263 lines
9.4 KiB
Python
263 lines
9.4 KiB
Python
|
import talib.abstract as ta
|
||
|
|
import freqtrade.vendor.qtpylib.indicators as qtpylib
|
||
|
|
from freqtrade.strategy.interface import IStrategy
|
||
|
|
from pandas import DataFrame
|
||
|
|
from hyperopt import hp
|
||
|
|
from functools import reduce
|
||
|
|
from typing import Dict, List
|
||
|
|
|
||
|
|
|
||
|
|
class_name = 'DefaultStrategy'
|
||
|
|
|
||
|
|
|
||
|
|
class DefaultStrategy(IStrategy):
|
||
|
|
"""
|
||
|
|
Default Strategy provided by freqtrade bot.
|
||
|
|
You can override it with your own strategy
|
||
|
|
"""
|
||
|
|
|
||
|
|
# Minimal ROI designed for the strategy
|
||
|
|
minimal_roi = {
|
||
|
|
"40": 0.0,
|
||
|
|
"30": 0.01,
|
||
|
|
"20": 0.02,
|
||
|
|
"0": 0.04
|
||
|
|
}
|
||
|
|
|
||
|
|
# Optimal stoploss designed for the strategy
|
||
|
|
stoploss = -0.10
|
||
|
|
|
||
|
|
|
||
|
|
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
|
||
|
|
"""
|
||
|
|
Adds several different TA indicators to the given DataFrame
|
||
|
|
|
||
|
|
Performance Note: For the best performance be frugal on the number of indicators
|
||
|
|
you are using. Let uncomment only the indicator you are using in your strategies
|
||
|
|
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
|
||
|
|
"""
|
||
|
|
|
||
|
|
# Momentum Indicator
|
||
|
|
# ------------------------------------
|
||
|
|
|
||
|
|
# ADX
|
||
|
|
dataframe['adx'] = ta.ADX(dataframe)
|
||
|
|
|
||
|
|
# Awesome oscillator
|
||
|
|
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
|
||
|
|
|
||
|
|
# MACD
|
||
|
|
macd = ta.MACD(dataframe)
|
||
|
|
dataframe['macd'] = macd['macd']
|
||
|
|
dataframe['macdsignal'] = macd['macdsignal']
|
||
|
|
dataframe['macdhist'] = macd['macdhist']
|
||
|
|
|
||
|
|
# MFI
|
||
|
|
dataframe['mfi'] = ta.MFI(dataframe)
|
||
|
|
|
||
|
|
# Minus Directional Indicator / Movement
|
||
|
|
dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
|
||
|
|
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
|
||
|
|
|
||
|
|
# Plus Directional Indicator / Movement
|
||
|
|
dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
|
||
|
|
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
|
||
|
|
|
||
|
|
# RSI
|
||
|
|
dataframe['rsi'] = ta.RSI(dataframe)
|
||
|
|
|
||
|
|
# Stoch fast
|
||
|
|
stoch_fast = ta.STOCHF(dataframe)
|
||
|
|
dataframe['fastd'] = stoch_fast['fastd']
|
||
|
|
dataframe['fastk'] = stoch_fast['fastk']
|
||
|
|
|
||
|
|
# Overlap Studies
|
||
|
|
# ------------------------------------
|
||
|
|
|
||
|
|
# Previous Bollinger bands
|
||
|
|
# Because ta.BBANDS implementation is broken with small numbers, it actually
|
||
|
|
# returns middle band for all the three bands. Switch to qtpylib.bollinger_bands
|
||
|
|
# and use middle band instead.
|
||
|
|
dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
|
||
|
|
"""
|
||
|
|
# Bollinger bands
|
||
|
|
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']
|
||
|
|
"""
|
||
|
|
|
||
|
|
# EMA - Exponential Moving Average
|
||
|
|
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
|
||
|
|
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
|
||
|
|
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
|
||
|
|
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
|
||
|
|
|
||
|
|
# SAR Parabol
|
||
|
|
dataframe['sar'] = ta.SAR(dataframe)
|
||
|
|
|
||
|
|
# SMA - Simple Moving Average
|
||
|
|
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
|
||
|
|
|
||
|
|
# TEMA - Triple Exponential Moving Average
|
||
|
|
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
|
||
|
|
|
||
|
|
# Cycle Indicator
|
||
|
|
# ------------------------------------
|
||
|
|
# Hilbert Transform Indicator - SineWave
|
||
|
|
hilbert = ta.HT_SINE(dataframe)
|
||
|
|
dataframe['htsine'] = hilbert['sine']
|
||
|
|
dataframe['htleadsine'] = hilbert['leadsine']
|
||
|
|
|
||
|
|
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[
|
||
|
|
(
|
||
|
|
(dataframe['rsi'] < 35) &
|
||
|
|
(dataframe['fastd'] < 35) &
|
||
|
|
(dataframe['adx'] > 30) &
|
||
|
|
(dataframe['plus_di'] > 0.5)
|
||
|
|
) |
|
||
|
|
(
|
||
|
|
(dataframe['adx'] > 65) &
|
||
|
|
(dataframe['plus_di'] > 0.5)
|
||
|
|
),
|
||
|
|
'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[
|
||
|
|
(
|
||
|
|
(
|
||
|
|
(qtpylib.crossed_above(dataframe['rsi'], 70)) |
|
||
|
|
(qtpylib.crossed_above(dataframe['fastd'], 70))
|
||
|
|
) &
|
||
|
|
(dataframe['adx'] > 10) &
|
||
|
|
(dataframe['minus_di'] > 0)
|
||
|
|
) |
|
||
|
|
(
|
||
|
|
(dataframe['adx'] > 70) &
|
||
|
|
(dataframe['minus_di'] > 0.5)
|
||
|
|
),
|
||
|
|
'sell'] = 1
|
||
|
|
return dataframe
|
||
|
|
|
||
|
|
def hyperopt_space(self) -> List[Dict]:
|
||
|
|
"""
|
||
|
|
Define your Hyperopt space for the strategy
|
||
|
|
"""
|
||
|
|
space = {
|
||
|
|
'mfi': hp.choice('mfi', [
|
||
|
|
{'enabled': False},
|
||
|
|
{'enabled': True, 'value': hp.quniform('mfi-value', 5, 25, 1)}
|
||
|
|
]),
|
||
|
|
'fastd': hp.choice('fastd', [
|
||
|
|
{'enabled': False},
|
||
|
|
{'enabled': True, 'value': hp.quniform('fastd-value', 10, 50, 1)}
|
||
|
|
]),
|
||
|
|
'adx': hp.choice('adx', [
|
||
|
|
{'enabled': False},
|
||
|
|
{'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)}
|
||
|
|
]),
|
||
|
|
'rsi': hp.choice('rsi', [
|
||
|
|
{'enabled': False},
|
||
|
|
{'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
|
||
|
|
]),
|
||
|
|
'uptrend_long_ema': hp.choice('uptrend_long_ema', [
|
||
|
|
{'enabled': False},
|
||
|
|
{'enabled': True}
|
||
|
|
]),
|
||
|
|
'uptrend_short_ema': hp.choice('uptrend_short_ema', [
|
||
|
|
{'enabled': False},
|
||
|
|
{'enabled': True}
|
||
|
|
]),
|
||
|
|
'over_sar': hp.choice('over_sar', [
|
||
|
|
{'enabled': False},
|
||
|
|
{'enabled': True}
|
||
|
|
]),
|
||
|
|
'green_candle': hp.choice('green_candle', [
|
||
|
|
{'enabled': False},
|
||
|
|
{'enabled': True}
|
||
|
|
]),
|
||
|
|
'uptrend_sma': hp.choice('uptrend_sma', [
|
||
|
|
{'enabled': False},
|
||
|
|
{'enabled': True}
|
||
|
|
]),
|
||
|
|
'trigger': hp.choice('trigger', [
|
||
|
|
{'type': 'lower_bb'},
|
||
|
|
{'type': 'faststoch10'},
|
||
|
|
{'type': 'ao_cross_zero'},
|
||
|
|
{'type': 'ema5_cross_ema10'},
|
||
|
|
{'type': 'macd_cross_signal'},
|
||
|
|
{'type': 'sar_reversal'},
|
||
|
|
{'type': 'stochf_cross'},
|
||
|
|
{'type': 'ht_sine'},
|
||
|
|
]),
|
||
|
|
'stoploss': hp.uniform('stoploss', -0.5, -0.02),
|
||
|
|
}
|
||
|
|
return space
|
||
|
|
|
||
|
|
def buy_strategy_generator(self, params) -> None:
|
||
|
|
"""
|
||
|
|
Define the buy strategy parameters to be used by hyperopt
|
||
|
|
"""
|
||
|
|
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
|
||
|
|
conditions = []
|
||
|
|
# GUARDS AND TRENDS
|
||
|
|
if params['uptrend_long_ema']['enabled']:
|
||
|
|
conditions.append(dataframe['ema50'] > dataframe['ema100'])
|
||
|
|
if params['uptrend_short_ema']['enabled']:
|
||
|
|
conditions.append(dataframe['ema5'] > dataframe['ema10'])
|
||
|
|
if params['mfi']['enabled']:
|
||
|
|
conditions.append(dataframe['mfi'] < params['mfi']['value'])
|
||
|
|
if params['fastd']['enabled']:
|
||
|
|
conditions.append(dataframe['fastd'] < params['fastd']['value'])
|
||
|
|
if params['adx']['enabled']:
|
||
|
|
conditions.append(dataframe['adx'] > params['adx']['value'])
|
||
|
|
if params['rsi']['enabled']:
|
||
|
|
conditions.append(dataframe['rsi'] < params['rsi']['value'])
|
||
|
|
if params['over_sar']['enabled']:
|
||
|
|
conditions.append(dataframe['close'] > dataframe['sar'])
|
||
|
|
if params['green_candle']['enabled']:
|
||
|
|
conditions.append(dataframe['close'] > dataframe['open'])
|
||
|
|
if params['uptrend_sma']['enabled']:
|
||
|
|
prevsma = dataframe['sma'].shift(1)
|
||
|
|
conditions.append(dataframe['sma'] > prevsma)
|
||
|
|
|
||
|
|
# TRIGGERS
|
||
|
|
triggers = {
|
||
|
|
'lower_bb': dataframe['tema'] <= dataframe['blower'],
|
||
|
|
'faststoch10': (qtpylib.crossed_above(dataframe['fastd'], 10.0)),
|
||
|
|
'ao_cross_zero': (qtpylib.crossed_above(dataframe['ao'], 0.0)),
|
||
|
|
'ema5_cross_ema10': (
|
||
|
|
qtpylib.crossed_above(dataframe['ema5'], dataframe['ema10'])
|
||
|
|
),
|
||
|
|
'macd_cross_signal': (
|
||
|
|
qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal'])
|
||
|
|
),
|
||
|
|
'sar_reversal': (qtpylib.crossed_above(dataframe['close'], dataframe['sar'])),
|
||
|
|
'stochf_cross': (qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd'])),
|
||
|
|
'ht_sine': (qtpylib.crossed_above(dataframe['htleadsine'], dataframe['htsine'])),
|
||
|
|
}
|
||
|
|
conditions.append(triggers.get(params['trigger']['type']))
|
||
|
|
|
||
|
|
dataframe.loc[
|
||
|
|
reduce(lambda x, y: x & y, conditions),
|
||
|
|
'buy'] = 1
|
||
|
|
|
||
|
|
return dataframe
|
||
|
|
|
||
|
|
return populate_buy_trend
|