# source: https://raw.githubusercontent.com/Cletus-Mccoy/custom_freqtrade/21504a94ba3c6406b30e9fe8b453c84f4ec83b47/user_data/strategies/ichiV1.py # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from pandas import DataFrame import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import pandas as pd # noqa pd.options.mode.chained_assignment = None # default='warn' import technical.indicators as ftt from functools import reduce from datetime import datetime, timedelta from freqtrade.strategy import merge_informative_pair import numpy as np from freqtrade.strategy import stoploss_from_open class Github_Cletus_Mccoy_custom_freqtrade__ichiV1__20250728_182526(IStrategy): # NOTE: settings as of the 25th july 21 # Buy hyperspace params: buy_params = { "buy_trend_above_senkou_level": 1, "buy_trend_bullish_level": 6, "buy_fan_magnitude_shift_value": 3, "buy_min_fan_magnitude_gain": 1.002 # NOTE: Good value (Win% ~70%), alot of trades #"buy_min_fan_magnitude_gain": 1.008 # NOTE: Very save value (Win% ~90%), only the biggest moves 1.008, } # Sell hyperspace params: # NOTE: was 15m but kept bailing out in dryrun sell_params = { "sell_trend_indicator": "trend_close_2h", } # ROI table: minimal_roi = { "0": 0.059, "10": 0.037, "41": 0.012, "114": 0 } # Stoploss: stoploss = -0.275 # Optimal timeframe for the strategy timeframe = '3m' # Updated to match config startup_candle_count = 96 process_only_new_candles = False trailing_stop = False #trailing_stop_positive = 0.002 #trailing_stop_positive_offset = 0.025 #trailing_only_offset_is_reached = True use_exit_signal = True # Updated from use_sell_signal exit_profit_only = False # Updated from sell_profit_only ignore_roi_if_entry_signal = False # Updated from ignore_roi_if_buy_signal plot_config = { 'main_plot': { # fill area between senkou_a and senkou_b 'senkou_a': { 'color': 'green', #optional 'fill_to': 'senkou_b', 'fill_label': 'Ichimoku Cloud', #optional 'fill_color': 'rgba(255,76,46,0.2)', #optional }, # plot senkou_b, too. Not only the area to it. 'senkou_b': {}, 'trend_close_5m': {'color': '#FF5733'}, 'trend_close_15m': {'color': '#FF8333'}, 'trend_close_30m': {'color': '#FFB533'}, 'trend_close_1h': {'color': '#FFE633'}, 'trend_close_2h': {'color': '#E3FF33'}, 'trend_close_4h': {'color': '#C4FF33'}, 'trend_close_6h': {'color': '#61FF33'}, 'trend_close_8h': {'color': '#33FF7D'} }, 'subplots': { 'fan_magnitude': { 'fan_magnitude': {} }, 'fan_magnitude_gain': { 'fan_magnitude_gain': {} } } } def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This function will automatically expand the defined features on the config defined `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`. """ # Add Ichimoku features dataframe[f"%-ichimoku_tenkan_period_{period}"] = ta.SMA(dataframe["close"], timeperiod=period) dataframe[f"%-ichimoku_kijun_period_{period}"] = ta.SMA(dataframe["close"], timeperiod=period * 3) # Add EMA features for different periods dataframe[f"%-ema_{period}"] = ta.EMA(dataframe["close"], timeperiod=period) dataframe[f"%-ema_open_{period}"] = ta.EMA(dataframe["open"], timeperiod=period) # Add fan magnitude features if period >= 8: short_ema = ta.EMA(dataframe["close"], timeperiod=max(1, period // 8)) long_ema = ta.EMA(dataframe["close"], timeperiod=period) dataframe[f"%-fan_magnitude_{period}"] = short_ema / long_ema dataframe[f"%-fan_magnitude_gain_{period}"] = dataframe[f"%-fan_magnitude_{period}"] / dataframe[f"%-fan_magnitude_{period}"].shift(1) # Add ATR dataframe[f"%-atr_{period}"] = ta.ATR(dataframe, timeperiod=period) return dataframe def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This function will automatically expand the defined features on the config defined `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`. All features must be prepended with `%` to be recognized by FreqAI internals. """ # Add basic Ichimoku cloud features dataframe["%-rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["%-mfi"] = ta.MFI(dataframe, timeperiod=14) dataframe["%-adx"] = ta.ADX(dataframe, timeperiod=14) dataframe["%-sma"] = ta.SMA(dataframe, timeperiod=14) dataframe["%-ema"] = ta.EMA(dataframe, timeperiod=14) # Bollinger bands bollinger = qtpylib.bollinger_bands(dataframe["close"], window=20, stds=2) dataframe["%-bb_lowerband"] = bollinger["lower"] dataframe["%-bb_middleband"] = bollinger["mid"] dataframe["%-bb_upperband"] = bollinger["upper"] dataframe["%-bb_percent"] = (dataframe["close"] - bollinger["lower"]) / (bollinger["upper"] - bollinger["lower"]) dataframe["%-bb_width"] = (bollinger["upper"] - bollinger["lower"]) / bollinger["mid"] return dataframe def feature_engineering_standard(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This function will be called once with the dataframe of the base timeframe. All features must be prepended with `%` to be recognized by FreqAI internals. """ # Add the original Ichimoku indicators as features ichimoku = ftt.ichimoku(dataframe, conversion_line_period=20, base_line_periods=60, laggin_span=120, displacement=30) dataframe['%-chikou_span'] = ichimoku['chikou_span'] dataframe['%-tenkan_sen'] = ichimoku['tenkan_sen'] dataframe['%-kijun_sen'] = ichimoku['kijun_sen'] dataframe['%-senkou_a'] = ichimoku['senkou_span_a'] dataframe['%-senkou_b'] = ichimoku['senkou_span_b'] dataframe['%-leading_senkou_span_a'] = ichimoku['leading_senkou_span_a'] dataframe['%-leading_senkou_span_b'] = ichimoku['leading_senkou_span_b'] dataframe['%-cloud_green'] = ichimoku['cloud_green'].astype(int) dataframe['%-cloud_red'] = ichimoku['cloud_red'].astype(int) # Add price position relative to cloud dataframe['%-price_above_cloud'] = ((dataframe['close'] > dataframe['%-senkou_a']) & (dataframe['close'] > dataframe['%-senkou_b'])).astype(int) # Add trend strength features dataframe['%-close_open_ratio'] = dataframe['close'] / dataframe['open'] return dataframe def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* Required function to set the targets for the machine learning model. All targets must be prepended with `&` to be recognized by FreqAI internals. """ # Predict future price movement (5 candles ahead) dataframe["&-target"] = dataframe["close"].shift(-5) / dataframe["close"] - 1 return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Start FreqAI first dataframe = self.freqai.start(dataframe, metadata, self) # Apply Heikin Ashi transformation (KEEPING THE ORIGINAL) heikinashi = qtpylib.heikinashi(dataframe) dataframe['open'] = heikinashi['open'] #dataframe['close'] = heikinashi['close'] # Keep original close for signals dataframe['high'] = heikinashi['high'] dataframe['low'] = heikinashi['low'] # Calculate ALL original trend indicators (adjusted periods for 3m timeframe) dataframe['trend_close_5m'] = dataframe['close'] dataframe['trend_close_15m'] = ta.EMA(dataframe['close'], timeperiod=5) # 15m equivalent for 3m dataframe['trend_close_30m'] = ta.EMA(dataframe['close'], timeperiod=10) # 30m equivalent for 3m dataframe['trend_close_1h'] = ta.EMA(dataframe['close'], timeperiod=20) # 1h equivalent for 3m dataframe['trend_close_2h'] = ta.EMA(dataframe['close'], timeperiod=40) # 2h equivalent for 3m dataframe['trend_close_4h'] = ta.EMA(dataframe['close'], timeperiod=80) # 4h equivalent for 3m dataframe['trend_close_6h'] = ta.EMA(dataframe['close'], timeperiod=120) # 6h equivalent for 3m dataframe['trend_close_8h'] = ta.EMA(dataframe['close'], timeperiod=160) # 8h equivalent for 3m dataframe['trend_open_5m'] = dataframe['open'] dataframe['trend_open_15m'] = ta.EMA(dataframe['open'], timeperiod=5) dataframe['trend_open_30m'] = ta.EMA(dataframe['open'], timeperiod=10) dataframe['trend_open_1h'] = ta.EMA(dataframe['open'], timeperiod=20) dataframe['trend_open_2h'] = ta.EMA(dataframe['open'], timeperiod=40) dataframe['trend_open_4h'] = ta.EMA(dataframe['open'], timeperiod=80) dataframe['trend_open_6h'] = ta.EMA(dataframe['open'], timeperiod=120) dataframe['trend_open_8h'] = ta.EMA(dataframe['open'], timeperiod=160) # Calculate fan magnitude (KEEPING THE ORIGINAL) dataframe['fan_magnitude'] = (dataframe['trend_close_1h'] / dataframe['trend_close_8h']) dataframe['fan_magnitude_gain'] = dataframe['fan_magnitude'] / dataframe['fan_magnitude'].shift(1) # Calculate Ichimoku indicators (KEEPING THE ORIGINAL) ichimoku = ftt.ichimoku(dataframe, conversion_line_period=20, base_line_periods=60, laggin_span=120, displacement=30) dataframe['chikou_span'] = ichimoku['chikou_span'] dataframe['tenkan_sen'] = ichimoku['tenkan_sen'] dataframe['kijun_sen'] = ichimoku['kijun_sen'] dataframe['senkou_a'] = ichimoku['senkou_span_a'] dataframe['senkou_b'] = ichimoku['senkou_span_b'] dataframe['leading_senkou_span_a'] = ichimoku['leading_senkou_span_a'] dataframe['leading_senkou_span_b'] = ichimoku['leading_senkou_span_b'] dataframe['cloud_green'] = ichimoku['cloud_green'] dataframe['cloud_red'] = ichimoku['cloud_red'] # Calculate ATR (KEEPING THE ORIGINAL) dataframe['atr'] = ta.ATR(dataframe) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # Use FreqAI prediction as primary signal (when available) freqai_conditions = [ dataframe['do_predict'] == 1, dataframe['&-target'] > 0.001 # Predict positive price movement (0.1%) ] # KEEP ALL ORIGINAL CONDITIONS # Trending market if self.buy_params['buy_trend_above_senkou_level'] >= 1: conditions.append(dataframe['trend_close_5m'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_5m'] > dataframe['senkou_b']) if self.buy_params['buy_trend_above_senkou_level'] >= 2: conditions.append(dataframe['trend_close_15m'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_15m'] > dataframe['senkou_b']) if self.buy_params['buy_trend_above_senkou_level'] >= 3: conditions.append(dataframe['trend_close_30m'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_30m'] > dataframe['senkou_b']) if self.buy_params['buy_trend_above_senkou_level'] >= 4: conditions.append(dataframe['trend_close_1h'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_1h'] > dataframe['senkou_b']) if self.buy_params['buy_trend_above_senkou_level'] >= 5: conditions.append(dataframe['trend_close_2h'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_2h'] > dataframe['senkou_b']) if self.buy_params['buy_trend_above_senkou_level'] >= 6: conditions.append(dataframe['trend_close_4h'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_4h'] > dataframe['senkou_b']) if self.buy_params['buy_trend_above_senkou_level'] >= 7: conditions.append(dataframe['trend_close_6h'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_6h'] > dataframe['senkou_b']) if self.buy_params['buy_trend_above_senkou_level'] >= 8: conditions.append(dataframe['trend_close_8h'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_8h'] > dataframe['senkou_b']) # Trends bullish if self.buy_params['buy_trend_bullish_level'] >= 1: conditions.append(dataframe['trend_close_5m'] > dataframe['trend_open_5m']) if self.buy_params['buy_trend_bullish_level'] >= 2: conditions.append(dataframe['trend_close_15m'] > dataframe['trend_open_15m']) if self.buy_params['buy_trend_bullish_level'] >= 3: conditions.append(dataframe['trend_close_30m'] > dataframe['trend_open_30m']) if self.buy_params['buy_trend_bullish_level'] >= 4: conditions.append(dataframe['trend_close_1h'] > dataframe['trend_open_1h']) if self.buy_params['buy_trend_bullish_level'] >= 5: conditions.append(dataframe['trend_close_2h'] > dataframe['trend_open_2h']) if self.buy_params['buy_trend_bullish_level'] >= 6: conditions.append(dataframe['trend_close_4h'] > dataframe['trend_open_4h']) if self.buy_params['buy_trend_bullish_level'] >= 7: conditions.append(dataframe['trend_close_6h'] > dataframe['trend_open_6h']) if self.buy_params['buy_trend_bullish_level'] >= 8: conditions.append(dataframe['trend_close_8h'] > dataframe['trend_open_8h']) # Trends magnitude (KEEPING THE ORIGINAL) conditions.append(dataframe['fan_magnitude_gain'] >= self.buy_params['buy_min_fan_magnitude_gain']) conditions.append(dataframe['fan_magnitude'] > 1) for x in range(self.buy_params['buy_fan_magnitude_shift_value']): conditions.append(dataframe['fan_magnitude'].shift(x+1) < dataframe['fan_magnitude']) # Combine FreqAI with original conditions if conditions: # Use FreqAI when available, otherwise fall back to original logic dataframe.loc[ (reduce(lambda x, y: x & y, freqai_conditions) & reduce(lambda x, y: x & y, conditions)) | (reduce(lambda x, y: x & y, conditions) & (dataframe['do_predict'] != 1)), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # Use FreqAI prediction when available freqai_exit = (dataframe['do_predict'] == 1) & (dataframe['&-target'] < -0.001) # Predict negative movement # Keep original exit condition original_exit = qtpylib.crossed_below(dataframe['trend_close_5m'], dataframe[self.sell_params['sell_trend_indicator']]) # Combine both conditions dataframe.loc[freqai_exit | original_exit, 'exit_long'] = 1 return dataframe