100 lines
3.3 KiB
Python
100 lines
3.3 KiB
Python
# --- Do not remove these libs ---
|
|
from freqtrade.strategy.interface import IStrategy
|
|
from typing import Dict, List
|
|
from hyperopt import hp
|
|
from functools import reduce
|
|
from pandas import DataFrame
|
|
# --------------------------------
|
|
|
|
import talib.abstract as ta
|
|
import freqtrade.vendor.qtpylib.indicators as qtpylib
|
|
|
|
|
|
class Quickie(IStrategy):
|
|
"""
|
|
|
|
author@: Gert Wohlgemuth
|
|
|
|
idea:
|
|
momentum based strategie. The main idea is that it closes trades very quickly, while avoiding excessive losses. Hence a rather moderate stop loss in this case
|
|
"""
|
|
|
|
# Minimal ROI designed for the strategy.
|
|
# This attribute will be overridden if the config file contains "minimal_roi"
|
|
minimal_roi = {
|
|
"60": 0.01,
|
|
"30": 0.03,
|
|
"20": 0.04,
|
|
"0": 0.05
|
|
}
|
|
|
|
# Optimal stoploss designed for the strategy
|
|
# This attribute will be overridden if the config file contains "stoploss"
|
|
stoploss = -0.3
|
|
|
|
# Optimal ticker interval for the strategy
|
|
ticker_interval = 5
|
|
|
|
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
|
|
macd = ta.MACD(dataframe)
|
|
dataframe['macd'] = macd['macd']
|
|
dataframe['macdsignal'] = macd['macdsignal']
|
|
dataframe['macdhist'] = macd['macdhist']
|
|
|
|
dataframe['cci'] = ta.CCI(dataframe)
|
|
dataframe['willr'] = ta.WILLR(dataframe)
|
|
|
|
dataframe['smaSlow'] = ta.SMA(dataframe, timeperiod=7)
|
|
dataframe['smaFast'] = ta.SMA(dataframe, timeperiod=13)
|
|
|
|
# required for graphing
|
|
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
|
|
dataframe['bb_lowerband'] = bollinger['lower']
|
|
dataframe['bb_middleband'] = bollinger['mid']
|
|
dataframe['bb_upperband'] = bollinger['upper']
|
|
|
|
bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=1.5)
|
|
dataframe['bb_lowerband_2'] = bollinger['lower']
|
|
dataframe['bb_middleband_2'] = bollinger['mid']
|
|
dataframe['bb_upperband_2'] = bollinger['upper']
|
|
|
|
return dataframe
|
|
|
|
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
|
|
"""
|
|
Based on TA indicators, populates the buy signal for the given dataframe
|
|
:param dataframe: DataFrame
|
|
:return: DataFrame with buy column
|
|
"""
|
|
dataframe.loc[
|
|
(
|
|
# we want to buy oversold assets
|
|
(dataframe['cci'] <= -50)
|
|
|
|
# some basic trend should have been established
|
|
& (dataframe['macd'] > dataframe['macdsignal'])
|
|
|
|
# which starts inside the band
|
|
& (dataframe['open'] > dataframe['bb_lowerband'])
|
|
)
|
|
,
|
|
'buy'] = 1
|
|
|
|
return dataframe
|
|
|
|
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
|
|
"""
|
|
Based on TA indicators, populates the sell signal for the given dataframe
|
|
:param dataframe: DataFrame
|
|
:return: DataFrame with buy column
|
|
"""
|
|
dataframe.loc[
|
|
(dataframe['close'] >= dataframe['bb_upperband']) |
|
|
(
|
|
(dataframe['macd'] < dataframe['macdsignal']) &
|
|
(dataframe['cci'] >= 100)
|
|
)
|
|
,
|
|
'sell'] = 1
|
|
return dataframe
|