diff --git a/requirements.txt b/requirements.txt
index 83f7e7a..6c82214 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,8 @@
-alpaca-py==0.30.1
+aiodns==3.2.0
+aiohappyeyeballs==2.4.3
+aiohttp==3.10.8
+aiosignal==1.3.1
+alpaca-py==0.18.1
annotated-types==0.7.0
anyio==4.6.0
appdirs==1.4.4
@@ -8,18 +12,21 @@ argon2-cffi-bindings==21.2.0
arrow==1.3.0
asttokens==2.4.1
async-lru==2.0.4
+async-timeout==4.0.3
attrs==24.2.0
babel==2.16.0
beautifulsoup4==4.12.3
bleach==6.1.0
blinker==1.8.2
bottle==0.12.25
+ccxt==4.4.10
certifi==2024.8.30
cffi==1.17.1
charset-normalizer==3.3.2
click==8.1.7
comm==0.2.2
contourpy==1.3.0
+cryptography==43.0.1
cycler==0.12.1
dash==2.17.1
dash-bootstrap-components==1.6.0
@@ -40,6 +47,7 @@ Flask==3.0.3
fonttools==4.53.1
fqdn==1.5.1
frozendict==2.4.4
+frozenlist==1.4.1
greenlet==3.0.3
h11==0.14.0
html5lib==1.1
@@ -53,8 +61,10 @@ ipykernel==6.29.5
ipython==8.27.0
ipywidgets==8.1.5
isoduration==20.11.0
-itables==2.2.1
+itables==2.0.1
itsdangerous==2.2.0
+jax==0.4.23
+jaxlib==0.4.23
jedi==0.19.1
Jinja2==3.1.4
joblib==1.4.2
@@ -64,18 +74,18 @@ jsonschema==4.23.0
jsonschema-specifications==2023.12.1
jupyter-events==0.10.0
jupyter-lsp==2.2.5
-jupyter_client==8.6.2
+jupyter_client==8.6.1
jupyter_core==5.7.2
-jupyter_server==2.14.2
+jupyter_server==2.14.0
jupyter_server_terminals==0.5.3
-jupyterlab==4.2.5
+jupyterlab==4.1.8
jupyterlab_pygments==0.3.0
-jupyterlab_server==2.27.3
+jupyterlab_server==2.27.1
jupyterlab_widgets==3.0.13
kiwisolver==1.4.5
korean-lunar-calendar==0.3.1
-lightweight_charts @ git+https://github.com/drew2323/lightweight-charts-python.git@2b9f238a4242d958bc863b6209bf6444786477c5
-llvmlite==0.43.0
+lightweight_charts @ git+https://github.com/drew2323/lightweight-charts-python.git@35f029714b23c18abe791b90a85447e959c72258
+llvmlite==0.39.1
lxml==5.3.0
markdown-it-py==3.0.0
MarkupSafe==2.1.5
@@ -83,7 +93,9 @@ matplotlib==3.9.2
matplotlib-inline==0.1.7
mdurl==0.1.2
mistune==3.0.2
+ml_dtypes==0.5.0
msgpack==1.0.8
+multidict==6.1.0
multitasking==0.0.11
mypy-extensions==1.0.0
nbclient==0.10.0
@@ -92,12 +104,13 @@ nbformat==5.10.4
nest-asyncio==1.6.0
notebook==7.2.2
notebook_shim==0.2.4
-numba==0.60.0
+numba==0.56.4
numpy==1.23.5
+opt_einsum==3.4.0
orjson==3.10.7
overrides==7.7.0
packaging==24.1
-pandas==2.2.2
+pandas==2.2.1
pandas_market_calendars==4.4.1
pandocfilters==1.5.1
parso==0.8.4
@@ -110,9 +123,11 @@ prometheus_client==0.21.0
prompt_toolkit==3.0.47
proxy-tools==0.1.0
psutil==6.0.0
+psycopg2==2.9.9
ptyprocess==0.7.0
pure_eval==0.2.3
-pyarrow==17.0.0
+pyarrow==11.0.0
+pycares==4.4.0
pycparser==2.22
pydantic==2.8.2
pydantic_core==2.20.1
@@ -142,7 +157,7 @@ rich==13.8.0
rpds-py==0.20.0
schedule==1.2.2
scikit-learn==1.5.1
-scipy==1.14.1
+scipy==1.11.2
seaborn==0.13.2
Send2Trash==1.8.3
six==1.16.0
@@ -164,20 +179,21 @@ tornado==6.4.1
tqdm==4.66.5
traitlets==5.14.3
tulipy==0.4.0
-types-python-dateutil==2.9.0.20240906
-typing_extensions==4.12.2
+types-python-dateutil==2.9.0.20240316
+typing_extensions==4.9.0
tzdata==2024.1
tzlocal==5.2
uri-template==1.3.0
urllib3==2.2.2
v2realbot @ git+https://github.com/drew2323/v2trading.git@700461033f2b816eeb1ee3a14b930f181cf57308
-vectorbtpro @ file:///Users/davidbrazda/Desktop/vectorbtpro-2024.2.22-py3-none-any.whl#sha256=d85431edc7c9df69886ad0b6ee2b32d3aaa40cdc0b456f1c8fa6aa26f01d2be3
+vectorbtpro @ file:///Users/davidbrazda/Downloads/vectorbt.pro-develop
wcwidth==0.2.13
webcolors==24.8.0
webencodings==0.5.1
websocket-client==1.8.0
-websockets==13.0.1
+websockets==11.0.3
Werkzeug==3.0.4
-widgetsnbextension==4.0.13
+widgetsnbextension==4.0.9
+yarl==1.13.1
yfinance==0.2.43
zipp==3.20.1
diff --git a/research/dbinsertdata.py b/research/dbinsertdata.py
new file mode 100644
index 0000000..0ea9073
--- /dev/null
+++ b/research/dbinsertdata.py
@@ -0,0 +1,74 @@
+"""Saves data from Kraken Futures, combining BTC and USD settled markets"""
+
+import logging
+from logging.config import fileConfig
+
+from vectorbtpro import pd, vbt
+
+from ext_lib.db import db_connect
+from ext_lib.util import find_earliest_date
+
+EXCHANGE = "1s_OHLCV"
+SYMBOLS = ("BTC/USD:BTC", "BTC/USD:USD")
+RESOLUTION = "1s"
+DB_ENGINE = db_connect("ohlcv_1m")
+DB_SYMBOL = "BTC/USD"
+
+#fileConfig("logging.ini", disable_existing_loggers=False)
+
+logging.basicConfig(
+ level=logging.INFO,
+ format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+ handlers=[logging.StreamHandler()]
+)
+
+log = logging.getLogger("main")
+
+vbt.CCXTData.set_custom_settings(exchange=EXCHANGE, timeframe=RESOLUTION, limit=6000000)
+vbt.SQLData.set_engine_settings(engine_name="pg", engine=DB_ENGINE, schema=EXCHANGE, populate_=True, chunksize=1000)
+vbt.SQLData.set_custom_settings(engine_name="pg", schema=EXCHANGE)
+
+
+def main():
+ if vbt.SQLData.has_table(DB_SYMBOL, schema=EXCHANGE):
+ data = vbt.SQLData.has_table(DB_SYMBOL, schema=EXCHANGE)
+
+
+
+ vbt.SQLData.create_schema(EXCHANGE)
+ db_last_tstamp = None
+ # TODO: figure out if it's possible to avoid using tables directly, but rather symbols
+ if vbt.SQLData.has_table(DB_SYMBOL, schema=EXCHANGE):
+ db_last_tstamp = vbt.SQLData.get_last_row_number(DB_SYMBOL, row_number_column="Open time")
+
+ dfs = []
+ for symbol in SYMBOLS:
+ if db_last_tstamp is None:
+ start = find_earliest_date(symbol, EXCHANGE)
+ else:
+ start = db_last_tstamp + pd.Timedelta(RESOLUTION)
+ log.info("Start date for %s is %s", symbol, start)
+
+ # Get data
+ df = vbt.CCXTData.pull(symbol, exchange=EXCHANGE, timeframe=RESOLUTION, start=start).get()
+ # If symbol is BTC/USD:BTC, convert volume from USD to BTC using the close price
+ if symbol == "BTC/USD:BTC":
+ df["Volume"] = df["Volume"].div(df["Close"])
+
+ dfs.append(df)
+
+ # Combine data from two symbols (last row is incomplete so removed)
+ concatenated_data = pd.concat(dfs, axis=0)
+ final_data = (
+ concatenated_data.groupby(concatenated_data.index)
+ .agg({"Open": "mean", "High": "mean", "Low": "mean", "Close": "mean", "Volume": "sum"})
+ .iloc[:-1]
+ )
+ data = vbt.SQLData.from_data({DB_SYMBOL: final_data})
+ # TODO: use custom method to prevent duplicate timestamps
+ log.info("Saving to DB")
+ vbt.SQLDataSaver(data).save_data(method="multi")
+
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/research/ext_lib/__init__.py b/research/ext_lib/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/research/ext_lib/db.py b/research/ext_lib/db.py
new file mode 100644
index 0000000..a75b28e
--- /dev/null
+++ b/research/ext_lib/db.py
@@ -0,0 +1,39 @@
+"""Database-related stuff"""
+
+import sqlalchemy
+from sqlalchemy import create_engine, text
+
+DB_USER = "postgres"
+DB_PW = "KzsZ0wz5tp2wUIPM"
+DB_HOST = "db.stratlab.dev"
+DB_PORT = 30543
+DB_URL = f"postgresql://{DB_USER}:{DB_PW}@{DB_HOST}:{DB_PORT}"
+print(DB_URL)
+#DB_ARGS = {"sslmode": "verify-full", "sslrootcert": "system"}
+DB_ARGS = {}
+
+def db_connect(db_name: str) -> sqlalchemy.engine.Engine:
+ """Connect to DB. Create it if it doesn't exist
+ Args:
+ db_name: name of the database to create
+ """
+ try:
+ engine = create_engine(f"{DB_URL}/{db_name}", connect_args=DB_ARGS)
+ engine.connect()
+ return engine
+ except sqlalchemy.exc.OperationalError:
+ # Database doesn't exist, create it
+ conn = create_engine(f"{DB_URL}/postgres", isolation_level="AUTOCOMMIT", connect_args=DB_ARGS).connect()
+ # TODO: figure out how to get rid of SQL injection. Standard parameterization adds quotes that breaks syntax
+ conn.execute(text(f"CREATE DATABASE {db_name}"))
+
+ return create_engine(f"{DB_URL}/{db_name}", connect_args=DB_ARGS)
+
+
+# list exchanges
+# list symbols
+# first date
+# last date
+
+# get data
+# save data
diff --git a/research/ext_lib/util.py b/research/ext_lib/util.py
new file mode 100644
index 0000000..12cda40
--- /dev/null
+++ b/research/ext_lib/util.py
@@ -0,0 +1,49 @@
+import logging
+from datetime import datetime, timedelta
+
+from ccxt.base.errors import ExchangeNotAvailable
+from vectorbtpro import pd, tp, vbt
+
+log = logging.getLogger(__name__)
+
+
+def find_earliest_date(symbol: str, exchange: str, **kwargs) -> tp.Optional[pd.Timestamp]:
+ """Wrapper around CCXTData.find_earliest_date to handle ExchangeNotAvailable error with binary search
+ Args:
+ symbol: The trading symbol to query
+ exchange: The exchange to query
+ **kwargs: Additional arguments to pass to the find_earliest_date method
+
+ Returns:
+ tp.Optional[pd.Timestamp]: The earliest available date if found, otherwise None
+ """
+ log.info("Searching for earliest date for %s", symbol)
+ start_date = pd.Timestamp(kwargs.pop("start", datetime(2010, 1, 1))).floor("D")
+ end_date = pd.Timestamp(kwargs.pop("end", datetime.now())).floor("D")
+
+ while start_date < end_date:
+ log.info("Trying %s to %s range", start_date, end_date)
+ mid_date = (start_date + (end_date - start_date) // 2).floor("D")
+ try:
+ found_date = vbt.CCXTData.find_earliest_date(
+ symbol, exchange=exchange, start=mid_date, end=end_date, limit=10, **kwargs
+ )
+ if found_date:
+ # Move the end date to mid_date to search the earlier half
+ end_date = mid_date
+ else:
+ # Move the start date to mid_date + 1 to search the later half
+ start_date = mid_date + timedelta(days=1)
+ except ExchangeNotAvailable:
+ # Move the start date to mid_date + 1 to search the later half
+ start_date = mid_date + timedelta(days=1)
+
+ # After the loop, start_date should be the earliest date with data
+ try:
+ found_date = vbt.CCXTData.find_earliest_date(
+ symbol, exchange=exchange, start=start_date, end=end_date, **kwargs
+ )
+ return found_date
+ except ExchangeNotAvailable as e:
+ log.error("ExchangeNotAvailable error encountered at final step... Error: %s", e)
+ return None
diff --git a/research/indicators/anchored_vwap.ipynb b/research/indicators/anchored_vwap.ipynb
new file mode 100644
index 0000000..9e981d8
--- /dev/null
+++ b/research/indicators/anchored_vwap.ipynb
@@ -0,0 +1,958 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Anchored VWAP\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "None\n",
+ "Loaded env variables from file None\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "Activating profile profile1\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "Activating profile profile1\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "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 lightweight_charts import Panel, chart\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": [
+ "5"
+ ]
+ },
+ "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\":\"2024-02-16 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": [
+ "\n",
+ "DatetimeIndex: 57966 entries, 2024-02-12 09:30:00-05:00 to 2024-02-16 15:59:59-05:00\n",
+ "Data columns (total 10 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 open 57966 non-null float64 \n",
+ " 1 high 57966 non-null float64 \n",
+ " 2 low 57966 non-null float64 \n",
+ " 3 close 57966 non-null float64 \n",
+ " 4 volume 57966 non-null float64 \n",
+ " 5 trades 57966 non-null float64 \n",
+ " 6 updated 57966 non-null datetime64[ns, US/Eastern]\n",
+ " 7 vwap 57966 non-null float64 \n",
+ " 8 buyvolume 57966 non-null float64 \n",
+ " 9 sellvolume 57966 non-null float64 \n",
+ "dtypes: datetime64[ns, US/Eastern](1), float64(9)\n",
+ "memory usage: 4.9 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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " open | \n",
+ " high | \n",
+ " low | \n",
+ " close | \n",
+ " volume | \n",
+ " vwap | \n",
+ " buyvolume | \n",
+ " trades | \n",
+ " sellvolume | \n",
+ "
\n",
+ " \n",
+ " | time | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 2024-02-12 09:30:00-05:00 | \n",
+ " 33.000 | \n",
+ " 33.00 | \n",
+ " 33.00 | \n",
+ " 33.000 | \n",
+ " 800.0 | \n",
+ " 33.000000 | \n",
+ " 0.0 | \n",
+ " 2.0 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " | 2024-02-12 09:30:01-05:00 | \n",
+ " 33.020 | \n",
+ " 33.05 | \n",
+ " 33.00 | \n",
+ " 33.010 | \n",
+ " 267365.0 | \n",
+ " 33.020039 | \n",
+ " 265765.0 | \n",
+ " 25.0 | \n",
+ " 1000.0 | \n",
+ "
\n",
+ " \n",
+ " | 2024-02-12 09:30:02-05:00 | \n",
+ " 33.000 | \n",
+ " 33.02 | \n",
+ " 33.00 | \n",
+ " 33.015 | \n",
+ " 1100.0 | \n",
+ " 33.009545 | \n",
+ " 500.0 | \n",
+ " 9.0 | \n",
+ " 600.0 | \n",
+ "
\n",
+ " \n",
+ " | 2024-02-12 09:30:03-05:00 | \n",
+ " 33.005 | \n",
+ " 33.08 | \n",
+ " 32.99 | \n",
+ " 33.080 | \n",
+ " 7508.0 | \n",
+ " 33.027898 | \n",
+ " 1970.0 | \n",
+ " 32.0 | \n",
+ " 3638.0 | \n",
+ "
\n",
+ " \n",
+ " | 2024-02-12 09:30:05-05:00 | \n",
+ " 33.060 | \n",
+ " 33.06 | \n",
+ " 33.06 | \n",
+ " 33.060 | \n",
+ " 500.0 | \n",
+ " 33.060000 | \n",
+ " 0.0 | \n",
+ " 5.0 | \n",
+ " 100.0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Activating profile profile1\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "Activating profile profile1\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"from dotenv import load_dotenv\n",
"\n",
@@ -22,6 +44,7 @@
"# Load the .env file\n",
"load_dotenv(env_file)\n",
"\n",
+ "from lightweight_charts import Panel, chart\n",
"from v2realbot.utils.utils import zoneNY\n",
"import pandas as pd\n",
"import numpy as np\n",
@@ -45,9 +68,37 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"metadata": {},
- "outputs": [],
+ "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": [
+ "5"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# Define the market open and close times\n",
"market_open = datetime.time(9, 30)\n",
@@ -66,7 +117,7 @@
"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",
+ "ohlcv_df = ohlcv_df.loc[\"2024-02-12 9:30\":\"2024-02-16 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",
@@ -78,9 +129,33 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "DatetimeIndex: 57966 entries, 2024-02-12 09:30:00-05:00 to 2024-02-16 15:59:59-05:00\n",
+ "Data columns (total 10 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 open 57966 non-null float64 \n",
+ " 1 high 57966 non-null float64 \n",
+ " 2 low 57966 non-null float64 \n",
+ " 3 close 57966 non-null float64 \n",
+ " 4 volume 57966 non-null float64 \n",
+ " 5 trades 57966 non-null float64 \n",
+ " 6 updated 57966 non-null datetime64[ns, US/Eastern]\n",
+ " 7 vwap 57966 non-null float64 \n",
+ " 8 buyvolume 57966 non-null float64 \n",
+ " 9 sellvolume 57966 non-null float64 \n",
+ "dtypes: datetime64[ns, US/Eastern](1), float64(9)\n",
+ "memory usage: 4.9 MB\n"
+ ]
+ }
+ ],
"source": [
"basic_data.data[\"BAC\"].info()"
]
@@ -94,7 +169,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -130,7 +205,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -150,8 +225,156 @@
"s1close = s1data.close\n",
"t1close = t1data.close\n",
"t30close = t30data.close\n",
- "t30volume = t30data.volume\n",
- "\n",
+ "t30volume = t30data.volume"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " open | \n",
+ " high | \n",
+ " low | \n",
+ " close | \n",
+ " volume | \n",
+ " vwap | \n",
+ " buyvolume | \n",
+ " trades | \n",
+ " sellvolume | \n",
+ "
\n",
+ " \n",
+ " | time | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 2024-02-12 09:30:00-05:00 | \n",
+ " 33.000 | \n",
+ " 33.00 | \n",
+ " 33.00 | \n",
+ " 33.000 | \n",
+ " 800.0 | \n",
+ " 33.000000 | \n",
+ " 0.0 | \n",
+ " 2.0 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " | 2024-02-12 09:30:01-05:00 | \n",
+ " 33.020 | \n",
+ " 33.05 | \n",
+ " 33.00 | \n",
+ " 33.010 | \n",
+ " 267365.0 | \n",
+ " 33.020039 | \n",
+ " 265765.0 | \n",
+ " 25.0 | \n",
+ " 1000.0 | \n",
+ "
\n",
+ " \n",
+ " | 2024-02-12 09:30:02-05:00 | \n",
+ " 33.000 | \n",
+ " 33.02 | \n",
+ " 33.00 | \n",
+ " 33.015 | \n",
+ " 1100.0 | \n",
+ " 33.009545 | \n",
+ " 500.0 | \n",
+ " 9.0 | \n",
+ " 600.0 | \n",
+ "
\n",
+ " \n",
+ " | 2024-02-12 09:30:03-05:00 | \n",
+ " 33.005 | \n",
+ " 33.08 | \n",
+ " 32.99 | \n",
+ " 33.080 | \n",
+ " 7508.0 | \n",
+ " 33.027898 | \n",
+ " 1970.0 | \n",
+ " 32.0 | \n",
+ " 3638.0 | \n",
+ "
\n",
+ " \n",
+ " | 2024-02-12 09:30:05-05:00 | \n",
+ " 33.060 | \n",
+ " 33.06 | \n",
+ " 33.06 | \n",
+ " 33.060 | \n",
+ " 500.0 | \n",
+ " 33.060000 | \n",
+ " 0.0 | \n",
+ " 5.0 | \n",
+ " 100.0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "