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>
67 lines
1.9 KiB
Python
67 lines
1.9 KiB
Python
from abc import ABC, abstractmethod
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from dataclasses import dataclass, field
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from typing import Optional
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import pandas as pd
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@dataclass
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class OrderBlockZone:
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"""Zone Order Block à surveiller."""
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id: str
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direction: str # "bullish" | "bearish"
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top: float # Haut de la zone
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bottom: float # Bas de la zone
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origin_time: pd.Timestamp
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mitigated: bool = False # True si le prix a traversé la zone
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@dataclass
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class LiquidityLevel:
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"""Niveau de liquidité (Equal H/L)."""
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id: str
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direction: str # "high" | "low"
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price: float
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origin_time: pd.Timestamp
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swept: bool = False # True si le prix a dépassé ce niveau
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@dataclass
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class TradeSignal:
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"""Signal de trading généré par la stratégie."""
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direction: str # "buy" | "sell"
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entry_price: float
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stop_loss: float
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take_profit: float
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signal_type: str # description du setup
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time: pd.Timestamp
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order_block: Optional[OrderBlockZone] = None
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liquidity_level: Optional[LiquidityLevel] = None
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@dataclass
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class AnalysisResult:
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"""Résultat complet de l'analyse de la stratégie."""
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order_blocks: list[OrderBlockZone] = field(default_factory=list)
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liquidity_levels: list[LiquidityLevel] = field(default_factory=list)
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signals: list[TradeSignal] = field(default_factory=list)
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class AbstractStrategy(ABC):
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"""Interface commune pour toutes les stratégies."""
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@abstractmethod
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def analyze(self, df: pd.DataFrame) -> AnalysisResult:
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"""
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Analyse un DataFrame de candles et retourne les zones,
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niveaux de liquidité et signaux d'entrée.
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df doit avoir les colonnes : time, open, high, low, close, volume
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"""
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...
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@abstractmethod
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def get_params(self) -> dict:
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"""Retourne les paramètres configurables de la stratégie."""
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...
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