# source: https://raw.githubusercontent.com/JC-Media-Arts/crypto-tracker-v3/72c791b90ea1fe25ac8c3bfe16b195147a337b76/freqtrade/user_data/strategies/hyperopt/SwingStrategyV1_HO.py """ SWING Strategy - Hyperoptable Version For finding optimal parameters through backtesting DO NOT USE FOR PRODUCTION - Use SwingStrategyV1.py instead """ import numpy as np import pandas as pd import talib.abstract as ta from pandas import DataFrame from datetime import datetime, timezone, timedelta from typing import Dict, Optional from functools import reduce from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter, CategoricalParameter from freqtrade.persistence import Trade class Github_JC_Media_Arts_crypto_tracker_v3__SwingStrategyV1_HO__20250902_031828(IStrategy): """ SWING Strategy - Hyperoptable Version This version is designed for hyperparameter optimization to find the best initial values for the SWING strategy parameters. After optimization, apply the discovered values to your Admin config and use the production SwingStrategyV1.py for actual trading. """ # Strategy interface version INTERFACE_VERSION = 3 # Disable ROI - we'll optimize stoploss and trailing stop instead minimal_roi = {"0": 100} # Hyperoptable stoploss stoploss = -0.12 # Will be optimized # Trailing stop parameters (will be optimized) trailing_stop = True trailing_stop_positive = 0.025 trailing_stop_positive_offset = 0.04 trailing_only_offset_is_reached = True # Optimal timeframe for the strategy timeframe = "1h" # Run "populate_indicators()" for all tickers in whitelist process_only_new_candles = False # ------------------------------------------------------------------------- # HYPEROPTABLE PARAMETERS - These will be optimized # ------------------------------------------------------------------------- # Buy space parameters (entry conditions) buy_breakout_threshold = DecimalParameter(1.005, 1.03, decimals=3, default=1.01, space='buy', optimize=True) buy_breakout_confirmation = DecimalParameter(0.005, 0.03, decimals=3, default=0.015, space='buy', optimize=True) buy_volume_surge = DecimalParameter(1.1, 2.0, decimals=1, default=1.3, space='buy', optimize=True) buy_rsi_min = IntParameter(35, 55, default=45, space='buy', optimize=True) buy_rsi_max = IntParameter(65, 85, default=75, space='buy', optimize=True) buy_momentum_score_min = IntParameter(30, 60, default=40, space='buy', optimize=True) buy_trend_strength_min = DecimalParameter(0.01, 0.05, decimals=3, default=0.02, space='buy', optimize=True) buy_volatility_max = DecimalParameter(0.05, 0.15, decimals=2, default=0.10, space='buy', optimize=True) # Sell space parameters (exit conditions) sell_take_profit = DecimalParameter(0.04, 0.20, decimals=2, default=0.08, space='sell', optimize=True) sell_rsi_high = IntParameter(75, 90, default=80, space='sell', optimize=True) sell_volume_ratio_low = DecimalParameter(0.3, 0.7, decimals=1, default=0.5, space='sell', optimize=True) sell_trend_reversal = DecimalParameter(-0.02, -0.005, decimals=3, default=-0.01, space='sell', optimize=True) # Protection/Stoploss parameters - no buy_ prefix for stoploss stoploss_value = DecimalParameter(-0.12, -0.03, decimals=2, default=-0.06, space='stoploss', optimize=True) trailing_stop_positive_value = DecimalParameter(0.01, 0.05, decimals=3, default=0.025, space='trailing', optimize=True) trailing_stop_positive_offset_value = DecimalParameter(0.02, 0.08, decimals=3, default=0.04, space='trailing', optimize=True) # Breakout detection parameters buy_breakout_lookback = IntParameter(10, 30, default=20, space='buy', optimize=True) # Technical indicator parameters (fixed for consistency) rsi_period = 14 macd_fast = 12 macd_slow = 26 macd_signal = 9 sma_fast = 20 sma_slow = 50 def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Calculate all technical indicators needed for the strategy """ # RSI dataframe["rsi"] = ta.RSI(dataframe, timeperiod=int(self.rsi_period)) # MACD macd = ta.MACD( dataframe, fastperiod=self.macd_fast, slowperiod=self.macd_slow, signalperiod=self.macd_signal ) dataframe["macd"] = macd["macd"] dataframe["macd_signal"] = macd["macdsignal"] dataframe["macd_hist"] = macd["macdhist"] # Moving Averages dataframe["sma_fast"] = ta.SMA(dataframe, timeperiod=self.sma_fast) dataframe["sma_slow"] = ta.SMA(dataframe, timeperiod=self.sma_slow) # Trend strength dataframe["trend_strength"] = ( (dataframe["sma_fast"] - dataframe["sma_slow"]) / dataframe["sma_slow"] ) # Volume indicators dataframe["volume_sma"] = dataframe["volume"].rolling(window=20).mean() dataframe["volume_ratio"] = dataframe["volume"] / dataframe["volume_sma"] dataframe["volume_usd"] = dataframe["volume"] * dataframe["close"] # Resistance levels (for breakout detection) - using hyperopt parameter # Note: We'll use the default value during indicator calculation # The actual value will be used in populate_entry_trend dataframe["resistance_20"] = dataframe["high"].rolling(window=20).max() # Support levels dataframe["support_20"] = dataframe["low"].rolling(window=20).min() # Price momentum dataframe["price_change_1h"] = dataframe["close"].pct_change(periods=1) dataframe["price_change_4h"] = dataframe["close"].pct_change(periods=4) dataframe["price_change_24h"] = dataframe["close"].pct_change(periods=24) # Volatility dataframe["volatility"] = dataframe["close"].pct_change().rolling(window=24).std() # Bollinger Bands (for volatility context) bollinger = ta.BBANDS( dataframe, timeperiod=20, nbdevup=2.0, nbdevdn=2.0, ) dataframe["bb_upper"] = bollinger["upperband"] dataframe["bb_middle"] = bollinger["middleband"] dataframe["bb_lower"] = bollinger["lowerband"] dataframe["bb_width"] = (dataframe["bb_upper"] - dataframe["bb_lower"]) / dataframe["bb_middle"] # Momentum score (simplified for hyperopt) dataframe["momentum_score"] = 0 # Add points for positive indicators dataframe.loc[dataframe["rsi"] > 50, "momentum_score"] += 10 dataframe.loc[dataframe["rsi"] > 60, "momentum_score"] += 10 dataframe.loc[dataframe["macd"] > dataframe["macd_signal"], "momentum_score"] += 15 dataframe.loc[dataframe["macd_hist"] > 0, "momentum_score"] += 10 dataframe.loc[dataframe["sma_fast"] > dataframe["sma_slow"], "momentum_score"] += 15 dataframe.loc[dataframe["trend_strength"] > 0.02, "momentum_score"] += 10 dataframe.loc[dataframe["volume_ratio"] > 1.2, "momentum_score"] += 10 dataframe.loc[dataframe["price_change_4h"] > 0, "momentum_score"] += 10 dataframe.loc[dataframe["price_change_24h"] > 0.03, "momentum_score"] += 10 return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Populate buy signals for the SWING strategy Uses hyperoptable parameters """ # Calculate dynamic resistance based on hyperopt parameter dataframe[f"resistance_dynamic"] = dataframe["high"].rolling( window=int(self.buy_breakout_lookback.value) ).max() dataframe["breakout"] = dataframe["close"] / dataframe["resistance_dynamic"] # SWING entry conditions using hyperoptable parameters conditions = [] # 1. Breakout detected (price above resistance) conditions.append(dataframe["breakout"] >= self.buy_breakout_threshold.value) # 2. Volume surge confirmation conditions.append(dataframe["volume_ratio"] >= self.buy_volume_surge.value) # 3. RSI in bullish range conditions.append(dataframe["rsi"] >= self.buy_rsi_min.value) conditions.append(dataframe["rsi"] <= self.buy_rsi_max.value) # 4. Momentum score above threshold conditions.append(dataframe["momentum_score"] >= self.buy_momentum_score_min.value) # 5. Positive trend (using hyperopt parameter) conditions.append(dataframe["trend_strength"] >= self.buy_trend_strength_min.value) # 6. MACD bullish conditions.append( (dataframe["macd"] > dataframe["macd_signal"]) | (dataframe["macd_hist"] > 0) ) # 7. Minimum USD volume (fixed for liquidity) conditions.append(dataframe["volume_usd"] >= 100000) # 8. Not too volatile conditions.append(dataframe["volatility"] < self.buy_volatility_max.value) # 9. Price momentum positive (confirming breakout) conditions.append(dataframe["price_change_4h"] > self.buy_breakout_confirmation.value) # Combine all conditions if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), ['enter_long', 'enter_tag'] ] = (1, 'swing_breakout_hyperopt') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Populate sell signals for the SWING strategy Uses hyperoptable parameters """ # Exit conditions using hyperoptable parameters conditions = [] # 1. Take profit reached (simplified) conditions.append( dataframe["price_change_24h"] >= self.sell_take_profit.value ) # 2. RSI overbought conditions.append(dataframe["rsi"] >= self.sell_rsi_high.value) # 3. MACD bearish crossover conditions.append( (dataframe["macd"] < dataframe["macd_signal"]) & (dataframe["macd_hist"] < 0) ) # 4. Trend reversal conditions.append( (dataframe["sma_fast"] < dataframe["sma_slow"]) & (dataframe["trend_strength"] < self.sell_trend_reversal.value) ) # 5. Volume drying up conditions.append( (dataframe["volume_ratio"] < self.sell_volume_ratio_low.value) & (dataframe["price_change_1h"] < 0) ) # Combine exit conditions (any of them triggers exit) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), ['exit_long', 'exit_tag'] ] = (1, 'swing_exit_hyperopt') return dataframe def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, after_fill: bool, **kwargs) -> Optional[float]: """ Custom stoploss logic using hyperoptable parameters """ # Use hyperoptable stoploss return self.stoploss_value.value @property def protections(self): """ Protection parameters for risk management """ return [ { "method": "StoplossGuard", "lookback_period_candles": 24, "trade_limit": 4, "stop_duration_candles": 12, "only_per_pair": False }, { "method": "MaxDrawdown", "lookback_period_candles": 48, "trade_limit": 20, "stop_duration_candles": 4, "max_allowed_drawdown": 0.2 }, { "method": "CooldownPeriod", "stop_duration_candles": 2 } ] def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: str, side: str, **kwargs) -> float: """ No leverage for SWING strategy (spot trading only) """ return 1.0