# source: https://raw.githubusercontent.com/Bldnr94/FreqTrade/d56ef86cf87c86b79643693dbf2686a2fcfc6c54/ft_userdata/user_data/strategies/LSv3.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 = True class Github_Bldnr94_FreqTrade__LSv3__20240620_104620(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, 6, default=3, space = "buy") buy_long_min_fan_magnitude_gain = DecimalParameter(1.000, 1.500, default=1.025, 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(0.500, 1.000, default=0.975, 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_2h'] / 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) 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_gain'] >= 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_gain'] <= 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.