91 lines
2.9 KiB
Python
91 lines
2.9 KiB
Python
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# --- Do not remove these libs ---
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from freqtrade.strategy.interface import IStrategy
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from typing import Dict, List
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from hyperopt import hp
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from functools import reduce
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from pandas import DataFrame
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# --------------------------------
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import talib.abstract as ta
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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class ZLC(IStrategy):
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"""
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author@: Gert Wohlgemuth
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"""
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# Minimal ROI designed for the strategy.
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# This attribute will be overridden if the config file contains "minimal_roi"
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minimal_roi = {
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"60": 0.01,
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"30": 0.03,
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"20": 0.04,
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"0": 0.01
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}
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# Optimal stoploss designed for the strategy
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# This attribute will be overridden if the config file contains "stoploss"
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stoploss = -0.3
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# Optimal ticker interval for the strategy
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ticker_interval = 5
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def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
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dataframe['cci-slow'] = ta.CCI(dataframe, timeperiod=25)
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dataframe['cci-fast'] = ta.CCI(dataframe, timeperiod=50)
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dataframe['expo'] = ta.EMA(dataframe, timeperiod=35)
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# required for graphing
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe['bb_lowerband'] = bollinger['lower']
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dataframe['bb_middleband'] = bollinger['mid']
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dataframe['bb_upperband'] = bollinger['upper']
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macd = ta.MACD(dataframe)
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dataframe['macd'] = macd['macd']
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dataframe['macdsignal'] = macd['macdsignal']
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dataframe['macdhist'] = macd['macdhist']
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
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"""
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Based on TA indicators, populates the buy signal for the given dataframe
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:param dataframe: DataFrame
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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(
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#don't buy on peak tops
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(dataframe['close'] < dataframe['bb_middleband'])
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# this is the main concept of evaluating buys
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& (dataframe['cci-fast'] > 0)
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& (dataframe['cci-slow'] > 0)
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& (dataframe['close'] > dataframe['expo'])
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)
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,
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'buy'] = 1
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return dataframe
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def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
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"""
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Based on TA indicators, populates the sell signal for the given dataframe
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:param dataframe: DataFrame
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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(dataframe['close'] >= dataframe['bb_upperband']) |
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(
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(dataframe['cci-fast'] < 0)
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& (dataframe['cci-slow'] < 0)
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& (dataframe['close'] < dataframe['expo'])
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)
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,
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'sell'] = 0
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return dataframe
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