fix module inits

* load_dotenv from python-dotenv library imported

* WEB_API_KEY is read as virtual environment variable specified in .env file

* env file referenced by variable imported from config.py

* env file directory and env file variables defined

* bash script to create env file

* Delete env_migration.sh

---------

Co-authored-by: David Brazda <davidbrazda61@gmail.com>

README.md

@@ -1,24 +1,29 @@
-# V2TRADING - Algorithmic Trading Platform with Frontend
+**README - V2TRADING - Advanced Algorithmic Trading Platform**
 
-## 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.
+**Overview**
+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
-- **Trading Engine**: Processes tick data in real time, aggregating data and managing trade execution.
+**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.
 
-- **Backtesting**: tick-by tick backtesting, down to millisecond accuracy, mirrors live trading environments and is vital for developing and testing high(er)-frequency trading strategies.
+- **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.
 
-- **Configuration**: robust configuration via TOML
-
-- **Frontend**: Frontend to support research to backtesting to paper trading workflow, including lightweight charts.
-
-- **Custom Data Aggregation:** Custom time based, volume based, dollar based and renko bars aggregators based on tick-by-tick data.
+- **Custom Data Aggregation:** The platform includes a data aggregator that allows for custom aggregation rules. This flexibility supports a variety of data analysis approaches, including non-time based bars and other unique criteria.
 
 - **Indicators** Contains inbuild [tulipy](https://tulipindicators.org/list) [ta-lib](https://ta-lib.github.io/ta-lib-python/) and templates for custom build multioutputs stateful indicators.
 
-- **Machine Learning Integration:** Includes modules for both training and inference, supporting the complete ML lifecycle. 
+- **Machine Learning Integration:** Recently, the platform has expanded to incorporate machine learning capabilities. This includes modules for both training and inference, supporting the complete ML lifecycle. These ML models can be utilized within trading strategies for classification and exploiting statistical advantages.
 
-**Gui examples**
+**Technology Stack**
+**Backend and API:** The backbone of the platform is built with Python, utilizing libraries such as FastAPI, NumPy, Keras, and JAX, ensuring high performance and scalability.
+**Frontend:** The client-side is developed with Vanilla JavaScript and jQuery, employing LightweightCharts for charting purposes. Additional modules enhance the platform's functionality. The frontend is slated for a future refactoring to modern frameworks like Vue.js and Vuetify for a more robust user interface.
+
+While the platform is fully functional and growing, ongoing development is planned, particularly in the realm of frontend enhancements and further integration of advanced machine learning techniques.
+
+**Contributions**
+Contributions to this project are welcome. Whether it's improving the frontend, enhancing the backend capabilities, or experimenting with new trading strategies and machine learning models, your input can help take this platform to the next level.
+
+This repository represents a sophisticated and evolving tool for algorithmic traders, offering precision, speed, and a level of customization that is unparalleled in open-source systems. Join us in shaping the future of algorithmic trading.
 
 <p align="center">
   Main screen with entry/exit points and stoploss lines<br>
@@ -45,83 +50,4 @@ Custom-built algorithmic trading platform for research, backtesting and live tra
   <img width="700" alt="Strategy analytical tools" src="https://github.com/drew2323/v2trading/assets/28433232/4bf8b3c3-e430-4250-831a-e5876bb6b743">
 </p>
 
-**Backend and API:** The backbone of the platform is built with Python, utilizing libraries such as FastAPI, NumPy, Keras, and JAX, ensuring high performance and scalability.
-**Frontend:** The client-side is developed with Vanilla JavaScript and jQuery, employing LightweightCharts for charting purposes. Additional modules enhance the platform's functionality. The frontend is slated for a future refactoring to modern frameworks like Vue.js and Vuetify for a more robust user interface.
-
-**Documentation** Public docs in in progress. Some can be found on [knowledge base](trading.mujdenik.eu) but first please request access. Some analysis documents can be found on [shared google doc folder](https://drive.google.com/drive/folders/1WmYG8oDGXO-lVTLVs9knAmMTmQL4dZt6?usp=drive_link).
-
-# 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

@@ -236,7 +236,7 @@ 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@78f2162d59753c243e374dd52c305e111affebb9#egg=v2realbot
 validators==0.20.0
 vectorbtpro @ file:///Users/davidbrazda/Downloads/vectorbt.pro-2024.2.22
 wcwidth==0.2.9

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

@@ -402,7 +402,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,

research/strat_LINREG_MULTI/v1_SINGLE.ipynb

@@ -44661,7 +44661,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,

research/strat_ORDER_IMBALANCE/v2_SINGLE.ipynb

@@ -211,6 +211,13 @@
     "t1data.data[\"BAC\"].info()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 20,
@@ -1691,7 +1698,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,

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 = ''
-SECRET_KEY = ''
+API_KEY = 'PKGGEWIEYZOVQFDRY70L'
+SECRET_KEY = 'O5Kt8X4RLceIOvM98i5LdbalItsX7hVZlbPYHy8Y'
 MAX_BATCH_SIZE = 1

v2realbot/config.py

@@ -11,7 +11,6 @@ _ml_module_loaded = False
 
 #directory for generated images and basic reports
 MEDIA_DIRECTORY = Path(__file__).parent.parent.parent / "media"
-VBT_DOC_DIRECTORY = Path(__file__).parent.parent.parent / "vbt-doc" #directory for vbt doc
 RUNNER_DETAIL_DIRECTORY = Path(__file__).parent.parent.parent / "runner_detail"
 
 #location of strat.log - it is used to fetch by gui

v2realbot/main.py

@@ -1,7 +1,7 @@
 import os,sys
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 os.environ["KERAS_BACKEND"] = "jax"
-from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY, LOG_PATH, MODEL_DIR, VBT_DOC_DIRECTORY
+from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY, LOG_PATH, MODEL_DIR
 from alpaca.data.timeframe import TimeFrame, TimeFrameUnit
 from datetime import datetime
 from rich import print
@@ -18,7 +18,6 @@ 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
@@ -122,22 +121,6 @@ async def static_files(request: Request, path: str, authenticated: bool = Depend
 
     return FileResponse(file_path)
 
-@app.get("/vbt-doc/{file_path:path}")
-async def serve_protected_docs(file_path: str, credentials: HTTPBasicCredentials = Depends(authenticate_user)):
-    file_location = VBT_DOC_DIRECTORY / file_path
-
-    if file_location.is_dir():  # If it's a directory, serve index.html
-        index_file = file_location / "index.html"
-        if index_file.exists():
-            return FileResponse(index_file)
-        else:
-            raise HTTPException(status_code=404, detail="Index file not found")
-    elif file_location.exists():
-        return FileResponse(file_location)
-    else:
-        raise HTTPException(status_code=404, detail="File not found")
-
-
 def get_current_username(
     credentials: Annotated[HTTPBasicCredentials, Depends(security)]
 ):
@@ -1042,25 +1025,7 @@ 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/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>
@@ -235,7 +215,7 @@
                 </div>
             </div>
             <div id="hist-trades" class="flex-items">
-                <div id="form-trades">               
+                <div id="form-trades">
                     <label data-bs-toggle="collapse" data-bs-target="#trades-data">
                         <h4>Trade History</h4>
                     </label>
@@ -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>

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()
-});