# source: https://raw.githubusercontent.com/hamidreza07/freqtrade/2d378aa9ceb8f44e420e507903706b75ac1afb32/freqtrade/templates/SupertrendAI.py """ Supertrend strategy: * Description: Generate a 3 supertrend indicators for 'buy' strategies & 3 supertrend indicators for 'sell' strategies Buys if the 3 'buy' indicators are 'up' Sells if the 3 'sell' indicators are 'down' * Author: @juankysoriano (Juan Carlos Soriano) * github: https://github.com/juankysoriano/ *** NOTE: This Supertrend strategy is just one of many possible strategies using `Supertrend` as indicator. It should on any case used at your own risk. It comes with at least a couple of caveats: 1. The implementation for the `supertrend` indicator is based on the following discussion: https://github.com/freqtrade/freqtrade-strategies/issues/30 . Concretelly https://github.com/freqtrade/freqtrade-strategies/issues/30#issuecomment-853042401 2. The implementation for `supertrend` on this strategy is not validated; meaning this that is not proven to match the results by the paper where it was originally introduced or any other trusted academic resources """ import logging from functools import reduce from typing import Dict import numpy as np import talib.abstract as ta from pandas import DataFrame from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy logger = logging.getLogger(__name__) class Github_hamidreza07_freqtrade__SupertrendAI__20231127_092233(IStrategy): # Buy params, Sell params, ROI, Stoploss and Trailing Stop are values generated by 'freqtrade hyperopt --strategy Supertrend --hyperopt-loss ShortTradeDurHyperOptLoss --timerange=20210101- --timeframe=1h --spaces all' # It's encourage you find the values that better suites your needs and risk management strategies plot_config = { "main_plot": {}, "subplots": { "prediction": {"prediction": {"color": "blue"}}, "do_predict": { "do_predict": {"color": "brown"}, }, }, } INTERFACE_VERSION: int = 3 # Buy hyperspace params: buy_params = { "buy_m1": 4, "buy_m2": 7, "buy_m3": 1, "buy_p1": 8, "buy_p2": 9, "buy_p3": 8, } # Sell hyperspace params: sell_params = { "sell_m1": 1, "sell_m2": 3, "sell_m3": 6, "sell_p1": 16, "sell_p2": 18, "sell_p3": 18, } # ROI table: minimal_roi = { "0": 0.01, "240": 0, } # Stoploss: stoploss = -0.05 # Trailing stop: trailing_stop = False trailing_stop_positive = 0.05 trailing_stop_positive_offset = 0.144 trailing_only_offset_is_reached = False timeframe = '3m' startup_candle_count = 18 buy_m1 = IntParameter(1, 7, default=2) buy_m2 = IntParameter(1, 7, default=3) buy_m3 = IntParameter(1, 7, default=4) buy_p1 = IntParameter(7, 21, default=8) buy_p2 = IntParameter(7, 21, default=10) buy_p3 = IntParameter(7, 21, default=12) sell_m1 = IntParameter(1, 7, default=2) sell_m2 = IntParameter(1, 7, default=3) sell_m3 = IntParameter(1, 7, default=4) sell_p1 = IntParameter(7, 21, default=8) sell_p2 = IntParameter(7, 21, default=10) sell_p3 = IntParameter(7, 21, default=12) 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 `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. More details on how these config defined parameters accelerate feature engineering in the documentation at: https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features :param dataframe: strategy dataframe which will receive the features :param period: period of the indicator - usage example: :param metadata: metadata of current pair dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period) """ dataframe["%-high"] = dataframe["high"] dataframe["%-close"] = dataframe["close"] dataframe["%-low"] = dataframe["low"] dataframe["%-open"] = dataframe["open"] return dataframe def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs): 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, metadata: Dict, **kwargs): dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek dataframe["%-hour_of_day"] = dataframe["date"].dt.hour 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 model. All targets must be prepended with `&` to be recognized by the FreqAI internals. More details about feature engineering available: https://www.freqtrade.io/en/latest/freqai-feature-engineering :param dataframe: strategy dataframe which will receive the targets :param metadata: metadata of current pair usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"] """ # self.freqai.class_names = ["down", "up","down2", "up2","down3", "up3","down4", "up4","down5", "up5","down6", "up6"] dataframe[f'&s-up_or_down'] = self.supertrend(dataframe, self.buy_m1.value, self.buy_p1.value)['STX'] dataframe[f'&s-up_or_down2'] = self.supertrend(dataframe, self.buy_m2.value, self.buy_p2.value)['STX'].replace({'up': 'up2', 'down': 'down2'}) dataframe[f'&s-up_or_down3'] = self.supertrend(dataframe, self.buy_m3.value, self.buy_p3.value)['STX'].replace({'up': 'up3', 'down': 'down3'}) dataframe[f'&s-up_or_down4'] = self.supertrend(dataframe, self.sell_m1.value, self.sell_p1.value)['STX'].replace({'up': 'up4', 'down': 'down4'}) dataframe[f'&s-up_or_down5'] = self.supertrend(dataframe, self.sell_m2.value, self.sell_p2.value)['STX'].replace({'up': 'up5', 'down': 'down5'}) dataframe[f'&s-up_or_down6'] = self.supertrend(dataframe, self.sell_m3.value, self.sell_p3.value)['STX'].replace({'up': 'up6', 'down': 'down6'}) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: self.freqai_info = self.config["freqai"] dataframe = self.freqai.start(dataframe, metadata, self) return dataframe def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: enter_long_conditions = [ df["do_predict"] == 1, df[f'&s-up_or_down'] == 'up' , df[f'&s-up_or_down2'] == 'up2' , df[f'&s-up_or_down3'] == 'up3' , df['volume'] > 0 ] if enter_long_conditions: df.loc[ reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"] ] = (1, "long") enter_short_conditions = [ df["do_predict"] == 1, df[f'&s-up_or_down'] == 'down' , df[f'&s-up_or_down2'] == 'down2' , df[f'&s-up_or_down3'] == 'down3' , # The three indicators are 'up' for the current candle df['volume'] > 0 # There is at least some trading volume ] if enter_short_conditions: df.loc[ reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"] ] = (1, "short") return df def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: exit_long_conditions = [ df["do_predict"] == 1, df[f'&s-up_or_down4'] == 'down4' , df[f'&s-up_or_down5'] == 'down5', df[f'&s-up_or_down6'] == 'down6' , # The three indicators are 'down' for the current candle df['volume'] > 0 # There is at least some trading volume ] if exit_long_conditions: df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1 exit_short_conditions = [ df["do_predict"] == 1, df[f'&s-up_or_down4'] == 'up4' , df[f'&s-up_or_down5'] == 'up5' , df[f'&s-up_or_down6'] == 'up6' , # The three indicators are 'down' for the current candle df['volume'] > 0 # There is at least some trading volume ] if exit_short_conditions: df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1 return df """ Supertrend Indicator; adapted for freqtrade from: https://github.com/freqtrade/freqtrade-strategies/issues/30 """ def supertrend(self, dataframe: DataFrame, multiplier, period): df = dataframe.copy() df['TR'] = ta.TRANGE(df) df['ATR'] = ta.SMA(df['TR'], period) st = 'ST_' + str(period) + '_' + str(multiplier) stx = 'STX_' + str(period) + '_' + str(multiplier) # Compute basic upper and lower bands df['basic_ub'] = (df['high'] + df['low']) / 2 + multiplier * df['ATR'] df['basic_lb'] = (df['high'] + df['low']) / 2 - multiplier * df['ATR'] # Compute final upper and lower bands df['final_ub'] = 0.00 df['final_lb'] = 0.00 for i in range(period, len(df)): df['final_ub'].iat[i] = df['basic_ub'].iat[i] if df['basic_ub'].iat[i] < df['final_ub'].iat[i - 1] or df['close'].iat[i - 1] > df['final_ub'].iat[i - 1] else df['final_ub'].iat[i - 1] df['final_lb'].iat[i] = df['basic_lb'].iat[i] if df['basic_lb'].iat[i] > df['final_lb'].iat[i - 1] or df['close'].iat[i - 1] < df['final_lb'].iat[i - 1] else df['final_lb'].iat[i - 1] # Set the Supertrend value df[st] = 0.00 for i in range(period, len(df)): df[st].iat[i] = df['final_ub'].iat[i] if df[st].iat[i - 1] == df['final_ub'].iat[i - 1] and df['close'].iat[i] <= df['final_ub'].iat[i] else \ df['final_lb'].iat[i] if df[st].iat[i - 1] == df['final_ub'].iat[i - 1] and df['close'].iat[i] > df['final_ub'].iat[i] else \ df['final_lb'].iat[i] if df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df['close'].iat[i] >= df['final_lb'].iat[i] else \ df['final_ub'].iat[i] if df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df['close'].iat[i] < df['final_lb'].iat[i] else 0.00 # Mark the trend direction up/down df[stx] = np.where((df[st] > 0.00), np.where((df['close'] < df[st]), 'down', 'up'), np.NaN) # Remove basic and final bands from the columns df.drop(['basic_ub', 'basic_lb', 'final_ub', 'final_lb'], inplace=True, axis=1) df.fillna(0, inplace=True) return DataFrame(index=df.index, data={ 'ST' : df[st], 'STX' : df[stx] })