# source: https://raw.githubusercontent.com/vaskosmihaylov/nfi-custom-strategies/19cd8ec4d7574ee3bad2096f4c5201a6a0e459bc/user_data/strategies/Best5m/CompleteIndicatorStrategy22.py import logging import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.strategy import IStrategy import talib.abstract as ta from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter, BooleanParameter # Optional for additional indicators import pandas_ta as pta logger = logging.getLogger(__name__) class Github_vaskosmihaylov_nfi_custom_strategies__CompleteIndicatorStrategy22__20260530_162717(IStrategy): """ A comprehensive strategy using TA-Lib candlestick patterns and multiple indicators, adjusted to avoid lookahead bias (e.g., Ichimoku shift). Uses .value for IntParameter/DecimalParameter, preventing “object not callable” errors. Explicitly supports shorting (can_short = True), but also requires: - `enable_short: true` in your config - An exchange that supports margin/futures - A negative stoploss """ # ===================================================================== # Strategy Core Settings # ===================================================================== # Allow short trades can_short = True # A negative stoploss is required to handle potential short trade losses stoploss = -0.20 # Example: 20% stop # ===================================================================== # Strategy Hyperopt Parameters # ===================================================================== # Leverage leverage_level = IntParameter(low=1, high=5, default=2, space="buy") # Score Thresholds (when to go long/short) long_threshold = IntParameter(low=1, high=30, default=11, space="buy") short_threshold = IntParameter(low=-30, high=-1, default=-4, space="sell") # Trailing Stop trailing_stop = True trailing_stop_positive = 0.03 trailing_stop_positive_opt = DecimalParameter(0.01, 0.10, default=0.03, space="sell") # Bullish Candlestick Pattern Weights hammer_weight = IntParameter(low=1, high=5, default=3, space="buy") morning_star_weight = IntParameter(low=1, high=5, default=3, space="buy") three_white_soldiers_weight = IntParameter(low=1, high=5, default=3, space="buy") piercing_weight = IntParameter(low=1, high=3, default=1, space="buy") # Bearish Candlestick Pattern Weights shooting_star_weight = IntParameter(low=-5, high=-1, default=-3, space="sell") evening_star_weight = IntParameter(low=-5, high=-1, default=-3, space="sell") three_black_crows_weight = IntParameter(low=-5, high=-1, default=-3, space="sell") dark_cloud_cover_weight = IntParameter(low=-3, high=-1, default=-1, space="sell") # Engulfing & Harami & 3 Inside & Rising/Falling 3 Methods bullish_engulfing_weight = IntParameter(low=1, high=5, default=3, space="buy") bearish_engulfing_weight = IntParameter(low=-5, high=-1, default=-3, space="sell") harami_bullish_weight = IntParameter(low=1, high=3, default=1, space="buy") harami_bearish_weight = IntParameter(low=-3, high=-1, default=-1, space="sell") three_inside_up_weight = IntParameter(low=1, high=3, default=1, space="buy") three_inside_down_weight = IntParameter(low=-3, high=-1, default=-1, space="sell") rising_three_methods_weight = IntParameter(low=1, high=3, default=1, space="buy") falling_three_methods_weight = IntParameter(low=-3, high=-1, default=-1, space="sell") # Indicator Weights macd_positive_weight = IntParameter(low=1, high=5, default=2, space="buy") macd_negative_weight = IntParameter(low=-5, high=-1, default=-2, space="sell") rsi_oversold_weight = IntParameter(low=1, high=5, default=3, space="buy") rsi_overbought_weight = IntParameter(low=-5, high=-1, default=-3, space="sell") stoch_oversold_weight = IntParameter(low=1, high=5, default=3, space="buy") stoch_overbought_weight = IntParameter(low=-5, high=-1, default=-3, space="sell") adx_strong_trend_weight = IntParameter(low=1, high=5, default=2, space="buy") adx_weak_trend_weight = IntParameter(low=-5, high=-1, default=-2, space="sell") sma_gap_positive_weight = IntParameter(low=1, high=5, default=2, space="buy") sma_gap_negative_weight = IntParameter(low=-5, high=-1, default=-2, space="sell") ichimoku_bullish_weight = IntParameter(low=1, high=5, default=2, space="buy") ichimoku_bearish_weight = IntParameter(low=-5, high=-1, default=-2, space="sell") # ===================================================================== # Timeframe & Startup # ===================================================================== timeframe = '1h' startup_candle_count = 52 # For Ichimoku + other extended indicators def bot_start(self, **kwargs) -> None: """ Sync hyperopt-backed values into the runtime config fields that Freqtrade validates as plain scalars during trading startup. """ super().bot_start(**kwargs) self.trailing_stop_positive = float(self.trailing_stop_positive_opt.value) logger.info("Using trailing_stop_positive=%s", self.trailing_stop_positive) # ===================================================================== # POPULATE INDICATORS # ===================================================================== def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Add indicators and candlestick patterns to the dataframe. Convert to numeric, handle missing data, shift Ichimoku to avoid lookahead. """ # Candlestick patterns (mostly 0 or +100) dataframe['hammer'] = pd.to_numeric(ta.CDLHAMMER(dataframe), errors='coerce').fillna(0) dataframe['shooting_star'] = pd.to_numeric(ta.CDLSHOOTINGSTAR(dataframe), errors='coerce').fillna(0) dataframe['morning_star'] = pd.to_numeric(ta.CDLMORNINGSTAR(dataframe), errors='coerce').fillna(0) dataframe['evening_star'] = pd.to_numeric(ta.CDLEVENINGSTAR(dataframe), errors='coerce').fillna(0) dataframe['three_white_soldiers'] = pd.to_numeric(ta.CDL3WHITESOLDIERS(dataframe), errors='coerce').fillna(0) dataframe['three_black_crows'] = pd.to_numeric(ta.CDL3BLACKCROWS(dataframe), errors='coerce').fillna(0) dataframe['piercing'] = pd.to_numeric(ta.CDLPIERCING(dataframe), errors='coerce').fillna(0) dataframe['dark_cloud_cover'] = pd.to_numeric(ta.CDLDARKCLOUDCOVER(dataframe), errors='coerce').fillna(0) # Candlestick patterns (± sign possible) dataframe['engulfing'] = pd.to_numeric(ta.CDLENGULFING(dataframe), errors='coerce').fillna(0) dataframe['harami'] = pd.to_numeric(ta.CDLHARAMI(dataframe), errors='coerce').fillna(0) dataframe['three_inside'] = pd.to_numeric(ta.CDL3INSIDE(dataframe), errors='coerce').fillna(0) dataframe['rise_fall_3_methods'] = pd.to_numeric(ta.CDLRISEFALL3METHODS(dataframe), errors='coerce').fillna(0) # SMAs dataframe['sma_50'] = pd.to_numeric(ta.SMA(dataframe, timeperiod=50), errors='coerce').fillna(0) dataframe['sma_200'] = pd.to_numeric(ta.SMA(dataframe, timeperiod=200), errors='coerce').fillna(0) # MACD macd = ta.MACD(dataframe) dataframe['macd'] = pd.to_numeric(macd['macd'], errors='coerce').fillna(0) dataframe['macd_signal'] = pd.to_numeric(macd['macdsignal'], errors='coerce').fillna(0) # RSI dataframe['rsi'] = pd.to_numeric(ta.RSI(dataframe), errors='coerce').fillna(0) # Stochastic stoch = ta.STOCH(dataframe) dataframe['stoch_k'] = pd.to_numeric(stoch['slowk'], errors='coerce').fillna(0) dataframe['stoch_d'] = pd.to_numeric(stoch['slowd'], errors='coerce').fillna(0) # ADX, +DI, -DI dataframe['adx'] = pd.to_numeric(ta.ADX(dataframe), errors='coerce').fillna(0) dataframe['plus_di'] = pd.to_numeric(ta.PLUS_DI(dataframe), errors='coerce').fillna(0) dataframe['minus_di'] = pd.to_numeric(ta.MINUS_DI(dataframe), errors='coerce').fillna(0) # Bollinger Bands bb = ta.BBANDS(dataframe) dataframe['bb_upper'] = pd.to_numeric(bb['upperband'], errors='coerce').fillna(0) dataframe['bb_middle'] = pd.to_numeric(bb['middleband'], errors='coerce').fillna(0) dataframe['bb_lower'] = pd.to_numeric(bb['lowerband'], errors='coerce').fillna(0) # Parabolic SAR dataframe['sar'] = pd.to_numeric(ta.SAR(dataframe), errors='coerce').fillna(0) # Ichimoku (shift leading spans 26 to avoid lookahead) high9 = dataframe['high'].rolling(window=9).max() low9 = dataframe['low'].rolling(window=9).min() conversion_line = (high9 + low9) / 2 high26 = dataframe['high'].rolling(window=26).max() low26 = dataframe['low'].rolling(window=26).min() base_line = (high26 + low26) / 2 high52 = dataframe['high'].rolling(window=52).max() low52 = dataframe['low'].rolling(window=52).min() leading_span_a = ((conversion_line + base_line) / 2).shift(26) leading_span_b = ((high52 + low52) / 2).shift(26) dataframe['conversion_line'] = pd.to_numeric(conversion_line, errors='coerce').fillna(0) dataframe['base_line'] = pd.to_numeric(base_line, errors='coerce').fillna(0) dataframe['ichimoku_span_a'] = pd.to_numeric(leading_span_a, errors='coerce').fillna(0) dataframe['ichimoku_span_b'] = pd.to_numeric(leading_span_b, errors='coerce').fillna(0) # Ensure volume is numeric dataframe['volume'] = pd.to_numeric(dataframe['volume'], errors='coerce').fillna(0) return dataframe # ===================================================================== # CALCULATE SCORE # ===================================================================== def calculate_score(self, row: pd.Series) -> float: """ Combine candlestick patterns and indicator signals into a single numeric score. +ve => bullish, -ve => bearish. Access IntParameter/DecimalParameter with .value. """ score = 0.0 # ---------- One-sided candlestick patterns ---------- if row['hammer'] > 0: score += self.hammer_weight.value if row['shooting_star'] > 0: score += self.shooting_star_weight.value if row['morning_star'] > 0: score += self.morning_star_weight.value if row['evening_star'] > 0: score += self.evening_star_weight.value if row['three_white_soldiers'] > 0: score += self.three_white_soldiers_weight.value if row['three_black_crows'] > 0: score += self.three_black_crows_weight.value if row['piercing'] > 0: score += self.piercing_weight.value if row['dark_cloud_cover'] > 0: score += self.dark_cloud_cover_weight.value # ---------- Two-sided candlestick patterns (check sign) ---------- if row['engulfing'] > 0: score += self.bullish_engulfing_weight.value elif row['engulfing'] < 0: score += self.bearish_engulfing_weight.value if row['harami'] > 0: score += self.harami_bullish_weight.value elif row['harami'] < 0: score += self.harami_bearish_weight.value if row['three_inside'] > 0: score += self.three_inside_up_weight.value elif row['three_inside'] < 0: score += self.three_inside_down_weight.value if row['rise_fall_3_methods'] > 0: score += self.rising_three_methods_weight.value elif row['rise_fall_3_methods'] < 0: score += self.falling_three_methods_weight.value # ---------- MACD ---------- if row['macd'] > row['macd_signal']: score += self.macd_positive_weight.value elif row['macd'] < row['macd_signal']: score += self.macd_negative_weight.value # ---------- RSI ---------- if row['rsi'] < 30: score += self.rsi_oversold_weight.value elif row['rsi'] > 70: score += self.rsi_overbought_weight.value # ---------- Stochastic ---------- if row['stoch_k'] < 20 and row['stoch_k'] > row['stoch_d']: score += self.stoch_oversold_weight.value elif row['stoch_k'] > 80 and row['stoch_k'] < row['stoch_d']: score += self.stoch_overbought_weight.value # ---------- ADX ---------- if row['adx'] > 25: if row['plus_di'] > row['minus_di']: score += self.adx_strong_trend_weight.value elif row['minus_di'] > row['plus_di']: score += self.adx_weak_trend_weight.value # ---------- SMA gap ---------- if row['sma_50'] != 0: gap_50 = (row['close'] - row['sma_50']) / row['sma_50'] if gap_50 > 0: score += self.sma_gap_positive_weight.value elif gap_50 < 0: score += self.sma_gap_negative_weight.value # ---------- Ichimoku ---------- if row['conversion_line'] > row['base_line'] and row['close'] > row['conversion_line']: score += self.ichimoku_bullish_weight.value elif row['conversion_line'] < row['base_line'] and row['close'] < row['conversion_line']: score += self.ichimoku_bearish_weight.value return score # ===================================================================== # POPULATE ENTRY TREND # ===================================================================== def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Create 'enter_long' and 'enter_short' signals based on the 'score' thresholds. Remember, you need `enable_short=true` in your config and an exchange that supports margin/futures. """ # Calculate the row-wise score dataframe['score'] = dataframe.apply(self.calculate_score, axis=1) # Long if score >= long_threshold dataframe['enter_long'] = 0 dataframe.loc[ dataframe['score'] >= self.long_threshold.value, 'enter_long' ] = 1 # Short if score <= short_threshold dataframe['enter_short'] = 0 dataframe.loc[ dataframe['score'] <= self.short_threshold.value, 'enter_short' ] = 1 return dataframe # ===================================================================== # POPULATE EXIT TREND # ===================================================================== def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Basic exit logic: exit positions if 'score' crosses the opposite side of zero. """ dataframe['exit_long'] = 0 dataframe['exit_short'] = 0 # Exit long if the current score is negative dataframe.loc[dataframe['score'] < 0, 'exit_long'] = 1 # Exit short if the current score is positive dataframe.loc[dataframe['score'] > 0, 'exit_short'] = 1 return dataframe # ===================================================================== # LEVERAGE (REQUIRED FOR FUTURES) # ===================================================================== def leverage( self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, side: str, **kwargs ) -> float: """ Optional leverage management for futures trading. Return a float, not an IntParameter object. """ # Make sure to return the .value so freqtrade receives a numeric value. return float(self.leverage_level.value)