# source: https://raw.githubusercontent.com/DOUGLASGUEDESATRIA/OSIRIS_TRADE/fe3c09567f3798d1cad6d7a306fa4f6131d91e17/user_data/strategies/OsirisGoldV2.py """ Github_DOUGLASGUEDESATRIA_OSIRIS_TRADE__OsirisGoldV2__20260329_060843 — High-Frequency Mean-Reversion Scalper (BTC/USDT 5m) ==================================================================== TARGET: 10+ trades/day | 65%+ Win Rate | 2:1+ R:R MATH: 288 candles/day on 5m. Need ~10 entries = trigger ~3.5% of candles. RSI(7) crosses above 40 from below = ~5% of candles + 1h trend alignment = ~2.5% + candle color confirmation = ~1.8% Multiple patterns combined = ~3-5% trigger rate = 10-14 trades/day PATTERNS: A) RSI_BOUNCE: RSI(7) crosses 40 from below + green candle + 1h up B) EMA_TOUCH: Price touches EMA(21) from above in uptrend + bounce C) BB_MEAN: Close below BB middle, bounces back + volume D) RSI_SHORT: RSI(7) crosses 60 from above + red candle + 1h down E) EMA_REJ: Price touches EMA(21) from below in downtrend + rejected RISK: SL = 0.35% (ATR-calibrated micro stop) TP = 0.70% (2:1 ratio) BE at 1x SL Max hold: 12 candles (1h) """ import logging import numpy as np import pandas as pd from datetime import datetime 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__OsirisGoldV2__20260329_060843(IStrategy): INTERFACE_VERSION = 3 can_short = True timeframe = "5m" stoploss = -0.02 minimal_roi = {"0": 100} trailing_stop = False use_custom_stoploss = True startup_candle_count = 100 process_only_new_candles = True # ── Risk params ────────────────────────────────────────────────────────── sl_pct = DecimalParameter( 0.15, 0.60, default=0.35, decimals=2, space="sell", optimize=True ) rr_ratio = DecimalParameter( 1.5, 3.0, default=2.0, decimals=1, space="sell", optimize=True ) max_hold = IntParameter(6, 24, default=12, space="sell", optimize=True) # ── Entry params ───────────────────────────────────────────────────────── rsi_bounce = IntParameter(30, 45, default=40, space="buy", optimize=True) rsi_reject = IntParameter(55, 70, default=60, space="buy", optimize=True) ema_period = IntParameter(15, 30, default=21, space="buy", optimize=True) ema_tol = DecimalParameter( 0.05, 0.30, default=0.15, decimals=2, space="buy", optimize=True ) cooldown = IntParameter(1, 4, default=2, space="buy", optimize=True) _last_entry_time: dict = {} # ── Informative pairs ──────────────────────────────────────────────────── def informative_pairs(self): pairs = self.dp.current_whitelist() if self.dp else [] return [(p, "1h") for p in pairs] # ── Indicators ─────────────────────────────────────────────────────────── def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: pair = metadata["pair"] ema_p = int(self.ema_period.value) # ── 5m ─────────────────────────────────────────────────────────────── dataframe["rsi7"] = ta.RSI(dataframe, timeperiod=7) dataframe["rsi7_prev"] = dataframe["rsi7"].shift(1) dataframe["ema"] = ta.EMA(dataframe, timeperiod=ema_p) dataframe["ema_prev"] = dataframe["ema"].shift(1) bb = ta.BBANDS(dataframe, timeperiod=20, nbdevup=2.0, nbdevdn=2.0) dataframe["bb_mid"] = bb["middleband"] dataframe["bb_lower"] = bb["lowerband"] dataframe["bb_upper"] = bb["upperband"] dataframe["volume_sma"] = ta.SMA(dataframe["volume"], timeperiod=20) dataframe["is_green"] = (dataframe["close"] > dataframe["open"]).astype(int) dataframe["is_red"] = (dataframe["close"] < dataframe["open"]).astype(int) # Price relative to EMA (%) dataframe["ema_dist"] = (dataframe["close"] - dataframe["ema"]) / dataframe["ema"] * 100 # ── 1h trend ───────────────────────────────────────────────────────── inf_1h = self.dp.get_pair_dataframe(pair=pair, timeframe="1h") if not inf_1h.empty: inf_1h["ema50_1h"] = ta.EMA(inf_1h, timeperiod=50) inf_1h["ema20_1h"] = ta.EMA(inf_1h, timeperiod=20) m1h = merge_informative_pair( dataframe, inf_1h[["date", "ema50_1h", "ema20_1h", "close"]], self.timeframe, "1h", ffill=True, ) dataframe["ema50_1h"] = m1h["ema50_1h_1h"].values dataframe["ema20_1h"] = m1h["ema20_1h_1h"].values dataframe["close_1h"] = m1h["close_1h"].values else: dataframe["ema50_1h"] = dataframe["close"] dataframe["ema20_1h"] = dataframe["close"] dataframe["close_1h"] = dataframe["close"] # 1h trend signals dataframe["trend_up"] = ( (dataframe["close_1h"] > dataframe["ema50_1h"]) & (dataframe["ema20_1h"] > dataframe["ema50_1h"]) ).astype(int) dataframe["trend_dn"] = ( (dataframe["close_1h"] < dataframe["ema50_1h"]) & (dataframe["ema20_1h"] < dataframe["ema50_1h"]) ).astype(int) return dataframe # ── Entry signals ──────────────────────────────────────────────────────── def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: bounce = int(self.rsi_bounce.value) reject = int(self.rsi_reject.value) tol = float(self.ema_tol.value) has_data = dataframe["rsi7"].notna() & (dataframe["volume"] > 0) # ═════════════════════════════════════════════════════════════════════ # LONG A: RSI(7) bounce from oversold + green + uptrend # RSI was below threshold, now crossing back above # ═════════════════════════════════════════════════════════════════════ long_rsi = ( has_data & (dataframe["trend_up"] == 1) & (dataframe["rsi7_prev"] < bounce) & (dataframe["rsi7"] >= bounce) & (dataframe["is_green"] == 1) ) # ═════════════════════════════════════════════════════════════════════ # LONG B: EMA touch (pullback to EMA in uptrend) # Price dipped close to EMA and bounced # ═════════════════════════════════════════════════════════════════════ long_ema = ( has_data & (dataframe["trend_up"] == 1) & (dataframe["low"] <= dataframe["ema"] * (1 + tol / 100)) & (dataframe["close"] > dataframe["ema"]) & (dataframe["is_green"] == 1) & (dataframe["rsi7"] > 35) & (dataframe["rsi7"] < 55) ) # ═════════════════════════════════════════════════════════════════════ # LONG C: BB middle bounce (mean reversion in uptrend) # Price was below BB middle, closing above # ═════════════════════════════════════════════════════════════════════ long_bb = ( has_data & (dataframe["trend_up"] == 1) & (dataframe["low"] <= dataframe["bb_mid"]) & (dataframe["close"] > dataframe["bb_mid"]) & (dataframe["is_green"] == 1) & (dataframe["volume"] > dataframe["volume_sma"] * 0.8) ) # ═════════════════════════════════════════════════════════════════════ # SHORT D: RSI(7) rejection from overbought + red + downtrend # ═════════════════════════════════════════════════════════════════════ short_rsi = ( has_data & (dataframe["trend_dn"] == 1) & (dataframe["rsi7_prev"] > reject) & (dataframe["rsi7"] <= reject) & (dataframe["is_red"] == 1) ) # ═════════════════════════════════════════════════════════════════════ # SHORT E: EMA rejection (bounce off EMA from below in downtrend) # ═════════════════════════════════════════════════════════════════════ short_ema = ( has_data & (dataframe["trend_dn"] == 1) & (dataframe["high"] >= dataframe["ema"] * (1 - tol / 100)) & (dataframe["close"] < dataframe["ema"]) & (dataframe["is_red"] == 1) & (dataframe["rsi7"] > 45) & (dataframe["rsi7"] < 65) ) # ═════════════════════════════════════════════════════════════════════ # COMBINE with tags # ═════════════════════════════════════════════════════════════════════ dataframe.loc[long_rsi, ["enter_long", "enter_tag"]] = [1, "rsi_bounce"] dataframe.loc[long_ema & ~long_rsi, ["enter_long", "enter_tag"]] = [1, "ema_touch"] dataframe.loc[long_bb & ~long_rsi & ~long_ema, ["enter_long", "enter_tag"]] = [1, "bb_mean"] dataframe.loc[short_rsi, ["enter_short", "enter_tag"]] = [1, "rsi_reject"] dataframe.loc[short_ema & ~short_rsi, ["enter_short", "enter_tag"]] = [1, "ema_reject"] 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: last = self._last_entry_time.get(pair) cd_seconds = int(self.cooldown.value) * 300 if last is not None: if (current_time - last).total_seconds() < cd_seconds: 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 # Breakeven at 1x SL profit if current_profit >= sl: is_short = getattr(trade, "is_short", False) return stoploss_from_open(0.001, current_profit, is_short=is_short) return -sl def custom_exit( self, pair, trade, current_time, current_rate, current_profit, **kwargs, ): sl = float(self.sl_pct.value) / 100 tp = sl * float(self.rr_ratio.value) if current_profit >= tp: return "tp_hit" if trade.open_date_utc: elapsed = (current_time - trade.open_date_utc).total_seconds() / 300 if elapsed >= 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