Compare commits
14 Commits
| 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 |
+16
-4
@@ -3,10 +3,22 @@ __pycache__/
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__OLD__/
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__OLD__/
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.specstory/
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.specstory/
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.history/
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.history/
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.cursorindexingignore
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.vscode/
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*.py[cod]
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.ipynb_checkpoints/
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.pytest_cache/
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# Local environments
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|
.venv/
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venv/
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|
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# Local test data and generated analysis results
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|
data/*
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!data/.gitkeep
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|
results/*
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!results/.gitkeep
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|
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data
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data
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|
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||||||
cvttpy
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cvttpy
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# SpecStory explanation file
|
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.specstory/.what-is-this.md
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results/
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tmp/
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tmp/
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Vendored
-1
@@ -1 +0,0 @@
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PYTHONPATH=/home/oleg/develop
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Vendored
-133
@@ -1,133 +0,0 @@
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{
|
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||||||
// Use IntelliSense to learn about possible attributes.
|
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||||||
// Hover to view descriptions of existing attributes.
|
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||||||
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
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"version": "0.2.0",
|
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"configurations": [
|
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||||||
|
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||||||
|
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||||||
{
|
|
||||||
"name": "Python Debugger: Current File",
|
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||||||
"type": "debugpy",
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"request": "launch",
|
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"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
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"program": "${file}",
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"console": "integratedTerminal",
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"env": {
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"PYTHONPATH": "${workspaceFolder}/lib:${workspaceFolder}/.."
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},
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},
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{
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"name": "-------- VECM --------",
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},
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{
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"name": "CRYPTO VECM BACKTEST (optimized)",
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"type": "debugpy",
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"request": "launch",
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"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
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"program": "${workspaceFolder}/research/backtest.py",
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||||||
"args": [
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||||||
"--config=http://cloud16.cvtt.vpn:6789/apps/pairs_trading/backtest",
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||||||
"--instruments=CRYPTO:BNBSPOT:PAIR-ADA-USDT,CRYPTO:BNBSPOT:PAIR-SOL-USDT",
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"--date_pattern=20250911",
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"--result_db=${workspaceFolder}/research/results/crypto/%T.vecm-opt.ADA-SOL.20250605.crypto_results.db",
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],
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"env": {
|
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"PYTHONPATH": "${workspaceFolder}/..",
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"CONFIG_SERVICE": "cloud16.cvtt.vpn:6789",
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"MODEL_CONFIG": "vecm-opt"
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},
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"console": "integratedTerminal"
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},
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// {
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||||||
// "name": "EQUITY VECM (rolling)",
|
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// "type": "debugpy",
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// "request": "launch",
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// "python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
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// "program": "${workspaceFolder}/research/backtest.py",
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// "args": [
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// "--config=${workspaceFolder}/configuration/vecm.cfg",
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// "--instruments=COIN:EQUITY:ALPACA,MSTR:EQUITY:ALPACA",
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// "--date_pattern=20250605",
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||||||
// "--result_db=${workspaceFolder}/research/results/equity/%T.vecm.COIN-MSTR.20250605.equity_results.db",
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||||||
// ],
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||||||
// "env": {
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||||||
// "PYTHONPATH": "${workspaceFolder}/lib"
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||||||
// },
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||||||
// "console": "integratedTerminal"
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||||||
// },
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||||||
// {
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||||||
// "name": "EQUITY-CRYPTO VECM (rolling)",
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||||||
// "type": "debugpy",
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||||||
// "request": "launch",
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||||||
// "python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
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||||||
// "program": "${workspaceFolder}/research/backtest.py",
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// "args": [
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// "--config=${workspaceFolder}/configuration/vecm.cfg",
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// "--instruments=COIN:EQUITY:ALPACA,BTC-USDT:CRYPTO:BNBSPOT",
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// "--date_pattern=20250605",
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||||||
// "--result_db=${workspaceFolder}/research/results/intermarket/%T.vecm.COIN-BTC.20250601.equity_results.db",
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||||||
// ],
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// "env": {
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||||||
// "PYTHONPATH": "${workspaceFolder}/lib"
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||||||
// },
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// "console": "integratedTerminal"
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// },
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{
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||||||
"name": "-------- B a t c h e s --------",
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||||||
},
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{
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||||||
"name": "CRYPTO OLS Batch (rolling)",
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|
||||||
"type": "debugpy",
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|
||||||
"request": "launch",
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|
||||||
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
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|
||||||
"program": "${workspaceFolder}/research/backtest.py",
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||||||
"args": [
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|
||||||
"--config=${workspaceFolder}/configuration/ols.cfg",
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|
||||||
"--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
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|
||||||
"--date_pattern=2025060*",
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|
||||||
"--result_db=${workspaceFolder}/research/results/crypto/%T.ols.ADA-SOL.2025060-.crypto_results.db",
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|
||||||
],
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||||||
"env": {
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|
||||||
"PYTHONPATH": "${workspaceFolder}/lib"
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|
||||||
},
|
|
||||||
"console": "integratedTerminal"
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|
||||||
},
|
|
||||||
{
|
|
||||||
"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",
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|
||||||
"--date_pattern=2025060*",
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|
||||||
"--result_db=${workspaceFolder}/research/results/crypto/%T.vecm.ADA-SOL.2025060-.crypto_results.db",
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|
||||||
],
|
|
||||||
"env": {
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|
||||||
"PYTHONPATH": "${workspaceFolder}/lib"
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|
||||||
},
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|
||||||
"console": "integratedTerminal"
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|
||||||
},
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|
||||||
{
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|
||||||
"name": "-------- Viz Test --------",
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|
||||||
},
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|
||||||
{
|
|
||||||
"name": "Viz Test",
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|
||||||
"type": "debugpy",
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|
||||||
"request": "launch",
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|
||||||
"python": "/home/oleg/.pyenv/python3.12-venv/bin/python",
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|
||||||
"program": "${workspaceFolder}/tests/viz_test.py",
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||||||
"args": [
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|
||||||
"--config=${workspaceFolder}/configuration/ols.cfg",
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|
||||||
"--instruments=ADA-USDT:CRYPTO:BNBSPOT,SOL-USDT:CRYPTO:BNBSPOT",
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|
||||||
"--date_pattern=20250605",
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|
||||||
],
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|
||||||
"env": {
|
|
||||||
"PYTHONPATH": "${workspaceFolder}/lib"
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|
||||||
},
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|
||||||
"console": "integratedTerminal"
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|
||||||
}
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|
||||||
]
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|
||||||
}
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||||||
Vendored
-10
@@ -1,10 +0,0 @@
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|||||||
{
|
|
||||||
"folders": [
|
|
||||||
{
|
|
||||||
"path": ".."
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|
||||||
}
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|
||||||
],
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|
||||||
"settings": {
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|
||||||
"workbench.colorTheme": "Dracula Theme"
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|
||||||
}
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|
||||||
}
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Vendored
-19
@@ -1,19 +0,0 @@
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|||||||
{
|
|
||||||
"python.testing.pytestEnabled": true,
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|
||||||
"python.testing.unittestEnabled": false,
|
|
||||||
"python.testing.pytestArgs": [
|
|
||||||
"unittests"
|
|
||||||
],
|
|
||||||
"python.testing.cwd": "${workspaceFolder}",
|
|
||||||
"python.testing.autoTestDiscoverOnSaveEnabled": true,
|
|
||||||
"python.testing.pytestPath": "python3",
|
|
||||||
"python.analysis.extraPaths": [
|
|
||||||
"${workspaceFolder}",
|
|
||||||
"${workspaceFolder}/..",
|
|
||||||
"${workspaceFolder}/unittests"
|
|
||||||
],
|
|
||||||
"python.envFile": "${workspaceFolder}/.env",
|
|
||||||
"python.testing.debugPort": 3000,
|
|
||||||
"python.testing.promptToConfigure": false,
|
|
||||||
"python.defaultInterpreterPath": "/home/oleg/.pyenv/python3.12-venv/bin/python"
|
|
||||||
}
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|
||||||
@@ -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*
|
||||||
|
|
||||||
@@ -1,937 +0,0 @@
|
|||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
import asyncio
|
|
||||||
import os
|
|
||||||
import sqlite3
|
|
||||||
from dataclasses import dataclass
|
|
||||||
from typing import Any, Dict, List, Optional, Sequence, Set, Tuple, Union
|
|
||||||
|
|
||||||
from aiohttp import web
|
|
||||||
import numpy as np
|
|
||||||
import pandas as pd
|
|
||||||
from statsmodels.tsa.stattools import adfuller, coint # type: ignore
|
|
||||||
from statsmodels.tsa.vector_ar.vecm import coint_johansen # type: ignore
|
|
||||||
|
|
||||||
|
|
||||||
from cvttpy_tools.app import App
|
|
||||||
from cvttpy_tools.base import NamedObject
|
|
||||||
from cvttpy_tools.config import Config, CvttAppConfig
|
|
||||||
from cvttpy_tools.logger import Log
|
|
||||||
from cvttpy_tools.timeutils import NanoPerSec, SecPerHour, current_nanoseconds
|
|
||||||
from cvttpy_tools.web.rest_client import RESTSender
|
|
||||||
from cvttpy_tools.web.rest_service import RestService
|
|
||||||
|
|
||||||
from cvttpy_trading.trading.exchange_config import ExchangeAccounts
|
|
||||||
from cvttpy_trading.trading.instrument import ExchangeInstrument
|
|
||||||
from cvttpy_trading.trading.mkt_data.md_summary import MdTradesAggregate, MdSummary
|
|
||||||
|
|
||||||
from pairs_trading.apps.pair_selector.renderer import HtmlRenderer
|
|
||||||
|
|
||||||
|
|
||||||
@dataclass
|
|
||||||
class BacktestAggregate:
|
|
||||||
aggr_time_ns_: int
|
|
||||||
num_trades_: Optional[int]
|
|
||||||
|
|
||||||
|
|
||||||
@dataclass
|
|
||||||
class InstrumentQuality(NamedObject):
|
|
||||||
instrument_: ExchangeInstrument
|
|
||||||
record_count_: int
|
|
||||||
latest_tstamp_: Optional[pd.Timestamp]
|
|
||||||
status_: str
|
|
||||||
reason_: str
|
|
||||||
|
|
||||||
|
|
||||||
@dataclass
|
|
||||||
class PairStats(NamedObject):
|
|
||||||
pair_name_: str
|
|
||||||
instrument_a_: ExchangeInstrument
|
|
||||||
instrument_b_: ExchangeInstrument
|
|
||||||
pvalue_eg_: Optional[float]
|
|
||||||
pvalue_adf_: Optional[float]
|
|
||||||
pvalue_j_: Optional[float]
|
|
||||||
trace_stat_j_: Optional[float]
|
|
||||||
rank_eg_: int = 0
|
|
||||||
rank_adf_: int = 0
|
|
||||||
rank_j_: int = 0
|
|
||||||
composite_rank_: int = 0
|
|
||||||
|
|
||||||
def as_dict(self) -> Dict[str, Any]:
|
|
||||||
return {
|
|
||||||
"exchange_a": self.instrument_a_.exchange_id_,
|
|
||||||
"exchange_b": self.instrument_b_.exchange_id_,
|
|
||||||
"pair_name": self.pair_name_,
|
|
||||||
"instrument_a": self.instrument_a_.instrument_id(),
|
|
||||||
"instrument_b": self.instrument_b_.instrument_id(),
|
|
||||||
"pvalue_eg": self.pvalue_eg_,
|
|
||||||
"pvalue_adf": self.pvalue_adf_,
|
|
||||||
"pvalue_j": self.pvalue_j_,
|
|
||||||
"trace_stat_j": self.trace_stat_j_,
|
|
||||||
"rank_eg": self.rank_eg_,
|
|
||||||
"rank_adf": self.rank_adf_,
|
|
||||||
"rank_j": self.rank_j_,
|
|
||||||
"composite_rank": self.composite_rank_,
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
def _extract_price_from_fields(
|
|
||||||
price_field: str,
|
|
||||||
inst: ExchangeInstrument,
|
|
||||||
open: Optional[float],
|
|
||||||
high: Optional[float],
|
|
||||||
low: Optional[float],
|
|
||||||
close: Optional[float],
|
|
||||||
vwap: Optional[float],
|
|
||||||
) -> float:
|
|
||||||
field_map = {
|
|
||||||
"open": open,
|
|
||||||
"high": high,
|
|
||||||
"low": low,
|
|
||||||
"close": close,
|
|
||||||
"vwap": vwap,
|
|
||||||
}
|
|
||||||
raw = field_map.get(price_field, close)
|
|
||||||
if raw is None:
|
|
||||||
raw = 0.0
|
|
||||||
return inst.get_price(raw)
|
|
||||||
|
|
||||||
|
|
||||||
class DataFetcher(NamedObject):
|
|
||||||
sender_: RESTSender
|
|
||||||
interval_sec_: int
|
|
||||||
history_depth_sec_: int
|
|
||||||
|
|
||||||
def __init__(
|
|
||||||
self,
|
|
||||||
base_url: str,
|
|
||||||
interval_sec: int,
|
|
||||||
history_depth_sec: int,
|
|
||||||
) -> None:
|
|
||||||
self.sender_ = RESTSender(base_url=base_url)
|
|
||||||
self.interval_sec_ = interval_sec
|
|
||||||
self.history_depth_sec_ = history_depth_sec
|
|
||||||
|
|
||||||
def fetch(
|
|
||||||
self, exch_acct: str, inst: ExchangeInstrument
|
|
||||||
) -> List[MdTradesAggregate]:
|
|
||||||
rqst_data = {
|
|
||||||
"exch_acct": exch_acct,
|
|
||||||
"instrument_id": inst.instrument_id(),
|
|
||||||
"interval_sec": self.interval_sec_,
|
|
||||||
"history_depth_sec": self.history_depth_sec_,
|
|
||||||
}
|
|
||||||
response = self.sender_.send_post(endpoint="md_summary", post_body=rqst_data)
|
|
||||||
if response.status_code not in (200, 201):
|
|
||||||
Log.error(
|
|
||||||
f"{self.fname()}: error {response.status_code} for {inst.details_short()}: {response.text}"
|
|
||||||
)
|
|
||||||
return []
|
|
||||||
mdsums: List[MdSummary] = MdSummary.from_REST_response(response=response)
|
|
||||||
return [
|
|
||||||
mdsum.create_md_trades_aggregate(
|
|
||||||
exch_acct=exch_acct, exch_inst=inst, interval_sec=self.interval_sec_
|
|
||||||
)
|
|
||||||
for mdsum in mdsums
|
|
||||||
]
|
|
||||||
|
|
||||||
|
|
||||||
AggregateLike = Union[MdTradesAggregate, BacktestAggregate]
|
|
||||||
|
|
||||||
|
|
||||||
class QualityChecker(NamedObject):
|
|
||||||
interval_sec_: int
|
|
||||||
|
|
||||||
def __init__(self, interval_sec: int) -> None:
|
|
||||||
self.interval_sec_ = interval_sec
|
|
||||||
|
|
||||||
def evaluate(
|
|
||||||
self,
|
|
||||||
inst: ExchangeInstrument,
|
|
||||||
aggr: Sequence[AggregateLike],
|
|
||||||
now_ts: Optional[pd.Timestamp] = None,
|
|
||||||
) -> InstrumentQuality:
|
|
||||||
if len(aggr) == 0:
|
|
||||||
return InstrumentQuality(
|
|
||||||
instrument_=inst,
|
|
||||||
record_count_=0,
|
|
||||||
latest_tstamp_=None,
|
|
||||||
status_="FAIL",
|
|
||||||
reason_="no records",
|
|
||||||
)
|
|
||||||
|
|
||||||
aggr_sorted = sorted(aggr, key=lambda a: a.aggr_time_ns_)
|
|
||||||
|
|
||||||
latest_ts = pd.to_datetime(aggr_sorted[-1].aggr_time_ns_, unit="ns", utc=True)
|
|
||||||
now_ts = now_ts or pd.Timestamp.utcnow()
|
|
||||||
recency_cutoff = now_ts - pd.Timedelta(seconds=2 * self.interval_sec_)
|
|
||||||
if latest_ts <= recency_cutoff:
|
|
||||||
return InstrumentQuality(
|
|
||||||
instrument_=inst,
|
|
||||||
record_count_=len(aggr_sorted),
|
|
||||||
latest_tstamp_=latest_ts,
|
|
||||||
status_="FAIL",
|
|
||||||
reason_=f"stale: latest {latest_ts} <= cutoff {recency_cutoff}",
|
|
||||||
)
|
|
||||||
|
|
||||||
gaps_ok, reason = self._check_gaps(aggr_sorted)
|
|
||||||
status = "PASS" if gaps_ok else "FAIL"
|
|
||||||
return InstrumentQuality(
|
|
||||||
instrument_=inst,
|
|
||||||
record_count_=len(aggr_sorted),
|
|
||||||
latest_tstamp_=latest_ts,
|
|
||||||
status_=status,
|
|
||||||
reason_=reason,
|
|
||||||
)
|
|
||||||
|
|
||||||
def _check_gaps(self, aggr: Sequence[AggregateLike]) -> Tuple[bool, str]:
|
|
||||||
NUM_TRADES_THRESHOLD = 50
|
|
||||||
if len(aggr) < 2:
|
|
||||||
return True, "ok"
|
|
||||||
|
|
||||||
interval_ns = self.interval_sec_ * NanoPerSec
|
|
||||||
for idx in range(1, len(aggr)):
|
|
||||||
prev = aggr[idx - 1]
|
|
||||||
curr = aggr[idx]
|
|
||||||
delta = curr.aggr_time_ns_ - prev.aggr_time_ns_
|
|
||||||
missing_intervals = int(delta // interval_ns) - 1
|
|
||||||
if missing_intervals <= 0:
|
|
||||||
continue
|
|
||||||
|
|
||||||
prev_nt = prev.num_trades_
|
|
||||||
next_nt = curr.num_trades_
|
|
||||||
estimate = self._approximate_num_trades(prev_nt, next_nt)
|
|
||||||
if estimate > NUM_TRADES_THRESHOLD:
|
|
||||||
return False, (
|
|
||||||
f"gap of {missing_intervals} interval(s), est num_trades={estimate} > {NUM_TRADES_THRESHOLD}"
|
|
||||||
)
|
|
||||||
return True, "ok"
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def _approximate_num_trades(prev_nt: Optional[int], next_nt: Optional[int]) -> float:
|
|
||||||
if prev_nt is None and next_nt is None:
|
|
||||||
return 0.0
|
|
||||||
if prev_nt is None:
|
|
||||||
return float(next_nt or 0)
|
|
||||||
if next_nt is None:
|
|
||||||
return float(prev_nt)
|
|
||||||
return (prev_nt + next_nt) / 2.0
|
|
||||||
|
|
||||||
|
|
||||||
class PairAnalyzer(NamedObject):
|
|
||||||
price_field_: str
|
|
||||||
interval_sec_: int
|
|
||||||
|
|
||||||
def __init__(self, price_field: str, interval_sec: int) -> None:
|
|
||||||
self.price_field_ = price_field
|
|
||||||
self.interval_sec_ = interval_sec
|
|
||||||
|
|
||||||
def analyze(
|
|
||||||
self, series: Dict[ExchangeInstrument, pd.DataFrame]
|
|
||||||
) -> Dict[str, PairStats]:
|
|
||||||
instruments = list(series.keys())
|
|
||||||
results: Dict[str, PairStats] = {}
|
|
||||||
for i in range(len(instruments)):
|
|
||||||
for j in range(i + 1, len(instruments)):
|
|
||||||
inst_a, inst_b, pair_name = self._normalized_pair(
|
|
||||||
instruments[i], instruments[j]
|
|
||||||
)
|
|
||||||
df_a = series[inst_a][["tstamp", "price"]].rename(
|
|
||||||
columns={"price": "price_a"}
|
|
||||||
)
|
|
||||||
df_b = series[inst_b][["tstamp", "price"]].rename(
|
|
||||||
columns={"price": "price_b"}
|
|
||||||
)
|
|
||||||
merged = pd.merge(df_a, df_b, on="tstamp", how="inner").sort_values(
|
|
||||||
"tstamp"
|
|
||||||
)
|
|
||||||
# Log.info(f"{self.fname()}: analyzing {pair_name}")
|
|
||||||
stats = self._compute_stats(inst_a, inst_b, pair_name, merged)
|
|
||||||
if stats:
|
|
||||||
results[pair_name] = stats
|
|
||||||
return self._rank(results)
|
|
||||||
|
|
||||||
def _compute_stats(
|
|
||||||
self,
|
|
||||||
inst_a: ExchangeInstrument,
|
|
||||||
inst_b: ExchangeInstrument,
|
|
||||||
pair_name: str,
|
|
||||||
merged: pd.DataFrame,
|
|
||||||
) -> Optional[PairStats]:
|
|
||||||
if len(merged) < 2:
|
|
||||||
return None
|
|
||||||
px_a = merged["price_a"].astype(float)
|
|
||||||
px_b = merged["price_b"].astype(float)
|
|
||||||
|
|
||||||
std_a = float(px_a.std())
|
|
||||||
std_b = float(px_b.std())
|
|
||||||
if std_a == 0 or std_b == 0:
|
|
||||||
return None
|
|
||||||
|
|
||||||
z_a = (px_a - float(px_a.mean())) / std_a
|
|
||||||
z_b = (px_b - float(px_b.mean())) / std_b
|
|
||||||
|
|
||||||
p_eg: Optional[float]
|
|
||||||
p_adf: Optional[float]
|
|
||||||
p_j: Optional[float]
|
|
||||||
trace_stat: Optional[float]
|
|
||||||
|
|
||||||
try:
|
|
||||||
p_eg = float(coint(z_a, z_b)[1])
|
|
||||||
except Exception as exc:
|
|
||||||
Log.warning(
|
|
||||||
f"{self.fname()}: EG failed for {inst_a.details_short()}/{inst_b.details_short()}: {exc}"
|
|
||||||
)
|
|
||||||
p_eg = None
|
|
||||||
|
|
||||||
try:
|
|
||||||
spread = z_a - z_b
|
|
||||||
p_adf = float(adfuller(spread, maxlag=1, regression="c")[1])
|
|
||||||
except Exception as exc:
|
|
||||||
Log.warning(
|
|
||||||
f"{self.fname()}: ADF failed for {inst_a.details_short()}/{inst_b.details_short()}: {exc}"
|
|
||||||
)
|
|
||||||
p_adf = None
|
|
||||||
|
|
||||||
try:
|
|
||||||
data = np.column_stack([z_a, z_b])
|
|
||||||
res = coint_johansen(data, det_order=0, k_ar_diff=1)
|
|
||||||
trace_stat = float(res.lr1[0])
|
|
||||||
cv10, cv5, cv1 = res.cvt[0]
|
|
||||||
if trace_stat > cv1:
|
|
||||||
p_j = 0.01
|
|
||||||
elif trace_stat > cv5:
|
|
||||||
p_j = 0.05
|
|
||||||
elif trace_stat > cv10:
|
|
||||||
p_j = 0.10
|
|
||||||
else:
|
|
||||||
p_j = 1.0
|
|
||||||
except Exception as exc:
|
|
||||||
Log.warning(
|
|
||||||
f"{self.fname()}: Johansen failed for {inst_a.details_short()}/{inst_b.details_short()}: {exc}"
|
|
||||||
)
|
|
||||||
p_j = None
|
|
||||||
trace_stat = None
|
|
||||||
|
|
||||||
return PairStats(
|
|
||||||
pair_name_=pair_name,
|
|
||||||
instrument_a_=inst_a,
|
|
||||||
instrument_b_=inst_b,
|
|
||||||
pvalue_eg_=p_eg,
|
|
||||||
pvalue_adf_=p_adf,
|
|
||||||
pvalue_j_=p_j,
|
|
||||||
trace_stat_j_=trace_stat,
|
|
||||||
)
|
|
||||||
|
|
||||||
def _rank(self, results: Dict[str, PairStats]) -> Dict[str, PairStats]:
|
|
||||||
ranked = list(results.values())
|
|
||||||
self._assign_ranks(ranked, key=lambda r: r.pvalue_eg_, attr="rank_eg_")
|
|
||||||
self._assign_ranks(ranked, key=lambda r: r.pvalue_adf_, attr="rank_adf_")
|
|
||||||
self._assign_ranks(ranked, key=lambda r: r.pvalue_j_, attr="rank_j_")
|
|
||||||
for res in ranked:
|
|
||||||
res.composite_rank_ = res.rank_eg_ + res.rank_adf_ # + res.rank_j_
|
|
||||||
ranked.sort(key=lambda r: r.composite_rank_)
|
|
||||||
return {res.pair_name_: res for res in ranked}
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def _normalized_pair(
|
|
||||||
inst_a: ExchangeInstrument, inst_b: ExchangeInstrument
|
|
||||||
) -> Tuple[ExchangeInstrument, ExchangeInstrument, str]:
|
|
||||||
inst_a_id = PairAnalyzer._pair_label(inst_a.instrument_id())
|
|
||||||
inst_b_id = PairAnalyzer._pair_label(inst_b.instrument_id())
|
|
||||||
if inst_a_id <= inst_b_id:
|
|
||||||
return inst_a, inst_b, f"{inst_a_id}<->{inst_b_id}"
|
|
||||||
return inst_b, inst_a, f"{inst_b_id}<->{inst_a_id}"
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def _pair_label(instrument_id: str) -> str:
|
|
||||||
if instrument_id.startswith("PAIR-"):
|
|
||||||
return instrument_id[len("PAIR-") :]
|
|
||||||
return instrument_id
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def _assign_ranks(results: List[PairStats], key, attr: str) -> None:
|
|
||||||
values = [key(r) for r in results]
|
|
||||||
sorted_vals = sorted([v for v in values if v is not None])
|
|
||||||
for res in results:
|
|
||||||
val = key(res)
|
|
||||||
if val is None:
|
|
||||||
setattr(res, attr, len(sorted_vals) + 1)
|
|
||||||
continue
|
|
||||||
rank = 1 + sum(1 for v in sorted_vals if v < val)
|
|
||||||
setattr(res, attr, rank)
|
|
||||||
|
|
||||||
|
|
||||||
class PairSelectionEngine(NamedObject):
|
|
||||||
config_: object
|
|
||||||
instruments_: List[ExchangeInstrument]
|
|
||||||
price_field_: str
|
|
||||||
fetcher_: DataFetcher
|
|
||||||
quality_: QualityChecker
|
|
||||||
analyzer_: PairAnalyzer
|
|
||||||
interval_sec_: int
|
|
||||||
history_depth_sec_: int
|
|
||||||
data_quality_cache_: List[InstrumentQuality]
|
|
||||||
pair_results_cache_: Dict[str, PairStats]
|
|
||||||
|
|
||||||
def __init__(
|
|
||||||
self,
|
|
||||||
config: Config,
|
|
||||||
instruments: List[ExchangeInstrument],
|
|
||||||
price_field: str,
|
|
||||||
) -> None:
|
|
||||||
self.config_ = config
|
|
||||||
self.instruments_ = instruments
|
|
||||||
self.price_field_ = price_field
|
|
||||||
|
|
||||||
interval_sec = int(config.get_value("interval_sec", 0))
|
|
||||||
history_depth_sec = int(config.get_value("history_depth_hours", 0)) * SecPerHour
|
|
||||||
base_url = config.get_value("cvtt_base_url", None)
|
|
||||||
assert interval_sec > 0, "interval_sec must be > 0"
|
|
||||||
assert history_depth_sec > 0, "history_depth_sec must be > 0"
|
|
||||||
assert base_url, "cvtt_base_url must be set"
|
|
||||||
|
|
||||||
self.fetcher_ = DataFetcher(
|
|
||||||
base_url=base_url,
|
|
||||||
interval_sec=interval_sec,
|
|
||||||
history_depth_sec=history_depth_sec,
|
|
||||||
)
|
|
||||||
self.quality_ = QualityChecker(interval_sec=interval_sec)
|
|
||||||
self.analyzer_ = PairAnalyzer(
|
|
||||||
price_field=price_field, interval_sec=interval_sec
|
|
||||||
)
|
|
||||||
|
|
||||||
self.interval_sec_ = interval_sec
|
|
||||||
self.history_depth_sec_ = history_depth_sec
|
|
||||||
|
|
||||||
self.data_quality_cache_ = []
|
|
||||||
self.pair_results_cache_ = {}
|
|
||||||
|
|
||||||
async def run_once(self) -> None:
|
|
||||||
quality_results: List[InstrumentQuality] = []
|
|
||||||
price_series: Dict[ExchangeInstrument, pd.DataFrame] = {}
|
|
||||||
|
|
||||||
for inst in self.instruments_:
|
|
||||||
exch_acct = inst.user_data_.get("exch_acct") or inst.exchange_id_
|
|
||||||
aggr = self.fetcher_.fetch(exch_acct=exch_acct, inst=inst)
|
|
||||||
q = self.quality_.evaluate(inst, aggr)
|
|
||||||
quality_results.append(q)
|
|
||||||
if q.status_ != "PASS":
|
|
||||||
continue
|
|
||||||
df = self._to_dataframe(aggr, inst)
|
|
||||||
if len(df) > 0:
|
|
||||||
price_series[inst] = df
|
|
||||||
self.data_quality_cache_ = quality_results
|
|
||||||
self.pair_results_cache_ = self.analyzer_.analyze(price_series)
|
|
||||||
|
|
||||||
def _to_dataframe(
|
|
||||||
self, aggr: List[MdTradesAggregate], inst: ExchangeInstrument
|
|
||||||
) -> pd.DataFrame:
|
|
||||||
rows: List[Dict[str, Any]] = []
|
|
||||||
for item in aggr:
|
|
||||||
rows.append(
|
|
||||||
{
|
|
||||||
"tstamp": pd.to_datetime(item.aggr_time_ns_, unit="ns", utc=True),
|
|
||||||
"price": self._extract_price(item, inst),
|
|
||||||
"num_trades": item.num_trades_,
|
|
||||||
}
|
|
||||||
)
|
|
||||||
df = pd.DataFrame(rows)
|
|
||||||
return df.sort_values("tstamp").reset_index(drop=True)
|
|
||||||
|
|
||||||
def _extract_price(
|
|
||||||
self, aggr: MdTradesAggregate, inst: ExchangeInstrument
|
|
||||||
) -> float:
|
|
||||||
return _extract_price_from_fields(
|
|
||||||
price_field=self.price_field_,
|
|
||||||
inst=inst,
|
|
||||||
open=aggr.open_,
|
|
||||||
high=aggr.high_,
|
|
||||||
low=aggr.low_,
|
|
||||||
close=aggr.close_,
|
|
||||||
vwap=aggr.vwap_,
|
|
||||||
)
|
|
||||||
|
|
||||||
def sleep_seconds_until_next_cycle(self) -> float:
|
|
||||||
now_ns = current_nanoseconds()
|
|
||||||
interval_ns = self.interval_sec_ * NanoPerSec
|
|
||||||
next_boundary = (now_ns // interval_ns + 1) * interval_ns
|
|
||||||
return max(0.0, (next_boundary - now_ns) / NanoPerSec)
|
|
||||||
|
|
||||||
def quality_dicts(self) -> List[Dict[str, Any]]:
|
|
||||||
res: List[Dict[str, Any]] = []
|
|
||||||
for q in self.data_quality_cache_:
|
|
||||||
res.append(
|
|
||||||
{
|
|
||||||
"instrument": q.instrument_.instrument_id(),
|
|
||||||
"record_count": q.record_count_,
|
|
||||||
"latest_tstamp": (
|
|
||||||
q.latest_tstamp_.isoformat() if q.latest_tstamp_ else None
|
|
||||||
),
|
|
||||||
"status": q.status_,
|
|
||||||
"reason": q.reason_,
|
|
||||||
}
|
|
||||||
)
|
|
||||||
return res
|
|
||||||
|
|
||||||
def pair_dicts(self) -> Dict[str, Dict[str, Any]]:
|
|
||||||
return {
|
|
||||||
pair_name: stats.as_dict()
|
|
||||||
for pair_name, stats in self.pair_results_cache_.items()
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
class PairSelectionBacktest(NamedObject):
|
|
||||||
config_: object
|
|
||||||
instruments_: List[ExchangeInstrument]
|
|
||||||
price_field_: str
|
|
||||||
input_db_: str
|
|
||||||
output_db_: str
|
|
||||||
interval_sec_: int
|
|
||||||
history_depth_hours_: int
|
|
||||||
quality_: QualityChecker
|
|
||||||
analyzer_: PairAnalyzer
|
|
||||||
inst_by_key_: Dict[Tuple[str, str], ExchangeInstrument]
|
|
||||||
inst_by_id_: Dict[str, Optional[ExchangeInstrument]]
|
|
||||||
ambiguous_ids_: Set[str]
|
|
||||||
|
|
||||||
def __init__(
|
|
||||||
self,
|
|
||||||
config: Config,
|
|
||||||
instruments: List[ExchangeInstrument],
|
|
||||||
price_field: str,
|
|
||||||
input_db: str,
|
|
||||||
output_db: str,
|
|
||||||
) -> None:
|
|
||||||
self.config_ = config
|
|
||||||
self.instruments_ = instruments
|
|
||||||
self.price_field_ = price_field
|
|
||||||
self.input_db_ = input_db
|
|
||||||
self.output_db_ = output_db
|
|
||||||
|
|
||||||
interval_sec = int(config.get_value("interval_sec", 0))
|
|
||||||
if interval_sec <= 0:
|
|
||||||
Log.warning(
|
|
||||||
f"{self.fname()}: interval_sec not set; defaulting to 60 seconds"
|
|
||||||
)
|
|
||||||
interval_sec = 60
|
|
||||||
history_depth_hours = int(config.get_value("history_depth_hours", 0))
|
|
||||||
assert history_depth_hours > 0, "history_depth_hours must be > 0"
|
|
||||||
|
|
||||||
self.interval_sec_ = interval_sec
|
|
||||||
self.history_depth_hours_ = history_depth_hours
|
|
||||||
self.quality_ = QualityChecker(interval_sec=interval_sec)
|
|
||||||
self.analyzer_ = PairAnalyzer(
|
|
||||||
price_field=price_field, interval_sec=interval_sec
|
|
||||||
)
|
|
||||||
|
|
||||||
self.inst_by_key_ = {
|
|
||||||
(inst.exchange_id_, inst.instrument_id()): inst for inst in instruments
|
|
||||||
}
|
|
||||||
self.inst_by_id_ = {}
|
|
||||||
self.ambiguous_ids_ = set()
|
|
||||||
for inst in instruments:
|
|
||||||
inst_id = inst.instrument_id()
|
|
||||||
if inst_id in self.inst_by_id_:
|
|
||||||
existing = self.inst_by_id_[inst_id]
|
|
||||||
if existing is not None and existing.exchange_id_ != inst.exchange_id_:
|
|
||||||
self.inst_by_id_[inst_id] = None
|
|
||||||
self.ambiguous_ids_.add(inst_id)
|
|
||||||
elif inst_id not in self.ambiguous_ids_:
|
|
||||||
self.inst_by_id_[inst_id] = inst
|
|
||||||
|
|
||||||
if self.ambiguous_ids_:
|
|
||||||
Log.warning(
|
|
||||||
f"{self.fname()}: ambiguous instrument_id(s) without exchange_id: "
|
|
||||||
f"{sorted(self.ambiguous_ids_)}"
|
|
||||||
)
|
|
||||||
|
|
||||||
def run(self) -> None:
|
|
||||||
df = self._load_input_df()
|
|
||||||
if df.empty:
|
|
||||||
Log.warning(f"{self.fname()}: no rows in md_1min_bars")
|
|
||||||
return
|
|
||||||
|
|
||||||
df = self._filter_instruments(df)
|
|
||||||
if df.empty:
|
|
||||||
Log.warning(f"{self.fname()}: no rows after instrument filtering")
|
|
||||||
return
|
|
||||||
|
|
||||||
conn = self._init_output_db()
|
|
||||||
try:
|
|
||||||
self._run_backtest(df, conn)
|
|
||||||
finally:
|
|
||||||
conn.commit()
|
|
||||||
conn.close()
|
|
||||||
|
|
||||||
def _load_input_df(self) -> pd.DataFrame:
|
|
||||||
if not os.path.exists(self.input_db_):
|
|
||||||
raise FileNotFoundError(f"input_db not found: {self.input_db_}")
|
|
||||||
with sqlite3.connect(self.input_db_) as conn:
|
|
||||||
df = pd.read_sql_query(
|
|
||||||
"""
|
|
||||||
SELECT
|
|
||||||
tstamp,
|
|
||||||
tstamp_ns,
|
|
||||||
exchange_id,
|
|
||||||
instrument_id,
|
|
||||||
open,
|
|
||||||
high,
|
|
||||||
low,
|
|
||||||
close,
|
|
||||||
volume,
|
|
||||||
vwap,
|
|
||||||
num_trades
|
|
||||||
FROM md_1min_bars
|
|
||||||
""",
|
|
||||||
conn,
|
|
||||||
)
|
|
||||||
if df.empty:
|
|
||||||
return df
|
|
||||||
|
|
||||||
ts_ns = pd.to_datetime(df["tstamp_ns"], unit="ns", utc=True, errors="coerce")
|
|
||||||
ts_txt = pd.to_datetime(df["tstamp"], utc=True, errors="coerce")
|
|
||||||
df["tstamp"] = ts_ns.fillna(ts_txt)
|
|
||||||
df = df.dropna(subset=["tstamp", "instrument_id"]).copy()
|
|
||||||
df["exchange_id"] = df["exchange_id"].fillna("")
|
|
||||||
df["instrument_id"] = df["instrument_id"].astype(str)
|
|
||||||
df["tstamp_ns"] = df["tstamp"].astype("int64")
|
|
||||||
return df.sort_values("tstamp").reset_index(drop=True)
|
|
||||||
|
|
||||||
def _filter_instruments(self, df: pd.DataFrame) -> pd.DataFrame:
|
|
||||||
instrument_ids = {inst.instrument_id() for inst in self.instruments_}
|
|
||||||
df = df[df["instrument_id"].isin(instrument_ids)].copy()
|
|
||||||
if "exchange_id" in df.columns:
|
|
||||||
exchange_ids = {inst.exchange_id_ for inst in self.instruments_}
|
|
||||||
df = df[
|
|
||||||
(df["exchange_id"].isin(exchange_ids)) | (df["exchange_id"] == "")
|
|
||||||
].copy()
|
|
||||||
return df
|
|
||||||
|
|
||||||
def _init_output_db(self) -> sqlite3.Connection:
|
|
||||||
if os.path.exists(self.output_db_):
|
|
||||||
os.remove(self.output_db_)
|
|
||||||
conn = sqlite3.connect(self.output_db_)
|
|
||||||
conn.execute(
|
|
||||||
"""
|
|
||||||
CREATE TABLE pair_selection_history (
|
|
||||||
tstamp TEXT,
|
|
||||||
tstamp_ns INTEGER,
|
|
||||||
pair_name TEXT,
|
|
||||||
exchange_a TEXT,
|
|
||||||
instrument_a TEXT,
|
|
||||||
exchange_b TEXT,
|
|
||||||
instrument_b TEXT,
|
|
||||||
pvalue_eg REAL,
|
|
||||||
pvalue_adf REAL,
|
|
||||||
pvalue_j REAL,
|
|
||||||
trace_stat_j REAL,
|
|
||||||
rank_eg INTEGER,
|
|
||||||
rank_adf INTEGER,
|
|
||||||
rank_j INTEGER,
|
|
||||||
composite_rank REAL
|
|
||||||
)
|
|
||||||
"""
|
|
||||||
)
|
|
||||||
conn.execute(
|
|
||||||
"""
|
|
||||||
CREATE INDEX idx_pair_selection_history_pair_name
|
|
||||||
ON pair_selection_history (pair_name)
|
|
||||||
"""
|
|
||||||
)
|
|
||||||
conn.execute(
|
|
||||||
"""
|
|
||||||
CREATE UNIQUE INDEX idx_pair_selection_history_tstamp_pair
|
|
||||||
ON pair_selection_history (tstamp, pair_name)
|
|
||||||
"""
|
|
||||||
)
|
|
||||||
conn.commit()
|
|
||||||
return conn
|
|
||||||
|
|
||||||
def _resolve_instrument(
|
|
||||||
self, exchange_id: str, instrument_id: str
|
|
||||||
) -> Optional[ExchangeInstrument]:
|
|
||||||
if exchange_id:
|
|
||||||
inst = self.inst_by_key_.get((exchange_id, instrument_id))
|
|
||||||
if inst is not None:
|
|
||||||
return inst
|
|
||||||
inst = self.inst_by_id_.get(instrument_id)
|
|
||||||
if inst is None and instrument_id in self.ambiguous_ids_:
|
|
||||||
return None
|
|
||||||
return inst
|
|
||||||
|
|
||||||
def _build_day_series(
|
|
||||||
self, df_day: pd.DataFrame
|
|
||||||
) -> Dict[ExchangeInstrument, pd.DataFrame]:
|
|
||||||
series: Dict[ExchangeInstrument, pd.DataFrame] = {}
|
|
||||||
group_cols = ["exchange_id", "instrument_id"]
|
|
||||||
for key, group in df_day.groupby(group_cols, dropna=False):
|
|
||||||
exchange_id, instrument_id = key
|
|
||||||
inst = self._resolve_instrument(str(exchange_id or ""), str(instrument_id))
|
|
||||||
if inst is None:
|
|
||||||
continue
|
|
||||||
df_inst = group.copy()
|
|
||||||
df_inst["price"] = [
|
|
||||||
_extract_price_from_fields(
|
|
||||||
price_field=self.price_field_,
|
|
||||||
inst=inst,
|
|
||||||
open=float(row.open), #type: ignore
|
|
||||||
high=float(row.high), #type: ignore
|
|
||||||
low=float(row.low), #type: ignore
|
|
||||||
close=float(row.close), #type: ignore
|
|
||||||
vwap=float(row.vwap),#type: ignore
|
|
||||||
)
|
|
||||||
for row in df_inst.itertuples(index=False)
|
|
||||||
]
|
|
||||||
df_inst = df_inst[["tstamp", "tstamp_ns", "price", "num_trades"]]
|
|
||||||
if inst in series:
|
|
||||||
series[inst] = pd.concat([series[inst], df_inst], ignore_index=True)
|
|
||||||
else:
|
|
||||||
series[inst] = df_inst
|
|
||||||
for inst in list(series.keys()):
|
|
||||||
series[inst] = series[inst].sort_values("tstamp").reset_index(drop=True)
|
|
||||||
return series
|
|
||||||
|
|
||||||
def _run_backtest(self, df: pd.DataFrame, conn: sqlite3.Connection) -> None:
|
|
||||||
window_minutes = self.history_depth_hours_ * 60
|
|
||||||
window_td = pd.Timedelta(minutes=window_minutes)
|
|
||||||
step_td = pd.Timedelta(seconds=self.interval_sec_)
|
|
||||||
|
|
||||||
df = df.copy()
|
|
||||||
df["day"] = df["tstamp"].dt.normalize()
|
|
||||||
days = sorted(df["day"].unique())
|
|
||||||
for day in days:
|
|
||||||
day_label = pd.Timestamp(day).date()
|
|
||||||
df_day = df[df["day"] == day]
|
|
||||||
t0 = df_day["tstamp"].min()
|
|
||||||
t_last = df_day["tstamp"].max()
|
|
||||||
if t_last - t0 < window_td:
|
|
||||||
Log.warning(
|
|
||||||
f"{self.fname()}: skipping {day_label} (insufficient data)"
|
|
||||||
)
|
|
||||||
continue
|
|
||||||
|
|
||||||
day_series = self._build_day_series(df_day)
|
|
||||||
if len(day_series) < 2:
|
|
||||||
Log.warning(
|
|
||||||
f"{self.fname()}: skipping {day_label} (insufficient instruments)"
|
|
||||||
)
|
|
||||||
continue
|
|
||||||
|
|
||||||
start = t0
|
|
||||||
expected_end = start + window_td
|
|
||||||
while expected_end <= t_last:
|
|
||||||
window_slices: Dict[ExchangeInstrument, pd.DataFrame] = {}
|
|
||||||
ts: Optional[pd.Timestamp] = None
|
|
||||||
for inst, df_inst in day_series.items():
|
|
||||||
df_win = df_inst[
|
|
||||||
(df_inst["tstamp"] >= start)
|
|
||||||
& (df_inst["tstamp"] < expected_end)
|
|
||||||
]
|
|
||||||
if df_win.empty:
|
|
||||||
continue
|
|
||||||
window_slices[inst] = df_win
|
|
||||||
last_ts = df_win["tstamp"].iloc[-1]
|
|
||||||
if ts is None or last_ts > ts:
|
|
||||||
ts = last_ts
|
|
||||||
|
|
||||||
if window_slices and ts is not None:
|
|
||||||
price_series: Dict[ExchangeInstrument, pd.DataFrame] = {}
|
|
||||||
for inst, df_win in window_slices.items():
|
|
||||||
aggr = self._to_backtest_aggregates(df_win)
|
|
||||||
q = self.quality_.evaluate(
|
|
||||||
inst=inst, aggr=aggr, now_ts=ts
|
|
||||||
)
|
|
||||||
if q.status_ != "PASS":
|
|
||||||
continue
|
|
||||||
price_series[inst] = df_win[["tstamp", "price"]]
|
|
||||||
pair_results = self.analyzer_.analyze(price_series)
|
|
||||||
Log.info(f"{self.fname()}: Saving Results for window ending {ts}")
|
|
||||||
self._insert_results(conn, ts, pair_results)
|
|
||||||
|
|
||||||
start = start + step_td
|
|
||||||
expected_end = start + window_td
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def _to_backtest_aggregates(df_win: pd.DataFrame) -> List[BacktestAggregate]:
|
|
||||||
aggr: List[BacktestAggregate] = []
|
|
||||||
for tstamp_ns, num_trades in zip(df_win["tstamp_ns"], df_win["num_trades"]):
|
|
||||||
nt = None if pd.isna(num_trades) else int(num_trades)
|
|
||||||
aggr.append(
|
|
||||||
BacktestAggregate(aggr_time_ns_=int(tstamp_ns), num_trades_=nt)
|
|
||||||
)
|
|
||||||
return aggr
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def _insert_results(
|
|
||||||
conn: sqlite3.Connection,
|
|
||||||
ts: pd.Timestamp,
|
|
||||||
pair_results: Dict[str, PairStats],
|
|
||||||
) -> None:
|
|
||||||
if not pair_results:
|
|
||||||
return
|
|
||||||
iso = ts.isoformat()
|
|
||||||
ns = int(ts.value)
|
|
||||||
rows = []
|
|
||||||
for pair_name in sorted(pair_results.keys()):
|
|
||||||
stats = pair_results[pair_name]
|
|
||||||
rows.append(
|
|
||||||
(
|
|
||||||
iso,
|
|
||||||
ns,
|
|
||||||
pair_name,
|
|
||||||
stats.instrument_a_.exchange_id_,
|
|
||||||
stats.instrument_a_.instrument_id(),
|
|
||||||
stats.instrument_b_.exchange_id_,
|
|
||||||
stats.instrument_b_.instrument_id(),
|
|
||||||
stats.pvalue_eg_,
|
|
||||||
stats.pvalue_adf_,
|
|
||||||
stats.pvalue_j_,
|
|
||||||
stats.trace_stat_j_,
|
|
||||||
stats.rank_eg_,
|
|
||||||
stats.rank_adf_,
|
|
||||||
stats.rank_j_,
|
|
||||||
stats.composite_rank_,
|
|
||||||
)
|
|
||||||
)
|
|
||||||
conn.executemany(
|
|
||||||
"""
|
|
||||||
INSERT INTO pair_selection_history (
|
|
||||||
tstamp,
|
|
||||||
tstamp_ns,
|
|
||||||
pair_name,
|
|
||||||
exchange_a,
|
|
||||||
instrument_a,
|
|
||||||
exchange_b,
|
|
||||||
instrument_b,
|
|
||||||
pvalue_eg,
|
|
||||||
pvalue_adf,
|
|
||||||
pvalue_j,
|
|
||||||
trace_stat_j,
|
|
||||||
rank_eg,
|
|
||||||
rank_adf,
|
|
||||||
rank_j,
|
|
||||||
composite_rank
|
|
||||||
) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
|
|
||||||
""",
|
|
||||||
rows,
|
|
||||||
)
|
|
||||||
conn.commit()
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
class PairSelector(NamedObject):
|
|
||||||
instruments_: List[ExchangeInstrument]
|
|
||||||
engine_: PairSelectionEngine
|
|
||||||
rest_service_: Optional[RestService]
|
|
||||||
backtest_: Optional[PairSelectionBacktest]
|
|
||||||
|
|
||||||
def __init__(self) -> None:
|
|
||||||
App.instance().add_cmdline_arg("--oneshot", action="store_true", default=False)
|
|
||||||
App.instance().add_cmdline_arg("--backtest", action="store_true", default=False)
|
|
||||||
App.instance().add_cmdline_arg("--input_db", default=None)
|
|
||||||
App.instance().add_cmdline_arg("--output_db", default=None)
|
|
||||||
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:
|
|
||||||
cfg = CvttAppConfig.instance()
|
|
||||||
self.instruments_ = self._load_instruments(cfg)
|
|
||||||
price_field = cfg.get_value("model/stat_model_price", "close")
|
|
||||||
|
|
||||||
self.backtest_ = None
|
|
||||||
self.rest_service_ = None
|
|
||||||
if App.instance().get_argument("backtest", False):
|
|
||||||
input_db = App.instance().get_argument("input_db", None)
|
|
||||||
output_db = App.instance().get_argument("output_db", None)
|
|
||||||
if not input_db or not output_db:
|
|
||||||
raise ValueError(
|
|
||||||
"--input_db and --output_db are required when --backtest is set"
|
|
||||||
)
|
|
||||||
self.backtest_ = PairSelectionBacktest(
|
|
||||||
config=cfg,
|
|
||||||
instruments=self.instruments_,
|
|
||||||
price_field=price_field,
|
|
||||||
input_db=input_db,
|
|
||||||
output_db=output_db,
|
|
||||||
)
|
|
||||||
return
|
|
||||||
|
|
||||||
self.engine_ = PairSelectionEngine(
|
|
||||||
config=cfg,
|
|
||||||
instruments=self.instruments_,
|
|
||||||
price_field=price_field,
|
|
||||||
)
|
|
||||||
|
|
||||||
self.rest_service_ = RestService(config_key="/api/REST")
|
|
||||||
self.rest_service_.add_handler("GET", "/data_quality", self._on_data_quality)
|
|
||||||
self.rest_service_.add_handler(
|
|
||||||
"GET", "/pair_selection", self._on_pair_selection
|
|
||||||
)
|
|
||||||
|
|
||||||
def _load_instruments(self, cfg: CvttAppConfig) -> List[ExchangeInstrument]:
|
|
||||||
instruments_cfg = cfg.get_value("instruments", [])
|
|
||||||
instruments: List[ExchangeInstrument] = []
|
|
||||||
assert len(instruments_cfg) >= 2, "at least two instruments required"
|
|
||||||
for item in instruments_cfg:
|
|
||||||
if isinstance(item, str):
|
|
||||||
parts = item.split(":", 1)
|
|
||||||
if len(parts) != 2:
|
|
||||||
raise ValueError(f"invalid instrument format: {item}")
|
|
||||||
exch_acct, instrument_id = parts
|
|
||||||
elif isinstance(item, dict):
|
|
||||||
exch_acct = item.get("exch_acct", "")
|
|
||||||
instrument_id = item.get("instrument_id", "")
|
|
||||||
if not exch_acct or not instrument_id:
|
|
||||||
raise ValueError(f"invalid instrument config: {item}")
|
|
||||||
else:
|
|
||||||
raise ValueError(f"unsupported instrument entry: {item}")
|
|
||||||
|
|
||||||
exch_inst = ExchangeAccounts.instance().get_exchange_instrument(
|
|
||||||
exch_acct=exch_acct, instrument_id=instrument_id
|
|
||||||
)
|
|
||||||
assert (
|
|
||||||
exch_inst is not None
|
|
||||||
), f"no ExchangeInstrument for {exch_acct}:{instrument_id}"
|
|
||||||
exch_inst.user_data_["exch_acct"] = exch_acct
|
|
||||||
instruments.append(exch_inst)
|
|
||||||
return instruments
|
|
||||||
|
|
||||||
async def run(self) -> None:
|
|
||||||
if App.instance().get_argument("backtest", False):
|
|
||||||
if self.backtest_ is None:
|
|
||||||
raise RuntimeError("backtest runner not initialized")
|
|
||||||
self.backtest_.run()
|
|
||||||
return
|
|
||||||
oneshot = App.instance().get_argument("oneshot", False)
|
|
||||||
while True:
|
|
||||||
await self.engine_.run_once()
|
|
||||||
if oneshot:
|
|
||||||
break
|
|
||||||
sleep_for = self.engine_.sleep_seconds_until_next_cycle()
|
|
||||||
await asyncio.sleep(sleep_for)
|
|
||||||
|
|
||||||
async def _on_data_quality(self, request: web.Request) -> web.Response:
|
|
||||||
fmt = request.query.get("format", "html").lower()
|
|
||||||
quality = self.engine_.quality_dicts()
|
|
||||||
if fmt == "json":
|
|
||||||
return web.json_response(quality)
|
|
||||||
return web.Response(
|
|
||||||
text=HtmlRenderer.render_data_quality(quality), content_type="text/html"
|
|
||||||
)
|
|
||||||
|
|
||||||
async def _on_pair_selection(self, request: web.Request) -> web.Response:
|
|
||||||
fmt = request.query.get("format", "html").lower()
|
|
||||||
pairs = self.engine_.pair_dicts()
|
|
||||||
if fmt == "json":
|
|
||||||
return web.json_response(pairs)
|
|
||||||
return web.Response(
|
|
||||||
text=HtmlRenderer.render_pairs(pairs), content_type="text/html"
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
App()
|
|
||||||
CvttAppConfig()
|
|
||||||
PairSelector()
|
|
||||||
App.instance().run()
|
|
||||||
@@ -1,138 +0,0 @@
|
|||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from typing import Any, Dict, List
|
|
||||||
|
|
||||||
|
|
||||||
from cvttpy_tools.app import App
|
|
||||||
from cvttpy_tools.base import NamedObject
|
|
||||||
from cvttpy_tools.config import CvttAppConfig
|
|
||||||
|
|
||||||
|
|
||||||
class HtmlRenderer(NamedObject):
|
|
||||||
def __init__(self) -> None:
|
|
||||||
pass
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def render_data_quality(quality: List[Dict[str, Any]]) -> str:
|
|
||||||
rows = "".join(
|
|
||||||
f"<tr>"
|
|
||||||
f"<td>{q.get('instrument','')}</td>"
|
|
||||||
f"<td>{q.get('record_count','')}</td>"
|
|
||||||
f"<td>{q.get('latest_tstamp','')}</td>"
|
|
||||||
f"<td>{q.get('status','')}</td>"
|
|
||||||
f"<td>{q.get('reason','')}</td>"
|
|
||||||
f"</tr>"
|
|
||||||
for q in sorted(quality, key=lambda x: str(x.get("instrument", "")))
|
|
||||||
)
|
|
||||||
return f"""
|
|
||||||
<!DOCTYPE html>
|
|
||||||
<html>
|
|
||||||
<head>
|
|
||||||
<meta charset='utf-8'/>
|
|
||||||
<title>Data Quality</title>
|
|
||||||
<style>
|
|
||||||
body {{ font-family: Arial, sans-serif; margin: 20px; }}
|
|
||||||
table {{ border-collapse: collapse; width: 100%; }}
|
|
||||||
th, td {{ border: 1px solid #ccc; padding: 8px; text-align: left; }}
|
|
||||||
th {{ background: #f2f2f2; }}
|
|
||||||
</style>
|
|
||||||
</head>
|
|
||||||
<body>
|
|
||||||
<h2>Data Quality</h2>
|
|
||||||
<table>
|
|
||||||
<thead>
|
|
||||||
<tr><th>Instrument</th><th>Records</th><th>Latest</th><th>Status</th><th>Reason</th></tr>
|
|
||||||
</thead>
|
|
||||||
<tbody>{rows}</tbody>
|
|
||||||
</table>
|
|
||||||
</body>
|
|
||||||
</html>
|
|
||||||
"""
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def render_pairs(pairs: Dict[str, Dict[str, Any]]) -> str:
|
|
||||||
if not pairs:
|
|
||||||
body = "<p>No pairs available. Check data quality and try again.</p>"
|
|
||||||
else:
|
|
||||||
body_rows = []
|
|
||||||
for pair_name, p in pairs.items():
|
|
||||||
body_rows.append(
|
|
||||||
"<tr>"
|
|
||||||
f"<td>{pair_name}</td>"
|
|
||||||
f"<td data-value='{p.get('rank_eg',0)}'>{p.get('rank_eg','')}</td>"
|
|
||||||
f"<td data-value='{p.get('rank_adf',0)}'>{p.get('rank_adf','')}</td>"
|
|
||||||
f"<td data-value='{p.get('rank_j',0)}'>{p.get('rank_j','')}</td>"
|
|
||||||
f"<td data-value='{p.get('pvalue_eg','')}'>{p.get('pvalue_eg','')}</td>"
|
|
||||||
f"<td data-value='{p.get('pvalue_adf','')}'>{p.get('pvalue_adf','')}</td>"
|
|
||||||
f"<td data-value='{p.get('pvalue_j','')}'>{p.get('pvalue_j','')}</td>"
|
|
||||||
"</tr>"
|
|
||||||
)
|
|
||||||
body = "\n".join(body_rows)
|
|
||||||
|
|
||||||
return f"""
|
|
||||||
<!DOCTYPE html>
|
|
||||||
<html>
|
|
||||||
<head>
|
|
||||||
<meta charset='utf-8'/>
|
|
||||||
<title>Pair Selection</title>
|
|
||||||
<style>
|
|
||||||
body {{ font-family: Arial, sans-serif; margin: 20px; }}
|
|
||||||
table {{ border-collapse: collapse; width: 100%; }}
|
|
||||||
th, td {{ border: 1px solid #ccc; padding: 8px; text-align: left; }}
|
|
||||||
th.sortable {{ cursor: pointer; background: #f2f2f2; }}
|
|
||||||
</style>
|
|
||||||
</head>
|
|
||||||
<body>
|
|
||||||
<h2>Pair Selection</h2>
|
|
||||||
<table id="pairs-table">
|
|
||||||
<thead>
|
|
||||||
<tr>
|
|
||||||
<th>Pair</th>
|
|
||||||
<th class="sortable" data-type="num">Rank-EG</th>
|
|
||||||
<th class="sortable" data-type="num">Rank-ADF</th>
|
|
||||||
<th class="sortable" data-type="num">Rank-J</th>
|
|
||||||
<th>EG p-value</th>
|
|
||||||
<th>ADF p-value</th>
|
|
||||||
<th>Johansen pseudo p</th>
|
|
||||||
</tr>
|
|
||||||
</thead>
|
|
||||||
<tbody>
|
|
||||||
{body}
|
|
||||||
</tbody>
|
|
||||||
</table>
|
|
||||||
<script>
|
|
||||||
(function() {{
|
|
||||||
const table = document.getElementById('pairs-table');
|
|
||||||
if (!table) return;
|
|
||||||
const getValue = (cell) => {{
|
|
||||||
const val = cell.getAttribute('data-value');
|
|
||||||
const num = parseFloat(val);
|
|
||||||
return isNaN(num) ? val : num;
|
|
||||||
}};
|
|
||||||
const toggleSort = (index, isNumeric) => {{
|
|
||||||
const tbody = table.querySelector('tbody');
|
|
||||||
const rows = Array.from(tbody.querySelectorAll('tr'));
|
|
||||||
const th = table.querySelectorAll('th')[index];
|
|
||||||
const dir = th.getAttribute('data-dir') === 'asc' ? 'desc' : 'asc';
|
|
||||||
th.setAttribute('data-dir', dir);
|
|
||||||
rows.sort((a, b) => {{
|
|
||||||
const va = getValue(a.children[index]);
|
|
||||||
const vb = getValue(b.children[index]);
|
|
||||||
if (isNumeric && !isNaN(va) && !isNaN(vb)) {{
|
|
||||||
return dir === 'asc' ? va - vb : vb - va;
|
|
||||||
}}
|
|
||||||
return dir === 'asc'
|
|
||||||
? String(va).localeCompare(String(vb))
|
|
||||||
: String(vb).localeCompare(String(va));
|
|
||||||
}});
|
|
||||||
tbody.innerHTML = '';
|
|
||||||
rows.forEach(r => tbody.appendChild(r));
|
|
||||||
}};
|
|
||||||
table.querySelectorAll('th.sortable').forEach((th, idx) => {{
|
|
||||||
th.addEventListener('click', () => toggleSort(idx, th.dataset.type === 'num'));
|
|
||||||
}});
|
|
||||||
}})();
|
|
||||||
</script>
|
|
||||||
</body>
|
|
||||||
</html>
|
|
||||||
"""
|
|
||||||
@@ -1,169 +0,0 @@
|
|||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
import asyncio
|
|
||||||
from typing import Callable, Coroutine, Dict, List
|
|
||||||
import aiohttp.web as web
|
|
||||||
|
|
||||||
from cvttpy_tools.app import App
|
|
||||||
from cvttpy_tools.config import Config
|
|
||||||
from cvttpy_tools.base import NamedObject
|
|
||||||
from cvttpy_tools.config import CvttAppConfig
|
|
||||||
from cvttpy_tools.logger import Log
|
|
||||||
from cvttpy_tools.settings.cvtt_types import BookIdT
|
|
||||||
from cvttpy_tools.web.rest_service import RestService
|
|
||||||
|
|
||||||
# ---
|
|
||||||
from cvttpy_trading.trading.instrument import ExchangeInstrument
|
|
||||||
from cvttpy_trading.trading.mkt_data.md_summary import MdTradesAggregate
|
|
||||||
from cvttpy_trading.trading.exchange_config import ExchangeAccounts
|
|
||||||
# ---
|
|
||||||
from pairs_trading.lib.live.mkt_data_client import CvttRestMktDataClient
|
|
||||||
|
|
||||||
'''
|
|
||||||
config http://cloud16.cvtt.vpn/apps/pairs_trading
|
|
||||||
'''
|
|
||||||
|
|
||||||
HistMdCbT = Callable[[List[MdTradesAggregate]], Coroutine]
|
|
||||||
UpdateMdCbT = Callable[[MdTradesAggregate], Coroutine]
|
|
||||||
|
|
||||||
class PairTrader(NamedObject):
|
|
||||||
config_: CvttAppConfig
|
|
||||||
instruments_: List[ExchangeInstrument]
|
|
||||||
book_id_: BookIdT
|
|
||||||
|
|
||||||
live_strategy_: "PtLiveStrategy" #type: ignore
|
|
||||||
ti_sender_: "TradingInstructionsSender" #type: ignore
|
|
||||||
pricer_client_: CvttRestMktDataClient
|
|
||||||
rest_service_: RestService
|
|
||||||
|
|
||||||
latest_history_: Dict[ExchangeInstrument, List[MdTradesAggregate]]
|
|
||||||
|
|
||||||
def __init__(self) -> None:
|
|
||||||
self.instruments_ = []
|
|
||||||
self.latest_history_ = {}
|
|
||||||
|
|
||||||
App.instance().add_cmdline_arg(
|
|
||||||
"--instrument_A",
|
|
||||||
type=str,
|
|
||||||
required=True,
|
|
||||||
help=(
|
|
||||||
" Instrument A in pair (e.g., COINBASE_AT:PAIR-BTC-USD)"
|
|
||||||
),
|
|
||||||
)
|
|
||||||
App.instance().add_cmdline_arg(
|
|
||||||
"--instrument_B",
|
|
||||||
type=str,
|
|
||||||
required=True,
|
|
||||||
help=(
|
|
||||||
" Instrument B in pair (e.g., COINBASE_AT:PAIR-ETH-USD)"
|
|
||||||
),
|
|
||||||
)
|
|
||||||
|
|
||||||
App.instance().add_cmdline_arg(
|
|
||||||
"--book_id",
|
|
||||||
type=str,
|
|
||||||
required=True,
|
|
||||||
help="Book ID"
|
|
||||||
)
|
|
||||||
App.instance().add_call(App.Stage.Config, self._on_config())
|
|
||||||
App.instance().add_call(App.Stage.Run, self.run())
|
|
||||||
|
|
||||||
async def _on_config(self) -> None:
|
|
||||||
self.config_ = CvttAppConfig.instance()
|
|
||||||
self.book_id_ = App.instance().get_argument(name="book_id")
|
|
||||||
|
|
||||||
# ------- PARSE INSTRUMENTS -------
|
|
||||||
instr_list: List[str] = []
|
|
||||||
instr_str = App.instance().get_argument("instrument_A", "")
|
|
||||||
assert instr_str != "", "Missing insrument A"
|
|
||||||
instr_list.append(instr_str)
|
|
||||||
instr_str = App.instance().get_argument("instrument_B", "")
|
|
||||||
assert instr_str != "", "Missing insrument B"
|
|
||||||
instr_list.append(instr_str)
|
|
||||||
|
|
||||||
for instr in instr_list:
|
|
||||||
instr_parts = instr.split(":")
|
|
||||||
if len(instr_parts) != 2:
|
|
||||||
raise ValueError(f"Invalid pair format: {instr}")
|
|
||||||
exch_acct = instr_parts[0]
|
|
||||||
instrument_id = instr_parts[1]
|
|
||||||
exch_inst = ExchangeAccounts.instance().get_exchange_instrument(exch_acct=exch_acct, instrument_id=instrument_id)
|
|
||||||
assert exch_inst is not None, f"No ExchangeInstrument for {instr}"
|
|
||||||
exch_inst.user_data_["exch_acct"] = exch_acct
|
|
||||||
self.instruments_.append(exch_inst)
|
|
||||||
|
|
||||||
Log.info(f"{self.fname()} Instruments: {self.instruments_[0].details_short()} <==> {self.instruments_[1].details_short()}")
|
|
||||||
|
|
||||||
# ------- CREATE STRATEGY -------
|
|
||||||
from pairs_trading.lib.pt_strategy.live.live_strategy import PtLiveStrategy
|
|
||||||
strategy_config = CvttAppConfig.instance() #self.config_.get_subconfig("strategy_config", Config({}))
|
|
||||||
self.live_strategy_ = PtLiveStrategy(
|
|
||||||
config=strategy_config,
|
|
||||||
pairs_trader=self,
|
|
||||||
)
|
|
||||||
Log.info(f"{self.fname()} Strategy created: {self.live_strategy_}")
|
|
||||||
model_name = self.config_.get_value("model/name", "?model/name?")
|
|
||||||
self.config_.set_value("strategy_id", f"{self.live_strategy_.__class__.__name__}:{model_name}")
|
|
||||||
|
|
||||||
# # ------- CREATE PRICER CLIENT -------
|
|
||||||
self.pricer_client_ = CvttRestMktDataClient(config=self.config_)
|
|
||||||
Log.info(f"{self.fname()} MD client created: {self.pricer_client_}")
|
|
||||||
|
|
||||||
# ------- CREATE TRADER CLIENT -------
|
|
||||||
from pairs_trading.lib.live.ti_sender import TradingInstructionsSender
|
|
||||||
self.ti_sender_ = TradingInstructionsSender(config=self.config_, pairs_trader=self)
|
|
||||||
Log.info(f"{self.fname()} TI sender created: {self.ti_sender_}")
|
|
||||||
|
|
||||||
# # ------- CREATE REST SERVER -------
|
|
||||||
self.rest_service_ = RestService(
|
|
||||||
config_key=f"/api/REST"
|
|
||||||
)
|
|
||||||
|
|
||||||
# --- Strategy Handlers
|
|
||||||
self.rest_service_.add_handler(
|
|
||||||
method="POST",
|
|
||||||
url="/api/strategy",
|
|
||||||
handler=self._on_api_request,
|
|
||||||
)
|
|
||||||
|
|
||||||
async def subscribe_md(self) -> None:
|
|
||||||
from functools import partial
|
|
||||||
for exch_inst in self.instruments_:
|
|
||||||
exch_acct = exch_inst.user_data_.get("exch_acct", "?exch_acct?")
|
|
||||||
instrument_id = exch_inst.instrument_id()
|
|
||||||
|
|
||||||
await self.pricer_client_.add_subscription(
|
|
||||||
exch_acct=exch_acct,
|
|
||||||
instrument_id=instrument_id,
|
|
||||||
interval_sec=self.live_strategy_.interval_sec(),
|
|
||||||
history_depth_sec=self.live_strategy_.history_depth_sec(),
|
|
||||||
callback=partial(self._on_md_summary, exch_inst=exch_inst)
|
|
||||||
)
|
|
||||||
|
|
||||||
async def _on_md_summary(self, history: List[MdTradesAggregate], exch_inst: ExchangeInstrument) -> None:
|
|
||||||
Log.info(f"{self.fname()}: got {exch_inst.details_short()} data")
|
|
||||||
self.latest_history_[exch_inst] = history
|
|
||||||
if len(self.latest_history_) == 2:
|
|
||||||
from itertools import chain
|
|
||||||
all_aggrs = sorted(list(chain.from_iterable(self.latest_history_.values())), key=lambda X: X.aggr_time_ns_)
|
|
||||||
|
|
||||||
await self.live_strategy_.on_mkt_data_hist_snapshot(hist_aggr=all_aggrs)
|
|
||||||
self.latest_history_ = {}
|
|
||||||
|
|
||||||
async def _on_api_request(self, request: web.Request) -> web.Response:
|
|
||||||
# TODO choose pair
|
|
||||||
# TODO confirm chosen pair (after selection is implemented)
|
|
||||||
return web.Response() # TODO API request handler implementation
|
|
||||||
|
|
||||||
|
|
||||||
async def run(self) -> None:
|
|
||||||
Log.info(f"{self.fname()} ...")
|
|
||||||
while True:
|
|
||||||
await asyncio.sleep(0.1)
|
|
||||||
pass
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
App()
|
|
||||||
CvttAppConfig()
|
|
||||||
PairTrader()
|
|
||||||
App.instance().run()
|
|
||||||
-186
@@ -1,186 +0,0 @@
|
|||||||
#!/usr/bin/env bash
|
|
||||||
|
|
||||||
# ---------------- Settings
|
|
||||||
|
|
||||||
repo=git@cloud21.cvtt.vpn:/works/git/cvtt2/research/pairs_trading.git
|
|
||||||
|
|
||||||
dist_root=/home/cvttdist/software/cvtt2
|
|
||||||
dist_user=cvttdist
|
|
||||||
dist_host="cloud21.cvtt.vpn"
|
|
||||||
dist_ssh_port="22"
|
|
||||||
|
|
||||||
dist_locations="cloud21.cvtt.vpn:22 hs01.cvtt.vpn:22"
|
|
||||||
version_file="VERSION"
|
|
||||||
|
|
||||||
prj=pairs_trading
|
|
||||||
brnch=master
|
|
||||||
interactive=N
|
|
||||||
|
|
||||||
# ---------------- Settings
|
|
||||||
|
|
||||||
# ---------------- cmdline
|
|
||||||
|
|
||||||
usage() {
|
|
||||||
echo "Usage: $0 [-b <branch (master)> -i (interactive)"
|
|
||||||
exit 1
|
|
||||||
}
|
|
||||||
|
|
||||||
while getopts "b:i" opt; do
|
|
||||||
case ${opt} in
|
|
||||||
b )
|
|
||||||
brnch=$OPTARG
|
|
||||||
;;
|
|
||||||
i )
|
|
||||||
interactive=Y
|
|
||||||
;;
|
|
||||||
\? )
|
|
||||||
echo "Invalid option: -$OPTARG" >&2
|
|
||||||
usage
|
|
||||||
;;
|
|
||||||
: )
|
|
||||||
echo "Option -$OPTARG requires an argument." >&2
|
|
||||||
usage
|
|
||||||
;;
|
|
||||||
esac
|
|
||||||
done
|
|
||||||
# ---------------- cmdline
|
|
||||||
|
|
||||||
confirm() {
|
|
||||||
if [ "${interactive}" == "Y" ]; then
|
|
||||||
echo "--------------------------------"
|
|
||||||
echo -n "Press <Enter> to continue" && read
|
|
||||||
fi
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
if [ "${interactive}" == "Y" ]; then
|
|
||||||
echo -n "Enter project [${prj}]: "
|
|
||||||
read project
|
|
||||||
if [ "${project}" == "" ]
|
|
||||||
then
|
|
||||||
project=${prj}
|
|
||||||
fi
|
|
||||||
else
|
|
||||||
project=${prj}
|
|
||||||
fi
|
|
||||||
|
|
||||||
# repo=${git_repo_arr[${project}]}
|
|
||||||
if [ -z ${repo} ]; then
|
|
||||||
echo "ERROR: Project repository for ${project} not found"
|
|
||||||
exit -1
|
|
||||||
fi
|
|
||||||
echo "Project repo: ${repo}"
|
|
||||||
|
|
||||||
if [ "${interactive}" == "Y" ]; then
|
|
||||||
echo -n "Enter branch to build release from [${brnch}]: "
|
|
||||||
read branch
|
|
||||||
if [ "${branch}" == "" ]
|
|
||||||
then
|
|
||||||
branch=${brnch}
|
|
||||||
fi
|
|
||||||
else
|
|
||||||
branch=${brnch}
|
|
||||||
fi
|
|
||||||
|
|
||||||
tmp_dir=$(mktemp -d)
|
|
||||||
function cleanup {
|
|
||||||
cd ${HOME}
|
|
||||||
rm -rf ${tmp_dir}
|
|
||||||
}
|
|
||||||
trap cleanup EXIT
|
|
||||||
|
|
||||||
|
|
||||||
prj_dir="${tmp_dir}/${prj}"
|
|
||||||
|
|
||||||
cmd_arr=()
|
|
||||||
Cmd="git clone ${repo} ${prj_dir}"
|
|
||||||
cmd_arr+=("${Cmd}")
|
|
||||||
|
|
||||||
Cmd="cd ${prj_dir}"
|
|
||||||
cmd_arr+=("${Cmd}")
|
|
||||||
|
|
||||||
if [ "${interactive}" == "Y" ]; then
|
|
||||||
echo "------------------------------------"
|
|
||||||
echo "The following commands will execute:"
|
|
||||||
echo "------------------------------------"
|
|
||||||
for cmd in "${cmd_arr[@]}"
|
|
||||||
do
|
|
||||||
echo ${cmd}
|
|
||||||
done
|
|
||||||
fi
|
|
||||||
|
|
||||||
confirm
|
|
||||||
|
|
||||||
for cmd in "${cmd_arr[@]}"
|
|
||||||
do
|
|
||||||
echo ${cmd} && eval ${cmd}
|
|
||||||
done
|
|
||||||
|
|
||||||
Cmd="git checkout ${branch}"
|
|
||||||
echo ${Cmd} && eval ${Cmd}
|
|
||||||
if [ "${?}" != "0" ]; then
|
|
||||||
echo "ERROR: Branch ${branch} is not found"
|
|
||||||
cd ${HOME} && rm -rf ${tmp_dir}
|
|
||||||
exit -1
|
|
||||||
fi
|
|
||||||
|
|
||||||
|
|
||||||
release_version=$(cat ${version_file} | awk -F',' '{print $1}')
|
|
||||||
whats_new=$(cat ${version_file} | awk -F',' '{print $2}')
|
|
||||||
|
|
||||||
|
|
||||||
echo "--------------------------------"
|
|
||||||
echo "Version file: ${version_file}"
|
|
||||||
echo "Release version: ${release_version}"
|
|
||||||
|
|
||||||
confirm
|
|
||||||
|
|
||||||
version_tag="v${release_version}"
|
|
||||||
if [ "$(git tag -l "${version_tag}")" != "" ]; then
|
|
||||||
version_tag="${version_tag}.$(date +%Y%m%d_%H%M)"
|
|
||||||
fi
|
|
||||||
version_comment="'${version_tag} ${project} ${branch} $(date +%Y-%m-%d)\n${whats_new}'"
|
|
||||||
|
|
||||||
cmd_arr=()
|
|
||||||
Cmd="git tag -a ${version_tag} -m ${version_comment}"
|
|
||||||
cmd_arr+=("${Cmd}")
|
|
||||||
|
|
||||||
Cmd="git push origin --tags"
|
|
||||||
cmd_arr+=("${Cmd}")
|
|
||||||
|
|
||||||
Cmd="rm -rf .git"
|
|
||||||
cmd_arr+=("${Cmd}")
|
|
||||||
|
|
||||||
SourceLoc=../${project}
|
|
||||||
|
|
||||||
dist_path="${dist_root}/${project}/${release_version}"
|
|
||||||
|
|
||||||
for dist_loc in ${dist_locations}; do
|
|
||||||
dhp=(${dist_loc//:/ })
|
|
||||||
dist_host=${dhp[0]}
|
|
||||||
dist_port=${dhp[1]}
|
|
||||||
Cmd="rsync -avzh"
|
|
||||||
Cmd="${Cmd} --rsync-path=\"mkdir -p ${dist_path}"
|
|
||||||
Cmd="${Cmd} && rsync\" -e \"ssh -p ${dist_ssh_port}\""
|
|
||||||
Cmd="${Cmd} $SourceLoc ${dist_user}@${dist_host}:${dist_path}/"
|
|
||||||
cmd_arr+=("${Cmd}")
|
|
||||||
done
|
|
||||||
|
|
||||||
if [ "${interactive}" == "Y" ]; then
|
|
||||||
echo "------------------------------------"
|
|
||||||
echo "The following commands will execute:"
|
|
||||||
echo "------------------------------------"
|
|
||||||
for cmd in "${cmd_arr[@]}"
|
|
||||||
do
|
|
||||||
echo ${cmd}
|
|
||||||
done
|
|
||||||
fi
|
|
||||||
|
|
||||||
confirm
|
|
||||||
|
|
||||||
for cmd in "${cmd_arr[@]}"
|
|
||||||
do
|
|
||||||
pwd && echo ${cmd} && eval ${cmd}
|
|
||||||
done
|
|
||||||
|
|
||||||
echo "$0 Done ${project} ${release_version}"
|
|
||||||
@@ -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": "pairs_trading.lib.pt_strategy.models.OLSModel",
|
|
||||||
"model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.ExpandingWindowDataPolicy",
|
|
||||||
|
|
||||||
# ====== Stop Conditions ======
|
|
||||||
"stop_close_conditions": {
|
|
||||||
"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": "pairs_trading.lib.pt_strategy.models.OLSModel",
|
|
||||||
|
|
||||||
# "model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.EGOptimizedWndDataPolicy",
|
|
||||||
# "model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.ADFOptimizedWndDataPolicy",
|
|
||||||
"model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.JohansenOptdWndDataPolicy",
|
|
||||||
"min_training_size": 60,
|
|
||||||
"max_training_size": 150,
|
|
||||||
|
|
||||||
# ====== 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": "pairs_trading.lib.pt_strategy.models.OLSModel",
|
|
||||||
|
|
||||||
"training_size": 120,
|
|
||||||
"model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.RollingWindowDataPolicy",
|
|
||||||
# "model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.OptimizedWindowDataPolicy",
|
|
||||||
# "min_training_size": 60,
|
|
||||||
# "max_training_size": 150,
|
|
||||||
|
|
||||||
# ====== 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": "pairs_trading.lib.pt_strategy.models.VECMModel",
|
|
||||||
|
|
||||||
"training_size": 120,
|
|
||||||
"model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.RollingWindowDataPolicy",
|
|
||||||
# "model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.OptimizedWindowDataPolicy",
|
|
||||||
# "min_training_size": 60,
|
|
||||||
# "max_training_size": 150,
|
|
||||||
|
|
||||||
# ====== 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",
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,46 +0,0 @@
|
|||||||
{
|
|
||||||
"refdata": {
|
|
||||||
"assets": @inc=http://@env{CONFIG_SERVICE}/refdata/assets
|
|
||||||
, "instruments": @inc=http://@env{CONFIG_SERVICE}/refdata/instruments
|
|
||||||
, "exchange_instruments": @inc=http://@env{CONFIG_SERVICE}/refdata/exchange_instruments
|
|
||||||
, "dynamic_instrument_exchanges": ["ALPACA"]
|
|
||||||
, "exchanges": @inc=http://@env{CONFIG_SERVICE}/refdata/exchanges
|
|
||||||
},
|
|
||||||
"market_data_loading": {
|
|
||||||
"CRYPTO": {
|
|
||||||
"data_directory": "./data/crypto",
|
|
||||||
"db_table_name": "md_1min_bars",
|
|
||||||
"instrument_id_pfx": "PAIR-",
|
|
||||||
},
|
|
||||||
"EQUITY": {
|
|
||||||
"data_directory": "./data/equity",
|
|
||||||
"db_table_name": "md_1min_bars",
|
|
||||||
"instrument_id_pfx": "STOCK-",
|
|
||||||
}
|
|
||||||
},
|
|
||||||
# ====== Funding ======
|
|
||||||
"funding_per_pair": 2000.0,
|
|
||||||
|
|
||||||
# ====== Model =======
|
|
||||||
"model": @inc=http://@env{CONFIG_SERVICE}/apps/common/models/@env{MODEL_CONFIG}
|
|
||||||
|
|
||||||
# ====== Trading =======
|
|
||||||
"execution_price": {
|
|
||||||
"column": "vwap",
|
|
||||||
"shift": 1,
|
|
||||||
},
|
|
||||||
# ====== 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/vecm-opt.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"
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,56 +0,0 @@
|
|||||||
{
|
|
||||||
# "refdata": {
|
|
||||||
# "assets": @inc=http://@env{CONFIG_SERVICE}/refdata/assets
|
|
||||||
# , "instruments": @inc=http://@env{CONFIG_SERVICE}/refdata/instruments
|
|
||||||
# , "exchange_instruments": @inc=http://@env{CONFIG_SERVICE}/refdata/exchange_instruments
|
|
||||||
# , "dynamic_instrument_exchanges": ["ALPACA"]
|
|
||||||
# , "exchanges": @inc=http://@env{CONFIG_SERVICE}/refdata/exchanges
|
|
||||||
# },
|
|
||||||
# "market_data_loading": {
|
|
||||||
# "CRYPTO": {
|
|
||||||
# "data_directory": "./data/crypto",
|
|
||||||
# "db_table_name": "md_1min_bars",
|
|
||||||
# "instrument_id_pfx": "PAIR-",
|
|
||||||
# },
|
|
||||||
# "EQUITY": {
|
|
||||||
# "data_directory": "./data/equity",
|
|
||||||
# "db_table_name": "md_1min_bars",
|
|
||||||
# "instrument_id_pfx": "STOCK-",
|
|
||||||
# }
|
|
||||||
# },
|
|
||||||
|
|
||||||
# # ====== Funding ======
|
|
||||||
# "funding_per_pair": 2000.0,
|
|
||||||
|
|
||||||
# ====== 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": "pairs_trading.lib.pt_strategy.models.VECMModel",
|
|
||||||
|
|
||||||
# "training_size": 120,
|
|
||||||
# "model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.RollingWindowDataPolicy",
|
|
||||||
"model_data_policy_class": "pairs_trading.lib.pt_strategy.model_data_policy.ADFOptimizedWndDataPolicy",
|
|
||||||
"min_training_size": 60,
|
|
||||||
"max_training_size": 150,
|
|
||||||
|
|
||||||
# # ====== Stop Conditions ======
|
|
||||||
# "stop_close_conditions": {
|
|
||||||
# "profit": 2.0,
|
|
||||||
# "loss": -0.5
|
|
||||||
# }
|
|
||||||
|
|
||||||
# # ====== 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",
|
|
||||||
# }
|
|
||||||
}
|
|
||||||
@@ -1,277 +0,0 @@
|
|||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
import asyncio
|
|
||||||
from typing import Dict, Any, List, Optional, Set
|
|
||||||
|
|
||||||
import requests
|
|
||||||
|
|
||||||
from cvttpy_tools.base import NamedObject
|
|
||||||
from cvttpy_tools.logger import Log
|
|
||||||
from cvttpy_tools.config import Config
|
|
||||||
from cvttpy_tools.timer import Timer
|
|
||||||
from cvttpy_tools.timeutils import NanosT, current_seconds
|
|
||||||
from cvttpy_tools.settings.cvtt_types import InstrumentIdT, IntervalSecT
|
|
||||||
from cvttpy_tools.web.rest_client import RESTSender
|
|
||||||
# ---
|
|
||||||
from cvttpy_trading.trading.instrument import ExchangeInstrument
|
|
||||||
from cvttpy_trading.trading.accounting.exch_account import ExchangeAccountNameT
|
|
||||||
from cvttpy_trading.trading.mkt_data.md_summary import MdTradesAggregate, MdSummary, MdSummaryCallbackT
|
|
||||||
from cvttpy_trading.trading.exchange_config import ExchangeAccounts
|
|
||||||
# ---
|
|
||||||
|
|
||||||
|
|
||||||
# class MdSummary(HistMdBar):
|
|
||||||
# def __init__(
|
|
||||||
# self,
|
|
||||||
# ts_ns: int,
|
|
||||||
# open: float,
|
|
||||||
# high: float,
|
|
||||||
# low: float,
|
|
||||||
# close: float,
|
|
||||||
# volume: float,
|
|
||||||
# vwap: float,
|
|
||||||
# num_trades: int,
|
|
||||||
# ):
|
|
||||||
# super().__init__(ts=ts_ns)
|
|
||||||
# self.open_ = open
|
|
||||||
# self.high_ = high
|
|
||||||
# self.low_ = low
|
|
||||||
# self.close_ = close
|
|
||||||
# self.volume_ = volume
|
|
||||||
# self.vwap_ = vwap
|
|
||||||
# self.num_trades_ = num_trades
|
|
||||||
|
|
||||||
# @classmethod
|
|
||||||
# def from_REST_response(cls, response: requests.Response) -> List[MdSummary]:
|
|
||||||
# res: List[MdSummary] = []
|
|
||||||
# jresp = response.json()
|
|
||||||
# hist_data = jresp.get("historical_data", [])
|
|
||||||
# for hd in hist_data:
|
|
||||||
# res.append(
|
|
||||||
# MdSummary(
|
|
||||||
# ts_ns=hd["time_ns"],
|
|
||||||
# open=hd["open"],
|
|
||||||
# high=hd["high"],
|
|
||||||
# low=hd["low"],
|
|
||||||
# close=hd["close"],
|
|
||||||
# volume=hd["volume"],
|
|
||||||
# vwap=hd["vwap"],
|
|
||||||
# num_trades=hd["num_trades"],
|
|
||||||
# )
|
|
||||||
# )
|
|
||||||
# return res
|
|
||||||
|
|
||||||
# def create_md_trades_aggregate(
|
|
||||||
# self,
|
|
||||||
# exch_acct: ExchangeAccountNameT,
|
|
||||||
# exch_inst: ExchangeInstrument,
|
|
||||||
# interval_sec: IntervalSecT,
|
|
||||||
# ) -> MdTradesAggregate:
|
|
||||||
# res = MdTradesAggregate(
|
|
||||||
# exch_acct=exch_acct,
|
|
||||||
# exch_inst=exch_inst,
|
|
||||||
# interval_ns=interval_sec * NanoPerSec,
|
|
||||||
# )
|
|
||||||
# res.set(mdbar=self)
|
|
||||||
# return res
|
|
||||||
|
|
||||||
|
|
||||||
# MdSummaryCallbackT = Callable[[List[MdTradesAggregate]], Coroutine]
|
|
||||||
|
|
||||||
|
|
||||||
class MdSummaryCollector(NamedObject):
|
|
||||||
sender_: RESTSender
|
|
||||||
exch_acct_: ExchangeAccountNameT
|
|
||||||
exch_inst_: ExchangeInstrument
|
|
||||||
interval_sec_: IntervalSecT
|
|
||||||
history_depth_sec_: IntervalSecT
|
|
||||||
|
|
||||||
history_: List[MdTradesAggregate]
|
|
||||||
|
|
||||||
callbacks_: List[MdSummaryCallbackT]
|
|
||||||
timer_: Optional[Timer]
|
|
||||||
|
|
||||||
def __init__(
|
|
||||||
self,
|
|
||||||
sender: RESTSender,
|
|
||||||
exch_acct: ExchangeAccountNameT,
|
|
||||||
instrument_id: InstrumentIdT,
|
|
||||||
interval_sec: IntervalSecT,
|
|
||||||
history_depth_sec: IntervalSecT,
|
|
||||||
) -> None:
|
|
||||||
self.sender_ = sender
|
|
||||||
self.exch_acct_ = exch_acct
|
|
||||||
|
|
||||||
exch_inst = ExchangeAccounts.instance().get_exchange_instrument(
|
|
||||||
exch_acct=exch_acct, instrument_id=instrument_id
|
|
||||||
)
|
|
||||||
assert exch_inst is not None, f"Unable to find Exchange instrument for {exch_acct}/{instrument_id}"
|
|
||||||
self.exch_inst_ = exch_inst
|
|
||||||
self.interval_sec_ = interval_sec
|
|
||||||
self.history_depth_sec_ = history_depth_sec
|
|
||||||
|
|
||||||
self.history_ = []
|
|
||||||
self.callbacks_ = []
|
|
||||||
self.timer_ = None
|
|
||||||
|
|
||||||
def add_callback(self, cb: MdSummaryCallbackT) -> None:
|
|
||||||
self.callbacks_.append(cb)
|
|
||||||
|
|
||||||
def __hash__(self):
|
|
||||||
return hash(
|
|
||||||
(
|
|
||||||
self.exch_acct_,
|
|
||||||
self.exch_inst_.instrument_id(),
|
|
||||||
self.interval_sec_,
|
|
||||||
self.history_depth_sec_,
|
|
||||||
)
|
|
||||||
)
|
|
||||||
|
|
||||||
def rqst_data(self) -> Dict[str, Any]:
|
|
||||||
return {
|
|
||||||
"exch_acct": self.exch_acct_,
|
|
||||||
"instrument_id": self.exch_inst_.instrument_id(),
|
|
||||||
"interval_sec": self.interval_sec_,
|
|
||||||
"history_depth_sec": self.history_depth_sec_,
|
|
||||||
}
|
|
||||||
|
|
||||||
def get_history(self) -> List[MdSummary]:
|
|
||||||
response: requests.Response = self.sender_.send_post(
|
|
||||||
endpoint="md_summary", post_body=self.rqst_data()
|
|
||||||
)
|
|
||||||
if response.status_code not in (200, 201):
|
|
||||||
Log.error(
|
|
||||||
f"{self.fname()}: Received error: {response.status_code} - {response.text}"
|
|
||||||
)
|
|
||||||
return []
|
|
||||||
return MdSummary.from_REST_response(response=response)
|
|
||||||
|
|
||||||
def get_last(self) -> Optional[MdSummary]:
|
|
||||||
Log.info(f"{self.fname()}: for {self.exch_inst_.details_short()}")
|
|
||||||
rqst_data = self.rqst_data()
|
|
||||||
rqst_data["history_depth_sec"] = self.interval_sec_ * 2
|
|
||||||
response: requests.Response = self.sender_.send_post(
|
|
||||||
endpoint="md_summary", post_body=rqst_data
|
|
||||||
)
|
|
||||||
if response.status_code not in (200, 201):
|
|
||||||
Log.error(
|
|
||||||
f"{self.fname()}: Received error: {response.status_code} - {response.text}"
|
|
||||||
)
|
|
||||||
return None
|
|
||||||
res = MdSummary.from_REST_response(response=response)
|
|
||||||
Log.info(f"DEBUG *** {self.exch_inst_.base_asset_id_}: {res[-1].tstamp_}")
|
|
||||||
return None if len(res) == 0 else res[-1]
|
|
||||||
|
|
||||||
def is_empty(self) -> bool:
|
|
||||||
return len(self.history_) == 0
|
|
||||||
|
|
||||||
async def start(self) -> None:
|
|
||||||
if self.timer_:
|
|
||||||
Log.error(f"{self.fname()}: Timer is already started")
|
|
||||||
return
|
|
||||||
mdsum_hist = self.get_history()
|
|
||||||
self.history_ = [
|
|
||||||
mdsum.create_md_trades_aggregate(
|
|
||||||
exch_acct=self.exch_acct_,
|
|
||||||
exch_inst=self.exch_inst_,
|
|
||||||
interval_sec=self.interval_sec_,
|
|
||||||
)
|
|
||||||
for mdsum in mdsum_hist
|
|
||||||
]
|
|
||||||
await self.run_callbacks()
|
|
||||||
self.set_timer()
|
|
||||||
|
|
||||||
def set_timer(self):
|
|
||||||
if self.timer_:
|
|
||||||
self.timer_.cancel()
|
|
||||||
start_in = self.next_load_time() - current_seconds()
|
|
||||||
self.timer_ = Timer(
|
|
||||||
start_in_sec=start_in,
|
|
||||||
func=self._load_new,
|
|
||||||
)
|
|
||||||
Log.info(f"{self.fname()} Timer for {self.exch_inst_.details_short()} is set to run in {start_in} sec")
|
|
||||||
|
|
||||||
def next_load_time(self) -> NanosT:
|
|
||||||
ALLOW_LAG_SEC = 1
|
|
||||||
curr_sec = int(current_seconds())
|
|
||||||
return (curr_sec - curr_sec % self.interval_sec_) + self.interval_sec_ + ALLOW_LAG_SEC
|
|
||||||
|
|
||||||
async def _load_new(self) -> None:
|
|
||||||
|
|
||||||
last: Optional[MdSummary] = self.get_last()
|
|
||||||
if not last:
|
|
||||||
Log.warning(f"{self.fname()}: did not get last update")
|
|
||||||
elif not self.is_empty() and last.ts_ns_ <= self.history_[-1].aggr_time_ns_:
|
|
||||||
Log.info(
|
|
||||||
f"{self.fname()}: Received {last}. Already Have: {self.history_[-1]}"
|
|
||||||
)
|
|
||||||
else:
|
|
||||||
self.history_.append(last.create_md_trades_aggregate(exch_acct=self.exch_acct_, exch_inst=self.exch_inst_, interval_sec=self.interval_sec_))
|
|
||||||
await self.run_callbacks()
|
|
||||||
self.set_timer()
|
|
||||||
|
|
||||||
async def run_callbacks(self) -> None:
|
|
||||||
[await cb(self.history_) for cb in self.callbacks_]
|
|
||||||
|
|
||||||
def stop(self) -> None:
|
|
||||||
if self.timer_:
|
|
||||||
self.timer_.cancel()
|
|
||||||
self.timer_ = None
|
|
||||||
|
|
||||||
|
|
||||||
class CvttRestMktDataClient(NamedObject):
|
|
||||||
config_: Config
|
|
||||||
sender_: RESTSender
|
|
||||||
collectors_: Set[MdSummaryCollector]
|
|
||||||
|
|
||||||
def __init__(self, config: Config) -> None:
|
|
||||||
self.config_ = config
|
|
||||||
base_url = self.config_.get_value("cvtt_base_url", default="")
|
|
||||||
assert base_url
|
|
||||||
self.sender_ = RESTSender(base_url=base_url)
|
|
||||||
self.collectors_ = set()
|
|
||||||
|
|
||||||
async def add_subscription(
|
|
||||||
self,
|
|
||||||
exch_acct: ExchangeAccountNameT,
|
|
||||||
instrument_id: InstrumentIdT,
|
|
||||||
interval_sec: IntervalSecT,
|
|
||||||
history_depth_sec: IntervalSecT,
|
|
||||||
callback: MdSummaryCallbackT,
|
|
||||||
) -> None:
|
|
||||||
mdsc = MdSummaryCollector(
|
|
||||||
sender=self.sender_,
|
|
||||||
exch_acct=exch_acct,
|
|
||||||
instrument_id=instrument_id,
|
|
||||||
interval_sec=interval_sec,
|
|
||||||
history_depth_sec=history_depth_sec,
|
|
||||||
)
|
|
||||||
mdsc.add_callback(callback)
|
|
||||||
self.collectors_.add(mdsc)
|
|
||||||
await mdsc.start()
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
config = Config(json_src={"cvtt_base_url": "http://cvtt-tester-01.cvtt.vpn:23456"})
|
|
||||||
# config = Config(json_src={"cvtt_base_url": "http://dev-server-02.cvtt.vpn:23456"})
|
|
||||||
|
|
||||||
async def _calback(history: List[MdTradesAggregate]) -> None:
|
|
||||||
Log.info(
|
|
||||||
f"MdSummary Hist Length is {len(history)}. Last summary: {history[-1] if len(history) > 0 else '[]'}"
|
|
||||||
)
|
|
||||||
|
|
||||||
async def __run() -> None:
|
|
||||||
Log.info("Starting...")
|
|
||||||
cvtt_client = CvttRestMktDataClient(config)
|
|
||||||
await cvtt_client.add_subscription(
|
|
||||||
exch_acct="COINBASE_AT",
|
|
||||||
instrument_id="PAIR-BTC-USD",
|
|
||||||
interval_sec=60,
|
|
||||||
history_depth_sec=24 * 3600,
|
|
||||||
callback=_calback,
|
|
||||||
)
|
|
||||||
while True:
|
|
||||||
await asyncio.sleep(5)
|
|
||||||
|
|
||||||
asyncio.run(__run())
|
|
||||||
pass
|
|
||||||
@@ -1,60 +0,0 @@
|
|||||||
```python
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from typing import Dict
|
|
||||||
import time
|
|
||||||
|
|
||||||
import requests
|
|
||||||
|
|
||||||
from cvttpy_tools.base import NamedObject
|
|
||||||
|
|
||||||
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
|
|
||||||
```
|
|
||||||
@@ -1,50 +0,0 @@
|
|||||||
from enum import Enum
|
|
||||||
|
|
||||||
import requests
|
|
||||||
|
|
||||||
# import aiohttp
|
|
||||||
from cvttpy_tools.base import NamedObject
|
|
||||||
from cvttpy_tools.config import Config
|
|
||||||
from cvttpy_tools.logger import Log
|
|
||||||
from cvttpy_tools.web.rest_client import RESTSender
|
|
||||||
# ---
|
|
||||||
from cvttpy_trading.trading.trading_instructions import TradingInstructions
|
|
||||||
# ---
|
|
||||||
from pairs_trading.apps.pair_trader import PairTrader
|
|
||||||
|
|
||||||
|
|
||||||
class TradingInstructionsSender(NamedObject):
|
|
||||||
config_: Config
|
|
||||||
sender_: RESTSender
|
|
||||||
pairs_trader_: PairTrader
|
|
||||||
|
|
||||||
class TradingInstType(str, Enum):
|
|
||||||
TARGET_POSITION = "TARGET_POSITION"
|
|
||||||
DIRECT_ORDER = "DIRECT_ORDER"
|
|
||||||
MARKET_MAKING = "MARKET_MAKING"
|
|
||||||
NONE = "NONE"
|
|
||||||
|
|
||||||
def __init__(self, config: Config, pairs_trader: PairTrader) -> None:
|
|
||||||
self.config_ = config
|
|
||||||
base_url = self.config_.get_value("cvtt_base_url", default="")
|
|
||||||
assert base_url
|
|
||||||
self.sender_ = RESTSender(base_url=base_url)
|
|
||||||
self.pairs_trader_ = pairs_trader
|
|
||||||
|
|
||||||
self.book_id_ = self.pairs_trader_.book_id_
|
|
||||||
assert self.book_id_, "book_id is required"
|
|
||||||
|
|
||||||
self.strategy_id_ = config.get_value("strategy_id", "")
|
|
||||||
assert self.strategy_id_, "strategy_id is required"
|
|
||||||
|
|
||||||
|
|
||||||
async def send_trading_instructions(self, ti: TradingInstructions) -> None:
|
|
||||||
Log.info(f"{self.fname()}: sending {ti=}")
|
|
||||||
response: requests.Response = self.sender_.send_post(
|
|
||||||
endpoint="trading_instructions", post_body=ti.to_dict()
|
|
||||||
)
|
|
||||||
if response.status_code not in (200, 201):
|
|
||||||
Log.error(
|
|
||||||
f"{self.fname()}: Received error: {response.status_code} - {response.text}"
|
|
||||||
)
|
|
||||||
|
|
||||||
@@ -1,351 +0,0 @@
|
|||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from typing import Any, Dict, List, Optional
|
|
||||||
|
|
||||||
import pandas as pd
|
|
||||||
|
|
||||||
# ---
|
|
||||||
from cvttpy_tools.base import NamedObject
|
|
||||||
from cvttpy_tools.app import App
|
|
||||||
from cvttpy_tools.config import Config
|
|
||||||
from cvttpy_tools.settings.cvtt_types import IntervalSecT
|
|
||||||
from cvttpy_tools.timeutils import NanosT, SecPerHour, current_nanoseconds, NanoPerSec, format_nanos_utc
|
|
||||||
from cvttpy_tools.logger import Log
|
|
||||||
|
|
||||||
# ---
|
|
||||||
from cvttpy_trading.trading.instrument import ExchangeInstrument
|
|
||||||
from cvttpy_trading.trading.mkt_data.md_summary import MdTradesAggregate
|
|
||||||
from cvttpy_trading.trading.trading_instructions import TradingInstructions
|
|
||||||
from cvttpy_trading.trading.trading_instructions import TargetPositionSignal
|
|
||||||
|
|
||||||
# ---
|
|
||||||
from pairs_trading.lib.pt_strategy.model_data_policy import ModelDataPolicy
|
|
||||||
from pairs_trading.lib.pt_strategy.pt_model import Prediction
|
|
||||||
from pairs_trading.lib.pt_strategy.trading_pair import LiveTradingPair
|
|
||||||
from pairs_trading.apps.pair_trader import PairTrader
|
|
||||||
from pairs_trading.lib.pt_strategy.pt_market_data import LiveMarketData
|
|
||||||
|
|
||||||
|
|
||||||
class PtLiveStrategy(NamedObject):
|
|
||||||
config_: Config
|
|
||||||
instruments_: List[ExchangeInstrument]
|
|
||||||
|
|
||||||
interval_sec_: IntervalSecT
|
|
||||||
history_depth_sec_: IntervalSecT
|
|
||||||
open_threshold_: float
|
|
||||||
close_threshold_: float
|
|
||||||
|
|
||||||
trading_pair_: LiveTradingPair
|
|
||||||
model_data_policy_: ModelDataPolicy
|
|
||||||
pairs_trader_: PairTrader
|
|
||||||
|
|
||||||
# for presentation: history of prediction values and trading signals
|
|
||||||
predictions_df_: pd.DataFrame
|
|
||||||
trading_signals_df_: pd.DataFrame
|
|
||||||
allowed_md_lag_sec_: int
|
|
||||||
|
|
||||||
|
|
||||||
def __init__(
|
|
||||||
self,
|
|
||||||
config: Config,
|
|
||||||
pairs_trader: PairTrader,
|
|
||||||
):
|
|
||||||
self.config_ = config
|
|
||||||
|
|
||||||
self.pairs_trader_ = pairs_trader
|
|
||||||
self.trading_pair_ = LiveTradingPair(
|
|
||||||
config=config,
|
|
||||||
instruments=self.pairs_trader_.instruments_,
|
|
||||||
)
|
|
||||||
self.model_data_policy_ = ModelDataPolicy.create(
|
|
||||||
self.config_,
|
|
||||||
is_real_time=True,
|
|
||||||
pair=self.trading_pair_,
|
|
||||||
)
|
|
||||||
assert (
|
|
||||||
self.model_data_policy_ is not None
|
|
||||||
), f"{self.fname()}: Unable to create ModelDataPolicy"
|
|
||||||
|
|
||||||
self.predictions_df_ = pd.DataFrame()
|
|
||||||
self.trading_signals_df_ = pd.DataFrame()
|
|
||||||
|
|
||||||
self.instruments_ = self.pairs_trader_.instruments_
|
|
||||||
|
|
||||||
App.instance().add_call(
|
|
||||||
stage=App.Stage.Config, func=self._on_config(), can_run_now=True
|
|
||||||
)
|
|
||||||
|
|
||||||
async def _on_config(self) -> None:
|
|
||||||
self.interval_sec_ = self.config_.get_value("interval_sec", 0)
|
|
||||||
assert self.interval_sec_ > 0, "interval_sec cannot be 0"
|
|
||||||
self.history_depth_sec_ = (
|
|
||||||
self.config_.get_value("history_depth_hours", 0) * SecPerHour
|
|
||||||
)
|
|
||||||
assert self.history_depth_sec_ > 0, "history_depth_hours cannot be 0"
|
|
||||||
|
|
||||||
self.allowed_md_lag_sec_ = self.config_.get_value("allowed_md_lag_sec", 3)
|
|
||||||
|
|
||||||
self.open_threshold_ = self.config_.get_value(
|
|
||||||
"model/disequilibrium/open_trshld", 0.0
|
|
||||||
)
|
|
||||||
self.close_threshold_ = self.config_.get_value(
|
|
||||||
"model/disequilibrium/close_trshld", 0.0
|
|
||||||
)
|
|
||||||
|
|
||||||
assert (
|
|
||||||
self.open_threshold_ > 0
|
|
||||||
), "disequilibrium/open_trshld must be greater than 0"
|
|
||||||
assert (
|
|
||||||
self.close_threshold_ > 0
|
|
||||||
), "disequilibrium/close_trshld must be greater than 0"
|
|
||||||
|
|
||||||
await self.pairs_trader_.subscribe_md()
|
|
||||||
|
|
||||||
|
|
||||||
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, hist_aggr: List[MdTradesAggregate]
|
|
||||||
) -> None:
|
|
||||||
if not self._is_md_actual(hist_aggr=hist_aggr):
|
|
||||||
return
|
|
||||||
|
|
||||||
market_data_df: pd.DataFrame = self._create_md_df(hist_aggr=hist_aggr)
|
|
||||||
if len(market_data_df) == 0:
|
|
||||||
Log.warning(f"{self.fname()} Unable to create market data df")
|
|
||||||
return
|
|
||||||
|
|
||||||
self.trading_pair_.market_data_ = market_data_df
|
|
||||||
|
|
||||||
Log.info(f"{self.fname()}: Running prediction for pair: {self.trading_pair_}")
|
|
||||||
prediction = self.trading_pair_.run(
|
|
||||||
market_data_df, self.model_data_policy_.advance()
|
|
||||||
)
|
|
||||||
self.predictions_df_ = pd.concat(
|
|
||||||
[self.predictions_df_, prediction.to_df()], ignore_index=True
|
|
||||||
)
|
|
||||||
|
|
||||||
trading_instructions: List[TradingInstructions] = (
|
|
||||||
self._create_trading_instructions(
|
|
||||||
prediction=prediction, last_row=market_data_df.iloc[-1]
|
|
||||||
)
|
|
||||||
)
|
|
||||||
if trading_instructions is not None:
|
|
||||||
await self._send_trading_instructions(trading_instructions)
|
|
||||||
|
|
||||||
def _is_md_actual(self, hist_aggr: List[MdTradesAggregate]) -> bool:
|
|
||||||
if len(hist_aggr) == 0:
|
|
||||||
Log.warning(f"{self.fname()} list of aggregates IS EMPTY")
|
|
||||||
return False
|
|
||||||
|
|
||||||
curr_ns = current_nanoseconds()
|
|
||||||
|
|
||||||
# MAYBE check market data length
|
|
||||||
|
|
||||||
# at 18:05:01 we should see data for 18:04:00
|
|
||||||
lag_sec = (curr_ns - hist_aggr[-1].aggr_time_ns_) / NanoPerSec - self.interval_sec()
|
|
||||||
if lag_sec > self.allowed_md_lag_sec_:
|
|
||||||
Log.warning(
|
|
||||||
f"{self.fname()} {hist_aggr[-1].exch_inst_.details_short()}"
|
|
||||||
f" Lagging {int(lag_sec)} > {self.allowed_md_lag_sec_} seconds:"
|
|
||||||
f"\n{len(hist_aggr)} records"
|
|
||||||
f"\n{hist_aggr[-1].exch_inst_.base_asset_id_}: {hist_aggr[-1].tstamp()}"
|
|
||||||
f"\n{hist_aggr[-2].exch_inst_.base_asset_id_}: {hist_aggr[-2].tstamp()}"
|
|
||||||
)
|
|
||||||
return False
|
|
||||||
else:
|
|
||||||
Log.info(
|
|
||||||
f"{self.fname()} {hist_aggr[-1].exch_inst_.details_short()}"
|
|
||||||
f" Lag {int(lag_sec)} <= {self.allowed_md_lag_sec_} seconds"
|
|
||||||
f"\n{len(hist_aggr)} records"
|
|
||||||
f"\n{hist_aggr[-1].exch_inst_.base_asset_id_}: {hist_aggr[-1].tstamp()}"
|
|
||||||
f"\n{hist_aggr[-2].exch_inst_.base_asset_id_}: {hist_aggr[-2].tstamp()}"
|
|
||||||
)
|
|
||||||
return True
|
|
||||||
|
|
||||||
def _create_md_df(self, hist_aggr: List[MdTradesAggregate]) -> pd.DataFrame:
|
|
||||||
"""
|
|
||||||
tstamp time_ns symbol open high low close volume num_trades vwap
|
|
||||||
0 2025-09-10 11:30:00 1757503800000000000 ADA-USDT 0.8750 0.8750 0.8743 0.8743 50710.500 0 0.874489
|
|
||||||
1 2025-09-10 11:30:00 1757503800000000000 SOL-USDT 219.9700 219.9800 219.6600 219.7000 2648.582 0 219.787847
|
|
||||||
2 2025-09-10 11:31:00 1757503860000000000 SOL-USDT 219.7000 219.7300 219.6200 219.6200 1134.886 0 219.663460
|
|
||||||
3 2025-09-10 11:31:00 1757503860000000000 ADA-USDT 0.8743 0.8745 0.8741 0.8741 10696.400 0 0.874234
|
|
||||||
4 2025-09-10 11:32:00 1757503920000000000 ADA-USDT 0.8742 0.8742 0.8739 0.8740 18546.900 0 0.874037
|
|
||||||
"""
|
|
||||||
|
|
||||||
rows: List[Dict[str, Any]] = []
|
|
||||||
|
|
||||||
for aggr in hist_aggr:
|
|
||||||
exch_inst = aggr.exch_inst_
|
|
||||||
|
|
||||||
rows.append(
|
|
||||||
{
|
|
||||||
# convert nanoseconds → tz-aware pandas timestamp
|
|
||||||
"tstamp": pd.to_datetime(aggr.aggr_time_ns_, unit="ns", utc=True),
|
|
||||||
"time_ns": aggr.aggr_time_ns_,
|
|
||||||
"symbol": exch_inst.instrument_id().split("-", 1)[1],
|
|
||||||
"exchange_id": exch_inst.exchange_id_,
|
|
||||||
"instrument_id": exch_inst.instrument_id(),
|
|
||||||
"open": exch_inst.get_price(aggr.open_),
|
|
||||||
"high": exch_inst.get_price(aggr.high_),
|
|
||||||
"low": exch_inst.get_price(aggr.low_),
|
|
||||||
"close": exch_inst.get_price(aggr.close_),
|
|
||||||
"volume": exch_inst.get_quantity(aggr.volume_),
|
|
||||||
"num_trades": aggr.num_trades_,
|
|
||||||
"vwap": exch_inst.get_price(aggr.vwap_),
|
|
||||||
}
|
|
||||||
)
|
|
||||||
|
|
||||||
source_md_df = pd.DataFrame(
|
|
||||||
rows,
|
|
||||||
columns=[
|
|
||||||
"tstamp",
|
|
||||||
"time_ns",
|
|
||||||
"symbol",
|
|
||||||
"exchange_id",
|
|
||||||
"instrument_id",
|
|
||||||
"open",
|
|
||||||
"high",
|
|
||||||
"low",
|
|
||||||
"close",
|
|
||||||
"volume",
|
|
||||||
"num_trades",
|
|
||||||
"vwap",
|
|
||||||
],
|
|
||||||
)
|
|
||||||
|
|
||||||
# automatic sorting
|
|
||||||
source_md_df.sort_values(
|
|
||||||
by=["time_ns", "symbol"],
|
|
||||||
ascending=True,
|
|
||||||
inplace=True,
|
|
||||||
kind="mergesort", # stable sort
|
|
||||||
)
|
|
||||||
|
|
||||||
source_md_df.reset_index(drop=True, inplace=True)
|
|
||||||
|
|
||||||
pt_mkt_data = LiveMarketData(config=self.config_, instruments=self.instruments_)
|
|
||||||
pt_mkt_data.origin_mkt_data_df_ = source_md_df
|
|
||||||
pt_mkt_data.set_market_data()
|
|
||||||
|
|
||||||
return pt_mkt_data.market_data_df_
|
|
||||||
|
|
||||||
def interval_sec(self) -> IntervalSecT:
|
|
||||||
return self.interval_sec_
|
|
||||||
|
|
||||||
def history_depth_sec(self) -> IntervalSecT:
|
|
||||||
return self.history_depth_sec_
|
|
||||||
|
|
||||||
async def _send_trading_instructions(
|
|
||||||
self, trading_instructions: List[TradingInstructions]
|
|
||||||
) -> None:
|
|
||||||
for ti in trading_instructions:
|
|
||||||
Log.info(f"{self.fname()} Sending trading instructions {ti}")
|
|
||||||
await self.pairs_trader_.ti_sender_.send_trading_instructions(ti)
|
|
||||||
|
|
||||||
def _create_trading_instructions(
|
|
||||||
self, prediction: Prediction, last_row: pd.Series
|
|
||||||
) -> List[TradingInstructions]:
|
|
||||||
trd_instructions: List[TradingInstructions] = []
|
|
||||||
pair = self.trading_pair_
|
|
||||||
|
|
||||||
scaled_disequilibrium = prediction.scaled_disequilibrium_
|
|
||||||
abs_scaled_disequilibrium = abs(scaled_disequilibrium)
|
|
||||||
|
|
||||||
if abs_scaled_disequilibrium >= self.open_threshold_:
|
|
||||||
trd_instructions = self._create_open_trade_instructions(
|
|
||||||
pair, row=last_row, prediction=prediction
|
|
||||||
)
|
|
||||||
|
|
||||||
elif abs_scaled_disequilibrium <= self.close_threshold_ or pair.to_stop_close_conditions(predicted_row=last_row):
|
|
||||||
trd_instructions = self._create_close_trade_instructions(
|
|
||||||
pair, row=last_row # , prediction=prediction
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
return trd_instructions
|
|
||||||
|
|
||||||
def _strength(self, scaled_disequilibrium: float) -> float:
|
|
||||||
# TODO PtLiveStrategy._strength()
|
|
||||||
return 1.0
|
|
||||||
|
|
||||||
def _create_open_trade_instructions(
|
|
||||||
self, pair: LiveTradingPair, row: pd.Series, prediction: Prediction
|
|
||||||
) -> List[TradingInstructions]:
|
|
||||||
diseqlbrm = prediction.disequilibrium_
|
|
||||||
scaled_disequilibrium = prediction.scaled_disequilibrium_
|
|
||||||
if diseqlbrm > 0:
|
|
||||||
side_a = -1
|
|
||||||
side_b = 1
|
|
||||||
else:
|
|
||||||
side_a = 1
|
|
||||||
side_b = -1
|
|
||||||
|
|
||||||
ti_a: Optional[TradingInstructions] = TradingInstructions(
|
|
||||||
book=self.pairs_trader_.book_id_,
|
|
||||||
strategy_id=self.__class__.__name__,
|
|
||||||
ti_type=TradingInstructions.Type.TARGET_POSITION,
|
|
||||||
issued_ts_ns=current_nanoseconds(),
|
|
||||||
data=TargetPositionSignal(
|
|
||||||
strength=side_a * self._strength(scaled_disequilibrium),
|
|
||||||
exchange_id=pair.get_instrument_a().exchange_id_,
|
|
||||||
base_asset=pair.get_instrument_a().base_asset_id_,
|
|
||||||
quote_asset=pair.get_instrument_a().quote_asset_id_,
|
|
||||||
user_data={}
|
|
||||||
),
|
|
||||||
)
|
|
||||||
if not ti_a:
|
|
||||||
return []
|
|
||||||
ti_b: Optional[TradingInstructions] = TradingInstructions(
|
|
||||||
book=self.pairs_trader_.book_id_,
|
|
||||||
strategy_id=self.__class__.__name__,
|
|
||||||
ti_type=TradingInstructions.Type.TARGET_POSITION,
|
|
||||||
issued_ts_ns=current_nanoseconds(),
|
|
||||||
data=TargetPositionSignal(
|
|
||||||
strength=side_b * self._strength(scaled_disequilibrium),
|
|
||||||
exchange_id=pair.get_instrument_b().exchange_id_,
|
|
||||||
base_asset=pair.get_instrument_b().base_asset_id_,
|
|
||||||
quote_asset=pair.get_instrument_b().quote_asset_id_,
|
|
||||||
user_data={}
|
|
||||||
),
|
|
||||||
)
|
|
||||||
if not ti_b:
|
|
||||||
return []
|
|
||||||
return [ti_a, ti_b]
|
|
||||||
|
|
||||||
|
|
||||||
def _create_close_trade_instructions(
|
|
||||||
self, pair: LiveTradingPair, row: pd.Series
|
|
||||||
) -> List[TradingInstructions]:
|
|
||||||
ti_a: Optional[TradingInstructions] = TradingInstructions(
|
|
||||||
book=self.pairs_trader_.book_id_,
|
|
||||||
strategy_id=self.__class__.__name__,
|
|
||||||
ti_type=TradingInstructions.Type.TARGET_POSITION,
|
|
||||||
issued_ts_ns=current_nanoseconds(),
|
|
||||||
data=TargetPositionSignal(
|
|
||||||
strength=0,
|
|
||||||
exchange_id=pair.get_instrument_a().exchange_id_,
|
|
||||||
base_asset=pair.get_instrument_a().base_asset_id_,
|
|
||||||
quote_asset=pair.get_instrument_a().quote_asset_id_,
|
|
||||||
user_data={}
|
|
||||||
),
|
|
||||||
)
|
|
||||||
if not ti_a:
|
|
||||||
return []
|
|
||||||
ti_b: Optional[TradingInstructions] = TradingInstructions(
|
|
||||||
book=self.pairs_trader_.book_id_,
|
|
||||||
strategy_id=self.__class__.__name__,
|
|
||||||
ti_type=TradingInstructions.Type.TARGET_POSITION,
|
|
||||||
issued_ts_ns=current_nanoseconds(),
|
|
||||||
data=TargetPositionSignal(
|
|
||||||
strength=0,
|
|
||||||
exchange_id=pair.get_instrument_b().exchange_id_,
|
|
||||||
base_asset=pair.get_instrument_b().base_asset_id_,
|
|
||||||
quote_asset=pair.get_instrument_b().quote_asset_id_,
|
|
||||||
user_data={}
|
|
||||||
),
|
|
||||||
)
|
|
||||||
if not ti_b:
|
|
||||||
return []
|
|
||||||
return [ti_a, ti_b]
|
|
||||||
@@ -1,253 +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
|
|
||||||
|
|
||||||
from cvttpy_tools.config import Config
|
|
||||||
|
|
||||||
@dataclass
|
|
||||||
class DataWindowParams:
|
|
||||||
training_size_: int
|
|
||||||
training_start_index_: int
|
|
||||||
|
|
||||||
|
|
||||||
class ModelDataPolicy(ABC):
|
|
||||||
config_: Config
|
|
||||||
current_data_params_: DataWindowParams
|
|
||||||
count_: int
|
|
||||||
is_real_time_: bool
|
|
||||||
|
|
||||||
def __init__(self, config: Config, *args: Any, **kwargs: Any):
|
|
||||||
self.config_ = config
|
|
||||||
self.current_data_params_ = DataWindowParams(
|
|
||||||
training_size_=config.get_value("model/training_size", 120),
|
|
||||||
training_start_index_=0,
|
|
||||||
)
|
|
||||||
self.count_ = 0
|
|
||||||
self.is_real_time_ = kwargs.get("is_real_time", False)
|
|
||||||
|
|
||||||
@abstractmethod
|
|
||||||
def advance(self, mkt_data_df: Optional[pd.DataFrame] = None) -> DataWindowParams:
|
|
||||||
self.count_ += 1
|
|
||||||
if not self.is_real_time_:
|
|
||||||
print(self.count_, end="\r")
|
|
||||||
return self.current_data_params_
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def create(config: Config, *args: Any, **kwargs: Any) -> ModelDataPolicy:
|
|
||||||
import importlib
|
|
||||||
|
|
||||||
model_data_policy_class_name = config.get_value("model/model_data_policy_class", None)
|
|
||||||
assert model_data_policy_class_name is not None
|
|
||||||
module_name, class_name = model_data_policy_class_name.rsplit(".", 1)
|
|
||||||
module = importlib.import_module(module_name)
|
|
||||||
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: Config, *args: Any, **kwargs: Any):
|
|
||||||
super().__init__(config, *args, **kwargs)
|
|
||||||
self.count_ = 1
|
|
||||||
|
|
||||||
def advance(self, mkt_data_df: Optional[pd.DataFrame] = None) -> DataWindowParams:
|
|
||||||
super().advance(mkt_data_df)
|
|
||||||
if self.is_real_time_:
|
|
||||||
self.current_data_params_.training_start_index_ = 0
|
|
||||||
if mkt_data_df and len(mkt_data_df) > self.curren_data_params_.training_size_:
|
|
||||||
self.current_data_params_.training_start_index_ = -self.curren_data_params_.training_size_
|
|
||||||
else:
|
|
||||||
self.current_data_params_.training_start_index_ += 1
|
|
||||||
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: Config, *args: Any, **kwargs: Any):
|
|
||||||
super().__init__(config, *args, **kwargs)
|
|
||||||
assert (
|
|
||||||
kwargs.get("pair") is not None
|
|
||||||
), "pair must be provided"
|
|
||||||
assert (config.key_exists("model/max_training_size") and config.key_exists("model/min_training_size")
|
|
||||||
), "min_training_size and max_training_size must be provided"
|
|
||||||
self.min_training_size_ = cast(int, config.get_value("model/min_training_size"))
|
|
||||||
self.max_training_size_ = cast(int, config.get_value("model/max_training_size"))
|
|
||||||
|
|
||||||
from pairs_trading.lib.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: Config, *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: Config, *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: Config, *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 pairs_trading.lib.pt_strategy.pt_model import PairsTradingModel, Prediction
|
|
||||||
from pairs_trading.lib.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()])
|
|
||||||
|
|
||||||
@@ -1,223 +0,0 @@
|
|||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from abc import ABC, abstractmethod
|
|
||||||
from typing import Any, Dict, List, Optional
|
|
||||||
|
|
||||||
import pandas as pd
|
|
||||||
|
|
||||||
# ---
|
|
||||||
from cvttpy_tools.base import NamedObject
|
|
||||||
from cvttpy_tools.config import Config
|
|
||||||
from cvttpy_tools.settings.cvtt_types import JsonDictT
|
|
||||||
|
|
||||||
# ---
|
|
||||||
from cvttpy_trading.trading.mkt_data.md_summary import MdTradesAggregate
|
|
||||||
from cvttpy_trading.trading.instrument import ExchangeInstrument
|
|
||||||
# ---
|
|
||||||
from pairs_trading.lib.tools.data_loader import load_market_data
|
|
||||||
|
|
||||||
|
|
||||||
class PtMarketData(NamedObject, ABC):
|
|
||||||
config_: Config
|
|
||||||
origin_mkt_data_df_: pd.DataFrame
|
|
||||||
market_data_df_: pd.DataFrame
|
|
||||||
stat_model_price_: str
|
|
||||||
instruments_: List[ExchangeInstrument]
|
|
||||||
symbol_a_: str
|
|
||||||
symbol_b_: str
|
|
||||||
|
|
||||||
def __init__(self, config: Config, instruments: List[ExchangeInstrument]):
|
|
||||||
self.config_ = config
|
|
||||||
self.origin_mkt_data_df_ = pd.DataFrame()
|
|
||||||
self.market_data_df_ = pd.DataFrame()
|
|
||||||
self.stat_model_price_ = self.config_.get_value("model/stat_model_price")
|
|
||||||
|
|
||||||
self.instruments_ = instruments
|
|
||||||
assert len(self.instruments_) > 0, "No instruments found in config"
|
|
||||||
self.symbol_a_ = self.instruments_[0].instrument_id().split("-", 1)[1]
|
|
||||||
self.symbol_b_ = self.instruments_[1].instrument_id().split("-", 1)[1]
|
|
||||||
|
|
||||||
@abstractmethod
|
|
||||||
def md_columns(self) -> List[str]: ...
|
|
||||||
|
|
||||||
@abstractmethod
|
|
||||||
def rename_columns(self, symbol_df: pd.DataFrame) -> pd.DataFrame: ...
|
|
||||||
|
|
||||||
@abstractmethod
|
|
||||||
def tranform_df_target_colnames(self) -> List[str]: ...
|
|
||||||
|
|
||||||
def set_market_data(self) -> None:
|
|
||||||
self.market_data_df_ = pd.DataFrame(
|
|
||||||
self._transform_dataframe(self.origin_mkt_data_df_)[
|
|
||||||
["tstamp"] + self.tranform_df_target_colnames()
|
|
||||||
]
|
|
||||||
)
|
|
||||||
|
|
||||||
self.market_data_df_ = self.market_data_df_.dropna().reset_index(drop=True)
|
|
||||||
self.market_data_df_["tstamp"] = pd.to_datetime(self.market_data_df_["tstamp"])
|
|
||||||
self.market_data_df_ = self.market_data_df_.sort_values("tstamp")
|
|
||||||
|
|
||||||
def colnames(self) -> List[str]:
|
|
||||||
return [
|
|
||||||
f"{self.stat_model_price_}_{self.symbol_a_}",
|
|
||||||
f"{self.stat_model_price_}_{self.symbol_b_}",
|
|
||||||
]
|
|
||||||
|
|
||||||
def _transform_dataframe(self, df: pd.DataFrame) -> pd.DataFrame:
|
|
||||||
df_selected: pd.DataFrame = pd.DataFrame(df[self.md_columns()])
|
|
||||||
result_df = (
|
|
||||||
pd.DataFrame(df_selected["tstamp"]).drop_duplicates().reset_index(drop=True)
|
|
||||||
)
|
|
||||||
|
|
||||||
# For each unique symbol, add a corresponding stat_model_price column
|
|
||||||
symbols = df_selected["symbol"].unique()
|
|
||||||
|
|
||||||
for symbol in symbols:
|
|
||||||
# Filter rows for this symbol
|
|
||||||
df_symbol = df_selected[df_selected["symbol"] == symbol].reset_index(
|
|
||||||
drop=True
|
|
||||||
)
|
|
||||||
# Create column name like "close-COIN"
|
|
||||||
temp_df: pd.DataFrame = self.rename_columns(df_symbol)
|
|
||||||
# Join with our result dataframe
|
|
||||||
result_df = pd.merge(result_df, temp_df, on="tstamp", how="left")
|
|
||||||
result_df = result_df.reset_index(
|
|
||||||
drop=True
|
|
||||||
) # do not dropna() since irrelevant symbol would affect dataset
|
|
||||||
|
|
||||||
return result_df.dropna()
|
|
||||||
|
|
||||||
class ResearchMarketData(PtMarketData):
|
|
||||||
current_index_: int
|
|
||||||
is_execution_price_: bool
|
|
||||||
|
|
||||||
def __init__(self, config: Config, instruments: List[ExchangeInstrument]):
|
|
||||||
super().__init__(config, instruments)
|
|
||||||
self.current_index_ = 0
|
|
||||||
self.is_execution_price_ = self.config_.key_exists("execution_price")
|
|
||||||
if self.is_execution_price_:
|
|
||||||
self.execution_price_column_ = self.config_.get_value("execution_price")["column"]
|
|
||||||
self.execution_price_shift_ = self.config_.get_value("execution_price")["shift"]
|
|
||||||
else:
|
|
||||||
self.execution_price_column_ = None
|
|
||||||
self.execution_price_shift_ = 0
|
|
||||||
|
|
||||||
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_value("datafiles", [])
|
|
||||||
assert len(datafiles) > 0, "No datafiles found in config"
|
|
||||||
|
|
||||||
extra_minutes: int = self.execution_price_shift_
|
|
||||||
|
|
||||||
for datafile in datafiles:
|
|
||||||
md_df = load_market_data(
|
|
||||||
datafile=datafile,
|
|
||||||
instruments=self.instruments_,
|
|
||||||
db_table_name=self.config_.get_value("market_data_loading")[
|
|
||||||
self.instruments_[0].user_data_.get("instrument_type", "?instrument_type?")
|
|
||||||
]["db_table_name"],
|
|
||||||
trading_hours=self.config_.get_value("trading_hours"),
|
|
||||||
extra_minutes=extra_minutes,
|
|
||||||
)
|
|
||||||
self.origin_mkt_data_df_ = pd.concat([self.origin_mkt_data_df_, md_df])
|
|
||||||
|
|
||||||
self.origin_mkt_data_df_ = self.origin_mkt_data_df_.sort_values(by="tstamp")
|
|
||||||
self.origin_mkt_data_df_ = self.origin_mkt_data_df_.dropna().reset_index(
|
|
||||||
drop=True
|
|
||||||
)
|
|
||||||
self.set_market_data()
|
|
||||||
self._set_execution_price_data()
|
|
||||||
|
|
||||||
def _set_execution_price_data(self) -> None:
|
|
||||||
if not self.is_execution_price_:
|
|
||||||
return
|
|
||||||
if not self.config_.key_exists("execution_price"):
|
|
||||||
self.market_data_df_[f"exec_price_{self.symbol_a_}"] = self.market_data_df_[
|
|
||||||
f"{self.stat_model_price_}_{self.symbol_a_}"
|
|
||||||
]
|
|
||||||
self.market_data_df_[f"exec_price_{self.symbol_b_}"] = self.market_data_df_[
|
|
||||||
f"{self.stat_model_price_}_{self.symbol_b_}"
|
|
||||||
]
|
|
||||||
return
|
|
||||||
execution_price_column = self.config_.get_value("execution_price")["column"]
|
|
||||||
execution_price_shift = self.config_.get_value("execution_price")["shift"]
|
|
||||||
self.market_data_df_[f"exec_price_{self.symbol_a_}"] = self.market_data_df_[
|
|
||||||
f"{execution_price_column}_{self.symbol_a_}"
|
|
||||||
].shift(-execution_price_shift)
|
|
||||||
self.market_data_df_[f"exec_price_{self.symbol_b_}"] = self.market_data_df_[
|
|
||||||
f"{execution_price_column}_{self.symbol_b_}"
|
|
||||||
].shift(-execution_price_shift)
|
|
||||||
self.market_data_df_ = self.market_data_df_.dropna().reset_index(drop=True)
|
|
||||||
|
|
||||||
def md_columns(self) -> List[str]:
|
|
||||||
# @abstractmethod
|
|
||||||
if self.is_execution_price_:
|
|
||||||
return ["tstamp", "symbol", self.stat_model_price_, self.execution_price_column_]
|
|
||||||
else:
|
|
||||||
return ["tstamp", "symbol", self.stat_model_price_]
|
|
||||||
|
|
||||||
def rename_columns(self, selected_symbol_df: pd.DataFrame) -> pd.DataFrame:
|
|
||||||
# @abstractmethod
|
|
||||||
symbol = selected_symbol_df.iloc[0]["symbol"]
|
|
||||||
new_price_column = f"{self.stat_model_price_}_{symbol}"
|
|
||||||
if self.is_execution_price_:
|
|
||||||
new_execution_price_column = f"{self.execution_price_column_}_{symbol}"
|
|
||||||
|
|
||||||
# Create temporary dataframe with timestamp and price
|
|
||||||
temp_df = pd.DataFrame(
|
|
||||||
{
|
|
||||||
"tstamp": selected_symbol_df["tstamp"],
|
|
||||||
new_price_column: selected_symbol_df[self.stat_model_price_],
|
|
||||||
new_execution_price_column: selected_symbol_df[self.execution_price_column_],
|
|
||||||
}
|
|
||||||
)
|
|
||||||
else:
|
|
||||||
temp_df = pd.DataFrame(
|
|
||||||
{
|
|
||||||
"tstamp": selected_symbol_df["tstamp"],
|
|
||||||
new_price_column: selected_symbol_df[self.stat_model_price_],
|
|
||||||
}
|
|
||||||
)
|
|
||||||
return temp_df
|
|
||||||
|
|
||||||
def tranform_df_target_colnames(self):
|
|
||||||
# @abstractmethod
|
|
||||||
return self.colnames() + self.orig_exec_prices_colnames()
|
|
||||||
|
|
||||||
def orig_exec_prices_colnames(self) -> List[str]:
|
|
||||||
return [
|
|
||||||
f"{self.execution_price_column_}_{self.symbol_a_}",
|
|
||||||
f"{self.execution_price_column_}_{self.symbol_b_}",
|
|
||||||
] if self.is_execution_price_ else []
|
|
||||||
|
|
||||||
class LiveMarketData(PtMarketData):
|
|
||||||
|
|
||||||
def __init__(self, config: Config, instruments: List[ExchangeInstrument]):
|
|
||||||
super().__init__(config, instruments)
|
|
||||||
|
|
||||||
def md_columns(self) -> List[str]:
|
|
||||||
# @abstractmethod
|
|
||||||
return ["tstamp", "symbol", self.stat_model_price_]
|
|
||||||
|
|
||||||
def rename_columns(self, selected_symbol_df: pd.DataFrame) -> pd.DataFrame:
|
|
||||||
# @abstractmethod
|
|
||||||
symbol = selected_symbol_df.iloc[0]["symbol"]
|
|
||||||
new_price_column = f"{self.stat_model_price_}_{symbol}"
|
|
||||||
temp_df = pd.DataFrame(
|
|
||||||
{
|
|
||||||
"tstamp": selected_symbol_df["tstamp"],
|
|
||||||
new_price_column: selected_symbol_df[self.stat_model_price_],
|
|
||||||
}
|
|
||||||
)
|
|
||||||
return temp_df
|
|
||||||
|
|
||||||
def tranform_df_target_colnames(self):
|
|
||||||
# @abstractmethod
|
|
||||||
return self.colnames()
|
|
||||||
@@ -1,30 +0,0 @@
|
|||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from abc import ABC, abstractmethod
|
|
||||||
from typing import Any, Dict, cast
|
|
||||||
|
|
||||||
# ---
|
|
||||||
from cvttpy_tools.config import Config
|
|
||||||
# ---
|
|
||||||
from pairs_trading.lib.pt_strategy.prediction import Prediction
|
|
||||||
from pairs_trading.lib.pt_strategy.trading_pair import TradingPair
|
|
||||||
|
|
||||||
class PairsTradingModel(ABC):
|
|
||||||
|
|
||||||
@abstractmethod
|
|
||||||
def predict(self, pair: TradingPair) -> Prediction: # type: ignore[assignment]
|
|
||||||
...
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def create(config: Config) -> PairsTradingModel:
|
|
||||||
import importlib
|
|
||||||
|
|
||||||
model_class_name = config.get_value("model/model_class", None)
|
|
||||||
assert model_class_name is not None
|
|
||||||
module_name, class_name = model_class_name.rsplit(".", 1)
|
|
||||||
module = importlib.import_module(module_name)
|
|
||||||
model_object = getattr(module, class_name)()
|
|
||||||
return cast(PairsTradingModel, model_object)
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
@@ -1,305 +0,0 @@
|
|||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from typing import Any, Dict, List, Optional, Tuple
|
|
||||||
|
|
||||||
import pandas as pd
|
|
||||||
# ---
|
|
||||||
from cvttpy_tools.config import Config
|
|
||||||
# ---
|
|
||||||
from cvttpy_trading.trading.instrument import ExchangeInstrument
|
|
||||||
# ---
|
|
||||||
from pairs_trading.lib.pt_strategy.model_data_policy import ModelDataPolicy
|
|
||||||
from pairs_trading.lib.pt_strategy.pt_market_data import ResearchMarketData
|
|
||||||
from pairs_trading.lib.pt_strategy.pt_model import Prediction
|
|
||||||
from pairs_trading.lib.pt_strategy.trading_pair import PairState, TradingPair, ResearchTradingPair
|
|
||||||
|
|
||||||
class PtResearchStrategy:
|
|
||||||
config_: Config
|
|
||||||
trading_pair_: ResearchTradingPair
|
|
||||||
model_data_policy_: ModelDataPolicy
|
|
||||||
pt_mkt_data_: ResearchMarketData
|
|
||||||
|
|
||||||
trades_: List[pd.DataFrame]
|
|
||||||
predictions_df_: pd.DataFrame
|
|
||||||
|
|
||||||
def __init__(
|
|
||||||
self,
|
|
||||||
config: Config,
|
|
||||||
instruments: List[ExchangeInstrument]
|
|
||||||
):
|
|
||||||
from pairs_trading.lib.pt_strategy.model_data_policy import ModelDataPolicy
|
|
||||||
from pairs_trading.lib.pt_strategy.trading_pair import TradingPair
|
|
||||||
|
|
||||||
self.config_ = config
|
|
||||||
self.trades_ = []
|
|
||||||
self.trading_pair_ = ResearchTradingPair(config=config, instruments=instruments)
|
|
||||||
self.predictions_df_ = pd.DataFrame()
|
|
||||||
|
|
||||||
import copy
|
|
||||||
|
|
||||||
# modified config must be passed to PtMarketData
|
|
||||||
config_copy = copy.deepcopy(config)
|
|
||||||
config_copy.set_value("instruments", instruments)
|
|
||||||
self.pt_mkt_data_ = ResearchMarketData(config=config_copy, instruments=instruments)
|
|
||||||
self.pt_mkt_data_.load()
|
|
||||||
self.model_data_policy_ = ModelDataPolicy.create(
|
|
||||||
config_copy, mkt_data=self.pt_mkt_data_.market_data_df_, pair=self.trading_pair_
|
|
||||||
)
|
|
||||||
|
|
||||||
def outstanding_positions(self) -> List[Dict[str, Any]]:
|
|
||||||
return list(self.trading_pair_.user_data_.get("outstanding_positions", []))
|
|
||||||
|
|
||||||
def run(self) -> None:
|
|
||||||
training_minutes = self.config_.get_value("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_df_ = pd.concat(
|
|
||||||
[self.predictions_df_, 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_.get_value("model/disequilibrium/open_trshld")
|
|
||||||
close_threshold = self.config_.get_value("model/disequilibrium/close_trshld")
|
|
||||||
scaled_disequilibrium = prediction.scaled_disequilibrium_
|
|
||||||
abs_scaled_disequilibrium = abs(scaled_disequilibrium)
|
|
||||||
|
|
||||||
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_.get_value("close_outstanding_positions", False):
|
|
||||||
close_position_row = pd.Series(pair.market_data_.iloc[-2])
|
|
||||||
# close_position_row["disequilibrium"] = 0.0
|
|
||||||
# close_position_row["scaled_disequilibrium"] = 0.0
|
|
||||||
# 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: ResearchTradingPair, 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: ResearchTradingPair, 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)
|
|
||||||
@@ -1,527 +0,0 @@
|
|||||||
import os
|
|
||||||
import sqlite3
|
|
||||||
from datetime import date, datetime
|
|
||||||
from typing import Any, Dict, List, Optional, Tuple
|
|
||||||
|
|
||||||
import pandas as pd
|
|
||||||
# ---
|
|
||||||
from cvttpy_tools.config import Config
|
|
||||||
# ---
|
|
||||||
from cvttpy_trading.trading.instrument import ExchangeInstrument
|
|
||||||
# ---
|
|
||||||
from pairs_trading.lib.pt_strategy.trading_pair import TradingPair
|
|
||||||
|
|
||||||
# Recommended replacement adapters and converters for Python 3.12+
|
|
||||||
# From: https://docs.python.org/3/library/sqlite3.html#sqlite3-adapter-converter-recipes
|
|
||||||
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: Config,
|
|
||||||
datafiles: List[Tuple[str, str]],
|
|
||||||
instruments: List[ExchangeInstrument],
|
|
||||||
) -> 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.data(), indent=2, default=str)
|
|
||||||
|
|
||||||
# Convert lists to comma-separated strings for storage
|
|
||||||
datafiles_str = ", ".join([f"{datafile}" for _, datafile in datafiles])
|
|
||||||
instruments_str = ", ".join(
|
|
||||||
[
|
|
||||||
inst.details_short()
|
|
||||||
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]]]
|
|
||||||
config_: Config
|
|
||||||
|
|
||||||
def __init__(self, config: Config) -> 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 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_value("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
|
|
||||||
|
|
||||||
@@ -1,226 +0,0 @@
|
|||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from abc import ABC, abstractmethod
|
|
||||||
from datetime import datetime
|
|
||||||
from enum import Enum
|
|
||||||
from typing import Any, Dict, List
|
|
||||||
|
|
||||||
import pandas as pd
|
|
||||||
|
|
||||||
# ---
|
|
||||||
from cvttpy_tools.base import NamedObject
|
|
||||||
from cvttpy_tools.config import Config
|
|
||||||
# ---
|
|
||||||
from cvttpy_trading.trading.instrument import ExchangeInstrument
|
|
||||||
# ---
|
|
||||||
from pairs_trading.lib.pt_strategy.model_data_policy import DataWindowParams
|
|
||||||
from pairs_trading.lib.pt_strategy.prediction import Prediction
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
class PairState(Enum):
|
|
||||||
INITIAL = 1
|
|
||||||
OPEN = 2
|
|
||||||
CLOSE = 3
|
|
||||||
CLOSE_POSITION = 4
|
|
||||||
CLOSE_STOP_LOSS = 5
|
|
||||||
CLOSE_STOP_PROFIT = 6
|
|
||||||
|
|
||||||
|
|
||||||
class TradingPair(NamedObject, ABC):
|
|
||||||
config_: Config
|
|
||||||
model_: Any # "PairsTradingModel"
|
|
||||||
market_data_: pd.DataFrame
|
|
||||||
|
|
||||||
user_data_: Dict[str, Any]
|
|
||||||
stat_model_price_: str
|
|
||||||
|
|
||||||
instruments_: List[ExchangeInstrument]
|
|
||||||
|
|
||||||
def __init__(
|
|
||||||
self,
|
|
||||||
config: Config,
|
|
||||||
instruments: List[ExchangeInstrument],
|
|
||||||
):
|
|
||||||
from pairs_trading.lib.pt_strategy.pt_model import PairsTradingModel
|
|
||||||
|
|
||||||
self.config_ = config
|
|
||||||
self.model_ = PairsTradingModel.create(config)
|
|
||||||
self.user_data_ = {}
|
|
||||||
self.instruments_ = instruments
|
|
||||||
self.instruments_[0].user_data_["symbol"] = instruments[0].instrument_id().split("-", 1)[1]
|
|
||||||
self.instruments_[1].user_data_["symbol"] = instruments[1].instrument_id().split("-", 1)[1]
|
|
||||||
self.stat_model_price_ = config.get_value("model/stat_model_price")
|
|
||||||
|
|
||||||
def run(self, market_data: pd.DataFrame, data_params: DataWindowParams) -> Prediction: # type: ignore[assignment]
|
|
||||||
self.market_data_ = market_data[
|
|
||||||
data_params.training_start_index_ : data_params.training_start_index_ + data_params.training_size_
|
|
||||||
]
|
|
||||||
return self.model_.predict(pair=self)
|
|
||||||
|
|
||||||
def colnames(self) -> List[str]:
|
|
||||||
return [
|
|
||||||
f"{self.stat_model_price_}_{self.symbol_a()}",
|
|
||||||
f"{self.stat_model_price_}_{self.symbol_b()}",
|
|
||||||
]
|
|
||||||
def symbol_a(self) -> str:
|
|
||||||
return self.get_instrument_a().user_data_["symbol"]
|
|
||||||
|
|
||||||
def symbol_b(self) -> str:
|
|
||||||
return self.get_instrument_b().user_data_["symbol"]
|
|
||||||
|
|
||||||
def get_instrument_a(self) -> ExchangeInstrument:
|
|
||||||
return self.instruments_[0]
|
|
||||||
|
|
||||||
def get_instrument_b(self) -> ExchangeInstrument:
|
|
||||||
return self.instruments_[1]
|
|
||||||
|
|
||||||
def __repr__(self) -> str:
|
|
||||||
return (
|
|
||||||
f"{self.__class__.__name__}:"
|
|
||||||
f" symbol_a={self.symbol_a()},"
|
|
||||||
f" symbol_b={self.symbol_b()},"
|
|
||||||
f" model={self.model_.__class__.__name__}"
|
|
||||||
)
|
|
||||||
|
|
||||||
class ResearchTradingPair(TradingPair):
|
|
||||||
|
|
||||||
def __init__(
|
|
||||||
self,
|
|
||||||
config: Config,
|
|
||||||
instruments: List[ExchangeInstrument],
|
|
||||||
):
|
|
||||||
assert len(instruments) == 2, "Trading pair must have exactly 2 instruments"
|
|
||||||
super().__init__(config=config, instruments=instruments)
|
|
||||||
|
|
||||||
self.user_data_ = {
|
|
||||||
"state": PairState.INITIAL,
|
|
||||||
}
|
|
||||||
|
|
||||||
def is_closed(self) -> bool:
|
|
||||||
return self.user_data_["state"] in [
|
|
||||||
PairState.CLOSE,
|
|
||||||
PairState.CLOSE_POSITION,
|
|
||||||
PairState.CLOSE_STOP_LOSS,
|
|
||||||
PairState.CLOSE_STOP_PROFIT,
|
|
||||||
]
|
|
||||||
|
|
||||||
def is_open(self) -> bool:
|
|
||||||
return not self.is_closed()
|
|
||||||
|
|
||||||
def exec_prices_colnames(self) -> List[str]:
|
|
||||||
return [
|
|
||||||
f"exec_price_{self.symbol_a()}",
|
|
||||||
f"exec_price_{self.symbol_b()}",
|
|
||||||
]
|
|
||||||
|
|
||||||
def to_stop_close_conditions(self, predicted_row: pd.Series) -> bool:
|
|
||||||
config = self.config_
|
|
||||||
if (
|
|
||||||
not config.key_exists("stop_close_conditions")
|
|
||||||
or config.get_value("stop_close_conditions") is None
|
|
||||||
):
|
|
||||||
return False
|
|
||||||
if "profit" in config.get_value("stop_close_conditions"):
|
|
||||||
current_return = self._current_return(predicted_row)
|
|
||||||
#
|
|
||||||
# print(f"time={predicted_row['tstamp']} current_return={current_return}")
|
|
||||||
#
|
|
||||||
if current_return >= config.get_value("stop_close_conditions")["profit"]:
|
|
||||||
print(f"STOP PROFIT: {current_return}")
|
|
||||||
self.user_data_["stop_close_state"] = PairState.CLOSE_STOP_PROFIT
|
|
||||||
return True
|
|
||||||
if "loss" in config.get_value("stop_close_conditions"):
|
|
||||||
if current_return <= config.get_value("stop_close_conditions")["loss"]:
|
|
||||||
print(f"STOP LOSS: {current_return}")
|
|
||||||
self.user_data_["stop_close_state"] = PairState.CLOSE_STOP_LOSS
|
|
||||||
return True
|
|
||||||
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_.get_value("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,
|
|
||||||
}
|
|
||||||
)
|
|
||||||
|
|
||||||
class LiveTradingPair(TradingPair):
|
|
||||||
|
|
||||||
def __init__(self, config: Config, instruments: List[ExchangeInstrument]):
|
|
||||||
super().__init__(config, instruments)
|
|
||||||
|
|
||||||
def to_stop_close_conditions(self, predicted_row: pd.Series) -> bool:
|
|
||||||
# TODO LiveTradingPair.to_stop_close_conditions()
|
|
||||||
return False
|
|
||||||
|
|
||||||
|
|
||||||
@@ -1,17 +0,0 @@
|
|||||||
import hjson
|
|
||||||
from typing import Dict
|
|
||||||
from datetime import datetime
|
|
||||||
# ---
|
|
||||||
from cvttpy_tools.config import Config
|
|
||||||
|
|
||||||
|
|
||||||
def load_config(config_path: str) -> Config:
|
|
||||||
return Config(json_src=f"file://{config_path}")
|
|
||||||
|
|
||||||
|
|
||||||
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"))
|
|
||||||
|
|
||||||
@@ -1,150 +0,0 @@
|
|||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
import sqlite3
|
|
||||||
from typing import Any, Dict, List, Tuple, cast
|
|
||||||
import pandas as pd
|
|
||||||
|
|
||||||
from cvttpy_trading.trading.instrument import ExchangeInstrument
|
|
||||||
|
|
||||||
def load_sqlite_to_dataframe(db_path:str, query:str) -> pd.DataFrame:
|
|
||||||
df: pd.DataFrame = pd.DataFrame()
|
|
||||||
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[ExchangeInstrument],
|
|
||||||
db_table_name: str,
|
|
||||||
trading_hours: Dict = {},
|
|
||||||
extra_minutes: int = 0,
|
|
||||||
) -> pd.DataFrame:
|
|
||||||
|
|
||||||
|
|
||||||
inst_ids = ['"' + exch_inst.instrument_id() + '"' for exch_inst in instruments]
|
|
||||||
instrument_ids = list(set(inst_ids))
|
|
||||||
exchange_ids = list(
|
|
||||||
set(['"' + instrument.exchange_id() + '"' for instrument in instruments])
|
|
||||||
)
|
|
||||||
|
|
||||||
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)
|
|
||||||
@@ -1,37 +0,0 @@
|
|||||||
import os
|
|
||||||
import glob
|
|
||||||
from typing import Dict, List, Tuple
|
|
||||||
# ---
|
|
||||||
from cvttpy_tools.config import Config
|
|
||||||
# ---
|
|
||||||
from cvttpy_trading.trading.instrument import ExchangeInstrument
|
|
||||||
|
|
||||||
DayT = str
|
|
||||||
DataFileNameT = str
|
|
||||||
|
|
||||||
def resolve_datafiles(
|
|
||||||
config: Config, date_pattern: str, instruments: List[ExchangeInstrument]
|
|
||||||
) -> List[Tuple[DayT, DataFileNameT]]:
|
|
||||||
resolved_files: List[Tuple[DayT, DataFileNameT]] = []
|
|
||||||
for exch_inst in instruments:
|
|
||||||
pattern = date_pattern
|
|
||||||
inst_type = exch_inst.user_data_.get("instrument_type", "?instrument_type?")
|
|
||||||
data_dir = config.get_value(f"market_data_loading/{inst_type}/data_directory")
|
|
||||||
if "*" in pattern or "?" in pattern:
|
|
||||||
# Handle wildcards
|
|
||||||
if not os.path.isabs(pattern):
|
|
||||||
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
|
|
||||||
|
|
||||||
@@ -1,79 +0,0 @@
|
|||||||
from pairs_trading.lib.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}")
|
|
||||||
|
|
||||||
@@ -1,502 +0,0 @@
|
|||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
|
|
||||||
from pairs_trading.lib.pt_strategy.results import (PairResearchResult)
|
|
||||||
from pairs_trading.lib.pt_strategy.research_strategy import PtResearchStrategy
|
|
||||||
|
|
||||||
|
|
||||||
def visualize_trades(strategy: PtResearchStrategy, results: PairResearchResult, trading_date: str) -> None:
|
|
||||||
|
|
||||||
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_df_[['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_.get_value('model/disequilibrium/open_trshld'),
|
|
||||||
y1=strategy.config_.get_value('model/disequilibrium/open_trshld'),
|
|
||||||
line=dict(color="purple", width=2, dash="dot"),
|
|
||||||
opacity=0.7,
|
|
||||||
row=1, col=1
|
|
||||||
)
|
|
||||||
|
|
||||||
fig.add_shape(
|
|
||||||
type="line",
|
|
||||||
x0=timeline_df['tstamp'].min(),
|
|
||||||
x1=timeline_df['tstamp'].max(),
|
|
||||||
y0=-strategy.config_.get_value('model/disequilibrium/open_trshld'),
|
|
||||||
y1=-strategy.config_.get_value('model/disequilibrium/open_trshld'),
|
|
||||||
line=dict(color="purple", width=2, dash="dot"),
|
|
||||||
opacity=0.7,
|
|
||||||
row=1, col=1
|
|
||||||
)
|
|
||||||
|
|
||||||
fig.add_shape(
|
|
||||||
type="line",
|
|
||||||
x0=timeline_df['tstamp'].min(),
|
|
||||||
x1=timeline_df['tstamp'].max(),
|
|
||||||
y0=strategy.config_.get_value('model/disequilibrium/close_trshld'),
|
|
||||||
y1=strategy.config_.get_value('model/disequilibrium/close_trshld'),
|
|
||||||
line=dict(color="brown", width=2, dash="dot"),
|
|
||||||
opacity=0.7,
|
|
||||||
row=1, col=1
|
|
||||||
)
|
|
||||||
|
|
||||||
fig.add_shape(
|
|
||||||
type="line",
|
|
||||||
x0=timeline_df['tstamp'].min(),
|
|
||||||
x1=timeline_df['tstamp'].max(),
|
|
||||||
y0=-strategy.config_.get_value('model/disequilibrium/close_trshld'),
|
|
||||||
y1=-strategy.config_.get_value('model/disequilibrium/close_trshld'),
|
|
||||||
line=dict(color="brown", width=2, dash="dot"),
|
|
||||||
opacity=0.7,
|
|
||||||
row=1, col=1
|
|
||||||
)
|
|
||||||
|
|
||||||
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)
|
||||||
+14
-201
@@ -1,201 +1,14 @@
|
|||||||
aiohttp>=3.8.4
|
# Interactive analysis
|
||||||
aiosignal>=1.3.1
|
ipykernel>=6.29,<7
|
||||||
async-timeout>=4.0.2
|
ipywidgets>=8.1,<9
|
||||||
attrs>=21.2.0
|
itables>=2.2,<3
|
||||||
beautifulsoup4>=4.10.0
|
jupyter>=1.1,<2
|
||||||
black>=23.3.0
|
jupyter_bokeh>=4.0,<5
|
||||||
flake8>=6.0.0
|
nbformat>=5.10,<6
|
||||||
certifi>=2020.6.20
|
pandas>=2.2,<3
|
||||||
chardet>=4.0.0
|
panel>=1.5,<2
|
||||||
charset-normalizer>=3.1.0
|
plotly>=5.24,<7
|
||||||
click>=8.0.3
|
|
||||||
colorama>=0.4.4
|
# Verification
|
||||||
configobj>=5.0.6
|
nbmake>=1.5,<2
|
||||||
cryptography>=3.4.8
|
pytest>=8,<9
|
||||||
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-seaborn>0.13.2
|
|
||||||
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
|
|
||||||
|
|||||||
@@ -1,139 +0,0 @@
|
|||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
import os
|
|
||||||
from typing import Any, Dict, List, Tuple
|
|
||||||
|
|
||||||
# ---
|
|
||||||
from cvttpy_tools.app import App
|
|
||||||
from cvttpy_tools.base import NamedObject
|
|
||||||
from cvttpy_tools.config import CvttAppConfig
|
|
||||||
|
|
||||||
# ---
|
|
||||||
from cvttpy_trading.trading.instrument import ExchangeInstrument
|
|
||||||
from cvttpy_trading.settings.instruments import Instruments
|
|
||||||
|
|
||||||
# ---
|
|
||||||
from pairs_trading.lib.pt_strategy.results import (
|
|
||||||
PairResearchResult,
|
|
||||||
create_result_database,
|
|
||||||
store_config_in_database,
|
|
||||||
)
|
|
||||||
from pairs_trading.lib.pt_strategy.research_strategy import PtResearchStrategy
|
|
||||||
from pairs_trading.lib.tools.filetools import resolve_datafiles
|
|
||||||
|
|
||||||
InstrumentTypeT = str
|
|
||||||
|
|
||||||
|
|
||||||
class Runner(NamedObject):
|
|
||||||
def __init__(self):
|
|
||||||
App()
|
|
||||||
CvttAppConfig()
|
|
||||||
|
|
||||||
# App.instance().add_cmdline_arg(
|
|
||||||
# "--config", type=str, required=True, help="Path to the configuration file."
|
|
||||||
# )
|
|
||||||
App.instance().add_cmdline_arg(
|
|
||||||
"--date_pattern",
|
|
||||||
type=str,
|
|
||||||
required=True,
|
|
||||||
help="Date YYYYMMDD, allows * and ? wildcards",
|
|
||||||
)
|
|
||||||
App.instance().add_cmdline_arg(
|
|
||||||
"--instruments",
|
|
||||||
type=str,
|
|
||||||
required=True,
|
|
||||||
help="Comma-separated list of instrument symbols (e.g., COIN:EQUITY,GBTC:CRYPTO)",
|
|
||||||
)
|
|
||||||
App.instance().add_cmdline_arg(
|
|
||||||
"--result_db",
|
|
||||||
type=str,
|
|
||||||
required=True,
|
|
||||||
help="Path to SQLite database for storing results. Use 'NONE' to disable database output.",
|
|
||||||
)
|
|
||||||
|
|
||||||
App.instance().add_call(stage=App.Stage.Config, func=self._on_config())
|
|
||||||
App.instance().add_call(stage=App.Stage.Run, func=self.run())
|
|
||||||
|
|
||||||
async def _on_config(self) -> None:
|
|
||||||
# Resolve data files (CLI takes priority over config)
|
|
||||||
instruments: List[ExchangeInstrument] = self._get_instruments()
|
|
||||||
datafiles = resolve_datafiles(
|
|
||||||
config=CvttAppConfig.instance(),
|
|
||||||
date_pattern=App.instance().get_argument("date_pattern"),
|
|
||||||
instruments=instruments,
|
|
||||||
)
|
|
||||||
|
|
||||||
days = list(set([day for day, _ in datafiles]))
|
|
||||||
print(f"Found {len(datafiles)} data files to process:")
|
|
||||||
for df in datafiles:
|
|
||||||
print(f" - {df}")
|
|
||||||
|
|
||||||
# Create result database if needed
|
|
||||||
if App.instance().get_argument("result_db").upper() != "NONE":
|
|
||||||
create_result_database(App.instance().get_argument("result_db"))
|
|
||||||
|
|
||||||
# Initialize a dictionary to store all trade results
|
|
||||||
all_results: Dict[str, Dict[str, Any]] = {}
|
|
||||||
is_config_stored = False
|
|
||||||
# Process each data file
|
|
||||||
|
|
||||||
results = PairResearchResult(config=CvttAppConfig.instance())
|
|
||||||
for day in sorted(days):
|
|
||||||
md_datafiles = [datafile for md_day, datafile in datafiles if md_day == day]
|
|
||||||
if not all([os.path.exists(datafile) for datafile in md_datafiles]):
|
|
||||||
print(f"WARNING: insufficient data files: {md_datafiles}")
|
|
||||||
exit(1)
|
|
||||||
print(f"\n====== Processing {day} ======")
|
|
||||||
|
|
||||||
if not is_config_stored:
|
|
||||||
store_config_in_database(
|
|
||||||
db_path=App.instance().get_argument("result_db"),
|
|
||||||
config_file_path=App.instance().get_argument("config"),
|
|
||||||
config=CvttAppConfig.instance(),
|
|
||||||
datafiles=datafiles,
|
|
||||||
instruments=instruments,
|
|
||||||
)
|
|
||||||
is_config_stored = True
|
|
||||||
|
|
||||||
CvttAppConfig.instance().set_value("datafiles", md_datafiles)
|
|
||||||
pt_strategy = PtResearchStrategy(
|
|
||||||
config=CvttAppConfig.instance(),
|
|
||||||
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()
|
|
||||||
|
|
||||||
def _get_instruments(self) -> List[ExchangeInstrument]:
|
|
||||||
res: List[ExchangeInstrument] = []
|
|
||||||
|
|
||||||
for inst in App.instance().get_argument("instruments").split(","):
|
|
||||||
instrument_type = inst.split(":")[0]
|
|
||||||
exchange_id = inst.split(":")[1]
|
|
||||||
instrument_id = inst.split(":")[2]
|
|
||||||
exch_inst: ExchangeInstrument = Instruments.instance().get_exch_inst(
|
|
||||||
exch_id=exchange_id, inst_id=instrument_id, src=f"{self.fname()}"
|
|
||||||
)
|
|
||||||
exch_inst.user_data_["instrument_type"] = instrument_type
|
|
||||||
res.append(exch_inst)
|
|
||||||
|
|
||||||
return res
|
|
||||||
|
|
||||||
async def run(self) -> None:
|
|
||||||
|
|
||||||
if App.instance().get_argument("result_db").upper() != "NONE":
|
|
||||||
print(
|
|
||||||
f'\nResults stored in database: {App.instance().get_argument("result_db")}'
|
|
||||||
)
|
|
||||||
else:
|
|
||||||
print("No results to display.")
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
Runner()
|
|
||||||
App.instance().run()
|
|
||||||
@@ -1,311 +0,0 @@
|
|||||||
{
|
|
||||||
"cells": [
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"# Pair Selection History\n",
|
|
||||||
"\n",
|
|
||||||
"Interactive notebook for exploring pair selection history from a SQLite database.\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"**Usage**\n",
|
|
||||||
"- Enter the SQLite `db_path` (file path).\n",
|
|
||||||
"- Click `Load pairs` to populate the dropdown.\n",
|
|
||||||
"- Select a `pair_name`, then click `Plot`.\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "668ebf19",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"# Settings"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 1,
|
|
||||||
"id": "c78db847",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"import sqlite3\n",
|
|
||||||
"from pathlib import Path\n",
|
|
||||||
"\n",
|
|
||||||
"import pandas as pd\n",
|
|
||||||
"import plotly.express as px\n",
|
|
||||||
"import ipywidgets as widgets\n",
|
|
||||||
"from IPython.display import display\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "e7ac6adc",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"# Data Loading"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"id": "766bcf9f",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"application/vnd.jupyter.widget-view+json": {
|
|
||||||
"model_id": "e0b30b1abd1b440b832fdaaa6cce8f76",
|
|
||||||
"version_major": 2,
|
|
||||||
"version_minor": 0
|
|
||||||
},
|
|
||||||
"text/plain": [
|
|
||||||
"VBox(children=(Text(value='', description='pair_db', layout=Layout(width='80%'), placeholder='/path/to/pairs.d…"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "display_data"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"application/vnd.jupyter.widget-view+json": {
|
|
||||||
"model_id": "15679f9015854d5fa7119210094fbbc8",
|
|
||||||
"version_major": 2,
|
|
||||||
"version_minor": 0
|
|
||||||
},
|
|
||||||
"text/plain": [
|
|
||||||
"Output()"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "display_data"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"db_path = widgets.Text(\n",
|
|
||||||
" value='',\n",
|
|
||||||
" placeholder='/path/to/pairs.db',\n",
|
|
||||||
" description='pair_db',\n",
|
|
||||||
" layout=widgets.Layout(width='80%')\n",
|
|
||||||
")\n",
|
|
||||||
"\n",
|
|
||||||
"md_db_path = widgets.Text(\n",
|
|
||||||
" value='',\n",
|
|
||||||
" placeholder='/path/to/market_data.db',\n",
|
|
||||||
" description='md_db',\n",
|
|
||||||
" layout=widgets.Layout(width='80%')\n",
|
|
||||||
")\n",
|
|
||||||
"\n",
|
|
||||||
"load_button = widgets.Button(description='Load pairs', button_style='info')\n",
|
|
||||||
"plot_button = widgets.Button(description='Plot', button_style='primary')\n",
|
|
||||||
"\n",
|
|
||||||
"pair_name = widgets.Dropdown(\n",
|
|
||||||
" options=[],\n",
|
|
||||||
" value=None,\n",
|
|
||||||
" description='pair_name',\n",
|
|
||||||
" layout=widgets.Layout(width='80%')\n",
|
|
||||||
")\n",
|
|
||||||
"\n",
|
|
||||||
"status = widgets.HTML(value='')\n",
|
|
||||||
"output = widgets.Output()\n",
|
|
||||||
"\n",
|
|
||||||
"controls = widgets.VBox([\n",
|
|
||||||
" db_path,\n",
|
|
||||||
" md_db_path,\n",
|
|
||||||
" widgets.HBox([load_button, plot_button]),\n",
|
|
||||||
" pair_name,\n",
|
|
||||||
" status,\n",
|
|
||||||
"])\n",
|
|
||||||
"\n",
|
|
||||||
"display(controls, output)\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "a4d47855",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"# Processing"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"id": "2c710f51",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"PLOT_WIDTH = 1100\n",
|
|
||||||
"PLOT_HEIGHT = 320\n",
|
|
||||||
"\n",
|
|
||||||
"def _connect(path: str):\n",
|
|
||||||
" if not path:\n",
|
|
||||||
" raise ValueError('Please provide db_path.')\n",
|
|
||||||
" p = Path(path).expanduser().resolve()\n",
|
|
||||||
" if not p.exists():\n",
|
|
||||||
" raise FileNotFoundError(f'Database not found: {p}')\n",
|
|
||||||
" return sqlite3.connect(p)\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"def _parse_tstamp(series: pd.Series) -> pd.Series:\n",
|
|
||||||
" return pd.to_datetime(series, utc=True, errors='coerce').dt.tz_convert(None)\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"def _style_fig(fig, tmin, tmax):\n",
|
|
||||||
" fig.update_layout(\n",
|
|
||||||
" legend=dict(orientation='h', yanchor='bottom', y=1.02, xanchor='left', x=0),\n",
|
|
||||||
" margin=dict(l=50, r=20, t=60, b=40),\n",
|
|
||||||
" height=PLOT_HEIGHT,\n",
|
|
||||||
" width=PLOT_WIDTH,\n",
|
|
||||||
" )\n",
|
|
||||||
" fig.update_xaxes(range=[tmin, tmax])\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"def _load_pairs(_=None):\n",
|
|
||||||
" status.value = ''\n",
|
|
||||||
" with output:\n",
|
|
||||||
" output.clear_output()\n",
|
|
||||||
" try:\n",
|
|
||||||
" with _connect(db_path.value) as conn:\n",
|
|
||||||
" rows = conn.execute(\n",
|
|
||||||
" \"SELECT pair_name \"\n",
|
|
||||||
" \"FROM pair_selection_history \"\n",
|
|
||||||
" \"GROUP BY pair_name \"\n",
|
|
||||||
" \"ORDER BY SUM(composite_rank), pair_name\"\n",
|
|
||||||
" ).fetchall()\n",
|
|
||||||
" options = [r[0] for r in rows]\n",
|
|
||||||
" pair_name.options = options\n",
|
|
||||||
" pair_name.value = options[0] if options else None\n",
|
|
||||||
" status.value = f'Loaded {len(options)} pairs.'\n",
|
|
||||||
" except Exception as exc:\n",
|
|
||||||
" status.value = f\"<span style='color:#b00'>Error: {exc}</span>\"\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"def _plot(_=None):\n",
|
|
||||||
" status.value = ''\n",
|
|
||||||
" with output:\n",
|
|
||||||
" output.clear_output()\n",
|
|
||||||
" try:\n",
|
|
||||||
" if not pair_name.value:\n",
|
|
||||||
" raise ValueError('Please select a pair_name.')\n",
|
|
||||||
" if not md_db_path.value:\n",
|
|
||||||
" raise ValueError('Please provide md_db path.')\n",
|
|
||||||
" query = (\n",
|
|
||||||
" 'SELECT tstamp, pvalue_eg, pvalue_adf, rank_eg, rank_adf, '\n",
|
|
||||||
" 'exchange_a, instrument_a, exchange_b, instrument_b '\n",
|
|
||||||
" 'FROM pair_selection_history '\n",
|
|
||||||
" 'WHERE pair_name = ? '\n",
|
|
||||||
" 'ORDER BY tstamp'\n",
|
|
||||||
" )\n",
|
|
||||||
" with _connect(db_path.value) as conn:\n",
|
|
||||||
" df = pd.read_sql_query(query, conn, params=(pair_name.value,))\n",
|
|
||||||
" if df.empty:\n",
|
|
||||||
" raise ValueError('No data for selected pair_name.')\n",
|
|
||||||
" df['tstamp'] = _parse_tstamp(df['tstamp'])\n",
|
|
||||||
" df = df.dropna(subset=['tstamp'])\n",
|
|
||||||
" if df.empty:\n",
|
|
||||||
" raise ValueError('No valid timestamps in pair selection data.')\n",
|
|
||||||
" tmin = df['tstamp'].min()\n",
|
|
||||||
" tmax = df['tstamp'].max()\n",
|
|
||||||
"\n",
|
|
||||||
" first_row = df.dropna(subset=['exchange_a', 'instrument_a', 'exchange_b', 'instrument_b']).iloc[0]\n",
|
|
||||||
" ex_a = first_row['exchange_a']\n",
|
|
||||||
" id_a = first_row['instrument_a']\n",
|
|
||||||
" ex_b = first_row['exchange_b']\n",
|
|
||||||
" id_b = first_row['instrument_b']\n",
|
|
||||||
"\n",
|
|
||||||
" fig_p = px.line(\n",
|
|
||||||
" df,\n",
|
|
||||||
" x='tstamp',\n",
|
|
||||||
" y=['pvalue_eg', 'pvalue_adf'],\n",
|
|
||||||
" title=f'P-Values Over Time: {pair_name.value}',\n",
|
|
||||||
" labels={'value': 'p-value', 'variable': 'metric', 'tstamp': 'timestamp'}\n",
|
|
||||||
" )\n",
|
|
||||||
" fig_p.update_layout(legend_title_text='metric')\n",
|
|
||||||
" _style_fig(fig_p, tmin, tmax)\n",
|
|
||||||
"\n",
|
|
||||||
" md_query = (\n",
|
|
||||||
" 'SELECT tstamp, close FROM md_1min_bars '\n",
|
|
||||||
" 'WHERE exchange_id = ? AND instrument_id = ? '\n",
|
|
||||||
" 'ORDER BY tstamp'\n",
|
|
||||||
" )\n",
|
|
||||||
" with _connect(md_db_path.value) as md_conn:\n",
|
|
||||||
" md_a = pd.read_sql_query(md_query, md_conn, params=(ex_a, id_a))\n",
|
|
||||||
" md_b = pd.read_sql_query(md_query, md_conn, params=(ex_b, id_b))\n",
|
|
||||||
" if md_a.empty or md_b.empty:\n",
|
|
||||||
" raise ValueError('Market data not found for selected instruments.')\n",
|
|
||||||
" md_a['tstamp'] = _parse_tstamp(md_a['tstamp'])\n",
|
|
||||||
" md_b['tstamp'] = _parse_tstamp(md_b['tstamp'])\n",
|
|
||||||
" md_a = md_a.dropna(subset=['tstamp', 'close'])\n",
|
|
||||||
" md_b = md_b.dropna(subset=['tstamp', 'close'])\n",
|
|
||||||
" md_a = md_a[(md_a['tstamp'] >= tmin) & (md_a['tstamp'] <= tmax)]\n",
|
|
||||||
" md_b = md_b[(md_b['tstamp'] >= tmin) & (md_b['tstamp'] <= tmax)]\n",
|
|
||||||
" if md_a.empty or md_b.empty:\n",
|
|
||||||
" raise ValueError('Market data is outside the pair selection time range.')\n",
|
|
||||||
" md_a = md_a.sort_values('tstamp')\n",
|
|
||||||
" md_b = md_b.sort_values('tstamp')\n",
|
|
||||||
" md_a['scaled_close'] = (md_a['close'] - md_a['close'].iloc[0]) / md_a['close'].iloc[0] * 100\n",
|
|
||||||
" md_b['scaled_close'] = (md_b['close'] - md_b['close'].iloc[0]) / md_b['close'].iloc[0] * 100\n",
|
|
||||||
"\n",
|
|
||||||
" md_plot = pd.DataFrame({\n",
|
|
||||||
" 'tstamp': md_a['tstamp'],\n",
|
|
||||||
" f'{ex_a}:{id_a}': md_a['scaled_close'],\n",
|
|
||||||
" })\n",
|
|
||||||
" md_plot = md_plot.merge(\n",
|
|
||||||
" pd.DataFrame({\n",
|
|
||||||
" 'tstamp': md_b['tstamp'],\n",
|
|
||||||
" f'{ex_b}:{id_b}': md_b['scaled_close'],\n",
|
|
||||||
" }),\n",
|
|
||||||
" on='tstamp',\n",
|
|
||||||
" how='outer'\n",
|
|
||||||
" ).sort_values('tstamp')\n",
|
|
||||||
"\n",
|
|
||||||
" fig_m = px.line(\n",
|
|
||||||
" md_plot,\n",
|
|
||||||
" x='tstamp',\n",
|
|
||||||
" y=[f'{ex_a}:{id_a}', f'{ex_b}:{id_b}'],\n",
|
|
||||||
" title='Scaled Close Price Change (%)',\n",
|
|
||||||
" labels={'value': 'scaled % change', 'variable': 'instrument', 'tstamp': 'timestamp'}\n",
|
|
||||||
" )\n",
|
|
||||||
" fig_m.update_layout(legend_title_text='instrument')\n",
|
|
||||||
" _style_fig(fig_m, tmin, tmax)\n",
|
|
||||||
"\n",
|
|
||||||
" with output:\n",
|
|
||||||
" display(fig_p)\n",
|
|
||||||
" display(fig_m)\n",
|
|
||||||
" except Exception as exc:\n",
|
|
||||||
" status.value = f\"<span style='color:#b00'>Error: {exc}</span>\"\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"load_button.on_click(_load_pairs)\n",
|
|
||||||
"plot_button.on_click(_plot)\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"metadata": {
|
|
||||||
"kernelspec": {
|
|
||||||
"display_name": "python3.12-venv",
|
|
||||||
"language": "python",
|
|
||||||
"name": "python3"
|
|
||||||
},
|
|
||||||
"language_info": {
|
|
||||||
"codemirror_mode": {
|
|
||||||
"name": "ipython",
|
|
||||||
"version": 3
|
|
||||||
},
|
|
||||||
"file_extension": ".py",
|
|
||||||
"mimetype": "text/x-python",
|
|
||||||
"name": "python",
|
|
||||||
"nbconvert_exporter": "python",
|
|
||||||
"pygments_lexer": "ipython3",
|
|
||||||
"version": "3.12.9"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"nbformat": 4,
|
|
||||||
"nbformat_minor": 5
|
|
||||||
}
|
|
||||||
File diff suppressed because one or more lines are too long
@@ -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"]
|
||||||
Reference in New Issue
Block a user