b5db15d6ab
Streamlit + VectorBT dashboard, Parquet harvester with nightly cron, Authentik header auth, SQLite strategy persistence, and Bugsink telemetry. Co-authored-by: Cursor <cursoragent@cursor.com>
88 lines
2.2 KiB
Python
88 lines
2.2 KiB
Python
"""VectorBT backtest engine reading local Parquet OHLCV data."""
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from __future__ import annotations
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import os
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from dataclasses import dataclass
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from pathlib import Path
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import pandas as pd
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import vectorbt as vbt
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@dataclass(frozen=True)
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class BacktestResult:
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ticker: str
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fast_window: int
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slow_window: int
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sharpe_ratio: float
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max_drawdown: float
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total_return: float
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equity_curve: pd.Series
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price: pd.Series
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def parquet_dir() -> Path:
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return Path(os.environ.get("PARQUET_DIR", "/data/parquet"))
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def load_ohlcv(ticker: str) -> pd.DataFrame:
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path = parquet_dir() / f"{ticker.upper()}.parquet"
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if not path.exists():
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raise FileNotFoundError(f"No Parquet file for {ticker.upper()} at {path}")
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df = pd.read_parquet(path)
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if "Date" in df.columns:
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df = df.set_index("Date")
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df.index = pd.to_datetime(df.index)
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df = df.sort_index()
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return df
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def run_ma_crossover(
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ticker: str,
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fast_window: int,
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slow_window: int,
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init_cash: float = 10_000.0,
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fees: float = 0.001,
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) -> BacktestResult:
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if fast_window >= slow_window:
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raise ValueError("Fast MA window must be smaller than slow MA window")
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ohlcv = load_ohlcv(ticker)
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close = ohlcv["Close"].astype(float)
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fast_ma = vbt.MA.run(close, fast_window, short_name="fast")
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slow_ma = vbt.MA.run(close, slow_window, short_name="slow")
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entries = fast_ma.ma_crossed_above(slow_ma)
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exits = fast_ma.ma_crossed_below(slow_ma)
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portfolio = vbt.Portfolio.from_signals(
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close,
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entries=entries,
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exits=exits,
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init_cash=init_cash,
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fees=fees,
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freq="1D",
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)
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stats = portfolio.stats()
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sharpe = float(stats.get("Sharpe Ratio", 0.0) or 0.0)
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max_dd = float(stats.get("Max Drawdown [%]", 0.0) or 0.0) / 100.0
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total_return = float(stats.get("Total Return [%]", 0.0) or 0.0) / 100.0
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equity = portfolio.value()
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if isinstance(equity, pd.DataFrame):
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equity = equity.iloc[:, 0]
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return BacktestResult(
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ticker=ticker.upper(),
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fast_window=fast_window,
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slow_window=slow_window,
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sharpe_ratio=sharpe,
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max_drawdown=max_dd,
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total_return=total_return,
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equity_curve=equity,
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price=close,
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)
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