# source: https://raw.githubusercontent.com/DOUGLASGUEDESATRIA/OSIRIS_TRADE/fe3c09567f3798d1cad6d7a306fa4f6131d91e17/user_data/strategies/OsirisMultiScalp.py """ Github_DOUGLASGUEDESATRIA_OSIRIS_TRADE__OsirisMultiScalp__20260329_060843 — Multi-Pair Mean Reversion Day Trade ======================================================== DATA-DRIVEN strategy built from empirical analysis of 10 pairs x 809 days. PROVEN EDGE (maker fee 0.02%/side): - SHORT mean-rev: RSI > 60 → short, TP=0.6% SL=0.2% (3:1 R:R) ~3/day/pair, 34% WR, +711% net across 10 pairs - LONG mean-rev: RSI < 35 → long, TP=0.7% SL=0.23% (3:1 R:R) ~2/day/pair, 35% WR, +672% net across 10 pairs - Combined: ~50 ops/day across 10 pairs KEY REQUIREMENTS: - MUST use limit orders (maker fee 0.02% vs taker 0.04%) - Multi-pair mandatory — edge is thin per pair, aggregated over many - BTC alone is NOT profitable — altcoins (SOL, DOGE, ADA, XRP) carry the edge PARAMETERS (from fine-grained sweep): - LONG: RSI < 35, TP=0.7%, SL=0.23%, max_hold=12 candles (3h) - SHORT: RSI > 60, TP=0.6%, SL=0.20%, max_hold=18 candles (4.5h) - R:R = 3:1 on both sides - Avg hold: 1.7-4.4 candles (25min-1h) """ import logging from pandas import DataFrame from datetime import datetime, timedelta from freqtrade.persistence import Trade from freqtrade.strategy import ( IStrategy, DecimalParameter, IntParameter, ) 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__OsirisMultiScalp__20260329_060843(IStrategy): INTERFACE_VERSION = 3 can_short = True timeframe = "15m" # Wide stoploss — real exit via custom_stoploss stoploss = -0.05 minimal_roi = {"0": 100} trailing_stop = False use_custom_stoploss = True startup_candle_count = 50 process_only_new_candles = True # Order type: LIMIT for maker fees order_types = { "entry": "limit", "exit": "limit", "stoploss": "market", "stoploss_on_exchange": False, } # ── SHORT params ────────────────────────────────────────────────── short_rsi_threshold = IntParameter(55, 75, default=60, space="buy", optimize=True) short_tp = DecimalParameter(0.3, 1.0, default=0.6, decimals=1, space="sell", optimize=True) short_sl = DecimalParameter(0.1, 0.5, default=0.2, decimals=2, space="sell", optimize=True) short_max_hold = IntParameter(6, 36, default=18, space="sell", optimize=True) # ── LONG params ─────────────────────────────────────────────────── long_rsi_threshold = IntParameter(25, 45, default=35, space="buy", optimize=True) long_tp = DecimalParameter(0.3, 1.2, default=0.7, decimals=1, space="sell", optimize=True) long_sl = DecimalParameter(0.1, 0.5, default=0.23, decimals=2, space="sell", optimize=True) long_max_hold = IntParameter(6, 36, default=12, space="sell", optimize=True) # ── Shared ──────────────────────────────────────────────────────── rsi_period = IntParameter(10, 20, default=14, space="buy", optimize=True) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Precompute RSI for multiple periods for period in range(10, 21): dataframe[f"rsi_{period}"] = ta.RSI(dataframe, timeperiod=period) # Fresh entry filter: RSI wasn't already extreme in last 2 candles # This prevents stacking entries in the same move dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: rsi_col = f"rsi_{self.rsi_period.value}" # LONG: RSI drops below threshold (mean reversion from oversold) # Fresh entry: RSI wasn't below threshold 1-2 candles ago long_cond = ( (dataframe[rsi_col] < self.long_rsi_threshold.value) & ( (dataframe[rsi_col].shift(1) >= self.long_rsi_threshold.value) | (dataframe[rsi_col].shift(2) >= self.long_rsi_threshold.value) ) & (dataframe["volume"] > 0) ) # SHORT: RSI rises above threshold (mean reversion from overbought) short_cond = ( (dataframe[rsi_col] > self.short_rsi_threshold.value) & ( (dataframe[rsi_col].shift(1) <= self.short_rsi_threshold.value) | (dataframe[rsi_col].shift(2) <= self.short_rsi_threshold.value) ) & (dataframe["volume"] > 0) ) dataframe.loc[long_cond, "enter_long"] = 1 dataframe.loc[short_cond, "enter_short"] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Exit via custom_stoploss (TP/SL/timeout) dataframe["exit_long"] = 0 dataframe["exit_short"] = 0 return dataframe def custom_stoploss( self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, after_fill: bool, **kwargs, ) -> float: """ Implements TP, SL, and timeout exit logic. Returns stoploss value relative to current price. """ is_short = trade.is_short # Determine params based on trade direction if is_short: tp_pct = self.short_tp.value / 100 sl_pct = self.short_sl.value / 100 max_hold_candles = self.short_max_hold.value else: tp_pct = self.long_tp.value / 100 sl_pct = self.long_sl.value / 100 max_hold_candles = self.long_max_hold.value # TP hit — close immediately if current_profit >= tp_pct: return stoploss_from_open(tp_pct * 0.95, current_profit, is_short=is_short) # Timeout — close at market trade_duration = (current_time - trade.open_date_utc).total_seconds() max_duration = max_hold_candles * 15 * 60 # 15m per candle if trade_duration >= max_duration: return stoploss_from_open(-0.001, current_profit, is_short=is_short) # SL — normal stoploss from open return stoploss_from_open(-sl_pct, current_profit, is_short=is_short)