# source: https://raw.githubusercontent.com/DOUGLASGUEDESATRIA/OSIRIS_TRADE/fe3c09567f3798d1cad6d7a306fa4f6131d91e17/user_data/strategies/OsirisScalper.py """ Github_DOUGLASGUEDESATRIA_OSIRIS_TRADE__OsirisScalper__20260329_060843 v6 — 1m Mean-Reversion Scalper ============================================== Pure mean-reversion on 1m with BB bands. LOGIC: Entry: Price crosses below BB lower → long (mean reversion) Price crosses above BB upper → short (mean reversion) Filter: RSI confirms extreme (oversold/overbought) 5m EMA alignment as trend sanity check Exit: Small fixed TP (0.2-0.5%) — quick scalps Tight SL (0.2-0.5%) — cut losses fast Breakeven when in profit Timeout: 15-30 candles max WHY 1m MEAN REVERSION: - BB bounces on 1m resolve within 5-15 candles (5-15 min) - High frequency: 10-30 signals per day on BTC - Mean reversion has naturally higher WR than momentum - Small TP captures the reversion reliably """ import logging from pandas import DataFrame from freqtrade.persistence import Trade from freqtrade.strategy import ( IStrategy, DecimalParameter, IntParameter, merge_informative_pair, ) import talib.abstract as ta try: from freqtrade.strategy import stoploss_from_open except ImportError: def stoploss_from_open(open_relative_stop, current_profit, is_short=False): if current_profit == 0: return 1 if is_short: return -1 + ((1 - open_relative_stop) / (1 - current_profit)) return 1 - ((1 + open_relative_stop) / (1 + current_profit)) logger = logging.getLogger(__name__) class Github_DOUGLASGUEDESATRIA_OSIRIS_TRADE__OsirisScalper__20260329_060843(IStrategy): INTERFACE_VERSION = 3 can_short = True timeframe = "1m" stoploss = -0.03 minimal_roi = {"0": 100} trailing_stop = False use_custom_stoploss = True startup_candle_count = 200 process_only_new_candles = True # ── Risk params (sell) ─────────────────────────────────────────────── sl_pct = DecimalParameter( 0.15, 0.60, default=0.35, decimals=2, space="sell", optimize=True ) tp_pct = DecimalParameter( 0.15, 0.80, default=0.30, decimals=2, space="sell", optimize=True ) be_trigger = DecimalParameter( 0.10, 0.40, default=0.15, decimals=2, space="sell", optimize=True ) max_hold = IntParameter(5, 45, default=20, space="sell", optimize=True) # ── Entry thresholds (buy) ─────────────────────────────────────────── rsi_os = IntParameter(15, 40, default=30, space="buy", optimize=True) rsi_ob = IntParameter(60, 85, default=70, space="buy", optimize=True) bb_std = DecimalParameter( 1.5, 2.8, default=2.0, decimals=1, space="buy", optimize=True ) _last_entry_time: dict = {} def informative_pairs(self): pairs = self.dp.current_whitelist() return [(pair, "5m") for pair in pairs] # ── Indicators ─────────────────────────────────────────────────────── def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: pair = metadata["pair"] # 1m indicators dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) # Precompute BB for multiple std dev values for std in [1.5, 2.0, 2.5]: s = int(std * 10) bb = ta.BBANDS(dataframe, timeperiod=20, nbdevup=std, nbdevdn=std) dataframe[f"bb_upper_{s}"] = bb["upperband"] dataframe[f"bb_mid_{s}"] = bb["middleband"] dataframe[f"bb_lower_{s}"] = bb["lowerband"] # 5m trend filter (informative) inf_5m = self.dp.get_pair_dataframe(pair=pair, timeframe="5m") if not inf_5m.empty: inf_5m["ema8_5m"] = ta.EMA(inf_5m, timeperiod=8) inf_5m["ema21_5m"] = ta.EMA(inf_5m, timeperiod=21) merged = merge_informative_pair( dataframe, inf_5m[["date", "ema8_5m", "ema21_5m"]], self.timeframe, "5m", ffill=True, ) dataframe["ema8_5m"] = merged["ema8_5m_5m"].values dataframe["ema21_5m"] = merged["ema21_5m_5m"].values else: dataframe["ema8_5m"] = dataframe["close"] dataframe["ema21_5m"] = dataframe["close"] return dataframe # ── Entry signals ──────────────────────────────────────────────────── def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: os_val = int(self.rsi_os.value) ob_val = int(self.rsi_ob.value) # Map bb_std to nearest precomputed column std_val = float(self.bb_std.value) std_key = min([15, 20, 25], key=lambda x: abs(x - int(std_val * 10))) bb_upper = dataframe[f"bb_upper_{std_key}"] bb_lower = dataframe[f"bb_lower_{std_key}"] bb_mid = dataframe[f"bb_mid_{std_key}"] has_data = dataframe["rsi"].notna() & (dataframe["volume"] > 0) # 5m trend (optional alignment - gives higher WR when active) trend_bull_5m = dataframe["ema8_5m"] > dataframe["ema21_5m"] trend_bear_5m = dataframe["ema8_5m"] < dataframe["ema21_5m"] # ═══════════════════════════════════════════════════════════════════ # LONG: Price at/below BB lower + RSI oversold = mean reversion up # ═══════════════════════════════════════════════════════════════════ long_bb = ( has_data & (dataframe["close"] <= bb_lower * 1.001) & (dataframe["rsi"] < os_val) ) # LONG with 5m trend alignment (higher quality) long_trend = ( has_data & trend_bull_5m & (dataframe["close"] <= bb_mid) & (dataframe["rsi"] < os_val + 10) & (dataframe["close"] > dataframe["close"].shift(1)) # bouncing ) # ═══════════════════════════════════════════════════════════════════ # SHORT: Price at/above BB upper + RSI overbought = mean reversion down # ═══════════════════════════════════════════════════════════════════ short_bb = ( has_data & (dataframe["close"] >= bb_upper * 0.999) & (dataframe["rsi"] > ob_val) ) # SHORT with 5m trend alignment (higher quality) short_trend = ( has_data & trend_bear_5m & (dataframe["close"] >= bb_mid) & (dataframe["rsi"] > ob_val - 10) & (dataframe["close"] < dataframe["close"].shift(1)) # falling ) dataframe.loc[long_bb | long_trend, "enter_long"] = 1 dataframe.loc[short_bb | short_trend, "enter_short"] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: return dataframe # ── Trade management ───────────────────────────────────────────────── def confirm_trade_entry( self, pair, order_type, amount, rate, time_in_force, current_time, entry_tag, side, **kwargs, ) -> bool: # Min 2 min cooldown between trades per pair last = self._last_entry_time.get(pair) if last is not None: if (current_time - last).total_seconds() < 120: return False self._last_entry_time[pair] = current_time return True def custom_stoploss( self, pair, trade: Trade, current_time, current_rate: float, current_profit: float, **kwargs, ) -> float: sl = float(self.sl_pct.value) / 100 be = float(self.be_trigger.value) / 100 is_short = getattr(trade, "is_short", False) # Breakeven when profit >= be_trigger if current_profit >= be: return stoploss_from_open(0.0001, current_profit, is_short=is_short) return -sl def custom_exit( self, pair, trade, current_time, current_rate, current_profit, **kwargs, ): tp = float(self.tp_pct.value) / 100 # Take profit if current_profit >= tp: return "tp_hit" # Timeout if trade.open_date_utc: candles = (current_time - trade.open_date_utc).total_seconds() / 60 if candles >= self.max_hold.value: return "timeout" return None def leverage( self, pair, current_time, current_rate, proposed_leverage, max_leverage, entry_tag, side, **kwargs, ) -> float: return 1.0