Timeframe
1m
Direction
Long Only
Stoploss
-27.7%
Trailing Stop
Yes
ROI
0m: 100000.0%
Interface Version
N/A
Startup Candles
N/A
Indicators
4
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
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame, DatetimeIndex, merge, Series
from technical.indicators import hull_moving_average
"""
Autor: https://github.com/werkkrew/freqtrade-strategies
"""
class FisherHull(IStrategy):
# Buy hyperspace params:
buy_params = {}
# Sell hyperspace params:
sell_params = {}
# ROI table:
minimal_roi = {'0': 1000}
# Stoploss:
stoploss = -0.27654
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.32606
trailing_stop_positive_offset = 0.33314
trailing_only_offset_is_reached = True
timeframe = '1m'
use_sell_signal = False
sell_profit_only = False
ignore_roi_if_buy_signal = True
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['hma'] = hull_moving_average(dataframe, 14, 'close')
dataframe['cci'] = ta.CCI(dataframe, timeperiod=14)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['hma'] < dataframe['hma'].shift()) &
(dataframe['cci'] <= -50.0) &
(dataframe['fisher_rsi'] < -0.5) &
(dataframe['volume'] > 0)
),
'buy'
] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['hma'] > dataframe['hma'].shift()) &
(dataframe['cci'] >= 100.0) &
(dataframe['fisher_rsi'] > 0.5) &
(dataframe['volume'] > 0)
),
'sell'
] = 1
return dataframe