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MyAwesomeStrategy.py
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MyAwesomeStrategy.py
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# Start hyperopt with the following command:
# freqtrade hyperopt --config config.json --hyperopt-loss SharpeHyperOptLoss --strategy RsiStrat -e 500 --spaces buy sell --random-state 8711
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from functools import reduce
from pandas import DataFrame
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,IStrategy, IntParameter)
# --- Add your lib to import here ---
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
# --- Generic strategy settings ---
class MyAwesomeStrategy(IStrategy):
INTERFACE_VERSION = 2
# Determine timeframe and # of candles before strategysignals becomes valid
timeframe = '1d'
startup_candle_count: int = 25
# Determine roi take profit and stop loss points
minimal_roi = {
"60": 0.01,
"30": 0.03,
"20": 0.04,
"0": 0.05
}
stoploss = -0.10
trailing_stop = False
use_sell_signal = True
sell_profit_only = False
sell_profit_offset = 0.0
ignore_roi_if_buy_signal = False
# --- Define spaces for the indicators ---
buy_adx = DecimalParameter(20, 40, decimals=1, default=30.1, space="buy")
buy_rsi = IntParameter(20, 40, default=30, space="buy")
buy_adx_enabled = BooleanParameter(default=True, space="buy")
buy_rsi_enabled = CategoricalParameter([True, False], default=False, space="buy")
buy_trigger = CategoricalParameter(["bb_lower", "macd_cross_signal"], default="bb_lower", space="buy")
# --- Used indicators of strategy code ----
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Generate all indicators used by the strategy
"""
dataframe['adx'] = ta.ADX(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
bollinger = ta.BBANDS(dataframe, timeperiod=20, nbdevup=2.0, nbdevdn=2.0)
dataframe['bb_lowerband'] = bollinger['lowerband']
dataframe['bb_middleband'] = bollinger['middleband']
dataframe['bb_upperband'] = bollinger['upperband']
return dataframe
# --- Buy settings ---
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
# GUARDS AND TRENDS
if self.buy_adx_enabled.value:
conditions.append(dataframe['adx'] > self.buy_adx.value)
if self.buy_rsi_enabled.value:
conditions.append(dataframe['rsi'] < self.buy_rsi.value)
# TRIGGERS
if self.buy_trigger.value == 'bb_lower':
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if self.buy_trigger.value == 'macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal']
))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
# --- Sell settings ---
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
# conditions.append(( ))
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
return dataframe