trader-bot/backend/app/api/routes/backtest.py

from datetime import datetime
from typing import Optional

from fastapi import APIRouter, Depends, HTTPException
from pydantic import BaseModel, Field
from sqlalchemy.ext.asyncio import AsyncSession

from app.core.backtester import Backtester
from app.core.database import get_db
from app.core.strategy.order_block_sweep import OrderBlockSweepParams, OrderBlockSweepStrategy
from app.models.backtest_result import BacktestResult
from app.services.market_data import MarketDataService

router = APIRouter(prefix="/backtest", tags=["backtest"])


class BacktestRequest(BaseModel):
    instrument: str = Field(default="EUR_USD")
    granularity: str = Field(default="H1")
    candle_count: int = Field(default=500, ge=100, le=5000)
    initial_balance: float = Field(default=10_000.0, gt=0)
    risk_percent: float = Field(default=1.0, gt=0, le=10)
    rr_ratio: float = Field(default=2.0, ge=1.0, le=10.0)
    swing_strength: int = Field(default=5, ge=2, le=20)
    liquidity_tolerance_pips: float = Field(default=2.0, ge=0.5)
    spread_pips: float = Field(default=1.5, ge=0)


@router.post("")
async def run_backtest(
    req: BacktestRequest,
    db: AsyncSession = Depends(get_db),
):
    service = MarketDataService(db)

    # Fetch données historiques (yfinance + TwelveData + cache DB)
    try:
        df = await service.get_candles(req.instrument, req.granularity, req.candle_count)
    except Exception as e:
        raise HTTPException(502, f"Erreur fetch données: {e}")

    if len(df) < 150:
        raise HTTPException(400, "Pas assez de données (min 150 bougies)")

    # Configurer la stratégie
    params = OrderBlockSweepParams(
        swing_strength=req.swing_strength,
        liquidity_tolerance_pips=req.liquidity_tolerance_pips,
        rr_ratio=req.rr_ratio,
    )
    strategy = OrderBlockSweepStrategy(params)

    # Lancer le backtest
    backtester = Backtester(
        strategy=strategy,
        initial_balance=req.initial_balance,
        risk_percent=req.risk_percent,
        spread_pips=req.spread_pips,
    )
    metrics = backtester.run(df)

    # Sauvegarder en BDD
    result = BacktestResult(
        instrument=req.instrument,
        granularity=req.granularity,
        start_date=df.iloc[0]["time"],
        end_date=df.iloc[-1]["time"],
        initial_balance=metrics.initial_balance,
        final_balance=metrics.final_balance,
        total_pnl=metrics.total_pnl,
        total_trades=metrics.total_trades,
        winning_trades=metrics.winning_trades,
        losing_trades=metrics.losing_trades,
        win_rate=metrics.win_rate,
        max_drawdown=metrics.max_drawdown,
        sharpe_ratio=metrics.sharpe_ratio,
        expectancy=metrics.expectancy,
        equity_curve=metrics.equity_curve,
        strategy_params=strategy.get_params(),
    )
    db.add(result)
    await db.commit()
    await db.refresh(result)

    # Formater les trades pour la réponse
    trades_out = [
        {
            "direction": t.direction,
            "entry_price": t.entry_price,
            "exit_price": t.exit_price,
            "stop_loss": t.stop_loss,
            "take_profit": t.take_profit,
            "entry_time": str(t.entry_time),
            "exit_time": str(t.exit_time) if t.exit_time else None,
            "pnl_pips": t.pnl_pips,
            "status": t.status,
            "signal_type": t.signal_type,
        }
        for t in metrics.trades
    ]

    return {
        "backtest_id": result.id,
        "instrument": req.instrument,
        "granularity": req.granularity,
        "period": {"start": str(df.iloc[0]["time"]), "end": str(df.iloc[-1]["time"])},
        "metrics": {
            "initial_balance": metrics.initial_balance,
            "final_balance": round(metrics.final_balance, 2),
            "total_pnl": round(metrics.total_pnl, 2),
            "total_pnl_pct": round(metrics.total_pnl_pct, 2),
            "total_trades": metrics.total_trades,
            "winning_trades": metrics.winning_trades,
            "losing_trades": metrics.losing_trades,
            "win_rate": round(metrics.win_rate, 1),
            "avg_win_pips": round(metrics.avg_win_pips, 1),
            "avg_loss_pips": round(metrics.avg_loss_pips, 1),
            "expectancy": round(metrics.expectancy, 2),
            "max_drawdown": round(metrics.max_drawdown, 2),
            "max_drawdown_pct": round(metrics.max_drawdown_pct, 2),
            "sharpe_ratio": round(metrics.sharpe_ratio, 3),
            "profit_factor": round(metrics.profit_factor, 2),
        },
        "equity_curve": metrics.equity_curve,
        "trades": trades_out,
    }


@router.get("/history")
async def get_backtest_history(db: AsyncSession = Depends(get_db)):
    from sqlalchemy import select
    stmt = select(BacktestResult).order_by(BacktestResult.created_at.desc()).limit(20)
    result = await db.execute(stmt)
    rows = result.scalars().all()
    return [
        {
            "id": r.id,
            "instrument": r.instrument,
            "granularity": r.granularity,
            "total_pnl": r.total_pnl,
            "win_rate": r.win_rate,
            "total_trades": r.total_trades,
            "sharpe_ratio": r.sharpe_ratio,
            "created_at": str(r.created_at),
        }
        for r in rows
    ]