Masoud AziziandGitHub
04dd805cfb
This strategy try to find best buy/sell position by deviding Simple Moving Averages *You can use other indicators and check the result*
79 lines
2.4 KiB
Python
79 lines
2.4 KiB
Python
# author: Masoud Azizi @mablue
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# --- Do not remove these libs ---
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from freqtrade.strategy.interface import IStrategy
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from pandas import DataFrame
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# --------------------------------
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# Add your lib to import here
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import talib.abstract as ta
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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FTF, STF = 5, 10
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class mabStra(IStrategy):
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# 100/100: 727 trades. 486/191/50 Wins/Draws/Losses. Avg profit 3.53 % . Median profit 5.97 % . Total profit 1502.52014358 USDT (2566.80Σ %). Avg duration 1396.1 min. Objective: -15.62092
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# Buy hyperspace params:
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buy_params = {
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'buy-div-max': 0.96451, 'buy-div-min': 0.22313
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}
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# Sell hyperspace params:
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sell_params = {
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'sell-div-max': 0.75476, 'sell-div-min': 0.16599
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}
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# ROI table:
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minimal_roi = {
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"0": 0.45574,
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"307": 0.21971,
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"428": 0.06762,
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"1387": 0
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}
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# Stoploss:
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stoploss = -0.34773
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# Trailing stop:
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trailing_stop = True
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trailing_stop_positive = 0.01573
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trailing_stop_positive_offset = 0.06651
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trailing_only_offset_is_reached = True
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# Optimal timeframe use it in your config
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timeframe = '1h'
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# SMA - ex Moving Average
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dataframe['buy-fastMA'] = ta.SMA(dataframe, timeperiod=FTF)
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dataframe['buy-slowMA'] = ta.SMA(dataframe, timeperiod=STF)
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dataframe['sell-fastMA'] = ta.SMA(dataframe, timeperiod=FTF)
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dataframe['sell-slowMA'] = ta.SMA(dataframe, timeperiod=STF)
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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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(dataframe['buy-fastMA'].div(dataframe['buy-slowMA'])
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> self.buy_params['buy-div-min']) &
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(dataframe['buy-fastMA'].div(dataframe['buy-slowMA'])
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< self.buy_params['buy-div-max'])
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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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(dataframe['sell-slowMA'].div(dataframe['sell-fastMA'])
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> self.sell_params['sell-div-min']) &
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(dataframe['sell-slowMA'].div(dataframe['sell-fastMA'])
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< self.sell_params['sell-div-max'])
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),
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'sell'] = 1
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return dataframe
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