# source: https://raw.githubusercontent.com/Bldnr94/FreqTrade/ac41453a47a5efd9aa04107c103b430ceddc2af7/ft_userdata/user_data/strategies/LSv3_F.py 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 pd.options.mode.chained_assignment = None 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 from freqtrade.strategy import DecimalParameter, IntParameter, CategoricalParameter ENABLE_FREQ_AI = False class Github_Bldnr94_FreqTrade__LSv3_F__20240622_175427(IStrategy): can_short = True # Define hyperparameters for optimization buy_long_trend_above_senkou_level = IntParameter(1, 8, default=4, space = "buy") buy_long_trend_bullish_level = IntParameter(1, 8, default=4, space = "buy") buy_long_fan_magnitude_shift_value = IntParameter(1, 5, default=3, space = "buy") buy_long_min_fan_magnitude_gain = DecimalParameter(0.950, 1.050, default=1.002, space = "buy") buy_short_trend_below_senkou_level = IntParameter(1, 8, default=4, space = "buy") buy_short_trend_bearish_level = IntParameter(1, 8, default=4, space = "buy") buy_short_fan_magnitude_shift_value = IntParameter(1, 5, default=3, space = "buy") buy_short_min_fan_magnitude_gain = DecimalParameter(0.950, 1.050, default=0.998, space = "buy") # Exit Long hyperspace params: exit_long_params = CategoricalParameter(["trend_close_1h", "trend_close_2h"], default= "trend_close_2h", space = "sell") # Exit Short hyperspace params: exit_short_params = CategoricalParameter(["trend_close_1h", "trend_close_2h"], default= "trend_close_2h", space = "sell") # ROI table: minimal_roi = { "0": 0.08, "10": 0.059, "25": 0.02, "40": 0.015, "60": 0 } # Stoploss: stoploss = -0.125 # Optimal timeframe for the strategy timeframe = '15m' startup_candle_count = 96 process_only_new_candles = False # Trailing stop: trailing_stop = True trailing_stop_positive = 0.05 trailing_stop_positive_offset = 0.1 trailing_only_offset_is_reached = False plot_config = { 'main_plot': { 'senkou_a': { 'color': 'green', 'fill_to': 'senkou_b', 'fill_label': 'Ichimoku Cloud', 'fill_color': 'rgba(255,76,46,0.2)', }, '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 populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: heikinashi = qtpylib.heikinashi(dataframe) dataframe['open'] = heikinashi['open'] dataframe['high'] = heikinashi['high'] dataframe['low'] = heikinashi['low'] dataframe['trend_close_5m'] = dataframe['close'] dataframe['trend_close_15m'] = ta.EMA(dataframe['close'], timeperiod=3) dataframe['trend_close_30m'] = ta.EMA(dataframe['close'], timeperiod=6) dataframe['trend_close_1h'] = ta.EMA(dataframe['close'], timeperiod=12) dataframe['trend_close_2h'] = ta.EMA(dataframe['close'], timeperiod=24) dataframe['trend_close_4h'] = ta.EMA(dataframe['close'], timeperiod=48) dataframe['trend_close_6h'] = ta.EMA(dataframe['close'], timeperiod=72) dataframe['trend_close_8h'] = ta.EMA(dataframe['close'], timeperiod=96) dataframe['trend_open_5m'] = dataframe['open'] dataframe['trend_open_15m'] = ta.EMA(dataframe['open'], timeperiod=3) dataframe['trend_open_30m'] = ta.EMA(dataframe['open'], timeperiod=6) dataframe['trend_open_1h'] = ta.EMA(dataframe['open'], timeperiod=12) dataframe['trend_open_2h'] = ta.EMA(dataframe['open'], timeperiod=24) dataframe['trend_open_4h'] = ta.EMA(dataframe['open'], timeperiod=48) dataframe['trend_open_6h'] = ta.EMA(dataframe['open'], timeperiod=72) dataframe['trend_open_8h'] = ta.EMA(dataframe['open'], timeperiod=96) dataframe['fan_magnitude'] = (dataframe['trend_close_1h'] / dataframe['trend_close_8h']) dataframe['fan_magnitude_gain'] = dataframe['fan_magnitude'] / dataframe['fan_magnitude'].shift(1) 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'] dataframe['atr'] = ta.ATR(dataframe) # Add additional indicators like RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) # if(ENABLE_FREQ_AI): # dataframe = self.freqai.start(dataframe, metadata, self) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions_long = [] conditions_short = [] # Long conditions if self.buy_long_trend_above_senkou_level.value >= 1: conditions_long.append(dataframe['trend_close_5m'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_5m'] > dataframe['senkou_b']) if self.buy_long_trend_above_senkou_level.value >= 2: conditions_long.append(dataframe['trend_close_15m'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_15m'] > dataframe['senkou_b']) if self.buy_long_trend_above_senkou_level.value >= 3: conditions_long.append(dataframe['trend_close_30m'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_30m'] > dataframe['senkou_b']) if self.buy_long_trend_above_senkou_level.value >= 4: conditions_long.append(dataframe['trend_close_1h'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_1h'] > dataframe['senkou_b']) if self.buy_long_trend_above_senkou_level.value >= 5: conditions_long.append(dataframe['trend_close_2h'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_2h'] > dataframe['senkou_b']) if self.buy_long_trend_above_senkou_level.value >= 6: conditions_long.append(dataframe['trend_close_4h'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_4h'] > dataframe['senkou_b']) if self.buy_long_trend_above_senkou_level.value >= 7: conditions_long.append(dataframe['trend_close_6h'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_6h'] > dataframe['senkou_b']) if self.buy_long_trend_above_senkou_level.value >= 8: conditions_long.append(dataframe['trend_close_8h'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_8h'] > dataframe['senkou_b']) # Trends bullish if self.buy_long_trend_bullish_level.value >= 1: conditions_long.append(dataframe['trend_close_5m'] > dataframe['trend_open_5m']) if self.buy_long_trend_bullish_level.value >= 2: conditions_long.append(dataframe['trend_close_15m'] > dataframe['trend_open_15m']) if self.buy_long_trend_bullish_level.value >= 3: conditions_long.append(dataframe['trend_close_30m'] > dataframe['trend_open_30m']) if self.buy_long_trend_bullish_level.value >= 4: conditions_long.append(dataframe['trend_close_1h'] > dataframe['trend_open_1h']) if self.buy_long_trend_bullish_level.value >= 5: conditions_long.append(dataframe['trend_close_2h'] > dataframe['trend_open_2h']) if self.buy_long_trend_bullish_level.value >= 6: conditions_long.append(dataframe['trend_close_4h'] > dataframe['trend_open_4h']) if self.buy_long_trend_bullish_level.value >= 7: conditions_long.append(dataframe['trend_close_6h'] > dataframe['trend_open_6h']) if self.buy_long_trend_bullish_level.value >= 8: conditions_long.append(dataframe['trend_close_8h'] > dataframe['trend_open_8h']) # Trends magnitude conditions_long.append(dataframe['fan_magnitude'] >= self.buy_long_min_fan_magnitude_gain.value) conditions_long.append(dataframe['fan_magnitude'] > 1) for x in range(self.buy_long_fan_magnitude_shift_value.value): conditions_long.append(dataframe['fan_magnitude'].shift(x+1) < dataframe['fan_magnitude']) # Short conditions if self.buy_short_trend_below_senkou_level.value >= 1: conditions_short.append(dataframe['trend_close_5m'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_5m'] < dataframe['senkou_b']) if self.buy_short_trend_below_senkou_level.value >= 2: conditions_short.append(dataframe['trend_close_15m'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_15m'] < dataframe['senkou_b']) if self.buy_short_trend_below_senkou_level.value >= 3: conditions_short.append(dataframe['trend_close_30m'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_30m'] < dataframe['senkou_b']) if self.buy_short_trend_below_senkou_level.value >= 4: conditions_short.append(dataframe['trend_close_1h'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_1h'] < dataframe['senkou_b']) if self.buy_short_trend_below_senkou_level.value >= 5: conditions_short.append(dataframe['trend_close_2h'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_2h'] < dataframe['senkou_b']) if self.buy_short_trend_below_senkou_level.value >= 6: conditions_short.append(dataframe['trend_close_4h'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_4h'] < dataframe['senkou_b']) if self.buy_short_trend_below_senkou_level.value >= 7: conditions_short.append(dataframe['trend_close_6h'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_6h'] < dataframe['senkou_b']) if self.buy_short_trend_below_senkou_level.value >= 8: conditions_short.append(dataframe['trend_close_8h'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_8h'] < dataframe['senkou_b']) # Trends bearish if self.buy_short_trend_bearish_level.value >= 1: conditions_short.append(dataframe['trend_close_5m'] < dataframe['trend_open_5m']) if self.buy_short_trend_bearish_level.value >= 2: conditions_short.append(dataframe['trend_close_15m'] < dataframe['trend_open_15m']) if self.buy_short_trend_bearish_level.value >= 3: conditions_short.append(dataframe['trend_close_30m'] < dataframe['trend_open_30m']) if self.buy_short_trend_bearish_level.value >= 4: conditions_short.append(dataframe['trend_close_1h'] < dataframe['trend_open_1h']) if self.buy_short_trend_bearish_level.value >= 5: conditions_short.append(dataframe['trend_close_2h'] < dataframe['trend_open_2h']) if self.buy_short_trend_bearish_level.value >= 6: conditions_short.append(dataframe['trend_close_4h'] < dataframe['trend_open_4h']) if self.buy_short_trend_bearish_level.value >= 7: conditions_short.append(dataframe['trend_close_6h'] < dataframe['trend_open_6h']) if self.buy_short_trend_bearish_level.value >= 8: conditions_short.append(dataframe['trend_close_8h'] < dataframe['trend_open_8h']) # Trends magnitude conditions_short.append(dataframe['fan_magnitude'] <= self.buy_short_min_fan_magnitude_gain.value) conditions_short.append(dataframe['fan_magnitude'] < 1) for x in range(self.buy_short_fan_magnitude_shift_value.value): conditions_short.append(dataframe['fan_magnitude'].shift(x+1) > dataframe['fan_magnitude']) if conditions_long: dataframe.loc[ reduce(lambda x, y: x & y, conditions_long), 'enter_long'] = 1 if conditions_short: dataframe.loc[ reduce(lambda x, y: x & y, conditions_short), 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions_long = [] conditions_short = [] # Exit long conditions conditions_long.append(qtpylib.crossed_below(dataframe['trend_close_5m'], dataframe[self.exit_long_params.value])) # Exit short conditions conditions_short.append(qtpylib.crossed_above(dataframe['trend_close_5m'], dataframe[self.exit_short_params.value])) if conditions_long: dataframe.loc[ reduce(lambda x, y: x & y, conditions_long), 'exit_long'] = 1 if conditions_short: dataframe.loc[ reduce(lambda x, y: x & y, conditions_short), 'exit_short'] = 1 return dataframe # def feature_engineering_expand_all(self, dataframe: DataFrame, period, **kwargs) -> DataFrame: # """ # *Only functional with FreqAI enabled strategies* # This function will automatically expand the defined features on the config defined # `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and # `include_corr_pairs`. In other words, a single feature defined in this function # will automatically expand to a total of # `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` * # `include_corr_pairs` numbers of features added to the model. # # All features must be prepended with `%` to be recognized by FreqAI internals. # # :param df: strategy dataframe which will receive the features # :param period: period of the indicator - usage example: # dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period) # """ # # dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period) # dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period) # dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period) # dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period) # dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period) # # return dataframe # # def feature_engineering_expand_basic(self, dataframe: DataFrame, **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`. # In other words, a single feature defined in this function # will automatically expand to a total of # `include_timeframes` * `include_shifted_candles` * `include_corr_pairs` # numbers of features added to the model. # # Features defined here will *not* be automatically duplicated on user defined # `indicator_periods_candles` # # All features must be prepended with `%` to be recognized by FreqAI internals. # # :param df: strategy dataframe which will receive the features # dataframe["%-pct-change"] = dataframe["close"].pct_change() # dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200) # """ # dataframe["%-pct-change"] = dataframe["close"].pct_change() # dataframe["%-raw_volume"] = dataframe["volume"] # dataframe["%-raw_price"] = dataframe["close"] # return dataframe # # def feature_engineering_standard(self, dataframe: DataFrame, **kwargs) -> DataFrame: # """ # *Only functional with FreqAI enabled strategies* # This optional function will be called once with the dataframe of the base timeframe. # This is the final function to be called, which means that the dataframe entering this # function will contain all the features and columns created by all other # freqai_feature_engineering_* functions. # # This function is a good place to do custom exotic feature extractions (e.g. tsfresh). # This function is a good place for any feature that should not be auto-expanded upon # (e.g. day of the week). # # All features must be prepended with `%` to be recognized by FreqAI internals. # # :param df: strategy dataframe which will receive the features # usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7 # """ # dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7 # dataframe["%-hour_of_day"] = (dataframe["date"].dt.hour + 1) / 25 # return dataframe # # def set_freqai_targets(self, dataframe: DataFrame, **kwargs) -> DataFrame: # """ # *Only functional with FreqAI enabled strategies* # Required function to set the targets for the model. # All targets must be prepended with `&` to be recognized by the FreqAI internals. # # :param df: strategy dataframe which will receive the targets # usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"] # """ # dataframe["&-s_close"] = ( # dataframe["close"] # .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) # .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) # .mean() # / dataframe["close"] # - 1 # ) # return dataframe # Have a working strategy at hand.