# source: https://raw.githubusercontent.com/aldrinmagno/FreqtradeStrategies/3d9c1b176faceca3c950f99c3c55c8a7abe77275/user_data/strategies/MeanReversionScalper.py """ Github_aldrinmagno_FreqtradeStrategies__MeanReversionScalper__20260317_150802 — Bollinger Band + RSI + Volume Spike Mean-Reversion Scalper Timeframe: 5m (with 1h informative for trend filter) Description: Enters when price touches the lower Bollinger Band with oversold RSI and a volume spike, targeting a 0.3-0.6% snap-back toward the midline. Entry conditions: - Close <= BB lower band - RSI < 30 (momentum exhaustion) - Volume > 1.5x 20-period average (capitulation spike) - BB width > 0.5% (enough room for profitable reversion) - 1h EMA50 slope > -0.3% (avoids buying into waterfall sell-offs) Risk management: - Tiered ROI: 0.6% immediately down to 0.1% at 40 minutes - Trailing stop activating at +0.3% - Time-based tightening custom stoploss (-0.8% -> -0.3%) - Signal exit when price hits BB midline - StoplossGuard after 3 consecutive losses + per-pair cooldown WARNING: This strategy has NOT been backtested. Run through at least 6 months of data on 8+ liquid pairs, verify win rate > ~55% with profit factor > 1.3, and paper trade 2-4 weeks before using real capital. Requires fees <= 0.075% per side. """ from datetime import datetime from freqtrade.persistence import Trade from freqtrade.strategy import IStrategy, DecimalParameter, IntParameter, merge_informative_pair from pandas import DataFrame import pandas_ta as pta class Github_aldrinmagno_FreqtradeStrategies__MeanReversionScalper__20260317_150802(IStrategy): INTERFACE_VERSION: int = 3 timeframe: str = "5m" # Tiered ROI — 0.6% at open, tapering to 0.1% at 40 min minimal_roi: dict = { "0": 0.006, "10": 0.005, "20": 0.003, "30": 0.002, "40": 0.001, } stoploss: float = -0.008 # -0.8% hard stoploss # Trailing stop — activates at +0.3% trailing_stop: bool = True trailing_stop_positive: float = 0.002 trailing_stop_positive_offset: float = 0.003 trailing_only_offset_is_reached: bool = True # Trade management max_open_trades: int = 6 use_exit_signal: bool = True exit_profit_only: bool = False # Informative timeframe for trend filter informative_timeframe: str = "1h" # Process only new candles for performance process_only_new_candles: bool = True # Enough candles for 1h EMA50 to warm up (50 * 12 five-min candles per hour) startup_candle_count: int = 600 # ----------------------------------------------------------------------- # Buy hyperopt parameters # ----------------------------------------------------------------------- buy_rsi_threshold = IntParameter(20, 40, default=30, space="buy", help="RSI must be below this to enter") buy_volume_factor = DecimalParameter(1.2, 2.5, default=1.5, space="buy", help="Volume must exceed SMA(20) * this factor") buy_bb_width_min = DecimalParameter(0.003, 0.01, default=0.005, space="buy", help="Minimum BB width (fraction) for entry") buy_ema_slope_min = DecimalParameter(-0.005, 0.0, default=-0.003, space="buy", help="Min 1h EMA50 slope (fraction) — trend filter") # ----------------------------------------------------------------------- # Sell hyperopt parameters # ----------------------------------------------------------------------- sell_bb_mid_offset = DecimalParameter(0.995, 1.005, default=1.0, space="sell", help="Exit at BB mid * this offset") # ----------------------------------------------------------------------- # Protection — StoplossGuard + CooldownPeriod # ----------------------------------------------------------------------- @property def protections(self): return [ { "method": "StoplossGuard", "lookback_period_candles": 12, # 1 hour of 5m candles "trade_limit": 3, "stop_duration_candles": 12, # pause for 1 hour "only_per_pair": False, }, { "method": "CooldownPeriod", "stop_duration_candles": 2, # 10 min cooldown per pair "only_per_pair": True, }, ] def informative_pairs(self): pairs = self.dp.current_whitelist() return [(pair, self.informative_timeframe) for pair in pairs] def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """Populate indicators for the 1h informative timeframe.""" dataframe["ema50"] = pta.ema(dataframe["close"], length=50) dataframe["ema50_slope"] = dataframe["ema50"].pct_change(periods=1) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # -- 5m indicators -- # RSI (14) dataframe["rsi"] = pta.rsi(dataframe["close"], length=14) # Bollinger Bands (20, 2 std) bbands = pta.bbands(dataframe["close"], length=20, std=2.0) dataframe["bb_lower"] = bbands["BBL_20_2.0"] dataframe["bb_mid"] = bbands["BBM_20_2.0"] dataframe["bb_upper"] = bbands["BBU_20_2.0"] # BB width as fraction of midline dataframe["bb_width"] = ( (dataframe["bb_upper"] - dataframe["bb_lower"]) / dataframe["bb_mid"] ) # Volume SMA (20) dataframe["volume_sma_20"] = pta.sma(dataframe["volume"], length=20) # -- 1h informative: EMA50 slope as trend filter -- if self.dp: inf_1h = self.dp.get_pair_dataframe( pair=metadata["pair"], timeframe=self.informative_timeframe ) inf_1h = self.populate_indicators_1h(inf_1h, metadata) dataframe = merge_informative_pair( dataframe, inf_1h, self.timeframe, self.informative_timeframe, ffill=True, ) dataframe.rename( columns={"ema50_slope_1h": "ema50_1h_slope"}, inplace=True ) else: dataframe["ema50_1h_slope"] = 0.0 return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( # Price at or below lower Bollinger Band (dataframe["close"] <= dataframe["bb_lower"]) # RSI confirms oversold exhaustion & (dataframe["rsi"] < self.buy_rsi_threshold.value) # Volume spike — capitulation, not low-conviction slide & (dataframe["volume"] > dataframe["volume_sma_20"] * self.buy_volume_factor.value) # Enough BB width for profitable reversion & (dataframe["bb_width"] > self.buy_bb_width_min.value) # 1h trend filter — not a waterfall sell-off & (dataframe["ema50_1h_slope"] > self.buy_ema_slope_min.value) # Sanity & (dataframe["volume"] > 0) ), "enter_long", ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( # Price reverted to BB midline (dataframe["close"] >= dataframe["bb_mid"] * self.sell_bb_mid_offset.value) & (dataframe["volume"] > 0) ), "exit_long", ] = 1 return dataframe def custom_stoploss( self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs, ) -> float: """ Time-based tightening stoploss: 0-10 min : -0.8% (use the static stoploss) 10-20 min: -0.5% 20-30 min: -0.4% 30+ min : -0.3% """ elapsed = (current_time - trade.open_date_utc).total_seconds() / 60.0 if elapsed > 30: return -0.003 elif elapsed > 20: return -0.004 elif elapsed > 10: return -0.005 # Return 1 to keep the default stoploss (-0.8%) return 1