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Long strategy generates about 1.81% a trade, at an average time of 277 min and a total returns of 0.018BTC over 20 days. Sell points are decent, could execute more buys technically

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Gert Wohlgemuth 2018-04-23 22:23:12 -07:00
parent df1295c74d
commit 15ce56fff1
1 changed files with 16 additions and 1 deletions
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17
user_data/strategies/Long.py
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@ -9,6 +9,7 @@ from pandas import DataFrame
import talib.abstract as ta import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa
class Long(IStrategy): class Long(IStrategy):
@ -47,6 +48,17 @@ class Long(IStrategy):
dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper'] dataframe['bb_upperband'] = bollinger['upper']
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
# SAR Parabol
dataframe['sar'] = ta.SAR(dataframe)
return dataframe return dataframe
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame: def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
@ -73,7 +85,10 @@ class Long(IStrategy):
""" """
dataframe.loc[ dataframe.loc[
( (
(dataframe['tema'] < dataframe['close']) # (dataframe['tema'] < dataframe['close'])
(dataframe['sar'] > dataframe['close']) &
(dataframe['fisher_rsi'] > 0.3)
), ),
'sell'] = 1 'sell'] = 1
return dataframe return dataframe