# source: https://raw.githubusercontent.com/DOUGLASGUEDESATRIA/OSIRIS_TRADE/fe3c09567f3798d1cad6d7a306fa4f6131d91e17/user_data/strategies/OsirisDayTradeV2.py """ OSIRIS DAY TRADE v2 — Multi-Timeframe Trend Pullback ================================================================ DAY TRADE puro em BTC/USDT — LONG e SHORT. MANIFESTO INVIOLÁVEL: ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Ativo: BTC/USDT APENAS Tipo: DAY TRADE (max 3h por operação) Meta: 10 operações/dia Direção: LONG e SHORT Timeframes: 5m (execução) + 15m (sinal) + 1h (tendência) Risco/Alvo: 1.5:1 mínimo, média 1.8:1 Max Loss/dia: -5R (hard stop) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ FILOSOFIA: Não tenta adivinhar topos e fundos. SEGUE A TENDÊNCIA e entra no PULLBACK. 1H diz PRA ONDE. 15m diz QUANDO. 5m diz O PREÇO EXATO. ENTRADAS (4 tipos, cada um ~2-3/dia = 10 total): A) EMA Pullback: Preço retorna à EMA21, candle de reversão B) BB Bounce: Preço toca BB oposta ao trend, reverte C) Momentum Cross: EMA9 cruza EMA21 na direção do 1H D) RSI Divergence: RSI volta de zona extrema na direção do trend STOPS: SL = abaixo/acima do swing recente (5 bars) + 0.1×ATR buffer Mínimo 0.3%, máximo 1.5% Trail: breakeven em 1R, lock 0.5R em 1.5R 100% proprietário. OSIRIS v2 — Trend Following, Not Guessing. """ import logging import numpy as np import pandas as pd from pandas import DataFrame from typing import Optional from datetime import datetime, timedelta from freqtrade.strategy import IStrategy, merge_informative_pair from freqtrade.strategy import DecimalParameter, IntParameter from freqtrade.persistence import Trade 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__OsirisDayTradeV2__20260329_060843(IStrategy): """ OSIRIS DAY TRADE v2 — Multi-TF Trend Following com Pullback 5m main | 15m signal | 1h trend | LONG + SHORT | 10 trades/dia """ INTERFACE_VERSION = 3 can_short = True timeframe = "5m" # ROI: wide — custom_exit handles the real exits minimal_roi = {"0": 0.10, "60": 0.03, "120": 0.01, "180": 0.003} # Safety net stoploss stoploss = -0.02 # Custom stoploss handles R-based trailing trailing_stop = False use_custom_stoploss = True startup_candle_count = 200 process_only_new_candles = True # Runtime state _daily_trades = {} _daily_losses_r = {} _consecutive_losses = 0 _last_loss_time = None # ═══════════════════════════════════════════════════════════════════ # HYPEROPT PARAMETERS # ═══════════════════════════════════════════════════════════════════ # Trend alignment strictness buy_trend_strict = IntParameter(0, 1, default=0, space="buy", optimize=True) # Pullback depth (how close to EMA for pullback entry) buy_pullback_atr_mult = DecimalParameter(0.2, 1.0, default=0.5, decimals=1, space="buy", optimize=True) # RSI thresholds buy_rsi_ob = IntParameter(65, 80, default=70, space="buy", optimize=True) buy_rsi_os = IntParameter(20, 35, default=30, space="buy", optimize=True) # Volume filter buy_vol_min = DecimalParameter(0.5, 1.5, default=0.7, decimals=1, space="buy", optimize=True) # R:R target buy_rr_target = DecimalParameter(1.2, 2.5, default=1.5, decimals=1, space="buy", optimize=True) # Max trades per day buy_max_daily = IntParameter(8, 15, default=10, space="buy", optimize=True) # Cooldown (candles between entries) buy_cooldown = IntParameter(2, 8, default=3, space="buy", optimize=True) # Max hold (candles of 5m = max 36 = 3 hours) buy_max_hold = IntParameter(18, 48, default=36, space="buy", optimize=True) # Stop buffer buy_sl_buffer_atr = DecimalParameter(0.05, 0.3, default=0.1, decimals=2, space="buy", optimize=True) # Exit sell_rsi_exit_long = IntParameter(72, 85, default=78, space="sell", optimize=True) sell_rsi_exit_short = IntParameter(15, 28, default=22, space="sell", optimize=True) # ═══════════════════════════════════════════════════════════════════ # INFORMATIVE PAIRS # ═══════════════════════════════════════════════════════════════════ def informative_pairs(self): return [ ("BTC/USDT:USDT", "15m"), ("BTC/USDT:USDT", "1h"), ] # ═══════════════════════════════════════════════════════════════════ # INDICATORS — 5m (main) # ═══════════════════════════════════════════════════════════════════ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # === EMAs === dataframe["ema9"] = ta.EMA(dataframe, timeperiod=9) dataframe["ema21"] = ta.EMA(dataframe, timeperiod=21) dataframe["ema50"] = ta.EMA(dataframe, timeperiod=50) # === RSI === dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) # === MACD === macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9) dataframe["macd"] = macd["macd"] dataframe["macd_signal"] = macd["macdsignal"] dataframe["macd_hist"] = macd["macdhist"] # === Bollinger Bands === bb = ta.BBANDS(dataframe, timeperiod=20, nbdevup=2.0, nbdevdn=2.0) dataframe["bb_upper"] = bb["upperband"] dataframe["bb_mid"] = bb["middleband"] dataframe["bb_lower"] = bb["lowerband"] # === ATR === dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) # === ADX === dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) dataframe["plus_di"] = ta.PLUS_DI(dataframe, timeperiod=14) dataframe["minus_di"] = ta.MINUS_DI(dataframe, timeperiod=14) # === Volume === dataframe["vol_sma"] = ta.SMA(dataframe["volume"], timeperiod=20) dataframe["vol_ratio"] = dataframe["volume"] / dataframe["vol_sma"].replace(0, 1) # === Swing levels (5 bar) === dataframe["swing_high_5"] = dataframe["high"].rolling(5, center=False).max().shift(1) dataframe["swing_low_5"] = dataframe["low"].rolling(5, center=False).min().shift(1) dataframe["swing_high_10"] = dataframe["high"].rolling(10, center=False).max().shift(1) dataframe["swing_low_10"] = dataframe["low"].rolling(10, center=False).min().shift(1) # === EMA previous values (for cross detection) === dataframe["ema9_prev"] = dataframe["ema9"].shift(1) dataframe["ema21_prev"] = dataframe["ema21"].shift(1) dataframe["rsi_prev"] = dataframe["rsi"].shift(1) dataframe["close_prev"] = dataframe["close"].shift(1) # === Candle patterns === dataframe["is_bullish"] = (dataframe["close"] > dataframe["open"]).astype(int) dataframe["is_bearish"] = (dataframe["close"] < dataframe["open"]).astype(int) dataframe["body"] = abs(dataframe["close"] - dataframe["open"]) dataframe["upper_wick"] = dataframe["high"] - dataframe[["close", "open"]].max(axis=1) dataframe["lower_wick"] = dataframe[["close", "open"]].min(axis=1) - dataframe["low"] dataframe["candle_range"] = dataframe["high"] - dataframe["low"] # === Distance to EMAs (in ATR units) === dataframe["dist_ema21"] = (dataframe["close"] - dataframe["ema21"]) / dataframe["atr"].replace(0, 1) dataframe["dist_ema50"] = (dataframe["close"] - dataframe["ema50"]) / dataframe["atr"].replace(0, 1) # === Multi-TF: 15m === if self.dp: pair = metadata["pair"] inf_15m = self.dp.get_pair_dataframe(pair=pair, timeframe="15m") if not inf_15m.empty: inf_15m["ema9"] = ta.EMA(inf_15m, timeperiod=9) inf_15m["ema21"] = ta.EMA(inf_15m, timeperiod=21) inf_15m["ema50"] = ta.EMA(inf_15m, timeperiod=50) inf_15m["rsi"] = ta.RSI(inf_15m, timeperiod=14) inf_15m["atr"] = ta.ATR(inf_15m, timeperiod=14) macd15 = ta.MACD(inf_15m, fastperiod=12, slowperiod=26, signalperiod=9) inf_15m["macd_hist"] = macd15["macdhist"] bb15 = ta.BBANDS(inf_15m, timeperiod=20, nbdevup=2.0, nbdevdn=2.0) inf_15m["bb_upper"] = bb15["upperband"] inf_15m["bb_lower"] = bb15["lowerband"] inf_15m["adx"] = ta.ADX(inf_15m, timeperiod=14) dataframe = merge_informative_pair( dataframe, inf_15m, self.timeframe, "15m", ffill=True ) # === Multi-TF: 1h === inf_1h = self.dp.get_pair_dataframe(pair=pair, timeframe="1h") if not inf_1h.empty: inf_1h["ema9"] = ta.EMA(inf_1h, timeperiod=9) inf_1h["ema21"] = ta.EMA(inf_1h, timeperiod=21) inf_1h["ema50"] = ta.EMA(inf_1h, timeperiod=50) inf_1h["rsi"] = ta.RSI(inf_1h, timeperiod=14) inf_1h["adx"] = ta.ADX(inf_1h, timeperiod=14) macd1h = ta.MACD(inf_1h, fastperiod=12, slowperiod=26, signalperiod=9) inf_1h["macd_hist"] = macd1h["macdhist"] dataframe = merge_informative_pair( dataframe, inf_1h, self.timeframe, "1h", ffill=True ) return dataframe # ═══════════════════════════════════════════════════════════════════ # ENTRY TREND # ═══════════════════════════════════════════════════════════════════ def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: pb_mult = self.buy_pullback_atr_mult.value vol_min = self.buy_vol_min.value rsi_ob = self.buy_rsi_ob.value rsi_os = self.buy_rsi_os.value # ─────────────────────────────────────────── # 1H TREND BIAS # ─────────────────────────────────────────── # Check if 1h columns exist has_1h = "ema9_1h" in dataframe.columns has_15m = "ema9_15m" in dataframe.columns if has_1h: trend_up_1h = dataframe["ema9_1h"] > dataframe["ema21_1h"] trend_dn_1h = dataframe["ema9_1h"] < dataframe["ema21_1h"] above_ema50_1h = dataframe["close"] > dataframe["ema50_1h"] below_ema50_1h = dataframe["close"] < dataframe["ema50_1h"] else: trend_up_1h = pd.Series(True, index=dataframe.index) trend_dn_1h = pd.Series(True, index=dataframe.index) above_ema50_1h = pd.Series(True, index=dataframe.index) below_ema50_1h = pd.Series(True, index=dataframe.index) # Strict mode: require close above/below 1h EMA50 too if self.buy_trend_strict.value == 1 and has_1h: bias_long = trend_up_1h & above_ema50_1h bias_short = trend_dn_1h & below_ema50_1h else: bias_long = trend_up_1h bias_short = trend_dn_1h # ─────────────────────────────────────────── # 15M MOMENTUM ALIGNMENT # ─────────────────────────────────────────── if has_15m: mom_long_15m = dataframe["ema9_15m"] > dataframe["ema21_15m"] mom_short_15m = dataframe["ema9_15m"] < dataframe["ema21_15m"] else: mom_long_15m = pd.Series(True, index=dataframe.index) mom_short_15m = pd.Series(True, index=dataframe.index) # ─────────────────────────────────────────── # 5M COMMON FILTERS # ─────────────────────────────────────────── vol_ok = dataframe["vol_ratio"] > vol_min has_volume = dataframe["volume"] > 0 atr_valid = dataframe["atr"] > 0.5 # Risk: stop distance must be between 0.3% and 1.5% stop_long = dataframe["swing_low_5"] stop_short = dataframe["swing_high_5"] risk_long_pct = (dataframe["close"] - stop_long) / dataframe["close"] risk_short_pct = (stop_short - dataframe["close"]) / dataframe["close"] risk_ok_long = (risk_long_pct > 0.003) & (risk_long_pct < 0.015) risk_ok_short = (risk_short_pct > 0.003) & (risk_short_pct < 0.015) # ─────────────────────────────────────────── # ENTRY TYPE A: EMA PULLBACK # Price touched EMA21 zone and bounced # ─────────────────────────────────────────── near_ema21_long = ( (dataframe["dist_ema21"] > -pb_mult) & (dataframe["dist_ema21"] < pb_mult * 0.5) & (dataframe["is_bullish"] == 1) ) near_ema21_short = ( (dataframe["dist_ema21"] < pb_mult) & (dataframe["dist_ema21"] > -pb_mult * 0.5) & (dataframe["is_bearish"] == 1) ) # EMA9 still above EMA21 (trend intact) ema_trend_up = dataframe["ema9"] > dataframe["ema21"] ema_trend_dn = dataframe["ema9"] < dataframe["ema21"] entry_a_long = near_ema21_long & ema_trend_up entry_a_short = near_ema21_short & ema_trend_dn # ─────────────────────────────────────────── # ENTRY TYPE B: BB BOUNCE # Price touched opposite BB and reverses # ─────────────────────────────────────────── touch_bb_lower = dataframe["low"] <= dataframe["bb_lower"] * 1.001 touch_bb_upper = dataframe["high"] >= dataframe["bb_upper"] * 0.999 entry_b_long = touch_bb_lower & (dataframe["is_bullish"] == 1) & (dataframe["rsi"] < 40) entry_b_short = touch_bb_upper & (dataframe["is_bearish"] == 1) & (dataframe["rsi"] > 60) # ─────────────────────────────────────────── # ENTRY TYPE C: EMA CROSS (momentum start) # EMA9 crosses EMA21 in direction of 1h trend # ─────────────────────────────────────────── ema_cross_up = (dataframe["ema9"] > dataframe["ema21"]) & (dataframe["ema9_prev"] <= dataframe["ema21_prev"]) ema_cross_dn = (dataframe["ema9"] < dataframe["ema21"]) & (dataframe["ema9_prev"] >= dataframe["ema21_prev"]) entry_c_long = ema_cross_up & (dataframe["adx"] > 18) entry_c_short = ema_cross_dn & (dataframe["adx"] > 18) # ─────────────────────────────────────────── # ENTRY TYPE D: RSI REVERSAL FROM EXTREME # RSI crosses back from OS/OB zone in trend direction # ─────────────────────────────────────────── rsi_bounce_long = (dataframe["rsi"] > rsi_os) & (dataframe["rsi_prev"] <= rsi_os) rsi_bounce_short = (dataframe["rsi"] < rsi_ob) & (dataframe["rsi_prev"] >= rsi_ob) entry_d_long = rsi_bounce_long & (dataframe["close"] > dataframe["ema50"]) entry_d_short = rsi_bounce_short & (dataframe["close"] < dataframe["ema50"]) # ─────────────────────────────────────────── # COMBINE: bias + momentum + entry + filters # ─────────────────────────────────────────── any_long_entry = entry_a_long | entry_b_long | entry_c_long | entry_d_long any_short_entry = entry_a_short | entry_b_short | entry_c_short | entry_d_short go_long = ( bias_long & mom_long_15m & any_long_entry & vol_ok & has_volume & atr_valid & risk_ok_long ) go_short = ( bias_short & mom_short_15m & any_short_entry & vol_ok & has_volume & atr_valid & risk_ok_short ) # Tag entries for analysis dataframe.loc[go_long, "enter_long"] = 1 dataframe.loc[go_short, "enter_short"] = 1 # Entry tags conditions_long = [ (go_long & entry_a_long, "ema_pullback_long"), (go_long & entry_b_long, "bb_bounce_long"), (go_long & entry_c_long, "ema_cross_long"), (go_long & entry_d_long, "rsi_reversal_long"), ] conditions_short = [ (go_short & entry_a_short, "ema_pullback_short"), (go_short & entry_b_short, "bb_bounce_short"), (go_short & entry_c_short, "ema_cross_short"), (go_short & entry_d_short, "rsi_reversal_short"), ] for cond, tag in conditions_long + conditions_short: dataframe.loc[cond, "enter_tag"] = tag return dataframe # ═══════════════════════════════════════════════════════════════════ # EXIT TREND # ═══════════════════════════════════════════════════════════════════ def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Long exit: RSI extreme overbought dataframe.loc[ dataframe["rsi"] > self.sell_rsi_exit_long.value, "exit_long", ] = 1 # Short exit: RSI extreme oversold dataframe.loc[ dataframe["rsi"] < self.sell_rsi_exit_short.value, "exit_short", ] = 1 return dataframe # ═══════════════════════════════════════════════════════════════════ # CUSTOM STOPLOSS — Structural + R-Multiple Trailing # ═══════════════════════════════════════════════════════════════════ def custom_stoploss( self, pair: str, trade: Trade, current_time, current_rate: float, current_profit: float, **kwargs, ) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if len(dataframe) == 0: return -0.015 last = dataframe.iloc[-1] atr = last.get("atr", 0) if atr == 0 or trade.open_rate == 0: return -0.015 is_short = trade.is_short if hasattr(trade, "is_short") else False # Calculate initial risk from structure if is_short: swing_ref = last.get("swing_high_5", 0) if swing_ref > 0: buf = self.buy_sl_buffer_atr.value * atr stop_price = swing_ref + buf stop_dist_pct = (stop_price - trade.open_rate) / trade.open_rate else: stop_dist_pct = 1.0 * atr / trade.open_rate else: swing_ref = last.get("swing_low_5", 0) if swing_ref > 0: buf = self.buy_sl_buffer_atr.value * atr stop_price = swing_ref - buf stop_dist_pct = (trade.open_rate - stop_price) / trade.open_rate else: stop_dist_pct = 1.0 * atr / trade.open_rate # Clamp stop distance stop_dist_pct = max(0.003, min(stop_dist_pct, 0.015)) # R-multiple trailing if current_profit > 0 and stop_dist_pct > 0: r_mult = current_profit / stop_dist_pct if r_mult >= 2.0: # Near/past target: lock 1R profit return stoploss_from_open( 1.0 * stop_dist_pct, current_profit, is_short=is_short ) elif r_mult >= 1.5: # Lock 0.5R return stoploss_from_open( 0.5 * stop_dist_pct, current_profit, is_short=is_short ) elif r_mult >= 1.0: # Breakeven return stoploss_from_open( 0.001, current_profit, is_short=is_short ) return -stop_dist_pct # ═══════════════════════════════════════════════════════════════════ # CUSTOM EXIT — R:R Target + Time + Momentum Fade # ═══════════════════════════════════════════════════════════════════ def custom_exit( self, pair: str, trade: Trade, current_time, current_rate: float, current_profit: float, **kwargs, ) -> Optional[str]: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if len(dataframe) == 0: return None last = dataframe.iloc[-1] atr = last.get("atr", 0) if atr == 0 or trade.open_rate == 0: return None is_short = trade.is_short if hasattr(trade, "is_short") else False # Estimate risk distance if is_short: swing = last.get("swing_high_5", 0) risk = (swing - trade.open_rate) if swing > 0 else 1.0 * atr else: swing = last.get("swing_low_5", 0) risk = (trade.open_rate - swing) if swing > 0 else 1.0 * atr risk_pct = max(risk / trade.open_rate, 0.003) target_pct = risk_pct * self.buy_rr_target.value # ── R:R TARGET HIT ── if current_profit >= target_pct: return "v2_tp_rr" # ── MOMENTUM FADE → early exit at 60% of target ── if current_profit >= target_pct * 0.6: rsi = last.get("rsi", 50) macd_h = last.get("macd_hist", 0) if is_short: # Short: momentum fades when RSI very low or MACD turns positive if rsi < 25 or macd_h > 0: return "v2_tp_fade" else: # Long: momentum fades when RSI very high or MACD turns negative if rsi > 75 or macd_h < 0: return "v2_tp_fade" # ── TIME EXIT ── minutes = (current_time - trade.open_date_utc).total_seconds() / 60 max_minutes = self.buy_max_hold.value * 5 # Small profit over time → take it if minutes > max_minutes * 0.7 and current_profit > 0.002: return "v2_time_profit" # Hard time limit if minutes > max_minutes: return "v2_time_force" return None # ═══════════════════════════════════════════════════════════════════ # CONFIRM ENTRY — Daily limits + cooldown + loss management # ═══════════════════════════════════════════════════════════════════ def confirm_trade_entry( self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, current_time: datetime, entry_tag: Optional[str], side: str, **kwargs, ) -> bool: today = current_time.strftime("%Y-%m-%d") # Reset daily counters if today not in self._daily_trades: self._daily_trades = {today: 0} self._daily_losses_r = {today: 0.0} # Daily trade limit if self._daily_trades.get(today, 0) >= self.buy_max_daily.value: return False # Daily loss limit: stop after -5R if self._daily_losses_r.get(today, 0) <= -5.0: return False # 3 consecutive losses → 30 min pause if self._consecutive_losses >= 3 and self._last_loss_time: pause_until = self._last_loss_time + timedelta(minutes=30) if current_time < pause_until: return False self._consecutive_losses = 0 self._daily_trades[today] = self._daily_trades.get(today, 0) + 1 return True def confirm_trade_exit( self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs, ) -> bool: profit = trade.calc_profit_ratio(rate) today = current_time.strftime("%Y-%m-%d") if profit < 0: self._consecutive_losses += 1 self._last_loss_time = current_time # Track daily losses in R self._daily_losses_r[today] = self._daily_losses_r.get(today, 0) - 1 else: self._consecutive_losses = 0 self._daily_losses_r[today] = self._daily_losses_r.get(today, 0) + profit * 100 return True