# source: https://raw.githubusercontent.com/ikuszka/hyperopt2/277d4d1863fbe0de2b539655b4bec53b4aefacf5/user_data/save/Strategy004.py # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame # -------------------------------- import talib.abstract as ta from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter class github_ikuszka_hyperopt2__Strategy004__20210618_093620(IStrategy): """ Strategy 004 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies How to use it? > python3 ./freqtrade/main.py -s github_ikuszka_hyperopt2__Strategy004__20210618_093620 """ #BUY params buy_adx = IntParameter(25, 75, default=50) buy_slowadx = IntParameter(20, 50, default=26) buy_cci = IntParameter(-100, -50, default=-100) #Buy params enabled buy_adx_enabled = CategoricalParameter([True, False], default=True) buy_cci_enabled = CategoricalParameter([True, False], default=True) #Sell params sell_slowadx = IntParameter(15, 35, default=25) sell_fastk_fastd = IntParameter(60, 80, default=70) #Sell params enabled sell_slowadx_enabled = CategoricalParameter([True, False], default=True) # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi" minimal_roi = { "60": 0.01, "30": 0.03, "20": 0.04, "0": 0.05 } # Optimal stoploss designed for the strategy # This attribute will be overridden if the config file contains "stoploss" stoploss = -0.10 # Optimal timeframe for the strategy timeframe = '5m' # trailing stoploss trailing_stop = False trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.02 # run "populate_indicators" only for new candle process_only_new_candles = False # Experimental settings (configuration will overide these if set) use_sell_signal = True sell_profit_only = True ignore_roi_if_buy_signal = False # Optional order type mapping order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. """ # ADX dataframe['adx'] = ta.ADX(dataframe) dataframe['slowadx'] = ta.ADX(dataframe, 35) # Commodity Channel Index: values Oversold:<-100, Overbought:>100 dataframe['cci'] = ta.CCI(dataframe) # Stoch stoch = ta.STOCHF(dataframe, 5) dataframe['fastd'] = stoch['fastd'] dataframe['fastk'] = stoch['fastk'] dataframe['fastk-previous'] = dataframe.fastk.shift(1) dataframe['fastd-previous'] = dataframe.fastd.shift(1) # Slow Stoch slowstoch = ta.STOCHF(dataframe, 50) dataframe['slowfastd'] = slowstoch['fastd'] dataframe['slowfastk'] = slowstoch['fastk'] dataframe['slowfastk-previous'] = dataframe.slowfastk.shift(1) dataframe['slowfastd-previous'] = dataframe.slowfastd.shift(1) # EMA - Exponential Moving Average dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) dataframe['mean-volume'] = dataframe['volume'].mean() return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the buy signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with buy column """ conditions = [] if self.buy_adx_enabled.value: conditions.append( (dataframe['adx'] > self.buy_adx.value) | (dataframe['slowadx'] > self.buy_slowadx.value) ) if self.buy_cci_enabled.value: conditions.append(dataframe['cci'] < self.buy_cci.value) #TODO add fastk, fastd, slowfastk, slow fastd and mean-volume as hyperopt params conditions.append(( (dataframe['fastk-previous'] < 20) & (dataframe['fastd-previous'] < 20) )) conditions.append(( (dataframe['slowfastk-previous'] < 30) & (dataframe['slowfastd-previous'] < 30) )) conditions.append((dataframe['fastk-previous'] < dataframe['fastd-previous'])) conditions.append((dataframe['fastk'] > dataframe['fastd'])) conditions.append((dataframe['mean-volume'] > 0.75)) #dataframe.loc[ # ( # ( # (dataframe['adx'] > 50) | # (dataframe['slowadx'] > 26) # ) & # (dataframe['cci'] < -100) & # ( # (dataframe['fastk-previous'] < 20) & # (dataframe['fastd-previous'] < 20) # ) & # ( # (dataframe['slowfastk-previous'] < 30) & # (dataframe['slowfastd-previous'] < 30) # ) & # (dataframe['fastk-previous'] < dataframe['fastd-previous']) & # (dataframe['fastk'] > dataframe['fastd']) & # (dataframe['mean-volume'] > 0.75) & # (dataframe['close'] > 0.00000100) # ), # 'buy'] = 1 if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] if self.sell_slowadx_enabled.value: conditions.append((dataframe['slowadx'] < self.sell_slowadx.value)) #TODO add these indicators to conditions.append((dataframe['fastk'] > 70) | (dataframe['fastd'] > 70)) conditions.append((dataframe['fastk-previous'] < dataframe['fastd-previous'])) conditions.append((dataframe['close'] > dataframe['ema5'])) # dataframe.loc[ # ( # (dataframe['slowadx'] < 25) & # ((dataframe['fastk'] > 70) | (dataframe['fastd'] > 70)) & # (dataframe['fastk-previous'] < dataframe['fastd-previous']) & # (dataframe['close'] > dataframe['ema5']) # ), # 'sell'] = 1 if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe