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Author SHA1 Message Date
Leonid Gloubchnin
809f46fe36 to discard 2025-11-04 18:02:38 +00:00
Leonid Gloubchnin
413abafe0f My First Commit 2025-11-04 17:55:08 +00:00
Oleg Sheynin
5d46c1e32c . 2025-10-27 18:46:26 -04:00
Oleg Sheynin
889f7ba1c3 . 2025-10-27 18:46:14 -04:00
Oleg Sheynin
1515b2d077 . 2025-10-27 18:39:51 -04:00
Oleg Sheynin
b4ae3e715d . 2025-10-27 18:36:26 -04:00
Cryptoval Trading Technologies
6f845d32c6 . 2025-07-25 22:13:49 +00:00
Cryptoval Trading Technologies
a04e8878fb lg_changes 2025-07-25 22:11:49 +00:00
61 changed files with 21984 additions and 18946 deletions
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2
.gitignore vendored
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# SpecStory explanation file
__pycache__/
__OLD__/
.specstory/
.history/
.cursorindexingignore
data
####.vscode/
cvttpy
# SpecStory explanation file
.specstory/.what-is-this.md

1
.vscode/.env vendored
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PYTHONPATH=/home/oleg/develop

9
.vscode/extensions.json vendored
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{
"recommendations": [
"ms-python.python",
"ms-python.pylance",
"ms-python.black-formatter",
"ms-python.mypy-type-checker",
"ms-python.isort"
]
}

271
.vscode/launch.json vendored
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{
// Use IntelliSense to learn about possible attributes.
// Hover to view descriptions of existing attributes.
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0",
"configurations": [
{
"name": "Python Debugger: Current File",
"type": "debugpy",
"request": "launch",
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
"program": "${file}",
"console": "integratedTerminal",
"env": {
"PYTHONPATH": "${workspaceFolder}/lib:${workspaceFolder}/.."
},
},
{
"name": "-------- Live Pair Trading --------",
},
{
"name": "PAIRS TRADER",
"type": "debugpy",
"request": "launch",
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
"program": "${workspaceFolder}/bin/pairs_trader.py",
"console": "integratedTerminal",
"env": {
"PYTHONPATH": "${workspaceFolder}/lib:${workspaceFolder}/.."
},
"args": [
"--config=${workspaceFolder}/configuration/pairs_trader.cfg",
"--pair=PAIR-ADA-USDT:BNBSPOT,PAIR-SOL-USDT:BNBSPOT",
],
},
{
"name": "-------- OLS --------",
},
{
"name": "CRYPTO OLS (rolling)",
"type": "debugpy",
"request": "launch",
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
"program": "${workspaceFolder}/research/backtest.py",
"args": [
"--config=${workspaceFolder}/configuration/ols.cfg",
"--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
"--date_pattern=20250605",
"--result_db=${workspaceFolder}/research/results/crypto/%T.ols.ADA-SOL.20250605.crypto_results.db",
],
"env": {
"PYTHONPATH": "${workspaceFolder}/lib"
},
"console": "integratedTerminal"
},
{
"name": "CRYPTO OLS (optimized)",
"type": "debugpy",
"request": "launch",
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
"program": "${workspaceFolder}/research/backtest.py",
"args": [
"--config=${workspaceFolder}/configuration/ols-opt.cfg",
"--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
"--date_pattern=20250605",
"--result_db=${workspaceFolder}/research/results/crypto/%T.ols-opt.ADA-SOL.20250605.crypto_results.db",
],
"env": {
"PYTHONPATH": "${workspaceFolder}/lib"
},
"console": "integratedTerminal"
},
// {
// "name": "CRYPTO OLS (expanding)",
// "type": "debugpy",
// "request": "launch",
// "python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
// "program": "${workspaceFolder}/research/backtest.py",
// "args": [
// "--config=${workspaceFolder}/configuration/ols-exp.cfg",
// "--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
// "--date_pattern=20250605",
// "--result_db=${workspaceFolder}/research/results/crypto/%T.ols-exp.ADA-SOL.20250605.crypto_results.db",
// ],
// "env": {
// "PYTHONPATH": "${workspaceFolder}/lib"
// },
// "console": "integratedTerminal"
// },
{
"name": "EQUITY OLS (rolling)",
"type": "debugpy",
"request": "launch",
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
"program": "${workspaceFolder}/research/backtest.py",
"args": [
"--config=${workspaceFolder}/configuration/ols.cfg",
"--instruments=COIN:EQUITY:ALPACA,MSTR:EQUITY:ALPACA",
"--date_pattern=20250605",
"--result_db=${workspaceFolder}/research/results/equity/%T.ols.COIN-MSTR.20250605.equity_results.db",
],
"env": {
"PYTHONPATH": "${workspaceFolder}/lib"
},
"console": "integratedTerminal"
},
{
"name": "EQUITY-CRYPTO OLS (rolling)",
"type": "debugpy",
"request": "launch",
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
"program": "${workspaceFolder}/research/backtest.py",
"args": [
"--config=${workspaceFolder}/configuration/ols.cfg",
"--instruments=COIN:EQUITY:ALPACA,BTC-USDT:CRYPTO:BNBSPOT",
"--date_pattern=20250605",
"--result_db=${workspaceFolder}/research/results/intermarket/%T.ols.COIN-BTC.20250605.equity_results.db",
],
"env": {
"PYTHONPATH": "${workspaceFolder}/lib"
},
"console": "integratedTerminal"
},
{
"name": "-------- VECM --------",
},
{
"name": "CRYPTO VECM (rolling)",
"type": "debugpy",
"request": "launch",
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
"program": "${workspaceFolder}/research/backtest.py",
"args": [
"--config=${workspaceFolder}/configuration/vecm.cfg",
"--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
"--date_pattern=20250605",
"--result_db=${workspaceFolder}/research/results/crypto/%T.vecm.ADA-SOL.20250605.crypto_results.db",
],
"env": {
"PYTHONPATH": "${workspaceFolder}/lib"
},
"console": "integratedTerminal"
},
{
"name": "CRYPTO VECM (optimized)",
"type": "debugpy",
"request": "launch",
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
"program": "${workspaceFolder}/research/backtest.py",
"args": [
"--config=${workspaceFolder}/configuration/vecm-opt.cfg",
"--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
"--date_pattern=20250605",
"--result_db=${workspaceFolder}/research/results/crypto/%T.vecm-opt.ADA-SOL.20250605.crypto_results.db",
],
"env": {
"PYTHONPATH": "${workspaceFolder}/lib"
},
"console": "integratedTerminal"
},
// {
// "name": "CRYPTO VECM (expanding)",
// "type": "debugpy",
// "request": "launch",
// "python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
// "program": "${workspaceFolder}/research/backtest.py",
// "args": [
// "--config=${workspaceFolder}/configuration/vecm-exp.cfg",
// "--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
// "--date_pattern=20250605",
// "--result_db=${workspaceFolder}/research/results/crypto/%T.vecm-exp.ADA-SOL.20250605.crypto_results.db",
// ],
// "env": {
// "PYTHONPATH": "${workspaceFolder}/lib"
// },
// "console": "integratedTerminal"
// },
{
"name": "EQUITY VECM (rolling)",
"type": "debugpy",
"request": "launch",
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
"program": "${workspaceFolder}/research/backtest.py",
"args": [
"--config=${workspaceFolder}/configuration/vecm.cfg",
"--instruments=COIN:EQUITY:ALPACA,MSTR:EQUITY:ALPACA",
"--date_pattern=20250605",
"--result_db=${workspaceFolder}/research/results/equity/%T.vecm.COIN-MSTR.20250605.equity_results.db",
],
"env": {
"PYTHONPATH": "${workspaceFolder}/lib"
},
"console": "integratedTerminal"
},
{
"name": "EQUITY-CRYPTO VECM (rolling)",
"type": "debugpy",
"request": "launch",
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
"program": "${workspaceFolder}/research/backtest.py",
"args": [
"--config=${workspaceFolder}/configuration/vecm.cfg",
"--instruments=COIN:EQUITY:ALPACA,BTC-USDT:CRYPTO:BNBSPOT",
"--date_pattern=20250605",
"--result_db=${workspaceFolder}/research/results/intermarket/%T.vecm.COIN-BTC.20250601.equity_results.db",
],
"env": {
"PYTHONPATH": "${workspaceFolder}/lib"
},
"console": "integratedTerminal"
},
{
"name": "-------- B a t c h e s --------",
},
{
"name": "CRYPTO OLS Batch (rolling)",
"type": "debugpy",
"request": "launch",
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
"program": "${workspaceFolder}/research/backtest.py",
"args": [
"--config=${workspaceFolder}/configuration/ols.cfg",
"--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
"--date_pattern=2025060*",
"--result_db=${workspaceFolder}/research/results/crypto/%T.ols.ADA-SOL.2025060-.crypto_results.db",
],
"env": {
"PYTHONPATH": "${workspaceFolder}/lib"
},
"console": "integratedTerminal"
},
{
"name": "CRYPTO VECM Batch (rolling)",
"type": "debugpy",
"request": "launch",
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
"program": "${workspaceFolder}/research/backtest.py",
"args": [
"--config=${workspaceFolder}/configuration/vecm.cfg",
"--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
"--date_pattern=2025060*",
"--result_db=${workspaceFolder}/research/results/crypto/%T.vecm.ADA-SOL.2025060-.crypto_results.db",
],
"env": {
"PYTHONPATH": "${workspaceFolder}/lib"
},
"console": "integratedTerminal"
},
{
"name": "-------- Viz Test --------",
},
{
"name": "Viz Test",
"type": "debugpy",
"request": "launch",
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
"program": "${workspaceFolder}/tests/viz_test.py",
"args": [
"--config=${workspaceFolder}/configuration/ols.cfg",
"--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
"--date_pattern=20250605",
],
"env": {
"PYTHONPATH": "${workspaceFolder}/lib"
},
"console": "integratedTerminal"
}
]
}

8
.vscode/pairs_trading.code-workspace vendored
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@ -1,8 +0,0 @@
{
"folders": [
{
"path": ".."
}
],
"settings": {}
}

112
.vscode/settings.json vendored
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{
"PythonVersion": "3.12",
"[python]": {
"editor.defaultFormatter": "ms-python.black-formatter"
},
// ===========================================================
"workbench.activityBar.orientation": "vertical",
// ===========================================================
// "markdown.styles": [
// "/home/oleg/develop/cvtt2/.vscode/light-theme.css"
// ],
"markdown.preview.background": "#ffffff",
"markdown.preview.textEditorTheme": "light",
"markdown-pdf.styles": [
"/home/oleg/develop/cvtt2/.vscode/light-theme.css"
],
"editor.detectIndentation": false,
// Configure editor settings to be overridden for [yaml] language.
"[yaml]": {
"editor.insertSpaces": true,
"editor.tabSize": 4,
},
"pylint.args": [
"--disable=missing-docstring"
, "--disable=invalid-name"
, "--disable=too-few-public-methods"
, "--disable=broad-exception-raised"
, "--disable=broad-exception-caught"
, "--disable=pointless-string-statement"
, "--disable=unused-argument"
, "--disable=line-too-long"
, "--disable=import-outside-toplevel"
, "--disable=fixme"
, "--disable=protected-access"
, "--disable=logging-fstring-interpolation"
],
// ===== TESTING CONFIGURATION =====
"python.testing.unittestEnabled": false,
"python.testing.pytestEnabled": true,
"python.testing.pytestArgs": [
"-v",
"--tb=short",
"--disable-warnings"
],
"python.testing.envVars": {
"PYTHONPATH": "${workspaceFolder}/lib:${workspaceFolder}/.."
},
"python.testing.cwd": "${workspaceFolder}",
"python.testing.autoTestDiscoverOnSaveEnabled": true,
"python.testing.pytestPath": "/home/oleg/.pyenv/python3.12-venv/bin/pytest",
"python.testing.promptToConfigure": false,
"python.testing.pytest.enabled": true,
// Python interpreter settings
"python.defaultInterpreterPath": "/home/oleg/.pyenv/python3.12-venv/bin/python3.12",
// Environment variables for Python execution
"python.envFile": "${workspaceFolder}/.vscode/.env",
"python.terminal.activateEnvironment": false,
"python.terminal.activateEnvInCurrentTerminal": false,
// Global environment variables for VS Code Python extension
"terminal.integrated.env.linux": {
"PYTHONPATH": "/home/oleg/develop/:${env:PYTHONPATH}"
},
"pylint.enabled": true,
"github.copilot.enable": false,
"markdown.extension.print.theme": "dark",
"python.analysis.extraPaths": [
"${workspaceFolder}/..",
"${workspaceFolder}/lib"
],
// Try enabling regular Python language server alongside CursorPyright
"python.languageServer": "None",
"python.analysis.diagnosticMode": "workspace",
"workbench.colorTheme": "Atom One Dark",
"cursorpyright.analysis.enable": false,
"cursorpyright.analysis.extraPaths": [
"${workspaceFolder}/..",
"${workspaceFolder}/lib"
],
// Enable quick fixes for unused imports
"python.analysis.autoImportCompletions": true,
"python.analysis.fixAll": ["source.unusedImports"],
"python.analysis.typeCheckingMode": "basic",
// Enable code actions for CursorPyright
"cursorpyright.analysis.autoImportCompletions": true,
"cursorpyright.analysis.typeCheckingMode": "off",
"cursorpyright.reportUnusedImport": "warning",
"cursorpyright.reportUnusedVariable": "warning",
"cursorpyright.analysis.diagnosticMode": "workspace",
// Force enable code actions
"editor.lightBulb.enabled": true,
"editor.codeActionsOnSave": {
"source.organizeImports": "explicit",
"source.fixAll": "explicit",
"source.unusedImports": "explicit"
},
// Enable Python-specific code actions
"python.analysis.completeFunctionParens": true,
"python.analysis.addImport.exactMatchOnly": false,
"workbench.tree.indent": 24,
}

181
__DELETE__/.vscode/launch.json vendored
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@ -1,181 +0,0 @@
{
// Use IntelliSense to learn about possible attributes.
// Hover to view descriptions of existing attributes.
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0",
"configurations": [
{
"name": "Python Debugger: Current File",
"type": "debugpy",
"request": "launch",
"program": "${file}",
"console": "integratedTerminal"
},
{
"name": "-------- Z-Score (OLS) --------",
},
{
"name": "CRYPTO z-score",
"type": "debugpy",
"request": "launch",
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
"program": "research/pt_backtest.py",
"args": [
"--config=${workspaceFolder}/configuration/zscore.cfg",
"--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
"--date_pattern=20250605",
"--result_db=${workspaceFolder}/research/results/crypto/%T.z-score.ADA-SOL.20250602.crypto_results.db",
],
"env": {
"PYTHONPATH": "${workspaceFolder}/lib"
},
"console": "integratedTerminal"
},
{
"name": "EQUITY z-score",
"type": "debugpy",
"request": "launch",
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
"program": "research/pt_backtest.py",
"args": [
"--config=${workspaceFolder}/configuration/zscore.cfg",
"--instruments=COIN:EQUITY:ALPACA,MSTR:EQUITY:ALPACA",
"--date_pattern=2025060*",
"--result_db=${workspaceFolder}/research/results/equity/%T.z-score.COIN-MSTR.20250602.equity_results.db",
],
"env": {
"PYTHONPATH": "${workspaceFolder}/lib"
},
"console": "integratedTerminal"
},
{
"name": "EQUITY-CRYPTO z-score",
"type": "debugpy",
"request": "launch",
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
"program": "research/pt_backtest.py",
"args": [
"--config=${workspaceFolder}/configuration/zscore.cfg",
"--instruments=COIN:EQUITY:ALPACA,BTC-USDT:CRYPTO:BNBSPOT",
"--date_pattern=2025060*",
"--result_db=${workspaceFolder}/research/results/intermarket/%T.z-score.COIN-BTC.20250601.equity_results.db",
],
"env": {
"PYTHONPATH": "${workspaceFolder}/lib"
},
"console": "integratedTerminal"
},
{
"name": "-------- VECM --------",
},
{
"name": "CRYPTO vecm",
"type": "debugpy",
"request": "launch",
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
"program": "research/pt_backtest.py",
"args": [
"--config=${workspaceFolder}/configuration/vecm.cfg",
"--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
"--date_pattern=2025060*",
"--result_db=${workspaceFolder}/research/results/crypto/%T.vecm.ADA-SOL.20250602.crypto_results.db",
],
"env": {
"PYTHONPATH": "${workspaceFolder}/lib"
},
"console": "integratedTerminal"
},
{
"name": "EQUITY vecm",
"type": "debugpy",
"request": "launch",
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
"program": "research/pt_backtest.py",
"args": [
"--config=${workspaceFolder}/configuration/vecm.cfg",
"--instruments=COIN:EQUITY:ALPACA,MSTR:EQUITY:ALPACA",
"--date_pattern=2025060*",
"--result_db=${workspaceFolder}/research/results/equity/%T.vecm.COIN-MSTR.20250602.equity_results.db",
],
"env": {
"PYTHONPATH": "${workspaceFolder}/lib"
},
"console": "integratedTerminal"
},
{
"name": "EQUITY-CRYPTO vecm",
"type": "debugpy",
"request": "launch",
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
"program": "research/pt_backtest.py",
"args": [
"--config=${workspaceFolder}/configuration/vecm.cfg",
"--instruments=COIN:EQUITY:ALPACA,BTC-USDT:CRYPTO:BNBSPOT",
"--date_pattern=2025060*",
"--result_db=${workspaceFolder}/research/results/intermarket/%T.vecm.COIN-BTC.20250601.equity_results.db",
],
"env": {
"PYTHONPATH": "${workspaceFolder}/lib"
},
"console": "integratedTerminal"
},
{
"name": "-------- New ZSCORE --------",
},
{
"name": "New CRYPTO z-score",
"type": "debugpy",
"request": "launch",
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
"program": "${workspaceFolder}/research/backtest_new.py",
"args": [
"--config=${workspaceFolder}/configuration/new_zscore.cfg",
"--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
"--date_pattern=2025060*",
"--result_db=${workspaceFolder}/research/results/crypto/%T.new_zscore.ADA-SOL.2025060-.crypto_results.db",
],
"env": {
"PYTHONPATH": "${workspaceFolder}/lib"
},
"console": "integratedTerminal"
},
{
"name": "New CRYPTO vecm",
"type": "debugpy",
"request": "launch",
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
"program": "${workspaceFolder}/research/backtest_new.py",
"args": [
"--config=${workspaceFolder}/configuration/new_vecm.cfg",
"--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
"--date_pattern=20250605",
"--result_db=${workspaceFolder}/research/results/crypto/%T.vecm.ADA-SOL.20250605.crypto_results.db",
],
"env": {
"PYTHONPATH": "${workspaceFolder}/lib"
},
"console": "integratedTerminal"
},
{
"name": "-------- Viz Test --------",
},
{
"name": "Viz Test",
"type": "debugpy",
"request": "launch",
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
"program": "${workspaceFolder}/research/viz_test.py",
"args": [
"--config=${workspaceFolder}/configuration/new_zscore.cfg",
"--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
"--date_pattern=20250605",
],
"env": {
"PYTHONPATH": "${workspaceFolder}/lib"
},
"console": "integratedTerminal"
}
]
}

44
__DELETE__/configuration/vecm.cfg
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@ -1,44 +0,0 @@
{
"market_data_loading": {
"CRYPTO": {
"data_directory": "./data/crypto",
"db_table_name": "md_1min_bars",
"instrument_id_pfx": "PAIR-",
},
"EQUITY": {
"data_directory": "./data/equity",
"db_table_name": "md_1min_bars",
"instrument_id_pfx": "STOCK-",
}
},
# ====== Funding ======
"funding_per_pair": 2000.0,
# ====== Trading Parameters ======
"stat_model_price": "close", # "vwap"
"execution_price": {
"column": "vwap",
"shift": 1,
},
"dis-equilibrium_open_trshld": 2.0,
"dis-equilibrium_close_trshld": 1.0,
"training_minutes": 120, # TODO Remove this
"training_size": 120,
"fit_method_class": "pt_trading.vecm_rolling_fit.VECMRollingFit",
# ====== Stop Conditions ======
"stop_close_conditions": {
"profit": 2.0,
"loss": -0.5
}
# ====== End of Session Closeout ======
"close_outstanding_positions": true,
# "close_outstanding_positions": false,
"trading_hours": {
"timezone": "America/New_York",
"begin_session": "7:30:00",
"end_session": "18:30:00",
}
}

43
__DELETE__/configuration/zscore_expanding.cfg
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@ -1,43 +0,0 @@
{
"market_data_loading": {
"CRYPTO": {
"data_directory": "./data/crypto",
"db_table_name": "md_1min_bars",
"instrument_id_pfx": "PAIR-",
},
"EQUITY": {
"data_directory": "./data/equity",
"db_table_name": "md_1min_bars",
"instrument_id_pfx": "STOCK-",
}
},
# ====== Funding ======
"funding_per_pair": 2000.0,
# ====== Trading Parameters ======
"stat_model_price": "close",
"execution_price": {
"column": "vwap",
"shift": 1,
},
"dis-equilibrium_open_trshld": 2.0,
"dis-equilibrium_close_trshld": 0.5,
"training_minutes": 120, # TODO Remove this
"training_size": 120,
"fit_method_class": "pt_trading.z-score_rolling_fit.ZScoreRollingFit",
# ====== Stop Conditions ======
"stop_close_conditions": {
"profit": 2.0,
"loss": -0.5
}
# ====== End of Session Closeout ======
"close_outstanding_positions": true,
# "close_outstanding_positions": false,
"trading_hours": {
"timezone": "America/New_York",
"begin_session": "9:30:00",
"end_session": "18:30:00",
}
}

304
__DELETE__/lib/pt_trading/expanding_window_fit.py
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@ -1,304 +0,0 @@
from abc import ABC, abstractmethod
from enum import Enum
from typing import Any, Dict, Optional, cast
import pandas as pd # type: ignore[import]
from pt_trading.fit_method import PairsTradingFitMethod
from pt_trading.results import BacktestResult
from pt_trading.trading_pair import PairState, TradingPair
NanoPerMin = 1e9
class ExpandingWindowFit(PairsTradingFitMethod):
"""
N O T E:
=========
- This class remains to be abstract
- The following methods are to be implemented in the subclass:
- create_trading_pair()
=========
"""
def __init__(self) -> None:
super().__init__()
def run_pair(
self, pair: TradingPair, bt_result: BacktestResult
) -> Optional[pd.DataFrame]:
print(f"***{pair}*** STARTING....")
config = pair.config_
start_idx = pair.get_begin_index()
end_index = pair.get_end_index()
pair.user_data_["state"] = PairState.INITIAL
# Initialize trades DataFrame with proper dtypes to avoid concatenation warnings
pair.user_data_["trades"] = pd.DataFrame(columns=self.TRADES_COLUMNS).astype(
{
"time": "datetime64[ns]",
"symbol": "string",
"side": "string",
"action": "string",
"price": "float64",
"disequilibrium": "float64",
"scaled_disequilibrium": "float64",
"pair": "object",
}
)
training_minutes = config["training_minutes"]
while training_minutes + 1 < end_index:
pair.get_datasets(
training_minutes=training_minutes,
training_start_index=start_idx,
testing_size=1,
)
# ================================ PREDICTION ================================
try:
self.pair_predict_result_ = pair.predict()
except Exception as e:
raise RuntimeError(
f"{pair}: TrainingPrediction failed: {str(e)}"
) from e
training_minutes += 1
self._create_trading_signals(pair, config, bt_result)
print(f"***{pair}*** FINISHED *** Num Trades:{len(pair.user_data_['trades'])}")
return pair.get_trades()
def _create_trading_signals(
self, pair: TradingPair, config: Dict, bt_result: BacktestResult
) -> None:
predicted_df = self.pair_predict_result_
assert predicted_df is not None
open_threshold = config["dis-equilibrium_open_trshld"]
close_threshold = config["dis-equilibrium_close_trshld"]
for curr_predicted_row_idx in range(len(predicted_df)):
pred_row = predicted_df.iloc[curr_predicted_row_idx]
scaled_disequilibrium = pred_row["scaled_disequilibrium"]
if pair.user_data_["state"] in [
PairState.INITIAL,
PairState.CLOSE,
PairState.CLOSE_POSITION,
PairState.CLOSE_STOP_LOSS,
PairState.CLOSE_STOP_PROFIT,
]:
if scaled_disequilibrium >= open_threshold:
open_trades = self._get_open_trades(
pair, row=pred_row, open_threshold=open_threshold
)
if open_trades is not None:
open_trades["status"] = PairState.OPEN.name
print(f"OPEN TRADES:\n{open_trades}")
pair.add_trades(open_trades)
pair.user_data_["state"] = PairState.OPEN
pair.on_open_trades(open_trades)
elif pair.user_data_["state"] == PairState.OPEN:
if scaled_disequilibrium <= close_threshold:
close_trades = self._get_close_trades(
pair, row=pred_row, close_threshold=close_threshold
)
if close_trades is not None:
close_trades["status"] = PairState.CLOSE.name
print(f"CLOSE TRADES:\n{close_trades}")
pair.add_trades(close_trades)
pair.user_data_["state"] = PairState.CLOSE
pair.on_close_trades(close_trades)
elif pair.to_stop_close_conditions(predicted_row=pred_row):
close_trades = self._get_close_trades(
pair, row=pred_row, close_threshold=close_threshold
)
if close_trades is not None:
close_trades["status"] = pair.user_data_[
"stop_close_state"
].name
print(f"STOP CLOSE TRADES:\n{close_trades}")
pair.add_trades(close_trades)
pair.user_data_["state"] = pair.user_data_["stop_close_state"]
pair.on_close_trades(close_trades)
# Outstanding positions
if pair.user_data_["state"] == PairState.OPEN:
print(f"{pair}: *** Position is NOT CLOSED. ***")
# outstanding positions
if config["close_outstanding_positions"]:
close_position_row = pd.Series(pair.market_data_.iloc[-2])
close_position_row["disequilibrium"] = 0.0
close_position_row["scaled_disequilibrium"] = 0.0
close_position_row["signed_scaled_disequilibrium"] = 0.0
close_position_trades = self._get_close_trades(
pair=pair, row=close_position_row, close_threshold=close_threshold
)
if close_position_trades is not None:
close_position_trades["status"] = PairState.CLOSE_POSITION.name
print(f"CLOSE_POSITION TRADES:\n{close_position_trades}")
pair.add_trades(close_position_trades)
pair.user_data_["state"] = PairState.CLOSE_POSITION
pair.on_close_trades(close_position_trades)
else:
if predicted_df is not None:
bt_result.handle_outstanding_position(
pair=pair,
pair_result_df=predicted_df,
last_row_index=0,
open_side_a=pair.user_data_["open_side_a"],
open_side_b=pair.user_data_["open_side_b"],
open_px_a=pair.user_data_["open_px_a"],
open_px_b=pair.user_data_["open_px_b"],
open_tstamp=pair.user_data_["open_tstamp"],
)
def _get_open_trades(
self, pair: TradingPair, row: pd.Series, open_threshold: float
) -> Optional[pd.DataFrame]:
colname_a, colname_b = pair.exec_prices_colnames()
open_row = row
open_tstamp = open_row["tstamp"]
open_disequilibrium = open_row["disequilibrium"]
open_scaled_disequilibrium = open_row["scaled_disequilibrium"]
signed_scaled_disequilibrium = open_row["signed_scaled_disequilibrium"]
open_px_a = open_row[f"{colname_a}"]
open_px_b = open_row[f"{colname_b}"]
# creating the trades
print(f"OPEN_TRADES: {row["tstamp"]} {open_scaled_disequilibrium=}")
if open_disequilibrium > 0:
open_side_a = "SELL"
open_side_b = "BUY"
close_side_a = "BUY"
close_side_b = "SELL"
else:
open_side_a = "BUY"
open_side_b = "SELL"
close_side_a = "SELL"
close_side_b = "BUY"
# save closing sides
pair.user_data_["open_side_a"] = open_side_a
pair.user_data_["open_side_b"] = open_side_b
pair.user_data_["open_px_a"] = open_px_a
pair.user_data_["open_px_b"] = open_px_b
pair.user_data_["open_tstamp"] = open_tstamp
pair.user_data_["close_side_a"] = close_side_a
pair.user_data_["close_side_b"] = close_side_b
# create opening trades
trd_signal_tuples = [
(
open_tstamp,
pair.symbol_a_,
open_side_a,
"OPEN",
open_px_a,
open_disequilibrium,
open_scaled_disequilibrium,
signed_scaled_disequilibrium,
pair,
),
(
open_tstamp,
pair.symbol_b_,
open_side_b,
"OPEN",
open_px_b,
open_disequilibrium,
open_scaled_disequilibrium,
signed_scaled_disequilibrium,
pair,
),
]
# Create DataFrame with explicit dtypes to avoid concatenation warnings
df = pd.DataFrame(
trd_signal_tuples,
columns=self.TRADES_COLUMNS,
)
# Ensure consistent dtypes
return df.astype(
{
"time": "datetime64[ns]",
"action": "string",
"symbol": "string",
"price": "float64",
"disequilibrium": "float64",
"scaled_disequilibrium": "float64",
"signed_scaled_disequilibrium": "float64",
"pair": "object",
}
)
def _get_close_trades(
self, pair: TradingPair, row: pd.Series, close_threshold: float
) -> Optional[pd.DataFrame]:
colname_a, colname_b = pair.exec_prices_colnames()
close_row = row
close_tstamp = close_row["tstamp"]
close_disequilibrium = close_row["disequilibrium"]
close_scaled_disequilibrium = close_row["scaled_disequilibrium"]
signed_scaled_disequilibrium = close_row["signed_scaled_disequilibrium"]
close_px_a = close_row[f"{colname_a}"]
close_px_b = close_row[f"{colname_b}"]
close_side_a = pair.user_data_["close_side_a"]
close_side_b = pair.user_data_["close_side_b"]
trd_signal_tuples = [
(
close_tstamp,
pair.symbol_a_,
close_side_a,
"CLOSE",
close_px_a,
close_disequilibrium,
close_scaled_disequilibrium,
signed_scaled_disequilibrium,
pair,
),
(
close_tstamp,
pair.symbol_b_,
close_side_b,
"CLOSE",
close_px_b,
close_disequilibrium,
close_scaled_disequilibrium,
signed_scaled_disequilibrium,
pair,
),
]
# Add tuples to data frame with explicit dtypes to avoid concatenation warnings
df = pd.DataFrame(
trd_signal_tuples,
columns=self.TRADES_COLUMNS,
)
# Ensure consistent dtypes
return df.astype(
{
"time": "datetime64[ns]",
"action": "string",
"symbol": "string",
"price": "float64",
"disequilibrium": "float64",
"scaled_disequilibrium": "float64",
"signed_scaled_disequilibrium": "float64",
"pair": "object",
}
)
def reset(self) -> None:
pass

122
__DELETE__/lib/pt_trading/vecm_rolling_fit.py
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from typing import Any, Dict, Optional, cast
import pandas as pd
from pt_trading.results import BacktestResult
from pt_trading.rolling_window_fit import RollingFit
from pt_trading.trading_pair import TradingPair
from statsmodels.tsa.vector_ar.vecm import VECM, VECMResults
NanoPerMin = 1e9
class VECMTradingPair(TradingPair):
vecm_fit_: Optional[VECMResults]
pair_predict_result_: Optional[pd.DataFrame]
def __init__(
self,
config: Dict[str, Any],
market_data: pd.DataFrame,
symbol_a: str,
symbol_b: str,
):
super().__init__(config, market_data, symbol_a, symbol_b)
self.vecm_fit_ = None
self.pair_predict_result_ = None
def _train_pair(self) -> None:
self._fit_VECM()
assert self.vecm_fit_ is not None
diseq_series = self.training_df_[self.colnames()] @ self.vecm_fit_.beta
# print(diseq_series.shape)
self.training_mu_ = float(diseq_series[0].mean())
self.training_std_ = float(diseq_series[0].std())
self.training_df_["dis-equilibrium"] = (
self.training_df_[self.colnames()] @ self.vecm_fit_.beta
)
# Normalize the dis-equilibrium
self.training_df_["scaled_dis-equilibrium"] = (
diseq_series - self.training_mu_
) / self.training_std_
def _fit_VECM(self) -> None:
assert self.training_df_ is not None
vecm_df = self.training_df_[self.colnames()].reset_index(drop=True)
vecm_model = VECM(vecm_df, coint_rank=1)
vecm_fit = vecm_model.fit()
assert vecm_fit is not None
# URGENT check beta and alpha
# Check if the model converged properly
if not hasattr(vecm_fit, "beta") or vecm_fit.beta is None:
print(f"{self}: VECM model failed to converge properly")
self.vecm_fit_ = vecm_fit
pass
def predict(self) -> pd.DataFrame:
self._train_pair()
assert self.testing_df_ is not None
assert self.vecm_fit_ is not None
predicted_prices = self.vecm_fit_.predict(steps=len(self.testing_df_))
# Convert prediction to a DataFrame for readability
predicted_df = pd.DataFrame(
predicted_prices, columns=pd.Index(self.colnames()), dtype=float
)
predicted_df = pd.merge(
self.testing_df_.reset_index(drop=True),
pd.DataFrame(
predicted_prices, columns=pd.Index(self.colnames()), dtype=float
),
left_index=True,
right_index=True,
suffixes=("", "_pred"),
).dropna()
predicted_df["disequilibrium"] = (
predicted_df[self.colnames()] @ self.vecm_fit_.beta
)
predicted_df["signed_scaled_disequilibrium"] = (
predicted_df["disequilibrium"] - self.training_mu_
) / self.training_std_
predicted_df["scaled_disequilibrium"] = abs(
predicted_df["signed_scaled_disequilibrium"]
)
predicted_df = predicted_df.reset_index(drop=True)
if self.pair_predict_result_ is None:
self.pair_predict_result_ = predicted_df
else:
self.pair_predict_result_ = pd.concat(
[self.pair_predict_result_, predicted_df], ignore_index=True
)
# Reset index to ensure proper indexing
self.pair_predict_result_ = self.pair_predict_result_.reset_index(drop=True)
return self.pair_predict_result_
class VECMRollingFit(RollingFit):
def __init__(self) -> None:
super().__init__()
def create_trading_pair(
self,
config: Dict,
market_data: pd.DataFrame,
symbol_a: str,
symbol_b: str,
) -> TradingPair:
return VECMTradingPair(
config=config,
market_data=market_data,
symbol_a=symbol_a,
symbol_b=symbol_b,
)

85
__DELETE__/lib/pt_trading/z-score_rolling_fit.py
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@ -1,85 +0,0 @@
from typing import Any, Dict, Optional, cast
import pandas as pd
from pt_trading.results import BacktestResult
from pt_trading.rolling_window_fit import RollingFit
from pt_trading.trading_pair import TradingPair
import statsmodels.api as sm
NanoPerMin = 1e9
class ZScoreTradingPair(TradingPair):
zscore_model_: Optional[sm.regression.linear_model.RegressionResultsWrapper]
pair_predict_result_: Optional[pd.DataFrame]
zscore_df_: Optional[pd.DataFrame]
def __init__(
self,
config: Dict[str, Any],
market_data: pd.DataFrame,
symbol_a: str,
symbol_b: str,
):
super().__init__(config, market_data, symbol_a, symbol_b)
self.zscore_model_ = None
self.pair_predict_result_ = None
self.zscore_df_ = None
def _fit_zscore(self) -> None:
assert self.training_df_ is not None
symbol_a_px_series = self.training_df_[self.colnames()].iloc[:, 0]
symbol_b_px_series = self.training_df_[self.colnames()].iloc[:, 1]
symbol_a_px_series, symbol_b_px_series = symbol_a_px_series.align(
symbol_b_px_series, axis=0
)
X = sm.add_constant(symbol_b_px_series)
self.zscore_model_ = sm.OLS(symbol_a_px_series, X).fit()
assert self.zscore_model_ is not None
hedge_ratio = self.zscore_model_.params.iloc[1]
# Calculate spread and Z-score
spread = symbol_a_px_series - hedge_ratio * symbol_b_px_series
self.zscore_df_ = (spread - spread.mean()) / spread.std()
def predict(self) -> pd.DataFrame:
self._fit_zscore()
assert self.zscore_df_ is not None
self.training_df_["dis-equilibrium"] = self.zscore_df_
self.training_df_["scaled_dis-equilibrium"] = abs(self.zscore_df_)
assert self.testing_df_ is not None
assert self.zscore_df_ is not None
predicted_df = self.testing_df_
predicted_df["disequilibrium"] = self.zscore_df_
predicted_df["signed_scaled_disequilibrium"] = self.zscore_df_
predicted_df["scaled_disequilibrium"] = abs(self.zscore_df_)
predicted_df = predicted_df.reset_index(drop=True)
if self.pair_predict_result_ is None:
self.pair_predict_result_ = predicted_df
else:
self.pair_predict_result_ = pd.concat(
[self.pair_predict_result_, predicted_df], ignore_index=True
)
# Reset index to ensure proper indexing
self.pair_predict_result_ = self.pair_predict_result_.reset_index(drop=True)
return self.pair_predict_result_.dropna()
class ZScoreRollingFit(RollingFit):
def __init__(self) -> None:
super().__init__()
def create_trading_pair(
self, config: Dict, market_data: pd.DataFrame, symbol_a: str, symbol_b: str
) -> TradingPair:
return ZScoreTradingPair(
config=config,
market_data=market_data,
symbol_a=symbol_a,
symbol_b=symbol_b,
)

7656
__DELETE__/research/notebooks/single_pair_test.ipynb
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101
__DELETE__/strategy/pair_strategy.py
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@ -1,101 +0,0 @@
import argparse
import asyncio
import glob
import importlib
import os
from datetime import date, datetime
from typing import Any, Dict, List, Optional
import hjson
import pandas as pd
from tools.data_loader import get_available_instruments_from_db, load_market_data
from pt_trading.results import (
BacktestResult,
create_result_database,
store_config_in_database,
store_results_in_database,
)
from pt_trading.fit_methods import PairsTradingFitMethod
from pt_trading.trading_pair import TradingPair
def run_strategy(
config: Dict,
datafile: str,
fit_method: PairsTradingFitMethod,
instruments: List[str],
) -> BacktestResult:
"""
Run backtest for all pairs using the specified instruments.
"""
bt_result: BacktestResult = BacktestResult(config=config)
def _create_pairs(config: Dict, instruments: List[str]) -> List[TradingPair]:
nonlocal datafile
all_indexes = range(len(instruments))
unique_index_pairs = [(i, j) for i in all_indexes for j in all_indexes if i < j]
pairs = []
# Update config to use the specified instruments
config_copy = config.copy()
config_copy["instruments"] = instruments
market_data_df = load_market_data(
datafile=datafile,
exchange_id=config_copy["exchange_id"],
instruments=config_copy["instruments"],
instrument_id_pfx=config_copy["instrument_id_pfx"],
db_table_name=config_copy["db_table_name"],
trading_hours=config_copy["trading_hours"],
)
for a_index, b_index in unique_index_pairs:
pair = fit_method.create_trading_pair(
market_data=market_data_df,
symbol_a=instruments[a_index],
symbol_b=instruments[b_index],
)
pairs.append(pair)
return pairs
pairs_trades = []
for pair in _create_pairs(config, instruments):
single_pair_trades = fit_method.run_pair(
pair=pair, config=config, bt_result=bt_result
)
if single_pair_trades is not None and len(single_pair_trades) > 0:
pairs_trades.append(single_pair_trades)
# Check if result_list has any data before concatenating
if len(pairs_trades) == 0:
print("No trading signals found for any pairs")
return bt_result
result = pd.concat(pairs_trades, ignore_index=True)
result["time"] = pd.to_datetime(result["time"])
result = result.set_index("time").sort_index()
bt_result.collect_single_day_results(result)
return bt_result
def main() -> None:
# Load config
# Subscribe to CVTT market data
# On snapshot (with historical data) - create trading strategy with market data dateframe
async def on_message(message_type: MessageTypeT, subscr_id: SubscriptionIdT, message: Dict, instrument_id: str) -> None:
print(f"{message_type=} {subscr_id=} {instrument_id}")
if message_type == "md_aggregate":
aggr = message.get("md_aggregate", [])
print(f"[{aggr['tstamp'][:19]}] *** RLTM *** {message}")
elif message_type == "historical_md_aggregate":
for aggr in message.get("historical_data", []):
print(f"[{aggr['tstamp'][:19]}] *** HIST *** {aggr}")
else:
print(f"Unknown message type: {message_type}")
if __name__ == "__main__":
asyncio.run(main())

105
bin/pairs_trader.py
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@ -1,105 +0,0 @@
from __future__ import annotations
from functools import partial
from typing import Dict, List
from cvttpy_tools.settings.cvtt_types import JsonDictT
from cvttpy_tools.tools.app import App
from cvttpy_tools.tools.base import NamedObject
from cvttpy_tools.tools.config import CvttAppConfig
from cvttpy_tools.tools.logger import Log
from pt_strategy.live.live_strategy import PtLiveStrategy
from pt_strategy.live.pricer_md_client import PtMktDataClient
from pt_strategy.live.ti_sender import TradingInstructionsSender
# import sys
# print("PYTHONPATH directories:")
# for path in sys.path:
# print(path)
# from cvtt_client.mkt_data import (CvttPricerWebSockClient,
# CvttPricesSubscription, MessageTypeT,
# SubscriptionIdT)
class PairTradingRunner(NamedObject):
config_: CvttAppConfig
instruments_: List[JsonDictT]
live_strategy_: PtLiveStrategy
pricer_client_: PtMktDataClient
def __init__(self) -> None:
self.instruments_ = []
App.instance().add_cmdline_arg(
"--pair",
type=str,
required=True,
help=(
"Comma-separated pair of instrument symbols"
" with exchange config name"
" (e.g., PAIR-BTC-USD:BNBSPOT,PAIR-ETH-USD:BNBSPOT)"
),
)
App.instance().add_call(App.Stage.Config, self._on_config())
App.instance().add_call(App.Stage.Run, self.run())
async def _on_config(self) -> None:
self.config_ = CvttAppConfig.instance()
# ------- PARSE INSTRUMENTS -------
instr_str = App.instance().get_argument("pair", "")
if not instr_str:
raise ValueError("Pair is required")
instr_list = instr_str.split(",")
for instr in instr_list:
instr_parts = instr.split(":")
if len(instr_parts) != 2:
raise ValueError(f"Invalid pair format: {instr}")
instrument_id = instr_parts[0]
exchange_config_name = instr_parts[1]
self.instruments_.append({
"exchange_config_name": exchange_config_name,
"instrument_id": instrument_id
})
assert len(self.instruments_) == 2, "Only two instruments are supported"
Log.info(f"{self.fname()} Instruments: {self.instruments_}")
# ------- CREATE TI (trading instructions) CLIENT -------
ti_config = self.config_.get_subconfig("ti_config", {})
self.ti_sender_ = TradingInstructionsSender(config=ti_config)
Log.info(f"{self.fname()} TI client created: {self.ti_sender_}")
# ------- CREATE STRATEGY -------
strategy_config = self.config_.get_value("strategy_config", {})
self.live_strategy_ = PtLiveStrategy(
config=strategy_config,
instruments=self.instruments_,
ti_sender=self.ti_sender_
)
Log.info(f"{self.fname()} Strategy created: {self.live_strategy_}")
# ------- CREATE PRICER CLIENT -------
pricer_config = self.config_.get_subconfig("pricer_config", {})
self.pricer_client_ = PtMktDataClient(
live_strategy=self.live_strategy_,
pricer_config=pricer_config
)
Log.info(f"{self.fname()} CVTT Pricer client created: {self.pricer_client_}")
async def run(self) -> None:
Log.info(f"{self.fname()} ...")
pass
if __name__ == "__main__":
App()
CvttAppConfig()
PairTradingRunner()
App.instance().run()

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configuration/equity.cfg Normal file
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@ -0,0 +1,27 @@
{
"security_type": "EQUITY",
"data_directory": "./data/equity",
"datafiles": [
"20250618.mktdata.ohlcv.db",
],
"db_table_name": "md_1min_bars",
"exchange_id": "ALPACA",
"instrument_id_pfx": "STOCK-",
"trading_hours": {
"begin_session": "9:30:00",
"end_session": "16:00:00",
"timezone": "America/New_York"
},
"price_column": "close",
"min_required_points": 30,
"zero_threshold": 1e-10,
"dis-equilibrium_open_trshld": 2.0,
"dis-equilibrium_close_trshld": 1.0,
"training_minutes": 120,
"funding_per_pair": 2000.0,
# "fit_method_class": "pt_trading.sliding_fit.SlidingFit",
"fit_method_class": "pt_trading.static_fit.StaticFit",
"exclude_instruments": ["CAN"],
"close_outstanding_positions": false
}

26
configuration/equity_lg.cfg Normal file
View File

@ -0,0 +1,26 @@
{
"security_type": "EQUITY",
"data_directory": "./data/equity",
"datafiles": [
"20250602.mktdata.ohlcv.db",
],
"db_table_name": "md_1min_bars",
"exchange_id": "ALPACA",
"instrument_id_pfx": "STOCK-",
"trading_hours": {
"begin_session": "9:30:00",
"end_session": "16:00:00",
"timezone": "America/New_York"
},
"price_column": "close",
"min_required_points": 30,
"zero_threshold": 1e-10,
"dis-equilibrium_open_trshld": 2.0,
"dis-equilibrium_close_trshld": 1.0,
"training_minutes": 120,
"funding_per_pair": 2000.0,
"fit_method_class": "pt_trading.fit_methods.StaticFit",
"exclude_instruments": ["CAN"]
}
# "fit_method_class": "pt_trading.fit_methods.SlidingFit",
# "fit_method_class": "pt_trading.fit_methods.StaticFit",

43
configuration/ols-exp.cfg
View File

@ -1,43 +0,0 @@
{
"market_data_loading": {
"CRYPTO": {
"data_directory": "./data/crypto",
"db_table_name": "md_1min_bars",
"instrument_id_pfx": "PAIR-",
},
"EQUITY": {
"data_directory": "./data/equity",
"db_table_name": "md_1min_bars",
"instrument_id_pfx": "STOCK-",
}
},
# ====== Funding ======
"funding_per_pair": 2000.0,
# ====== Trading Parameters ======
"stat_model_price": "close",
"execution_price": {
"column": "vwap",
"shift": 1,
},
"dis-equilibrium_open_trshld": 2.0,
"dis-equilibrium_close_trshld": 0.5,
"training_size": 120,
"model_class": "pt_strategy.models.OLSModel",
"model_data_policy_class": "pt_strategy.model_data_policy.ExpandingWindowDataPolicy",
# ====== Stop Conditions ======
"stop_close_conditions": {
"profit": 2.0,
"loss": -0.5
}
# ====== End of Session Closeout ======
"close_outstanding_positions": true,
# "close_outstanding_positions": false,
"trading_hours": {
"timezone": "America/New_York",
"begin_session": "7:30:00",
"end_session": "18:30:00",
}
}

47
configuration/ols-opt.cfg
View File

@ -1,47 +0,0 @@
{
"market_data_loading": {
"CRYPTO": {
"data_directory": "./data/crypto",
"db_table_name": "md_1min_bars",
"instrument_id_pfx": "PAIR-",
},
"EQUITY": {
"data_directory": "./data/equity",
"db_table_name": "md_1min_bars",
"instrument_id_pfx": "STOCK-",
}
},
# ====== Funding ======
"funding_per_pair": 2000.0,
# ====== Trading Parameters ======
"stat_model_price": "close",
"execution_price": {
"column": "vwap",
"shift": 1,
},
"dis-equilibrium_open_trshld": 1.75,
"dis-equilibrium_close_trshld": 0.9,
"model_class": "pt_strategy.models.OLSModel",
# "model_data_policy_class": "pt_strategy.model_data_policy.EGOptimizedWndDataPolicy",
# "model_data_policy_class": "pt_strategy.model_data_policy.ADFOptimizedWndDataPolicy",
"model_data_policy_class": "pt_strategy.model_data_policy.JohansenOptdWndDataPolicy",
"min_training_size": 60,
"max_training_size": 150,
# ====== Stop Conditions ======
"stop_close_conditions": {
"profit": 2.0,
"loss": -0.5
}
# ====== End of Session Closeout ======
"close_outstanding_positions": true,
# "close_outstanding_positions": false,
"trading_hours": {
"timezone": "America/New_York",
"begin_session": "7:30:00",
"end_session": "18:30:00",
}
}

47
configuration/ols.cfg
View File

@ -1,47 +0,0 @@
{
"market_data_loading": {
"CRYPTO": {
"data_directory": "./data/crypto",
"db_table_name": "md_1min_bars",
"instrument_id_pfx": "PAIR-",
},
"EQUITY": {
"data_directory": "./data/equity",
"db_table_name": "md_1min_bars",
"instrument_id_pfx": "STOCK-",
}
},
# ====== Funding ======
"funding_per_pair": 2000.0,
# ====== Trading Parameters ======
"stat_model_price": "close",
"execution_price": {
"column": "vwap",
"shift": 1,
},
"dis-equilibrium_open_trshld": 1.75,
"dis-equilibrium_close_trshld": 0.9,
"model_class": "pt_strategy.models.OLSModel",
"training_size": 120,
"model_data_policy_class": "pt_strategy.model_data_policy.RollingWindowDataPolicy",
# "model_data_policy_class": "pt_strategy.model_data_policy.OptimizedWindowDataPolicy",
# "min_training_size": 60,
# "max_training_size": 150,
# ====== Stop Conditions ======
"stop_close_conditions": {
"profit": 2.0,
"loss": -0.5
}
# ====== End of Session Closeout ======
"close_outstanding_positions": true,
# "close_outstanding_positions": false,
"trading_hours": {
"timezone": "America/New_York",
"begin_session": "7:30:00",
"end_session": "18:30:00",
}
}

21
configuration/pairs_trader.cfg
View File

@ -1,21 +0,0 @@
{
"strategy_config": @inc=file:///home/oleg/develop/pairs_trading/configuration/ols.cfg
"pricer_config": {
"pricer_url": "ws://localhost:12346/ws",
"history_depth_sec": 86400 #"60*60*24", # use simpleeval
"interval_sec": 60
},
"ti_config": {
"cvtt_base_url": "http://localhost:23456"
"book_id": "XXXXXXXXX",
"strategy_id": "XXXXXXXXX",
"ti_endpoint": {
"method": "POST",
"url": "/trading_instructions"
},
"health_check_endpoint": {
"method": "GET",
"url": "/ping"
}
}
}

49
configuration/vecm-opt.cfg
View File

@ -1,49 +0,0 @@
{
"market_data_loading": {
"CRYPTO": {
"data_directory": "./data/crypto",
"db_table_name": "md_1min_bars",
"instrument_id_pfx": "PAIR-",
},
"EQUITY": {
"data_directory": "./data/equity",
"db_table_name": "md_1min_bars",
"instrument_id_pfx": "STOCK-",
}
},
# ====== Funding ======
"funding_per_pair": 2000.0,
# ====== Trading Parameters ======
"stat_model_price": "close", # "vwap"
"execution_price": {
"column": "vwap",
"shift": 1,
},
"dis-equilibrium_open_trshld": 1.75,
"dis-equilibrium_close_trshld": 1.0,
"model_class": "pt_strategy.models.VECMModel",
# "training_size": 120,
# "model_data_policy_class": "pt_strategy.model_data_policy.RollingWindowDataPolicy",
"model_data_policy_class": "pt_strategy.model_data_policy.ADFOptimizedWndDataPolicy",
"min_training_size": 60,
"max_training_size": 150,
# ====== Stop Conditions ======
"stop_close_conditions": {
"profit": 2.0,
"loss": -0.5
}
# ====== End of Session Closeout ======
"close_outstanding_positions": true,
# "close_outstanding_positions": false,
"trading_hours": {
"timezone": "America/New_York",
"begin_session": "7:30:00",
"end_session": "18:30:00",
}
}

13
configuration/vecm.cfg
View File

@ -21,15 +21,10 @@
"column": "vwap",
"shift": 1,
},
"dis-equilibrium_open_trshld": 1.75,
"dis-equilibrium_open_trshld": 2.0,
"dis-equilibrium_close_trshld": 1.0,
"model_class": "pt_strategy.models.VECMModel",
"training_size": 120,
"model_data_policy_class": "pt_strategy.model_data_policy.RollingWindowDataPolicy",
# "model_data_policy_class": "pt_strategy.model_data_policy.OptimizedWindowDataPolicy",
# "min_training_size": 60,
# "max_training_size": 150,
"training_minutes": 120,
"fit_method_class": "pt_trading.vecm_rolling_fit.VECMRollingFit",
# ====== Stop Conditions ======
"stop_close_conditions": {
@ -42,7 +37,7 @@
# "close_outstanding_positions": false,
"trading_hours": {
"timezone": "America/New_York",
"begin_session": "7:30:00",
"begin_session": "9:30:00",
"end_session": "18:30:00",
}
}

13
__DELETE__/configuration/zscore.cfg → configuration/zscore.cfg
View File

@ -16,14 +16,13 @@
"funding_per_pair": 2000.0,
# ====== Trading Parameters ======
"stat_model_price": "close",
# "execution_price": {
# "column": "vwap",
# "shift": 1,
# },
"execution_price": {
"column": "vwap",
"shift": 1,
},
"dis-equilibrium_open_trshld": 2.0,
"dis-equilibrium_close_trshld": 0.5,
"training_minutes": 120, # TODO Remove this
"training_size": 120,
"training_minutes": 120,
"fit_method_class": "pt_trading.z-score_rolling_fit.ZScoreRollingFit",
# ====== Stop Conditions ======
@ -37,7 +36,7 @@
# "close_outstanding_positions": false,
"trading_hours": {
"timezone": "America/New_York",
"begin_session": "7:30:00",
"begin_session": "9:30:00",
"end_session": "18:30:00",
}
}

115
lg_notes.md Normal file
View File

@ -0,0 +1,115 @@
07.11.2025
pairs_trading/configuration <---- directory for config
equity_lg.cfg <-------- copy of equity.cfg
How to run a Program: TRIANGLEsquare ----> triangle EQUITY backtest
Results are in > results (timestamp table for all runs)
table "...timestamp... .pt_backtest_results.equity.db"
going to table using sqlite
> sqlite3 '/home/coder/results/20250721_175750.pt_backtest_results.equity.db'
sqlite> .databases
main: /home/coder/results/20250717_180122.pt_backtest_results.equity.db r/w
sqlite> .tables
config outstanding_positions pt_bt_results
sqlite> PRAGMA table_info('pt_bt_results');
0|date|DATE|0||0
1|pair|TEXT|0||0
2|symbol|TEXT|0||0
3|open_time|DATETIME|0||0
4|open_side|TEXT|0||0
5|open_price|REAL|0||0
6|open_quantity|INTEGER|0||0
7|open_disequilibrium|REAL|0||0
8|close_time|DATETIME|0||0
9|close_side|TEXT|0||0
10|close_price|REAL|0||0
11|close_quantity|INTEGER|0||0
12|close_disequilibrium|REAL|0||0
13|symbol_return|REAL|0||0
14|pair_return|REAL|0||0
select count(*) as cnt from pt_bt_results;
8
select * from pt_bt_results;
select
date, close_time, pair, symbol, symbol_return, pair_return
from pt_bt_results ;
select date, sum(symbol_return) as daily_return
from pt_bt_results where date = '2025-06-18' group by date;
.quit
sqlite3 '/home/coder/results/20250717_172435.pt_backtest_results.equity.db'
sqlite> select date, sum(symbol_return) as daily_return
from pt_bt_results group by date;
2025-06-02|1.29845390060828
...
2025-06-18|-43.5084977104115 <========== ????? ==========>
2025-06-20|11.8605547517183
select
date, close_time, pair, symbol, symbol_return, pair_return
from pt_bt_results ;
select date, close_time, pair, symbol, symbol_return, pair_return
from pt_bt_results where date = '2025-06-18';
./scripts/load_equity_pair_intraday.sh -A NVDA -B QQQ -d 20250701 -T ./intraday_md
to inspect exactly what sources, formats, and processing steps you can open the script with:
head -n 50 ./scripts/load_equity_pair_intraday.sh
✓ Data file found: /home/coder/pairs_trading/data/crypto/20250605.mktdata.ohlcv.db
sqlite3 '/home/coder/results/20250722_201930.pt_backtest_results.crypto.db'
sqlite3 '/home/coder/results/xxxxxxxx_yyyyyy.pt_backtest_results.pseudo.db'
11111111
=== At your terminal, run these commands:
sqlite3 '/home/coder/results/20250722_201930.pt_backtest_results.crypto.db'
=== Then inside the SQLite prompt:
.mode csv
.headers on
.output results_20250722.csv
SELECT * FROM pt_bt_results;
.output stdout
.quit
cd /home/coder/
# === mode csv formats output as CSV
# === headers on includes column names
# === output my_table.csv directs output to that file
# === Run your SELECT query, then revert output
# === Open my_table.csv in Excel directly
# ======== Using scp (Secure Copy)
# === On your local machine, open a terminal and run:
scp cvtt@953f6e8df266:/home/coder/results_20250722.csv ~/Downloads/
# ===== convert cvs pandas dataframe ====== -->
import pandas as pd
# Replace with the actual path to your CSV file
file_path = '/home/coder/results_20250722.csv'
# Read the CSV file into a DataFrame
df = pd.read_csv(file_path)
# Show the first few rows
print(df.head())

96
lib/cvtt_client/mkt_data.py → lib/cvtt/mkt_data.py
View File

@ -1,15 +1,17 @@
#!/usr/bin/env python3
import argparse
from ast import Sub
import asyncio
from functools import partial
import json
import logging
import uuid
from dataclasses import dataclass
from functools import partial
from typing import Callable, Coroutine, Dict, Optional
from typing import Callable, Coroutine, Dict, List, Optional
from numpy.strings import str_len
import websockets
from cvttpy_tools.settings.cvtt_types import JsonDictT
from cvttpy_tools.tools.logger import Log
from websockets.asyncio.client import ClientConnection
MessageTypeT = str
@ -46,56 +48,22 @@ class CvttPricesSubscription:
self.is_subscribed_ = False
self.is_historical_ = history_depth_sec > 0
class CvttWebSockClient:
class CvttPricerWebSockClient:
# Class members with type hints
ws_url_: UrlT
websocket_: Optional[ClientConnection]
subscriptions_: Dict[SubscriptionIdT, CvttPricesSubscription]
is_connected_: bool
logger_: logging.Logger
def __init__(self, url: str):
self.ws_url_ = url
self.websocket_ = None
self.is_connected_ = False
async def connect(self) -> None:
self.websocket_ = await websockets.connect(self.ws_url_)
self.is_connected_ = True
async def close(self) -> None:
if self.websocket_ is not None:
await self.websocket_.close()
self.is_connected_ = False
async def receive_message(self) -> JsonDictT:
assert self.websocket_ is not None
assert self.is_connected_
message = await self.websocket_.recv()
message_str = (
message.decode("utf-8")
if isinstance(message, bytes)
else message
)
res = json.loads(message_str)
assert res is not None
assert isinstance(res, dict)
return res
@classmethod
async def check_connection(cls, url: str) -> bool:
try:
async with websockets.connect(url) as websocket:
result = True
except Exception as e:
Log.error(f"Unable to connect to {url}: {str(e)}")
result = False
return result
class CvttPricerWebSockClient(CvttWebSockClient):
# Class members with type hints
subscriptions_: Dict[SubscriptionIdT, CvttPricesSubscription]
def __init__(self, url: str):
super().__init__(url)
self.subscriptions_ = {}
self.logger_ = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
async def subscribe(
self, subscription: CvttPricesSubscription
@ -103,10 +71,11 @@ class CvttPricerWebSockClient(CvttWebSockClient):
if not self.is_connected_:
try:
Log.info(f"Connecting to {self.ws_url_}")
await self.connect()
self.logger_.info(f"Connecting to {self.ws_url_}")
self.websocket_ = await websockets.connect(self.ws_url_)
self.is_connected_ = True
except Exception as e:
Log.error(f"Unable to connect to {self.ws_url_}: {str(e)}")
self.logger_.error(f"Unable to connect to {self.ws_url_}: {str(e)}")
raise e
subscr_msg = {
@ -140,10 +109,10 @@ class CvttPricerWebSockClient(CvttWebSockClient):
return False
if response.get("status") == "success":
Log.info(f"Subscription successful: {json.dumps(response)}")
self.logger_.info(f"Subscription successful: {json.dumps(response)}")
return True
elif response.get("status") == "error":
Log.error(f"Subscription failed: {response.get('reason')}")
self.logger_.error(f"Subscription failed: {response.get('reason')}")
return False
return False
@ -152,20 +121,19 @@ class CvttPricerWebSockClient(CvttWebSockClient):
try:
while self.is_connected_:
try:
msg_dict: JsonDictT = await self.receive_message()
message = await self.websocket_.recv()
message_str = (
message.decode("utf-8")
if isinstance(message, bytes)
else message
)
await self.process_message(json.loads(message_str))
except websockets.ConnectionClosed:
Log.warning("Connection closed")
self.logger_.warning("Connection closed")
self.is_connected_ = False
break
except Exception as e:
Log.error(f"Error occurred: {str(e)}")
self.is_connected_ = False
await asyncio.sleep(5) # Wait before reconnecting
await self.process_message(msg_dict)
except Exception as e:
Log.error(f"Error occurred: {str(e)}")
self.logger_.error(f"Error occurred: {str(e)}")
self.is_connected_ = False
await asyncio.sleep(5) # Wait before reconnecting
@ -174,13 +142,13 @@ class CvttPricerWebSockClient(CvttWebSockClient):
if message_type in ["md_aggregate", "historical_md_aggregate"]:
subscription_id = message.get("subscr_id")
if subscription_id not in self.subscriptions_:
Log.warning(f"Unknown subscription id: {subscription_id}")
self.logger_.warning(f"Unknown subscription id: {subscription_id}")
return
subscription = self.subscriptions_[subscription_id]
await subscription.callback_(message_type, subscription_id, message)
else:
Log.warning(f"Unknown message type: {message.get('type')}")
self.logger_.warning(f"Unknown message type: {message.get('type')}")
async def main() -> None:
@ -188,10 +156,10 @@ async def main() -> None:
print(f"{message_type=} {subscr_id=} {instrument_id}")
if message_type == "md_aggregate":
aggr = message.get("md_aggregate", [])
print(f"[{aggr['tstamp'][:19]}] *** RLTM *** {message}")
print(f"[{aggr['tstmp'][:19]}] *** RLTM *** {message}")
elif message_type == "historical_md_aggregate":
for aggr in message.get("historical_data", []):
print(f"[{aggr['tstamp'][:19]}] *** HIST *** {aggr}")
print(f"[{aggr['tstmp'][:19]}] *** HIST *** {aggr}")
else:
print(f"Unknown message type: {message_type}")

346
lib/pt_strategy/live/live_strategy.py
View File

@ -1,346 +0,0 @@
from __future__ import annotations
from dataclasses import dataclass
from typing import Any, Dict, List, Optional
import pandas as pd
from cvttpy_tools.settings.cvtt_types import JsonDictT
from cvttpy_tools.tools.base import NamedObject
from cvttpy_tools.tools.logger import Log
from pt_strategy.live.ti_sender import TradingInstructionsSender
from pt_strategy.model_data_policy import ModelDataPolicy
from pt_strategy.pt_market_data import RealTimeMarketData
from pt_strategy.pt_model import Prediction
from pt_strategy.trading_pair import PairState, TradingPair
"""
--config=pair.cfg
--pair=PAIR-BTC-USDT:COINBASE_AT,PAIR-ETH-USDT:COINBASE_AT
"""
class TradingInstructionType(Enum):
TARGET_POSITION = "TARGET_POSITION"
@dataclass
class TradingInstruction(NamedObject):
type_: TradingInstructionType
exch_instr_: ExchangeInstrument
specifics_: Dict[str, Any]
class PtLiveStrategy(NamedObject):
config_: Dict[str, Any]
trading_pair_: TradingPair
model_data_policy_: ModelDataPolicy
pt_mkt_data_: RealTimeMarketData
ti_sender_: TradingInstructionsSender
# for presentation: history of prediction values and trading signals
predictions_: pd.DataFrame
trading_signals_: pd.DataFrame
def __init__(
self,
config: Dict[str, Any],
instruments: List[Dict[str, str]],
ti_sender: TradingInstructionsSender,
):
self.config_ = config
self.trading_pair_ = TradingPair(config=config, instruments=instruments)
self.predictions_ = pd.DataFrame()
self.trading_signals_ = pd.DataFrame()
self.ti_sender_ = ti_sender
import copy
# modified config must be passed to PtMarketData
config_copy = copy.deepcopy(config)
config_copy["instruments"] = instruments
self.pt_mkt_data_ = RealTimeMarketData(config=config_copy)
self.model_data_policy_ = ModelDataPolicy.create(
config, is_real_time=True, pair=self.trading_pair_
)
self.open_threshold_ = self.config_.get("dis-equilibrium_open_trshld", 0.0)
assert self.open_threshold_ > 0, "open_threshold must be greater than 0"
self.close_threshold_ = self.config_.get("dis-equilibrium_close_trshld", 0.0)
assert self.close_threshold_ > 0, "close_threshold must be greater than 0"
def __repr__(self) -> str:
return f"{self.classname()}: trading_pair={self.trading_pair_}, mdp={self.model_data_policy_.__class__.__name__}, "
async def on_mkt_data_hist_snapshot(self, aggr: JsonDictT) -> None:
Log.info(f"on_mkt_data_hist_snapshot: {aggr}")
await self.pt_mkt_data_.on_mkt_data_hist_snapshot(snapshot=aggr)
pass
async def on_mkt_data_update(self, aggr: JsonDictT) -> None:
market_data_df = await self.pt_mkt_data_.on_mkt_data_update(update=aggr)
if market_data_df is not None:
self.trading_pair_.market_data_ = market_data_df
self.model_data_policy_.advance()
prediction = self.trading_pair_.run(
market_data_df, self.model_data_policy_.advance()
)
self.predictions_ = pd.concat(
[self.predictions_, prediction.to_df()], ignore_index=True
)
trading_instructions: List[TradingInstruction] = (
self._create_trading_instructions(
prediction=prediction, last_row=market_data_df.iloc[-1]
)
)
if len(trading_instructions) > 0:
await self._send_trading_instructions(trading_instructions)
# trades = self._create_trades(prediction=prediction, last_row=market_data_df.iloc[-1])
# URGENT implement this
pass
async def _send_trading_instructions(
self, trading_instructions: pd.DataFrame
) -> None:
pass
def _create_trading_instructions(
self, prediction: Prediction, last_row: pd.Series
) -> List[TradingInstruction]:
pair = self.trading_pair_
trd_instructions: List[TradingInstruction] = []
scaled_disequilibrium = prediction.scaled_disequilibrium_
abs_scaled_disequilibrium = abs(scaled_disequilibrium)
if pair.is_closed():
if abs_scaled_disequilibrium >= self.open_threshold_:
trd_instructions = self._create_open_trade_instructions(
pair, row=last_row, prediction=prediction
)
elif pair.is_open():
if abs_scaled_disequilibrium <= self.close_threshold_:
trd_instructions = self._create_close_trade_instructions(
pair, row=last_row, prediction=prediction
)
elif pair.to_stop_close_conditions(predicted_row=last_row):
trd_instructions = self._create_close_trade_instructions(
pair, row=last_row
)
return trd_instructions
def _create_open_trade_instructions(
self, pair: TradingPair, row: pd.Series, prediction: Prediction
) -> List[TradingInstruction]:
scaled_disequilibrium = prediction.scaled_disequilibrium_
if scaled_disequilibrium > 0:
side_a = "SELL"
trd_inst_a = TradingInstruction(
type=TradingInstructionType.TARGET_POSITION,
exch_instr=pair.get_instrument_a(),
specifics={"side": "SELL", "strength": -1},
)
side_b = "BUY"
else:
side_a = "BUY"
side_b = "SELL"
# save closing sides
pair.user_data_["open_side_a"] = side_a # used in oustanding positions
pair.user_data_["open_side_b"] = side_b
pair.user_data_["open_px_a"] = px_a
pair.user_data_["open_px_b"] = px_b
pair.user_data_["open_tstamp"] = tstamp
pair.user_data_["close_side_a"] = side_b # used for closing trades
pair.user_data_["close_side_b"] = side_a
# create opening trades
df.loc[len(df)] = {
"time": tstamp,
"symbol": pair.symbol_a_,
"side": side_a,
"action": "OPEN",
"price": px_a,
"disequilibrium": diseqlbrm,
"signed_scaled_disequilibrium": scaled_disequilibrium,
"scaled_disequilibrium": abs(scaled_disequilibrium),
# "pair": pair,
}
df.loc[len(df)] = {
"time": tstamp,
"symbol": pair.symbol_b_,
"side": side_b,
"action": "OPEN",
"price": px_b,
"disequilibrium": diseqlbrm,
"scaled_disequilibrium": abs(scaled_disequilibrium),
"signed_scaled_disequilibrium": scaled_disequilibrium,
# "pair": pair,
}
return df
def _handle_outstanding_positions(self) -> Optional[pd.DataFrame]:
trades = None
pair = self.trading_pair_
# Outstanding positions
if pair.user_data_["state"] == PairState.OPEN:
print(f"{pair}: *** Position is NOT CLOSED. ***")
# outstanding positions
if self.config_["close_outstanding_positions"]:
close_position_row = pd.Series(pair.market_data_.iloc[-2])
# close_position_row["disequilibrium"] = 0.0
# close_position_row["scaled_disequilibrium"] = 0.0
# close_position_row["signed_scaled_disequilibrium"] = 0.0
trades = self._create_close_trades(
pair=pair, row=close_position_row, prediction=None
)
if trades is not None:
trades["status"] = PairState.CLOSE_POSITION.name
print(f"CLOSE_POSITION TRADES:\n{trades}")
pair.user_data_["state"] = PairState.CLOSE_POSITION
pair.on_close_trades(trades)
else:
pair.add_outstanding_position(
symbol=pair.symbol_a_,
open_side=pair.user_data_["open_side_a"],
open_px=pair.user_data_["open_px_a"],
open_tstamp=pair.user_data_["open_tstamp"],
last_mkt_data_row=pair.market_data_.iloc[-1],
)
pair.add_outstanding_position(
symbol=pair.symbol_b_,
open_side=pair.user_data_["open_side_b"],
open_px=pair.user_data_["open_px_b"],
open_tstamp=pair.user_data_["open_tstamp"],
last_mkt_data_row=pair.market_data_.iloc[-1],
)
return trades
def _trades_df(self) -> pd.DataFrame:
types = {
"time": "datetime64[ns]",
"action": "string",
"symbol": "string",
"side": "string",
"price": "float64",
"disequilibrium": "float64",
"scaled_disequilibrium": "float64",
"signed_scaled_disequilibrium": "float64",
# "pair": "object",
}
columns = list(types.keys())
return pd.DataFrame(columns=columns).astype(types)
def _create_open_trades(
self, pair: TradingPair, row: pd.Series, prediction: Prediction
) -> Optional[pd.DataFrame]:
colname_a, colname_b = pair.exec_prices_colnames()
tstamp = row["tstamp"]
diseqlbrm = prediction.disequilibrium_
scaled_disequilibrium = prediction.scaled_disequilibrium_
px_a = row[f"{colname_a}"]
px_b = row[f"{colname_b}"]
# creating the trades
df = self._trades_df()
print(f"OPEN_TRADES: {row["tstamp"]} {scaled_disequilibrium=}")
if diseqlbrm > 0:
side_a = "SELL"
side_b = "BUY"
else:
side_a = "BUY"
side_b = "SELL"
# save closing sides
pair.user_data_["open_side_a"] = side_a # used in oustanding positions
pair.user_data_["open_side_b"] = side_b
pair.user_data_["open_px_a"] = px_a
pair.user_data_["open_px_b"] = px_b
pair.user_data_["open_tstamp"] = tstamp
pair.user_data_["close_side_a"] = side_b # used for closing trades
pair.user_data_["close_side_b"] = side_a
# create opening trades
df.loc[len(df)] = {
"time": tstamp,
"symbol": pair.symbol_a_,
"side": side_a,
"action": "OPEN",
"price": px_a,
"disequilibrium": diseqlbrm,
"signed_scaled_disequilibrium": scaled_disequilibrium,
"scaled_disequilibrium": abs(scaled_disequilibrium),
# "pair": pair,
}
df.loc[len(df)] = {
"time": tstamp,
"symbol": pair.symbol_b_,
"side": side_b,
"action": "OPEN",
"price": px_b,
"disequilibrium": diseqlbrm,
"scaled_disequilibrium": abs(scaled_disequilibrium),
"signed_scaled_disequilibrium": scaled_disequilibrium,
# "pair": pair,
}
return df
def _create_close_trades(
self, pair: TradingPair, row: pd.Series, prediction: Optional[Prediction] = None
) -> Optional[pd.DataFrame]:
colname_a, colname_b = pair.exec_prices_colnames()
tstamp = row["tstamp"]
if prediction is not None:
diseqlbrm = prediction.disequilibrium_
signed_scaled_disequilibrium = prediction.scaled_disequilibrium_
scaled_disequilibrium = abs(prediction.scaled_disequilibrium_)
else:
diseqlbrm = 0.0
signed_scaled_disequilibrium = 0.0
scaled_disequilibrium = 0.0
px_a = row[f"{colname_a}"]
px_b = row[f"{colname_b}"]
# creating the trades
df = self._trades_df()
# create opening trades
df.loc[len(df)] = {
"time": tstamp,
"symbol": pair.symbol_a_,
"side": pair.user_data_["close_side_a"],
"action": "CLOSE",
"price": px_a,
"disequilibrium": diseqlbrm,
"scaled_disequilibrium": scaled_disequilibrium,
"signed_scaled_disequilibrium": signed_scaled_disequilibrium,
# "pair": pair,
}
df.loc[len(df)] = {
"time": tstamp,
"symbol": pair.symbol_b_,
"side": pair.user_data_["close_side_b"],
"action": "CLOSE",
"price": px_b,
"disequilibrium": diseqlbrm,
"scaled_disequilibrium": scaled_disequilibrium,
"signed_scaled_disequilibrium": signed_scaled_disequilibrium,
# "pair": pair,
}
del pair.user_data_["close_side_a"]
del pair.user_data_["close_side_b"]
del pair.user_data_["open_tstamp"]
del pair.user_data_["open_px_a"]
del pair.user_data_["open_px_b"]
del pair.user_data_["open_side_a"]
del pair.user_data_["open_side_b"]
return df

85
lib/pt_strategy/live/pricer_md_client.py
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@ -1,85 +0,0 @@
from __future__ import annotations
from functools import partial
from typing import Dict, List
from cvtt_client.mkt_data import (CvttPricerWebSockClient,
CvttPricesSubscription, MessageTypeT,
SubscriptionIdT)
from cvttpy_tools.settings.cvtt_types import JsonDictT
from cvttpy_tools.tools.app import App
from cvttpy_tools.tools.base import NamedObject
from cvttpy_tools.tools.config import Config
from cvttpy_tools.tools.logger import Log
from pt_strategy.live.live_strategy import PtLiveStrategy
from pt_strategy.trading_pair import TradingPair
"""
--config=pair.cfg
--pair=PAIR-BTC-USDT:COINBASE_AT,PAIR-ETH-USDT:COINBASE_AT
"""
class PtMktDataClient(NamedObject):
config_: Config
live_strategy_: PtLiveStrategy
pricer_client_: CvttPricerWebSockClient
subscriptions_: List[CvttPricesSubscription]
def __init__(self, live_strategy: PtLiveStrategy, pricer_config: Config):
self.config_ = pricer_config
self.live_strategy_ = live_strategy
App.instance().add_call(App.Stage.Start, self._on_start())
App.instance().add_call(App.Stage.Run, self.run())
async def _on_start(self) -> None:
pricer_url = self.config_.get_value("pricer_url")
assert pricer_url is not None, "pricer_url is not found in config"
self.pricer_client_ = CvttPricerWebSockClient(url=pricer_url)
async def _subscribe(self) -> None:
history_depth_sec = self.config_.get_value("history_depth_sec", 86400)
interval_sec = self.config_.get_value("interval_sec", 60)
pair: TradingPair = self.live_strategy_.trading_pair_
subscriptions = [CvttPricesSubscription(
exchange_config_name=instrument["exchange_config_name"],
instrument_id=instrument["instrument_id"],
interval_sec=interval_sec,
history_depth_sec=history_depth_sec,
callback=partial(
self.on_message, instrument_id=instrument["instrument_id"]
),
) for instrument in pair.instruments_]
for subscription in subscriptions:
Log.info(f"{self.fname()} Subscribing to {subscription}")
await self.pricer_client_.subscribe(subscription)
async def on_message(
self,
message_type: MessageTypeT,
subscr_id: SubscriptionIdT,
message: Dict,
instrument_id: str,
) -> None:
Log.info(f"{self.fname()}: {message_type=} {subscr_id=} {instrument_id}")
aggr: JsonDictT
if message_type == "md_aggregate":
aggr = message.get("md_aggregate", {})
await self.live_strategy_.on_mkt_data_update(aggr)
elif message_type == "historical_md_aggregate":
aggr = message.get("historical_data", {})
await self.live_strategy_.on_mkt_data_hist_snapshot(aggr)
else:
Log.info(f"Unknown message type: {message_type}")
async def run(self) -> None:
if not await CvttPricerWebSockClient.check_connection(self.pricer_client_.ws_url_):
Log.error(f"Unable to connect to {self.pricer_client_.ws_url_}")
raise Exception(f"Unable to connect to {self.pricer_client_.ws_url_}")
await self._subscribe()
await self.pricer_client_.run()

86
lib/pt_strategy/live/ti_sender.py
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@ -1,86 +0,0 @@
import time
from enum import Enum
from typing import Tuple
# import aiohttp
from cvttpy_tools.tools.app import App
from cvttpy_tools.tools.base import NamedObject
from cvttpy_tools.tools.config import Config
from cvttpy_tools.tools.logger import Log
from cvttpy_tools.tools.timer import Timer
from cvttpy_tools.tools.timeutils import NanoPerSec
from cvttpy_tools.tools.web.rest_client import REST_RequestProcessor
class TradingInstructionsSender(NamedObject):
class TradingInstType(str, Enum):
TARGET_POSITION = "TARGET_POSITION"
DIRECT_ORDER = "DIRECT_ORDER"
MARKET_MAKING = "MARKET_MAKING"
NONE = "NONE"
config_: Config
ti_method_: str
ti_url_: str
health_check_method_: str
health_check_url_: str
def __init__(self, config: Config):
self.config_ = config
base_url = config.get_value("url", "ws://localhost:12346/ws")
self.book_id_ = config.get_value("book_id", "")
assert self.book_id_, "book_id is required"
self.strategy_id_ = config.get_value("strategy_id", "")
assert self.strategy_id_, "strategy_id is required"
endpoint_uri = config.get_value("ti_endpoint/url", "/trading_instructions")
endpoint_method = config.get_value("ti_endpoint/method", "POST")
health_check_uri = config.get_value("health_check_endpoint/url", "/ping")
health_check_method = config.get_value("health_check_endpoint/method", "GET")
self.ti_method_ = endpoint_method
self.ti_url_ = f"{base_url}{endpoint_uri}"
self.health_check_method_ = health_check_method
self.health_check_url_ = f"{base_url}{health_check_uri}"
App.instance().add_call(App.Stage.Start, self._set_health_check_timer(), can_run_now=True)
async def _set_health_check_timer(self) -> None:
# TODO: configurable interval
self.health_check_timer_ = Timer(is_periodic=True, period_interval=15, start_in_sec=0, func=self._health_check)
Log.info(f"{self.fname()} Health check timer set to 15 seconds")
async def _health_check(self) -> None:
rqst = REST_RequestProcessor(method=self.health_check_method_, url=self.health_check_url_)
async with rqst as (status, msg, headers):
if status != 200:
Log.error(f"{self.fname()} CVTT Service is not responding")
async def send_tgt_positions(self, strength: float, base_asset: str, quote_asset: str) -> Tuple[int, str]:
instr = {
"type": self.TradingInstType.TARGET_POSITION.value,
"book_id": self.book_id_,
"strategy_id": self.strategy_id_,
"issued_ts_ns": int(time.time() * NanoPerSec),
"data": {
"strength": strength,
"base_asset": base_asset,
"quote_asset": quote_asset,
"user_data": {},
},
}
rqst = REST_RequestProcessor(method=self.ti_method_, url=self.ti_url_, params=instr)
async with rqst as (status, msg, headers):
if status != 200:
raise ConnectionError(f"Failed to send trading instructions: {msg}")
return (status, msg)

257
lib/pt_strategy/model_data_policy.py
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@ -1,257 +0,0 @@
from __future__ import annotations
import copy
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Any, Dict, Optional, cast
import numpy as np
import pandas as pd
@dataclass
class DataWindowParams:
training_size: int
training_start_index: int
class ModelDataPolicy(ABC):
config_: Dict[str, Any]
current_data_params_: DataWindowParams
count_: int
is_real_time_: bool
def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
self.config_ = config
training_size = config.get("training_size", 120)
training_start_index = 0
if kwargs.get("is_real_time", False):
training_size = 120
training_start_index = 0
else:
training_size = config.get("training_size", 120)
self.current_data_params_ = DataWindowParams(
training_size=config.get("training_size", 120),
training_start_index=0,
)
self.count_ = 0
self.is_real_time_ = kwargs.get("is_real_time", False)
@abstractmethod
def advance(self, mkt_data_df: Optional[pd.DataFrame] = None) -> DataWindowParams:
self.count_ += 1
print(self.count_, end="\r")
return self.current_data_params_
@staticmethod
def create(config: Dict[str, Any], *args: Any, **kwargs: Any) -> ModelDataPolicy:
import importlib
model_data_policy_class_name = config.get("model_data_policy_class", None)
assert model_data_policy_class_name is not None
module_name, class_name = model_data_policy_class_name.rsplit(".", 1)
module = importlib.import_module(module_name)
model_training_data_policy_object = getattr(module, class_name)(
config=config, *args, **kwargs
)
return cast(ModelDataPolicy, model_training_data_policy_object)
class RollingWindowDataPolicy(ModelDataPolicy):
def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
super().__init__(config, *args, **kwargs)
self.count_ = 1
def advance(self, mkt_data_df: Optional[pd.DataFrame] = None) -> DataWindowParams:
super().advance(mkt_data_df)
if self.is_real_time_:
self.current_data_params_.training_start_index = -self.current_data_params_.training_size
else:
self.current_data_params_.training_start_index += 1
return self.current_data_params_
class OptimizedWndDataPolicy(ModelDataPolicy, ABC):
mkt_data_df_: pd.DataFrame
pair_: TradingPair # type: ignore
min_training_size_: int
max_training_size_: int
end_index_: int
prices_a_: np.ndarray
prices_b_: np.ndarray
def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
super().__init__(config, *args, **kwargs)
assert (
kwargs.get("pair") is not None
), "pair must be provided"
assert (
"min_training_size" in config and "max_training_size" in config
), "min_training_size and max_training_size must be provided"
self.min_training_size_ = cast(int, config.get("min_training_size"))
self.max_training_size_ = cast(int, config.get("max_training_size"))
from pt_strategy.trading_pair import TradingPair
self.pair_ = cast(TradingPair, kwargs.get("pair"))
if "mkt_data" in kwargs:
self.mkt_data_df_ = cast(pd.DataFrame, kwargs.get("mkt_data"))
col_a, col_b = self.pair_.colnames()
self.prices_a_ = np.array(self.mkt_data_df_[col_a])
self.prices_b_ = np.array(self.mkt_data_df_[col_b])
assert self.min_training_size_ < self.max_training_size_
def advance(self, mkt_data_df: Optional[pd.DataFrame] = None) -> DataWindowParams:
super().advance(mkt_data_df)
if mkt_data_df is not None:
self.mkt_data_df_ = mkt_data_df
if self.is_real_time_:
self.end_index_ = len(self.mkt_data_df_) - 1
else:
self.end_index_ = self.current_data_params_.training_start_index + self.max_training_size_
if self.end_index_ > len(self.mkt_data_df_) - 1:
self.end_index_ = len(self.mkt_data_df_) - 1
self.current_data_params_.training_start_index = self.end_index_ - self.max_training_size_
if self.current_data_params_.training_start_index < 0:
self.current_data_params_.training_start_index = 0
col_a, col_b = self.pair_.colnames()
self.prices_a_ = np.array(self.mkt_data_df_[col_a])
self.prices_b_ = np.array(self.mkt_data_df_[col_b])
self.current_data_params_ = self.optimize_window_size()
return self.current_data_params_
@abstractmethod
def optimize_window_size(self) -> DataWindowParams:
...
class EGOptimizedWndDataPolicy(OptimizedWndDataPolicy):
'''
# Engle-Granger cointegration test
*** VERY SLOW ***
'''
def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
super().__init__(config, *args, **kwargs)
def optimize_window_size(self) -> DataWindowParams:
# Run Engle-Granger cointegration test
last_pvalue = 1.0
result = copy.copy(self.current_data_params_)
for trn_size in range(self.min_training_size_, self.max_training_size_):
if self.end_index_ - trn_size < 0:
break
from statsmodels.tsa.stattools import coint # type: ignore
start_index = self.end_index_ - trn_size
series_a = self.prices_a_[start_index : self.end_index_]
series_b = self.prices_b_[start_index : self.end_index_]
eg_pvalue = float(coint(series_a, series_b)[1])
if eg_pvalue < last_pvalue:
last_pvalue = eg_pvalue
result.training_size = trn_size
result.training_start_index = start_index
# print(
# f"*** DEBUG *** end_index={self.end_index_}, best_trn_size={self.current_data_params_.training_size}, {last_pvalue=}"
# )
return result
class ADFOptimizedWndDataPolicy(OptimizedWndDataPolicy):
# Augmented Dickey-Fuller test
def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
super().__init__(config, *args, **kwargs)
def optimize_window_size(self) -> DataWindowParams:
from statsmodels.regression.linear_model import OLS
from statsmodels.tools.tools import add_constant
from statsmodels.tsa.stattools import adfuller
last_pvalue = 1.0
result = copy.copy(self.current_data_params_)
for trn_size in range(self.min_training_size_, self.max_training_size_):
if self.end_index_ - trn_size < 0:
break
start_index = self.end_index_ - trn_size
y = self.prices_a_[start_index : self.end_index_]
x = self.prices_b_[start_index : self.end_index_]
# Add constant to x for intercept
x_with_const = add_constant(x)
# OLS regression: y = a + b*x + e
model = OLS(y, x_with_const).fit()
residuals = y - model.predict(x_with_const)
# ADF test on residuals
try:
adf_result = adfuller(residuals, maxlag=1, regression="c")
adf_pvalue = float(adf_result[1])
except Exception as e:
# Handle edge cases with exception (e.g., constant series, etc.)
adf_pvalue = 1.0
if adf_pvalue < last_pvalue:
last_pvalue = adf_pvalue
result.training_size = trn_size
result.training_start_index = start_index
# print(
# f"*** DEBUG *** end_index={self.end_index_},"
# f" best_trn_size={self.current_data_params_.training_size},"
# f" {last_pvalue=}"
# )
return result
class JohansenOptdWndDataPolicy(OptimizedWndDataPolicy):
# Johansen test
def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
super().__init__(config, *args, **kwargs)
def optimize_window_size(self) -> DataWindowParams:
from statsmodels.tsa.vector_ar.vecm import coint_johansen
import numpy as np
best_stat = -np.inf
best_trn_size = 0
best_start_index = -1
result = copy.copy(self.current_data_params_)
for trn_size in range(self.min_training_size_, self.max_training_size_):
if self.end_index_ - trn_size < 0:
break
start_index = self.end_index_ - trn_size
series_a = self.prices_a_[start_index:self.end_index_]
series_b = self.prices_b_[start_index:self.end_index_]
# Combine into 2D matrix for Johansen test
try:
data = np.column_stack([series_a, series_b])
# Johansen test: det_order=0 (no deterministic trend), k_ar_diff=1 (lag)
res = coint_johansen(data, det_order=0, k_ar_diff=1)
# Trace statistic for cointegration rank 1
trace_stat = res.lr1[0] # test stat for rank=0 vs >=1
critical_value = res.cvt[0, 1] # 5% critical value
if trace_stat > best_stat:
best_stat = trace_stat
best_trn_size = trn_size
best_start_index = start_index
except Exception:
continue
if best_trn_size > 0:
result.training_size = best_trn_size
result.training_start_index = best_start_index
else:
print("*** WARNING: No valid cointegration window found.")
# print(
# f"*** DEBUG *** end_index={self.end_index_}, best_trn_size={best_trn_size}, trace_stat={best_stat}"
# )
return result

104
lib/pt_strategy/models.py
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@ -1,104 +0,0 @@
from __future__ import annotations
from typing import Optional
import pandas as pd
import statsmodels.api as sm
from pt_strategy.pt_model import PairsTradingModel, Prediction
from pt_strategy.trading_pair import TradingPair
class OLSModel(PairsTradingModel):
model_: Optional[sm.regression.linear_model.RegressionResultsWrapper]
pair_predict_result_: Optional[pd.DataFrame]
zscore_df_: Optional[pd.DataFrame]
def predict(self, pair: TradingPair) -> Prediction:
self.training_df_ = pair.market_data_.copy()
zscore_df = self._fit_zscore(pair=pair)
assert zscore_df is not None
# zscore is both disequilibrium and scaled_disequilibrium
self.training_df_["dis-equilibrium"] = zscore_df[0]
self.training_df_["scaled_dis-equilibrium"] = zscore_df[0]
assert zscore_df is not None
return Prediction(
tstamp=pair.market_data_.iloc[-1]["tstamp"],
disequilibrium=self.training_df_["dis-equilibrium"].iloc[-1],
scaled_disequilibrium=self.training_df_["scaled_dis-equilibrium"].iloc[-1],
)
def _fit_zscore(self, pair: TradingPair) -> pd.DataFrame:
assert self.training_df_ is not None
symbol_a_px_series = self.training_df_[pair.colnames()].iloc[:, 0]
symbol_b_px_series = self.training_df_[pair.colnames()].iloc[:, 1]
symbol_a_px_series, symbol_b_px_series = symbol_a_px_series.align(
symbol_b_px_series, axis=0
)
X = sm.add_constant(symbol_b_px_series)
self.model_ = sm.OLS(symbol_a_px_series, X).fit()
assert self.model_ is not None
# alternate way would be to use models residuals (will give identical results)
# alpha, beta = self.model_.params
# spread = symbol_a_px_series - (alpha + beta * symbol_b_px_series)
spread = self.model_.resid
return pd.DataFrame((spread - spread.mean()) / spread.std())
class VECMModel(PairsTradingModel):
def predict(self, pair: TradingPair) -> Prediction:
self.training_df_ = pair.market_data_.copy()
assert self.training_df_ is not None
vecm_fit = self._fit_VECM(pair=pair)
assert vecm_fit is not None
predicted_prices = vecm_fit.predict(steps=1)
# Convert prediction to a DataFrame for readability
predicted_df = pd.DataFrame(
predicted_prices, columns=pd.Index(pair.colnames()), dtype=float
)
disequilibrium = (predicted_df[pair.colnames()] @ vecm_fit.beta)[0][0]
scaled_disequilibrium = (disequilibrium - self.training_mu_) / self.training_std_
return Prediction(
tstamp=pair.market_data_.iloc[-1]["tstamp"],
disequilibrium=disequilibrium,
scaled_disequilibrium=scaled_disequilibrium,
)
def _fit_VECM(self, pair: TradingPair) -> VECMResults: # type: ignore
from statsmodels.tsa.vector_ar.vecm import VECM, VECMResults
vecm_df = self.training_df_[pair.colnames()].reset_index(drop=True)
vecm_model = VECM(vecm_df, coint_rank=1)
vecm_fit = vecm_model.fit()
assert vecm_fit is not None
# Check if the model converged properly
if not hasattr(vecm_fit, "beta") or vecm_fit.beta is None:
print(f"{self}: VECM model failed to converge properly")
diseq_series = self.training_df_[pair.colnames()] @ vecm_fit.beta
# print(diseq_series.shape)
self.training_mu_ = float(diseq_series[0].mean())
self.training_std_ = float(diseq_series[0].std())
self.training_df_["dis-equilibrium"] = (
self.training_df_[pair.colnames()] @ vecm_fit.beta
)
# Normalize the dis-equilibrium
self.training_df_["scaled_dis-equilibrium"] = (
diseq_series - self.training_mu_
) / self.training_std_
return vecm_fit

28
lib/pt_strategy/prediction.py
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@ -1,28 +0,0 @@
from __future__ import annotations
from typing import Any, Dict
import pandas as pd
class Prediction:
tstamp_: pd.Timestamp
disequilibrium_: float
scaled_disequilibrium_: float
def __init__(self, tstamp: pd.Timestamp, disequilibrium: float, scaled_disequilibrium: float):
self.tstamp_ = tstamp
self.disequilibrium_ = disequilibrium
self.scaled_disequilibrium_ = scaled_disequilibrium
def to_dict(self) -> Dict[str, Any]:
return {
"tstamp": self.tstamp_,
"disequilibrium": self.disequilibrium_,
"signed_scaled_disequilibrium": self.scaled_disequilibrium_,
"scaled_disequilibrium": abs(self.scaled_disequilibrium_),
# "pair": self.pair_,
}
def to_df(self) -> pd.DataFrame:
return pd.DataFrame([self.to_dict()])

229
lib/pt_strategy/pt_market_data.py
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@ -1,229 +0,0 @@
from __future__ import annotations
from typing import Any, Dict, List, Optional
import pandas as pd
from cvttpy_tools.settings.cvtt_types import JsonDictT
from tools.data_loader import load_market_data
class PtMarketData():
config_: Dict[str, Any]
origin_mkt_data_df_: pd.DataFrame
market_data_df_: pd.DataFrame
def __init__(self, config: Dict[str, Any]):
self.config_ = config
self.origin_mkt_data_df_ = pd.DataFrame()
self.market_data_df_ = pd.DataFrame()
class ResearchMarketData(PtMarketData):
current_index_: int
is_execution_price_: bool
def __init__(self, config: Dict[str, Any]):
super().__init__(config)
self.current_index_ = 0
self.is_execution_price_ = "execution_price" in self.config_
if self.is_execution_price_:
self.execution_price_column_ = self.config_["execution_price"]["column"]
self.execution_price_shift_ = self.config_["execution_price"]["shift"]
else:
self.execution_price_column_ = None
self.execution_price_shift_ = 0
def has_next(self) -> bool:
return self.current_index_ < len(self.market_data_df_)
def get_next(self) -> pd.Series:
result = self.market_data_df_.iloc[self.current_index_]
self.current_index_ += 1
return result
def load(self) -> None:
datafiles: List[str] = self.config_.get("datafiles", [])
instruments: List[Dict[str, str]] = self.config_.get("instruments", [])
assert len(instruments) > 0, "No instruments found in config"
assert len(datafiles) > 0, "No datafiles found in config"
self.symbol_a_ = instruments[0]["symbol"]
self.symbol_b_ = instruments[1]["symbol"]
self.stat_model_price_ = self.config_["stat_model_price"]
extra_minutes: int
extra_minutes = self.execution_price_shift_
for datafile in datafiles:
md_df = load_market_data(
datafile=datafile,
instruments=instruments,
db_table_name=self.config_["market_data_loading"][instruments[0]["instrument_type"]]["db_table_name"],
trading_hours=self.config_["trading_hours"],
extra_minutes=extra_minutes,
)
self.origin_mkt_data_df_ = pd.concat([self.origin_mkt_data_df_, md_df])
self.origin_mkt_data_df_ = self.origin_mkt_data_df_.sort_values(by="tstamp")
self.origin_mkt_data_df_ = self.origin_mkt_data_df_.dropna().reset_index(drop=True)
self._set_market_data()
def _set_market_data(self, ) -> None:
if self.is_execution_price_:
self.market_data_df_ = pd.DataFrame(
self._transform_dataframe(self.origin_mkt_data_df_)[["tstamp"] + self.colnames() + self.orig_exec_prices_colnames()]
)
else:
self.market_data_df_ = pd.DataFrame(
self._transform_dataframe(self.origin_mkt_data_df_)[["tstamp"] + self.colnames()]
)
self.market_data_df_ = self.market_data_df_.dropna().reset_index(drop=True)
self.market_data_df_["tstamp"] = pd.to_datetime(self.market_data_df_["tstamp"])
self.market_data_df_ = self.market_data_df_.sort_values("tstamp")
self._set_execution_price_data()
def _transform_dataframe(self, df: pd.DataFrame) -> pd.DataFrame:
df_selected: pd.DataFrame
if self.is_execution_price_:
execution_price_column = self.config_["execution_price"]["column"]
df_selected = pd.DataFrame(
df[["tstamp", "symbol", self.stat_model_price_, execution_price_column]]
)
else:
df_selected = pd.DataFrame(
df[["tstamp", "symbol", self.stat_model_price_]]
)
result_df = pd.DataFrame(df_selected["tstamp"]).drop_duplicates().reset_index(drop=True)
# For each unique symbol, add a corresponding stat_model_price column
symbols = df_selected["symbol"].unique()
for symbol in symbols:
# Filter rows for this symbol
df_symbol = df_selected[df_selected["symbol"] == symbol].reset_index(
drop=True
)
# Create column name like "close-COIN"
new_price_column = f"{self.stat_model_price_}_{symbol}"
if self.is_execution_price_:
new_execution_price_column = f"{self.execution_price_column_}_{symbol}"
# Create temporary dataframe with timestamp and price
temp_df = pd.DataFrame(
{
"tstamp": df_symbol["tstamp"],
new_price_column: df_symbol[self.stat_model_price_],
new_execution_price_column: df_symbol[execution_price_column],
}
)
else:
temp_df = pd.DataFrame(
{
"tstamp": df_symbol["tstamp"],
new_price_column: df_symbol[self.stat_model_price_],
}
)
# Join with our result dataframe
result_df = pd.merge(result_df, temp_df, on="tstamp", how="left")
result_df = result_df.reset_index(
drop=True
) # do not dropna() since irrelevant symbol would affect dataset
return result_df.dropna()
def _set_execution_price_data(self) -> None:
if "execution_price" not in self.config_:
self.market_data_df_[f"exec_price_{self.symbol_a_}"] = self.market_data_df_[f"{self.stat_model_price_}_{self.symbol_a_}"]
self.market_data_df_[f"exec_price_{self.symbol_b_}"] = self.market_data_df_[f"{self.stat_model_price_}_{self.symbol_b_}"]
return
execution_price_column = self.config_["execution_price"]["column"]
execution_price_shift = self.config_["execution_price"]["shift"]
self.market_data_df_[f"exec_price_{self.symbol_a_}"] = self.market_data_df_[f"{execution_price_column}_{self.symbol_a_}"].shift(-execution_price_shift)
self.market_data_df_[f"exec_price_{self.symbol_b_}"] = self.market_data_df_[f"{execution_price_column}_{self.symbol_b_}"].shift(-execution_price_shift)
self.market_data_df_ = self.market_data_df_.dropna().reset_index(drop=True)
def colnames(self) -> List[str]:
return [
f"{self.stat_model_price_}_{self.symbol_a_}",
f"{self.stat_model_price_}_{self.symbol_b_}",
]
def orig_exec_prices_colnames(self) -> List[str]:
return [
f"{self.execution_price_column_}_{self.symbol_a_}",
f"{self.execution_price_column_}_{self.symbol_b_}",
]
def exec_prices_colnames(self) -> List[str]:
return [
f"exec_price_{self.symbol_a_}",
f"exec_price_{self.symbol_b_}",
]
class RealTimeMarketData(PtMarketData):
def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
super().__init__(config, *args, **kwargs)
async def on_mkt_data_hist_snapshot(self, snapshot: JsonDictT) -> None:
# URGENT
# create origin_mkt_data_df_ from snapshot
# verify that the data for both instruments are present
# transform it to market_data_df_ tstamp, close_symbolA, close_symbolB
'''
# from cvttpy/exchanges/binance/spot/mkt_data.py
values = {
"time_ns": time_ns,
"tstamp": format_nanos_utc(time_ns),
"exchange_id": exch_inst.exchange_id_,
"instrument_id": exch_inst.instrument_id(),
"interval_ns": interval_sec * 1_000_000_000,
"open": float(kline[1]),
"high": float(kline[2]),
"low": float(kline[3]),
"close": float(kline[4]),
"volume": float(kline[5]),
"num_trades": kline[8],
"vwap": float(kline[7]) / float(kline[5]) if float(kline[5]) > 0 else 0.0 # Calculate VWAP
}
'''
pass
async def on_mkt_data_update(self, update: JsonDictT) -> Optional[pd.DataFrame]:
# URGENT
# make sure update has both instruments
# create DataFrame tmp1 from update
# transform tmp1 into temp. datframe tmp2
# add tmp1 to origin_mkt_data_df_
# add tmp2 to market_data_df_
# return market_data_df_
'''
class MdTradesAggregate(NamedObject):
def to_dict(self) -> Dict[str, Any]:
return {
"time_ns": self.time_ns_,
"tstamp": format_nanos_utc(self.time_ns_),
"exchange_id": self.exch_inst_.exchange_id_,
"instrument_id": self.exch_inst_.instrument_id(),
"interval_ns": self.interval_ns_,
"open": self.exch_inst_.get_price(self.open_),
"high": self.exch_inst_.get_price(self.high_),
"low": self.exch_inst_.get_price(self.low_),
"close": self.exch_inst_.get_price(self.close_),
"volume": self.exch_inst_.get_quantity(self.volume_),
"vwap": self.exch_inst_.get_price(self.vwap_),
"num_trades": self.exch_inst_.get_quantity(self.num_trades_),
}
'''
return pd.DataFrame()

27
lib/pt_strategy/pt_model.py
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@ -1,27 +0,0 @@
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Any, Dict, cast
from pt_strategy.prediction import Prediction
class PairsTradingModel(ABC):
@abstractmethod
def predict(self, pair: TradingPair) -> Prediction: # type: ignore[assignment]
...
@staticmethod
def create(config: Dict[str, Any]) -> PairsTradingModel:
import importlib
model_class_name = config.get("model_class", None)
assert model_class_name is not None
module_name, class_name = model_class_name.rsplit(".", 1)
module = importlib.import_module(module_name)
model_object = getattr(module, class_name)()
return cast(PairsTradingModel, model_object)

303
lib/pt_strategy/research_strategy.py
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@ -1,303 +0,0 @@
from __future__ import annotations
from typing import Any, Dict, List, Optional
import pandas as pd
from pt_strategy.model_data_policy import ModelDataPolicy
from pt_strategy.pt_market_data import ResearchMarketData
from pt_strategy.pt_model import Prediction
from pt_strategy.trading_pair import PairState, TradingPair
class PtResearchStrategy:
config_: Dict[str, Any]
trading_pair_: TradingPair
model_data_policy_: ModelDataPolicy
pt_mkt_data_: ResearchMarketData
trades_: List[pd.DataFrame]
predictions_: pd.DataFrame
def __init__(
self,
config: Dict[str, Any],
datafiles: List[str],
instruments: List[Dict[str, str]],
):
from pt_strategy.model_data_policy import ModelDataPolicy
from pt_strategy.trading_pair import TradingPair
self.config_ = config
self.trades_ = []
self.trading_pair_ = TradingPair(config=config, instruments=instruments)
self.predictions_ = pd.DataFrame()
import copy
# modified config must be passed to PtMarketData
config_copy = copy.deepcopy(config)
config_copy["instruments"] = instruments
config_copy["datafiles"] = datafiles
self.pt_mkt_data_ = ResearchMarketData(config=config_copy)
self.pt_mkt_data_.load()
self.model_data_policy_ = ModelDataPolicy.create(
config, mkt_data=self.pt_mkt_data_.market_data_df_, pair=self.trading_pair_
)
def outstanding_positions(self) -> List[Dict[str, Any]]:
return list(self.trading_pair_.user_data_.get("outstanding_positions", []))
def run(self) -> None:
training_minutes = self.config_.get("training_minutes", 120)
market_data_series: pd.Series
market_data_df = pd.DataFrame()
idx = 0
while self.pt_mkt_data_.has_next():
market_data_series = self.pt_mkt_data_.get_next()
new_row = pd.DataFrame([market_data_series])
market_data_df = pd.concat([market_data_df, new_row], ignore_index=True)
if idx >= training_minutes:
break
idx += 1
assert idx >= training_minutes, "Not enough training data"
while self.pt_mkt_data_.has_next():
market_data_series = self.pt_mkt_data_.get_next()
new_row = pd.DataFrame([market_data_series])
market_data_df = pd.concat([market_data_df, new_row], ignore_index=True)
prediction = self.trading_pair_.run(
market_data_df, self.model_data_policy_.advance(mkt_data_df=market_data_df)
)
self.predictions_ = pd.concat(
[self.predictions_, prediction.to_df()], ignore_index=True
)
assert prediction is not None
trades = self._create_trades(
prediction=prediction, last_row=market_data_df.iloc[-1]
)
if trades is not None:
self.trades_.append(trades)
trades = self._handle_outstanding_positions()
if trades is not None:
self.trades_.append(trades)
def _create_trades(
self, prediction: Prediction, last_row: pd.Series
) -> Optional[pd.DataFrame]:
pair = self.trading_pair_
trades = None
open_threshold = self.config_["dis-equilibrium_open_trshld"]
close_threshold = self.config_["dis-equilibrium_close_trshld"]
scaled_disequilibrium = prediction.scaled_disequilibrium_
abs_scaled_disequilibrium = abs(scaled_disequilibrium)
if pair.user_data_["state"] in [
PairState.INITIAL,
PairState.CLOSE,
PairState.CLOSE_POSITION,
PairState.CLOSE_STOP_LOSS,
PairState.CLOSE_STOP_PROFIT,
]:
if abs_scaled_disequilibrium >= open_threshold:
trades = self._create_open_trades(
pair, row=last_row, prediction=prediction
)
if trades is not None:
trades["status"] = PairState.OPEN.name
print(f"OPEN TRADES:\n{trades}")
pair.user_data_["state"] = PairState.OPEN
pair.on_open_trades(trades)
elif pair.user_data_["state"] == PairState.OPEN:
if abs_scaled_disequilibrium <= close_threshold:
trades = self._create_close_trades(
pair, row=last_row, prediction=prediction
)
if trades is not None:
trades["status"] = PairState.CLOSE.name
print(f"CLOSE TRADES:\n{trades}")
pair.user_data_["state"] = PairState.CLOSE
pair.on_close_trades(trades)
elif pair.to_stop_close_conditions(predicted_row=last_row):
trades = self._create_close_trades(pair, row=last_row)
if trades is not None:
trades["status"] = pair.user_data_["stop_close_state"].name
print(f"STOP CLOSE TRADES:\n{trades}")
pair.user_data_["state"] = pair.user_data_["stop_close_state"]
pair.on_close_trades(trades)
return trades
def _handle_outstanding_positions(self) -> Optional[pd.DataFrame]:
trades = None
pair = self.trading_pair_
# Outstanding positions
if pair.user_data_["state"] == PairState.OPEN:
print(f"{pair}: *** Position is NOT CLOSED. ***")
# outstanding positions
if self.config_["close_outstanding_positions"]:
close_position_row = pd.Series(pair.market_data_.iloc[-2])
# close_position_row["disequilibrium"] = 0.0
# close_position_row["scaled_disequilibrium"] = 0.0
# close_position_row["signed_scaled_disequilibrium"] = 0.0
trades = self._create_close_trades(
pair=pair, row=close_position_row, prediction=None
)
if trades is not None:
trades["status"] = PairState.CLOSE_POSITION.name
print(f"CLOSE_POSITION TRADES:\n{trades}")
pair.user_data_["state"] = PairState.CLOSE_POSITION
pair.on_close_trades(trades)
else:
pair.add_outstanding_position(
symbol=pair.symbol_a_,
open_side=pair.user_data_["open_side_a"],
open_px=pair.user_data_["open_px_a"],
open_tstamp=pair.user_data_["open_tstamp"],
last_mkt_data_row=pair.market_data_.iloc[-1],
)
pair.add_outstanding_position(
symbol=pair.symbol_b_,
open_side=pair.user_data_["open_side_b"],
open_px=pair.user_data_["open_px_b"],
open_tstamp=pair.user_data_["open_tstamp"],
last_mkt_data_row=pair.market_data_.iloc[-1],
)
return trades
def _trades_df(self) -> pd.DataFrame:
types = {
"time": "datetime64[ns]",
"action": "string",
"symbol": "string",
"side": "string",
"price": "float64",
"disequilibrium": "float64",
"scaled_disequilibrium": "float64",
"signed_scaled_disequilibrium": "float64",
# "pair": "object",
}
columns = list(types.keys())
return pd.DataFrame(columns=columns).astype(types)
def _create_open_trades(
self, pair: TradingPair, row: pd.Series, prediction: Prediction
) -> Optional[pd.DataFrame]:
colname_a, colname_b = pair.exec_prices_colnames()
tstamp = row["tstamp"]
diseqlbrm = prediction.disequilibrium_
scaled_disequilibrium = prediction.scaled_disequilibrium_
px_a = row[f"{colname_a}"]
px_b = row[f"{colname_b}"]
# creating the trades
df = self._trades_df()
print(f"OPEN_TRADES: {row["tstamp"]} {scaled_disequilibrium=}")
if diseqlbrm > 0:
side_a = "SELL"
side_b = "BUY"
else:
side_a = "BUY"
side_b = "SELL"
# save closing sides
pair.user_data_["open_side_a"] = side_a # used in oustanding positions
pair.user_data_["open_side_b"] = side_b
pair.user_data_["open_px_a"] = px_a
pair.user_data_["open_px_b"] = px_b
pair.user_data_["open_tstamp"] = tstamp
pair.user_data_["close_side_a"] = side_b # used for closing trades
pair.user_data_["close_side_b"] = side_a
# create opening trades
df.loc[len(df)] = {
"time": tstamp,
"symbol": pair.symbol_a_,
"side": side_a,
"action": "OPEN",
"price": px_a,
"disequilibrium": diseqlbrm,
"signed_scaled_disequilibrium": scaled_disequilibrium,
"scaled_disequilibrium": abs(scaled_disequilibrium),
# "pair": pair,
}
df.loc[len(df)] = {
"time": tstamp,
"symbol": pair.symbol_b_,
"side": side_b,
"action": "OPEN",
"price": px_b,
"disequilibrium": diseqlbrm,
"scaled_disequilibrium": abs(scaled_disequilibrium),
"signed_scaled_disequilibrium": scaled_disequilibrium,
# "pair": pair,
}
return df
def _create_close_trades(
self, pair: TradingPair, row: pd.Series, prediction: Optional[Prediction] = None
) -> Optional[pd.DataFrame]:
colname_a, colname_b = pair.exec_prices_colnames()
tstamp = row["tstamp"]
if prediction is not None:
diseqlbrm = prediction.disequilibrium_
signed_scaled_disequilibrium = prediction.scaled_disequilibrium_
scaled_disequilibrium = abs(prediction.scaled_disequilibrium_)
else:
diseqlbrm = 0.0
signed_scaled_disequilibrium = 0.0
scaled_disequilibrium = 0.0
px_a = row[f"{colname_a}"]
px_b = row[f"{colname_b}"]
# creating the trades
df = self._trades_df()
# create opening trades
df.loc[len(df)] = {
"time": tstamp,
"symbol": pair.symbol_a_,
"side": pair.user_data_["close_side_a"],
"action": "CLOSE",
"price": px_a,
"disequilibrium": diseqlbrm,
"scaled_disequilibrium": scaled_disequilibrium,
"signed_scaled_disequilibrium": signed_scaled_disequilibrium,
# "pair": pair,
}
df.loc[len(df)] = {
"time": tstamp,
"symbol": pair.symbol_b_,
"side": pair.user_data_["close_side_b"],
"action": "CLOSE",
"price": px_b,
"disequilibrium": diseqlbrm,
"scaled_disequilibrium": scaled_disequilibrium,
"signed_scaled_disequilibrium": signed_scaled_disequilibrium,
# "pair": pair,
}
del pair.user_data_["close_side_a"]
del pair.user_data_["close_side_b"]
del pair.user_data_["open_tstamp"]
del pair.user_data_["open_px_a"]
del pair.user_data_["open_px_b"]
del pair.user_data_["open_side_a"]
del pair.user_data_["open_side_b"]
return df
def day_trades(self) -> pd.DataFrame:
return pd.concat(self.trades_, ignore_index=True)

532
lib/pt_strategy/results.py
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import os
import sqlite3
from datetime import date, datetime
from typing import Any, Dict, List, Optional, Tuple
import pandas as pd
from pt_strategy.trading_pair import TradingPair
# Recommended replacement adapters and converters for Python 3.12+
# From: https://docs.python.org/3/library/sqlite3.html#sqlite3-adapter-converter-recipes
def adapt_date_iso(val: date) -> str:
"""Adapt datetime.date to ISO 8601 date."""
return val.isoformat()
def adapt_datetime_iso(val: datetime) -> str:
"""Adapt datetime.datetime to timezone-naive ISO 8601 date."""
return val.isoformat()
def convert_date(val: bytes) -> date:
"""Convert ISO 8601 date to datetime.date object."""
return datetime.fromisoformat(val.decode()).date()
def convert_datetime(val: bytes) -> datetime:
"""Convert ISO 8601 datetime to datetime.datetime object."""
return datetime.fromisoformat(val.decode())
# Register the adapters and converters
sqlite3.register_adapter(date, adapt_date_iso)
sqlite3.register_adapter(datetime, adapt_datetime_iso)
sqlite3.register_converter("date", convert_date)
sqlite3.register_converter("datetime", convert_datetime)
def create_result_database(db_path: str) -> None:
"""
Create the SQLite database and required tables if they don't exist.
"""
try:
# Create directory if it doesn't exist
db_dir = os.path.dirname(db_path)
if db_dir and not os.path.exists(db_dir):
os.makedirs(db_dir, exist_ok=True)
print(f"Created directory: {db_dir}")
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
# Create the pt_bt_results table for completed trades
cursor.execute(
"""
CREATE TABLE IF NOT EXISTS pt_bt_results (
date DATE,
pair TEXT,
symbol TEXT,
open_time DATETIME,
open_side TEXT,
open_price REAL,
open_quantity INTEGER,
open_disequilibrium REAL,
close_time DATETIME,
close_side TEXT,
close_price REAL,
close_quantity INTEGER,
close_disequilibrium REAL,
symbol_return REAL,
pair_return REAL,
close_condition TEXT
)
"""
)
cursor.execute("DELETE FROM pt_bt_results;")
# Create the outstanding_positions table for open positions
cursor.execute(
"""
CREATE TABLE IF NOT EXISTS outstanding_positions (
date DATE,
pair TEXT,
symbol TEXT,
position_quantity REAL,
last_price REAL,
unrealized_return REAL,
open_price REAL,
open_side TEXT
)
"""
)
cursor.execute("DELETE FROM outstanding_positions;")
# Create the config table for storing configuration JSON for reference
cursor.execute(
"""
CREATE TABLE IF NOT EXISTS config (
id INTEGER PRIMARY KEY AUTOINCREMENT,
run_timestamp DATETIME,
config_file_path TEXT,
config_json TEXT,
datafiles TEXT,
instruments TEXT
)
"""
)
cursor.execute("DELETE FROM config;")
conn.commit()
conn.close()
except Exception as e:
print(f"Error creating result database: {str(e)}")
raise
def store_config_in_database(
db_path: str,
config_file_path: str,
config: Dict,
datafiles: List[Tuple[str, str]],
instruments: List[Dict[str, str]],
) -> None:
"""
Store configuration information in the database for reference.
"""
import json
if db_path.upper() == "NONE":
return
try:
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
# Convert config to JSON string
config_json = json.dumps(config, indent=2, default=str)
# Convert lists to comma-separated strings for storage
datafiles_str = ", ".join([f"{datafile}" for _, datafile in datafiles])
instruments_str = ", ".join(
[
f"{inst['symbol']}:{inst['instrument_type']}:{inst['exchange_id']}"
for inst in instruments
]
)
# Insert configuration record
cursor.execute(
"""
INSERT INTO config (
run_timestamp, config_file_path, config_json, datafiles, instruments
) VALUES (?, ?, ?, ?, ?)
""",
(
datetime.now(),
config_file_path,
config_json,
datafiles_str,
instruments_str,
),
)
conn.commit()
conn.close()
print(f"Configuration stored in database")
except Exception as e:
print(f"Error storing configuration in database: {str(e)}")
import traceback
traceback.print_exc()
def convert_timestamp(timestamp: Any) -> Optional[datetime]:
"""Convert pandas Timestamp to Python datetime object for SQLite compatibility."""
if timestamp is None:
return None
if isinstance(timestamp, pd.Timestamp):
return timestamp.to_pydatetime()
elif isinstance(timestamp, datetime):
return timestamp
elif isinstance(timestamp, date):
return datetime.combine(timestamp, datetime.min.time())
elif isinstance(timestamp, str):
return datetime.strptime(timestamp, "%Y-%m-%d %H:%M:%S")
elif isinstance(timestamp, int):
return datetime.fromtimestamp(timestamp)
else:
raise ValueError(f"Unsupported timestamp type: {type(timestamp)}")
DayT = str
TradeT = Dict[str, Any]
OutstandingPositionT = Dict[str, Any]
class PairResearchResult:
"""
Class to handle pair research results for a single pair across multiple days.
Simplified version of BacktestResult focused on single pair analysis.
"""
trades_: Dict[DayT, pd.DataFrame]
outstanding_positions_: Dict[DayT, List[OutstandingPositionT]]
symbol_roundtrip_trades_: Dict[str, List[Dict[str, Any]]]
def __init__(self, config: Dict[str, Any]) -> None:
self.config_ = config
self.trades_ = {}
self.outstanding_positions_ = {}
self.total_realized_pnl = 0.0
self.symbol_roundtrip_trades_ = {}
def add_day_results(self, day: DayT, trades: pd.DataFrame, outstanding_positions: List[Dict[str, Any]]) -> None:
assert isinstance(trades, pd.DataFrame)
self.trades_[day] = trades
self.outstanding_positions_[day] = outstanding_positions
# def all_trades(self) -> List[TradeT]:
# """Get all trades across all days as a flat list."""
# all_trades_list: List[TradeT] = []
# for day_trades in self.trades_.values():
# all_trades_list.extend(day_trades.to_dict(orient="records"))
# return all_trades_list
def outstanding_positions(self) -> List[OutstandingPositionT]:
"""Get all outstanding positions across all days as a flat list."""
res: List[Dict[str, Any]] = []
for day in self.outstanding_positions_.keys():
res.extend(self.outstanding_positions_[day])
return res
def calculate_returns(self) -> None:
"""Calculate and store total returns for the single pair across all days."""
self.extract_roundtrip_trades()
self.total_realized_pnl = 0.0
for day, day_trades in self.symbol_roundtrip_trades_.items():
for trade in day_trades:
self.total_realized_pnl += trade['symbol_return']
def extract_roundtrip_trades(self) -> None:
"""
Extract round-trip trades by day, grouping open/close pairs for each symbol.
Returns a dictionary with day as key and list of completed round-trip trades.
"""
def _symbol_return(trade1_side: str, trade1_px: float, trade2_side: str, trade2_px: float) -> float:
if trade1_side == "BUY" and trade2_side == "SELL":
return (trade2_px - trade1_px) / trade1_px * 100
elif trade1_side == "SELL" and trade2_side == "BUY":
return (trade1_px - trade2_px) / trade1_px * 100
else:
return 0
# Process each day separately
for day, day_trades in self.trades_.items():
# Sort trades by timestamp for the day
sorted_trades = day_trades #sorted(day_trades, key=lambda x: x["timestamp"] if x["timestamp"] else pd.Timestamp.min)
day_roundtrips = []
# Process trades in groups of 4 (open A, open B, close A, close B)
for idx in range(0, len(sorted_trades), 4):
if idx + 3 >= len(sorted_trades):
break
trade_a_1 = sorted_trades.iloc[idx] # Open A
trade_b_1 = sorted_trades.iloc[idx + 1] # Open B
trade_a_2 = sorted_trades.iloc[idx + 2] # Close A
trade_b_2 = sorted_trades.iloc[idx + 3] # Close B
# Validate trade sequence
if not (trade_a_1["action"] == "OPEN" and trade_a_2["action"] == "CLOSE"):
continue
if not (trade_b_1["action"] == "OPEN" and trade_b_2["action"] == "CLOSE"):
continue
# Calculate individual symbol returns
symbol_a_return = _symbol_return(
trade_a_1["side"], trade_a_1["price"],
trade_a_2["side"], trade_a_2["price"]
)
symbol_b_return = _symbol_return(
trade_b_1["side"], trade_b_1["price"],
trade_b_2["side"], trade_b_2["price"]
)
pair_return = symbol_a_return + symbol_b_return
# Create round-trip records for both symbols
funding_per_position = self.config_.get("funding_per_pair", 10000) / 2
# Symbol A round-trip
day_roundtrips.append({
"symbol": trade_a_1["symbol"],
"open_side": trade_a_1["side"],
"open_price": trade_a_1["price"],
"open_time": trade_a_1["time"],
"close_side": trade_a_2["side"],
"close_price": trade_a_2["price"],
"close_time": trade_a_2["time"],
"symbol_return": symbol_a_return,
"pair_return": pair_return,
"shares": funding_per_position / trade_a_1["price"],
"close_condition": trade_a_2.get("status", "UNKNOWN"),
"open_disequilibrium": trade_a_1.get("disequilibrium"),
"close_disequilibrium": trade_a_2.get("disequilibrium"),
})
# Symbol B round-trip
day_roundtrips.append({
"symbol": trade_b_1["symbol"],
"open_side": trade_b_1["side"],
"open_price": trade_b_1["price"],
"open_time": trade_b_1["time"],
"close_side": trade_b_2["side"],
"close_price": trade_b_2["price"],
"close_time": trade_b_2["time"],
"symbol_return": symbol_b_return,
"pair_return": pair_return,
"shares": funding_per_position / trade_b_1["price"],
"close_condition": trade_b_2.get("status", "UNKNOWN"),
"open_disequilibrium": trade_b_1.get("disequilibrium"),
"close_disequilibrium": trade_b_2.get("disequilibrium"),
})
if day_roundtrips:
self.symbol_roundtrip_trades_[day] = day_roundtrips
def print_returns_by_day(self) -> None:
"""
Print detailed return information for each day, grouped by day.
Shows individual symbol round-trips and daily totals.
"""
print("\n====== PAIR RESEARCH RETURNS BY DAY ======")
total_return_all_days = 0.0
for day, day_trades in sorted(self.symbol_roundtrip_trades_.items()):
print(f"\n--- {day} ---")
day_total_return = 0.0
pair_returns = []
# Group trades by pair (every 2 trades form a pair)
for idx in range(0, len(day_trades), 2):
if idx + 1 < len(day_trades):
trade_a = day_trades[idx]
trade_b = day_trades[idx + 1]
# Print individual symbol results
print(f" {trade_a['open_time'].time()}-{trade_a['close_time'].time()}")
print(f" {trade_a['symbol']}: {trade_a['open_side']} @ ${trade_a['open_price']:.2f} → "
f"{trade_a['close_side']} @ ${trade_a['close_price']:.2f} | "
f"Return: {trade_a['symbol_return']:+.2f}% | Shares: {trade_a['shares']:.2f}")
print(f" {trade_b['symbol']}: {trade_b['open_side']} @ ${trade_b['open_price']:.2f} → "
f"{trade_b['close_side']} @ ${trade_b['close_price']:.2f} | "
f"Return: {trade_b['symbol_return']:+.2f}% | Shares: {trade_b['shares']:.2f}")
# Show disequilibrium info if available
if trade_a.get('open_disequilibrium') is not None:
print(f" Disequilibrium: Open: {trade_a['open_disequilibrium']:.4f}, "
f"Close: {trade_a['close_disequilibrium']:.4f}")
pair_return = trade_a['pair_return']
print(f" Pair Return: {pair_return:+.2f}% | Close Condition: {trade_a['close_condition']}")
print()
pair_returns.append(pair_return)
day_total_return += pair_return
print(f" Day Total Return: {day_total_return:+.2f}% ({len(pair_returns)} pairs)")
total_return_all_days += day_total_return
print(f"\n====== TOTAL RETURN ACROSS ALL DAYS ======")
print(f"Total Return: {total_return_all_days:+.2f}%")
print(f"Total Days: {len(self.symbol_roundtrip_trades_)}")
if len(self.symbol_roundtrip_trades_) > 0:
print(f"Average Daily Return: {total_return_all_days / len(self.symbol_roundtrip_trades_):+.2f}%")
def get_return_summary(self) -> Dict[str, Any]:
"""
Get a summary of returns across all days.
Returns a dictionary with key metrics.
"""
if len(self.symbol_roundtrip_trades_) == 0:
return {
"total_return": 0.0,
"total_days": 0,
"total_pairs": 0,
"average_daily_return": 0.0,
"best_day": None,
"worst_day": None,
"daily_returns": {}
}
daily_returns = {}
total_return = 0.0
total_pairs = 0
for day, day_trades in self.symbol_roundtrip_trades_.items():
day_return = 0.0
day_pairs = len(day_trades) // 2 # Each pair has 2 symbol trades
for trade in day_trades:
day_return += trade['symbol_return']
daily_returns[day] = {
"return": day_return,
"pairs": day_pairs
}
total_return += day_return
total_pairs += day_pairs
best_day = max(daily_returns.items(), key=lambda x: x[1]["return"]) if daily_returns else None
worst_day = min(daily_returns.items(), key=lambda x: x[1]["return"]) if daily_returns else None
return {
"total_return": total_return,
"total_days": len(self.symbol_roundtrip_trades_),
"total_pairs": total_pairs,
"average_daily_return": total_return / len(self.symbol_roundtrip_trades_) if self.symbol_roundtrip_trades_ else 0.0,
"best_day": best_day,
"worst_day": worst_day,
"daily_returns": daily_returns
}
def print_grand_totals(self) -> None:
"""Print grand totals for the single pair analysis."""
summary = self.get_return_summary()
print(f"\n====== PAIR RESEARCH GRAND TOTALS ======")
print('---')
print(f"Total Return: {summary['total_return']:+.2f}%")
print('---')
print(f"Total Days Traded: {summary['total_days']}")
print(f"Total Open-Close Actions: {summary['total_pairs']}")
print(f"Total Trades: 4 * {summary['total_pairs']} = {4 * summary['total_pairs']}")
if summary['total_days'] > 0:
print(f"Average Daily Return: {summary['average_daily_return']:+.2f}%")
if summary['best_day']:
best_day, best_data = summary['best_day']
print(f"Best Day: {best_day} ({best_data['return']:+.2f}%)")
if summary['worst_day']:
worst_day, worst_data = summary['worst_day']
print(f"Worst Day: {worst_day} ({worst_data['return']:+.2f}%)")
# Update the total_realized_pnl for backward compatibility
self.total_realized_pnl = summary['total_return']
def analyze_pair_performance(self) -> None:
"""
Main method to perform comprehensive pair research analysis.
Extracts round-trip trades, calculates returns, groups by day, and prints results.
"""
print(f"\n{'='*60}")
print(f"PAIR RESEARCH PERFORMANCE ANALYSIS")
print(f"{'='*60}")
self.calculate_returns()
self.print_returns_by_day()
self.print_outstanding_positions()
self._print_additional_metrics()
self.print_grand_totals()
def _print_additional_metrics(self) -> None:
"""Print additional performance metrics."""
summary = self.get_return_summary()
if summary['total_days'] == 0:
return
print(f"\n====== ADDITIONAL METRICS ======")
# Calculate win rate
winning_days = sum(1 for day_data in summary['daily_returns'].values() if day_data['return'] > 0)
win_rate = (winning_days / summary['total_days']) * 100
print(f"Winning Days: {winning_days}/{summary['total_days']} ({win_rate:.1f}%)")
# Calculate average trade return
if summary['total_pairs'] > 0:
# Each pair has 2 symbol trades, so total symbol trades = total_pairs * 2
total_symbol_trades = summary['total_pairs'] * 2
avg_symbol_return = summary['total_return'] / total_symbol_trades
print(f"Average Symbol Return: {avg_symbol_return:+.2f}%")
avg_pair_return = summary['total_return'] / summary['total_pairs'] / 2 # Divide by 2 since we sum both symbols
print(f"Average Pair Return: {avg_pair_return:+.2f}%")
# Show daily return distribution
daily_returns_list = [data['return'] for data in summary['daily_returns'].values()]
if daily_returns_list:
print(f"Daily Return Range: {min(daily_returns_list):+.2f}% to {max(daily_returns_list):+.2f}%")
def print_outstanding_positions(self) -> None:
"""Print outstanding positions for the single pair."""
all_positions: List[OutstandingPositionT] = self.outstanding_positions()
if not all_positions:
print("\n====== NO OUTSTANDING POSITIONS ======")
return
print(f"\n====== OUTSTANDING POSITIONS ======")
print(f"{'Symbol':<10} {'Side':<4} {'Shares':<10} {'Open $':<8} {'Current $':<10} {'Value $':<12}")
print("-" * 70)
total_value = 0.0
for pos in all_positions:
current_value = pos.get("last_value", 0.0)
print(f"{pos['symbol']:<10} {pos['open_side']:<4} {pos['shares']:<10.2f} "
f"{pos['open_px']:<8.2f} {pos['last_px']:<10.2f} {current_value:<12.2f}")
total_value += current_value
print("-" * 70)
print(f"{'TOTAL VALUE':<60} ${total_value:<12.2f}")
def get_total_realized_pnl(self) -> float:
"""Get total realized PnL."""
return self.total_realized_pnl

199
lib/pt_strategy/trading_pair.py
View File

@ -1,199 +0,0 @@
from __future__ import annotations
from datetime import datetime
from enum import Enum
from typing import Any, Dict, List
import pandas as pd
from pt_strategy.model_data_policy import DataWindowParams
from pt_strategy.prediction import Prediction
class PairState(Enum):
INITIAL = 1
OPEN = 2
CLOSE = 3
CLOSE_POSITION = 4
CLOSE_STOP_LOSS = 5
CLOSE_STOP_PROFIT = 6
def get_symbol(instrument: Dict[str, str]) -> str:
if "symbol" in instrument:
return instrument["symbol"]
elif "instrument_id" in instrument:
instrument_id = instrument["instrument_id"]
instrument_pfx = instrument_id[:instrument_id.find("-") + 1]
symbol = instrument_id[len(instrument_pfx):]
instrument["symbol"] = symbol
instrument["instrument_id_pfx"] = instrument_pfx
return symbol
else:
raise ValueError(f"Invalid instrument: {instrument}, missing symbol or instrument_id")
class TradingPair:
config_: Dict[str, Any]
market_data_: pd.DataFrame
instruments_: List[Dict[str, str]]
symbol_a_: str
symbol_b_: str
stat_model_price_: str
model_: PairsTradingModel # type: ignore[assignment]
user_data_: Dict[str, Any]
def __init__(
self,
config: Dict[str, Any],
instruments: List[Dict[str, str]],
):
from pt_strategy.pt_model import PairsTradingModel
assert len(instruments) == 2, "Trading pair must have exactly 2 instruments"
self.config_ = config
self.instruments_ = instruments
self.symbol_a_ = get_symbol(instruments[0])
self.symbol_b_ = get_symbol(instruments[1])
self.model_ = PairsTradingModel.create(config)
self.stat_model_price_ = config["stat_model_price"]
self.user_data_ = {
"state": PairState.INITIAL,
}
def __repr__(self) -> str:
return (
f"{self.__class__.__name__}:"
f" symbol_a={self.symbol_a_},"
f" symbol_b={self.symbol_b_},"
f" model={self.model_.__class__.__name__}"
)
def is_closed(self) -> bool:
return self.user_data_["state"] in [
PairState.CLOSE,
PairState.CLOSE_POSITION,
PairState.CLOSE_STOP_LOSS,
PairState.CLOSE_STOP_PROFIT,
]
def is_open(self) -> bool:
return self.user_data_["state"] == PairState.OPEN
def colnames(self) -> List[str]:
return [
f"{self.stat_model_price_}_{self.symbol_a_}",
f"{self.stat_model_price_}_{self.symbol_b_}",
]
def exec_prices_colnames(self) -> List[str]:
return [
f"exec_price_{self.symbol_a_}",
f"exec_price_{self.symbol_b_}",
]
def to_stop_close_conditions(self, predicted_row: pd.Series) -> bool:
config = self.config_
if (
"stop_close_conditions" not in config
or config["stop_close_conditions"] is None
):
return False
if "profit" in config["stop_close_conditions"]:
current_return = self._current_return(predicted_row)
#
# print(f"time={predicted_row['tstamp']} current_return={current_return}")
#
if current_return >= config["stop_close_conditions"]["profit"]:
print(f"STOP PROFIT: {current_return}")
self.user_data_["stop_close_state"] = PairState.CLOSE_STOP_PROFIT
return True
if "loss" in config["stop_close_conditions"]:
if current_return <= config["stop_close_conditions"]["loss"]:
print(f"STOP LOSS: {current_return}")
self.user_data_["stop_close_state"] = PairState.CLOSE_STOP_LOSS
return True
return False
def _current_return(self, predicted_row: pd.Series) -> float:
if "open_trades" in self.user_data_:
open_trades = self.user_data_["open_trades"]
if len(open_trades) == 0:
return 0.0
def _single_instrument_return(symbol: str) -> float:
instrument_open_trades = open_trades[open_trades["symbol"] == symbol]
instrument_open_price = instrument_open_trades["price"].iloc[0]
sign = -1 if instrument_open_trades["side"].iloc[0] == "SELL" else 1
instrument_price = predicted_row[f"{self.stat_model_price_}_{symbol}"]
instrument_return = (
sign
* (instrument_price - instrument_open_price)
/ instrument_open_price
)
return float(instrument_return) * 100.0
instrument_a_return = _single_instrument_return(self.symbol_a_)
instrument_b_return = _single_instrument_return(self.symbol_b_)
return instrument_a_return + instrument_b_return
return 0.0
def on_open_trades(self, trades: pd.DataFrame) -> None:
if "close_trades" in self.user_data_:
del self.user_data_["close_trades"]
self.user_data_["open_trades"] = trades
def on_close_trades(self, trades: pd.DataFrame) -> None:
del self.user_data_["open_trades"]
self.user_data_["close_trades"] = trades
def add_outstanding_position(
self,
symbol: str,
open_side: str,
open_px: float,
open_tstamp: datetime,
last_mkt_data_row: pd.Series,
) -> None:
assert symbol in [self.symbol_a_, self.symbol_b_], "Symbol must be one of the pair's symbols"
assert open_side in ["BUY", "SELL"], "Open side must be either BUY or SELL"
assert open_px > 0, "Open price must be greater than 0"
assert open_tstamp is not None, "Open timestamp must be provided"
assert last_mkt_data_row is not None, "Last market data row must be provided"
exec_prices_col_a, exec_prices_col_b = self.exec_prices_colnames()
if symbol == self.symbol_a_:
last_px = last_mkt_data_row[exec_prices_col_a]
else:
last_px = last_mkt_data_row[exec_prices_col_b]
funding_per_position = self.config_["funding_per_pair"] / 2
shares = funding_per_position / open_px
if open_side == "SELL":
shares = -shares
if "outstanding_positions" not in self.user_data_:
self.user_data_["outstanding_positions"] = []
self.user_data_["outstanding_positions"].append({
"symbol": symbol,
"open_side": open_side,
"open_px": open_px,
"shares": shares,
"open_tstamp": open_tstamp,
"last_px": last_px,
"last_tstamp": last_mkt_data_row["tstamp"],
"last_value": last_px * shares,
})
def run(self, market_data: pd.DataFrame, data_params: DataWindowParams) -> Prediction: # type: ignore[assignment]
self.market_data_ = market_data[data_params.training_start_index:data_params.training_start_index + data_params.training_size]
return self.model_.predict(pair=self)

0
__DELETE__/lib/pt_trading/fit_method.py → lib/pt_trading/fit_method.py
View File

38
__DELETE__/lib/pt_trading/results.py → lib/pt_trading/results.py
View File

@ -195,16 +195,6 @@ def convert_timestamp(timestamp: Any) -> Optional[datetime]:
raise ValueError(f"Unsupported timestamp type: {type(timestamp)}")
class PairResarchResult:
pair_: TradingPair
trades_: Dict[str, Dict[str, Any]]
outstanding_positions_: List[Dict[str, Any]]
def __init__(self, config: Dict[str, Any], pair: TradingPair, trades: Dict[str, Dict[str, Any]], outstanding_positions: List[Dict[str, Any]]):
self.config = config
self.pair_ = pair
self.trades_ = trades
self.outstanding_positions_ = outstanding_positions
class BacktestResult:
"""
@ -216,7 +206,7 @@ class BacktestResult:
self.trades: Dict[str, Dict[str, Any]] = {}
self.total_realized_pnl = 0.0
self.outstanding_positions: List[Dict[str, Any]] = []
self.symbol_roundtrip_trades_: Dict[str, List[Dict[str, Any]]] = {}
self.pairs_trades_: Dict[str, List[Dict[str, Any]]] = {}
def add_trade(
self,
@ -344,7 +334,7 @@ class BacktestResult:
for filename, data in all_results.items():
pairs = list(data["trades"].keys())
for pair in pairs:
self.symbol_roundtrip_trades_[pair] = []
self.pairs_trades_[pair] = []
trades_dict = data["trades"][pair]
for symbol in trades_dict.keys():
trades.extend(trades_dict[symbol])
@ -379,7 +369,7 @@ class BacktestResult:
pair_return = symbol_a_return + symbol_b_return
self.symbol_roundtrip_trades_[pair].append(
self.pairs_trades_[pair].append(
{
"symbol": symbol_a,
"open_side": trade_a_1["side"],
@ -401,7 +391,7 @@ class BacktestResult:
"pair_return": pair_return
}
)
self.symbol_roundtrip_trades_[pair].append(
self.pairs_trades_[pair].append(
{
"symbol": symbol_b,
"open_side": trade_b_1["side"],
@ -427,24 +417,26 @@ class BacktestResult:
# Print pair returns with disequilibrium information
day_return = 0.0
if pair in self.symbol_roundtrip_trades_:
if pair in self.pairs_trades_:
print(f"{pair}:")
pair_return = 0.0
for trd in self.symbol_roundtrip_trades_[pair]:
for trd in self.pairs_trades_[pair]:
disequil_info = ""
if (
trd["open_scaled_disequilibrium"] is not None
and trd["open_scaled_disequilibrium"] is not None
):
disequil_info = f" | Open Dis-eq: {trd['open_scaled_disequilibrium']:.2f},"
f" Close Dis-eq: {trd['open_scaled_disequilibrium']:.2f}"
disequil_info = (
f' | Open Dis-eq: {trd["open_scaled_disequilibrium"]:.2f},'
f' Close Dis-eq: {trd["close_scaled_disequilibrium"]:.2f}'
)
print(
f" {trd['open_time'].time()}-{trd['close_time'].time()} {trd['symbol']}: "
f" {trd['open_side']} @ ${trd['open_price']:.2f},"
f" {trd["close_side"]} @ ${trd["close_price"]:.2f},"
f" Return: {trd['symbol_return']:.2f}%{disequil_info}"
f' {trd["open_time"].time()}-{trd["close_time"].time()} {trd["symbol"]}: '
f' {trd["open_side"]} @ ${trd["open_price"]:.2f},'
f' {trd["close_side"]} @ ${trd["close_price"]:.2f},'
f' Return: {trd["symbol_return"]:.2f}%{disequil_info}'
)
pair_return += trd["symbol_return"]
@ -651,7 +643,7 @@ class BacktestResult:
for pair_name, _ in trades.items():
# Second pass: insert completed trade records into database
for trade_pair in sorted(self.symbol_roundtrip_trades_[pair_name], key=lambda x: x["open_time"]):
for trade_pair in sorted(self.pairs_trades_[pair_name], key=lambda x: x["open_time"]):
# Only store completed trades in pt_bt_results table
cursor.execute(
"""

10
__DELETE__/lib/pt_trading/rolling_window_fit.py → lib/pt_trading/rolling_window_fit.py
View File

@ -188,7 +188,8 @@ class RollingFit(PairsTradingFitMethod):
open_px_b = open_row[f"{colname_b}"]
# creating the trades
print(f"OPEN_TRADES: {row["tstamp"]} {open_scaled_disequilibrium=}")
# use outer single quotes so we can reference DataFrame keys with double quotes inside
print(f'OPEN_TRADES: {open_tstamp} open_scaled_disequilibrium={open_scaled_disequilibrium}')
if open_disequilibrium > 0:
open_side_a = "SELL"
open_side_b = "BUY"
@ -237,10 +238,7 @@ class RollingFit(PairsTradingFitMethod):
),
]
# Create DataFrame with explicit dtypes to avoid concatenation warnings
df = pd.DataFrame(
trd_signal_tuples,
columns=self.TRADES_COLUMNS,
)
df = pd.DataFrame(trd_signal_tuples, columns=self.TRADES_COLUMNS)
# Ensure consistent dtypes
return df.astype(
{
@ -316,4 +314,4 @@ class RollingFit(PairsTradingFitMethod):
)
def reset(self) -> None:
pass
curr_training_start_idx = 0

4
__DELETE__/lib/pt_trading/trading_pair.py → lib/pt_trading/trading_pair.py
View File

@ -119,8 +119,8 @@ class TradingPair(ABC):
return
execution_price_column = self.config_["execution_price"]["column"]
execution_price_shift = self.config_["execution_price"]["shift"]
self.market_data_[f"exec_price_{self.symbol_a_}"] = self.market_data_[f"{execution_price_column}_{self.symbol_a_}"].shift(-execution_price_shift)
self.market_data_[f"exec_price_{self.symbol_b_}"] = self.market_data_[f"{execution_price_column}_{self.symbol_b_}"].shift(-execution_price_shift)
self.market_data_[f"exec_price_{self.symbol_a_}"] = self.market_data_[f"{self.stat_model_price_}_{self.symbol_a_}"].shift(-execution_price_shift)
self.market_data_[f"exec_price_{self.symbol_b_}"] = self.market_data_[f"{self.stat_model_price_}_{self.symbol_b_}"].shift(-execution_price_shift)
self.market_data_ = self.market_data_.dropna().reset_index(drop=True)

193
lib/pt_trading/vecm_rolling_fit.py Normal file
View File

@ -0,0 +1,193 @@
# original script moved to vecm_rolling_fit_01.py
# 09.09.25 Added GARCH model - predicting volatility
# Rule of thumb:
# alpha + beta ≈ 1 → strong volatility clustering, persistence.
# If much lower → volatility mean reverts quickly.
# If > 1 → model is unstable / non-stationary (bad).
# the VECM disequilibrium (mean reversion signal) and
# the GARCH volatility forecast (risk measure).
# combine them → e.g., only enter trades when:
# high_volatility = 1 → persistence > 0.95 or volatility > 2 (rule of thumb: unstable / risky regime).
# high_volatility = 0 → stable regime.
# VECM disequilibrium z-score > threshold and
# GARCH-forecasted volatility is not too high (avoid noise-driven signals).
# This creates a volatility-adjusted pairs trading strategy, more robust than plain VECM
# now pair_predict_result_ DataFrame includes:
# disequilibrium, scaled_disequilibrium, z-scores, garch_alpha, garch_beta, garch_persistence (α+β rule-of-thumb)
# garch_vol_forecast (1-step volatility forecast)
# Would you like me to also add a warning flag column
# (e.g., "high_volatility" = 1 if persistence > 0.95 or vol_forecast > threshold)
# so you can easily detect unstable regimes?
# VECM/GARCH
# vecm_rolling_fit.py:
from typing import Any, Dict, Optional, cast
import numpy as np
import pandas as pd
from typing import Any, Dict, Optional
from pt_trading.results import BacktestResult
from pt_trading.rolling_window_fit import RollingFit
from pt_trading.trading_pair import TradingPair
from statsmodels.tsa.vector_ar.vecm import VECM, VECMResults
from arch import arch_model
NanoPerMin = 1e9
class VECMTradingPair(TradingPair):
vecm_fit_: Optional[VECMResults]
pair_predict_result_: Optional[pd.DataFrame]
def __init__(
self,
config: Dict[str, Any],
market_data: pd.DataFrame,
symbol_a: str,
symbol_b: str,
):
super().__init__(config, market_data, symbol_a, symbol_b)
self.vecm_fit_ = None
self.pair_predict_result_ = None
self.garch_fit_ = None
self.sigma_spread_forecast_ = None
self.garch_alpha_ = None
self.garch_beta_ = None
self.garch_persistence_ = None
self.high_volatility_flag_ = None
def _train_pair(self) -> None:
self._fit_VECM()
assert self.vecm_fit_ is not None
diseq_series = self.training_df_[self.colnames()] @ self.vecm_fit_.beta
self.training_mu_ = float(diseq_series[0].mean())
self.training_std_ = float(diseq_series[0].std())
self.training_df_["disequilibrium"] = diseq_series
self.training_df_["scaled_disequilibrium"] = (
diseq_series - self.training_mu_
) / self.training_std_
def _fit_VECM(self) -> None:
assert self.training_df_ is not None
vecm_df = self.training_df_[self.colnames()].reset_index(drop=True)
vecm_model = VECM(vecm_df, coint_rank=1)
vecm_fit = vecm_model.fit()
self.vecm_fit_ = vecm_fit
# Error Correction Term (spread)
ect_series = (vecm_df @ vecm_fit.beta).iloc[:, 0]
# Difference the spread for stationarity
dz = ect_series.diff().dropna()
if len(dz) < 30:
print("Not enough data for GARCH fitting.")
return
# Rescale if variance too small
if dz.std() < 0.1:
dz = dz * 1000
# print("Scale check:", dz.std())
try:
garch = arch_model(dz, vol="GARCH", p=1, q=1, mean="Zero", dist="normal")
garch_fit = garch.fit(disp="off")
self.garch_fit_ = garch_fit
# Extract parameters
params = garch_fit.params
self.garch_alpha_ = params.get("alpha[1]", np.nan)
self.garch_beta_ = params.get("beta[1]", np.nan)
self.garch_persistence_ = self.garch_alpha_ + self.garch_beta_
# print (f"GARCH α: {self.garch_alpha_:.4f}, β: {self.garch_beta_:.4f}, "
# f"α+β (persistence): {self.garch_persistence_:.4f}")
# One-step-ahead volatility forecast
forecast = garch_fit.forecast(horizon=1)
sigma_next = np.sqrt(forecast.variance.iloc[-1, 0])
self.sigma_spread_forecast_ = float(sigma_next)
# print("GARCH sigma forecast:", self.sigma_spread_forecast_)
# Rule of thumb: persistence close to 1 or large volatility forecast
self.high_volatility_flag_ = int(
(self.garch_persistence_ is not None and self.garch_persistence_ > 0.95)
or (self.sigma_spread_forecast_ is not None and self.sigma_spread_forecast_ > 2)
)
except Exception as e:
print(f"GARCH fit failed: {e}")
self.garch_fit_ = None
self.sigma_spread_forecast_ = None
self.high_volatility_flag_ = None
def predict(self) -> pd.DataFrame:
self._train_pair()
assert self.testing_df_ is not None
assert self.vecm_fit_ is not None
# VECM predictions
predicted_prices = self.vecm_fit_.predict(steps=len(self.testing_df_))
predicted_df = pd.merge(
self.testing_df_.reset_index(drop=True),
pd.DataFrame(predicted_prices, columns=pd.Index(self.colnames()), dtype=float),
left_index=True,
right_index=True,
suffixes=("", "_pred"),
).dropna()
# Disequilibrium and z-scores
predicted_df["disequilibrium"] = (
predicted_df[self.colnames()] @ self.vecm_fit_.beta
)
predicted_df["signed_scaled_disequilibrium"] = (
predicted_df["disequilibrium"] - self.training_mu_
) / self.training_std_
predicted_df["scaled_disequilibrium"] = abs(
predicted_df["signed_scaled_disequilibrium"]
)
# Add GARCH parameters + volatility forecast
predicted_df["garch_alpha"] = self.garch_alpha_
predicted_df["garch_beta"] = self.garch_beta_
predicted_df["garch_persistence"] = self.garch_persistence_
predicted_df["garch_vol_forecast"] = self.sigma_spread_forecast_
predicted_df["high_volatility"] = self.high_volatility_flag_
# Save results
if self.pair_predict_result_ is None:
self.pair_predict_result_ = predicted_df
else:
self.pair_predict_result_ = pd.concat(
[self.pair_predict_result_, predicted_df], ignore_index=True
)
return self.pair_predict_result_
class VECMRollingFit(RollingFit):
def __init__(self) -> None:
super().__init__()
def create_trading_pair(
self,
config: Dict,
market_data: pd.DataFrame,
symbol_a: str,
symbol_b: str,
) -> TradingPair:
return VECMTradingPair(
config=config,
market_data=market_data,
symbol_a = symbol_a,
symbol_b = symbol_b,
)

124
lib/pt_trading/z-score_rolling_fit.py Normal file
View File

@ -0,0 +1,124 @@
from typing import Any, Dict, Optional
import pandas as pd
import statsmodels.api as sm
from pt_trading.rolling_window_fit import RollingFit
from pt_trading.trading_pair import TradingPair
NanoPerMin = 1e9
class ZScoreTradingPair(TradingPair):
"""TradingPair implementation that fits a hedge ratio with OLS and
computes a standardized spread (z-score).
The class stores training spread mean/std and hedge ratio so the model
can be applied to testing data consistently.
"""
zscore_model_: Optional[sm.regression.linear_model.RegressionResultsWrapper]
pair_predict_result_: Optional[pd.DataFrame]
zscore_df_: Optional[pd.Series]
hedge_ratio_: Optional[float]
spread_mean_: Optional[float]
spread_std_: Optional[float]
def __init__(
self,
config: Dict[str, Any],
market_data: pd.DataFrame,
symbol_a: str,
symbol_b: str,
):
super().__init__(config, market_data, symbol_a, symbol_b)
self.zscore_model_ = None
self.pair_predict_result_ = None
self.zscore_df_ = None
self.hedge_ratio_ = None
self.spread_mean_ = None
self.spread_std_ = None
def _fit_zscore(self) -> None:
"""Fit OLS on the training window and compute training z-score."""
assert self.training_df_ is not None
# Extract price series for the two symbols from the training frame.
px_df = self.training_df_[self.colnames()]
symbol_a_px = px_df.iloc[:, 0]
symbol_b_px = px_df.iloc[:, 1]
# Align indexes and fit OLS: symbol_a ~ const + symbol_b
symbol_a_px, symbol_b_px = symbol_a_px.align(symbol_b_px, join="inner")
X = sm.add_constant(symbol_b_px)
self.zscore_model_ = sm.OLS(symbol_a_px, X).fit()
# Hedge ratio is the slope on symbol_b
params = self.zscore_model_.params
self.hedge_ratio_ = float(params.iloc[1]) if len(params) > 1 else 0.0
# Training spread and its standardized z-score
spread = symbol_a_px - self.hedge_ratio_ * symbol_b_px
self.spread_mean_ = float(spread.mean())
self.spread_std_ = float(spread.std(ddof=0)) if spread.std(ddof=0) != 0 else 1.0
self.zscore_df_ = (spread - self.spread_mean_) / self.spread_std_
def predict(self) -> pd.DataFrame:
"""Apply fitted hedge ratio to the testing frame and return a
dataframe with canonical columns:
- disequilibrium: signed z-score
- scaled_disequilibrium: absolute z-score
- signed_scaled_disequilibrium: same as disequilibrium (keeps sign)
"""
# Fit on training window
self._fit_zscore()
assert self.zscore_df_ is not None
assert self.hedge_ratio_ is not None
assert self.spread_mean_ is not None and self.spread_std_ is not None
# Keep training columns for inspection
self.training_df_["disequilibrium"] = self.zscore_df_
self.training_df_["scaled_disequilibrium"] = self.zscore_df_.abs()
# Apply model to testing frame
assert self.testing_df_ is not None
test_df = self.testing_df_.copy()
px_test = test_df[self.colnames()]
a_test = px_test.iloc[:, 0]
b_test = px_test.iloc[:, 1]
a_test, b_test = a_test.align(b_test, join="inner")
# Compute test spread and standardize using training mean/std
test_spread = a_test - self.hedge_ratio_ * b_test
test_zscore = (test_spread - self.spread_mean_) / self.spread_std_
# Attach canonical columns
# Align back to test_df index if needed
test_zscore = test_zscore.reindex(test_df.index)
test_df["disequilibrium"] = test_zscore
test_df["signed_scaled_disequilibrium"] = test_zscore
test_df["scaled_disequilibrium"] = test_zscore.abs()
# Reset index and accumulate results across windows
test_df = test_df.reset_index(drop=True)
if self.pair_predict_result_ is None:
self.pair_predict_result_ = test_df
else:
self.pair_predict_result_ = pd.concat(
[self.pair_predict_result_, test_df], ignore_index=True
)
self.pair_predict_result_ = self.pair_predict_result_.reset_index(drop=True)
return self.pair_predict_result_.dropna()
class ZScoreRollingFit(RollingFit):
def __init__(self) -> None:
super().__init__()
def create_trading_pair(
self, config: Dict, market_data: pd.DataFrame, symbol_a: str, symbol_b: str
) -> TradingPair:
return ZScoreTradingPair(
config=config, market_data=market_data, symbol_a=symbol_a, symbol_b=symbol_b
)

33
lib/tools/filetools.py
View File

@ -1,33 +0,0 @@
import os
import glob
from typing import Dict, List, Tuple
DayT = str
DataFileNameT = str
def resolve_datafiles(
config: Dict, date_pattern: str, instruments: List[Dict[str, str]]
) -> List[Tuple[DayT, DataFileNameT]]:
resolved_files: List[Tuple[DayT, DataFileNameT]] = []
for inst in instruments:
pattern = date_pattern
inst_type = inst["instrument_type"]
data_dir = config["market_data_loading"][inst_type]["data_directory"]
if "*" in pattern or "?" in pattern:
# Handle wildcards
if not os.path.isabs(pattern):
pattern = os.path.join(data_dir, f"{pattern}.mktdata.ohlcv.db")
matched_files = glob.glob(pattern)
for matched_file in matched_files:
import re
match = re.search(r"(\d{8})\.mktdata\.ohlcv\.db$", matched_file)
assert match is not None
day = match.group(1)
resolved_files.append((day, matched_file))
else:
# Handle explicit file path
if not os.path.isabs(pattern):
pattern = os.path.join(data_dir, f"{pattern}.mktdata.ohlcv.db")
resolved_files.append((date_pattern, pattern))
return sorted(list(set(resolved_files))) # Remove duplicates and sort

21
lib/tools/instruments.py
View File

@ -1,21 +0,0 @@
import argparse
from typing import Dict, List
def get_instruments(args: argparse.Namespace, config: Dict) -> List[Dict[str, str]]:
instruments = [
{
"symbol": inst.split(":")[0],
"instrument_type": inst.split(":")[1],
"exchange_id": inst.split(":")[2],
"instrument_id_pfx": config["market_data_loading"][inst.split(":")[1]][
"instrument_id_pfx"
],
"db_table_name": config["market_data_loading"][inst.split(":")[1]][
"db_table_name"
],
}
for inst in args.instruments.split(",")
]
return instruments

79
lib/tools/viz/viz_prices.py
View File

@ -1,79 +0,0 @@
from pt_strategy.research_strategy import PtResearchStrategy
def visualize_prices(strategy: PtResearchStrategy, trading_date: str) -> None:
# Plot raw price data
import matplotlib.pyplot as plt
# Set plotting style
import seaborn as sns
pair = strategy.trading_pair_
SYMBOL_A = pair.symbol_a_
SYMBOL_B = pair.symbol_b_
TRD_DATE = f"{trading_date[0:4]}-{trading_date[4:6]}-{trading_date[6:8]}"
plt.style.use('seaborn-v0_8')
sns.set_palette("husl")
plt.rcParams['figure.figsize'] = (15, 10)
# Get column names for the trading pair
colname_a, colname_b = pair.colnames()
price_data = strategy.pt_mkt_data_.market_data_df_.copy()
# Create separate subplots for better visibility
fig_price, price_axes = plt.subplots(2, 1, figsize=(18, 10))
# Plot SYMBOL_A
price_axes[0].plot(price_data['tstamp'], price_data[colname_a], alpha=0.7,
label=f'{SYMBOL_A}', linewidth=1, color='blue')
price_axes[0].set_title(f'{SYMBOL_A} Price Data ({TRD_DATE})')
price_axes[0].set_ylabel(f'{SYMBOL_A} Price')
price_axes[0].legend()
price_axes[0].grid(True)
# Plot SYMBOL_B
price_axes[1].plot(price_data['tstamp'], price_data[colname_b], alpha=0.7,
label=f'{SYMBOL_B}', linewidth=1, color='red')
price_axes[1].set_title(f'{SYMBOL_B} Price Data ({TRD_DATE})')
price_axes[1].set_ylabel(f'{SYMBOL_B} Price')
price_axes[1].set_xlabel('Time')
price_axes[1].legend()
price_axes[1].grid(True)
plt.tight_layout()
plt.show()
# Plot individual prices
fig, axes = plt.subplots(2, 1, figsize=(18, 12))
# Normalized prices for comparison
norm_a = price_data[colname_a] / price_data[colname_a].iloc[0]
norm_b = price_data[colname_b] / price_data[colname_b].iloc[0]
axes[0].plot(price_data['tstamp'], norm_a, label=f'{SYMBOL_A} (normalized)', alpha=0.8, linewidth=1)
axes[0].plot(price_data['tstamp'], norm_b, label=f'{SYMBOL_B} (normalized)', alpha=0.8, linewidth=1)
axes[0].set_title(f'Normalized Price Comparison (Base = 1.0) ({TRD_DATE})')
axes[0].set_ylabel('Normalized Price')
axes[0].legend()
axes[0].grid(True)
# Price ratio
price_ratio = price_data[colname_a] / price_data[colname_b]
axes[1].plot(price_data['tstamp'], price_ratio, label=f'{SYMBOL_A}/{SYMBOL_B} Ratio', color='green', alpha=0.8, linewidth=1)
axes[1].set_title(f'Price Ratio Px({SYMBOL_A})/Px({SYMBOL_B}) ({TRD_DATE})')
axes[1].set_ylabel('Ratio')
axes[1].set_xlabel('Time')
axes[1].legend()
axes[1].grid(True)
plt.tight_layout()
plt.show()
# Print basic statistics
print(f"\nPrice Statistics:")
print(f" {SYMBOL_A}: Mean=${price_data[colname_a].mean():.2f}, Std=${price_data[colname_a].std():.2f}")
print(f" {SYMBOL_B}: Mean=${price_data[colname_b].mean():.2f}, Std=${price_data[colname_b].std():.2f}")
print(f" Price Ratio: Mean={price_ratio.mean():.2f}, Std={price_ratio.std():.2f}")
print(f" Correlation: {price_data[colname_a].corr(price_data[colname_b]):.4f}")

507
lib/tools/viz/viz_trades.py
View File

@ -1,507 +0,0 @@
from __future__ import annotations
import os
from typing import Any, Dict
from pt_strategy.results import (PairResearchResult, create_result_database,
store_config_in_database)
from pt_strategy.research_strategy import PtResearchStrategy
from tools.filetools import resolve_datafiles
from tools.instruments import get_instruments
def visualize_trades(strategy: PtResearchStrategy, results: PairResearchResult, trading_date: str) -> None:
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import plotly.offline as pyo
from IPython.display import HTML
from plotly.subplots import make_subplots
pair = strategy.trading_pair_
trades = results.trades_[trading_date].copy()
origin_mkt_data_df = strategy.pt_mkt_data_.origin_mkt_data_df_
mkt_data_df = strategy.pt_mkt_data_.market_data_df_
TRD_DATE = f"{trading_date[0:4]}-{trading_date[4:6]}-{trading_date[6:8]}"
SYMBOL_A = pair.symbol_a_
SYMBOL_B = pair.symbol_b_
print(f"\nCreated trading pair: {pair}")
print(f"Market data shape: {pair.market_data_.shape}")
print(f"Column names: {pair.colnames()}")
# Configure plotly for offline mode
pyo.init_notebook_mode(connected=True)
# Strategy-specific interactive visualization
assert strategy.config_ is not None
print("=== SLIDING FIT INTERACTIVE VISUALIZATION ===")
print("Note: Rolling Fit strategy visualization with interactive plotly charts")
# Create consistent timeline - superset of timestamps from both dataframes
all_timestamps = sorted(set(mkt_data_df['tstamp']))
# Create a unified timeline dataframe for consistent plotting
timeline_df = pd.DataFrame({'tstamp': all_timestamps})
# Merge with predicted data to get dis-equilibrium values
timeline_df = timeline_df.merge(strategy.predictions_[['tstamp', 'disequilibrium', 'scaled_disequilibrium', 'signed_scaled_disequilibrium']],
on='tstamp', how='left')
# Get Symbol_A and Symbol_B market data
colname_a, colname_b = pair.colnames()
symbol_a_data = mkt_data_df[['tstamp', colname_a]].copy()
symbol_b_data = mkt_data_df[['tstamp', colname_b]].copy()
norm_a = symbol_a_data[colname_a] / symbol_a_data[colname_a].iloc[0]
norm_b = symbol_b_data[colname_b] / symbol_b_data[colname_b].iloc[0]
print(f"Using consistent timeline with {len(timeline_df)} timestamps")
print(f"Timeline range: {timeline_df['tstamp'].min()} to {timeline_df['tstamp'].max()}")
# Create subplots with price charts at bottom
fig = make_subplots(
rows=4, cols=1,
row_heights=[0.3, 0.4, 0.15, 0.15],
subplot_titles=[
f'Dis-equilibrium with Trading Thresholds ({TRD_DATE})',
f'Normalized Price Comparison with BUY/SELL Signals - {SYMBOL_A}&{SYMBOL_B} ({TRD_DATE})',
f'{SYMBOL_A} Market Data with Trading Signals ({TRD_DATE})',
f'{SYMBOL_B} Market Data with Trading Signals ({TRD_DATE})',
],
vertical_spacing=0.06,
specs=[[{"secondary_y": False}],
[{"secondary_y": False}],
[{"secondary_y": False}],
[{"secondary_y": False}]]
)
# 1. Scaled dis-equilibrium with thresholds - using consistent timeline
fig.add_trace(
go.Scatter(
x=timeline_df['tstamp'],
y=timeline_df['scaled_disequilibrium'],
name='Absolute Scaled Dis-equilibrium',
line=dict(color='green', width=2),
opacity=0.8
),
row=1, col=1
)
fig.add_trace(
go.Scatter(
x=timeline_df['tstamp'],
y=timeline_df['signed_scaled_disequilibrium'],
name='Scaled Dis-equilibrium',
line=dict(color='darkmagenta', width=2),
opacity=0.8
),
row=1, col=1
)
# Add threshold lines to first subplot
fig.add_shape(
type="line",
x0=timeline_df['tstamp'].min(),
x1=timeline_df['tstamp'].max(),
y0=strategy.config_['dis-equilibrium_open_trshld'],
y1=strategy.config_['dis-equilibrium_open_trshld'],
line=dict(color="purple", width=2, dash="dot"),
opacity=0.7,
row=1, col=1
)
fig.add_shape(
type="line",
x0=timeline_df['tstamp'].min(),
x1=timeline_df['tstamp'].max(),
y0=-strategy.config_['dis-equilibrium_open_trshld'],
y1=-strategy.config_['dis-equilibrium_open_trshld'],
line=dict(color="purple", width=2, dash="dot"),
opacity=0.7,
row=1, col=1
)
fig.add_shape(
type="line",
x0=timeline_df['tstamp'].min(),
x1=timeline_df['tstamp'].max(),
y0=strategy.config_['dis-equilibrium_close_trshld'],
y1=strategy.config_['dis-equilibrium_close_trshld'],
line=dict(color="brown", width=2, dash="dot"),
opacity=0.7,
row=1, col=1
)
fig.add_shape(
type="line",
x0=timeline_df['tstamp'].min(),
x1=timeline_df['tstamp'].max(),
y0=-strategy.config_['dis-equilibrium_close_trshld'],
y1=-strategy.config_['dis-equilibrium_close_trshld'],
line=dict(color="brown", width=2, dash="dot"),
opacity=0.7,
row=1, col=1
)
fig.add_shape(
type="line",
x0=timeline_df['tstamp'].min(),
x1=timeline_df['tstamp'].max(),
y0=0,
y1=0,
line=dict(color="black", width=1, dash="solid"),
opacity=0.5,
row=1, col=1
)
# Add normalized price lines
fig.add_trace(
go.Scatter(
x=mkt_data_df['tstamp'],
y=norm_a,
name=f'{SYMBOL_A} (Normalized)',
line=dict(color='blue', width=2),
opacity=0.8
),
row=2, col=1
)
fig.add_trace(
go.Scatter(
x=mkt_data_df['tstamp'],
y=norm_b,
name=f'{SYMBOL_B} (Normalized)',
line=dict(color='orange', width=2),
opacity=0.8,
),
row=2, col=1
)
# Add BUY and SELL signals if available
if trades is not None and len(trades) > 0:
# Define signal groups to avoid legend repetition
signal_groups = {}
# Process all trades and group by signal type (ignore OPEN/CLOSE status)
for _, trade in trades.iterrows():
symbol = trade['symbol']
side = trade['side']
# status = trade['status']
action = trade['action']
# Create signal group key (without status to combine OPEN/CLOSE)
signal_key = f"{symbol} {side} {action}"
# Find normalized price for this trade
trade_time = trade['time']
if symbol == SYMBOL_A:
closest_idx = mkt_data_df['tstamp'].searchsorted(trade_time)
if closest_idx < len(norm_a):
norm_price = norm_a.iloc[closest_idx]
else:
norm_price = norm_a.iloc[-1]
else: # SYMBOL_B
closest_idx = mkt_data_df['tstamp'].searchsorted(trade_time)
if closest_idx < len(norm_b):
norm_price = norm_b.iloc[closest_idx]
else:
norm_price = norm_b.iloc[-1]
# Initialize group if not exists
if signal_key not in signal_groups:
signal_groups[signal_key] = {
'times': [],
'prices': [],
'actual_prices': [],
'symbol': symbol,
'side': side,
# 'status': status,
'action': trade['action']
}
# Add to group
signal_groups[signal_key]['times'].append(trade_time)
signal_groups[signal_key]['prices'].append(norm_price)
signal_groups[signal_key]['actual_prices'].append(trade['price'])
# Add each signal group as a single trace
for signal_key, group_data in signal_groups.items():
symbol = group_data['symbol']
side = group_data['side']
# status = group_data['status']
# Determine marker properties (same for all OPEN/CLOSE of same side)
is_close: bool = (group_data['action'] == "CLOSE")
if 'BUY' in side:
marker_color = 'green'
marker_symbol = 'triangle-up'
marker_size = 14
else: # SELL
marker_color = 'red'
marker_symbol = 'triangle-down'
marker_size = 14
# Create hover text for each point in the group
hover_texts = []
for i, (time, norm_price, actual_price) in enumerate(zip(group_data['times'],
group_data['prices'],
group_data['actual_prices'])):
# Find the corresponding trade to get the status for hover text
trade_info = trades[(trades['time'] == time) &
(trades['symbol'] == symbol) &
(trades['side'] == side)]
if len(trade_info) > 0:
action = trade_info.iloc[0]['action']
hover_texts.append(f'<b>{signal_key} {action}</b><br>' +
f'Time: {time}<br>' +
f'Normalized Price: {norm_price:.4f}<br>' +
f'Actual Price: ${actual_price:.2f}')
else:
hover_texts.append(f'<b>{signal_key}</b><br>' +
f'Time: {time}<br>' +
f'Normalized Price: {norm_price:.4f}<br>' +
f'Actual Price: ${actual_price:.2f}')
fig.add_trace(
go.Scatter(
x=group_data['times'],
y=group_data['prices'],
mode='markers',
name=signal_key,
marker=dict(
color=marker_color,
size=marker_size,
symbol=marker_symbol,
line=dict(width=2, color='black') if is_close else None
),
showlegend=True,
hovertemplate='%{text}<extra></extra>',
text=hover_texts
),
row=2, col=1
)
# -----------------------------
fig.add_trace(
go.Scatter(
x=symbol_a_data['tstamp'],
y=symbol_a_data[colname_a],
name=f'{SYMBOL_A} Price',
line=dict(color='blue', width=2),
opacity=0.8
),
row=3, col=1
)
# Filter trades for Symbol_A
symbol_a_trades = trades[trades['symbol'] == SYMBOL_A]
print(f"\nSymbol_A trades:\n{symbol_a_trades}")
if len(symbol_a_trades) > 0:
# Separate trades by action and status for different colors
buy_open_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('BUY', na=False)) &
(symbol_a_trades['action'].str.contains('OPEN', na=False))]
buy_close_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('BUY', na=False)) &
(symbol_a_trades['action'].str.contains('CLOSE', na=False))]
sell_open_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('SELL', na=False)) &
(symbol_a_trades['action'].str.contains('OPEN', na=False))]
sell_close_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('SELL', na=False)) &
(symbol_a_trades['action'].str.contains('CLOSE', na=False))]
# Add BUY OPEN signals
if len(buy_open_trades) > 0:
fig.add_trace(
go.Scatter(
x=buy_open_trades['time'],
y=buy_open_trades['price'],
mode='markers',
name=f'{SYMBOL_A} BUY OPEN',
marker=dict(color='green', size=12, symbol='triangle-up'),
showlegend=True
),
row=3, col=1
)
# Add BUY CLOSE signals
if len(buy_close_trades) > 0:
fig.add_trace(
go.Scatter(
x=buy_close_trades['time'],
y=buy_close_trades['price'],
mode='markers',
name=f'{SYMBOL_A} BUY CLOSE',
marker=dict(color='green', size=12, symbol='triangle-up'),
line=dict(width=2, color='black'),
showlegend=True
),
row=3, col=1
)
# Add SELL OPEN signals
if len(sell_open_trades) > 0:
fig.add_trace(
go.Scatter(
x=sell_open_trades['time'],
y=sell_open_trades['price'],
mode='markers',
name=f'{SYMBOL_A} SELL OPEN',
marker=dict(color='red', size=12, symbol='triangle-down'),
showlegend=True
),
row=3, col=1
)
# Add SELL CLOSE signals
if len(sell_close_trades) > 0:
fig.add_trace(
go.Scatter(
x=sell_close_trades['time'],
y=sell_close_trades['price'],
mode='markers',
name=f'{SYMBOL_A} SELL CLOSE',
marker=dict(color='red', size=12, symbol='triangle-down'),
line=dict(width=2, color='black'),
showlegend=True
),
row=3, col=1
)
# 4. Symbol_B Market Data with Trading Signals
fig.add_trace(
go.Scatter(
x=symbol_b_data['tstamp'],
y=symbol_b_data[colname_b],
name=f'{SYMBOL_B} Price',
line=dict(color='orange', width=2),
opacity=0.8
),
row=4, col=1
)
# Add trading signals for Symbol_B if available
symbol_b_trades = trades[trades['symbol'] == SYMBOL_B]
print(f"\nSymbol_B trades:\n{symbol_b_trades}")
if len(symbol_b_trades) > 0:
# Separate trades by action and status for different colors
buy_open_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('BUY', na=False)) &
(symbol_b_trades['action'].str.startswith('OPEN', na=False))]
buy_close_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('BUY', na=False)) &
(symbol_b_trades['action'].str.startswith('CLOSE', na=False))]
sell_open_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('SELL', na=False)) &
(symbol_b_trades['action'].str.contains('OPEN', na=False))]
sell_close_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('SELL', na=False)) &
(symbol_b_trades['action'].str.contains('CLOSE', na=False))]
# Add BUY OPEN signals
if len(buy_open_trades) > 0:
fig.add_trace(
go.Scatter(
x=buy_open_trades['time'],
y=buy_open_trades['price'],
mode='markers',
name=f'{SYMBOL_B} BUY OPEN',
marker=dict(color='darkgreen', size=12, symbol='triangle-up'),
showlegend=True
),
row=4, col=1
)
# Add BUY CLOSE signals
if len(buy_close_trades) > 0:
fig.add_trace(
go.Scatter(
x=buy_close_trades['time'],
y=buy_close_trades['price'],
mode='markers',
name=f'{SYMBOL_B} BUY CLOSE',
marker=dict(color='green', size=12, symbol='triangle-up'),
line=dict(width=2, color='black'),
showlegend=True
),
row=4, col=1
)
# Add SELL OPEN signals
if len(sell_open_trades) > 0:
fig.add_trace(
go.Scatter(
x=sell_open_trades['time'],
y=sell_open_trades['price'],
mode='markers',
name=f'{SYMBOL_B} SELL OPEN',
marker=dict(color='red', size=12, symbol='triangle-down'),
showlegend=True
),
row=4, col=1
)
# Add SELL CLOSE signals
if len(sell_close_trades) > 0:
fig.add_trace(
go.Scatter(
x=sell_close_trades['time'],
y=sell_close_trades['price'],
mode='markers',
name=f'{SYMBOL_B} SELL CLOSE',
marker=dict(color='red', size=12, symbol='triangle-down'),
line=dict(width=2, color='black'),
showlegend=True
),
row=4, col=1
)
# Update layout
fig.update_layout(
height=1600,
title_text=f"Strategy Analysis - {SYMBOL_A} & {SYMBOL_B} ({TRD_DATE})",
showlegend=True,
template="plotly_white",
plot_bgcolor='lightgray',
)
# Update y-axis labels
fig.update_yaxes(title_text="Scaled Dis-equilibrium", row=1, col=1)
fig.update_yaxes(title_text=f"{SYMBOL_A} Price ($)", row=2, col=1)
fig.update_yaxes(title_text=f"{SYMBOL_B} Price ($)", row=3, col=1)
fig.update_yaxes(title_text="Normalized Price (Base = 1.0)", row=4, col=1)
# Update x-axis labels and ensure consistent time range
time_range = [timeline_df['tstamp'].min(), timeline_df['tstamp'].max()]
fig.update_xaxes(range=time_range, row=1, col=1)
fig.update_xaxes(range=time_range, row=2, col=1)
fig.update_xaxes(range=time_range, row=3, col=1)
fig.update_xaxes(title_text="Time", range=time_range, row=4, col=1)
# Display using plotly offline mode
# pyo.iplot(fig)
fig.show()
else:
print("No interactive visualization data available - strategy may not have run successfully")
print(f"\nChart shows:")
print(f"- {SYMBOL_A} and {SYMBOL_B} prices normalized to start at 1.0")
print(f"- BUY signals shown as green triangles pointing up")
print(f"- SELL signals shown as orange triangles pointing down")
print(f"- All BUY signals per symbol grouped together, all SELL signals per symbol grouped together")
print(f"- Hover over markers to see individual trade details (OPEN/CLOSE status)")
if trades is not None and len(trades) > 0:
print(f"- Total signals displayed: {len(trades)}")
print(f"- {SYMBOL_A} signals: {len(trades[trades['symbol'] == SYMBOL_A])}")
print(f"- {SYMBOL_B} signals: {len(trades[trades['symbol'] == SYMBOL_B])}")
else:
print("- No trading signals to display")

169
lib/utils/db_inspector.py Normal file
View File

@ -0,0 +1,169 @@
#!/usr/bin/env python3
"""
Database inspector utility for pairs trading results database.
Provides functionality to view all tables and their contents.
"""
import sqlite3
import sys
import json
import os
from typing import List, Dict, Any
def list_tables(db_path: str) -> List[str]:
"""List all tables in the database."""
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
cursor.execute("""
SELECT name FROM sqlite_master
WHERE type='table'
ORDER BY name
""")
tables = [row[0] for row in cursor.fetchall()]
conn.close()
return tables
def view_table_schema(db_path: str, table_name: str) -> None:
"""View the schema of a specific table."""
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
cursor.execute(f"PRAGMA table_info({table_name})")
columns = cursor.fetchall()
print(f"\nTable: {table_name}")
print("-" * 50)
print("Column Name".ljust(20) + "Type".ljust(15) + "Not Null".ljust(10) + "Default")
print("-" * 50)
for col in columns:
cid, name, type_, not_null, default_value, pk = col
print(f"{name}".ljust(20) + f"{type_}".ljust(15) + f"{bool(not_null)}".ljust(10) + f"{default_value or ''}")
conn.close()
def view_config_table(db_path: str, limit: int = 10) -> None:
"""View entries from the config table."""
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
cursor.execute(f"""
SELECT id, run_timestamp, config_file_path, fit_method_class,
datafiles, instruments, config_json
FROM config
ORDER BY run_timestamp DESC
LIMIT {limit}
""")
rows = cursor.fetchall()
if not rows:
print("No configuration entries found.")
return
print(f"\nMost recent {len(rows)} configuration entries:")
print("=" * 80)
for row in rows:
id, run_timestamp, config_file_path, fit_method_class, datafiles, instruments, config_json = row
print(f"ID: {id} | {run_timestamp}")
print(f"Config: {config_file_path} | Strategy: {fit_method_class}")
print(f"Files: {datafiles}")
print(f"Instruments: {instruments}")
print("-" * 80)
conn.close()
def view_results_summary(db_path: str) -> None:
"""View summary of trading results."""
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
# Get results summary
cursor.execute("""
SELECT date, COUNT(*) as trade_count,
ROUND(SUM(symbol_return), 2) as total_return
FROM pt_bt_results
GROUP BY date
ORDER BY date DESC
""")
results = cursor.fetchall()
if not results:
print("No trading results found.")
return
print(f"\nTrading Results Summary:")
print("-" * 50)
print("Date".ljust(15) + "Trades".ljust(10) + "Total Return %")
print("-" * 50)
for date, trade_count, total_return in results:
print(f"{date}".ljust(15) + f"{trade_count}".ljust(10) + f"{total_return}")
# Get outstanding positions summary
cursor.execute("""
SELECT COUNT(*) as position_count,
ROUND(SUM(unrealized_return), 2) as total_unrealized
FROM outstanding_positions
""")
outstanding = cursor.fetchone()
if outstanding and outstanding[0] > 0:
print(f"\nOutstanding Positions: {outstanding[0]} positions")
print(f"Total Unrealized Return: {outstanding[1]}%")
conn.close()
def main() -> None:
if len(sys.argv) < 2:
print("Usage: python db_inspector.py <database_path> [command]")
print("Commands:")
print(" tables - List all tables")
print(" schema - Show schema for all tables")
print(" config - View configuration entries")
print(" results - View trading results summary")
print(" all - Show everything (default)")
print("\nExample: python db_inspector.py results/equity.db config")
sys.exit(1)
db_path = sys.argv[1]
command = sys.argv[2] if len(sys.argv) > 2 else "all"
if not os.path.exists(db_path):
print(f"Database file not found: {db_path}")
sys.exit(1)
try:
if command in ["tables", "all"]:
tables = list_tables(db_path)
print(f"Tables in database: {', '.join(tables)}")
if command in ["schema", "all"]:
tables = list_tables(db_path)
for table in tables:
view_table_schema(db_path, table)
if command in ["config", "all"]:
if "config" in list_tables(db_path):
view_config_table(db_path)
else:
print("Config table not found.")
if command in ["results", "all"]:
if "pt_bt_results" in list_tables(db_path):
view_results_summary(db_path)
else:
print("Results table not found.")
except Exception as e:
print(f"Error inspecting database: {str(e)}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
main()

3
pyrightconfig.json
View File

@ -16,8 +16,7 @@
"autoImportCompletions": true,
"autoSearchPaths": true,
"extraPaths": [
"lib",
".."
"lib"
],
"stubPath": "./typings",
"venvPath": ".",

213
requirements.txt
View File

@ -61,7 +61,7 @@ protobuf>=3.12.4
psutil>=5.9.0
ptyprocess>=0.7.0
pycurl>=7.44.1
pyelftools>=0.27
# pyelftools>=0.27
Pygments>=2.11.2
pyparsing>=2.4.7
pyrsistent>=0.18.1
@ -69,7 +69,7 @@ python-debian>=0.1.43 #+ubuntu1.1
python-dotenv>=0.19.2
python-magic>=0.4.24
python-xlib>=0.29
pyxdg>=0.27
# pyxdg>=0.27
PyYAML>=6.0
reportlab>=3.6.8
requests>=2.25.1
@ -78,118 +78,117 @@ scipy<1.13.0
seaborn>=0.13.2
SecretStorage>=3.3.1
setproctitle>=1.2.2
simpleeval>=1.0.3
six>=1.16.0
soupsieve>=2.3.1
ssh-import-id>=5.11
statsmodels>=0.14.4
texttable>=1.6.4
# texttable>=1.6.4
tldextract>=3.1.2
tomli>=1.2.2
######## typed-ast>=1.4.3
types-aiofiles>=0.1
types-annoy>=1.17
types-appdirs>=1.4
types-atomicwrites>=1.4
types-aws-xray-sdk>=2.8
types-babel>=2.9
types-backports-abc>=0.5
types-backports.ssl-match-hostname>=3.7
types-beautifulsoup4>=4.10
types-bleach>=4.1
types-boto>=2.49
types-braintree>=4.11
types-cachetools>=4.2
types-caldav>=0.8
types-certifi>=2020.4
types-characteristic>=14.3
types-chardet>=4.0
types-click>=7.1
types-click-spinner>=0.1
types-colorama>=0.4
types-commonmark>=0.9
types-contextvars>=0.1
types-croniter>=1.0
types-cryptography>=3.3
types-dataclasses>=0.1
types-dateparser>=1.0
types-DateTimeRange>=0.1
types-decorator>=0.1
types-Deprecated>=1.2
types-docopt>=0.6
types-docutils>=0.17
types-editdistance>=0.5
types-emoji>=1.2
types-entrypoints>=0.3
types-enum34>=1.1
types-filelock>=3.2
types-first>=2.0
types-Flask>=1.1
types-freezegun>=1.1
types-frozendict>=0.1
types-futures>=3.3
types-html5lib>=1.1
types-httplib2>=0.19
types-humanfriendly>=9.2
types-ipaddress>=1.0
types-itsdangerous>=1.1
types-JACK-Client>=0.1
types-Jinja2>=2.11
types-jmespath>=0.10
types-jsonschema>=3.2
types-Markdown>=3.3
types-MarkupSafe>=1.1
types-mock>=4.0
types-mypy-extensions>=0.4
types-mysqlclient>=2.0
types-oauthlib>=3.1
types-orjson>=3.6
types-paramiko>=2.7
types-Pillow>=8.3
types-polib>=1.1
types-prettytable>=2.1
types-protobuf>=3.17
types-psutil>=5.8
types-psycopg2>=2.9
types-pyaudio>=0.2
types-pycurl>=0.1
types-pyfarmhash>=0.2
types-Pygments>=2.9
types-PyMySQL>=1.0
types-pyOpenSSL>=20.0
types-pyRFC3339>=0.1
types-pysftp>=0.2
types-pytest-lazy-fixture>=0.6
types-python-dateutil>=2.8
types-python-gflags>=3.1
types-python-nmap>=0.6
types-python-slugify>=5.0
types-pytz>=2021.1
types-pyvmomi>=7.0
types-PyYAML>=5.4
types-redis>=3.5
types-requests>=2.25
types-retry>=0.9
types-selenium>=3.141
types-Send2Trash>=1.8
types-setuptools>=57.4
types-simplejson>=3.17
types-singledispatch>=3.7
types-six>=1.16
types-slumber>=0.7
types-stripe>=2.59
types-tabulate>=0.8
types-termcolor>=1.1
types-toml>=0.10
types-toposort>=1.6
types-ttkthemes>=3.2
types-typed-ast>=1.4
types-tzlocal>=0.1
types-ujson>=0.1
types-vobject>=0.9
types-waitress>=0.1
types-Werkzeug>=1.0
types-xxhash>=2.0
# types-aiofiles>=0.1
# types-annoy>=1.17
# types-appdirs>=1.4
# types-atomicwrites>=1.4
# types-aws-xray-sdk>=2.8
# types-babel>=2.9
# types-backports-abc>=0.5
# types-backports.ssl-match-hostname>=3.7
# types-beautifulsoup4>=4.10
# types-bleach>=4.1
# types-boto>=2.49
# types-braintree>=4.11
# types-cachetools>=4.2
# types-caldav>=0.8
# types-certifi>=2020.4
# types-characteristic>=14.3
# types-chardet>=4.0
# types-click>=7.1
# types-click-spinner>=0.1
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# types-commonmark>=0.9
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# types-croniter>=1.0
# types-cryptography>=3.3
# types-dataclasses>=0.1
# types-dateparser>=1.0
# types-DateTimeRange>=0.1
# types-decorator>=0.1
# types-Deprecated>=1.2
# types-docopt>=0.6
# types-docutils>=0.17
# types-editdistance>=0.5
# types-emoji>=1.2
# types-entrypoints>=0.3
# types-enum34>=1.1
# types-filelock>=3.2
# types-first>=2.0
# types-Flask>=1.1
# types-freezegun>=1.1
# types-frozendict>=0.1
# types-futures>=3.3
# types-html5lib>=1.1
# types-httplib2>=0.19
# types-humanfriendly>=9.2
# types-ipaddress>=1.0
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# types-JACK-Client>=0.1
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# types-jmespath>=0.10
# types-jsonschema>=3.2
# types-Markdown>=3.3
# types-MarkupSafe>=1.1
# types-mock>=4.0
# types-mypy-extensions>=0.4
# types-mysqlclient>=2.0
# types-oauthlib>=3.1
# types-orjson>=3.6
# types-paramiko>=2.7
# types-Pillow>=8.3
# types-polib>=1.1
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# types-protobuf>=3.17
# types-psutil>=5.8
# types-psycopg2>=2.9
# types-pyaudio>=0.2
# types-pycurl>=0.1
# types-pyfarmhash>=0.2
# types-Pygments>=2.9
# types-PyMySQL>=1.0
# types-pyOpenSSL>=20.0
# types-pyRFC3339>=0.1
# types-pysftp>=0.2
# types-pytest-lazy-fixture>=0.6
# types-python-dateutil>=2.8
# types-python-gflags>=3.1
# types-python-nmap>=0.6
# types-python-slugify>=5.0
# types-pytz>=2021.1
# types-pyvmomi>=7.0
# types-PyYAML>=5.4
# types-redis>=3.5
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# types-simplejson>=3.17
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# types-toml>=0.10
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# types-ujson>=0.1
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# types-waitress>=0.1
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#types-xxhash>=2.0
typing-extensions>=3.10.0.2
Unidecode>=1.3.3
urllib3>=1.26.5

106
research/backtest.py
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@ -1,106 +0,0 @@
from __future__ import annotations
import os
from typing import Any, Dict
from pt_strategy.results import (
PairResearchResult,
create_result_database,
store_config_in_database,
)
from pt_strategy.research_strategy import PtResearchStrategy
from tools.filetools import resolve_datafiles
from tools.instruments import get_instruments
def main() -> None:
import argparse
from tools.config import expand_filename, load_config
parser = argparse.ArgumentParser(description="Run pairs trading backtest.")
parser.add_argument(
"--config", type=str, required=True, help="Path to the configuration file."
)
parser.add_argument(
"--date_pattern",
type=str,
required=True,
help="Date YYYYMMDD, allows * and ? wildcards",
)
parser.add_argument(
"--instruments",
type=str,
required=True,
help="Comma-separated list of instrument symbols (e.g., COIN:EQUITY,GBTC:CRYPTO)",
)
parser.add_argument(
"--result_db",
type=str,
required=True,
help="Path to SQLite database for storing results. Use 'NONE' to disable database output.",
)
args = parser.parse_args()
config: Dict = load_config(args.config)
# Resolve data files (CLI takes priority over config)
instruments = get_instruments(args, config)
datafiles = resolve_datafiles(config, args.date_pattern, instruments)
days = list(set([day for day, _ in datafiles]))
print(f"Found {len(datafiles)} data files to process:")
for df in datafiles:
print(f" - {df}")
# Create result database if needed
if args.result_db.upper() != "NONE":
args.result_db = expand_filename(args.result_db)
create_result_database(args.result_db)
# Initialize a dictionary to store all trade results
all_results: Dict[str, Dict[str, Any]] = {}
is_config_stored = False
# Process each data file
results = PairResearchResult(config=config)
for day in sorted(days):
md_datafiles = [datafile for md_day, datafile in datafiles if md_day == day]
if not all([os.path.exists(datafile) for datafile in md_datafiles]):
print(f"WARNING: insufficient data files: {md_datafiles}")
continue
print(f"\n====== Processing {day} ======")
if not is_config_stored:
store_config_in_database(
db_path=args.result_db,
config_file_path=args.config,
config=config,
datafiles=datafiles,
instruments=instruments,
)
is_config_stored = True
pt_strategy = PtResearchStrategy(
config=config, datafiles=md_datafiles, instruments=instruments
)
pt_strategy.run()
results.add_day_results(
day=day,
trades=pt_strategy.day_trades(),
outstanding_positions=pt_strategy.outstanding_positions(),
)
results.analyze_pair_performance()
if args.result_db.upper() != "NONE":
print(f"\nResults stored in database: {args.result_db}")
else:
print("No results to display.")
if __name__ == "__main__":
main()

3
__DELETE__/research/cointegration_test.py → research/cointegration_test.py
View File

@ -9,18 +9,19 @@ import pandas as pd
from tools.config import expand_filename, load_config
from tools.data_loader import get_available_instruments_from_db
from pt_trading.results import (
BacktestResult,
create_result_database,
store_config_in_database,
store_results_in_database,
)
from pt_trading.fit_method import PairsTradingFitMethod
from pt_trading.trading_pair import TradingPair
from research.research_tools import create_pairs, resolve_datafiles
def main() -> None:
parser = argparse.ArgumentParser(description="Run pairs trading backtest.")
parser.add_argument(

6416
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16
research/notebooks/pt_pair_backtest.ipynb Normal file
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@ -0,0 +1,16 @@
{
"cells": [],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.12.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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6832
research/notebooks/single_pair_test.ipynb Normal file
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6685
research/notebooks/single_pair_test_lg.ipynb Normal file
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0
__DELETE__/research/pt_backtest.py → research/pt_backtest.py
View File

1
research/tools/research_tools.py → research/research_tools.py
View File

@ -5,7 +5,6 @@ from typing import Dict, List, Optional
import pandas as pd
from pt_trading.fit_method import PairsTradingFitMethod
def resolve_datafiles(config: Dict, cli_datafiles: Optional[str] = None) -> List[str]:
"""
Resolve the list of data files to process.

221
strategy/pair_strategy.py Normal file
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@ -0,0 +1,221 @@
import argparse
import asyncio
import glob
import importlib
import os
from datetime import date, datetime
from typing import Any, Dict, List, Optional
import hjson
import pandas as pd
from tools.data_loader import get_available_instruments_from_db, load_market_data
from pt_trading.results import (
BacktestResult,
create_result_database,
store_config_in_database,
store_results_in_database,
)
from pt_trading.fit_methods import PairsTradingFitMethod
from pt_trading.trading_pair import TradingPair
def run_strategy(
config: Dict,
datafile: str,
fit_method: PairsTradingFitMethod,
instruments: List[str],
) -> BacktestResult:
"""
Run backtest for all pairs using the specified instruments.
"""
bt_result: BacktestResult = BacktestResult(config=config)
def _create_pairs(config: Dict, instruments: List[str]) -> List[TradingPair]:
nonlocal datafile
all_indexes = range(len(instruments))
unique_index_pairs = [(i, j) for i in all_indexes for j in all_indexes if i < j]
pairs = []
# Update config to use the specified instruments
config_copy = config.copy()
config_copy["instruments"] = instruments
market_data_df = load_market_data(
datafile=datafile,
exchange_id=config_copy["exchange_id"],
instruments=config_copy["instruments"],
instrument_id_pfx=config_copy["instrument_id_pfx"],
db_table_name=config_copy["db_table_name"],
trading_hours=config_copy["trading_hours"],
)
for a_index, b_index in unique_index_pairs:
pair = fit_method.create_trading_pair(
market_data=market_data_df,
symbol_a=instruments[a_index],
symbol_b=instruments[b_index],
)
pairs.append(pair)
return pairs
pairs_trades = []
for pair in _create_pairs(config, instruments):
single_pair_trades = fit_method.run_pair(
pair=pair, config=config, bt_result=bt_result
)
if single_pair_trades is not None and len(single_pair_trades) > 0:
pairs_trades.append(single_pair_trades)
# Check if result_list has any data before concatenating
if len(pairs_trades) == 0:
print("No trading signals found for any pairs")
return bt_result
result = pd.concat(pairs_trades, ignore_index=True)
result["time"] = pd.to_datetime(result["time"])
result = result.set_index("time").sort_index()
bt_result.collect_single_day_results(result)
return bt_result
def main() -> None:
parser = argparse.ArgumentParser(description="Run pairs trading backtest.")
parser.add_argument(
"--config", type=str, required=True, help="Path to the configuration file."
)
parser.add_argument(
"--datafiles",
type=str,
required=False,
help="Comma-separated list of data files (overrides config). No wildcards supported.",
)
parser.add_argument(
"--instruments",
type=str,
required=False,
help="Comma-separated list of instrument symbols (e.g., COIN,GBTC). If not provided, auto-detects from database.",
)
parser.add_argument(
"--result_db",
type=str,
required=True,
help="Path to SQLite database for storing results. Use 'NONE' to disable database output.",
)
args = parser.parse_args()
config: Dict = load_config(args.config)
# Dynamically instantiate fit method class
fit_method_class_name = config.get("fit_method_class", None)
assert fit_method_class_name is not None
module_name, class_name = fit_method_class_name.rsplit(".", 1)
module = importlib.import_module(module_name)
fit_method = getattr(module, class_name)()
# Resolve data files (CLI takes priority over config)
datafiles = resolve_datafiles(config, args.datafiles)
if not datafiles:
print("No data files found to process.")
return
print(f"Found {len(datafiles)} data files to process:")
for df in datafiles:
print(f" - {df}")
# Create result database if needed
if args.result_db.upper() != "NONE":
create_result_database(args.result_db)
# Initialize a dictionary to store all trade results
all_results: Dict[str, Dict[str, Any]] = {}
# Store configuration in database for reference
if args.result_db.upper() != "NONE":
# Get list of all instruments for storage
all_instruments = []
for datafile in datafiles:
if args.instruments:
file_instruments = [
inst.strip() for inst in args.instruments.split(",")
]
else:
file_instruments = get_available_instruments_from_db(datafile, config)
all_instruments.extend(file_instruments)
# Remove duplicates while preserving order
unique_instruments = list(dict.fromkeys(all_instruments))
store_config_in_database(
db_path=args.result_db,
config_file_path=args.config,
config=config,
fit_method_class=fit_method_class_name,
datafiles=datafiles,
instruments=unique_instruments,
)
# Process each data file
for datafile in datafiles:
print(f"\n====== Processing {os.path.basename(datafile)} ======")
# Determine instruments to use
if args.instruments:
# Use CLI-specified instruments
instruments = [inst.strip() for inst in args.instruments.split(",")]
print(f"Using CLI-specified instruments: {instruments}")
else:
# Auto-detect instruments from database
instruments = get_available_instruments_from_db(datafile, config)
print(f"Auto-detected instruments: {instruments}")
if not instruments:
print(f"No instruments found for {datafile}, skipping...")
continue
# Process data for this file
try:
fit_method.reset()
bt_results = run_strategy(
config=config,
datafile=datafile,
fit_method=fit_method,
instruments=instruments,
)
# Store results with file name as key
filename = os.path.basename(datafile)
all_results[filename] = {"trades": bt_results.trades.copy()}
# Store results in database
if args.result_db.upper() != "NONE":
store_results_in_database(args.result_db, datafile, bt_results)
print(f"Successfully processed {filename}")
except Exception as err:
print(f"Error processing {datafile}: {str(err)}")
import traceback
traceback.print_exc()
# Calculate and print results using a new BacktestResult instance for aggregation
if all_results:
aggregate_bt_results = BacktestResult(config=config)
aggregate_bt_results.calculate_returns(all_results)
aggregate_bt_results.print_grand_totals()
aggregate_bt_results.print_outstanding_positions()
if args.result_db.upper() != "NONE":
print(f"\nResults stored in database: {args.result_db}")
else:
print("No results to display.")
if __name__ == "__main__":
asyncio.run(main())

111
tests/viz_test.py
View File

@ -1,111 +0,0 @@
from __future__ import annotations
import os
from typing import Any, Dict
from pt_strategy.results import (PairResearchResult, create_result_database,
store_config_in_database)
from pt_strategy.research_strategy import PtResearchStrategy
from tools.filetools import resolve_datafiles
from tools.instruments import get_instruments
from tools.viz.viz_trades import visualize_trades
def main() -> None:
import argparse
from tools.config import expand_filename, load_config
parser = argparse.ArgumentParser(description="Run pairs trading backtest.")
parser.add_argument(
"--config", type=str, required=True, help="Path to the configuration file."
)
parser.add_argument(
"--date_pattern",
type=str,
required=True,
help="Date YYYYMMDD, allows * and ? wildcards",
)
parser.add_argument(
"--instruments",
type=str,
required=True,
help="Comma-separated list of instrument symbols (e.g., COIN:EQUITY,GBTC:CRYPTO)",
)
parser.add_argument(
"--result_db",
type=str,
required=False,
default="NONE",
help="Path to SQLite database for storing results. Use 'NONE' to disable database output.",
)
args = parser.parse_args()
config: Dict = load_config(args.config)
# Resolve data files (CLI takes priority over config)
instruments = get_instruments(args, config)
datafiles = resolve_datafiles(config, args.date_pattern, instruments)
days = list(set([day for day, _ in datafiles]))
print(f"Found {len(datafiles)} data files to process:")
for df in datafiles:
print(f" - {df}")
# Create result database if needed
if args.result_db.upper() != "NONE":
args.result_db = expand_filename(args.result_db)
create_result_database(args.result_db)
# Initialize a dictionary to store all trade results
all_results: Dict[str, Dict[str, Any]] = {}
is_config_stored = False
# Process each data file
results = PairResearchResult(config=config)
for day in sorted(days):
md_datafiles = [datafile for md_day, datafile in datafiles if md_day == day]
if not all([os.path.exists(datafile) for datafile in md_datafiles]):
print(f"WARNING: insufficient data files: {md_datafiles}")
continue
print(f"\n====== Processing {day} ======")
if not is_config_stored:
store_config_in_database(
db_path=args.result_db,
config_file_path=args.config,
config=config,
datafiles=datafiles,
instruments=instruments,
)
is_config_stored = True
pt_strategy = PtResearchStrategy(
config=config, datafiles=md_datafiles, instruments=instruments
)
pt_strategy.run()
results.add_day_results(
day=day,
trades=pt_strategy.day_trades(),
outstanding_positions=pt_strategy.outstanding_positions(),
)
results.analyze_pair_performance()
visualize_trades(pt_strategy, results, day)
if args.result_db.upper() != "NONE":
print(f"\nResults stored in database: {args.result_db}")
else:
print("No results to display.")
if __name__ == "__main__":
main()