# ══════════════════════════════════════════════════════════════ # anis solidscale - Elite Spot Trading Suite # STRATEGIE : RSI2Connors # CATEGORIE : Mean-Reversion — RSI(2) de Larry Connors adapte crypto # ══════════════════════════════════════════════════════════════ # # LOGIQUE : # RSI(2) ultra-court terme detecte les conditions de survente extreme. # Filtre tendance SMA(200) pour n'acheter que dans un uptrend. # 1. RSI(2) < seuil_entree (ex: 5) + prix > SMA(200) → long # 2. Sortie : RSI(2) > seuil_sortie (ex: 70) # SOURCE : QuantifiedStrategies — WR 70-80% sur actions depuis 1990 # ══════════════════════════════════════════════════════════════ import sys from pathlib import Path from pandas import DataFrame from freqtrade.strategy import IStrategy, IntParameter sys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent)) from utils.indicators import CommonIndicators from utils.logging_utils import TradeLogger from utils.telegram_notifier import TelegramNotifier class RSI2Connors(IStrategy): INTERFACE_VERSION = 3 can_short = False timeframe = "4h" startup_candle_count = 210 minimal_roi = {"0": 0.08, "120": 0.04, "360": 0.02, "720": 0.01} stoploss = -0.05 trailing_stop = True trailing_stop_positive = 0.015 trailing_stop_positive_offset = 0.025 trailing_only_offset_is_reached = True # ── Buy params ── rsi_period = IntParameter(2, 5, default=2, space="buy") rsi_entry = IntParameter(3, 15, default=5, space="buy") sma_period = IntParameter(150, 250, default=200, space="buy") # ── Sell params ── rsi_exit = IntParameter(50, 90, default=70, space="sell") _logger = None _notifier = None def __getstate__(self): state = self.__dict__.copy() state["_logger"] = None state["_notifier"] = None return state def __setstate__(self, state): self.__dict__.update(state) def _init_utils(self) -> None: if self._logger is None: self._logger = TradeLogger(strategy_name="RSI2Connors") self._notifier = TelegramNotifier() def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: self._init_utils() # Pre-calc RSI pour TOUTES les valeurs possibles for rsi_p in range(self.rsi_period.low, self.rsi_period.high + 1): dataframe = CommonIndicators.add_rsi(dataframe, period=rsi_p) # Pre-calc SMA pour TOUTES les valeurs possibles for sma_p in range(self.sma_period.low, self.sma_period.high + 1): dataframe = CommonIndicators.add_sma(dataframe, period=sma_p) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: rsi_col = f"rsi_{self.rsi_period.value}" sma_col = f"sma_{self.sma_period.value}" conditions = ( (dataframe[rsi_col] < self.rsi_entry.value) & (dataframe["close"] > dataframe[sma_col]) & (dataframe["volume"] > 0) ) dataframe.loc[conditions, "enter_long"] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: rsi_col = f"rsi_{self.rsi_period.value}" conditions = ( dataframe[rsi_col] > self.rsi_exit.value ) dataframe.loc[conditions, "exit_long"] = 1 return dataframe