remote fetch
This commit is contained in:
David Brazda
22
README.md
22
README.md
@ -97,6 +97,28 @@ python3 prepare_cache.py --symbols BAC AAPL --day_start 2024-10-14 --day_stop 20
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```
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## remote loaders
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Remote bars of given resolutions from Alpaca.
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Available resolutions Minute, Hours, Day. It s not possible to limit included trades.
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Use only when no precision required.
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```python
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from ttools.external_loaders import load_history_bars
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from ttools.config import zoneNY
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from datetime import datetime, time
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from alpaca.data.timeframe import TimeFrame, TimeFrameUnit
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symbol = "AAPL"
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start_date = zoneNY.localize(datetime(2023, 2, 27, 18, 51, 38))
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end_date = zoneNY.localize(datetime(2023, 4, 27, 21, 51, 39))
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timeframe = TimeFrame(amount=1,unit=TimeFrameUnit.Minute)
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df = load_history_bars(symbol, start_date, end_date, timeframe, main_session_only=True)
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df.loc[('AAPL',)]
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```
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# vbtutils
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Contains helpers for vbtpro
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2
setup.py
2
setup.py
@ -2,7 +2,7 @@ from setuptools import setup, find_packages
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setup(
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name='ttools',
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version='0.7.1',
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version='0.7.2',
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packages=find_packages(),
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install_requires=[
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# list your dependencies here
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460
tests/alpaca_loader.ipynb
Normal file
460
tests/alpaca_loader.ipynb
Normal file
@ -0,0 +1,460 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"TTOOLS: Loaded env variables from file /Users/davidbrazda/Documents/Development/python/.env\n"
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]
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}
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],
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"source": [
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"from ttools.external_loaders import load_history_bars\n",
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"from ttools.config import zoneNY\n",
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"from datetime import datetime, time\n",
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"from alpaca.data.timeframe import TimeFrame, TimeFrameUnit\n",
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"\n",
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"symbol = \"AAPL\"\n",
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"start_date = zoneNY.localize(datetime(2023, 2, 27, 18, 51, 38))\n",
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"end_date = zoneNY.localize(datetime(2023, 4, 27, 21, 51, 39))\n",
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"timeframe = TimeFrame(amount=1,unit=TimeFrameUnit.Minute)\n",
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"\n",
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"df = load_history_bars(symbol, start_date, end_date, timeframe, True)\n",
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"df.loc[('AAPL',)]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>open</th>\n",
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" <th>high</th>\n",
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" <th>low</th>\n",
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" <th>close</th>\n",
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" <th>volume</th>\n",
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" <th>trade_count</th>\n",
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" <th>vwap</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>timestamp</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>2023-02-28 09:30:00-05:00</th>\n",
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" <td>147.050</td>\n",
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" <td>147.380</td>\n",
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" <td>146.830</td>\n",
|
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" <td>147.2700</td>\n",
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" <td>1554100.0</td>\n",
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" <td>6447.0</td>\n",
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" <td>146.914560</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2023-02-28 09:31:00-05:00</th>\n",
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" <td>147.250</td>\n",
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" <td>147.320</td>\n",
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" <td>147.180</td>\n",
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" <td>147.2942</td>\n",
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" <td>159387.0</td>\n",
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" <td>6855.0</td>\n",
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" <td>147.252171</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2023-02-28 09:32:00-05:00</th>\n",
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" <td>147.305</td>\n",
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" <td>147.330</td>\n",
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" <td>147.090</td>\n",
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" <td>147.1600</td>\n",
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" <td>214536.0</td>\n",
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" <td>7435.0</td>\n",
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" <td>147.210128</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2023-02-28 09:33:00-05:00</th>\n",
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" <td>147.140</td>\n",
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" <td>147.230</td>\n",
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" <td>147.090</td>\n",
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" <td>147.1500</td>\n",
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" <td>171487.0</td>\n",
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" <td>7235.0</td>\n",
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" <td>147.154832</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2023-02-28 09:34:00-05:00</th>\n",
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" <td>147.160</td>\n",
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" <td>147.160</td>\n",
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" <td>146.880</td>\n",
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" <td>146.9850</td>\n",
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" <td>235915.0</td>\n",
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" <td>4965.0</td>\n",
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" <td>147.001762</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2023-04-27 15:26:00-04:00</th>\n",
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" <td>168.400</td>\n",
|
||||
" <td>168.415</td>\n",
|
||||
" <td>168.340</td>\n",
|
||||
" <td>168.3601</td>\n",
|
||||
" <td>163973.0</td>\n",
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||||
" <td>1398.0</td>\n",
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" <td>168.368809</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2023-04-27 15:27:00-04:00</th>\n",
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" <td>168.360</td>\n",
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" <td>168.400</td>\n",
|
||||
" <td>168.330</td>\n",
|
||||
" <td>168.3800</td>\n",
|
||||
" <td>130968.0</td>\n",
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||||
" <td>1420.0</td>\n",
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||||
" <td>168.364799</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2023-04-27 15:28:00-04:00</th>\n",
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" <td>168.380</td>\n",
|
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" <td>168.430</td>\n",
|
||||
" <td>168.320</td>\n",
|
||||
" <td>168.3285</td>\n",
|
||||
" <td>152193.0</td>\n",
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||||
" <td>1361.0</td>\n",
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" <td>168.372671</td>\n",
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||||
" </tr>\n",
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" <tr>\n",
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" <th>2023-04-27 15:29:00-04:00</th>\n",
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" <td>168.325</td>\n",
|
||||
" <td>168.330</td>\n",
|
||||
" <td>168.260</td>\n",
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||||
" <td>168.2850</td>\n",
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||||
" <td>208426.0</td>\n",
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||||
" <td>1736.0</td>\n",
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||||
" <td>168.297379</td>\n",
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||||
" </tr>\n",
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||||
" <tr>\n",
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" <th>2023-04-27 15:30:00-04:00</th>\n",
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" <td>168.280</td>\n",
|
||||
" <td>168.350</td>\n",
|
||||
" <td>168.255</td>\n",
|
||||
" <td>168.3450</td>\n",
|
||||
" <td>218077.0</td>\n",
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||||
" <td>1694.0</td>\n",
|
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" <td>168.308873</td>\n",
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||||
" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>15162 rows × 7 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" open high low close volume \\\n",
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"timestamp \n",
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"2023-02-28 09:30:00-05:00 147.050 147.380 146.830 147.2700 1554100.0 \n",
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||||
"2023-02-28 09:31:00-05:00 147.250 147.320 147.180 147.2942 159387.0 \n",
|
||||
"2023-02-28 09:32:00-05:00 147.305 147.330 147.090 147.1600 214536.0 \n",
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||||
"2023-02-28 09:33:00-05:00 147.140 147.230 147.090 147.1500 171487.0 \n",
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||||
"2023-02-28 09:34:00-05:00 147.160 147.160 146.880 146.9850 235915.0 \n",
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||||
"... ... ... ... ... ... \n",
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"2023-04-27 15:26:00-04:00 168.400 168.415 168.340 168.3601 163973.0 \n",
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||||
"2023-04-27 15:27:00-04:00 168.360 168.400 168.330 168.3800 130968.0 \n",
|
||||
"2023-04-27 15:28:00-04:00 168.380 168.430 168.320 168.3285 152193.0 \n",
|
||||
"2023-04-27 15:29:00-04:00 168.325 168.330 168.260 168.2850 208426.0 \n",
|
||||
"2023-04-27 15:30:00-04:00 168.280 168.350 168.255 168.3450 218077.0 \n",
|
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"\n",
|
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" trade_count vwap \n",
|
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"timestamp \n",
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"2023-02-28 09:30:00-05:00 6447.0 146.914560 \n",
|
||||
"2023-02-28 09:31:00-05:00 6855.0 147.252171 \n",
|
||||
"2023-02-28 09:32:00-05:00 7435.0 147.210128 \n",
|
||||
"2023-02-28 09:33:00-05:00 7235.0 147.154832 \n",
|
||||
"2023-02-28 09:34:00-05:00 4965.0 147.001762 \n",
|
||||
"... ... ... \n",
|
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"2023-04-27 15:26:00-04:00 1398.0 168.368809 \n",
|
||||
"2023-04-27 15:27:00-04:00 1420.0 168.364799 \n",
|
||||
"2023-04-27 15:28:00-04:00 1361.0 168.372671 \n",
|
||||
"2023-04-27 15:29:00-04:00 1736.0 168.297379 \n",
|
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"2023-04-27 15:30:00-04:00 1694.0 168.308873 \n",
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"\n",
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"[15162 rows x 7 columns]"
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||||
]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.loc[('AAPL',)]"
|
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]
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},
|
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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||||
{
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||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
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||||
" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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||||
"</style>\n",
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||||
"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th>open</th>\n",
|
||||
" <th>high</th>\n",
|
||||
" <th>low</th>\n",
|
||||
" <th>close</th>\n",
|
||||
" <th>volume</th>\n",
|
||||
" <th>trade_count</th>\n",
|
||||
" <th>vwap</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>symbol</th>\n",
|
||||
" <th>timestamp</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
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||||
" <th></th>\n",
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" <th></th>\n",
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||||
" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th rowspan=\"11\" valign=\"top\">AAPL</th>\n",
|
||||
" <th>2023-02-27 18:52:00-05:00</th>\n",
|
||||
" <td>148.0200</td>\n",
|
||||
" <td>148.02</td>\n",
|
||||
" <td>148.0200</td>\n",
|
||||
" <td>148.02</td>\n",
|
||||
" <td>112.0</td>\n",
|
||||
" <td>7.0</td>\n",
|
||||
" <td>148.020000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2023-02-27 18:56:00-05:00</th>\n",
|
||||
" <td>148.0200</td>\n",
|
||||
" <td>148.02</td>\n",
|
||||
" <td>148.0200</td>\n",
|
||||
" <td>148.02</td>\n",
|
||||
" <td>175.0</td>\n",
|
||||
" <td>10.0</td>\n",
|
||||
" <td>148.020000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2023-02-27 19:00:00-05:00</th>\n",
|
||||
" <td>148.0299</td>\n",
|
||||
" <td>148.03</td>\n",
|
||||
" <td>148.0299</td>\n",
|
||||
" <td>148.03</td>\n",
|
||||
" <td>1957.0</td>\n",
|
||||
" <td>10.0</td>\n",
|
||||
" <td>148.029993</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2023-02-27 19:06:00-05:00</th>\n",
|
||||
" <td>148.0600</td>\n",
|
||||
" <td>148.06</td>\n",
|
||||
" <td>148.0600</td>\n",
|
||||
" <td>148.06</td>\n",
|
||||
" <td>122.0</td>\n",
|
||||
" <td>7.0</td>\n",
|
||||
" <td>148.060000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2023-02-27 19:09:00-05:00</th>\n",
|
||||
" <td>148.0500</td>\n",
|
||||
" <td>148.10</td>\n",
|
||||
" <td>148.0500</td>\n",
|
||||
" <td>148.10</td>\n",
|
||||
" <td>1604.0</td>\n",
|
||||
" <td>33.0</td>\n",
|
||||
" <td>148.075109</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>...</th>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2023-04-27 19:54:00-04:00</th>\n",
|
||||
" <td>167.8000</td>\n",
|
||||
" <td>167.80</td>\n",
|
||||
" <td>167.8000</td>\n",
|
||||
" <td>167.80</td>\n",
|
||||
" <td>534.0</td>\n",
|
||||
" <td>15.0</td>\n",
|
||||
" <td>167.800000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2023-04-27 19:56:00-04:00</th>\n",
|
||||
" <td>167.8800</td>\n",
|
||||
" <td>167.88</td>\n",
|
||||
" <td>167.8800</td>\n",
|
||||
" <td>167.88</td>\n",
|
||||
" <td>1386.0</td>\n",
|
||||
" <td>28.0</td>\n",
|
||||
" <td>167.880000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2023-04-27 19:57:00-04:00</th>\n",
|
||||
" <td>167.8000</td>\n",
|
||||
" <td>167.80</td>\n",
|
||||
" <td>167.8000</td>\n",
|
||||
" <td>167.80</td>\n",
|
||||
" <td>912.0</td>\n",
|
||||
" <td>60.0</td>\n",
|
||||
" <td>167.800000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2023-04-27 19:58:00-04:00</th>\n",
|
||||
" <td>167.8000</td>\n",
|
||||
" <td>167.88</td>\n",
|
||||
" <td>167.8000</td>\n",
|
||||
" <td>167.88</td>\n",
|
||||
" <td>3311.0</td>\n",
|
||||
" <td>22.0</td>\n",
|
||||
" <td>167.877333</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2023-04-27 19:59:00-04:00</th>\n",
|
||||
" <td>167.9000</td>\n",
|
||||
" <td>167.94</td>\n",
|
||||
" <td>167.9000</td>\n",
|
||||
" <td>167.94</td>\n",
|
||||
" <td>1969.0</td>\n",
|
||||
" <td>64.0</td>\n",
|
||||
" <td>167.918150</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>31217 rows × 7 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" open high low close volume \\\n",
|
||||
"symbol timestamp \n",
|
||||
"AAPL 2023-02-27 18:52:00-05:00 148.0200 148.02 148.0200 148.02 112.0 \n",
|
||||
" 2023-02-27 18:56:00-05:00 148.0200 148.02 148.0200 148.02 175.0 \n",
|
||||
" 2023-02-27 19:00:00-05:00 148.0299 148.03 148.0299 148.03 1957.0 \n",
|
||||
" 2023-02-27 19:06:00-05:00 148.0600 148.06 148.0600 148.06 122.0 \n",
|
||||
" 2023-02-27 19:09:00-05:00 148.0500 148.10 148.0500 148.10 1604.0 \n",
|
||||
"... ... ... ... ... ... \n",
|
||||
" 2023-04-27 19:54:00-04:00 167.8000 167.80 167.8000 167.80 534.0 \n",
|
||||
" 2023-04-27 19:56:00-04:00 167.8800 167.88 167.8800 167.88 1386.0 \n",
|
||||
" 2023-04-27 19:57:00-04:00 167.8000 167.80 167.8000 167.80 912.0 \n",
|
||||
" 2023-04-27 19:58:00-04:00 167.8000 167.88 167.8000 167.88 3311.0 \n",
|
||||
" 2023-04-27 19:59:00-04:00 167.9000 167.94 167.9000 167.94 1969.0 \n",
|
||||
"\n",
|
||||
" trade_count vwap \n",
|
||||
"symbol timestamp \n",
|
||||
"AAPL 2023-02-27 18:52:00-05:00 7.0 148.020000 \n",
|
||||
" 2023-02-27 18:56:00-05:00 10.0 148.020000 \n",
|
||||
" 2023-02-27 19:00:00-05:00 10.0 148.029993 \n",
|
||||
" 2023-02-27 19:06:00-05:00 7.0 148.060000 \n",
|
||||
" 2023-02-27 19:09:00-05:00 33.0 148.075109 \n",
|
||||
"... ... ... \n",
|
||||
" 2023-04-27 19:54:00-04:00 15.0 167.800000 \n",
|
||||
" 2023-04-27 19:56:00-04:00 28.0 167.880000 \n",
|
||||
" 2023-04-27 19:57:00-04:00 60.0 167.800000 \n",
|
||||
" 2023-04-27 19:58:00-04:00 22.0 167.877333 \n",
|
||||
" 2023-04-27 19:59:00-04:00 64.0 167.918150 \n",
|
||||
"\n",
|
||||
"[31217 rows x 7 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
@ -729,7 +729,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -741,7 +741,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -919,7 +919,7 @@
|
||||
"[222754 rows x 6 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
from .vbtutils import AnchoredIndicator, create_mask_from_window, isrising, isfalling, isrisingc, isfallingc, trades2entries_exits, figs2cell
|
||||
from .vbtindicators import register_custom_inds
|
||||
from .utils import AggType, zoneNY, zonePRG, zoneUTC
|
||||
from .loaders import load_data, prepare_trade_cache
|
||||
from .loaders import load_data, prepare_trade_cache
|
||||
from .external_loaders import load_history_bars
|
||||
55
ttools/external_loaders.py
Normal file
55
ttools/external_loaders.py
Normal file
@ -0,0 +1,55 @@
|
||||
from ctypes import Union
|
||||
from ttools import zoneUTC
|
||||
from ttools.config import *
|
||||
from datetime import datetime
|
||||
from alpaca.data.historical import StockHistoricalDataClient
|
||||
from ttools.config import ACCOUNT1_LIVE_API_KEY, ACCOUNT1_LIVE_SECRET_KEY
|
||||
from datetime import timedelta, datetime, time
|
||||
from alpaca.data.enums import DataFeed
|
||||
from typing import List, Union
|
||||
import pandas as pd
|
||||
from alpaca.data.historical import StockHistoricalDataClient
|
||||
from alpaca.data.requests import StockBarsRequest
|
||||
from alpaca.data.enums import DataFeed
|
||||
from alpaca.data.timeframe import TimeFrame, TimeFrameUnit
|
||||
|
||||
def load_history_bars(symbol: Union[str, List[str]], datetime_object_from: datetime, datetime_object_to: datetime, timeframe: TimeFrame, main_session_only: bool = True):
|
||||
"""Returns dataframe fetched remotely from Alpaca.
|
||||
|
||||
Args:
|
||||
symbol: symbol or list of symbols
|
||||
datetime_object_from: datetime in zoneNY
|
||||
datetime_object_to: datetime in zoneNY
|
||||
timeframe: timeframe
|
||||
main_session_only: boolean to fetch only main session data
|
||||
|
||||
Returns:
|
||||
dataframe
|
||||
|
||||
Example:
|
||||
```python
|
||||
from ttools.external_loaders import load_history_bars
|
||||
from ttools.config import zoneNY
|
||||
from datetime import datetime
|
||||
from alpaca.data.timeframe import TimeFrame, TimeFrameUnit
|
||||
|
||||
symbol = "AAPL"
|
||||
start_date = zoneNY.localize(datetime(2023, 2, 27, 18, 51, 38))
|
||||
end_date = zoneNY.localize(datetime(2023, 4, 27, 21, 51, 39))
|
||||
timeframe = TimeFrame(amount=1,unit=TimeFrameUnit.Minute)
|
||||
|
||||
df = load_history_bars(symbol, start_date, end_date, timeframe)
|
||||
```
|
||||
"""
|
||||
client = StockHistoricalDataClient(ACCOUNT1_LIVE_API_KEY, ACCOUNT1_LIVE_SECRET_KEY, raw_data=False)
|
||||
#datetime_object_from = datetime(2023, 2, 27, 18, 51, 38, tzinfo=datetime.timezone.utc)
|
||||
#datetime_object_to = datetime(2023, 2, 27, 21, 51, 39, tzinfo=datetime.timezone.utc)
|
||||
bar_request = StockBarsRequest(symbol_or_symbols=symbol,timeframe=timeframe, start=datetime_object_from, end=datetime_object_to, feed=DataFeed.SIP)
|
||||
#print("before df")
|
||||
df = client.get_stock_bars(bar_request).df
|
||||
df.index = df.index.set_levels(df.index.get_level_values(1).tz_convert(zoneNY), level=1)
|
||||
if main_session_only:
|
||||
start_time = time(9, 30, 0)
|
||||
end_time = time(15, 30, 0)
|
||||
df = df.loc[(df.index.get_level_values(1).time >= start_time) & (df.index.get_level_values(1).time <= end_time)]
|
||||
return df
|
||||
Reference in New Issue
Block a user