103 lines
4.1 KiB
Python
103 lines
4.1 KiB
Python
# --- Do not remove these libs ---
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from freqtrade.strategy.interface import IStrategy
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from freqtrade.strategy import timeframe_to_minutes
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from pandas import DataFrame
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from technical.util import resample_to_interval, resampled_merge
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import numpy # noqa
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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 ReinforcedSmoothScalp(IStrategy):
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"""
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this strategy is based around the idea of generating a lot of potentatils buys and make tiny profits on each trade
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we recommend to have at least 60 parallel trades at any time to cover non avoidable losses
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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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"0": 0.02
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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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# should not be below 3% loss
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stoploss = -0.1
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# Optimal timeframe for the strategy
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# the shorter the better
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timeframe = '1m'
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# resample factor to establish our general trend. Basically don't buy if a trend is not given
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resample_factor = 5
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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tf_res = timeframe_to_minutes(self.timeframe) * 5
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df_res = resample_to_interval(dataframe, tf_res)
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df_res['sma'] = ta.SMA(df_res, 50, price='close')
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dataframe = resampled_merge(dataframe, df_res, fill_na=True)
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dataframe['resample_sma'] = dataframe[f'resample_{tf_res}_sma']
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dataframe['ema_high'] = ta.EMA(dataframe, timeperiod=5, price='high')
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dataframe['ema_close'] = ta.EMA(dataframe, timeperiod=5, price='close')
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dataframe['ema_low'] = ta.EMA(dataframe, timeperiod=5, price='low')
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stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
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dataframe['fastd'] = stoch_fast['fastd']
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dataframe['fastk'] = stoch_fast['fastk']
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dataframe['adx'] = ta.ADX(dataframe)
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dataframe['cci'] = ta.CCI(dataframe, timeperiod=20)
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dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
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dataframe['mfi'] = ta.MFI(dataframe)
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# required for graphing
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bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
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dataframe['bb_lowerband'] = bollinger['lower']
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dataframe['bb_upperband'] = bollinger['upper']
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dataframe['bb_middleband'] = bollinger['mid']
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe.loc[
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(
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(
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(dataframe['open'] < dataframe['ema_low']) &
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(dataframe['adx'] > 30) &
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(dataframe['mfi'] < 30) &
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(
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(dataframe['fastk'] < 30) &
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(dataframe['fastd'] < 30) &
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(qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd']))
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) &
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(dataframe['resample_sma'] < dataframe['close'])
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)
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# |
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# # try to get some sure things independent of resample
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# ((dataframe['rsi'] - dataframe['mfi']) < 10) &
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# (dataframe['mfi'] < 30) &
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# (dataframe['cci'] < -200)
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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, metadata: dict) -> DataFrame:
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dataframe.loc[
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(
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(
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(
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(dataframe['open'] >= dataframe['ema_high'])
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) |
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(
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(qtpylib.crossed_above(dataframe['fastk'], 70)) |
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(qtpylib.crossed_above(dataframe['fastd'], 70))
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)
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) & (dataframe['cci'] > 100)
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)
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,
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'sell'] = 1
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return dataframe
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