# source: https://raw.githubusercontent.com/DOUGLASGUEDESATRIA/OSIRIS_TRADE/fe3c09567f3798d1cad6d7a306fa4f6131d91e17/user_data/strategies/OsirisGoldV5.py """ Github_DOUGLASGUEDESATRIA_OSIRIS_TRADE__OsirisGoldV5__20260329_060843 — Pure Mean-Reversion + Volatility Scalper (BTC 5m) ================================================================= INSIGHT: Traditional indicators have ZERO directional edge on 5m. V4 proved WR=49% at all score levels = coin flip. NEW APPROACH: Don't predict direction. Exploit mean-reversion. When price stretches beyond BB bands + volume spike → it snaps back. Entry: Mean-reversion (at extremes, bet on reversion) Exit: Quick scalp to BB middle or small fixed target PHYSICS: BTC 5m candle avg range = ~$300 (0.3%) BB(20,2) captures 95% of moves → touch of band = 2.5% of candles = ~7/day With volume filter = ~4-5/day Mean-reversion to BB mid = ~$150-200 profit = 0.15-0.2% SL = below/above the extreme wick = ~0.3-0.5% ENTRIES: LONG: Close <= BB lower (or wick below) + volume spike + green candle SHORT: Close >= BB upper (or wick above) + volume spike + red candle NO TREND FILTER (mean-reversion works in ALL regimes) EXITS: TP: BB middle (natural mean-reversion target) SL: Fixed % below entry (wider than noise) Timeout: 6 candles (30min) — if no reversion, exit """ import logging import numpy as np from pandas import DataFrame from freqtrade.persistence import Trade from freqtrade.strategy import ( IStrategy, DecimalParameter, IntParameter, ) import talib.abstract as ta logger = logging.getLogger(__name__) class Github_DOUGLASGUEDESATRIA_OSIRIS_TRADE__OsirisGoldV5__20260329_060843(IStrategy): INTERFACE_VERSION = 3 can_short = True timeframe = "5m" stoploss = -0.015 # Emergency SL 1.5% minimal_roi = {"0": 100} # Disabled — custom_exit handles TP trailing_stop = False use_custom_stoploss = False startup_candle_count = 50 process_only_new_candles = True # ── Mean-reversion params ──────────────────────────────────────────────── bb_period = IntParameter(15, 30, default=20, space="buy", optimize=True) bb_std = DecimalParameter(1.5, 2.5, default=2.0, decimals=1, space="buy", optimize=True) vol_mult = DecimalParameter(1.0, 2.5, default=1.3, decimals=1, space="buy", optimize=True) rsi_extreme_low = IntParameter(20, 40, default=30, space="buy", optimize=True) rsi_extreme_high = IntParameter(60, 80, default=70, space="buy", optimize=True) # ── Exit params ────────────────────────────────────────────────────────── sl_pct = DecimalParameter(0.3, 1.5, default=0.8, decimals=1, space="sell", optimize=True) tp_to_mid_pct = DecimalParameter(0.3, 1.0, default=0.7, decimals=1, space="sell", optimize=True) timeout_candles = IntParameter(3, 12, default=6, space="sell", optimize=True) _last_entry_time: dict = {} def informative_pairs(self): return [] # No need for multi-TF — pure price action def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Bollinger Bands for period in [15, 20, 25, 30]: for std in [1.5, 2.0, 2.5]: bb = ta.BBANDS(dataframe, timeperiod=period, nbdevup=std, nbdevdn=std) col_suffix = f"_{period}_{str(std).replace('.','')}" dataframe[f"bb_upper{col_suffix}"] = bb["upperband"] dataframe[f"bb_lower{col_suffix}"] = bb["lowerband"] dataframe[f"bb_mid{col_suffix}"] = bb["middleband"] # Current active BB (we'll use populate_entry_trend to select) bb = ta.BBANDS(dataframe, timeperiod=20, nbdevup=2.0, nbdevdn=2.0) dataframe["bb_upper"] = bb["upperband"] dataframe["bb_lower"] = bb["lowerband"] dataframe["bb_mid"] = bb["middleband"] dataframe["bb_width"] = (bb["upperband"] - bb["lowerband"]) / bb["middleband"] # RSI for confirmation dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) # Volume spike detection dataframe["volume_sma"] = ta.SMA(dataframe["volume"], timeperiod=20) dataframe["rvol"] = dataframe["volume"] / dataframe["volume_sma"].replace(0, np.nan) # Candle properties dataframe["is_green"] = (dataframe["close"] > dataframe["open"]).astype(int) dataframe["is_red"] = (dataframe["close"] < dataframe["open"]).astype(int) # Distance from BB bands (normalized) dataframe["dist_lower"] = (dataframe["close"] - dataframe["bb_lower"]) / dataframe["close"] * 100 dataframe["dist_upper"] = (dataframe["bb_upper"] - dataframe["close"]) / dataframe["close"] * 100 # ATR for dynamic SL dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: bb_p = int(self.bb_period.value) bb_s = float(self.bb_std.value) col = f"_{bb_p}_{str(bb_s).replace('.','')}" vol_m = float(self.vol_mult.value) rsi_low = int(self.rsi_extreme_low.value) rsi_high = int(self.rsi_extreme_high.value) has_data = dataframe["rsi"].notna() & (dataframe["volume"] > 0) bb_lower = dataframe[f"bb_lower{col}"] bb_upper = dataframe[f"bb_upper{col}"] bb_mid = dataframe[f"bb_mid{col}"] # ═════════════════════════════════════════════════════════════════════ # LONG: Price at/below lower BB + volume spike + oversold RSI # Mean-reversion: extreme low → expect bounce to middle # ═════════════════════════════════════════════════════════════════════ long_signal = ( has_data & (dataframe["low"] <= bb_lower) # Wick touches lower BB & (dataframe["close"] > bb_lower) # But closes above (bounce) & (dataframe["is_green"] == 1) # Green candle (buying pressure) & (dataframe["rsi"] < rsi_low) # Oversold & (dataframe["rvol"] > vol_m) # Volume spike (participation) ) # ═════════════════════════════════════════════════════════════════════ # SHORT: Price at/above upper BB + volume spike + overbought RSI # Mean-reversion: extreme high → expect drop to middle # ═════════════════════════════════════════════════════════════════════ short_signal = ( has_data & (dataframe["high"] >= bb_upper) # Wick touches upper BB & (dataframe["close"] < bb_upper) # Closes below (rejection) & (dataframe["is_red"] == 1) # Red candle (selling pressure) & (dataframe["rsi"] > rsi_high) # Overbought & (dataframe["rvol"] > vol_m) # Volume spike ) dataframe.loc[long_signal, "enter_long"] = 1 dataframe.loc[long_signal, "enter_tag"] = "bb_bounce" dataframe.loc[short_signal, "enter_short"] = 1 dataframe.loc[short_signal, "enter_tag"] = "bb_reject" return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: return dataframe def custom_exit( self, pair, trade, current_time, current_rate, current_profit, **kwargs, ): # Get BB middle for target dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if dataframe.empty: return None bb_p = int(self.bb_period.value) bb_s = float(self.bb_std.value) col = f"_{bb_p}_{str(bb_s).replace('.','')}" last = dataframe.iloc[-1] bb_mid = last.get(f"bb_mid{col}", 0) is_short = getattr(trade, "is_short", False) # TP: Price reaches BB middle (mean-reversion target) if not is_short and current_rate >= bb_mid and current_profit > 0: return "mr_tp_mid" if is_short and current_rate <= bb_mid and current_profit > 0: return "mr_tp_mid" # SL: Fixed percentage sl = float(self.sl_pct.value) / 100 if current_profit <= -sl: return "mr_sl" # Timeout: Exit after N candles regardless if trade.open_date_utc: elapsed = (current_time - trade.open_date_utc).total_seconds() / 300 if elapsed >= self.timeout_candles.value: return "mr_timeout" return None def confirm_trade_entry( self, pair, order_type, amount, rate, time_in_force, current_time, entry_tag, side, **kwargs, ) -> bool: # Cooldown: 2 candles minimum between entries last = self._last_entry_time.get(pair) if last is not None and (current_time - last).total_seconds() < 600: return False self._last_entry_time[pair] = current_time return True def leverage(self, pair, current_time, current_rate, proposed_leverage, max_leverage, entry_tag, side, **kwargs) -> float: return 1.0