# source: https://raw.githubusercontent.com/Eksz3lsi0r/DOLLAMNY/f6178a8e9385159556a6443262fc338e43d6f88c/example_hyperopt_strategy.py """ Example Strategy with Comprehensive Hyperopt Parameters This strategy demonstrates how to use the hyperopt parameters defined in hyperopt_parameters.py for a complete trading strategy with RSI, EMA, MACD, ROI, Stoploss, and ML integration. """ from pandas import DataFrame import talib.abstract as ta from freqtrade.persistence import Trade from freqtrade.strategy import BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, IStrategy import freqtrade.vendor.qtpylib.indicators as qtpylib class Github_Eksz3lsi0r_DOLLAMNY__example_hyperopt_strategy__20250916_061219(IStrategy): """ Example strategy demonstrating comprehensive hyperopt parameter usage. This strategy includes: - RSI thresholds for buy/sell signals - EMA crossovers for trend following - MACD signals for momentum - Custom ROI table generation - Stoploss and trailing stop optimization - ML threshold integration for FreqAI """ # Strategy interface version INTERFACE_VERSION = 3 # Timeframe timeframe = "5m" # Startup candle count startup_candle_count: int = 30 # Order types order_types = {"entry": "limit", "exit": "limit", "stoploss": "market", "stoploss_on_exchange": False} # Order time in force order_time_in_force = {"entry": "gtc", "exit": "gtc"} # ============================================================================= # HYPEROPT PARAMETERS # ============================================================================= # RSI Parameters buy_rsi = IntParameter(20, 40, default=30, space="buy") sell_rsi = IntParameter(60, 80, default=70, space="sell") rsi_period = IntParameter(10, 21, default=14, space="buy") buy_rsi_enabled = BooleanParameter(default=True, space="buy") sell_rsi_enabled = BooleanParameter(default=True, space="sell") # EMA Parameters ema_short_period = IntParameter(5, 15, default=9, space="buy") ema_long_period = IntParameter(30, 50, default=34, space="buy") ema_short_enabled = BooleanParameter(default=True, space="buy") ema_long_enabled = BooleanParameter(default=True, space="buy") ema_cross_threshold = DecimalParameter(0.001, 0.01, default=0.005, decimals=3, space="buy") # MACD Parameters macd_fast_period = IntParameter(8, 16, default=12, space="buy") macd_slow_period = IntParameter(20, 30, default=26, space="buy") macd_signal_period = IntParameter(7, 12, default=9, space="buy") macd_histogram_threshold = DecimalParameter(0.0001, 0.01, default=0.001, decimals=4, space="buy") macd_buy_trigger = CategoricalParameter( ["macd_cross_signal", "macd_histogram_positive", "macd_both"], default="macd_cross_signal", space="buy" ) # ROI Parameters roi_t1 = IntParameter(5, 30, default=10, space="roi") roi_t2 = IntParameter(10, 60, default=20, space="roi") roi_t3 = IntParameter(20, 120, default=40, space="roi") roi_p1 = DecimalParameter(0.01, 0.05, default=0.02, decimals=3, space="roi") roi_p2 = DecimalParameter(0.01, 0.08, default=0.03, decimals=3, space="roi") roi_p3 = DecimalParameter(0.01, 0.15, default=0.05, decimals=3, space="roi") # Stoploss Parameters stoploss = DecimalParameter(-0.15, -0.02, default=-0.10, decimals=3, space="stoploss") # Trailing Stop Parameters trailing_stop = BooleanParameter(default=True, space="trailing") trailing_stop_positive = DecimalParameter(0.01, 0.05, default=0.02, decimals=3, space="trailing") trailing_stop_positive_offset = DecimalParameter(0.01, 0.10, default=0.05, decimals=3, space="trailing") trailing_only_offset_is_reached = BooleanParameter(default=True, space="trailing") # ML Parameters (for FreqAI integration) ml_buy_threshold = DecimalParameter(0.5, 0.9, default=0.7, decimals=2, space="buy") ml_sell_threshold = DecimalParameter(0.1, 0.5, default=0.3, decimals=2, space="sell") ml_confidence_threshold = DecimalParameter(0.6, 0.95, default=0.8, decimals=2, space="buy") ml_model_type = CategoricalParameter( ["RandomForest", "XGBoost", "LightGBM", "CatBoost"], default="RandomForest", space="buy" ) # Additional Technical Indicators bb_period = IntParameter(15, 25, default=20, space="buy") bb_std = DecimalParameter(1.5, 2.5, default=2.0, decimals=1, space="buy") adx_period = IntParameter(10, 20, default=14, space="buy") adx_threshold = IntParameter(20, 40, default=25, space="buy") # Risk Management max_risk_per_trade = DecimalParameter(0.01, 0.05, default=0.02, decimals=3, space="buy") position_sizing_method = CategoricalParameter( ["fixed", "percentage", "volatility"], default="percentage", space="buy" ) # Market Condition Filters trend_filter_enabled = BooleanParameter(default=True, space="buy") volatility_filter_enabled = BooleanParameter(default=True, space="buy") volume_filter_enabled = BooleanParameter(default=True, space="buy") # ============================================================================= # CUSTOM ROI TABLE GENERATION # ============================================================================= def custom_roi_table(self, params: dict) -> dict: """ Generate custom ROI table based on hyperopt parameters. """ roi_table = {} roi_table[0] = params["roi_p1"] + params["roi_p2"] + params["roi_p3"] roi_table[params["roi_t3"]] = params["roi_p1"] + params["roi_p2"] roi_table[params["roi_t3"] + params["roi_t2"]] = params["roi_p1"] roi_table[params["roi_t3"] + params["roi_t2"] + params["roi_t1"]] = 0 return roi_table # ============================================================================= # INDICATOR CALCULATION # ============================================================================= def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Calculate all technical indicators used in the strategy. """ # RSI dataframe["rsi"] = ta.RSI(dataframe, timeperiod=self.rsi_period.value) # EMAs if self.ema_short_enabled.value: dataframe["ema_short"] = ta.EMA(dataframe, timeperiod=self.ema_short_period.value) if self.ema_long_enabled.value: dataframe["ema_long"] = ta.EMA(dataframe, timeperiod=self.ema_long_period.value) # MACD macd = ta.MACD( dataframe, fastperiod=self.macd_fast_period.value, slowperiod=self.macd_slow_period.value, signalperiod=self.macd_signal_period.value, ) dataframe["macd"] = macd["macd"] dataframe["macdsignal"] = macd["macdsignal"] dataframe["macdhist"] = macd["macdhist"] # Bollinger Bands bollinger = qtpylib.bollinger_bands(dataframe["close"], window=self.bb_period.value, stds=self.bb_std.value) dataframe["bb_lowerband"] = bollinger["lower"] dataframe["bb_middleband"] = bollinger["mid"] dataframe["bb_upperband"] = bollinger["upper"] # ADX dataframe["adx"] = ta.ADX(dataframe, timeperiod=self.adx_period.value) # Volume indicators dataframe["volume_sma"] = ta.SMA(dataframe["volume"], timeperiod=20) # ML predictions (if using FreqAI) # These would be populated by FreqAI in a real implementation if hasattr(self, "freqai_info"): dataframe["prediction"] = dataframe.get("prediction", 0.5) dataframe["confidence"] = dataframe.get("confidence", 0.8) else: # Placeholder values for backtesting without FreqAI dataframe["prediction"] = 0.5 dataframe["confidence"] = 0.8 return dataframe # ============================================================================= # BUY SIGNAL LOGIC # ============================================================================= def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Define buy signal logic using hyperopt parameters. """ conditions = [] # RSI condition if self.buy_rsi_enabled.value: conditions.append(dataframe["rsi"] < self.buy_rsi.value) # EMA crossover condition if self.ema_short_enabled.value and self.ema_long_enabled.value: conditions.append(dataframe["ema_short"] > dataframe["ema_long"]) # Additional threshold for crossover strength conditions.append((dataframe["ema_short"] - dataframe["ema_long"]) > self.ema_cross_threshold.value) # MACD condition if self.macd_buy_trigger.value == "macd_cross_signal": conditions.append(dataframe["macd"] > dataframe["macdsignal"]) elif self.macd_buy_trigger.value == "macd_histogram_positive": conditions.append(dataframe["macdhist"] > self.macd_histogram_threshold.value) elif self.macd_buy_trigger.value == "macd_both": conditions.append( (dataframe["macd"] > dataframe["macdsignal"]) & (dataframe["macdhist"] > self.macd_histogram_threshold.value) ) # Bollinger Bands condition (price near lower band) conditions.append(dataframe["close"] <= dataframe["bb_lowerband"]) # ADX condition (trend strength) if self.trend_filter_enabled.value: conditions.append(dataframe["adx"] > self.adx_threshold.value) # Volume condition if self.volume_filter_enabled.value: conditions.append(dataframe["volume"] > dataframe["volume_sma"]) # ML condition (if using FreqAI) conditions.append(dataframe["prediction"] > self.ml_buy_threshold.value) conditions.append(dataframe["confidence"] > self.ml_confidence_threshold.value) # Combine all conditions if conditions: dataframe.loc[ qtpylib.crossed_above(dataframe["rsi"], 30) # Additional momentum check & qtpylib.AND(*conditions), "buy", ] = 1 return dataframe # ============================================================================= # SELL SIGNAL LOGIC # ============================================================================= def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Define sell signal logic using hyperopt parameters. """ conditions = [] # RSI condition if self.sell_rsi_enabled.value: conditions.append(dataframe["rsi"] > self.sell_rsi.value) # EMA crossover condition (opposite of buy) if self.ema_short_enabled.value and self.ema_long_enabled.value: conditions.append(dataframe["ema_short"] < dataframe["ema_long"]) # MACD condition (opposite of buy) conditions.append(dataframe["macd"] < dataframe["macdsignal"]) # Bollinger Bands condition (price near upper band) conditions.append(dataframe["close"] >= dataframe["bb_upperband"]) # ML condition conditions.append(dataframe["prediction"] < self.ml_sell_threshold.value) # Combine all conditions if conditions: dataframe.loc[ qtpylib.crossed_below(dataframe["rsi"], 70) # Additional momentum check & qtpylib.AND(*conditions), "sell", ] = 1 return dataframe # ============================================================================= # CUSTOM STOPLOSS # ============================================================================= def custom_stoploss( self, pair: str, trade: Trade, current_time, current_rate, current_profit, after_fill: bool, **kwargs ) -> float: """ Custom stoploss logic using hyperopt parameters. """ # Use the hyperopt-optimized stoploss value return self.stoploss.value # ============================================================================= # CUSTOM EXIT # ============================================================================= def custom_exit(self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs) -> str: """ Custom exit logic for additional sell conditions. """ # Exit if ML confidence drops below threshold if hasattr(trade, "ml_confidence") and trade.ml_confidence < self.ml_confidence_threshold.value: return "ml_confidence_low" # Exit if profit target reached and RSI overbought if current_profit > 0.05 and trade.calc_profit_ratio(current_rate) > 0.02: return "profit_target_reached" return None # ============================================================================= # PLOT CONFIGURATION # ============================================================================= plot_config = { "main_plot": { "ema_short": {"color": "blue"}, "ema_long": {"color": "orange"}, "bb_upperband": {"color": "red"}, "bb_lowerband": {"color": "red"}, }, "subplots": { "RSI": { "rsi": {"color": "purple"}, }, "MACD": { "macd": {"color": "blue"}, "macdsignal": {"color": "red"}, "macdhist": {"type": "bar", "plotly": {"opacity": 0.9}}, }, "ML": { "prediction": {"color": "green"}, "confidence": {"color": "orange"}, }, }, } # ============================================================================= # HYPEROPT CONFIGURATION CLASS # ============================================================================= class Github_Eksz3lsi0r_DOLLAMNY__example_hyperopt_strategy__20250916_061219Hyperopt: """ Custom hyperopt configuration for the example strategy. This class can be used to override default hyperopt spaces. """ @staticmethod def generate_roi_table(params: dict) -> dict: """ Generate ROI table for hyperopt optimization. """ roi_table = {} roi_table[0] = params["roi_p1"] + params["roi_p2"] + params["roi_p3"] roi_table[params["roi_t3"]] = params["roi_p1"] + params["roi_p2"] roi_table[params["roi_t3"] + params["roi_t2"]] = params["roi_p1"] roi_table[params["roi_t3"] + params["roi_t2"] + params["roi_t1"]] = 0 return roi_table @staticmethod def generate_trailing_params(params: dict) -> dict: """ Generate trailing stop parameters for hyperopt optimization. """ return { "trailing_stop": params["trailing_stop"], "trailing_stop_positive": params["trailing_stop_positive"], "trailing_stop_positive_offset": ( params["trailing_stop_positive"] + params["trailing_stop_positive_offset"] ), "trailing_only_offset_is_reached": params["trailing_only_offset_is_reached"], } # ============================================================================= # USAGE INSTRUCTIONS # ============================================================================= """ To use this strategy with hyperopt: 1. Save this file as your strategy (e.g., in user_data/strategies/) 2. Run hyperopt with the strategy: freqtrade hyperopt --strategy Github_Eksz3lsi0r_DOLLAMNY__example_hyperopt_strategy__20250916_061219 \ --hyperopt-loss SharpeHyperOptLoss \ --spaces buy sell roi stoploss trailing --epochs 100 3. For ML integration with FreqAI: freqtrade hyperopt --strategy Github_Eksz3lsi0r_DOLLAMNY__example_hyperopt_strategy__20250916_061219 \ --freqai-model-type RandomForest \ --freqai-backtest-live-models \ --spaces buy sell roi stoploss trailing --epochs 100 4. The hyperopt results will show optimized parameters for: - RSI thresholds - EMA periods - MACD parameters - ROI table values - Stoploss and trailing stop values - ML thresholds (if using FreqAI) 5. Copy the best parameters from hyperopt results into your strategy or config file. Example hyperopt command with all spaces: freqtrade hyperopt --strategy Github_Eksz3lsi0r_DOLLAMNY__example_hyperopt_strategy__20250916_061219 \ --hyperopt-loss SharpeHyperOptLoss \ --spaces buy sell roi stoploss trailing --epochs 200 \ --timerange 20230101-20231201 """