92 lines
2.9 KiB
Python
92 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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# Update this variable if you change the class name
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class Strategy001(IStrategy):
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"""
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Strategy 001
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author@: Gerald Lonlas
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github@: https://github.com/freqtrade/freqtrade-strategies
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How to use it?
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> python3 ./freqtrade/main.py -s Strategy001
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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": 0.05
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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 = '5m'
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def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
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"""
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Adds several different TA indicators to the given DataFrame
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Performance Note: For the best performance be frugal on the number of indicators
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you are using. Let uncomment only the indicator you are using in your strategies
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or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
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"""
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dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)
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dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
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dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
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heikinashi = qtpylib.heikinashi(dataframe)
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dataframe['ha_open'] = heikinashi['open']
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dataframe['ha_close'] = heikinashi['close']
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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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qtpylib.crossed_above(dataframe['ema20'], dataframe['ema50']) &
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(dataframe['ha_close'] > dataframe['ema20']) &
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(dataframe['ha_open'] < dataframe['ha_close']) # green bar
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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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qtpylib.crossed_above(dataframe['ema50'], dataframe['ema100']) &
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(dataframe['ha_close'] < dataframe['ema20']) &
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(dataframe['ha_open'] > dataframe['ha_close']) # red bar
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),
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'sell'] = 1
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return dataframe
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