tika
4df8d53b1a
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>
135 lines
4.1 KiB
Python
135 lines
4.1 KiB
Python
"""
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Provider yfinance — données OHLCV gratuites.
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Limites :
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- M1 : 7 derniers jours
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- M5/M15/M30 : 60 derniers jours
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- H1/H4 : 730 derniers jours
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- D : illimité
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"""
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import asyncio
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import logging
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from datetime import datetime, timedelta, timezone
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from typing import Optional
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import pandas as pd
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from app.services.data_providers.constants import (
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GRANULARITY_TO_YF,
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INSTRUMENT_TO_YF,
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YF_MAX_DAYS,
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)
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logger = logging.getLogger(__name__)
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def _normalize(df: pd.DataFrame) -> pd.DataFrame:
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"""Normalise un DataFrame yfinance vers le format interne."""
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df = df.copy()
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df.index = pd.to_datetime(df.index, utc=True)
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df.index = df.index.tz_localize(None) if df.index.tz is not None else df.index
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df.columns = [c.lower() for c in df.columns]
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# yfinance peut retourner des colonnes multi-index
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if isinstance(df.columns, pd.MultiIndex):
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df.columns = df.columns.get_level_values(0)
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df = df.rename(columns={"adj close": "close"})[["open", "high", "low", "close", "volume"]]
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df = df.dropna(subset=["open", "high", "low", "close"])
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df.index.name = "time"
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df = df.reset_index()
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df["time"] = pd.to_datetime(df["time"]).dt.tz_localize(None)
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return df
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def _fetch_sync(
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yf_symbol: str,
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yf_interval: str,
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start: datetime,
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end: datetime,
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) -> pd.DataFrame:
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"""Exécution synchrone de yfinance (sera appelée dans un thread)."""
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import yfinance as yf
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ticker = yf.Ticker(yf_symbol)
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df = ticker.history(
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interval=yf_interval,
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start=start.strftime("%Y-%m-%d"),
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end=(end + timedelta(days=1)).strftime("%Y-%m-%d"),
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auto_adjust=True,
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prepost=False,
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)
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return df
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class YFinanceProvider:
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"""Fetche des candles depuis Yahoo Finance."""
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def yf_cutoff(self, granularity: str) -> Optional[datetime]:
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"""Retourne la date la plus ancienne que yfinance peut fournir."""
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max_days = YF_MAX_DAYS.get(granularity)
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if max_days is None:
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return None
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return datetime.utcnow() - timedelta(days=max_days - 1)
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def can_provide(self, granularity: str, start: datetime) -> bool:
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"""Vérifie si yfinance peut fournir des données pour cette période."""
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cutoff = self.yf_cutoff(granularity)
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if cutoff is None:
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return False
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return start >= cutoff
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async def fetch(
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self,
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instrument: str,
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granularity: str,
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start: datetime,
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end: Optional[datetime] = None,
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) -> pd.DataFrame:
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"""
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Fetche les candles pour la période [start, end].
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Tronque start à la limite yfinance si nécessaire.
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"""
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yf_symbol = INSTRUMENT_TO_YF.get(instrument)
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yf_interval = GRANULARITY_TO_YF.get(granularity)
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if not yf_symbol or not yf_interval:
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logger.warning("yfinance : instrument ou granularité non supporté — %s %s", instrument, granularity)
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return pd.DataFrame()
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# Tronquer start à la limite yfinance
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cutoff = self.yf_cutoff(granularity)
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if cutoff and start < cutoff:
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logger.debug("yfinance : start tronqué de %s à %s", start, cutoff)
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start = cutoff
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if end is None:
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end = datetime.utcnow()
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if start >= end:
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return pd.DataFrame()
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logger.info(
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"yfinance fetch : %s (%s) %s → %s",
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instrument, granularity, start.strftime("%Y-%m-%d"), end.strftime("%Y-%m-%d"),
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)
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try:
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loop = asyncio.get_event_loop()
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raw = await loop.run_in_executor(
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None, _fetch_sync, yf_symbol, yf_interval, start, end
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)
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except Exception as e:
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logger.error("yfinance erreur : %s", e)
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return pd.DataFrame()
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if raw.empty:
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logger.warning("yfinance : aucune donnée pour %s %s", instrument, granularity)
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return pd.DataFrame()
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df = _normalize(raw)
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df = df[(df["time"] >= start) & (df["time"] <= end)]
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logger.info("yfinance : %d bougies récupérées pour %s %s", len(df), instrument, granularity)
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return df
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