Timeframe
1h
Direction
Long Only
Stoploss
-25.0%
Trailing Stop
No
ROI
0m: 10.0%
Interface Version
3
Startup Candles
N/A
Indicators
3
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
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
# --------------------------------
class AwesomeMacd(IStrategy):
INTERFACE_VERSION = 3
'\n\n author@: Gert Wohlgemuth\n\n converted from:\n\n https://github.com/sthewissen/Mynt/blob/master/src/Mynt.Core/Strategies/AwesomeMacd.cs\n\n '
# 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"
minimal_roi = {'0': 0.1}
# Optimal stoploss designed for the strategy
stoploss = -0.25
# Optimal timeframe for the strategy
timeframe = '1h'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[(dataframe['macd'] > 0) & (dataframe['ao'] > 0) & (dataframe['ao'].shift() < 0), 'enter_long'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[(dataframe['macd'] < 0) & (dataframe['ao'] < 0) & (dataframe['ao'].shift() > 0), 'exit_long'] = 1
return dataframe