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This commit is contained in:
David Brazda
2024-10-03 17:05:49 +02:00
parent 978cd7e2be
commit 48db2bc9de
6 changed files with 3703 additions and 39 deletions
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8
requirements.txt
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@ -84,7 +84,7 @@ 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@35f029714b23c18abe791b90a85447e959c72258
lightweight_charts @ git+https://github.com/drew2323/lightweight-charts-python.git@7986aa9195d9d3204a998d1a8f5778d95219a08e
llvmlite==0.39.1
lxml==5.3.0
markdown-it-py==3.0.0
@ -121,9 +121,11 @@ platformdirs==4.2.2
plotly==5.24.0
prometheus_client==0.21.0
prompt_toolkit==3.0.47
protobuf==5.28.2
proxy-tools==0.1.0
psutil==6.0.0
psycopg2==2.9.9
psycopg2-binary==2.9.9
ptyprocess==0.7.0
pure_eval==0.2.3
pyarrow==11.0.0
@ -147,6 +149,7 @@ PyWavelets==1.7.0
pywebview==5.2
PyYAML==6.0.2
pyzmq==26.2.0
ray==2.37.0
referencing==0.35.1
regex==2024.7.24
requests==2.32.3
@ -166,6 +169,7 @@ soupsieve==2.6
SQLAlchemy==2.0.32
sseclient-py==1.8.0
stack-data==0.6.3
stratlab_db @ git+https://gitea.stratlab.dev/Stratlab/db.git@0bbe486de7ac410a9375f2ccf7d557a658a662ea
stumpy==1.13.0
TA-Lib==0.4.32
tenacity==9.0.0
@ -193,7 +197,7 @@ webencodings==0.5.1
websocket-client==1.8.0
websockets==11.0.3
Werkzeug==3.0.4
widgetsnbextension==4.0.9
widgetsnbextension==4.0.13
yarl==1.13.1
yfinance==0.2.43
zipp==3.20.1

64
research/get_trades_at_once.ipynb
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@ -13,9 +13,38 @@
},
{
"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-2023-01-01T09:30:00-2024-10-02T16:00:00-['4', '7', 'B', 'C', 'F', 'O', 'P', 'U', 'V', 'W', 'Z']-100.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": [
"['4', '7', 'B', 'C', 'F', 'O', 'P', 'U', 'V', 'W', 'Z']"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
@ -45,10 +74,10 @@
"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",
"symbol = \"BAC\"\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 = datetime(2023, 1, 1, 9, 30, 0)\n",
"day_stop = datetime(2024, 10, 2, 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",
@ -56,13 +85,15 @@
"#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",
"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')}-{str(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)"
" print(f)\n",
"\n",
"exclude_conditions"
]
},
{
@ -71,13 +102,26 @@
"metadata": {},
"outputs": [],
"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"
"#fetch trades in one go\n",
"#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",
"#fetch trades in parallel - for longer intervals\n",
"#trades_df = fetch_trades_parallel(symbol, day_start, day_stop, exclude_conditions=exclude_conditions, minsize=minsize, force_remote=False, max_workers=None)\n",
" \n",
"##trades_df.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"#trades_df.to_parquet(file_trades, engine='pyarrow', compression='gzip')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [

1662
research/indicators/eval_robustness.ipynb Normal file
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42
research/strat_SUPERTREND/SUPERTREND_v1_SINGLE.ipynb
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@ -11,7 +11,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 2,
"metadata": {},
"outputs": [
{
@ -68,7 +68,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 3,
"metadata": {},
"outputs": [
{
@ -80,7 +80,9 @@
"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-2023-01-01T09:30:00-2024-10-02T16:00:00-['4', '7', 'B', 'C', 'F', 'O', 'P', 'U', 'V', 'W', 'Z']-100.parquet\n",
"trades_df-BAC-2024-05-15T09_30_00-2024-05-25T16_00_00-47BCFOPUVWZ-100.parquet\n",
"ohlcv_df-BAC-2023-01-01T09:30:00-2024-10-02T16:00:00-['4', '7', 'B', 'C', 'F', 'O', 'P', 'U', 'V', 'W', 'Z']-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",
@ -94,7 +96,7 @@
"5"
]
},
"execution_count": 2,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@ -129,36 +131,22 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\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"
"ename": "NameError",
"evalue": "name 'basic_data' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mbasic_data\u001b[49m\u001b[38;5;241m.\u001b[39mdata[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBAC\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39minfo()\n\u001b[1;32m 3\u001b[0m df \u001b[38;5;241m=\u001b[39m basic_data\u001b[38;5;241m.\u001b[39mdata[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBAC\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 5\u001b[0m nan_rows \u001b[38;5;241m=\u001b[39m df[df\u001b[38;5;241m.\u001b[39misna()\u001b[38;5;241m.\u001b[39many(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)]\n",
"\u001b[0;31mNameError\u001b[0m: name 'basic_data' is not defined"
]
}
],
"source": [
"basic_data.data[\"BAC\"].info()"
]
"source": []
},
{
"cell_type": "markdown",

1840
research/tests/database_saver.ipynb Normal file
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126
research/tests/ray_test.ipynb Normal file
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@ -0,0 +1,126 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-10-03 09:43:41,741\tINFO worker.py:1786 -- Started a local Ray instance.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"waiting for ray\n",
"ray returned all\n",
"ray finsihed 0.4031927839969285\n",
"worker 0 started\n",
"worker 0 finsihed\n",
"worker 1 started\n",
"worker 1 finsihed\n",
"worker 2 started\n",
"worker 2 finsihed\n",
"worker 3 started\n",
"worker 3 finsihed\n",
"serial function finsihed 0.21200023603159934\n",
"Ray with 4 parts: 0.4031927839969285 seconds\n",
"Serial: 0.21200023603159934 seconds\n",
"Serial computation is faster than Ray with 4 parts\n"
]
}
],
"source": [
"import numpy as np\n",
"import timeit\n",
"import ray\n",
"\n",
"# Define the expensive function\n",
"@ray.remote\n",
"def expensive_function(n):\n",
" # Generate a large random matrix\n",
" A = np.random.rand(1000, 1000)\n",
" B = np.random.rand(1000, 1000)\n",
"\n",
" # Perform the matrix multiplication\n",
" C = np.dot(A, B)\n",
" # Return the result\n",
" return C\n",
"\n",
"def expensive_function_serial(n):\n",
" print(f\"worker {n} started\")\n",
" # Generate a large random matrix\n",
" A = np.random.rand(1000, 1000)\n",
" B = np.random.rand(1000, 1000)\n",
"\n",
" # Perform the matrix multiplication\n",
" C = np.dot(A, B)\n",
"\n",
" # Return the result\n",
" print(f\"worker {n} finsihed\")\n",
" return C\n",
"\n",
"# Initialize Ray\n",
"ray.init()\n",
"\n",
"# Create 4 remote actors to distribute the work\n",
"futures = [expensive_function.remote(_) for _ in range(4)]\n",
"\n",
"# Time the function using Ray with 4 parts\n",
"start_time = timeit.default_timer()\n",
"print(\"waiting for ray\")\n",
"results = ray.get(futures)\n",
"print(\"ray returned all\")\n",
"end_time = timeit.default_timer()\n",
"ray_time = end_time - start_time\n",
"print(\"ray finsihed\", ray_time)\n",
"\n",
"# Time the function serially\n",
"start_time = timeit.default_timer()\n",
"results = [expensive_function_serial(_) for _ in range(4)]\n",
"end_time = timeit.default_timer()\n",
"serial_time = end_time - start_time\n",
"print(\"serial function finsihed\", serial_time)\n",
"\n",
"\n",
"# Print the results\n",
"print(f\"Ray with 4 parts: {ray_time} seconds\")\n",
"print(f\"Serial: {serial_time} seconds\")\n",
"\n",
"# Compare the results\n",
"if ray_time < serial_time:\n",
" print(\"Ray with 4 parts is faster than serial computation\")\n",
"else:\n",
" print(\"Serial computation is faster than Ray with 4 parts\")\n",
"\n",
"# Shutdown Ray\n",
"ray.shutdown()\n"
]
}
],
"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
}