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https://github.com/freqtrade/freqtrade.git
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88 lines
2.7 KiB
Python
88 lines
2.7 KiB
Python
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# --- Do not remove these libs ---
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# Add your lib to import here
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import talib.abstract as ta
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from pandas import DataFrame
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from freqtrade.strategy import IStrategy
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# --------------------------------
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# This class is a sample. Feel free to customize it.
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class TestStrategyLegacyV1(IStrategy):
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"""
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This is a test strategy using the legacy function headers, which will be
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removed in a future update.
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Please do not use this as a template, but refer to user_data/strategy/sample_strategy.py
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for a uptodate version of this template.
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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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"40": 0.0,
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"30": 0.01,
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"20": 0.02,
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"0": 0.04
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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 = -0.10
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# Optimal timeframe for the strategy
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# Keep the legacy value here to test compatibility
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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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# Momentum Indicator
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# ------------------------------------
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# ADX
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dataframe['adx'] = ta.ADX(dataframe)
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# TEMA - Triple Exponential Moving Average
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dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
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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['adx'] > 30) &
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(dataframe['tema'] > dataframe['tema'].shift(1)) &
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(dataframe['volume'] > 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['adx'] > 70) &
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(dataframe['tema'] < dataframe['tema'].shift(1)) &
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(dataframe['volume'] > 0)
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),
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'sell'] = 1
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return dataframe
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