# source: https://raw.githubusercontent.com/DOUGLASGUEDESATRIA/OSIRIS_TRADE/fe3c09567f3798d1cad6d7a306fa4f6131d91e17/user_data/strategies/OsirisFunnelV3.py """ OSIRIS FUNNEL STRATEGY v3.0 — Ultra-Selective 80% WR Target ============================================================= Entry: Pullback-to-trend with multi-timeframe alignment. Base: EMA9 cross close on 5m (entry trigger) Filter 1: MACD histogram aligned + growing momentum Filter 2: 1H RSI aligned (>55 long, <45 short) — stronger bias Filter 3: ADX(14) > 30 (strong trend only, not weak 25-30) Filter 4: EMA alignment on 5m (9>21>50 bull, 9<21<50 bear) Filter 5: 1H EMA alignment (trend confirmation) Filter 6: Volume > 1.0× SMA(20) — above average activity Filter 7: RSI directional (long: RSI not overbought, short: RSI not oversold) Filter 8: BB position (long: not top, short: not bottom) Exit: RR 3:1 — TP = 3 × SL via stoploss + ROI. """ import logging import numpy as np from pandas import DataFrame from freqtrade.strategy import IStrategy, merge_informative_pair import talib.abstract as ta logger = logging.getLogger(__name__) class Github_DOUGLASGUEDESATRIA_OSIRIS_TRADE__OsirisFunnelV3__20260329_060843(IStrategy): INTERFACE_VERSION = 3 can_short = True timeframe = "5m" # RR 3:1: SL=1.0%, TP=3.0% — grid-searchable minimal_roi = {"0": 0.03} stoploss = -0.01 # Trailing: OFF trailing_stop = False trailing_stop_positive = 0.0 trailing_stop_positive_offset = 0.0 trailing_only_offset_is_reached = False use_custom_stoploss = False use_exit_signal = False startup_candle_count = 200 process_only_new_candles = True def informative_pairs(self): pairs = self.dp.current_whitelist() return [(pair, "1h") for pair in pairs] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # === 5m indicators === dataframe["ema9"] = ta.EMA(dataframe, timeperiod=9) dataframe["ema21"] = ta.EMA(dataframe, timeperiod=21) dataframe["ema50"] = ta.EMA(dataframe, timeperiod=50) dataframe["adx14"] = ta.ADX(dataframe, timeperiod=14) dataframe["rsi14"] = ta.RSI(dataframe, timeperiod=14) macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9) dataframe["macd_hist"] = macd["macdhist"] dataframe["macd_hist_prev"] = dataframe["macd_hist"].shift(1) # Bollinger Bands bb = ta.BBANDS(dataframe, timeperiod=20) dataframe["bb_upper"] = bb["upperband"] dataframe["bb_lower"] = bb["lowerband"] dataframe["bb_mid"] = bb["middleband"] # Volume SMA dataframe["vol_sma20"] = ta.SMA(dataframe["volume"], timeperiod=20) # EMA9 cross detection dataframe["prev_close"] = dataframe["close"].shift(1) dataframe["prev_ema9"] = dataframe["ema9"].shift(1) # EMA alignment flags dataframe["ema_bull"] = ( (dataframe["ema9"] > dataframe["ema21"]) & (dataframe["ema21"] > dataframe["ema50"]) ).astype(int) dataframe["ema_bear"] = ( (dataframe["ema9"] < dataframe["ema21"]) & (dataframe["ema21"] < dataframe["ema50"]) ).astype(int) # === 1H indicators === if self.dp: inf_1h = self.dp.get_pair_dataframe( pair=metadata["pair"], timeframe="1h" ) if not inf_1h.empty: inf_1h["rsi_1h"] = ta.RSI(inf_1h, timeperiod=14) inf_1h["adx_1h"] = ta.ADX(inf_1h, timeperiod=14) inf_1h["ema9_1h"] = ta.EMA(inf_1h, timeperiod=9) inf_1h["ema21_1h"] = ta.EMA(inf_1h, timeperiod=21) inf_1h["ema50_1h"] = ta.EMA(inf_1h, timeperiod=50) inf_1h["trend_1h"] = ( ((inf_1h["ema9_1h"] > inf_1h["ema21_1h"]) & (inf_1h["ema21_1h"] > inf_1h["ema50_1h"])).astype(int) - ((inf_1h["ema9_1h"] < inf_1h["ema21_1h"]) & (inf_1h["ema21_1h"] < inf_1h["ema50_1h"])).astype(int) ) inf_1h = inf_1h[["date", "rsi_1h", "adx_1h", "trend_1h"]].copy() dataframe = merge_informative_pair( dataframe, inf_1h, self.timeframe, "1h", ffill=True ) for col in ["rsi_1h_1h", "adx_1h_1h", "trend_1h_1h"]: if col not in dataframe.columns: dataframe[col] = 50.0 if "rsi" in col else (20.0 if "adx" in col else 0.0) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ ULTRA-SELECTIVE ENTRY — 8 filters for maximum quality. """ # === BASE SIGNAL: EMA9 cross === cross_above = ( (dataframe["prev_close"] < dataframe["prev_ema9"]) & (dataframe["close"] > dataframe["ema9"]) ) cross_below = ( (dataframe["prev_close"] > dataframe["prev_ema9"]) & (dataframe["close"] < dataframe["ema9"]) ) # === FILTER 1: MACD histogram aligned + growing === macd_bull = (dataframe["macd_hist"] > 0) macd_bear = (dataframe["macd_hist"] < 0) # === FILTER 2: 1H RSI strong bias === rsi1h_bull = dataframe["rsi_1h_1h"] > 55 rsi1h_bear = dataframe["rsi_1h_1h"] < 45 # === FILTER 3: ADX > 30 (strong trend only) === adx_strong = dataframe["adx14"] > 30 # === FILTER 4: 5m EMA alignment === ema_aligned_bull = dataframe["ema_bull"] == 1 ema_aligned_bear = dataframe["ema_bear"] == 1 # === FILTER 5: 1H trend alignment === trend_1h_bull = dataframe["trend_1h_1h"] == 1 trend_1h_bear = dataframe["trend_1h_1h"] == -1 # === FILTER 6: Volume above average === vol_above = dataframe["volume"] > dataframe["vol_sma20"] # === FILTER 7: RSI directional (not overextended) === rsi_long_ok = (dataframe["rsi14"] > 40) & (dataframe["rsi14"] < 65) rsi_short_ok = (dataframe["rsi14"] > 35) & (dataframe["rsi14"] < 60) # === FILTER 8: BB position (room to run) === bb_range = dataframe["bb_upper"] - dataframe["bb_lower"] bb_range = bb_range.replace(0, 1) bb_pos = (dataframe["close"] - dataframe["bb_lower"]) / bb_range bb_long_ok = bb_pos < 0.7 # Not at top bb_short_ok = bb_pos > 0.3 # Not at bottom # === LONG === dataframe.loc[ cross_above & macd_bull & rsi1h_bull & adx_strong & ema_aligned_bull & trend_1h_bull & vol_above & rsi_long_ok & bb_long_ok, "enter_long", ] = 1 # === SHORT === dataframe.loc[ cross_below & macd_bear & rsi1h_bear & adx_strong & ema_aligned_bear & trend_1h_bear & vol_above & rsi_short_ok & bb_short_ok, "enter_short", ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: return dataframe