author@: Gert Wohlgemuth
Timeframe
1h
Direction
Long Only
Stoploss
-25.0%
Trailing Stop
No
ROI
0m: 10.0%
Interface Version
N/A
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 ---
import talib.abstract as ta
from pandas import DataFrame
import coingro.vendor.qtpylib.indicators as qtpylib
from coingro.strategy.interface import IStrategy
# --------------------------------
class AwesomeMacd(IStrategy):
"""
author@: Gert Wohlgemuth
converted from:
https://github.com/sthewissen/Mynt/blob/master/src/Mynt.Core/Strategies/AwesomeMacd.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"
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_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
((dataframe["macd"] > 0) & (dataframe["ao"] > 0) & (dataframe["ao"].shift() < 0)), "buy"
] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
((dataframe["macd"] < 0) & (dataframe["ao"] < 0) & (dataframe["ao"].shift() > 0)),
"sell",
] = 1
return dataframe