Initial commit - AHAD QUANT v1
This commit is contained in:
Abdoul Ahad Binizi
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"""
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AHAD QUANT — Configuration (Forex Edition)
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Loads all settings from environment variables with sensible defaults.
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"""
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import os
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from dotenv import load_dotenv
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load_dotenv(override=True)
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# ─── Exchange / Broker selection ─────────────────────────────────────────────
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# Valeurs acceptées : oanda | mt5 | alpaca | ccxt | ib | paper
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EXCHANGE: str = os.getenv("EXCHANGE", "oanda")
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# ─── Source de données historiques (INDÉPENDANTE du broker d'exécution) ───────
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# auto → OANDA si clé disponible, sinon yfinance
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# oanda → OANDA v20 REST API (clé requise)
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# yfinance → Yahoo Finance (gratuit, sans clé, 2 ans max)
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# twelvedata → Twelve Data (800 req/j gratuit, clé recommandée)
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# alphavantage→ Alpha Vantage (25 req/j gratuit)
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# mt5 → MT5 Python SDK (Windows uniquement)
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# ccxt → via broker CCXT_BROKER
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DATA_SOURCE: str = os.getenv("DATA_SOURCE", "auto")
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# ─── Paper Mode (simulate trades, no real money) ─────────────────────────────
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PAPER_MODE: bool = os.getenv("PAPER_MODE", "false").lower() == "true"
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PAPER_INITIAL_BALANCE: float = float(os.getenv("PAPER_INITIAL_BALANCE", "100.0"))
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# ─── OANDA credentials (primary Forex broker) ────────────────────────────────
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OANDA_API_KEY: str = os.getenv("OANDA_API_KEY", "")
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OANDA_ACCOUNT_ID: str = os.getenv("OANDA_ACCOUNT_ID", "")
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OANDA_PRACTICE: bool = os.getenv("OANDA_PRACTICE", "true").lower() == "true"
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# ─── MetaTrader 5 credentials ─────────────────────────────────────────────────
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MT5_LOGIN: int = int(os.getenv("MT5_LOGIN", "0"))
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MT5_PASSWORD: str = os.getenv("MT5_PASSWORD", "")
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MT5_SERVER: str = os.getenv("MT5_SERVER", "")
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# ─── MT5 CSV Bridge ────────────────────────────────────────────────────────────
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# Activer le bridge CSV pour connecter AHAD QUANT à un EA MetaTrader 5
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# Mettre EXCHANGE=mt5 dans .env pour utiliser ce mode
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MT5_BRIDGE_ENABLED: bool = os.getenv("MT5_BRIDGE_ENABLED", "false").lower() == "true"
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MT5_FILES_PATH: str = os.getenv("MT5_FILES_PATH", "") # Chemin vers MQL5/Files/
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MT5_POLL_INTERVAL: float = float(os.getenv("MT5_POLL_INTERVAL", "0.2")) # secondes
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MT5_SIGNAL_TIMEOUT: int = int(os.getenv("MT5_SIGNAL_TIMEOUT", "300")) # secondes (5 min)
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# Suffixe symbole MT5 — certains brokers ajoutent .m, .r, .pro, etc.
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# Ex: ICMarkets, Pepperstone → "USDJPY.m" | La plupart → ""
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MT5_SYMBOL_SUFFIX: str = os.getenv("MT5_SYMBOL_SUFFIX", "") # Ex: ".m"
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# ─── Alpaca Markets (paper + live, API REST, très accessible) ────────────────
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# Paper trading : https://paper-api.alpaca.markets
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# Live trading : https://api.alpaca.markets
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ALPACA_API_KEY: str = os.getenv("ALPACA_API_KEY", "")
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ALPACA_SECRET: str = os.getenv("ALPACA_SECRET", "")
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ALPACA_PAPER: bool = os.getenv("ALPACA_PAPER", "true").lower() == "true"
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# ─── CCXT — broker générique (IG, GAIN Capital, Binance, Bybit, etc.) ────────
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# Définir EXCHANGE=ccxt puis CCXT_BROKER=nom_du_broker (ex: "ig", "okcoin")
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CCXT_BROKER: str = os.getenv("CCXT_BROKER", "")
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CCXT_API_KEY: str = os.getenv("CCXT_API_KEY", "")
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CCXT_API_SECRET: str = os.getenv("CCXT_API_SECRET", "")
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CCXT_PASSPHRASE: str = os.getenv("CCXT_PASSPHRASE", "")
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CCXT_SANDBOX: bool = os.getenv("CCXT_SANDBOX", "false").lower() == "true"
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# ─── Twelve Data (données historiques — 800 req/j gratuit) ───────────────────
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# Inscription : https://twelvedata.com/ (plan Free suffisant pour backtests)
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TWELVE_DATA_API_KEY: str = os.getenv("TWELVE_DATA_API_KEY", "")
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# ─── Alpha Vantage (données historiques — 25 req/j gratuit) ──────────────────
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# Inscription : https://www.alphavantage.co/support/#api-key
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ALPHA_VANTAGE_API_KEY: str = os.getenv("ALPHA_VANTAGE_API_KEY", "")
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# ─── Interactive Brokers (TWS / IB Gateway) ───────────────────────────────────
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IB_HOST: str = os.getenv("IB_HOST", "127.0.0.1")
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IB_PORT: int = int(os.getenv("IB_PORT", "7497"))
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IB_CLIENT_ID: int = int(os.getenv("IB_CLIENT_ID", "1"))
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# ─── Fee rates (Forex spread equivalent) ─────────────────────────────────────
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# ~0.2 pip for majors on ECN accounts ≈ 0.00002 (2 pips round-trip = 0.00004)
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FEE_RATE: float = float(os.getenv("FEE_RATE", "0.00004"))
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MAKER_FEE_RATE: float = float(os.getenv("MAKER_FEE_RATE", "0.00002"))
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# ─── Trading parameters ───────────────────────────────────────────────────────
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# ESMA: 30:1 majors, 20:1 minors/gold, 10:1 commodities
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# Offshore brokers: up to 500:1 — use responsibly
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LEVERAGE: int = int(os.getenv("LEVERAGE", "30"))
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MAX_POSITIONS: int = int(os.getenv("MAX_POSITIONS", "5"))
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RISK_PER_TRADE: float = float(os.getenv("RISK_PER_TRADE", "0.01")) # 1% of equity (Forex standard)
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MAX_DAILY_LOSS_PCT: float = float(os.getenv("MAX_DAILY_LOSS_PCT", "0.03"))
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# ─── Forex lot sizing ─────────────────────────────────────────────────────────
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# 1 standard lot = 100,000 units of base currency
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# 1 mini lot = 10,000 units
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# 1 micro lot = 1,000 units
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UNITS_PER_LOT: int = 100_000
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MIN_LOT_SIZE: float = float(os.getenv("MIN_LOT_SIZE", "0.01")) # micro lot
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MAX_LOT_SIZE: float = float(os.getenv("MAX_LOT_SIZE", "10.0")) # 10 standard lots
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# ─── Risk management ──────────────────────────────────────────────────────────
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STOP_LOSS_PCT: float = float(os.getenv("STOP_LOSS_PCT", "0.0050")) # 50 pips on EUR/USD ≈ 0.5%
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TAKE_PROFIT_PCT: float = float(os.getenv("TAKE_PROFIT_PCT", "0.0075")) # 75 pips — R:R 1:1.5
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CIRCUIT_BREAKER_LOSSES: int = int(os.getenv("CIRCUIT_BREAKER_LOSSES", "3"))
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CIRCUIT_BREAKER_COOLDOWN: int = int(os.getenv("CIRCUIT_BREAKER_COOLDOWN", "3600"))
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# ─── Multi-target TP (ATR-based) ──────────────────────────────────────────────
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MULTI_TP_ENABLED: bool = os.getenv("MULTI_TP_ENABLED", "true").lower() == "true"
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TP1_ATR_MULT: float = float(os.getenv("TP1_ATR_MULT", "0.8"))
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TP2_ATR_MULT: float = float(os.getenv("TP2_ATR_MULT", "1.3"))
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# ─── Auto-Unstuck ─────────────────────────────────────────────────────────────
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AUTO_UNSTUCK_ENABLED: bool = os.getenv("AUTO_UNSTUCK_ENABLED", "true").lower() == "true"
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_UNSTUCK_DEFAULT = "[[-0.02, 0.25], [-0.03, 0.25], [-0.04, 0.25], [-0.05, 1.0]]"
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try:
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import json as _json
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UNSTUCK_LEVELS: list = [tuple(x) for x in _json.loads(os.getenv("UNSTUCK_LEVELS", _UNSTUCK_DEFAULT))]
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except Exception:
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UNSTUCK_LEVELS: list = [(-0.02, 0.25), (-0.03, 0.25), (-0.04, 0.25), (-0.05, 1.0)]
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# ─── Model / AI ───────────────────────────────────────────────────────────────
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MODEL_PATH: str = os.getenv("MODEL_PATH", "model.pkl")
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ENSEMBLE_MODEL_PATH: str = os.getenv("ENSEMBLE_MODEL_PATH", "model_ensemble.pkl")
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MIN_CONFIDENCE: float = float(os.getenv("MIN_CONFIDENCE", "0.72"))
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USE_ENSEMBLE: bool = os.getenv("USE_ENSEMBLE", "true").lower() == "true"
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USE_REGIME_FILTER: bool = os.getenv("USE_REGIME_FILTER", "false").lower() == "true"
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# ─── Sizing & capital controls ────────────────────────────────────────────────
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COMPOUND_ENABLED: bool = os.getenv("COMPOUND_ENABLED", "true").lower() == "true"
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MAX_MARGIN_USAGE: float = float(os.getenv("MAX_MARGIN_USAGE", "0.20"))
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CAPITAL_CAP_PER_TRADE: float = float(os.getenv("CAPITAL_CAP_PER_TRADE", "1000000"))
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# ─── Timeout / hold duration ──────────────────────────────────────────────────
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TIMEOUT_ENABLED: bool = os.getenv("TIMEOUT_ENABLED", "true").lower() == "true"
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MAX_HOLD_CANDLES: int = int(os.getenv("MAX_HOLD_CANDLES", "3"))
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# ─── Auto-Retraining ──────────────────────────────────────────────────────────
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# AUTO_RETRAIN_ENABLED/AUTO_RETRAIN_INTERVAL_HOURS gouvernent UNIQUEMENT le
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# ré-entraînement complet (download_data.py + train.py) — lourd, dépendant du
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# réseau (téléchargement de nouvelles bougies), donc volontairement PLUS
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# automatique par défaut (reste disponible en appel manuel).
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AUTO_RETRAIN_ENABLED: bool = os.getenv("AUTO_RETRAIN_ENABLED", "false").lower() == "true"
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AUTO_RETRAIN_INTERVAL_HOURS: int = int(os.getenv("AUTO_RETRAIN_INTERVAL_HOURS", "24"))
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AUTO_RETRAIN_MIN_ACCURACY: float = float(os.getenv("AUTO_RETRAIN_MIN_ACCURACY", "0.55"))
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# DAILY_LOCAL_RETRAIN_ENABLED gouverne le cycle UNIFIÉ quotidien et 100% LOCAL
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# (aucun téléchargement réseau) : warm-start ML + fine-tune RL, tous deux sur
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# les données déjà sur disque + le buffer de trades réels, avec acceptation
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# uniquement si le nouveau modèle est meilleur (sinon l'ancien est conservé).
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# C'est ce cycle qui tourne automatiquement en arrière-plan par défaut.
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DAILY_LOCAL_RETRAIN_ENABLED: bool = os.getenv("DAILY_LOCAL_RETRAIN_ENABLED", "true").lower() == "true"
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DAILY_LOCAL_RETRAIN_INTERVAL_HOURS: int = int(os.getenv("DAILY_LOCAL_RETRAIN_INTERVAL_HOURS", "24"))
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# ─── Grid Bot ─────────────────────────────────────────────────────────────────
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GRID_BOT_ENABLED: bool = os.getenv("GRID_BOT_ENABLED", "false").lower() == "true"
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GRID_PAIR: str = os.getenv("GRID_PAIR", "EURUSD")
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GRID_COIN: str = GRID_PAIR # backward-compat alias
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GRID_STRATEGY: str = os.getenv("GRID_STRATEGY", "neutral")
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GRID_LOWER: float = float(os.getenv("GRID_LOWER", "0"))
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GRID_UPPER: float = float(os.getenv("GRID_UPPER", "0"))
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GRID_LEVELS: int = int(os.getenv("GRID_LEVELS", "10"))
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GRID_TOTAL_USDT: float = float(os.getenv("GRID_TOTAL_USDT", "1000"))
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GRID_LEVERAGE: int = int(os.getenv("GRID_LEVERAGE", "10"))
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# ─── DCA Bot ──────────────────────────────────────────────────────────────────
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DCA_BOT_ENABLED: bool = os.getenv("DCA_BOT_ENABLED", "false").lower() == "true"
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DCA_PAIR: str = os.getenv("DCA_PAIR", "EURUSD")
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DCA_COIN: str = DCA_PAIR # backward-compat alias
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DCA_STRATEGY: str = os.getenv("DCA_STRATEGY", "classic")
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DCA_BASE_ORDER_USDT: float = float(os.getenv("DCA_BASE_ORDER_USDT", "100"))
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DCA_SAFETY_ORDER_USDT: float = float(os.getenv("DCA_SAFETY_ORDER_USDT", "50"))
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DCA_MAX_SAFETY_ORDERS: int = int(os.getenv("DCA_MAX_SAFETY_ORDERS", "5"))
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DCA_PRICE_DEVIATION: float = float(os.getenv("DCA_PRICE_DEVIATION", "0.0020")) # 20 pips
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DCA_TAKE_PROFIT_PCT: float = float(os.getenv("DCA_TAKE_PROFIT_PCT", "0.0030")) # 30 pips
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# ─── Session Scanner (replaces Pump Scanner in Forex) ─────────────────────────
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PUMP_SCANNER_ENABLED: bool = os.getenv("SESSION_SCANNER_ENABLED", "false").lower() == "true"
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PUMP_VOLUME_MULT: float = float(os.getenv("BREAKOUT_VOLUME_MULT", "2.0"))
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PUMP_PRICE_PCT: float = float(os.getenv("BREAKOUT_PRICE_PCT", "0.003")) # 30 pips
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PUMP_LEVERAGE: int = int(os.getenv("BREAKOUT_LEVERAGE", "10"))
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PUMP_RISK_PCT: float = float(os.getenv("BREAKOUT_RISK_PCT", "0.01"))
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# ─── Session filter ───────────────────────────────────────────────────────────
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# Trade only during high-liquidity Forex sessions (UTC hours)
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SESSION_FILTER_ENABLED: bool = os.getenv("SESSION_FILTER_ENABLED", "false").lower() == "true"
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# London: 07:00-16:00 UTC | New York: 12:00-21:00 UTC | Overlap: 12:00-16:00
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SESSION_LONDON_START: int = int(os.getenv("SESSION_LONDON_START", "7"))
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SESSION_LONDON_END: int = int(os.getenv("SESSION_LONDON_END", "16"))
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SESSION_NY_START: int = int(os.getenv("SESSION_NY_START", "12"))
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SESSION_NY_END: int = int(os.getenv("SESSION_NY_END", "21"))
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# ─── Data ─────────────────────────────────────────────────────────────────────
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CANDLE_INTERVAL: str = os.getenv("CANDLE_INTERVAL", "1h")
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DATA_DIR: str = os.getenv("DATA_DIR", "data")
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# ─── Forex pairs to trade ──────────────────────────────────────────────────────
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# Majors (USD pairs)
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# Crosses (non-USD)
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_PAIRS_DEFAULT = (
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"EURUSD,GBPUSD,USDJPY,USDCHF,AUDUSD,NZDUSD,USDCAD,"
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"EURGBP,EURJPY,EURCAD,EURCHF,EURAUD,EURNZD,"
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"GBPJPY,GBPCAD,GBPCHF,GBPAUD,"
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"AUDCAD,AUDNZD,AUDJPY,AUDCHF,"
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"CADJPY,CHFJPY,NZDJPY,NZDCAD"
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)
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PAIRS: list[str] = [p.strip() for p in os.getenv("PAIRS", _PAIRS_DEFAULT).split(",") if p.strip()]
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# Backward-compat alias (backtest.py, train.py, pump_scanner.py use config.COINS)
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COINS: list[str] = PAIRS
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# ─── Loop timing ──────────────────────────────────────────────────────────────
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MAIN_LOOP_SECONDS: int = int(os.getenv("MAIN_LOOP_SECONDS", "60"))
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# ─── Reinforcement Learning (RL Agent) ───────────────────────────────────────
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# Activer le filtre RL (nécessite rl_agent.zip + rl_scaler.pkl entraînés)
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USE_RL_AGENT: bool = os.getenv("USE_RL_AGENT", "false").lower() == "true"
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# Chemin vers le modèle PPO sauvegardé par rl_train.py
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RL_MODEL_PATH: str = os.getenv("RL_MODEL_PATH", "rl_agent")
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UNIFIED_MODEL_PATH: str = os.getenv("UNIFIED_MODEL_PATH", "ahad_quant_unified.zip")
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# Chemin vers le scaler (mean/std features) sauvegardé par rl_train.py
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RL_SCALER_PATH: str = os.getenv("RL_SCALER_PATH", "rl_scaler.pkl")
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# Mode du filtre RL :
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# "filter" → RL valide/rejette les signaux ML (recommandé en production)
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# "override" → RL génère ses propres signaux, ML ignoré (expérimental)
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RL_MODE: str = os.getenv("RL_MODE", "filter")
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# Confidence boost quand RL et ML sont en accord (+5% par défaut)
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RL_CONFIDENCE_BOOST: float = float(os.getenv("RL_CONFIDENCE_BOOST", "0.05"))
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# Seuil ML pour override le NEUTRAL RL (signal passe même si RL dit HOLD)
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RL_OVERRIDE_THRESHOLD: float = float(os.getenv("RL_OVERRIDE_THRESHOLD", "0.82"))
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# Re-entraînement RL hebdomadaire (fine-tuning sur nouvelles données)
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RL_AUTO_RETRAIN_ENABLED: bool = os.getenv("RL_AUTO_RETRAIN_ENABLED", "true").lower() == "true"
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RL_RETRAIN_INTERVAL_HOURS: int = int(os.getenv("RL_RETRAIN_INTERVAL_HOURS", "24")) # 1 jour (était 7 jours)
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RL_FINETUNE_STEPS: int = int(os.getenv("RL_FINETUNE_STEPS", "200000")) # 200k steps
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# ─── Apprentissage Continu des Erreurs (V7) ───────────────────────────────────
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# Système d'apprentissage permanent à partir de chaque trade fermé.
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# Comprend : replay buffer, warm-start LGB quotidien, calibration seuil,
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# injection trades réels dans PPO, monitoring drift.
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# Activer/désactiver tout le système d'apprentissage continu
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CONTINUOUS_LEARNING_ENABLED: bool = os.getenv("CONTINUOUS_LEARNING_ENABLED", "true").lower() == "true"
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# Taille max du replay buffer (trades, FIFO)
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EXPERIENCE_BUFFER_MAX_SIZE: int = int(os.getenv("EXPERIENCE_BUFFER_MAX_SIZE", "10000"))
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# LightGBM warm-start quotidien sur les trades LOSS + TIMEOUT
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WARMSTART_ENABLED: bool = os.getenv("WARMSTART_ENABLED", "true").lower() == "true"
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# Nombre minimum de trades échoués pour déclencher le warm-start
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WARMSTART_MIN_TRADES: int = int(os.getenv("WARMSTART_MIN_TRADES", "5")) # [FIX] 20→5 : ne plus bloquer en phase de bonne performance
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# Injection des vrais trades dans le fine-tune PPO quotidien
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# [21/06/2026] Devenu le SEUL mode d'entraînement RL du cycle quotidien :
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# fine_tune_real_only() s'entraîne à 100% sur ces trades réels, zéro
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# simulation. RL_REPLAY_MIN_TRADES = volume minimum requis avant de
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# lancer ce fine-tune ; sous ce seuil, le cycle RL est skip (pas de repli
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# sur AhadQuantForexEnv).
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RL_REAL_REPLAY_ENABLED: bool = os.getenv("RL_REAL_REPLAY_ENABLED", "true").lower() == "true"
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RL_REPLAY_MIN_TRADES: int = int(os.getenv("RL_REPLAY_MIN_TRADES", "10")) # [FIX] 50→10 : ne plus bloquer au démarrage du bot
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# Surveillance des métriques (drift, alertes, pause auto)
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MONITOR_ENABLED: bool = os.getenv("MONITOR_ENABLED", "true").lower() == "true"
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# Pause auto du trading si win_rate < 35% (basculement PAPER_MODE=true)
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EMERGENCY_PAUSE_ENABLED: bool = os.getenv("EMERGENCY_PAUSE_ENABLED", "true").lower() == "true"
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