Compare commits
39 Commits
e97f76222c
..
v1.0.4
| Author | SHA1 | Date | |
|---|---|---|---|
 Oleg Sheynin
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6e0789d614 | ||
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Oleg Sheynin
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4ddf4017bd | ||
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Oleg Sheynin
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f49a10f54e | ||
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Oleg Sheynin
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9d553dcf1a | ||
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Oleg Sheynin
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a3e5acd765 | ||
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Oleg Sheynin
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49c91e5d85 | ||
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Oleg Sheynin
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400bd41e56 | ||
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Oleg Sheynin
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1d1ebd385e | ||
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Oleg Sheynin
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c5ed951b2a | ||
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Oleg Sheynin
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c77377f67e | ||
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Oleg Sheynin
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8ccebf81f5 | ||
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Oleg Sheynin
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dc38176529 | ||
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Oleg Sheynin
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3f29717b64 | ||
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Oleg Sheynin
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ecc1c1de5d | ||
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Oleg Sheynin
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2a118d4600 | ||
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Oleg Sheynin
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98f6defe96 | ||
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Oleg Sheynin
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2819fd536a | ||
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Oleg Sheynin
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73135ee8c2 | ||
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Oleg Sheynin
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e4a3795793 | ||
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Oleg Sheynin
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f311315ef8 | ||
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Oleg Sheynin
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76f9a80ad6 | ||
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Oleg Sheynin
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bf25eb7fb5 | ||
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Oleg Sheynin
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f2a5d6a7ad | ||
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Oleg Sheynin
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b9d479ae8c | ||
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Oleg Sheynin
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e6ae62ebb6 | ||
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Oleg Sheynin
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170e48d646 | ||
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Oleg Sheynin
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d5f00f557b | ||
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Oleg Sheynin
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c0fabcb429 | ||
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Oleg Sheynin
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bd6cf1d4d0 | ||
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Oleg Sheynin
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b196863a34 | ||
 oleg
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6dd0f97d74 | ||
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oleg
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002f797751 | ||
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oleg
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4bf1d46208 | ||
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oleg
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842eb3ec62 | ||
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oleg
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69a0b19e9f | ||
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oleg
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121c85def0 | ||
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Oleg Sheynin
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2e32b26fad | ||
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Oleg Sheynin
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ba2a6cd2eb | ||
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Oleg Sheynin
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8b115cee75 |
+17
-4
@@ -3,9 +3,22 @@ __pycache__/
|
||||
__OLD__/
|
||||
.specstory/
|
||||
.history/
|
||||
.cursorindexingignore
|
||||
.vscode/
|
||||
*.py[cod]
|
||||
.ipynb_checkpoints/
|
||||
.pytest_cache/
|
||||
|
||||
# Local environments
|
||||
.venv/
|
||||
venv/
|
||||
|
||||
# Local test data and generated analysis results
|
||||
data/*
|
||||
!data/.gitkeep
|
||||
results/*
|
||||
!results/.gitkeep
|
||||
|
||||
data
|
||||
|
||||
cvttpy
|
||||
# SpecStory explanation file
|
||||
.specstory/.what-is-this.md
|
||||
results/
|
||||
tmp/
|
||||
|
||||
Vendored
-1
@@ -1 +0,0 @@
|
||||
PYTHONPATH=/home/oleg/develop
|
||||
Vendored
-271
@@ -1,271 +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",
|
||||
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
|
||||
"program": "${file}",
|
||||
"console": "integratedTerminal",
|
||||
"env": {
|
||||
"PYTHONPATH": "${workspaceFolder}/lib:${workspaceFolder}/.."
|
||||
},
|
||||
},
|
||||
{
|
||||
"name": "-------- Live Pair Trading --------",
|
||||
},
|
||||
{
|
||||
"name": "PAIRS TRADER",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
|
||||
"program": "${workspaceFolder}/bin/pairs_trader.py",
|
||||
"console": "integratedTerminal",
|
||||
"env": {
|
||||
"PYTHONPATH": "${workspaceFolder}/.."
|
||||
},
|
||||
"args": [
|
||||
"--config=${workspaceFolder}/configuration/pairs_trader.cfg",
|
||||
"--pair=PAIR-ADA-USDT:BNBSPOT,PAIR-SOL-USDT:BNBSPOT",
|
||||
],
|
||||
},
|
||||
{
|
||||
"name": "-------- OLS --------",
|
||||
},
|
||||
{
|
||||
"name": "CRYPTO OLS (rolling)",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
|
||||
"program": "${workspaceFolder}/research/backtest.py",
|
||||
"args": [
|
||||
"--config=${workspaceFolder}/configuration/ols.cfg",
|
||||
"--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
|
||||
"--date_pattern=20250605",
|
||||
"--result_db=${workspaceFolder}/research/results/crypto/%T.ols.ADA-SOL.20250605.crypto_results.db",
|
||||
],
|
||||
"env": {
|
||||
"PYTHONPATH": "${workspaceFolder}/lib"
|
||||
},
|
||||
"console": "integratedTerminal"
|
||||
},
|
||||
{
|
||||
"name": "CRYPTO OLS (optimized)",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
|
||||
"program": "${workspaceFolder}/research/backtest.py",
|
||||
"args": [
|
||||
"--config=${workspaceFolder}/configuration/ols-opt.cfg",
|
||||
"--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
|
||||
"--date_pattern=20250605",
|
||||
"--result_db=${workspaceFolder}/research/results/crypto/%T.ols-opt.ADA-SOL.20250605.crypto_results.db",
|
||||
],
|
||||
"env": {
|
||||
"PYTHONPATH": "${workspaceFolder}/lib"
|
||||
},
|
||||
"console": "integratedTerminal"
|
||||
},
|
||||
// {
|
||||
// "name": "CRYPTO OLS (expanding)",
|
||||
// "type": "debugpy",
|
||||
// "request": "launch",
|
||||
// "python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
|
||||
// "program": "${workspaceFolder}/research/backtest.py",
|
||||
// "args": [
|
||||
// "--config=${workspaceFolder}/configuration/ols-exp.cfg",
|
||||
// "--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
|
||||
// "--date_pattern=20250605",
|
||||
// "--result_db=${workspaceFolder}/research/results/crypto/%T.ols-exp.ADA-SOL.20250605.crypto_results.db",
|
||||
// ],
|
||||
// "env": {
|
||||
// "PYTHONPATH": "${workspaceFolder}/lib"
|
||||
// },
|
||||
// "console": "integratedTerminal"
|
||||
// },
|
||||
{
|
||||
"name": "EQUITY OLS (rolling)",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
|
||||
"program": "${workspaceFolder}/research/backtest.py",
|
||||
"args": [
|
||||
"--config=${workspaceFolder}/configuration/ols.cfg",
|
||||
"--instruments=COIN:EQUITY:ALPACA,MSTR:EQUITY:ALPACA",
|
||||
"--date_pattern=20250605",
|
||||
"--result_db=${workspaceFolder}/research/results/equity/%T.ols.COIN-MSTR.20250605.equity_results.db",
|
||||
],
|
||||
"env": {
|
||||
"PYTHONPATH": "${workspaceFolder}/lib"
|
||||
},
|
||||
"console": "integratedTerminal"
|
||||
},
|
||||
{
|
||||
"name": "EQUITY-CRYPTO OLS (rolling)",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
|
||||
"program": "${workspaceFolder}/research/backtest.py",
|
||||
"args": [
|
||||
"--config=${workspaceFolder}/configuration/ols.cfg",
|
||||
"--instruments=COIN:EQUITY:ALPACA,BTC-USDT:CRYPTO:BNBSPOT",
|
||||
"--date_pattern=20250605",
|
||||
"--result_db=${workspaceFolder}/research/results/intermarket/%T.ols.COIN-BTC.20250605.equity_results.db",
|
||||
],
|
||||
"env": {
|
||||
"PYTHONPATH": "${workspaceFolder}/lib"
|
||||
},
|
||||
"console": "integratedTerminal"
|
||||
},
|
||||
{
|
||||
"name": "-------- VECM --------",
|
||||
},
|
||||
{
|
||||
"name": "CRYPTO VECM (rolling)",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
|
||||
"program": "${workspaceFolder}/research/backtest.py",
|
||||
"args": [
|
||||
"--config=${workspaceFolder}/configuration/vecm.cfg",
|
||||
"--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
|
||||
"--date_pattern=20250605",
|
||||
"--result_db=${workspaceFolder}/research/results/crypto/%T.vecm.ADA-SOL.20250605.crypto_results.db",
|
||||
],
|
||||
"env": {
|
||||
"PYTHONPATH": "${workspaceFolder}/lib"
|
||||
},
|
||||
"console": "integratedTerminal"
|
||||
},
|
||||
{
|
||||
"name": "CRYPTO VECM (optimized)",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
|
||||
"program": "${workspaceFolder}/research/backtest.py",
|
||||
"args": [
|
||||
"--config=${workspaceFolder}/configuration/vecm-opt.cfg",
|
||||
"--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
|
||||
"--date_pattern=20250605",
|
||||
"--result_db=${workspaceFolder}/research/results/crypto/%T.vecm-opt.ADA-SOL.20250605.crypto_results.db",
|
||||
],
|
||||
"env": {
|
||||
"PYTHONPATH": "${workspaceFolder}/lib"
|
||||
},
|
||||
"console": "integratedTerminal"
|
||||
},
|
||||
// {
|
||||
// "name": "CRYPTO VECM (expanding)",
|
||||
// "type": "debugpy",
|
||||
// "request": "launch",
|
||||
// "python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
|
||||
// "program": "${workspaceFolder}/research/backtest.py",
|
||||
// "args": [
|
||||
// "--config=${workspaceFolder}/configuration/vecm-exp.cfg",
|
||||
// "--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
|
||||
// "--date_pattern=20250605",
|
||||
// "--result_db=${workspaceFolder}/research/results/crypto/%T.vecm-exp.ADA-SOL.20250605.crypto_results.db",
|
||||
// ],
|
||||
// "env": {
|
||||
// "PYTHONPATH": "${workspaceFolder}/lib"
|
||||
// },
|
||||
// "console": "integratedTerminal"
|
||||
// },
|
||||
{
|
||||
"name": "EQUITY VECM (rolling)",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
|
||||
"program": "${workspaceFolder}/research/backtest.py",
|
||||
"args": [
|
||||
"--config=${workspaceFolder}/configuration/vecm.cfg",
|
||||
"--instruments=COIN:EQUITY:ALPACA,MSTR:EQUITY:ALPACA",
|
||||
"--date_pattern=20250605",
|
||||
"--result_db=${workspaceFolder}/research/results/equity/%T.vecm.COIN-MSTR.20250605.equity_results.db",
|
||||
],
|
||||
"env": {
|
||||
"PYTHONPATH": "${workspaceFolder}/lib"
|
||||
},
|
||||
"console": "integratedTerminal"
|
||||
},
|
||||
{
|
||||
"name": "EQUITY-CRYPTO VECM (rolling)",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
|
||||
"program": "${workspaceFolder}/research/backtest.py",
|
||||
"args": [
|
||||
"--config=${workspaceFolder}/configuration/vecm.cfg",
|
||||
"--instruments=COIN:EQUITY:ALPACA,BTC-USDT:CRYPTO:BNBSPOT",
|
||||
"--date_pattern=20250605",
|
||||
"--result_db=${workspaceFolder}/research/results/intermarket/%T.vecm.COIN-BTC.20250601.equity_results.db",
|
||||
],
|
||||
"env": {
|
||||
"PYTHONPATH": "${workspaceFolder}/lib"
|
||||
},
|
||||
"console": "integratedTerminal"
|
||||
},
|
||||
{
|
||||
"name": "-------- B a t c h e s --------",
|
||||
},
|
||||
{
|
||||
"name": "CRYPTO OLS Batch (rolling)",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
|
||||
"program": "${workspaceFolder}/research/backtest.py",
|
||||
"args": [
|
||||
"--config=${workspaceFolder}/configuration/ols.cfg",
|
||||
"--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
|
||||
"--date_pattern=2025060*",
|
||||
"--result_db=${workspaceFolder}/research/results/crypto/%T.ols.ADA-SOL.2025060-.crypto_results.db",
|
||||
],
|
||||
"env": {
|
||||
"PYTHONPATH": "${workspaceFolder}/lib"
|
||||
},
|
||||
"console": "integratedTerminal"
|
||||
},
|
||||
{
|
||||
"name": "CRYPTO VECM Batch (rolling)",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
|
||||
"program": "${workspaceFolder}/research/backtest.py",
|
||||
"args": [
|
||||
"--config=${workspaceFolder}/configuration/vecm.cfg",
|
||||
"--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
|
||||
"--date_pattern=2025060*",
|
||||
"--result_db=${workspaceFolder}/research/results/crypto/%T.vecm.ADA-SOL.2025060-.crypto_results.db",
|
||||
],
|
||||
"env": {
|
||||
"PYTHONPATH": "${workspaceFolder}/lib"
|
||||
},
|
||||
"console": "integratedTerminal"
|
||||
},
|
||||
{
|
||||
"name": "-------- Viz Test --------",
|
||||
},
|
||||
{
|
||||
"name": "Viz Test",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
|
||||
"program": "${workspaceFolder}/tests/viz_test.py",
|
||||
"args": [
|
||||
"--config=${workspaceFolder}/configuration/ols.cfg",
|
||||
"--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
|
||||
"--date_pattern=20250605",
|
||||
],
|
||||
"env": {
|
||||
"PYTHONPATH": "${workspaceFolder}/lib"
|
||||
},
|
||||
"console": "integratedTerminal"
|
||||
}
|
||||
]
|
||||
}
|
||||
Vendored
-10
@@ -1,10 +0,0 @@
|
||||
{
|
||||
"folders": [
|
||||
{
|
||||
"path": ".."
|
||||
}
|
||||
],
|
||||
"settings": {
|
||||
"workbench.colorTheme": "Noctis Minimus"
|
||||
}
|
||||
}
|
||||
Vendored
-112
@@ -1,112 +0,0 @@
|
||||
{
|
||||
"PythonVersion": "3.12",
|
||||
"[python]": {
|
||||
"editor.defaultFormatter": "ms-python.black-formatter"
|
||||
},
|
||||
// ===========================================================
|
||||
"workbench.activityBar.orientation": "vertical",
|
||||
// ===========================================================
|
||||
|
||||
// "markdown.styles": [
|
||||
// "/home/oleg/develop/cvtt2/.vscode/light-theme.css"
|
||||
// ],
|
||||
"markdown.preview.background": "#ffffff",
|
||||
"markdown.preview.textEditorTheme": "light",
|
||||
"markdown-pdf.styles": [
|
||||
"/home/oleg/develop/cvtt2/.vscode/light-theme.css"
|
||||
],
|
||||
"editor.detectIndentation": false,
|
||||
// Configure editor settings to be overridden for [yaml] language.
|
||||
"[yaml]": {
|
||||
"editor.insertSpaces": true,
|
||||
"editor.tabSize": 4,
|
||||
},
|
||||
"pylint.args": [
|
||||
"--disable=missing-docstring"
|
||||
, "--disable=invalid-name"
|
||||
, "--disable=too-few-public-methods"
|
||||
, "--disable=broad-exception-raised"
|
||||
, "--disable=broad-exception-caught"
|
||||
, "--disable=pointless-string-statement"
|
||||
, "--disable=unused-argument"
|
||||
, "--disable=line-too-long"
|
||||
, "--disable=import-outside-toplevel"
|
||||
, "--disable=fixme"
|
||||
, "--disable=protected-access"
|
||||
, "--disable=logging-fstring-interpolation"
|
||||
],
|
||||
|
||||
// ===== TESTING CONFIGURATION =====
|
||||
"python.testing.unittestEnabled": false,
|
||||
"python.testing.pytestEnabled": true,
|
||||
"python.testing.pytestArgs": [
|
||||
"-v",
|
||||
"--tb=short",
|
||||
"--disable-warnings"
|
||||
],
|
||||
"python.testing.envVars": {
|
||||
"PYTHONPATH": "${workspaceFolder}/lib:${workspaceFolder}/.."
|
||||
},
|
||||
"python.testing.cwd": "${workspaceFolder}",
|
||||
"python.testing.autoTestDiscoverOnSaveEnabled": true,
|
||||
"python.testing.pytestPath": "/home/oleg/.pyenv/python3.12-venv/bin/pytest",
|
||||
"python.testing.promptToConfigure": false,
|
||||
"python.testing.pytest.enabled": true,
|
||||
|
||||
|
||||
// Python interpreter settings
|
||||
"python.defaultInterpreterPath": "/home/oleg/.pyenv/python3.12-venv/bin/python3.12",
|
||||
|
||||
// Environment variables for Python execution
|
||||
"python.envFile": "${workspaceFolder}/.vscode/.env",
|
||||
"python.terminal.activateEnvironment": false,
|
||||
"python.terminal.activateEnvInCurrentTerminal": false,
|
||||
|
||||
// Global environment variables for VS Code Python extension
|
||||
"terminal.integrated.env.linux": {
|
||||
"PYTHONPATH": "/home/oleg/develop/:${env:PYTHONPATH}"
|
||||
},
|
||||
|
||||
"pylint.enabled": true,
|
||||
"github.copilot.enable": false,
|
||||
"markdown.extension.print.theme": "dark",
|
||||
"python.analysis.extraPaths": [
|
||||
"${workspaceFolder}/..",
|
||||
"${workspaceFolder}/lib"
|
||||
],
|
||||
|
||||
// Try enabling regular Python language server alongside CursorPyright
|
||||
"python.languageServer": "None",
|
||||
"python.analysis.diagnosticMode": "workspace",
|
||||
"workbench.colorTheme": "Atom One Dark",
|
||||
"cursorpyright.analysis.enable": false,
|
||||
"cursorpyright.analysis.extraPaths": [
|
||||
"${workspaceFolder}/..",
|
||||
"${workspaceFolder}/lib"
|
||||
],
|
||||
|
||||
// Enable quick fixes for unused imports
|
||||
"python.analysis.autoImportCompletions": true,
|
||||
"python.analysis.fixAll": ["source.unusedImports"],
|
||||
"python.analysis.typeCheckingMode": "basic",
|
||||
|
||||
// Enable code actions for CursorPyright
|
||||
"cursorpyright.analysis.autoImportCompletions": true,
|
||||
"cursorpyright.analysis.typeCheckingMode": "off",
|
||||
"cursorpyright.reportUnusedImport": "warning",
|
||||
"cursorpyright.reportUnusedVariable": "warning",
|
||||
"cursorpyright.analysis.diagnosticMode": "workspace",
|
||||
|
||||
// Force enable code actions
|
||||
"editor.lightBulb.enabled": true,
|
||||
"editor.codeActionsOnSave": {
|
||||
"source.organizeImports": "explicit",
|
||||
"source.fixAll": "explicit",
|
||||
"source.unusedImports": "explicit"
|
||||
},
|
||||
|
||||
// Enable Python-specific code actions
|
||||
"python.analysis.completeFunctionParens": true,
|
||||
"python.analysis.addImport.exactMatchOnly": false,
|
||||
"workbench.tree.indent": 24,
|
||||
}
|
||||
Vendored
-26
@@ -1,26 +0,0 @@
|
||||
{
|
||||
"python.testing.pytestEnabled": true,
|
||||
"python.testing.unittestEnabled": false,
|
||||
"python.testing.pytestArgs": [
|
||||
"unittests"
|
||||
],
|
||||
"python.testing.cwd": "${workspaceFolder}",
|
||||
"python.testing.autoTestDiscoverOnSaveEnabled": true,
|
||||
"python.defaultInterpreterPath": "/usr/bin/python3",
|
||||
"python.testing.pytestPath": "python3",
|
||||
"python.analysis.extraPaths": [
|
||||
"${workspaceFolder}",
|
||||
"${workspaceFolder}/..",
|
||||
"${workspaceFolder}/unittests"
|
||||
],
|
||||
"python.envFile": "${workspaceFolder}/.env",
|
||||
"python.testing.debugPort": 3000,
|
||||
"python.linting.enabled": true,
|
||||
"python.linting.pylintEnabled": false,
|
||||
"python.linting.mypyEnabled": true,
|
||||
"files.associations": {
|
||||
"*.py": "python"
|
||||
},
|
||||
"python.testing.promptToConfigure": false,
|
||||
"workbench.colorTheme": "Dracula Theme Soft"
|
||||
}
|
||||
@@ -0,0 +1,156 @@
|
||||
# Agent Instructions
|
||||
|
||||
## Repository purpose
|
||||
|
||||
This repository analyzes test results with Jupyter notebooks and Python or
|
||||
Bash scripts. Inputs are commonly SQLite databases containing time-series data
|
||||
and JSON columns, but analyses may use other test-result formats.
|
||||
|
||||
Ignore `__SAV__/`. It is unrelated legacy material, is not part of the active
|
||||
project, and must not be read, edited, moved, or used as a source of conventions
|
||||
unless the user explicitly requests it.
|
||||
|
||||
## Active layout
|
||||
|
||||
- `notebooks/`: exploratory and report-oriented Jupyter notebooks.
|
||||
- `scripts/`: reusable Python and Bash analysis utilities.
|
||||
- `data/`: local input data. Contents are ignored except for `.gitkeep`.
|
||||
- `results/`: generated tables, figures, exports, and reports. Contents are
|
||||
ignored except for `.gitkeep`.
|
||||
- `requirements.txt`: Python dependencies needed to reproduce repository work.
|
||||
|
||||
Keep reusable logic in `scripts/` and use notebooks to orchestrate analysis,
|
||||
explain decisions, and present results. Do not create a separate `analysis/`
|
||||
tree.
|
||||
|
||||
## Python environment
|
||||
|
||||
The intended virtual environment is `~/.pyenv/python3.12-venv`.
|
||||
|
||||
```bash
|
||||
source ~/.pyenv/python3.12-venv/bin/activate
|
||||
python -m pip install -r requirements.txt
|
||||
```
|
||||
|
||||
Agents may install packages in this environment when needed. Whenever a package
|
||||
is installed for repository work, update `requirements.txt` in the same change
|
||||
with a suitable direct dependency declaration. Use `python -m pip`, not bare
|
||||
`pip`, in documented commands.
|
||||
|
||||
Do not create an in-repository virtual environment unless the user asks for
|
||||
one.
|
||||
|
||||
## Data handling
|
||||
|
||||
- Treat files in `data/` as local, potentially large, and potentially
|
||||
sensitive.
|
||||
- Do not commit SQLite databases, raw test results, or generated results.
|
||||
- Do not modify source data in place. Write transformed data and exports under
|
||||
`results/`.
|
||||
- Use parameterized SQL for values. Do not construct SQL by interpolating
|
||||
untrusted data.
|
||||
- Parse JSON columns defensively and preserve missing, malformed, and unexpected
|
||||
values unless the analysis explicitly defines another policy.
|
||||
- State assumptions about timestamps, time zones, ordering, units, and duplicate
|
||||
observations in the notebook or script that relies on them.
|
||||
- Avoid loading entire databases into memory when a filtered query or chunked
|
||||
read is practical.
|
||||
|
||||
## Notebook conventions
|
||||
|
||||
- A notebook must run from a fresh kernel, top to bottom, without relying on
|
||||
hidden interactive state.
|
||||
- Set random seeds where nondeterminism affects results.
|
||||
- Keep data paths relative to the repository root and avoid machine-specific
|
||||
absolute paths.
|
||||
- Move logic that is reused or substantial enough to test into `scripts/`.
|
||||
- Clear cell outputs before committing notebooks. Never commit embedded source
|
||||
data, credentials, or bulky generated output.
|
||||
- Keep concise Markdown context near analyses: purpose, input assumptions,
|
||||
method, and interpretation.
|
||||
|
||||
## Scripts
|
||||
|
||||
- Python scripts should expose reusable functions and use a guarded CLI entry
|
||||
point when executable.
|
||||
- Bash scripts must start with `#!/usr/bin/env bash` and use
|
||||
`set -euo pipefail`.
|
||||
- Prefer explicit CLI arguments over hard-coded paths or parameters.
|
||||
- Fail with actionable error messages when required data, tables, columns, or
|
||||
configuration are missing.
|
||||
|
||||
## Verification
|
||||
|
||||
Verification should be proportional to the change. At minimum:
|
||||
|
||||
- Run `pytest` for Python script changes.
|
||||
- Add or update tests for reusable parsing, transformation, query, and
|
||||
calculation logic.
|
||||
- Execute changed notebooks from a fresh kernel with `nbmake`.
|
||||
- Run changed Bash scripts against a safe fixture or exercise their
|
||||
non-destructive validation/help path.
|
||||
- Clear notebook outputs after execution and before committing.
|
||||
|
||||
Useful commands:
|
||||
|
||||
```bash
|
||||
python -m pytest
|
||||
python -m pytest --nbmake notebooks
|
||||
jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace path/to/notebook.ipynb
|
||||
```
|
||||
|
||||
If verification cannot be run, report exactly what was skipped and why.
|
||||
|
||||
## Release rules
|
||||
|
||||
- Update `CHANGELOG.md` for every release with the release version, release
|
||||
date, Git tag, and a concise summary of notable changes.
|
||||
- Keep an `Unreleased` section at the top of `CHANGELOG.md` for changes that
|
||||
have not been included in a tagged release yet.
|
||||
- Move relevant entries from `Unreleased` into the dated release section when
|
||||
creating a release, and leave `Unreleased` present for future changes.
|
||||
- Use release headers in `YYYY-MM-DD vMAJOR.MINOR.PATCH` form.
|
||||
- Use version numbers in `MAJOR.MINOR.PATCH` form. Start this repository at
|
||||
`0.0.1`.
|
||||
- Use Git tags in `vMAJOR.MINOR.PATCH` form, matching the changelog version
|
||||
exactly. For example, version `0.0.1` must be tagged as `v0.0.1`.
|
||||
- Create the Git tag only after the changelog and any release-related version
|
||||
changes are complete.
|
||||
- When the user requests creating a release, treat that as explicit permission
|
||||
to commit the release changes, create the matching Git tag, and push both the
|
||||
branch and tag.
|
||||
- Do not push release commits or tags unless the user explicitly requests it.
|
||||
|
||||
## Mandatory background review
|
||||
|
||||
Changes to Python scripts, Bash scripts, or notebook code cells require approval
|
||||
from a separate background reviewer agent before the implementing agent may
|
||||
declare the work complete.
|
||||
|
||||
The implementing agent must:
|
||||
|
||||
1. Finish the implementation and run the relevant verification.
|
||||
2. Ask a separate background agent to review the diff for correctness,
|
||||
reproducibility, data safety, and test coverage.
|
||||
3. Address every material finding, rerun affected checks, and request follow-up
|
||||
review when the fix materially changes the code.
|
||||
4. Report the reviewer outcome in the final response.
|
||||
|
||||
The reviewer must inspect the actual diff and relevant surrounding files; a
|
||||
self-review does not satisfy this requirement. Documentation-only,
|
||||
configuration-only, dependency-only, and ignore-rule-only changes do not
|
||||
require background approval unless they also alter Python, Bash, or notebook
|
||||
code cells.
|
||||
|
||||
If no background reviewer is available, complete all other work but do not
|
||||
claim reviewer approval. End the handoff with the exact status:
|
||||
|
||||
`review pending`
|
||||
|
||||
## Change discipline
|
||||
|
||||
- Preserve user changes and avoid unrelated cleanup.
|
||||
- Do not edit or commit generated files from `data/` or `results/`.
|
||||
- Do not push or commit unless the user explicitly requests it. The `master`
|
||||
branch being unprotected does not imply permission to push directly.
|
||||
- Keep changes focused and explain any new assumptions or dependencies.
|
||||
@@ -0,0 +1,95 @@
|
||||
# Changelog
|
||||
|
||||
All notable changes to this project are documented in this file.
|
||||
|
||||
## Unreleased
|
||||
|
||||
No unreleased changes yet.
|
||||
|
||||
## 2026-07-30 v1.0.4
|
||||
|
||||
- Updated notebook and Panel analysis for the SP Quant result database schema,
|
||||
including explicit `trading_instructions` columns for action, assets,
|
||||
scaled disequilibrium, and beta.
|
||||
- Changed selected-pair market charts to read from the `market` table and kept
|
||||
legacy packed instruction JSON support for older result databases.
|
||||
- Added `scaled_disequilibrium` and `beta` to selected-pair theoretical
|
||||
execution displays.
|
||||
- Improved VS Code notebook usability with the `jupyter_bokeh` dependency,
|
||||
direct Plotly figure rendering, and a dropdown Analyze control for individual
|
||||
pair selection.
|
||||
- Made the Panel app use the dark theme by default and reduced the sidebar
|
||||
width from 430 px to 215 px with responsive sidebar controls.
|
||||
- Expanded tests and notebook verification coverage for the new database schema
|
||||
and Panel layout defaults.
|
||||
|
||||
## 2026-07-29 v1.0.3
|
||||
|
||||
- Removed invalid fixed sizing mode from Panel Tabulator grids to avoid Bokeh
|
||||
layout warnings while preserving compact table layout.
|
||||
- Changed the Panel Calculate action to refresh the result-file list before
|
||||
loading data and removed the standalone Panel Refresh button.
|
||||
|
||||
## 2026-07-29 v1.0.2
|
||||
|
||||
- Added a Panel application for single-day SPBT result analysis with result-file
|
||||
selection, minimum TARGET-change input, pair TheoRet table, pair selector,
|
||||
selected-pair execution table, and market/trade chart.
|
||||
- Added a launcher script for the Panel application.
|
||||
- Changed notebook and Panel pair analysis to use per-row Analyze actions from
|
||||
the Pair TheoRet grid, deferring selected-pair calculations until clicked.
|
||||
- Adjusted Panel sizing so key controls use compact widths and Pair TheoRet uses
|
||||
content width with vertical scrolling instead of full-width paginated layout.
|
||||
- Added a FastListTemplate shell to the Panel application for sidebar controls
|
||||
and configurable app color accents.
|
||||
- Made Plotly chart panes use all available horizontal space.
|
||||
|
||||
## 2026-07-28 v1.0.1
|
||||
|
||||
- Added the `spbt_day` notebook for interactive single-day backtest result
|
||||
analysis, including SQLite result file selection from the local data
|
||||
directory.
|
||||
- Added selector-pair loading and dense ranking by `mr_score.final`, preserving
|
||||
rows with invalid score JSON for inspection.
|
||||
- Added theoretical return calculation for ranked pairs from
|
||||
`trading_instructions`, including reusable helper functions and tests.
|
||||
- Added a Plotly histogram for visual analysis of total theoretical return by
|
||||
pair.
|
||||
- Moved notebook support code into reusable `scripts/spbt_day.py` helpers.
|
||||
- Adjusted notebook table outputs to show all relevant rows and reduce
|
||||
redundant intermediate displays.
|
||||
- Added an alphabetically sorted pair selector for individual pair analysis.
|
||||
- Added selected-pair theoretical execution tables and aligned TheoRet
|
||||
calculations with target-delta trade generation.
|
||||
- Added per-asset `strength` values to selected-pair theoretical execution
|
||||
tables.
|
||||
- Corrected theoretical execution size to use
|
||||
`10000 * strength / reference_price`.
|
||||
- Removed `:USD` quote suffixes from displayed pair names in notebook tables,
|
||||
chart hovers, and the pair selector dropdown while preserving full internal
|
||||
pair keys for calculations.
|
||||
- Added `num_trades` to pair TheoRet summaries, counting asset-level theoretical
|
||||
trades from effective `TARGET` and `CLOSE` instructions.
|
||||
- Added sortable interactive grids for the pair TheoRet and selected-pair
|
||||
theoretical execution tables.
|
||||
- Styled interactive dataframe grids with black text on white backgrounds for
|
||||
readability across notebook themes.
|
||||
- Added a selected-pair Plotly chart that overlays theoretical BUY/SELL
|
||||
executions on relative 1-minute market close data for both instruments.
|
||||
- Anchored the selected-pair market chart at trading-day midnight and normalized
|
||||
relative prices to each instrument's close at that timestamp.
|
||||
- Added a `min_pctg_change` threshold for ranked pair TheoRet calculations to
|
||||
skip small target-strength changes after a position is acquired.
|
||||
- Added a notebook input field for the minimum TARGET strength-change threshold.
|
||||
|
||||
## 2026-07-25 v0.0.9
|
||||
|
||||
- Added contributing guidance and Python dependency declarations.
|
||||
- Added placeholder files for active project directories.
|
||||
- Updated ignore rules for local data, generated results, caches, and local
|
||||
environments.
|
||||
- Documented unreleased changelog handling and release push behavior.
|
||||
|
||||
## 2026-07-25 v0.0.1
|
||||
|
||||
- Established the initial repository structure and project guidance.
|
||||
@@ -0,0 +1,54 @@
|
||||
# Contributing
|
||||
|
||||
## Setup
|
||||
|
||||
Use the shared Python 3.12 virtual environment:
|
||||
|
||||
```bash
|
||||
source ~/.pyenv/python3.12-venv/bin/activate
|
||||
python -m pip install -r requirements.txt
|
||||
```
|
||||
|
||||
If you install another package for repository work, add its direct dependency
|
||||
to `requirements.txt`.
|
||||
|
||||
## Repository layout
|
||||
|
||||
- Put notebooks in `notebooks/`.
|
||||
- Put reusable Python and Bash utilities in `scripts/`.
|
||||
- Put local input files in `data/`.
|
||||
- Put generated artifacts in `results/`.
|
||||
|
||||
The contents of `data/` and `results/` are ignored. Do not force-add test
|
||||
databases, raw test results, generated exports, or notebook outputs.
|
||||
|
||||
`__SAV__/` is unrelated legacy material and is outside the active project.
|
||||
|
||||
## Working with notebooks
|
||||
|
||||
Notebooks must execute from top to bottom in a fresh kernel. Use relative paths,
|
||||
document data assumptions, and move reusable logic into tested scripts.
|
||||
|
||||
Before handing off a change:
|
||||
|
||||
```bash
|
||||
python -m pytest
|
||||
python -m pytest --nbmake notebooks
|
||||
jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace path/to/notebook.ipynb
|
||||
```
|
||||
|
||||
Run only the checks relevant to the files present in the repository, and report
|
||||
anything that could not be run.
|
||||
|
||||
## Review requirement
|
||||
|
||||
Python scripts, Bash scripts, and notebook code-cell changes require review and
|
||||
approval by a separate background agent. Address material findings and rerun
|
||||
affected checks before completion. If a reviewer is unavailable, the change may
|
||||
be handed off only with the status `review pending`.
|
||||
|
||||
Documentation, dependency declarations, and ignore rules do not require this
|
||||
background review when no Python, Bash, or notebook code cells changed.
|
||||
|
||||
The `master` branch is not protected. That does not remove the review
|
||||
requirement or authorize an agent to commit or push without an explicit request.
|
||||
@@ -1,185 +0,0 @@
|
||||
# Enhanced Pairs Trading Backtest Usage Guide
|
||||
|
||||
## Overview
|
||||
|
||||
The enhanced `pt_backtest.py` script now supports multi-day and multi-instrument backtesting with SQLite database output. This guide explains how to use the new features.
|
||||
|
||||
## New Features
|
||||
|
||||
### 1. Multi-Day Data Processing
|
||||
- Process multiple data files in a single run
|
||||
- Support for wildcard patterns in configuration files
|
||||
- CLI override for data file specification
|
||||
|
||||
|
||||
### 2. Dynamic Instrument Selection
|
||||
- Auto-detection of instruments from database
|
||||
- CLI override for instrument specification
|
||||
- No need to manually update configuration files
|
||||
|
||||
### 3. SQLite Database Output
|
||||
- Automated storage of backtest results
|
||||
- Structured data format for analysis
|
||||
- Optional database output (can be disabled)
|
||||
|
||||
## Command Line Arguments
|
||||
|
||||
### Required Arguments
|
||||
- `--config`: Path to configuration file
|
||||
- `--result_db`: Path to SQLite database for results (use "NONE" to disable)
|
||||
|
||||
### Optional Arguments
|
||||
- `--datafiles`: Comma-separated list of data files (overrides config)
|
||||
- `--instruments`: Comma-separated list of instruments (overrides auto-detection)
|
||||
|
||||
## Usage Examples
|
||||
|
||||
### Basic Usage (Auto-detect instruments, use config datafiles)
|
||||
```bash
|
||||
python src/pt_backtest.py --config configuration/crypto.cfg --result_db results.db
|
||||
```
|
||||
|
||||
### Specify Instruments via CLI
|
||||
```bash
|
||||
python src/pt_backtest.py \
|
||||
--config configuration/crypto.cfg \
|
||||
--result_db results.db \
|
||||
--instruments "BTC-USDT,ETH-USDT,ADA-USDT"
|
||||
```
|
||||
|
||||
### Override Data Files via CLI
|
||||
```bash
|
||||
python src/pt_backtest.py \
|
||||
--config configuration/crypto.cfg \
|
||||
--result_db results.db \
|
||||
--datafiles "20250528.mktdata.ohlcv.db,20250529.mktdata.ohlcv.db"
|
||||
```
|
||||
|
||||
### Complete Override (Custom instruments and data files)
|
||||
```bash
|
||||
python src/pt_backtest.py \
|
||||
--config configuration/crypto.cfg \
|
||||
--result_db results.db \
|
||||
--instruments "BTC-USDT,ETH-USDT" \
|
||||
--datafiles "20250528.mktdata.ohlcv.db,20250529.mktdata.ohlcv.db"
|
||||
```
|
||||
|
||||
### Disable Database Output
|
||||
```bash
|
||||
python src/pt_backtest.py \
|
||||
--config configuration/crypto.cfg \
|
||||
--result_db NONE
|
||||
```
|
||||
|
||||
## Configuration File Updates
|
||||
|
||||
### Wildcard Support in Data Files
|
||||
The configuration file now supports wildcards in the `datafiles` array:
|
||||
|
||||
```json
|
||||
{
|
||||
"datafiles": [
|
||||
"2025*.mktdata.ohlcv.db",
|
||||
"specific_file.db",
|
||||
"202405*.mktdata.ohlcv.db"
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
### Multiple Patterns
|
||||
You can specify multiple wildcard patterns:
|
||||
|
||||
```json
|
||||
{
|
||||
"datafiles": [
|
||||
"202405*.mktdata.ohlcv.db",
|
||||
"202406*.mktdata.ohlcv.db",
|
||||
"special_data.db"
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
## Database Schema
|
||||
|
||||
The script creates a `pt_bt_results` table with the following schema:
|
||||
|
||||
| Column | Type | Description |
|
||||
|--------|------|-------------|
|
||||
| date | DATE | Trading date extracted from filename |
|
||||
| pair | TEXT | Trading pair name (e.g., "BTC-USDT & ETH-USDT") |
|
||||
| symbol | TEXT | Individual symbol (e.g., "BTC-USDT") |
|
||||
| open_time | DATETIME | Trade opening time |
|
||||
| open_side | TEXT | Opening side (BUY/SELL) |
|
||||
| open_price | REAL | Opening price |
|
||||
| open_quantity | INTEGER | Opening quantity |
|
||||
| open_disequilibrium | REAL | Disequilibrium at opening |
|
||||
| close_time | DATETIME | Trade closing time |
|
||||
| close_side | TEXT | Closing side (BUY/SELL) |
|
||||
| close_price | REAL | Closing price |
|
||||
| close_quantity | INTEGER | Closing quantity |
|
||||
| close_disequilibrium | REAL | Disequilibrium at closing |
|
||||
| symbol_return | REAL | Individual symbol return (%) |
|
||||
| pair_return | REAL | Combined pair return (%) |
|
||||
|
||||
## Auto-Detection Logic
|
||||
|
||||
### Instrument Auto-Detection
|
||||
When `--instruments` is not specified, the script:
|
||||
1. Connects to each data file
|
||||
2. Queries distinct `instrument_id` values from the configured table
|
||||
3. Removes the configured prefix (`instrument_id_pfx`)
|
||||
4. Uses the resulting symbols for pair generation
|
||||
|
||||
### Data File Resolution
|
||||
The script resolves data files in this order:
|
||||
1. If `--datafiles` is specified, use those files
|
||||
2. Otherwise, process each pattern in config `datafiles`:
|
||||
- Expand wildcards using `glob.glob()`
|
||||
- Resolve relative paths using `data_directory`
|
||||
- Remove duplicates and sort
|
||||
|
||||
## Output
|
||||
|
||||
### Console Output
|
||||
- Lists all data files to be processed
|
||||
- Shows auto-detected or specified instruments
|
||||
- Displays trade signals for each file
|
||||
- Prints returns by day and pair
|
||||
- Shows grand totals and outstanding positions
|
||||
|
||||
### Database Output
|
||||
- Creates database and table automatically
|
||||
- Stores detailed trade information
|
||||
- Includes calculated returns
|
||||
- One record per symbol per trade
|
||||
|
||||
## Error Handling
|
||||
|
||||
The script includes comprehensive error handling:
|
||||
- Invalid data files are skipped with warnings
|
||||
- Database connection errors are reported
|
||||
- Auto-detection failures fall back gracefully
|
||||
- Processing errors are logged with stack traces
|
||||
|
||||
## Performance Considerations
|
||||
|
||||
- Wildcard expansion happens once at startup
|
||||
- Database connections are opened/closed per operation
|
||||
- Large numbers of files are processed sequentially
|
||||
- Memory usage scales with the number of instruments and data points
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Common Issues
|
||||
|
||||
1. **No instruments found**: Check that the database contains data for the specified exchange_id
|
||||
2. **No data files found**: Verify wildcard patterns and data_directory path
|
||||
3. **Database errors**: Ensure write permissions for the result database path
|
||||
4. **Memory issues**: Consider processing fewer files at once or reducing instrument count
|
||||
|
||||
### Debug Tips
|
||||
|
||||
- Use `--result_db NONE` to disable database output during testing
|
||||
- Start with a small set of instruments using `--instruments`
|
||||
- Test with explicit file lists using `--datafiles` before using wildcards
|
||||
- Check console output for detailed processing information
|
||||
@@ -1,132 +0,0 @@
|
||||
# Pairs Trading Backtest
|
||||
|
||||
This document provides a guide to understanding, configuring, and running the pairs trading backtest system.
|
||||
|
||||
## Overview
|
||||
|
||||
The system is designed to backtest pairs trading strategies on historical market data.
|
||||
It allows users to select different strategies, configure parameters, and analyze the
|
||||
performance of these strategies.
|
||||
|
||||
## Core Concepts
|
||||
|
||||
### Trading Pair
|
||||
A trading pair consists of two financial instruments (e.g., stocks or cryptocurrencies)
|
||||
whose prices are believed to have a long-term statistical relationship (cointegration).
|
||||
The strategy aims to profit from temporary deviations from this relationship.
|
||||
|
||||
### Strategy
|
||||
The system supports different strategies for identifying and exploiting trading opportunities. Each strategy has its own set of configurable parameters.
|
||||
|
||||
### Trading Signals
|
||||
Trading signals indicate when to open or close a position based on the configured strategy
|
||||
and parameters. These signals are typically generated when the "dis-equilibrium" (the
|
||||
deviation from the long-term relationship) crosses certain thresholds.
|
||||
|
||||
## Running a Backtest
|
||||
|
||||
### 1. Configuration
|
||||
|
||||
The primary configuration for the backtest is managed in the `src/pt_backtest.py` file. Here, you will define which dataset to use (cryptocurrencies or equities) and which strategy to employ.
|
||||
|
||||
#### Choosing a Dataset:
|
||||
You can switch between `CRYPTO_CONFIG` and `EQT_CONFIG` by uncommenting the desired configuration block:
|
||||
|
||||
```python
|
||||
# CONFIG = CRYPTO_CONFIG # For cryptocurrency data
|
||||
CONFIG = EQT_CONFIG # For equity data
|
||||
```
|
||||
|
||||
Each configuration dictionary specifies:
|
||||
- `data_directory`: Path to the data files.
|
||||
- `datafiles`: A list of database files to process. You can comment/uncomment specific files to include/exclude them from the backtest.
|
||||
- `db_table_name`: The name of the table within the SQLite database.
|
||||
- `instruments`: A list of symbols to consider for forming trading pairs.
|
||||
- `trading_hours`: Defines the session start and end times, crucial for equity markets.
|
||||
- `stat_model_price`: The column in the data to be used as the price (e.g., "close").
|
||||
- `dis-equilibrium_open_trshld`: The threshold (in standard deviations) of the dis-equilibrium for opening a trade.
|
||||
- `dis-equilibrium_close_trshld`: The threshold (in standard deviations) of the dis-equilibrium for closing an open trade.
|
||||
- `training_minutes`: The length of the rolling window (in minutes) used to train the model (e.g., calculate cointegration, mean, and standard deviation of the dis-equilibrium).
|
||||
- `funding_per_pair`: The amount of capital allocated to each trading pair.
|
||||
|
||||
#### Choosing a Strategy:
|
||||
The system currently offers two main strategies: `StaticFitStrategy` and `SlidingFitStrategy`. You select a strategy by instantiating it:
|
||||
|
||||
```python
|
||||
# STRATEGY = StaticFitStrategy()
|
||||
STRATEGY = SlidingFitStrategy()
|
||||
```
|
||||
|
||||
- **`StaticFitStrategy`**: This strategy fits the cointegration model once at the beginning
|
||||
of each trading day (or for the entire dataset if run on a single file without a rolling
|
||||
window logic in the strategy itself). The parameters (mean, standard deviation of
|
||||
dis-equilibrium) derived from this initial fit are used for generating trading signals
|
||||
throughout the day.
|
||||
- **Pros**: Simpler, computationally less intensive.
|
||||
- **Cons**: May not adapt well to changing market conditions during the day.
|
||||
|
||||
- **`SlidingFitStrategy`**: This strategy uses a rolling window approach. The cointegration model and its parameters are re-estimated at regular intervals (defined by `training_minutes` and how the strategy implements the sliding window). This allows the strategy to adapt to evolving market dynamics.
|
||||
- **Pros**: More adaptive to changing market conditions.
|
||||
- **Cons**: Computationally more intensive. The `training_minutes` parameter is crucial here as it defines the look-back period for each re-estimation.
|
||||
|
||||
### 2. Parameters for Trading Signals
|
||||
|
||||
The key parameters that determine trading signals are primarily found within the `CONFIG` dictionaries:
|
||||
|
||||
- **`dis-equilibrium_open_trshld`**: This is the number of standard deviations the current dis-equilibrium must move away from its mean (calculated during the training period) to trigger an opening signal.
|
||||
- A *higher* value means the strategy will wait for a more significant deviation before entering a trade, leading to fewer but potentially more robust signals.
|
||||
- A *lower* value means the strategy will enter trades on smaller deviations, leading to more frequent signals but potentially more false positives.
|
||||
|
||||
- **`dis-equilibrium_close_trshld`**: This is the number of standard deviations the current dis-equilibrium must revert towards its mean (from its peak deviation) to trigger a closing signal.
|
||||
- A *higher* value (closer to the `dis-equilibrium_open_trshld`) means the strategy will close trades more quickly as the dis-equilibrium starts to revert.
|
||||
- A *lower* value (closer to zero) means the strategy will hold onto trades longer, waiting for the dis-equilibrium to revert more significantly towards the mean.
|
||||
|
||||
- **`training_minutes`**:
|
||||
- For `StaticFitStrategy`, this determines the initial period of data used to establish the cointegration relationship and calculate the baseline dis-equilibrium statistics for the entire trading day (or dataset portion being processed).
|
||||
- For `SlidingFitStrategy`, this defines the length of the rolling window. The model is refit using data from the most recent `training_minutes` period. A shorter window makes the strategy more responsive to recent price action but might be more prone to noise. A longer window provides a more stable model but might be slower to adapt to new trends.
|
||||
|
||||
### 3. Running the Script
|
||||
|
||||
Once the configuration is set, you can run the backtest from your terminal:
|
||||
|
||||
```bash
|
||||
python src/pt_backtest.py
|
||||
```
|
||||
|
||||
The script will process each datafile specified in the `CONFIG`, create all possible unique pairs from the `instruments` list, and apply the chosen strategy.
|
||||
|
||||
### 4. Interpreting Results
|
||||
|
||||
The script will output:
|
||||
- Progress messages for each datafile being processed.
|
||||
- A summary of trades taken.
|
||||
- Grand totals of performance metrics (PnL, etc.).
|
||||
- A list of any outstanding positions at the end of the backtest.
|
||||
|
||||
The core logic for a pair involves:
|
||||
1. **Data Preparation**: For each pair, relevant price series are extracted.
|
||||
2. **Training Phase** (for `SlidingFitStrategy`, this happens repeatedly; for `StaticFitStrategy`, typically once per day/file):
|
||||
* The `get_datasets()` method in `TradingPair` splits data into training and testing sets.
|
||||
* `check_cointegration()` uses the Johansen test to see if the pair's price series are cointegrated within the current training window. If not, the pair is often skipped for that window.
|
||||
* If cointegrated, `fit_VECM()` estimates a Vector Error Correction Model (VECM). The `beta` coefficients from this model define the cointegrating relationship (the "spread" or "dis-equilibrium series").
|
||||
* `training_mu_` (mean) and `training_std_` (standard deviation) of this dis-equilibrium series are calculated. These are crucial for scaling the dis-equilibrium and setting trade thresholds.
|
||||
3. **Prediction/Trading Phase**:
|
||||
* The strategy iterates through the "testing" data points.
|
||||
* For each point, the current dis-equilibrium is calculated using the `beta` from the VECM.
|
||||
* This dis-equilibrium is then scaled: `(current_disequilibrium - training_mu_) / training_std_`.
|
||||
* This scaled value is compared against `dis-equilibrium_open_trshld` and `dis-equilibrium_close_trshld` to generate buy/sell/close signals.
|
||||
|
||||
## Customizing and Extending
|
||||
|
||||
- **Adding New Strategies**: Create a new class that inherits from a base strategy class (if one exists) or implements a similar interface to `StaticFitStrategy` or `SlidingFitStrategy`. The core method to implement would be `run_pair()`.
|
||||
- **Modifying Data Loading**: The `tools/data_loader.py` can be modified to support different data formats or sources.
|
||||
- **Changing Cointegration/Model Parameters**: The `TradingPair` class houses the VECM fitting and cointegration checks. You can adjust parameters like `k_ar_diff` in `coint_johansen` or the `VECM` model itself.
|
||||
|
||||
## Important Considerations
|
||||
|
||||
- **Data Quality**: Ensure your market data is clean, accurate, and properly formatted. Gaps or errors in data can significantly impact backtest results.
|
||||
- **Transaction Costs**: The current backtest might not explicitly model transaction costs (brokerage fees, slippage). These can have a significant impact on the profitability of high-frequency strategies. Consider adding a cost model to `BacktestResult` or within the strategy execution.
|
||||
- **Look-ahead Bias**: Be extremely careful to avoid look-ahead bias. Ensure that decisions at any point in time are made using only information that would have been available at that time. The use of `training_df_` and `testing_df_` in `TradingPair` is designed to help prevent this.
|
||||
- **Overfitting**: When optimizing parameters (`dis-equilibrium_open_trshld`, `training_minutes`, etc.), be mindful of overfitting to the historical data. A strategy that performs exceptionally well on past data may not perform well in the future. Use out-of-sample testing or walk-forward optimization for more robust validation.
|
||||
|
||||
This tutorial should provide a solid foundation for working with the pairs trading backtest system. Experiment with different configurations and strategies to find what works best for your chosen markets and instruments.
|
||||
@@ -0,0 +1,9 @@
|
||||
- [ ] Add disequilibrium chart
|
||||
- [ ] Add scatter chart for `mr-rank <--> realized pnl`
|
||||
|
||||
# DONE
|
||||
|
||||
## 2026-07-29
|
||||
|
||||
- [x] Change notebook and panel (stat_pairs_backtest) to use sp_quant's database tables `trading_instructions` and `market`, to have *disequilibrium* and *beta*
|
||||
|
||||
@@ -1,103 +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.app import App
|
||||
from cvttpy_tools.base import NamedObject
|
||||
from cvttpy_tools.config import CvttAppConfig
|
||||
from cvttpy_tools.logger import Log
|
||||
from pairs_trading.lib.pt_strategy.live.live_strategy import PtLiveStrategy
|
||||
from pairs_trading.lib.pt_strategy.live.pricer_md_client import PtMktDataClient
|
||||
from pairs_trading.lib.pt_strategy.live.ti_sender import TradingInstructionsSender
|
||||
|
||||
# import sys
|
||||
# print("PYTHONPATH directories:")
|
||||
# for path in sys.path:
|
||||
# print(path)
|
||||
|
||||
|
||||
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 CVTT 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()
|
||||
@@ -1,43 +0,0 @@
|
||||
{
|
||||
"market_data_loading": {
|
||||
"CRYPTO": {
|
||||
"data_directory": "./data/crypto",
|
||||
"db_table_name": "md_1min_bars",
|
||||
"instrument_id_pfx": "PAIR-",
|
||||
},
|
||||
"EQUITY": {
|
||||
"data_directory": "./data/equity",
|
||||
"db_table_name": "md_1min_bars",
|
||||
"instrument_id_pfx": "STOCK-",
|
||||
}
|
||||
},
|
||||
|
||||
# ====== Funding ======
|
||||
"funding_per_pair": 2000.0,
|
||||
# ====== Trading Parameters ======
|
||||
"stat_model_price": "close",
|
||||
"execution_price": {
|
||||
"column": "vwap",
|
||||
"shift": 1,
|
||||
},
|
||||
"dis-equilibrium_open_trshld": 2.0,
|
||||
"dis-equilibrium_close_trshld": 0.5,
|
||||
"training_size": 120,
|
||||
"model_class": "pt_strategy.models.OLSModel",
|
||||
"model_data_policy_class": "pt_strategy.model_data_policy.ExpandingWindowDataPolicy",
|
||||
|
||||
# ====== Stop Conditions ======
|
||||
"stop_close_conditions": {
|
||||
"profit": 2.0,
|
||||
"loss": -0.5
|
||||
}
|
||||
|
||||
# ====== End of Session Closeout ======
|
||||
"close_outstanding_positions": true,
|
||||
# "close_outstanding_positions": false,
|
||||
"trading_hours": {
|
||||
"timezone": "America/New_York",
|
||||
"begin_session": "7:30:00",
|
||||
"end_session": "18:30:00",
|
||||
}
|
||||
}
|
||||
@@ -1,47 +0,0 @@
|
||||
{
|
||||
"market_data_loading": {
|
||||
"CRYPTO": {
|
||||
"data_directory": "./data/crypto",
|
||||
"db_table_name": "md_1min_bars",
|
||||
"instrument_id_pfx": "PAIR-",
|
||||
},
|
||||
"EQUITY": {
|
||||
"data_directory": "./data/equity",
|
||||
"db_table_name": "md_1min_bars",
|
||||
"instrument_id_pfx": "STOCK-",
|
||||
}
|
||||
},
|
||||
|
||||
# ====== Funding ======
|
||||
"funding_per_pair": 2000.0,
|
||||
# ====== Trading Parameters ======
|
||||
"stat_model_price": "close",
|
||||
"execution_price": {
|
||||
"column": "vwap",
|
||||
"shift": 1,
|
||||
},
|
||||
"dis-equilibrium_open_trshld": 1.75,
|
||||
"dis-equilibrium_close_trshld": 0.9,
|
||||
"model_class": "pt_strategy.models.OLSModel",
|
||||
|
||||
# "model_data_policy_class": "pt_strategy.model_data_policy.EGOptimizedWndDataPolicy",
|
||||
# "model_data_policy_class": "pt_strategy.model_data_policy.ADFOptimizedWndDataPolicy",
|
||||
"model_data_policy_class": "pt_strategy.model_data_policy.JohansenOptdWndDataPolicy",
|
||||
"min_training_size": 60,
|
||||
"max_training_size": 150,
|
||||
|
||||
# ====== Stop Conditions ======
|
||||
"stop_close_conditions": {
|
||||
"profit": 2.0,
|
||||
"loss": -0.5
|
||||
}
|
||||
|
||||
# ====== End of Session Closeout ======
|
||||
"close_outstanding_positions": true,
|
||||
# "close_outstanding_positions": false,
|
||||
"trading_hours": {
|
||||
"timezone": "America/New_York",
|
||||
"begin_session": "7:30:00",
|
||||
"end_session": "18:30:00",
|
||||
}
|
||||
}
|
||||
@@ -1,47 +0,0 @@
|
||||
{
|
||||
"market_data_loading": {
|
||||
"CRYPTO": {
|
||||
"data_directory": "./data/crypto",
|
||||
"db_table_name": "md_1min_bars",
|
||||
"instrument_id_pfx": "PAIR-",
|
||||
},
|
||||
"EQUITY": {
|
||||
"data_directory": "./data/equity",
|
||||
"db_table_name": "md_1min_bars",
|
||||
"instrument_id_pfx": "STOCK-",
|
||||
}
|
||||
},
|
||||
|
||||
# ====== Funding ======
|
||||
"funding_per_pair": 2000.0,
|
||||
# ====== Trading Parameters ======
|
||||
"stat_model_price": "close",
|
||||
"execution_price": {
|
||||
"column": "vwap",
|
||||
"shift": 1,
|
||||
},
|
||||
"dis-equilibrium_open_trshld": 1.75,
|
||||
"dis-equilibrium_close_trshld": 0.9,
|
||||
"model_class": "pt_strategy.models.OLSModel",
|
||||
|
||||
"training_size": 120,
|
||||
"model_data_policy_class": "pt_strategy.model_data_policy.RollingWindowDataPolicy",
|
||||
# "model_data_policy_class": "pt_strategy.model_data_policy.OptimizedWindowDataPolicy",
|
||||
# "min_training_size": 60,
|
||||
# "max_training_size": 150,
|
||||
|
||||
# ====== Stop Conditions ======
|
||||
"stop_close_conditions": {
|
||||
"profit": 2.0,
|
||||
"loss": -0.5
|
||||
}
|
||||
|
||||
# ====== End of Session Closeout ======
|
||||
"close_outstanding_positions": true,
|
||||
# "close_outstanding_positions": false,
|
||||
"trading_hours": {
|
||||
"timezone": "America/New_York",
|
||||
"begin_session": "7:30:00",
|
||||
"end_session": "18:30:00",
|
||||
}
|
||||
}
|
||||
@@ -1,21 +0,0 @@
|
||||
{
|
||||
"strategy_config": @inc=file:///home/oleg/develop/pairs_trading/configuration/ols.cfg
|
||||
"pricer_config": {
|
||||
"pricer_url": "ws://localhost:12346/ws",
|
||||
"history_depth_sec": 86400 #"60*60*24", # use simpleeval
|
||||
"interval_sec": 60
|
||||
},
|
||||
"ti_config": {
|
||||
"cvtt_base_url": "http://localhost:23456"
|
||||
"book_id": "XXXXXXXXX",
|
||||
"strategy_id": "XXXXXXXXX",
|
||||
"ti_endpoint": {
|
||||
"method": "POST",
|
||||
"url": "/trading_instructions"
|
||||
},
|
||||
"health_check_endpoint": {
|
||||
"method": "GET",
|
||||
"url": "/ping"
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,49 +0,0 @@
|
||||
{
|
||||
"market_data_loading": {
|
||||
"CRYPTO": {
|
||||
"data_directory": "./data/crypto",
|
||||
"db_table_name": "md_1min_bars",
|
||||
"instrument_id_pfx": "PAIR-",
|
||||
},
|
||||
"EQUITY": {
|
||||
"data_directory": "./data/equity",
|
||||
"db_table_name": "md_1min_bars",
|
||||
"instrument_id_pfx": "STOCK-",
|
||||
}
|
||||
},
|
||||
|
||||
# ====== Funding ======
|
||||
"funding_per_pair": 2000.0,
|
||||
|
||||
# ====== Trading Parameters ======
|
||||
"stat_model_price": "close", # "vwap"
|
||||
"execution_price": {
|
||||
"column": "vwap",
|
||||
"shift": 1,
|
||||
},
|
||||
"dis-equilibrium_open_trshld": 1.75,
|
||||
"dis-equilibrium_close_trshld": 1.0,
|
||||
|
||||
"model_class": "pt_strategy.models.VECMModel",
|
||||
|
||||
# "training_size": 120,
|
||||
# "model_data_policy_class": "pt_strategy.model_data_policy.RollingWindowDataPolicy",
|
||||
"model_data_policy_class": "pt_strategy.model_data_policy.ADFOptimizedWndDataPolicy",
|
||||
"min_training_size": 60,
|
||||
"max_training_size": 150,
|
||||
|
||||
# ====== Stop Conditions ======
|
||||
"stop_close_conditions": {
|
||||
"profit": 2.0,
|
||||
"loss": -0.5
|
||||
}
|
||||
|
||||
# ====== End of Session Closeout ======
|
||||
"close_outstanding_positions": true,
|
||||
# "close_outstanding_positions": false,
|
||||
"trading_hours": {
|
||||
"timezone": "America/New_York",
|
||||
"begin_session": "7:30:00",
|
||||
"end_session": "18:30:00",
|
||||
}
|
||||
}
|
||||
@@ -1,48 +0,0 @@
|
||||
{
|
||||
"market_data_loading": {
|
||||
"CRYPTO": {
|
||||
"data_directory": "./data/crypto",
|
||||
"db_table_name": "md_1min_bars",
|
||||
"instrument_id_pfx": "PAIR-",
|
||||
},
|
||||
"EQUITY": {
|
||||
"data_directory": "./data/equity",
|
||||
"db_table_name": "md_1min_bars",
|
||||
"instrument_id_pfx": "STOCK-",
|
||||
}
|
||||
},
|
||||
|
||||
# ====== Funding ======
|
||||
"funding_per_pair": 2000.0,
|
||||
|
||||
# ====== Trading Parameters ======
|
||||
"stat_model_price": "close", # "vwap"
|
||||
"execution_price": {
|
||||
"column": "vwap",
|
||||
"shift": 1,
|
||||
},
|
||||
"dis-equilibrium_open_trshld": 1.75,
|
||||
"dis-equilibrium_close_trshld": 1.0,
|
||||
"model_class": "pt_strategy.models.VECMModel",
|
||||
|
||||
"training_size": 120,
|
||||
"model_data_policy_class": "pt_strategy.model_data_policy.RollingWindowDataPolicy",
|
||||
# "model_data_policy_class": "pt_strategy.model_data_policy.OptimizedWindowDataPolicy",
|
||||
# "min_training_size": 60,
|
||||
# "max_training_size": 150,
|
||||
|
||||
# ====== Stop Conditions ======
|
||||
"stop_close_conditions": {
|
||||
"profit": 2.0,
|
||||
"loss": -0.5
|
||||
}
|
||||
|
||||
# ====== End of Session Closeout ======
|
||||
"close_outstanding_positions": true,
|
||||
# "close_outstanding_positions": false,
|
||||
"trading_hours": {
|
||||
"timezone": "America/New_York",
|
||||
"begin_session": "7:30:00",
|
||||
"end_session": "18:30:00",
|
||||
}
|
||||
}
|
||||
@@ -1,213 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import 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_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
|
||||
|
||||
|
||||
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
|
||||
|
||||
|
||||
class MdSummaryCollector(NamedObject):
|
||||
sender_: RESTSender
|
||||
exch_acct_: str
|
||||
instrument_id_: str
|
||||
interval_sec_: int
|
||||
history_depth_sec_: int
|
||||
|
||||
history_: List[MdSummary]
|
||||
timer_: Optional[Timer]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
sender: RESTSender,
|
||||
exch_acct: str,
|
||||
instrument_id: str,
|
||||
interval_sec: int,
|
||||
history_depth_sec: int,
|
||||
) -> None:
|
||||
self.sender_ = sender
|
||||
self.exch_acct_ = exch_acct
|
||||
self.instrument_id_ = instrument_id
|
||||
self.interval_sec_ = interval_sec
|
||||
self.history_depth_sec_ = history_depth_sec
|
||||
|
||||
self.history_depth_sec_ = []
|
||||
self.timer_ = None
|
||||
|
||||
def rqst_data(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"exch_acct": self.exch_acct_,
|
||||
"instrument_id": self.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()
|
||||
)
|
||||
return MdSummary.from_REST_response(response=response)
|
||||
|
||||
def get_last(self) -> Optional[MdSummary]:
|
||||
rqst_data = self.rqst_data()
|
||||
rqst_data["history_depth_sec"] = self.interval_sec_
|
||||
response: requests.Response = self.sender_.send_post(
|
||||
endpoint="md_summary", post_body=rqst_data
|
||||
)
|
||||
res = MdSummary.from_REST_response(response=response)
|
||||
return None if len(res) == 0 else res[-1]
|
||||
|
||||
async def start(self) -> None:
|
||||
if self.timer_:
|
||||
Log.error(f"{self.fname()}: Timer is already started")
|
||||
return
|
||||
self.history_ = self.get_history()
|
||||
self.timer_ = Timer(
|
||||
start_in_sec=self.interval_sec_,
|
||||
is_periodic=True,
|
||||
period_interval=self.interval_sec_,
|
||||
func=self._load_new,
|
||||
)
|
||||
|
||||
async def _load_new(self) -> None:
|
||||
last: Optional[MdSummary] = self.get_last()
|
||||
if not last:
|
||||
# URGENT logging
|
||||
return
|
||||
if last.ts_ns_ <= self.history_[-1].ts_ns_:
|
||||
# URGENT logging
|
||||
return
|
||||
self.history_.append(last)
|
||||
# URGENT implement notification
|
||||
|
||||
def stop(self) -> None:
|
||||
if self.timer_:
|
||||
self.timer_.cancel()
|
||||
self.timer_ = None
|
||||
|
||||
class CvttRESTClient(NamedObject):
|
||||
config_: Config
|
||||
sender_: RESTSender
|
||||
|
||||
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)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
config = Config(json_src={"cvtt_base_url": "http://cvtt-tester-01.cvtt.vpn:23456"})
|
||||
|
||||
cvtt_client = CvttRESTClient(config)
|
||||
|
||||
mdsc = MdSummaryCollector(
|
||||
sender=cvtt_client.sender_,
|
||||
exch_acct="COINBASE_AT",
|
||||
instrument_id="PAIR-BTC-USD",
|
||||
interval_sec=60,
|
||||
history_depth_sec=24 * 3600,
|
||||
)
|
||||
|
||||
hist = mdsc.get_history()
|
||||
last = mdsc.get_last()
|
||||
pass
|
||||
@@ -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.logger import Log
|
||||
from cvttpy_tools.settings.cvtt_types import JsonDictT
|
||||
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())
|
||||
@@ -1,351 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Dict, List, Optional
|
||||
from enum import Enum
|
||||
|
||||
import pandas as pd
|
||||
# ---
|
||||
from cvttpy_tools.base import NamedObject
|
||||
from cvttpy_tools.logger import Log
|
||||
from cvttpy_tools.settings.cvtt_types import JsonDictT
|
||||
# ---
|
||||
from cvttpy_trading.trading.instrument import ExchangeInstrument
|
||||
# ---
|
||||
from pt_strategy.live.ti_sender import TradingInstructionsSender
|
||||
from pt_strategy.model_data_policy import ModelDataPolicy
|
||||
from pt_strategy.pt_market_data import RealTimeMarketData
|
||||
from pt_strategy.pt_model import Prediction
|
||||
from pt_strategy.trading_pair import PairState, TradingPair
|
||||
|
||||
"""
|
||||
--config=pair.cfg
|
||||
--pair=PAIR-BTC-USDT:COINBASE_AT,PAIR-ETH-USDT:COINBASE_AT
|
||||
"""
|
||||
|
||||
|
||||
class TradingInstructionType(Enum):
|
||||
TARGET_POSITION = "TARGET_POSITION"
|
||||
|
||||
@dataclass
|
||||
class TradingInstruction(NamedObject):
|
||||
type_: TradingInstructionType
|
||||
exch_instr_: ExchangeInstrument
|
||||
specifics_: Dict[str, Any]
|
||||
|
||||
|
||||
class PtLiveStrategy(NamedObject):
|
||||
config_: Dict[str, Any]
|
||||
trading_pair_: TradingPair
|
||||
model_data_policy_: ModelDataPolicy
|
||||
pt_mkt_data_: RealTimeMarketData
|
||||
ti_sender_: TradingInstructionsSender
|
||||
|
||||
# for presentation: history of prediction values and trading signals
|
||||
predictions_: pd.DataFrame
|
||||
trading_signals_: pd.DataFrame
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: Dict[str, Any],
|
||||
instruments: List[Dict[str, str]],
|
||||
ti_sender: TradingInstructionsSender,
|
||||
):
|
||||
|
||||
self.config_ = config
|
||||
self.trading_pair_ = TradingPair(config=config, instruments=instruments)
|
||||
self.predictions_ = pd.DataFrame()
|
||||
self.trading_signals_ = pd.DataFrame()
|
||||
self.ti_sender_ = ti_sender
|
||||
|
||||
import copy
|
||||
|
||||
# modified config must be passed to PtMarketData
|
||||
config_copy = copy.deepcopy(config)
|
||||
config_copy["instruments"] = instruments
|
||||
self.pt_mkt_data_ = RealTimeMarketData(config=config_copy)
|
||||
self.model_data_policy_ = ModelDataPolicy.create(
|
||||
config, is_real_time=True, pair=self.trading_pair_
|
||||
)
|
||||
self.open_threshold_ = self.config_.get("dis-equilibrium_open_trshld", 0.0)
|
||||
assert self.open_threshold_ > 0, "open_threshold must be greater than 0"
|
||||
self.close_threshold_ = self.config_.get("dis-equilibrium_close_trshld", 0.0)
|
||||
assert self.close_threshold_ > 0, "close_threshold must be greater than 0"
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return f"{self.classname()}: trading_pair={self.trading_pair_}, mdp={self.model_data_policy_.__class__.__name__}, "
|
||||
|
||||
async def on_mkt_data_hist_snapshot(self, aggr: JsonDictT) -> None:
|
||||
Log.info(f"on_mkt_data_hist_snapshot: {aggr}")
|
||||
await self.pt_mkt_data_.on_mkt_data_hist_snapshot(snapshot=aggr)
|
||||
pass
|
||||
|
||||
async def on_mkt_data_update(self, aggr: JsonDictT) -> None:
|
||||
market_data_df = await self.pt_mkt_data_.on_mkt_data_update(update=aggr)
|
||||
if market_data_df is not None:
|
||||
self.trading_pair_.market_data_ = market_data_df
|
||||
self.model_data_policy_.advance()
|
||||
prediction = self.trading_pair_.run(
|
||||
market_data_df, self.model_data_policy_.advance()
|
||||
)
|
||||
self.predictions_ = pd.concat(
|
||||
[self.predictions_, prediction.to_df()], ignore_index=True
|
||||
)
|
||||
|
||||
trading_instructions: List[TradingInstruction] = (
|
||||
self._create_trading_instructions(
|
||||
prediction=prediction, last_row=market_data_df.iloc[-1]
|
||||
)
|
||||
)
|
||||
if len(trading_instructions) > 0:
|
||||
await self._send_trading_instructions(trading_instructions)
|
||||
# trades = self._create_trades(prediction=prediction, last_row=market_data_df.iloc[-1])
|
||||
# URGENT implement this
|
||||
pass
|
||||
|
||||
async def _send_trading_instructions(
|
||||
self, trading_instructions: pd.DataFrame
|
||||
) -> None:
|
||||
pass
|
||||
|
||||
def _create_trading_instructions(
|
||||
self, prediction: Prediction, last_row: pd.Series
|
||||
) -> List[TradingInstruction]:
|
||||
pair = self.trading_pair_
|
||||
trd_instructions: List[TradingInstruction] = []
|
||||
|
||||
scaled_disequilibrium = prediction.scaled_disequilibrium_
|
||||
abs_scaled_disequilibrium = abs(scaled_disequilibrium)
|
||||
|
||||
if pair.is_closed():
|
||||
if abs_scaled_disequilibrium >= self.open_threshold_:
|
||||
trd_instructions = self._create_open_trade_instructions(
|
||||
pair, row=last_row, prediction=prediction
|
||||
)
|
||||
elif pair.is_open():
|
||||
if abs_scaled_disequilibrium <= self.close_threshold_:
|
||||
trd_instructions = self._create_close_trade_instructions(
|
||||
pair, row=last_row, prediction=prediction
|
||||
)
|
||||
elif pair.to_stop_close_conditions(predicted_row=last_row):
|
||||
trd_instructions = self._create_close_trade_instructions(
|
||||
pair, row=last_row
|
||||
)
|
||||
|
||||
return trd_instructions
|
||||
|
||||
def _create_open_trade_instructions(
|
||||
self, pair: TradingPair, row: pd.Series, prediction: Prediction
|
||||
) -> List[TradingInstruction]:
|
||||
scaled_disequilibrium = prediction.scaled_disequilibrium_
|
||||
|
||||
if scaled_disequilibrium > 0:
|
||||
side_a = "SELL"
|
||||
trd_inst_a = TradingInstruction(
|
||||
type=TradingInstructionType.TARGET_POSITION,
|
||||
exch_instr=pair.get_instrument_a(),
|
||||
specifics={"side": "SELL", "strength": -1},
|
||||
)
|
||||
side_b = "BUY"
|
||||
else:
|
||||
side_a = "BUY"
|
||||
side_b = "SELL"
|
||||
|
||||
# save closing sides
|
||||
pair.user_data_["open_side_a"] = side_a # used in oustanding positions
|
||||
pair.user_data_["open_side_b"] = side_b
|
||||
pair.user_data_["open_px_a"] = px_a
|
||||
pair.user_data_["open_px_b"] = px_b
|
||||
pair.user_data_["open_tstamp"] = tstamp
|
||||
|
||||
pair.user_data_["close_side_a"] = side_b # used for closing trades
|
||||
pair.user_data_["close_side_b"] = side_a
|
||||
|
||||
# create opening trades
|
||||
df.loc[len(df)] = {
|
||||
"time": tstamp,
|
||||
"symbol": pair.symbol_a_,
|
||||
"side": side_a,
|
||||
"action": "OPEN",
|
||||
"price": px_a,
|
||||
"disequilibrium": diseqlbrm,
|
||||
"signed_scaled_disequilibrium": scaled_disequilibrium,
|
||||
"scaled_disequilibrium": abs(scaled_disequilibrium),
|
||||
# "pair": pair,
|
||||
}
|
||||
df.loc[len(df)] = {
|
||||
"time": tstamp,
|
||||
"symbol": pair.symbol_b_,
|
||||
"side": side_b,
|
||||
"action": "OPEN",
|
||||
"price": px_b,
|
||||
"disequilibrium": diseqlbrm,
|
||||
"scaled_disequilibrium": abs(scaled_disequilibrium),
|
||||
"signed_scaled_disequilibrium": scaled_disequilibrium,
|
||||
# "pair": pair,
|
||||
}
|
||||
return df
|
||||
|
||||
def _handle_outstanding_positions(self) -> Optional[pd.DataFrame]:
|
||||
trades = None
|
||||
pair = self.trading_pair_
|
||||
|
||||
# Outstanding positions
|
||||
if pair.user_data_["state"] == PairState.OPEN:
|
||||
print(f"{pair}: *** Position is NOT CLOSED. ***")
|
||||
# outstanding positions
|
||||
if self.config_["close_outstanding_positions"]:
|
||||
close_position_row = pd.Series(pair.market_data_.iloc[-2])
|
||||
# close_position_row["disequilibrium"] = 0.0
|
||||
# close_position_row["scaled_disequilibrium"] = 0.0
|
||||
# close_position_row["signed_scaled_disequilibrium"] = 0.0
|
||||
|
||||
trades = self._create_close_trades(
|
||||
pair=pair, row=close_position_row, prediction=None
|
||||
)
|
||||
if trades is not None:
|
||||
trades["status"] = PairState.CLOSE_POSITION.name
|
||||
print(f"CLOSE_POSITION TRADES:\n{trades}")
|
||||
pair.user_data_["state"] = PairState.CLOSE_POSITION
|
||||
pair.on_close_trades(trades)
|
||||
else:
|
||||
pair.add_outstanding_position(
|
||||
symbol=pair.symbol_a_,
|
||||
open_side=pair.user_data_["open_side_a"],
|
||||
open_px=pair.user_data_["open_px_a"],
|
||||
open_tstamp=pair.user_data_["open_tstamp"],
|
||||
last_mkt_data_row=pair.market_data_.iloc[-1],
|
||||
)
|
||||
pair.add_outstanding_position(
|
||||
symbol=pair.symbol_b_,
|
||||
open_side=pair.user_data_["open_side_b"],
|
||||
open_px=pair.user_data_["open_px_b"],
|
||||
open_tstamp=pair.user_data_["open_tstamp"],
|
||||
last_mkt_data_row=pair.market_data_.iloc[-1],
|
||||
)
|
||||
return trades
|
||||
|
||||
def _trades_df(self) -> pd.DataFrame:
|
||||
types = {
|
||||
"time": "datetime64[ns]",
|
||||
"action": "string",
|
||||
"symbol": "string",
|
||||
"side": "string",
|
||||
"price": "float64",
|
||||
"disequilibrium": "float64",
|
||||
"scaled_disequilibrium": "float64",
|
||||
"signed_scaled_disequilibrium": "float64",
|
||||
# "pair": "object",
|
||||
}
|
||||
columns = list(types.keys())
|
||||
return pd.DataFrame(columns=columns).astype(types)
|
||||
|
||||
def _create_open_trades(
|
||||
self, pair: TradingPair, row: pd.Series, prediction: Prediction
|
||||
) -> Optional[pd.DataFrame]:
|
||||
colname_a, colname_b = pair.exec_prices_colnames()
|
||||
|
||||
tstamp = row["tstamp"]
|
||||
diseqlbrm = prediction.disequilibrium_
|
||||
scaled_disequilibrium = prediction.scaled_disequilibrium_
|
||||
px_a = row[f"{colname_a}"]
|
||||
px_b = row[f"{colname_b}"]
|
||||
|
||||
# creating the trades
|
||||
df = self._trades_df()
|
||||
|
||||
print(f"OPEN_TRADES: {row["tstamp"]} {scaled_disequilibrium=}")
|
||||
if diseqlbrm > 0:
|
||||
side_a = "SELL"
|
||||
side_b = "BUY"
|
||||
else:
|
||||
side_a = "BUY"
|
||||
side_b = "SELL"
|
||||
|
||||
# save closing sides
|
||||
pair.user_data_["open_side_a"] = side_a # used in oustanding positions
|
||||
pair.user_data_["open_side_b"] = side_b
|
||||
pair.user_data_["open_px_a"] = px_a
|
||||
pair.user_data_["open_px_b"] = px_b
|
||||
pair.user_data_["open_tstamp"] = tstamp
|
||||
|
||||
pair.user_data_["close_side_a"] = side_b # used for closing trades
|
||||
pair.user_data_["close_side_b"] = side_a
|
||||
|
||||
# create opening trades
|
||||
df.loc[len(df)] = {
|
||||
"time": tstamp,
|
||||
"symbol": pair.symbol_a_,
|
||||
"side": side_a,
|
||||
"action": "OPEN",
|
||||
"price": px_a,
|
||||
"disequilibrium": diseqlbrm,
|
||||
"signed_scaled_disequilibrium": scaled_disequilibrium,
|
||||
"scaled_disequilibrium": abs(scaled_disequilibrium),
|
||||
# "pair": pair,
|
||||
}
|
||||
df.loc[len(df)] = {
|
||||
"time": tstamp,
|
||||
"symbol": pair.symbol_b_,
|
||||
"side": side_b,
|
||||
"action": "OPEN",
|
||||
"price": px_b,
|
||||
"disequilibrium": diseqlbrm,
|
||||
"scaled_disequilibrium": abs(scaled_disequilibrium),
|
||||
"signed_scaled_disequilibrium": scaled_disequilibrium,
|
||||
# "pair": pair,
|
||||
}
|
||||
return df
|
||||
|
||||
def _create_close_trades(
|
||||
self, pair: TradingPair, row: pd.Series, prediction: Optional[Prediction] = None
|
||||
) -> Optional[pd.DataFrame]:
|
||||
colname_a, colname_b = pair.exec_prices_colnames()
|
||||
|
||||
tstamp = row["tstamp"]
|
||||
if prediction is not None:
|
||||
diseqlbrm = prediction.disequilibrium_
|
||||
signed_scaled_disequilibrium = prediction.scaled_disequilibrium_
|
||||
scaled_disequilibrium = abs(prediction.scaled_disequilibrium_)
|
||||
else:
|
||||
diseqlbrm = 0.0
|
||||
signed_scaled_disequilibrium = 0.0
|
||||
scaled_disequilibrium = 0.0
|
||||
px_a = row[f"{colname_a}"]
|
||||
px_b = row[f"{colname_b}"]
|
||||
|
||||
# creating the trades
|
||||
df = self._trades_df()
|
||||
|
||||
# create opening trades
|
||||
df.loc[len(df)] = {
|
||||
"time": tstamp,
|
||||
"symbol": pair.symbol_a_,
|
||||
"side": pair.user_data_["close_side_a"],
|
||||
"action": "CLOSE",
|
||||
"price": px_a,
|
||||
"disequilibrium": diseqlbrm,
|
||||
"scaled_disequilibrium": scaled_disequilibrium,
|
||||
"signed_scaled_disequilibrium": signed_scaled_disequilibrium,
|
||||
# "pair": pair,
|
||||
}
|
||||
df.loc[len(df)] = {
|
||||
"time": tstamp,
|
||||
"symbol": pair.symbol_b_,
|
||||
"side": pair.user_data_["close_side_b"],
|
||||
"action": "CLOSE",
|
||||
"price": px_b,
|
||||
"disequilibrium": diseqlbrm,
|
||||
"scaled_disequilibrium": scaled_disequilibrium,
|
||||
"signed_scaled_disequilibrium": signed_scaled_disequilibrium,
|
||||
# "pair": pair,
|
||||
}
|
||||
del pair.user_data_["close_side_a"]
|
||||
del pair.user_data_["close_side_b"]
|
||||
|
||||
del pair.user_data_["open_tstamp"]
|
||||
del pair.user_data_["open_px_a"]
|
||||
del pair.user_data_["open_px_b"]
|
||||
del pair.user_data_["open_side_a"]
|
||||
del pair.user_data_["open_side_b"]
|
||||
return df
|
||||
@@ -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.app import App
|
||||
from cvttpy_tools.base import NamedObject
|
||||
from cvttpy_tools.config import Config
|
||||
from cvttpy_tools.logger import Log
|
||||
from cvttpy_tools.settings.cvtt_types import JsonDictT
|
||||
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.app import App
|
||||
from cvttpy_tools.base import NamedObject
|
||||
from cvttpy_tools.config import Config
|
||||
from cvttpy_tools.logger import Log
|
||||
from cvttpy_tools.timer import Timer
|
||||
from cvttpy_tools.timeutils import NanoPerSec
|
||||
from cvttpy_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)
|
||||
|
||||
|
||||
@@ -1,257 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import copy
|
||||
from abc import ABC, abstractmethod
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Dict, Optional, cast
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
|
||||
@dataclass
|
||||
class DataWindowParams:
|
||||
training_size: int
|
||||
training_start_index: int
|
||||
|
||||
|
||||
class ModelDataPolicy(ABC):
|
||||
config_: Dict[str, Any]
|
||||
current_data_params_: DataWindowParams
|
||||
count_: int
|
||||
is_real_time_: bool
|
||||
|
||||
def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
|
||||
self.config_ = config
|
||||
training_size = config.get("training_size", 120)
|
||||
training_start_index = 0
|
||||
if kwargs.get("is_real_time", False):
|
||||
training_size = 120
|
||||
training_start_index = 0
|
||||
else:
|
||||
training_size = config.get("training_size", 120)
|
||||
self.current_data_params_ = DataWindowParams(
|
||||
training_size=config.get("training_size", 120),
|
||||
training_start_index=0,
|
||||
)
|
||||
self.count_ = 0
|
||||
self.is_real_time_ = kwargs.get("is_real_time", False)
|
||||
|
||||
@abstractmethod
|
||||
def advance(self, mkt_data_df: Optional[pd.DataFrame] = None) -> DataWindowParams:
|
||||
self.count_ += 1
|
||||
print(self.count_, end="\r")
|
||||
return self.current_data_params_
|
||||
|
||||
@staticmethod
|
||||
def create(config: Dict[str, Any], *args: Any, **kwargs: Any) -> ModelDataPolicy:
|
||||
import importlib
|
||||
|
||||
model_data_policy_class_name = config.get("model_data_policy_class", None)
|
||||
assert model_data_policy_class_name is not None
|
||||
module_name, class_name = model_data_policy_class_name.rsplit(".", 1)
|
||||
module = importlib.import_module(module_name)
|
||||
model_training_data_policy_object = getattr(module, class_name)(
|
||||
config=config, *args, **kwargs
|
||||
)
|
||||
return cast(ModelDataPolicy, model_training_data_policy_object)
|
||||
|
||||
|
||||
class RollingWindowDataPolicy(ModelDataPolicy):
|
||||
def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
|
||||
super().__init__(config, *args, **kwargs)
|
||||
self.count_ = 1
|
||||
|
||||
def advance(self, mkt_data_df: Optional[pd.DataFrame] = None) -> DataWindowParams:
|
||||
super().advance(mkt_data_df)
|
||||
if self.is_real_time_:
|
||||
self.current_data_params_.training_start_index = -self.current_data_params_.training_size
|
||||
else:
|
||||
self.current_data_params_.training_start_index += 1
|
||||
return self.current_data_params_
|
||||
|
||||
|
||||
class OptimizedWndDataPolicy(ModelDataPolicy, ABC):
|
||||
mkt_data_df_: pd.DataFrame
|
||||
pair_: TradingPair # type: ignore
|
||||
min_training_size_: int
|
||||
max_training_size_: int
|
||||
end_index_: int
|
||||
prices_a_: np.ndarray
|
||||
prices_b_: np.ndarray
|
||||
|
||||
def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
|
||||
super().__init__(config, *args, **kwargs)
|
||||
assert (
|
||||
kwargs.get("pair") is not None
|
||||
), "pair must be provided"
|
||||
assert (
|
||||
"min_training_size" in config and "max_training_size" in config
|
||||
), "min_training_size and max_training_size must be provided"
|
||||
self.min_training_size_ = cast(int, config.get("min_training_size"))
|
||||
self.max_training_size_ = cast(int, config.get("max_training_size"))
|
||||
|
||||
from pt_strategy.trading_pair import TradingPair
|
||||
self.pair_ = cast(TradingPair, kwargs.get("pair"))
|
||||
|
||||
if "mkt_data" in kwargs:
|
||||
self.mkt_data_df_ = cast(pd.DataFrame, kwargs.get("mkt_data"))
|
||||
col_a, col_b = self.pair_.colnames()
|
||||
self.prices_a_ = np.array(self.mkt_data_df_[col_a])
|
||||
self.prices_b_ = np.array(self.mkt_data_df_[col_b])
|
||||
assert self.min_training_size_ < self.max_training_size_
|
||||
|
||||
|
||||
def advance(self, mkt_data_df: Optional[pd.DataFrame] = None) -> DataWindowParams:
|
||||
super().advance(mkt_data_df)
|
||||
if mkt_data_df is not None:
|
||||
self.mkt_data_df_ = mkt_data_df
|
||||
|
||||
if self.is_real_time_:
|
||||
self.end_index_ = len(self.mkt_data_df_) - 1
|
||||
else:
|
||||
self.end_index_ = self.current_data_params_.training_start_index + self.max_training_size_
|
||||
if self.end_index_ > len(self.mkt_data_df_) - 1:
|
||||
self.end_index_ = len(self.mkt_data_df_) - 1
|
||||
self.current_data_params_.training_start_index = self.end_index_ - self.max_training_size_
|
||||
if self.current_data_params_.training_start_index < 0:
|
||||
self.current_data_params_.training_start_index = 0
|
||||
|
||||
col_a, col_b = self.pair_.colnames()
|
||||
self.prices_a_ = np.array(self.mkt_data_df_[col_a])
|
||||
self.prices_b_ = np.array(self.mkt_data_df_[col_b])
|
||||
|
||||
self.current_data_params_ = self.optimize_window_size()
|
||||
return self.current_data_params_
|
||||
|
||||
@abstractmethod
|
||||
def optimize_window_size(self) -> DataWindowParams:
|
||||
...
|
||||
|
||||
class EGOptimizedWndDataPolicy(OptimizedWndDataPolicy):
|
||||
'''
|
||||
# Engle-Granger cointegration test
|
||||
*** VERY SLOW ***
|
||||
'''
|
||||
def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
|
||||
super().__init__(config, *args, **kwargs)
|
||||
|
||||
def optimize_window_size(self) -> DataWindowParams:
|
||||
# Run Engle-Granger cointegration test
|
||||
last_pvalue = 1.0
|
||||
result = copy.copy(self.current_data_params_)
|
||||
for trn_size in range(self.min_training_size_, self.max_training_size_):
|
||||
if self.end_index_ - trn_size < 0:
|
||||
break
|
||||
|
||||
from statsmodels.tsa.stattools import coint # type: ignore
|
||||
|
||||
start_index = self.end_index_ - trn_size
|
||||
series_a = self.prices_a_[start_index : self.end_index_]
|
||||
series_b = self.prices_b_[start_index : self.end_index_]
|
||||
eg_pvalue = float(coint(series_a, series_b)[1])
|
||||
if eg_pvalue < last_pvalue:
|
||||
last_pvalue = eg_pvalue
|
||||
result.training_size = trn_size
|
||||
result.training_start_index = start_index
|
||||
|
||||
# print(
|
||||
# f"*** DEBUG *** end_index={self.end_index_}, best_trn_size={self.current_data_params_.training_size}, {last_pvalue=}"
|
||||
# )
|
||||
return result
|
||||
|
||||
class ADFOptimizedWndDataPolicy(OptimizedWndDataPolicy):
|
||||
# Augmented Dickey-Fuller test
|
||||
def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
|
||||
super().__init__(config, *args, **kwargs)
|
||||
|
||||
def optimize_window_size(self) -> DataWindowParams:
|
||||
from statsmodels.regression.linear_model import OLS
|
||||
from statsmodels.tools.tools import add_constant
|
||||
from statsmodels.tsa.stattools import adfuller
|
||||
|
||||
last_pvalue = 1.0
|
||||
result = copy.copy(self.current_data_params_)
|
||||
for trn_size in range(self.min_training_size_, self.max_training_size_):
|
||||
if self.end_index_ - trn_size < 0:
|
||||
break
|
||||
start_index = self.end_index_ - trn_size
|
||||
y = self.prices_a_[start_index : self.end_index_]
|
||||
x = self.prices_b_[start_index : self.end_index_]
|
||||
|
||||
# Add constant to x for intercept
|
||||
x_with_const = add_constant(x)
|
||||
|
||||
# OLS regression: y = a + b*x + e
|
||||
model = OLS(y, x_with_const).fit()
|
||||
residuals = y - model.predict(x_with_const)
|
||||
|
||||
# ADF test on residuals
|
||||
try:
|
||||
adf_result = adfuller(residuals, maxlag=1, regression="c")
|
||||
adf_pvalue = float(adf_result[1])
|
||||
except Exception as e:
|
||||
# Handle edge cases with exception (e.g., constant series, etc.)
|
||||
adf_pvalue = 1.0
|
||||
|
||||
if adf_pvalue < last_pvalue:
|
||||
last_pvalue = adf_pvalue
|
||||
result.training_size = trn_size
|
||||
result.training_start_index = start_index
|
||||
|
||||
# print(
|
||||
# f"*** DEBUG *** end_index={self.end_index_},"
|
||||
# f" best_trn_size={self.current_data_params_.training_size},"
|
||||
# f" {last_pvalue=}"
|
||||
# )
|
||||
return result
|
||||
|
||||
class JohansenOptdWndDataPolicy(OptimizedWndDataPolicy):
|
||||
# Johansen test
|
||||
def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
|
||||
super().__init__(config, *args, **kwargs)
|
||||
|
||||
def optimize_window_size(self) -> DataWindowParams:
|
||||
from statsmodels.tsa.vector_ar.vecm import coint_johansen
|
||||
import numpy as np
|
||||
|
||||
best_stat = -np.inf
|
||||
best_trn_size = 0
|
||||
best_start_index = -1
|
||||
|
||||
result = copy.copy(self.current_data_params_)
|
||||
for trn_size in range(self.min_training_size_, self.max_training_size_):
|
||||
if self.end_index_ - trn_size < 0:
|
||||
break
|
||||
start_index = self.end_index_ - trn_size
|
||||
series_a = self.prices_a_[start_index:self.end_index_]
|
||||
series_b = self.prices_b_[start_index:self.end_index_]
|
||||
|
||||
# Combine into 2D matrix for Johansen test
|
||||
try:
|
||||
data = np.column_stack([series_a, series_b])
|
||||
|
||||
# Johansen test: det_order=0 (no deterministic trend), k_ar_diff=1 (lag)
|
||||
res = coint_johansen(data, det_order=0, k_ar_diff=1)
|
||||
|
||||
# Trace statistic for cointegration rank 1
|
||||
trace_stat = res.lr1[0] # test stat for rank=0 vs >=1
|
||||
critical_value = res.cvt[0, 1] # 5% critical value
|
||||
|
||||
if trace_stat > best_stat:
|
||||
best_stat = trace_stat
|
||||
best_trn_size = trn_size
|
||||
best_start_index = start_index
|
||||
except Exception:
|
||||
continue
|
||||
|
||||
if best_trn_size > 0:
|
||||
result.training_size = best_trn_size
|
||||
result.training_start_index = best_start_index
|
||||
else:
|
||||
print("*** WARNING: No valid cointegration window found.")
|
||||
|
||||
# print(
|
||||
# f"*** DEBUG *** end_index={self.end_index_}, best_trn_size={best_trn_size}, trace_stat={best_stat}"
|
||||
# )
|
||||
return result
|
||||
@@ -1,104 +0,0 @@
|
||||
from __future__ import annotations
|
||||
from typing import Optional
|
||||
|
||||
import pandas as pd
|
||||
import statsmodels.api as sm
|
||||
|
||||
|
||||
|
||||
from pt_strategy.pt_model import PairsTradingModel, Prediction
|
||||
from pt_strategy.trading_pair import TradingPair
|
||||
|
||||
|
||||
class OLSModel(PairsTradingModel):
|
||||
model_: Optional[sm.regression.linear_model.RegressionResultsWrapper]
|
||||
pair_predict_result_: Optional[pd.DataFrame]
|
||||
zscore_df_: Optional[pd.DataFrame]
|
||||
|
||||
def predict(self, pair: TradingPair) -> Prediction:
|
||||
self.training_df_ = pair.market_data_.copy()
|
||||
|
||||
zscore_df = self._fit_zscore(pair=pair)
|
||||
|
||||
assert zscore_df is not None
|
||||
# zscore is both disequilibrium and scaled_disequilibrium
|
||||
self.training_df_["dis-equilibrium"] = zscore_df[0]
|
||||
self.training_df_["scaled_dis-equilibrium"] = zscore_df[0]
|
||||
|
||||
assert zscore_df is not None
|
||||
return Prediction(
|
||||
tstamp=pair.market_data_.iloc[-1]["tstamp"],
|
||||
disequilibrium=self.training_df_["dis-equilibrium"].iloc[-1],
|
||||
scaled_disequilibrium=self.training_df_["scaled_dis-equilibrium"].iloc[-1],
|
||||
)
|
||||
|
||||
def _fit_zscore(self, pair: TradingPair) -> pd.DataFrame:
|
||||
assert self.training_df_ is not None
|
||||
symbol_a_px_series = self.training_df_[pair.colnames()].iloc[:, 0]
|
||||
symbol_b_px_series = self.training_df_[pair.colnames()].iloc[:, 1]
|
||||
|
||||
symbol_a_px_series, symbol_b_px_series = symbol_a_px_series.align(
|
||||
symbol_b_px_series, axis=0
|
||||
)
|
||||
|
||||
X = sm.add_constant(symbol_b_px_series)
|
||||
self.model_ = sm.OLS(symbol_a_px_series, X).fit()
|
||||
assert self.model_ is not None
|
||||
|
||||
# alternate way would be to use models residuals (will give identical results)
|
||||
# alpha, beta = self.model_.params
|
||||
# spread = symbol_a_px_series - (alpha + beta * symbol_b_px_series)
|
||||
spread = self.model_.resid
|
||||
return pd.DataFrame((spread - spread.mean()) / spread.std())
|
||||
|
||||
|
||||
class VECMModel(PairsTradingModel):
|
||||
def predict(self, pair: TradingPair) -> Prediction:
|
||||
self.training_df_ = pair.market_data_.copy()
|
||||
assert self.training_df_ is not None
|
||||
vecm_fit = self._fit_VECM(pair=pair)
|
||||
|
||||
assert vecm_fit is not None
|
||||
predicted_prices = vecm_fit.predict(steps=1)
|
||||
|
||||
# Convert prediction to a DataFrame for readability
|
||||
predicted_df = pd.DataFrame(
|
||||
predicted_prices, columns=pd.Index(pair.colnames()), dtype=float
|
||||
)
|
||||
|
||||
disequilibrium = (predicted_df[pair.colnames()] @ vecm_fit.beta)[0][0]
|
||||
scaled_disequilibrium = (disequilibrium - self.training_mu_) / self.training_std_
|
||||
return Prediction(
|
||||
tstamp=pair.market_data_.iloc[-1]["tstamp"],
|
||||
disequilibrium=disequilibrium,
|
||||
scaled_disequilibrium=scaled_disequilibrium,
|
||||
)
|
||||
|
||||
def _fit_VECM(self, pair: TradingPair) -> VECMResults: # type: ignore
|
||||
from statsmodels.tsa.vector_ar.vecm import VECM, VECMResults
|
||||
|
||||
vecm_df = self.training_df_[pair.colnames()].reset_index(drop=True)
|
||||
vecm_model = VECM(vecm_df, coint_rank=1)
|
||||
vecm_fit = vecm_model.fit()
|
||||
|
||||
assert vecm_fit is not None
|
||||
|
||||
# Check if the model converged properly
|
||||
if not hasattr(vecm_fit, "beta") or vecm_fit.beta is None:
|
||||
print(f"{self}: VECM model failed to converge properly")
|
||||
|
||||
diseq_series = self.training_df_[pair.colnames()] @ vecm_fit.beta
|
||||
# print(diseq_series.shape)
|
||||
self.training_mu_ = float(diseq_series[0].mean())
|
||||
self.training_std_ = float(diseq_series[0].std())
|
||||
|
||||
self.training_df_["dis-equilibrium"] = (
|
||||
self.training_df_[pair.colnames()] @ vecm_fit.beta
|
||||
)
|
||||
# Normalize the dis-equilibrium
|
||||
self.training_df_["scaled_dis-equilibrium"] = (
|
||||
diseq_series - self.training_mu_
|
||||
) / self.training_std_
|
||||
|
||||
return vecm_fit
|
||||
|
||||
@@ -1,28 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any, Dict
|
||||
|
||||
import pandas as pd
|
||||
|
||||
|
||||
class Prediction:
|
||||
tstamp_: pd.Timestamp
|
||||
disequilibrium_: float
|
||||
scaled_disequilibrium_: float
|
||||
|
||||
def __init__(self, tstamp: pd.Timestamp, disequilibrium: float, scaled_disequilibrium: float):
|
||||
self.tstamp_ = tstamp
|
||||
self.disequilibrium_ = disequilibrium
|
||||
self.scaled_disequilibrium_ = scaled_disequilibrium
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"tstamp": self.tstamp_,
|
||||
"disequilibrium": self.disequilibrium_,
|
||||
"signed_scaled_disequilibrium": self.scaled_disequilibrium_,
|
||||
"scaled_disequilibrium": abs(self.scaled_disequilibrium_),
|
||||
# "pair": self.pair_,
|
||||
}
|
||||
def to_df(self) -> pd.DataFrame:
|
||||
return pd.DataFrame([self.to_dict()])
|
||||
|
||||
@@ -1,229 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
import pandas as pd
|
||||
from cvttpy_tools.settings.cvtt_types import JsonDictT
|
||||
from tools.data_loader import load_market_data
|
||||
|
||||
|
||||
class PtMarketData():
|
||||
config_: Dict[str, Any]
|
||||
origin_mkt_data_df_: pd.DataFrame
|
||||
market_data_df_: pd.DataFrame
|
||||
|
||||
def __init__(self, config: Dict[str, Any]):
|
||||
self.config_ = config
|
||||
self.origin_mkt_data_df_ = pd.DataFrame()
|
||||
self.market_data_df_ = pd.DataFrame()
|
||||
|
||||
|
||||
class ResearchMarketData(PtMarketData):
|
||||
current_index_: int
|
||||
|
||||
is_execution_price_: bool
|
||||
|
||||
def __init__(self, config: Dict[str, Any]):
|
||||
super().__init__(config)
|
||||
self.current_index_ = 0
|
||||
self.is_execution_price_ = "execution_price" in self.config_
|
||||
if self.is_execution_price_:
|
||||
self.execution_price_column_ = self.config_["execution_price"]["column"]
|
||||
self.execution_price_shift_ = self.config_["execution_price"]["shift"]
|
||||
else:
|
||||
self.execution_price_column_ = None
|
||||
self.execution_price_shift_ = 0
|
||||
|
||||
def has_next(self) -> bool:
|
||||
return self.current_index_ < len(self.market_data_df_)
|
||||
|
||||
def get_next(self) -> pd.Series:
|
||||
result = self.market_data_df_.iloc[self.current_index_]
|
||||
self.current_index_ += 1
|
||||
return result
|
||||
|
||||
def load(self) -> None:
|
||||
datafiles: List[str] = self.config_.get("datafiles", [])
|
||||
instruments: List[Dict[str, str]] = self.config_.get("instruments", [])
|
||||
assert len(instruments) > 0, "No instruments found in config"
|
||||
assert len(datafiles) > 0, "No datafiles found in config"
|
||||
self.symbol_a_ = instruments[0]["symbol"]
|
||||
self.symbol_b_ = instruments[1]["symbol"]
|
||||
self.stat_model_price_ = self.config_["stat_model_price"]
|
||||
|
||||
extra_minutes: int
|
||||
extra_minutes = self.execution_price_shift_
|
||||
|
||||
for datafile in datafiles:
|
||||
md_df = load_market_data(
|
||||
datafile=datafile,
|
||||
instruments=instruments,
|
||||
db_table_name=self.config_["market_data_loading"][instruments[0]["instrument_type"]]["db_table_name"],
|
||||
trading_hours=self.config_["trading_hours"],
|
||||
extra_minutes=extra_minutes,
|
||||
)
|
||||
self.origin_mkt_data_df_ = pd.concat([self.origin_mkt_data_df_, md_df])
|
||||
|
||||
self.origin_mkt_data_df_ = self.origin_mkt_data_df_.sort_values(by="tstamp")
|
||||
self.origin_mkt_data_df_ = self.origin_mkt_data_df_.dropna().reset_index(drop=True)
|
||||
self._set_market_data()
|
||||
|
||||
def _set_market_data(self, ) -> None:
|
||||
if self.is_execution_price_:
|
||||
self.market_data_df_ = pd.DataFrame(
|
||||
self._transform_dataframe(self.origin_mkt_data_df_)[["tstamp"] + self.colnames() + self.orig_exec_prices_colnames()]
|
||||
)
|
||||
else:
|
||||
self.market_data_df_ = pd.DataFrame(
|
||||
self._transform_dataframe(self.origin_mkt_data_df_)[["tstamp"] + self.colnames()]
|
||||
)
|
||||
|
||||
self.market_data_df_ = self.market_data_df_.dropna().reset_index(drop=True)
|
||||
self.market_data_df_["tstamp"] = pd.to_datetime(self.market_data_df_["tstamp"])
|
||||
self.market_data_df_ = self.market_data_df_.sort_values("tstamp")
|
||||
self._set_execution_price_data()
|
||||
|
||||
def _transform_dataframe(self, df: pd.DataFrame) -> pd.DataFrame:
|
||||
df_selected: pd.DataFrame
|
||||
if self.is_execution_price_:
|
||||
execution_price_column = self.config_["execution_price"]["column"]
|
||||
|
||||
df_selected = pd.DataFrame(
|
||||
df[["tstamp", "symbol", self.stat_model_price_, execution_price_column]]
|
||||
)
|
||||
else:
|
||||
df_selected = pd.DataFrame(
|
||||
df[["tstamp", "symbol", self.stat_model_price_]]
|
||||
)
|
||||
|
||||
result_df = pd.DataFrame(df_selected["tstamp"]).drop_duplicates().reset_index(drop=True)
|
||||
|
||||
# For each unique symbol, add a corresponding stat_model_price column
|
||||
symbols = df_selected["symbol"].unique()
|
||||
|
||||
|
||||
|
||||
for symbol in symbols:
|
||||
# Filter rows for this symbol
|
||||
df_symbol = df_selected[df_selected["symbol"] == symbol].reset_index(
|
||||
drop=True
|
||||
)
|
||||
|
||||
# Create column name like "close-COIN"
|
||||
new_price_column = f"{self.stat_model_price_}_{symbol}"
|
||||
if self.is_execution_price_:
|
||||
new_execution_price_column = f"{self.execution_price_column_}_{symbol}"
|
||||
|
||||
# Create temporary dataframe with timestamp and price
|
||||
temp_df = pd.DataFrame(
|
||||
{
|
||||
"tstamp": df_symbol["tstamp"],
|
||||
new_price_column: df_symbol[self.stat_model_price_],
|
||||
new_execution_price_column: df_symbol[execution_price_column],
|
||||
}
|
||||
)
|
||||
else:
|
||||
temp_df = pd.DataFrame(
|
||||
{
|
||||
"tstamp": df_symbol["tstamp"],
|
||||
new_price_column: df_symbol[self.stat_model_price_],
|
||||
}
|
||||
)
|
||||
|
||||
# Join with our result dataframe
|
||||
result_df = pd.merge(result_df, temp_df, on="tstamp", how="left")
|
||||
result_df = result_df.reset_index(
|
||||
drop=True
|
||||
) # do not dropna() since irrelevant symbol would affect dataset
|
||||
|
||||
return result_df.dropna()
|
||||
|
||||
def _set_execution_price_data(self) -> None:
|
||||
if "execution_price" not in self.config_:
|
||||
self.market_data_df_[f"exec_price_{self.symbol_a_}"] = self.market_data_df_[f"{self.stat_model_price_}_{self.symbol_a_}"]
|
||||
self.market_data_df_[f"exec_price_{self.symbol_b_}"] = self.market_data_df_[f"{self.stat_model_price_}_{self.symbol_b_}"]
|
||||
return
|
||||
execution_price_column = self.config_["execution_price"]["column"]
|
||||
execution_price_shift = self.config_["execution_price"]["shift"]
|
||||
self.market_data_df_[f"exec_price_{self.symbol_a_}"] = self.market_data_df_[f"{execution_price_column}_{self.symbol_a_}"].shift(-execution_price_shift)
|
||||
self.market_data_df_[f"exec_price_{self.symbol_b_}"] = self.market_data_df_[f"{execution_price_column}_{self.symbol_b_}"].shift(-execution_price_shift)
|
||||
self.market_data_df_ = self.market_data_df_.dropna().reset_index(drop=True)
|
||||
|
||||
def colnames(self) -> List[str]:
|
||||
return [
|
||||
f"{self.stat_model_price_}_{self.symbol_a_}",
|
||||
f"{self.stat_model_price_}_{self.symbol_b_}",
|
||||
]
|
||||
|
||||
def orig_exec_prices_colnames(self) -> List[str]:
|
||||
return [
|
||||
f"{self.execution_price_column_}_{self.symbol_a_}",
|
||||
f"{self.execution_price_column_}_{self.symbol_b_}",
|
||||
]
|
||||
|
||||
def exec_prices_colnames(self) -> List[str]:
|
||||
return [
|
||||
f"exec_price_{self.symbol_a_}",
|
||||
f"exec_price_{self.symbol_b_}",
|
||||
]
|
||||
|
||||
class RealTimeMarketData(PtMarketData):
|
||||
|
||||
def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
|
||||
super().__init__(config, *args, **kwargs)
|
||||
|
||||
async def on_mkt_data_hist_snapshot(self, snapshot: JsonDictT) -> None:
|
||||
# URGENT
|
||||
# create origin_mkt_data_df_ from snapshot
|
||||
# verify that the data for both instruments are present
|
||||
|
||||
# transform it to market_data_df_ tstamp, close_symbolA, close_symbolB
|
||||
'''
|
||||
# from cvttpy/exchanges/binance/spot/mkt_data.py
|
||||
values = {
|
||||
"time_ns": time_ns,
|
||||
"tstamp": format_nanos_utc(time_ns),
|
||||
"exchange_id": exch_inst.exchange_id_,
|
||||
"instrument_id": exch_inst.instrument_id(),
|
||||
"interval_ns": interval_sec * 1_000_000_000,
|
||||
"open": float(kline[1]),
|
||||
"high": float(kline[2]),
|
||||
"low": float(kline[3]),
|
||||
"close": float(kline[4]),
|
||||
"volume": float(kline[5]),
|
||||
"num_trades": kline[8],
|
||||
"vwap": float(kline[7]) / float(kline[5]) if float(kline[5]) > 0 else 0.0 # Calculate VWAP
|
||||
}
|
||||
'''
|
||||
|
||||
|
||||
pass
|
||||
|
||||
async def on_mkt_data_update(self, update: JsonDictT) -> Optional[pd.DataFrame]:
|
||||
# URGENT
|
||||
# make sure update has both instruments
|
||||
# create DataFrame tmp1 from update
|
||||
# transform tmp1 into temp. datframe tmp2
|
||||
# add tmp1 to origin_mkt_data_df_
|
||||
# add tmp2 to market_data_df_
|
||||
# return market_data_df_
|
||||
'''
|
||||
class MdTradesAggregate(NamedObject):
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"time_ns": self.time_ns_,
|
||||
"tstamp": format_nanos_utc(self.time_ns_),
|
||||
"exchange_id": self.exch_inst_.exchange_id_,
|
||||
"instrument_id": self.exch_inst_.instrument_id(),
|
||||
"interval_ns": self.interval_ns_,
|
||||
"open": self.exch_inst_.get_price(self.open_),
|
||||
"high": self.exch_inst_.get_price(self.high_),
|
||||
"low": self.exch_inst_.get_price(self.low_),
|
||||
"close": self.exch_inst_.get_price(self.close_),
|
||||
"volume": self.exch_inst_.get_quantity(self.volume_),
|
||||
"vwap": self.exch_inst_.get_price(self.vwap_),
|
||||
"num_trades": self.exch_inst_.get_quantity(self.num_trades_),
|
||||
}
|
||||
'''
|
||||
|
||||
return pd.DataFrame()
|
||||
@@ -1,27 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Any, Dict, cast
|
||||
|
||||
from pt_strategy.prediction import Prediction
|
||||
|
||||
|
||||
class PairsTradingModel(ABC):
|
||||
|
||||
@abstractmethod
|
||||
def predict(self, pair: TradingPair) -> Prediction: # type: ignore[assignment]
|
||||
...
|
||||
|
||||
@staticmethod
|
||||
def create(config: Dict[str, Any]) -> PairsTradingModel:
|
||||
import importlib
|
||||
|
||||
model_class_name = config.get("model_class", None)
|
||||
assert model_class_name is not None
|
||||
module_name, class_name = model_class_name.rsplit(".", 1)
|
||||
module = importlib.import_module(module_name)
|
||||
model_object = getattr(module, class_name)()
|
||||
return cast(PairsTradingModel, model_object)
|
||||
|
||||
|
||||
|
||||
@@ -1,303 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
import pandas as pd
|
||||
from pt_strategy.model_data_policy import ModelDataPolicy
|
||||
from pt_strategy.pt_market_data import ResearchMarketData
|
||||
from pt_strategy.pt_model import Prediction
|
||||
from pt_strategy.trading_pair import PairState, TradingPair
|
||||
|
||||
|
||||
class PtResearchStrategy:
|
||||
config_: Dict[str, Any]
|
||||
trading_pair_: TradingPair
|
||||
model_data_policy_: ModelDataPolicy
|
||||
pt_mkt_data_: ResearchMarketData
|
||||
|
||||
trades_: List[pd.DataFrame]
|
||||
predictions_: pd.DataFrame
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: Dict[str, Any],
|
||||
datafiles: List[str],
|
||||
instruments: List[Dict[str, str]],
|
||||
):
|
||||
from pt_strategy.model_data_policy import ModelDataPolicy
|
||||
from pt_strategy.trading_pair import TradingPair
|
||||
|
||||
self.config_ = config
|
||||
self.trades_ = []
|
||||
self.trading_pair_ = TradingPair(config=config, instruments=instruments)
|
||||
self.predictions_ = pd.DataFrame()
|
||||
|
||||
import copy
|
||||
|
||||
# modified config must be passed to PtMarketData
|
||||
config_copy = copy.deepcopy(config)
|
||||
config_copy["instruments"] = instruments
|
||||
config_copy["datafiles"] = datafiles
|
||||
self.pt_mkt_data_ = ResearchMarketData(config=config_copy)
|
||||
self.pt_mkt_data_.load()
|
||||
self.model_data_policy_ = ModelDataPolicy.create(
|
||||
config, mkt_data=self.pt_mkt_data_.market_data_df_, pair=self.trading_pair_
|
||||
)
|
||||
|
||||
def outstanding_positions(self) -> List[Dict[str, Any]]:
|
||||
return list(self.trading_pair_.user_data_.get("outstanding_positions", []))
|
||||
|
||||
def run(self) -> None:
|
||||
training_minutes = self.config_.get("training_minutes", 120)
|
||||
market_data_series: pd.Series
|
||||
market_data_df = pd.DataFrame()
|
||||
|
||||
idx = 0
|
||||
while self.pt_mkt_data_.has_next():
|
||||
market_data_series = self.pt_mkt_data_.get_next()
|
||||
new_row = pd.DataFrame([market_data_series])
|
||||
market_data_df = pd.concat([market_data_df, new_row], ignore_index=True)
|
||||
if idx >= training_minutes:
|
||||
break
|
||||
idx += 1
|
||||
|
||||
assert idx >= training_minutes, "Not enough training data"
|
||||
|
||||
while self.pt_mkt_data_.has_next():
|
||||
|
||||
market_data_series = self.pt_mkt_data_.get_next()
|
||||
new_row = pd.DataFrame([market_data_series])
|
||||
market_data_df = pd.concat([market_data_df, new_row], ignore_index=True)
|
||||
|
||||
prediction = self.trading_pair_.run(
|
||||
market_data_df, self.model_data_policy_.advance(mkt_data_df=market_data_df)
|
||||
)
|
||||
self.predictions_ = pd.concat(
|
||||
[self.predictions_, prediction.to_df()], ignore_index=True
|
||||
)
|
||||
assert prediction is not None
|
||||
|
||||
trades = self._create_trades(
|
||||
prediction=prediction, last_row=market_data_df.iloc[-1]
|
||||
)
|
||||
if trades is not None:
|
||||
self.trades_.append(trades)
|
||||
|
||||
trades = self._handle_outstanding_positions()
|
||||
if trades is not None:
|
||||
self.trades_.append(trades)
|
||||
|
||||
def _create_trades(
|
||||
self, prediction: Prediction, last_row: pd.Series
|
||||
) -> Optional[pd.DataFrame]:
|
||||
pair = self.trading_pair_
|
||||
trades = None
|
||||
|
||||
open_threshold = self.config_["dis-equilibrium_open_trshld"]
|
||||
close_threshold = self.config_["dis-equilibrium_close_trshld"]
|
||||
scaled_disequilibrium = prediction.scaled_disequilibrium_
|
||||
abs_scaled_disequilibrium = abs(scaled_disequilibrium)
|
||||
|
||||
if pair.user_data_["state"] in [
|
||||
PairState.INITIAL,
|
||||
PairState.CLOSE,
|
||||
PairState.CLOSE_POSITION,
|
||||
PairState.CLOSE_STOP_LOSS,
|
||||
PairState.CLOSE_STOP_PROFIT,
|
||||
]:
|
||||
if abs_scaled_disequilibrium >= open_threshold:
|
||||
trades = self._create_open_trades(
|
||||
pair, row=last_row, prediction=prediction
|
||||
)
|
||||
if trades is not None:
|
||||
trades["status"] = PairState.OPEN.name
|
||||
print(f"OPEN TRADES:\n{trades}")
|
||||
pair.user_data_["state"] = PairState.OPEN
|
||||
pair.on_open_trades(trades)
|
||||
|
||||
elif pair.user_data_["state"] == PairState.OPEN:
|
||||
if abs_scaled_disequilibrium <= close_threshold:
|
||||
trades = self._create_close_trades(
|
||||
pair, row=last_row, prediction=prediction
|
||||
)
|
||||
if trades is not None:
|
||||
trades["status"] = PairState.CLOSE.name
|
||||
print(f"CLOSE TRADES:\n{trades}")
|
||||
pair.user_data_["state"] = PairState.CLOSE
|
||||
pair.on_close_trades(trades)
|
||||
elif pair.to_stop_close_conditions(predicted_row=last_row):
|
||||
trades = self._create_close_trades(pair, row=last_row)
|
||||
if trades is not None:
|
||||
trades["status"] = pair.user_data_["stop_close_state"].name
|
||||
print(f"STOP CLOSE TRADES:\n{trades}")
|
||||
pair.user_data_["state"] = pair.user_data_["stop_close_state"]
|
||||
pair.on_close_trades(trades)
|
||||
|
||||
return trades
|
||||
|
||||
def _handle_outstanding_positions(self) -> Optional[pd.DataFrame]:
|
||||
trades = None
|
||||
pair = self.trading_pair_
|
||||
|
||||
# Outstanding positions
|
||||
if pair.user_data_["state"] == PairState.OPEN:
|
||||
print(f"{pair}: *** Position is NOT CLOSED. ***")
|
||||
# outstanding positions
|
||||
if self.config_["close_outstanding_positions"]:
|
||||
close_position_row = pd.Series(pair.market_data_.iloc[-2])
|
||||
# close_position_row["disequilibrium"] = 0.0
|
||||
# close_position_row["scaled_disequilibrium"] = 0.0
|
||||
# close_position_row["signed_scaled_disequilibrium"] = 0.0
|
||||
|
||||
trades = self._create_close_trades(
|
||||
pair=pair, row=close_position_row, prediction=None
|
||||
)
|
||||
if trades is not None:
|
||||
trades["status"] = PairState.CLOSE_POSITION.name
|
||||
print(f"CLOSE_POSITION TRADES:\n{trades}")
|
||||
pair.user_data_["state"] = PairState.CLOSE_POSITION
|
||||
pair.on_close_trades(trades)
|
||||
else:
|
||||
pair.add_outstanding_position(
|
||||
symbol=pair.symbol_a_,
|
||||
open_side=pair.user_data_["open_side_a"],
|
||||
open_px=pair.user_data_["open_px_a"],
|
||||
open_tstamp=pair.user_data_["open_tstamp"],
|
||||
last_mkt_data_row=pair.market_data_.iloc[-1],
|
||||
)
|
||||
pair.add_outstanding_position(
|
||||
symbol=pair.symbol_b_,
|
||||
open_side=pair.user_data_["open_side_b"],
|
||||
open_px=pair.user_data_["open_px_b"],
|
||||
open_tstamp=pair.user_data_["open_tstamp"],
|
||||
last_mkt_data_row=pair.market_data_.iloc[-1],
|
||||
)
|
||||
return trades
|
||||
|
||||
def _trades_df(self) -> pd.DataFrame:
|
||||
types = {
|
||||
"time": "datetime64[ns]",
|
||||
"action": "string",
|
||||
"symbol": "string",
|
||||
"side": "string",
|
||||
"price": "float64",
|
||||
"disequilibrium": "float64",
|
||||
"scaled_disequilibrium": "float64",
|
||||
"signed_scaled_disequilibrium": "float64",
|
||||
# "pair": "object",
|
||||
}
|
||||
columns = list(types.keys())
|
||||
return pd.DataFrame(columns=columns).astype(types)
|
||||
|
||||
def _create_open_trades(
|
||||
self, pair: TradingPair, row: pd.Series, prediction: Prediction
|
||||
) -> Optional[pd.DataFrame]:
|
||||
colname_a, colname_b = pair.exec_prices_colnames()
|
||||
|
||||
tstamp = row["tstamp"]
|
||||
diseqlbrm = prediction.disequilibrium_
|
||||
scaled_disequilibrium = prediction.scaled_disequilibrium_
|
||||
px_a = row[f"{colname_a}"]
|
||||
px_b = row[f"{colname_b}"]
|
||||
|
||||
# creating the trades
|
||||
df = self._trades_df()
|
||||
|
||||
print(f"OPEN_TRADES: {row["tstamp"]} {scaled_disequilibrium=}")
|
||||
if diseqlbrm > 0:
|
||||
side_a = "SELL"
|
||||
side_b = "BUY"
|
||||
else:
|
||||
side_a = "BUY"
|
||||
side_b = "SELL"
|
||||
|
||||
# save closing sides
|
||||
pair.user_data_["open_side_a"] = side_a # used in oustanding positions
|
||||
pair.user_data_["open_side_b"] = side_b
|
||||
pair.user_data_["open_px_a"] = px_a
|
||||
pair.user_data_["open_px_b"] = px_b
|
||||
pair.user_data_["open_tstamp"] = tstamp
|
||||
|
||||
pair.user_data_["close_side_a"] = side_b # used for closing trades
|
||||
pair.user_data_["close_side_b"] = side_a
|
||||
|
||||
# create opening trades
|
||||
df.loc[len(df)] = {
|
||||
"time": tstamp,
|
||||
"symbol": pair.symbol_a_,
|
||||
"side": side_a,
|
||||
"action": "OPEN",
|
||||
"price": px_a,
|
||||
"disequilibrium": diseqlbrm,
|
||||
"signed_scaled_disequilibrium": scaled_disequilibrium,
|
||||
"scaled_disequilibrium": abs(scaled_disequilibrium),
|
||||
# "pair": pair,
|
||||
}
|
||||
df.loc[len(df)] = {
|
||||
"time": tstamp,
|
||||
"symbol": pair.symbol_b_,
|
||||
"side": side_b,
|
||||
"action": "OPEN",
|
||||
"price": px_b,
|
||||
"disequilibrium": diseqlbrm,
|
||||
"scaled_disequilibrium": abs(scaled_disequilibrium),
|
||||
"signed_scaled_disequilibrium": scaled_disequilibrium,
|
||||
# "pair": pair,
|
||||
}
|
||||
return df
|
||||
|
||||
def _create_close_trades(
|
||||
self, pair: TradingPair, row: pd.Series, prediction: Optional[Prediction] = None
|
||||
) -> Optional[pd.DataFrame]:
|
||||
colname_a, colname_b = pair.exec_prices_colnames()
|
||||
|
||||
tstamp = row["tstamp"]
|
||||
if prediction is not None:
|
||||
diseqlbrm = prediction.disequilibrium_
|
||||
signed_scaled_disequilibrium = prediction.scaled_disequilibrium_
|
||||
scaled_disequilibrium = abs(prediction.scaled_disequilibrium_)
|
||||
else:
|
||||
diseqlbrm = 0.0
|
||||
signed_scaled_disequilibrium = 0.0
|
||||
scaled_disequilibrium = 0.0
|
||||
px_a = row[f"{colname_a}"]
|
||||
px_b = row[f"{colname_b}"]
|
||||
|
||||
# creating the trades
|
||||
df = self._trades_df()
|
||||
|
||||
# create opening trades
|
||||
df.loc[len(df)] = {
|
||||
"time": tstamp,
|
||||
"symbol": pair.symbol_a_,
|
||||
"side": pair.user_data_["close_side_a"],
|
||||
"action": "CLOSE",
|
||||
"price": px_a,
|
||||
"disequilibrium": diseqlbrm,
|
||||
"scaled_disequilibrium": scaled_disequilibrium,
|
||||
"signed_scaled_disequilibrium": signed_scaled_disequilibrium,
|
||||
# "pair": pair,
|
||||
}
|
||||
df.loc[len(df)] = {
|
||||
"time": tstamp,
|
||||
"symbol": pair.symbol_b_,
|
||||
"side": pair.user_data_["close_side_b"],
|
||||
"action": "CLOSE",
|
||||
"price": px_b,
|
||||
"disequilibrium": diseqlbrm,
|
||||
"scaled_disequilibrium": scaled_disequilibrium,
|
||||
"signed_scaled_disequilibrium": signed_scaled_disequilibrium,
|
||||
# "pair": pair,
|
||||
}
|
||||
del pair.user_data_["close_side_a"]
|
||||
del pair.user_data_["close_side_b"]
|
||||
|
||||
del pair.user_data_["open_tstamp"]
|
||||
del pair.user_data_["open_px_a"]
|
||||
del pair.user_data_["open_px_b"]
|
||||
del pair.user_data_["open_side_a"]
|
||||
del pair.user_data_["open_side_b"]
|
||||
return df
|
||||
|
||||
def day_trades(self) -> pd.DataFrame:
|
||||
return pd.concat(self.trades_, ignore_index=True)
|
||||
@@ -1,532 +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_strategy.trading_pair import TradingPair
|
||||
|
||||
|
||||
# Recommended replacement adapters and converters for Python 3.12+
|
||||
# From: https://docs.python.org/3/library/sqlite3.html#sqlite3-adapter-converter-recipes
|
||||
def adapt_date_iso(val: date) -> str:
|
||||
"""Adapt datetime.date to ISO 8601 date."""
|
||||
return val.isoformat()
|
||||
|
||||
|
||||
def adapt_datetime_iso(val: datetime) -> str:
|
||||
"""Adapt datetime.datetime to timezone-naive ISO 8601 date."""
|
||||
return val.isoformat()
|
||||
|
||||
|
||||
def convert_date(val: bytes) -> date:
|
||||
"""Convert ISO 8601 date to datetime.date object."""
|
||||
return datetime.fromisoformat(val.decode()).date()
|
||||
|
||||
|
||||
def convert_datetime(val: bytes) -> datetime:
|
||||
"""Convert ISO 8601 datetime to datetime.datetime object."""
|
||||
return datetime.fromisoformat(val.decode())
|
||||
|
||||
|
||||
# Register the adapters and converters
|
||||
sqlite3.register_adapter(date, adapt_date_iso)
|
||||
sqlite3.register_adapter(datetime, adapt_datetime_iso)
|
||||
sqlite3.register_converter("date", convert_date)
|
||||
sqlite3.register_converter("datetime", convert_datetime)
|
||||
|
||||
|
||||
def create_result_database(db_path: str) -> None:
|
||||
"""
|
||||
Create the SQLite database and required tables if they don't exist.
|
||||
"""
|
||||
try:
|
||||
# Create directory if it doesn't exist
|
||||
db_dir = os.path.dirname(db_path)
|
||||
if db_dir and not os.path.exists(db_dir):
|
||||
os.makedirs(db_dir, exist_ok=True)
|
||||
print(f"Created directory: {db_dir}")
|
||||
|
||||
conn = sqlite3.connect(db_path)
|
||||
cursor = conn.cursor()
|
||||
|
||||
# Create the pt_bt_results table for completed trades
|
||||
cursor.execute(
|
||||
"""
|
||||
CREATE TABLE IF NOT EXISTS pt_bt_results (
|
||||
date DATE,
|
||||
pair TEXT,
|
||||
symbol TEXT,
|
||||
open_time DATETIME,
|
||||
open_side TEXT,
|
||||
open_price REAL,
|
||||
open_quantity INTEGER,
|
||||
open_disequilibrium REAL,
|
||||
close_time DATETIME,
|
||||
close_side TEXT,
|
||||
close_price REAL,
|
||||
close_quantity INTEGER,
|
||||
close_disequilibrium REAL,
|
||||
symbol_return REAL,
|
||||
pair_return REAL,
|
||||
close_condition TEXT
|
||||
)
|
||||
"""
|
||||
)
|
||||
cursor.execute("DELETE FROM pt_bt_results;")
|
||||
|
||||
# Create the outstanding_positions table for open positions
|
||||
cursor.execute(
|
||||
"""
|
||||
CREATE TABLE IF NOT EXISTS outstanding_positions (
|
||||
date DATE,
|
||||
pair TEXT,
|
||||
symbol TEXT,
|
||||
position_quantity REAL,
|
||||
last_price REAL,
|
||||
unrealized_return REAL,
|
||||
open_price REAL,
|
||||
open_side TEXT
|
||||
)
|
||||
"""
|
||||
)
|
||||
cursor.execute("DELETE FROM outstanding_positions;")
|
||||
|
||||
# Create the config table for storing configuration JSON for reference
|
||||
cursor.execute(
|
||||
"""
|
||||
CREATE TABLE IF NOT EXISTS config (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
run_timestamp DATETIME,
|
||||
config_file_path TEXT,
|
||||
config_json TEXT,
|
||||
datafiles TEXT,
|
||||
instruments TEXT
|
||||
)
|
||||
"""
|
||||
)
|
||||
cursor.execute("DELETE FROM config;")
|
||||
|
||||
conn.commit()
|
||||
conn.close()
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error creating result database: {str(e)}")
|
||||
raise
|
||||
|
||||
|
||||
def store_config_in_database(
|
||||
db_path: str,
|
||||
config_file_path: str,
|
||||
config: Dict,
|
||||
datafiles: List[Tuple[str, str]],
|
||||
instruments: List[Dict[str, str]],
|
||||
) -> None:
|
||||
"""
|
||||
Store configuration information in the database for reference.
|
||||
"""
|
||||
import json
|
||||
|
||||
if db_path.upper() == "NONE":
|
||||
return
|
||||
|
||||
try:
|
||||
conn = sqlite3.connect(db_path)
|
||||
cursor = conn.cursor()
|
||||
|
||||
# Convert config to JSON string
|
||||
config_json = json.dumps(config, indent=2, default=str)
|
||||
|
||||
# Convert lists to comma-separated strings for storage
|
||||
datafiles_str = ", ".join([f"{datafile}" for _, datafile in datafiles])
|
||||
instruments_str = ", ".join(
|
||||
[
|
||||
f"{inst['symbol']}:{inst['instrument_type']}:{inst['exchange_id']}"
|
||||
for inst in instruments
|
||||
]
|
||||
)
|
||||
|
||||
# Insert configuration record
|
||||
cursor.execute(
|
||||
"""
|
||||
INSERT INTO config (
|
||||
run_timestamp, config_file_path, config_json, datafiles, instruments
|
||||
) VALUES (?, ?, ?, ?, ?)
|
||||
""",
|
||||
(
|
||||
datetime.now(),
|
||||
config_file_path,
|
||||
config_json,
|
||||
datafiles_str,
|
||||
instruments_str,
|
||||
),
|
||||
)
|
||||
|
||||
conn.commit()
|
||||
conn.close()
|
||||
|
||||
print(f"Configuration stored in database")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error storing configuration in database: {str(e)}")
|
||||
import traceback
|
||||
|
||||
traceback.print_exc()
|
||||
|
||||
|
||||
def convert_timestamp(timestamp: Any) -> Optional[datetime]:
|
||||
"""Convert pandas Timestamp to Python datetime object for SQLite compatibility."""
|
||||
if timestamp is None:
|
||||
return None
|
||||
if isinstance(timestamp, pd.Timestamp):
|
||||
return timestamp.to_pydatetime()
|
||||
elif isinstance(timestamp, datetime):
|
||||
return timestamp
|
||||
elif isinstance(timestamp, date):
|
||||
return datetime.combine(timestamp, datetime.min.time())
|
||||
elif isinstance(timestamp, str):
|
||||
return datetime.strptime(timestamp, "%Y-%m-%d %H:%M:%S")
|
||||
elif isinstance(timestamp, int):
|
||||
return datetime.fromtimestamp(timestamp)
|
||||
else:
|
||||
raise ValueError(f"Unsupported timestamp type: {type(timestamp)}")
|
||||
|
||||
|
||||
|
||||
DayT = str
|
||||
TradeT = Dict[str, Any]
|
||||
OutstandingPositionT = Dict[str, Any]
|
||||
class PairResearchResult:
|
||||
"""
|
||||
Class to handle pair research results for a single pair across multiple days.
|
||||
Simplified version of BacktestResult focused on single pair analysis.
|
||||
"""
|
||||
trades_: Dict[DayT, pd.DataFrame]
|
||||
outstanding_positions_: Dict[DayT, List[OutstandingPositionT]]
|
||||
symbol_roundtrip_trades_: Dict[str, List[Dict[str, Any]]]
|
||||
|
||||
|
||||
def __init__(self, config: Dict[str, Any]) -> None:
|
||||
self.config_ = config
|
||||
self.trades_ = {}
|
||||
self.outstanding_positions_ = {}
|
||||
self.total_realized_pnl = 0.0
|
||||
self.symbol_roundtrip_trades_ = {}
|
||||
|
||||
def add_day_results(self, day: DayT, trades: pd.DataFrame, outstanding_positions: List[Dict[str, Any]]) -> None:
|
||||
assert isinstance(trades, pd.DataFrame)
|
||||
self.trades_[day] = trades
|
||||
self.outstanding_positions_[day] = outstanding_positions
|
||||
|
||||
# def all_trades(self) -> List[TradeT]:
|
||||
# """Get all trades across all days as a flat list."""
|
||||
# all_trades_list: List[TradeT] = []
|
||||
# for day_trades in self.trades_.values():
|
||||
# all_trades_list.extend(day_trades.to_dict(orient="records"))
|
||||
# return all_trades_list
|
||||
|
||||
def outstanding_positions(self) -> List[OutstandingPositionT]:
|
||||
"""Get all outstanding positions across all days as a flat list."""
|
||||
res: List[Dict[str, Any]] = []
|
||||
for day in self.outstanding_positions_.keys():
|
||||
res.extend(self.outstanding_positions_[day])
|
||||
return res
|
||||
|
||||
def calculate_returns(self) -> None:
|
||||
"""Calculate and store total returns for the single pair across all days."""
|
||||
self.extract_roundtrip_trades()
|
||||
|
||||
self.total_realized_pnl = 0.0
|
||||
|
||||
for day, day_trades in self.symbol_roundtrip_trades_.items():
|
||||
for trade in day_trades:
|
||||
self.total_realized_pnl += trade['symbol_return']
|
||||
|
||||
def extract_roundtrip_trades(self) -> None:
|
||||
"""
|
||||
Extract round-trip trades by day, grouping open/close pairs for each symbol.
|
||||
Returns a dictionary with day as key and list of completed round-trip trades.
|
||||
"""
|
||||
def _symbol_return(trade1_side: str, trade1_px: float, trade2_side: str, trade2_px: float) -> float:
|
||||
if trade1_side == "BUY" and trade2_side == "SELL":
|
||||
return (trade2_px - trade1_px) / trade1_px * 100
|
||||
elif trade1_side == "SELL" and trade2_side == "BUY":
|
||||
return (trade1_px - trade2_px) / trade1_px * 100
|
||||
else:
|
||||
return 0
|
||||
|
||||
# Process each day separately
|
||||
for day, day_trades in self.trades_.items():
|
||||
|
||||
# Sort trades by timestamp for the day
|
||||
sorted_trades = day_trades #sorted(day_trades, key=lambda x: x["timestamp"] if x["timestamp"] else pd.Timestamp.min)
|
||||
|
||||
day_roundtrips = []
|
||||
|
||||
# Process trades in groups of 4 (open A, open B, close A, close B)
|
||||
for idx in range(0, len(sorted_trades), 4):
|
||||
if idx + 3 >= len(sorted_trades):
|
||||
break
|
||||
|
||||
trade_a_1 = sorted_trades.iloc[idx] # Open A
|
||||
trade_b_1 = sorted_trades.iloc[idx + 1] # Open B
|
||||
trade_a_2 = sorted_trades.iloc[idx + 2] # Close A
|
||||
trade_b_2 = sorted_trades.iloc[idx + 3] # Close B
|
||||
|
||||
# Validate trade sequence
|
||||
if not (trade_a_1["action"] == "OPEN" and trade_a_2["action"] == "CLOSE"):
|
||||
continue
|
||||
if not (trade_b_1["action"] == "OPEN" and trade_b_2["action"] == "CLOSE"):
|
||||
continue
|
||||
|
||||
# Calculate individual symbol returns
|
||||
symbol_a_return = _symbol_return(
|
||||
trade_a_1["side"], trade_a_1["price"],
|
||||
trade_a_2["side"], trade_a_2["price"]
|
||||
)
|
||||
symbol_b_return = _symbol_return(
|
||||
trade_b_1["side"], trade_b_1["price"],
|
||||
trade_b_2["side"], trade_b_2["price"]
|
||||
)
|
||||
|
||||
pair_return = symbol_a_return + symbol_b_return
|
||||
|
||||
# Create round-trip records for both symbols
|
||||
funding_per_position = self.config_.get("funding_per_pair", 10000) / 2
|
||||
|
||||
# Symbol A round-trip
|
||||
day_roundtrips.append({
|
||||
"symbol": trade_a_1["symbol"],
|
||||
"open_side": trade_a_1["side"],
|
||||
"open_price": trade_a_1["price"],
|
||||
"open_time": trade_a_1["time"],
|
||||
"close_side": trade_a_2["side"],
|
||||
"close_price": trade_a_2["price"],
|
||||
"close_time": trade_a_2["time"],
|
||||
"symbol_return": symbol_a_return,
|
||||
"pair_return": pair_return,
|
||||
"shares": funding_per_position / trade_a_1["price"],
|
||||
"close_condition": trade_a_2.get("status", "UNKNOWN"),
|
||||
"open_disequilibrium": trade_a_1.get("disequilibrium"),
|
||||
"close_disequilibrium": trade_a_2.get("disequilibrium"),
|
||||
})
|
||||
|
||||
# Symbol B round-trip
|
||||
day_roundtrips.append({
|
||||
"symbol": trade_b_1["symbol"],
|
||||
"open_side": trade_b_1["side"],
|
||||
"open_price": trade_b_1["price"],
|
||||
"open_time": trade_b_1["time"],
|
||||
"close_side": trade_b_2["side"],
|
||||
"close_price": trade_b_2["price"],
|
||||
"close_time": trade_b_2["time"],
|
||||
"symbol_return": symbol_b_return,
|
||||
"pair_return": pair_return,
|
||||
"shares": funding_per_position / trade_b_1["price"],
|
||||
"close_condition": trade_b_2.get("status", "UNKNOWN"),
|
||||
"open_disequilibrium": trade_b_1.get("disequilibrium"),
|
||||
"close_disequilibrium": trade_b_2.get("disequilibrium"),
|
||||
})
|
||||
|
||||
if day_roundtrips:
|
||||
self.symbol_roundtrip_trades_[day] = day_roundtrips
|
||||
|
||||
|
||||
def print_returns_by_day(self) -> None:
|
||||
"""
|
||||
Print detailed return information for each day, grouped by day.
|
||||
Shows individual symbol round-trips and daily totals.
|
||||
"""
|
||||
|
||||
print("\n====== PAIR RESEARCH RETURNS BY DAY ======")
|
||||
|
||||
total_return_all_days = 0.0
|
||||
|
||||
for day, day_trades in sorted(self.symbol_roundtrip_trades_.items()):
|
||||
|
||||
print(f"\n--- {day} ---")
|
||||
|
||||
day_total_return = 0.0
|
||||
pair_returns = []
|
||||
|
||||
# Group trades by pair (every 2 trades form a pair)
|
||||
for idx in range(0, len(day_trades), 2):
|
||||
if idx + 1 < len(day_trades):
|
||||
trade_a = day_trades[idx]
|
||||
trade_b = day_trades[idx + 1]
|
||||
|
||||
# Print individual symbol results
|
||||
print(f" {trade_a['open_time'].time()}-{trade_a['close_time'].time()}")
|
||||
print(f" {trade_a['symbol']}: {trade_a['open_side']} @ ${trade_a['open_price']:.2f} → "
|
||||
f"{trade_a['close_side']} @ ${trade_a['close_price']:.2f} | "
|
||||
f"Return: {trade_a['symbol_return']:+.2f}% | Shares: {trade_a['shares']:.2f}")
|
||||
|
||||
print(f" {trade_b['symbol']}: {trade_b['open_side']} @ ${trade_b['open_price']:.2f} → "
|
||||
f"{trade_b['close_side']} @ ${trade_b['close_price']:.2f} | "
|
||||
f"Return: {trade_b['symbol_return']:+.2f}% | Shares: {trade_b['shares']:.2f}")
|
||||
|
||||
# Show disequilibrium info if available
|
||||
if trade_a.get('open_disequilibrium') is not None:
|
||||
print(f" Disequilibrium: Open: {trade_a['open_disequilibrium']:.4f}, "
|
||||
f"Close: {trade_a['close_disequilibrium']:.4f}")
|
||||
|
||||
pair_return = trade_a['pair_return']
|
||||
print(f" Pair Return: {pair_return:+.2f}% | Close Condition: {trade_a['close_condition']}")
|
||||
print()
|
||||
|
||||
pair_returns.append(pair_return)
|
||||
day_total_return += pair_return
|
||||
|
||||
print(f" Day Total Return: {day_total_return:+.2f}% ({len(pair_returns)} pairs)")
|
||||
total_return_all_days += day_total_return
|
||||
|
||||
print(f"\n====== TOTAL RETURN ACROSS ALL DAYS ======")
|
||||
print(f"Total Return: {total_return_all_days:+.2f}%")
|
||||
print(f"Total Days: {len(self.symbol_roundtrip_trades_)}")
|
||||
if len(self.symbol_roundtrip_trades_) > 0:
|
||||
print(f"Average Daily Return: {total_return_all_days / len(self.symbol_roundtrip_trades_):+.2f}%")
|
||||
|
||||
def get_return_summary(self) -> Dict[str, Any]:
|
||||
"""
|
||||
Get a summary of returns across all days.
|
||||
Returns a dictionary with key metrics.
|
||||
"""
|
||||
if len(self.symbol_roundtrip_trades_) == 0:
|
||||
return {
|
||||
"total_return": 0.0,
|
||||
"total_days": 0,
|
||||
"total_pairs": 0,
|
||||
"average_daily_return": 0.0,
|
||||
"best_day": None,
|
||||
"worst_day": None,
|
||||
"daily_returns": {}
|
||||
}
|
||||
|
||||
daily_returns = {}
|
||||
total_return = 0.0
|
||||
total_pairs = 0
|
||||
|
||||
for day, day_trades in self.symbol_roundtrip_trades_.items():
|
||||
day_return = 0.0
|
||||
day_pairs = len(day_trades) // 2 # Each pair has 2 symbol trades
|
||||
|
||||
for trade in day_trades:
|
||||
day_return += trade['symbol_return']
|
||||
|
||||
daily_returns[day] = {
|
||||
"return": day_return,
|
||||
"pairs": day_pairs
|
||||
}
|
||||
total_return += day_return
|
||||
total_pairs += day_pairs
|
||||
|
||||
best_day = max(daily_returns.items(), key=lambda x: x[1]["return"]) if daily_returns else None
|
||||
worst_day = min(daily_returns.items(), key=lambda x: x[1]["return"]) if daily_returns else None
|
||||
|
||||
return {
|
||||
"total_return": total_return,
|
||||
"total_days": len(self.symbol_roundtrip_trades_),
|
||||
"total_pairs": total_pairs,
|
||||
"average_daily_return": total_return / len(self.symbol_roundtrip_trades_) if self.symbol_roundtrip_trades_ else 0.0,
|
||||
"best_day": best_day,
|
||||
"worst_day": worst_day,
|
||||
"daily_returns": daily_returns
|
||||
}
|
||||
|
||||
|
||||
def print_grand_totals(self) -> None:
|
||||
"""Print grand totals for the single pair analysis."""
|
||||
summary = self.get_return_summary()
|
||||
|
||||
print(f"\n====== PAIR RESEARCH GRAND TOTALS ======")
|
||||
print('---')
|
||||
print(f"Total Return: {summary['total_return']:+.2f}%")
|
||||
print('---')
|
||||
print(f"Total Days Traded: {summary['total_days']}")
|
||||
print(f"Total Open-Close Actions: {summary['total_pairs']}")
|
||||
print(f"Total Trades: 4 * {summary['total_pairs']} = {4 * summary['total_pairs']}")
|
||||
|
||||
if summary['total_days'] > 0:
|
||||
print(f"Average Daily Return: {summary['average_daily_return']:+.2f}%")
|
||||
|
||||
if summary['best_day']:
|
||||
best_day, best_data = summary['best_day']
|
||||
print(f"Best Day: {best_day} ({best_data['return']:+.2f}%)")
|
||||
|
||||
if summary['worst_day']:
|
||||
worst_day, worst_data = summary['worst_day']
|
||||
print(f"Worst Day: {worst_day} ({worst_data['return']:+.2f}%)")
|
||||
|
||||
# Update the total_realized_pnl for backward compatibility
|
||||
self.total_realized_pnl = summary['total_return']
|
||||
|
||||
def analyze_pair_performance(self) -> None:
|
||||
"""
|
||||
Main method to perform comprehensive pair research analysis.
|
||||
Extracts round-trip trades, calculates returns, groups by day, and prints results.
|
||||
"""
|
||||
print(f"\n{'='*60}")
|
||||
print(f"PAIR RESEARCH PERFORMANCE ANALYSIS")
|
||||
print(f"{'='*60}")
|
||||
|
||||
self.calculate_returns()
|
||||
self.print_returns_by_day()
|
||||
self.print_outstanding_positions()
|
||||
self._print_additional_metrics()
|
||||
self.print_grand_totals()
|
||||
|
||||
def _print_additional_metrics(self) -> None:
|
||||
"""Print additional performance metrics."""
|
||||
summary = self.get_return_summary()
|
||||
|
||||
if summary['total_days'] == 0:
|
||||
return
|
||||
|
||||
print(f"\n====== ADDITIONAL METRICS ======")
|
||||
|
||||
# Calculate win rate
|
||||
winning_days = sum(1 for day_data in summary['daily_returns'].values() if day_data['return'] > 0)
|
||||
win_rate = (winning_days / summary['total_days']) * 100
|
||||
print(f"Winning Days: {winning_days}/{summary['total_days']} ({win_rate:.1f}%)")
|
||||
|
||||
# Calculate average trade return
|
||||
if summary['total_pairs'] > 0:
|
||||
# Each pair has 2 symbol trades, so total symbol trades = total_pairs * 2
|
||||
total_symbol_trades = summary['total_pairs'] * 2
|
||||
avg_symbol_return = summary['total_return'] / total_symbol_trades
|
||||
print(f"Average Symbol Return: {avg_symbol_return:+.2f}%")
|
||||
|
||||
avg_pair_return = summary['total_return'] / summary['total_pairs'] / 2 # Divide by 2 since we sum both symbols
|
||||
print(f"Average Pair Return: {avg_pair_return:+.2f}%")
|
||||
|
||||
# Show daily return distribution
|
||||
daily_returns_list = [data['return'] for data in summary['daily_returns'].values()]
|
||||
if daily_returns_list:
|
||||
print(f"Daily Return Range: {min(daily_returns_list):+.2f}% to {max(daily_returns_list):+.2f}%")
|
||||
|
||||
|
||||
def print_outstanding_positions(self) -> None:
|
||||
"""Print outstanding positions for the single pair."""
|
||||
all_positions: List[OutstandingPositionT] = self.outstanding_positions()
|
||||
if not all_positions:
|
||||
print("\n====== NO OUTSTANDING POSITIONS ======")
|
||||
return
|
||||
|
||||
print(f"\n====== OUTSTANDING POSITIONS ======")
|
||||
print(f"{'Symbol':<10} {'Side':<4} {'Shares':<10} {'Open $':<8} {'Current $':<10} {'Value $':<12}")
|
||||
print("-" * 70)
|
||||
|
||||
total_value = 0.0
|
||||
for pos in all_positions:
|
||||
current_value = pos.get("last_value", 0.0)
|
||||
print(f"{pos['symbol']:<10} {pos['open_side']:<4} {pos['shares']:<10.2f} "
|
||||
f"{pos['open_px']:<8.2f} {pos['last_px']:<10.2f} {current_value:<12.2f}")
|
||||
total_value += current_value
|
||||
|
||||
print("-" * 70)
|
||||
print(f"{'TOTAL VALUE':<60} ${total_value:<12.2f}")
|
||||
|
||||
def get_total_realized_pnl(self) -> float:
|
||||
"""Get total realized PnL."""
|
||||
return self.total_realized_pnl
|
||||
|
||||
@@ -1,199 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from datetime import datetime
|
||||
from enum import Enum
|
||||
from typing import Any, Dict, List
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from pt_strategy.model_data_policy import DataWindowParams
|
||||
from pt_strategy.prediction import Prediction
|
||||
|
||||
|
||||
class PairState(Enum):
|
||||
INITIAL = 1
|
||||
OPEN = 2
|
||||
CLOSE = 3
|
||||
CLOSE_POSITION = 4
|
||||
CLOSE_STOP_LOSS = 5
|
||||
CLOSE_STOP_PROFIT = 6
|
||||
|
||||
|
||||
def get_symbol(instrument: Dict[str, str]) -> str:
|
||||
if "symbol" in instrument:
|
||||
return instrument["symbol"]
|
||||
elif "instrument_id" in instrument:
|
||||
instrument_id = instrument["instrument_id"]
|
||||
instrument_pfx = instrument_id[:instrument_id.find("-") + 1]
|
||||
symbol = instrument_id[len(instrument_pfx):]
|
||||
instrument["symbol"] = symbol
|
||||
instrument["instrument_id_pfx"] = instrument_pfx
|
||||
return symbol
|
||||
else:
|
||||
raise ValueError(f"Invalid instrument: {instrument}, missing symbol or instrument_id")
|
||||
|
||||
class TradingPair:
|
||||
config_: Dict[str, Any]
|
||||
market_data_: pd.DataFrame
|
||||
instruments_: List[Dict[str, str]]
|
||||
symbol_a_: str
|
||||
symbol_b_: str
|
||||
|
||||
stat_model_price_: str
|
||||
model_: PairsTradingModel # type: ignore[assignment]
|
||||
|
||||
user_data_: Dict[str, Any]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: Dict[str, Any],
|
||||
instruments: List[Dict[str, str]],
|
||||
):
|
||||
|
||||
from pt_strategy.pt_model import PairsTradingModel
|
||||
|
||||
assert len(instruments) == 2, "Trading pair must have exactly 2 instruments"
|
||||
|
||||
self.config_ = config
|
||||
self.instruments_ = instruments
|
||||
self.symbol_a_ = get_symbol(instruments[0])
|
||||
self.symbol_b_ = get_symbol(instruments[1])
|
||||
self.model_ = PairsTradingModel.create(config)
|
||||
self.stat_model_price_ = config["stat_model_price"]
|
||||
self.user_data_ = {
|
||||
"state": PairState.INITIAL,
|
||||
}
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return (
|
||||
f"{self.__class__.__name__}:"
|
||||
f" symbol_a={self.symbol_a_},"
|
||||
f" symbol_b={self.symbol_b_},"
|
||||
f" model={self.model_.__class__.__name__}"
|
||||
)
|
||||
|
||||
def is_closed(self) -> bool:
|
||||
return self.user_data_["state"] in [
|
||||
PairState.CLOSE,
|
||||
PairState.CLOSE_POSITION,
|
||||
PairState.CLOSE_STOP_LOSS,
|
||||
PairState.CLOSE_STOP_PROFIT,
|
||||
]
|
||||
def is_open(self) -> bool:
|
||||
return self.user_data_["state"] == PairState.OPEN
|
||||
|
||||
def colnames(self) -> List[str]:
|
||||
return [
|
||||
f"{self.stat_model_price_}_{self.symbol_a_}",
|
||||
f"{self.stat_model_price_}_{self.symbol_b_}",
|
||||
]
|
||||
|
||||
def exec_prices_colnames(self) -> List[str]:
|
||||
return [
|
||||
f"exec_price_{self.symbol_a_}",
|
||||
f"exec_price_{self.symbol_b_}",
|
||||
]
|
||||
|
||||
def to_stop_close_conditions(self, predicted_row: pd.Series) -> bool:
|
||||
config = self.config_
|
||||
if (
|
||||
"stop_close_conditions" not in config
|
||||
or config["stop_close_conditions"] is None
|
||||
):
|
||||
return False
|
||||
if "profit" in config["stop_close_conditions"]:
|
||||
current_return = self._current_return(predicted_row)
|
||||
#
|
||||
# print(f"time={predicted_row['tstamp']} current_return={current_return}")
|
||||
#
|
||||
if current_return >= config["stop_close_conditions"]["profit"]:
|
||||
print(f"STOP PROFIT: {current_return}")
|
||||
self.user_data_["stop_close_state"] = PairState.CLOSE_STOP_PROFIT
|
||||
return True
|
||||
if "loss" in config["stop_close_conditions"]:
|
||||
if current_return <= config["stop_close_conditions"]["loss"]:
|
||||
print(f"STOP LOSS: {current_return}")
|
||||
self.user_data_["stop_close_state"] = PairState.CLOSE_STOP_LOSS
|
||||
return True
|
||||
return False
|
||||
|
||||
def _current_return(self, predicted_row: pd.Series) -> float:
|
||||
if "open_trades" in self.user_data_:
|
||||
open_trades = self.user_data_["open_trades"]
|
||||
if len(open_trades) == 0:
|
||||
return 0.0
|
||||
|
||||
def _single_instrument_return(symbol: str) -> float:
|
||||
instrument_open_trades = open_trades[open_trades["symbol"] == symbol]
|
||||
instrument_open_price = instrument_open_trades["price"].iloc[0]
|
||||
|
||||
sign = -1 if instrument_open_trades["side"].iloc[0] == "SELL" else 1
|
||||
instrument_price = predicted_row[f"{self.stat_model_price_}_{symbol}"]
|
||||
instrument_return = (
|
||||
sign
|
||||
* (instrument_price - instrument_open_price)
|
||||
/ instrument_open_price
|
||||
)
|
||||
return float(instrument_return) * 100.0
|
||||
|
||||
instrument_a_return = _single_instrument_return(self.symbol_a_)
|
||||
instrument_b_return = _single_instrument_return(self.symbol_b_)
|
||||
return instrument_a_return + instrument_b_return
|
||||
return 0.0
|
||||
|
||||
def on_open_trades(self, trades: pd.DataFrame) -> None:
|
||||
if "close_trades" in self.user_data_:
|
||||
del self.user_data_["close_trades"]
|
||||
self.user_data_["open_trades"] = trades
|
||||
|
||||
def on_close_trades(self, trades: pd.DataFrame) -> None:
|
||||
del self.user_data_["open_trades"]
|
||||
self.user_data_["close_trades"] = trades
|
||||
|
||||
def add_outstanding_position(
|
||||
self,
|
||||
symbol: str,
|
||||
open_side: str,
|
||||
open_px: float,
|
||||
open_tstamp: datetime,
|
||||
last_mkt_data_row: pd.Series,
|
||||
) -> None:
|
||||
assert symbol in [self.symbol_a_, self.symbol_b_], "Symbol must be one of the pair's symbols"
|
||||
assert open_side in ["BUY", "SELL"], "Open side must be either BUY or SELL"
|
||||
assert open_px > 0, "Open price must be greater than 0"
|
||||
assert open_tstamp is not None, "Open timestamp must be provided"
|
||||
assert last_mkt_data_row is not None, "Last market data row must be provided"
|
||||
|
||||
exec_prices_col_a, exec_prices_col_b = self.exec_prices_colnames()
|
||||
if symbol == self.symbol_a_:
|
||||
last_px = last_mkt_data_row[exec_prices_col_a]
|
||||
else:
|
||||
last_px = last_mkt_data_row[exec_prices_col_b]
|
||||
|
||||
|
||||
funding_per_position = self.config_["funding_per_pair"] / 2
|
||||
shares = funding_per_position / open_px
|
||||
if open_side == "SELL":
|
||||
shares = -shares
|
||||
|
||||
if "outstanding_positions" not in self.user_data_:
|
||||
self.user_data_["outstanding_positions"] = []
|
||||
|
||||
self.user_data_["outstanding_positions"].append({
|
||||
"symbol": symbol,
|
||||
"open_side": open_side,
|
||||
"open_px": open_px,
|
||||
"shares": shares,
|
||||
"open_tstamp": open_tstamp,
|
||||
"last_px": last_px,
|
||||
"last_tstamp": last_mkt_data_row["tstamp"],
|
||||
"last_value": last_px * shares,
|
||||
})
|
||||
|
||||
|
||||
def run(self, market_data: pd.DataFrame, data_params: DataWindowParams) -> Prediction: # type: ignore[assignment]
|
||||
self.market_data_ = market_data[data_params.training_start_index:data_params.training_start_index + data_params.training_size]
|
||||
return self.model_.predict(pair=self)
|
||||
|
||||
|
||||
|
||||
@@ -1,17 +0,0 @@
|
||||
import hjson
|
||||
from typing import Dict
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
def load_config(config_path: str) -> Dict:
|
||||
with open(config_path, "r") as f:
|
||||
config = hjson.load(f)
|
||||
return dict(config)
|
||||
|
||||
|
||||
def expand_filename(filename: str) -> str:
|
||||
# expand %T
|
||||
res = filename.replace("%T", datetime.now().strftime("%Y%m%d_%H%M%S"))
|
||||
# expand %D
|
||||
return res.replace("%D", datetime.now().strftime("%Y%m%d"))
|
||||
|
||||
@@ -1,151 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import sqlite3
|
||||
from typing import Dict, List, cast
|
||||
import pandas as pd
|
||||
|
||||
|
||||
def load_sqlite_to_dataframe(db_path:str, query:str) -> pd.DataFrame:
|
||||
df: pd.DataFrame = pd.DataFrame()
|
||||
import os
|
||||
if not os.path.exists(db_path):
|
||||
print(f"WARNING: database file {db_path} does not exist")
|
||||
return df
|
||||
|
||||
try:
|
||||
conn = sqlite3.connect(db_path)
|
||||
|
||||
df = pd.read_sql_query(query, conn)
|
||||
return df
|
||||
except sqlite3.Error as excpt:
|
||||
print(f"SQLite error: {excpt}")
|
||||
raise
|
||||
except Exception as excpt:
|
||||
print(f"Error: {excpt}")
|
||||
raise Exception() from excpt
|
||||
finally:
|
||||
if "conn" in locals():
|
||||
conn.close()
|
||||
|
||||
|
||||
def convert_time_to_UTC(value: str, timezone: str, extra_minutes: int = 0) -> str:
|
||||
|
||||
from zoneinfo import ZoneInfo
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
# Parse it to naive datetime object
|
||||
local_dt = datetime.strptime(value, "%Y-%m-%d %H:%M:%S")
|
||||
local_dt = local_dt + timedelta(minutes=extra_minutes)
|
||||
|
||||
zinfo = ZoneInfo(timezone)
|
||||
result: datetime = local_dt.replace(tzinfo=zinfo).astimezone(ZoneInfo("UTC"))
|
||||
|
||||
return result.strftime("%Y-%m-%d %H:%M:%S")
|
||||
|
||||
|
||||
def load_market_data(
|
||||
datafile: str,
|
||||
instruments: List[Dict[str, str]],
|
||||
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))
|
||||
exchange_ids = list(
|
||||
set(['"' + instrument["exchange_id"] + '"' for instrument in instruments])
|
||||
)
|
||||
|
||||
query = "select"
|
||||
query += " tstamp"
|
||||
query += ", tstamp_ns as time_ns"
|
||||
|
||||
query += f", substr(instrument_id, instr(instrument_id, '-') + 1) as symbol"
|
||||
query += ", open"
|
||||
query += ", high"
|
||||
query += ", low"
|
||||
query += ", close"
|
||||
query += ", volume"
|
||||
query += ", num_trades"
|
||||
query += ", vwap"
|
||||
|
||||
query += f" from {db_table_name}"
|
||||
query += f" where exchange_id in ({','.join(exchange_ids)})"
|
||||
query += f" and instrument_id in ({','.join(instrument_ids)})"
|
||||
|
||||
df = load_sqlite_to_dataframe(db_path=datafile, query=query)
|
||||
|
||||
# Trading Hours
|
||||
if len(df) > 0 and len(trading_hours) > 0:
|
||||
date_str = df["tstamp"][0][0:10]
|
||||
|
||||
start_time = convert_time_to_UTC(
|
||||
f"{date_str} {trading_hours['begin_session']}", trading_hours["timezone"]
|
||||
)
|
||||
end_time = convert_time_to_UTC(
|
||||
f"{date_str} {trading_hours['end_session']}", trading_hours["timezone"], extra_minutes=extra_minutes # to get execution price
|
||||
)
|
||||
|
||||
# Perform boolean selection
|
||||
df = df[(df["tstamp"] >= start_time) & (df["tstamp"] <= end_time)]
|
||||
df["tstamp"] = pd.to_datetime(df["tstamp"])
|
||||
|
||||
return cast(pd.DataFrame, df)
|
||||
|
||||
|
||||
# def get_available_instruments_from_db(datafile: str, config: Dict) -> List[str]:
|
||||
# """
|
||||
# Auto-detect available instruments from the database by querying distinct instrument_id values.
|
||||
# Returns instruments without the configured prefix.
|
||||
# """
|
||||
# try:
|
||||
# conn = sqlite3.connect(datafile)
|
||||
|
||||
# # Build exclusion list with full instrument_ids
|
||||
# exclude_instruments = config.get("exclude_instruments", [])
|
||||
# prefix = config.get("instrument_id_pfx", "")
|
||||
# exclude_instrument_ids = [f"{prefix}{inst}" for inst in exclude_instruments]
|
||||
|
||||
# # Query to get distinct instrument_ids
|
||||
# query = f"""
|
||||
# SELECT DISTINCT instrument_id
|
||||
# FROM {config['db_table_name']}
|
||||
# WHERE exchange_id = ?
|
||||
# """
|
||||
|
||||
# # Add exclusion clause if there are instruments to exclude
|
||||
# if exclude_instrument_ids:
|
||||
# placeholders = ",".join(["?" for _ in exclude_instrument_ids])
|
||||
# query += f" AND instrument_id NOT IN ({placeholders})"
|
||||
# cursor = conn.execute(
|
||||
# query, (config["exchange_id"],) + tuple(exclude_instrument_ids)
|
||||
# )
|
||||
# else:
|
||||
# cursor = conn.execute(query, (config["exchange_id"],))
|
||||
# instrument_ids = [row[0] for row in cursor.fetchall()]
|
||||
# conn.close()
|
||||
|
||||
# # Remove the configured prefix to get instrument symbols
|
||||
# instruments = []
|
||||
# for instrument_id in instrument_ids:
|
||||
# if instrument_id.startswith(prefix):
|
||||
# symbol = instrument_id[len(prefix) :]
|
||||
# instruments.append(symbol)
|
||||
# else:
|
||||
# instruments.append(instrument_id)
|
||||
|
||||
# return sorted(instruments)
|
||||
|
||||
# except Exception as e:
|
||||
# print(f"Error auto-detecting instruments from {datafile}: {str(e)}")
|
||||
# return []
|
||||
|
||||
|
||||
# if __name__ == "__main__":
|
||||
# df1 = load_sqlite_to_dataframe(sys.argv[1], table_name="md_1min_bars")
|
||||
|
||||
# print(df1)
|
||||
@@ -1,33 +0,0 @@
|
||||
import os
|
||||
import glob
|
||||
from typing import Dict, List, Tuple
|
||||
|
||||
DayT = str
|
||||
DataFileNameT = str
|
||||
|
||||
def resolve_datafiles(
|
||||
config: Dict, date_pattern: str, instruments: List[Dict[str, str]]
|
||||
) -> List[Tuple[DayT, DataFileNameT]]:
|
||||
resolved_files: List[Tuple[DayT, DataFileNameT]] = []
|
||||
for inst in instruments:
|
||||
pattern = date_pattern
|
||||
inst_type = inst["instrument_type"]
|
||||
data_dir = config["market_data_loading"][inst_type]["data_directory"]
|
||||
if "*" in pattern or "?" in pattern:
|
||||
# Handle wildcards
|
||||
if not os.path.isabs(pattern):
|
||||
pattern = os.path.join(data_dir, f"{pattern}.mktdata.ohlcv.db")
|
||||
matched_files = glob.glob(pattern)
|
||||
for matched_file in matched_files:
|
||||
import re
|
||||
match = re.search(r"(\d{8})\.mktdata\.ohlcv\.db$", matched_file)
|
||||
assert match is not None
|
||||
day = match.group(1)
|
||||
resolved_files.append((day, matched_file))
|
||||
else:
|
||||
# Handle explicit file path
|
||||
if not os.path.isabs(pattern):
|
||||
pattern = os.path.join(data_dir, f"{pattern}.mktdata.ohlcv.db")
|
||||
resolved_files.append((date_pattern, pattern))
|
||||
return sorted(list(set(resolved_files))) # Remove duplicates and sort
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -1,79 +0,0 @@
|
||||
from pt_strategy.research_strategy import PtResearchStrategy
|
||||
|
||||
|
||||
def visualize_prices(strategy: PtResearchStrategy, trading_date: str) -> None:
|
||||
# Plot raw price data
|
||||
import matplotlib.pyplot as plt
|
||||
# Set plotting style
|
||||
import seaborn as sns
|
||||
|
||||
pair = strategy.trading_pair_
|
||||
SYMBOL_A = pair.symbol_a_
|
||||
SYMBOL_B = pair.symbol_b_
|
||||
TRD_DATE = f"{trading_date[0:4]}-{trading_date[4:6]}-{trading_date[6:8]}"
|
||||
|
||||
plt.style.use('seaborn-v0_8')
|
||||
sns.set_palette("husl")
|
||||
plt.rcParams['figure.figsize'] = (15, 10)
|
||||
|
||||
# Get column names for the trading pair
|
||||
colname_a, colname_b = pair.colnames()
|
||||
price_data = strategy.pt_mkt_data_.market_data_df_.copy()
|
||||
|
||||
# Create separate subplots for better visibility
|
||||
fig_price, price_axes = plt.subplots(2, 1, figsize=(18, 10))
|
||||
|
||||
# Plot SYMBOL_A
|
||||
price_axes[0].plot(price_data['tstamp'], price_data[colname_a], alpha=0.7,
|
||||
label=f'{SYMBOL_A}', linewidth=1, color='blue')
|
||||
price_axes[0].set_title(f'{SYMBOL_A} Price Data ({TRD_DATE})')
|
||||
price_axes[0].set_ylabel(f'{SYMBOL_A} Price')
|
||||
price_axes[0].legend()
|
||||
price_axes[0].grid(True)
|
||||
|
||||
# Plot SYMBOL_B
|
||||
price_axes[1].plot(price_data['tstamp'], price_data[colname_b], alpha=0.7,
|
||||
label=f'{SYMBOL_B}', linewidth=1, color='red')
|
||||
price_axes[1].set_title(f'{SYMBOL_B} Price Data ({TRD_DATE})')
|
||||
price_axes[1].set_ylabel(f'{SYMBOL_B} Price')
|
||||
price_axes[1].set_xlabel('Time')
|
||||
price_axes[1].legend()
|
||||
price_axes[1].grid(True)
|
||||
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
|
||||
# Plot individual prices
|
||||
fig, axes = plt.subplots(2, 1, figsize=(18, 12))
|
||||
|
||||
# Normalized prices for comparison
|
||||
norm_a = price_data[colname_a] / price_data[colname_a].iloc[0]
|
||||
norm_b = price_data[colname_b] / price_data[colname_b].iloc[0]
|
||||
|
||||
axes[0].plot(price_data['tstamp'], norm_a, label=f'{SYMBOL_A} (normalized)', alpha=0.8, linewidth=1)
|
||||
axes[0].plot(price_data['tstamp'], norm_b, label=f'{SYMBOL_B} (normalized)', alpha=0.8, linewidth=1)
|
||||
axes[0].set_title(f'Normalized Price Comparison (Base = 1.0) ({TRD_DATE})')
|
||||
axes[0].set_ylabel('Normalized Price')
|
||||
axes[0].legend()
|
||||
axes[0].grid(True)
|
||||
|
||||
# Price ratio
|
||||
price_ratio = price_data[colname_a] / price_data[colname_b]
|
||||
axes[1].plot(price_data['tstamp'], price_ratio, label=f'{SYMBOL_A}/{SYMBOL_B} Ratio', color='green', alpha=0.8, linewidth=1)
|
||||
axes[1].set_title(f'Price Ratio Px({SYMBOL_A})/Px({SYMBOL_B}) ({TRD_DATE})')
|
||||
axes[1].set_ylabel('Ratio')
|
||||
axes[1].set_xlabel('Time')
|
||||
axes[1].legend()
|
||||
axes[1].grid(True)
|
||||
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
# Print basic statistics
|
||||
print(f"\nPrice Statistics:")
|
||||
print(f" {SYMBOL_A}: Mean=${price_data[colname_a].mean():.2f}, Std=${price_data[colname_a].std():.2f}")
|
||||
print(f" {SYMBOL_B}: Mean=${price_data[colname_b].mean():.2f}, Std=${price_data[colname_b].std():.2f}")
|
||||
print(f" Price Ratio: Mean={price_ratio.mean():.2f}, Std={price_ratio.std():.2f}")
|
||||
print(f" Correlation: {price_data[colname_a].corr(price_data[colname_b]):.4f}")
|
||||
|
||||
@@ -1,507 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
from typing import Any, Dict
|
||||
|
||||
from pt_strategy.results import (PairResearchResult, create_result_database,
|
||||
store_config_in_database)
|
||||
from pt_strategy.research_strategy import PtResearchStrategy
|
||||
from tools.filetools import resolve_datafiles
|
||||
from tools.instruments import get_instruments
|
||||
|
||||
|
||||
def visualize_trades(strategy: PtResearchStrategy, results: PairResearchResult, trading_date: str) -> None:
|
||||
|
||||
import pandas as pd
|
||||
import plotly.express as px
|
||||
import plotly.graph_objects as go
|
||||
import plotly.offline as pyo
|
||||
from IPython.display import HTML
|
||||
from plotly.subplots import make_subplots
|
||||
|
||||
|
||||
pair = strategy.trading_pair_
|
||||
trades = results.trades_[trading_date].copy()
|
||||
origin_mkt_data_df = strategy.pt_mkt_data_.origin_mkt_data_df_
|
||||
mkt_data_df = strategy.pt_mkt_data_.market_data_df_
|
||||
TRD_DATE = f"{trading_date[0:4]}-{trading_date[4:6]}-{trading_date[6:8]}"
|
||||
SYMBOL_A = pair.symbol_a_
|
||||
SYMBOL_B = pair.symbol_b_
|
||||
|
||||
|
||||
print(f"\nCreated trading pair: {pair}")
|
||||
print(f"Market data shape: {pair.market_data_.shape}")
|
||||
print(f"Column names: {pair.colnames()}")
|
||||
|
||||
# Configure plotly for offline mode
|
||||
pyo.init_notebook_mode(connected=True)
|
||||
|
||||
# Strategy-specific interactive visualization
|
||||
assert strategy.config_ is not None
|
||||
|
||||
print("=== SLIDING FIT INTERACTIVE VISUALIZATION ===")
|
||||
print("Note: Rolling Fit strategy visualization with interactive plotly charts")
|
||||
|
||||
|
||||
# Create consistent timeline - superset of timestamps from both dataframes
|
||||
all_timestamps = sorted(set(mkt_data_df['tstamp']))
|
||||
|
||||
|
||||
# Create a unified timeline dataframe for consistent plotting
|
||||
timeline_df = pd.DataFrame({'tstamp': all_timestamps})
|
||||
|
||||
# Merge with predicted data to get dis-equilibrium values
|
||||
timeline_df = timeline_df.merge(strategy.predictions_[['tstamp', 'disequilibrium', 'scaled_disequilibrium', 'signed_scaled_disequilibrium']],
|
||||
on='tstamp', how='left')
|
||||
|
||||
# Get Symbol_A and Symbol_B market data
|
||||
colname_a, colname_b = pair.colnames()
|
||||
symbol_a_data = mkt_data_df[['tstamp', colname_a]].copy()
|
||||
symbol_b_data = mkt_data_df[['tstamp', colname_b]].copy()
|
||||
|
||||
norm_a = symbol_a_data[colname_a] / symbol_a_data[colname_a].iloc[0]
|
||||
norm_b = symbol_b_data[colname_b] / symbol_b_data[colname_b].iloc[0]
|
||||
|
||||
print(f"Using consistent timeline with {len(timeline_df)} timestamps")
|
||||
print(f"Timeline range: {timeline_df['tstamp'].min()} to {timeline_df['tstamp'].max()}")
|
||||
|
||||
# Create subplots with price charts at bottom
|
||||
fig = make_subplots(
|
||||
rows=4, cols=1,
|
||||
row_heights=[0.3, 0.4, 0.15, 0.15],
|
||||
subplot_titles=[
|
||||
f'Dis-equilibrium with Trading Thresholds ({TRD_DATE})',
|
||||
f'Normalized Price Comparison with BUY/SELL Signals - {SYMBOL_A}&{SYMBOL_B} ({TRD_DATE})',
|
||||
f'{SYMBOL_A} Market Data with Trading Signals ({TRD_DATE})',
|
||||
f'{SYMBOL_B} Market Data with Trading Signals ({TRD_DATE})',
|
||||
],
|
||||
vertical_spacing=0.06,
|
||||
specs=[[{"secondary_y": False}],
|
||||
[{"secondary_y": False}],
|
||||
[{"secondary_y": False}],
|
||||
[{"secondary_y": False}]]
|
||||
)
|
||||
|
||||
# 1. Scaled dis-equilibrium with thresholds - using consistent timeline
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=timeline_df['tstamp'],
|
||||
y=timeline_df['scaled_disequilibrium'],
|
||||
name='Absolute Scaled Dis-equilibrium',
|
||||
line=dict(color='green', width=2),
|
||||
opacity=0.8
|
||||
),
|
||||
row=1, col=1
|
||||
)
|
||||
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=timeline_df['tstamp'],
|
||||
y=timeline_df['signed_scaled_disequilibrium'],
|
||||
name='Scaled Dis-equilibrium',
|
||||
line=dict(color='darkmagenta', width=2),
|
||||
opacity=0.8
|
||||
),
|
||||
row=1, col=1
|
||||
)
|
||||
|
||||
# Add threshold lines to first subplot
|
||||
fig.add_shape(
|
||||
type="line",
|
||||
x0=timeline_df['tstamp'].min(),
|
||||
x1=timeline_df['tstamp'].max(),
|
||||
y0=strategy.config_['dis-equilibrium_open_trshld'],
|
||||
y1=strategy.config_['dis-equilibrium_open_trshld'],
|
||||
line=dict(color="purple", width=2, dash="dot"),
|
||||
opacity=0.7,
|
||||
row=1, col=1
|
||||
)
|
||||
|
||||
fig.add_shape(
|
||||
type="line",
|
||||
x0=timeline_df['tstamp'].min(),
|
||||
x1=timeline_df['tstamp'].max(),
|
||||
y0=-strategy.config_['dis-equilibrium_open_trshld'],
|
||||
y1=-strategy.config_['dis-equilibrium_open_trshld'],
|
||||
line=dict(color="purple", width=2, dash="dot"),
|
||||
opacity=0.7,
|
||||
row=1, col=1
|
||||
)
|
||||
|
||||
fig.add_shape(
|
||||
type="line",
|
||||
x0=timeline_df['tstamp'].min(),
|
||||
x1=timeline_df['tstamp'].max(),
|
||||
y0=strategy.config_['dis-equilibrium_close_trshld'],
|
||||
y1=strategy.config_['dis-equilibrium_close_trshld'],
|
||||
line=dict(color="brown", width=2, dash="dot"),
|
||||
opacity=0.7,
|
||||
row=1, col=1
|
||||
)
|
||||
|
||||
fig.add_shape(
|
||||
type="line",
|
||||
x0=timeline_df['tstamp'].min(),
|
||||
x1=timeline_df['tstamp'].max(),
|
||||
y0=-strategy.config_['dis-equilibrium_close_trshld'],
|
||||
y1=-strategy.config_['dis-equilibrium_close_trshld'],
|
||||
line=dict(color="brown", width=2, dash="dot"),
|
||||
opacity=0.7,
|
||||
row=1, col=1
|
||||
)
|
||||
|
||||
fig.add_shape(
|
||||
type="line",
|
||||
x0=timeline_df['tstamp'].min(),
|
||||
x1=timeline_df['tstamp'].max(),
|
||||
y0=0,
|
||||
y1=0,
|
||||
line=dict(color="black", width=1, dash="solid"),
|
||||
opacity=0.5,
|
||||
row=1, col=1
|
||||
)
|
||||
|
||||
# Add normalized price lines
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=mkt_data_df['tstamp'],
|
||||
y=norm_a,
|
||||
name=f'{SYMBOL_A} (Normalized)',
|
||||
line=dict(color='blue', width=2),
|
||||
opacity=0.8
|
||||
),
|
||||
row=2, col=1
|
||||
)
|
||||
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=mkt_data_df['tstamp'],
|
||||
y=norm_b,
|
||||
name=f'{SYMBOL_B} (Normalized)',
|
||||
line=dict(color='orange', width=2),
|
||||
opacity=0.8,
|
||||
),
|
||||
row=2, col=1
|
||||
)
|
||||
|
||||
# Add BUY and SELL signals if available
|
||||
if trades is not None and len(trades) > 0:
|
||||
# Define signal groups to avoid legend repetition
|
||||
signal_groups = {}
|
||||
|
||||
# Process all trades and group by signal type (ignore OPEN/CLOSE status)
|
||||
for _, trade in trades.iterrows():
|
||||
symbol = trade['symbol']
|
||||
side = trade['side']
|
||||
# status = trade['status']
|
||||
action = trade['action']
|
||||
|
||||
# Create signal group key (without status to combine OPEN/CLOSE)
|
||||
signal_key = f"{symbol} {side} {action}"
|
||||
|
||||
# Find normalized price for this trade
|
||||
trade_time = trade['time']
|
||||
if symbol == SYMBOL_A:
|
||||
closest_idx = mkt_data_df['tstamp'].searchsorted(trade_time)
|
||||
if closest_idx < len(norm_a):
|
||||
norm_price = norm_a.iloc[closest_idx]
|
||||
else:
|
||||
norm_price = norm_a.iloc[-1]
|
||||
else: # SYMBOL_B
|
||||
closest_idx = mkt_data_df['tstamp'].searchsorted(trade_time)
|
||||
if closest_idx < len(norm_b):
|
||||
norm_price = norm_b.iloc[closest_idx]
|
||||
else:
|
||||
norm_price = norm_b.iloc[-1]
|
||||
|
||||
# Initialize group if not exists
|
||||
if signal_key not in signal_groups:
|
||||
signal_groups[signal_key] = {
|
||||
'times': [],
|
||||
'prices': [],
|
||||
'actual_prices': [],
|
||||
'symbol': symbol,
|
||||
'side': side,
|
||||
# 'status': status,
|
||||
'action': trade['action']
|
||||
}
|
||||
|
||||
# Add to group
|
||||
signal_groups[signal_key]['times'].append(trade_time)
|
||||
signal_groups[signal_key]['prices'].append(norm_price)
|
||||
signal_groups[signal_key]['actual_prices'].append(trade['price'])
|
||||
|
||||
# Add each signal group as a single trace
|
||||
for signal_key, group_data in signal_groups.items():
|
||||
symbol = group_data['symbol']
|
||||
side = group_data['side']
|
||||
# status = group_data['status']
|
||||
|
||||
# Determine marker properties (same for all OPEN/CLOSE of same side)
|
||||
is_close: bool = (group_data['action'] == "CLOSE")
|
||||
|
||||
if 'BUY' in side:
|
||||
marker_color = 'green'
|
||||
marker_symbol = 'triangle-up'
|
||||
marker_size = 14
|
||||
else: # SELL
|
||||
marker_color = 'red'
|
||||
marker_symbol = 'triangle-down'
|
||||
marker_size = 14
|
||||
|
||||
# Create hover text for each point in the group
|
||||
hover_texts = []
|
||||
for i, (time, norm_price, actual_price) in enumerate(zip(group_data['times'],
|
||||
group_data['prices'],
|
||||
group_data['actual_prices'])):
|
||||
# Find the corresponding trade to get the status for hover text
|
||||
trade_info = trades[(trades['time'] == time) &
|
||||
(trades['symbol'] == symbol) &
|
||||
(trades['side'] == side)]
|
||||
if len(trade_info) > 0:
|
||||
action = trade_info.iloc[0]['action']
|
||||
hover_texts.append(f'<b>{signal_key} {action}</b><br>' +
|
||||
f'Time: {time}<br>' +
|
||||
f'Normalized Price: {norm_price:.4f}<br>' +
|
||||
f'Actual Price: ${actual_price:.2f}')
|
||||
else:
|
||||
hover_texts.append(f'<b>{signal_key}</b><br>' +
|
||||
f'Time: {time}<br>' +
|
||||
f'Normalized Price: {norm_price:.4f}<br>' +
|
||||
f'Actual Price: ${actual_price:.2f}')
|
||||
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=group_data['times'],
|
||||
y=group_data['prices'],
|
||||
mode='markers',
|
||||
name=signal_key,
|
||||
marker=dict(
|
||||
color=marker_color,
|
||||
size=marker_size,
|
||||
symbol=marker_symbol,
|
||||
line=dict(width=2, color='black') if is_close else None
|
||||
),
|
||||
showlegend=True,
|
||||
hovertemplate='%{text}<extra></extra>',
|
||||
text=hover_texts
|
||||
),
|
||||
row=2, col=1
|
||||
)
|
||||
|
||||
# -----------------------------
|
||||
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=symbol_a_data['tstamp'],
|
||||
y=symbol_a_data[colname_a],
|
||||
name=f'{SYMBOL_A} Price',
|
||||
line=dict(color='blue', width=2),
|
||||
opacity=0.8
|
||||
),
|
||||
row=3, col=1
|
||||
)
|
||||
|
||||
# Filter trades for Symbol_A
|
||||
symbol_a_trades = trades[trades['symbol'] == SYMBOL_A]
|
||||
print(f"\nSymbol_A trades:\n{symbol_a_trades}")
|
||||
|
||||
if len(symbol_a_trades) > 0:
|
||||
# Separate trades by action and status for different colors
|
||||
buy_open_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('BUY', na=False)) &
|
||||
(symbol_a_trades['action'].str.contains('OPEN', na=False))]
|
||||
buy_close_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('BUY', na=False)) &
|
||||
(symbol_a_trades['action'].str.contains('CLOSE', na=False))]
|
||||
|
||||
sell_open_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('SELL', na=False)) &
|
||||
(symbol_a_trades['action'].str.contains('OPEN', na=False))]
|
||||
sell_close_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('SELL', na=False)) &
|
||||
(symbol_a_trades['action'].str.contains('CLOSE', na=False))]
|
||||
|
||||
# Add BUY OPEN signals
|
||||
if len(buy_open_trades) > 0:
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=buy_open_trades['time'],
|
||||
y=buy_open_trades['price'],
|
||||
mode='markers',
|
||||
name=f'{SYMBOL_A} BUY OPEN',
|
||||
marker=dict(color='green', size=12, symbol='triangle-up'),
|
||||
showlegend=True
|
||||
),
|
||||
row=3, col=1
|
||||
)
|
||||
|
||||
# Add BUY CLOSE signals
|
||||
if len(buy_close_trades) > 0:
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=buy_close_trades['time'],
|
||||
y=buy_close_trades['price'],
|
||||
mode='markers',
|
||||
name=f'{SYMBOL_A} BUY CLOSE',
|
||||
marker=dict(color='green', size=12, symbol='triangle-up'),
|
||||
line=dict(width=2, color='black'),
|
||||
showlegend=True
|
||||
),
|
||||
row=3, col=1
|
||||
)
|
||||
|
||||
# Add SELL OPEN signals
|
||||
if len(sell_open_trades) > 0:
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=sell_open_trades['time'],
|
||||
y=sell_open_trades['price'],
|
||||
mode='markers',
|
||||
name=f'{SYMBOL_A} SELL OPEN',
|
||||
marker=dict(color='red', size=12, symbol='triangle-down'),
|
||||
showlegend=True
|
||||
),
|
||||
row=3, col=1
|
||||
)
|
||||
|
||||
# Add SELL CLOSE signals
|
||||
if len(sell_close_trades) > 0:
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=sell_close_trades['time'],
|
||||
y=sell_close_trades['price'],
|
||||
mode='markers',
|
||||
name=f'{SYMBOL_A} SELL CLOSE',
|
||||
marker=dict(color='red', size=12, symbol='triangle-down'),
|
||||
line=dict(width=2, color='black'),
|
||||
showlegend=True
|
||||
),
|
||||
row=3, col=1
|
||||
)
|
||||
|
||||
# 4. Symbol_B Market Data with Trading Signals
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=symbol_b_data['tstamp'],
|
||||
y=symbol_b_data[colname_b],
|
||||
name=f'{SYMBOL_B} Price',
|
||||
line=dict(color='orange', width=2),
|
||||
opacity=0.8
|
||||
),
|
||||
row=4, col=1
|
||||
)
|
||||
|
||||
# Add trading signals for Symbol_B if available
|
||||
symbol_b_trades = trades[trades['symbol'] == SYMBOL_B]
|
||||
print(f"\nSymbol_B trades:\n{symbol_b_trades}")
|
||||
|
||||
if len(symbol_b_trades) > 0:
|
||||
# Separate trades by action and status for different colors
|
||||
buy_open_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('BUY', na=False)) &
|
||||
(symbol_b_trades['action'].str.startswith('OPEN', na=False))]
|
||||
buy_close_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('BUY', na=False)) &
|
||||
(symbol_b_trades['action'].str.startswith('CLOSE', na=False))]
|
||||
|
||||
sell_open_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('SELL', na=False)) &
|
||||
(symbol_b_trades['action'].str.contains('OPEN', na=False))]
|
||||
sell_close_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('SELL', na=False)) &
|
||||
(symbol_b_trades['action'].str.contains('CLOSE', na=False))]
|
||||
|
||||
# Add BUY OPEN signals
|
||||
if len(buy_open_trades) > 0:
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=buy_open_trades['time'],
|
||||
y=buy_open_trades['price'],
|
||||
mode='markers',
|
||||
name=f'{SYMBOL_B} BUY OPEN',
|
||||
marker=dict(color='darkgreen', size=12, symbol='triangle-up'),
|
||||
showlegend=True
|
||||
),
|
||||
row=4, col=1
|
||||
)
|
||||
|
||||
# Add BUY CLOSE signals
|
||||
if len(buy_close_trades) > 0:
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=buy_close_trades['time'],
|
||||
y=buy_close_trades['price'],
|
||||
mode='markers',
|
||||
name=f'{SYMBOL_B} BUY CLOSE',
|
||||
marker=dict(color='green', size=12, symbol='triangle-up'),
|
||||
line=dict(width=2, color='black'),
|
||||
showlegend=True
|
||||
),
|
||||
row=4, col=1
|
||||
)
|
||||
|
||||
# Add SELL OPEN signals
|
||||
if len(sell_open_trades) > 0:
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=sell_open_trades['time'],
|
||||
y=sell_open_trades['price'],
|
||||
mode='markers',
|
||||
name=f'{SYMBOL_B} SELL OPEN',
|
||||
marker=dict(color='red', size=12, symbol='triangle-down'),
|
||||
showlegend=True
|
||||
),
|
||||
row=4, col=1
|
||||
)
|
||||
|
||||
# Add SELL CLOSE signals
|
||||
if len(sell_close_trades) > 0:
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=sell_close_trades['time'],
|
||||
y=sell_close_trades['price'],
|
||||
mode='markers',
|
||||
name=f'{SYMBOL_B} SELL CLOSE',
|
||||
marker=dict(color='red', size=12, symbol='triangle-down'),
|
||||
line=dict(width=2, color='black'),
|
||||
showlegend=True
|
||||
),
|
||||
row=4, col=1
|
||||
)
|
||||
|
||||
# Update layout
|
||||
fig.update_layout(
|
||||
height=1600,
|
||||
title_text=f"Strategy Analysis - {SYMBOL_A} & {SYMBOL_B} ({TRD_DATE})",
|
||||
showlegend=True,
|
||||
template="plotly_white",
|
||||
plot_bgcolor='lightgray',
|
||||
)
|
||||
|
||||
# Update y-axis labels
|
||||
fig.update_yaxes(title_text="Scaled Dis-equilibrium", row=1, col=1)
|
||||
fig.update_yaxes(title_text=f"{SYMBOL_A} Price ($)", row=2, col=1)
|
||||
fig.update_yaxes(title_text=f"{SYMBOL_B} Price ($)", row=3, col=1)
|
||||
fig.update_yaxes(title_text="Normalized Price (Base = 1.0)", row=4, col=1)
|
||||
|
||||
# Update x-axis labels and ensure consistent time range
|
||||
time_range = [timeline_df['tstamp'].min(), timeline_df['tstamp'].max()]
|
||||
fig.update_xaxes(range=time_range, row=1, col=1)
|
||||
fig.update_xaxes(range=time_range, row=2, col=1)
|
||||
fig.update_xaxes(range=time_range, row=3, col=1)
|
||||
fig.update_xaxes(title_text="Time", range=time_range, row=4, col=1)
|
||||
|
||||
# Display using plotly offline mode
|
||||
# pyo.iplot(fig)
|
||||
fig.show()
|
||||
|
||||
else:
|
||||
print("No interactive visualization data available - strategy may not have run successfully")
|
||||
|
||||
print(f"\nChart shows:")
|
||||
print(f"- {SYMBOL_A} and {SYMBOL_B} prices normalized to start at 1.0")
|
||||
print(f"- BUY signals shown as green triangles pointing up")
|
||||
print(f"- SELL signals shown as orange triangles pointing down")
|
||||
print(f"- All BUY signals per symbol grouped together, all SELL signals per symbol grouped together")
|
||||
print(f"- Hover over markers to see individual trade details (OPEN/CLOSE status)")
|
||||
|
||||
if trades is not None and len(trades) > 0:
|
||||
print(f"- Total signals displayed: {len(trades)}")
|
||||
print(f"- {SYMBOL_A} signals: {len(trades[trades['symbol'] == SYMBOL_A])}")
|
||||
print(f"- {SYMBOL_B} signals: {len(trades[trades['symbol'] == SYMBOL_B])}")
|
||||
else:
|
||||
print("- No trading signals to display")
|
||||
|
||||
@@ -0,0 +1 @@
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -0,0 +1,303 @@
|
||||
"""Panel application for single-day SPBT result analysis."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from pathlib import Path
|
||||
import sys
|
||||
from typing import Any
|
||||
|
||||
import pandas as pd
|
||||
import panel as pn
|
||||
|
||||
|
||||
APP_DIR = Path(__file__).resolve().parent
|
||||
REPO_ROOT = APP_DIR.parent
|
||||
if str(REPO_ROOT) not in sys.path:
|
||||
sys.path.insert(0, str(REPO_ROOT))
|
||||
|
||||
from scripts import spbt_day
|
||||
|
||||
|
||||
pn.extension("tabulator", "plotly")
|
||||
|
||||
|
||||
PAIR_THEO_RET_SORT_COLUMNS = ["total_pnl", "pair_name"]
|
||||
PAIR_THEO_RET_DISPLAY_DROP_COLUMNS = ["total_pnl"]
|
||||
APP_TITLE = "SPBT Day Analysis"
|
||||
APP_ACCENT_COLOR = "#226c67"
|
||||
APP_HEADER_COLOR = "#184c47"
|
||||
APP_SIDEBAR_WIDTH = 215
|
||||
APP_SIDEBAR_CONTROL_WIDTH = 200
|
||||
|
||||
|
||||
class SpbtDayPanelApp:
|
||||
"""Stateful Panel UI for single-day SPBT analysis."""
|
||||
|
||||
def __init__(self, repo_root: Path | None = None) -> None:
|
||||
self.repo_root = (repo_root or spbt_day.find_repo_root(REPO_ROOT)).resolve()
|
||||
self.selector_pair_rankings = pd.DataFrame()
|
||||
self.trading_instructions = pd.DataFrame()
|
||||
self.pair_theo_ret = pd.DataFrame()
|
||||
self.selected_pair_theo_executions = pd.DataFrame()
|
||||
self.selected_pair_name: str | None = None
|
||||
self.min_pctg_change = 0.0
|
||||
|
||||
self.directory_input = pn.widgets.TextInput(
|
||||
label="Directory",
|
||||
value=str(self.repo_root / "data"),
|
||||
sizing_mode="stretch_width",
|
||||
width=None,
|
||||
)
|
||||
self.show_all_files = pn.widgets.Checkbox(label="Show all files", value=False)
|
||||
self.file_select = pn.widgets.Select(
|
||||
label="SQLite result file",
|
||||
options={},
|
||||
sizing_mode="stretch_width",
|
||||
width=None,
|
||||
)
|
||||
self.min_pctg_change_input = pn.widgets.FloatInput(
|
||||
label="Mininal TARGET change (%)",
|
||||
value=0.0,
|
||||
step=1.0,
|
||||
sizing_mode="stretch_width",
|
||||
width=None,
|
||||
)
|
||||
self.calculate_button = pn.widgets.Button(
|
||||
label="Calculate",
|
||||
color="primary",
|
||||
width=110,
|
||||
)
|
||||
|
||||
self.status = pn.pane.Markdown("")
|
||||
self.pair_theo_ret_table = spbt_day.create_pair_theo_ret_analyze_grid(
|
||||
pd.DataFrame(),
|
||||
height=420,
|
||||
)
|
||||
self.total_pnl_histogram = pn.pane.Plotly(
|
||||
None,
|
||||
height=360,
|
||||
sizing_mode="stretch_width",
|
||||
)
|
||||
self.selected_pair_message = pn.pane.Markdown(
|
||||
"Click Analyze in the Pair TheoRet grid to load individual-pair details."
|
||||
)
|
||||
self.selected_pair_executions_table = spbt_day.create_selected_pair_executions_grid(
|
||||
height=320,
|
||||
)
|
||||
self.selected_pair_market_plot = pn.pane.Plotly(
|
||||
None,
|
||||
height=520,
|
||||
sizing_mode="stretch_width",
|
||||
)
|
||||
|
||||
self.calculate_button.on_click(self.calculate)
|
||||
self.directory_input.param.watch(self.refresh_files, "value")
|
||||
self.show_all_files.param.watch(self.refresh_files, "value")
|
||||
self.pair_theo_ret_table.on_click(
|
||||
self.analyze_pair_click,
|
||||
column=spbt_day.ANALYZE_BUTTON_COLUMN,
|
||||
)
|
||||
self.refresh_files()
|
||||
|
||||
def set_status(self, message: str, *, error: bool = False) -> None:
|
||||
"""Update visible status text."""
|
||||
prefix = "**Error:** " if error else ""
|
||||
self.status.object = f"{prefix}{message}" if message else ""
|
||||
|
||||
def selected_database_path(self) -> Path:
|
||||
"""Return the selected result database path."""
|
||||
if not self.file_select.value:
|
||||
raise ValueError("Select a SQLite result file before calculating.")
|
||||
db_path = Path(str(self.file_select.value)).resolve()
|
||||
if not db_path.exists():
|
||||
raise FileNotFoundError(f"Selected database does not exist: {db_path}")
|
||||
if not db_path.is_file():
|
||||
raise ValueError(f"Selected database path is not a file: {db_path}")
|
||||
return db_path
|
||||
|
||||
def refresh_files(self, *_events: Any) -> bool:
|
||||
"""Refresh selectable SQLite files from the configured directory."""
|
||||
try:
|
||||
directory = spbt_day.normalize_directory(
|
||||
self.directory_input.value,
|
||||
self.repo_root,
|
||||
)
|
||||
candidates = spbt_day.list_candidate_files(
|
||||
directory,
|
||||
show_all=self.show_all_files.value,
|
||||
)
|
||||
except Exception as exc:
|
||||
self.file_select.options = {}
|
||||
self.file_select.value = None
|
||||
self.set_status(str(exc), error=True)
|
||||
return False
|
||||
|
||||
options = {path.name: str(path) for path in candidates}
|
||||
previous_value = self.file_select.value
|
||||
self.file_select.options = options
|
||||
if previous_value in options.values():
|
||||
self.file_select.value = previous_value
|
||||
elif options:
|
||||
self.file_select.value = next(iter(options.values()))
|
||||
else:
|
||||
self.file_select.value = None
|
||||
|
||||
if options:
|
||||
self.set_status(f"Found {len(options):,} file(s) in {directory}.")
|
||||
else:
|
||||
self.set_status(f"No selectable files found in {directory}.")
|
||||
return True
|
||||
|
||||
def calculate(self, *_events: Any) -> None:
|
||||
"""Load selected data and calculate all-pair TheoRet."""
|
||||
self.calculate_button.loading = True
|
||||
try:
|
||||
if not self.refresh_files():
|
||||
return
|
||||
db_path = self.selected_database_path()
|
||||
self.min_pctg_change = float(self.min_pctg_change_input.value)
|
||||
|
||||
conn = spbt_day.connect_sqlite_read_only(db_path)
|
||||
try:
|
||||
self.selector_pair_rankings = spbt_day.load_selector_pair_rankings(conn)
|
||||
self.trading_instructions = spbt_day.load_trading_instructions(conn)
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
self.pair_theo_ret = (
|
||||
spbt_day.add_total_pnl(
|
||||
spbt_day.calculate_ranked_pairs_theo_ret(
|
||||
self.selector_pair_rankings,
|
||||
self.trading_instructions,
|
||||
min_pctg_change=self.min_pctg_change,
|
||||
)
|
||||
)
|
||||
.sort_values(
|
||||
PAIR_THEO_RET_SORT_COLUMNS,
|
||||
ascending=[True, True],
|
||||
kind="mergesort",
|
||||
)
|
||||
.drop(columns=PAIR_THEO_RET_DISPLAY_DROP_COLUMNS)
|
||||
.reset_index(drop=True)
|
||||
)
|
||||
self.pair_theo_ret_table.value = spbt_day.format_pair_theo_ret_for_analyze_grid(
|
||||
self.pair_theo_ret
|
||||
)
|
||||
self.total_pnl_histogram.object = spbt_day.create_total_pnl_histogram(
|
||||
self.pair_theo_ret
|
||||
)
|
||||
self.clear_selected_pair_analysis()
|
||||
|
||||
self.set_status(
|
||||
f"Calculated {len(self.pair_theo_ret):,} pair row(s) from {db_path.name}."
|
||||
)
|
||||
except Exception as exc:
|
||||
self.set_status(str(exc), error=True)
|
||||
finally:
|
||||
self.calculate_button.loading = False
|
||||
|
||||
def clear_selected_pair_analysis(self) -> None:
|
||||
"""Clear individual-pair outputs until a row Analyze button is clicked."""
|
||||
self.selected_pair_name = None
|
||||
self.selected_pair_theo_executions = pd.DataFrame()
|
||||
self.selected_pair_message.object = (
|
||||
"Click Analyze in the Pair TheoRet grid to load individual-pair details."
|
||||
)
|
||||
self.selected_pair_executions_table.value = pd.DataFrame(
|
||||
columns=spbt_day.SELECTED_PAIR_EXECUTION_DISPLAY_COLUMNS
|
||||
)
|
||||
self.selected_pair_market_plot.object = None
|
||||
|
||||
def analyze_pair_click(self, event: Any) -> None:
|
||||
"""Run selected-pair analysis from a Pair TheoRet Analyze button click."""
|
||||
self.update_selected_pair(
|
||||
spbt_day.pair_name_from_analyze_event(self.pair_theo_ret_table, event)
|
||||
)
|
||||
|
||||
def analyze_pair_row(self, row: int) -> None:
|
||||
"""Run selected-pair analysis for a Pair TheoRet table row."""
|
||||
event = type("AnalyzeEvent", (), {"row": row})()
|
||||
self.analyze_pair_click(event)
|
||||
|
||||
def update_selected_pair(self, pair_name: str) -> None:
|
||||
"""Calculate selected-pair executions and market plot."""
|
||||
if self.trading_instructions.empty:
|
||||
self.clear_selected_pair_analysis()
|
||||
return
|
||||
|
||||
self.selected_pair_name = pair_name
|
||||
self.selected_pair_message.object = (
|
||||
f"Selected pair: **{spbt_day.format_pair_name_for_display(pair_name)}**"
|
||||
)
|
||||
self.selected_pair_theo_executions = spbt_day.calculate_pair_theo_executions(
|
||||
pair_name,
|
||||
self.trading_instructions,
|
||||
min_pctg_change=self.min_pctg_change,
|
||||
)
|
||||
self.selected_pair_executions_table.value = (
|
||||
self.selected_pair_theo_executions.reindex(
|
||||
columns=spbt_day.SELECTED_PAIR_EXECUTION_DISPLAY_COLUMNS
|
||||
)
|
||||
)
|
||||
|
||||
try:
|
||||
trading_day_start_ns = spbt_day.infer_trading_day_start_ns(
|
||||
self.trading_instructions
|
||||
)
|
||||
conn = spbt_day.connect_sqlite_read_only(self.selected_database_path())
|
||||
try:
|
||||
selected_pair_market_data = spbt_day.load_pair_market_data(
|
||||
conn,
|
||||
pair_name,
|
||||
trading_day_start_ns=trading_day_start_ns,
|
||||
)
|
||||
finally:
|
||||
conn.close()
|
||||
self.selected_pair_market_plot.object = spbt_day.create_pair_trades_market_plot(
|
||||
pair_name,
|
||||
selected_pair_market_data,
|
||||
self.selected_pair_theo_executions,
|
||||
)
|
||||
except Exception as exc:
|
||||
self.selected_pair_market_plot.object = None
|
||||
self.set_status(str(exc), error=True)
|
||||
|
||||
@property
|
||||
def view(self) -> pn.template.FastListTemplate:
|
||||
"""Return the app layout."""
|
||||
controls = pn.Column(
|
||||
"## Inputs",
|
||||
self.directory_input,
|
||||
self.show_all_files,
|
||||
self.file_select,
|
||||
self.min_pctg_change_input,
|
||||
self.calculate_button,
|
||||
self.status,
|
||||
width=APP_SIDEBAR_CONTROL_WIDTH,
|
||||
)
|
||||
main = pn.Column(
|
||||
"## Pair TheoRet",
|
||||
self.pair_theo_ret_table,
|
||||
self.total_pnl_histogram,
|
||||
"## Individual Pair",
|
||||
self.selected_pair_message,
|
||||
"### Theoretical Executions",
|
||||
self.selected_pair_executions_table,
|
||||
"### Trades on Market Data",
|
||||
self.selected_pair_market_plot,
|
||||
)
|
||||
return pn.template.FastListTemplate(
|
||||
title=APP_TITLE,
|
||||
sidebar=[controls],
|
||||
main=[main],
|
||||
sidebar_width=APP_SIDEBAR_WIDTH,
|
||||
accent_base_color=APP_ACCENT_COLOR,
|
||||
header_background=APP_HEADER_COLOR,
|
||||
main_layout=None,
|
||||
theme=pn.template.DarkTheme,
|
||||
)
|
||||
|
||||
|
||||
app_controller = SpbtDayPanelApp()
|
||||
app = app_controller.view
|
||||
app.servable(title=APP_TITLE)
|
||||
@@ -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"
|
||||
}
|
||||
+14
-200
@@ -1,200 +1,14 @@
|
||||
aiohttp>=3.8.4
|
||||
aiosignal>=1.3.1
|
||||
async-timeout>=4.0.2
|
||||
attrs>=21.2.0
|
||||
beautifulsoup4>=4.10.0
|
||||
black>=23.3.0
|
||||
flake8>=6.0.0
|
||||
certifi>=2020.6.20
|
||||
chardet>=4.0.0
|
||||
charset-normalizer>=3.1.0
|
||||
click>=8.0.3
|
||||
colorama>=0.4.4
|
||||
configobj>=5.0.6
|
||||
cryptography>=3.4.8
|
||||
distro>=1.7.0
|
||||
docker>=5.0.3
|
||||
dockerpty>=0.4.1
|
||||
docopt>=0.6.2
|
||||
eyeD3>=0.8.10
|
||||
filelock>=3.6.0
|
||||
frozenlist>=1.3.3
|
||||
grpcio>=1.30.2
|
||||
hjson>=3.0.2
|
||||
html5lib>=1.1
|
||||
httplib2>=0.20.2
|
||||
idna>=3.3
|
||||
ipython>=8.18.1
|
||||
ipywidgets>=8.1.1
|
||||
ifaddr>=0.1.7
|
||||
IMDbPY>=2021.4.18
|
||||
ipykernel>=6.29.5
|
||||
jeepney>=0.7.1
|
||||
jsonschema>=3.2.0
|
||||
jupyter>=1.0.0
|
||||
keyring>=23.5.0
|
||||
launchpadlib>=1.10.16
|
||||
lazr.restfulclient>=0.14.4
|
||||
lazr.uri>=1.0.6
|
||||
lxml>=4.8.0
|
||||
Mako>=1.1.3
|
||||
Markdown>=3.3.6
|
||||
MarkupSafe>=2.0.1
|
||||
matplotlib>=3.10.3
|
||||
more-itertools>=8.10.0
|
||||
multidict>=6.0.4
|
||||
mypy>=0.942
|
||||
mypy-extensions>=0.4.3
|
||||
nbformat>=5.10.2
|
||||
netaddr>=0.8.0
|
||||
######### netifaces>=0.11.0
|
||||
numpy>=1.26.4,<2.3.0
|
||||
oauthlib>=3.2.0
|
||||
packaging>=23.1
|
||||
pandas>=2.2.3
|
||||
pathspec>=0.11.1
|
||||
pexpect>=4.8.0
|
||||
Pillow>=9.0.1
|
||||
platformdirs>=3.2.0
|
||||
plotly>=5.19.0
|
||||
protobuf>=3.12.4
|
||||
psutil>=5.9.0
|
||||
ptyprocess>=0.7.0
|
||||
pycurl>=7.44.1
|
||||
pyelftools>=0.27
|
||||
Pygments>=2.11.2
|
||||
pyparsing>=2.4.7
|
||||
pyrsistent>=0.18.1
|
||||
python-debian>=0.1.43 #+ubuntu1.1
|
||||
python-dotenv>=0.19.2
|
||||
python-magic>=0.4.24
|
||||
python-xlib>=0.29
|
||||
pyxdg>=0.27
|
||||
PyYAML>=6.0
|
||||
reportlab>=3.6.8
|
||||
requests>=2.25.1
|
||||
requests-file>=1.5.1
|
||||
scipy<1.13.0
|
||||
seaborn>=0.13.2
|
||||
SecretStorage>=3.3.1
|
||||
setproctitle>=1.2.2
|
||||
simpleeval>=1.0.3
|
||||
six>=1.16.0
|
||||
soupsieve>=2.3.1
|
||||
ssh-import-id>=5.11
|
||||
statsmodels>=0.14.4
|
||||
texttable>=1.6.4
|
||||
tldextract>=3.1.2
|
||||
tomli>=1.2.2
|
||||
######## typed-ast>=1.4.3
|
||||
types-aiofiles>=0.1
|
||||
types-annoy>=1.17
|
||||
types-appdirs>=1.4
|
||||
types-atomicwrites>=1.4
|
||||
types-aws-xray-sdk>=2.8
|
||||
types-babel>=2.9
|
||||
types-backports-abc>=0.5
|
||||
types-backports.ssl-match-hostname>=3.7
|
||||
types-beautifulsoup4>=4.10
|
||||
types-bleach>=4.1
|
||||
types-boto>=2.49
|
||||
types-braintree>=4.11
|
||||
types-cachetools>=4.2
|
||||
types-caldav>=0.8
|
||||
types-certifi>=2020.4
|
||||
types-characteristic>=14.3
|
||||
types-chardet>=4.0
|
||||
types-click>=7.1
|
||||
types-click-spinner>=0.1
|
||||
types-colorama>=0.4
|
||||
types-commonmark>=0.9
|
||||
types-contextvars>=0.1
|
||||
types-croniter>=1.0
|
||||
types-cryptography>=3.3
|
||||
types-dataclasses>=0.1
|
||||
types-dateparser>=1.0
|
||||
types-DateTimeRange>=0.1
|
||||
types-decorator>=0.1
|
||||
types-Deprecated>=1.2
|
||||
types-docopt>=0.6
|
||||
types-docutils>=0.17
|
||||
types-editdistance>=0.5
|
||||
types-emoji>=1.2
|
||||
types-entrypoints>=0.3
|
||||
types-enum34>=1.1
|
||||
types-filelock>=3.2
|
||||
types-first>=2.0
|
||||
types-Flask>=1.1
|
||||
types-freezegun>=1.1
|
||||
types-frozendict>=0.1
|
||||
types-futures>=3.3
|
||||
types-html5lib>=1.1
|
||||
types-httplib2>=0.19
|
||||
types-humanfriendly>=9.2
|
||||
types-ipaddress>=1.0
|
||||
types-itsdangerous>=1.1
|
||||
types-JACK-Client>=0.1
|
||||
types-Jinja2>=2.11
|
||||
types-jmespath>=0.10
|
||||
types-jsonschema>=3.2
|
||||
types-Markdown>=3.3
|
||||
types-MarkupSafe>=1.1
|
||||
types-mock>=4.0
|
||||
types-mypy-extensions>=0.4
|
||||
types-mysqlclient>=2.0
|
||||
types-oauthlib>=3.1
|
||||
types-orjson>=3.6
|
||||
types-paramiko>=2.7
|
||||
types-Pillow>=8.3
|
||||
types-polib>=1.1
|
||||
types-prettytable>=2.1
|
||||
types-protobuf>=3.17
|
||||
types-psutil>=5.8
|
||||
types-psycopg2>=2.9
|
||||
types-pyaudio>=0.2
|
||||
types-pycurl>=0.1
|
||||
types-pyfarmhash>=0.2
|
||||
types-Pygments>=2.9
|
||||
types-PyMySQL>=1.0
|
||||
types-pyOpenSSL>=20.0
|
||||
types-pyRFC3339>=0.1
|
||||
types-pysftp>=0.2
|
||||
types-pytest-lazy-fixture>=0.6
|
||||
types-python-dateutil>=2.8
|
||||
types-python-gflags>=3.1
|
||||
types-python-nmap>=0.6
|
||||
types-python-slugify>=5.0
|
||||
types-pytz>=2021.1
|
||||
types-pyvmomi>=7.0
|
||||
types-PyYAML>=5.4
|
||||
types-redis>=3.5
|
||||
types-requests>=2.25
|
||||
types-retry>=0.9
|
||||
types-selenium>=3.141
|
||||
types-Send2Trash>=1.8
|
||||
types-setuptools>=57.4
|
||||
types-simplejson>=3.17
|
||||
types-singledispatch>=3.7
|
||||
types-six>=1.16
|
||||
types-slumber>=0.7
|
||||
types-stripe>=2.59
|
||||
types-tabulate>=0.8
|
||||
types-termcolor>=1.1
|
||||
types-toml>=0.10
|
||||
types-toposort>=1.6
|
||||
types-ttkthemes>=3.2
|
||||
types-typed-ast>=1.4
|
||||
types-tzlocal>=0.1
|
||||
types-ujson>=0.1
|
||||
types-vobject>=0.9
|
||||
types-waitress>=0.1
|
||||
types-Werkzeug>=1.0
|
||||
types-xxhash>=2.0
|
||||
typing-extensions>=3.10.0.2
|
||||
Unidecode>=1.3.3
|
||||
urllib3>=1.26.5
|
||||
wadllib>=1.3.6
|
||||
webencodings>=0.5.1
|
||||
websocket-client>=1.2.3
|
||||
yarl>=1.9.1
|
||||
zipp>=1.0.0
|
||||
# Interactive analysis
|
||||
ipykernel>=6.29,<7
|
||||
ipywidgets>=8.1,<9
|
||||
itables>=2.2,<3
|
||||
jupyter>=1.1,<2
|
||||
jupyter_bokeh>=4.0,<5
|
||||
nbformat>=5.10,<6
|
||||
pandas>=2.2,<3
|
||||
panel>=1.5,<2
|
||||
plotly>=5.24,<7
|
||||
|
||||
# Verification
|
||||
nbmake>=1.5,<2
|
||||
pytest>=8,<9
|
||||
|
||||
@@ -1,106 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
from typing import Any, Dict
|
||||
|
||||
from pt_strategy.results import (
|
||||
PairResearchResult,
|
||||
create_result_database,
|
||||
store_config_in_database,
|
||||
)
|
||||
from pt_strategy.research_strategy import PtResearchStrategy
|
||||
from tools.filetools import resolve_datafiles
|
||||
from tools.instruments import get_instruments
|
||||
|
||||
|
||||
def main() -> None:
|
||||
import argparse
|
||||
|
||||
from tools.config import expand_filename, load_config
|
||||
|
||||
parser = argparse.ArgumentParser(description="Run pairs trading backtest.")
|
||||
parser.add_argument(
|
||||
"--config", type=str, required=True, help="Path to the configuration file."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--date_pattern",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Date YYYYMMDD, allows * and ? wildcards",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--instruments",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Comma-separated list of instrument symbols (e.g., COIN:EQUITY,GBTC:CRYPTO)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--result_db",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path to SQLite database for storing results. Use 'NONE' to disable database output.",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
config: Dict = load_config(args.config)
|
||||
|
||||
# Resolve data files (CLI takes priority over config)
|
||||
instruments = get_instruments(args, config)
|
||||
datafiles = resolve_datafiles(config, args.date_pattern, instruments)
|
||||
|
||||
days = list(set([day for day, _ in datafiles]))
|
||||
print(f"Found {len(datafiles)} data files to process:")
|
||||
for df in datafiles:
|
||||
print(f" - {df}")
|
||||
|
||||
# Create result database if needed
|
||||
if args.result_db.upper() != "NONE":
|
||||
args.result_db = expand_filename(args.result_db)
|
||||
create_result_database(args.result_db)
|
||||
|
||||
# Initialize a dictionary to store all trade results
|
||||
all_results: Dict[str, Dict[str, Any]] = {}
|
||||
is_config_stored = False
|
||||
# Process each data file
|
||||
|
||||
results = PairResearchResult(config=config)
|
||||
for day in sorted(days):
|
||||
md_datafiles = [datafile for md_day, datafile in datafiles if md_day == day]
|
||||
if not all([os.path.exists(datafile) for datafile in md_datafiles]):
|
||||
print(f"WARNING: insufficient data files: {md_datafiles}")
|
||||
continue
|
||||
print(f"\n====== Processing {day} ======")
|
||||
|
||||
if not is_config_stored:
|
||||
store_config_in_database(
|
||||
db_path=args.result_db,
|
||||
config_file_path=args.config,
|
||||
config=config,
|
||||
datafiles=datafiles,
|
||||
instruments=instruments,
|
||||
)
|
||||
is_config_stored = True
|
||||
|
||||
pt_strategy = PtResearchStrategy(
|
||||
config=config, datafiles=md_datafiles, instruments=instruments
|
||||
)
|
||||
pt_strategy.run()
|
||||
results.add_day_results(
|
||||
day=day,
|
||||
trades=pt_strategy.day_trades(),
|
||||
outstanding_positions=pt_strategy.outstanding_positions(),
|
||||
)
|
||||
|
||||
|
||||
results.analyze_pair_performance()
|
||||
|
||||
|
||||
if args.result_db.upper() != "NONE":
|
||||
print(f"\nResults stored in database: {args.result_db}")
|
||||
else:
|
||||
print("No results to display.")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
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
|
||||
@@ -0,0 +1 @@
|
||||
|
||||
@@ -0,0 +1 @@
|
||||
|
||||
@@ -1,42 +0,0 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
# -------------------------------------
|
||||
# --- Given month, specific dates
|
||||
# -------------------------------------
|
||||
|
||||
# for dt in 20250528 20250529 20250530 20250531; do
|
||||
# rsync -ahvv cvtt@hs01.cvtt.vpn:/works/cvtt/md_archive/crypto/sim/2025/2025-05/${dt}.*.gz ./
|
||||
# done
|
||||
# -------------------------------------
|
||||
|
||||
# -------------------------------------
|
||||
# --- Current month - all files
|
||||
# -------------------------------------
|
||||
cd $(realpath $(dirname $0))/..
|
||||
mkdir -p ./data/crypto
|
||||
pushd ./data/crypto
|
||||
|
||||
Files=$1
|
||||
if [ -z "$Files" ]; then
|
||||
Files="*.gz"
|
||||
fi
|
||||
|
||||
Cmd="rsync -ahvv cvtt@hs01.cvtt.vpn:/works/cvtt/md_archive/crypto/sim/${Files} ./"
|
||||
echo $Cmd
|
||||
eval $Cmd
|
||||
# -------------------------------------
|
||||
|
||||
for srcfname in $(ls *.db.gz); do
|
||||
dt="${srcfname:0:8}"
|
||||
tgtfile=${dt}.mktdata.ohlcv.db
|
||||
echo "${srcfname} -> ${tgtfile}"
|
||||
|
||||
Cmd="gunzip -c $srcfname > temp.db"
|
||||
echo $Cmd
|
||||
eval $Cmd
|
||||
Cmd="rm -f ${tgtfile} && sqlite3 temp.db \".dump md_1min_bars\" | sqlite3 ${tgtfile} && rm ${srcfname}"
|
||||
echo $Cmd
|
||||
eval $Cmd
|
||||
done
|
||||
rm temp.db
|
||||
popd
|
||||
@@ -1,37 +0,0 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
usage() {
|
||||
echo "Usage: $0 [DatePattern]"
|
||||
echo "DatePattern: YYYYMM or YYYYM or YYYYMMD"
|
||||
exit 1
|
||||
}
|
||||
|
||||
DatePattern="${1}"
|
||||
if [ -z "${DatePattern}" ]; then
|
||||
usage
|
||||
fi
|
||||
FilePattern="${DatePattern}*.alpaca_sim_md.db.gz"
|
||||
|
||||
cd $(realpath $(dirname $0))/..
|
||||
mkdir -p ./data/equity
|
||||
pushd ./data/equity
|
||||
|
||||
Cmd="rsync -ahvv cvtt@hs01.cvtt.vpn:/works/cvtt/md_archive/equity/alpaca_md/sim/${FilePattern} ./"
|
||||
echo ${Cmd}
|
||||
eval ${Cmd}
|
||||
# -------------------------------------
|
||||
|
||||
for srcfname in $(ls *.db.gz); do
|
||||
dt="${srcfname:0:8}"
|
||||
tgtfile=${dt}.mktdata.ohlcv.db
|
||||
echo "${srcfname} -> ${tgtfile}"
|
||||
|
||||
Cmd="gunzip -c $srcfname > temp.db && rm $srcfname"
|
||||
echo ${Cmd}
|
||||
eval ${Cmd}
|
||||
Cmd="rm -f ${tgtfile} && sqlite3 temp.db '.dump md_1min_bars' | sqlite3 ${tgtfile}"
|
||||
echo ${Cmd}
|
||||
eval ${Cmd}
|
||||
done
|
||||
rm temp.db
|
||||
popd
|
||||
Executable
+7
@@ -0,0 +1,7 @@
|
||||
#!/usr/bin/env bash
|
||||
set -euo pipefail
|
||||
|
||||
repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
|
||||
cd "$repo_root"
|
||||
|
||||
panel serve panel/spbt_day_panel.py --show "$@"
|
||||
+1369
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,243 @@
|
||||
import importlib.util
|
||||
import sqlite3
|
||||
from pathlib import Path
|
||||
|
||||
import pandas as pd
|
||||
|
||||
|
||||
def load_panel_app_module():
|
||||
module_path = Path("panel/spbt_day_panel.py").resolve()
|
||||
spec = importlib.util.spec_from_file_location("spbt_day_panel_app", module_path)
|
||||
module = importlib.util.module_from_spec(spec)
|
||||
assert spec.loader is not None
|
||||
spec.loader.exec_module(module)
|
||||
return module
|
||||
|
||||
|
||||
def create_panel_fixture_db(db_path: Path) -> None:
|
||||
trading_day_start_ns = pd.Timestamp("2026-06-17T00:00:00Z").value
|
||||
conn = sqlite3.connect(db_path)
|
||||
try:
|
||||
conn.execute(
|
||||
"""
|
||||
CREATE TABLE selector_pairs (
|
||||
time_ns INTEGER,
|
||||
tstamp TEXT,
|
||||
pair_name TEXT,
|
||||
instrument_a TEXT,
|
||||
instrument_b TEXT,
|
||||
mr_score TEXT
|
||||
)
|
||||
"""
|
||||
)
|
||||
conn.execute(
|
||||
"""
|
||||
CREATE TABLE trading_instructions (
|
||||
tstamp TEXT,
|
||||
tstamp_ns INTEGER,
|
||||
type TEXT,
|
||||
book_id TEXT,
|
||||
strategy_id TEXT,
|
||||
action TEXT,
|
||||
quote_asset TEXT,
|
||||
assets TEXT,
|
||||
scaled_disequilibrium REAL,
|
||||
beta REAL
|
||||
)
|
||||
"""
|
||||
)
|
||||
conn.execute(
|
||||
"""
|
||||
CREATE TABLE market (
|
||||
tstamp TEXT,
|
||||
tstamp_ns INTEGER,
|
||||
exch_acct TEXT,
|
||||
instrument_id TEXT,
|
||||
open REAL,
|
||||
high REAL,
|
||||
low REAL,
|
||||
close REAL,
|
||||
volume REAL,
|
||||
vwap REAL,
|
||||
num_trades INTEGER
|
||||
)
|
||||
"""
|
||||
)
|
||||
conn.execute(
|
||||
"INSERT INTO selector_pairs VALUES (?, ?, ?, ?, ?, ?)",
|
||||
(
|
||||
10,
|
||||
"2026-06-17T00:00:00Z",
|
||||
"AAA:USD-BBB:USD",
|
||||
"EXCH:PAIR-AAA-USD",
|
||||
"EXCH:PAIR-BBB-USD",
|
||||
'{"final":"0.5"}',
|
||||
),
|
||||
)
|
||||
conn.executemany(
|
||||
"INSERT INTO trading_instructions VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
|
||||
[
|
||||
(
|
||||
"2026-06-17T00:00:00Z",
|
||||
trading_day_start_ns,
|
||||
"TARGET_POSITION",
|
||||
"book",
|
||||
"strategy-AAA:USD-BBB:USD",
|
||||
"TARGET",
|
||||
"USD",
|
||||
'{"AAA":{"reference_price":"100","strength":"0.5"},'
|
||||
'"BBB":{"reference_price":"50","strength":"-0.5"}}',
|
||||
-1.25,
|
||||
0.75,
|
||||
),
|
||||
(
|
||||
"2026-06-17T00:01:00Z",
|
||||
trading_day_start_ns + 60_000_000_000,
|
||||
"CLOSE_POSITION",
|
||||
"book",
|
||||
"strategy-AAA:USD-BBB:USD",
|
||||
"CLOSE",
|
||||
"USD",
|
||||
'{"AAA":{"reference_price":"110"},'
|
||||
'"BBB":{"reference_price":"45"}}',
|
||||
-0.5,
|
||||
0.75,
|
||||
),
|
||||
],
|
||||
)
|
||||
conn.executemany(
|
||||
"INSERT INTO market VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
|
||||
[
|
||||
(
|
||||
"2026-06-17T00:00:00Z",
|
||||
trading_day_start_ns,
|
||||
"EXCH",
|
||||
"PAIR-AAA-USD",
|
||||
100.0,
|
||||
100.0,
|
||||
100.0,
|
||||
100.0,
|
||||
1.0,
|
||||
100.0,
|
||||
1,
|
||||
),
|
||||
(
|
||||
"2026-06-17T00:00:00Z",
|
||||
trading_day_start_ns,
|
||||
"EXCH",
|
||||
"PAIR-BBB-USD",
|
||||
50.0,
|
||||
50.0,
|
||||
50.0,
|
||||
50.0,
|
||||
1.0,
|
||||
50.0,
|
||||
1,
|
||||
),
|
||||
],
|
||||
)
|
||||
conn.commit()
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
|
||||
def test_pair_analyze_grid_keeps_clean_labels_and_full_pair_values():
|
||||
module = load_panel_app_module()
|
||||
pair_theo_ret = pd.DataFrame(
|
||||
{
|
||||
"pair_name": ["BTC:USD-ETH:USD", "ADA:USD-BTC:USD"],
|
||||
"mr_ranking": [2, 1],
|
||||
"realized_pnl": [0.0, 0.0],
|
||||
"unrealized_pnl": [0.0, 0.0],
|
||||
}
|
||||
)
|
||||
|
||||
formatted = module.spbt_day.format_pair_theo_ret_for_analyze_grid(pair_theo_ret)
|
||||
|
||||
assert formatted["pair_name"].tolist() == ["BTC-ETH", "ADA-BTC"]
|
||||
assert formatted[module.spbt_day.PAIR_NAME_VALUE_COLUMN].tolist() == [
|
||||
"BTC:USD-ETH:USD",
|
||||
"ADA:USD-BTC:USD",
|
||||
]
|
||||
|
||||
|
||||
def test_panel_app_uses_fast_list_template(tmp_path):
|
||||
module = load_panel_app_module()
|
||||
app = module.SpbtDayPanelApp(repo_root=tmp_path)
|
||||
view = app.view
|
||||
|
||||
assert not hasattr(app, "refresh_button")
|
||||
assert isinstance(view, module.pn.template.FastListTemplate)
|
||||
assert view.title == module.APP_TITLE
|
||||
assert view.theme is module.pn.template.DarkTheme
|
||||
assert view.sidebar_width == module.APP_SIDEBAR_WIDTH
|
||||
assert view.accent_base_color == module.APP_ACCENT_COLOR
|
||||
assert view.header_background == module.APP_HEADER_COLOR
|
||||
assert len(view.sidebar) == 1
|
||||
assert len(view.main) == 1
|
||||
|
||||
|
||||
def test_panel_app_calculates_pairs_and_selected_pair_outputs(tmp_path):
|
||||
module = load_panel_app_module()
|
||||
data_dir = tmp_path / "data"
|
||||
data_dir.mkdir()
|
||||
db_path = data_dir / "20260617.spbt_results.db"
|
||||
create_panel_fixture_db(db_path)
|
||||
|
||||
app = module.SpbtDayPanelApp(repo_root=tmp_path)
|
||||
app.directory_input.value = str(data_dir)
|
||||
app.refresh_files()
|
||||
app.min_pctg_change_input.value = 0.0
|
||||
|
||||
app.calculate()
|
||||
|
||||
assert app.file_select.value == str(db_path)
|
||||
assert app.directory_input.sizing_mode == "stretch_width"
|
||||
assert app.directory_input.width is None
|
||||
assert app.file_select.sizing_mode == "stretch_width"
|
||||
assert app.file_select.width is None
|
||||
assert app.min_pctg_change_input.sizing_mode == "stretch_width"
|
||||
assert app.min_pctg_change_input.width is None
|
||||
assert app.calculate_button.width == 110
|
||||
assert app.total_pnl_histogram.sizing_mode == "stretch_width"
|
||||
assert app.selected_pair_market_plot.sizing_mode == "stretch_width"
|
||||
assert app.pair_theo_ret_table.pagination is None
|
||||
assert app.pair_theo_ret_table.layout == "fit_data_table"
|
||||
assert app.pair_theo_ret_table.value["pair_name"].tolist() == ["AAA-BBB"]
|
||||
assert (
|
||||
app.pair_theo_ret_table.value[module.spbt_day.PAIR_NAME_VALUE_COLUMN].tolist()
|
||||
== ["AAA:USD-BBB:USD"]
|
||||
)
|
||||
assert app.selected_pair_name is None
|
||||
assert app.selected_pair_executions_table.value.empty
|
||||
assert app.selected_pair_market_plot.object is None
|
||||
|
||||
app.analyze_pair_row(0)
|
||||
|
||||
assert app.selected_pair_name == "AAA:USD-BBB:USD"
|
||||
assert app.selected_pair_executions_table.value["action"].tolist() == [
|
||||
"TARGET",
|
||||
"TARGET",
|
||||
"CLOSE",
|
||||
"CLOSE",
|
||||
]
|
||||
assert app.selected_pair_market_plot.object is not None
|
||||
|
||||
|
||||
def test_calculate_refreshes_file_list_before_loading(tmp_path):
|
||||
module = load_panel_app_module()
|
||||
data_dir = tmp_path / "data"
|
||||
data_dir.mkdir()
|
||||
|
||||
app = module.SpbtDayPanelApp(repo_root=tmp_path)
|
||||
app.directory_input.value = str(data_dir)
|
||||
app.refresh_files()
|
||||
assert app.file_select.value is None
|
||||
|
||||
db_path = data_dir / "20260617.spbt_results.db"
|
||||
create_panel_fixture_db(db_path)
|
||||
|
||||
app.calculate()
|
||||
|
||||
assert app.file_select.value == str(db_path)
|
||||
assert app.pair_theo_ret_table.value["pair_name"].tolist() == ["AAA-BBB"]
|
||||
@@ -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