# source: https://raw.githubusercontent.com/DOUGLASGUEDESATRIA/OSIRIS_TRADE/fe3c09567f3798d1cad6d7a306fa4f6131d91e17/user_data/strategies/OsirisDNA.py """ Github_DOUGLASGUEDESATRIA_OSIRIS_TRADE__OsirisDNA__20260329_060843 v4 — Session Flow with Flat SL ========================================== v3 confirmed: the session direction signal WORKS (53.5% WR confirmed) but graduated/trailing SL was killing good trades (79/106 exits were premature trailing_stop_loss). v4 FIX: FLAT stoploss — no trailing, no graduation. Let the session develop its full move without premature tightening. KEY INSIGHT from hyperopt: - TP exits: 13 trades, 100% WR, +1.84% avg ← signal is profitable - trailing_stop_loss: 79 trades, 10% WR ← SL management destroying edge v4 APPROACH: - Flat SL (no custom_stoploss — just use strategy stoploss) - TP via custom_exit - Timeout via custom_exit - EOD close - ATR-multiple SL/TP option via stoploss parameter MATH (at 1:1 R:R with 53.5% WR): EV = 0.535 * TP - 0.465 * SL = 0.07 * TP per trade At TP=SL=1.0%, 2x leverage: 0.14% per trade per day 200 trading days: 28% annual """ import logging from datetime import datetime from pandas import DataFrame import numpy as np 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__OsirisDNA__20260329_060843(IStrategy): INTERFACE_VERSION = 3 can_short = True timeframe = '15m' startup_candle_count = 200 process_only_new_candles = True # FLAT stoploss — no custom_stoploss, no trailing stoploss = -0.03 # 3% account max (with 2x leverage = 1.5% price move) use_custom_stoploss = False trailing_stop = False minimal_roi = {"0": 100} # === SESSION FLOW params === london_thresh = DecimalParameter(0.05, 1.00, default=0.10, decimals=2, space='buy', optimize=True, load=False) ny_entry_hour = IntParameter(12, 14, default=14, space='buy', optimize=True) # TP as percentage (applies before leverage) sess_tp_pct = DecimalParameter(0.30, 3.00, default=1.51, decimals=2, space='sell', optimize=True) # Max hold in 15m candles (48 = 12h) sess_max_hold = IntParameter(8, 48, default=32, space='sell', optimize=True) # Leverage sess_leverage = IntParameter(2, 5, default=2, space='buy', optimize=True) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['hour'] = dataframe['date'].dt.hour dataframe['minute'] = dataframe['date'].dt.minute dataframe['weekday'] = dataframe['date'].dt.weekday dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) dataframe['atr_pct'] = dataframe['atr'] / dataframe['close'] * 100 dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) # EMA200 for trend context dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200) # London session return tracking hours = dataframe['hour'].values minutes = dataframe['minute'].values closes = dataframe['close'].values opens = dataframe['open'].values london_rets = np.zeros(len(dataframe)) london_base = np.nan for i in range(len(dataframe)): h, m = int(hours[i]), int(minutes[i]) if h == 8 and m == 0: london_base = opens[i] if not np.isnan(london_base) and london_base > 0: london_rets[i] = (closes[i] - london_base) / london_base * 100 if h == 22 and m == 0: london_base = np.nan dataframe['london_ret'] = london_rets return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'enter_long'] = 0 dataframe.loc[:, 'enter_short'] = 0 dataframe.loc[:, 'enter_tag'] = '' entry_h = self.ny_entry_hour.value thresh = self.london_thresh.value weekday = dataframe['weekday'] <= 4 ny_candle = (dataframe['hour'] == entry_h) & (dataframe['minute'] == 0) london_up = dataframe['london_ret'] > thresh london_dn = dataframe['london_ret'] < -thresh # Session continuation: LONG ONLY # DNA evidence: longs +29.18% over 2.2y, shorts -33.68% # BTC has natural long bias in session flow dataframe.loc[weekday & ny_candle & london_up, 'enter_long'] = 1 dataframe.loc[weekday & ny_candle & london_up, 'enter_tag'] = 'sess_long' return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'exit_long'] = 0 dataframe.loc[:, 'exit_short'] = 0 return dataframe def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> str | bool: tp = self.sess_tp_pct.value / 100 hours_held = (current_time - trade.open_date_utc).total_seconds() / 3600 # Take profit if current_profit >= tp: return 'tp' # Cut losers: if still losing >2% after 3h, session thesis failed if hours_held >= 3.0 and current_profit < -0.02: return 'cut_loss_3h' # Reduce further: if losing >1% after 5h, cut if hours_held >= 5.0 and current_profit < -0.01: return 'cut_loss_5h' # Max hold timeout if hours_held >= 8.0: return 'timeout_profit' if current_profit > 0 else 'timeout_loss' # EOD close if current_time.hour >= 21: if current_profit > 0.001: return 'eod_profit' return 'eod_close' return False def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: str | None, side: str, **kwargs) -> float: return min(float(self.sess_leverage.value), max_leverage)