Compare commits
15 Commits
7d137a1a0e
..
v0.0.2
| Author | SHA1 | Date | |
|---|---|---|---|
 Oleg Sheynin
|
d5f00f557b | ||
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Oleg Sheynin
|
c0fabcb429 | ||
|
Oleg Sheynin
|
bd6cf1d4d0 | ||
|
Oleg Sheynin
|
b196863a34 | ||
 oleg
|
6dd0f97d74 | ||
|
oleg
|
002f797751 | ||
|
oleg
|
4bf1d46208 | ||
|
oleg
|
842eb3ec62 | ||
|
oleg
|
69a0b19e9f | ||
|
oleg
|
121c85def0 | ||
|
Oleg Sheynin
|
2e32b26fad | ||
|
Oleg Sheynin
|
ba2a6cd2eb | ||
|
Oleg Sheynin
|
8b115cee75 | ||
|
Oleg Sheynin
|
e97f76222c | ||
|
Oleg Sheynin
|
38e1621b2f |
Vendored
-9
@@ -1,9 +0,0 @@
|
||||
{
|
||||
"recommendations": [
|
||||
"ms-python.python",
|
||||
"ms-python.pylance",
|
||||
"ms-python.black-formatter",
|
||||
"ms-python.mypy-type-checker",
|
||||
"ms-python.isort"
|
||||
]
|
||||
}
|
||||
Vendored
+40
-153
@@ -21,196 +21,83 @@
|
||||
"name": "-------- Live Pair Trading --------",
|
||||
},
|
||||
{
|
||||
"name": "PAIRS TRADER",
|
||||
"name": "PAIR TRADER",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
|
||||
"program": "${workspaceFolder}/bin/pairs_trader.py",
|
||||
"program": "${workspaceFolder}/apps/pair_trader.py",
|
||||
"console": "integratedTerminal",
|
||||
"env": {
|
||||
"PYTHONPATH": "${workspaceFolder}/lib:${workspaceFolder}/.."
|
||||
"PYTHONPATH": "${workspaceFolder}/..",
|
||||
"CONFIG_SERVICE": "cloud16.cvtt.vpn:6789",
|
||||
"MODEL_CONFIG": "vecm",
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||||
"CVTT_URL": "http://cvtt-tester-01.cvtt.vpn:23456",
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||||
// "CVTT_URL": "http://dev-server-02.cvtt.vpn:23456",
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},
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"args": [
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"--config=${workspaceFolder}/configuration/pairs_trader.cfg",
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"--pair=PAIR-ADA-USDT:BNBSPOT,PAIR-SOL-USDT:BNBSPOT",
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// "--config=${workspaceFolder}/configuration/pair_trader.cfg",
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"--config=http://cloud16.cvtt.vpn:6789/apps/pairs_trading/pair_trader",
|
||||
"--book_id=TSTBOOK_PT_20260113",
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"--instrument_A=COINBASE_AT:PAIR-ADA-USD",
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"--instrument_B=COINBASE_AT:PAIR-SOL-USD",
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],
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||||
},
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||||
{
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||||
"name": "-------- OLS --------",
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||||
},
|
||||
{
|
||||
"name": "CRYPTO OLS (rolling)",
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"type": "debugpy",
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||||
"request": "launch",
|
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"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
|
||||
"program": "${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",
|
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],
|
||||
"env": {
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||||
"PYTHONPATH": "${workspaceFolder}/lib"
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||||
},
|
||||
"console": "integratedTerminal"
|
||||
},
|
||||
{
|
||||
"name": "CRYPTO OLS (optimized)",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
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"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
|
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"program": "${workspaceFolder}/research/backtest.py",
|
||||
"args": [
|
||||
"--config=${workspaceFolder}/configuration/ols-opt.cfg",
|
||||
"--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
|
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"--date_pattern=20250605",
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||||
"--result_db=${workspaceFolder}/research/results/crypto/%T.ols-opt.ADA-SOL.20250605.crypto_results.db",
|
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],
|
||||
"env": {
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"PYTHONPATH": "${workspaceFolder}/lib"
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},
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"console": "integratedTerminal"
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},
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// {
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||||
// "name": "CRYPTO OLS (expanding)",
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// "type": "debugpy",
|
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// "request": "launch",
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||||
// "python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
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// "program": "${workspaceFolder}/research/backtest.py",
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// "args": [
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||||
// "--config=${workspaceFolder}/configuration/ols-exp.cfg",
|
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// "--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
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||||
// "--date_pattern=20250605",
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||||
// "--result_db=${workspaceFolder}/research/results/crypto/%T.ols-exp.ADA-SOL.20250605.crypto_results.db",
|
||||
// ],
|
||||
// "env": {
|
||||
// "PYTHONPATH": "${workspaceFolder}/lib"
|
||||
// },
|
||||
// "console": "integratedTerminal"
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||||
// },
|
||||
{
|
||||
"name": "EQUITY OLS (rolling)",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
|
||||
"program": "${workspaceFolder}/research/backtest.py",
|
||||
"args": [
|
||||
"--config=${workspaceFolder}/configuration/ols.cfg",
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||||
"--instruments=COIN:EQUITY:ALPACA,MSTR:EQUITY:ALPACA",
|
||||
"--date_pattern=20250605",
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||||
"--result_db=${workspaceFolder}/research/results/equity/%T.ols.COIN-MSTR.20250605.equity_results.db",
|
||||
],
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||||
"env": {
|
||||
"PYTHONPATH": "${workspaceFolder}/lib"
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||||
},
|
||||
"console": "integratedTerminal"
|
||||
},
|
||||
{
|
||||
"name": "EQUITY-CRYPTO OLS (rolling)",
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||||
"type": "debugpy",
|
||||
"request": "launch",
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||||
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
|
||||
"program": "${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": "${workspaceFolder}/lib"
|
||||
},
|
||||
"console": "integratedTerminal"
|
||||
},
|
||||
{
|
||||
"name": "-------- VECM --------",
|
||||
},
|
||||
{
|
||||
"name": "CRYPTO VECM (rolling)",
|
||||
"name": "CRYPTO VECM BACKTEST (optimized)",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
|
||||
"program": "${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=${workspaceFolder}/configuration/vecm-opt.cfg",
|
||||
"--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
|
||||
"--date_pattern=20250605",
|
||||
"--config=http://cloud16.cvtt.vpn:6789/apps/pairs_trading/backtest",
|
||||
"--instruments=CRYPTO:BNBSPOT:PAIR-ADA-USDT,CRYPTO:BNBSPOT:PAIR-SOL-USDT",
|
||||
"--date_pattern=20250911",
|
||||
"--result_db=${workspaceFolder}/research/results/crypto/%T.vecm-opt.ADA-SOL.20250605.crypto_results.db",
|
||||
],
|
||||
"env": {
|
||||
"PYTHONPATH": "${workspaceFolder}/lib"
|
||||
"PYTHONPATH": "${workspaceFolder}/..",
|
||||
"CONFIG_SERVICE": "cloud16.cvtt.vpn:6789",
|
||||
"MODEL_CONFIG": "vecm-opt"
|
||||
},
|
||||
"console": "integratedTerminal"
|
||||
},
|
||||
// {
|
||||
// "name": "CRYPTO VECM (expanding)",
|
||||
// "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-exp.cfg",
|
||||
// "--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
|
||||
// "--config=${workspaceFolder}/configuration/vecm.cfg",
|
||||
// "--instruments=COIN:EQUITY:ALPACA,MSTR:EQUITY:ALPACA",
|
||||
// "--date_pattern=20250605",
|
||||
// "--result_db=${workspaceFolder}/research/results/crypto/%T.vecm-exp.ADA-SOL.20250605.crypto_results.db",
|
||||
// "--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": "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 --------",
|
||||
},
|
||||
|
||||
Vendored
+3
-1
@@ -4,5 +4,7 @@
|
||||
"path": ".."
|
||||
}
|
||||
],
|
||||
"settings": {}
|
||||
"settings": {
|
||||
"workbench.colorTheme": "Dracula Theme"
|
||||
}
|
||||
}
|
||||
Vendored
+9
-102
@@ -1,112 +1,19 @@
|
||||
{
|
||||
"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.unittestEnabled": false,
|
||||
"python.testing.pytestArgs": [
|
||||
"-v",
|
||||
"--tb=short",
|
||||
"--disable-warnings"
|
||||
"unittests"
|
||||
],
|
||||
"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/:${env:PYTHONPATH}"
|
||||
},
|
||||
|
||||
"pylint.enabled": true,
|
||||
"github.copilot.enable": false,
|
||||
"markdown.extension.print.theme": "dark",
|
||||
"python.defaultInterpreterPath": "/home/oleg/.pyenv/python3.12-venv/bin/python3",
|
||||
"python.testing.pytestPath": "python3",
|
||||
"python.analysis.extraPaths": [
|
||||
"${workspaceFolder}",
|
||||
"${workspaceFolder}/..",
|
||||
"${workspaceFolder}/lib"
|
||||
"${workspaceFolder}/unittests"
|
||||
],
|
||||
|
||||
// Try enabling regular Python language server alongside CursorPyright
|
||||
"python.languageServer": "None",
|
||||
"python.analysis.diagnosticMode": "workspace",
|
||||
"workbench.colorTheme": "Atom One Dark",
|
||||
"cursorpyright.analysis.enable": false,
|
||||
"cursorpyright.analysis.extraPaths": [
|
||||
"${workspaceFolder}/..",
|
||||
"${workspaceFolder}/lib"
|
||||
],
|
||||
|
||||
// Enable quick fixes for unused imports
|
||||
"python.analysis.autoImportCompletions": true,
|
||||
"python.analysis.fixAll": ["source.unusedImports"],
|
||||
"python.analysis.typeCheckingMode": "basic",
|
||||
|
||||
// Enable code actions for CursorPyright
|
||||
"cursorpyright.analysis.autoImportCompletions": true,
|
||||
"cursorpyright.analysis.typeCheckingMode": "off",
|
||||
"cursorpyright.reportUnusedImport": "warning",
|
||||
"cursorpyright.reportUnusedVariable": "warning",
|
||||
"cursorpyright.analysis.diagnosticMode": "workspace",
|
||||
|
||||
// Force enable code actions
|
||||
"editor.lightBulb.enabled": true,
|
||||
"editor.codeActionsOnSave": {
|
||||
"source.organizeImports": "explicit",
|
||||
"source.fixAll": "explicit",
|
||||
"source.unusedImports": "explicit"
|
||||
},
|
||||
|
||||
// Enable Python-specific code actions
|
||||
"python.analysis.completeFunctionParens": true,
|
||||
"python.analysis.addImport.exactMatchOnly": false,
|
||||
"workbench.tree.indent": 24,
|
||||
"python.envFile": "${workspaceFolder}/.env",
|
||||
"python.testing.debugPort": 3000,
|
||||
"python.testing.promptToConfigure": false,
|
||||
}
|
||||
Vendored
-181
@@ -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"
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -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",
|
||||
}
|
||||
}
|
||||
@@ -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": "7:30:00",
|
||||
"end_session": "18:30:00",
|
||||
}
|
||||
}
|
||||
@@ -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
|
||||
@@ -1,52 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from enum import Enum
|
||||
from typing import Dict, Optional, cast
|
||||
|
||||
import pandas as pd
|
||||
from pt_trading.results import BacktestResult
|
||||
from pt_trading.trading_pair import TradingPair
|
||||
|
||||
NanoPerMin = 1e9
|
||||
|
||||
|
||||
class PairsTradingFitMethod(ABC):
|
||||
TRADES_COLUMNS = [
|
||||
"time",
|
||||
"symbol",
|
||||
"side",
|
||||
"action",
|
||||
"price",
|
||||
"disequilibrium",
|
||||
"scaled_disequilibrium",
|
||||
"signed_scaled_disequilibrium",
|
||||
"pair",
|
||||
]
|
||||
@staticmethod
|
||||
def create(config: Dict) -> PairsTradingFitMethod:
|
||||
import importlib
|
||||
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)()
|
||||
return cast(PairsTradingFitMethod, fit_method)
|
||||
|
||||
@abstractmethod
|
||||
def run_pair(
|
||||
self, pair: TradingPair, bt_result: BacktestResult
|
||||
) -> Optional[pd.DataFrame]: ...
|
||||
|
||||
@abstractmethod
|
||||
def reset(self) -> None: ...
|
||||
|
||||
@abstractmethod
|
||||
def create_trading_pair(
|
||||
self,
|
||||
config: Dict,
|
||||
market_data: pd.DataFrame,
|
||||
symbol_a: str,
|
||||
symbol_b: str,
|
||||
) -> TradingPair: ...
|
||||
|
||||
@@ -1,751 +0,0 @@
|
||||
import os
|
||||
import sqlite3
|
||||
from datetime import date, datetime
|
||||
from typing import Any, Dict, List, Optional, Tuple
|
||||
|
||||
import pandas as pd
|
||||
from pt_trading.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,
|
||||
fit_method_class 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,
|
||||
fit_method_class: str,
|
||||
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, fit_method_class, datafiles, instruments
|
||||
) VALUES (?, ?, ?, ?, ?, ?)
|
||||
""",
|
||||
(
|
||||
datetime.now(),
|
||||
config_file_path,
|
||||
config_json,
|
||||
fit_method_class,
|
||||
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)}")
|
||||
|
||||
|
||||
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:
|
||||
"""
|
||||
Class to handle backtest results, trades tracking, PnL calculations, and reporting.
|
||||
"""
|
||||
|
||||
def __init__(self, config: Dict[str, Any]):
|
||||
self.config = config
|
||||
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]]] = {}
|
||||
|
||||
def add_trade(
|
||||
self,
|
||||
pair_nm: str,
|
||||
symbol: str,
|
||||
side: str,
|
||||
action: str,
|
||||
price: Any,
|
||||
disequilibrium: Optional[float] = None,
|
||||
scaled_disequilibrium: Optional[float] = None,
|
||||
timestamp: Optional[datetime] = None,
|
||||
status: Optional[str] = None,
|
||||
) -> None:
|
||||
"""Add a trade to the results tracking."""
|
||||
pair_nm = str(pair_nm)
|
||||
|
||||
if pair_nm not in self.trades:
|
||||
self.trades[pair_nm] = {symbol: []}
|
||||
if symbol not in self.trades[pair_nm]:
|
||||
self.trades[pair_nm][symbol] = []
|
||||
self.trades[pair_nm][symbol].append(
|
||||
{
|
||||
"symbol": symbol,
|
||||
"side": side,
|
||||
"action": action,
|
||||
"price": price,
|
||||
"disequilibrium": disequilibrium,
|
||||
"scaled_disequilibrium": scaled_disequilibrium,
|
||||
"timestamp": timestamp,
|
||||
"status": status,
|
||||
}
|
||||
)
|
||||
|
||||
def add_outstanding_position(self, position: Dict[str, Any]) -> None:
|
||||
"""Add an outstanding position to tracking."""
|
||||
self.outstanding_positions.append(position)
|
||||
|
||||
def add_realized_pnl(self, realized_pnl: float) -> None:
|
||||
"""Add realized PnL to the total."""
|
||||
self.total_realized_pnl += realized_pnl
|
||||
|
||||
def get_total_realized_pnl(self) -> float:
|
||||
"""Get total realized PnL."""
|
||||
return self.total_realized_pnl
|
||||
|
||||
def get_outstanding_positions(self) -> List[Dict[str, Any]]:
|
||||
"""Get all outstanding positions."""
|
||||
return self.outstanding_positions
|
||||
|
||||
def get_trades(self) -> Dict[str, Dict[str, Any]]:
|
||||
"""Get all trades."""
|
||||
return self.trades
|
||||
|
||||
def clear_trades(self) -> None:
|
||||
"""Clear all trades (used when processing new files)."""
|
||||
self.trades.clear()
|
||||
|
||||
def collect_single_day_results(self, pairs_trades: List[pd.DataFrame]) -> None:
|
||||
"""Collect and process single day trading results."""
|
||||
result = pd.concat(pairs_trades, ignore_index=True)
|
||||
result["time"] = pd.to_datetime(result["time"])
|
||||
result = result.set_index("time").sort_index()
|
||||
|
||||
print("\n -------------- Suggested Trades ")
|
||||
print(result)
|
||||
|
||||
for row in result.itertuples():
|
||||
side = row.side
|
||||
action = row.action
|
||||
symbol = row.symbol
|
||||
price = row.price
|
||||
disequilibrium = getattr(row, "disequilibrium", None)
|
||||
scaled_disequilibrium = getattr(row, "scaled_disequilibrium", None)
|
||||
if hasattr(row, "time"):
|
||||
timestamp = getattr(row, "time")
|
||||
else:
|
||||
timestamp = convert_timestamp(row.Index)
|
||||
status = row.status
|
||||
self.add_trade(
|
||||
pair_nm=str(row.pair),
|
||||
symbol=str(symbol),
|
||||
side=str(side),
|
||||
action=str(action),
|
||||
price=float(str(price)),
|
||||
disequilibrium=disequilibrium,
|
||||
scaled_disequilibrium=scaled_disequilibrium,
|
||||
timestamp=timestamp,
|
||||
status=str(status) if status is not None else "?",
|
||||
)
|
||||
|
||||
def print_single_day_results(self) -> None:
|
||||
"""Print single day results summary."""
|
||||
for pair, symbols in self.trades.items():
|
||||
print(f"\n--- {pair} ---")
|
||||
for symbol, trades in symbols.items():
|
||||
for trade_data in trades:
|
||||
if len(trade_data) >= 2:
|
||||
side, price = trade_data[:2]
|
||||
print(f"{symbol} {side} at ${price}")
|
||||
|
||||
def print_results_summary(self, all_results: Dict[str, Dict[str, Any]]) -> None:
|
||||
"""Print summary of all processed files."""
|
||||
print("\n====== Summary of All Processed Files ======")
|
||||
for filename, data in all_results.items():
|
||||
trade_count = sum(
|
||||
len(trades)
|
||||
for symbol_trades in data["trades"].values()
|
||||
for trades in symbol_trades.values()
|
||||
)
|
||||
print(f"{filename}: {trade_count} trades")
|
||||
|
||||
def calculate_returns(self, all_results: Dict[str, Dict[str, Any]]) -> None:
|
||||
"""Calculate and print returns by day and pair."""
|
||||
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
|
||||
|
||||
print("\n====== Returns By Day and Pair ======")
|
||||
|
||||
trades = []
|
||||
for filename, data in all_results.items():
|
||||
pairs = list(data["trades"].keys())
|
||||
for pair in pairs:
|
||||
self.symbol_roundtrip_trades_[pair] = []
|
||||
trades_dict = data["trades"][pair]
|
||||
for symbol in trades_dict.keys():
|
||||
trades.extend(trades_dict[symbol])
|
||||
trades = sorted(trades, key=lambda x: (x["timestamp"], x["symbol"]))
|
||||
|
||||
print(f"\n--- {filename} ---")
|
||||
|
||||
self.outstanding_positions = data["outstanding_positions"]
|
||||
|
||||
day_return = 0.0
|
||||
for idx in range(0, len(trades), 4):
|
||||
symbol_a = trades[idx]["symbol"]
|
||||
trade_a_1 = trades[idx]
|
||||
trade_a_2 = trades[idx + 2]
|
||||
|
||||
symbol_b = trades[idx + 1]["symbol"]
|
||||
trade_b_1 = trades[idx + 1]
|
||||
trade_b_2 = trades[idx + 3]
|
||||
|
||||
symbol_return = 0
|
||||
assert (
|
||||
trade_a_1["timestamp"] < trade_a_2["timestamp"]
|
||||
), f"Trade 1: {trade_a_1['timestamp']} is not less than Trade 2: {trade_a_2['timestamp']}"
|
||||
assert (
|
||||
trade_a_1["action"] == "OPEN" and trade_a_2["action"] == "CLOSE"
|
||||
), f"Trade 1: {trade_a_1['action']} and Trade 2: {trade_a_2['action']} are the same"
|
||||
|
||||
# Calculate return based on action combination
|
||||
trade_return = 0
|
||||
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
|
||||
|
||||
self.symbol_roundtrip_trades_[pair].append(
|
||||
{
|
||||
"symbol": symbol_a,
|
||||
"open_side": trade_a_1["side"],
|
||||
"open_action": trade_a_1["action"],
|
||||
"open_price": trade_a_1["price"],
|
||||
"close_side": trade_a_2["side"],
|
||||
"close_action": trade_a_2["action"],
|
||||
"close_price": trade_a_2["price"],
|
||||
"symbol_return": symbol_a_return,
|
||||
"open_disequilibrium": trade_a_1["disequilibrium"],
|
||||
"open_scaled_disequilibrium": trade_a_1["scaled_disequilibrium"],
|
||||
"close_disequilibrium": trade_a_2["disequilibrium"],
|
||||
"close_scaled_disequilibrium": trade_a_2["scaled_disequilibrium"],
|
||||
"open_time": trade_a_1["timestamp"],
|
||||
"close_time": trade_a_2["timestamp"],
|
||||
"shares": self.config["funding_per_pair"] / 2 / trade_a_1["price"],
|
||||
"is_completed": True,
|
||||
"close_condition": trade_a_2["status"],
|
||||
"pair_return": pair_return
|
||||
}
|
||||
)
|
||||
self.symbol_roundtrip_trades_[pair].append(
|
||||
{
|
||||
"symbol": symbol_b,
|
||||
"open_side": trade_b_1["side"],
|
||||
"open_action": trade_b_1["action"],
|
||||
"open_price": trade_b_1["price"],
|
||||
"close_side": trade_b_2["side"],
|
||||
"close_action": trade_b_2["action"],
|
||||
"close_price": trade_b_2["price"],
|
||||
"symbol_return": symbol_b_return,
|
||||
"open_disequilibrium": trade_b_1["disequilibrium"],
|
||||
"open_scaled_disequilibrium": trade_b_1["scaled_disequilibrium"],
|
||||
"close_disequilibrium": trade_b_2["disequilibrium"],
|
||||
"close_scaled_disequilibrium": trade_b_2["scaled_disequilibrium"],
|
||||
"open_time": trade_b_1["timestamp"],
|
||||
"close_time": trade_b_2["timestamp"],
|
||||
"shares": self.config["funding_per_pair"] / 2 / trade_b_1["price"],
|
||||
"is_completed": True,
|
||||
"close_condition": trade_b_2["status"],
|
||||
"pair_return": pair_return
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
# Print pair returns with disequilibrium information
|
||||
day_return = 0.0
|
||||
if pair in self.symbol_roundtrip_trades_:
|
||||
|
||||
print(f"{pair}:")
|
||||
pair_return = 0.0
|
||||
for trd in self.symbol_roundtrip_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}"
|
||||
|
||||
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}"
|
||||
)
|
||||
pair_return += trd["symbol_return"]
|
||||
|
||||
print(f" Pair Total Return: {pair_return:.2f}%")
|
||||
day_return += pair_return
|
||||
|
||||
# Print day total return and add to global realized PnL
|
||||
if day_return != 0:
|
||||
print(f" Day Total Return: {day_return:.2f}%")
|
||||
self.add_realized_pnl(day_return)
|
||||
|
||||
def print_outstanding_positions(self) -> None:
|
||||
"""Print all outstanding positions with share quantities and current values."""
|
||||
if not self.get_outstanding_positions():
|
||||
print("\n====== NO OUTSTANDING POSITIONS ======")
|
||||
return
|
||||
|
||||
print(f"\n====== OUTSTANDING POSITIONS ======")
|
||||
print(
|
||||
f"{'Pair':<15}"
|
||||
f" {'Symbol':<10}"
|
||||
f" {'Side':<4}"
|
||||
f" {'Shares':<10}"
|
||||
f" {'Open $':<8}"
|
||||
f" {'Current $':<10}"
|
||||
f" {'Value $':<12}"
|
||||
f" {'Disequilibrium':<15}"
|
||||
)
|
||||
print("-" * 100)
|
||||
|
||||
total_value = 0.0
|
||||
|
||||
for pos in self.get_outstanding_positions():
|
||||
# Print position A
|
||||
print(
|
||||
f"{pos['pair']:<15}"
|
||||
f" {pos['symbol_a']:<10}"
|
||||
f" {pos['side_a']:<4}"
|
||||
f" {pos['shares_a']:<10.2f}"
|
||||
f" {pos['open_px_a']:<8.2f}"
|
||||
f" {pos['current_px_a']:<10.2f}"
|
||||
f" {pos['current_value_a']:<12.2f}"
|
||||
f" {'':<15}"
|
||||
)
|
||||
|
||||
# Print position B
|
||||
print(
|
||||
f"{'':<15}"
|
||||
f" {pos['symbol_b']:<10}"
|
||||
f" {pos['side_b']:<4}"
|
||||
f" {pos['shares_b']:<10.2f}"
|
||||
f" {pos['open_px_b']:<8.2f}"
|
||||
f" {pos['current_px_b']:<10.2f}"
|
||||
f" {pos['current_value_b']:<12.2f}"
|
||||
)
|
||||
|
||||
# Print pair totals with disequilibrium info
|
||||
print(
|
||||
f"{'':<15}"
|
||||
f" {'PAIR TOTAL':<10}"
|
||||
f" {'':<4}"
|
||||
f" {'':<10}"
|
||||
f" {'':<8}"
|
||||
f" {'':<10}"
|
||||
f" {pos['total_current_value']:<12.2f}"
|
||||
)
|
||||
|
||||
# Print disequilibrium details
|
||||
print(
|
||||
f"{'':<15}"
|
||||
f" {'DISEQUIL':<10}"
|
||||
f" {'':<4}"
|
||||
f" {'':<10}"
|
||||
f" {'':<8}"
|
||||
f" {'':<10}"
|
||||
f" Raw: {pos['current_disequilibrium']:<6.4f}"
|
||||
f" Scaled: {pos['current_scaled_disequilibrium']:<6.4f}"
|
||||
)
|
||||
|
||||
print("-" * 100)
|
||||
|
||||
total_value += pos["total_current_value"]
|
||||
|
||||
print(f"{'TOTAL OUTSTANDING VALUE':<80} ${total_value:<12.2f}")
|
||||
|
||||
def print_grand_totals(self) -> None:
|
||||
"""Print grand totals across all pairs."""
|
||||
print(f"\n====== GRAND TOTALS ACROSS ALL PAIRS ======")
|
||||
print(f"Total Realized PnL: {self.get_total_realized_pnl():.2f}%")
|
||||
|
||||
def handle_outstanding_position(
|
||||
self,
|
||||
pair: TradingPair,
|
||||
pair_result_df: pd.DataFrame,
|
||||
last_row_index: int,
|
||||
open_side_a: str,
|
||||
open_side_b: str,
|
||||
open_px_a: float,
|
||||
open_px_b: float,
|
||||
open_tstamp: datetime,
|
||||
) -> Tuple[float, float, float]:
|
||||
"""
|
||||
Handle calculation and tracking of outstanding positions when no close signal is found.
|
||||
|
||||
Args:
|
||||
pair: TradingPair object
|
||||
pair_result_df: DataFrame with pair results
|
||||
last_row_index: Index of the last row in the data
|
||||
open_side_a, open_side_b: Trading sides for symbols A and B
|
||||
open_px_a, open_px_b: Opening prices for symbols A and B
|
||||
open_tstamp: Opening timestamp
|
||||
"""
|
||||
if pair_result_df is None or pair_result_df.empty:
|
||||
return 0, 0, 0
|
||||
|
||||
last_row = pair_result_df.loc[last_row_index]
|
||||
last_tstamp = last_row["tstamp"]
|
||||
colname_a, colname_b = pair.exec_prices_colnames()
|
||||
last_px_a = last_row[colname_a]
|
||||
last_px_b = last_row[colname_b]
|
||||
|
||||
# Calculate share quantities based on funding per pair
|
||||
# Split funding equally between the two positions
|
||||
funding_per_position = self.config["funding_per_pair"] / 2
|
||||
shares_a = funding_per_position / open_px_a
|
||||
shares_b = funding_per_position / open_px_b
|
||||
|
||||
# Calculate current position values (shares * current price)
|
||||
current_value_a = shares_a * last_px_a * (-1 if open_side_a == "SELL" else 1)
|
||||
current_value_b = shares_b * last_px_b * (-1 if open_side_b == "SELL" else 1)
|
||||
total_current_value = current_value_a + current_value_b
|
||||
|
||||
# Get disequilibrium information
|
||||
current_disequilibrium = last_row["disequilibrium"]
|
||||
current_scaled_disequilibrium = last_row["scaled_disequilibrium"]
|
||||
|
||||
# Store outstanding positions
|
||||
self.add_outstanding_position(
|
||||
{
|
||||
"pair": str(pair),
|
||||
"symbol_a": pair.symbol_a_,
|
||||
"symbol_b": pair.symbol_b_,
|
||||
"side_a": open_side_a,
|
||||
"side_b": open_side_b,
|
||||
"shares_a": shares_a,
|
||||
"shares_b": shares_b,
|
||||
"open_px_a": open_px_a,
|
||||
"open_px_b": open_px_b,
|
||||
"current_px_a": last_px_a,
|
||||
"current_px_b": last_px_b,
|
||||
"current_value_a": current_value_a,
|
||||
"current_value_b": current_value_b,
|
||||
"total_current_value": total_current_value,
|
||||
"open_time": open_tstamp,
|
||||
"last_time": last_tstamp,
|
||||
"current_abs_term": current_scaled_disequilibrium,
|
||||
"current_disequilibrium": current_disequilibrium,
|
||||
"current_scaled_disequilibrium": current_scaled_disequilibrium,
|
||||
}
|
||||
)
|
||||
|
||||
# Print position details
|
||||
print(f"{pair}: NO CLOSE SIGNAL FOUND - Position held until end of session")
|
||||
print(f" Open: {open_tstamp} | Last: {last_tstamp}")
|
||||
print(
|
||||
f" {pair.symbol_a_}: {open_side_a} {shares_a:.2f} shares @ ${open_px_a:.2f} -> ${last_px_a:.2f} | Value: ${current_value_a:.2f}"
|
||||
)
|
||||
print(
|
||||
f" {pair.symbol_b_}: {open_side_b} {shares_b:.2f} shares @ ${open_px_b:.2f} -> ${last_px_b:.2f} | Value: ${current_value_b:.2f}"
|
||||
)
|
||||
print(f" Total Value: ${total_current_value:.2f}")
|
||||
print(
|
||||
f" Disequilibrium: {current_disequilibrium:.4f} | Scaled: {current_scaled_disequilibrium:.4f}"
|
||||
)
|
||||
|
||||
return current_value_a, current_value_b, total_current_value
|
||||
|
||||
def store_results_in_database(
|
||||
self, db_path: str, day: str
|
||||
) -> None:
|
||||
"""
|
||||
Store backtest results in the SQLite database.
|
||||
"""
|
||||
if db_path.upper() == "NONE":
|
||||
return
|
||||
|
||||
try:
|
||||
# Extract date from datafile name (assuming format like 20250528.mktdata.ohlcv.db)
|
||||
date_str = day
|
||||
|
||||
# Convert to proper date format
|
||||
try:
|
||||
date_obj = datetime.strptime(date_str, "%Y%m%d").date()
|
||||
except ValueError:
|
||||
# If date parsing fails, use current date
|
||||
date_obj = datetime.now().date()
|
||||
|
||||
conn = sqlite3.connect(db_path)
|
||||
cursor = conn.cursor()
|
||||
|
||||
# Process each trade from bt_result
|
||||
trades = self.get_trades()
|
||||
|
||||
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"]):
|
||||
# Only store completed trades in pt_bt_results table
|
||||
cursor.execute(
|
||||
"""
|
||||
INSERT INTO pt_bt_results (
|
||||
date, pair, symbol, open_time, open_side, open_price,
|
||||
open_quantity, open_disequilibrium, close_time, close_side,
|
||||
close_price, close_quantity, close_disequilibrium,
|
||||
symbol_return, pair_return, close_condition
|
||||
) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
|
||||
""",
|
||||
(
|
||||
date_obj,
|
||||
pair_name,
|
||||
trade_pair["symbol"],
|
||||
trade_pair["open_time"],
|
||||
trade_pair["open_side"],
|
||||
trade_pair["open_price"],
|
||||
trade_pair["shares"],
|
||||
trade_pair["open_scaled_disequilibrium"],
|
||||
trade_pair["close_time"],
|
||||
trade_pair["close_side"],
|
||||
trade_pair["close_price"],
|
||||
trade_pair["shares"],
|
||||
trade_pair["close_scaled_disequilibrium"],
|
||||
trade_pair["symbol_return"],
|
||||
trade_pair["pair_return"],
|
||||
trade_pair["close_condition"]
|
||||
),
|
||||
)
|
||||
|
||||
# Store outstanding positions in separate table
|
||||
outstanding_positions = self.get_outstanding_positions()
|
||||
for pos in outstanding_positions:
|
||||
# Calculate position quantity (negative for SELL positions)
|
||||
position_qty_a = (
|
||||
pos["shares_a"] if pos["side_a"] == "BUY" else -pos["shares_a"]
|
||||
)
|
||||
position_qty_b = (
|
||||
pos["shares_b"] if pos["side_b"] == "BUY" else -pos["shares_b"]
|
||||
)
|
||||
|
||||
# Calculate unrealized returns
|
||||
# For symbol A: (current_price - open_price) / open_price * 100 * position_direction
|
||||
unrealized_return_a = (
|
||||
(pos["current_px_a"] - pos["open_px_a"]) / pos["open_px_a"] * 100
|
||||
) * (1 if pos["side_a"] == "BUY" else -1)
|
||||
unrealized_return_b = (
|
||||
(pos["current_px_b"] - pos["open_px_b"]) / pos["open_px_b"] * 100
|
||||
) * (1 if pos["side_b"] == "BUY" else -1)
|
||||
|
||||
# Store outstanding position for symbol A
|
||||
cursor.execute(
|
||||
"""
|
||||
INSERT INTO outstanding_positions (
|
||||
date, pair, symbol, position_quantity, last_price, unrealized_return, open_price, open_side
|
||||
) VALUES (?, ?, ?, ?, ?, ?, ?, ?)
|
||||
""",
|
||||
(
|
||||
date_obj,
|
||||
pos["pair"],
|
||||
pos["symbol_a"],
|
||||
position_qty_a,
|
||||
pos["current_px_a"],
|
||||
unrealized_return_a,
|
||||
pos["open_px_a"],
|
||||
pos["side_a"],
|
||||
),
|
||||
)
|
||||
|
||||
# Store outstanding position for symbol B
|
||||
cursor.execute(
|
||||
"""
|
||||
INSERT INTO outstanding_positions (
|
||||
date, pair, symbol, position_quantity, last_price, unrealized_return, open_price, open_side
|
||||
) VALUES (?, ?, ?, ?, ?, ?, ?, ?)
|
||||
""",
|
||||
(
|
||||
date_obj,
|
||||
pos["pair"],
|
||||
pos["symbol_b"],
|
||||
position_qty_b,
|
||||
pos["current_px_b"],
|
||||
unrealized_return_b,
|
||||
pos["open_px_b"],
|
||||
pos["side_b"],
|
||||
),
|
||||
)
|
||||
|
||||
conn.commit()
|
||||
conn.close()
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error storing results in database: {str(e)}")
|
||||
import traceback
|
||||
|
||||
traceback.print_exc()
|
||||
|
||||
@@ -1,319 +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
|
||||
from statsmodels.tsa.vector_ar.vecm import VECM, VECMResults
|
||||
|
||||
NanoPerMin = 1e9
|
||||
|
||||
|
||||
class RollingFit(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_
|
||||
|
||||
curr_training_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"]
|
||||
curr_predicted_row_idx = 0
|
||||
while True:
|
||||
print(curr_training_start_idx, end="\r")
|
||||
pair.get_datasets(
|
||||
training_minutes=training_minutes,
|
||||
training_start_index=curr_training_start_idx,
|
||||
testing_size=1,
|
||||
)
|
||||
|
||||
if len(pair.training_df_) < training_minutes:
|
||||
print(
|
||||
f"{pair}: current offset={curr_training_start_idx}"
|
||||
f" * Training data length={len(pair.training_df_)} < {training_minutes}"
|
||||
" * Not enough training data. Completing the job."
|
||||
)
|
||||
break
|
||||
|
||||
try:
|
||||
# ================================ PREDICTION ================================
|
||||
self.pair_predict_result_ = pair.predict()
|
||||
except Exception as e:
|
||||
raise RuntimeError(
|
||||
f"{pair}: TrainingPrediction failed: {str(e)}"
|
||||
) from e
|
||||
|
||||
# break
|
||||
|
||||
curr_training_start_idx += 1
|
||||
if curr_training_start_idx > end_index:
|
||||
break
|
||||
curr_predicted_row_idx += 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
|
||||
@@ -1,380 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from enum import Enum
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
import pandas as pd # type:ignore
|
||||
|
||||
|
||||
class PairState(Enum):
|
||||
INITIAL = 1
|
||||
OPEN = 2
|
||||
CLOSE = 3
|
||||
CLOSE_POSITION = 4
|
||||
CLOSE_STOP_LOSS = 5
|
||||
CLOSE_STOP_PROFIT = 6
|
||||
|
||||
class CointegrationData:
|
||||
EG_PVALUE_THRESHOLD = 0.05
|
||||
|
||||
tstamp_: pd.Timestamp
|
||||
pair_: str
|
||||
eg_pvalue_: float
|
||||
johansen_lr1_: float
|
||||
johansen_cvt_: float
|
||||
eg_is_cointegrated_: bool
|
||||
johansen_is_cointegrated_: bool
|
||||
|
||||
def __init__(self, pair: TradingPair):
|
||||
training_df = pair.training_df_
|
||||
|
||||
assert training_df is not None
|
||||
from statsmodels.tsa.vector_ar.vecm import coint_johansen
|
||||
|
||||
df = training_df[pair.colnames()].reset_index(drop=True)
|
||||
|
||||
# Run Johansen cointegration test
|
||||
result = coint_johansen(df, det_order=0, k_ar_diff=1)
|
||||
self.johansen_lr1_ = result.lr1[0]
|
||||
self.johansen_cvt_ = result.cvt[0, 1]
|
||||
self.johansen_is_cointegrated_ = self.johansen_lr1_ > self.johansen_cvt_
|
||||
|
||||
# Run Engle-Granger cointegration test
|
||||
from statsmodels.tsa.stattools import coint # type: ignore
|
||||
|
||||
col1, col2 = pair.colnames()
|
||||
assert training_df is not None
|
||||
series1 = training_df[col1].reset_index(drop=True)
|
||||
series2 = training_df[col2].reset_index(drop=True)
|
||||
|
||||
self.eg_pvalue_ = float(coint(series1, series2)[1])
|
||||
self.eg_is_cointegrated_ = bool(self.eg_pvalue_ < self.EG_PVALUE_THRESHOLD)
|
||||
|
||||
self.tstamp_ = training_df.index[-1]
|
||||
self.pair_ = pair.name()
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"tstamp": self.tstamp_,
|
||||
"pair": self.pair_,
|
||||
"eg_pvalue": self.eg_pvalue_,
|
||||
"johansen_lr1": self.johansen_lr1_,
|
||||
"johansen_cvt": self.johansen_cvt_,
|
||||
"eg_is_cointegrated": self.eg_is_cointegrated_,
|
||||
"johansen_is_cointegrated": self.johansen_is_cointegrated_,
|
||||
}
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return f"CointegrationData(tstamp={self.tstamp_}, pair={self.pair_}, eg_pvalue={self.eg_pvalue_}, johansen_lr1={self.johansen_lr1_}, johansen_cvt={self.johansen_cvt_}, eg_is_cointegrated={self.eg_is_cointegrated_}, johansen_is_cointegrated={self.johansen_is_cointegrated_})"
|
||||
|
||||
|
||||
class TradingPair(ABC):
|
||||
market_data_: pd.DataFrame
|
||||
symbol_a_: str
|
||||
symbol_b_: str
|
||||
stat_model_price_: str
|
||||
|
||||
training_mu_: float
|
||||
training_std_: float
|
||||
|
||||
training_df_: pd.DataFrame
|
||||
testing_df_: pd.DataFrame
|
||||
|
||||
user_data_: Dict[str, Any]
|
||||
|
||||
# predicted_df_: Optional[pd.DataFrame]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: Dict[str, Any],
|
||||
market_data: pd.DataFrame,
|
||||
symbol_a: str,
|
||||
symbol_b: str,
|
||||
):
|
||||
self.symbol_a_ = symbol_a
|
||||
self.symbol_b_ = symbol_b
|
||||
self.stat_model_price_ = config["stat_model_price"]
|
||||
self.user_data_ = {}
|
||||
self.predicted_df_ = None
|
||||
self.config_ = config
|
||||
|
||||
self._set_market_data(market_data)
|
||||
|
||||
def _set_market_data(self, market_data: pd.DataFrame) -> None:
|
||||
self.market_data_ = pd.DataFrame(
|
||||
self._transform_dataframe(market_data)[["tstamp"] + self.colnames()]
|
||||
)
|
||||
|
||||
self.market_data_ = self.market_data_.dropna().reset_index(drop=True)
|
||||
self.market_data_["tstamp"] = pd.to_datetime(self.market_data_["tstamp"])
|
||||
self.market_data_ = self.market_data_.sort_values("tstamp")
|
||||
self._set_execution_price_data()
|
||||
pass
|
||||
|
||||
def _set_execution_price_data(self) -> None:
|
||||
if "execution_price" not in self.config_:
|
||||
self.market_data_[f"exec_price_{self.symbol_a_}"] = self.market_data_[f"{self.stat_model_price_}_{self.symbol_a_}"]
|
||||
self.market_data_[f"exec_price_{self.symbol_b_}"] = self.market_data_[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_[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_ = self.market_data_.dropna().reset_index(drop=True)
|
||||
|
||||
|
||||
|
||||
|
||||
def get_begin_index(self) -> int:
|
||||
if "trading_hours" not in self.config_:
|
||||
return 0
|
||||
assert "timezone" in self.config_["trading_hours"]
|
||||
assert "begin_session" in self.config_["trading_hours"]
|
||||
start_time = (
|
||||
pd.to_datetime(self.config_["trading_hours"]["begin_session"])
|
||||
.tz_localize(self.config_["trading_hours"]["timezone"])
|
||||
.time()
|
||||
)
|
||||
mask = self.market_data_["tstamp"].dt.time >= start_time
|
||||
return int(self.market_data_.index[mask].min())
|
||||
|
||||
def get_end_index(self) -> int:
|
||||
if "trading_hours" not in self.config_:
|
||||
return 0
|
||||
assert "timezone" in self.config_["trading_hours"]
|
||||
assert "end_session" in self.config_["trading_hours"]
|
||||
end_time = (
|
||||
pd.to_datetime(self.config_["trading_hours"]["end_session"])
|
||||
.tz_localize(self.config_["trading_hours"]["timezone"])
|
||||
.time()
|
||||
)
|
||||
mask = self.market_data_["tstamp"].dt.time <= end_time
|
||||
return int(self.market_data_.index[mask].max())
|
||||
|
||||
def _transform_dataframe(self, df: pd.DataFrame) -> pd.DataFrame:
|
||||
# Select only the columns we need
|
||||
df_selected: pd.DataFrame = pd.DataFrame(
|
||||
df[["tstamp", "symbol", self.stat_model_price_]]
|
||||
)
|
||||
|
||||
# Start with unique timestamps
|
||||
result_df: pd.DataFrame = (
|
||||
pd.DataFrame(df_selected["tstamp"]).drop_duplicates().reset_index(drop=True)
|
||||
)
|
||||
|
||||
# For each unique symbol, add a corresponding close 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}"
|
||||
|
||||
# Create temporary dataframe with timestamp and price
|
||||
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 get_datasets(
|
||||
self,
|
||||
training_minutes: int,
|
||||
training_start_index: int = 0,
|
||||
testing_size: Optional[int] = None,
|
||||
) -> None:
|
||||
|
||||
testing_start_index = training_start_index + training_minutes
|
||||
self.training_df_ = self.market_data_.iloc[
|
||||
training_start_index:testing_start_index, :training_minutes
|
||||
].copy()
|
||||
assert self.training_df_ is not None
|
||||
self.training_df_ = self.training_df_.dropna().reset_index(drop=True)
|
||||
|
||||
testing_start_index = training_start_index + training_minutes
|
||||
if testing_size is None:
|
||||
self.testing_df_ = self.market_data_.iloc[testing_start_index:, :].copy()
|
||||
else:
|
||||
self.testing_df_ = self.market_data_.iloc[
|
||||
testing_start_index : testing_start_index + testing_size, :
|
||||
].copy()
|
||||
assert self.testing_df_ is not None
|
||||
self.testing_df_ = self.testing_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 exec_prices_colnames(self) -> List[str]:
|
||||
return [
|
||||
f"exec_price_{self.symbol_a_}",
|
||||
f"exec_price_{self.symbol_b_}",
|
||||
]
|
||||
|
||||
def add_trades(self, trades: pd.DataFrame) -> None:
|
||||
if self.user_data_["trades"] is None or len(self.user_data_["trades"]) == 0:
|
||||
# If trades is empty or None, just assign the new trades directly
|
||||
self.user_data_["trades"] = trades.copy()
|
||||
else:
|
||||
# Ensure both DataFrames have the same columns and dtypes before concatenation
|
||||
existing_trades = self.user_data_["trades"]
|
||||
|
||||
# If existing trades is empty, just assign the new trades
|
||||
if len(existing_trades) == 0:
|
||||
self.user_data_["trades"] = trades.copy()
|
||||
else:
|
||||
# Ensure both DataFrames have the same columns
|
||||
if set(existing_trades.columns) != set(trades.columns):
|
||||
# Add missing columns to trades with appropriate default values
|
||||
for col in existing_trades.columns:
|
||||
if col not in trades.columns:
|
||||
if col == "time":
|
||||
trades[col] = pd.Timestamp.now()
|
||||
elif col in ["action", "symbol"]:
|
||||
trades[col] = ""
|
||||
elif col in [
|
||||
"price",
|
||||
"disequilibrium",
|
||||
"scaled_disequilibrium",
|
||||
]:
|
||||
trades[col] = 0.0
|
||||
elif col == "pair":
|
||||
trades[col] = None
|
||||
else:
|
||||
trades[col] = None
|
||||
|
||||
# Concatenate with explicit dtypes to avoid warnings
|
||||
self.user_data_["trades"] = pd.concat(
|
||||
[existing_trades, trades], ignore_index=True, copy=False
|
||||
)
|
||||
|
||||
def get_trades(self) -> pd.DataFrame:
|
||||
return (
|
||||
self.user_data_["trades"] if "trades" in self.user_data_ else pd.DataFrame()
|
||||
)
|
||||
|
||||
def cointegration_check(self) -> Optional[pd.DataFrame]:
|
||||
print(f"***{self}*** STARTING....")
|
||||
config = self.config_
|
||||
|
||||
curr_training_start_idx = 0
|
||||
|
||||
COINTEGRATION_DATA_COLUMNS = {
|
||||
"tstamp": "datetime64[ns]",
|
||||
"pair": "string",
|
||||
"eg_pvalue": "float64",
|
||||
"johansen_lr1": "float64",
|
||||
"johansen_cvt": "float64",
|
||||
"eg_is_cointegrated": "bool",
|
||||
"johansen_is_cointegrated": "bool",
|
||||
}
|
||||
# Initialize trades DataFrame with proper dtypes to avoid concatenation warnings
|
||||
result: pd.DataFrame = pd.DataFrame(
|
||||
columns=[col for col in COINTEGRATION_DATA_COLUMNS.keys()]
|
||||
) # .astype(COINTEGRATION_DATA_COLUMNS)
|
||||
|
||||
training_minutes = config["training_minutes"]
|
||||
while True:
|
||||
print(curr_training_start_idx, end="\r")
|
||||
self.get_datasets(
|
||||
training_minutes=training_minutes,
|
||||
training_start_index=curr_training_start_idx,
|
||||
testing_size=1,
|
||||
)
|
||||
|
||||
if len(self.training_df_) < training_minutes:
|
||||
print(
|
||||
f"{self}: current offset={curr_training_start_idx}"
|
||||
f" * Training data length={len(self.training_df_)} < {training_minutes}"
|
||||
" * Not enough training data. Completing the job."
|
||||
)
|
||||
break
|
||||
new_row = pd.Series(CointegrationData(self).to_dict())
|
||||
result.loc[len(result)] = new_row
|
||||
curr_training_start_idx += 1
|
||||
return result
|
||||
|
||||
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 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 _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 __repr__(self) -> str:
|
||||
return self.name()
|
||||
|
||||
def name(self) -> str:
|
||||
return f"{self.symbol_a_} & {self.symbol_b_}"
|
||||
# return f"{self.symbol_a_} & {self.symbol_b_}"
|
||||
|
||||
@abstractmethod
|
||||
def predict(self) -> pd.DataFrame: ...
|
||||
|
||||
# @abstractmethod
|
||||
# def predicted_df(self) -> Optional[pd.DataFrame]: ...
|
||||
@@ -1,122 +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
|
||||
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,
|
||||
)
|
||||
@@ -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,
|
||||
)
|
||||
@@ -1,126 +0,0 @@
|
||||
import argparse
|
||||
import glob
|
||||
import importlib
|
||||
import os
|
||||
from datetime import date, datetime
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
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(
|
||||
"--config", type=str, required=True, help="Path to the configuration file."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--datafile",
|
||||
type=str,
|
||||
required=False,
|
||||
help="Market data file to process.",
|
||||
)
|
||||
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.",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
config: Dict = load_config(args.config)
|
||||
|
||||
# Resolve data files (CLI takes priority over config)
|
||||
datafile = resolve_datafiles(config, args.datafile)[0]
|
||||
|
||||
if not datafile:
|
||||
print("No data files found to process.")
|
||||
return
|
||||
|
||||
print(f"Found {datafile} data files to process:")
|
||||
|
||||
# # 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]] = {}
|
||||
|
||||
# # 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
|
||||
stat_model_price = config["stat_model_price"]
|
||||
|
||||
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 in {datafile}...")
|
||||
return
|
||||
# Process data for this file
|
||||
try:
|
||||
cointegration_data: pd.DataFrame = pd.DataFrame()
|
||||
for pair in create_pairs(datafile, stat_model_price, config, instruments):
|
||||
cointegration_data = pd.concat([cointegration_data, pair.cointegration_check()])
|
||||
|
||||
pd.set_option('display.width', 400)
|
||||
pd.set_option('display.max_colwidth', None)
|
||||
pd.set_option('display.max_columns', None)
|
||||
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
|
||||
print(f"cointegration_data:\n{cointegration_data}")
|
||||
|
||||
except Exception as err:
|
||||
print(f"Error processing {datafile}: {str(err)}")
|
||||
import traceback
|
||||
|
||||
traceback.print_exc()
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
File diff suppressed because one or more lines are too long
@@ -1,232 +0,0 @@
|
||||
import argparse
|
||||
import glob
|
||||
import importlib
|
||||
import os
|
||||
from datetime import date, datetime
|
||||
from typing import Any, Dict, List, Optional, Tuple
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from research.research_tools import create_pairs
|
||||
from tools.config import expand_filename, load_config
|
||||
from pt_trading.results import (
|
||||
BacktestResult,
|
||||
create_result_database,
|
||||
store_config_in_database,
|
||||
)
|
||||
from pt_trading.fit_method import PairsTradingFitMethod
|
||||
from pt_trading.trading_pair import TradingPair
|
||||
|
||||
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
|
||||
|
||||
|
||||
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
|
||||
|
||||
|
||||
def run_backtest(
|
||||
config: Dict,
|
||||
datafiles: List[str],
|
||||
fit_method: PairsTradingFitMethod,
|
||||
instruments: List[Dict[str, str]],
|
||||
) -> BacktestResult:
|
||||
"""
|
||||
Run backtest for all pairs using the specified instruments.
|
||||
"""
|
||||
bt_result: BacktestResult = BacktestResult(config=config)
|
||||
# if len(datafiles) < 2:
|
||||
# print(f"WARNING: insufficient data files: {datafiles}")
|
||||
# return bt_result
|
||||
|
||||
if not all([os.path.exists(datafile) for datafile in datafiles]):
|
||||
print(f"WARNING: data file {datafiles} does not exist")
|
||||
return bt_result
|
||||
|
||||
pairs_trades = []
|
||||
|
||||
pairs = create_pairs(
|
||||
datafiles=datafiles,
|
||||
fit_method=fit_method,
|
||||
config=config,
|
||||
instruments=instruments,
|
||||
)
|
||||
for pair in pairs:
|
||||
single_pair_trades = fit_method.run_pair(pair=pair, bt_result=bt_result)
|
||||
if single_pair_trades is not None and len(single_pair_trades) > 0:
|
||||
pairs_trades.append(single_pair_trades)
|
||||
print(f"pairs_trades:\n{pairs_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
|
||||
|
||||
bt_result.collect_single_day_results(pairs_trades)
|
||||
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(
|
||||
"--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)
|
||||
|
||||
# Dynamically instantiate fit method class
|
||||
fit_method = PairsTradingFitMethod.create(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
|
||||
|
||||
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,
|
||||
fit_method_class=config["fit_method_class"],
|
||||
datafiles=datafiles,
|
||||
instruments=instruments,
|
||||
)
|
||||
is_config_stored = True
|
||||
|
||||
# Process data for this file
|
||||
try:
|
||||
fit_method.reset()
|
||||
|
||||
bt_results = run_backtest(
|
||||
config=config,
|
||||
datafiles=md_datafiles,
|
||||
fit_method=fit_method,
|
||||
instruments=instruments,
|
||||
)
|
||||
|
||||
if bt_results.trades is None or len(bt_results.trades) == 0:
|
||||
print(f"No trades found for {day}")
|
||||
continue
|
||||
|
||||
# Store results with day name as key
|
||||
filename = os.path.basename(day)
|
||||
all_results[filename] = {
|
||||
"trades": bt_results.trades.copy(),
|
||||
"outstanding_positions": bt_results.outstanding_positions.copy(),
|
||||
}
|
||||
|
||||
# Store results in database
|
||||
if args.result_db.upper() != "NONE":
|
||||
bt_results.calculate_returns(
|
||||
{
|
||||
filename: {
|
||||
"trades": bt_results.trades.copy(),
|
||||
"outstanding_positions": bt_results.outstanding_positions.copy(),
|
||||
}
|
||||
}
|
||||
)
|
||||
bt_results.store_results_in_database(db_path=args.result_db, day=day)
|
||||
|
||||
print(f"Successfully processed {filename}")
|
||||
|
||||
except Exception as err:
|
||||
print(f"Error processing {day}: {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__":
|
||||
main()
|
||||
@@ -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())
|
||||
@@ -0,0 +1,170 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
from typing import Callable, Coroutine, Dict, List
|
||||
import aiohttp.web as web
|
||||
|
||||
from cvttpy_tools.app import App
|
||||
from cvttpy_tools.config import Config
|
||||
from cvttpy_tools.base import NamedObject
|
||||
from cvttpy_tools.config import CvttAppConfig
|
||||
from cvttpy_tools.logger import Log
|
||||
from cvttpy_tools.settings.cvtt_types import BookIdT
|
||||
from cvttpy_tools.web.rest_service import RestService
|
||||
|
||||
# ---
|
||||
from cvttpy_trading.trading.instrument import ExchangeInstrument
|
||||
from cvttpy_trading.trading.mkt_data.md_summary import MdTradesAggregate
|
||||
from cvttpy_trading.trading.exchange_config import ExchangeAccounts
|
||||
# ---
|
||||
from pairs_trading.lib.live.mkt_data_client import CvttRestMktDataClient
|
||||
|
||||
'''
|
||||
config http://cloud16.cvtt.vpn/apps/pairs_trading
|
||||
'''
|
||||
|
||||
HistMdCbT = Callable[[List[MdTradesAggregate]], Coroutine]
|
||||
UpdateMdCbT = Callable[[MdTradesAggregate], Coroutine]
|
||||
|
||||
class PairTrader(NamedObject):
|
||||
config_: CvttAppConfig
|
||||
instruments_: List[ExchangeInstrument]
|
||||
book_id_: BookIdT
|
||||
|
||||
live_strategy_: "PtLiveStrategy" #type: ignore
|
||||
ti_sender_: "TradingInstructionsSender" #type: ignore
|
||||
pricer_client_: CvttRestMktDataClient
|
||||
rest_service_: RestService
|
||||
|
||||
latest_history_: Dict[ExchangeInstrument, List[MdTradesAggregate]]
|
||||
|
||||
def __init__(self) -> None:
|
||||
self.instruments_ = []
|
||||
self.latest_history_ = {}
|
||||
|
||||
App.instance().add_cmdline_arg(
|
||||
"--instrument_A",
|
||||
type=str,
|
||||
required=True,
|
||||
help=(
|
||||
" Instrument A in pair (e.g., COINBASE_AT:PAIR-BTC-USD)"
|
||||
),
|
||||
)
|
||||
App.instance().add_cmdline_arg(
|
||||
"--instrument_B",
|
||||
type=str,
|
||||
required=True,
|
||||
help=(
|
||||
" Instrument B in pair (e.g., COINBASE_AT:PAIR-ETH-USD)"
|
||||
),
|
||||
)
|
||||
|
||||
App.instance().add_cmdline_arg(
|
||||
"--book_id",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Book ID"
|
||||
)
|
||||
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()
|
||||
self.book_id_ = App.instance().get_argument(name="book_id")
|
||||
|
||||
# ------- PARSE INSTRUMENTS -------
|
||||
instr_list: List[str] = []
|
||||
instr_str = App.instance().get_argument("instrument_A", "")
|
||||
assert instr_str != "", "Missing insrument A"
|
||||
instr_list.append(instr_str)
|
||||
instr_str = App.instance().get_argument("instrument_B", "")
|
||||
assert instr_str != "", "Missing insrument B"
|
||||
instr_list.append(instr_str)
|
||||
|
||||
for instr in instr_list:
|
||||
instr_parts = instr.split(":")
|
||||
if len(instr_parts) != 2:
|
||||
raise ValueError(f"Invalid pair format: {instr}")
|
||||
exch_acct = instr_parts[0]
|
||||
instrument_id = instr_parts[1]
|
||||
exch_inst = ExchangeAccounts.instance().get_exchange_instrument(exch_acct=exch_acct, instrument_id=instrument_id)
|
||||
assert exch_inst is not None, f"No ExchangeInstrument for {instr}"
|
||||
exch_inst.user_data_["exch_acct"] = exch_acct
|
||||
self.instruments_.append(exch_inst)
|
||||
|
||||
Log.info(f"{self.fname()} Instruments: {self.instruments_[0].details_short()} <==> {self.instruments_[1].details_short()}")
|
||||
|
||||
# ------- CREATE STRATEGY -------
|
||||
from pairs_trading.lib.pt_strategy.live.live_strategy import PtLiveStrategy
|
||||
strategy_config = CvttAppConfig.instance() #self.config_.get_subconfig("strategy_config", Config({}))
|
||||
self.live_strategy_ = PtLiveStrategy(
|
||||
config=strategy_config,
|
||||
pairs_trader=self,
|
||||
)
|
||||
Log.info(f"{self.fname()} Strategy created: {self.live_strategy_}")
|
||||
model_name = self.config_.get_value("model/name", "?model/name?")
|
||||
self.config_.set_value("strategy_id", f"{self.live_strategy_.__class__.__name__}:{model_name}")
|
||||
|
||||
# # ------- CREATE PRICER CLIENT -------
|
||||
self.pricer_client_ = CvttRestMktDataClient(config=self.config_)
|
||||
Log.info(f"{self.fname()} MD client created: {self.pricer_client_}")
|
||||
|
||||
# ------- CREATE TRADER CLIENT -------
|
||||
from pairs_trading.lib.live.ti_sender import TradingInstructionsSender
|
||||
self.ti_sender_ = TradingInstructionsSender(config=self.config_, pairs_trader=self)
|
||||
Log.info(f"{self.fname()} TI sender created: {self.ti_sender_}")
|
||||
|
||||
# # ------- CREATE REST SERVER -------
|
||||
self.rest_service_ = RestService(
|
||||
config_key=f"/api/REST"
|
||||
)
|
||||
|
||||
# --- Strategy Handlers
|
||||
self.rest_service_.add_handler(
|
||||
method="POST",
|
||||
url="/api/strategy",
|
||||
handler=self._on_api_request,
|
||||
)
|
||||
|
||||
async def subscribe_md(self) -> None:
|
||||
from functools import partial
|
||||
for exch_inst in self.instruments_:
|
||||
exch_acct = exch_inst.user_data_.get("exch_acct", "?exch_acct?")
|
||||
instrument_id = exch_inst.instrument_id()
|
||||
|
||||
await self.pricer_client_.add_subscription(
|
||||
exch_acct=exch_acct,
|
||||
instrument_id=instrument_id,
|
||||
interval_sec=self.live_strategy_.interval_sec(),
|
||||
history_depth_sec=self.live_strategy_.history_depth_sec(),
|
||||
callback=partial(self._on_md_summary, exch_inst=exch_inst)
|
||||
)
|
||||
|
||||
async def _on_md_summary(self, history: List[MdTradesAggregate], exch_inst: ExchangeInstrument) -> None:
|
||||
# URGENT before calling stragegy, make sure that **BOTH** instruments market data is combined.
|
||||
Log.info(f"{self.fname()}: got {exch_inst.details_short()} data")
|
||||
self.latest_history_[exch_inst] = history
|
||||
if len(self.latest_history_) == 2:
|
||||
from itertools import chain
|
||||
all_aggrs = sorted(list(chain.from_iterable(self.latest_history_.values())), key=lambda X: X.time_ns_)
|
||||
|
||||
await self.live_strategy_.on_mkt_data_hist_snapshot(hist_aggr=all_aggrs)
|
||||
self.latest_history_ = {}
|
||||
|
||||
async def _on_api_request(self, request: web.Request) -> web.Response:
|
||||
# TODO choose pair
|
||||
# TODO confirm chosen pair (after selection is implemented)
|
||||
return web.Response() # TODO API request handler implementation
|
||||
|
||||
|
||||
async def run(self) -> None:
|
||||
Log.info(f"{self.fname()} ...")
|
||||
while True:
|
||||
await asyncio.sleep(0.1)
|
||||
pass
|
||||
|
||||
if __name__ == "__main__":
|
||||
App()
|
||||
CvttAppConfig()
|
||||
PairTrader()
|
||||
App.instance().run()
|
||||
@@ -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()
|
||||
@@ -0,0 +1,183 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
# ---------------- Settings
|
||||
|
||||
repo=git@cloud21.cvtt.vpn:/works/git/cvtt2/research/pairs_trading.git
|
||||
|
||||
dist_root=/home/cvttdist/software/cvtt2
|
||||
dist_user=cvttdist
|
||||
dist_host="cloud21.cvtt.vpn"
|
||||
dist_ssh_port="22"
|
||||
|
||||
dist_locations="cloud21.cvtt.vpn:22 hs01.cvtt.vpn:22"
|
||||
version_file="VERSION"
|
||||
|
||||
prj=pairs_trading
|
||||
brnch=master
|
||||
interactive=N
|
||||
|
||||
# ---------------- Settings
|
||||
|
||||
# ---------------- cmdline
|
||||
|
||||
usage() {
|
||||
echo "Usage: $0 [-b <branch (master)> -i (interactive)"
|
||||
exit 1
|
||||
}
|
||||
|
||||
while getopts "b:i" opt; do
|
||||
case ${opt} in
|
||||
b )
|
||||
brnch=$OPTARG
|
||||
;;
|
||||
i )
|
||||
interactive=Y
|
||||
;;
|
||||
\? )
|
||||
echo "Invalid option: -$OPTARG" >&2
|
||||
usage
|
||||
;;
|
||||
: )
|
||||
echo "Option -$OPTARG requires an argument." >&2
|
||||
usage
|
||||
;;
|
||||
esac
|
||||
done
|
||||
# ---------------- cmdline
|
||||
|
||||
confirm() {
|
||||
if [ "${interactive}" == "Y" ]; then
|
||||
echo "--------------------------------"
|
||||
echo -n "Press <Enter> to continue" && read
|
||||
fi
|
||||
}
|
||||
|
||||
|
||||
if [ "${interactive}" == "Y" ]; then
|
||||
echo -n "Enter project [${prj}]: "
|
||||
read project
|
||||
if [ "${project}" == "" ]
|
||||
then
|
||||
project=${prj}
|
||||
fi
|
||||
else
|
||||
project=${prj}
|
||||
fi
|
||||
|
||||
# repo=${git_repo_arr[${project}]}
|
||||
if [ -z ${repo} ]; then
|
||||
echo "ERROR: Project repository for ${project} not found"
|
||||
exit -1
|
||||
fi
|
||||
echo "Project repo: ${repo}"
|
||||
|
||||
if [ "${interactive}" == "Y" ]; then
|
||||
echo -n "Enter branch to build release from [${brnch}]: "
|
||||
read branch
|
||||
if [ "${branch}" == "" ]
|
||||
then
|
||||
branch=${brnch}
|
||||
fi
|
||||
else
|
||||
branch=${brnch}
|
||||
fi
|
||||
|
||||
tmp_dir=$(mktemp -d)
|
||||
function cleanup {
|
||||
cd ${HOME}
|
||||
rm -rf ${tmp_dir}
|
||||
}
|
||||
trap cleanup EXIT
|
||||
|
||||
|
||||
prj_dir="${tmp_dir}/${prj}"
|
||||
|
||||
cmd_arr=()
|
||||
Cmd="git clone ${repo} ${prj_dir}"
|
||||
cmd_arr+=("${Cmd}")
|
||||
|
||||
Cmd="cd ${prj_dir}"
|
||||
cmd_arr+=("${Cmd}")
|
||||
|
||||
if [ "${interactive}" == "Y" ]; then
|
||||
echo "------------------------------------"
|
||||
echo "The following commands will execute:"
|
||||
echo "------------------------------------"
|
||||
for cmd in "${cmd_arr[@]}"
|
||||
do
|
||||
echo ${cmd}
|
||||
done
|
||||
fi
|
||||
|
||||
confirm
|
||||
|
||||
for cmd in "${cmd_arr[@]}"
|
||||
do
|
||||
echo ${cmd} && eval ${cmd}
|
||||
done
|
||||
|
||||
Cmd="git checkout ${branch}"
|
||||
echo ${Cmd} && eval ${Cmd}
|
||||
if [ "${?}" != "0" ]; then
|
||||
echo "ERROR: Branch ${branch} is not found"
|
||||
cd ${HOME} && rm -rf ${tmp_dir}
|
||||
exit -1
|
||||
fi
|
||||
|
||||
|
||||
release_version=$(cat ${version_file} | awk -F',' '{print $1}')
|
||||
whats_new=$(cat ${version_file} | awk -F',' '{print $2}')
|
||||
|
||||
|
||||
echo "--------------------------------"
|
||||
echo "Version file: ${version_file}"
|
||||
echo "Release version: ${release_version}"
|
||||
|
||||
confirm
|
||||
|
||||
version_tag="v${release_version}"
|
||||
version_comment="'${version_tag} ${project} ${branch} $(date +%Y-%m-%d)\n${whats_new}'"
|
||||
|
||||
cmd_arr=()
|
||||
Cmd="git tag -a ${version_tag} -m ${version_comment}"
|
||||
cmd_arr+=("${Cmd}")
|
||||
|
||||
Cmd="git push origin --tags"
|
||||
cmd_arr+=("${Cmd}")
|
||||
|
||||
Cmd="rm -rf .git"
|
||||
cmd_arr+=("${Cmd}")
|
||||
|
||||
SourceLoc=../${project}
|
||||
|
||||
dist_path="${dist_root}/${project}/${release_version}"
|
||||
|
||||
for dist_loc in ${dist_locations}; do
|
||||
dhp=(${dist_loc//:/ })
|
||||
dist_host=${dhp[0]}
|
||||
dist_port=${dhp[1]}
|
||||
Cmd="rsync -avzh"
|
||||
Cmd="${Cmd} --rsync-path=\"mkdir -p ${dist_path}"
|
||||
Cmd="${Cmd} && rsync\" -e \"ssh -p ${dist_ssh_port}\""
|
||||
Cmd="${Cmd} $SourceLoc ${dist_user}@${dist_host}:${dist_path}/"
|
||||
cmd_arr+=("${Cmd}")
|
||||
done
|
||||
|
||||
if [ "${interactive}" == "Y" ]; then
|
||||
echo "------------------------------------"
|
||||
echo "The following commands will execute:"
|
||||
echo "------------------------------------"
|
||||
for cmd in "${cmd_arr[@]}"
|
||||
do
|
||||
echo ${cmd}
|
||||
done
|
||||
fi
|
||||
|
||||
confirm
|
||||
|
||||
for cmd in "${cmd_arr[@]}"
|
||||
do
|
||||
pwd && echo ${cmd} && eval ${cmd}
|
||||
done
|
||||
|
||||
echo "$0 Done ${project} ${release_version}"
|
||||
@@ -23,8 +23,8 @@
|
||||
"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",
|
||||
"model_class": "pairs_trading.lib.pt_strategy.models.OLSModel",
|
||||
"model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.ExpandingWindowDataPolicy",
|
||||
|
||||
# ====== Stop Conditions ======
|
||||
"stop_close_conditions": {
|
||||
@@ -22,11 +22,11 @@
|
||||
},
|
||||
"dis-equilibrium_open_trshld": 1.75,
|
||||
"dis-equilibrium_close_trshld": 0.9,
|
||||
"model_class": "pt_strategy.models.OLSModel",
|
||||
"model_class": "pairs_trading.lib.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",
|
||||
# "model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.EGOptimizedWndDataPolicy",
|
||||
# "model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.ADFOptimizedWndDataPolicy",
|
||||
"model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.JohansenOptdWndDataPolicy",
|
||||
"min_training_size": 60,
|
||||
"max_training_size": 150,
|
||||
|
||||
@@ -22,11 +22,11 @@
|
||||
},
|
||||
"dis-equilibrium_open_trshld": 1.75,
|
||||
"dis-equilibrium_close_trshld": 0.9,
|
||||
"model_class": "pt_strategy.models.OLSModel",
|
||||
"model_class": "pairs_trading.lib.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",
|
||||
"model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.RollingWindowDataPolicy",
|
||||
# "model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.OptimizedWindowDataPolicy",
|
||||
# "min_training_size": 60,
|
||||
# "max_training_size": 150,
|
||||
|
||||
@@ -23,11 +23,11 @@
|
||||
},
|
||||
"dis-equilibrium_open_trshld": 1.75,
|
||||
"dis-equilibrium_close_trshld": 1.0,
|
||||
"model_class": "pt_strategy.models.VECMModel",
|
||||
"model_class": "pairs_trading.lib.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",
|
||||
"model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.RollingWindowDataPolicy",
|
||||
# "model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.OptimizedWindowDataPolicy",
|
||||
# "min_training_size": 60,
|
||||
# "max_training_size": 150,
|
||||
|
||||
@@ -1,4 +1,11 @@
|
||||
{
|
||||
"refdata": {
|
||||
"assets": @inc=http://@env{CONFIG_SERVICE}/refdata/assets
|
||||
, "instruments": @inc=http://@env{CONFIG_SERVICE}/refdata/instruments
|
||||
, "exchange_instruments": @inc=http://@env{CONFIG_SERVICE}/refdata/exchange_instruments
|
||||
, "dynamic_instrument_exchanges": ["ALPACA"]
|
||||
, "exchanges": @inc=http://@env{CONFIG_SERVICE}/refdata/exchanges
|
||||
},
|
||||
"market_data_loading": {
|
||||
"CRYPTO": {
|
||||
"data_directory": "./data/crypto",
|
||||
@@ -11,21 +18,17 @@
|
||||
"instrument_id_pfx": "STOCK-",
|
||||
}
|
||||
},
|
||||
|
||||
# ====== Funding ======
|
||||
"funding_per_pair": 2000.0,
|
||||
# ====== Trading Parameters ======
|
||||
"stat_model_price": "close",
|
||||
|
||||
# ====== Model =======
|
||||
"model": @inc=http://@env{CONFIG_SERVICE}/apps/common/models/@env{MODEL_CONFIG}
|
||||
|
||||
# ====== Trading =======
|
||||
"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,
|
||||
@@ -37,7 +40,7 @@
|
||||
# "close_outstanding_positions": false,
|
||||
"trading_hours": {
|
||||
"timezone": "America/New_York",
|
||||
"begin_session": "9:30:00",
|
||||
"begin_session": "7:30:00",
|
||||
"end_session": "18:30:00",
|
||||
}
|
||||
}
|
||||
@@ -1,5 +1,5 @@
|
||||
{
|
||||
"strategy_config": @inc=file:///home/oleg/develop/pairs_trading/configuration/ols.cfg
|
||||
"strategy_config": @inc=file:///home/oleg/develop/pairs_trading/configuration/vecm-opt.cfg
|
||||
"pricer_config": {
|
||||
"pricer_url": "ws://localhost:12346/ws",
|
||||
"history_depth_sec": 86400 #"60*60*24", # use simpleeval
|
||||
+37
-30
@@ -1,19 +1,26 @@
|
||||
{
|
||||
"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-",
|
||||
}
|
||||
},
|
||||
# "refdata": {
|
||||
# "assets": @inc=http://@env{CONFIG_SERVICE}/refdata/assets
|
||||
# , "instruments": @inc=http://@env{CONFIG_SERVICE}/refdata/instruments
|
||||
# , "exchange_instruments": @inc=http://@env{CONFIG_SERVICE}/refdata/exchange_instruments
|
||||
# , "dynamic_instrument_exchanges": ["ALPACA"]
|
||||
# , "exchanges": @inc=http://@env{CONFIG_SERVICE}/refdata/exchanges
|
||||
# },
|
||||
# "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,
|
||||
# # ====== Funding ======
|
||||
# "funding_per_pair": 2000.0,
|
||||
|
||||
# ====== Trading Parameters ======
|
||||
"stat_model_price": "close", # "vwap"
|
||||
@@ -24,26 +31,26 @@
|
||||
"dis-equilibrium_open_trshld": 1.75,
|
||||
"dis-equilibrium_close_trshld": 1.0,
|
||||
|
||||
"model_class": "pt_strategy.models.VECMModel",
|
||||
"model_class": "pairs_trading.lib.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",
|
||||
# "model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.RollingWindowDataPolicy",
|
||||
"model_data_policy_class": "pairs_trading.lib.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
|
||||
}
|
||||
# # ====== 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",
|
||||
}
|
||||
# # ====== 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",
|
||||
# }
|
||||
}
|
||||
@@ -1,220 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import uuid
|
||||
from dataclasses import dataclass
|
||||
from functools import partial
|
||||
from typing import Callable, Coroutine, Dict, Optional
|
||||
|
||||
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
|
||||
SubscriptionIdT = str
|
||||
MessageT = Dict
|
||||
UrlT = str
|
||||
CallbackT = Callable[[MessageTypeT, SubscriptionIdT, MessageT], Coroutine[None, str, None]]
|
||||
|
||||
@dataclass
|
||||
class CvttPricesSubscription:
|
||||
id_: str
|
||||
exchange_config_name_: str
|
||||
instrument_id_: str
|
||||
interval_sec_: int
|
||||
history_depth_sec_: int
|
||||
is_subscribed_: bool
|
||||
is_historical_: bool
|
||||
callback_: CallbackT
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
exchange_config_name: str,
|
||||
instrument_id: str,
|
||||
interval_sec: int,
|
||||
history_depth_sec: int,
|
||||
callback: CallbackT,
|
||||
):
|
||||
self.exchange_config_name_ = exchange_config_name
|
||||
self.instrument_id_ = instrument_id
|
||||
self.interval_sec_ = interval_sec
|
||||
self.history_depth_sec_ = history_depth_sec
|
||||
self.callback_ = callback
|
||||
self.id_ = str(uuid.uuid4())
|
||||
self.is_subscribed_ = False
|
||||
self.is_historical_ = history_depth_sec > 0
|
||||
|
||||
class CvttWebSockClient:
|
||||
ws_url_: UrlT
|
||||
websocket_: Optional[ClientConnection]
|
||||
is_connected_: bool
|
||||
|
||||
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_ = {}
|
||||
|
||||
async def subscribe(
|
||||
self, subscription: CvttPricesSubscription
|
||||
) -> str: # returns subscription id
|
||||
|
||||
if not self.is_connected_:
|
||||
try:
|
||||
Log.info(f"Connecting to {self.ws_url_}")
|
||||
await self.connect()
|
||||
except Exception as e:
|
||||
Log.error(f"Unable to connect to {self.ws_url_}: {str(e)}")
|
||||
raise e
|
||||
|
||||
subscr_msg = {
|
||||
"type": "subscr",
|
||||
"id": subscription.id_,
|
||||
"subscr_type": "MD_AGGREGATE",
|
||||
"exchange_config_name": subscription.exchange_config_name_,
|
||||
"instrument_id": subscription.instrument_id_,
|
||||
"interval_sec": subscription.interval_sec_,
|
||||
}
|
||||
if subscription.is_historical_:
|
||||
subscr_msg["history_depth_sec"] = subscription.history_depth_sec_
|
||||
|
||||
assert self.websocket_ is not None
|
||||
await self.websocket_.send(json.dumps(subscr_msg))
|
||||
|
||||
response = await self.websocket_.recv()
|
||||
response_data = json.loads(response)
|
||||
if not await self.handle_subscription_response(subscription, response_data):
|
||||
await self.websocket_.close()
|
||||
self.is_connected_ = False
|
||||
raise Exception(f"Subscription failed: {str(response)}")
|
||||
|
||||
self.subscriptions_[subscription.id_] = subscription
|
||||
return subscription.id_
|
||||
|
||||
async def handle_subscription_response(
|
||||
self, subscription: CvttPricesSubscription, response: dict
|
||||
) -> bool:
|
||||
if response.get("type") != "subscr" or response.get("id") != subscription.id_:
|
||||
return False
|
||||
|
||||
if response.get("status") == "success":
|
||||
Log.info(f"Subscription successful: {json.dumps(response)}")
|
||||
return True
|
||||
elif response.get("status") == "error":
|
||||
Log.error(f"Subscription failed: {response.get('reason')}")
|
||||
return False
|
||||
return False
|
||||
|
||||
async def run(self) -> None:
|
||||
assert self.websocket_
|
||||
try:
|
||||
while self.is_connected_:
|
||||
try:
|
||||
msg_dict: JsonDictT = await self.receive_message()
|
||||
except websockets.ConnectionClosed:
|
||||
Log.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.is_connected_ = False
|
||||
await asyncio.sleep(5) # Wait before reconnecting
|
||||
|
||||
async def process_message(self, message: Dict) -> None:
|
||||
message_type = message.get("type")
|
||||
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}")
|
||||
return
|
||||
|
||||
subscription = self.subscriptions_[subscription_id]
|
||||
await subscription.callback_(message_type, subscription_id, message)
|
||||
else:
|
||||
Log.warning(f"Unknown message type: {message.get('type')}")
|
||||
|
||||
|
||||
async def main() -> None:
|
||||
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}")
|
||||
|
||||
pricer_client = CvttPricerWebSockClient(
|
||||
"ws://localhost:12346/ws"
|
||||
)
|
||||
await pricer_client.subscribe(CvttPricesSubscription(
|
||||
exchange_config_name="COINBASE_AT",
|
||||
instrument_id="PAIR-BTC-USD",
|
||||
interval_sec=60,
|
||||
history_depth_sec=60*60*24,
|
||||
callback=partial(on_message, instrument_id="PAIR-BTC-USD")
|
||||
))
|
||||
await pricer_client.subscribe(CvttPricesSubscription(
|
||||
exchange_config_name="COINBASE_AT",
|
||||
instrument_id="PAIR-ETH-USD",
|
||||
interval_sec=60,
|
||||
history_depth_sec=60*60*24,
|
||||
callback=partial(on_message, instrument_id="PAIR-ETH-USD")
|
||||
))
|
||||
|
||||
await pricer_client.run()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -0,0 +1,279 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
from typing import Callable, Coroutine, Dict, Any, List, Optional, Set
|
||||
|
||||
import requests
|
||||
|
||||
from cvttpy_tools.base import NamedObject
|
||||
from cvttpy_tools.app import App
|
||||
from cvttpy_tools.logger import Log
|
||||
from cvttpy_tools.config import Config
|
||||
from cvttpy_tools.timer import Timer
|
||||
from cvttpy_tools.timeutils import NanosT, current_seconds, NanoPerSec
|
||||
from cvttpy_tools.settings.cvtt_types import InstrumentIdT, IntervalSecT
|
||||
|
||||
# ---
|
||||
from cvttpy_trading.trading.mkt_data.historical_md import HistMdBar
|
||||
from cvttpy_trading.trading.instrument import ExchangeInstrument
|
||||
from cvttpy_trading.trading.accounting.exch_account import ExchangeAccountNameT
|
||||
from cvttpy_trading.trading.mkt_data.md_summary import MdTradesAggregate
|
||||
from cvttpy_trading.trading.exchange_config import ExchangeAccounts
|
||||
|
||||
# ---
|
||||
from pairs_trading.lib.live.rest_client import RESTSender
|
||||
|
||||
|
||||
class MdSummary(HistMdBar):
|
||||
def __init__(
|
||||
self,
|
||||
ts_ns: int,
|
||||
open: float,
|
||||
high: float,
|
||||
low: float,
|
||||
close: float,
|
||||
volume: float,
|
||||
vwap: float,
|
||||
num_trades: int,
|
||||
):
|
||||
super().__init__(ts=ts_ns)
|
||||
self.open_ = open
|
||||
self.high_ = high
|
||||
self.low_ = low
|
||||
self.close_ = close
|
||||
self.volume_ = volume
|
||||
self.vwap_ = vwap
|
||||
self.num_trades_ = num_trades
|
||||
|
||||
@classmethod
|
||||
def from_REST_response(cls, response: requests.Response) -> List[MdSummary]:
|
||||
res: List[MdSummary] = []
|
||||
jresp = response.json()
|
||||
hist_data = jresp.get("historical_data", [])
|
||||
for hd in hist_data:
|
||||
res.append(
|
||||
MdSummary(
|
||||
ts_ns=hd["time_ns"],
|
||||
open=hd["open"],
|
||||
high=hd["high"],
|
||||
low=hd["low"],
|
||||
close=hd["close"],
|
||||
volume=hd["volume"],
|
||||
vwap=hd["vwap"],
|
||||
num_trades=hd["num_trades"],
|
||||
)
|
||||
)
|
||||
return res
|
||||
|
||||
def create_md_trades_aggregate(
|
||||
self,
|
||||
exch_acct: ExchangeAccountNameT,
|
||||
exch_inst: ExchangeInstrument,
|
||||
interval_sec: IntervalSecT,
|
||||
) -> MdTradesAggregate:
|
||||
res = MdTradesAggregate(
|
||||
exch_acct=exch_acct,
|
||||
exch_inst=exch_inst,
|
||||
interval_ns=interval_sec * NanoPerSec,
|
||||
)
|
||||
res.set(mdbar=self)
|
||||
return res
|
||||
|
||||
|
||||
MdSummaryCallbackT = Callable[[List[MdTradesAggregate]], Coroutine]
|
||||
|
||||
|
||||
class MdSummaryCollector(NamedObject):
|
||||
sender_: RESTSender
|
||||
exch_acct_: ExchangeAccountNameT
|
||||
exch_inst_: ExchangeInstrument
|
||||
interval_sec_: IntervalSecT
|
||||
history_depth_sec_: IntervalSecT
|
||||
|
||||
history_: List[MdTradesAggregate]
|
||||
|
||||
callbacks_: List[MdSummaryCallbackT]
|
||||
timer_: Optional[Timer]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
sender: RESTSender,
|
||||
exch_acct: ExchangeAccountNameT,
|
||||
instrument_id: InstrumentIdT,
|
||||
interval_sec: IntervalSecT,
|
||||
history_depth_sec: IntervalSecT,
|
||||
) -> None:
|
||||
self.sender_ = sender
|
||||
self.exch_acct_ = exch_acct
|
||||
|
||||
exch_inst = ExchangeAccounts.instance().get_exchange_instrument(
|
||||
exch_acct=exch_acct, instrument_id=instrument_id
|
||||
)
|
||||
assert exch_inst is not None, f"Unable to find Exchange instrument for {exch_acct}/{instrument_id}"
|
||||
self.exch_inst_ = exch_inst
|
||||
self.interval_sec_ = interval_sec
|
||||
self.history_depth_sec_ = history_depth_sec
|
||||
|
||||
self.history_ = []
|
||||
self.callbacks_ = []
|
||||
self.timer_ = None
|
||||
|
||||
def add_callback(self, cb: MdSummaryCallbackT) -> None:
|
||||
self.callbacks_.append(cb)
|
||||
|
||||
def __hash__(self):
|
||||
return hash(
|
||||
(
|
||||
self.exch_acct_,
|
||||
self.exch_inst_.instrument_id(),
|
||||
self.interval_sec_,
|
||||
self.history_depth_sec_,
|
||||
)
|
||||
)
|
||||
|
||||
def rqst_data(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"exch_acct": self.exch_acct_,
|
||||
"instrument_id": self.exch_inst_.instrument_id(),
|
||||
"interval_sec": self.interval_sec_,
|
||||
"history_depth_sec": self.history_depth_sec_,
|
||||
}
|
||||
|
||||
def get_history(self) -> List[MdSummary]:
|
||||
response: requests.Response = self.sender_.send_post(
|
||||
endpoint="md_summary", post_body=self.rqst_data()
|
||||
)
|
||||
if response.status_code not in (200, 201):
|
||||
Log.error(
|
||||
f"{self.fname()}: Received error: {response.status_code} - {response.text}"
|
||||
)
|
||||
return []
|
||||
return MdSummary.from_REST_response(response=response)
|
||||
|
||||
def get_last(self) -> Optional[MdSummary]:
|
||||
Log.info(f"{self.fname()}: for {self.exch_inst_.details_short()}")
|
||||
rqst_data = self.rqst_data()
|
||||
rqst_data["history_depth_sec"] = self.interval_sec_ * 2
|
||||
response: requests.Response = self.sender_.send_post(
|
||||
endpoint="md_summary", post_body=rqst_data
|
||||
)
|
||||
if response.status_code not in (200, 201):
|
||||
Log.error(
|
||||
f"{self.fname()}: Received error: {response.status_code} - {response.text}"
|
||||
)
|
||||
return None
|
||||
res = MdSummary.from_REST_response(response=response)
|
||||
return None if len(res) == 0 else res[-1]
|
||||
|
||||
def is_empty(self) -> bool:
|
||||
return len(self.history_) == 0
|
||||
|
||||
async def start(self) -> None:
|
||||
if self.timer_:
|
||||
Log.error(f"{self.fname()}: Timer is already started")
|
||||
return
|
||||
mdsum_hist = self.get_history()
|
||||
self.history_ = [
|
||||
mdsum.create_md_trades_aggregate(
|
||||
exch_acct=self.exch_acct_,
|
||||
exch_inst=self.exch_inst_,
|
||||
interval_sec=self.interval_sec_,
|
||||
)
|
||||
for mdsum in mdsum_hist
|
||||
]
|
||||
await self.run_callbacks()
|
||||
self.set_timer()
|
||||
|
||||
def set_timer(self):
|
||||
if self.timer_:
|
||||
self.timer_.cancel()
|
||||
start_in = self.next_load_time() - current_seconds()
|
||||
self.timer_ = Timer(
|
||||
start_in_sec=start_in,
|
||||
func=self._load_new,
|
||||
)
|
||||
Log.info(f"{self.fname()} Timer for {self.exch_inst_.details_short()} is set to run in {start_in} sec")
|
||||
|
||||
def next_load_time(self) -> NanosT:
|
||||
curr_sec = int(current_seconds())
|
||||
return (curr_sec - curr_sec % self.interval_sec_) + self.interval_sec_ + 2
|
||||
|
||||
async def _load_new(self) -> None:
|
||||
|
||||
last: Optional[MdSummary] = self.get_last()
|
||||
if not last:
|
||||
Log.warning(f"{self.fname()}: did not get last update")
|
||||
elif not self.is_empty() and last.ts_ns_ <= self.history_[-1].time_ns_:
|
||||
Log.info(
|
||||
f"{self.fname()}: Received {last}. Already Have: {self.history_[-1]}"
|
||||
)
|
||||
else:
|
||||
self.history_.append(last.create_md_trades_aggregate(exch_acct=self.exch_acct_, exch_inst=self.exch_inst_, interval_sec=self.interval_sec_))
|
||||
await self.run_callbacks()
|
||||
self.set_timer()
|
||||
|
||||
async def run_callbacks(self) -> None:
|
||||
[await cb(self.history_) for cb in self.callbacks_]
|
||||
|
||||
def stop(self) -> None:
|
||||
if self.timer_:
|
||||
self.timer_.cancel()
|
||||
self.timer_ = None
|
||||
|
||||
|
||||
class CvttRestMktDataClient(NamedObject):
|
||||
config_: Config
|
||||
sender_: RESTSender
|
||||
collectors_: Set[MdSummaryCollector]
|
||||
|
||||
def __init__(self, config: Config) -> None:
|
||||
self.config_ = config
|
||||
base_url = self.config_.get_value("cvtt_base_url", default="")
|
||||
assert base_url
|
||||
self.sender_ = RESTSender(base_url=base_url)
|
||||
self.collectors_ = set()
|
||||
|
||||
async def add_subscription(
|
||||
self,
|
||||
exch_acct: ExchangeAccountNameT,
|
||||
instrument_id: InstrumentIdT,
|
||||
interval_sec: IntervalSecT,
|
||||
history_depth_sec: IntervalSecT,
|
||||
callback: MdSummaryCallbackT,
|
||||
) -> None:
|
||||
mdsc = MdSummaryCollector(
|
||||
sender=self.sender_,
|
||||
exch_acct=exch_acct,
|
||||
instrument_id=instrument_id,
|
||||
interval_sec=interval_sec,
|
||||
history_depth_sec=history_depth_sec,
|
||||
)
|
||||
mdsc.add_callback(callback)
|
||||
self.collectors_.add(mdsc)
|
||||
await mdsc.start()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
config = Config(json_src={"cvtt_base_url": "http://cvtt-tester-01.cvtt.vpn:23456"})
|
||||
# config = Config(json_src={"cvtt_base_url": "http://dev-server-02.cvtt.vpn:23456"})
|
||||
|
||||
async def _calback(history: List[MdTradesAggregate]) -> None:
|
||||
Log.info(
|
||||
f"MdSummary Hist Length is {len(history)}. Last summary: {history[-1] if len(history) > 0 else '[]'}"
|
||||
)
|
||||
|
||||
async def __run() -> None:
|
||||
Log.info("Starting...")
|
||||
cvtt_client = CvttRestMktDataClient(config)
|
||||
await cvtt_client.add_subscription(
|
||||
exch_acct="COINBASE_AT",
|
||||
instrument_id="PAIR-BTC-USD",
|
||||
interval_sec=60,
|
||||
history_depth_sec=24 * 3600,
|
||||
callback=_calback,
|
||||
)
|
||||
while True:
|
||||
await asyncio.sleep(5)
|
||||
|
||||
asyncio.run(__run())
|
||||
pass
|
||||
@@ -0,0 +1,67 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
from typing import Callable, Dict, Any, List, Optional
|
||||
import time
|
||||
|
||||
import requests
|
||||
|
||||
from cvttpy_tools.base import NamedObject
|
||||
from cvttpy_tools.logger import Log
|
||||
from cvttpy_tools.config import Config
|
||||
from cvttpy_tools.timer import Timer
|
||||
|
||||
from cvttpy_tools.timeutils import NanoPerSec, NanosT, current_nanoseconds, current_seconds
|
||||
from cvttpy_trading.trading.mkt_data.historical_md import HistMdBar
|
||||
|
||||
|
||||
class RESTSender(NamedObject):
|
||||
session_: requests.Session
|
||||
base_url_: str
|
||||
|
||||
def __init__(self, base_url: str) -> None:
|
||||
self.base_url_ = base_url
|
||||
self.session_ = requests.Session()
|
||||
|
||||
def is_ready(self) -> bool:
|
||||
"""Checks if the server is up and responding"""
|
||||
url = f"{self.base_url_}/ping"
|
||||
try:
|
||||
response = self.session_.get(url)
|
||||
response.raise_for_status()
|
||||
return True
|
||||
except requests.exceptions.RequestException:
|
||||
return False
|
||||
|
||||
def send_post(self, endpoint: str, post_body: Dict) -> requests.Response:
|
||||
|
||||
while not self.is_ready():
|
||||
print("Waiting for FrontGateway to start...")
|
||||
time.sleep(5)
|
||||
|
||||
url = f"{self.base_url_}/{endpoint}"
|
||||
try:
|
||||
return self.session_.request(
|
||||
method="POST",
|
||||
url=url,
|
||||
json=post_body,
|
||||
headers={"Content-Type": "application/json"},
|
||||
)
|
||||
except requests.exceptions.RequestException as excpt:
|
||||
raise ConnectionError(
|
||||
f"Failed to send status={excpt.response.status_code} {excpt.response.text}" # type: ignore
|
||||
) from excpt
|
||||
|
||||
def send_get(self, endpoint: str) -> requests.Response:
|
||||
while not self.is_ready():
|
||||
print("Waiting for FrontGateway to start...")
|
||||
time.sleep(5)
|
||||
|
||||
url = f"{self.base_url_}/{endpoint}"
|
||||
try:
|
||||
return self.session_.request(method="GET", url=url)
|
||||
except requests.exceptions.RequestException as excpt:
|
||||
raise ConnectionError(
|
||||
f"Failed to send status={excpt.response.status_code} {excpt.response.text}" # type: ignore
|
||||
) from excpt
|
||||
|
||||
@@ -0,0 +1,50 @@
|
||||
from enum import Enum
|
||||
|
||||
import requests
|
||||
|
||||
# import aiohttp
|
||||
from cvttpy_tools.base import NamedObject
|
||||
from cvttpy_tools.config import Config
|
||||
from cvttpy_tools.logger import Log
|
||||
# ---
|
||||
from cvttpy_trading.trading.trading_instructions import TradingInstructions
|
||||
# ---
|
||||
from pairs_trading.lib.live.rest_client import RESTSender
|
||||
from pairs_trading.apps.pair_trader import PairTrader
|
||||
|
||||
|
||||
class TradingInstructionsSender(NamedObject):
|
||||
config_: Config
|
||||
sender_: RESTSender
|
||||
pairs_trader_: PairTrader
|
||||
|
||||
class TradingInstType(str, Enum):
|
||||
TARGET_POSITION = "TARGET_POSITION"
|
||||
DIRECT_ORDER = "DIRECT_ORDER"
|
||||
MARKET_MAKING = "MARKET_MAKING"
|
||||
NONE = "NONE"
|
||||
|
||||
def __init__(self, config: Config, pairs_trader: PairTrader) -> None:
|
||||
self.config_ = config
|
||||
base_url = self.config_.get_value("cvtt_base_url", default="")
|
||||
assert base_url
|
||||
self.sender_ = RESTSender(base_url=base_url)
|
||||
self.pairs_trader_ = pairs_trader
|
||||
|
||||
self.book_id_ = self.pairs_trader_.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"
|
||||
|
||||
|
||||
async def send_trading_instructions(self, ti: TradingInstructions) -> None:
|
||||
Log.info(f"{self.fname()}: sending {ti=}")
|
||||
response: requests.Response = self.sender_.send_post(
|
||||
endpoint="trading_instructions", post_body=ti.to_dict()
|
||||
)
|
||||
if response.status_code not in (200, 201):
|
||||
Log.error(
|
||||
f"{self.fname()}: Received error: {response.status_code} - {response.text}"
|
||||
)
|
||||
|
||||
@@ -1,346 +1,332 @@
|
||||
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
|
||||
"""
|
||||
# ---
|
||||
from cvttpy_tools.base import NamedObject
|
||||
from cvttpy_tools.app import App
|
||||
from cvttpy_tools.config import Config
|
||||
from cvttpy_tools.settings.cvtt_types import IntervalSecT
|
||||
from cvttpy_tools.timeutils import SecPerHour, current_nanoseconds, NanoPerSec
|
||||
from cvttpy_tools.logger import Log
|
||||
|
||||
# ---
|
||||
from cvttpy_trading.trading.instrument import ExchangeInstrument
|
||||
from cvttpy_trading.trading.mkt_data.md_summary import MdTradesAggregate
|
||||
from cvttpy_trading.trading.trading_instructions import TradingInstructions
|
||||
from cvttpy_trading.trading.trading_instructions import TargetPositionSignal
|
||||
|
||||
class TradingInstructionType(Enum):
|
||||
TARGET_POSITION = "TARGET_POSITION"
|
||||
|
||||
@dataclass
|
||||
class TradingInstruction(NamedObject):
|
||||
type_: TradingInstructionType
|
||||
exch_instr_: ExchangeInstrument
|
||||
specifics_: Dict[str, Any]
|
||||
# ---
|
||||
from pairs_trading.lib.pt_strategy.model_data_policy import ModelDataPolicy
|
||||
from pairs_trading.lib.pt_strategy.pt_model import Prediction
|
||||
from pairs_trading.lib.pt_strategy.trading_pair import LiveTradingPair
|
||||
from pairs_trading.apps.pair_trader import PairTrader
|
||||
from pairs_trading.lib.pt_strategy.pt_market_data import LiveMarketData
|
||||
|
||||
|
||||
class PtLiveStrategy(NamedObject):
|
||||
config_: Dict[str, Any]
|
||||
trading_pair_: TradingPair
|
||||
config_: Config
|
||||
instruments_: List[ExchangeInstrument]
|
||||
|
||||
interval_sec_: IntervalSecT
|
||||
history_depth_sec_: IntervalSecT
|
||||
open_threshold_: float
|
||||
close_threshold_: float
|
||||
|
||||
trading_pair_: LiveTradingPair
|
||||
model_data_policy_: ModelDataPolicy
|
||||
pt_mkt_data_: RealTimeMarketData
|
||||
ti_sender_: TradingInstructionsSender
|
||||
pairs_trader_: PairTrader
|
||||
|
||||
# for presentation: history of prediction values and trading signals
|
||||
predictions_: pd.DataFrame
|
||||
trading_signals_: pd.DataFrame
|
||||
predictions_df_: pd.DataFrame
|
||||
trading_signals_df_: pd.DataFrame
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: Dict[str, Any],
|
||||
instruments: List[Dict[str, str]],
|
||||
ti_sender: TradingInstructionsSender,
|
||||
config: Config,
|
||||
pairs_trader: PairTrader,
|
||||
):
|
||||
|
||||
# import copy
|
||||
# self.config_ = Config(json_src=copy.deepcopy(config.data()))
|
||||
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.pairs_trader_ = pairs_trader
|
||||
self.trading_pair_ = LiveTradingPair(
|
||||
config=config,
|
||||
instruments=self.pairs_trader_.instruments_,
|
||||
)
|
||||
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"
|
||||
self.model_data_policy_ = ModelDataPolicy.create(
|
||||
self.config_,
|
||||
is_real_time=True,
|
||||
pair=self.trading_pair_,
|
||||
)
|
||||
assert (
|
||||
self.model_data_policy_ is not None
|
||||
), f"{self.fname()}: Unable to create ModelDataPolicy"
|
||||
|
||||
self.predictions_df_ = pd.DataFrame()
|
||||
self.trading_signals_df_ = pd.DataFrame()
|
||||
|
||||
self.instruments_ = self.pairs_trader_.instruments_
|
||||
|
||||
App.instance().add_call(
|
||||
stage=App.Stage.Config, func=self._on_config(), can_run_now=True
|
||||
)
|
||||
|
||||
async def _on_config(self) -> None:
|
||||
self.interval_sec_ = self.config_.get_value("interval_sec", 0)
|
||||
assert self.interval_sec_ > 0, "interval_sec cannot be 0"
|
||||
self.history_depth_sec_ = (
|
||||
self.config_.get_value("history_depth_hours", 0) * SecPerHour
|
||||
)
|
||||
assert self.history_depth_sec_ > 0, "history_depth_hours cannot be 0"
|
||||
|
||||
await self.pairs_trader_.subscribe_md()
|
||||
|
||||
self.open_threshold_ = self.config_.get_value(
|
||||
"model/disequilibrium/open_trshld", 0.0
|
||||
)
|
||||
self.close_threshold_ = self.config_.get_value(
|
||||
"model/disequilibrium/close_trshld", 0.0
|
||||
)
|
||||
|
||||
assert (
|
||||
self.open_threshold_ > 0
|
||||
), "disequilibrium/open_trshld must be greater than 0"
|
||||
assert (
|
||||
self.close_threshold_ > 0
|
||||
), "disequilibrium/close_trshld 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_hist_snapshot(
|
||||
self, hist_aggr: List[MdTradesAggregate]
|
||||
) -> None:
|
||||
if not self._is_md_actual(hist_aggr=hist_aggr):
|
||||
return
|
||||
|
||||
market_data_df: pd.DataFrame = self._create_md_df(hist_aggr=hist_aggr)
|
||||
if len(market_data_df) == 0:
|
||||
Log.warning(f"{self.fname()} Unable to create market data df")
|
||||
return
|
||||
|
||||
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()
|
||||
|
||||
Log.info(f"{self.fname()}: Running prediction for pair: {self.trading_pair_}")
|
||||
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
|
||||
self.predictions_df_ = pd.concat(
|
||||
[self.predictions_df_, prediction.to_df()], ignore_index=True
|
||||
)
|
||||
|
||||
trading_instructions: List[TradingInstruction] = (
|
||||
trading_instructions: List[TradingInstructions] = (
|
||||
self._create_trading_instructions(
|
||||
prediction=prediction, last_row=market_data_df.iloc[-1]
|
||||
)
|
||||
)
|
||||
if len(trading_instructions) > 0:
|
||||
if trading_instructions is not None:
|
||||
await self._send_trading_instructions(trading_instructions)
|
||||
# trades = self._create_trades(prediction=prediction, last_row=market_data_df.iloc[-1])
|
||||
# URGENT implement this
|
||||
pass
|
||||
|
||||
def _is_md_actual(self, hist_aggr: List[MdTradesAggregate]) -> bool:
|
||||
curr_ns = current_nanoseconds()
|
||||
LAG_THRESHOLD = 5 * NanoPerSec
|
||||
|
||||
if len(hist_aggr) == 0:
|
||||
Log.warning(f"{self.fname()} list of aggregates IS EMPTY")
|
||||
return False
|
||||
# MAYBE check market data length
|
||||
lag_ns = curr_ns - hist_aggr[-1].time_ns_
|
||||
if lag_ns > LAG_THRESHOLD:
|
||||
Log.warning(f"{self.fname()} {hist_aggr[-1].exch_inst_.details_short()} Lagging {int(lag_ns/NanoPerSec)} seconds")
|
||||
return False
|
||||
return True
|
||||
|
||||
def _create_md_df(self, hist_aggr: List[MdTradesAggregate]) -> pd.DataFrame:
|
||||
"""
|
||||
tstamp time_ns symbol open high low close volume num_trades vwap
|
||||
0 2025-09-10 11:30:00 1757503800000000000 ADA-USDT 0.8750 0.8750 0.8743 0.8743 50710.500 0 0.874489
|
||||
1 2025-09-10 11:30:00 1757503800000000000 SOL-USDT 219.9700 219.9800 219.6600 219.7000 2648.582 0 219.787847
|
||||
2 2025-09-10 11:31:00 1757503860000000000 SOL-USDT 219.7000 219.7300 219.6200 219.6200 1134.886 0 219.663460
|
||||
3 2025-09-10 11:31:00 1757503860000000000 ADA-USDT 0.8743 0.8745 0.8741 0.8741 10696.400 0 0.874234
|
||||
4 2025-09-10 11:32:00 1757503920000000000 ADA-USDT 0.8742 0.8742 0.8739 0.8740 18546.900 0 0.874037
|
||||
"""
|
||||
|
||||
rows: List[Dict[str, Any]] = []
|
||||
|
||||
for aggr in hist_aggr:
|
||||
exch_inst = aggr.exch_inst_
|
||||
|
||||
rows.append(
|
||||
{
|
||||
# convert nanoseconds → tz-aware pandas timestamp
|
||||
"tstamp": pd.to_datetime(aggr.time_ns_, unit="ns", utc=True),
|
||||
"time_ns": aggr.time_ns_,
|
||||
"symbol": exch_inst.instrument_id().split("-", 1)[1],
|
||||
"exchange_id": exch_inst.exchange_id_,
|
||||
"instrument_id": exch_inst.instrument_id(),
|
||||
"open": exch_inst.get_price(aggr.open_),
|
||||
"high": exch_inst.get_price(aggr.high_),
|
||||
"low": exch_inst.get_price(aggr.low_),
|
||||
"close": exch_inst.get_price(aggr.close_),
|
||||
"volume": exch_inst.get_quantity(aggr.volume_),
|
||||
"num_trades": aggr.num_trades_,
|
||||
"vwap": exch_inst.get_price(aggr.vwap_),
|
||||
}
|
||||
)
|
||||
|
||||
source_md_df = pd.DataFrame(
|
||||
rows,
|
||||
columns=[
|
||||
"tstamp",
|
||||
"time_ns",
|
||||
"symbol",
|
||||
"exchange_id",
|
||||
"instrument_id",
|
||||
"open",
|
||||
"high",
|
||||
"low",
|
||||
"close",
|
||||
"volume",
|
||||
"num_trades",
|
||||
"vwap",
|
||||
],
|
||||
)
|
||||
|
||||
# automatic sorting
|
||||
source_md_df.sort_values(
|
||||
by=["time_ns", "symbol"],
|
||||
ascending=True,
|
||||
inplace=True,
|
||||
kind="mergesort", # stable sort
|
||||
)
|
||||
|
||||
source_md_df.reset_index(drop=True, inplace=True)
|
||||
|
||||
pt_mkt_data = LiveMarketData(config=self.config_, instruments=self.instruments_)
|
||||
pt_mkt_data.origin_mkt_data_df_ = source_md_df
|
||||
pt_mkt_data.set_market_data()
|
||||
|
||||
return pt_mkt_data.market_data_df_
|
||||
|
||||
def interval_sec(self) -> IntervalSecT:
|
||||
return self.interval_sec_
|
||||
|
||||
def history_depth_sec(self) -> IntervalSecT:
|
||||
return self.history_depth_sec_
|
||||
|
||||
async def _send_trading_instructions(
|
||||
self, trading_instructions: pd.DataFrame
|
||||
self, trading_instructions: List[TradingInstructions]
|
||||
) -> None:
|
||||
pass
|
||||
for ti in trading_instructions:
|
||||
Log.info(f"{self.fname()} Sending trading instructions {ti}")
|
||||
await self.pairs_trader_.ti_sender_.send_trading_instructions(ti)
|
||||
|
||||
def _create_trading_instructions(
|
||||
self, prediction: Prediction, last_row: pd.Series
|
||||
) -> List[TradingInstruction]:
|
||||
) -> List[TradingInstructions]:
|
||||
trd_instructions: List[TradingInstructions] = []
|
||||
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_:
|
||||
|
||||
elif abs_scaled_disequilibrium <= self.close_threshold_ or pair.to_stop_close_conditions(predicted_row=last_row):
|
||||
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
|
||||
pair, row=last_row # , prediction=prediction
|
||||
)
|
||||
|
||||
|
||||
return trd_instructions
|
||||
|
||||
def _strength(self, scaled_disequilibrium: float) -> float:
|
||||
# TODO PtLiveStrategy._strength()
|
||||
return 1.0
|
||||
|
||||
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"]
|
||||
self, pair: LiveTradingPair, row: pd.Series, prediction: Prediction
|
||||
) -> List[TradingInstructions]:
|
||||
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"
|
||||
side_a = -1
|
||||
side_b = 1
|
||||
else:
|
||||
side_a = "BUY"
|
||||
side_b = "SELL"
|
||||
side_a = 1
|
||||
side_b = -1
|
||||
|
||||
# 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
|
||||
ti_a: Optional[TradingInstructions] = TradingInstructions(
|
||||
book=self.pairs_trader_.book_id_,
|
||||
strategy_id=self.__class__.__name__,
|
||||
ti_type=TradingInstructions.Type.TARGET_POSITION,
|
||||
issued_ts_ns=current_nanoseconds(),
|
||||
data=TargetPositionSignal(
|
||||
strength=side_a * self._strength(scaled_disequilibrium),
|
||||
exchange_id=pair.get_instrument_a().exchange_id_,
|
||||
base_asset=pair.get_instrument_a().base_asset_id_,
|
||||
quote_asset=pair.get_instrument_a().quote_asset_id_,
|
||||
user_data={}
|
||||
),
|
||||
)
|
||||
if not ti_a:
|
||||
return []
|
||||
ti_b: Optional[TradingInstructions] = TradingInstructions(
|
||||
book=self.pairs_trader_.book_id_,
|
||||
strategy_id=self.__class__.__name__,
|
||||
ti_type=TradingInstructions.Type.TARGET_POSITION,
|
||||
issued_ts_ns=current_nanoseconds(),
|
||||
data=TargetPositionSignal(
|
||||
strength=side_b * self._strength(scaled_disequilibrium),
|
||||
exchange_id=pair.get_instrument_b().exchange_id_,
|
||||
base_asset=pair.get_instrument_b().base_asset_id_,
|
||||
quote_asset=pair.get_instrument_b().quote_asset_id_,
|
||||
user_data={}
|
||||
),
|
||||
)
|
||||
if not ti_b:
|
||||
return []
|
||||
return [ti_a, ti_b]
|
||||
|
||||
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 _create_close_trade_instructions(
|
||||
self, pair: LiveTradingPair, row: pd.Series
|
||||
) -> List[TradingInstructions]:
|
||||
ti_a: Optional[TradingInstructions] = TradingInstructions(
|
||||
book=self.pairs_trader_.book_id_,
|
||||
strategy_id=self.__class__.__name__,
|
||||
ti_type=TradingInstructions.Type.TARGET_POSITION,
|
||||
issued_ts_ns=current_nanoseconds(),
|
||||
data=TargetPositionSignal(
|
||||
strength=0,
|
||||
exchange_id=pair.get_instrument_a().exchange_id_,
|
||||
base_asset=pair.get_instrument_a().base_asset_id_,
|
||||
quote_asset=pair.get_instrument_a().quote_asset_id_,
|
||||
user_data={}
|
||||
),
|
||||
)
|
||||
if not ti_a:
|
||||
return []
|
||||
ti_b: Optional[TradingInstructions] = TradingInstructions(
|
||||
book=self.pairs_trader_.book_id_,
|
||||
strategy_id=self.__class__.__name__,
|
||||
ti_type=TradingInstructions.Type.TARGET_POSITION,
|
||||
issued_ts_ns=current_nanoseconds(),
|
||||
data=TargetPositionSignal(
|
||||
strength=0,
|
||||
exchange_id=pair.get_instrument_b().exchange_id_,
|
||||
base_asset=pair.get_instrument_b().base_asset_id_,
|
||||
quote_asset=pair.get_instrument_b().quote_asset_id_,
|
||||
user_data={}
|
||||
),
|
||||
)
|
||||
if not ti_b:
|
||||
return []
|
||||
return [ti_a, ti_b]
|
||||
|
||||
@@ -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()
|
||||
@@ -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)
|
||||
|
||||
|
||||
@@ -8,31 +8,25 @@ from typing import Any, Dict, Optional, cast
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from cvttpy_tools.config import Config
|
||||
|
||||
@dataclass
|
||||
class DataWindowParams:
|
||||
training_size: int
|
||||
training_start_index: int
|
||||
training_size_: int
|
||||
training_start_index_: int
|
||||
|
||||
|
||||
class ModelDataPolicy(ABC):
|
||||
config_: Dict[str, Any]
|
||||
config_: Config
|
||||
current_data_params_: DataWindowParams
|
||||
count_: int
|
||||
is_real_time_: bool
|
||||
|
||||
def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
|
||||
def __init__(self, config: Config, *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,
|
||||
training_size_=config.get_value("model/training_size", 120),
|
||||
training_start_index_=0,
|
||||
)
|
||||
self.count_ = 0
|
||||
self.is_real_time_ = kwargs.get("is_real_time", False)
|
||||
@@ -40,14 +34,15 @@ class ModelDataPolicy(ABC):
|
||||
@abstractmethod
|
||||
def advance(self, mkt_data_df: Optional[pd.DataFrame] = None) -> DataWindowParams:
|
||||
self.count_ += 1
|
||||
if not self.is_real_time_:
|
||||
print(self.count_, end="\r")
|
||||
return self.current_data_params_
|
||||
|
||||
@staticmethod
|
||||
def create(config: Dict[str, Any], *args: Any, **kwargs: Any) -> ModelDataPolicy:
|
||||
def create(config: Config, *args: Any, **kwargs: Any) -> ModelDataPolicy:
|
||||
import importlib
|
||||
|
||||
model_data_policy_class_name = config.get("model_data_policy_class", None)
|
||||
model_data_policy_class_name = config.get_value("model/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)
|
||||
@@ -58,16 +53,18 @@ class ModelDataPolicy(ABC):
|
||||
|
||||
|
||||
class RollingWindowDataPolicy(ModelDataPolicy):
|
||||
def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
|
||||
def __init__(self, config: Config, *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
|
||||
self.current_data_params_.training_start_index_ = 0
|
||||
if mkt_data_df and len(mkt_data_df) > self.curren_data_params_.training_size_:
|
||||
self.current_data_params_.training_start_index_ = -self.curren_data_params_.training_size_
|
||||
else:
|
||||
self.current_data_params_.training_start_index += 1
|
||||
self.current_data_params_.training_start_index_ += 1
|
||||
return self.current_data_params_
|
||||
|
||||
|
||||
@@ -80,18 +77,17 @@ class OptimizedWndDataPolicy(ModelDataPolicy, ABC):
|
||||
prices_a_: np.ndarray
|
||||
prices_b_: np.ndarray
|
||||
|
||||
def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
|
||||
def __init__(self, config: Config, *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
|
||||
assert (config.key_exists("model/max_training_size") and config.key_exists("model/min_training_size")
|
||||
), "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"))
|
||||
self.min_training_size_ = cast(int, config.get_value("model/min_training_size"))
|
||||
self.max_training_size_ = cast(int, config.get_value("model/max_training_size"))
|
||||
|
||||
from pt_strategy.trading_pair import TradingPair
|
||||
from pairs_trading.lib.pt_strategy.trading_pair import TradingPair
|
||||
self.pair_ = cast(TradingPair, kwargs.get("pair"))
|
||||
|
||||
if "mkt_data" in kwargs:
|
||||
@@ -110,12 +106,12 @@ class OptimizedWndDataPolicy(ModelDataPolicy, ABC):
|
||||
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_
|
||||
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
|
||||
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])
|
||||
@@ -133,7 +129,7 @@ class EGOptimizedWndDataPolicy(OptimizedWndDataPolicy):
|
||||
# Engle-Granger cointegration test
|
||||
*** VERY SLOW ***
|
||||
'''
|
||||
def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
|
||||
def __init__(self, config: Config, *args: Any, **kwargs: Any):
|
||||
super().__init__(config, *args, **kwargs)
|
||||
|
||||
def optimize_window_size(self) -> DataWindowParams:
|
||||
@@ -152,8 +148,8 @@ class EGOptimizedWndDataPolicy(OptimizedWndDataPolicy):
|
||||
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
|
||||
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=}"
|
||||
@@ -162,7 +158,7 @@ class EGOptimizedWndDataPolicy(OptimizedWndDataPolicy):
|
||||
|
||||
class ADFOptimizedWndDataPolicy(OptimizedWndDataPolicy):
|
||||
# Augmented Dickey-Fuller test
|
||||
def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
|
||||
def __init__(self, config: Config, *args: Any, **kwargs: Any):
|
||||
super().__init__(config, *args, **kwargs)
|
||||
|
||||
def optimize_window_size(self) -> DataWindowParams:
|
||||
@@ -196,8 +192,8 @@ class ADFOptimizedWndDataPolicy(OptimizedWndDataPolicy):
|
||||
|
||||
if adf_pvalue < last_pvalue:
|
||||
last_pvalue = adf_pvalue
|
||||
result.training_size = trn_size
|
||||
result.training_start_index = start_index
|
||||
result.training_size_ = trn_size
|
||||
result.training_start_index_ = start_index
|
||||
|
||||
# print(
|
||||
# f"*** DEBUG *** end_index={self.end_index_},"
|
||||
@@ -208,7 +204,7 @@ class ADFOptimizedWndDataPolicy(OptimizedWndDataPolicy):
|
||||
|
||||
class JohansenOptdWndDataPolicy(OptimizedWndDataPolicy):
|
||||
# Johansen test
|
||||
def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
|
||||
def __init__(self, config: Config, *args: Any, **kwargs: Any):
|
||||
super().__init__(config, *args, **kwargs)
|
||||
|
||||
def optimize_window_size(self) -> DataWindowParams:
|
||||
@@ -246,8 +242,8 @@ class JohansenOptdWndDataPolicy(OptimizedWndDataPolicy):
|
||||
continue
|
||||
|
||||
if best_trn_size > 0:
|
||||
result.training_size = best_trn_size
|
||||
result.training_start_index = best_start_index
|
||||
result.training_size_ = best_trn_size
|
||||
result.training_start_index_ = best_start_index
|
||||
else:
|
||||
print("*** WARNING: No valid cointegration window found.")
|
||||
|
||||
|
||||
@@ -6,8 +6,8 @@ import statsmodels.api as sm
|
||||
|
||||
|
||||
|
||||
from pt_strategy.pt_model import PairsTradingModel, Prediction
|
||||
from pt_strategy.trading_pair import TradingPair
|
||||
from pairs_trading.lib.pt_strategy.pt_model import PairsTradingModel, Prediction
|
||||
from pairs_trading.lib.pt_strategy.trading_pair import TradingPair
|
||||
|
||||
|
||||
class OLSModel(PairsTradingModel):
|
||||
|
||||
+148
-154
@@ -1,35 +1,103 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
import pandas as pd
|
||||
|
||||
# ---
|
||||
from cvttpy_tools.base import NamedObject
|
||||
from cvttpy_tools.config import Config
|
||||
from cvttpy_tools.settings.cvtt_types import JsonDictT
|
||||
from tools.data_loader import load_market_data
|
||||
|
||||
# ---
|
||||
from cvttpy_trading.trading.mkt_data.md_summary import MdTradesAggregate
|
||||
from cvttpy_trading.trading.instrument import ExchangeInstrument
|
||||
# ---
|
||||
from pairs_trading.lib.tools.data_loader import load_market_data
|
||||
|
||||
|
||||
class PtMarketData():
|
||||
config_: Dict[str, Any]
|
||||
class PtMarketData(NamedObject, ABC):
|
||||
config_: Config
|
||||
origin_mkt_data_df_: pd.DataFrame
|
||||
market_data_df_: pd.DataFrame
|
||||
stat_model_price_: str
|
||||
instruments_: List[ExchangeInstrument]
|
||||
symbol_a_: str
|
||||
symbol_b_: str
|
||||
|
||||
def __init__(self, config: Dict[str, Any]):
|
||||
def __init__(self, config: Config, instruments: List[ExchangeInstrument]):
|
||||
self.config_ = config
|
||||
self.origin_mkt_data_df_ = pd.DataFrame()
|
||||
self.market_data_df_ = pd.DataFrame()
|
||||
self.stat_model_price_ = self.config_.get_value("model/stat_model_price")
|
||||
|
||||
self.instruments_ = instruments
|
||||
assert len(self.instruments_) > 0, "No instruments found in config"
|
||||
self.symbol_a_ = self.instruments_[0].instrument_id().split("-", 1)[1]
|
||||
self.symbol_b_ = self.instruments_[1].instrument_id().split("-", 1)[1]
|
||||
|
||||
@abstractmethod
|
||||
def md_columns(self) -> List[str]: ...
|
||||
|
||||
@abstractmethod
|
||||
def rename_columns(self, symbol_df: pd.DataFrame) -> pd.DataFrame: ...
|
||||
|
||||
@abstractmethod
|
||||
def tranform_df_target_colnames(self) -> List[str]: ...
|
||||
|
||||
def set_market_data(self) -> None:
|
||||
self.market_data_df_ = pd.DataFrame(
|
||||
self._transform_dataframe(self.origin_mkt_data_df_)[
|
||||
["tstamp"] + self.tranform_df_target_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")
|
||||
|
||||
def colnames(self) -> List[str]:
|
||||
return [
|
||||
f"{self.stat_model_price_}_{self.symbol_a_}",
|
||||
f"{self.stat_model_price_}_{self.symbol_b_}",
|
||||
]
|
||||
|
||||
def _transform_dataframe(self, df: pd.DataFrame) -> pd.DataFrame:
|
||||
df_selected: pd.DataFrame = pd.DataFrame(df[self.md_columns()])
|
||||
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"
|
||||
temp_df: pd.DataFrame = self.rename_columns(df_symbol)
|
||||
# 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()
|
||||
|
||||
class ResearchMarketData(PtMarketData):
|
||||
current_index_: int
|
||||
|
||||
is_execution_price_: bool
|
||||
|
||||
def __init__(self, config: Dict[str, Any]):
|
||||
super().__init__(config)
|
||||
def __init__(self, config: Config, instruments: List[ExchangeInstrument]):
|
||||
super().__init__(config, instruments)
|
||||
self.current_index_ = 0
|
||||
self.is_execution_price_ = "execution_price" in self.config_
|
||||
self.is_execution_price_ = self.config_.key_exists("execution_price")
|
||||
if self.is_execution_price_:
|
||||
self.execution_price_column_ = self.config_["execution_price"]["column"]
|
||||
self.execution_price_shift_ = self.config_["execution_price"]["shift"]
|
||||
self.execution_price_column_ = self.config_.get_value("execution_price")["column"]
|
||||
self.execution_price_shift_ = self.config_.get_value("execution_price")["shift"]
|
||||
else:
|
||||
self.execution_price_column_ = None
|
||||
self.execution_price_shift_ = 0
|
||||
@@ -43,73 +111,61 @@ class ResearchMarketData(PtMarketData):
|
||||
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"
|
||||
datafiles: List[str] = self.config_.get_value("datafiles", [])
|
||||
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_
|
||||
extra_minutes: int = 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"],
|
||||
instruments=self.instruments_,
|
||||
db_table_name=self.config_.get_value("market_data_loading")[
|
||||
self.instruments_[0].user_data_.get("instrument_type", "?instrument_type?")
|
||||
]["db_table_name"],
|
||||
trading_hours=self.config_.get_value("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(
|
||||
self.origin_mkt_data_df_ = self.origin_mkt_data_df_.dropna().reset_index(
|
||||
drop=True
|
||||
)
|
||||
self.set_market_data()
|
||||
self._set_execution_price_data()
|
||||
|
||||
# Create column name like "close-COIN"
|
||||
def _set_execution_price_data(self) -> None:
|
||||
if not self.is_execution_price_:
|
||||
return
|
||||
if not self.config_.key_exists("execution_price"):
|
||||
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_.get_value("execution_price")["column"]
|
||||
execution_price_shift = self.config_.get_value("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 md_columns(self) -> List[str]:
|
||||
# @abstractmethod
|
||||
if self.is_execution_price_:
|
||||
return ["tstamp", "symbol", self.stat_model_price_, self.execution_price_column_]
|
||||
else:
|
||||
return ["tstamp", "symbol", self.stat_model_price_]
|
||||
|
||||
def rename_columns(self, selected_symbol_df: pd.DataFrame) -> pd.DataFrame:
|
||||
# @abstractmethod
|
||||
symbol = selected_symbol_df.iloc[0]["symbol"]
|
||||
new_price_column = f"{self.stat_model_price_}_{symbol}"
|
||||
if self.is_execution_price_:
|
||||
new_execution_price_column = f"{self.execution_price_column_}_{symbol}"
|
||||
@@ -117,113 +173,51 @@ class ResearchMarketData(PtMarketData):
|
||||
# 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],
|
||||
"tstamp": selected_symbol_df["tstamp"],
|
||||
new_price_column: selected_symbol_df[self.stat_model_price_],
|
||||
new_execution_price_column: selected_symbol_df[self.execution_price_column_],
|
||||
}
|
||||
)
|
||||
else:
|
||||
temp_df = pd.DataFrame(
|
||||
{
|
||||
"tstamp": df_symbol["tstamp"],
|
||||
new_price_column: df_symbol[self.stat_model_price_],
|
||||
"tstamp": selected_symbol_df["tstamp"],
|
||||
new_price_column: selected_symbol_df[self.stat_model_price_],
|
||||
}
|
||||
)
|
||||
return temp_df
|
||||
|
||||
# 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 tranform_df_target_colnames(self):
|
||||
# @abstractmethod
|
||||
return self.colnames() + self.orig_exec_prices_colnames()
|
||||
|
||||
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_}",
|
||||
]
|
||||
] if self.is_execution_price_ else []
|
||||
|
||||
def exec_prices_colnames(self) -> List[str]:
|
||||
return [
|
||||
f"exec_price_{self.symbol_a_}",
|
||||
f"exec_price_{self.symbol_b_}",
|
||||
]
|
||||
class LiveMarketData(PtMarketData):
|
||||
|
||||
class RealTimeMarketData(PtMarketData):
|
||||
def __init__(self, config: Config, instruments: List[ExchangeInstrument]):
|
||||
super().__init__(config, instruments)
|
||||
|
||||
def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
|
||||
super().__init__(config, *args, **kwargs)
|
||||
def md_columns(self) -> List[str]:
|
||||
# @abstractmethod
|
||||
return ["tstamp", "symbol", self.stat_model_price_]
|
||||
|
||||
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
|
||||
def rename_columns(self, selected_symbol_df: pd.DataFrame) -> pd.DataFrame:
|
||||
# @abstractmethod
|
||||
symbol = selected_symbol_df.iloc[0]["symbol"]
|
||||
new_price_column = f"{self.stat_model_price_}_{symbol}"
|
||||
temp_df = pd.DataFrame(
|
||||
{
|
||||
"tstamp": selected_symbol_df["tstamp"],
|
||||
new_price_column: selected_symbol_df[self.stat_model_price_],
|
||||
}
|
||||
'''
|
||||
)
|
||||
return temp_df
|
||||
|
||||
|
||||
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()
|
||||
def tranform_df_target_colnames(self):
|
||||
# @abstractmethod
|
||||
return self.colnames()
|
||||
|
||||
@@ -3,8 +3,11 @@ from __future__ import annotations
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Any, Dict, cast
|
||||
|
||||
from pt_strategy.prediction import Prediction
|
||||
|
||||
# ---
|
||||
from cvttpy_tools.config import Config
|
||||
# ---
|
||||
from pairs_trading.lib.pt_strategy.prediction import Prediction
|
||||
from pairs_trading.lib.pt_strategy.trading_pair import TradingPair
|
||||
|
||||
class PairsTradingModel(ABC):
|
||||
|
||||
@@ -13,10 +16,10 @@ class PairsTradingModel(ABC):
|
||||
...
|
||||
|
||||
@staticmethod
|
||||
def create(config: Dict[str, Any]) -> PairsTradingModel:
|
||||
def create(config: Config) -> PairsTradingModel:
|
||||
import importlib
|
||||
|
||||
model_class_name = config.get("model_class", None)
|
||||
model_class_name = config.get_value("model/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)
|
||||
|
||||
@@ -1,54 +1,56 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any, Dict, List, Optional
|
||||
from typing import Any, Dict, List, Optional, Tuple
|
||||
|
||||
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
|
||||
|
||||
# ---
|
||||
from cvttpy_tools.config import Config
|
||||
# ---
|
||||
from cvttpy_trading.trading.instrument import ExchangeInstrument
|
||||
# ---
|
||||
from pairs_trading.lib.pt_strategy.model_data_policy import ModelDataPolicy
|
||||
from pairs_trading.lib.pt_strategy.pt_market_data import ResearchMarketData
|
||||
from pairs_trading.lib.pt_strategy.pt_model import Prediction
|
||||
from pairs_trading.lib.pt_strategy.trading_pair import PairState, TradingPair, ResearchTradingPair
|
||||
|
||||
class PtResearchStrategy:
|
||||
config_: Dict[str, Any]
|
||||
trading_pair_: TradingPair
|
||||
config_: Config
|
||||
trading_pair_: ResearchTradingPair
|
||||
model_data_policy_: ModelDataPolicy
|
||||
pt_mkt_data_: ResearchMarketData
|
||||
|
||||
trades_: List[pd.DataFrame]
|
||||
predictions_: pd.DataFrame
|
||||
predictions_df_: pd.DataFrame
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: Dict[str, Any],
|
||||
datafiles: List[str],
|
||||
instruments: List[Dict[str, str]],
|
||||
config: Config,
|
||||
instruments: List[ExchangeInstrument]
|
||||
):
|
||||
from pt_strategy.model_data_policy import ModelDataPolicy
|
||||
from pt_strategy.trading_pair import TradingPair
|
||||
from pairs_trading.lib.pt_strategy.model_data_policy import ModelDataPolicy
|
||||
from pairs_trading.lib.pt_strategy.trading_pair import TradingPair
|
||||
|
||||
self.config_ = config
|
||||
self.trades_ = []
|
||||
self.trading_pair_ = TradingPair(config=config, instruments=instruments)
|
||||
self.predictions_ = pd.DataFrame()
|
||||
self.trading_pair_ = ResearchTradingPair(config=config, instruments=instruments)
|
||||
self.predictions_df_ = 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)
|
||||
config_copy.set_value("instruments", instruments)
|
||||
self.pt_mkt_data_ = ResearchMarketData(config=config_copy, instruments=instruments)
|
||||
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_
|
||||
config_copy, 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)
|
||||
training_minutes = self.config_.get_value("training_minutes", 120)
|
||||
market_data_series: pd.Series
|
||||
market_data_df = pd.DataFrame()
|
||||
|
||||
@@ -72,8 +74,8 @@ class PtResearchStrategy:
|
||||
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
|
||||
self.predictions_df_ = pd.concat(
|
||||
[self.predictions_df_, prediction.to_df()], ignore_index=True
|
||||
)
|
||||
assert prediction is not None
|
||||
|
||||
@@ -93,8 +95,8 @@ class PtResearchStrategy:
|
||||
pair = self.trading_pair_
|
||||
trades = None
|
||||
|
||||
open_threshold = self.config_["dis-equilibrium_open_trshld"]
|
||||
close_threshold = self.config_["dis-equilibrium_close_trshld"]
|
||||
open_threshold = self.config_.get_value("model/disequilibrium/open_trshld")
|
||||
close_threshold = self.config_.get_value("model/disequilibrium/close_trshld")
|
||||
scaled_disequilibrium = prediction.scaled_disequilibrium_
|
||||
abs_scaled_disequilibrium = abs(scaled_disequilibrium)
|
||||
|
||||
@@ -143,7 +145,7 @@ class PtResearchStrategy:
|
||||
if pair.user_data_["state"] == PairState.OPEN:
|
||||
print(f"{pair}: *** Position is NOT CLOSED. ***")
|
||||
# outstanding positions
|
||||
if self.config_["close_outstanding_positions"]:
|
||||
if self.config_.get_value("close_outstanding_positions", False):
|
||||
close_position_row = pd.Series(pair.market_data_.iloc[-2])
|
||||
# close_position_row["disequilibrium"] = 0.0
|
||||
# close_position_row["scaled_disequilibrium"] = 0.0
|
||||
@@ -159,14 +161,14 @@ class PtResearchStrategy:
|
||||
pair.on_close_trades(trades)
|
||||
else:
|
||||
pair.add_outstanding_position(
|
||||
symbol=pair.symbol_a_,
|
||||
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_,
|
||||
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"],
|
||||
@@ -190,7 +192,7 @@ class PtResearchStrategy:
|
||||
return pd.DataFrame(columns=columns).astype(types)
|
||||
|
||||
def _create_open_trades(
|
||||
self, pair: TradingPair, row: pd.Series, prediction: Prediction
|
||||
self, pair: ResearchTradingPair, row: pd.Series, prediction: Prediction
|
||||
) -> Optional[pd.DataFrame]:
|
||||
colname_a, colname_b = pair.exec_prices_colnames()
|
||||
|
||||
@@ -224,7 +226,7 @@ class PtResearchStrategy:
|
||||
# create opening trades
|
||||
df.loc[len(df)] = {
|
||||
"time": tstamp,
|
||||
"symbol": pair.symbol_a_,
|
||||
"symbol": pair.symbol_a(),
|
||||
"side": side_a,
|
||||
"action": "OPEN",
|
||||
"price": px_a,
|
||||
@@ -235,7 +237,7 @@ class PtResearchStrategy:
|
||||
}
|
||||
df.loc[len(df)] = {
|
||||
"time": tstamp,
|
||||
"symbol": pair.symbol_b_,
|
||||
"symbol": pair.symbol_b(),
|
||||
"side": side_b,
|
||||
"action": "OPEN",
|
||||
"price": px_b,
|
||||
@@ -247,7 +249,7 @@ class PtResearchStrategy:
|
||||
return df
|
||||
|
||||
def _create_close_trades(
|
||||
self, pair: TradingPair, row: pd.Series, prediction: Optional[Prediction] = None
|
||||
self, pair: ResearchTradingPair, row: pd.Series, prediction: Optional[Prediction] = None
|
||||
) -> Optional[pd.DataFrame]:
|
||||
colname_a, colname_b = pair.exec_prices_colnames()
|
||||
|
||||
@@ -269,7 +271,7 @@ class PtResearchStrategy:
|
||||
# create opening trades
|
||||
df.loc[len(df)] = {
|
||||
"time": tstamp,
|
||||
"symbol": pair.symbol_a_,
|
||||
"symbol": pair.symbol_a(),
|
||||
"side": pair.user_data_["close_side_a"],
|
||||
"action": "CLOSE",
|
||||
"price": px_a,
|
||||
@@ -280,7 +282,7 @@ class PtResearchStrategy:
|
||||
}
|
||||
df.loc[len(df)] = {
|
||||
"time": tstamp,
|
||||
"symbol": pair.symbol_b_,
|
||||
"symbol": pair.symbol_b(),
|
||||
"side": pair.user_data_["close_side_b"],
|
||||
"action": "CLOSE",
|
||||
"price": px_b,
|
||||
|
||||
+13
-18
@@ -4,8 +4,12 @@ from datetime import date, datetime
|
||||
from typing import Any, Dict, List, Optional, Tuple
|
||||
|
||||
import pandas as pd
|
||||
from pt_strategy.trading_pair import TradingPair
|
||||
|
||||
# ---
|
||||
from cvttpy_tools.config import Config
|
||||
# ---
|
||||
from cvttpy_trading.trading.instrument import ExchangeInstrument
|
||||
# ---
|
||||
from pairs_trading.lib.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
|
||||
@@ -18,12 +22,10 @@ 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())
|
||||
@@ -118,9 +120,9 @@ def create_result_database(db_path: str) -> None:
|
||||
def store_config_in_database(
|
||||
db_path: str,
|
||||
config_file_path: str,
|
||||
config: Dict,
|
||||
config: Config,
|
||||
datafiles: List[Tuple[str, str]],
|
||||
instruments: List[Dict[str, str]],
|
||||
instruments: List[ExchangeInstrument],
|
||||
) -> None:
|
||||
"""
|
||||
Store configuration information in the database for reference.
|
||||
@@ -135,13 +137,13 @@ def store_config_in_database(
|
||||
cursor = conn.cursor()
|
||||
|
||||
# Convert config to JSON string
|
||||
config_json = json.dumps(config, indent=2, default=str)
|
||||
config_json = json.dumps(config.data(), 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']}"
|
||||
inst.details_short()
|
||||
for inst in instruments
|
||||
]
|
||||
)
|
||||
@@ -204,9 +206,9 @@ class PairResearchResult:
|
||||
trades_: Dict[DayT, pd.DataFrame]
|
||||
outstanding_positions_: Dict[DayT, List[OutstandingPositionT]]
|
||||
symbol_roundtrip_trades_: Dict[str, List[Dict[str, Any]]]
|
||||
config_: Config
|
||||
|
||||
|
||||
def __init__(self, config: Dict[str, Any]) -> None:
|
||||
def __init__(self, config: Config) -> None:
|
||||
self.config_ = config
|
||||
self.trades_ = {}
|
||||
self.outstanding_positions_ = {}
|
||||
@@ -218,13 +220,6 @@ class PairResearchResult:
|
||||
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]] = []
|
||||
@@ -292,7 +287,7 @@ class PairResearchResult:
|
||||
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
|
||||
funding_per_position = self.config_.get_value("funding_per_pair", 10000) / 2
|
||||
|
||||
# Symbol A round-trip
|
||||
day_roundtrips.append({
|
||||
|
||||
@@ -1,13 +1,21 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
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
|
||||
# ---
|
||||
from cvttpy_tools.base import NamedObject
|
||||
from cvttpy_tools.config import Config
|
||||
# ---
|
||||
from cvttpy_trading.trading.instrument import ExchangeInstrument
|
||||
# ---
|
||||
from pairs_trading.lib.pt_strategy.model_data_policy import DataWindowParams
|
||||
from pairs_trading.lib.pt_strategy.prediction import Prediction
|
||||
|
||||
|
||||
|
||||
class PairState(Enum):
|
||||
@@ -19,59 +27,76 @@ class PairState(Enum):
|
||||
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]
|
||||
class TradingPair(NamedObject, ABC):
|
||||
config_: Config
|
||||
model_: Any # "PairsTradingModel"
|
||||
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]
|
||||
stat_model_price_: str
|
||||
|
||||
instruments_: List[ExchangeInstrument]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: Dict[str, Any],
|
||||
instruments: List[Dict[str, str]],
|
||||
config: Config,
|
||||
instruments: List[ExchangeInstrument],
|
||||
):
|
||||
|
||||
from pt_strategy.pt_model import PairsTradingModel
|
||||
|
||||
assert len(instruments) == 2, "Trading pair must have exactly 2 instruments"
|
||||
from pairs_trading.lib.pt_strategy.pt_model import PairsTradingModel
|
||||
|
||||
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,
|
||||
}
|
||||
self.user_data_ = {}
|
||||
self.instruments_ = instruments
|
||||
self.instruments_[0].user_data_["symbol"] = instruments[0].instrument_id().split("-", 1)[1]
|
||||
self.instruments_[1].user_data_["symbol"] = instruments[1].instrument_id().split("-", 1)[1]
|
||||
self.stat_model_price_ = config.get_value("model/stat_model_price")
|
||||
|
||||
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)
|
||||
|
||||
def colnames(self) -> List[str]:
|
||||
return [
|
||||
f"{self.stat_model_price_}_{self.symbol_a()}",
|
||||
f"{self.stat_model_price_}_{self.symbol_b()}",
|
||||
]
|
||||
def symbol_a(self) -> str:
|
||||
return self.get_instrument_a().user_data_["symbol"]
|
||||
|
||||
def symbol_b(self) -> str:
|
||||
return self.get_instrument_b().user_data_["symbol"]
|
||||
|
||||
def get_instrument_a(self) -> ExchangeInstrument:
|
||||
return self.instruments_[0]
|
||||
|
||||
def get_instrument_b(self) -> ExchangeInstrument:
|
||||
return self.instruments_[1]
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return (
|
||||
f"{self.__class__.__name__}:"
|
||||
f" symbol_a={self.symbol_a_},"
|
||||
f" symbol_b={self.symbol_b_},"
|
||||
f" symbol_a={self.symbol_a()},"
|
||||
f" symbol_b={self.symbol_b()},"
|
||||
f" model={self.model_.__class__.__name__}"
|
||||
)
|
||||
|
||||
class ResearchTradingPair(TradingPair):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: Config,
|
||||
instruments: List[ExchangeInstrument],
|
||||
):
|
||||
assert len(instruments) == 2, "Trading pair must have exactly 2 instruments"
|
||||
super().__init__(config=config, instruments=instruments)
|
||||
|
||||
self.user_data_ = {
|
||||
"state": PairState.INITIAL,
|
||||
}
|
||||
|
||||
def is_closed(self) -> bool:
|
||||
return self.user_data_["state"] in [
|
||||
PairState.CLOSE,
|
||||
@@ -79,39 +104,34 @@ class TradingPair:
|
||||
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 is_open(self) -> bool:
|
||||
return not self.is_closed()
|
||||
|
||||
def exec_prices_colnames(self) -> List[str]:
|
||||
return [
|
||||
f"exec_price_{self.symbol_a_}",
|
||||
f"exec_price_{self.symbol_b_}",
|
||||
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
|
||||
not config.key_exists("stop_close_conditions")
|
||||
or config.get_value("stop_close_conditions") is None
|
||||
):
|
||||
return False
|
||||
if "profit" in config["stop_close_conditions"]:
|
||||
if "profit" in config.get_value("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"]:
|
||||
if current_return >= config.get_value("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"]:
|
||||
if "loss" in config.get_value("stop_close_conditions"):
|
||||
if current_return <= config.get_value("stop_close_conditions")["loss"]:
|
||||
print(f"STOP LOSS: {current_return}")
|
||||
self.user_data_["stop_close_state"] = PairState.CLOSE_STOP_LOSS
|
||||
return True
|
||||
@@ -136,8 +156,8 @@ class TradingPair:
|
||||
)
|
||||
return float(instrument_return) * 100.0
|
||||
|
||||
instrument_a_return = _single_instrument_return(self.symbol_a_)
|
||||
instrument_b_return = _single_instrument_return(self.symbol_b_)
|
||||
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
|
||||
|
||||
@@ -158,20 +178,22 @@ class TradingPair:
|
||||
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 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_:
|
||||
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
|
||||
funding_per_position = self.config_.get_value("funding_per_pair") / 2
|
||||
shares = funding_per_position / open_px
|
||||
if open_side == "SELL":
|
||||
shares = -shares
|
||||
@@ -179,7 +201,8 @@ class TradingPair:
|
||||
if "outstanding_positions" not in self.user_data_:
|
||||
self.user_data_["outstanding_positions"] = []
|
||||
|
||||
self.user_data_["outstanding_positions"].append({
|
||||
self.user_data_["outstanding_positions"].append(
|
||||
{
|
||||
"symbol": symbol,
|
||||
"open_side": open_side,
|
||||
"open_px": open_px,
|
||||
@@ -188,12 +211,16 @@ class TradingPair:
|
||||
"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)
|
||||
|
||||
}
|
||||
)
|
||||
|
||||
class LiveTradingPair(TradingPair):
|
||||
|
||||
def __init__(self, config: Config, instruments: List[ExchangeInstrument]):
|
||||
super().__init__(config, instruments)
|
||||
|
||||
def to_stop_close_conditions(self, predicted_row: pd.Series) -> bool:
|
||||
# TODO LiveTradingPair.to_stop_close_conditions()
|
||||
return False
|
||||
|
||||
|
||||
|
||||
+4
-4
@@ -1,12 +1,12 @@
|
||||
import hjson
|
||||
from typing import Dict
|
||||
from datetime import datetime
|
||||
# ---
|
||||
from cvttpy_tools.config import Config
|
||||
|
||||
|
||||
def load_config(config_path: str) -> Dict:
|
||||
with open(config_path, "r") as f:
|
||||
config = hjson.load(f)
|
||||
return dict(config)
|
||||
def load_config(config_path: str) -> Config:
|
||||
return Config(json_src=f"file://{config_path}")
|
||||
|
||||
|
||||
def expand_filename(filename: str) -> str:
|
||||
|
||||
@@ -1,9 +1,10 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import sqlite3
|
||||
from typing import Dict, List, cast
|
||||
from typing import Any, Dict, List, Tuple, cast
|
||||
import pandas as pd
|
||||
|
||||
from cvttpy_trading.trading.instrument import ExchangeInstrument
|
||||
|
||||
def load_sqlite_to_dataframe(db_path:str, query:str) -> pd.DataFrame:
|
||||
df: pd.DataFrame = pd.DataFrame()
|
||||
@@ -45,19 +46,17 @@ def convert_time_to_UTC(value: str, timezone: str, extra_minutes: int = 0) -> st
|
||||
|
||||
def load_market_data(
|
||||
datafile: str,
|
||||
instruments: List[Dict[str, str]],
|
||||
instruments: List[ExchangeInstrument],
|
||||
db_table_name: str,
|
||||
trading_hours: Dict = {},
|
||||
extra_minutes: int = 0,
|
||||
) -> pd.DataFrame:
|
||||
|
||||
insts = [
|
||||
'"' + instrument["instrument_id_pfx"] + instrument["symbol"] + '"'
|
||||
for instrument in instruments
|
||||
]
|
||||
instrument_ids = list(set(insts))
|
||||
|
||||
inst_ids = ['"' + exch_inst.instrument_id() + '"' for exch_inst in instruments]
|
||||
instrument_ids = list(set(inst_ids))
|
||||
exchange_ids = list(
|
||||
set(['"' + instrument["exchange_id"] + '"' for instrument in instruments])
|
||||
set(['"' + instrument.exchange_id() + '"' for instrument in instruments])
|
||||
)
|
||||
|
||||
query = "select"
|
||||
|
||||
@@ -1,18 +1,22 @@
|
||||
import os
|
||||
import glob
|
||||
from typing import Dict, List, Tuple
|
||||
# ---
|
||||
from cvttpy_tools.config import Config
|
||||
# ---
|
||||
from cvttpy_trading.trading.instrument import ExchangeInstrument
|
||||
|
||||
DayT = str
|
||||
DataFileNameT = str
|
||||
|
||||
def resolve_datafiles(
|
||||
config: Dict, date_pattern: str, instruments: List[Dict[str, str]]
|
||||
config: Config, date_pattern: str, instruments: List[ExchangeInstrument]
|
||||
) -> List[Tuple[DayT, DataFileNameT]]:
|
||||
resolved_files: List[Tuple[DayT, DataFileNameT]] = []
|
||||
for inst in instruments:
|
||||
for exch_inst in instruments:
|
||||
pattern = date_pattern
|
||||
inst_type = inst["instrument_type"]
|
||||
data_dir = config["market_data_loading"][inst_type]["data_directory"]
|
||||
inst_type = exch_inst.user_data_.get("instrument_type", "?instrument_type?")
|
||||
data_dir = config.get_value(f"market_data_loading/{inst_type}/data_directory")
|
||||
if "*" in pattern or "?" in pattern:
|
||||
# Handle wildcards
|
||||
if not os.path.isabs(pattern):
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
from pt_strategy.research_strategy import PtResearchStrategy
|
||||
from pairs_trading.lib.pt_strategy.research_strategy import PtResearchStrategy
|
||||
|
||||
|
||||
def visualize_prices(strategy: PtResearchStrategy, trading_date: str) -> None:
|
||||
@@ -8,8 +8,8 @@ def visualize_prices(strategy: PtResearchStrategy, trading_date: str) -> None:
|
||||
import seaborn as sns
|
||||
|
||||
pair = strategy.trading_pair_
|
||||
SYMBOL_A = pair.symbol_a_
|
||||
SYMBOL_B = pair.symbol_b_
|
||||
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')
|
||||
|
||||
+13
-18
@@ -1,13 +1,8 @@
|
||||
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 pairs_trading.lib.pt_strategy.results import (PairResearchResult)
|
||||
from pairs_trading.lib.pt_strategy.research_strategy import PtResearchStrategy
|
||||
|
||||
|
||||
def visualize_trades(strategy: PtResearchStrategy, results: PairResearchResult, trading_date: str) -> None:
|
||||
@@ -25,8 +20,8 @@ def visualize_trades(strategy: PtResearchStrategy, results: PairResearchResult,
|
||||
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_
|
||||
SYMBOL_A = pair.symbol_a()
|
||||
SYMBOL_B = pair.symbol_b()
|
||||
|
||||
|
||||
print(f"\nCreated trading pair: {pair}")
|
||||
@@ -51,7 +46,7 @@ def visualize_trades(strategy: PtResearchStrategy, results: PairResearchResult,
|
||||
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']],
|
||||
timeline_df = timeline_df.merge(strategy.predictions_df_[['tstamp', 'disequilibrium', 'scaled_disequilibrium', 'signed_scaled_disequilibrium']],
|
||||
on='tstamp', how='left')
|
||||
|
||||
# Get Symbol_A and Symbol_B market data
|
||||
@@ -110,8 +105,8 @@ def visualize_trades(strategy: PtResearchStrategy, results: PairResearchResult,
|
||||
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'],
|
||||
y0=strategy.config_.get_value('model/disequilibrium/open_trshld'),
|
||||
y1=strategy.config_.get_value('model/disequilibrium/open_trshld'),
|
||||
line=dict(color="purple", width=2, dash="dot"),
|
||||
opacity=0.7,
|
||||
row=1, col=1
|
||||
@@ -121,8 +116,8 @@ def visualize_trades(strategy: PtResearchStrategy, results: PairResearchResult,
|
||||
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'],
|
||||
y0=-strategy.config_.get_value('model/disequilibrium/open_trshld'),
|
||||
y1=-strategy.config_.get_value('model/disequilibrium/open_trshld'),
|
||||
line=dict(color="purple", width=2, dash="dot"),
|
||||
opacity=0.7,
|
||||
row=1, col=1
|
||||
@@ -132,8 +127,8 @@ def visualize_trades(strategy: PtResearchStrategy, results: PairResearchResult,
|
||||
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'],
|
||||
y0=strategy.config_.get_value('model/disequilibrium/close_trshld'),
|
||||
y1=strategy.config_.get_value('model/disequilibrium/close_trshld'),
|
||||
line=dict(color="brown", width=2, dash="dot"),
|
||||
opacity=0.7,
|
||||
row=1, col=1
|
||||
@@ -143,8 +138,8 @@ def visualize_trades(strategy: PtResearchStrategy, results: PairResearchResult,
|
||||
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'],
|
||||
y0=-strategy.config_.get_value('model/disequilibrium/close_trshld'),
|
||||
y1=-strategy.config_.get_value('model/disequilibrium/close_trshld'),
|
||||
line=dict(color="brown", width=2, dash="dot"),
|
||||
opacity=0.7,
|
||||
row=1, col=1
|
||||
|
||||
@@ -1,66 +0,0 @@
|
||||
[build-system]
|
||||
requires = ["setuptools>=45", "wheel"]
|
||||
build-backend = "setuptools.build_meta"
|
||||
|
||||
[project]
|
||||
name = "pairs-trading"
|
||||
version = "0.1.0"
|
||||
description = "Pairs Trading Backtesting Framework"
|
||||
requires-python = ">=3.8"
|
||||
|
||||
[tool.black]
|
||||
line-length = 88
|
||||
target-version = ['py38']
|
||||
include = '\.pyi?$'
|
||||
extend-exclude = '''
|
||||
/(
|
||||
# directories
|
||||
\.eggs
|
||||
| \.git
|
||||
| \.hg
|
||||
| \.mypy_cache
|
||||
| \.tox
|
||||
| \.venv
|
||||
| build
|
||||
| dist
|
||||
)/
|
||||
'''
|
||||
|
||||
[tool.flake8]
|
||||
max-line-length = 88
|
||||
extend-ignore = ["E203", "W503"]
|
||||
exclude = [
|
||||
".git",
|
||||
"__pycache__",
|
||||
"build",
|
||||
"dist",
|
||||
".venv",
|
||||
".mypy_cache",
|
||||
".tox"
|
||||
]
|
||||
|
||||
[tool.mypy]
|
||||
python_version = "3.8"
|
||||
warn_return_any = true
|
||||
warn_unused_configs = true
|
||||
disallow_untyped_defs = true
|
||||
disallow_incomplete_defs = true
|
||||
check_untyped_defs = true
|
||||
disallow_untyped_decorators = true
|
||||
no_implicit_optional = true
|
||||
warn_redundant_casts = true
|
||||
warn_unused_ignores = true
|
||||
warn_no_return = true
|
||||
warn_unreachable = true
|
||||
strict_equality = true
|
||||
|
||||
[[tool.mypy.overrides]]
|
||||
module = [
|
||||
"numpy.*",
|
||||
"pandas.*",
|
||||
"matplotlib.*",
|
||||
"seaborn.*",
|
||||
"scipy.*",
|
||||
"sklearn.*"
|
||||
]
|
||||
ignore_missing_imports = true
|
||||
@@ -1,25 +0,0 @@
|
||||
{
|
||||
"include": [
|
||||
"lib"
|
||||
],
|
||||
"exclude": [
|
||||
"**/node_modules",
|
||||
"**/__pycache__",
|
||||
"**/.*",
|
||||
"results",
|
||||
"data"
|
||||
],
|
||||
"ignore": [],
|
||||
"defineConstant": {},
|
||||
"typeCheckingMode": "basic",
|
||||
"useLibraryCodeForTypes": true,
|
||||
"autoImportCompletions": true,
|
||||
"autoSearchPaths": true,
|
||||
"extraPaths": [
|
||||
"lib",
|
||||
".."
|
||||
],
|
||||
"stubPath": "./typings",
|
||||
"venvPath": ".",
|
||||
"venv": "python3.12-venv"
|
||||
}
|
||||
@@ -170,6 +170,7 @@ types-PyYAML>=5.4
|
||||
types-redis>=3.5
|
||||
types-requests>=2.25
|
||||
types-retry>=0.9
|
||||
types-seaborn>0.13.2
|
||||
types-selenium>=3.141
|
||||
types-Send2Trash>=1.8
|
||||
types-setuptools>=57.4
|
||||
|
||||
+67
-34
@@ -1,53 +1,67 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
from typing import Any, Dict
|
||||
from typing import Any, Dict, List, Tuple
|
||||
|
||||
from pt_strategy.results import (
|
||||
# ---
|
||||
from cvttpy_tools.app import App
|
||||
from cvttpy_tools.base import NamedObject
|
||||
from cvttpy_tools.config import CvttAppConfig
|
||||
|
||||
# ---
|
||||
from cvttpy_trading.trading.instrument import ExchangeInstrument
|
||||
from cvttpy_trading.settings.instruments import Instruments
|
||||
|
||||
# ---
|
||||
from pairs_trading.lib.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 pairs_trading.lib.pt_strategy.research_strategy import PtResearchStrategy
|
||||
from pairs_trading.lib.tools.filetools import resolve_datafiles
|
||||
|
||||
InstrumentTypeT = str
|
||||
|
||||
|
||||
def main() -> None:
|
||||
import argparse
|
||||
class Runner(NamedObject):
|
||||
def __init__(self):
|
||||
App()
|
||||
CvttAppConfig()
|
||||
|
||||
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(
|
||||
# App.instance().add_cmdline_arg(
|
||||
# "--config", type=str, required=True, help="Path to the configuration file."
|
||||
# )
|
||||
App.instance().add_cmdline_arg(
|
||||
"--date_pattern",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Date YYYYMMDD, allows * and ? wildcards",
|
||||
)
|
||||
parser.add_argument(
|
||||
App.instance().add_cmdline_arg(
|
||||
"--instruments",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Comma-separated list of instrument symbols (e.g., COIN:EQUITY,GBTC:CRYPTO)",
|
||||
)
|
||||
parser.add_argument(
|
||||
App.instance().add_cmdline_arg(
|
||||
"--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)
|
||||
App.instance().add_call(stage=App.Stage.Config, func=self._on_config())
|
||||
App.instance().add_call(stage=App.Stage.Run, func=self.run())
|
||||
|
||||
async def _on_config(self) -> None:
|
||||
# Resolve data files (CLI takes priority over config)
|
||||
instruments = get_instruments(args, config)
|
||||
datafiles = resolve_datafiles(config, args.date_pattern, instruments)
|
||||
instruments: List[ExchangeInstrument] = self._get_instruments()
|
||||
datafiles = resolve_datafiles(
|
||||
config=CvttAppConfig.instance(),
|
||||
date_pattern=App.instance().get_argument("date_pattern"),
|
||||
instruments=instruments,
|
||||
)
|
||||
|
||||
days = list(set([day for day, _ in datafiles]))
|
||||
print(f"Found {len(datafiles)} data files to process:")
|
||||
@@ -55,35 +69,36 @@ def main() -> None:
|
||||
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)
|
||||
if App.instance().get_argument("result_db").upper() != "NONE":
|
||||
create_result_database(App.instance().get_argument("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)
|
||||
results = PairResearchResult(config=CvttAppConfig.instance())
|
||||
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
|
||||
exit(1)
|
||||
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,
|
||||
db_path=App.instance().get_argument("result_db"),
|
||||
config_file_path=App.instance().get_argument("config"),
|
||||
config=CvttAppConfig.instance(),
|
||||
datafiles=datafiles,
|
||||
instruments=instruments,
|
||||
)
|
||||
is_config_stored = True
|
||||
|
||||
CvttAppConfig.instance().set_value("datafiles", md_datafiles)
|
||||
pt_strategy = PtResearchStrategy(
|
||||
config=config, datafiles=md_datafiles, instruments=instruments
|
||||
config=CvttAppConfig.instance(),
|
||||
instruments=instruments,
|
||||
)
|
||||
pt_strategy.run()
|
||||
results.add_day_results(
|
||||
@@ -92,15 +107,33 @@ def main() -> None:
|
||||
outstanding_positions=pt_strategy.outstanding_positions(),
|
||||
)
|
||||
|
||||
|
||||
results.analyze_pair_performance()
|
||||
|
||||
def _get_instruments(self) -> List[ExchangeInstrument]:
|
||||
res: List[ExchangeInstrument] = []
|
||||
|
||||
if args.result_db.upper() != "NONE":
|
||||
print(f"\nResults stored in database: {args.result_db}")
|
||||
for inst in App.instance().get_argument("instruments").split(","):
|
||||
instrument_type = inst.split(":")[0]
|
||||
exchange_id = inst.split(":")[1]
|
||||
instrument_id = inst.split(":")[2]
|
||||
exch_inst: ExchangeInstrument = Instruments.instance().get_exch_inst(
|
||||
exch_id=exchange_id, inst_id=instrument_id, src=f"{self.fname()}"
|
||||
)
|
||||
exch_inst.user_data_["instrument_type"] = instrument_type
|
||||
res.append(exch_inst)
|
||||
|
||||
return res
|
||||
|
||||
async def run(self) -> None:
|
||||
|
||||
if App.instance().get_argument("result_db").upper() != "NONE":
|
||||
print(
|
||||
f'\nResults stored in database: {App.instance().get_argument("result_db")}'
|
||||
)
|
||||
else:
|
||||
print("No results to display.")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Runner()
|
||||
App.instance().run()
|
||||
|
||||
+5188
-4519
File diff suppressed because one or more lines are too long
@@ -1,94 +0,0 @@
|
||||
import glob
|
||||
import os
|
||||
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.
|
||||
CLI datafiles take priority over config datafiles.
|
||||
Supports wildcards in config but not in CLI.
|
||||
"""
|
||||
if cli_datafiles:
|
||||
# CLI override - comma-separated list, no wildcards
|
||||
datafiles = [f.strip() for f in cli_datafiles.split(",")]
|
||||
# Make paths absolute relative to data directory
|
||||
data_dir = config.get("data_directory", "./data")
|
||||
resolved_files = []
|
||||
for df in datafiles:
|
||||
if not os.path.isabs(df):
|
||||
df = os.path.join(data_dir, df)
|
||||
resolved_files.append(df)
|
||||
return resolved_files
|
||||
|
||||
# Use config datafiles with wildcard support
|
||||
config_datafiles = config.get("datafiles", [])
|
||||
data_dir = config.get("data_directory", "./data")
|
||||
resolved_files = []
|
||||
|
||||
for pattern in config_datafiles:
|
||||
if "*" in pattern or "?" in pattern:
|
||||
# Handle wildcards
|
||||
if not os.path.isabs(pattern):
|
||||
pattern = os.path.join(data_dir, pattern)
|
||||
matched_files = glob.glob(pattern)
|
||||
resolved_files.extend(matched_files)
|
||||
else:
|
||||
# Handle explicit file path
|
||||
if not os.path.isabs(pattern):
|
||||
pattern = os.path.join(data_dir, pattern)
|
||||
resolved_files.append(pattern)
|
||||
|
||||
return sorted(list(set(resolved_files))) # Remove duplicates and sort
|
||||
|
||||
|
||||
def create_pairs(
|
||||
datafiles: List[str],
|
||||
fit_method: PairsTradingFitMethod,
|
||||
config: Dict,
|
||||
instruments: List[Dict[str, str]],
|
||||
) -> List:
|
||||
from pt_trading.trading_pair import TradingPair
|
||||
from tools.data_loader import load_market_data
|
||||
|
||||
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 = pd.DataFrame()
|
||||
extra_minutes = 0
|
||||
if "execution_price" in config_copy:
|
||||
extra_minutes = config_copy["execution_price"]["shift"]
|
||||
|
||||
for datafile in datafiles:
|
||||
md_df = load_market_data(
|
||||
datafile=datafile,
|
||||
instruments=instruments,
|
||||
db_table_name=config_copy["market_data_loading"][instruments[0]["instrument_type"]]["db_table_name"],
|
||||
trading_hours=config_copy["trading_hours"],
|
||||
extra_minutes=extra_minutes,
|
||||
)
|
||||
market_data_df = pd.concat([market_data_df, md_df])
|
||||
|
||||
if len(set(market_data_df["symbol"])) != 2: # both symbols must be present for a pair
|
||||
print(f"WARNING: insufficient data in files: {datafiles}")
|
||||
return []
|
||||
|
||||
for a_index, b_index in unique_index_pairs:
|
||||
symbol_a=instruments[a_index]["symbol"]
|
||||
symbol_b=instruments[b_index]["symbol"]
|
||||
pair = fit_method.create_trading_pair(
|
||||
config=config_copy,
|
||||
market_data=market_data_df,
|
||||
symbol_a=symbol_a,
|
||||
symbol_b=symbol_b,
|
||||
)
|
||||
pairs.append(pair)
|
||||
return pairs
|
||||
@@ -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()
|
||||
Reference in New Issue
Block a user