95 lines
2.8 KiB
Python
95 lines
2.8 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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import numpy # noqa
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class Long(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": 0.05,
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"30": 0.06,
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"20": 0.07,
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"0": 0.08
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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 = -0.15
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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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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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dataframe['cci'] = ta.CCI(dataframe)
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dataframe['tema'] = ta.TEMA(dataframe, timeperiod=50)
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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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# RSI
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dataframe['rsi'] = ta.RSI(dataframe)
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# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
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rsi = 0.1 * (dataframe['rsi'] - 50)
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dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
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# SAR Parabol
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dataframe['sar'] = ta.SAR(dataframe)
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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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(dataframe['macd'] > dataframe['macdsignal']) &
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(dataframe['macd'] > 0) &
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(dataframe['cci'] <= 0.0)
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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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(
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# (dataframe['tema'] < dataframe['close'])
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(dataframe['sar'] > dataframe['close']) &
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(dataframe['fisher_rsi'] > 0.3)
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),
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'sell'] = 1
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return dataframe
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