d256b65d30
Stack: FastAPI + React/TypeScript + SQLite + GPT-4o Features: Radar géopolitique, Marchés, Régime Macro, Journal de Bord MTM, Rapport IA, Super Contexte (base de raisonnement évolutive), Boucle feedback IA. Deploy: Docker + docker-compose + nginx pour openfin.open-squared.tech Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
127 lines
4.5 KiB
Python
127 lines
4.5 KiB
Python
from fastapi import APIRouter
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from pydantic import BaseModel
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from typing import Optional, List
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import yfinance as yf
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import numpy as np
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import pandas as pd
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from datetime import datetime
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from services.options_pricer import black_scholes
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router = APIRouter(prefix="/api/backtest", tags=["backtest"])
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class BacktestRequest(BaseModel):
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symbol: str
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start_date: str
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end_date: str
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strategy: str # "long_call" | "long_put" | "bull_call_spread" | "bear_put_spread" | "straddle"
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strike_offset_pct: float = 0.05 # e.g. 5% OTM
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expiry_days: int = 90
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capital: float = 1000.0
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geo_filter: Optional[str] = None # optional pattern id to filter
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@router.post("/run")
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def run_backtest(req: BacktestRequest):
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try:
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ticker = yf.Ticker(req.symbol)
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hist = ticker.history(start=req.start_date, end=req.end_date, interval="1d")
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if hist.empty or len(hist) < 20:
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return {"error": "Insufficient data for the period"}
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hist = hist.reset_index()
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returns = np.log(hist["Close"] / hist["Close"].shift(1)).dropna()
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trades = []
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equity = [req.capital]
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capital = req.capital
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r = 0.05
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T_open = req.expiry_days / 365
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step = max(1, req.expiry_days // 3)
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for i in range(0, len(hist) - req.expiry_days, step):
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row = hist.iloc[i]
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S = float(row["Close"])
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date_str = str(row["Date"])[:10]
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sigma_window = returns.iloc[max(0, i - 30):i]
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if len(sigma_window) < 5:
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continue
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sigma = float(sigma_window.std() * np.sqrt(252))
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if sigma < 0.01:
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sigma = 0.20
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if req.strategy in ["long_call", "bull_call_spread"]:
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K = S * (1 + req.strike_offset_pct)
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else:
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K = S * (1 - req.strike_offset_pct)
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result = black_scholes(S, K, T_open, r, sigma, "call" if "call" in req.strategy else "put")
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premium = result["price"]
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contracts = max(1, int((capital * 0.1) / (premium * 100)))
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cost = contracts * premium * 100
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expiry_idx = min(i + req.expiry_days, len(hist) - 1)
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S_expiry = float(hist.iloc[expiry_idx]["Close"])
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date_expiry = str(hist.iloc[expiry_idx]["Date"])[:10]
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if req.strategy in ["long_call", "bull_call_spread"]:
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intrinsic = max(0, S_expiry - K)
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else:
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intrinsic = max(0, K - S_expiry)
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pnl = (intrinsic - premium) * contracts * 100
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capital += pnl
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equity.append(round(capital, 2))
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trades.append({
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"entry_date": date_str,
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"exit_date": date_expiry,
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"strategy": req.strategy,
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"S_entry": round(S, 2),
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"K": round(K, 2),
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"premium": round(premium, 4),
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"contracts": contracts,
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"cost": round(cost, 2),
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"S_expiry": round(S_expiry, 2),
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"intrinsic": round(intrinsic, 4),
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"pnl": round(pnl, 2),
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"capital": round(capital, 2),
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})
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if not trades:
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return {"error": "No trades generated"}
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wins = [t for t in trades if t["pnl"] > 0]
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losses = [t for t in trades if t["pnl"] <= 0]
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total_pnl = sum(t["pnl"] for t in trades)
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gross_profit = sum(t["pnl"] for t in wins) if wins else 0
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gross_loss = abs(sum(t["pnl"] for t in losses)) if losses else 1
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eq = np.array(equity)
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peak = np.maximum.accumulate(eq)
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drawdown = (eq - peak) / peak
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max_dd = float(drawdown.min()) * 100
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equity_curve = [{"index": i, "capital": v} for i, v in enumerate(equity)]
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return {
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"symbol": req.symbol,
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"strategy": req.strategy,
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"period": f"{req.start_date} → {req.end_date}",
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"total_trades": len(trades),
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"wins": len(wins),
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"losses": len(losses),
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"win_rate": round(len(wins) / len(trades) * 100, 1) if trades else 0,
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"total_pnl": round(total_pnl, 2),
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"total_return_pct": round((capital - req.capital) / req.capital * 100, 2),
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"max_drawdown_pct": round(max_dd, 2),
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"profit_factor": round(gross_profit / gross_loss, 2) if gross_loss else 0,
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"final_capital": round(capital, 2),
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"equity_curve": equity_curve,
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"trades": trades[-20:],
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}
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except Exception as e:
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return {"error": str(e)}
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