# ══════════════════════════════════════════════════════════════ # anis solidscale - Elite Spot Trading Suite # STRATEGIE : OBVTrendConfirm # CATEGORIE : Volume-trend — OBV Accumulation Confirmee # ══════════════════════════════════════════════════════════════ # # LOGIQUE : # OBV (On-Balance Volume) precede le prix de 2-5 bougies (Granville). # 1. OBV > OBV_SMA (accumulation en cours) # 2. OBV rising sur N bougies (momentum volume) # 3. EMA trend filter + RSI pas en surachat # 4. Sortie : OBV < OBV_SMA (distribution) # ══════════════════════════════════════════════════════════════ 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 OBVTrendConfirm(IStrategy): INTERFACE_VERSION = 3 can_short = False timeframe = "4h" startup_candle_count = 80 minimal_roi = {"0": 0.10, "240": 0.05, "720": 0.03, "1440": 0.01} stoploss = -0.06 trailing_stop = True trailing_stop_positive = 0.02 trailing_stop_positive_offset = 0.03 trailing_only_offset_is_reached = True # ── Buy params ── obv_sma_period = IntParameter(10, 30, default=20, space="buy") obv_rising = IntParameter(2, 6, default=3, space="buy") ema_period = IntParameter(30, 70, default=50, space="buy") rsi_period = IntParameter(7, 21, default=14, space="buy") rsi_max = IntParameter(60, 75, default=70, space="buy") # ── Sell params ── rsi_exit = IntParameter(65, 80, default=75, 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="OBVTrendConfirm") self._notifier = TelegramNotifier() def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: self._init_utils() for p in range(self.obv_sma_period.low, self.obv_sma_period.high + 1): dataframe = CommonIndicators.add_obv(dataframe, sma_period=p) for p in range(self.ema_period.low, self.ema_period.high + 1): dataframe = CommonIndicators.add_ema(dataframe, period=p) for p in range(self.rsi_period.low, self.rsi_period.high + 1): dataframe = CommonIndicators.add_rsi(dataframe, period=p) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: obv_sma_col = f"obv_sma_{self.obv_sma_period.value}" ema_col = f"ema_{self.ema_period.value}" rsi_col = f"rsi_{self.rsi_period.value}" rising_n = self.obv_rising.value # OBV rising pendant N bougies obv_up = dataframe["obv"] > dataframe["obv"].shift(1) for i in range(2, rising_n + 1): obv_up = obv_up & (dataframe["obv"].shift(i - 1) > dataframe["obv"].shift(i)) conditions = ( (dataframe["obv"] > dataframe[obv_sma_col]) & obv_up & (dataframe["close"] > dataframe[ema_col]) & (dataframe[rsi_col] < self.rsi_max.value) & (dataframe["volume"] > 0) ) dataframe.loc[conditions, "enter_long"] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: obv_sma_col = f"obv_sma_{self.obv_sma_period.value}" rsi_col = f"rsi_{self.rsi_period.value}" conditions = ( (dataframe["obv"] < dataframe[obv_sma_col]) | (dataframe[rsi_col] > self.rsi_exit.value) ) dataframe.loc[conditions, "exit_long"] = 1 return dataframe