# source: https://raw.githubusercontent.com/nicolasasauer/tradingbot-v8/e23da3fb0dc4b7b247e9ce4b97645b7916e2702f/user_data/strategies/SmartAlphaStrategy.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 from datetime import datetime from typing import Optional import numpy as np import pandas as pd import talib.abstract as ta from pandas import DataFrame from freqtrade.strategy import ( BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter, merge_informative_pair, ) class Github_nicolasasauer_tradingbot_v8__SmartAlphaStrategy__20260311_135647(IStrategy): """ Github_nicolasasauer_tradingbot_v8__SmartAlphaStrategy__20260311_135647 – RSI + EMA Trend + Bollinger Bands Entry logic: - RSI is oversold (below buy_rsi threshold) - Price is above the EMA-200 (long-term uptrend filter) - EMA-20 is above EMA-50 (medium-term uptrend confirmation) - Price touches or crosses the lower Bollinger Band (mean-reversion entry) Exit logic: - RSI is overbought (above sell_rsi threshold) - Price crosses above the upper Bollinger Band - Trailing stop-loss as a safety net Protective mechanisms: - Hard stop-loss at 5 % below entry - Trailing stop-loss that kicks in after 2 % of profit """ # ------------------------------------------------------------------ # Strategy metadata # ------------------------------------------------------------------ INTERFACE_VERSION = 3 timeframe = "5m" can_short = False use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # ------------------------------------------------------------------ # ROI – fallback exits (time-based) # ------------------------------------------------------------------ minimal_roi = { "0": 0.03, # 3 % at any time "15": 0.015, # 1.5 % after 15 minutes "30": 0.01, # 1 % after 30 minutes "60": 0.005, # 0.5 % after 60 minutes } # ------------------------------------------------------------------ # Stop-loss & trailing # ------------------------------------------------------------------ stoploss = -0.05 # Hard stop: -5 % trailing_stop = True trailing_stop_positive = 0.02 # Trailing activates after +2 % trailing_stop_positive_offset = 0.03 # Trailing trails from +3 % trailing_only_offset_is_reached = True # ------------------------------------------------------------------ # Hyperopt-tunable parameters # ------------------------------------------------------------------ # Buy parameters buy_rsi = IntParameter(20, 50, default=30, space="buy", optimize=True) buy_rsi_enabled = BooleanParameter(default=True, space="buy", optimize=True) buy_ema_short = IntParameter(10, 30, default=20, space="buy", optimize=True) buy_ema_long = IntParameter(40, 70, default=50, space="buy", optimize=True) buy_bb_enabled = BooleanParameter(default=True, space="buy", optimize=True) # Sell parameters sell_rsi = IntParameter(60, 90, default=70, space="sell", optimize=True) sell_rsi_enabled = BooleanParameter(default=True, space="sell", optimize=True) sell_bb_enabled = BooleanParameter(default=True, space="sell", optimize=True) # ------------------------------------------------------------------ # Startup candle count (needed so indicators are warmed up) # ------------------------------------------------------------------ startup_candle_count: int = 200 def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """Calculate all technical indicators.""" # ---- RSI -------------------------------------------------------- dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) # ---- EMA family ------------------------------------------------- for period in (20, 50, 200): dataframe[f"ema_{period}"] = ta.EMA(dataframe, timeperiod=period) # ---- Dynamic EMA windows (hyperopt) ------------------------------ for val in self.buy_ema_short.range: dataframe[f"ema_short_{val}"] = ta.EMA(dataframe, timeperiod=val) for val in self.buy_ema_long.range: dataframe[f"ema_long_{val}"] = ta.EMA(dataframe, timeperiod=val) # ---- Bollinger Bands (20, 2σ) ------------------------------------ bollinger = ta.BBANDS(dataframe, timeperiod=20, nbdevup=2.0, nbdevdn=2.0) dataframe["bb_upperband"] = bollinger["upperband"] dataframe["bb_middleband"] = bollinger["middleband"] dataframe["bb_lowerband"] = bollinger["lowerband"] dataframe["bb_width"] = ( (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"] ) # ---- MACD (auxiliary confirmation) ------------------------------- macd = ta.MACD(dataframe) dataframe["macd"] = macd["macd"] dataframe["macdsignal"] = macd["macdsignal"] dataframe["macdhist"] = macd["hist"] # ---- Volume indicator -------------------------------------------- dataframe["volume_mean"] = dataframe["volume"].rolling(window=20).mean() return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """Define entry (buy) conditions.""" conditions = [] # RSI oversold if self.buy_rsi_enabled.value: conditions.append(dataframe["rsi"] < self.buy_rsi.value) # Dynamic EMA: short EMA above long EMA (uptrend) conditions.append( dataframe[f"ema_short_{self.buy_ema_short.value}"] > dataframe[f"ema_long_{self.buy_ema_long.value}"] ) # Price above EMA-200 (macro uptrend filter) conditions.append(dataframe["close"] > dataframe["ema_200"]) # Price touches or is below lower Bollinger Band (mean-reversion) if self.buy_bb_enabled.value: conditions.append(dataframe["close"] <= dataframe["bb_lowerband"]) # Minimum volume filter (avoid thin markets) conditions.append(dataframe["volume"] > dataframe["volume_mean"] * 0.5) # Candle must be valid conditions.append(dataframe["volume"] > 0) if conditions: dataframe.loc[ np.logical_and.reduce(conditions), "enter_long", ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """Define exit (sell) conditions.""" conditions = [] # RSI overbought if self.sell_rsi_enabled.value: conditions.append(dataframe["rsi"] > self.sell_rsi.value) # Price crosses above upper Bollinger Band if self.sell_bb_enabled.value: conditions.append(dataframe["close"] >= dataframe["bb_upperband"]) # Candle must be valid conditions.append(dataframe["volume"] > 0) if conditions: # Use OR logic for exits: trigger on any exit condition dataframe.loc[ np.logical_or.reduce(conditions) & (dataframe["volume"] > 0), "exit_long", ] = 1 return dataframe def custom_stoploss( self, pair: str, trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs, ) -> float: """ Dynamic protective stop-loss: - If profit > 5 %, lock in at least 2 % profit. - If profit > 10 %, lock in at least 5 % profit. - Otherwise fall back to the static stoploss. """ if current_profit > 0.10: return max(self.stoploss, -0.05 + current_profit - 0.05) if current_profit > 0.05: return max(self.stoploss, -0.02) return self.stoploss