime import datetime\n",
-    "from v2realbot.utils.utils import parse_alpaca_timestamp, ltp, zoneNY, send_to_telegram, fetch_calendar_data\n",
-    "import pyarrow\n",
-    "from v2realbot.loader.aggregator_vectorized import fetch_daily_stock_trades, fetch_trades_parallel, generate_time_bars_nb, aggregate_trades\n",
-    "import vectorbtpro as vbt\n",
-    "import v2realbot.utils.config_handler as cfh\n",
-    "\n",
-    "vbt.settings.set_theme(\"dark\")\n",
-    "vbt.settings['plotting']['layout']['width'] = 1280\n",
-    "vbt.settings.plotting.auto_rangebreaks = True\n",
-    "# Set the option to display with pagination\n",
-    "pd.set_option('display.notebook_repr_html', True)\n",
-    "pd.set_option('display.max_rows', 20)  # Number of rows per page\n",
-    "# pd.set_option('display.float_format', '{:.9f}'.format)\n",
-    "\n",
-    "\n",
-    "#trade filtering\n",
-    "exclude_conditions = cfh.config_handler.get_val('AGG_EXCLUDED_TRADES') #standard ['C','O','4','B','7','V','P','W','U','Z','F']\n",
-    "minsize = 100\n",
-    "\n",
-    "symbol = \"SPY\"\n",
-    "#datetime in zoneNY \n",
-    "day_start = datetime(2024, 1, 1, 9, 30, 0)\n",
-    "day_stop = datetime(2024, 1, 14, 16, 00, 0)\n",
-    "day_start = zoneNY.localize(day_start)\n",
-    "day_stop = zoneNY.localize(day_stop)\n",
-    "#filename of trades_df parquet, date are in isoformat but without time zone part\n",
-    "dir = DATA_DIR + \"/notebooks/\"\n",
-    "#parquet interval cache contains exclude conditions and minsize filtering\n",
-    "file_trades = dir + f\"trades_df-{symbol}-{day_start.strftime('%Y-%m-%dT%H:%M:%S')}-{day_stop.strftime('%Y-%m-%dT%H:%M:%S')}-{exclude_conditions}-{minsize}.parquet\"\n",
-    "#file_trades = dir + f\"trades_df-{symbol}-{day_start.strftime('%Y-%m-%dT%H:%M:%S')}-{day_stop.strftime('%Y-%m-%dT%H:%M:%S')}.parquet\"\n",
-    "file_ohlcv = dir + f\"ohlcv_df-{symbol}-{day_start.strftime('%Y-%m-%dT%H:%M:%S')}-{day_stop.strftime('%Y-%m-%dT%H:%M:%S')}-{exclude_conditions}-{minsize}.parquet\"\n",
-    "\n",
-    "#PRINT all parquet in directory\n",
-    "import os\n",
-    "files = [f for f in os.listdir(dir) if f.endswith(\".parquet\")]\n",
-    "for f in files:\n",
-    "    print(f)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "NOT FOUND. Fetching from remote\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m trades_df \u001b[38;5;241m=\u001b[39m \u001b[43mfetch_daily_stock_trades\u001b[49m\u001b[43m(\u001b[49m\u001b[43msymbol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mday_start\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mday_stop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexclude_conditions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexclude_conditions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mminsize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mminsize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforce_remote\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbackoff_factor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m trades_df\n",
-      "File \u001b[0;32m~/Documents/Development/python/v2trading/v2realbot/loader/aggregator_vectorized.py:200\u001b[0m, in \u001b[0;36mfetch_daily_stock_trades\u001b[0;34m(symbol, start, end, exclude_conditions, minsize, force_remote, max_retries, backoff_factor)\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m attempt \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(max_retries):\n\u001b[1;32m    199\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 200\u001b[0m         tradesResponse \u001b[38;5;241m=\u001b[39m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_stock_trades\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstockTradeRequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    201\u001b[0m         is_empty \u001b[38;5;241m=\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m tradesResponse[symbol]\n\u001b[1;32m    202\u001b[0m         \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRemote fetched: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mis_empty\u001b[38;5;132;01m=}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, start, end)\n",
-      "File \u001b[0;32m~/Documents/Development/python/v2trading/.venv/lib/python3.10/site-packages/alpaca/data/historical/stock.py:144\u001b[0m, in \u001b[0;36mStockHistoricalDataClient.get_stock_trades\u001b[0;34m(self, request_params)\u001b[0m\n\u001b[1;32m    141\u001b[0m params \u001b[38;5;241m=\u001b[39m request_params\u001b[38;5;241m.\u001b[39mto_request_fields()\n\u001b[1;32m    143\u001b[0m \u001b[38;5;66;03m# paginated get request for market data api\u001b[39;00m\n\u001b[0;32m--> 144\u001b[0m raw_trades \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data_get\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    145\u001b[0m \u001b[43m    \u001b[49m\u001b[43mendpoint_data_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtrades\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    146\u001b[0m \u001b[43m    \u001b[49m\u001b[43mendpoint_asset_class\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstocks\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    147\u001b[0m \u001b[43m    \u001b[49m\u001b[43mapi_version\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mv2\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    148\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    149\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_use_raw_data:\n\u001b[1;32m    152\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m raw_trades\n",
-      "File \u001b[0;32m~/Documents/Development/python/v2trading/.venv/lib/python3.10/site-packages/alpaca/data/historical/stock.py:338\u001b[0m, in \u001b[0;36mStockHistoricalDataClient._data_get\u001b[0;34m(self, endpoint_asset_class, endpoint_data_type, api_version, symbol_or_symbols, limit, page_limit, extension, **kwargs)\u001b[0m\n\u001b[1;32m    335\u001b[0m params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlimit\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m actual_limit\n\u001b[1;32m    336\u001b[0m params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpage_token\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m page_token\n\u001b[0;32m--> 338\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mapi_version\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mapi_version\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    340\u001b[0m \u001b[38;5;66;03m# TODO: Merge parsing if possible\u001b[39;00m\n\u001b[1;32m    341\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m extension \u001b[38;5;241m==\u001b[39m DataExtensionType\u001b[38;5;241m.\u001b[39mSNAPSHOT:\n",
-      "File \u001b[0;32m~/Documents/Development/python/v2trading/.venv/lib/python3.10/site-packages/alpaca/common/rest.py:221\u001b[0m, in \u001b[0;36mRESTClient.get\u001b[0;34m(self, path, data, **kwargs)\u001b[0m\n\u001b[1;32m    210\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget\u001b[39m(\u001b[38;5;28mself\u001b[39m, path: \u001b[38;5;28mstr\u001b[39m, data: Union[\u001b[38;5;28mdict\u001b[39m, \u001b[38;5;28mstr\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m HTTPResult:\n\u001b[1;32m    211\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Performs a single GET request\u001b[39;00m\n\u001b[1;32m    212\u001b[0m \n\u001b[1;32m    213\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    219\u001b[0m \u001b[38;5;124;03m        dict: The response\u001b[39;00m\n\u001b[1;32m    220\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 221\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mGET\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/Documents/Development/python/v2trading/.venv/lib/python3.10/site-packages/alpaca/common/rest.py:129\u001b[0m, in \u001b[0;36mRESTClient._request\u001b[0;34m(self, method, path, data, base_url, api_version)\u001b[0m\n\u001b[1;32m    127\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m retry \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    128\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 129\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_one_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretry\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m RetryException:\n\u001b[1;32m    131\u001b[0m         time\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_retry_wait)\n",
-      "File \u001b[0;32m~/Documents/Development/python/v2trading/.venv/lib/python3.10/site-packages/alpaca/common/rest.py:193\u001b[0m, in \u001b[0;36mRESTClient._one_request\u001b[0;34m(self, method, url, opts, retry)\u001b[0m\n\u001b[1;32m    174\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_one_request\u001b[39m(\u001b[38;5;28mself\u001b[39m, method: \u001b[38;5;28mstr\u001b[39m, url: \u001b[38;5;28mstr\u001b[39m, opts: \u001b[38;5;28mdict\u001b[39m, retry: \u001b[38;5;28mint\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mdict\u001b[39m:\n\u001b[1;32m    175\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Perform one request, possibly raising RetryException in the case\u001b[39;00m\n\u001b[1;32m    176\u001b[0m \u001b[38;5;124;03m    the response is 429. Otherwise, if error text contain \"code\" string,\u001b[39;00m\n\u001b[1;32m    177\u001b[0m \u001b[38;5;124;03m    then it decodes to json object and returns APIError.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    191\u001b[0m \u001b[38;5;124;03m        dict: The response data\u001b[39;00m\n\u001b[1;32m    192\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 193\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_session\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mopts\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    195\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    196\u001b[0m         response\u001b[38;5;241m.\u001b[39mraise_for_status()\n",
-      "File \u001b[0;32m~/Documents/Development/python/v2trading/.venv/lib/python3.10/site-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    584\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    585\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[1;32m    586\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[1;32m    587\u001b[0m }\n\u001b[1;32m    588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msend_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
-      "File \u001b[0;32m~/Documents/Development/python/v2trading/.venv/lib/python3.10/site-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    700\u001b[0m start \u001b[38;5;241m=\u001b[39m preferred_clock()\n\u001b[1;32m    702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43madapter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[1;32m    706\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m preferred_clock() \u001b[38;5;241m-\u001b[39m start\n",
-      "File \u001b[0;32m~/Documents/Development/python/v2trading/.venv/lib/python3.10/site-packages/requests/adapters.py:486\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    483\u001b[0m     timeout \u001b[38;5;241m=\u001b[39m TimeoutSauce(connect\u001b[38;5;241m=\u001b[39mtimeout, read\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[1;32m    485\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 486\u001b[0m     resp \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    487\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    488\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    489\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    490\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    491\u001b[0m \u001b[43m        \u001b[49m\u001b[43mredirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    492\u001b[0m \u001b[43m        \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    493\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    494\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    495\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    496\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    497\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    498\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    500\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m    501\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(err, request\u001b[38;5;241m=\u001b[39mrequest)\n",
-      "File \u001b[0;32m~/Documents/Development/python/v2trading/.venv/lib/python3.10/site-packages/urllib3/connectionpool.py:703\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m    700\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_proxy(conn)\n\u001b[1;32m    702\u001b[0m \u001b[38;5;66;03m# Make the request on the httplib connection object.\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    704\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    705\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    706\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    707\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    708\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    709\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    710\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    711\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    713\u001b[0m \u001b[38;5;66;03m# If we're going to release the connection in ``finally:``, then\u001b[39;00m\n\u001b[1;32m    714\u001b[0m \u001b[38;5;66;03m# the response doesn't need to know about the connection. Otherwise\u001b[39;00m\n\u001b[1;32m    715\u001b[0m \u001b[38;5;66;03m# it will also try to release it and we'll have a double-release\u001b[39;00m\n\u001b[1;32m    716\u001b[0m \u001b[38;5;66;03m# mess.\u001b[39;00m\n\u001b[1;32m    717\u001b[0m response_conn \u001b[38;5;241m=\u001b[39m conn \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m release_conn \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "File \u001b[0;32m~/Documents/Development/python/v2trading/.venv/lib/python3.10/site-packages/urllib3/connectionpool.py:449\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    444\u001b[0m             httplib_response \u001b[38;5;241m=\u001b[39m conn\u001b[38;5;241m.\u001b[39mgetresponse()\n\u001b[1;32m    445\u001b[0m         \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    446\u001b[0m             \u001b[38;5;66;03m# Remove the TypeError from the exception chain in\u001b[39;00m\n\u001b[1;32m    447\u001b[0m             \u001b[38;5;66;03m# Python 3 (including for exceptions like SystemExit).\u001b[39;00m\n\u001b[1;32m    448\u001b[0m             \u001b[38;5;66;03m# Otherwise it looks like a bug in the code.\u001b[39;00m\n\u001b[0;32m--> 449\u001b[0m             \u001b[43msix\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_from\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    450\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (SocketTimeout, BaseSSLError, SocketError) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    451\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_timeout(err\u001b[38;5;241m=\u001b[39me, url\u001b[38;5;241m=\u001b[39murl, timeout_value\u001b[38;5;241m=\u001b[39mread_timeout)\n",
-      "File \u001b[0;32m<string>:3\u001b[0m, in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n",
-      "File \u001b[0;32m~/Documents/Development/python/v2trading/.venv/lib/python3.10/site-packages/urllib3/connectionpool.py:444\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    441\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m    442\u001b[0m     \u001b[38;5;66;03m# Python 3\u001b[39;00m\n\u001b[1;32m    443\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 444\u001b[0m         httplib_response \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    445\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    446\u001b[0m         \u001b[38;5;66;03m# Remove the TypeError from the exception chain in\u001b[39;00m\n\u001b[1;32m    447\u001b[0m         \u001b[38;5;66;03m# Python 3 (including for exceptions like SystemExit).\u001b[39;00m\n\u001b[1;32m    448\u001b[0m         \u001b[38;5;66;03m# Otherwise it looks like a bug in the code.\u001b[39;00m\n\u001b[1;32m    449\u001b[0m         six\u001b[38;5;241m.\u001b[39mraise_from(e, \u001b[38;5;28;01mNone\u001b[39;00m)\n",
-      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/http/client.py:1375\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1373\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1374\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1375\u001b[0m         \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbegin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1376\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m:\n\u001b[1;32m   1377\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose()\n",
-      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/http/client.py:318\u001b[0m, in \u001b[0;36mHTTPResponse.begin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    316\u001b[0m \u001b[38;5;66;03m# read until we get a non-100 response\u001b[39;00m\n\u001b[1;32m    317\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m--> 318\u001b[0m     version, status, reason \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_read_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    319\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m status \u001b[38;5;241m!=\u001b[39m CONTINUE:\n\u001b[1;32m    320\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
-      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/http/client.py:279\u001b[0m, in \u001b[0;36mHTTPResponse._read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    278\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_read_status\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 279\u001b[0m     line \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreadline\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_MAXLINE\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miso-8859-1\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    280\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(line) \u001b[38;5;241m>\u001b[39m _MAXLINE:\n\u001b[1;32m    281\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m LineTooLong(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstatus line\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/socket.py:705\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    703\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m    704\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 705\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecv_into\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    706\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m timeout:\n\u001b[1;32m    707\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_timeout_occurred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
-      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/ssl.py:1274\u001b[0m, in \u001b[0;36mSSLSocket.recv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1270\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m flags \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m   1271\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1272\u001b[0m           \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnon-zero flags not allowed in calls to recv_into() on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[1;32m   1273\u001b[0m           \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m)\n\u001b[0;32m-> 1274\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnbytes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1275\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1276\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mrecv_into(buffer, nbytes, flags)\n",
-      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/ssl.py:1130\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m   1128\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1129\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m buffer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1130\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sslobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1131\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1132\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sslobj\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;28mlen\u001b[39m)\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "trades_df = fetch_daily_stock_trades(symbol, day_start, day_stop, exclude_conditions=exclude_conditions, minsize=minsize, force_remote=False, max_retries=5, backoff_factor=1)\n",
-    "trades_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Either load trades or ohlcv from parquet if exists\n",
-    "\n",
-    "#trades_df = fetch_trades_parallel(symbol, day_start, day_stop, exclude_conditions=exclude_conditions, minsize=50, max_workers=20) #exclude_conditions=['C','O','4','B','7','V','P','W','U','Z','F'])\n",
-    "# trades_df.to_parquet(file_trades, engine='pyarrow', compression='gzip')\n",
-    "\n",
-    "trades_df = pd.read_parquet(file_trades,engine='pyarrow')\n",
-    "ohlcv_df = aggregate_trades(symbol=symbol, trades_df=trades_df, resolution=1, type=BarType.TIME)\n",
-    "ohlcv_df.to_parquet(file_ohlcv, engine='pyarrow', compression='gzip')\n",
-    "\n",
-    "# ohlcv_df = pd.read_parquet(file_ohlcv,engine='pyarrow')\n",
-    "# trades_df = pd.read_parquet(file_trades,engine='pyarrow')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#list all files is dir directory with parquet extension\n",
-    "dir = DATA_DIR + \"/notebooks/\"\n",
-    "import os\n",
-    "files = [f for f in os.listdir(dir) if f.endswith(\".parquet\")]\n",
-    "file_name = \"\"\n",
-    "ohlcv_df = pd.read_parquet(file_ohlcv,engine='pyarrow')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ohlcv_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "# Calculate daily returns\n",
-    "ohlcv_df['returns'] = ohlcv_df['close'].pct_change().dropna()\n",
-    "#same as above but pct_change is from 3 datapoints back, but only if it is the same date, else na\n",
-    "\n",
-    "\n",
-    "# Plot the probability distribution curve\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.histplot(df['returns'].dropna(), kde=True, stat='probability', bins=30)\n",
-    "plt.title('Probability Distribution of Daily Returns')\n",
-    "plt.xlabel('Daily Returns')\n",
-    "plt.ylabel('Probability')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "\n",
-    "# Define the intervals from 5 to 20 s, returns for each interval\n",
-    "#maybe use rolling window?\n",
-    "intervals = range(5, 21, 5)\n",
-    "\n",
-    "# Create columns for percentage returns\n",
-    "rolling_window = 50\n",
-    "\n",
-    "# Normalize the returns using rolling mean and std\n",
-    "for N in intervals:\n",
-    "    column_name = f'returns_{N}'\n",
-    "    rolling_mean = ohlcv_df[column_name].rolling(window=rolling_window).mean()\n",
-    "    rolling_std = ohlcv_df[column_name].rolling(window=rolling_window).std()\n",
-    "    ohlcv_df[f'norm_{column_name}'] = (ohlcv_df[column_name] - rolling_mean) / rolling_std\n",
-    "\n",
-    "# Display the dataframe with normalized return columns\n",
-    "ohlcv_df\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Calculate the sum of the normalized return columns for each row\n",
-    "ohlcv_df['sum_norm_returns'] = ohlcv_df[[f'norm_returns_{N}' for N in intervals]].sum(axis=1)\n",
-    "\n",
-    "# Sort the DataFrame based on the sum of normalized returns in descending order\n",
-    "df_sorted = ohlcv_df.sort_values(by='sum_norm_returns', ascending=False)\n",
-    "\n",
-    "# Display the top rows with the highest sum of normalized returns\n",
-    "df_sorted\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Drop initial rows with NaN values due to pct_change\n",
-    "ohlcv_df.dropna(inplace=True)\n",
-    "\n",
-    "# Plotting the probability distribution curves\n",
-    "plt.figure(figsize=(14, 8))\n",
-    "for N in intervals:\n",
-    "    sns.kdeplot(ohlcv_df[f'returns_{N}'].dropna(), label=f'Returns {N}', fill=True)\n",
-    "\n",
-    "plt.title('Probability Distribution of Percentage Returns')\n",
-    "plt.xlabel('Percentage Return')\n",
-    "plt.ylabel('Density')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "# Plot the probability distribution curve\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.histplot(ohlcv_df['returns'].dropna(), kde=True, stat='probability', bins=30)\n",
-    "plt.title('Probability Distribution of Daily Returns')\n",
-    "plt.xlabel('Daily Returns')\n",
-    "plt.ylabel('Probability')\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#show only rows from ohlcv_df where returns > 0.005\n",
-    "ohlcv_df[ohlcv_df['returns'] > 0.0005]\n",
-    "\n",
-    "#ohlcv_df[ohlcv_df['returns'] < -0.005]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#ohlcv where index = date 2024-03-13 and between hour 12\n",
-    "\n",
-    "a = ohlcv_df.loc['2024-03-13 12:00:00':'2024-03-13 13:00:00']\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ohlcv_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trades_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ohlcv_df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trades_df.to_parquet(\"trades_df-spy-0111-0111.parquett\", engine='pyarrow', compression='gzip')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trades_df.to_parquet(\"trades_df-spy-111-0516.parquett\", engine='pyarrow', compression='gzip', allow_truncated_timestamps=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ohlcv_df.to_parquet(\"ohlcv_df-spy-111-0516.parquett\", engine='pyarrow', compression='gzip')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "basic_data = vbt.Data.from_data(vbt.symbol_dict({symbol: ohlcv_df}), tz_convert=zoneNY)\n",
-    "vbt.settings['plotting']['auto_rangebreaks'] = True\n",
-    "basic_data.ohlcv.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#access just BCA\n",
-    "#df_filtered = df.loc[\"BAC\"]"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

research/strat_SUPERTREND/SUPERTREND_v1_MULTI.ipynb

@@ -1,932 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from v2realbot.tools.loadbatch import load_batch\n",
-    "from v2realbot.utils.utils import zoneNY\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import vectorbtpro as vbt\n",
-    "from itables import init_notebook_mode, show\n",
-    "import datetime\n",
-    "from itertools import product\n",
-    "from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, DATA_DIR\n",
-    "\n",
-    "init_notebook_mode(all_interactive=True)\n",
-    "\n",
-    "vbt.settings.set_theme(\"dark\")\n",
-    "vbt.settings['plotting']['layout']['width'] = 1280\n",
-    "vbt.settings.plotting.auto_rangebreaks = True\n",
-    "# Set the option to display with pagination\n",
-    "pd.set_option('display.notebook_repr_html', True)\n",
-    "pd.set_option('display.max_rows', 10)  # Number of rows per page\n",
-    "\n",
-    "# Define the market open and close times\n",
-    "market_open = datetime.time(9, 30)\n",
-    "market_close = datetime.time(16, 0)\n",
-    "entry_window_opens = 1\n",
-    "entry_window_closes = 370\n",
-    "\n",
-    "forced_exit_start = 380\n",
-    "forced_exit_end = 390\n",
-    "\n",
-    "#LOAD FROM PARQUET\n",
-    "#list all files is dir directory with parquet extension\n",
-    "dir = DATA_DIR + \"/notebooks/\"\n",
-    "import os\n",
-    "files = [f for f in os.listdir(dir) if f.endswith(\".parquet\")]\n",
-    "#print('\\n'.join(map(str, files)))\n",
-    "file_name = \"ohlcv_df-SPY-2024-01-01T09:30:00-2024-05-14T16:00:00.parquet\"\n",
-    "ohlcv_df = pd.read_parquet(dir+file_name,engine='pyarrow')\n",
-    "basic_data = vbt.Data.from_data(vbt.symbol_dict({\"SPY\": ohlcv_df}), tz_convert=zoneNY)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#parameters (primary y line, secondary y line, close)\n",
-    "def plot_2y_close(priminds, secinds, close):\n",
-    "    fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"MOM\", \"Price\" ))\n",
-    "    close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
-    "    for ind in priminds:\n",
-    "        ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
-    "    for ind in secinds:\n",
-    "        ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
-    "    return fig\n",
-    "\n",
-    "# close = basic_data.xloc[\"09:30\":\"10:00\"].close"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#PIPELINE - FOR - LOOP\n",
-    "\n",
-    "#indicator parameters\n",
-    "mom_timeperiod = list(range(2, 12))\n",
-    "\n",
-    "#uzavreni okna od 1 do 200\n",
-    "#entry_window_closes = list(range(2, 50, 3))\n",
-    "entry_window_closes = [5, 10, 30, 45]\n",
-    "#entry_window_closes = 30\n",
-    "#threshold  entries parameters\n",
-    "#long\n",
-    "mom_th = np.round(np.arange(0.01, 0.5 + 0.02, 0.02),4).tolist()#-0.02\n",
-    "# short\n",
-    "#mom_th = np.round(np.arange(-0.01, -0.3 - 0.02, -0.02),4).tolist()#-0.02\n",
-    "roc_th = np.round(np.arange(-0.2, -0.8 - 0.05, -0.05),4).tolist()#-0.2\n",
-    "#print(mom_th, roc_th)\n",
-    "\n",
-    "#portfolio simulation parameters\n",
-    "sl_stop =np.round(np.arange(0.02/100, 0.7/100, 0.05/100),4).tolist()\n",
-    "tp_stop = np.round(np.arange(0.02/100, 0.7/100, 0.05/100),4).tolist()\n",
-    "\n",
-    "combs = list(product(mom_timeperiod, mom_th, roc_th, sl_stop, tp_stop))\n",
-    "\n",
-    "@vbt.parameterized(merge_func = \"concat\", random_subset = 2000, show_progress=True)  \n",
-    "def test_strat(entry_window_closes=60,\n",
-    "               mom_timeperiod=2,\n",
-    "               mom_th=-0.04,\n",
-    "               #roc_th=-0.2,\n",
-    "               sl_stop=0.19/100,\n",
-    "               tp_stop=0.19/100):\n",
-    "    # mom_timeperiod=2\n",
-    "    # mom_th=-0.06\n",
-    "    # roc_th=-0.2\n",
-    "    # sl_stop=0.04/100\n",
-    "    # tp_stop=0.04/100\n",
-    "\n",
-    "    momshort = vbt.indicator(\"talib:MOM\").run(basic_data.close, timeperiod=mom_timeperiod, short_name = \"slope_short\")\n",
-    "    rocp = vbt.indicator(\"talib:ROC\").run(basic_data.close, short_name = \"rocp\")\n",
-    "    #rate of change + momentum\n",
-    "\n",
-    "    #momshort.plot rocp.real_crossed_below(roc_th) & \n",
-    "    #short_signal = momshort.real_crossed_below(mom_th)\n",
-    "    long_signal = momshort.real_crossed_above(mom_th)\n",
-    "    # print(\"short signal\")\n",
-    "    # print(short_signal.value_counts())\n",
-    "\n",
-    "    #forced_exit = pd.Series(False, index=close.index)\n",
-    "    forced_exit = basic_data.symbol_wrapper.fill(False)\n",
-    "    #entry_window_open = pd.Series(False, index=close.index)\n",
-    "    entry_window_open=  basic_data.symbol_wrapper.fill(False)\n",
-    "\n",
-    "    #print(entry_window_closes, \"entry window closes\")\n",
-    "    # Calculate the time difference in minutes from market open for each timestamp\n",
-    "    elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
-    "\n",
-    "    entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
-    "\n",
-    "    #print(entry_window_open.value_counts())\n",
-    "\n",
-    "    forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
-    "    #short_entries = (short_signal & entry_window_open)\n",
-    "    #short_exits = forced_exit\n",
-    "    entries = (long_signal & entry_window_open)\n",
-    "    exits = forced_exit\n",
-    "    #long_entries.info()\n",
-    "    #number of trues and falses in long_entries\n",
-    "    #print(short_exits.value_counts())\n",
-    "    #print(short_entries.value_counts())\n",
-    "\n",
-    "    #fig = plot_2y_close([],[momshort, rocp], close)\n",
-    "    #short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
-    "    #print(sl_stop)\n",
-    "    #tsl_th=sl_stop, \n",
-    "    #short_entries=short_entries, short_exits=short_exits,\n",
-    "    pf = vbt.Portfolio.from_signals(close=basic_data.close, entries=entries, exits=exits, tsl_stop=sl_stop, tp_stop = tp_stop, fees=0.0167/100, freq=\"1s\", price=\"close\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
-    "    \n",
-    "    return pf.stats([\n",
-    "        'total_return',\n",
-    "        'max_dd', \n",
-    "        'total_trades', \n",
-    "        'win_rate', \n",
-    "        'expectancy'\n",
-    "    ])\n",
-    "\n",
-    "pf_results = test_strat(vbt.Param(entry_window_closes),\n",
-    "                        vbt.Param(mom_timeperiod),\n",
-    "                        vbt.Param(mom_th),\n",
-    "                        #vbt.Param(roc_th)\n",
-    "                        vbt.Param(sl_stop),\n",
-    "                        vbt.Param(tp_stop, condition=\"tp_stop > sl_stop\"))\n",
-    "pf_results = pf_results.unstack(level=-1)\n",
-    "pf_results.sort_values(by=[\"Total Return [%]\", \"Max Drawdown [%]\"], ascending=[False, True])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#pf_results.load(\"10tiscomb.pickle\")\n",
-    "#pf_results.info()\n",
-    "\n",
-    "vbt.save(pf_results, \"8tiscomb_tsl.pickle\")\n",
-    "\n",
-    "# pf_results = vbt.load(\"8tiscomb_tsl.pickle\")\n",
-    "# pf_results\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# parallel_coordinates method¶\n",
-    "\n",
-    "# attach_px_methods.<locals>.plot_func(\n",
-    "#     *args,\n",
-    "#     layout=None,\n",
-    "#     **kwargs\n",
-    "# )\n",
-    "\n",
-    "# pf_results.vbt.px.parallel_coordinates() #ocdf\n",
-    "\n",
-    "res = pf_results.reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pf_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from sklearn.decomposition import PCA\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Assuming pf_results is your DataFrame\n",
-    "# Convert columns to numeric, assuming NaNs where conversion fails\n",
-    "metrics = ['Total Return [%]', 'Max Drawdown [%]', 'Total Trades']\n",
-    "for metric in metrics:\n",
-    "    pf_results[metric] = pd.to_numeric(pf_results[metric], errors='coerce')\n",
-    "\n",
-    "# Handle missing values, for example filling with the median\n",
-    "pf_results['Max Drawdown [%]'].fillna(pf_results['Max Drawdown [%]'].median(), inplace=True)\n",
-    "\n",
-    "# Extract the metrics into a new DataFrame\n",
-    "data_for_pca = pf_results[metrics]\n",
-    "\n",
-    "# Standardize the data before applying PCA\n",
-    "scaler = StandardScaler()\n",
-    "data_scaled = scaler.fit_transform(data_for_pca)\n",
-    "\n",
-    "# Apply PCA\n",
-    "pca = PCA(n_components=2)  # Adjust components as needed\n",
-    "principal_components = pca.fit_transform(data_scaled)\n",
-    "\n",
-    "# Create a DataFrame with the principal components\n",
-    "pca_results = pd.DataFrame(data=principal_components, columns=['PC1', 'PC2'])\n",
-    "\n",
-    "# Visualize the results\n",
-    "plt.figure(figsize=(8,6))\n",
-    "plt.scatter(pca_results['PC1'], pca_results['PC2'], alpha=0.5)\n",
-    "plt.xlabel('Principal Component 1')\n",
-    "plt.ylabel('Principal Component 2')\n",
-    "plt.title('PCA of Strategy Optimization Results')\n",
-    "plt.grid(True)\n",
-    "plt.savefig(\"ddd.png\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Check if there is any unnamed level and rename it\n",
-    "if None in df.index.names:\n",
-    "    # Generate new names list replacing None with 'stat'\n",
-    "    new_names = ['stat' if name is None else name for name in df.index.names]\n",
-    "    df.index.set_names(new_names, inplace=True)\n",
-    "\n",
-    "rs= df\n",
-    "\n",
-    "rs.info()\n",
-    "\n",
-    "\n",
-    "# # Now, 'stat' is the name of the previously unnamed level\n",
-    "\n",
-    "# # Filter for 'Total Return' assuming it is a correct identifier in the 'stat' level\n",
-    "# total_return_series = df.xs('Total Return [%]', level='stat')\n",
-    "\n",
-    "# # Sort the Series to get the largest 'Total Return' values\n",
-    "# sorted_series = total_return_series.sort_values(ascending=False)\n",
-    "\n",
-    "# # Print the sorted filtered data\n",
-    "# sorted_series.head(20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sorted_series.vbt.save()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#df.info()\n",
-    "total_return_series = df.xs('Total Return [%]')\n",
-    "sorted_series = total_return_series.sort_values(ascending=False)\n",
-    "\n",
-    "# Display the top N entries, e.g., top 5\n",
-    "sorted_series.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comb_stats_df.nlargest(10, 'Total Return [%]')\n",
-    "#stats_df.info()\n",
-    "\n",
-    "\n",
-    "8\t-0.06\t-0.2\t0.0028\t0.0048\t4.156254\n",
-    "4               -0.02   -0.25   0.0028   0.0048      0.84433\n",
-    "3   -0.02   -0.25   0.0033   0.0023   Total Return [%]     0.846753\n",
-    "#2\t-0.04\t-0.2\t0.0019\t0.0019\n",
-    "# 2\t-0.04\t-0.2\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
-    "# 2\t-0.04\t-0.25\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
-    "# 2\t-0.04\t-0.3\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
-    "# 2\t-0.04\t-0.35\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
-    "# 2\t-0.04\t-0.4\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
-    "# 2\t-0.04\t-0.2\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
-    "# 2\t-0.04\t-0.25\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
-    "# 2\t-0.04\t-0.3\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
-    "# 2\t-0.04\t-0.35\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
-    "# 2\t-0.04\t-0.4\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pf.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "basic_data.symbols"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    ">>> def apply_func(ts, entries, exits, fastw, sloww, minp=None):\n",
-    "...     fast_ma = vbt.nb.rolling_mean_nb(ts, fastw, minp=minp)\n",
-    "...     slow_ma = vbt.nb.rolling_mean_nb(ts, sloww, minp=minp)\n",
-    "...     entries[:] = vbt.nb.crossed_above_nb(fast_ma, slow_ma)  \n",
-    "...     exits[:] = vbt.nb.crossed_above_nb(slow_ma, fast_ma)\n",
-    "...     return (fast_ma, slow_ma)  \n",
-    "\n",
-    ">>> CrossSig = vbt.IF(\n",
-    "...     class_name=\"CrossSig\",\n",
-    "...     input_names=['ts'],\n",
-    "...     in_output_names=['entries', 'exits'],\n",
-    "...     param_names=['fastw', 'sloww'],\n",
-    "...     output_names=['fast_ma', 'slow_ma']\n",
-    "... ).with_apply_func(\n",
-    "...     apply_func,\n",
-    "...     in_output_settings=dict(\n",
-    "...         entries=dict(dtype=np.bool_),  #initialize output with bool\n",
-    "...         exits=dict(dtype=np.bool_)\n",
-    "...     )\n",
-    "... )\n",
-    ">>> cross_sig = CrossSig.run(ts2, 2, 4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#PIPELINE - parameters in one go\n",
-    "\n",
-    "\n",
-    "#TOTO prepsat do FOR-LOOPu\n",
-    "\n",
-    "\n",
-    "#indicator parameters\n",
-    "mom_timeperiod = list(range(2, 6))\n",
-    "\n",
-    "#threshold  entries parameters\n",
-    "mom_th = np.round(np.arange(-0.02, -0.1 - 0.02, -0.02),4).tolist()#-0.02\n",
-    "roc_th = np.round(np.arange(-0.2, -0.4 - 0.05, -0.05),4).tolist()#-0.2\n",
-    "#print(mom_th, roc_th)\n",
-    "#jejich product\n",
-    "# mom_th_prod, roc_th_prod = zip(*product(mom_th, roc_th))\n",
-    "\n",
-    "# #convert threshold to vbt param\n",
-    "# mom_th_index = vbt.Param(mom_th_prod, name='mom_th_th') \n",
-    "# roc_th_index = vbt.Param(roc_th_prod, name='roc_th_th')\n",
-    "\n",
-    "mom_th = vbt.Param(mom_th, name='mom_th')\n",
-    "roc_th = vbt.Param(roc_th, name='roc_th')\n",
-    "\n",
-    "#portfolio simulation parameters\n",
-    "sl_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
-    "# Using the round function\n",
-    "sl_stop = [round(val, 4) for val in sl_stop]\n",
-    "tp_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
-    "# Using the round function\n",
-    "tp_stop = [round(val, 4) for val in tp_stop]\n",
-    "sl_stop = vbt.Param(sl_stop) #np.nan mean s no stoploss\n",
-    "tp_stop = vbt.Param(tp_stop) #np.nan mean s no stoploss\n",
-    "\n",
-    "\n",
-    "#def test_mom(window=14, mom_th=0.2, roc_th=0.2, sl_stop=0.03/100, tp_stop=0.03/100):\n",
-    "#close = basic_data.xloc[\"09:30\":\"10:00\"].close\n",
-    "momshort = vbt.indicator(\"talib:MOM\").run(basic_data.get(\"Close\"), timeperiod=mom_timeperiod, short_name = \"slope_short\")\n",
-    "\n",
-    "#ht_trendline = vbt.indicator(\"talib:HT_TRENDLINE\").run(close, short_name = \"httrendline\")\n",
-    "rocp = vbt.indicator(\"talib:ROC\").run(basic_data.get(\"Close\"), short_name = \"rocp\")\n",
-    "#rate of change + momentum\n",
-    "\n",
-    "rocp_signal = rocp.real_crossed_below(mom_th)\n",
-    "mom_signal = momshort.real_crossed_below(roc_th)\n",
-    "\n",
-    "#mom_signal\n",
-    "print(rocp_signal.info())\n",
-    "print(mom_signal.info())\n",
-    "#print(rocp.real)\n",
-    "\n",
-    "\n",
-    "short_signal = (mom_signal.vbt & rocp_signal)\n",
-    "\n",
-    "# #short_signal = (rocp.real_crossed_below(roc_th_index) & momshort.real_crossed_below(mom_th_index))\n",
-    "# forced_exit = m1_data.symbol_wrapper.fill(False)\n",
-    "# entry_window_open=  m1_data.symbol_wrapper.fill(False)\n",
-    "\n",
-    "\n",
-    "# # Calculate the time difference in minutes from market open for each timestamp\n",
-    "# elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
-    "\n",
-    "# entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
-    "# forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
-    "# short_entries = (short_signal & entry_window_open)\n",
-    "# short_exits = forced_exit\n",
-    "# #long_entries.info()\n",
-    "# #number of trues and falses in long_entries\n",
-    "# #short_exits.value_counts()\n",
-    "# #short_entries.value_counts()\n",
-    "\n",
-    "\n",
-    "# pf = vbt.Portfolio.from_signals(close=close, short_entries=short_entries, short_exits=short_exits, sl_stop=sl_stop, tp_stop = tp_stop, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# filter dates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#filter na dny\n",
-    "dates_of_interest = pd.to_datetime(['2024-04-22']).tz_localize('US/Eastern')\n",
-    "filtered_df = df.loc[df.index.normalize().isin(dates_of_interest)]\n",
-    "\n",
-    "df = filtered_df\n",
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import plotly.io as pio\n",
-    "# pio.renderers.default = 'notebook'\n",
-    "\n",
-    "#naloadujeme do vbt symbol as column\n",
-    "basic_data = vbt.Data.from_data({\"BAC\": df}, tz_convert=zoneNY)\n",
-    "\n",
-    "vbt.settings.plotting.auto_rangebreaks = True\n",
-    "#basic_data.data[\"BAC\"].vbt.ohlcv.plot()\n",
-    "\n",
-    "#basic_data.data[\"BAC\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m1_data = basic_data[['Open', 'High', 'Low', 'Close', 'Volume']]\n",
-    "\n",
-    "m1_data.data[\"BAC\"]\n",
-    "#m5_data = m1_data.resample(\"5T\")\n",
-    "\n",
-    "#m5_data.data[\"BAC\"].head(10)\n",
-    "\n",
-    "# m15_data = m1_data.resample(\"15T\")\n",
-    "\n",
-    "# m15 = m15_data.data[\"BAC\"]\n",
-    "\n",
-    "# m15.vbt.ohlcv.plot()\n",
-    "\n",
-    "# m1_data.wrapper.index\n",
-    "\n",
-    "# m1_resampler = m1_data.wrapper.get_resampler(\"1T\")\n",
-    "# m1_resampler.index_difference(reverse=True)\n",
-    "\n",
-    "\n",
-    "# m5_resampler.prettify()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# MOM indicator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vbt.phelp(vbt.indicator(\"talib:ROCP\").run)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "vyuzití rychleho klesani na sekundove urovni behem open rush\n",
-    "- MOM + ROC during open rush\n",
-    "- short signal\n",
-    "- pipeline kombinace thresholdu pro vstup mom_th, roc_th + hodnota sl_stop a tp_stop (pripadne trailing) - nalezeni optimalni kombinace atributu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# fig = plot_2y_close([ht_trendline],[momshort, rocp], close)\n",
-    "# short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False))   \n",
-    "\n",
-    "#parameters (primary y line, secondary y line, close)\n",
-    "def plot_2y_close(priminds, secinds, close):\n",
-    "    fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"MOM\", \"Price\" ))\n",
-    "    close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
-    "    for ind in priminds:\n",
-    "        ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
-    "    for ind in secinds:\n",
-    "        ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
-    "    return fig\n",
-    "\n",
-    "close = m1_data.xloc[\"09:30\":\"10:00\"].close\n",
-    "momshort = vbt.indicator(\"talib:MOM\").run(close, timeperiod=3, short_name = \"slope_short\")\n",
-    "ht_trendline = vbt.indicator(\"talib:HT_TRENDLINE\").run(close, short_name = \"httrendline\")\n",
-    "rocp = vbt.indicator(\"talib:ROC\").run(close, short_name = \"rocp\")\n",
-    "#rate of change + momentum\n",
-    "short_signal = (rocp.real_crossed_below(-0.2) & momshort.real_crossed_below(-0.02))\n",
-    "#indlong = vbt.indicator(\"talib:MOM\").run(close, timeperiod=10, short_name = \"slope_long\")\n",
-    "fig = plot_2y_close([ht_trendline],[momshort, rocp], close)\n",
-    "short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False))   "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "close = m1_data.close\n",
-    "#vbt.phelp(vbt.OLS.run)\n",
-    "\n",
-    "#oer steepmnes of regression line\n",
-    "#talib.LINEARREG_SLOPE(close, timeperiod=timeperiod)\n",
-    "#a také ON BALANCE VOLUME - http://5.161.179.223:8000/static/js/vbt/api/indicators/custom/obv/index.html\n",
-    "\n",
-    "\n",
-    "\n",
-    "mom_ind = vbt.indicator(\"talib:MOM\") \n",
-    "#vbt.phelp(mom_ind.run)\n",
-    "\n",
-    "mom = mom_ind.run(close, timeperiod=10)\n",
-    "\n",
-    "plot_2y_close(mom, close)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# defining ENTRY WINDOW and forced EXIT window"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#m1_data.data[\"BAC\"].info()\n",
-    "import datetime\n",
-    "# Define the market open and close times\n",
-    "market_open = datetime.time(9, 30)\n",
-    "market_close = datetime.time(16, 0)\n",
-    "entry_window_opens = 2\n",
-    "entry_window_closes = 30\n",
-    "\n",
-    "forced_exit_start = 380\n",
-    "forced_exit_end = 390\n",
-    "\n",
-    "forced_exit = m1_data.symbol_wrapper.fill(False)\n",
-    "entry_window_open=  m1_data.symbol_wrapper.fill(False)\n",
-    "\n",
-    "# Calculate the time difference in minutes from market open for each timestamp\n",
-    "elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
-    "\n",
-    "entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
-    "forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
-    "\n",
-    "#entry_window_open.info()\n",
-    "# forced_exit.tail(100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "close = m1_data.close\n",
-    "\n",
-    "#rsi = vbt.RSI.run(close, window=14)\n",
-    "\n",
-    "short_entries = (short_signal & entry_window_open)\n",
-    "short_exits = forced_exit\n",
-    "#long_entries.info()\n",
-    "#number of trues and falses in long_entries\n",
-    "#short_exits.value_counts()\n",
-    "short_entries.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_rsi(close, entries, exits):\n",
-    "    fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"RSI\", \"Price\" ))\n",
-    "    close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
-    "    #rsi.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
-    "    entries.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False))  \n",
-    "    exits.vbt.signals.plot_as_exits(close, fig=fig, add_trace_kwargs=dict(secondary_y=False))  \n",
-    "    return fig\n",
-    "\n",
-    "plot_rsi(close, short_entries, short_exits)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vbt.phelp(vbt.Portfolio.from_signals)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sl_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
-    "# Using the round function\n",
-    "sl_stop = [round(val, 4) for val in sl_stop]\n",
-    "print(sl_stop)\n",
-    "sl_stop = vbt.Param(sl_stop) #np.nan mean s no stoploss\n",
-    "\n",
-    "pf = vbt.Portfolio.from_signals(close=close, short_entries=short_entries, short_exits=short_exits, sl_stop=0.03/100, tp_stop = 0.03/100, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
-    "\n",
-    "#pf.stats()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#list of orders\n",
-    "#pf.orders.records_readable\n",
-    "#pf.orders.plots()\n",
-    "#pf.stats()\n",
-    "pf.stats()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pf.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pf[(0.0015,0.0013)].plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pf[0.03].plot_trade_signals()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# pristup k pf jako multi index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#pf[0.03].plot()\n",
-    "#pf.order_records\n",
-    "pf[(0.03)].stats()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#zgrupovane statistiky\n",
-    "stats_df = pf.stats([\n",
-    "    'total_return',\n",
-    "    'total_trades',\n",
-    "    'win_rate',\n",
-    "    'expectancy'\n",
-    "], agg_func=None)\n",
-    "stats_df\n",
-    "\n",
-    "\n",
-    "stats_df.nlargest(10, 'Total Return [%]')\n",
-    "#stats_df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pf[(0.0011,0.0013000000000000002)].plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pandas.tseries.offsets import DateOffset\n",
-    "\n",
-    "temp_data = basic_data['2024-4-22']\n",
-    "temp_data\n",
-    "res1m = temp_data[[\"Open\", \"High\", \"Low\", \"Close\", \"Volume\"]]\n",
-    "\n",
-    "# Define a custom date offset that starts at 9:30 AM and spans 4 hours\n",
-    "custom_offset = DateOffset(hours=4, minutes=30)\n",
-    "\n",
-    "# res1m = res1m.get().resample(\"4H\").agg({  \n",
-    "#     \"Open\": \"first\",\n",
-    "#     \"High\": \"max\",\n",
-    "#     \"Low\": \"min\",\n",
-    "#     \"Close\": \"last\",\n",
-    "#     \"Volume\": \"sum\"\n",
-    "# })\n",
-    "\n",
-    "res4h = res1m.resample(\"1h\", resample_kwargs=dict(origin=\"start\"))\n",
-    "\n",
-    "res4h.data\n",
-    "\n",
-    "res15m = res1m.resample(\"15T\", resample_kwargs=dict(origin=\"start\"))\n",
-    "\n",
-    "res15m.data[\"BAC\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@vbt.njit\n",
-    "def long_entry_place_func_nb(c, low, close, time_in_ns, rsi14, window_open, window_close):\n",
-    "    market_open_minutes = 570  # 9 hours * 60 minutes + 30 minutes\n",
-    "\n",
-    "    for out_i in range(len(c.out)):\n",
-    "        i = c.from_i + out_i\n",
-    "\n",
-    "        current_minutes = vbt.dt_nb.hour_nb(time_in_ns[i]) * 60 + vbt.dt_nb.minute_nb(time_in_ns[i])\n",
-    "        #print(\"current_minutes\", current_minutes)\n",
-    "        # Calculate elapsed minutes since market open at 9:30 AM\n",
-    "        elapsed_from_open = current_minutes - market_open_minutes\n",
-    "        elapsed_from_open = elapsed_from_open if elapsed_from_open >= 0 else 0\n",
-    "        #print( \"elapsed_from_open\", elapsed_from_open)\n",
-    "\n",
-    "        #elapsed_from_open = elapsed_minutes_from_open_nb(time_in_ns) \n",
-    "        in_window = elapsed_from_open > window_open and elapsed_from_open < window_close\n",
-    "        #print(\"in_window\", in_window)\n",
-    "        # if in_window:\n",
-    "        #     print(\"in window\")\n",
-    "\n",
-    "        if in_window and rsi14[i] > 60: # and low[i, c.col] <= hit_price: # and hour == 9:  # (4)!\n",
-    "            return out_i\n",
-    "    return -1\n",
-    "\n",
-    "@vbt.njit\n",
-    "def long_exit_place_func_nb(c, high, close, time_index, tp, sl):  # (5)!\n",
-    "    entry_i = c.from_i - c.wait\n",
-    "    entry_price = close[entry_i, c.col]\n",
-    "    hit_price = entry_price * (1 + tp)\n",
-    "    stop_price = entry_price * (1 - sl)\n",
-    "    for out_i in range(len(c.out)):\n",
-    "        i = c.from_i + out_i\n",
-    "        last_bar_of_day = vbt.dt_nb.day_changed_nb(time_index[i], time_index[i + 1])\n",
-    "\n",
-    "        #print(next_day)\n",
-    "        if last_bar_of_day: #pokud je dalsi next day, tak zavirame posledni\n",
-    "            print(\"ted\",out_i)\n",
-    "            return out_i\n",
-    "        if close[i, c.col] >= hit_price or close[i, c.col] <= stop_price :\n",
-    "            return out_i\n",
-    "    return -1\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.DataFrame(np.random.random(size=(5, 10)), columns=list('abcdefghij'))\n",
-    "\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.sum()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

research/strat_SUPERTREND/SUPERTREND_v1_SINGLE.ipynb

@@ -1,782 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# SUPERTREND\n",
-    "\n",
-    "* kombinace supertrendu na vice urovnich"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from dotenv import load_dotenv\n",
-    "\n",
-    "#as V2realbot is client , load env variables here\n",
-    "env_file = \"/Users/davidbrazda/Documents/Development/python/.env\"\n",
-    "# Load the .env file\n",
-    "load_dotenv(env_file)\n",
-    "\n",
-    "from v2realbot.utils.utils import zoneNY\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import vectorbtpro as vbt\n",
-    "# from itables import init_notebook_mode, show\n",
-    "import datetime\n",
-    "from itertools import product\n",
-    "from v2realbot.config import DATA_DIR\n",
-    "from lightweight_charts import JupyterChart, chart, Panel, PlotAccessor\n",
-    "from IPython.display import display\n",
-    "\n",
-    "# init_notebook_mode(all_interactive=True)\n",
-    "\n",
-    "vbt.settings.set_theme(\"dark\")\n",
-    "vbt.settings['plotting']['layout']['width'] = 1280\n",
-    "vbt.settings.plotting.auto_rangebreaks = True\n",
-    "# Set the option to display with pagination\n",
-    "pd.set_option('display.notebook_repr_html', True)\n",
-    "pd.set_option('display.max_rows', 10)  # Number of rows per page"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "trades_df-BAC-2024-01-01T09_30_00-2024-05-14T16_00_00-CO4B7VPWUZF-100.parquet\n",
-      "trades_df-BAC-2024-01-11T09:30:00-2024-01-12T16:00:00.parquet\n",
-      "trades_df-SPY-2024-01-01T09:30:00-2024-05-14T16:00:00.parquet\n",
-      "trades_df-BAC-2023-01-01T09_30_00-2024-05-25T16_00_00-47BCFOPUVWZ-100.parquet\n",
-      "ohlcv_df-BAC-2024-01-11T09:30:00-2024-01-12T16:00:00.parquet\n",
-      "trades_df-BAC-2024-05-15T09_30_00-2024-05-25T16_00_00-47BCFOPUVWZ-100.parquet\n",
-      "ohlcv_df-BAC-2024-01-01T09_30_00-2024-05-25T16_00_00-47BCFOPUVWZ-100.parquet\n",
-      "ohlcv_df-SPY-2024-01-01T09:30:00-2024-05-14T16:00:00.parquet\n",
-      "ohlcv_df-BAC-2024-01-01T09_30_00-2024-05-14T16_00_00-CO4B7VPWUZF-100.parquet\n",
-      "ohlcv_df-BAC-2023-01-01T09_30_00-2024-05-25T16_00_00-47BCFOPUVWZ-100.parquet\n",
-      "ohlcv_df-BAC-2023-01-01T09_30_00-2024-05-25T15_30_00-47BCFOPUVWZ-100.parquet\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "351"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Define the market open and close times\n",
-    "market_open = datetime.time(9, 30)\n",
-    "market_close = datetime.time(16, 0)\n",
-    "entry_window_opens = 1\n",
-    "entry_window_closes = 370\n",
-    "forced_exit_start = 380\n",
-    "forced_exit_end = 390\n",
-    "\n",
-    "#LOAD FROM PARQUET\n",
-    "#list all files is dir directory with parquet extension\n",
-    "dir = DATA_DIR + \"/notebooks/\"\n",
-    "import os\n",
-    "files = [f for f in os.listdir(dir) if f.endswith(\".parquet\")]\n",
-    "print('\\n'.join(map(str, files)))\n",
-    "file_name = \"ohlcv_df-BAC-2023-01-01T09_30_00-2024-05-25T15_30_00-47BCFOPUVWZ-100.parquet\"\n",
-    "ohlcv_df = pd.read_parquet(dir+file_name,engine='pyarrow')\n",
-    "#filter ohlcv_df to certain date range (assuming datetime index)\n",
-    "#ohlcv_df = ohlcv_df.loc[\"2024-02-12 9:30\":\"2024-02-14 16:00\"]\n",
-    "\n",
-    "#add vwap column to ohlcv_df\n",
-    "#ohlcv_df[\"hlcc4\"] = (ohlcv_df[\"close\"] + ohlcv_df[\"high\"] + ohlcv_df[\"low\"] + ohlcv_df[\"close\"]) / 4\n",
-    "\n",
-    "basic_data = vbt.Data.from_data(vbt.symbol_dict({\"BAC\": ohlcv_df}), tz_convert=zoneNY)\n",
-    "ohlcv_df= None\n",
-    "basic_data.wrapper.index.normalize().nunique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "DatetimeIndex: 4549772 entries, 2023-01-03 09:30:01-05:00 to 2024-05-24 15:59:59-04:00\n",
-      "Data columns (total 10 columns):\n",
-      " #   Column      Dtype                     \n",
-      "---  ------      -----                     \n",
-      " 0   open        float64                   \n",
-      " 1   high        float64                   \n",
-      " 2   low         float64                   \n",
-      " 3   close       float64                   \n",
-      " 4   volume      float64                   \n",
-      " 5   trades      float64                   \n",
-      " 6   updated     datetime64[ns, US/Eastern]\n",
-      " 7   vwap        float64                   \n",
-      " 8   buyvolume   float64                   \n",
-      " 9   sellvolume  float64                   \n",
-      "dtypes: datetime64[ns, US/Eastern](1), float64(9)\n",
-      "memory usage: 381.8 MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "basic_data.data[\"BAC\"].info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Add resample function to custom columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from vectorbtpro.utils.config import merge_dicts, Config, HybridConfig\n",
-    "from vectorbtpro import _typing as tp\n",
-    "from vectorbtpro.generic import nb as generic_nb\n",
-    "\n",
-    "_feature_config: tp.ClassVar[Config] = HybridConfig(\n",
-    "    {\n",
-    "        \"buyvolume\": dict(\n",
-    "            resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(\n",
-    "                resampler,\n",
-    "                generic_nb.sum_reduce_nb,\n",
-    "            )\n",
-    "        ),\n",
-    "        \"sellvolume\": dict(\n",
-    "            resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(\n",
-    "                resampler,\n",
-    "                generic_nb.sum_reduce_nb,\n",
-    "            )\n",
-    "        ),\n",
-    "        \"trades\": dict(\n",
-    "            resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(\n",
-    "                resampler,\n",
-    "                generic_nb.sum_reduce_nb,\n",
-    "            )\n",
-    "        )\n",
-    "    }\n",
-    ")\n",
-    "\n",
-    "basic_data._feature_config = _feature_config"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','trades','sellvolume']]\n",
-    "\n",
-    "s5data = s1data.resample(\"5s\")\n",
-    "s5data = s5data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
-    "\n",
-    "t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','trades','sellvolume']].resample(\"1T\")\n",
-    "t1data = t1data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
-    "# t1data.data[\"BAC\"].info()\n",
-    "\n",
-    "t30data  = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','trades','sellvolume']].resample(\"30T\")\n",
-    "t30data = t30data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
-    "# t30data.data[\"BAC\"].info()\n",
-    "\n",
-    "s1close = s1data.close\n",
-    "t1close = t1data.close\n",
-    "t30close = t30data.close\n",
-    "t30volume = t30data.volume\n",
-    "\n",
-    "#resample on specific index \n",
-    "resampler = vbt.Resampler(t30data.index, s1data.index, source_freq=\"30T\", target_freq=\"1s\")\n",
-    "t30close_realigned = t30close.vbt.realign_closing(resampler)\n",
-    "\n",
-    "#resample 1min to s\n",
-    "resampler_s = vbt.Resampler(t1data.index, s1data.index, source_freq=\"1T\", target_freq=\"1s\")\n",
-    "t1close_realigned = t1close.vbt.realign_closing(resampler_s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "datetime64[ns, US/Eastern]\n",
-      "datetime64[ns, US/Eastern]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(t30data.index.dtype)\n",
-    "print(s1data.index.dtype)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "DatetimeIndex: 4551 entries, 2023-01-03 09:30:00-05:00 to 2024-05-24 15:30:00-04:00\n",
-      "Data columns (total 9 columns):\n",
-      " #   Column      Non-Null Count  Dtype  \n",
-      "---  ------      --------------  -----  \n",
-      " 0   open        4551 non-null   float64\n",
-      " 1   high        4551 non-null   float64\n",
-      " 2   low         4551 non-null   float64\n",
-      " 3   close       4551 non-null   float64\n",
-      " 4   volume      4551 non-null   float64\n",
-      " 5   vwap        4551 non-null   float64\n",
-      " 6   buyvolume   4551 non-null   float64\n",
-      " 7   trades      4551 non-null   float64\n",
-      " 8   sellvolume  4551 non-null   float64\n",
-      "dtypes: float64(9)\n",
-      "memory usage: 355.5 KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "t30data.data[\"BAC\"].info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vbt.IF.list_indicators(\"*vwap\")\n",
-    "vbt.phelp(vbt.VWAP.run)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# VWAP"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "t1vwap_h = vbt.VWAP.run(t1data.high, t1data.low, t1data.close, t1data.volume, anchor=\"H\")\n",
-    "t1vwap_d = vbt.VWAP.run(t1data.high, t1data.low, t1data.close, t1data.volume, anchor=\"D\")\n",
-    "t1vwap_t = vbt.VWAP.run(t1data.high, t1data.low, t1data.close, t1data.volume, anchor=\"T\")\n",
-    "\n",
-    "t1vwap_h_real = t1vwap_h.vwap.vbt.realign_closing(resampler_s)\n",
-    "t1vwap_d_real = t1vwap_d.vwap.vbt.realign_closing(resampler_s)\n",
-    "t1vwap_t_real = t1vwap_t.vwap.vbt.realign_closing(resampler_s)\n",
-    "\n",
-    "#t1vwap_5t.xloc[\"2024-01-3 09:30:00\":\"2024-01-03 16:00:00\"].plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#m30data.close.lw.plot()\n",
-    "#quick few liner\n",
-    "pane1 = Panel(\n",
-    "    histogram=[\n",
-    "               #(s1data.volume, \"volume\",None, 0.8),\n",
-    "               #(m30volume, \"m30volume\",None, 1)\n",
-    "               ], # [(series, name, \"rgba(53, 94, 59, 0.6)\", opacity)]\n",
-    "    right=[\n",
-    "          (s1data.close, \"1s close\"),\n",
-    "            (t1data.close, \"1min close\"),\n",
-    "            (t1vwap_t, \"1mvwap_t\"),\n",
-    "            (t1vwap_h, \"1mvwap_h\"),\n",
-    "            (t1vwap_d, \"1mvwap_d\"),\n",
-    "            (t1vwap_t_real, \"1mvwap_t_real\"),\n",
-    "            (t1vwap_h_real, \"1mvwap_h_real\"),\n",
-    "            (t1vwap_d_real, \"1mvwap_d_real\")\n",
-    "        #    (t1close_realigned, \"1min close realigned\"),\n",
-    "        #    (m30data.close, \"30min-close\"),\n",
-    "        #    (m30close_realigned, \"30min close realigned\"),\n",
-    "           ],\n",
-    ")\n",
-    "ch = chart([pane1], size=\"s\", xloc=slice(\"2024-05-1 09:30:00\",\"2024-05-25 16:00:00\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# SUPERTREND"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "supertrend_s1 = vbt.SUPERTREND.run(s1data.high, s1data.low, s1data.close, period=5, multiplier=3)\n",
-    "direction_series_s1 = supertrend_s1.direction\n",
-    "supertrend_t1 = vbt.SUPERTREND.run(t1data.high, t1data.low, t1data.close, period=14, multiplier=3)\n",
-    "direction_series_t1 = supertrend_t1.direction\n",
-    "supertrend_t30 = vbt.SUPERTREND.run(t30data.high, t30data.low, t30data.close, period=14, multiplier=3)\n",
-    "direction_series_t30 = supertrend_t30.direction\n",
-    "\n",
-    "resampler_1t_sec = vbt.Resampler(direction_series_t1.index, direction_series_s1.index, source_freq=\"1T\", target_freq=\"1s\")\n",
-    "resampler_30t_sec = vbt.Resampler(direction_series_t30.index, direction_series_s1.index, source_freq=\"30T\", target_freq=\"1s\")\n",
-    "direction_series_t1_realigned = direction_series_t1.vbt.realign_closing(resampler_1t_sec)\n",
-    "direction_series_t30_realigned = direction_series_t30.vbt.realign_closing(resampler_30t_sec)\n",
-    "\n",
-    "#supertrend_s1.xloc[\"2024-01-3 09:30:00\":\"2024-01-03 16:00:00\"].plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# aligned_ups= pd.Series(False, index=direction_real.index)\n",
-    "# aligned_downs= pd.Series(False, index=direction_real.index)\n",
-    "\n",
-    "# aligned_ups = direction_real == 1 & supertrend.direction == 1\n",
-    "# aligned_ups"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s5close = s5data.data[\"BAC\"].close\n",
-    "s5open = s5data.data[\"BAC\"].open\n",
-    "s5high = s5data.data[\"BAC\"].high\n",
-    "s5close_prev = s5close.shift(1)\n",
-    "s5open_prev = s5open.shift(1)\n",
-    "s5high_prev = s5high.shift(1)\n",
-    "#gap nahoru od byci svicky a nevraci se zpet na jeji uroven\n",
-    "entry_ups = (s5close_prev > s5open_prev) & (s5open > s5high_prev + 0.010) & (s5close > s5close_prev)\n",
-    "\n",
-    "entry_ups.value_counts()\n",
-    "\n",
-    "#entry_ups.info()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Entry window"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "market_open = datetime.time(9, 30)\n",
-    "market_close = datetime.time(16, 0)\n",
-    "entry_window_opens = 10\n",
-    "entry_window_closes = 370"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "entry_window_open= pd.Series(False, index=entry_ups.index)\n",
-    "# Calculate the time difference in minutes from market open for each timestamp\n",
-    "elapsed_min_from_open = (entry_ups.index.hour - market_open.hour) * 60 + (entry_ups.index.minute - market_open.minute)\n",
-    "entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
-    "#entry_window_open\n",
-    "\n",
-    "entry_ups = entry_ups & entry_window_open\n",
-    "# entry_ups\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s5vwap_h = vbt.VWAP.run(s5data.high, s5data.low, s5data.close, s5data.volume, anchor=\"H\")\n",
-    "s5vwap_d = vbt.VWAP.run(s5data.high, s5data.low, s5data.close, s5data.volume, anchor=\"D\")\n",
-    "\n",
-    "# s5vwap_h_real = s5vwap_h.vwap.vbt.realign_closing(resampler_s)\n",
-    "# s5vwap_d_real = s5vwap_d.vwap.vbt.realign_closing(resampler_s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pane1 = Panel(\n",
-    "    ohlcv=(s5data.data[\"BAC\"],), #(series, entries, exits, other_markers)\n",
-    "    histogram=[], # [(series, name, \"rgba(53, 94, 59, 0.6), opacity\")]\n",
-    "    right=[#(bbands,), #[(series, name, entries, exits, other_markers)]\n",
-    "        (s5data.data[\"BAC\"].close, \"close\", entry_ups),\n",
-    "        (s5data.data[\"BAC\"].open, \"open\"),\n",
-    "        (s5vwap_h, \"vwap5s_H\",),\n",
-    "        (s5vwap_d, \"vwap5s_D\",)\n",
-    "        #    (t1data.data[\"BAC\"].vwap, \"vwap\"),\n",
-    "        #   (t1data.close, \"1min close\"),\n",
-    "        #    (supertrend_s1.trend,\"STtrend\"),\n",
-    "        #    (supertrend_s1.long,\"STlong\"),\n",
-    "        #    (supertrend_s1.short,\"STshort\")\n",
-    "           ],\n",
-    "    left = [\n",
-    "        #(direction_series_s1,\"direction_s1\"),\n",
-    "        #    (direction_series_t1,\"direction_t1\"),\n",
-    "        #    (direction_series_t30,\"direction_t30\")\n",
-    "            \n",
-    "            ],\n",
-    "    # right=[(bbands.upperband, \"upperband\",),\n",
-    "    #       (bbands.lowerband, \"lowerband\",),\n",
-    "    #       (bbands.middleband, \"middleband\",)\n",
-    "    #       ], #[(series, name, entries, exits, other_markers)]\n",
-    "    middle1=[],\n",
-    "    middle2=[],\n",
-    ")\n",
-    "\n",
-    "# pane2 = Panel(\n",
-    "#     ohlcv=(t1data.data[\"BAC\"],uptrend_m30, downtrend_m30), #(series, entries, exits, other_markers)\n",
-    "#     histogram=[], # [(series, name, \"rgba(53, 94, 59, 0.6), opacity\")]\n",
-    "#     left=[#(bbands,), #[(series, name, entries, exits, other_markers)]\n",
-    "#            (direction_real,\"direction30min_real\"),\n",
-    "#            ],\n",
-    "#     # left = [(supertrendm30.direction,\"STdirection30\")],\n",
-    "#     # # right=[(bbands.upperband, \"upperband\",),\n",
-    "#     # #       (bbands.lowerband, \"lowerband\",),\n",
-    "#     # #       (bbands.middleband, \"middleband\",)\n",
-    "#     # #       ], #[(series, name, entries, exits, other_markers)]\n",
-    "#     middle1=[],\n",
-    "#     middle2=[],\n",
-    "#     title = \"1m\")\n",
-    "\n",
-    "ch = chart([pane1], sync=True, size=\"s\", xloc=slice(\"2024-02-20 09:30:00\",\"2024-02-22 16:00:00\"), precision=6)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# data = s5data.xloc[\"2024-01-03 09:30:00\":\"2024-03-10 16:00:00\"]\n",
-    "# entry = entry_ups.vbt.xloc[\"2024-01-03 09:30:00\":\"2024-03-10 16:00:00\"].obj\n",
-    "\n",
-    "pf = vbt.Portfolio.from_signals(close=s5data, entries=entry_ups, direction=\"longonly\", sl_stop=0.05/100, tp_stop = 0.05/100, fees=0.0167/100, freq=\"5s\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pf.xloc[\"2024-01-26 09:30:00\":\"2024-02-28 16:00:00\"].positions.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pf.xloc[\"2024-01-26 09:30:00\":\"2024-01-28 16:00:00\"].plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.set_option('display.max_rows', None)\n",
-    "pf.stats()\n",
-    "# pf.xloc[\"monday\"].stats()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "buyvolume = t1data.data[\"BAC\"].buyvolume\n",
-    "sellvolume = t1data.data[\"BAC\"].sellvolume\n",
-    "totalvolume = buyvolume + sellvolume\n",
-    "\n",
-    "#adjust to minimal value to avoid division by zero\n",
-    "sellvolume_adjusted = sellvolume.replace(0, 1e-10)\n",
-    "oibratio = buyvolume / sellvolume\n",
-    "\n",
-    "#cumulative order flow (net difference)\n",
-    "cof = buyvolume - sellvolume\n",
-    "\n",
-    "# Calculate the order imbalance (net differene) normalize the order imbalance by calculating the difference between buy and sell volumes and then scaling it by the total volume.\n",
-    "order_imbalance = cof / totalvolume\n",
-    "order_imbalance = order_imbalance.fillna(0) #nan nahradime 0\n",
-    "\n",
-    "order_imbalance_allvolume = cof / t1data.data[\"BAC\"].volume\n",
-    "\n",
-    "order_imbalance_sma = vbt.indicator(\"talib:EMA\").run(order_imbalance, timeperiod=5)\n",
-    "short_signals = order_imbalance.vbt < -0.5\n",
-    "#short_entries = oibratio.vbt < 0.01\n",
-    "short_signals.value_counts()\n",
-    "short_signals.name = \"short_entries\"\n",
-    "#.fillna(False)\n",
-    "short_exits = short_signals.shift(-2).fillna(False).astype(bool)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pane1 = Panel(\n",
-    "    ohlcv=(t1data.data[\"BAC\"],), #(series, entries, exits, other_markers)\n",
-    "    histogram=[(order_imbalance_allvolume, \"oib_allvolume\", \"rgba(53, 94, 59, 0.6)\",0.5),\n",
-    "               (t1data.data[\"BAC\"].trades, \"trades\",None,0.4),\n",
-    "               ], # [(series, name, \"rgba(53, 94, 59, 0.6)\", opacity)]\n",
-    "    # right=[\n",
-    "    #        (supertrend.trend,\"STtrend\"),\n",
-    "    #        (supertrend.long,\"STlong\"),\n",
-    "    #        (supertrend.short,\"STshort\")\n",
-    "    #        ],\n",
-    "    # left = [(supertrend.direction,\"STdirection\")],\n",
-    "    # right=[(bbands.upperband, \"upperband\",),\n",
-    "    #       (bbands.lowerband, \"lowerband\",),\n",
-    "    #       (bbands.middleband, \"middleband\",)\n",
-    "    #       ], #[(series, name, entries, exits, other_markers)]\n",
-    "    middle1=[],\n",
-    "    middle2=[],\n",
-    ")\n",
-    "\n",
-    "pane2 = Panel(\n",
-    "    ohlcv=(basic_data.data[\"BAC\"],), #(series, entries, exits, other_markers)\n",
-    "    left=[(basic_data.data[\"BAC\"].trades, \"trades\")],\n",
-    "    histogram=[(basic_data.data[\"BAC\"].trades, \"trades_hist\", \"white\", 0.5)], #\"rgba(53, 94, 59, 0.6)\"\n",
-    "    #           ], # [(series, name, \"rgba(53, 94, 59, 0.6)\")]\n",
-    "    # right=[\n",
-    "    #        (supertrend.trend,\"STtrend\"),\n",
-    "    #        (supertrend.long,\"STlong\"),\n",
-    "    #        (supertrend.short,\"STshort\")\n",
-    "    #        ],\n",
-    "    # left = [(supertrend.direction,\"STdirection\")],\n",
-    "    # right=[(bbands.upperband, \"upperband\",),\n",
-    "    #       (bbands.lowerband, \"lowerband\",),\n",
-    "    #       (bbands.middleband, \"middleband\",)\n",
-    "    #       ], #[(series, name, entries, exits, other_markers)]\n",
-    "    middle1=[],\n",
-    "    middle2=[],\n",
-    ")\n",
-    "\n",
-    "\n",
-    "ch = chart([pane1, pane2], size=\"m\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#short_signal = t1slope.real_below(t1_th) & t2slope.real_below(t2_th) & t3slope.real_below(t3_th) & t4slope.real_below(t4_th)\n",
-    "#long_signal = t1slope.real_above(t1_th) & t2slope.real_above(t2_th) & t3slope.real_above(t3_th) & t4slope.real_above(t4_th)\n",
-    "\n",
-    "#test na daily s reversem  crossed 0\n",
-    "short_signal = t2slope.vbt < -0.01 & t3slope.vbt < -0.01  #min value of threshold\n",
-    "long_signal = t2slope.vbt > 0.01 & t3slope.vbt > 0.01  #min\n",
-    "\n",
-    "# thirty_up_signal = t3slope.vbt.crossed_above(0.01)\n",
-    "# thirty_down_signal = t3slope.vbt.crossed_below(-0.01)\n",
-    "\n",
-    "fig = plot_2y_close(priminds=[], secinds=[t3slope], close=t1data.close)\n",
-    "#short_signal.vbt.signals.plot_as_entries(basic_data.close, fig=fig)\n",
-    "\n",
-    "short_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"SHORTS\",\n",
-    "                                                                                    line=dict(color=\"#ffe476\"),\n",
-    "                                                                                    marker=dict(color=\"red\", symbol=\"triangle-down\"),\n",
-    "                                                                                    fill=None,\n",
-    "                                                                                    connectgaps=True,\n",
-    "                                                                                    ))\n",
-    "long_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"LONGS\",\n",
-    "                                                                                    line=dict(color=\"#ffe476\"),\n",
-    "                                                                                    marker=dict(color=\"limegreen\"),\n",
-    "                                                                                    fill=None,\n",
-    "                                                                                    connectgaps=True,\n",
-    "                                                                                    ))\n",
-    "\n",
-    "# thirty_down_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"DOWN30\",\n",
-    "#                                                                                     line=dict(color=\"#ffe476\"),\n",
-    "#                                                                                     marker=dict(color=\"yellow\", symbol=\"triangle-down\"),\n",
-    "#                                                                                     fill=None,\n",
-    "#                                                                                     connectgaps=True,\n",
-    "#                                                                                     ))\n",
-    "# thirty_up_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"UP30\",\n",
-    "#                                                                                     line=dict(color=\"#ffe476\"),\n",
-    "#                                                                                     marker=dict(color=\"grey\"),\n",
-    "#                                                                                     fill=None,\n",
-    "#                                                                                     connectgaps=True,\n",
-    "#                                                                                     ))\n",
-    "\n",
-    "# thirtymin_slope_to_compare.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True), trace_kwargs=dict(name=\"30min slope\",\n",
-    "#                                                                                         line=dict(color=\"yellow\"),                                                                                     \n",
-    "#                                                                                         fill=None,\n",
-    "#                                                                                         connectgaps=True,\n",
-    "#                                                                                         ))\n",
-    "\n",
-    "fig.show()\n",
-    "# print(\"short signal\")\n",
-    "# print(short_signal.value_counts())\n",
-    "\n",
-    "#forced_exit = pd.Series(False, index=close.index)\n",
-    "forced_exit = basic_data.symbol_wrapper.fill(False)\n",
-    "#entry_window_open = pd.Series(False, index=close.index)\n",
-    "entry_window_open=  basic_data.symbol_wrapper.fill(False)\n",
-    "\n",
-    "# Calculate the time difference in minutes from market open for each timestamp\n",
-    "elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
-    "\n",
-    "entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
-    "\n",
-    "#print(entry_window_open.value_counts())\n",
-    "\n",
-    "forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
-    "short_entries = (short_signal & entry_window_open)\n",
-    "short_exits = forced_exit\n",
-    "\n",
-    "entries = (long_signal & entry_window_open)\n",
-    "exits = forced_exit\n",
-    "#long_entries.info()\n",
-    "#number of trues and falses in long_entries\n",
-    "# print(short_exits.value_counts())\n",
-    "# print(short_entries.value_counts())\n",
-    "\n",
-    "#fig = plot_2y_close([],[momshort, rocp], close)\n",
-    "#short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
-    "#print(sl_stop)\n",
-    "#short_entries=short_entries, short_exits=short_exits,\n",
-    "# pf = vbt.Portfolio.from_signals(close=basic_data, entries=short_entries, exits=exits, tsl_stop=0.005, tp_stop = 0.05, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
-    "\n",
-    "# pf.stats()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "forced_exit = t1data.symbol_wrapper.fill(False)\n",
-    "#entry_window_open = pd.Series(False, index=close.index)\n",
-    "entry_window_open=  t1data.symbol_wrapper.fill(False)\n",
-    "\n",
-    "# Calculate the time difference in minutes from market open for each timestamp\n",
-    "elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
-    "\n",
-    "entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
-    "\n",
-    "#print(entry_window_open.value_counts())\n",
-    "\n",
-    "forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
-    "short_entries = (short_signals & entry_window_open)\n",
-    "short_exits = forced_exit\n",
-    "\n",
-    "entries = (long_signals & entry_window_open)\n",
-    "exits = forced_exit\n",
-    "\n",
-    "pf = vbt.Portfolio.from_signals(close=t1data, entries=entries, exits=exits, short_entries=short_entries, short_exits=exits,\n",
-    "td_stop=2, time_delta_format=\"rows\",\n",
-    "tsl_stop=0.005, tp_stop = 0.005, fees=0.0167/100)#, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
-    "\n",
-    "pf.stats()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pf.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pf.get_drawdowns().records_readable"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pf.orders.records_readable"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

research/test1sbars.ipynb

@@ -1,421 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from v2realbot.tools.loadbatch import load_batch\n",
-    "from v2realbot.utils.utils import zoneNY\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import vectorbtpro as vbt\n",
-    "from itables import init_notebook_mode, show\n",
-    "\n",
-    "init_notebook_mode(all_interactive=True)\n",
-    "\n",
-    "vbt.settings.set_theme(\"dark\")\n",
-    "vbt.settings['plotting']['layout']['width'] = 1280\n",
-    "vbt.settings.plotting.auto_rangebreaks = True\n",
-    "# Set the option to display with pagination\n",
-    "pd.set_option('display.notebook_repr_html', True)\n",
-    "pd.set_option('display.max_rows', 10)  # Number of rows per page\n",
-    "\n",
-    "res, df = load_batch(batch_id=\"0fb5043a\", #46 days 1.3 - 6.5.\n",
-    "                     space_resolution_evenly=False,\n",
-    "                     indicators_columns=[\"Rsi14\"],\n",
-    "                     main_session_only=True,\n",
-    "                     verbose = False)\n",
-    "if res < 0:\n",
-    "    print(\"Error\" + str(res) + str(df))\n",
-    "df = df[\"bars\"]\n",
-    "\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# filter dates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#filter na dny\n",
-    "# dates_of_interest = pd.to_datetime(['2024-04-22', '2024-04-23']).tz_localize('US/Eastern')\n",
-    "# filtered_df = df.loc[df.index.normalize().isin(dates_of_interest)]\n",
-    "\n",
-    "# df = filtered_df\n",
-    "# df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import plotly.io as pio\n",
-    "pio.renderers.default = 'notebook'\n",
-    "\n",
-    "#naloadujeme do vbt symbol as column\n",
-    "basic_data = vbt.Data.from_data({\"BAC\": df}, tz_convert=zoneNY)\n",
-    "start_date = pd.Timestamp('2024-03-12 09:30', tz=zoneNY)\n",
-    "end_date = pd.Timestamp('2024-03-13 16:00', tz=zoneNY)\n",
-    "\n",
-    "#basic_data = basic_data.transform(lambda df: df[df.index.date == start_date.date()])\n",
-    "#basic_data = basic_data.transform(lambda df: df[(df.index >= start_date) & (df.index <= end_date)])\n",
-    "#basic_data.data[\"BAC\"].info()\n",
-    "\n",
-    "# fig = basic_data.plot(plot_volume=False)\n",
-    "# pivot_info = basic_data.run(\"pivotinfo\", up_th=0.003, down_th=0.002)\n",
-    "# #pivot_info.plot()\n",
-    "# pivot_info.plot(fig=fig, conf_value_trace_kwargs=dict(visible=True))\n",
-    "# fig.show()\n",
-    "\n",
-    "\n",
-    "# rsi14 = basic_data.data[\"BAC\"][\"Rsi14\"].rename(\"Rsi14\")\n",
-    "\n",
-    "# rsi14.vbt.plot().show()\n",
-    "#basic_data.xloc[\"09:30\":\"10:00\"].data[\"BAC\"].vbt.ohlcv.plot().show()\n",
-    "\n",
-    "vbt.settings.plotting.auto_rangebreaks = True\n",
-    "#basic_data.data[\"BAC\"].vbt.ohlcv.plot()\n",
-    "\n",
-    "#basic_data.data[\"BAC\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m1_data = basic_data[['Open', 'High', 'Low', 'Close', 'Volume']]\n",
-    "\n",
-    "m1_data.data[\"BAC\"]\n",
-    "#m5_data = m1_data.resample(\"5T\")\n",
-    "\n",
-    "#m5_data.data[\"BAC\"].head(10)\n",
-    "\n",
-    "# m15_data = m1_data.resample(\"15T\")\n",
-    "\n",
-    "# m15 = m15_data.data[\"BAC\"]\n",
-    "\n",
-    "# m15.vbt.ohlcv.plot()\n",
-    "\n",
-    "# m1_data.wrapper.index\n",
-    "\n",
-    "# m1_resampler = m1_data.wrapper.get_resampler(\"1T\")\n",
-    "# m1_resampler.index_difference(reverse=True)\n",
-    "\n",
-    "\n",
-    "# m5_resampler.prettify()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# defining ENTRY WINDOW and forced EXIT window"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#m1_data.data[\"BAC\"].info()\n",
-    "import datetime\n",
-    "# Define the market open and close times\n",
-    "market_open = datetime.time(9, 30)\n",
-    "market_close = datetime.time(16, 0)\n",
-    "entry_window_opens = 1\n",
-    "entry_window_closes = 350\n",
-    "\n",
-    "forced_exit_start = 380\n",
-    "forced_exit_end = 390\n",
-    "\n",
-    "forced_exit = m1_data.symbol_wrapper.fill(False)\n",
-    "entry_window_open=  m1_data.symbol_wrapper.fill(False)\n",
-    "\n",
-    "# Calculate the time difference in minutes from market open for each timestamp\n",
-    "elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
-    "\n",
-    "entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
-    "forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
-    "\n",
-    "#entry_window_open.info()\n",
-    "# forced_exit.tail(100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "close = m1_data.close\n",
-    "\n",
-    "rsi = vbt.RSI.run(close, window=14)\n",
-    "\n",
-    "long_entries = (rsi.rsi.vbt.crossed_below(20) & entry_window_open)\n",
-    "long_exits = (rsi.rsi.vbt.crossed_above(70) | forced_exit)\n",
-    "#long_entries.info()\n",
-    "#number of trues and falses in long_entries\n",
-    "long_entries.value_counts()\n",
-    "#long_exits.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_rsi(rsi, close, entries, exits):\n",
-    "    fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"RSI\", \"Price\" ))\n",
-    "    close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
-    "    rsi.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
-    "    entries.vbt.signals.plot_as_entries(rsi.rsi, fig=fig, add_trace_kwargs=dict(secondary_y=False))  \n",
-    "    exits.vbt.signals.plot_as_exits(rsi.rsi, fig=fig, add_trace_kwargs=dict(secondary_y=False))  \n",
-    "    return fig\n",
-    "\n",
-    "plot_rsi(rsi, close, long_entries, long_exits)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vbt.phelp(vbt.Portfolio.from_signals)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sl_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
-    "# Using the round function\n",
-    "sl_stop = [round(val, 4) for val in sl_stop]\n",
-    "print(sl_stop)\n",
-    "sl_stop = vbt.Param(sl_stop) #np.nan mean s no stoploss\n",
-    "\n",
-    "pf = vbt.Portfolio.from_signals(close=close, entries=long_entries, sl_stop=sl_stop, tp_stop = sl_stop, exits=long_exits,fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop, \n",
-    "\n",
-    "#pf.stats()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pf.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pf[(0.0015,0.0013)].plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pf[0.03].plot_trade_signals()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# pristup k pf jako multi index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#pf[0.03].plot()\n",
-    "#pf.order_records\n",
-    "pf[(0.03)].stats()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#zgrupovane statistiky\n",
-    "stats_df = pf.stats([\n",
-    "    'total_return',\n",
-    "    'total_trades',\n",
-    "    'win_rate',\n",
-    "    'expectancy'\n",
-    "], agg_func=None)\n",
-    "stats_df\n",
-    "\n",
-    "\n",
-    "stats_df.nlargest(50, 'Total Return [%]')\n",
-    "#stats_df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pf[(0.0011,0.0013)].plot()\n",
-    "\n",
-    "#pf[(0.0011,0.0013000000000000002)].plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pandas.tseries.offsets import DateOffset\n",
-    "\n",
-    "temp_data = basic_data['2024-4-22']\n",
-    "temp_data\n",
-    "res1m = temp_data[[\"Open\", \"High\", \"Low\", \"Close\", \"Volume\"]]\n",
-    "\n",
-    "# Define a custom date offset that starts at 9:30 AM and spans 4 hours\n",
-    "custom_offset = DateOffset(hours=4, minutes=30)\n",
-    "\n",
-    "# res1m = res1m.get().resample(\"4H\").agg({  \n",
-    "#     \"Open\": \"first\",\n",
-    "#     \"High\": \"max\",\n",
-    "#     \"Low\": \"min\",\n",
-    "#     \"Close\": \"last\",\n",
-    "#     \"Volume\": \"sum\"\n",
-    "# })\n",
-    "\n",
-    "res4h = res1m.resample(\"1h\", resample_kwargs=dict(origin=\"start\"))\n",
-    "\n",
-    "res4h.data\n",
-    "\n",
-    "res15m = res1m.resample(\"15T\", resample_kwargs=dict(origin=\"start\"))\n",
-    "\n",
-    "res15m.data[\"BAC\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@vbt.njit\n",
-    "def long_entry_place_func_nb(c, low, close, time_in_ns, rsi14, window_open, window_close):\n",
-    "    market_open_minutes = 570  # 9 hours * 60 minutes + 30 minutes\n",
-    "\n",
-    "    for out_i in range(len(c.out)):\n",
-    "        i = c.from_i + out_i\n",
-    "\n",
-    "        current_minutes = vbt.dt_nb.hour_nb(time_in_ns[i]) * 60 + vbt.dt_nb.minute_nb(time_in_ns[i])\n",
-    "        #print(\"current_minutes\", current_minutes)\n",
-    "        # Calculate elapsed minutes since market open at 9:30 AM\n",
-    "        elapsed_from_open = current_minutes - market_open_minutes\n",
-    "        elapsed_from_open = elapsed_from_open if elapsed_from_open >= 0 else 0\n",
-    "        #print( \"elapsed_from_open\", elapsed_from_open)\n",
-    "\n",
-    "        #elapsed_from_open = elapsed_minutes_from_open_nb(time_in_ns) \n",
-    "        in_window = elapsed_from_open > window_open and elapsed_from_open < window_close\n",
-    "        #print(\"in_window\", in_window)\n",
-    "        # if in_window:\n",
-    "        #     print(\"in window\")\n",
-    "\n",
-    "        if in_window and rsi14[i] > 60: # and low[i, c.col] <= hit_price: # and hour == 9:  # (4)!\n",
-    "            return out_i\n",
-    "    return -1\n",
-    "\n",
-    "@vbt.njit\n",
-    "def long_exit_place_func_nb(c, high, close, time_index, tp, sl):  # (5)!\n",
-    "    entry_i = c.from_i - c.wait\n",
-    "    entry_price = close[entry_i, c.col]\n",
-    "    hit_price = entry_price * (1 + tp)\n",
-    "    stop_price = entry_price * (1 - sl)\n",
-    "    for out_i in range(len(c.out)):\n",
-    "        i = c.from_i + out_i\n",
-    "        last_bar_of_day = vbt.dt_nb.day_changed_nb(time_index[i], time_index[i + 1])\n",
-    "\n",
-    "        #print(next_day)\n",
-    "        if last_bar_of_day: #pokud je dalsi next day, tak zavirame posledni\n",
-    "            print(\"ted\",out_i)\n",
-    "            return out_i\n",
-    "        if close[i, c.col] >= hit_price or close[i, c.col] <= stop_price :\n",
-    "            return out_i\n",
-    "    return -1\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.DataFrame(np.random.random(size=(5, 10)), columns=list('abcdefghij'))\n",
-    "\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.sum()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

testdoc.md

@@ -1,107 +0,0 @@
-# Plotly
-
-* MAKE_SUBPLOT Defines layout (if more then 1x1 or secondary y axis are required)
-
-```python
-fig = vbt.make_subplots(rows=2, cols=1, shared_xaxes=True, 
-                        specs=[[{"secondary_y": True}], [{"secondary_y": False}]], 
-                        vertical_spacing=0.02, subplot_titles=("Row 1 title", "Row 2 title"))
-```
-
-Then the different [sr/df generic accessor](http://5.161.179.223:8000/static/js/vbt/api/generic/accessors/index.html#vectorbtpro.generic.accessors.GenericAccessor.areaplot) are added with ADD_TRACE_KWARGS and TRACE_KWARGS. Other types of plot available in [plotting module](http://5.161.179.223:8000/static/js/vbt/api/generic/plotting/index.html)
-
-```python
-#using accessor
-close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False,row=1, col=1), trace_kwargs=dict(line=dict(color="blue")))
-indvolume.vbt.barplot(fig=fig, add_trace_kwargs=dict(secondary_y=False, row=2, col=1))
-#using plotting module
-vbt.Bar(indvolume, fig=fig, add_trace_kwargs=dict(secondary_y=False, row=2, col=1))
-```
-
-* ADD_TRACE_KWARGS - determines positioning withing subplot
-```python
-add_trace_kwargs=dict(secondary_y=False,row=1, col=1)
-```
-* TRACE_KWARGS - other styling of trace
-```python
-trace_kwargs=dict(name="LONGS",
-                  line=dict(color="#ffe476"),
-                  marker=dict(color="limegreen"),
-                  fill=None,
-                  connectgaps=True)
-```
-
-## Example
-
-```python
-    fig = vbt.make_subplots(rows=2, cols=1, shared_xaxes=True, 
-                            specs=[[{"secondary_y": True}], [{"secondary_y": False}]], 
-                            vertical_spacing=0.02, subplot_titles=("Price and Indicators", "Volume"))
-
-    # Plotting the close price
-    close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False,row=1, col=1), trace_kwargs=dict(line=dict(color="blue")))
-```
-
-# Data
-## Resampling
-```python
-t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']].resample("1T")
-t1data = t1data.transform(lambda df: df.between_time('09:30', '16:00').dropna()) #main session data only, no nans
-
-t5data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']].resample("5T")
-t5data = t5data.transform(lambda df: df.between_time('09:30', '16:00').dropna())
-
-dailydata = basic_data[['open', 'high', 'low', 'close', 'volume', 'vwap']].resample("D").dropna()
-
-#realign 5min close to 1min so it can be compared with 1min
-t5data_close_realigned = t5data.close.vbt.realign_closing("1T").between_time('09:30', '16:00').dropna() 
-#same with open
-t5data.open.vbt.realign_opening("1h")
-```
-### Define resample function for custom column
-Example of custom feature config [Binance Data](http://5.161.179.223:8000/static/js/vbt/api/data/custom/binance/index.html#vectorbtpro.data.custom.binance.BinanceData.feature_config).
-Other [reduced functions available](http://5.161.179.223:8000/static/js/vbt/api/generic/nb/apply_reduce/index.html). (mean, min, max, median, nth ...)
-```python
-from vectorbtpro.utils.config import merge_dicts, Config, HybridConfig
-from vectorbtpro import _typing as tp
-from vectorbtpro.generic import nb as generic_nb
-
-_feature_config: tp.ClassVar[Config] = HybridConfig(
-    {
-        "buyvolume": dict(
-            resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(
-                resampler,
-                generic_nb.sum_reduce_nb,
-            )
-        ),
-        "sellvolume": dict(
-            resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(
-                resampler,
-                generic_nb.sum_reduce_nb,
-            )
-        )
-    }
-)
-
-basic_data._feature_config = _feature_config
-```
-
-### Validate resample
-```python
-t2dataclose = t2data.close.rename("15MIN - realigned").vbt.realign_closing("1T")
-fig = t1data.close.rename("1MIN").vbt.plot()
-t2data.close.rename("15MIN").vbt.plot(fig=fig)
-t2dataclose.vbt.plot(fig=fig)
-```
-## Persisting
-```python
-basic_data.to_parquet(partition_by="day", compression="gzip")  
-day_data = vbt.ParquetData.pull("BAC", filters=[("group", "==", "2024-05-03")]) 
-vbt.print_dir_tree("BTC-USD")#overeni directory structure
-```
-# Discover
-```python
-vbt.phelp(vbt.talib(“atr”).run) #parameters it accepts
-vbt.pdir(pf) - get available properties and methods
-vbt.pprint(basic_data) #to get correct shape, info about instance
-```

testy/archive/config.py

@@ -1,3 +1,3 @@
-API_KEY = ''
+API_KEY = 'PKGGEWIEYZOVQFDRY70L'
-SECRET_KEY = ''
+SECRET_KEY = 'O5Kt8X4RLceIOvM98i5LdbalItsX7hVZlbPYHy8Y'
 MAX_BATCH_SIZE = 1

testy/fibonaccistoploss.py

@@ -1,9 +1,9 @@
 import numpy as np
-from v2realbot.common.model import Trade, TradeDirection, TradeStatus
+from v2realbot.common.PrescribedTradeModel import Trade, TradeDirection, TradeStatus
 from typing import Tuple
 from copy import deepcopy
 from v2realbot.strategy.base import StrategyState
-from v2realbot.strategyblocks.activetrade.helpers import get_max_profit_price, get_profit_target_price, get_signal_section_directive, keyword_conditions_met
+from v2realbot.strategyblocks.activetrade.helpers import get_max_profit_price, get_profit_target_price, get_override_for_active_trade, keyword_conditions_met
 from v2realbot.utils.utils import safe_get
 # FIBONACCI PRO PROFIT A SL
 
@@ -63,10 +63,10 @@ class SLOptimizer:
 
     def initialize_levels(self, state):
         directive_name = 'SL_opt_exit_levels_'+str(self.direction)
-        SL_opt_exit_levels = get_signal_section_directive(state=state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, None))
+        SL_opt_exit_levels = get_override_for_active_trade(state=state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, None))
 
         directive_name = 'SL_opt_exit_sizes_'+str(self.direction)
-        SL_opt_exit_sizes = get_signal_section_directive(state=state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, None))
+        SL_opt_exit_sizes = get_override_for_active_trade(state=state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, None))
 
         if SL_opt_exit_levels is None or SL_opt_exit_sizes is not None:
             print("no directives found: SL_opt_exit_levels/SL_opt_exit_sizes")

testy/gethistorytrades.py

@@ -1,9 +1,7 @@
 import os,sys
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 print(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-import pandas as pd
+from alpaca.data.historical import CryptoHistoricalDataClient, StockHistoricalDataClient
-import numpy as np
-from alpaca.data.historical import StockHistoricalDataClient
 from alpaca.data.requests import CryptoLatestTradeRequest, StockLatestTradeRequest, StockLatestBarRequest, StockTradesRequest
 from alpaca.data.enums import DataFeed
 from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY

testy/testtt.py

@@ -1,2 +0,0 @@
-import locale
-print(locale.getdefaultlocale())

testy/vectorbt/testHtml2MD.py

@@ -1,66 +0,0 @@
-import os
-from bs4 import BeautifulSoup
-import html2text
-
-def convert_html_to_markdown(html_content, link_mapping):
-    h = html2text.HTML2Text()
-    h.ignore_links = False
-
-    # Update internal links to point to the relevant sections in the Markdown
-    soup = BeautifulSoup(html_content, 'html.parser')
-    for a in soup.find_all('a', href=True):
-        href = a['href']
-        if href in link_mapping:
-            a['href'] = f"#{link_mapping[href]}"
-    
-    return h.handle(str(soup))
-
-def create_link_mapping(root_dir):
-    link_mapping = {}
-    for subdir, _, files in os.walk(root_dir):
-        for file in files:
-            if file == "index.html":
-                relative_path = os.path.relpath(os.path.join(subdir, file), root_dir)
-                chapter_id = relative_path.replace(os.sep, '-').replace('index.html', '')
-                link_mapping[relative_path] = chapter_id
-                link_mapping[relative_path.replace(os.sep, '/')] = chapter_id  # for URLs with slashes
-    return link_mapping
-
-def read_html_files(root_dir, link_mapping):
-    markdown_content = []
-    
-    for subdir, _, files in os.walk(root_dir):
-        relative_path = os.path.relpath(subdir, root_dir)
-        if files and any(file == "index.html" for file in files):
-            # Add directory as a heading based on its depth
-            heading_level = relative_path.count(os.sep) + 1
-            markdown_content.append(f"{'#' * heading_level} {relative_path}\n")
-        
-        for file in files:
-            if file == "index.html":
-                file_path = os.path.join(subdir, file)
-                with open(file_path, 'r', encoding='utf-8') as f:
-                    html_content = f.read()
-                    soup = BeautifulSoup(html_content, 'html.parser')
-                    title = soup.title.string if soup.title else "No Title"
-                    chapter_id = os.path.relpath(file_path, root_dir).replace(os.sep, '-').replace('index.html', '')
-                    markdown_content.append(f"<a id='{chapter_id}'></a>\n")
-                    markdown_content.append(f"{'#' * (heading_level + 1)} {title}\n")
-                    markdown_content.append(convert_html_to_markdown(html_content, link_mapping))
-    
-    return "\n".join(markdown_content)
-
-def save_to_markdown_file(content, output_file):
-    with open(output_file, 'w', encoding='utf-8') as f:
-        f.write(content)
-
-def main():
-    root_dir =  "./v2realbot/static/js/vbt/"
-    output_file = "output.md"
-    link_mapping = create_link_mapping(root_dir)
-    markdown_content = read_html_files(root_dir, link_mapping)
-    save_to_markdown_file(markdown_content, output_file)
-    print(f"Markdown document created at {output_file}")
-
-if __name__ == "__main__":
-    main()

v2realbot/ENTRY_ClassicSL_v01.py

@@ -59,11 +59,24 @@ Hlavní loop:
 
 """
 def next(data, state: StrategyState):
-    print(10*"*", state.account_variables)
+    ##print(10*"*","NEXT START",10*"*")
+    # important vars state.avgp, state.positions, state.vars, data
     
+    #indicators moved to call_next in upper class
+
+    #pokud mame prazdne pozice a neceka se na nic
+    if state.positions == 0 and state.vars.pending is None:
+        #vykoname trady ve fronte
         execute_prescribed_trades(state, data)
+        #pokud se neaktivoval nejaky trade, poustime signal search - ale jen jednou za bar?
+        #if conf_bar == 1:
+        if state.vars.pending is None:
             signal_search(state, data)
-    execute_prescribed_trades(state, data) #pro jistotu ihned zpracujeme
+            #pro jistotu ihned zpracujeme
+            execute_prescribed_trades(state, data)
+
+    #mame aktivni trade a neceka se n anic
+    elif state.vars.activeTrade and state.vars.pending is None:
             manage_active_trade(state, data)
 
 def init(state: StrategyState):
@@ -75,13 +88,13 @@ def init(state: StrategyState):
 
     #nove atributy na rizeni tradu
     #identifikuje provedenou změnu na Tradu (neděláme změny dokud nepřijde potvrzeni z notifikace)
-    #state.vars.pending = None #nahrazeno pebnding pod accountem state.account_variables[account.name].pending
+    state.vars.pending = None
     #obsahuje aktivni Trade a jeho nastaveni
-    #state.vars.activeTrade = None #pending/Trade moved to account_variables
+    state.vars.activeTrade = None #pending/Trade
     #obsahuje pripravene Trady ve frontě
     state.vars.prescribedTrades = []
     #flag pro reversal
-    #state.vars.requested_followup = None #nahrazeno pod accountem
+    state.vars.requested_followup = None
 
     #TODO presunout inicializaci work_dict u podminek - sice hodnoty nepujdou zmenit, ale zlepsi se performance
     #pripadne udelat refresh kazdych x-iterací
@@ -89,8 +102,9 @@ def init(state: StrategyState):
     state.vars.mode = None
     state.vars.last_50_deltas = []
     state.vars.next_new = 0
-    state.vars.last_entry_index = None #mponechano obecne pro vsechny accounty
+    state.vars.last_buy_index = None
-    state.vars.last_exit_index = None #obecna varianta ponechana
+    state.vars.last_exit_index = None
+    state.vars.last_in_index = None
     state.vars.last_update_time = 0
     state.vars.reverse_position_waiting_amount = 0
     #INIT promenne, ktere byly zbytecne ve stratvars

v2realbot/backtesting/backtester.py

@@ -39,7 +39,7 @@
 """
 from uuid import UUID, uuid4
 from alpaca.trading.enums import OrderSide, OrderStatus, TradeEvent, OrderType
-from v2realbot.common.model import TradeUpdate, Order, Account
+from v2realbot.common.model import TradeUpdate, Order
 from rich import print as printanyway
 import threading
 import asyncio
@@ -61,7 +61,6 @@ import dash_bootstrap_components as dbc
 from dash.dependencies import Input, Output
 from dash import dcc, html, dash_table, Dash
 import v2realbot.utils.config_handler as cfh
-from typing import Set
 """"
 LATENCY DELAYS
 .000 trigger - last_trade_time (.4246266)
@@ -75,20 +74,7 @@ lock = threading.Lock
 #todo nejspis dat do classes, aby se mohlo backtestovat paralelne
 #ted je globalni promena last_time_now a self.account a cash
 class Backtester:
-    """
+    def __init__(self, symbol: str, order_fill_callback: callable, btdata: list, bp_from: datetime, bp_to: datetime, cash: float = 100000):
-    Initializes a new instance of the Backtester class.
-    Args:
-        symbol (str): The symbol of the security being backtested.
-        accounts (set): A set of accounts to use for backtesting.
-        order_fill_callback (callable): A callback function to handle order fills.
-        btdata (list): A list of backtesting data.
-        bp_from (datetime): The start date of the backtesting period.
-        bp_to (datetime): The end date of the backtesting period.
-        cash (float, optional): The initial cash balance. Defaults to 100000.
-    Returns:
-        None
-    """
-    def __init__(self, symbol: str, accounts: Set, order_fill_callback: callable, btdata: list, bp_from: datetime, bp_to: datetime, cash: float = 100000):
         #this TIME value determines true time for submit, replace, cancel order should happen (allowing past)
         #it is set by every iteration of BT or before fill callback - allowing past events to happen
         self.time = None
@@ -97,7 +83,6 @@ class Backtester:
         self.btdata = btdata
         self.backtest_start = None
         self.backtest_end = None
-        self.accounts = accounts
         self.cash_init = cash
         #backtesting period
         self.bp_from = bp_from
@@ -105,10 +90,9 @@ class Backtester:
         self.cash = cash
         self.cash_reserved_for_shorting = 0
         self.trades = []
-        self.internal_account = { account.name:{self.symbol: [0, 0]} for account in accounts }
+        self.account = { "BAC": [0, 0] }
-        # { "ACCOUNT1": {}"BAC": [avgp, size]}, .... }
+        # { "BAC": [avgp, size] }
-        self.open_orders =[] #open orders shared for all accounts, account being an attribute
+        self.open_orders =[]
-
         # self.open_orders = [Order(id=uuid4(),
         #                         submitted_at = datetime(2023, 3, 17, 9, 30, 0, 0, tzinfo=zoneNY),
         #                         symbol = "BAC",
@@ -126,8 +110,6 @@ class Backtester:
         #                         side = OrderSide.BUY)]
     
     #
-  
-
     def execute_orders_and_callbacks(self, intime: float):
         """""
         Voláno ze strategie před každou iterací s časem T.
@@ -184,7 +166,7 @@ class Backtester:
 
         for order in self.open_orders:
             #pokud je vyplneny symbol, tak jedeme jen tyto, jinak vsechny
-            print(order.account.name, order.id, datetime.timestamp(order.submitted_at), order.symbol, order.side, order.order_type, order.qty, order.limit_price, order.submitted_at)
+            print(order.id, datetime.timestamp(order.submitted_at), order.symbol, order.side, order.order_type, order.qty, order.limit_price, order.submitted_at)
             if order.canceled_at:
                 #ic("deleting canceled order",order.id)
                 todel.append(order)
@@ -366,22 +348,21 @@ class Backtester:
 
             #ic(o.filled_at, o.filled_avg_price)
 
-            a = self.update_internal_account(o = o)
+            a = self.update_account(o = o)
             if a < 0:
                 tlog("update_account ERROR")
                 return -1
 
-            trade = TradeUpdate(account=o.account,
+            trade = TradeUpdate(order = o,
-                                order = o,
                                 event = TradeEvent.FILL,
                                 execution_id = str(uuid4()),
                                 timestamp = datetime.fromtimestamp(fill_time),
-                                position_qty= self.internal_account[o.account.name][o.symbol][0],
+                                position_qty= self.account[o.symbol][0],
                                 price=float(fill_price),
                                 qty = o.qty,
                                 value = float(o.qty*fill_price),
                                 cash = self.cash,
-                                pos_avg_price = self.internal_account[o.account.name][o.symbol][1])
+                                pos_avg_price = self.account[o.symbol][1])
             
             self.trades.append(trade)
 
@@ -398,49 +379,49 @@ class Backtester:
         self.time = time + float(cfh.config_handler.get_val('BT_DELAYS','fill_to_not'))
         print("current bt.time",self.time)
         #print("FILL NOTIFICATION: ", tradeupdate)
-        res = asyncio.run(self.order_fill_callback(tradeupdate, tradeupdate.account))
+        res = asyncio.run(self.order_fill_callback(tradeupdate))
         return 0
 
-    def update_internal_account(self, o: Order):
+    def update_account(self, o: Order):
         #updatujeme self.account
         #pokud neexistuje klic v accountu vytvorime si ho
-        if o.symbol not in self.internal_account[o.account.name]:
+        if o.symbol not in self.account:
             # { "BAC": [size, avgp] }
-            self.internal_account[o.account.name][o.symbol] = [0,0]
+            self.account[o.symbol] = [0,0]
 
         if o.side == OrderSide.BUY:
             #[size, avgp]
-            newsize = (self.internal_account[o.account.name][o.symbol][0] + o.qty)
+            newsize = (self.account[o.symbol][0] + o.qty)
             #JPLNE UZAVRENI SHORT  (avgp 0)
             if newsize == 0: newavgp = 0
             #CASTECNE UZAVRENI SHORT (avgp puvodni)
-            elif newsize < 0: newavgp = self.internal_account[o.account.name][o.symbol][1]
+            elif newsize < 0: newavgp = self.account[o.symbol][1]
             #JDE O LONG (avgp nove)
             else:
-                newavgp = ((self.internal_account[o.account.name][o.symbol][0] * self.internal_account[o.account.name][o.symbol][1]) + (o.qty * o.filled_avg_price)) / (self.internal_account[o.account.name][o.symbol][0] + o.qty)
+                newavgp = ((self.account[o.symbol][0] * self.account[o.symbol][1]) + (o.qty * o.filled_avg_price)) / (self.account[o.symbol][0] + o.qty)
             
-            self.internal_account[o.account.name][o.symbol] = [newsize, newavgp]
+            self.account[o.symbol] = [newsize, newavgp]
             self.cash = self.cash - (o.qty * o.filled_avg_price)
             return 1
         #sell
         elif o.side == OrderSide.SELL:
-            newsize = self.internal_account[o.account.name][o.symbol][0]-o.qty
+            newsize = self.account[o.symbol][0]-o.qty
             #UPLNE UZAVRENI LONGU (avgp 0)
             if newsize == 0: newavgp = 0
             #CASTECNE UZAVRENI LONGU (avgp puvodni)
-            elif newsize > 0: newavgp = self.internal_account[o.account.name][o.symbol][1]
+            elif newsize > 0: newavgp = self.account[o.symbol][1]
             #jde o SHORT (avgp nove)
             else:
                 #pokud je predchozi 0 - tzn. jde o prvni short
-                if self.internal_account[o.account.name][o.symbol][1] == 0:
+                if self.account[o.symbol][1] == 0:
                     newavgp = o.filled_avg_price
                 else:
-                    newavgp = ((abs(self.internal_account[o.account.name][o.symbol][0]) * self.internal_account[o.account.name][o.symbol][1]) + (o.qty * o.filled_avg_price)) / (abs(self.internal_account[o.account.name][o.symbol][0]) + o.qty)
+                    newavgp = ((abs(self.account[o.symbol][0]) * self.account[o.symbol][1]) + (o.qty * o.filled_avg_price)) / (abs(self.account[o.symbol][0]) + o.qty)
 
-            self.internal_account[o.account.name][o.symbol] = [newsize, newavgp]
+            self.account[o.symbol] = [newsize, newavgp]
 
             #pokud jde o prodej longu(nova pozice je>=0) upravujeme cash
-            if self.internal_account[o.account.name][o.symbol][0] >= 0:
+            if self.account[o.symbol][0] >= 0:
                 self.cash = float(self.cash + (o.qty * o.filled_avg_price))
                 print("uprava cashe, jde o prodej longu")
             else:
@@ -485,7 +466,7 @@ class Backtester:
         # #ic("get last price")
         # return self.btdata[i-1][1]
 
-    def submit_order(self, time: float, symbol: str, side: OrderSide, size: int, order_type: OrderType, account: Account, price: float = None):
+    def submit_order(self, time: float, symbol: str, side: OrderSide, size: int, order_type: OrderType, price: float = None):
         """submit order
         - zakladni validace
         - vloží do self.open_orders s daným časem
@@ -518,9 +499,9 @@ class Backtester:
             return -1
     
         #pokud neexistuje klic v accountu vytvorime si ho
-        if symbol not in self.internal_account[account.name]:
+        if symbol not in self.account:
             # { "BAC": [size, avgp] }
-            self.internal_account[account.name][symbol] = [0,0]
+            self.account[symbol] = [0,0]
 
         #check for available quantity
         if side == OrderSide.SELL:
@@ -528,15 +509,15 @@ class Backtester:
             reserved_price = 0
             #with lock:
             for o in self.open_orders:
-                if o.side == OrderSide.SELL and o.symbol == symbol and o.canceled_at is None and o.account==account:
+                if o.side == OrderSide.SELL and o.symbol == symbol and o.canceled_at is None:
                     reserved += o.qty
                     cena = o.limit_price if o.limit_price else self.get_last_price(time, o.symbol)
                     reserved_price += o.qty * cena
                 print("blokovano v open orders pro sell qty: ", reserved, "celkem:", reserved_price)
             
-            actual_minus_reserved = int(self.internal_account[account.name][symbol][0]) - reserved 
+            actual_minus_reserved = int(self.account[symbol][0]) - reserved 
             if actual_minus_reserved > 0 and actual_minus_reserved - int(size) < 0:
-                printanyway("not enough shares available to sell or shorting while long position",self.internal_account[account.name][symbol][0],"reserved",reserved,"available",int(self.internal_account[account.name][symbol][0]) - reserved,"selling",size)
+                printanyway("not enough shares available to sell or shorting while long position",self.account[symbol][0],"reserved",reserved,"available",int(self.account[symbol][0]) - reserved,"selling",size)
                 return -1
             
             #if is shorting - check available cash to short
@@ -552,13 +533,13 @@ class Backtester:
             reserved_price = 0
             #with lock:
             for o in self.open_orders:
-                if o.side == OrderSide.BUY and o.canceled_at is None and o.account==account:
+                if o.side == OrderSide.BUY and o.canceled_at is None:
                     cena = o.limit_price if o.limit_price else self.get_last_price(time, o.symbol)
                     reserved_price += o.qty * cena
                     reserved_qty += o.qty
                     print("blokovano v open orders for buy: qty, price", reserved_qty, reserved_price)
 
-            actual_plus_reserved_qty = int(self.internal_account[account.name][symbol][0]) + reserved_qty
+            actual_plus_reserved_qty = int(self.account[symbol][0]) + reserved_qty
             
             #jde o uzavreni shortu
             if actual_plus_reserved_qty < 0 and (actual_plus_reserved_qty + int(size)) > 0:
@@ -574,7 +555,6 @@ class Backtester:
 
         id = str(uuid4())
         order = Order(id=id,
-                    account=account,
                     submitted_at = datetime.fromtimestamp(float(time)),
                     symbol=symbol,
                     order_type = order_type,
@@ -589,7 +569,7 @@ class Backtester:
         return id
 
 
-    def replace_order(self, id: str, time: float, account: Account, size: int = None, price: float = None):
+    def replace_order(self, id: str, time: float, size: int = None, price: float = None):
         """replace order
         - zakladni validace vrací synchronně
         - vrací číslo nové objednávky
@@ -606,7 +586,7 @@ class Backtester:
         #with lock:
         for o in self.open_orders:
             print(o.id)
-            if str(o.id) == str(id) and o.account == account:
+            if str(o.id) == str(id):
                 newid = str(uuid4())
                 o.id = newid
                 o.submitted_at = datetime.fromtimestamp(time)
@@ -617,7 +597,7 @@ class Backtester:
         print("BT: replacement order doesnt exist")
         return 0
     
-    def cancel_order(self, time: float, id: str, account: Account):
+    def cancel_order(self, time: float, id: str):
         """cancel order
         - základní validace vrací synchronně
         - vymaže objednávku z open orders
@@ -633,22 +613,22 @@ class Backtester:
             return 0
         #with lock:
         for o in self.open_orders:
-            if str(o.id) == id and o.account == account:
+            if str(o.id) == id:
                 o.canceled_at = time
                 print("set as canceled in self.open_orders")
                 return 1
         print("BTC: cantchange. open order doesnt exist")
         return 0
     
-    def get_open_position(self, symbol: str, account: Account):
+    def get_open_position(self, symbol: str):
         """get positions ->(avg,size)"""
         #print("BT:get open positions entry")
         try:
-            return self.internal_account[account.name][symbol][1], self.internal_account[account.name][symbol][0]
+            return self.account[symbol][1], self.account[symbol][0]
         except:
             return (0,0)
 
-    def get_open_orders(self, side: OrderSide, symbol: str, account: Account):
+    def get_open_orders(self, side: OrderSide, symbol: str):
         """get open orders ->list(OrderNotification)""" 
         print("BT:get open orders entry")
         if len(self.open_orders) == 0:
@@ -658,7 +638,7 @@ class Backtester:
         #with lock:
         for o in self.open_orders:
             #print(o)
-            if o.symbol == symbol and o.canceled_at is None and o.account == account:
+            if o.symbol == symbol and o.canceled_at is None:
                 if side is None or o.side == side:
                     res.append(o)
         return res

v2realbot/common/PrescribedTradeModel.py

@@ -0,0 +1,41 @@
+from enum import Enum
+from datetime import datetime
+from pydantic import BaseModel
+from typing import Any, Optional, List, Union
+from uuid import UUID
+class TradeStatus(str, Enum):
+    READY = "ready"
+    ACTIVATED = "activated"
+    CLOSED = "closed"
+    #FINISHED = "finished"
+
+class TradeDirection(str, Enum):
+    LONG = "long"
+    SHORT = "short"
+
+class TradeStoplossType(str, Enum):
+    FIXED = "fixed"
+    TRAILING = "trailing"
+
+#Predpis obchodu vygenerovany signalem, je to zastresujici jednotka
+#ke kteremu jsou pak navazany jednotlivy FILLy (reprezentovany model.TradeUpdate) - napr. castecne exity atp.
+class Trade(BaseModel):
+    id: UUID
+    last_update: datetime
+    entry_time: Optional[datetime] = None
+    exit_time: Optional[datetime] = None
+    status: TradeStatus
+    generated_by: Optional[str] = None
+    direction: TradeDirection
+    entry_price: Optional[float] = None
+    goal_price: Optional[float] = None
+    size: Optional[int] = None
+    # size_multiplier je pomocna promenna pro pocitani relativniho denniho profit
+    size_multiplier: Optional[float] = None    
+    # stoploss_type: TradeStoplossType
+    stoploss_value: Optional[float] = None
+    profit: Optional[float] = 0
+    profit_sum: Optional[float] = 0
+    rel_profit: Optional[float] = 0
+    rel_profit_cum: Optional[float] = 0
+    

v2realbot/common/model.py

@@ -5,75 +5,10 @@ from rich import print
 from typing import Any, Optional, List, Union
 from datetime import datetime, date
 from pydantic import BaseModel, Field
-from v2realbot.enums.enums import Mode, Account, SchedulerStatus, Moddus, Market, Followup
+from v2realbot.enums.enums import Mode, Account, SchedulerStatus, Moddus
 from alpaca.data.enums import Exchange
-from enum import Enum
-from datetime import datetime
-from pydantic import BaseModel
-from typing import Any, Optional, List, Union
-from uuid import UUID
 
 
-#prescribed model
-#from prescribed model
-class InstantIndicator(BaseModel):
-    name: str
-    toml: str
-
-
-class TradeStatus(str, Enum):
-    READY = "ready"
-    ACTIVATED = "activated"
-    CLOSED = "closed"
-    #FINISHED = "finished"
-
-class TradeDirection(str, Enum):
-    LONG = "long"
-    SHORT = "short"
-
-class TradeStoplossType(str, Enum):
-    FIXED = "fixed"
-    TRAILING = "trailing"
-
-#Predpis obchodu vygenerovany signalem, je to zastresujici jednotka
-#ke kteremu jsou pak navazany jednotlivy FILLy (reprezentovany model.TradeUpdate) - napr. castecne exity atp.
-class Trade(BaseModel):
-    account: Account
-    id: UUID
-    last_update: datetime
-    entry_time: Optional[datetime] = None
-    exit_time: Optional[datetime] = None
-    status: TradeStatus
-    generated_by: Optional[str] = None
-    direction: TradeDirection
-    entry_price: Optional[float] = None
-    goal_price: Optional[float] = None
-    size: Optional[int] = None
-    # size_multiplier je pomocna promenna pro pocitani relativniho denniho profit
-    size_multiplier: Optional[float] = None    
-    # stoploss_type: TradeStoplossType
-    stoploss_value: Optional[float] = None
-    profit: Optional[float] = 0
-    profit_sum: Optional[float] = 0
-    rel_profit: Optional[float] = 0
-    rel_profit_cum: Optional[float] = 0
-
-#account variables that can be accessed by ACCOUNT key dictionary
-class AccountVariables(BaseModel):
-    positions: float = 0
-    avgp: float = 0
-    pending: str = None
-    blockbuy: int = 0
-    wait_for_fill: float = None
-    profit: float = 0
-    docasny_rel_profit: list = []
-    rel_profit_cum: list = []
-    last_entry_index: int = None #acc varianta, mame taky obnecnou state.vars.last_entry_index
-    requested_followup: Followup = None
-    activeTrade: Trade = None
-    dont_exit_already_activated: bool = False
-    #activeTrade, prescribedTrades
-    #tbd transferables?
 
 
 #models for server side datatables
@@ -156,7 +91,7 @@ class TestList(BaseModel):
     dates: List[Intervals]
 
 #for GUI to fetch historical trades on given symbol
-class TradeView(BaseModel):
+class Trade(BaseModel):
     symbol: str
     timestamp: datetime
     exchange: Optional[Union[Exchange, str]] = None
@@ -224,7 +159,6 @@ class RunManagerRecord(BaseModel):
     mode: Mode
     note: Optional[str] = None
     ilog_save: bool = False
-    market: Optional[Market] = Market.US
     bt_from: Optional[datetime] = None
     bt_to: Optional[datetime] = None
     #weekdays filter
@@ -254,8 +188,8 @@ class RunnerView(BaseModel):
     run_symbol: Optional[str] = None
     run_trade_count: Optional[int] = 0
     run_profit: Optional[float] = 0
-    run_positions: Optional[dict] = 0
+    run_positions: Optional[int] = 0
-    run_avgp: Optional[dict] = 0
+    run_avgp: Optional[float] = 0
     run_stopped: Optional[datetime] = None
     run_paused: Optional[datetime] = None    
  
@@ -273,8 +207,8 @@ class Runner(BaseModel):
     run_ilog_save: Optional[bool] = False
     run_trade_count: Optional[int] = None
     run_profit: Optional[float] = None
-    run_positions: Optional[dict] = None
+    run_positions: Optional[int] = None
-    run_avgp: Optional[dict] = None
+    run_avgp: Optional[float] = None
     run_strat_json: Optional[str] = None
     run_stopped: Optional[datetime] = None
     run_paused: Optional[datetime] = None   
@@ -312,7 +246,6 @@ class Bar(BaseModel):
     vwap: Optional[float] = 0
 
 class Order(BaseModel):
-    account: Account
     id: UUID
     submitted_at: datetime
     filled_at: Optional[datetime] = None
@@ -328,7 +261,6 @@ class Order(BaseModel):
 
 #entita pro kazdy kompletni FILL, je navazana na prescribed_trade 
 class TradeUpdate(BaseModel):
-    account: Account
     event: Union[TradeEvent, str]
     execution_id: Optional[UUID] = None
     order: Order
@@ -374,8 +306,8 @@ class RunArchive(BaseModel):
     ilog_save: Optional[bool] = False
     profit: float = 0
     trade_count: int = 0
-    end_positions: Union[dict,str] = None
+    end_positions: int = 0
-    end_positions_avgp: Union[dict,str] = None
+    end_positions_avgp: float = 0
     metrics: Union[dict, str] = None
     stratvars_toml: Optional[str] = None
 
@@ -396,8 +328,8 @@ class RunArchiveView(BaseModel):
     ilog_save: Optional[bool] = False
     profit: float = 0
     trade_count: int = 0
-    end_positions: Union[dict,int] = None
+    end_positions: int = 0
-    end_positions_avgp: Union[dict,float] = None
+    end_positions_avgp: float = 0
     metrics: Union[dict, str] = None
     batch_profit: float = 0  # Total profit for the batch - now calculated during query
     batch_count: int = 0  # Count of runs in the batch - now calculated during query
@@ -414,8 +346,6 @@ class SLHistory(BaseModel):
     id: Optional[UUID] = None
     time: datetime
     sl_val: float
-    direction: TradeDirection
-    account: Account
 
 #Contains archive of running strategies (runner) - detail data
 class RunArchiveDetail(BaseModel):
@@ -428,3 +358,9 @@ class RunArchiveDetail(BaseModel):
     trades: List[TradeUpdate]
     ext_data: Optional[dict] = None
     
+
+class InstantIndicator(BaseModel):
+    name: str
+    toml: str
+
+

v2realbot/common/transform.py

@@ -5,7 +5,9 @@ import v2realbot.controller.services as cs
 
 #prevede dict radku zpatky na objekt vcetme retypizace
 def row_to_runmanager(row: dict) -> RunManagerRecord:
+
     is_running = cs.is_runner_running(row['runner_id']) if row['runner_id'] else False
+
     res = RunManagerRecord(
         moddus=row['moddus'],
         id=row['id'],
@@ -15,7 +17,6 @@ def row_to_runmanager(row: dict) -> RunManagerRecord:
         account=row['account'],
         note=row['note'],
         ilog_save=bool(row['ilog_save']),
-        market=row['market'] if row['market'] is not None else None,
         bt_from=datetime.fromisoformat(row['bt_from']) if row['bt_from'] else None,
         bt_to=datetime.fromisoformat(row['bt_to']) if row['bt_to'] else None,
         weekdays_filter=[int(x) for x in row['weekdays_filter'].split(',')] if row['weekdays_filter'] else [],
@@ -51,8 +52,8 @@ def row_to_runarchiveview(row: dict) -> RunArchiveView:
         ilog_save=bool(row['ilog_save']),
         profit=float(row['profit']),
         trade_count=int(row['trade_count']),
-        end_positions=orjson.loads(row['end_positions']),
+        end_positions=int(row['end_positions']),
-        end_positions_avgp=orjson.loads(row['end_positions_avgp']),
+        end_positions_avgp=float(row['end_positions_avgp']),
         metrics=orjson.loads(row['metrics']) if row['metrics'] else None,
         batch_profit=int(row['batch_profit']) if row['batch_profit'] and row['batch_id'] else 0,
         batch_count=int(row['batch_count']) if row['batch_count'] and row['batch_id'] else 0,
@@ -79,8 +80,8 @@ def row_to_runarchive(row: dict) -> RunArchive:
         ilog_save=bool(row['ilog_save']),
         profit=float(row['profit']),
         trade_count=int(row['trade_count']),
-        end_positions=str(row['end_positions']),
+        end_positions=int(row['end_positions']),
-        end_positions_avgp=str(row['end_positions_avgp']),
+        end_positions_avgp=float(row['end_positions_avgp']),
         metrics=orjson.loads(row['metrics']),
         stratvars_toml=row['stratvars_toml'],
         transferables=orjson.loads(row['transferables']) if row['transferables'] else None

v2realbot/config.py

@@ -4,7 +4,6 @@ from appdirs import user_data_dir
 from pathlib import Path
 import os
 from collections import defaultdict
-from dotenv import load_dotenv
 # Global flag to track if the ml module has been imported (solution for long import times of tensorflow)
 #the first occurence of using it will load it globally
 _ml_module_loaded = False
@@ -27,32 +26,6 @@ MODEL_DIR = Path(DATA_DIR)/"models"
 PROFILING_NEXT_ENABLED = False
 PROFILING_OUTPUT_DIR = DATA_DIR
 
-def find_dotenv(start_path):
-    """
-    Searches for a .env file in the given directory or its parents and returns the path.
-
-    Args:
-        start_path: The directory to start searching from.
-
-    Returns:
-        Path to the .env file if found, otherwise None.
-    """
-    current_path = Path(start_path)
-    for _ in range(6):  # Limit search depth to 5 levels
-        dotenv_path = current_path / '.env'
-        if dotenv_path.exists():
-            return dotenv_path
-        current_path = current_path.parent
-    return None
-
-ENV_FILE = find_dotenv(__file__)
-
-#NALOADUJEME DOTENV ENV VARIABLES
-if load_dotenv(ENV_FILE, verbose=True) is False:
-    print(f"Error loading.env file {ENV_FILE}. Now depending on ENV VARIABLES set externally.")
-else:
-    print(f"Loaded env variables from file {ENV_FILE}")
-
 #WIP - FILL CONFIGURATION CLASS FOR BACKTESTING
 class BT_FILL_CONF:
     """"
@@ -83,10 +56,10 @@ def get_key(mode: Mode, account: Account):
         return None
     dict = globals()
     try:
-        API_KEY = dict[str.upper(str(account.name)) + "_" + str.upper(str(mode.name)) + "_API_KEY" ]
+        API_KEY = dict[str.upper(str(account.value)) + "_" + str.upper(str(mode.value)) + "_API_KEY" ]
-        SECRET_KEY = dict[str.upper(str(account.name)) + "_" + str.upper(str(mode.name)) + "_SECRET_KEY" ]
+        SECRET_KEY = dict[str.upper(str(account.value)) + "_" + str.upper(str(mode.value)) + "_SECRET_KEY" ]
-        PAPER = dict[str.upper(str(account.name)) + "_" + str.upper(str(mode.name)) + "_PAPER" ]
+        PAPER = dict[str.upper(str(account.value)) + "_" + str.upper(str(mode.value)) + "_PAPER" ]
-        FEED = dict[str.upper(str(account.name)) + "_" + str.upper(str(mode.name)) + "_FEED" ]
+        FEED = dict[str.upper(str(account.value)) + "_" + str.upper(str(mode.value)) + "_FEED" ]
         return Keys(API_KEY, SECRET_KEY, PAPER, FEED)
     except KeyError:
         print("Not valid combination to get keys for", mode, account)
@@ -95,7 +68,7 @@ def get_key(mode: Mode, account: Account):
 #strategy instance main loop heartbeat
 HEARTBEAT_TIMEOUT=5
 
-WEB_API_KEY=os.environ.get('WEB_API_KEY')
+WEB_API_KEY="david"
 
 #PRIMARY PAPER
 ACCOUNT1_PAPER_API_KEY = os.environ.get('ACCOUNT1_PAPER_API_KEY')
@@ -110,7 +83,7 @@ data_feed_type_str = os.environ.get('ACCOUNT1_PAPER_FEED', 'iex')  # Default to
 # Convert the string to DataFeed enum
 try:
     ACCOUNT1_PAPER_FEED = DataFeed(data_feed_type_str)
-except nameError:
+except ValueError:
     # Handle the case where the environment variable does not match any enum member
     print(f"Invalid data feed type: {data_feed_type_str} in ACCOUNT1_PAPER_FEED defaulting to 'iex'")
     ACCOUNT1_PAPER_FEED = DataFeed.SIP
@@ -128,7 +101,7 @@ data_feed_type_str = os.environ.get('ACCOUNT1_LIVE_FEED', 'iex')  # Default to '
 # Convert the string to DataFeed enum
 try:
     ACCOUNT1_LIVE_FEED = DataFeed(data_feed_type_str)
-except nameError:
+except ValueError:
     # Handle the case where the environment variable does not match any enum member
     print(f"Invalid data feed type: {data_feed_type_str} in ACCOUNT1_LIVE_FEED defaulting to 'iex'")
     ACCOUNT1_LIVE_FEED = DataFeed.IEX
@@ -146,7 +119,7 @@ data_feed_type_str = os.environ.get('ACCOUNT2_PAPER_FEED', 'iex')  # Default to
 # Convert the string to DataFeed enum
 try:
     ACCOUNT2_PAPER_FEED = DataFeed(data_feed_type_str)
-except nameError:
+except ValueError:
     # Handle the case where the environment variable does not match any enum member
     print(f"Invalid data feed type: {data_feed_type_str} in ACCOUNT2_PAPER_FEED defaulting to 'iex'")
     ACCOUNT2_PAPER_FEED = DataFeed.IEX
@@ -165,7 +138,7 @@ except nameError:
 # # Convert the string to DataFeed enum
 # try:
 #     ACCOUNT2_LIVE_FEED = DataFeed(data_feed_type_str)
-# except nameError:
+# except ValueError:
 #     # Handle the case where the environment variable does not match any enum member
 #     print(f"Invalid data feed type: {data_feed_type_str} in ACCOUNT2_LIVE_FEED defaulting to 'iex'")
 #     ACCOUNT2_LIVE_FEED = DataFeed.IEX

v2realbot/controller/run_manager.py

@@ -3,7 +3,7 @@ from uuid import UUID, uuid4
 from v2realbot.common.model import RunManagerRecord, StrategyInstance, RunDay, StrategyInstance, Runner, RunRequest, RunArchive, RunArchiveView, RunArchiveViewPagination, RunArchiveDetail, RunArchiveChange, Bar, TradeEvent, TestList, Intervals, ConfigItem, InstantIndicator, DataTablesRequest
 from v2realbot.utils.utils import validate_and_format_time, AttributeDict, zoneNY, zonePRG, safe_get, dict_replace_value, Store, parse_toml_string, json_serial, is_open_hours, send_to_telegram, concatenate_weekdays, transform_data
 from v2realbot.utils.ilog import delete_logs
-from v2realbot.common.model import Trade, TradeDirection, TradeStatus, TradeStoplossType
+from v2realbot.common.PrescribedTradeModel import Trade, TradeDirection, TradeStatus, TradeStoplossType
 from datetime import datetime
 from v2realbot.loader.trade_offline_streamer import Trade_Offline_Streamer
 from threading import Thread, current_thread, Event, enumerate
@@ -172,14 +172,14 @@ def add_run_manager_record(new_record: RunManagerRecord):
         # Construct a suitable INSERT query based on your RunManagerRecord fields
         insert_query = """
             INSERT INTO run_manager (moddus, id, strat_id, symbol,account, mode, note,ilog_save,
-                                    market, bt_from, bt_to, weekdays_filter, batch_id,
+                                    bt_from, bt_to, weekdays_filter, batch_id,
                                     start_time, stop_time, status, last_processed,
                                     history, valid_from, valid_to, testlist_id)
-            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?,?) 
+            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) 
         """
         values = [
             new_record.moddus, str(new_record.id), str(new_record.strat_id), new_record.symbol, new_record.account, new_record.mode, new_record.note,
-            int(new_record.ilog_save), new_record.market,
+            int(new_record.ilog_save),
             new_record.bt_from.isoformat() if new_record.bt_from is not None else None,
             new_record.bt_to.isoformat() if new_record.bt_to is not None else None,
             ",".join(str(x) for x in new_record.weekdays_filter) if new_record.weekdays_filter else None,

v2realbot/controller/services.py

@@ -8,9 +8,9 @@ from alpaca.data.timeframe import TimeFrame
 from v2realbot.strategy.base import StrategyState
 from v2realbot.enums.enums import RecordType, StartBarAlign, Mode, Account, OrderSide
 from v2realbot.common.model import RunDay, StrategyInstance, Runner, RunRequest, RunArchive, RunArchiveView, RunArchiveViewPagination, RunArchiveDetail, RunArchiveChange, Bar, TradeEvent, TestList, Intervals, ConfigItem, InstantIndicator, DataTablesRequest
-from v2realbot.utils.utils import AttributeDict, zoneNY, zonePRG, safe_get, dict_replace_value, Store, parse_toml_string, json_serial, is_open_hours, send_to_telegram, concatenate_weekdays, transform_data, gaka
+from v2realbot.utils.utils import AttributeDict, zoneNY, zonePRG, safe_get, dict_replace_value, Store, parse_toml_string, json_serial, is_open_hours, send_to_telegram, concatenate_weekdays, transform_data
 from v2realbot.utils.ilog import delete_logs
-from v2realbot.common.model import Trade, TradeDirection, TradeStatus, TradeStoplossType
+from v2realbot.common.PrescribedTradeModel import Trade, TradeDirection, TradeStatus, TradeStoplossType
 from datetime import datetime
 from v2realbot.loader.trade_offline_streamer import Trade_Offline_Streamer
 from threading import Thread, current_thread, Event, enumerate
@@ -71,8 +71,8 @@ def get_all_runners():
             if i.run_instance:
                 i.run_profit = round(float(i.run_instance.state.profit),2)
                 i.run_trade_count = len(i.run_instance.state.tradeList)
-                i.run_positions = gaka(i.run_instance.state.account_variables, "positions")
+                i.run_positions = i.run_instance.state.positions
-                i.run_avgp = gaka(i.run_instance.state.account_variables, "avgp", lambda x: round(float(x),3))
+                i.run_avgp = round(float(i.run_instance.state.avgp),3)
         return (0, db.runners)
     else:
         return (0, [])
@@ -94,8 +94,8 @@ def get_runner(id: UUID):
         if str(i.id) == str(id):
             i.run_profit = round(float(i.run_instance.state.profit),2)
             i.run_trade_count = len(i.run_instance.state.tradeList)
-            i.run_positions =gaka(i.run_instance.state.account_variables, "positions")
+            i.run_positions = i.run_instance.state.positions
-            i.run_avgp = gaka(i.run_instance.state.account_variables, "avgp", lambda x: round(float(x),3))
+            i.run_avgp = round(float(i.run_instance.state.avgp),3)
             return (0, i)
     return (-2, "not found")
 
@@ -738,14 +738,13 @@ def populate_metrics_output_directory(strat: StrategyInstance, inter_batch_param
 
     tradeList = strat.state.tradeList
 
-    trade_dict = AttributeDict(account=[],orderid=[],timestamp=[],symbol=[],side=[],order_type=[],qty=[],price=[],position_qty=[])
+    trade_dict = AttributeDict(orderid=[],timestamp=[],symbol=[],side=[],order_type=[],qty=[],price=[],position_qty=[])
     if strat.mode == Mode.BT:
         trade_dict["value"] = []
         trade_dict["cash"] = []
         trade_dict["pos_avg_price"] = []
     for t in tradeList:
         if t.event == TradeEvent.FILL:
-            trade_dict.account.append(t.account)
             trade_dict.orderid.append(str(t.order.id))
             trade_dict.timestamp.append(t.timestamp)
             trade_dict.symbol.append(t.order.symbol)
@@ -769,12 +768,10 @@ def populate_metrics_output_directory(strat: StrategyInstance, inter_batch_param
     max_positions = max_positions[max_positions['side'] == OrderSide.SELL]
     max_positions = max_positions.drop(columns=['side'], axis=1)
 
-    res = dict(account_variables={}, profit={})
+    res = dict(profit={})
     #filt = max_positions['side'] == 'OrderSide.BUY'
-
-    res["account_variables"] = transform_data(strat.state.account_variables, json_serial)
-
     res["pos_cnt"] = dict(zip(str(max_positions['qty']), max_positions['count']))
+
     #naplneni batch sum profitu
     if inter_batch_params is not None:
         res["profit"]["batch_sum_profit"] = int(inter_batch_params["batch_profit"])
@@ -926,8 +923,8 @@ def archive_runner(runner: Runner, strat: StrategyInstance, inter_batch_params:
                                             settings = settings,
                                             profit=round(float(strat.state.profit),2),
                                             trade_count=len(strat.state.tradeList),
-                                            end_positions=gaka(strat.state.account_variables, "positions"),
+                                            end_positions=strat.state.positions,
-                                            end_positions_avgp=gaka(strat.state.account_variables, "avgp", lambda x: round(float(x),3)),
+                                            end_positions_avgp=round(float(strat.state.avgp),3),
                                             metrics=results_metrics,
                                             stratvars_toml=runner.run_stratvars_toml,
                                             transferables=strat.state.vars["transferables"]
@@ -1267,7 +1264,6 @@ def insert_archive_header(archeader: RunArchive):
     try:
         c = conn.cursor()
         #json_string = orjson.dumps(archeader, default=json_serial, option=orjson.OPT_PASSTHROUGH_DATETIME)
-        #print(archeader)
 
         res = c.execute("""
             INSERT INTO runner_header 
@@ -1275,7 +1271,7 @@ def insert_archive_header(archeader: RunArchive):
             VALUES
             (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
             """,
-            (str(archeader.id), str(archeader.strat_id), archeader.batch_id, archeader.symbol, archeader.name, archeader.note, archeader.started, archeader.stopped, archeader.mode, archeader.account, archeader.bt_from, archeader.bt_to, orjson.dumps(archeader.strat_json).decode('utf-8'), orjson.dumps(archeader.settings).decode('utf-8'), archeader.ilog_save, archeader.profit, archeader.trade_count, orjson.dumps(archeader.end_positions).decode('utf-8'), orjson.dumps(archeader.end_positions_avgp).decode('utf-8'), orjson.dumps(archeader.metrics, default=json_serial, option=orjson.OPT_PASSTHROUGH_DATETIME).decode('utf-8'), archeader.stratvars_toml, orjson.dumps(archeader.transferables).decode('utf-8')))
+            (str(archeader.id), str(archeader.strat_id), archeader.batch_id, archeader.symbol, archeader.name, archeader.note, archeader.started, archeader.stopped, archeader.mode, archeader.account, archeader.bt_from, archeader.bt_to, orjson.dumps(archeader.strat_json).decode('utf-8'), orjson.dumps(archeader.settings).decode('utf-8'), archeader.ilog_save, archeader.profit, archeader.trade_count, archeader.end_positions, archeader.end_positions_avgp, orjson.dumps(archeader.metrics, default=json_serial, option=orjson.OPT_PASSTHROUGH_DATETIME).decode('utf-8'), archeader.stratvars_toml, orjson.dumps(archeader.transferables).decode('utf-8')))
 
         #retry not yet supported for statement format above
         #res = execute_with_retry(c,statement)
@@ -1650,14 +1646,13 @@ def preview_indicator_byTOML(id: UUID, indicator: InstantIndicator, save: bool =
         new_inds = AttributeDict(**new_inds)
         new_tick_inds = {key: [] for key in detail.indicators[1].keys()}
         new_tick_inds = AttributeDict(**new_tick_inds)
-        def_account = Account("ACCOUNT1")
+        interface = BacktestInterface(symbol="X", bt=None)
-        interface = BacktestInterface(symbol="X", bt=None, account=def_account)
 
         ##dame nastaveni indikatoru do tvaru, ktery stratvars ocekava (pro dynmaicke inicializace)
         stratvars = AttributeDict(indicators=AttributeDict(**{jmeno:toml_parsed}))
         #print("stratvars", stratvars)
 
-        state = StrategyState(name="XX", symbol = "X", stratvars = AttributeDict(**stratvars), interface=interface, accounts=[def_account], account=def_account)
+        state = StrategyState(name="XX", symbol = "X", stratvars = AttributeDict(**stratvars), interface=interface)
 
         #inicializujeme stavove promenne a novy indikator v cilovem dict
         if output == "bar":

v2realbot/enums/enums.py

@@ -1,11 +1,6 @@
 from enum import Enum
 from alpaca.trading.enums import OrderSide, OrderStatus, OrderType
 
-class BarType(str, Enum):
-    TIME = "time"
-    VOLUME = "volume"
-    DOLLAR = "dollar"
-
 class Env(str, Enum):
     PROD = "prod"
     TEST = "test"
@@ -109,9 +104,3 @@ class StartBarAlign(str, Enum):
     """ 
     ROUND = "round"
     RANDOM = "random"
-
-class Market(str, Enum):
-    US = "US"
-    CRYPTO = "CRYPTO"
-
-     

v2realbot/interfaces/backtest_interface.py

@@ -5,7 +5,6 @@ from v2realbot.backtesting.backtester import Backtester
 from datetime import datetime
 from v2realbot.utils.utils import zoneNY
 import v2realbot.utils.config_handler as cfh
-from v2realbot.common.model import Account
 
 """"
 backtester methods can be called
@@ -17,9 +16,8 @@ both should be backtestable
 if method are called for the past self.time must be set accordingly
 """
 class BacktestInterface(GeneralInterface):
-    def __init__(self, symbol, bt: Backtester, account: Account) -> None:
+    def __init__(self, symbol, bt: Backtester) -> None:
         self.symbol = symbol
-        self.account = account
         self.bt = bt
         self.count_api_requests = cfh.config_handler.get_val('COUNT_API_REQUESTS')
         self.mincnt = list([dict(minute=0,count=0)])
@@ -45,48 +43,48 @@ class BacktestInterface(GeneralInterface):
     def buy(self, size = 1, repeat: bool = False):
         self.count()
         #add REST API latency
-        return self.bt.submit_order(time=self.bt.time + cfh.config_handler.get_val('BT_DELAYS','strat_to_sub'),symbol=self.symbol,side=OrderSide.BUY,size=size,order_type = OrderType.MARKET, account=self.account) 
+        return self.bt.submit_order(time=self.bt.time + cfh.config_handler.get_val('BT_DELAYS','strat_to_sub'),symbol=self.symbol,side=OrderSide.BUY,size=size,order_type = OrderType.MARKET) 
     
     """buy limit"""
     def buy_l(self, price: float, size: int = 1, repeat: bool = False, force: int = 0):
         self.count()
-        return self.bt.submit_order(time=self.bt.time + cfh.config_handler.get_val('BT_DELAYS','strat_to_sub'),symbol=self.symbol,side=OrderSide.BUY,size=size,price=price,order_type = OrderType.LIMIT, account=self.account) 
+        return self.bt.submit_order(time=self.bt.time + cfh.config_handler.get_val('BT_DELAYS','strat_to_sub'),symbol=self.symbol,side=OrderSide.BUY,size=size,price=price,order_type = OrderType.LIMIT) 
     
     """sell market"""
     def sell(self, size = 1, repeat: bool = False):
         self.count()
-        return self.bt.submit_order(time=self.bt.time + cfh.config_handler.get_val('BT_DELAYS','strat_to_sub'),symbol=self.symbol,side=OrderSide.SELL,size=size,order_type = OrderType.MARKET, account=self.account) 
+        return self.bt.submit_order(time=self.bt.time + cfh.config_handler.get_val('BT_DELAYS','strat_to_sub'),symbol=self.symbol,side=OrderSide.SELL,size=size,order_type = OrderType.MARKET) 
 
     """sell limit"""
     async def sell_l(self, price: float, size = 1, repeat: bool = False):
         self.count()
-        return self.bt.submit_order(time=self.bt.time + cfh.config_handler.get_val('BT_DELAYS','strat_to_sub'),symbol=self.symbol,side=OrderSide.SELL,size=size,price=price,order_type = OrderType.LIMIT, account=self.account)        
+        return self.bt.submit_order(time=self.bt.time + cfh.config_handler.get_val('BT_DELAYS','strat_to_sub'),symbol=self.symbol,side=OrderSide.SELL,size=size,price=price,order_type = OrderType.LIMIT)        
 
     """replace order"""
     async def repl(self, orderid: str, price: float = None, size: int = None, repeat: bool = False):
         self.count()
-        return self.bt.replace_order(time=self.bt.time + cfh.config_handler.get_val('BT_DELAYS','strat_to_sub'),id=orderid,size=size,price=price, account=self.account)
+        return self.bt.replace_order(time=self.bt.time + cfh.config_handler.get_val('BT_DELAYS','strat_to_sub'),id=orderid,size=size,price=price)
     
     """cancel order"""
     #TBD exec predtim?
     def cancel(self, orderid: str):
         self.count()
-        return self.bt.cancel_order(time=self.bt.time + cfh.config_handler.get_val('BT_DELAYS','strat_to_sub'), id=orderid, account=self.account)
+        return self.bt.cancel_order(time=self.bt.time + cfh.config_handler.get_val('BT_DELAYS','strat_to_sub'), id=orderid)
 
     """get positions ->(size,avgp)"""
     #TBD exec predtim?
     def pos(self):
         self.count()
-        return self.bt.get_open_position(symbol=self.symbol, account=self.account)
+        return self.bt.get_open_position(symbol=self.symbol)
 
     """get open orders ->list(Order)"""      
     def get_open_orders(self, side: OrderSide, symbol: str):
         self.count()
-        return self.bt.get_open_orders(side=side, symbol=symbol, account=self.account)
+        return self.bt.get_open_orders(side=side, symbol=symbol)
     
     def get_last_price(self, symbol: str):
         self.count()
-        return self.bt.get_last_price(time=self.bt.time, account=self.account)
+        return self.bt.get_last_price(time=self.bt.time)
 
 
     

v2realbot/interfaces/live_interface.py

@@ -97,7 +97,7 @@ class LiveInterface(GeneralInterface):
             return -1
 
     """sell limit"""
-    def sell_l(self, price: float, size = 1, repeat: bool = False):
+    async def sell_l(self, price: float, size = 1, repeat: bool = False):
         self.size = size
         self.repeat = repeat
 
@@ -124,7 +124,7 @@ class LiveInterface(GeneralInterface):
             return -1
         
     """order replace"""
-    def repl(self, orderid: str, price: float = None, size: int = None, repeatl: bool = False):
+    async def repl(self, orderid: str, price: float = None, size: int = None, repeatl: bool = False):
         
         if not price and not size:
             print("price or size has to be filled")

v2realbot/loader/aggregator_vectorized.py

@@ -1,570 +0,0 @@
-import pandas as pd
-import numpy as np
-from numba import jit
-from alpaca.data.historical import StockHistoricalDataClient
-from sqlalchemy import column
-from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, DATA_DIR
-from alpaca.data.requests import StockTradesRequest
-import time as time_module
-from v2realbot.utils.utils import parse_alpaca_timestamp, ltp, zoneNY, send_to_telegram, fetch_calendar_data
-import pyarrow
-from traceback import format_exc
-from datetime import timedelta, datetime, time
-from concurrent.futures import ThreadPoolExecutor
-import os
-import gzip
-import pickle
-import random
-from alpaca.data.models import BarSet, QuoteSet, TradeSet
-import v2realbot.utils.config_handler as cfh
-from v2realbot.enums.enums import BarType
-from tqdm import tqdm
-""""
-Module used for vectorized aggregation of trades.
-
-Includes fetch (remote/cached) methods and numba aggregator function for TIME BASED, VOLUME BASED and DOLLAR BARS
-
-"""""
-
-def aggregate_trades(symbol: str, trades_df: pd.DataFrame, resolution: int, type: BarType = BarType.TIME):
-    """"
-    Accepts dataframe with trades keyed by symbol. Preparess dataframe to 
-    numpy and calls Numba optimized aggregator for given bar type. (time/volume/dollar)
-    """""
-    trades_df = trades_df.loc[symbol]
-    trades_df= trades_df.reset_index()
-    ticks = trades_df[['timestamp', 'price', 'size']].to_numpy()
-    # Extract the timestamps column (assuming it's the first column)
-    timestamps = ticks[:, 0]
-    # Convert the timestamps to Unix timestamps in seconds with microsecond precision
-    unix_timestamps_s = np.array([ts.timestamp() for ts in timestamps], dtype='float64')
-    # Replace the original timestamps in the NumPy array with the converted Unix timestamps
-    ticks[:, 0] = unix_timestamps_s
-    ticks = ticks.astype(np.float64)
-    #based on type, specific aggregator function is called
-    match type:
-        case BarType.TIME:
-            ohlcv_bars = generate_time_bars_nb(ticks, resolution)
-        case BarType.VOLUME:
-            ohlcv_bars = generate_volume_bars_nb(ticks, resolution)
-        case BarType.DOLLAR:
-            ohlcv_bars = generate_dollar_bars_nb(ticks, resolution)
-        case _:
-            raise ValueError("Invalid bar type. Supported types are 'time', 'volume' and 'dollar'.")
-    # Convert the resulting array back to a DataFrame
-    columns = ['time', 'open', 'high', 'low', 'close', 'volume', 'trades']
-    if type == BarType.DOLLAR:
-        columns.append('amount')
-    columns.append('updated')
-    if type == BarType.TIME:
-        columns.append('vwap')
-        columns.append('buyvolume')
-        columns.append('sellvolume')
-    if type == BarType.VOLUME:
-        columns.append('buyvolume')
-        columns.append('sellvolume')
-    ohlcv_df = pd.DataFrame(ohlcv_bars, columns=columns)
-    ohlcv_df['time'] = pd.to_datetime(ohlcv_df['time'], unit='s').dt.tz_localize('UTC').dt.tz_convert(zoneNY)
-    #print(ohlcv_df['updated'])
-    ohlcv_df['updated'] = pd.to_datetime(ohlcv_df['updated'], unit="s").dt.tz_localize('UTC').dt.tz_convert(zoneNY)
-    # Round to microseconds to maintain six decimal places
-    ohlcv_df['updated'] = ohlcv_df['updated'].dt.round('us')
-
-    ohlcv_df.set_index('time', inplace=True)
-    #ohlcv_df.index = ohlcv_df.index.tz_localize('UTC').tz_convert(zoneNY)
-    return ohlcv_df
-
-# Function to ensure fractional seconds are present
-def ensure_fractional_seconds(timestamp):
-    if '.' not in timestamp:
-        # Inserting .000000 before the timezone indicator 'Z'
-        return timestamp.replace('Z', '.000000Z')
-    else:
-        return timestamp
-
-def convert_dict_to_multiindex_df(tradesResponse):
-    """"
-    Converts dictionary from cache or from remote (raw input) to multiindex dataframe.
-    with microsecond precision (from nanoseconds in the raw data)
-    """""
-    # Create a DataFrame for each key and add the key as part of the MultiIndex
-    dfs = []
-    for key, values in tradesResponse.items():
-        df = pd.DataFrame(values)
-        # Rename columns
-        # Select and order columns explicitly
-        #print(df)
-        df = df[['t', 'x', 'p', 's', 'i', 'c','z']]
-        df.rename(columns={'t': 'timestamp', 'c': 'conditions', 'p': 'price', 's': 'size', 'x': 'exchange', 'z':'tape', 'i':'id'}, inplace=True)
-        df['symbol'] = key  # Add ticker as a column
-
-        # Apply the function to ensure all timestamps have fractional seconds
-        #zvazit zda toto ponechat a nebo dat jen pri urcitem erroru pri to_datetime
-        #pripadne pak pridelat efektivnejsi pristup, aneb nahrazeni NaT - https://chatgpt.com/c/d2be6f87-b38f-4050-a1c6-541d100b1474
-        df['timestamp'] = df['timestamp'].apply(ensure_fractional_seconds)
-
-        df['timestamp'] = pd.to_datetime(df['timestamp'], errors='coerce')  # Convert 't' from string to datetime before setting it as an index
-
-        #Adjust to microsecond precision
-        df.loc[df['timestamp'].notna(), 'timestamp'] = df['timestamp'].dt.floor('us')
-
-        df.set_index(['symbol', 'timestamp'], inplace=True)  # Set the multi-level index using both 'ticker' and 't'
-        df = df.tz_convert(zoneNY, level='timestamp')
-        dfs.append(df)
-
-    # Concatenate all DataFrames into a single DataFrame with MultiIndex
-    final_df = pd.concat(dfs)
-
-    return final_df
-
-def dict_to_df(tradesResponse, start, end, exclude_conditions = None, minsize = None):
-    """"
-    Transforms dict to Tradeset, then df and to zone aware
-    Also filters to start and end if necessary (ex. 9:30 to 15:40 is required only)
-   
-   NOTE: prepodkladame, ze tradesResponse je dict from Raw data (cached/remote)
-   """""
-
-    df = convert_dict_to_multiindex_df(tradesResponse)
-
-    #REQUIRED FILTERING
-    #pokud je zacatek pozdeji nebo konec driv tak orizneme
-    if (start.time() > time(9, 30) or end.time() < time(16, 0)):
-        print(f"filtrujeme {start.time()} {end.time()}")
-        # Define the time range
-        # start_time = pd.Timestamp(start.time(), tz=zoneNY).time()
-        # end_time = pd.Timestamp(end.time(), tz=zoneNY).time()
-
-        # Create a mask to filter rows within the specified time range
-        mask = (df.index.get_level_values('timestamp') >= start) & \
-            (df.index.get_level_values('timestamp') <= end)
-
-        # Apply the mask to the DataFrame
-        df = df[mask]
-
-    if exclude_conditions is not None:
-        print(f"excluding conditions {exclude_conditions}")
-        # Create a mask to exclude rows with any of the specified conditions
-        mask = df['conditions'].apply(lambda x: any(cond in exclude_conditions for cond in x))
-
-        # Filter out the rows with specified conditions
-        df = df[~mask]
-
-    if minsize is not None:
-        print(f"minsize {minsize}")
-        #exclude conditions
-        df = df[df['size'] >= minsize]
-    return df
-
-def fetch_daily_stock_trades(symbol, start, end, exclude_conditions=None, minsize=None, force_remote=False, max_retries=5, backoff_factor=1):
-    #doc for this function
-    """
-    Attempts to fetch stock trades either from cache or remote. When remote, it uses retry mechanism with exponential backoff.
-    Also it stores the data to cache if it is not already there.
-    by using force_remote - forcess using remote data always and thus refreshing cache for these dates
-        Attributes:
-        :param symbol: The stock symbol to fetch trades for.
-        :param start: The start time for the trade data.
-        :param end: The end time for the trade data.
-        :exclude_conditions: list of string conditions to exclude from the data
-        :minsize minimum size of trade to be included in the data
-        :force_remote will always use remote data and refresh cache
-        :param max_retries: Maximum number of retries.
-        :param backoff_factor: Factor to determine the next sleep time.
-        :return: TradesResponse object.
-        :raises: ConnectionError if all retries fail.
-    
-    We use tradecache only for main sessison requests = 9:30 to 16:00
-    Do budoucna ukládat celý den BAC-20240203.cache.gz a z toho si pak filtrovat bud main sesssionu a extended
-    Ale zatim je uloženo jen main session v BAC-timestampopenu-timestampclose.cache.gz
-    """
-    is_same_day = start.date() == end.date()
-    # Determine if the requested times fall within the main session
-    in_main_session = (time(9, 30) <= start.time() < time(16, 0)) and (time(9, 30) <= end.time() <= time(16, 0))
-    file_path = ''
-    
-    if in_main_session:
-        filename_start = zoneNY.localize(datetime.combine(start.date(), time(9, 30)))
-        filename_end = zoneNY.localize(datetime.combine(end.date(), time(16, 0)))
-        daily_file = f"{symbol}-{int(filename_start.timestamp())}-{int(filename_end.timestamp())}.cache.gz"
-        file_path = f"{DATA_DIR}/tradecache/{daily_file}"
-        if not force_remote and os.path.exists(file_path):
-            print(f"Searching {str(start.date())} cache: " + daily_file)
-            with gzip.open(file_path, 'rb') as fp:
-                tradesResponse = pickle.load(fp)
-                print("FOUND in CACHE", daily_file)
-                return dict_to_df(tradesResponse, start, end, exclude_conditions, minsize)
-
-    print("NOT FOUND. Fetching from remote")
-    client = StockHistoricalDataClient(ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, raw_data=True)
-    stockTradeRequest = StockTradesRequest(symbol_or_symbols=symbol, start=start, end=end)
-    last_exception = None
-
-    for attempt in range(max_retries):
-        try:
-            tradesResponse = client.get_stock_trades(stockTradeRequest)
-            is_empty = not tradesResponse[symbol]
-            print(f"Remote fetched: {is_empty=}", start, end)
-            if in_main_session and not is_empty:
-                current_time = datetime.now().astimezone(zoneNY)
-                if not (start < current_time < end):
-                    with gzip.open(file_path, 'wb') as fp:
-                        pickle.dump(tradesResponse, fp)
-                        print("Saving to Trade CACHE", file_path)
-
-                else:  # Don't save the cache if the market is still open
-                    print("Not saving trade cache, market still open today")
-            return pd.DataFrame() if is_empty else dict_to_df(tradesResponse, start, end, exclude_conditions, minsize) 
-        except Exception as e:
-            print(f"Attempt {attempt + 1} failed: {e}")
-            last_exception = e
-            time_module.sleep(backoff_factor * (2 ** attempt) + random.uniform(0, 1))  # Adding random jitter
-
-    print("All attempts to fetch data failed.")
-    raise ConnectionError(f"Failed to fetch stock trades after {max_retries} retries. Last exception: {str(last_exception)} and {format_exc()}")
-
-
-def fetch_trades_parallel(symbol, start_date, end_date, exclude_conditions = cfh.config_handler.get_val('AGG_EXCLUDED_TRADES'), minsize = 100, force_remote = False, max_workers=None):
-    """
-    Fetches trades for each day between start_date and end_date during market hours (9:30-16:00) in parallel and concatenates them into a single DataFrame.
-
-    :param symbol: Stock symbol.
-    :param start_date: Start date as datetime.
-    :param end_date: End date as datetime.
-    :return: DataFrame containing all trades from start_date to end_date.
-    """
-    futures = []
-    results = []
-    
-    market_open_days = fetch_calendar_data(start_date, end_date)
-    day_count = len(market_open_days)
-    print("Contains", day_count, " market days")
-    max_workers = min(10, max(2, day_count // 2)) if max_workers is None else max_workers  # Heuristic: half the days to process, but at least 1 and no more than 10
-
-    with ThreadPoolExecutor(max_workers=max_workers) as executor:
-        #for single_date in (start_date + timedelta(days=i) for i in range((end_date - start_date).days + 1)):
-        for market_day in tqdm(market_open_days, desc="Processing market days"):
-            #start = datetime.combine(single_date, time(9, 30))  # Market opens at 9:30 AM
-            #end = datetime.combine(single_date, time(16, 0))   # Market closes at 4:00 PM
-            
-            interval_from = zoneNY.localize(market_day.open)
-            interval_to = zoneNY.localize(market_day.close)
-
-            #pripadne orizneme pokud je pozadovane pozdejsi zacatek a drivejsi konek
-            start = start_date if interval_from < start_date else interval_from
-            #start = max(start_date, interval_from)
-            end = end_date if interval_to > end_date else interval_to
-            #end = min(end_date, interval_to)
-
-            future = executor.submit(fetch_daily_stock_trades, symbol, start, end, exclude_conditions, minsize, force_remote)
-            futures.append(future)
-        
-        for future in tqdm(futures, desc="Fetching data"):
-            try:
-                result = future.result()
-                results.append(result)
-            except Exception as e:
-                print(f"Error fetching data for a day: {e}")
-
-    # Batch concatenation to improve speed
-    batch_size = 10
-    batches = [results[i:i + batch_size] for i in range(0, len(results), batch_size)]
-    final_df = pd.concat([pd.concat(batch, ignore_index=False) for batch in batches], ignore_index=False)
-
-    return final_df
-
-    #original version
-    #return pd.concat(results, ignore_index=False)
-
-@jit(nopython=True)
-def generate_dollar_bars_nb(ticks, amount_per_bar):
-    """"
-    Generates Dollar based bars from ticks.
-
-    There is also simple prevention of aggregation from different days
-    as described here https://chatgpt.com/c/17804fc1-a7bc-495d-8686-b8392f3640a2
-    Downside: split days by UTC (which is ok for main session, but when extended hours it should be reworked by preprocessing new column identifying session)
-    
-    
-    When trade is split into multiple bars it is counted as trade in each of the bars.
-    Other option: trade count can be proportionally distributed by weight (0.2 to 1st bar, 0.8 to 2nd bar) - but this is not implemented yet
-    https://chatgpt.com/c/ff4802d9-22a2-4b72-8ab7-97a91e7a515f
-    """""
-    ohlcv_bars = []
-    remaining_amount = amount_per_bar
-
-    # Initialize bar values based on the first tick to avoid uninitialized values
-    open_price = ticks[0, 1]
-    high_price = ticks[0, 1]
-    low_price = ticks[0, 1]
-    close_price = ticks[0, 1]
-    volume = 0
-    trades_count = 0
-    current_day = np.floor(ticks[0, 0] / 86400)  # Calculate the initial day from the first tick timestamp
-    bar_time = ticks[0, 0]  # Initialize bar time with the time of the first tick
-
-    for tick in ticks:
-        tick_time = tick[0]
-        price = tick[1]
-        tick_volume = tick[2]
-        tick_amount = price * tick_volume
-        tick_day = np.floor(tick_time / 86400)  # Calculate the day of the current tick
-
-        # Check if the new tick is from a different day, then close the current bar
-        if tick_day != current_day:
-            if trades_count > 0:
-                ohlcv_bars.append([bar_time, open_price, high_price, low_price, close_price, volume, trades_count, amount_per_bar, tick_time])
-            # Reset for the new day using the current tick data
-            open_price = price
-            high_price = price
-            low_price = price
-            close_price = price
-            volume = 0
-            trades_count = 0
-            remaining_amount = amount_per_bar
-            current_day = tick_day
-            bar_time = tick_time
-
-        # Start new bar if needed because of the dollar value
-        while tick_amount > 0:
-            if tick_amount < remaining_amount:
-                # Add the entire tick to the current bar
-                high_price = max(high_price, price)
-                low_price = min(low_price, price)
-                close_price = price
-                volume += tick_volume
-                remaining_amount -= tick_amount
-                trades_count += 1
-                tick_amount = 0
-            else:
-                # Calculate the amount of volume that fits within the remaining dollar amount
-                volume_to_add = remaining_amount / price
-                volume += volume_to_add  # Update the volume here before appending and resetting
-
-                # Append the partially filled bar to the list
-                ohlcv_bars.append([bar_time, open_price, high_price, low_price, close_price, volume, trades_count + 1, amount_per_bar, tick_time])
-
-                # Fill the current bar and continue with a new bar
-                tick_volume -= volume_to_add
-                tick_amount -= remaining_amount
-
-                # Reset bar values for the new bar using the current tick data
-                open_price = price
-                high_price = price
-                low_price = price
-                close_price = price
-                volume = 0  # Reset volume for the new bar
-                trades_count = 0
-                remaining_amount = amount_per_bar
-                
-                # Increment bar time if splitting a trade
-                if tick_volume > 0: #pokud v tradu je jeste zbytek nastavujeme cas o nanosekundu vetsi
-                    bar_time = tick_time + 1e-6
-                else:
-                    bar_time = tick_time #jinak nastavujeme cas ticku
-                #bar_time = tick_time
-
-    # Add the last bar if it contains any trades
-    if trades_count > 0:
-        ohlcv_bars.append([bar_time, open_price, high_price, low_price, close_price, volume, trades_count, amount_per_bar, tick_time])
-
-    return np.array(ohlcv_bars)
-
-
-@jit(nopython=True)
-def generate_volume_bars_nb(ticks, volume_per_bar):
-    """"
-    Generates Volume based bars from ticks.
-    
-    NOTE: UTC day split here (doesnt aggregate trades from different days)
-    but realized from UTC (ok for main session) - but needs rework for extension by preprocessing ticks_df and introduction sesssion column
-    
-    When trade is split into multiple bars it is counted as trade in each of the bars.
-    Other option: trade count can be proportionally distributed by weight (0.2 to 1st bar, 0.8 to 2nd bar) - but this is not implemented yet
-    https://chatgpt.com/c/ff4802d9-22a2-4b72-8ab7-97a91e7a515f
-    """""
-    ohlcv_bars = []
-    remaining_volume = volume_per_bar
-
-    # Initialize bar values based on the first tick to avoid uninitialized values
-    open_price = ticks[0, 1]
-    high_price = ticks[0, 1]
-    low_price = ticks[0, 1]
-    close_price = ticks[0, 1]
-    volume = 0
-    trades_count = 0
-    current_day = np.floor(ticks[0, 0] / 86400)  # Calculate the initial day from the first tick timestamp
-    bar_time = ticks[0, 0]  # Initialize bar time with the time of the first tick
-    buy_volume = 0  # Volume of buy trades
-    sell_volume = 0  # Volume of sell trades
-    prev_price = ticks[0, 1]  # Initialize previous price for the first tick
-
-    for tick in ticks:
-        tick_time = tick[0]
-        price = tick[1]
-        tick_volume = tick[2]
-        tick_day = np.floor(tick_time / 86400)  # Calculate the day of the current tick
-
-        # Check if the new tick is from a different day, then close the current bar
-        if tick_day != current_day:
-            if trades_count > 0:
-                ohlcv_bars.append([bar_time, open_price, high_price, low_price, close_price, volume, trades_count, tick_time, buy_volume, sell_volume])
-            # Reset for the new day using the current tick data
-            open_price = price
-            high_price = price
-            low_price = price
-            close_price = price
-            volume = 0
-            trades_count = 0
-            remaining_volume = volume_per_bar
-            current_day = tick_day
-            bar_time = tick_time  # Update bar time to the current tick time
-            buy_volume = 0
-            sell_volume = 0
-            # Reset previous tick price (calulating imbalance for each day from the start)
-            prev_price = price
-
-        # Start new bar if needed because of the volume
-        while tick_volume > 0:
-            if tick_volume < remaining_volume:
-                # Add the entire tick to the current bar
-                high_price = max(high_price, price)
-                low_price = min(low_price, price)
-                close_price = price
-                volume += tick_volume
-                remaining_volume -= tick_volume
-                trades_count += 1
-                
-                # Update buy and sell volumes
-                if price > prev_price:
-                    buy_volume += tick_volume
-                elif price < prev_price:
-                    sell_volume += tick_volume
-                
-                tick_volume = 0
-            else:
-                # Fill the current bar and continue with a new bar
-                volume_to_add = remaining_volume
-                volume += volume_to_add
-                tick_volume -= volume_to_add
-                trades_count += 1
-                
-                # Update buy and sell volumes
-                if price > prev_price:
-                    buy_volume += volume_to_add
-                elif price < prev_price:
-                    sell_volume += volume_to_add
-                
-                # Append the completed bar to the list
-                ohlcv_bars.append([bar_time, open_price, high_price, low_price, close_price, volume, trades_count, tick_time, buy_volume, sell_volume])
-
-                # Reset bar values for the new bar using the current tick data
-                open_price = price
-                high_price = price
-                low_price = price
-                close_price = price
-                volume = 0
-                trades_count = 0
-                remaining_volume = volume_per_bar
-                buy_volume = 0
-                sell_volume = 0
-                
-                # Increment bar time if splitting a trade
-                if tick_volume > 0: # If there's remaining volume in the trade, set bar time slightly later
-                    bar_time = tick_time + 1e-6
-                else:
-                    bar_time = tick_time # Otherwise, set bar time to the tick time
-
-        prev_price = price
-
-    # Add the last bar if it contains any trades
-    if trades_count > 0:
-        ohlcv_bars.append([bar_time, open_price, high_price, low_price, close_price, volume, trades_count, tick_time, buy_volume, sell_volume])
-
-    return np.array(ohlcv_bars)
-
-@jit(nopython=True)
-def generate_time_bars_nb(ticks, resolution):
-    # Initialize the start and end time
-    start_time = np.floor(ticks[0, 0] / resolution) * resolution
-    end_time = np.floor(ticks[-1, 0] / resolution) * resolution
-    
-    # # Calculate number of bars
-    # num_bars = int((end_time - start_time) // resolution + 1)
-    
-    # Using a list to append data only when trades exist
-    ohlcv_bars = []
-    
-    # Variables to track the current bar
-    current_bar_index = -1
-    open_price = 0
-    high_price = -np.inf
-    low_price = np.inf
-    close_price = 0
-    volume = 0
-    trades_count = 0
-    vwap_cum_volume_price = 0  # Cumulative volume * price
-    cum_volume = 0  # Cumulative volume for VWAP
-    buy_volume = 0  # Volume of buy trades
-    sell_volume = 0  # Volume of sell trades
-    prev_price = ticks[0, 1]  # Initialize previous price for the first tick
-    prev_day = np.floor(ticks[0, 0] / 86400)  # Calculate the initial day from the first tick timestamp
-    
-    for tick in ticks:
-        curr_time = tick[0] #updated time
-        tick_time = np.floor(tick[0] / resolution) * resolution
-        price = tick[1]
-        tick_volume = tick[2]
-        tick_day = np.floor(tick_time / 86400)  # Calculate the day of the current tick
-
-        #if the new tick is from a new day, reset previous tick price (calculating imbalance starts over)
-        if tick_day != prev_day:
-            prev_price = price
-            prev_day = tick_day
-
-        # Check if the tick belongs to a new bar
-        if tick_time != start_time + current_bar_index * resolution:
-            if current_bar_index >= 0 and trades_count > 0:  # Save the previous bar if trades happened
-                vwap = vwap_cum_volume_price / cum_volume if cum_volume > 0 else 0
-                ohlcv_bars.append([start_time + current_bar_index * resolution, open_price, high_price, low_price, close_price, volume, trades_count, curr_time, vwap, buy_volume, sell_volume])
-            
-            # Reset bar values
-            current_bar_index = int((tick_time - start_time) / resolution)
-            open_price = price
-            high_price = price
-            low_price = price
-            volume = 0
-            trades_count = 0
-            vwap_cum_volume_price = 0
-            cum_volume = 0
-            buy_volume = 0
-            sell_volume = 0
-        
-        # Update the OHLCV values for the current bar
-        high_price = max(high_price, price)
-        low_price = min(low_price, price)
-        close_price = price
-        volume += tick_volume
-        trades_count += 1
-        vwap_cum_volume_price += price * tick_volume
-        cum_volume += tick_volume
-
-        # Update buy and sell volumes
-        if price > prev_price:
-            buy_volume += tick_volume
-        elif price < prev_price:
-            sell_volume += tick_volume
-        
-        prev_price = price
-
-    # Save the last processed bar
-    if trades_count > 0:
-        vwap = vwap_cum_volume_price / cum_volume if cum_volume > 0 else 0
-        ohlcv_bars.append([start_time + current_bar_index * resolution, open_price, high_price, low_price, close_price, volume, trades_count, curr_time, vwap, buy_volume, sell_volume])
-    
-    return np.array(ohlcv_bars)
-
-# Example usage
-if __name__ == '__main__':
-    pass
-    #example in agg_vect.ipynb

v2realbot/loader/order_updates_streamer.py

@@ -1,7 +1,6 @@
-from threading import Thread, current_thread
+from threading import Thread
 from alpaca.trading.stream import TradingStream
 from v2realbot.config import Keys
-from v2realbot.common.model import Account
 
 #jelikoz Alpaca podporuje pripojeni libovolneho poctu websocket instanci na order updates
 #vytvorime pro kazdou bezici instanci vlastni webservisu (jinak bychom museli delat instanci pro kombinaci ACCOUNT1 - LIVE, ACCOUNT1 - PAPER, ACCOUNT2 - PAPER ..)
@@ -15,16 +14,15 @@ As Alpaca supports connecting of any number of trade updates clients
 new instance of this websocket thread is created for each strategy instance.
 """""
 class LiveOrderUpdatesStreamer(Thread):
-    def __init__(self, key: Keys, name: str, account: Account) -> None:
+    def __init__(self, key: Keys, name: str) -> None:
         self.key = key
-        self.account = account
         self.strategy = None
         self.client = TradingStream(api_key=key.API_KEY, secret_key=key.SECRET_KEY, paper=key.PAPER)
         Thread.__init__(self, name=name)
     
     #notif dispatcher - pouze 1 strategie
     async def distributor(self,data):     
-        if self.strategy.symbol == data.order.symbol: await self.strategy.order_updates(data, self.account)
+        if self.strategy.symbol == data.order.symbol: await self.strategy.order_updates(data)
 
    # connects callback to interface object - responses for given symbol are routed to interface callback
     def connect_callback(self, st):
@@ -41,6 +39,6 @@ class LiveOrderUpdatesStreamer(Thread):
             print("connect strategy first")
             return
         self.client.subscribe_trade_updates(self.distributor)
-        print("*"*10, "WS Order Update Streamer started for", current_thread().name,"*"*10)
+        print("*"*10, "WS Order Update Streamer started for", self.strategy.name, "*"*10)
         self.client.run()
         

v2realbot/main.py

@@ -10,15 +10,14 @@ from fastapi.security import APIKeyHeader
 import uvicorn
 from uuid import UUID
 from v2realbot.utils.ilog import get_log_window
-from v2realbot.common.model import RunManagerRecord, StrategyInstance, RunnerView, RunRequest, TradeView, RunArchive, RunArchiveView, RunArchiveViewPagination, RunArchiveDetail, Bar, RunArchiveChange, TestList, ConfigItem, InstantIndicator, DataTablesRequest, AnalyzerInputs
+from v2realbot.common.model import RunManagerRecord, StrategyInstance, RunnerView, RunRequest, Trade, RunArchive, RunArchiveView, RunArchiveViewPagination, RunArchiveDetail, Bar, RunArchiveChange, TestList, ConfigItem, InstantIndicator, DataTablesRequest, AnalyzerInputs
-from fastapi import FastAPI, WebSocket, WebSocketDisconnect, Depends, HTTPException, status, WebSocketException, Cookie, Query, Request
+from fastapi import FastAPI, WebSocket, WebSocketDisconnect, Depends, HTTPException, status, WebSocketException, Cookie, Query
 from fastapi.responses import FileResponse, StreamingResponse, JSONResponse
 from fastapi.staticfiles import StaticFiles
 from fastapi.security import HTTPBasic, HTTPBasicCredentials
 from v2realbot.enums.enums import Env, Mode
 from typing import Annotated
 import os
-import psutil
 import uvicorn
 import orjson
 from queue import Queue, Empty
@@ -36,7 +35,7 @@ from traceback import format_exc
 #from v2realbot.reporting.optimizecutoffs import find_optimal_cutoff
 import v2realbot.reporting.analyzer as ci
 import shutil
-from starlette.responses import JSONResponse, HTMLResponse, FileResponse, RedirectResponse
+from starlette.responses import JSONResponse
 import mlroom
 import mlroom.utils.mlutils as ml
 from typing import List
@@ -76,51 +75,13 @@ def api_key_auth(api_key: str = Depends(X_API_KEY)):
             detail="Forbidden"
         )  
      
-def authenticate_user(credentials: HTTPBasicCredentials = Depends(HTTPBasic())):
-    correct_username = "david"
-    correct_password = "david"
-    
-    if credentials.username == correct_username and credentials.password == correct_password:
-        return True
-    else:
-        raise HTTPException(
-            status_code=status.HTTP_401_UNAUTHORIZED,
-            detail="Incorrect username or password",
-            headers={"WWW-Authenticate": "Basic"},
-        )
-
-
 app = FastAPI()
 root = os.path.dirname(os.path.abspath(__file__))
-#app.mount("/static", StaticFiles(html=True, directory=os.path.join(root, 'static')), name="static")
+app.mount("/static", StaticFiles(html=True, directory=os.path.join(root, 'static')), name="static")
 app.mount("/media", StaticFiles(directory=str(MEDIA_DIRECTORY)), name="media")
 #app.mount("/", StaticFiles(html=True, directory=os.path.join(root, 'static')), name="www")
 
 security = HTTPBasic()
-@app.get("/static/{path:path}")
-async def static_files(request: Request, path: str, authenticated: bool = Depends(authenticate_user)):
-    root = os.path.dirname(os.path.abspath(__file__))
-    static_dir = os.path.join(root, 'static')
-
-    if not path or path == "/":
-        file_path = os.path.join(static_dir, 'index.html')
-    else:
-        file_path = os.path.join(static_dir, path)
-
-    # Check if path is a directory
-    if os.path.isdir(file_path):
-        # If it's a directory, try to serve index.html within that directory
-        index_path = os.path.join(file_path, 'index.html')
-        if os.path.exists(index_path):
-            return FileResponse(index_path)
-        else:
-            # Optionally, you can return a directory listing or a custom 404 page here
-            return HTMLResponse("Directory listing not enabled.", status_code=403) 
-
-    if not os.path.exists(file_path):
-        raise HTTPException(status_code=404, detail="File not found")
-
-    return FileResponse(file_path)
 
 def get_current_username(
     credentials: Annotated[HTTPBasicCredentials, Depends(security)]
@@ -142,9 +103,9 @@ async def get_api_key(
     return session or api_key
 
 #TODO predelat z Async?
-# @app.get("/static")
+@app.get("/static")
-# async def get(username: Annotated[str, Depends(get_current_username)]):
+async def get(username: Annotated[str, Depends(get_current_username)]):
-#     return FileResponse("index.html")
+    return FileResponse("index.html")
 
 @app.websocket("/runners/{runner_id}/ws")
 async def websocket_endpoint(
@@ -334,7 +295,7 @@ def stop_all_runners():
         raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=f"Error: {res}:{id}")
 
 @app.get("/tradehistory/{symbol}", dependencies=[Depends(api_key_auth)])
-def get_trade_history(symbol: str, timestamp_from: float, timestamp_to:float) -> list[TradeView]:
+def get_trade_history(symbol: str, timestamp_from: float, timestamp_to:float) -> list[Trade]:
     res, set = cs.get_trade_history(symbol, timestamp_from, timestamp_to)
     if res == 0:
         return set
@@ -1026,25 +987,7 @@ def get_metadata(model_name: str):
     #         "last_modified": os.path.getmtime(model_path),
     #         # ... other metadata fields ...
     #     }
-@app.get("/system-info")
+
-def get_system_info():
-    """Get system info, e.g. disk free space, used percentage ... """
-    disk_total = round(psutil.disk_usage('/').total / 1024**3, 1)
-    disk_used = round(psutil.disk_usage('/').used / 1024**3, 1)
-    disk_free = round(psutil.disk_usage('/').free / 1024**3, 1)
-    disk_used_percentage = round(psutil.disk_usage('/').percent, 1)
-    # memory_total = round(psutil.virtual_memory().total / 1024**3, 1)
-    # memory_perc = round(psutil.virtual_memory().percent, 1)
-    # cpu_time_user = round(psutil.cpu_times().user,1)
-    # cpu_time_system = round(psutil.cpu_times().system,1)
-    # cpu_time_idle = round(psutil.cpu_times().idle,1)
-    # network_sent = round(psutil.net_io_counters().bytes_sent / 1024**3, 6)
-    # network_recv = round(psutil.net_io_counters().bytes_recv / 1024**3, 6)
-    return {"disk_space": {"total": disk_total, "used": disk_used, "free" : disk_free, "used_percentage" : disk_used_percentage},
-            # "memory": {"total": memory_total, "used_percentage": memory_perc},
-            # "cpu_time" : {"user": cpu_time_user, "system": cpu_time_system, "idle": cpu_time_idle},
-            # "network": {"sent": network_sent, "received": network_recv}
-            }
 
 # Thread function to insert data from the queue into the database
 def insert_queue2db():

v2realbot/reporting/analyzer/WIP_daily_profit_distribution.py

@@ -12,7 +12,7 @@ import numpy as np
 import v2realbot.controller.services as cs
 from rich import print
 from v2realbot.common.model import AnalyzerInputs
-from v2realbot.common.model import TradeDirection, TradeStatus, Trade, TradeStoplossType
+from v2realbot.common.PrescribedTradeModel import TradeDirection, TradeStatus, Trade, TradeStoplossType
 from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, safe_get#, print
 from pathlib import Path
 from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY

v2realbot/reporting/analyzer/daily_profit_distribution.py

@@ -12,7 +12,7 @@ import numpy as np
 import v2realbot.controller.services as cs
 from rich import print
 from v2realbot.common.model import AnalyzerInputs
-from v2realbot.common.model import TradeDirection, TradeStatus, Trade, TradeStoplossType
+from v2realbot.common.PrescribedTradeModel import TradeDirection, TradeStatus, Trade, TradeStoplossType
 from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, safe_get#, print
 from pathlib import Path
 from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY

v2realbot/reporting/analyzer/example_plugin.py

@@ -11,7 +11,7 @@ import numpy as np
 import v2realbot.controller.services as cs
 from rich import print
 from v2realbot.common.model import AnalyzerInputs
-from v2realbot.common.model import TradeDirection, TradeStatus, Trade, TradeStoplossType
+from v2realbot.common.PrescribedTradeModel import TradeDirection, TradeStatus, Trade, TradeStoplossType
 from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, safe_get#, print
 from pathlib import Path
 from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY

v2realbot/reporting/analyzer/find_optimal_cutoff.py

@@ -11,7 +11,7 @@ import numpy as np
 import v2realbot.controller.services as cs
 from rich import print
 from v2realbot.common.model import AnalyzerInputs
-from v2realbot.common.model import TradeDirection, TradeStatus, Trade, TradeStoplossType
+from v2realbot.common.PrescribedTradeModel import TradeDirection, TradeStatus, Trade, TradeStoplossType
 from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, safe_get#, print
 from pathlib import Path
 from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY

v2realbot/reporting/analyzer/find_optimal_cutoff_REL.py

@@ -11,7 +11,7 @@ import numpy as np
 import v2realbot.controller.services as cs
 from rich import print
 from v2realbot.common.model import AnalyzerInputs
-from v2realbot.common.model import TradeDirection, TradeStatus, Trade, TradeStoplossType
+from v2realbot.common.PrescribedTradeModel import TradeDirection, TradeStatus, Trade, TradeStoplossType
 from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, safe_get#, print
 from pathlib import Path
 from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY

v2realbot/reporting/analyzer/ls_profit_distribution.py

@@ -11,7 +11,7 @@ import numpy as np
 import v2realbot.controller.services as cs
 from rich import print
 from v2realbot.common.model import AnalyzerInputs
-from v2realbot.common.model import TradeDirection, TradeStatus, Trade, TradeStoplossType
+from v2realbot.common.PrescribedTradeModel import TradeDirection, TradeStatus, Trade, TradeStoplossType
 from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, safe_get#, print
 from pathlib import Path
 from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY

v2realbot/reporting/analyzer/profit_distribution_by_month.py

@@ -11,7 +11,7 @@ import numpy as np
 import v2realbot.controller.services as cs
 from rich import print
 from v2realbot.common.model import AnalyzerInputs
-from v2realbot.common.model import TradeDirection, TradeStatus, Trade, TradeStoplossType
+from v2realbot.common.PrescribedTradeModel import TradeDirection, TradeStatus, Trade, TradeStoplossType
 from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, safe_get#, print
 from pathlib import Path
 from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY

v2realbot/reporting/analyzer/profit_sum_by_hour.py

@@ -11,7 +11,7 @@ import numpy as np
 import v2realbot.controller.services as cs
 from rich import print
 from v2realbot.common.model import AnalyzerInputs
-from v2realbot.common.model import TradeDirection, TradeStatus, Trade, TradeStoplossType
+from v2realbot.common.PrescribedTradeModel import TradeDirection, TradeStatus, Trade, TradeStoplossType
 from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, safe_get#, print
 from pathlib import Path
 from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY

v2realbot/reporting/analyzer/summarize_trade_metrics.py

@@ -12,7 +12,7 @@ import numpy as np
 import v2realbot.controller.services as cs
 from rich import print
 from v2realbot.common.model import AnalyzerInputs
-from v2realbot.common.model import TradeDirection, TradeStatus, Trade, TradeStoplossType
+from v2realbot.common.PrescribedTradeModel import TradeDirection, TradeStatus, Trade, TradeStoplossType
 from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, safe_get#, print
 from pathlib import Path
 from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY

v2realbot/reporting/archive/optimizecutoff.py

@@ -10,7 +10,7 @@ from enum import Enum
 import numpy as np
 import v2realbot.controller.services as cs
 from rich import print
-from v2realbot.common.model import TradeDirection, TradeStatus, Trade, TradeStoplossType
+from v2realbot.common.PrescribedTradeModel import TradeDirection, TradeStatus, Trade, TradeStoplossType
 from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, safe_get#, print
 from pathlib import Path
 from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY

v2realbot/reporting/archive/optimizecutoffprofloss.py

@@ -10,7 +10,7 @@ from enum import Enum
 import numpy as np
 import v2realbot.controller.services as cs
 from rich import print
-from v2realbot.common.model import TradeDirection, TradeStatus, Trade, TradeStoplossType
+from v2realbot.common.PrescribedTradeModel import TradeDirection, TradeStatus, Trade, TradeStoplossType
 from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, safe_get#, print
 from pathlib import Path
 from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY

v2realbot/reporting/archive/optimizecutoffv2.py

@@ -10,7 +10,7 @@ from enum import Enum
 import numpy as np
 import v2realbot.controller.services as cs
 from rich import print
-from v2realbot.common.model import TradeDirection, TradeStatus, Trade, TradeStoplossType
+from v2realbot.common.PrescribedTradeModel import TradeDirection, TradeStatus, Trade, TradeStoplossType
 from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, safe_get#, print
 from pathlib import Path
 from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY

v2realbot/reporting/load_trades.py

@@ -11,7 +11,7 @@ import numpy as np
 import v2realbot.controller.services as cs
 from rich import print
 from v2realbot.common.model import AnalyzerInputs
-from v2realbot.common.model import TradeDirection, TradeStatus, Trade, TradeStoplossType
+from v2realbot.common.PrescribedTradeModel import TradeDirection, TradeStatus, Trade, TradeStoplossType
 from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, safe_get#, print
 from pathlib import Path
 from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY
@@ -23,7 +23,7 @@ from collections import defaultdict
 from scipy.stats import zscore
 from io import BytesIO
 from typing import Tuple, Optional, List
-from v2realbot.common.model import TradeDirection, TradeStatus, Trade, TradeStoplossType
+from v2realbot.common.PrescribedTradeModel import TradeDirection, TradeStatus, Trade, TradeStoplossType
 
 def load_trades(runner_ids: List = None, batch_id: str = None) -> Tuple[int, List[Trade], int]:
     if runner_ids is None and batch_id is None:

v2realbot/reporting/metricstoolsimage.py

@@ -10,7 +10,7 @@ from enum import Enum
 import numpy as np
 import v2realbot.controller.services as cs
 from rich import print
-from v2realbot.common.model import TradeDirection, TradeStatus, Trade, TradeStoplossType
+from v2realbot.common.PrescribedTradeModel import TradeDirection, TradeStatus, Trade, TradeStoplossType
 from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, safe_get, print
 from pathlib import Path
 from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY

v2realbot/scheduler/ap_scheduler.py

@@ -2,9 +2,9 @@ from uuid import UUID
 from typing import Any, List, Tuple
 from uuid import UUID, uuid4
 from v2realbot.enums.enums import Moddus, SchedulerStatus, RecordType, StartBarAlign, Mode, Account, OrderSide
-from v2realbot.common.model import RunManagerRecord, StrategyInstance, RunDay, StrategyInstance, Runner, RunRequest, RunArchive, RunArchiveView, RunArchiveViewPagination, RunArchiveDetail, RunArchiveChange, Bar, TradeEvent, TestList, Intervals, ConfigItem, InstantIndicator, DataTablesRequest, Market
+from v2realbot.common.model import RunManagerRecord, StrategyInstance, RunDay, StrategyInstance, Runner, RunRequest, RunArchive, RunArchiveView, RunArchiveViewPagination, RunArchiveDetail, RunArchiveChange, Bar, TradeEvent, TestList, Intervals, ConfigItem, InstantIndicator, DataTablesRequest
 from v2realbot.utils.utils import validate_and_format_time, AttributeDict, zoneNY, zonePRG, safe_get, dict_replace_value, Store, parse_toml_string, json_serial, is_open_hours, send_to_telegram, concatenate_weekdays, transform_data
-from v2realbot.common.model import Trade, TradeDirection, TradeStatus, TradeStoplossType
+from v2realbot.common.PrescribedTradeModel import Trade, TradeDirection, TradeStatus, TradeStoplossType
 from datetime import datetime
 from v2realbot.config import JOB_LOG_FILE, STRATVARS_UNCHANGEABLES, ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, ACCOUNT1_LIVE_API_KEY, ACCOUNT1_LIVE_SECRET_KEY, DATA_DIR, MEDIA_DIRECTORY, RUNNER_DETAIL_DIRECTORY
 import numpy as np
@@ -116,8 +116,7 @@ def initialize_jobs(run_manager_records: RunManagerRecord = None):
             scheduler.add_job(start_runman_record, start_trigger, id=f"scheduler_start_{record.id}", args=[record.id])
             scheduler.add_job(stop_runman_record, stop_trigger, id=f"scheduler_stop_{record.id}", args=[record.id])
                         
-    #scheduler.add_job(print_hello, 'interval', seconds=10, id=
+    #scheduler.add_job(print_hello, 'interval', seconds=10, id=f"scheduler_testinterval")
-    # f"scheduler_testinterval")
     scheduled_jobs = scheduler.get_jobs()
     print(f"APS jobs refreshed ({len(scheduled_jobs)})")
     current_jobs_dict = format_apscheduler_jobs(scheduled_jobs)
@@ -125,9 +124,9 @@ def initialize_jobs(run_manager_records: RunManagerRecord = None):
     return 0, current_jobs_dict
 
 #zastresovaci funkce resici error handling a printing
-def start_runman_record(id: UUID, debug_date = None):
+def start_runman_record(id: UUID, market = "US", debug_date = None):
     record = None
-    res, record, msg = _start_runman_record(id=id, debug_date=debug_date)
+    res, record, msg = _start_runman_record(id=id, market=market, debug_date=debug_date)
 
     if record is not None:
         market_time_now = datetime.now().astimezone(zoneNY) if debug_date is None else debug_date
@@ -166,8 +165,8 @@ def update_runman_record(record: RunManagerRecord):
         err_msg= f"STOP: Error updating {record.id} errir {set} with values {record}"
         return -2, err_msg#toto stopne zpracovani dalsich zaznamu pri chybe, zvazit continue
 
-def stop_runman_record(id: UUID, debug_date = None):
+def stop_runman_record(id: UUID, market = "US", debug_date = None):
-    res, record, msg = _stop_runman_record(id=id, debug_date=debug_date)
+    res, record, msg = _stop_runman_record(id=id, market=market, debug_date=debug_date)
     #results : 0 - ok, -1 not running/already running/not specific, -2 error
 
     #report vzdy zapiseme do history, pokud je record not None, pripadna chyba se stala po dotazeni recordu
@@ -197,7 +196,7 @@ def stop_runman_record(id: UUID, debug_date = None):
         print(f"STOP JOB: {id} FINISHED")
 
 #start function that is called from the job
-def _start_runman_record(id: UUID, debug_date = None):
+def _start_runman_record(id: UUID, market = "US", debug_date = None):
     print(f"Start scheduled record {id}")
 
     record : RunManagerRecord = None
@@ -208,16 +207,15 @@ def _start_runman_record(id: UUID, debug_date = None):
     
     record = result
 
-    if record.market == Market.US or record.market == Market.CRYPTO:
+    if market is not None and market == "US":
-        res, sada = sch.get_todays_market_times(market=record.market, debug_date=debug_date)
+        res, sada = sch.get_todays_market_times(market=market, debug_date=debug_date)
         if res == 0:
             market_time_now, market_open_datetime, market_close_datetime = sada
             print(f"OPEN:{market_open_datetime} CLOSE:{market_close_datetime}")
         else:
-            sada = f"Market {record.market} Error getting market times (CLOSED): " + str(sada)
+            sada = f"Market {market} Error getting market times (CLOSED): " + str(sada)
             return res, record, sada
-    else:
+    
-        print("Market type is unknown.")
     if cs.is_stratin_running(record.strat_id):
         return -1, record, f"Stratin {record.strat_id} is already running"
 
@@ -231,7 +229,7 @@ def _start_runman_record(id: UUID, debug_date = None):
     return 0, record, record.runner_id
 
 #stop function that is called from the job
-def _stop_runman_record(id: UUID, debug_date = None):
+def _stop_runman_record(id: UUID, market = "US", debug_date = None):
     record = None
     #get all records
     print(f"Stopping record {id}")
@@ -306,5 +304,5 @@ if __name__ == "__main__":
     # print(f"CALL FINISHED, with {debug_date} RESULT: {res}, {result}")
 
 
-    res, result = stop_runman_record(id=id, debug_date = debug_date)
+    res, result = stop_runman_record(id=id, market = "US", debug_date = debug_date)
     print(f"CALL FINISHED, with {debug_date} RESULT: {res}, {result}")

v2realbot/scheduler/scheduler.py

@@ -2,10 +2,10 @@ import json
 import datetime
 import v2realbot.controller.services as cs
 import v2realbot.controller.run_manager as rm
-from v2realbot.common.model import RunnerView, RunManagerRecord, StrategyInstance, Runner, RunRequest, Trade, RunArchive, RunArchiveView, RunArchiveViewPagination, RunArchiveDetail, Bar, RunArchiveChange, TestList, ConfigItem, InstantIndicator, DataTablesRequest, AnalyzerInputs, Market
+from v2realbot.common.model import RunnerView, RunManagerRecord, StrategyInstance, Runner, RunRequest, Trade, RunArchive, RunArchiveView, RunArchiveViewPagination, RunArchiveDetail, Bar, RunArchiveChange, TestList, ConfigItem, InstantIndicator, DataTablesRequest, AnalyzerInputs
 from uuid import uuid4, UUID
-from v2realbot.utils.utils import json_serial, send_to_telegram, zoneNY, zonePRG, zoneUTC, fetch_calendar_data
+from v2realbot.utils.utils import json_serial, send_to_telegram, zoneNY, zonePRG, fetch_calendar_data
-from datetime import datetime, timedelta, time
+from datetime import datetime, timedelta
 from traceback import format_exc
 from rich import print
 import requests
@@ -18,18 +18,9 @@ from v2realbot.config import WEB_API_KEY
 #naplanovany jako samostatni job a triggerován pouze jednou v daný čas pro start a stop 
 #novy kod v aps_scheduler.py
 
-def is_US_market_day(date):
+def get_todays_market_times(market = "US", debug_date = None):
-    cal_dates = fetch_calendar_data(date, date)
-    if len(cal_dates) == 0:
-        print("Today is not a market day.")
-        return False, cal_dates
-    else:
-        print("Market is open")
-        return True, cal_dates
-
-def get_todays_market_times(market, debug_date = None):
     try:
-        if market == Market.US:
+        if market == "US":
             #zjistit vsechny podminky - mozna loopovat - podminky jsou vlevo
             if debug_date is not None:
                 nowNY = debug_date
@@ -37,20 +28,17 @@ def get_todays_market_times(market, debug_date = None):
                 nowNY = datetime.now().astimezone(zoneNY)
             nowNY_date = nowNY.date()
             #is market open - nyni pouze US
-            stat, calendar_dates = is_US_market_day(nowNY_date)
+            cal_dates = fetch_calendar_data(nowNY_date, nowNY_date)
-            if stat:
+
+            if len(cal_dates) == 0:
+                print("No Market Day today")
+                return -1, "Market Closed"
             #zatim podpora pouze main session
+
             #pouze main session
-                market_open_datetime = zoneNY.localize(calendar_dates[0].open)
+            market_open_datetime = zoneNY.localize(cal_dates[0].open)
-                market_close_datetime = zoneNY.localize(calendar_dates[0].close)
+            market_close_datetime = zoneNY.localize(cal_dates[0].close)
             return 0, (nowNY, market_open_datetime, market_close_datetime)
-            else:
-                return -1, "Market is closed."
-        elif market == Market.CRYPTO:
-            now_market_datetime = datetime.now().astimezone(zoneUTC)
-            market_open_datetime = datetime.combine(datetime.now(), time.min)
-            matket_close_datetime = datetime.combine(datetime.now(), time.max)
-            return 0, (now_market_datetime, market_open_datetime, matket_close_datetime)
         else:
             return -1, "Market not supported"
     except Exception as e:

v2realbot/static/index.html

@@ -131,29 +131,9 @@
 
         <!-- <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/monaco-editor/0.41.0/min/vs/editor/editor.main.js"></script>  -->
         <!-- <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/monaco-editor/0.41.0/min/vs/loader.min.js"></script>   -->
-        <script src="/static/js/systeminfo.js"> </script>
     </head>
     <body>
         <div id="main" class="mainConteiner flex-container content">
-            <div id="system-info" class="flex-items"> 
-                <label data-bs-toggle="collapse" data-bs-target="#system-info-inner" aria-expanded="true">
-                    <h4>System Info </h4>
-                </label>
-                <div id="system-info-inner" class="collapse">
-                    <div id="system-info-output"></div>
-                    <div id="graphical-output"> 
-                        <div id="disk-gauge-container">
-                            <span id="title"> Disk Space: </span>
-                            <span id="free-space">Free: -- GB</span> | 
-                            <span id="total-space">Total: -- GB</span> | 
-                            <span id="used-percent">Used: -- %</span>
-                            <div id="disk-gauge">
-                            <div id="disk-gauge-bar"></div>
-                            </div>
-                        </div>
-                    </div>
-                </div>
-            </div>
             <div id="chartContainer" class="flex-items">
                 <label data-bs-toggle="collapse" data-bs-target="#chartContainerInner" aria-expanded="true">
                     <h4>Chart</h4>
@@ -250,7 +230,6 @@
                         <!-- <table id="trades-data-table" class="dataTable no-footer" style="width: 300px;display: contents;"></table> -->
                 </div>
             </div>
-
             <div id="runner-table" class="flex-items">
                 <label data-bs-toggle="collapse" data-bs-target="#runner-table-inner">
                     <h4>Running Strategies</h4>
@@ -368,7 +347,6 @@
                                 <th>testlist_id</th>
                                 <th>Running</th>
                                 <th>RunnerId</th>
-                                <th>Market</th>
                             </tr>
                         </thead>
                         <tbody></tbody>
@@ -1171,7 +1149,7 @@
         <script src="/static/js/config.js?v=1.04"></script>
         <!-- tady zacina polska docasna lokalizace -->
         <!-- <script type="text/javascript" src="https://unpkg.com/lightweight-charts/dist/lightweight-charts.standalone.production.js"></script> -->
-        <script type="text/javascript" src="/static/js/libs/lightweightcharts/lightweight-charts.standalone.production413.js"></script>
+        <script type="text/javascript" src="/static/js/libs/lightweightcharts/lightweight-charts.standalone.production410.js"></script>
         <script src="/static/js/dynamicbuttons.js?v=1.05"></script>
     
        

v2realbot/static/js/archivechart.js

@@ -11,44 +11,6 @@ var markersLine = null
 var avgBuyLine = null
 var profitLine = null
 var slLine = []
-
-//create function which for each ACCOUNT1, ACCOUNT2 or ACCOUNT3 returns color for buy and color for sell - which can be strings representing color
-//HELPERS FUNCTION - will go to utils
-/**
- * Returns an object containing the colors for buy and sell for the specified account.
- *
- * Parameters:
- *     account (string): The account for which to retrieve the colors (ACCOUNT1, ACCOUNT2, or ACCOUNT3).
- *
- * Returns:
- *     object: An object with 'buy' and 'sell' properties containing the corresponding color strings.
- * 
- * Account 1:
-#FF6B6B, #FF9999
-Account 2:
-#4ECDC4, #83E8E1
-Account 3:
-#FFD93D, #FFE787
-Account 4:
-#6C5CE7, #A29BFE
-Another option for colors:
-
-#1F77B4 (Entry) and #AEC7E8 (Exit)
-#FF7F0E (Entry) and #FFBB78 (Exit)
-#2CA02C (Entry) and #98DF8A (Exit)
-#D62728 (Entry) and #FF9896 (Exit)
-    */
-function getAccountColors(account) {
-    const accountColors = {
-        ACCOUNT1: { accid: 'A1', buy: '#FF7F0E', sell: '#FFBB78' },
-        ACCOUNT2: { accid: 'A2',buy: '#1F77B4', sell: '#AEC7E8' },
-        ACCOUNT3: { accid: 'A3',buy: '#2CA02C', sell: '#98DF8A' },
-        ACCOUNT4: { accid: 'A4',buy: '#D62728', sell: '#FF9896' },
-        ACCOUNT5: { accid: 'A5',buy: 'purple', sell: 'orange' }
-    };
-    return accountColors[account] || { buy: '#37cade', sell: 'red' };
-}  
-
 //TRANSFORM object returned from REST API get_arch_run_detail
 //to series and markers required by lightweigth chart
 //input array object bars = { high: [1,2,3], time: [1,2,3], close: [2,2,2]...}
@@ -72,11 +34,6 @@ function transform_data(data) {
     //cas of first record, nekdy jsou stejny - musim pridat setinku
     prev_cas = 0
     if ((data.ext_data !== null) && (data.ext_data.sl_history)) {
-        ///sort sl_history according to order id string - i need all same order id together
-        data.ext_data.sl_history.sort(function (a, b) {
-            return a.id.localeCompare(b.id);
-          });
-
         data.ext_data.sl_history.forEach((histRecord, index, array) => {
             
             //console.log("plnime")
@@ -91,7 +48,6 @@ function transform_data(data) {
                 //init nova sada
                 sl_line_sada = []
                 sl_line_markers_sada = []
-                sline_color = "#f5aa42"
             }
 
             prev_id = histRecord.id
@@ -109,21 +65,12 @@ function transform_data(data) {
             sline = {}
             sline["time"] = cas
             sline["value"] = histRecord.sl_val
-            if (histRecord.account) {
-                const accColors = getAccountColors(histRecord.account)
-                sline_color = histRecord.direction == "long" ? accColors.buy : accColors.sell //idealne
-                sline["color"] = sline_color
-            }
-
             sl_line_sada.push(sline)
 
-            //ZDE JSEM SKONCIL
-            //COLOR SE NASTAVUJE V SERIES OPTIONS POZDEJI - nejak vymyslet
-
             sline_markers = {}
             sline_markers["time"] = cas
             sline_markers["position"] = "inBar" 
-            sline_markers["color"] = sline_color
+            sline_markers["color"] = "#f5aa42"
             //sline_markers["shape"] = "circle"
             //console.log("SHOW_SL_DIGITS",SHOW_SL_DIGITS)
             sline_markers["text"] = SHOW_SL_DIGITS ? histRecord.sl_val.toFixed(3) : ""
@@ -294,31 +241,29 @@ function transform_data(data) {
             }
         }
         
-        const { accid: accountId,buy: buyColor, sell: sellColor } = getAccountColors(trade.account);
 
         //buy sell markery
         marker = {}
         marker["time"] = timestamp;
         // marker["position"] = (trade.order.side == "buy") ? "belowBar" : "aboveBar" 
         marker["position"] = (trade.order.side == "buy") ? "aboveBar" : "aboveBar" 
-        marker["color"] = (trade.order.side == "buy") ? buyColor : sellColor
+        marker["color"] = (trade.order.side == "buy") ? "#37cade" : "red"
         //marker["shape"] = (trade.order.side == "buy") ? "arrowUp" : "arrowDown"
         marker["shape"] = (trade.order.side == "buy") ? "arrowUp" : "arrowDown"
         //marker["text"] =  trade.qty + "/" + trade.price
         qt_optimized = (trade.order.qty % 1000 === 0) ? (trade.order.qty / 1000).toFixed(1) + 'K' : trade.order.qty
   
-        marker["text"] = accountId + " " //account shortcut
         if (CHART_SHOW_TEXT) {
             //včetně qty
             //marker["text"] =  qt_optimized + "@" + trade.price
                 
             //bez qty
-            marker["text"] +=  trade.price
+            marker["text"] =  trade.price
             closed_trade_marker_and_profit = (trade.profit) ? "c" + trade.profit.toFixed(1) + "/" + trade.profit_sum.toFixed(1) : "c"
             marker["text"] += (trade.position_qty == 0) ? closed_trade_marker_and_profit : ""
         } else {
             closed_trade_marker_and_profit = (trade.profit) ? "c" + trade.profit.toFixed(1) + "/" + trade.profit_sum.toFixed(1) : "c"
-            marker["text"] += (trade.position_qty == 0) ? closed_trade_marker_and_profit : trade.price.toFixed(3)
+            marker["text"] = (trade.position_qty == 0) ? closed_trade_marker_and_profit : trade.price.toFixed(3)
         }
 
         markers.push(marker)
@@ -899,7 +844,7 @@ function display_buy_markers(data) {
         //console.log("uvnitr")
         slLine_temp = chart.addLineSeries({
             //    title: "avgpbuyline",
-                color: slRecord[0]["color"] ? slRecord[0]["color"] : '#e4c76d',
+                color: '#e4c76d',
             //    color: 'transparent',
                 lineWidth: 1,
                 lastValueVisible: false

v2realbot/static/js/systeminfo.js

@@ -1,31 +0,0 @@
-    function get_system_info() {
-        console.log('Button get system status clicked')
-        $.ajax({
-            url: '/system-info',
-            type: 'GET',
-            beforeSend: function (xhr) {
-                xhr.setRequestHeader('X-API-Key',
-                API_KEY); },
-            success: function(response) {
-                $.each(response, function(index, item) {
-                    if (index=="disk_space") {
-                        $('#disk-gauge-bar').css('width', response.disk_space.used_percentage + '%');
-                        $('#free-space').text('Free: ' + response.disk_space.free + ' GB');
-                        $('#total-space').text('Total: ' + response.disk_space.total + ' GB');
-                        $('#used-percent').text('Used: ' + response.disk_space.used_percentage + '%');
-                    } else {
-                        var formatted_item = JSON.stringify(item, null, 4)
-                        $('#system-info-output').append('<p>' + index + ': ' + formatted_item + '</p>');
-                    }
-                });            
-            },
-            error: function(xhr, status, error) {
-                $('#disk-gauge-bar').html('An error occurred: ' + error + xhr.responseText + status);
-            }
-        });
-    }
-
-
-$(document).ready(function(){
-  get_system_info()
-});

v2realbot/static/js/tables/archivetable/init.js

@@ -172,7 +172,7 @@ function initialize_archiveRecords() {
                     {
                         targets: [13,14,15],
                         render: function ( data, type, row ) {
-                            return '<div class="tdsmall">'+JSON.stringify(data, null, 2)+'</div>'
+                            return '<div class="tdsmall">'+data+'</div>'
                         },
                     },
                     {

v2realbot/static/js/tables/runmanager/init.js

@@ -46,7 +46,6 @@ function initialize_runmanagerRecords() {
                         {data: 'testlist_id', visible: true},
                         {data: 'strat_running', visible: true},
                         {data: 'runner_id', visible: true},                  
-                        {data: 'market', visible: true},                  
                     ],
             paging: true,
             processing: true,

v2realbot/static/js/utils/utils.js

@@ -371,10 +371,9 @@ function initialize_chart() {
     }
 
     chart = LightweightCharts.createChart(document.getElementById('chart'), chartOptions);
-    chart.applyOptions({ timeScale: { visible: true, timeVisible: true, secondsVisible: true, minBarSpacing: 0.003}, crosshair: {
+    chart.applyOptions({ timeScale: { visible: true, timeVisible: true, secondsVisible: true }, crosshair: {
         mode: LightweightCharts.CrosshairMode.Normal, labelVisible: true
     }})
-    console.log("chart intiialized")
 }
 
 //mozna atributy last value visible

v2realbot/static/main.css

@@ -994,24 +994,3 @@ pre {
 #datepicker:disabled {
   background-color: #f2f2f2;
 }
-
-#disk-gauge-container {
-  text-align: center;
-  width: 400px;
-}
-
-
-#disk-gauge {
-  width: 100%;
-  height: 20px;
-  background-color: #ddd;
-  border-radius: 10px;
-  overflow: hidden;
-}
-
-#disk-gauge-bar {
-  height: 100%;
-  background-color: #4285F4;
-  width: 0%; /* Initial state */
-  border-radius: 10px;
-}

v2realbot/strategy/StrategyClassicSL.py

@@ -4,7 +4,7 @@ from v2realbot.utils.tlog import tlog, tlog_exception
 from v2realbot.enums.enums import Mode, Order, Account, RecordType, Followup
 #from alpaca.trading.models import TradeUpdate
 from  v2realbot.common.model import TradeUpdate
-from v2realbot.common.model import Trade, TradeDirection, TradeStatus
+from v2realbot.common.PrescribedTradeModel import Trade, TradeDirection, TradeStatus
 from alpaca.trading.enums import TradeEvent, OrderStatus
 from v2realbot.indicators.indicators import ema
 import orjson
@@ -44,8 +44,7 @@ class StrategyClassicSL(Strategy):
                 msg = f"QUITTING {hard_cutoff=} MAX SUM REL PROFIT REACHED {max_sum_profit_to_quit_rel=} {self.state.profit=} {rel_profit=} relprofits:{str(self.state.rel_profit_cum)}"
                 printanyway(msg)
                 self.state.ilog(e=msg)
-                for account in self.accounts:
+                self.state.vars.pending = "max_sum_profit_to_quit_rel"
-                    self.state.account_variables[account.name].pending = "max_sum_profit_to_quit_rel"
                 if self.mode not in [Mode.BT, Mode.PREP]:
                     send_to_telegram(msg)
                 if hard_cutoff:
@@ -58,8 +57,7 @@ class StrategyClassicSL(Strategy):
                 msg=f"QUITTING {hard_cutoff=} MAX SUM REL LOSS REACHED {max_sum_loss_to_quit_rel=} {self.state.profit=} {rel_profit=} relprofits:{str(self.state.rel_profit_cum)}"
                 printanyway(msg)
                 self.state.ilog(e=msg)
-                for account in self.accounts:
+                self.state.vars.pending = "max_sum_loss_to_quit_rel"
-                    self.state.account_variables[account.name].pending = "max_sum_loss_to_quit_rel"
                 if self.mode not in [Mode.BT, Mode.PREP]:
                     send_to_telegram(msg)
                 if hard_cutoff:
@@ -73,8 +71,7 @@ class StrategyClassicSL(Strategy):
                 msg = f"QUITTING {hard_cutoff=} MAX SUM ABS PROFIT REACHED {max_sum_profit_to_quit=} {self.state.profit=} {rel_profit=} relprofits:{str(self.state.rel_profit_cum)}"
                 printanyway(msg)
                 self.state.ilog(e=msg)
-                for account in self.accounts:
+                self.state.vars.pending = "max_sum_profit_to_quit"
-                    self.state.account_variables[account.name].pending = "max_sum_profit_to_quit"
                 if self.mode not in [Mode.BT, Mode.PREP]:
                     send_to_telegram(msg)
                 if hard_cutoff:
@@ -87,8 +84,7 @@ class StrategyClassicSL(Strategy):
                 msg = f"QUITTING {hard_cutoff=} MAX SUM ABS LOSS REACHED {max_sum_loss_to_quit=} {self.state.profit=} {rel_profit=} relprofits:{str(self.state.rel_profit_cum)}"
                 printanyway(msg)
                 self.state.ilog(e=msg)
-                for account in self.accounts:
+                self.state.vars.pending = "max_sum_loss_to_quit"
-                    self.state.account_variables[account.name].pending = "max_sum_loss_to_quit"
                 if self.mode not in [Mode.BT, Mode.PREP]:
                     send_to_telegram(msg)
                 if hard_cutoff:
@@ -99,10 +95,9 @@ class StrategyClassicSL(Strategy):
 
         return False
 
-    async def add_followup(self, direction: TradeDirection, size: int, signal_name: str, account: Account):
+    async def add_followup(self, direction: TradeDirection, size: int, signal_name: str):
         trade_to_add = Trade(
             id=uuid4(),
-            account=account,
             last_update=datetime.fromtimestamp(self.state.time).astimezone(zoneNY),
             status=TradeStatus.READY,
             size=size,
@@ -113,48 +108,45 @@ class StrategyClassicSL(Strategy):
 
         self.state.vars.prescribedTrades.append(trade_to_add)
         
-        self.state.account_variables[account.name].requested_followup = None
+        self.state.vars.requested_followup = None
 
-        self.state.ilog(e=f"FOLLOWUP {direction} - {account} added to prescr.trades {signal_name=} {size=}", trade=trade_to_add)
+        self.state.ilog(e=f"FOLLOWUP {direction} added to prescr.trades {signal_name=} {size=}", trade=trade_to_add)
 
     async def orderUpdateBuy(self, data: TradeUpdate):
         o: Order = data.order
         signal_name = None
-        account = data.account
         ##nejak to vymyslet, aby se dal poslat cely Trade a serializoval se
-        self.state.ilog(e="Příchozí BUY notif"+account, msg=o.status, trade=transform_data(data, json_serial))
+        self.state.ilog(e="Příchozí BUY notif", msg=o.status, trade=transform_data(data, json_serial))
 
         if data.event == TradeEvent.FILL or data.event == TradeEvent.PARTIAL_FILL:
 
            #pokud jde o fill pred kterym je partail, muze se stat, ze uz budou vynulovany pozice, toto je pojistka
             #jde o uzavření short pozice - počítáme PROFIT
-            if int(self.state.account_variables[account.name].positions) < 0 or (int(self.state.account_variables[account.name].positions) == 0 and self.state.account_variables[account.name].wait_for_fill is not None):
+            if int(self.state.positions) < 0 or (int(self.state.positions) == 0 and self.state.wait_for_fill is not None):
 
-                if data.event == TradeEvent.PARTIAL_FILL and self.state.account_variables[account.name].wait_for_fill is None:
+                if data.event == TradeEvent.PARTIAL_FILL and self.state.wait_for_fill is None:
                     #timto si oznacime, ze po partialu s vlivem na PROFIT musime cekat na FILL a zaroven ukladame prum cenu, kterou potrebujeme na vypocet profitu u fillu
-                    self.state.account_variables[account.name].wait_for_fill = float(self.state.account_variables[account.name].avgp)
+                    self.state.wait_for_fill = float(self.state.avgp)
 
                 #PROFIT pocitame z TradeUpdate.price a TradeUpdate.qty - aktualne provedene mnozstvi a cena
                 #naklady vypocteme z prumerne ceny, kterou mame v pozicich
                 bought_amount = data.qty * data.price
                 #podle prumerne vstupni ceny, kolik stalo toto mnozstvi
-                if float(self.state.account_variables[account.name].avgp) > 0:
+                if float(self.state.avgp) > 0:
-                    vstup_cena = float(self.state.account_variables[account.name].avgp)
+                    vstup_cena = float(self.state.avgp)
-                elif float(self.state.account_variables[account.name].avgp) == 0 and self.state.account_variables[account.name].wait_for_fill is not None:
+                elif float(self.state.avgp) == 0 and self.state.wait_for_fill is not None:
-                    vstup_cena = float(self.state.account_variables[account.name].wait_for_fill)
+                    vstup_cena = float(self.state.wait_for_fill)
                 else:
                     vstup_cena = 0
 
                 avg_costs = vstup_cena * float(data.qty)
                 
                 if avg_costs == 0:
-                    self.state.ilog(e="ERR: Nemame naklady na PROFIT, AVGP je nula. Zaznamenano jako 0"+account, msg="naklady=utrzena cena. TBD opravit.")
+                    self.state.ilog(e="ERR: Nemame naklady na PROFIT, AVGP je nula. Zaznamenano jako 0", msg="naklady=utrzena cena. TBD opravit.")
                     avg_costs = bought_amount
 
                 trade_profit = round((avg_costs-bought_amount),2)
-                #celkovy profit
+                self.state.profit += trade_profit
-                self.state.profit += trade_profit #overall abs profit
-                self.state.account_variables[account.name].profit += trade_profit #account profit
 
                 rel_profit = 0
                 #spoctene celkovy relativni profit za trade v procentech ((trade_profit/vstup_naklady)*100)
@@ -168,32 +160,30 @@ class StrategyClassicSL(Strategy):
                 if data.event == TradeEvent.FILL:
                     #jde o partial EXIT dvááme si rel.profit do docasne promenne, po poslednim exitu z nich vypocteme skutecny rel.profit
                     if data.position_qty != 0:
-                        self.state.account_variables[account.name].docasny_rel_profit.append(rel_profit)
+                        self.state.docasny_rel_profit.append(rel_profit)
                         partial_exit = True
                     else:
                         #jde o posledni z PARTIAL EXITU tzn.data.position_qty == 0
-                        if len(self.state.account_variables[account.name].docasny_rel_profit) > 0:
+                        if len(self.state.docasny_rel_profit) > 0:
                             #pricteme aktualni rel profit
-                            self.state.account_variables[account.name].docasny_rel_profit.append(rel_profit)
+                            self.state.docasny_rel_profit.append(rel_profit)
                             #a z rel profitu tohoto tradu vypocteme prumer, ktery teprve ulozime
-                            rel_profit = round(np.mean(self.state.account_variables[account.name].docasny_rel_profit),5)
+                            rel_profit = round(np.mean(self.state.docasny_rel_profit),5)
-                            self.state.account_variables[account.name].docasny_rel_profit = []
+                            self.state.docasny_rel_profit = []
                             partial_last = True
 
-                        self.state.rel_profit_cum.append(rel_profit) #overall cum rel profit
+                        self.state.rel_profit_cum.append(rel_profit)
-                        self.state.account_variables[account.name].rel_profit_cum.append(rel_profit) #account cum rel profit
-
                         rel_profit_cum_calculated = round(np.sum(self.state.rel_profit_cum),5)
 
-                        #pro martingale updatujeme loss_series_cnt - 
+                        #pro martingale updatujeme loss_series_cnt
                         self.state.vars["transferables"]["martingale"]["cont_loss_series_cnt"] = 0 if rel_profit > 0 else self.state.vars["transferables"]["martingale"]["cont_loss_series_cnt"]+1
                         self.state.ilog(lvl=1, e=f"update cont_loss_series_cnt na {self.state.vars['transferables']['martingale']['cont_loss_series_cnt']}")
 
-                self.state.ilog(e=f"BUY notif {account} - SHORT PROFIT: {partial_exit=} {partial_last=} {round(float(trade_profit),3)} celkem abs:{round(float(self.state.profit),3)} rel:{float(rel_profit)} rel_cum:{round(rel_profit_cum_calculated,7)}", msg=str(data.event), rel_profit_cum=str(self.state.rel_profit_cum), bought_amount=bought_amount, avg_costs=avg_costs, trade_qty=data.qty, trade_price=data.price, orderid=str(data.order.id))
+                self.state.ilog(e=f"BUY notif - SHORT PROFIT: {partial_exit=} {partial_last=} {round(float(trade_profit),3)} celkem:{round(float(self.state.profit),3)} rel:{float(rel_profit)} rel_cum:{round(rel_profit_cum_calculated,7)}", msg=str(data.event), rel_profit_cum=str(self.state.rel_profit_cum), bought_amount=bought_amount, avg_costs=avg_costs, trade_qty=data.qty, trade_price=data.price, orderid=str(data.order.id))
 
                 #zapsat profit do prescr.trades
                 for trade in self.state.vars.prescribedTrades:
-                    if trade.id == self.state.account_variables[account.name].pending:
+                    if trade.id == self.state.vars.pending:
                         trade.last_update = datetime.fromtimestamp(self.state.time).astimezone(zoneNY)
                         trade.profit += trade_profit
                         #pro ulozeni do tradeData scitame vsechen zisk z tohoto tradu (kvuli partialum)
@@ -211,7 +201,7 @@ class StrategyClassicSL(Strategy):
 
                 if data.event == TradeEvent.FILL:
                     #mazeme self.state.
-                    self.state.account_variables[account.name].wait_for_fill = None
+                    self.state.wait_for_fill = None
                     #zapsat update profitu do tradeList
                     for tradeData in self.state.tradeList:
                         if tradeData.execution_id == data.execution_id:
@@ -219,7 +209,7 @@ class StrategyClassicSL(Strategy):
                             setattr(tradeData, "profit", trade_profit)
                             setattr(tradeData, "profit_sum", self.state.profit)
                             setattr(tradeData, "signal_name", signal_name)
-                            setattr(tradeData, "prescribed_trade_id", self.state.account_variables[account.name].pending)
+                            setattr(tradeData, "prescribed_trade_id", self.state.vars.pending)
                             #self.state.ilog(f"updatnut tradeList o profit", tradeData=orjson.loads(orjson.dumps(tradeData, default=json_serial, option=orjson.OPT_PASSTHROUGH_DATETIME)))
                             setattr(tradeData, "rel_profit", rel_profit)
                             setattr(tradeData, "rel_profit_cum", rel_profit_cum_calculated)
@@ -230,16 +220,16 @@ class StrategyClassicSL(Strategy):
 
                     #pIF REVERSAL REQUIRED - reverse position is added to prescr.Trades with same signal name
                     #jen při celém FILLU
-                    if data.event == TradeEvent.FILL and self.state.account_variables[account.name].requested_followup is not None:
+                    if data.event == TradeEvent.FILL and self.state.vars.requested_followup is not None:
-                            if self.state.account_variables[account.name].requested_followup == Followup.REVERSE:
+                            if self.state.vars.requested_followup == Followup.REVERSE:
-                                await self.add_followup(direction=TradeDirection.LONG, size=o.qty, signal_name=signal_name, account=account)
+                                await self.add_followup(direction=TradeDirection.LONG, size=o.qty, signal_name=signal_name)
-                            elif self.state.account_variables[account.name].requested_followup == Followup.ADD:
+                            elif self.state.vars.requested_followup == Followup.ADD:
                                 #zatim stejna SIZE
-                                await self.add_followup(direction=TradeDirection.SHORT, size=o.qty, signal_name=signal_name, account=account)
+                                await self.add_followup(direction=TradeDirection.SHORT, size=o.qty, signal_name=signal_name)
             else:
                 #zjistime nazev signalu a updatneme do tradeListu - abychom meli svazano
                 for trade in self.state.vars.prescribedTrades:
-                    if trade.id == self.state.account_variables[account.name].pending:
+                    if trade.id == self.state.vars.pending:
                         signal_name = trade.generated_by
                         #zapiseme entry_time (jen pokud to neni partial add) - tzn. jen poprvé
                         if data.event == TradeEvent.FILL and trade.entry_time is None:
@@ -249,7 +239,7 @@ class StrategyClassicSL(Strategy):
                 for tradeData in self.state.tradeList:
                     if tradeData.execution_id == data.execution_id:
                         setattr(tradeData, "signal_name", signal_name)
-                        setattr(tradeData, "prescribed_trade_id", self.state.account_variables[account.name].pending)
+                        setattr(tradeData, "prescribed_trade_id", self.state.vars.pending)
 
                 self.state.ilog(e="BUY: Jde o LONG nakuú nepocitame profit zatim")
 
@@ -258,47 +248,43 @@ class StrategyClassicSL(Strategy):
                     self.state.last_entry_price["long"] = data.price
 
                     #pokud neni nastaveno goal_price tak vyplnujeme defaultem
-                    if self.state.account_variables[account.name].activeTrade.goal_price is None:
+                    if self.state.vars.activeTrade.goal_price is None:
                         dat = dict(close=data.price)
-                        self.state.account_variables[account.name].activeTrade.goal_price = get_profit_target_price(self.state, dat, self.state.account_variables[account.name].activeTrade, TradeDirection.LONG)
+                        self.state.vars.activeTrade.goal_price = get_profit_target_price(self.state, dat, TradeDirection.LONG)
 
             #ic("vstupujeme do orderupdatebuy")
             print(data)
             #dostavame zde i celkové akutální množství - ukládáme
-            self.state.account_variables[account.name].positions = data.position_qty
+            self.state.positions = data.position_qty
-            self.state.account_variables[account.name].avgp, self.state.account_variables[account.name].positions = self.state.interface[account.name].pos()
+            self.state.avgp, self.state.positions = self.state.interface.pos()
 
         if o.status == OrderStatus.FILLED or o.status == OrderStatus.CANCELED:
             #davame pryc pending
-            self.state.account_variables[account.name].pending = None
+            self.state.vars.pending = None
 
 
     async def orderUpdateSell(self, data: TradeUpdate): 
         
-        account = data.account
+        self.state.ilog(e="Příchozí SELL notif", msg=data.order.status, trade=transform_data(data, json_serial))
-        #TODO tady jsem skoncil, pak projit vsechen kod na state.avgp a prehodit
-
-
-        self.state.ilog(e=f"Příchozí SELL notif - {account}", msg=data.order.status, trade=transform_data(data, json_serial))
 
         #naklady vypocteme z prumerne ceny, kterou mame v pozicich
         if data.event == TradeEvent.FILL or data.event == TradeEvent.PARTIAL_FILL:            
 
            #pokud jde o fill pred kterym je partail, muze se stat, ze uz budou vynulovany pozice, toto je pojistka
            #jde o uzavření long pozice - počítáme PROFIT
-            if int(self.state.account_variables[account.name].positions) > 0 or (int(self.state.account_variables[account.name].positions) == 0 and self.state.account_variables[account.name].wait_for_fill is not None):
+            if int(self.state.positions) > 0 or (int(self.state.positions) == 0 and self.state.wait_for_fill is not None):
 
-                if data.event == TradeEvent.PARTIAL_FILL and self.state.account_variables[account.name].wait_for_fill is None:
+                if data.event == TradeEvent.PARTIAL_FILL and self.state.wait_for_fill is None:
                     #timto si oznacime, ze po partialu s vlivem na PROFIT musime cekat na FILL a zaroven ukladame prum cenu, kterou potrebujeme na vypocet profitu u fillu
-                    self.state.account_variables[account.name].wait_for_fill = float(self.state.account_variables[account.name].avgp)
+                    self.state.wait_for_fill = float(self.state.avgp)
 
                 #PROFIT pocitame z TradeUpdate.price a TradeUpdate.qty - aktualne provedene mnozstvi a cena
                 #naklady vypocteme z prumerne ceny, kterou mame v pozicich
                 sold_amount = data.qty * data.price
-                if float(self.state.account_variables[account.name].avgp) > 0:
+                if float(self.state.avgp) > 0:
-                    vstup_cena = float(self.state.account_variables[account.name].avgp)
+                    vstup_cena = float(self.state.avgp)
-                elif float(self.state.account_variables[account.name].avgp) == 0 and self.state.account_variables[account.name].wait_for_fill is not None:
+                elif float(self.state.avgp) == 0 and self.state.wait_for_fill is not None:
-                    vstup_cena = float(self.state.account_variables[account.name].wait_for_fill)
+                    vstup_cena = float(self.state.wait_for_fill)
                 else:
                     vstup_cena = 0
 
@@ -306,12 +292,11 @@ class StrategyClassicSL(Strategy):
                 avg_costs = vstup_cena * float(data.qty)
  
                 if avg_costs == 0:
-                    self.state.ilog(e=f"ERR: {account} Nemame naklady na PROFIT, AVGP je nula. Zaznamenano jako 0", msg="naklady=utrzena cena. TBD opravit.")
+                    self.state.ilog(e="ERR: Nemame naklady na PROFIT, AVGP je nula. Zaznamenano jako 0", msg="naklady=utrzena cena. TBD opravit.")
                     avg_costs = sold_amount
                 
                 trade_profit = round((sold_amount - avg_costs),2)
-                self.state.profit += trade_profit #celkový profit
+                self.state.profit += trade_profit
-                self.state.account_variables[account.name].profit += trade_profit #account specific profit
 
                 rel_profit = 0
                 #spoctene celkovy relativni profit za trade v procentech ((trade_profit/vstup_naklady)*100)
@@ -324,31 +309,30 @@ class StrategyClassicSL(Strategy):
                 if data.event == TradeEvent.FILL:
                     #jde o partial EXIT dvááme si rel.profit do docasne promenne, po poslednim exitu z nich vypocteme skutecny rel.profit
                     if data.position_qty != 0:
-                        self.state.account_variables[account.name].docasny_rel_profit.append(rel_profit)
+                        self.state.docasny_rel_profit.append(rel_profit)
                         partial_exit = True
                     else:
                         #jde o posledni z PARTIAL EXITU tzn.data.position_qty == 0
-                        if len(self.state.account_variables[account.name].docasny_rel_profit) > 0:
+                        if len(self.state.docasny_rel_profit) > 0:
                             #pricteme aktualni rel profit
-                            self.state.account_variables[account.name].docasny_rel_profit.append(rel_profit)
+                            self.state.docasny_rel_profit.append(rel_profit)
                             #a z rel profitu tohoto tradu vypocteme prumer, ktery teprve ulozime
-                            rel_profit = round(np.mean(self.state.account_variables[account.name].docasny_rel_profit),5)
+                            rel_profit = round(np.mean(self.state.docasny_rel_profit),5)
-                            self.state.account_variables[account.name].docasny_rel_profit = []
+                            self.state.docasny_rel_profit = []
                             partial_last = True
 
-                        self.state.rel_profit_cum.append(rel_profit) #overall rel profit
+                        self.state.rel_profit_cum.append(rel_profit)
-                        self.state.account_variables[account.name].rel_profit_cum.append(rel_profit) #account cum rel profit
                         rel_profit_cum_calculated = round(np.sum(self.state.rel_profit_cum),5)
 
                         #pro martingale updatujeme loss_series_cnt
                         self.state.vars["transferables"]["martingale"]["cont_loss_series_cnt"] = 0 if rel_profit > 0 else self.state.vars["transferables"]["martingale"]["cont_loss_series_cnt"]+1
                         self.state.ilog(lvl=1, e=f"update cont_loss_series_cnt na {self.state.vars['transferables']['martingale']['cont_loss_series_cnt']}")
 
-                self.state.ilog(e=f"SELL notif {account.name}- LONG PROFIT {partial_exit=} {partial_last=}:{round(float(trade_profit),3)} celkem:{round(float(self.state.profit),3)} rel:{float(rel_profit)} rel_cum:{round(rel_profit_cum_calculated,7)}", msg=str(data.event), rel_profit_cum = str(self.state.rel_profit_cum), sold_amount=sold_amount, avg_costs=avg_costs, trade_qty=data.qty, trade_price=data.price, orderid=str(data.order.id))
+                self.state.ilog(e=f"SELL notif - LONG PROFIT {partial_exit=} {partial_last=}:{round(float(trade_profit),3)} celkem:{round(float(self.state.profit),3)} rel:{float(rel_profit)} rel_cum:{round(rel_profit_cum_calculated,7)}", msg=str(data.event), rel_profit_cum = str(self.state.rel_profit_cum), sold_amount=sold_amount, avg_costs=avg_costs, trade_qty=data.qty, trade_price=data.price, orderid=str(data.order.id))
 
                 #zapsat profit do prescr.trades
                 for trade in self.state.vars.prescribedTrades:
-                    if trade.id == self.state.account_variables[account.name].pending:
+                    if trade.id == self.state.vars.pending:
                         trade.last_update = datetime.fromtimestamp(self.state.time).astimezone(zoneNY)
                         trade.profit += trade_profit
                         #pro ulozeni do tradeData scitame vsechen zisk z tohoto tradu (kvuli partialum)
@@ -365,7 +349,7 @@ class StrategyClassicSL(Strategy):
 
                 if data.event == TradeEvent.FILL:
                     #mazeme self.state.
-                    self.state.account_variables[account.name].wait_for_fill = None
+                    self.state.wait_for_fill = None
                     #zapsat update profitu do tradeList
                     for tradeData in self.state.tradeList:
                         if tradeData.execution_id == data.execution_id:
@@ -373,7 +357,7 @@ class StrategyClassicSL(Strategy):
                             setattr(tradeData, "profit", trade_profit)
                             setattr(tradeData, "profit_sum", self.state.profit)
                             setattr(tradeData, "signal_name", signal_name)
-                            setattr(tradeData, "prescribed_trade_id", self.state.account_variables[account.name].pending)
+                            setattr(tradeData, "prescribed_trade_id", self.state.vars.pending)
                             #self.state.ilog(f"updatnut tradeList o profi {str(tradeData)}")
                             setattr(tradeData, "rel_profit", rel_profit)
                             setattr(tradeData, "rel_profit_cum", rel_profit_cum_calculated)
@@ -383,17 +367,17 @@ class StrategyClassicSL(Strategy):
                 if data.event == TradeEvent.FILL and await self.stop_when_max_profit_loss() is False:
 
                     #IF REVERSAL REQUIRED - reverse position is added to prescr.Trades with same signal name
-                    if data.event == TradeEvent.FILL and self.state.account_variables[account.name].requested_followup is not None:
+                    if data.event == TradeEvent.FILL and self.state.vars.requested_followup is not None:
-                            if self.state.account_variables[account.name].requested_followup == Followup.REVERSE:
+                            if self.state.vars.requested_followup == Followup.REVERSE:
                                 await self.add_followup(direction=TradeDirection.SHORT, size=data.order.qty, signal_name=signal_name)
-                            elif self.state.account_variables[account.name].requested_followup == Followup.ADD:
+                            elif self.state.vars.requested_followup == Followup.ADD:
                                 #zatim stejna SIZE
                                 await self.add_followup(direction=TradeDirection.LONG, size=data.order.qty, signal_name=signal_name)                            
 
             else:
                 #zjistime nazev signalu a updatneme do tradeListu - abychom meli svazano
                 for trade in self.state.vars.prescribedTrades:
-                    if trade.id == self.state.account_variables[account.name].pending:
+                    if trade.id == self.state.vars.pending:
                         signal_name = trade.generated_by
                         #zapiseme entry_time (jen pokud to neni partial add) - tzn. jen poprvé
                         if data.event == TradeEvent.FILL and trade.entry_time is None:
@@ -403,7 +387,7 @@ class StrategyClassicSL(Strategy):
                 for tradeData in self.state.tradeList:
                     if tradeData.execution_id == data.execution_id:
                         setattr(tradeData, "signal_name", signal_name)
-                        setattr(tradeData, "prescribed_trade_id", self.state.account_variables[account.name].pending)
+                        setattr(tradeData, "prescribed_trade_id", self.state.vars.pending)
 
                 self.state.ilog(e="SELL: Jde o SHORT nepocitame profit zatim")
 
@@ -411,32 +395,32 @@ class StrategyClassicSL(Strategy):
                     #zapisujeme last entry price
                     self.state.last_entry_price["short"] = data.price
                     #pokud neni nastaveno goal_price tak vyplnujeme defaultem
-                    if self.state.account_variables[account.name].activeTrade.goal_price is None:
+                    if self.state.vars.activeTrade.goal_price is None:
                         dat = dict(close=data.price)
-                        self.state.account_variables[account.name].activeTrade.goal_price = get_profit_target_price(self.state, dat, self.state.account_variables[account.name].activeTrade, TradeDirection.SHORT)
+                        self.state.vars.activeTrade.goal_price = get_profit_target_price(self.state, dat, TradeDirection.SHORT)
                     #sem v budoucnu dat i update SL
                     #if self.state.vars.activeTrade.stoploss_value is None:
 
 
             #update pozic, v trade update je i pocet zbylych pozic
-            old_avgp = self.state.account_variables[account.name].avgp
+            old_avgp = self.state.avgp
-            old_pos = self.state.account_variables[account.name].positions
+            old_pos = self.state.positions
-            self.state.account_variables[account.name].positions = int(data.position_qty)
+            self.state.positions = int(data.position_qty)
             if int(data.position_qty) == 0:
-                self.state.account_variables[account.name].avgp = 0
+                self.state.avgp = 0
 
-            self.state.ilog(e="SELL notifikace "+str(data.order.status), msg="update pozic", old_avgp=old_avgp, old_pos=old_pos, avgp=self.state.account_variables[account.name].avgp, pos=self.state.account_variables[account.name].positions, orderid=str(data.order.id))
+            self.state.ilog(e="SELL notifikace "+str(data.order.status), msg="update pozic", old_avgp=old_avgp, old_pos=old_pos, avgp=self.state.avgp, pos=self.state.positions, orderid=str(data.order.id))
-            #self.state.account_variables[account.name].avgp, self.state.account_variables[account.name].positions = self.interface.pos()
+            #self.state.avgp, self.state.positions = self.interface.pos()
 
         if data.event == TradeEvent.FILL or data.event == TradeEvent.CANCELED:
             print("Příchozí SELL notifikace - complete FILL nebo CANCEL", data.event)
-            self.state.account_variables[account.name].pending = None
+            self.state.vars.pending = None
-            a,p = self.interface[account.name].pos() #TBD maybe optimize for speed
+            a,p = self.interface.pos()
             #pri chybe api nechavame puvodni hodnoty
             if a != -1:
-                self.state.account_variables[account.name].avgp, self.state.account_variables[account.name].positions = a,p
+                self.state.avgp, self.state.positions = a,p
             else: self.state.ilog(e=f"Chyba pri dotažení self.interface.pos() {a}")
-            #ic(self.state.account_variables[account.name].avgp, self.state.account_variables[account.name].positions)
+            #ic(self.state.avgp, self.state.positions)
 
     #this parent method is called by strategy just once before waiting for first data
     def strat_init(self):
@@ -457,47 +441,48 @@ class StrategyClassicSL(Strategy):
         else:
             self.next(item, self.state)
 
-
     #overidden methods
     # pouziva se pri vstupu long nebo exitu short
     # osetrit uzavreni s vice nez mam
-    def buy(self, account: Account, size = None, repeat: bool = False):
+    def buy(self, size = None, repeat: bool = False):
         print("overriden buy method")
         if size is None:
             sizer = self.state.vars.chunk
         else:
             sizer = size
         #jde o uzavreni short pozice
-        if int(self.state.account_variables[account.name].positions) < 0 and (int(self.state.account_variables[account.name].positions) + int(sizer)) > 0:
+        if int(self.state.positions) < 0 and (int(self.state.positions) + int(sizer)) > 0:
-            self.state.ilog(e="buy nelze nakoupit vic nez shortuji", positions=self.state.account_variables[account.name].positions, size=size)
+            self.state.ilog(e="buy nelze nakoupit vic nez shortuji", positions=self.state.positions, size=size)
             printanyway("buy nelze nakoupit vic nez shortuji") 
             return -2
 
-        if int(self.state.account_variables[account.name].positions) >= self.state.vars.maxpozic:
+        if int(self.state.positions) >= self.state.vars.maxpozic:
-            self.state.ilog(e="buy Maxim mnozstvi naplneno", positions=self.state.account_variables[account.name].positions)
+            self.state.ilog(e="buy Maxim mnozstvi naplneno", positions=self.state.positions)
             printanyway("max mnostvi naplneno")
             return 0
 
+        self.state.blockbuy = 1
+        self.state.vars.lastbuyindex = self.state.bars['index'][-1]
         #self.state.ilog(e="send MARKET buy to if", msg="S:"+str(size), ltp=self.state.interface.get_last_price(self.state.symbol))
         self.state.ilog(e="send MARKET buy to if", msg="S:"+str(size), ltp=self.state.bars['close'][-1])
-        return self.state.interface[account.name].buy(size=sizer)
+        return self.state.interface.buy(size=sizer)
 
     #overidden methods
     # pouziva se pri vstupu short nebo exitu long
-    def sell(self, account: Account, size = None, repeat: bool = False):
+    def sell(self, size = None, repeat: bool = False):
         print("overriden sell method")
         if size is None:
-            size = abs(int(self.state.account_variables[account.name].positions))
+            size = abs(int(self.state.positions))
 
         #jde o uzavreni long pozice
-        if int(self.state.account_variables[account.name].positions) > 0 and (int(self.state.account_variables[account.name].positions) - int(size)) < 0:
+        if int(self.state.positions) > 0 and (int(self.state.positions) - int(size)) < 0:
-            self.state.ilog(e="nelze prodat vic nez longuji", positions=self.state.account_variables[account.name].positions, size=size)
+            self.state.ilog(e="nelze prodat vic nez longuji", positions=self.state.positions, size=size)
             printanyway("nelze prodat vic nez longuji") 
             return -2
 
         #pokud shortuji a mam max pozic
-        if int(self.state.account_variables[account.name].positions) < 0 and abs(int(self.state.account_variables[account.name].positions)) >= self.state.vars.maxpozic:
+        if int(self.state.positions) < 0 and abs(int(self.state.positions)) >= self.state.vars.maxpozic:
-            self.state.ilog(e="short - Maxim mnozstvi naplneno", positions=self.state.account_variables[account.name].positions, size=size)
+            self.state.ilog(e="short - Maxim mnozstvi naplneno", positions=self.state.positions, size=size)
             printanyway("short - Maxim mnozstvi naplneno") 
             return 0
 
@@ -505,4 +490,4 @@ class StrategyClassicSL(Strategy):
         #self.state.vars.lastbuyindex = self.state.bars['index'][-1]
         #self.state.ilog(e="send MARKET SELL to if", msg="S:"+str(size), ltp=self.state.interface.get_last_price(self.state.symbol))
         self.state.ilog(e="send MARKET SELL to if", msg="S:"+str(size), ltp=self.state.bars['close'][-1])
-        return self.state.interface[account.name].sell(size=size)
+        return self.state.interface.sell(size=size)

v2realbot/strategy/base.py

@@ -2,7 +2,7 @@
     Strategy base class
 """
 from datetime import datetime
-from v2realbot.utils.utils import AttributeDict, zoneNY, is_open_rush, is_close_rush, json_serial, print, gaka
+from v2realbot.utils.utils import AttributeDict, zoneNY, is_open_rush, is_close_rush, json_serial, print
 from v2realbot.utils.tlog import tlog
 from v2realbot.utils.ilog import insert_log, insert_log_multiple_queue
 from v2realbot.enums.enums import RecordType, StartBarAlign, Mode, Order, Account
@@ -16,11 +16,11 @@ from v2realbot.loader.trade_ws_streamer import Trade_WS_Streamer
 from v2realbot.interfaces.general_interface import GeneralInterface
 from v2realbot.interfaces.backtest_interface import BacktestInterface
 from v2realbot.interfaces.live_interface import LiveInterface
-import v2realbot.common.model as ptm
+import v2realbot.common.PrescribedTradeModel as ptm
 from alpaca.trading.enums import OrderSide
 from v2realbot.backtesting.backtester import Backtester
 #from alpaca.trading.models import TradeUpdate
-from v2realbot.common.model import TradeUpdate, AccountVariables
+from v2realbot.common.model import TradeUpdate
 from alpaca.trading.enums import TradeEvent, OrderStatus
 from threading import Event, current_thread
 import orjson
@@ -30,7 +30,6 @@ from collections import defaultdict
 import v2realbot.strategyblocks.activetrade.sl.optimsl as optimsl
 from tqdm import tqdm
 import v2realbot.utils.config_handler as cfh
-from typing import Dict, Set
 
 if PROFILING_NEXT_ENABLED:
     from pyinstrument import Profiler
@@ -51,8 +50,7 @@ class Strategy:
         self.rectype: RecordType = None
         self.nextnew = 1
         self.btdata: list = []
-        self.interface: Dict[str, GeneralInterface] = {}
+        self.interface: GeneralInterface = None
-        self.order_notifs: Dict[str, LiveOrderUpdatesStreamer] = {}
         self.state: StrategyState = None
         self.bt: Backtester = None
         self.debug = False
@@ -62,8 +60,8 @@ class Strategy:
         self.open_rush = open_rush
         self.close_rush = close_rush
         self._streams = []
-        #primary account from runReqs
         self.account = account
+        self.key = get_key(mode=self.mode, account=self.account)
         self.rtqueue = None
         self.runner_id = runner_id
         self.ilog_save = ilog_save
@@ -71,9 +69,6 @@ class Strategy:
         self.secondary_res_start_index = dict()
         self.last_index = -1
 
-        #set of all accounts (Account) including those from stratvars
-        self.accounts = self.get_accounts_in_stratvars_and_reqs()
-
         #TODO predelat na dynamické queues
         self.q1 = queue.Queue()
         self.q2 = queue.Queue()
@@ -88,25 +83,6 @@ class Strategy:
         self.hard_stop = False #indikuje hard stop, tedy vypnuti strategie
         self.soft_stop = False #indikuje soft stop (napr. při dosažení max zisku/ztráty), tedy pokracovani strategie, vytvareni dat, jen bez obchodu
 
-    def get_accounts_in_stratvars_and_reqs(self) -> Set:
-        """
-        Helper that retrieves distinct account values used in stratvars and in runRequest.
-
-        Returns:
-            set: A set of unique account values.
-        """
-        account_keywords = ['account', 'account_long', 'account_short']
-        account_values = set()
-
-        for signal_value in self.stratvars.get('signals', {}).values():
-            for key in account_keywords:
-                if key in signal_value:
-                    account_values.add(Account(signal_value[key]))
-
-        account_values.add(Account(self.account))
-        printnow("Distinct account values:", account_values)
-        return account_values
-
     #prdelat queue na dynamic - podle toho jak bud uchtit pracovat s multiresolutions
     #zatim jen jedna q1
     #TODO zaroven strategie musi vedet o rectypu, protoze je zpracovava
@@ -140,33 +116,25 @@ class Strategy:
             return -1
         
         self.debug = debug
+        self.key = get_key(mode=mode, account=self.account)
         
         if mode == Mode.LIVE or mode == Mode.PAPER:
             #data loader thread
             self.dataloader = Trade_WS_Streamer(name="WS-LDR-"+self.name)
-            #populate interfaces for each account
+            self.interface = LiveInterface(symbol=self.symbol, key=self.key)
-            for account in self.accounts:
-                #get key for account
-                key = get_key(mode=mode, account=Account(account))
-                self.interface[account.name] = LiveInterface(symbol=self.symbol, key=key)
             # order notif thread
-                self.order_notifs[account.name] = LiveOrderUpdatesStreamer(key=key, name="WS-STRMR-" + account.name + "-" + self.name, account=account)
+            self.order_notifs = LiveOrderUpdatesStreamer(key=self.key, name="WS-STRMR-" + self.name)
             #propojujeme notifice s interfacem (pro callback)
-                self.order_notifs[account.name].connect_callback(self)
+            self.order_notifs.connect_callback(self)
-
+            self.state = StrategyState(name=self.name, symbol = self.symbol, stratvars = self.stratvars, interface=self.interface, rectype=self.rectype, runner_id=self.runner_id, ilog_save=self.ilog_save)
-            self.state = StrategyState(name=self.name, accounts=self.accounts, account=self.account, symbol = self.symbol, stratvars = self.stratvars, interface=self.interface, rectype=self.rectype, runner_id=self.runner_id, ilog_save=self.ilog_save)
 
         elif mode == Mode.BT:
 
             self.dataloader = Trade_Offline_Streamer(start, end, btdata=self.btdata)
-            self.bt = Backtester(symbol = self.symbol, accounts=self.accounts, order_fill_callback= self.order_updates, btdata=self.btdata, cash=cash, bp_from=start, bp_to=end)
+            self.bt = Backtester(symbol = self.symbol, order_fill_callback= self.order_updates, btdata=self.btdata, cash=cash, bp_from=start, bp_to=end)
             
-            #populate interfaces for each account
+            self.interface = BacktestInterface(symbol=self.symbol, bt=self.bt)
-            for account in self.accounts:
+            self.state = StrategyState(name=self.name, symbol = self.symbol, stratvars = self.stratvars, interface=self.interface, rectype=self.rectype, runner_id=self.runner_id, bt=self.bt, ilog_save=self.ilog_save)
-                #pro backtest volame stejne oklicujeme interface
-                self.interface[account.name] = BacktestInterface(symbol=self.symbol, bt=self.bt, account=account)
-            self.state = StrategyState(name=self.name, accounts=self.accounts, account=self.account, symbol = self.symbol, stratvars = self.stratvars, interface=self.interface, rectype=self.rectype, runner_id=self.runner_id, bt=self.bt, ilog_save=self.ilog_save)
-            #no callback from bt, it is called directly
             self.order_notifs = None
             
             ##streamer bude plnit trady do listu trades - nad kterym bude pracovat paper trade
@@ -174,10 +142,10 @@ class Strategy:
             self.dataloader.add_stream(TradeAggregator2List(symbol=self.symbol,btdata=self.btdata,rectype=RecordType.TRADE))
         elif mode == Mode.PREP:
             #bt je zde jen pro udrzeni BT casu v logu atp. JInak jej nepouzivame.
-            self.bt = Backtester(symbol = self.symbol, accounts=self.accounts, order_fill_callback= self.order_updates, btdata=self.btdata, cash=cash, bp_from=start, bp_to=end)
+            self.bt = Backtester(symbol = self.symbol, order_fill_callback= self.order_updates, btdata=self.btdata, cash=cash, bp_from=start, bp_to=end)
             self.interface = None
             #self.interface = BacktestInterface(symbol=self.symbol, bt=self.bt)
-            self.state = StrategyState(name=self.name, accounts=self.accounts, account=self.account, symbol = self.symbol, stratvars = self.stratvars, interface=self.interface, rectype=self.rectype, runner_id=self.runner_id, bt=self.bt, ilog_save=self.ilog_save)
+            self.state = StrategyState(name=self.name, symbol = self.symbol, stratvars = self.stratvars, interface=self.interface, rectype=self.rectype, runner_id=self.runner_id, bt=self.bt, ilog_save=self.ilog_save)
             self.order_notifs = None        
         
         else:
@@ -346,15 +314,13 @@ class Strategy:
     """"refresh positions and avgp - for CBAR once per confirmed, for BARS each time"""
     def refresh_positions(self, item):
         if self.rectype == RecordType.BAR:
-            for account in self.accounts:
+            a,p = self.interface.pos()
-                a,p = self.interface[account.name].pos()
             if a != -1:
-                    self.state.account_variables[account.name].avgp, self.state.account_variables[account.name].positions = a, p
+                self.state.avgp, self.state.positions = a,p
         elif self.rectype in (RecordType.CBAR, RecordType.CBARVOLUME, RecordType.CBARDOLLAR, RecordType.CBARRENKO) and item['confirmed'] == 1:
-            for account in self.accounts:
+            a,p = self.interface.pos()
-                a,p = self.interface[account.name].pos()
             if a != -1:
-                    self.state.account_variables[account.name].avgp, self.state.account_variables[account.name].positions = a, p
+                self.state.avgp, self.state.positions = a,p
 
     """update state.last_trade_time a time of iteration"""
     def update_times(self, item):
@@ -450,11 +416,7 @@ class Strategy:
 
         if self.mode == Mode.LIVE or self.mode == Mode.PAPER:
             #live notification thread
-            #for all keys in self.order_notifs call start()
+            self.order_notifs.start()
-            for key in self.order_notifs:
-                self.order_notifs[key].start()
-
-                #self.order_notifs.start()
         elif self.mode == Mode.BT or self.mode == Mode.PREP:
             self.bt.backtest_start = datetime.now()
 
@@ -524,7 +486,7 @@ class Strategy:
         self.stop()
 
         if self.mode == Mode.BT:
-            print("REQUEST COUNT:", {account_str:self.interface[account_str].mincnt for account_str in self.interface})
+            print("REQUEST COUNT:", self.interface.mincnt)
 
             self.bt.backtest_end = datetime.now()
             #print(40*"*",self.mode, "BACKTEST RESULTS",40*"*")
@@ -538,9 +500,7 @@ class Strategy:
 
         #disconnect strategy from websocket trader updates
         if self.mode == Mode.LIVE or self.mode == Mode.PAPER:
-            for key in self.order_notifs:
+            self.order_notifs.disconnect_callback(self)
-                self.order_notifs[key].disconnect_callback(self)
-            #self.order_notifs.disconnect_callback(self)
 
         #necessary only for shared loaders (to keep it running for other stratefies)
         for i in self._streams:
@@ -581,11 +541,11 @@ class Strategy:
     #for order updates from LIVE or BACKTEST
     #updates are sent only for SYMBOL of strategy
 
-    async def order_updates(self, data: TradeUpdate, account: Account):
+    async def order_updates(self, data: TradeUpdate):
         if self.mode == Mode.LIVE or self.mode == Mode.PAPER:
             now = datetime.now().timestamp()
             #z alpakýho TradeEvent si udelame svuj rozsireny TradeEvent (obsahujici navic profit atp.)
-            data = TradeUpdate(**data.dict(), account=account)
+            data = TradeUpdate(**data.dict())
         else:
             now = self.bt.time
 
@@ -673,13 +633,16 @@ class Strategy:
                 for key, value in self.state.statinds.items():
                     rt_out["statinds"][key] = value
 
+            #vkladame average price and positions, pokud existuji
+            #self.state.avgp , self.state.positions
+            
             #pro typ strategie Classic, posilame i vysi stoploss
             try:
-                sl_value = gaka(self.state.account_variables, "activeTrade", lambda x: x.stoploss_value)
+                sl_value = self.state.vars["activeTrade"].stoploss_value
             except (KeyError, AttributeError):
                 sl_value = None
 
-            rt_out["positions"] = dict(time=self.state.time, positions=gaka(self.state.account_variables, "positions"), avgp=gaka(self.state.account_variables,), sl_value=sl_value)
+            rt_out["positions"] = dict(time=self.state.time, positions=self.state.positions, avgp=self.state.avgp, sl_value=sl_value)
 
             #vkladame limitku a pendingbuys
             try:
@@ -755,21 +718,17 @@ class StrategyState:
     """Strategy Stat object that is passed to callbacks
         note: 
           state.time
-          state.interface[account.name].time
+          state.interface.time
-          accounts = set of all accounts (strings)
-          account = enum of primary account (Account)
           většinou mají stejnou hodnotu, ale lišit se mužou např. v případě BT callbacku - kdy se v rámci okna končící state.time realizují objednávky, které
           triggerují callback, který následně vyvolá např. buy (ten se musí ale udít v čase fillu, tzn. callback si nastaví čas interfacu na filltime)
           po dokončení bt kroků před zahájením iterace "NEXT" se časy znovu updatnout na původni state.time
     """
-    def __init__(self, name: str, symbol: str, accounts: set, account: Account, stratvars: AttributeDict, bars: AttributeDict = {}, trades: AttributeDict = {}, interface: GeneralInterface = None, rectype: RecordType = RecordType.BAR, runner_id: UUID = None, bt: Backtester = None, ilog_save: bool = False):
+    def __init__(self, name: str, symbol: str, stratvars: AttributeDict, bars: AttributeDict = {}, trades: AttributeDict = {}, interface: GeneralInterface = None, rectype: RecordType = RecordType.BAR, runner_id: UUID = None, bt: Backtester = None, ilog_save: bool = False):
         self.vars = stratvars
         self.interface = interface
-        self.account = account #primary account
+        self.positions = 0
-        self.accounts = accounts
+        self.avgp = 0
-        #populate account variables dictionary
+        self.blockbuy = 0
-        self.account_variables: Dict[str, AccountVariables] = {account.name: AccountVariables() for account in self.accounts}
-    
         self.name = name
         self.symbol = symbol
         self.rectype = rectype
@@ -778,10 +737,11 @@ class StrategyState:
         self.time = None
         #time of last trade processed
         self.last_trade_time = 0
-        self.last_entry_price={key:dict(long=0,short=999) for key in self.accounts}
+        self.last_entry_price=dict(long=0,short=999)
         self.resolution = None
         self.runner_id = runner_id
         self.bt = bt
+        self.dont_exit_already_activated = False
         self.docasny_rel_profit = []
         self.ilog_save = ilog_save
         self.sl_optimizer_short = optimsl.SLOptimizer(ptm.TradeDirection.SHORT)
@@ -819,14 +779,18 @@ class StrategyState:
         #secondary resolution indicators
         #self.secondary_indicators = AttributeDict(time=[], sec_price=[])
         self.statinds = AttributeDict()
-
+        #these methods can be overrided by StrategyType (to add or alter its functionality)
+        self.buy = self.interface.buy
+        self.buy_l = self.interface.buy_l
+        self.sell = self.interface.sell
+        self.sell_l = self.interface.sell_l
         self.cancel_pending_buys = None
         self.iter_log_list = []
         self.dailyBars = defaultdict(dict)
         #celkovy profit (prejmennovat na profit_cum)
-        self.profit = 0 #TODO key by account?
+        self.profit = 0
         #celkovy relativni profit (obsahuje pole relativnich zisku, z jeho meanu se spocita celkovy rel_profit_cu,)
-        self.rel_profit_cum = []#TODO key by account?
+        self.rel_profit_cum = []
         self.tradeList = []
         #nova promenna pro externi data do ArchiveDetaili, napr. pro zobrazeni v grafu, je zde např. SL history
         self.extData = defaultdict(dict)
@@ -835,25 +799,6 @@ class StrategyState:
         self.today_market_close = None
         self.classed_indicators = {}
     
-    #quick interface actions to access from state without having to write interface[account.name].buy_l
-    def buy_l(self, account: Account, price: float, size: int = 1, repeat: bool = False, force: int = 0):
-        self.interface[account.name].buy_l(price, size, repeat, force)
-
-    def buy(self, account: Account, size = 1, repeat: bool = False):
-        self.interface[account.name].buy(size, repeat)
-
-    def sell_l(self, account: Account, price: float, size: int = 1, repeat: bool = False):
-        self.interface[account.name].sell_l(price, size, repeat)
-
-    def sell(self, account: Account, size = 1, repeat: bool = False):
-        self.interface[account.name].sell(size, repeat)
-    
-    def repl(self, account: Account, orderid: str, price: float = None, size: int = 1, repeat: bool = False):
-        self.interface[account.name].repl(orderid, price, size, repeat)
-    
-    def cancel(self, account: Account, orderid: str):
-        self.interface[account.name].cancel(orderid)
-
     def release(self):
         #release large variables
         self.bars = None

v2realbot/strategyblocks/activetrade/activetrade_hub.py

@@ -1,13 +1,9 @@
 from v2realbot.strategyblocks.activetrade.sl.trailsl import trail_SL_management
 from v2realbot.strategyblocks.activetrade.close.evaluate_close import eval_close_position
-from v2realbot.utils.utils import gaka
+
 def manage_active_trade(state, data):
-    accountsWithActiveTrade = gaka(state.account_variables, "activeTrade", None, lambda x: x is not None)
+    trade = state.vars.activeTrade
-    # {"account1": activeTrade,
+    if trade is None:
-    #  "account2": activeTrade}
+        return -1
-
+    trail_SL_management(state, data)
-    if len(accountsWithActiveTrade.values()) == 0:
+    eval_close_position(state, data)
-        return
-    
-    trail_SL_management(state, accountsWithActiveTrade, data)
-    eval_close_position(state, accountsWithActiveTrade, data)

v2realbot/strategyblocks/activetrade/close/close_position.py

@@ -1,7 +1,7 @@
 from v2realbot.strategy.base import StrategyState
 from v2realbot.strategy.StrategyOrderLimitVykladaciNormalizedMYSELL import StrategyOrderLimitVykladaciNormalizedMYSELL
 from v2realbot.enums.enums import RecordType, StartBarAlign, Mode, Account, Followup
-from v2realbot.common.model import Trade, TradeDirection, TradeStatus
+from v2realbot.common.PrescribedTradeModel import Trade, TradeDirection, TradeStatus
 from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, print, safe_get, is_still, is_window_open, eval_cond_dict, crossed_down, crossed_up, crossed, is_pivot, json_serial, pct_diff, create_new_bars, slice_dict_lists
 from v2realbot.utils.directive_utils import get_conditions_from_configuration
 from v2realbot.common.model import SLHistory
@@ -18,61 +18,52 @@ import os
 from traceback import format_exc
 from v2realbot.strategyblocks.activetrade.helpers import insert_SL_history
 
-#TODO tady jsem taky skoncil a pak zpetna evaluate_close (mozna zde staci jen account?)
-
 # - close means change status in prescribed Trends,update profit, delete from activeTrade
-def close_position(state: StrategyState, activeTrade: Trade, data, direction: TradeDirection, reason: str, followup: Followup = None):
+def close_position(state, data, direction: TradeDirection, reason: str, followup: Followup = None):
     followup_text = str(followup) if followup is not None else ""
-    positions = state.account_variables[activeTrade.account.name].positions
+    state.ilog(lvl=1,e=f"CLOSING TRADE {followup_text} {reason} {str(direction)}", curr_price=data["close"], trade=state.vars.activeTrade)
-    state.ilog(lvl=1,e=f"CLOSING TRADE {followup_text} {reason} {str(direction)}", curr_price=data["close"], trade=activeTrade)
     if direction == TradeDirection.SHORT:
-        res = state.buy(account=activeTrade.account, size=abs(int(positions)))
+        res = state.buy(size=abs(int(state.positions)))
         if isinstance(res, int) and res < 0:
             raise Exception(f"error in required operation {reason} {res}")
 
     elif direction == TradeDirection.LONG:
-        res = state.sell(account=activeTrade.account, size=positions)
+        res = state.sell(size=state.positions)
         if isinstance(res, int) and res < 0:
-            raise Exception(f"error in required operation STOPLOSS SELL {res}") #TBD error handling
+            raise Exception(f"error in required operation STOPLOSS SELL {res}")
     
     else:
         raise Exception(f"unknow TradeDirection in close_position")
     
     #pri uzavreni tradu zapisujeme SL history - lepsi zorbazeni v grafu
-    insert_SL_history(state, activeTrade)
+    insert_SL_history(state)
-    state.account_variables[activeTrade.account.name].pending = activeTrade.id
+    state.dont_exit_already_activated = False
-    state.account_variables[activeTrade.account.name].activeTrade = None
+    state.vars.pending = state.vars.activeTrade.id
-    #state.account_variables[activeTrade.account.name].last_exit_index = data["index"]
+    state.vars.activeTrade = None   
     state.vars.last_exit_index = data["index"]    
-    state.account_variables[activeTrade.account.name].dont_exit_already_activated = False
     if followup is not None:
-        state.account_variables[activeTrade.account.name].requested_followup = followup
+        state.vars.requested_followup = followup
 
 #close only partial position - no followup here, size multiplier must be between 0 and 1
-def close_position_partial(state, activeTrade: Trade,data, direction: TradeDirection, reason: str, size: float):
+def close_position_partial(state, data, direction: TradeDirection, reason: str, size: float):
-    positions = state.account_variables[activeTrade.account.name].positions
     if size <= 0 or size >=1:
         raise Exception(f"size must be betweem 0 and 1")
-    size_abs = abs(int(int(positions)*size))
+    size_abs = abs(int(int(state.positions)*size))
-    state.ilog(lvl=1,e=f"CLOSING TRADE PART: {size_abs} {size} {reason} {str(direction)}", curr_price=data["close"], trade=activeTrade)
+    state.ilog(lvl=1,e=f"CLOSING TRADE PART: {size_abs} {size} {reason} {str(direction)}", curr_price=data["close"], trade=state.vars.activeTrade)
     if direction == TradeDirection.SHORT:
-        res = state.buy(account=activeTrade.account, size=size_abs)
+        res = state.buy(size=size_abs)
         if isinstance(res, int) and res < 0:
             raise Exception(f"error in required operation STOPLOSS PARTIAL BUY {reason} {res}")
 
     elif direction == TradeDirection.LONG:
-        res = state.sell(account=activeTrade.account, size=size_abs)
+        res = state.sell(size=size_abs)
         if isinstance(res, int) and res < 0:
             raise Exception(f"error in required operation STOPLOSS PARTIAL SELL {res}")
     else:
         raise Exception(f"unknow TradeDirection in close_position")
     
     #pri uzavreni tradu zapisujeme SL history - lepsi zorbazeni v grafu
-    insert_SL_history(state, activeTrade)
+    insert_SL_history(state)
-    state.account_variables[activeTrade.account.name].pending = activeTrade.id
+    state.vars.pending = state.vars.activeTrade.id
-    state.account_variables[activeTrade.account.name].activeTrade = None
-    state.account_variables[activeTrade.account.name].dont_exit_already_activated = False
-    #state.account_variables[activeTrade.account.name].last_exit_index = data["index"]  
-    
     #state.vars.activeTrade = None   
-    state.vars.last_exit_index = data["index"] #ponechano mimo account
+    #state.vars.last_exit_index = data["index"]    

v2realbot/strategyblocks/activetrade/close/conditions.py

@@ -1,7 +1,7 @@
 from v2realbot.strategy.base import StrategyState
 from v2realbot.strategy.StrategyOrderLimitVykladaciNormalizedMYSELL import StrategyOrderLimitVykladaciNormalizedMYSELL
 from v2realbot.enums.enums import RecordType, StartBarAlign, Mode, Account, Followup
-from v2realbot.common.model import Trade, TradeDirection, TradeStatus
+from v2realbot.common.PrescribedTradeModel import Trade, TradeDirection, TradeStatus
 from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, print, safe_get, is_still, is_window_open, eval_cond_dict, crossed_down, crossed_up, crossed, is_pivot, json_serial, pct_diff, create_new_bars, slice_dict_lists
 from v2realbot.utils.directive_utils import get_conditions_from_configuration
 from v2realbot.common.model import SLHistory
@@ -12,9 +12,9 @@ from threading import Event
 import os
 from traceback import format_exc
 from v2realbot.strategyblocks.indicators.helpers import evaluate_directive_conditions
-from v2realbot.strategyblocks.activetrade.helpers import get_signal_section_directive, normalize_tick
+from v2realbot.strategyblocks.activetrade.helpers import get_override_for_active_trade, normalize_tick
 
-def dontexit_protection_met(state, activeTrade: Trade, data, direction: TradeDirection):
+def dontexit_protection_met(state, data, direction: TradeDirection):
     if direction == TradeDirection.LONG:
         smer = "long"
     else:
@@ -24,64 +24,58 @@ def dontexit_protection_met(state, activeTrade: Trade, data, direction: TradeDir
     #vyreseno pri kazde aktivaci se vyplni flag already_activated
     #pri naslednem false podminky se v pripade, ze je aktivovany flag posle True - 
     #take se vyrusi v closu
-    def process_result(result, account):
+    def process_result(result):
         if result:
-            state.account_variables[account.name].dont_exit_already_activated = True
+            state.dont_exit_already_activated = True
             return True
         else:
             return False
 
     def evaluate_result():
-        mother_signal = activeTrade.generated_by
+        mother_signal = state.vars.activeTrade.generated_by
-        dont_exit_already_activated = state.account_variables[activeTrade.account.name].dont_exit_already_activated
 
         if mother_signal is not None:
             #TESTUJEME DONT_EXIT_
             cond_dict = state.vars.conditions[KW.dont_exit][mother_signal][smer]
             #OR 
             result, conditions_met = evaluate_directive_conditions(state, cond_dict, "OR")
-            state.ilog(lvl=1,e=f"DONT_EXIT {mother_signal} {smer} =OR= {result}", **conditions_met, cond_dict=cond_dict, already_activated=str(dont_exit_already_activated))
+            state.ilog(lvl=1,e=f"DONT_EXIT {mother_signal} {smer} =OR= {result}", **conditions_met, cond_dict=cond_dict, already_activated=str(state.dont_exit_already_activated))
             if result:
                 return True
             
             #OR neprosly testujeme AND
             result, conditions_met = evaluate_directive_conditions(state, cond_dict, "AND")
-            state.ilog(lvl=1,e=f"DONT_EXIT {mother_signal}  {smer} =AND= {result}", **conditions_met, cond_dict=cond_dict, already_activated=str(dont_exit_already_activated))
+            state.ilog(lvl=1,e=f"DONT_EXIT {mother_signal}  {smer} =AND= {result}", **conditions_met, cond_dict=cond_dict, already_activated=str(state.dont_exit_already_activated))
             if result:
                 return True
             
         cond_dict = state.vars.conditions[KW.dont_exit]["common"][smer]            
         #OR 
         result, conditions_met = evaluate_directive_conditions(state, cond_dict, "OR")
-        state.ilog(lvl=1,e=f"DONT_EXIT common {smer} =OR= {result}", **conditions_met, cond_dict=cond_dict, already_activated=str(dont_exit_already_activated))
+        state.ilog(lvl=1,e=f"DONT_EXIT common {smer} =OR= {result}", **conditions_met, cond_dict=cond_dict, already_activated=str(state.dont_exit_already_activated))
         if result:
             return True
         
         #OR neprosly testujeme AND
         result, conditions_met = evaluate_directive_conditions(state, cond_dict, "AND")
-        state.ilog(lvl=1,e=f"DONT_EXIT common {smer} =AND= {result}", **conditions_met, cond_dict=cond_dict, already_activated=str(dont_exit_already_activated))
+        state.ilog(lvl=1,e=f"DONT_EXIT common {smer} =AND= {result}", **conditions_met, cond_dict=cond_dict, already_activated=str(state.dont_exit_already_activated))
         return result
 
     #nejprve evaluujeme vsechny podminky
     result = evaluate_result()
 
     #pak evaluujeme vysledek a vracíme
-    return process_result(result, activeTrade.account)
+    return process_result(result)
 
 
-def exit_conditions_met(state: StrategyState, activeTrade: Trade, data, direction: TradeDirection):
+def exit_conditions_met(state, data, direction: TradeDirection):
     if direction == TradeDirection.LONG:
         smer = "long"
     else:
         smer = "short"
 
-    signal_name = activeTrade.generated_by
-    last_entry_index = state.account_variables[activeTrade.account.name].last_entry_index
-    avgp = state.account_variables[activeTrade.account.name].avgp
-    positions = state.account_variables[activeTrade.account.name].positions
-
     directive_name = "exit_cond_only_on_confirmed"
-    exit_cond_only_on_confirmed = get_signal_section_directive(state, signal_name=signal_name, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, False))
+    exit_cond_only_on_confirmed = get_override_for_active_trade(state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, False))
 
     if exit_cond_only_on_confirmed and data['confirmed'] == 0:
         state.ilog(lvl=0,e="EXIT COND ONLY ON CONFIRMED BAR")
@@ -89,20 +83,20 @@ def exit_conditions_met(state: StrategyState, activeTrade: Trade, data, directio
     
     ## minimální počet barů od vstupu
     directive_name = "exit_cond_req_bars"
-    exit_cond_req_bars = get_signal_section_directive(state, signal_name=signal_name, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, 1))
+    exit_cond_req_bars = get_override_for_active_trade(state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, 1))
 
-    if last_entry_index is not None:
+    if state.vars.last_in_index is not None:
-        index_to_compare = int(last_entry_index)+int(exit_cond_req_bars) 
+        index_to_compare = int(state.vars.last_in_index)+int(exit_cond_req_bars) 
         if int(data["index"]) < index_to_compare:
-            state.ilog(lvl=1,e=f"EXIT COND WAITING on required bars from IN {exit_cond_req_bars} TOO SOON", currindex=data["index"], index_to_compare=index_to_compare, last_entry_index=last_entry_index)
+            state.ilog(lvl=1,e=f"EXIT COND WAITING on required bars from IN {exit_cond_req_bars} TOO SOON", currindex=data["index"], index_to_compare=index_to_compare, last_in_index=state.vars.last_in_index)
             return False
 
     #POKUD je nastaven MIN PROFIT, zkontrolujeme ho a az pripadne pustime CONDITIONY
     directive_name = "exit_cond_min_profit"
-    exit_cond_min_profit_nodir = get_signal_section_directive(state, signal_name=signal_name, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, None))
+    exit_cond_min_profit_nodir = get_override_for_active_trade(state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, None))
 
     directive_name = "exit_cond_min_profit_" + str(smer)
-    exit_cond_min_profit = get_signal_section_directive(state, signal_name=signal_name,directive_name=directive_name, default_value=exit_cond_min_profit_nodir)
+    exit_cond_min_profit = get_override_for_active_trade(state, directive_name=directive_name, default_value=exit_cond_min_profit_nodir)
 
 
     #máme nastavený exit_cond_min_profit
@@ -111,10 +105,10 @@ def exit_conditions_met(state: StrategyState, activeTrade: Trade, data, directio
 
     if exit_cond_min_profit is not None:
         exit_cond_min_profit_normalized = normalize_tick(state, data, float(exit_cond_min_profit))
-        exit_cond_goal_price = price2dec(float(avgp)+exit_cond_min_profit_normalized,3) if int(positions) > 0 else price2dec(float(avgp)-exit_cond_min_profit_normalized,3) 
+        exit_cond_goal_price = price2dec(float(state.avgp)+exit_cond_min_profit_normalized,3) if int(state.positions) > 0 else price2dec(float(state.avgp)-exit_cond_min_profit_normalized,3) 
         curr_price = float(data["close"])
         state.ilog(lvl=1,e=f"EXIT COND min profit {exit_cond_goal_price=} {exit_cond_min_profit=} {exit_cond_min_profit_normalized=} {curr_price=}")
-        if (int(positions) < 0 and curr_price<=exit_cond_goal_price) or (int(positions) > 0 and curr_price>=exit_cond_goal_price):
+        if (int(state.positions) < 0 and curr_price<=exit_cond_goal_price) or (int(state.positions) > 0 and curr_price>=exit_cond_goal_price):
             state.ilog(lvl=1,e=f"EXIT COND min profit PASS - POKRACUJEME")
         else:
             state.ilog(lvl=1,e=f"EXIT COND min profit NOT PASS")
@@ -143,10 +137,10 @@ def exit_conditions_met(state: StrategyState, activeTrade: Trade, data, directio
     #bereme bud exit condition signalu, ktery activeTrade vygeneroval+ fallback na general
     state.ilog(lvl=0,e=f"EXIT CONDITIONS ENTRY {smer}", conditions=state.vars.conditions[KW.exit])
 
-    mother_signal = signal_name
+    mother_signal = state.vars.activeTrade.generated_by
 
     if mother_signal is not None:
-        cond_dict = state.vars.conditions[KW.exit][signal_name][smer]
+        cond_dict = state.vars.conditions[KW.exit][state.vars.activeTrade.generated_by][smer]
         result, conditions_met = evaluate_directive_conditions(state, cond_dict, "OR")
         state.ilog(lvl=1,e=f"EXIT CONDITIONS of {mother_signal} =OR= {result}", **conditions_met, cond_dict=cond_dict)
         if result:

v2realbot/strategyblocks/activetrade/close/eod_exit.py

@@ -1,7 +1,7 @@
 from v2realbot.strategy.base import StrategyState
 from v2realbot.strategy.StrategyOrderLimitVykladaciNormalizedMYSELL import StrategyOrderLimitVykladaciNormalizedMYSELL
 from v2realbot.enums.enums import RecordType, StartBarAlign, Mode, Account, Followup
-from v2realbot.common.model import Trade, TradeDirection, TradeStatus
+from v2realbot.common.PrescribedTradeModel import Trade, TradeDirection, TradeStatus
 from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, print, safe_get, is_still, is_window_open, eval_cond_dict, crossed_down, crossed_up, crossed, is_pivot, json_serial, pct_diff, create_new_bars, slice_dict_lists
 from v2realbot.utils.directive_utils import get_conditions_from_configuration
 from v2realbot.common.model import SLHistory
@@ -18,10 +18,10 @@ import os
 from traceback import format_exc
 from v2realbot.strategyblocks.activetrade.helpers import insert_SL_history
 from v2realbot.strategyblocks.activetrade.close.conditions import dontexit_protection_met, exit_conditions_met
-from v2realbot.strategyblocks.activetrade.helpers import get_max_profit_price, get_profit_target_price, get_signal_section_directive
+from v2realbot.strategyblocks.activetrade.helpers import get_max_profit_price, get_profit_target_price, get_override_for_active_trade, keyword_conditions_met
 
 
-def eod_exit_activated(state: StrategyState, activeTrade: Trade, data, direction: TradeDirection):
+def eod_exit_activated(state: StrategyState, data, direction: TradeDirection):
     """
     Function responsible for end of day management
 
@@ -38,10 +38,8 @@ def eod_exit_activated(state: StrategyState, activeTrade: Trade, data, direction
     - 1 min forced immediate
     """
 
-    avgp = state.account_variables[activeTrade.account.name].avgp
-
     directive_name = "forced_exit_window_start"
-    forced_exit_window_start = get_signal_section_directive(state, signal_name=activeTrade.generated_by, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, None))
+    forced_exit_window_start = get_override_for_active_trade(state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, None))
 
     if forced_exit_window_start is None:
         state.ilog(lvl=0,e="Forced exit not required.")
@@ -49,7 +47,7 @@ def eod_exit_activated(state: StrategyState, activeTrade: Trade, data, direction
 
     
     directive_name = "forced_exit_window_end"
-    forced_exit_window_end = get_signal_section_directive(state, signal_name=activeTrade.generated_by,directive_name=directive_name, default_value=safe_get(state.vars, directive_name, 389))
+    forced_exit_window_end = get_override_for_active_trade(state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, 389))
 
     if forced_exit_window_start>389:
         state.ilog(lvl=0,e="Forced exit window end max is 389")
@@ -62,7 +60,7 @@ def eod_exit_activated(state: StrategyState, activeTrade: Trade, data, direction
 
     # #dokdy konci okno snizujiciho se profitu (zbytek je breakeven a posledni minuta forced) - default pulka okna
     # directive_name = "forced_exit_decreasing_profit_window_end"
-    # forced_exit_decreasing_profit_window_end = get_signal_section_directive(state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, (forced_exit_window_end-forced_exit_window_end)/2))
+    # forced_exit_decreasing_profit_window_end = get_override_for_active_trade(state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, (forced_exit_window_end-forced_exit_window_end)/2))
 
     # if forced_exit_decreasing_profit_window_end > forced_exit_window_end-1:
     #     state.ilog(lvl=0,e="Decreasing profit window must be less than window end -1.")
@@ -74,7 +72,7 @@ def eod_exit_activated(state: StrategyState, activeTrade: Trade, data, direction
         state.ilog(lvl=1,e=f"Forced Exit Window OPEN - breakeven check", msg=f"{forced_exit_window_start=} {forced_exit_window_end=} ", time=str(datetime.fromtimestamp(data['updated']).astimezone(zoneNY)))
  
         directive_name = "forced_exit_breakeven_period"
-        forced_exit_breakeven_period = get_signal_section_directive(state, signal_name=activeTrade.generated_by, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, True))
+        forced_exit_breakeven_period = get_override_for_active_trade(state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, True))
 
         if forced_exit_breakeven_period is False:
             return False
@@ -82,11 +80,11 @@ def eod_exit_activated(state: StrategyState, activeTrade: Trade, data, direction
         #zatim krom posledni minuty cekame alespon na breakeven
         curr_price = float(data['close'])
         #short smer
-        if direction == TradeDirection.SHORT and curr_price<=float(avgp):
+        if direction == TradeDirection.SHORT and curr_price<=float(state.avgp):
             state.ilog(lvl=1,e=f"Forced Exit - price better than avgp, dir SHORT")
             return True
     
-        if direction == TradeDirection.LONG and curr_price>=float(avgp):
+        if direction == TradeDirection.LONG and curr_price>=float(state.avgp):
             state.ilog(lvl=1,e=f"Forced Exit - price better than avgp, dir LONG")
             return True
         

v2realbot/strategyblocks/activetrade/close/evaluate_close.py

@@ -1,98 +1,89 @@
 from v2realbot.strategyblocks.activetrade.close.close_position import close_position, close_position_partial
 from v2realbot.strategy.base import StrategyState
 from v2realbot.enums.enums import  Followup
-from v2realbot.common.model import Trade, TradeDirection, TradeStatus
+from v2realbot.common.PrescribedTradeModel import Trade, TradeDirection, TradeStatus
 from v2realbot.utils.utils import safe_get
 from v2realbot.config import KW
 #from icecream import install, ic
 from rich import print as printanyway
 from threading import Event
-#import gaka
-from v2realbot.utils.utils import gaka
 import os
 from traceback import format_exc
 from v2realbot.strategyblocks.activetrade.close.eod_exit import eod_exit_activated
 from v2realbot.strategyblocks.activetrade.close.conditions import dontexit_protection_met, exit_conditions_met
-from v2realbot.strategyblocks.activetrade.helpers import get_max_profit_price, get_profit_target_price, get_signal_section_directive, keyword_conditions_met
+from v2realbot.strategyblocks.activetrade.helpers import get_max_profit_price, get_profit_target_price, get_override_for_active_trade, keyword_conditions_met
 from v2realbot.strategyblocks.activetrade.sl.optimsl import SLOptimizer
 
-#TODO tady odsud
+def eval_close_position(state: StrategyState, data):
-def eval_close_position(state: StrategyState, accountsWithActiveTrade, data):
-
     curr_price = float(data['close'])
-    state.ilog(lvl=0,e="Eval CLOSE", price=curr_price, pos=gaka(state.account_variables, "positions"), avgp=gaka(state.account_variables, "avgp"), pending=gaka(state.account_variables, "pending"), activeTrade=str(gaka(state.account_variables, "activeTrade")))
+    state.ilog(lvl=0,e="Eval CLOSE", price=curr_price, pos=state.positions, avgp=state.avgp, pending=state.vars.pending, activeTrade=str(state.vars.activeTrade))
 
-    #iterate over accountsWithActiveTrade
+    if int(state.positions) != 0 and float(state.avgp)>0 and state.vars.pending is None:
-    for account_str, activeTrade in accountsWithActiveTrade.items():
-        positions = state.account_variables[account_str].positions
-        avgp = state.account_variables[account_str].avgp
-        pending = state.account_variables[account_str].pending
-        if int(positions) != 0 and float(avgp)>0 and pending is None:
         
         #close position handling
         #TBD pridat OPTIMALIZACI POZICE - EXIT 1/2
 
         #mame short pozice - (IDEA: rozlisovat na zaklade aktivniho tradu - umozni mi spoustet i pri soucasne long pozicemi)
-            if int(positions) < 0:
+        if int(state.positions) < 0:
             #get TARGET PRICE pro dany smer a signal
 
             #pokud existujeme bereme z nastaveni tradu a nebo z defaultu
-                if activeTrade.goal_price is not None:
+            if state.vars.activeTrade.goal_price is not None:
-                    goal_price = activeTrade.goal_price
+                 goal_price = state.vars.activeTrade.goal_price
             else:
-                    goal_price = get_profit_target_price(state,  data, activeTrade, TradeDirection.SHORT)
+                goal_price = get_profit_target_price(state, data, TradeDirection.SHORT)
 
-                max_price = get_max_profit_price(state, activeTrade, data, TradeDirection.SHORT)
+            max_price = get_max_profit_price(state, data, TradeDirection.SHORT)
             state.ilog(lvl=1,e=f"Def Goal price {str(TradeDirection.SHORT)} {goal_price} max price {max_price}")                
             
             #SL OPTIMALIZATION - PARTIAL EXIT
-                level_met, exit_adjustment = state.sl_optimizer_short.eval_position(state, data, activeTrade)
+            level_met, exit_adjustment = state.sl_optimizer_short.eval_position(state, data)
             if level_met is not None and exit_adjustment is not None:
-                    position = positions * exit_adjustment
+                position = state.positions * exit_adjustment
-                    state.ilog(lvl=1,e=f"SL OPTIMIZATION ENGAGED {str(TradeDirection.SHORT)} {position=} {level_met=} {exit_adjustment}", initial_levels=str(state.sl_optimizer_short.get_initial_abs_levels(state, activeTrade)), rem_levels=str(state.sl_optimizer_short.get_remaining_abs_levels(state, activeTrade)), exit_levels=str(state.sl_optimizer_short.exit_levels), exit_sizes=str(state.sl_optimizer_short.exit_sizes))
+                state.ilog(lvl=1,e=f"SL OPTIMIZATION ENGAGED {str(TradeDirection.SHORT)} {position=} {level_met=} {exit_adjustment}", initial_levels=str(state.sl_optimizer_short.get_initial_abs_levels(state)), rem_levels=str(state.sl_optimizer_short.get_remaining_abs_levels(state)), exit_levels=str(state.sl_optimizer_short.exit_levels), exit_sizes=str(state.sl_optimizer_short.exit_sizes))
                 printanyway(f"SL OPTIMIZATION ENGAGED {str(TradeDirection.SHORT)} {position=} {level_met=} {exit_adjustment}")
-                    close_position_partial(state=state, activeTrade=activeTrade, data=data, direction=TradeDirection.SHORT, reason=F"SL OPT LEVEL {level_met} REACHED", size=exit_adjustment)
+                close_position_partial(state=state, data=data, direction=TradeDirection.SHORT, reason=F"SL OPT LEVEL {level_met} REACHED", size=exit_adjustment)
                 return
             
             #FULL SL reached - execution
-                if curr_price > activeTrade.stoploss_value:
+            if curr_price > state.vars.activeTrade.stoploss_value:
 
                 directive_name = 'reverse_for_SL_exit_short'
-                    reverse_for_SL_exit = get_signal_section_directive(state=state, signal_name=activeTrade.generated_by, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, "no"))
+                reverse_for_SL_exit = get_override_for_active_trade(state=state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, "no"))
 
                 if reverse_for_SL_exit == "always":
                     followup_action = Followup.REVERSE
                 elif reverse_for_SL_exit == "cond":
-                        followup_action = Followup.REVERSE if keyword_conditions_met(state, data=data, activeTrade=activeTrade, direction=TradeDirection.SHORT, keyword=KW.slreverseonly, skip_conf_validation=True) else None
+                    followup_action = Followup.REVERSE if keyword_conditions_met(state, data, direction=TradeDirection.SHORT, keyword=KW.slreverseonly, skip_conf_validation=True) else None
                 else:
                     followup_action = None
-                    close_position(state=state, activeTrade=activeTrade, data=data, direction=TradeDirection.SHORT, reason="SL REACHED", followup=followup_action)
+                close_position(state=state, data=data, direction=TradeDirection.SHORT, reason="SL REACHED", followup=followup_action)
                 return
                 
             
             #REVERSE BASED ON REVERSE CONDITIONS
-                if keyword_conditions_met(state, data, activeTrade=activeTrade, direction=TradeDirection.SHORT, keyword=KW.reverse):
+            if keyword_conditions_met(state, data, direction=TradeDirection.SHORT, keyword=KW.reverse):
-                        close_position(state=state, activeTrade=activeTrade,data=data, direction=TradeDirection.SHORT, reason="REVERSE COND MET", followup=Followup.REVERSE)
+                    close_position(state=state, data=data, direction=TradeDirection.SHORT, reason="REVERSE COND MET", followup=Followup.REVERSE)
                     return  
 
             #EXIT ADD CONDITIONS MET (exit and add)
-                if keyword_conditions_met(state, data, activeTrade=activeTrade, direction=TradeDirection.SHORT, keyword=KW.exitadd):
+            if keyword_conditions_met(state, data, direction=TradeDirection.SHORT, keyword=KW.exitadd):
-                        close_position(state=state, activeTrade=activeTrade, data=data, direction=TradeDirection.SHORT, reason="EXITADD COND MET", followup=Followup.ADD)
+                    close_position(state=state, data=data, direction=TradeDirection.SHORT, reason="EXITADD COND MET", followup=Followup.ADD)
                     return  
 
             #CLOSING BASED ON EXIT CONDITIONS
-                if exit_conditions_met(state, activeTrade, data, TradeDirection.SHORT):
+            if exit_conditions_met(state, data, TradeDirection.SHORT):
                     directive_name = 'reverse_for_cond_exit_short'
-                        reverse_for_cond_exit_short = get_signal_section_directive(state=state, signal_name=activeTrade.generated_by, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, False))
+                    reverse_for_cond_exit_short = get_override_for_active_trade(state=state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, False))
                     directive_name = 'add_for_cond_exit_short'
-                        add_for_cond_exit_short = get_signal_section_directive(state=state, signal_name=activeTrade.generated_by, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, False))
+                    add_for_cond_exit_short = get_override_for_active_trade(state=state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, False))
                     if reverse_for_cond_exit_short:
                         followup_action = Followup.REVERSE
                     elif add_for_cond_exit_short: 
                         followup_action = Followup.ADD
                     else:
                         followup_action = None
-                        close_position(state=state, activeTrae=activeTrade, data=data, direction=TradeDirection.SHORT, reason="EXIT COND MET", followup=followup_action)
+                    close_position(state=state, data=data, direction=TradeDirection.SHORT, reason="EXIT COND MET", followup=followup_action)
                     return                   
 
             #PROFIT
@@ -102,85 +93,85 @@ def eval_close_position(state: StrategyState, accountsWithActiveTrade, data):
                 #TODO pripadne pokud dosahne TGTBB prodat ihned
                 max_price_signal = curr_price<=max_price
                 #OPTIMALIZACE pri stoupajícím angle
-                    if max_price_signal or dontexit_protection_met(state=state, activeTrade=activeTrade, data=data,direction=TradeDirection.SHORT) is False:
+                if max_price_signal or dontexit_protection_met(state=state, data=data,direction=TradeDirection.SHORT) is False:
-                        close_position(state=state, activeTrade=activeTrade, data=data, direction=TradeDirection.SHORT, reason=f"PROFIT or MAXPROFIT REACHED {max_price_signal=}")
+                    close_position(state=state, data=data, direction=TradeDirection.SHORT, reason=f"PROFIT or MAXPROFIT REACHED {max_price_signal=}")
                     return
             #pokud je cena horsi, ale byla uz dont exit aktivovany - pak prodavame také
-                elif state.account_variables[activeTrade.account.name].dont_exit_already_activated == True:
+            elif state.dont_exit_already_activated == True:
                 #TODO toto mozna take na direktivu, timto neprodavame pokud porkacuje trend - EXIT_PROT_BOUNCE_IMMEDIATE
                 #if dontexit_protection_met(state=state, data=data,direction=TradeDirection.SHORT) is False:
-                    close_position(state=state, activeTrade=activeTrade, data=data, direction=TradeDirection.SHORT, reason=f"EXIT PROTECTION BOUNCE {state.account_variables[activeTrade.account.name].dont_exit_already_activated=}")
+                close_position(state=state, data=data, direction=TradeDirection.SHORT, reason=f"EXIT PROTECTION BOUNCE {state.dont_exit_already_activated=}")
-                    state.account_variables[activeTrade.account.name].dont_exit_already_activated = False
+                state.dont_exit_already_activated = False
                 return
 
             #FORCED EXIT PRI KONCI DNE
-                if eod_exit_activated(state, activeTrade=activeTrade, data=data, direction=TradeDirection.SHORT):
+            if eod_exit_activated(state, data, TradeDirection.SHORT):
-                        close_position(state=state, activeTrade=activeTrade,data=data, direction=TradeDirection.SHORT, reason="EOD EXIT ACTIVATED")
+                    close_position(state=state, data=data, direction=TradeDirection.SHORT, reason="EOD EXIT ACTIVATED")
                     return           
                
         #mame long
-            elif int(positions) > 0:
+        elif int(state.positions) > 0:
 
             #get TARGET PRICE pro dany smer a signal
             #pokud existujeme bereme z nastaveni tradu a nebo z defaultu
-                if activeTrade.goal_price is not None:
+            if state.vars.activeTrade.goal_price is not None:
-                    goal_price = activeTrade.goal_price
+                 goal_price = state.vars.activeTrade.goal_price
             else:
-                    goal_price = get_profit_target_price(state, data, activeTrade, TradeDirection.LONG)
+                goal_price = get_profit_target_price(state, data, TradeDirection.LONG)
         
-                max_price = get_max_profit_price(state, activeTrade, data, TradeDirection.LONG)
+            max_price = get_max_profit_price(state, data, TradeDirection.LONG)
             state.ilog(lvl=1,e=f"Goal price {str(TradeDirection.LONG)} {goal_price} max price {max_price}")
 
             #SL OPTIMALIZATION - PARTIAL EXIT
-                level_met, exit_adjustment = state.sl_optimizer_long.eval_position(state, data, activeTrade)
+            level_met, exit_adjustment = state.sl_optimizer_long.eval_position(state, data)
             if level_met is not None and exit_adjustment is not None:
-                    position = positions * exit_adjustment
+                position = state.positions * exit_adjustment
-                    state.ilog(lvl=1,e=f"SL OPTIMIZATION ENGAGED {str(TradeDirection.LONG)} {position=} {level_met=} {exit_adjustment}", initial_levels=str(state.sl_optimizer_long.get_initial_abs_levels(state, activeTrade)), rem_levels=str(state.sl_optimizer_long.get_remaining_abs_levels(state, activeTrade)), exit_levels=str(state.sl_optimizer_long.exit_levels), exit_sizes=str(state.sl_optimizer_long.exit_sizes))
+                state.ilog(lvl=1,e=f"SL OPTIMIZATION ENGAGED {str(TradeDirection.LONG)} {position=} {level_met=} {exit_adjustment}", initial_levels=str(state.sl_optimizer_long.get_initial_abs_levels(state)), rem_levels=str(state.sl_optimizer_long.get_remaining_abs_levels(state)), exit_levels=str(state.sl_optimizer_long.exit_levels), exit_sizes=str(state.sl_optimizer_long.exit_sizes))
                 printanyway(f"SL OPTIMIZATION ENGAGED {str(TradeDirection.LONG)} {position=} {level_met=} {exit_adjustment}")
-                    close_position_partial(state=state, activeTrade=activeTrade, data=data, direction=TradeDirection.LONG, reason=f"SL OPT LEVEL {level_met} REACHED", size=exit_adjustment)
+                close_position_partial(state=state, data=data, direction=TradeDirection.LONG, reason=f"SL OPT LEVEL {level_met} REACHED", size=exit_adjustment)
                 return
 
             #SL FULL execution
-                if curr_price < activeTrade.stoploss_value:
+            if curr_price < state.vars.activeTrade.stoploss_value:
                 directive_name = 'reverse_for_SL_exit_long'
-                    reverse_for_SL_exit = get_signal_section_directive(state, signal_name=activeTrade.generated_by, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, "no"))
+                reverse_for_SL_exit = get_override_for_active_trade(state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, "no"))
 
                 state.ilog(lvl=1, e=f"reverse_for_SL_exit {reverse_for_SL_exit}")
 
                 if reverse_for_SL_exit == "always":
                     followup_action = Followup.REVERSE
                 elif reverse_for_SL_exit == "cond":
-                        followup_action = Followup.REVERSE if keyword_conditions_met(state, data, activeTrade, direction=TradeDirection.LONG, keyword=KW.slreverseonly, skip_conf_validation=True) else None
+                    followup_action = Followup.REVERSE if keyword_conditions_met(state, data, direction=TradeDirection.LONG, keyword=KW.slreverseonly, skip_conf_validation=True) else None
                 else:
                     followup_action = None
 
-                    close_position(state=state, activeTrade=activeTrade, data=data, direction=TradeDirection.LONG, reason="SL REACHED", followup=followup_action)
+                close_position(state=state, data=data, direction=TradeDirection.LONG, reason="SL REACHED", followup=followup_action)
                 return
             
 
             #REVERSE BASED ON REVERSE CONDITIONS
-                if keyword_conditions_met(state, data, activeTrade, TradeDirection.LONG, KW.reverse):
+            if keyword_conditions_met(state, data,TradeDirection.LONG, KW.reverse):
-                        close_position(state=state, activeTrade=activeTrade, data=data, direction=TradeDirection.LONG, reason="REVERSE COND MET", followup=Followup.REVERSE)
+                    close_position(state=state, data=data, direction=TradeDirection.LONG, reason="REVERSE COND MET", followup=Followup.REVERSE)
                     return  
 
             #EXIT ADD CONDITIONS MET (exit and add)
-                if keyword_conditions_met(state, data, activeTrade, TradeDirection.LONG, KW.exitadd):
+            if keyword_conditions_met(state, data, TradeDirection.LONG, KW.exitadd):
-                        close_position(state=state, activeTrade=activeTrade, data=data, direction=TradeDirection.LONG, reason="EXITADD COND MET", followup=Followup.ADD)
+                    close_position(state=state, data=data, direction=TradeDirection.LONG, reason="EXITADD COND MET", followup=Followup.ADD)
                     return  
 
             #EXIT CONDITIONS
-                if exit_conditions_met(state, activeTrade, data, TradeDirection.LONG):
+            if exit_conditions_met(state, data, TradeDirection.LONG):
                     directive_name = 'reverse_for_cond_exit_long'
-                        reverse_for_cond_exit_long = get_signal_section_directive(state, signal_name=activeTrade.generated_by, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, False))
+                    reverse_for_cond_exit_long = get_override_for_active_trade(state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, False))
                     directive_name = 'add_for_cond_exit_long'
-                        add_for_cond_exit_long = get_signal_section_directive(state, signal_name=activeTrade.generated_by, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, False))
+                    add_for_cond_exit_long = get_override_for_active_trade(state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, False))
                     if reverse_for_cond_exit_long:
                         followup_action = Followup.REVERSE
                     elif add_for_cond_exit_long: 
                         followup_action = Followup.ADD
                     else:
                         followup_action = None
-                        close_position(state=state, activeTrade=activeTrade, data=data, direction=TradeDirection.LONG, reason="EXIT CONDS MET", followup=followup_action)
+                    close_position(state=state, data=data, direction=TradeDirection.LONG, reason="EXIT CONDS MET", followup=followup_action)
                     return    
 
             #PROFIT
@@ -190,18 +181,18 @@ def eval_close_position(state: StrategyState, accountsWithActiveTrade, data):
                 #TODO pripadne pokud dosahne TGTBB prodat ihned
                 max_price_signal = curr_price>=max_price
                 #OPTIMALIZACE pri stoupajícím angle
-                    if max_price_signal or dontexit_protection_met(state, activeTrade=activeTrade, data=data, direction=TradeDirection.LONG) is False:
+                if max_price_signal or dontexit_protection_met(state, data, direction=TradeDirection.LONG) is False:
-                        close_position(state=state, activeTrade=activeTrade, data=data, direction=TradeDirection.LONG, reason=f"PROFIT or MAXPROFIT REACHED {max_price_signal=}")
+                    close_position(state=state, data=data, direction=TradeDirection.LONG, reason=f"PROFIT or MAXPROFIT REACHED {max_price_signal=}")
                     return
             #pokud je cena horsi, ale byl uz dont exit aktivovany - pak prodavame také
-                elif state.account_variables[activeTrade.account.name].dont_exit_already_activated == True:
+            elif state.dont_exit_already_activated == True:
                 #TODO toto mozna take na direktivu, timto neprodavame pokud porkacuje trend - EXIT_PROT_BOUNCE_IMMEDIATE
                 # if dontexit_protection_met(state=state, data=data,direction=TradeDirection.LONG) is False:
-                    close_position(state=state, activeTrade=activeTrade, data=data, direction=TradeDirection.LONG, reason=f"EXIT PROTECTION BOUNCE {state.account_variables[activeTrade.account.name].dont_exit_already_activated=}")
+                close_position(state=state, data=data, direction=TradeDirection.LONG, reason=f"EXIT PROTECTION BOUNCE {state.dont_exit_already_activated=}")
-                    state.account_variables[activeTrade.account.name].dont_exit_already_activated = False
+                state.dont_exit_already_activated = False
                 return
             
             #FORCED EXIT PRI KONCI DNE
-                if eod_exit_activated(state=state, activeTrade=activeTrade, data=data, direction=TradeDirection.LONG):
+            if eod_exit_activated(state, data, TradeDirection.LONG):
-                        close_position(state=state, activeTrade=activeTrade, data=data, direction=TradeDirection.LONG, reason="EOD EXIT ACTIVATED")
+                    close_position(state=state, data=data, direction=TradeDirection.LONG, reason="EOD EXIT ACTIVATED")
                     return      

v2realbot/strategyblocks/activetrade/helpers.py

@@ -1,5 +1,5 @@
 from v2realbot.enums.enums import RecordType, StartBarAlign, Mode, Account, Followup
-from v2realbot.common.model import Trade, TradeDirection, TradeStatus
+from v2realbot.common.PrescribedTradeModel import Trade, TradeDirection, TradeStatus
 from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, print, safe_get, is_still, is_window_open, eval_cond_dict, crossed_down, crossed_up, crossed, is_pivot, json_serial, pct_diff, create_new_bars, slice_dict_lists
 from v2realbot.utils.directive_utils import get_conditions_from_configuration
 from v2realbot.common.model import SLHistory
@@ -18,20 +18,17 @@ from traceback import format_exc
 from v2realbot.strategyblocks.helpers import normalize_tick
 from v2realbot.strategyblocks.indicators.helpers import evaluate_directive_conditions
 
-#TODO zde dodelat viz nize get get_signal_section_directive a pak pokracovat v close positions
 #otestuje keyword podminky (napr. reverse_if, nebo exitadd_if)
-def keyword_conditions_met(state, data, activeTrade: Trade, direction: TradeDirection, keyword: KW, skip_conf_validation: bool = False):
+def keyword_conditions_met(state, data, direction: TradeDirection, keyword: KW, skip_conf_validation: bool = False):
         action = str(keyword).upper()
         if direction == TradeDirection.LONG:
             smer = "long"
         else:
             smer = "short"
 
-        mother_signal = activeTrade.generated_by
-
         if skip_conf_validation is False: 
             directive_name = "exit_cond_only_on_confirmed"
-            exit_cond_only_on_confirmed = get_signal_section_directive(state, signal_name=activeTrade.generated_by, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, False))
+            exit_cond_only_on_confirmed = get_override_for_active_trade(state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, False))
 
             if exit_cond_only_on_confirmed and data['confirmed'] == 0:
                 state.ilog(lvl=0,e=f"{action} CHECK COND ONLY ON CONFIRMED BAR")
@@ -40,7 +37,7 @@ def keyword_conditions_met(state, data, activeTrade: Trade, direction: TradeDire
         #TOTO zatim u REVERSU neresime
         # #POKUD je nastaven MIN PROFIT, zkontrolujeme ho a az pripadne pustime CONDITIONY
         # directive_name = "exit_cond_min_profit"
-        # exit_cond_min_profit = get_signal_section_directive(directive_name=directive_name, default_value=safe_get(state.vars, directive_name, None))
+        # exit_cond_min_profit = get_override_for_active_trade(directive_name=directive_name, default_value=safe_get(state.vars, directive_name, None))
 
         # #máme nastavený exit_cond_min_profit
         # # zjistíme, zda jsme v daném profit a případně nepustíme dál
@@ -80,6 +77,8 @@ def keyword_conditions_met(state, data, activeTrade: Trade, direction: TradeDire
         #bereme bud exit condition signalu, ktery activeTrade vygeneroval+ fallback na general
         state.ilog(lvl=0,e=f"{action} CONDITIONS ENTRY {smer}", conditions=state.vars.conditions[KW.reverse])
 
+        mother_signal = state.vars.activeTrade.generated_by
+
         if mother_signal is not None:
             cond_dict = state.vars.conditions[keyword][mother_signal][smer]
             result, conditions_met = evaluate_directive_conditions(state, cond_dict, "OR")
@@ -109,12 +108,12 @@ def keyword_conditions_met(state, data, activeTrade: Trade, direction: TradeDire
 
 
 #mozna do SL helpers tuto
-def insert_SL_history(state, activeTrade: Trade):
+def insert_SL_history(state):
 #insert stoploss history as key sl_history into runner archive extended data
-    state.extData["sl_history"].append(SLHistory(id=activeTrade.id, time=state.time, sl_val=activeTrade.stoploss_value, direction=activeTrade.direction, account=activeTrade.account))
+    state.extData["sl_history"].append(SLHistory(id=state.vars.activeTrade.id, time=state.time, sl_val=state.vars.activeTrade.stoploss_value))
 
 
-def get_default_sl_value(state, signal_name, direction: TradeDirection):
+def get_default_sl_value(state, direction: TradeDirection):
 
     if direction == TradeDirection.LONG:
         smer = "long"
@@ -129,16 +128,15 @@ def get_default_sl_value(state, signal_name, direction: TradeDirection):
         state.ilog(lvl=1,e="No options for exit in stratvars. Fallback.")
         return 0.01
     directive_name = 'SL_defval_'+str(smer)
-    val = get_signal_section_directive(state, signal_name, directive_name=directive_name, default_value=safe_get(options, directive_name, 0.01))
+    val = get_override_for_active_trade(state, directive_name=directive_name, default_value=safe_get(options, directive_name, 0.01))
     return val
 #funkce pro direktivy, ktere muzou byt overridnute v signal sekci
 #tato funkce vyhleda signal sekci aktivniho tradu a pokusi se danou direktivu vyhledat tam,
 #pokud nenajde tak vrati default, ktery byl poskytnut
-#TODO toto predelat na jiny nazev get_overide_for_directive_section (vstup muze byt opuze signal_name)
+def get_override_for_active_trade(state, directive_name: str, default_value: str):
-def get_signal_section_directive(state, signal_name: str, directive_name: str, default_value: str):
     val = default_value
     override = "NO"
-    mother_signal = signal_name
+    mother_signal = state.vars.activeTrade.generated_by
 
     if mother_signal is not None:
         override = "YES "+mother_signal
@@ -147,30 +145,30 @@ def get_signal_section_directive(state, signal_name: str, directive_name: str, d
     state.ilog(lvl=0,e=f"{directive_name} OVERRIDE {override} NEWVAL:{val} ORIGINAL:{default_value} {mother_signal}", mother_signal=mother_signal,default_value=default_value)
     return val
 
-def get_profit_target_price(state, data, activeTrade, direction: TradeDirection):
+def get_profit_target_price(state, data, direction: TradeDirection):
     if direction == TradeDirection.LONG:
         smer = "long"
     else:
         smer = "short"
 
     directive_name = "profit"
-    def_profit_both_directions = get_signal_section_directive(state, signal_name=activeTrade.generated_by, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, 0.50))
+    def_profit_both_directions = get_override_for_active_trade(state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, 0.50))
 
     #profit pro dany smer
     directive_name = 'profit_'+str(smer)
-    def_profit = get_signal_section_directive(state, signal_name=activeTrade.generated_by, directive_name=directive_name, default_value=def_profit_both_directions)
+    def_profit = get_override_for_active_trade(state, directive_name=directive_name, default_value=def_profit_both_directions)
 
     #mame v direktivve ticky
     if isinstance(def_profit, (float, int)):
-        to_return = get_normalized_profitprice_from_tick(state, data, def_profit, activeTrade.account, direction)
+        to_return = get_normalized_profitprice_from_tick(state, data, def_profit, direction)
     #mame v direktive indikator
     elif isinstance(def_profit, str):
         to_return = float(value_or_indicator(state, def_profit))
 
         #min profit (ochrana extremnich hodnot indikatoru)
         directive_name = 'profit_min_ind_tick_value'
-        profit_min_ind_tick_value = get_signal_section_directive(state, signal_name=activeTrade.generated_by, directive_name=directive_name, default_value=def_profit_both_directions)
+        profit_min_ind_tick_value = get_override_for_active_trade(state, directive_name=directive_name, default_value=def_profit_both_directions)
-        profit_min_ind_price_value = get_normalized_profitprice_from_tick(state, data, profit_min_ind_tick_value, activeTrade.account, direction)
+        profit_min_ind_price_value = get_normalized_profitprice_from_tick(state, data, profit_min_ind_tick_value, direction)
 
         #ochrana pri nastaveni profitu prilis nizko
         if direction == TradeDirection.LONG and to_return < profit_min_ind_price_value or direction == TradeDirection.SHORT and to_return > profit_min_ind_price_value:
@@ -181,32 +179,28 @@ def get_profit_target_price(state, data, activeTrade, direction: TradeDirection)
     return to_return
 
 ##based on tick a direction, returns normalized prfoit price (LONG = avgp(nebo currprice)+norm.tick, SHORT=avgp(or currprice)-norm.tick)
-def get_normalized_profitprice_from_tick(state, data, tick, account: Account, direction: TradeDirection):
+def get_normalized_profitprice_from_tick(state, data, tick, direction: TradeDirection):
-        avgp = state.account_variables[account.name].avgp
         normalized_tick = normalize_tick(state, data, float(tick))
-        base_price = avgp if avgp != 0 else data["close"]
+        base_price = state.avgp if state.avgp != 0 else data["close"]
         returned_price = price2dec(float(base_price)+normalized_tick,3) if direction == TradeDirection.LONG else price2dec(float(base_price)-normalized_tick,3)
         state.ilog(lvl=0,e=f"NORMALIZED TICK {tick=} {normalized_tick=} NORM.PRICE {returned_price}")
         return returned_price
 
-def get_max_profit_price(state, activeTrade: Trade, data, direction: TradeDirection):
+def get_max_profit_price(state, data, direction: TradeDirection):
     if direction == TradeDirection.LONG:
         smer = "long"
     else:
         smer = "short"
 
     directive_name = "max_profit"
-    max_profit_both_directions = get_signal_section_directive(state, signal_name=activeTrade.generated_by, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, 0.35))
+    max_profit_both_directions = get_override_for_active_trade(state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, 0.35))
-
-    avgp = state.account_variables[activeTrade.account.name].avgp
-    positions = state.account_variables[activeTrade.account.name].positions
 
     #max profit pro dany smer, s fallbackem na bez smeru
     directive_name = 'max_profit_'+str(smer)
-    max_profit = get_signal_section_directive(state, signal_name=activeTrade.generated_by, directive_name=directive_name, default_value=max_profit_both_directions)
+    max_profit = get_override_for_active_trade(state, directive_name=directive_name, default_value=max_profit_both_directions)
 
     normalized_max_profit = normalize_tick(state,data,float(max_profit))
 
     state.ilog(lvl=0,e=f"MAX PROFIT {max_profit=} {normalized_max_profit=}")
 
-    return price2dec(float(avgp)+normalized_max_profit,3) if int(positions) > 0 else price2dec(float(avgp)-normalized_max_profit,3)    
+    return price2dec(float(state.avgp)+normalized_max_profit,3) if int(state.positions) > 0 else price2dec(float(state.avgp)-normalized_max_profit,3)    

v2realbot/strategyblocks/activetrade/sl/optimsl.py

@@ -1,8 +1,8 @@
 import numpy as np
-from v2realbot.common.model import Trade, TradeDirection, TradeStatus
+from v2realbot.common.PrescribedTradeModel import Trade, TradeDirection, TradeStatus
 from typing import Tuple
 from copy import deepcopy
-from v2realbot.strategyblocks.activetrade.helpers import get_signal_section_directive
+from v2realbot.strategyblocks.activetrade.helpers import get_override_for_active_trade
 from v2realbot.utils.utils import safe_get
 # FIBONACCI PRO PROFIT A SL
 
@@ -49,23 +49,23 @@ class SLOptimizer:
     #      self.exit_levels = self.init_exit_levels
     #      self.exit_sizes = self.init_exit_sizes
 
-    def get_trade_details(self, state, activeTrade):
+    def get_trade_details(self, state):
-        trade: Trade = activeTrade
+        trade: Trade = state.vars.activeTrade
         #jde o novy trade - resetujeme levely
         if trade.id != self.last_trade:
             #inicializujeme a vymazeme pripadne puvodni
-            if self.initialize_levels(state, activeTrade) is False:
+            if self.initialize_levels(state) is False:
                 return None, None
             self.last_trade = trade.id
         #return cost_price, sl_price
-        return  state.account_variables[trade.account.name].avgp, trade.stoploss_value
+        return  state.avgp, trade.stoploss_value
 
-    def initialize_levels(self, state, activeTrade):
+    def initialize_levels(self, state):
         directive_name = 'SL_opt_exit_levels_'+str(self.direction.value)
-        SL_opt_exit_levels = get_signal_section_directive(state=state, signal_name=activeTrade.generated_by, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, None))
+        SL_opt_exit_levels = get_override_for_active_trade(state=state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, None))
 
         directive_name = 'SL_opt_exit_sizes_'+str(self.direction.value)
-        SL_opt_exit_sizes = get_signal_section_directive(state=state,  signal_name=activeTrade.generated_by, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, None))
+        SL_opt_exit_sizes = get_override_for_active_trade(state=state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, None))
 
         if SL_opt_exit_levels is None or SL_opt_exit_sizes is None:
             #print("no directives found: SL_opt_exit_levels/SL_opt_exit_sizes")
@@ -83,11 +83,11 @@ class SLOptimizer:
         print(f"new levels initialized {self.exit_levels=} {self.exit_sizes=}")
         return True
 
-    def get_initial_abs_levels(self, state, activeTrade):
+    def get_initial_abs_levels(self, state):
          """
          Returns price levels corresponding to initial setting of exit_levels
          """
-         cost_price, sl_price = self.get_trade_details(state, activeTrade)
+         cost_price, sl_price = self.get_trade_details(state)
          if cost_price is None or sl_price is None:
              return []
          curr_sl_distance = np.abs(cost_price - sl_price)
@@ -96,11 +96,11 @@ class SLOptimizer:
          else:
             return [cost_price - exit_level * curr_sl_distance for exit_level in self.init_exit_levels]
 
-    def get_remaining_abs_levels(self, state, activeTrade):
+    def get_remaining_abs_levels(self, state):
          """
          Returns price levels corresponding to remaing exit_levels for current trade
          """
-         cost_price, sl_price = self.get_trade_details(state, activeTrade)
+         cost_price, sl_price = self.get_trade_details(state)
          if cost_price is None or sl_price is None:
              return []
          curr_sl_distance = np.abs(cost_price - sl_price)
@@ -109,11 +109,11 @@ class SLOptimizer:
          else:
             return [cost_price - exit_level * curr_sl_distance for exit_level in self.exit_levels]
 
-    def eval_position(self, state, data, activeTrade) -> Tuple[float, float]:
+    def eval_position(self, state, data) -> Tuple[float, float]:
         """Evaluates optimalization for current position and returns if the given level was
            met and how to adjust exit position.
         """    
-        cost_price, sl_price = self.get_trade_details(state, activeTrade)
+        cost_price, sl_price = self.get_trade_details(state)
         if cost_price is None or sl_price is None:
              #print("no settings found")
              return (None, None)

v2realbot/strategyblocks/activetrade/sl/trailsl.py

@@ -1,12 +1,13 @@
 from v2realbot.strategy.base import StrategyState
-from v2realbot.common.model import Trade, TradeDirection, TradeStatus
+from v2realbot.common.PrescribedTradeModel import Trade, TradeDirection, TradeStatus
-from v2realbot.utils.utils import gaka, isrising, isfalling,zoneNY, price2dec, print, safe_get
+from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, print, safe_get
 #from icecream import install, ic
 from rich import print as printanyway
 from threading import Event
 import os
 from traceback import format_exc
-from v2realbot.strategyblocks.activetrade.helpers import get_signal_section_directive, normalize_tick, insert_SL_history
+from v2realbot.strategyblocks.activetrade.helpers import get_override_for_active_trade, normalize_tick, insert_SL_history
+
 
 #pokud se cena posouva nasim smerem olespon o (0.05) nad (SL + 0.09val), posuneme SL o offset
 #+ varianta - skoncit breakeven
@@ -24,17 +25,10 @@ from v2realbot.strategyblocks.activetrade.helpers import get_signal_section_dire
 # SL_trailing_stop_at_breakeven_short = true
 # SL_trailing_stop_at_breakeven_long = true
     
-def trail_SL_management(state: StrategyState, accountsWithActiveTrade, data):
+def trail_SL_management(state: StrategyState, data):
-    #iterate over accountsWithActiveTrade
+    if int(state.positions) != 0 and float(state.avgp)>0 and state.vars.pending is None:
-    for account_str, activeTrade in accountsWithActiveTrade.items():
-        positions = state.account_variables[account_str].positions
-        avgp = state.account_variables[account_str].avgp
-        pending = state.account_variables[account_str].pending    
-        signal_name = activeTrade.generated_by
-        last_entry_index = state.account_variables[account_str].last_entry_index
-        if int(positions) != 0 and float(avgp)>0 and pending is None:
 
-            if int(positions) < 0:
+        if int(state.positions) < 0:
             direction = TradeDirection.SHORT
             smer = "short"
         else:
@@ -49,32 +43,32 @@ def trail_SL_management(state: StrategyState, accountsWithActiveTrade, data):
             return
         
         directive_name = 'SL_trailing_enabled_'+str(smer)
-            sl_trailing_enabled = get_signal_section_directive(state=state, signal_name=signal_name, directive_name=directive_name, default_value=safe_get(options, directive_name, False))
+        sl_trailing_enabled = get_override_for_active_trade(state=state, directive_name=directive_name, default_value=safe_get(options, directive_name, False))
     
 
         #SL_trailing_protection_window_short
         directive_name = 'SL_trailing_protection_window_'+str(smer)
-            SL_trailing_protection_window = get_signal_section_directive(state=state, signal_name=signal_name, directive_name=directive_name, default_value=safe_get(options, directive_name, 0))
+        SL_trailing_protection_window = get_override_for_active_trade(state=state, directive_name=directive_name, default_value=safe_get(options, directive_name, 0))
-            index_to_compare = int(last_entry_index)+int(SL_trailing_protection_window) 
+        index_to_compare = int(state.vars.last_in_index)+int(SL_trailing_protection_window) 
         if index_to_compare > int(data["index"]):
-                state.ilog(lvl=1,e=f"SL trail PROTECTION WINDOW {SL_trailing_protection_window} - TOO SOON", currindex=data["index"], index_to_compare=index_to_compare, last_entry_index=last_entry_index)
+            state.ilog(lvl=1,e=f"SL trail PROTECTION WINDOW {SL_trailing_protection_window} - TOO SOON", currindex=data["index"], index_to_compare=index_to_compare, last_in_index=state.vars.last_in_index)
             return
 
 
         
         if sl_trailing_enabled is True:
             directive_name = 'SL_trailing_stop_at_breakeven_'+str(smer)
-                stop_breakeven = get_signal_section_directive(state=state, signal_name=signal_name, directive_name=directive_name, default_value=safe_get(options, directive_name, False))
+            stop_breakeven = get_override_for_active_trade(state=state, directive_name=directive_name, default_value=safe_get(options, directive_name, False))
             directive_name = 'SL_defval_'+str(smer)
-                def_SL = get_signal_section_directive(state=state, signal_name=signal_name, directive_name=directive_name, default_value=safe_get(options, directive_name, 0.01))
+            def_SL = get_override_for_active_trade(state=state, directive_name=directive_name, default_value=safe_get(options, directive_name, 0.01))
             directive_name = "SL_trailing_offset_"+str(smer)
-                offset = get_signal_section_directive(state=state, signal_name=signal_name, directive_name=directive_name, default_value=safe_get(options, directive_name, 0.01))
+            offset = get_override_for_active_trade(state=state, directive_name=directive_name, default_value=safe_get(options, directive_name, 0.01))
             directive_name = "SL_trailing_step_"+str(smer)
-                step = get_signal_section_directive(state=state, signal_name=signal_name, directive_name=directive_name, default_value=safe_get(options, directive_name, offset))
+            step = get_override_for_active_trade(state=state, directive_name=directive_name, default_value=safe_get(options, directive_name, offset))
 
             #pokud je pozadovan trail jen do breakeven a uz prekroceno
-                if (direction == TradeDirection.LONG and stop_breakeven and activeTrade.stoploss_value >= float(avgp)) or (direction == TradeDirection.SHORT and stop_breakeven and activeTrade.stoploss_value <= float(avgp)):
+            if (direction == TradeDirection.LONG and stop_breakeven and state.vars.activeTrade.stoploss_value >= float(state.avgp)) or (direction == TradeDirection.SHORT and stop_breakeven and state.vars.activeTrade.stoploss_value <= float(state.avgp)):
-                    state.ilog(lvl=1,e=f"SL trail STOP at breakeven {str(smer)} SL:{activeTrade.stoploss_value} UNCHANGED", stop_breakeven=stop_breakeven)
+                state.ilog(lvl=1,e=f"SL trail STOP at breakeven {str(smer)} SL:{state.vars.activeTrade.stoploss_value} UNCHANGED", stop_breakeven=stop_breakeven)
                 return
             
             #Aktivace SL pokud vystoupa na "offset", a nasledne posunuti o "step"
@@ -83,16 +77,16 @@ def trail_SL_management(state: StrategyState, accountsWithActiveTrade, data):
             step_normalized = normalize_tick(state, data, step)
             def_SL_normalized = normalize_tick(state, data, def_SL)
             if direction == TradeDirection.LONG:
-                    move_SL_threshold = activeTrade.stoploss_value + offset_normalized + def_SL_normalized
+                move_SL_threshold = state.vars.activeTrade.stoploss_value + offset_normalized + def_SL_normalized
-                    state.ilog(lvl=1,e=f"SL TRAIL EVAL {smer} SL:{round(activeTrade.stoploss_value,3)} TRAILGOAL:{move_SL_threshold}", def_SL=def_SL, offset=offset, offset_normalized=offset_normalized, step_normalized=step_normalized, def_SL_normalized=def_SL_normalized)
+                state.ilog(lvl=1,e=f"SL TRAIL EVAL {smer} SL:{round(state.vars.activeTrade.stoploss_value,3)} TRAILGOAL:{move_SL_threshold}", def_SL=def_SL, offset=offset, offset_normalized=offset_normalized, step_normalized=step_normalized, def_SL_normalized=def_SL_normalized)
                 if (move_SL_threshold) < data['close']:
-                        activeTrade.stoploss_value += step_normalized
+                    state.vars.activeTrade.stoploss_value += step_normalized
-                        insert_SL_history(state, activeTrade)
+                    insert_SL_history(state)
-                        state.ilog(lvl=1,e=f"SL TRAIL TH {smer} reached {move_SL_threshold} SL moved to {activeTrade.stoploss_value}", offset_normalized=offset_normalized, step_normalized=step_normalized, def_SL_normalized=def_SL_normalized)
+                    state.ilog(lvl=1,e=f"SL TRAIL TH {smer} reached {move_SL_threshold} SL moved to {state.vars.activeTrade.stoploss_value}", offset_normalized=offset_normalized, step_normalized=step_normalized, def_SL_normalized=def_SL_normalized)
             elif direction == TradeDirection.SHORT:
-                    move_SL_threshold = activeTrade.stoploss_value - offset_normalized - def_SL_normalized
+                move_SL_threshold = state.vars.activeTrade.stoploss_value - offset_normalized - def_SL_normalized
-                    state.ilog(lvl=0,e=f"SL TRAIL EVAL {smer} SL:{round(activeTrade.stoploss_value,3)} TRAILGOAL:{move_SL_threshold}", def_SL=def_SL, offset=offset, offset_normalized=offset_normalized, step_normalized=step_normalized, def_SL_normalized=def_SL_normalized)
+                state.ilog(lvl=0,e=f"SL TRAIL EVAL {smer} SL:{round(state.vars.activeTrade.stoploss_value,3)} TRAILGOAL:{move_SL_threshold}", def_SL=def_SL, offset=offset, offset_normalized=offset_normalized, step_normalized=step_normalized, def_SL_normalized=def_SL_normalized)
                 if (move_SL_threshold) > data['close']:
-                        activeTrade.stoploss_value -= step_normalized
+                    state.vars.activeTrade.stoploss_value -= step_normalized
-                        insert_SL_history(state, activeTrade)
+                    insert_SL_history(state)
-                        state.ilog(lvl=1,e=f"SL TRAIL GOAL {smer} reached {move_SL_threshold} SL moved to {activeTrade.stoploss_value}", offset_normalized=offset_normalized, step_normalized=step_normalized, def_SL_normalized=def_SL_normalized)                            
+                    state.ilog(lvl=1,e=f"SL TRAIL GOAL {smer} reached {move_SL_threshold} SL moved to {state.vars.activeTrade.stoploss_value}", offset_normalized=offset_normalized, step_normalized=step_normalized, def_SL_normalized=def_SL_normalized)                            

v2realbot/strategyblocks/indicators/indicators_hub.py

@@ -55,11 +55,7 @@ def populate_all_indicators(data, state: StrategyState):
     #TODO tento lof patri spis do nextu classic SL - je poplatny typu stratefie
     #TODO na toto se podivam, nejak moc zajasonovani a zpatky -
     #PERF PROBLEM
-    positions = state.account_variables[state.account].positions
+    state.ilog(lvl=1,e="ENTRY", msg=f"LP:{lp} P:{state.positions}/{round(float(state.avgp),3)} SL:{state.vars.activeTrade.stoploss_value if state.vars.activeTrade is not None else None} GP:{state.vars.activeTrade.goal_price if state.vars.activeTrade is not None else None} profit:{round(float(state.profit),2)} profit_rel:{round(np.sum(state.rel_profit_cum),6) if len(state.rel_profit_cum)>0 else 0} Trades:{len(state.tradeList)} pend:{state.vars.pending}", rel_profit_cum=str(state.rel_profit_cum), activeTrade=transform_data(state.vars.activeTrade, json_serial), prescribedTrades=transform_data(state.vars.prescribedTrades, json_serial), pending=str(state.vars.pending))
-    avgp = state.account_variables[state.account].avgp
-    #state.ilog(lvl=1,e="ENTRY", msg=f"LP:{lp} P:{positions}/{round(float(avgp),3)} SL:{state.vars.activeTrade.stoploss_value if state.vars.activeTrade is not None else None} GP:{state.vars.activeTrade.goal_price if state.vars.activeTrade is not None else None} profit:{round(float(state.profit),2)} profit_rel:{round(np.sum(state.rel_profit_cum),6) if len(state.rel_profit_cum)>0 else 0} Trades:{len(state.tradeList)} pend:{state.vars.pending}", rel_profit_cum=str(state.rel_profit_cum), activeTrade=transform_data(state.vars.activeTrade, json_serial), prescribedTrades=transform_data(state.vars.prescribedTrades, json_serial), pending=str(state.vars.pending))
-
-    state.ilog(lvl=1,e="ENTRY", msg=f"LP:{lp} ", accountVars=transform_data(state.account_variables, json_serial), prescribedTrades=transform_data(state.vars.prescribedTrades, json_serial))
     
     #kroky pro CONFIRMED BAR only
     if conf_bar == 1:

v2realbot/strategyblocks/indicators/slopeLP.py

@@ -46,10 +46,6 @@ def populate_dynamic_slopeLP_indicator(data, state: StrategyState, name):
             #typ leveho bodu [lastbuy - cena posledniho nakupu, baropen - cena otevreni baru]
             leftpoint = safe_get(options, 'leftpoint', "lastbuy")
 
-            #REFACTOR multiaccount
-            #avgp bereme z primarni accountu (state.account)
-            avgp = state.account_variables[state.account].avgp          
-
             #lookback has to be even
             if lookback_offset % 2 != 0:
                 lookback_offset += 1
@@ -69,8 +65,8 @@ def populate_dynamic_slopeLP_indicator(data, state: StrategyState, name):
 
                     #pokud mame aktivni pozice, nastavime lookbackprice a time podle posledniho tradu
                     #pokud se ale dlouho nenakupuje (uplynulo od posledniho nakupu vic nez back_to_standard_after baru), tak se vracime k prumeru
-                    if avgp > 0 and state.bars.index[-1] < int(state.vars.last_entry_index)+back_to_standard_after:
+                    if state.avgp > 0 and state.bars.index[-1] < int(state.vars.last_buy_index)+back_to_standard_after:
-                        lb_index = -1 - (state.bars.index[-1] - int(state.vars.last_entry_index))
+                        lb_index = -1 - (state.bars.index[-1] - int(state.vars.last_buy_index))
                         lookbackprice = state.bars.vwap[lb_index]
                         state.ilog(lvl=0,e=f"IND {name} slope {leftpoint}- LEFT POINT OVERRIDE bereme ajko cenu lastbuy {lookbackprice=} {lookbacktime=} {lb_index=}")
                     else:

v2realbot/strategyblocks/inits/init_directives.py

@@ -1,7 +1,7 @@
 from v2realbot.strategy.base import StrategyState
 from v2realbot.strategy.StrategyOrderLimitVykladaciNormalizedMYSELL import StrategyOrderLimitVykladaciNormalizedMYSELL
 from v2realbot.enums.enums import RecordType, StartBarAlign, Mode, Account, Followup
-from v2realbot.common.model import Trade, TradeDirection, TradeStatus
+from v2realbot.common.PrescribedTradeModel import Trade, TradeDirection, TradeStatus
 from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, print, safe_get, is_still, is_window_open, eval_cond_dict, crossed_down, crossed_up, crossed, is_pivot, json_serial, pct_diff, create_new_bars, slice_dict_lists
 from v2realbot.utils.directive_utils import get_conditions_from_configuration
 from v2realbot.common.model import SLHistory

v2realbot/strategyblocks/inits/init_indicators.py

@@ -1,7 +1,7 @@
 from v2realbot.strategy.base import StrategyState
 from v2realbot.strategy.StrategyOrderLimitVykladaciNormalizedMYSELL import StrategyOrderLimitVykladaciNormalizedMYSELL
 from v2realbot.enums.enums import RecordType, StartBarAlign, Mode, Account, Followup
-from v2realbot.common.model import Trade, TradeDirection, TradeStatus
+from v2realbot.common.PrescribedTradeModel import Trade, TradeDirection, TradeStatus
 from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, print, safe_get, is_still, is_window_open, eval_cond_dict, crossed_down, crossed_up, crossed, is_pivot, json_serial, pct_diff, create_new_bars, slice_dict_lists
 from v2realbot.utils.directive_utils import get_conditions_from_configuration
 #import mlroom.utils.mlutils as ml

v2realbot/strategyblocks/newtrade/conditions.py

@@ -1,7 +1,7 @@
 from v2realbot.strategy.base import StrategyState
 from v2realbot.strategy.StrategyOrderLimitVykladaciNormalizedMYSELL import StrategyOrderLimitVykladaciNormalizedMYSELL
 from v2realbot.enums.enums import RecordType, StartBarAlign, Mode, Account, Followup
-from v2realbot.common.model import Trade, TradeDirection, TradeStatus
+from v2realbot.common.PrescribedTradeModel import Trade, TradeDirection, TradeStatus
 from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, print, safe_get, is_still, is_window_open, eval_cond_dict, crossed_down, crossed_up, crossed, is_pivot, json_serial, pct_diff, create_new_bars, slice_dict_lists
 from v2realbot.utils.directive_utils import get_conditions_from_configuration
 from v2realbot.common.model import SLHistory

v2realbot/strategyblocks/newtrade/prescribedtrades.py

@@ -1,6 +1,6 @@
 from v2realbot.strategy.base import StrategyState
-from v2realbot.common.model import TradeDirection, TradeStatus
+from v2realbot.common.PrescribedTradeModel import TradeDirection, TradeStatus
-from v2realbot.utils.utils import zoneNY, json_serial,transform_data, gaka
+from v2realbot.utils.utils import zoneNY, json_serial,transform_data
 from datetime import datetime
 #import random
 import orjson
@@ -11,108 +11,96 @@ from v2realbot.strategyblocks.indicators.helpers import value_or_indicator
 def execute_prescribed_trades(state: StrategyState, data):
     ##evaluate prescribed trade, prvni eligible presuneme do activeTrade, zmenime stav and vytvorime objednavky
     
-    #for multiaccount setup we check if there is active trade for each account
+    if state.vars.activeTrade is not None or len(state.vars.prescribedTrades) == 0:
-    
-    if len(state.vars.prescribedTrades) == 0 :
         return
-    
-    accountsWithNoActiveTrade = gaka(state.account_variables, "activeTrade", None, lambda x: x is None)
-
-    if len(accountsWithNoActiveTrade.values()) == 0:
-        #print("active trades on all accounts")
-        return
-
-    #returns true if all values are not None
-    #all(v is not None for v in d.keys())
-
     #evaluate long (price/market)
-    #support multiaccount trades
     state.ilog(lvl=1,e="evaluating prescr trades", trades=transform_data(state.vars.prescribedTrades, json_serial))
     for trade in state.vars.prescribedTrades:
-        if trade.account.name not in accountsWithNoActiveTrade.keys() or state.account_variables[trade.account.name].pending is not None: #availability or pending
-            continue
         if trade.status == TradeStatus.READY and trade.direction == TradeDirection.LONG and (trade.entry_price is None or trade.entry_price >= data['close']):
             trade.status = TradeStatus.ACTIVATED
             trade.last_update = datetime.fromtimestamp(state.time).astimezone(zoneNY)
             state.ilog(lvl=1,e=f"evaluated LONG", trade=transform_data(trade, json_serial), prescrTrades=transform_data(state.vars.prescribedTrades, json_serial))
-            execute_trade(state, data, trade) #TBD ERROR HANDLING
+            state.vars.activeTrade = trade
-            del accountsWithNoActiveTrade[trade.account.name] #to avoid other entries on the same account
+            state.vars.last_buy_index = data["index"]
-            continue
+            state.vars.last_in_index = data["index"]
+            break
     #evaluate shorts
+    if not state.vars.activeTrade:
+        for trade in state.vars.prescribedTrades:
             if trade.status == TradeStatus.READY and trade.direction == TradeDirection.SHORT and (trade.entry_price is None or trade.entry_price <= data['close']):
                 state.ilog(lvl=1,e=f"evaluaed SHORT", trade=transform_data(trade, json_serial), prescrTrades=transform_data(state.vars.prescribedTrades, json_serial))
                 trade.status = TradeStatus.ACTIVATED
                 trade.last_update = datetime.fromtimestamp(state.time).astimezone(zoneNY)
-            execute_trade(state, data, trade) #TBD ERROR HANDLING
+                state.vars.activeTrade = trade
-            del accountsWithNoActiveTrade[trade.account.name] #to avoid other entries on the same account
+                state.vars.last_buy_index = data["index"]
-            continue
+                state.vars.last_in_index = data["index"]
+                break
 
-
-    #TODO konzolidovat nize na spolecny kod pro short a long
     #odeslani ORDER + NASTAVENI STOPLOSS (zatim hardcoded)
-#TODO doplnit error management
+    if state.vars.activeTrade:
-def execute_trade(state, data, trade):
+        if state.vars.activeTrade.direction == TradeDirection.LONG:
-            if trade.direction == TradeDirection.LONG:
+            state.ilog(lvl=1,e="odesilame LONG ORDER", trade=transform_data(state.vars.activeTrade, json_serial))
-                state.ilog(lvl=1,e="odesilame LONG ORDER", trade=transform_data(trade, json_serial))
+            if state.vars.activeTrade.size is not None:
-                size = trade.size  if trade.size is not None else state.vars.chunk
+                size = state.vars.activeTrade.size
-                res = state.buy(size=size, account=trade.account)
+            else:
-                #TODO ukládáme někam ID objednávky? už zde je vráceno v res
+                size = state.vars.chunk
-                #TODO error handling
+            res = state.buy(size=size)
             if isinstance(res, int) and res < 0:
                 raise Exception(f"error in required operation LONG {res}")
-                    #TODO error handling
+
             #defaultni goal price pripadne nastavujeme az v notifikaci
-                state.account_variables[trade.account.name].activeTrade = trade
 
             #TODO nastaveni SL az do notifikace, kdy je známá
             #pokud neni nastaveno SL v prescribe, tak nastavuji default dle stratvars
-                if trade.stoploss_value is None:
+            if state.vars.activeTrade.stoploss_value is None:
-                    sl_defvalue = get_default_sl_value(state=state, signal_name=trade.generated_by, direction=trade.direction)
+                sl_defvalue = get_default_sl_value(state, direction=state.vars.activeTrade.direction)
 
                 if isinstance(sl_defvalue, (float, int)):
                     #normalizuji dle aktualni ceny 
                     sl_defvalue_normalized = normalize_tick(state, data,sl_defvalue)
-                        state.account_variables[trade.account.name].activeTrade.stoploss_value = float(data['close']) - sl_defvalue_normalized
+                    state.vars.activeTrade.stoploss_value = float(data['close']) - sl_defvalue_normalized
-                        state.ilog(lvl=1,e=f"Nastaveno SL na {sl_defvalue}, priced normalized: {sl_defvalue_normalized} price: {state.account_variables[trade.account.name].activeTrade.stoploss_value }")
+                    state.ilog(lvl=1,e=f"Nastaveno SL na {sl_defvalue}, priced normalized: {sl_defvalue_normalized} price: {state.vars.activeTrade.stoploss_value }")
                 elif isinstance(sl_defvalue, str):
                     #from indicator
                     ind = sl_defvalue
                     sl_defvalue_abs = float(value_or_indicator(state, sl_defvalue))
                     if sl_defvalue_abs >= float(data['close']):
                         raise Exception(f"error in stoploss {ind} {sl_defvalue_abs} >= curr price")
-                        state.account_variables[trade.account.name].activeTrade.stoploss_value = sl_defvalue_abs
+                    state.vars.activeTrade.stoploss_value = sl_defvalue_abs
                     state.ilog(lvl=1,e=f"Nastaveno SL na {sl_defvalue_abs} dle indikatoru {ind}")
-                    insert_SL_history(state, state.account_variables[trade.account.name].activeTrade)
+                insert_SL_history(state)
-            elif trade.direction == TradeDirection.SHORT:
+            state.vars.pending = state.vars.activeTrade.id
-                state.ilog(lvl=1,e="odesilame SHORT ORDER", trade=transform_data(trade, json_serial))
+        elif state.vars.activeTrade.direction == TradeDirection.SHORT:
-                size = trade.size if trade.size is not None else state.vars.chunk
+            state.ilog(lvl=1,e="odesilame SHORT ORDER", trade=transform_data(state.vars.activeTrade, json_serial))
-                res = state.sell(size=size, account=trade.account)
+            if state.vars.activeTrade.size is not None:
+                size = state.vars.activeTrade.size
+            else:
+                size = state.vars.chunk
+            res = state.sell(size=size)
             if isinstance(res, int) and res < 0:
                 print(f"error in required operation SHORT {res}")
                 raise Exception(f"error in required operation SHORT {res}")
             #defaultní goalprice nastavujeme az v notifikaci
 
-                state.account_variables[trade.account.name].activeTrade = trade
             #pokud neni nastaveno SL v prescribe, tak nastavuji default dle stratvars
-                if trade.stoploss_value is None:
+            if state.vars.activeTrade.stoploss_value is None:
-                    sl_defvalue = get_default_sl_value(state, signal_name=trade.generated_by,direction=trade.direction)
+                sl_defvalue = get_default_sl_value(state, direction=state.vars.activeTrade.direction)
 
                 if isinstance(sl_defvalue, (float, int)):
                     #normalizuji dle aktualni ceny 
                     sl_defvalue_normalized = normalize_tick(state, data,sl_defvalue)
-                        state.account_variables[trade.account.name].activeTrade.stoploss_value = float(data['close']) + sl_defvalue_normalized
+                    state.vars.activeTrade.stoploss_value = float(data['close']) + sl_defvalue_normalized
-                        state.ilog(lvl=1,e=f"Nastaveno SL na {sl_defvalue}, priced normalized: {sl_defvalue_normalized} price: {state.account_variables[trade.account.name].activeTrade.stoploss_value }")
+                    state.ilog(lvl=1,e=f"Nastaveno SL na {sl_defvalue}, priced normalized: {sl_defvalue_normalized} price: {state.vars.activeTrade.stoploss_value }")
                 elif isinstance(sl_defvalue, str):
                     #from indicator
                     ind = sl_defvalue
                     sl_defvalue_abs = float(value_or_indicator(state, sl_defvalue))
                     if sl_defvalue_abs <= float(data['close']):
                         raise Exception(f"error in stoploss {ind} {sl_defvalue_abs} <= curr price")
-                        state.account_variables[trade.account.name].activeTrade.stoploss_value = sl_defvalue_abs
+                    state.vars.activeTrade.stoploss_value = sl_defvalue_abs
                     state.ilog(lvl=1,e=f"Nastaveno SL na {sl_defvalue_abs} dle indikatoru {ind}")
-                    insert_SL_history(state, state.account_variables[trade.account.name].activeTrade)
+                insert_SL_history(state)
-
+            state.vars.pending = state.vars.activeTrade.id
-            state.account_variables[trade.account.name].pending = trade.id
+        else:
-            state.account_variables[trade.account.name].activeTrade = trade
+            state.ilog(lvl=1,e="unknow direction")
-            state.account_variables[trade.account.name].last_entry_index =data["index"] #last_entry_index per account
+            state.vars.activeTrade = None
-            state.vars.last_entry_index = data["index"] #spolecne pro vsechny accounty

v2realbot/strategyblocks/newtrade/signals.py

@@ -1,6 +1,6 @@
 from v2realbot.strategy.base import StrategyState
-from v2realbot.common.model import Trade, TradeDirection, TradeStatus
+from v2realbot.common.PrescribedTradeModel import Trade, TradeDirection, TradeStatus
-from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, print, safe_get, gaka
+from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, print, safe_get
 from v2realbot.config import KW
 from uuid import uuid4
 from datetime import datetime
@@ -31,12 +31,6 @@ def signal_search(state: StrategyState, data):
     # slope10.out_short_if_above = 0
     # ema.AND.short_if_below = 28
 
-    accountsWithNoActiveTrade = gaka(state.account_variables, "activeTrade", None, lambda x: x is None)
-
-    if len(accountsWithNoActiveTrade.values()) == 0:
-        #print("active trades on all accounts")
-        return
-
     for signalname, signalsettings in state.vars.signals.items():
         execute_signal_generator(state, data, signalname)
 
@@ -44,21 +38,14 @@ def signal_search(state: StrategyState, data):
     # pokud je s cenou ceka se na cenu, pokud immmediate tak se hned provede
     # to vse za predpokladu, ze neni aktivni trade
 
-def execute_signal_generator(state: StrategyState, data, name):
+def execute_signal_generator(state, data, name):
     state.ilog(lvl=1,e=f"SIGNAL SEARCH for {name}", cond_go=state.vars.conditions[KW.go][name], cond_dontgo=state.vars.conditions[KW.dont_go][name], cond_activate=state.vars.conditions[KW.activate][name] )
     options = safe_get(state.vars.signals, name, None)
 
-    #add account from stratvars (if there) or default to self.state.account
-
     if options is None:
         state.ilog(lvl=1,e=f"No options for {name} in stratvars")
         return
     
-    #get account of the signal, fallback to default
-    account = safe_get(options, "account", state.account)
-    account_long = safe_get(options, "account_long", account)
-    account_short = safe_get(options, "account_short", account)
-
     if common_go_preconditions_check(state, data, signalname=name, options=options) is False:
         return
 
@@ -84,12 +71,9 @@ def execute_signal_generator(state: StrategyState, data, name):
             state.ilog(lvl=1,e=f"{name} SHORT DISABLED")
         if long_enabled is False:
             state.ilog(lvl=1,e=f"{name} LONG DISABLED")
-        trade_made = None
+        if long_enabled and go_conditions_met(state, data,signalname=name, direction=TradeDirection.LONG):
-        #predkontroloa zda neni pending na accountu nebo aktivni trade
-        if state.account_variables[account_long].pending is None and state.account_variables[account_long].activeTrade is None and long_enabled and go_conditions_met(state, data,signalname=name, direction=TradeDirection.LONG):
             multiplier = get_multiplier(state, data, options, TradeDirection.LONG)
             state.vars.prescribedTrades.append(Trade(
-                                    account = account_long,
                                     id=uuid4(),
                                     last_update=datetime.fromtimestamp(state.time).astimezone(zoneNY),
                                     status=TradeStatus.READY,
@@ -99,12 +83,9 @@ def execute_signal_generator(state: StrategyState, data, name):
                                     direction=TradeDirection.LONG,
                                     entry_price=None,
                                     stoploss_value = None))
-            trade_made = account_long
+        elif short_enabled and go_conditions_met(state, data, signalname=name, direction=TradeDirection.SHORT):
-        #pri multiaccountu muzeme udelat v jedne iteraci vice tradu avsak vzdy na ruznych accountech
-        if (trade_made is None or trade_made != account_short) and state.account_variables[account_short].pending is None and state.account_variables[account_short].activeTrade is None and short_enabled and go_conditions_met(state, data, signalname=name, direction=TradeDirection.SHORT):
             multiplier = get_multiplier(state, data, options, TradeDirection.SHORT)
             state.vars.prescribedTrades.append(Trade(
-                    account=account_short,
                     id=uuid4(),
                     last_update=datetime.fromtimestamp(state.time).astimezone(zoneNY),
                     status=TradeStatus.READY,
@@ -114,5 +95,5 @@ def execute_signal_generator(state: StrategyState, data, name):
                     direction=TradeDirection.SHORT,
                     entry_price=None,
                     stoploss_value = None))
-            return
+        else:
             state.ilog(lvl=0,e=f"{name} NO SIGNAL")

v2realbot/strategyblocks/newtrade/sizing.py

@@ -1,5 +1,5 @@
 from v2realbot.strategy.base import StrategyState
-from v2realbot.common.model import Trade, TradeDirection, TradeStatus
+from v2realbot.common.PrescribedTradeModel import Trade, TradeDirection, TradeStatus
 import v2realbot.utils.utils as utls
 from v2realbot.config import KW
 from uuid import uuid4
@@ -99,8 +99,7 @@ def get_multiplier(state: StrategyState, data, signaloptions: dict, direction: T
 
     if probe_enabled:
         #zatim pouze probe number 1 natvrdo, tzn. nesmi byt trade pro aktivace
-        #zatim funguje pouze pro primarni
+        if state.vars.last_in_index is None:
-        if state.account_variables[state.account].last_entry_index is None:
             #probe_number = utls.safe_get(options, "probe_number",1)
             probe_size = float(utls.safe_get(options, "probe_size", 0.1))
             state.ilog(lvl=1,e=f"SIZER - PROBE - setting multiplier to {probe_size}", options=options)

v2realbot/tools/createbatchimage.py

@@ -12,7 +12,7 @@ from enum import Enum
 import numpy as np
 import v2realbot.controller.services as cs
 #from rich import print
-from v2realbot.common.model import TradeDirection, TradeStatus, Trade, TradeStoplossType
+from v2realbot.common.PrescribedTradeModel import TradeDirection, TradeStatus, Trade, TradeStoplossType
 from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, safe_get
 from pathlib import Path
 from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY

v2realbot/tools/loadbatch.py

@@ -1,320 +0,0 @@
-import matplotlib
-import matplotlib.dates as mdates
-#matplotlib.use('Agg')  # Set the Matplotlib backend to 'Agg'
-import matplotlib.pyplot as plt
-import seaborn as sns
-import pandas as pd
-from datetime import datetime
-from typing import List
-from enum import Enum
-import numpy as np
-import v2realbot.controller.services as cs
-from rich import print as richprint
-from v2realbot.common.model import AnalyzerInputs
-from v2realbot.common.model import TradeDirection, TradeStatus, Trade, TradeStoplossType
-from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, safe_get#, print
-from pathlib import Path
-from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY
-from v2realbot.enums.enums import RecordType, StartBarAlign, Mode, Account, OrderSide
-from io import BytesIO
-from v2realbot.utils.historicals import get_historical_bars
-from alpaca.data.timeframe import TimeFrame, TimeFrameUnit
-from collections import defaultdict
-from scipy.stats import zscore
-from io import BytesIO
-from typing import Tuple, Optional, List
-from v2realbot.common.model import TradeDirection, TradeStatus, Trade, TradeStoplossType
-from collections import Counter
-import vectorbtpro as vbt
-
-    # Function to add 23 seconds to the last datetime (if it exists and is the same day)
-def adjust_datetime_iteratively(df, resolution):
-    adjusted_times = []
-    for i, current_time in enumerate(df.index):
-        if i == 0:
-            # The first entry is unchanged
-            adjusted_times.append(current_time)
-            continue
-        
-        previous_time = adjusted_times[-1]
-        # Check if it's the same day
-        if previous_time.date() == current_time.date():
-            # Add resolution to the previous datetime
-            adjusted_time = previous_time + pd.Timedelta(seconds=resolution)
-        else:
-            # Different day, leave it as is
-            adjusted_time = current_time
-        
-        adjusted_times.append(adjusted_time)
-    
-    # Update DataFrame index
-    df.index = pd.DatetimeIndex(adjusted_times)
-    return df
-
-def convert_to_dataframe(ohlcv):
-    """
-    Convert a dictionary containing OHLCV data into a pandas DataFrame.
-    
-    Parameters:
-        ohlcv (dict): Dictionary containing OHLCV data.
-                      It should have keys 'time', 'open', 'high', 'low', 'close', 'volume', 'updated'.
-                      'time' should be a list of float timestamps.
-                      'updated' should be a list of Python datetimes in UTC time zone.
-    
-    Returns:
-        pd.DataFrame: DataFrame containing the OHLCV data with the index converted to East coast US time.
-    """
-
-    #pokud existuje key index, tak menime na custom_index, aby nedelal neplechu v pd
-    try:
-        if ohlcv.get('index', False):
-            ohlcv['custom_index'] = ohlcv.pop('index')
-    except Exception as e:
-        pass
-
-    #keys that should not go uppercase letter first 
-    keys_not_to_upper = ["time", "updated"]
-
-    # Update keys not in the exclusion list
-    for key in list(ohlcv.keys()):  # Iterate over a copy of the keys
-        if key not in keys_not_to_upper:
-            ohlcv[key.title()] = ohlcv.pop(key) 
-
-    # Create DataFrame from the dictionary
-    df = pd.DataFrame(ohlcv)
-    
-    # Convert 'time' to datetime and set as index
-    df['time'] = pd.to_datetime(df['time'], unit='s', utc=True)
-    df.set_index('time', inplace=True)
-    # Convert index to East coast US time zone
-    df.index = df.index.tz_convert('US/Eastern')
-    if 'updated' in df.columns:
-        df['updated'] = pd.to_datetime(df['updated'], unit='s', utc=True)
-        df['updated'] = df['updated'].dt.tz_convert('US/Eastern')
-    
-    return df
-
-def print(v, *args, **kwargs):
-    if v:
-        richprint(*args, **kwargs)
-
-def load_batch(runner_ids: List = None, batch_id: str = None, space_resolution_evenly = False, main_session_only = True, merge_ind2bars = True, bars_columns = ['Open', 'High', 'Low', 'Close', 'Volume', 'Vwap'], indicators_columns = [], verbose = False) -> Tuple[int, dict]:
-    """Load batches (all runners from single batch) into pandas dataframes
-
-    Args:
-        runner_ids (List, optional): A list of runner identifiers (e.g., stock tickers). Defaults to None.
-        batch_id (str, optional): The ID of a specific batch to retrieve. Defaults to None.
-        merge_ind2bars (bool, optional): merge indicator into bars dataframe. Defaults to True.
-        bars_columns (list, optional):  List of columns to keep in bars df. Defaults to ['Open', 'High', 'Low', 'Close', 'Volume', 'Vwap'].
-        indicators_columns (list, optional): List of columns to keep in indicators df. Defaults to an empty list.
-        space_resoution_evenly: If True then, it alters index so it is spaced evenly in given resolution in ['resooution']
-    Returns:
-        Tuple[int, dict]: A tuple containing:
-            * An integer potentially representing a status code or data count.
-            * A dictionary with keys bars, indicators and cbar_indicators - with pandas dataframe
-    """
-
-    if runner_ids is None and batch_id is None:
-        return -2, f"runner_id or batch_id must be present", 0
-
-    if batch_id is not None:
-        res, runner_ids =cs.get_archived_runnerslist_byBatchID(batch_id)
-
-        if res != 0:
-            print(f"no batch {batch_id} found")
-            return -1, f"no batch {batch_id} found", 0
-
-    #DATA PREPARATION
-    bars = None
-    indicators = None
-    cnt = 0
-    dfs = dict(bars=[], indicators=[],cbar_indicators=[])
-    resolution = None
-    for id in runner_ids:
-        cnt += 1
-        #get runner detail
-        res, sada =cs.get_archived_runner_details_byID(id)
-        if res != 0:
-            print(f"no runner {id} found")
-            return -1, f"no runner {id} found", 0
-        
-        if resolution is None:
-            resolution  = sada["bars"]["resolution"][0]
-            print(verbose, f"Resolution : {resolution}")
-
-        #add daily bars limited to required columns, we keep updated as its mapping column to indicators
-        bars = convert_to_dataframe(sada["bars"])[bars_columns + ["updated"]]
-        #bars = bars.loc[:, bars_columns]
-
-        indicators = convert_to_dataframe(sada["indicators"][0])[indicators_columns]
-
-        #join indicators to bars dataframe
-        if merge_ind2bars:
-            #merge, time v indicators odpovida udpated v bars
-            bars = bars.reset_index()
-            bars = pd.merge(bars, indicators, left_on="updated", right_on="time", how="left")
-            bars = bars.set_index("time")
-        else:
-            dfs["indicators"].append(indicators)
-
-        #drop updated as mapping column
-        #bars = bars.drop("updated", axis=1)
-        dfs["bars"].append(bars)
-
-        #indicators = sada["indicators"][0]
-        #cbar_indicators = sada["indicators"][1]
-    #merge all days into single df
-    for key in dfs:
-        if len(dfs[key])>0:
-            concat_df = pd.concat(dfs[key], axis=0)
-            concat_df = concat_df.between_time('9:30', '16:00') if main_session_only else concat_df
-
-            # Count the number of duplicates (excluding the first occurrence)
-            num_duplicates = concat_df.index.duplicated().sum()
-
-            if num_duplicates > 0:
-                print(verbose, f"NOTE: DUPLICATES {num_duplicates}/{len(concat_df)} in {key}. REMOVING.")
-                concat_df = concat_df[~concat_df.index.duplicated()]
-
-                num_duplicates = concat_df.index.duplicated().sum()
-                print(verbose, f"Now there are {num_duplicates}/{len(concat_df)}")
-
-            if space_resolution_evenly and key != "cbar_indicators":
-                # Apply rounding to the datetime index according to resolution (in seconds)
-                concat_df = adjust_datetime_iteratively(concat_df, resolution)
-
-            dfs[key] = concat_df
-    return 0, dfs
-
-if __name__ == "__main__":
-    res, df = load_batch(batch_id="e44a5075", space_resolution_evenly=True, indicators_columns=["Rsi14"], main_session_only=False)
-    if res < 0:
-        print("Error" + str(res) + str(df))
-    print(df)
-    df = df["bars"]
-    print(df.info(), df.head())
-    #filter columns
-    #columns_to_keep = ['Open', 'High', 'Low', 'Close', 'Volume', 'Vwap']
-    #df = df.loc[:, columns_to_keep]
-    #df = df.rename(columns={'index': 'custom_index'})
-    print(df.info(), df.head(), df.describe())
-    #filter times
-    #df = df.between_time('9:30', '16:00')
-    print(df.info())
-    # Set the frequency to 23 seconds
-    #df.index.freq = pd.tseries.offsets.Second(23)
-    # Check the frequency of the index
-
-    # Resample and aggregate the data
-    # resampled_df = df.resample('23S').agg({
-    #         'open': 'first',
-    #         'high': 'max',
-    #         'low': 'min',
-    #         'close': 'last',
-    #         'volume': 'sum'
-    #     })
-
-    #df.index.freq = pd.infer_freq(df.index)
-    #print(df.index.freq)
-
-
-    # Set the frequency of the index explicitly - if it exists like 1T etc, if doesnt exists then custom_frequency will be used
-    #df.index.freq = pd.date_range(start=df.index[0], periods=len(df), freq='23S')
-
-    print(df.info())
-
-    vbt.settings.set_theme("dark")
-    vbt.settings['plotting']['layout']['width'] = 1280
-    vbt.settings.plotting.auto_rangebreaks = True
-
-    #naloadujeme do vbt symbol as column
-    bar_data = vbt.Data.from_data({"BAC": df}, tz_convert="US/Eastern")
-    print(bar_data)
-    print(bar_data.close)
-    
-    print(bar_data.data["BAC"]["Rsi14"])
-    bar_data.data["BAC"]["Rsi14"].vbt.plot().show()
-    print(bar_data["Rsi14"])
-    
-    
-    #ohlcv plot (sublot 2x1)
-    bar_data.data["BAC"].vbt.ohlcv.plot().show()
-
-    #create two subplots 3x1 (ohlcv + RSI)
-    # fig = vbt.make_subplots(rows=3, cols=1)
-    # bar_data.data["BAC"].vbt.ohlcv.plot(add_trace_kwargs=dict(row=1, col=1),fig=fig)
-    # bar_data.data["BAC"]["Rsi14"].vbt.plot(add_trace_kwargs=dict(row=3, col=1),fig=fig)
-    # fig.show()
-
-    #create subplots with alternate Y axis - RSI overlay
-    fig1 = vbt.make_subplots(specs=[[{"secondary_y": True}]])
-    bar_data.data["BAC"]["Close"].vbt.plot(add_trace_kwargs=dict(secondary_y=False),fig=fig1)
-    bar_data.data["BAC"].vbt.plot(add_trace_kwargs=dict(secondary_y=True),fig=fig1)
-    fig1.show()
-
-    puv_df = bar_data.data["BAC"]
-
-    bar_data23s = bar_data[["Open", "High", "Low", "Close", "Volume"]]
-    print(bar_data23s)
-    #resample by vbt
-    bar_data46s = bar_data23s.get().resample("46s").agg({
-        "Open": "first",
-        "High": "max",
-        "Low": "min",
-        "Close": "last",
-        "Volume": "sum"
-    })
-
-    print(bar_data46s)
-    res_data = bar_data46s.data["BAC"]
-    #bar_data23s.data["BAC"].ptable()
-    #bar_data23s = bar_data.resample("23S")
-    print(bar_data46s)
-    print(bar_data46s.close)
-    vbt.settings.plotting.auto_rangebreaks = True
-    bar_data46s.data["BAC"].vbt.ohlcv.plot().show()
-
-    #TARGET DAYS - only one day or range
-    # Target Date
-    #target_date = pd.to_datetime('2023-10-12', tz='US/Eastern')
-
-    # Date Range
-    start_date = pd.to_datetime('2024-03-12')
-    #end_date = pd.to_datetime('2023-10-14')
-
-    new_data = bar_data.transform(lambda df: df[df.index.date == start_date.date()])
-    #range filtered_data = data[(data.index >= start_date) & (data.index <= end_date)
-
-    print(new_data)
-    new_data.data["BAC"].vbt.ohlcv.plot().show()
-
-
-    # Filtering RANGE or DAY
-    # filtered_data = data[(data.index >= start_date) & (data.index <= end_date)]g
-    # filtered_data = data[data.index.date == target_date.date()]
-
-
-
-    #custom aggregagation
-    # ohlcv_agg = pd.DataFrame({
-    #     'Open': df.resample('1T')['Open'].first(),
-    #     'High': df.resample('1T')['High'].max(),
-    #     'Low': df.resample('1T')['Low'].min(),
-    #     'Close': df.resample('1T')['Close'].last(),
-    #     'Volume': df.resample('1T')['Volume'].sum()
-    # })
-
-    #Define a custom frequency with a timedelta of 23 seconds
-    # custom_frequency = pd.tseries.offsets.DateOffset(seconds=23)
-
-    # # Create a new DataFrame with the desired frequency
-    # new_index = pd.date_range(start=df.index[0], end=df.index[-1], freq=custom_frequency)
-    # new_df = pd.DataFrame(index=new_index)
-
-    # # Reindex the DataFrame
-    # df = df.reindex(new_df.index)
-
-    # # Now you can check the frequency of the index
-    # print(df.index.freq)
-
-

v2realbot/utils/config_handler.py

@@ -5,7 +5,6 @@ from alpaca.data.enums import DataFeed
 import v2realbot.utils.config_defaults as config_defaults
 from v2realbot.enums.enums import FillCondition
 from rich import print
-# from v2realbot.utils.utils import print
 
 def aggregate_configurations(module):
     return {key: getattr(module, key) for key in dir(module) if key.isupper()}
@@ -49,8 +48,8 @@ class ConfigHandler:
                     self.active_config = self.default_config.copy() 
                     self.active_config.update(override_configuration)
                     self.active_profile = profile_name
-                    #print(f"Profile {profile_name} loaded successfully.")
+                    print(f"Profile {profile_name} loaded successfully.")
-                    #print("Current values:", self.active_config)
+                    print("Current values:", self.active_config)
                 else:
                     print(f"Profile {profile_name} does not exist in config item: {config_directive}")
         except Exception as e:
@@ -94,9 +93,7 @@ class ConfigHandler:
                 return FillCondition(value)
             case "BT_FILL_CONDITION_SELL_LIMIT":
                 return FillCondition(value)
-            case "AGG_EXCLUDED_TRADES":
+            # Add cases for other enumeration conversions as needed
-                return sorted(value)  # Convert to sorted
-            # Add cases for other enumeration conversions or transformations as needed
             case _:
                 return value
 
@@ -105,8 +102,8 @@ class ConfigHandler:
 
 # Global configuratio - it is imported by modules that need it. In the future can be changed to Dependency Ingestion (each service will have the config instance  as input parameter)
 config_handler = ConfigHandler()
-#print(f"{config_handler.active_profile=}")
+print(f"{config_handler.active_profile=}")
-#print("config handler initialized")
+print("config handler initialized")
 
 #this is how to get value
 #config_handler.get_val('BT_FILL_PRICE_MARKET_ORDER_PREMIUM')

v2realbot/utils/utils.py

@@ -9,8 +9,8 @@ import decimal
 from v2realbot.enums.enums import RecordType, Mode, StartBarAlign
 import pickle
 import os
-from v2realbot.common.model import StrategyInstance, Runner, RunArchive, RunArchiveDetail, Intervals, SLHistory, InstantIndicator, AccountVariables
+from v2realbot.common.model import StrategyInstance, Runner, RunArchive, RunArchiveDetail, Intervals, SLHistory, InstantIndicator
-from v2realbot.common.model import Trade, TradeDirection, TradeStatus, TradeStoplossType
+from v2realbot.common.PrescribedTradeModel import Trade, TradeDirection, TradeStatus, TradeStoplossType
 from typing import List
 import tomli
 from v2realbot.config import DATA_DIR, ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY
@@ -20,7 +20,7 @@ from uuid import UUID
 from enum import Enum
 #from v2realbot.enums.enums import Order
 from v2realbot.common.model import Order as btOrder, TradeUpdate as btTradeUpdate
-from alpaca.trading.models import Order, TradeUpdate, Calendar
+from alpaca.trading.models import Order, TradeUpdate
 import numpy as np
 import pandas as pd
 from collections import deque
@@ -35,221 +35,6 @@ import tempfile
 import shutil
 from filelock import FileLock
 import v2realbot.utils.config_handler as cfh
-import pandas_market_calendars as mcal
-from typing import Dict, Any, Callable, Optional
-from pydantic import BaseModel
-
-def get_attribute(obj: Any, attr: str) -> Any:
-    """
-    Returns the value of given attribute from the object being it dict or BaseModel
-    """
-    if isinstance(obj, dict):
-        return obj.get(attr)
-    if isinstance(obj, BaseModel):
-        return getattr(obj, attr, None)
-    return None
-
-def gaka(
-    account_variables: Dict[str, Any],
-    name_of_attribute: str,
-    transform_function: Optional[Callable[[Any], Any]] = None,
-    condition_function: Optional[Callable[[Any], bool]] = None
-) -> Dict[str, Any]:
-    """
-    Gets Account Keyed Attribute
-    Extracts the specified attribute from each account variable in the given dictionary.
-    It also contains transformation function and condition function.
-
-    ```
-    avgps = gaka(account_variables, "avgp", 
-                 transform_function=lambda x: round(x, 3),
-                 condition_function=lambda x: x > 3)
-
-    returns:
-    {
-        'account2': 5000.654,
-        'account3': 3000.789,
-        'account4': 8000.235
-    }
-    ```
-
-    Args:
-        account_variables (Dict[str, BaseModel]): A dictionary of account variables.
-        name_of_attribute (str): The name of the attribute to extract.
-        transform_function (Optional[Callable[[Any], Any]]): Optional function to transform the attribute value.
-        condition_function (Optional[Callable[[Any], bool]]): Optional function to filter the results.
-
-    Returns:
-        Dict[str, Any]: A dictionary containing the extracted attribute for each account that meets the condition.
-    """
-    result = {}
-    for account_str, acc_vars in account_variables.items():
-        value = get_attribute(acc_vars, name_of_attribute)
-        
-        if value is None and not hasattr(acc_vars, name_of_attribute):
-            continue
-
-        transformed_value = transform_function(value) if transform_function else value
-        
-        if condition_function is None or condition_function(transformed_value):
-            result[account_str] = transformed_value
-
-    return result
-
-def gaka_unoptimized(
-    account_variables: Dict[str, Any],
-    name_of_attribute: str,
-    transform_function: Optional[Callable[[Any], Any]] = None,
-    condition_function: Optional[Callable[[Any], bool]] = None
-) -> Dict[str, Any]:
-    """
-    Gets Account Keyed Attribute
-    Extracts the specified attribute from each account variable in the given dictionary.
-    It also contains transformation function and condition function.
-
-        ```
-    avgps = gaka(account_variables, "avgp", 
-                 transform_function=lambda x: round(x, 3),
-                 condition_function=lambda x: x > 3)
-
-    returns:
-    {
-        'account2': 5000.654,
-        'account3': 3000.789,
-        'account4': 8000.235
-    }
-    ```
-
-    Args:
-        account_variables (Dict[str, BaseModel]): A dictionary of account variables.
-        name_of_attribute (str): The name of the attribute to extract.
-        transform_function (Optional[Callable[[Any], Any]]): Optional function to transform the attribute value.
-        condition_function (Optional[Callable[[Any], bool]]): Optional function to filter the results.
-
-    Returns:
-        Dict[str, Any]: A dictionary containing the extracted attribute for each account that meets the condition.
-    """
-    result = {}
-    for account, acc_vars in account_variables.items():
-        if isinstance(acc_vars, BaseModel):
-            value = getattr(acc_vars, name_of_attribute, None)
-        elif isinstance(acc_vars, dict):
-            value = acc_vars.get(name_of_attribute, None)
-        else:
-            continue  # Skip if acc_vars is neither BaseModel nor dict
-
-        if value is None and not hasattr(acc_vars, name_of_attribute):
-            continue  # Skip if attribute doesn't exist
-
-        transformed_value = transform_function(value) if transform_function else value
-
-        if condition_function is None or condition_function(transformed_value):
-            result[account] = transformed_value
-
-    return result
-
-def gaka_old_with_comprehesion(
-    account_variables: Dict[str, Any],
-    name_of_attribute: str,
-    transform_function: Optional[Callable[[Any], Any]] = None,
-    condition_function: Optional[Callable[[Any], bool]] = None
-) -> Dict[str, Any]:
-    """
-    Gets Account Keyed Attribute
-    Extracts the specified attribute from each account variable in the given dictionary.
-
-    It also contains transformation function and condition function.
-
-    ```
-    avgps = gaka(account_variables, "avgp", 
-                 transform_function=lambda x: round(x, 3),
-                 condition_function=lambda x: x > 3)
-
-    returns:
-    {
-        'account2': 5000.654,
-        'account3': 3000.789,
-        'account4': 8000.235
-    }
-    ```
-
-    Args:
-        account_variables (Dict[str, BaseModel]): A dictionary of account variables.
-        name_of_attribute (str): The name of the attribute to extract.
-        transform_function (Optional[Callable[[Any], Any]]): Optional function to transform the attribute value.
-        condition_function (Optional[Callable[[Any], bool]]): Optional function to filter the results.
-
-    Returns:
-        Dict[str, Any]: A dictionary containing the extracted attribute for each account that meets the condition.
-    """
-    return {
-        account: transformed_value
-        for account, acc_vars in account_variables.items()
-        if (value := (
-            getattr(acc_vars, name_of_attribute, None) 
-            if isinstance(acc_vars, BaseModel) 
-            else acc_vars.get(name_of_attribute, None) 
-            if isinstance(acc_vars, dict) 
-            else None
-        )) is not None or name_of_attribute in acc_vars.__dict__
-        and (transformed_value := (
-            transform_function(value) if transform_function else value
-        )) is not None
-        and (not condition_function or condition_function(transformed_value))
-    }
-
-def gaka_old(account_variables: Dict[str, Any], name_of_attribute: str, transform_function: Optional[Callable[[Any], Any]] = None) -> Dict[str, Any]:
-    """
-    Gets Account Keyed Attribute
-    Extracts the specified attribute from each account variable in the given dictionary.
-
-    It also contain transformation function.
-
-    ```
-    avgps = extract_attribute(account_variables, "avgp", lambda x: round(x, 3))
-
-    returns:
-    {
-        'account1': 1000.123,
-        'account2': 5000.654,
-        'account3': 3000.789,
-        'account4': 8000.235
-    }
-    ```
-
-    Args:
-        account_variables (Dict[str, BaseModel]): A dictionary of account variables.
-        name_of_attribute (str): The name of the attribute to extract.
-
-    Returns:
-        Dict[str, Any]: A dictionary containing the extracted attribute for each account.
-    """
-    #return {account: getattr(acc_vars, name_of_attribute) for account, acc_vars in account_variables.items()}
-    return {
-        account: (
-            transform_function(value) if transform_function and value is not None else value
-        )
-        for account, acc_vars in account_variables.items()
-        if (value := (
-            getattr(acc_vars, name_of_attribute, None) 
-            if isinstance(acc_vars, BaseModel) 
-            else acc_vars.get(name_of_attribute, None) 
-            if isinstance(acc_vars, dict) 
-            else None
-        )) is not None
-    }
-
-def empty_lists_in_dict(d: dict):
-    """
-    Assumes all values of dict are list. Returns true if all lists are empty.
-
-    Args:
-        d (dict): The dictionary to check.
-
-    Returns:
-        bool: True if all lists in the dictionary are empty, False otherwise.
-    """
-    return all(len(v) == 0 for v in d.values())     
 
 def validate_and_format_time(time_string):
     """
@@ -274,32 +59,8 @@ def validate_and_format_time(time_string):
     else:
         return None
 
-def fetch_calendar_data(start: datetime, end: datetime) -> List[Calendar]:
-    """
-    Fetches the trading schedule for the NYSE (New York Stock Exchange) between the specified start and end dates.
-    Args:
-        start (datetime): The start date for the trading schedule.
-        end (datetime): The end date for the trading schedule.
-    Returns:
-        List[Calendar]: A list of Calendar objects containing the trading dates and market open/close times. 
-                        Returns an empty list if no trading days are found within the specified range.
-    """ 
-    nyse = mcal.get_calendar('NYSE')
-    schedule = nyse.schedule(start_date=start, end_date=end, tz='America/New_York')
-    if not schedule.empty: 
-        schedule = (schedule.reset_index()
-                        .rename(columns={"index": "date", "market_open": "open", "market_close": "close"})
-                        .assign(date=lambda day: day['date'].dt.date.astype(str),
-                                open=lambda day: day['open'].dt.strftime('%H:%M'), 
-                                close=lambda day: day['close'].dt.strftime('%H:%M'))
-                        .to_dict(orient="records"))
-        cal_dates = [Calendar(**record) for record in schedule]
-        return cal_dates
-    else:
-        return []
-
 #Alpaca Calendar wrapper with retry
-def fetch_calendar_data_from_alpaca(start, end, max_retries=5, backoff_factor=1):
+def fetch_calendar_data(start, end, max_retries=5, backoff_factor=1):
     """
     Attempts to fetch calendar data with exponential backoff. Raises an exception if all retries fail.
 
@@ -889,7 +650,6 @@ def json_serial(obj):
         SLHistory: lambda obj: obj.__dict__,
         InstantIndicator: lambda obj: obj.__dict__,
         StrategyInstance: lambda obj: obj.__dict__,
-        AccountVariables: lambda obj: obj.__dict__
     }
 
     serializer = type_map.get(type(obj))