feat: trading bot MVP — ICT Order Block + Liquidity Sweep strategy
Full-stack trading bot with: - FastAPI backend with ICT strategy (Order Block + Liquidity Sweep detection) - Backtester engine with rolling window, spread simulation, and performance metrics - Hybrid market data service (yfinance + TwelveData with rate limiting + SQLite cache) - Simulated exchange for paper trading - React/TypeScript frontend with TradingView lightweight-charts v5 - Live dashboard with candlestick chart, OHLC legend, trade markers - Backtest page with configurable parameters, equity curve, and trade table - WebSocket support for real-time updates - Bot runner with asyncio loop for automated trading Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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147
backend/app/api/routes/backtest.py
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147
backend/app/api/routes/backtest.py
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from datetime import datetime
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from typing import Optional
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from fastapi import APIRouter, Depends, HTTPException
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from pydantic import BaseModel, Field
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.core.backtester import Backtester
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from app.core.database import get_db
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from app.core.strategy.order_block_sweep import OrderBlockSweepParams, OrderBlockSweepStrategy
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from app.models.backtest_result import BacktestResult
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from app.services.market_data import MarketDataService
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router = APIRouter(prefix="/backtest", tags=["backtest"])
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class BacktestRequest(BaseModel):
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instrument: str = Field(default="EUR_USD")
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granularity: str = Field(default="H1")
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candle_count: int = Field(default=500, ge=100, le=5000)
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initial_balance: float = Field(default=10_000.0, gt=0)
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risk_percent: float = Field(default=1.0, gt=0, le=10)
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rr_ratio: float = Field(default=2.0, ge=1.0, le=10.0)
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swing_strength: int = Field(default=5, ge=2, le=20)
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liquidity_tolerance_pips: float = Field(default=2.0, ge=0.5)
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spread_pips: float = Field(default=1.5, ge=0)
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@router.post("")
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async def run_backtest(
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req: BacktestRequest,
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db: AsyncSession = Depends(get_db),
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):
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service = MarketDataService(db)
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# Fetch données historiques (yfinance + TwelveData + cache DB)
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try:
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df = await service.get_candles(req.instrument, req.granularity, req.candle_count)
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except Exception as e:
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raise HTTPException(502, f"Erreur fetch données: {e}")
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if len(df) < 150:
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raise HTTPException(400, "Pas assez de données (min 150 bougies)")
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# Configurer la stratégie
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params = OrderBlockSweepParams(
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swing_strength=req.swing_strength,
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liquidity_tolerance_pips=req.liquidity_tolerance_pips,
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rr_ratio=req.rr_ratio,
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)
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strategy = OrderBlockSweepStrategy(params)
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# Lancer le backtest
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backtester = Backtester(
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strategy=strategy,
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initial_balance=req.initial_balance,
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risk_percent=req.risk_percent,
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spread_pips=req.spread_pips,
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)
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metrics = backtester.run(df)
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# Sauvegarder en BDD
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result = BacktestResult(
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instrument=req.instrument,
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granularity=req.granularity,
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start_date=df.iloc[0]["time"],
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end_date=df.iloc[-1]["time"],
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initial_balance=metrics.initial_balance,
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final_balance=metrics.final_balance,
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total_pnl=metrics.total_pnl,
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total_trades=metrics.total_trades,
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winning_trades=metrics.winning_trades,
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losing_trades=metrics.losing_trades,
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win_rate=metrics.win_rate,
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max_drawdown=metrics.max_drawdown,
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sharpe_ratio=metrics.sharpe_ratio,
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expectancy=metrics.expectancy,
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equity_curve=metrics.equity_curve,
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strategy_params=strategy.get_params(),
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)
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db.add(result)
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await db.commit()
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await db.refresh(result)
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# Formater les trades pour la réponse
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trades_out = [
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{
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"direction": t.direction,
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"entry_price": t.entry_price,
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"exit_price": t.exit_price,
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"stop_loss": t.stop_loss,
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"take_profit": t.take_profit,
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"entry_time": str(t.entry_time),
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"exit_time": str(t.exit_time) if t.exit_time else None,
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"pnl_pips": t.pnl_pips,
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"status": t.status,
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"signal_type": t.signal_type,
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}
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for t in metrics.trades
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]
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return {
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"backtest_id": result.id,
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"instrument": req.instrument,
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"granularity": req.granularity,
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"period": {"start": str(df.iloc[0]["time"]), "end": str(df.iloc[-1]["time"])},
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"metrics": {
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"initial_balance": metrics.initial_balance,
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"final_balance": round(metrics.final_balance, 2),
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"total_pnl": round(metrics.total_pnl, 2),
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"total_pnl_pct": round(metrics.total_pnl_pct, 2),
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"total_trades": metrics.total_trades,
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"winning_trades": metrics.winning_trades,
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"losing_trades": metrics.losing_trades,
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"win_rate": round(metrics.win_rate, 1),
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"avg_win_pips": round(metrics.avg_win_pips, 1),
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"avg_loss_pips": round(metrics.avg_loss_pips, 1),
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"expectancy": round(metrics.expectancy, 2),
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"max_drawdown": round(metrics.max_drawdown, 2),
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"max_drawdown_pct": round(metrics.max_drawdown_pct, 2),
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"sharpe_ratio": round(metrics.sharpe_ratio, 3),
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"profit_factor": round(metrics.profit_factor, 2),
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},
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"equity_curve": metrics.equity_curve,
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"trades": trades_out,
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}
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@router.get("/history")
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async def get_backtest_history(db: AsyncSession = Depends(get_db)):
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from sqlalchemy import select
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stmt = select(BacktestResult).order_by(BacktestResult.created_at.desc()).limit(20)
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result = await db.execute(stmt)
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rows = result.scalars().all()
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return [
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{
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"id": r.id,
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"instrument": r.instrument,
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"granularity": r.granularity,
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"total_pnl": r.total_pnl,
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"win_rate": r.win_rate,
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"total_trades": r.total_trades,
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"sharpe_ratio": r.sharpe_ratio,
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"created_at": str(r.created_at),
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}
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for r in rows
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]
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