author@: Gert Wohlgemuth converted from: https://github.com/sthewissen/Mynt/blob/master/src/Mynt.Core/Strategies/AdxMomentum.cs
Timeframe
N/A
Direction
Long Only
Stoploss
N/A
Trailing Stop
No
ROI
N/A
Interface Version
N/A
Startup Candles
N/A
Indicators
2
freqtrade/freqtrade-strategies
freqtrade/freqtrade-strategies
this is an example class, implementing a PSAR based trailing stop loss you are supposed to take the `custom_stoploss()` and `populate_indicators()` parts and adapt it to your own strategy
freqtrade/freqtrade-strategies
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
import talib.abstract as ta
# --------------------------------
class ADXMomentum_opt(IStrategy):
"""
author@: Gert Wohlgemuth
converted from:
https://github.com/sthewissen/Mynt/blob/master/src/Mynt.Core/Strategies/AdxMomentum.cs
"""
# Minimal ROI designed for the strategy.
# adjust based on market conditions. We would recommend to keep it low for quick turn arounds
# This attribute will be overridden if the config file contains "minimal_roi"
# Optimal stoploss designed for the strategy
# Trailing stoploss
# Optimal ticker interval for the strategy
ticker_interval = '1m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
dataframe['plus_di'] = ta.PLUS_DI(dataframe, timeperiod=25)
dataframe['minus_di'] = ta.MINUS_DI(dataframe, timeperiod=25)
dataframe['sar'] = ta.SAR(dataframe)
dataframe['mom'] = ta.MOM(dataframe, timeperiod=14)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['adx'] > 47) &
(dataframe['mom'] < 20) &
# (dataframe['minus_di'] > 45) &
(dataframe['plus_di'] < dataframe['minus_di'])
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['adx'] > 47) &
(dataframe['mom'] > 20) &
# (dataframe['minus_di'] > 45) &
(dataframe['plus_di'] > dataframe['minus_di'])
),
'sell'] = 1
return dataframe