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https://github.com/shawnkim1997/All-in-one-Financial-Analysis.git
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shawnkim1997
38c56a5a43
Add asset-type aware market/overview flows, portfolio OCR reverse-engineering with exchange overrides, and interactive index heatmap features. Update README with recent updates and wire backend/frontend APIs for FX matrix, exchange options, and improved portfolio editing flows. Made-with: Cursor
48 lines
1.8 KiB
Python
48 lines
1.8 KiB
Python
"""Backtesting service for simple strategies."""
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from __future__ import annotations
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async def run_backtest(
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ticker: str,
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strategy: str,
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start_date: str,
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end_date: str,
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initial_capital: float = 10000.0,
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) -> dict:
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"""Run a basic backtest for selected strategy."""
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import yfinance as yf
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import ta
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df = yf.Ticker(ticker.upper()).history(start=start_date, end=end_date)
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if df is None or df.empty:
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return {"error": "No price data"}
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if strategy == "sma_crossover":
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df["sma50"] = ta.trend.sma_indicator(df["Close"], 50)
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df["sma200"] = ta.trend.sma_indicator(df["Close"], 200)
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df["signal"] = (df["sma50"] > df["sma200"]).astype(int)
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elif strategy == "rsi_oversold":
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df["rsi"] = ta.momentum.rsi(df["Close"], 14)
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df["signal"] = 0
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df.loc[df["rsi"] < 30, "signal"] = 1
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df.loc[df["rsi"] > 70, "signal"] = 0
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else:
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df["signal"] = 1
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df["returns"] = df["Close"].pct_change().fillna(0)
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df["strategy_returns"] = (df["returns"] * df["signal"].shift(1)).fillna(0)
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cumulative = (1 + df["strategy_returns"]).cumprod()
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benchmark = (1 + df["returns"]).cumprod()
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return {
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"total_return_pct": round((float(cumulative.iloc[-1]) - 1) * 100, 2),
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"benchmark_return_pct": round((float(benchmark.iloc[-1]) - 1) * 100, 2),
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"alpha": round((float(cumulative.iloc[-1]) - float(benchmark.iloc[-1])) * 100, 2),
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"max_drawdown_pct": round(float(((cumulative / cumulative.cummax()) - 1).min()) * 100, 2),
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"sharpe_ratio": round(float(df["strategy_returns"].mean() / (df["strategy_returns"].std() + 1e-10) * (252 ** 0.5)), 2),
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"equity_curve": [float(x) for x in cumulative.tolist()],
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"benchmark_curve": [float(x) for x in benchmark.tolist()],
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"dates": df.index.strftime("%Y-%m-%d").tolist(),
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}
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