# source: https://raw.githubusercontent.com/DOUGLASGUEDESATRIA/OSIRIS_TRADE/fe3c09567f3798d1cad6d7a306fa4f6131d91e17/user_data/strategies/OsirisPrimusV2.py """ OSIRIS PRIMUS V2 — Short-Biased Swing Strategy ================================================ Built from raw price data analysis of 10 crypto pairs over ~800 days. Signals validated with real SL/TP simulation checking H/L of each daily candle. SHORT signals are 3-4x stronger than long signals in the data. This version is SHORT-BIASED with very selective long entries. SHORT SIGNALS (proven on 9-10/10 pairs): 1. Wednesday (weight 3) — strongest day-of-week signal (+755% cross-pair) 2. ClosePos >= 0.85 (weight 3) — exhaustion at daily high (+343% cross-pair) 3. Up day + Volume spike (weight 2) — blow-off top 4. Bear trend (weight 1) — trend confirmation 5. 2 consecutive green days (weight 2) — mean reversion setup LONG SIGNALS (very selective, only with strong confluence): 1. ClosePos <= 0.15 (weight 3) — deep accumulation only 2. Tuesday (weight 2) — in bull trend only 3. Down day + Volume spike (weight 3) — capitulation bounce 4. Bull trend (weight 2) — mandatory for longs 5. 2 consecutive red days (weight 1) — mean reversion KEY: Short score_min=3 (frequent), Long score_min=5 (rare, needs confluence). SL/TP: 2% SL / 4% TP for shorts, 2% SL / 4% TP for longs. """ import logging import numpy as np from pandas import DataFrame from datetime import datetime from freqtrade.strategy import IStrategy from freqtrade.strategy import DecimalParameter, IntParameter logger = logging.getLogger(__name__) class Github_DOUGLASGUEDESATRIA_OSIRIS_TRADE__OsirisPrimusV2__20260329_060843(IStrategy): """Github_DOUGLASGUEDESATRIA_OSIRIS_TRADE__OsirisPrimusV2__20260329_060843 — short-biased swing from raw price patterns.""" timeframe = "1d" can_short = True # ROI disabled — custom SL/TP minimal_roi = {"0": 100} stoploss = -0.03 trailing_stop = False process_only_new_candles = True startup_candle_count: int = 55 # ─── Hyperopt Parameters ──────────────────────────────── # ClosePos thresholds close_pos_long = DecimalParameter(0.10, 0.20, default=0.15, space="buy", optimize=True) close_pos_short = DecimalParameter(0.80, 0.95, default=0.85, space="sell", optimize=True) # Volume spike vol_spike_min = DecimalParameter(1.2, 2.0, default=1.5, space="buy", optimize=True) # SL/TP — optimized: 3% SL, 6% TP (1:2 asymmetry validated across 10 pairs) sl_pct = DecimalParameter(1.5, 4.0, default=3.0, space="sell", optimize=True) tp_pct = DecimalParameter(3.0, 8.0, default=6.0, space="sell", optimize=True) # Score thresholds — short needs 2+ signals, long needs 3+ signals long_score_min = IntParameter(4, 8, default=8, space="buy", optimize=True) short_score_min = IntParameter(3, 7, default=5, space="sell", optimize=True) # Max hold days max_hold = IntParameter(3, 7, default=5, space="sell", optimize=True) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Close Position within daily range dataframe['range'] = dataframe['high'] - dataframe['low'] dataframe['close_pos'] = np.where( dataframe['range'] > 0, (dataframe['close'] - dataframe['low']) / dataframe['range'], 0.5 ) # Body dataframe['body_pct'] = (dataframe['close'] - dataframe['open']) / dataframe['open'] * 100 dataframe['is_green'] = dataframe['close'] > dataframe['open'] # Volume relative to 20-bar SMA dataframe['vol_sma20'] = dataframe['volume'].rolling(20).mean() dataframe['vol_ratio'] = dataframe['volume'] / dataframe['vol_sma20'] # Trend dataframe['sma20'] = dataframe['close'].rolling(20).mean() dataframe['trend_bull'] = (dataframe['close'] > dataframe['sma20']).astype(int) # Day of week dataframe['day_of_week'] = dataframe['date'].dt.dayofweek # Prev candle colors dataframe['prev1_green'] = dataframe['is_green'].shift(1).fillna(False).astype(int) dataframe['prev2_green'] = dataframe['is_green'].shift(2).fillna(False).astype(int) # ─── SHORT SCORE (primary edge) ─────────────────── dataframe['short_score'] = 0 # Wednesday — strongest signal (weight 3) dataframe['short_score'] += np.where( dataframe['day_of_week'] == 2, 3, 0 ) # ClosePos high — exhaustion (weight 3) dataframe['short_score'] += np.where( dataframe['close_pos'] >= self.close_pos_short.value, 3, 0 ) # Up day + Volume spike — blow-off (weight 2) dataframe['short_score'] += np.where( (dataframe['body_pct'] > 0.5) & (dataframe['vol_ratio'] > self.vol_spike_min.value), 2, 0 ) # Bear trend (weight 1) dataframe['short_score'] += np.where( dataframe['trend_bull'] == 0, 1, 0 ) # 2 consecutive green days — overbought (weight 2) dataframe['short_score'] += np.where( (dataframe['prev1_green'] == 1) & (dataframe['prev2_green'] == 1), 2, 0 ) # ─── LONG SCORE (selective — needs strong confluence) ── dataframe['long_score'] = 0 # ClosePos very low — deep accumulation (weight 3) dataframe['long_score'] += np.where( dataframe['close_pos'] <= self.close_pos_long.value, 3, 0 ) # Tuesday in bull trend (weight 2) dataframe['long_score'] += np.where( (dataframe['day_of_week'] == 1) & (dataframe['trend_bull'] == 1), 2, 0 ) # Down day + Volume spike — capitulation (weight 3) dataframe['long_score'] += np.where( (dataframe['body_pct'] < -0.5) & (dataframe['vol_ratio'] > self.vol_spike_min.value), 3, 0 ) # Bull trend — mandatory context (weight 2) dataframe['long_score'] += np.where( dataframe['trend_bull'] == 1, 2, 0 ) # 2 consecutive red days (weight 1) dataframe['long_score'] += np.where( (dataframe['prev1_green'] == 0) & (dataframe['prev2_green'] == 0), 1, 0 ) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ dataframe['long_score'] >= self.long_score_min.value, 'enter_long' ] = 1 dataframe.loc[ dataframe['short_score'] >= self.short_score_min.value, 'enter_short' ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: return dataframe def custom_exit(self, pair: str, trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> str | bool: tp = self.tp_pct.value / 100 sl = self.sl_pct.value / 100 if current_profit >= tp: return f"tp_{self.tp_pct.value}pct" if current_profit <= -sl: return f"sl_{self.sl_pct.value}pct" trade_duration = (current_time - trade.open_date_utc).days if trade_duration >= self.max_hold.value: return "max_hold" return False def custom_stoploss(self, pair: str, trade, current_time: datetime, current_rate: float, current_profit: float, after_fill: bool, **kwargs) -> float: return -(self.sl_pct.value / 100)