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Merge pull request #430 from gcarq/include_indicators_in_hyperopt
Separate strategy and hyperopt
This commit is contained in:
Gérald LONLAS
GitHub
commit
38101d433b
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@ -3,21 +3,28 @@
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import json
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import logging
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import sys
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import os
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import pickle
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import signal
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import os
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import sys
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from functools import reduce
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from math import exp
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from operator import itemgetter
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from typing import Dict, List
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from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, space_eval, tpe
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import numpy
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import talib.abstract as ta
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from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, hp, space_eval, tpe
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from hyperopt.mongoexp import MongoTrials
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from pandas import DataFrame
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from freqtrade import main, misc # noqa
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from freqtrade import exchange, optimize
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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# Monkey patch config
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from freqtrade import main # noqa; noqa
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from freqtrade import exchange, misc, optimize
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from freqtrade.exchange import Bittrex
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from freqtrade.misc import load_config
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from freqtrade.optimize import backtesting
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from freqtrade.optimize.backtesting import backtest
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from freqtrade.strategy.strategy import Strategy
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from user_data.hyperopt_conf import hyperopt_optimize_conf
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@ -51,11 +58,129 @@ OPTIMIZE_CONFIG = hyperopt_optimize_conf()
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TRIALS_FILE = os.path.join('user_data', 'hyperopt_trials.pickle')
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TRIALS = Trials()
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# Monkey patch config
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from freqtrade import main # noqa
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main._CONF = OPTIMIZE_CONFIG
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def populate_indicators(dataframe: DataFrame) -> DataFrame:
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"""
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Adds several different TA indicators to the given DataFrame
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"""
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dataframe['adx'] = ta.ADX(dataframe)
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dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
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dataframe['cci'] = ta.CCI(dataframe)
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macd = ta.MACD(dataframe)
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dataframe['macd'] = macd['macd']
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dataframe['macdsignal'] = macd['macdsignal']
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dataframe['macdhist'] = macd['macdhist']
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dataframe['mfi'] = ta.MFI(dataframe)
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dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
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dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
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dataframe['plus_di'] = ta.PLUS_DI(dataframe)
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dataframe['roc'] = ta.ROC(dataframe)
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dataframe['rsi'] = ta.RSI(dataframe)
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# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
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rsi = 0.1 * (dataframe['rsi'] - 50)
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dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
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# Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
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dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
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# Stoch
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stoch = ta.STOCH(dataframe)
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dataframe['slowd'] = stoch['slowd']
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dataframe['slowk'] = stoch['slowk']
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# Stoch fast
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stoch_fast = ta.STOCHF(dataframe)
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dataframe['fastd'] = stoch_fast['fastd']
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dataframe['fastk'] = stoch_fast['fastk']
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# Stoch RSI
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stoch_rsi = ta.STOCHRSI(dataframe)
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dataframe['fastd_rsi'] = stoch_rsi['fastd']
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dataframe['fastk_rsi'] = stoch_rsi['fastk']
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# Bollinger bands
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe['bb_lowerband'] = bollinger['lower']
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dataframe['bb_middleband'] = bollinger['mid']
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dataframe['bb_upperband'] = bollinger['upper']
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# EMA - Exponential Moving Average
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dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
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dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
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dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
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dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
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dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
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# SAR Parabolic
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dataframe['sar'] = ta.SAR(dataframe)
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# SMA - Simple Moving Average
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dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
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# TEMA - Triple Exponential Moving Average
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dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
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# Hilbert Transform Indicator - SineWave
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hilbert = ta.HT_SINE(dataframe)
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dataframe['htsine'] = hilbert['sine']
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dataframe['htleadsine'] = hilbert['leadsine']
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# Pattern Recognition - Bullish candlestick patterns
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# ------------------------------------
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"""
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# Hammer: values [0, 100]
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dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
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# Inverted Hammer: values [0, 100]
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dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
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# Dragonfly Doji: values [0, 100]
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dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
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# Piercing Line: values [0, 100]
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dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
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# Morningstar: values [0, 100]
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dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
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# Three White Soldiers: values [0, 100]
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dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
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"""
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# Pattern Recognition - Bearish candlestick patterns
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# ------------------------------------
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"""
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# Hanging Man: values [0, 100]
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dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
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# Shooting Star: values [0, 100]
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dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
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# Gravestone Doji: values [0, 100]
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dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
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# Dark Cloud Cover: values [0, 100]
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dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
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# Evening Doji Star: values [0, 100]
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dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
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# Evening Star: values [0, 100]
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dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
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"""
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# Pattern Recognition - Bullish/Bearish candlestick patterns
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# ------------------------------------
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"""
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# Three Line Strike: values [0, -100, 100]
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dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
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# Spinning Top: values [0, -100, 100]
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dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
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# Engulfing: values [0, -100, 100]
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dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
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# Harami: values [0, -100, 100]
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dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
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# Three Outside Up/Down: values [0, -100, 100]
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dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
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# Three Inside Up/Down: values [0, -100, 100]
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dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
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"""
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# Chart type
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# ------------------------------------
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# Heikinashi stategy
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heikinashi = qtpylib.heikinashi(dataframe)
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dataframe['ha_open'] = heikinashi['open']
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dataframe['ha_close'] = heikinashi['close']
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dataframe['ha_high'] = heikinashi['high']
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dataframe['ha_low'] = heikinashi['low']
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return dataframe
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def save_trials(trials, trials_path=TRIALS_FILE):
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"""Save hyperopt trials to file"""
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logger.info('Saving Trials to \'{}\''.format(trials_path))
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@ -100,13 +225,146 @@ def calculate_loss(total_profit: float, trade_count: int, trade_duration: float)
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return trade_loss + profit_loss + duration_loss
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def hyperopt_space() -> List[Dict]:
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"""
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Define your Hyperopt space for searching strategy parameters
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"""
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space = {
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'macd_below_zero': hp.choice('macd_below_zero', [
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{'enabled': False},
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{'enabled': True}
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]),
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'mfi': hp.choice('mfi', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('mfi-value', 5, 25, 1)}
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]),
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'fastd': hp.choice('fastd', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('fastd-value', 10, 50, 1)}
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]),
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'adx': hp.choice('adx', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)}
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]),
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'rsi': hp.choice('rsi', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
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]),
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'uptrend_long_ema': hp.choice('uptrend_long_ema', [
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{'enabled': False},
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{'enabled': True}
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]),
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'uptrend_short_ema': hp.choice('uptrend_short_ema', [
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{'enabled': False},
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{'enabled': True}
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]),
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'over_sar': hp.choice('over_sar', [
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{'enabled': False},
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{'enabled': True}
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]),
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'green_candle': hp.choice('green_candle', [
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{'enabled': False},
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{'enabled': True}
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]),
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'uptrend_sma': hp.choice('uptrend_sma', [
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{'enabled': False},
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{'enabled': True}
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]),
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'trigger': hp.choice('trigger', [
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{'type': 'lower_bb'},
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{'type': 'lower_bb_tema'},
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{'type': 'faststoch10'},
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{'type': 'ao_cross_zero'},
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{'type': 'ema3_cross_ema10'},
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{'type': 'macd_cross_signal'},
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{'type': 'sar_reversal'},
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{'type': 'ht_sine'},
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{'type': 'heiken_reversal_bull'},
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{'type': 'di_cross'},
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]),
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'stoploss': hp.uniform('stoploss', -0.5, -0.02),
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}
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return space
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def buy_strategy_generator(params) -> None:
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"""
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Define the buy strategy parameters to be used by hyperopt
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"""
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def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
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conditions = []
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# GUARDS AND TRENDS
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if 'uptrend_long_ema' in params and params['uptrend_long_ema']['enabled']:
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conditions.append(dataframe['ema50'] > dataframe['ema100'])
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if 'macd_below_zero' in params and params['macd_below_zero']['enabled']:
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conditions.append(dataframe['macd'] < 0)
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if 'uptrend_short_ema' in params and params['uptrend_short_ema']['enabled']:
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conditions.append(dataframe['ema5'] > dataframe['ema10'])
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if 'mfi' in params and params['mfi']['enabled']:
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conditions.append(dataframe['mfi'] < params['mfi']['value'])
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if 'fastd' in params and params['fastd']['enabled']:
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conditions.append(dataframe['fastd'] < params['fastd']['value'])
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if 'adx' in params and params['adx']['enabled']:
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conditions.append(dataframe['adx'] > params['adx']['value'])
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if 'rsi' in params and params['rsi']['enabled']:
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conditions.append(dataframe['rsi'] < params['rsi']['value'])
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if 'over_sar' in params and params['over_sar']['enabled']:
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conditions.append(dataframe['close'] > dataframe['sar'])
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if 'green_candle' in params and params['green_candle']['enabled']:
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conditions.append(dataframe['close'] > dataframe['open'])
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if 'uptrend_sma' in params and params['uptrend_sma']['enabled']:
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prevsma = dataframe['sma'].shift(1)
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conditions.append(dataframe['sma'] > prevsma)
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# TRIGGERS
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triggers = {
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'lower_bb': (
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dataframe['close'] < dataframe['bb_lowerband']
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),
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'lower_bb_tema': (
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dataframe['tema'] < dataframe['bb_lowerband']
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),
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'faststoch10': (qtpylib.crossed_above(
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dataframe['fastd'], 10.0
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)),
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'ao_cross_zero': (qtpylib.crossed_above(
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dataframe['ao'], 0.0
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)),
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'ema3_cross_ema10': (qtpylib.crossed_above(
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dataframe['ema3'], dataframe['ema10']
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)),
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'macd_cross_signal': (qtpylib.crossed_above(
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dataframe['macd'], dataframe['macdsignal']
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)),
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'sar_reversal': (qtpylib.crossed_above(
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dataframe['close'], dataframe['sar']
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)),
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'ht_sine': (qtpylib.crossed_above(
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dataframe['htleadsine'], dataframe['htsine']
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)),
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'heiken_reversal_bull': (
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(qtpylib.crossed_above(dataframe['ha_close'], dataframe['ha_open'])) &
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(dataframe['ha_low'] == dataframe['ha_open'])
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),
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'di_cross': (qtpylib.crossed_above(
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dataframe['plus_di'], dataframe['minus_di']
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)),
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}
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conditions.append(triggers.get(params['trigger']['type']))
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'buy'] = 1
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return dataframe
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return populate_buy_trend
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def optimizer(params):
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global _CURRENT_TRIES
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from freqtrade.optimize import backtesting
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strategy = Strategy()
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backtesting.populate_buy_trend = strategy.buy_strategy_generator(params)
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backtesting.populate_buy_trend = buy_strategy_generator(params)
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results = backtest({'stake_amount': OPTIMIZE_CONFIG['stake_amount'],
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'processed': PROCESSED,
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@ -179,6 +437,7 @@ def start(args):
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data = optimize.load_data(args.datadir, pairs=pairs,
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ticker_interval=args.ticker_interval,
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timerange=timerange)
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optimize.populate_indicators = populate_indicators
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PROCESSED = optimize.tickerdata_to_dataframe(data)
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if args.mongodb:
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@ -203,7 +462,7 @@ def start(args):
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try:
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best_parameters = fmin(
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fn=optimizer,
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space=strategy.hyperopt_space(),
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space=hyperopt_space(),
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algo=tpe.suggest,
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max_evals=TOTAL_TRIES,
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trials=TRIALS
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@ -220,7 +479,7 @@ def start(args):
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# Improve best parameter logging display
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if best_parameters:
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best_parameters = space_eval(
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strategy.hyperopt_space(),
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hyperopt_space(),
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best_parameters
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)
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|
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@ -2,9 +2,6 @@ import talib.abstract as ta
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from freqtrade.strategy.interface import IStrategy
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from pandas import DataFrame
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from hyperopt import hp
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from functools import reduce
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from typing import Dict, List
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class_name = 'DefaultStrategy'
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@ -239,137 +236,3 @@ class DefaultStrategy(IStrategy):
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),
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'sell'] = 1
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return dataframe
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def hyperopt_space(self) -> List[Dict]:
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"""
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Define your Hyperopt space for the strategy
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"""
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space = {
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'macd_below_zero': hp.choice('macd_below_zero', [
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{'enabled': False},
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{'enabled': True}
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]),
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'mfi': hp.choice('mfi', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('mfi-value', 5, 25, 1)}
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]),
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'fastd': hp.choice('fastd', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('fastd-value', 10, 50, 1)}
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]),
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'adx': hp.choice('adx', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)}
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]),
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'rsi': hp.choice('rsi', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
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]),
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'uptrend_long_ema': hp.choice('uptrend_long_ema', [
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{'enabled': False},
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{'enabled': True}
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]),
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'uptrend_short_ema': hp.choice('uptrend_short_ema', [
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{'enabled': False},
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{'enabled': True}
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]),
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'over_sar': hp.choice('over_sar', [
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{'enabled': False},
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{'enabled': True}
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]),
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'green_candle': hp.choice('green_candle', [
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{'enabled': False},
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{'enabled': True}
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]),
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'uptrend_sma': hp.choice('uptrend_sma', [
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{'enabled': False},
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{'enabled': True}
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]),
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'trigger': hp.choice('trigger', [
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{'type': 'lower_bb'},
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{'type': 'lower_bb_tema'},
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{'type': 'faststoch10'},
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{'type': 'ao_cross_zero'},
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{'type': 'ema3_cross_ema10'},
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{'type': 'macd_cross_signal'},
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{'type': 'sar_reversal'},
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{'type': 'ht_sine'},
|
||||
{'type': 'heiken_reversal_bull'},
|
||||
{'type': 'di_cross'},
|
||||
]),
|
||||
'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 'uptrend_long_ema' in params and params['uptrend_long_ema']['enabled']:
|
||||
conditions.append(dataframe['ema50'] > dataframe['ema100'])
|
||||
if 'macd_below_zero' in params and params['macd_below_zero']['enabled']:
|
||||
conditions.append(dataframe['macd'] < 0)
|
||||
if 'uptrend_short_ema' in params and params['uptrend_short_ema']['enabled']:
|
||||
conditions.append(dataframe['ema5'] > dataframe['ema10'])
|
||||
if 'mfi' in params and params['mfi']['enabled']:
|
||||
conditions.append(dataframe['mfi'] < params['mfi']['value'])
|
||||
if 'fastd' in params and params['fastd']['enabled']:
|
||||
conditions.append(dataframe['fastd'] < params['fastd']['value'])
|
||||
if 'adx' in params and params['adx']['enabled']:
|
||||
conditions.append(dataframe['adx'] > params['adx']['value'])
|
||||
if 'rsi' in params and params['rsi']['enabled']:
|
||||
conditions.append(dataframe['rsi'] < params['rsi']['value'])
|
||||
if 'over_sar' in params and params['over_sar']['enabled']:
|
||||
conditions.append(dataframe['close'] > dataframe['sar'])
|
||||
if 'green_candle' in params and params['green_candle']['enabled']:
|
||||
conditions.append(dataframe['close'] > dataframe['open'])
|
||||
if 'uptrend_sma' in params and params['uptrend_sma']['enabled']:
|
||||
prevsma = dataframe['sma'].shift(1)
|
||||
conditions.append(dataframe['sma'] > prevsma)
|
||||
|
||||
# TRIGGERS
|
||||
triggers = {
|
||||
'lower_bb': (
|
||||
dataframe['close'] < dataframe['bb_lowerband']
|
||||
),
|
||||
'lower_bb_tema': (
|
||||
dataframe['tema'] < dataframe['bb_lowerband']
|
||||
),
|
||||
'faststoch10': (qtpylib.crossed_above(
|
||||
dataframe['fastd'], 10.0
|
||||
)),
|
||||
'ao_cross_zero': (qtpylib.crossed_above(
|
||||
dataframe['ao'], 0.0
|
||||
)),
|
||||
'ema3_cross_ema10': (qtpylib.crossed_above(
|
||||
dataframe['ema3'], dataframe['ema10']
|
||||
)),
|
||||
'macd_cross_signal': (qtpylib.crossed_above(
|
||||
dataframe['macd'], dataframe['macdsignal']
|
||||
)),
|
||||
'sar_reversal': (qtpylib.crossed_above(
|
||||
dataframe['close'], dataframe['sar']
|
||||
)),
|
||||
'ht_sine': (qtpylib.crossed_above(
|
||||
dataframe['htleadsine'], dataframe['htsine']
|
||||
)),
|
||||
'heiken_reversal_bull': (
|
||||
(qtpylib.crossed_above(dataframe['ha_close'], dataframe['ha_open'])) &
|
||||
(dataframe['ha_low'] == dataframe['ha_open'])
|
||||
),
|
||||
'di_cross': (qtpylib.crossed_above(
|
||||
dataframe['plus_di'], dataframe['minus_di']
|
||||
)),
|
||||
}
|
||||
conditions.append(triggers.get(params['trigger']['type']))
|
||||
|
||||
dataframe.loc[
|
||||
reduce(lambda x, y: x & y, conditions),
|
||||
'buy'] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
return populate_buy_trend
|
||||
|
|
|
|||
|
|
@ -1,6 +1,5 @@
|
|||
from abc import ABC, abstractmethod
|
||||
from pandas import DataFrame
|
||||
from typing import Dict
|
||||
|
||||
|
||||
class IStrategy(ABC):
|
||||
|
|
@ -43,15 +42,3 @@ class IStrategy(ABC):
|
|||
:param dataframe: DataFrame
|
||||
:return: DataFrame with buy column
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def hyperopt_space(self) -> Dict:
|
||||
"""
|
||||
Define your Hyperopt space for the strategy
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def buy_strategy_generator(self, params) -> None:
|
||||
"""
|
||||
Define the buy strategy parameters to be used by hyperopt
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -164,15 +164,3 @@ class Strategy(object):
|
|||
:return: DataFrame with buy column
|
||||
"""
|
||||
return self.custom_strategy.populate_sell_trend(dataframe)
|
||||
|
||||
def hyperopt_space(self) -> Dict:
|
||||
"""
|
||||
Define your Hyperopt space for the strategy
|
||||
"""
|
||||
return self.custom_strategy.hyperopt_space()
|
||||
|
||||
def buy_strategy_generator(self, params) -> None:
|
||||
"""
|
||||
Define the buy strategy parameters to be used by hyperopt
|
||||
"""
|
||||
return self.custom_strategy.buy_strategy_generator(params)
|
||||
|
|
|
|||
|
|
@ -22,8 +22,6 @@ def test_default_strategy_structure():
|
|||
assert hasattr(DefaultStrategy, 'populate_indicators')
|
||||
assert hasattr(DefaultStrategy, 'populate_buy_trend')
|
||||
assert hasattr(DefaultStrategy, 'populate_sell_trend')
|
||||
assert hasattr(DefaultStrategy, 'hyperopt_space')
|
||||
assert hasattr(DefaultStrategy, 'buy_strategy_generator')
|
||||
|
||||
|
||||
def test_default_strategy(result):
|
||||
|
|
@ -36,5 +34,3 @@ def test_default_strategy(result):
|
|||
assert type(indicators) is DataFrame
|
||||
assert type(strategy.populate_buy_trend(indicators)) is DataFrame
|
||||
assert type(strategy.populate_sell_trend(indicators)) is DataFrame
|
||||
assert type(strategy.hyperopt_space()) is dict
|
||||
assert callable(strategy.buy_strategy_generator({}))
|
||||
|
|
|
|||
|
|
@ -33,8 +33,6 @@ def test_strategy_structure():
|
|||
assert hasattr(Strategy, 'populate_indicators')
|
||||
assert hasattr(Strategy, 'populate_buy_trend')
|
||||
assert hasattr(Strategy, 'populate_sell_trend')
|
||||
assert hasattr(Strategy, 'hyperopt_space')
|
||||
assert hasattr(Strategy, 'buy_strategy_generator')
|
||||
|
||||
|
||||
def test_load_strategy(result):
|
||||
|
|
@ -71,12 +69,6 @@ def test_strategy(result):
|
|||
dataframe = strategy.populate_sell_trend(strategy.populate_indicators(result))
|
||||
assert 'sell' in dataframe.columns
|
||||
|
||||
assert hasattr(strategy.custom_strategy, 'hyperopt_space')
|
||||
assert 'adx' in strategy.hyperopt_space()
|
||||
|
||||
assert hasattr(strategy.custom_strategy, 'buy_strategy_generator')
|
||||
assert callable(strategy.buy_strategy_generator({}))
|
||||
|
||||
|
||||
def test_strategy_override_minimal_roi(caplog):
|
||||
config = {
|
||||
|
|
|
|||
|
|
@ -1,9 +1,6 @@
|
|||
|
||||
# --- 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
|
||||
# --------------------------------
|
||||
|
||||
|
|
@ -122,15 +119,6 @@ class TestStrategy(IStrategy):
|
|||
# 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']
|
||||
|
|
@ -235,7 +223,7 @@ class TestStrategy(IStrategy):
|
|||
dataframe.loc[
|
||||
(
|
||||
(dataframe['adx'] > 30) &
|
||||
(dataframe['tema'] <= dataframe['blower']) &
|
||||
(dataframe['tema'] <= dataframe['bb_middleband']) &
|
||||
(dataframe['tema'] > dataframe['tema'].shift(1))
|
||||
),
|
||||
'buy'] = 1
|
||||
|
|
@ -251,143 +239,8 @@ class TestStrategy(IStrategy):
|
|||
dataframe.loc[
|
||||
(
|
||||
(dataframe['adx'] > 70) &
|
||||
(dataframe['tema'] > dataframe['blower']) &
|
||||
(dataframe['tema'] > dataframe['bb_middleband']) &
|
||||
(dataframe['tema'] < dataframe['tema'].shift(1))
|
||||
),
|
||||
'sell'] = 1
|
||||
return dataframe
|
||||
|
||||
def hyperopt_space(self) -> List[Dict]:
|
||||
"""
|
||||
Define your Hyperopt space for the strategy
|
||||
:return: Dict
|
||||
"""
|
||||
space = {
|
||||
'macd_below_zero': hp.choice('macd_below_zero', [
|
||||
{'enabled': False},
|
||||
{'enabled': True}
|
||||
]),
|
||||
'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': 'lower_bb_tema'},
|
||||
{'type': 'faststoch10'},
|
||||
{'type': 'ao_cross_zero'},
|
||||
{'type': 'ema3_cross_ema10'},
|
||||
{'type': 'macd_cross_signal'},
|
||||
{'type': 'sar_reversal'},
|
||||
{'type': 'ht_sine'},
|
||||
{'type': 'heiken_reversal_bull'},
|
||||
{'type': 'di_cross'},
|
||||
]),
|
||||
'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 'uptrend_long_ema' in params and params['uptrend_long_ema']['enabled']:
|
||||
conditions.append(dataframe['ema50'] > dataframe['ema100'])
|
||||
if 'macd_below_zero' in params and params['macd_below_zero']['enabled']:
|
||||
conditions.append(dataframe['macd'] < 0)
|
||||
if 'uptrend_short_ema' in params and params['uptrend_short_ema']['enabled']:
|
||||
conditions.append(dataframe['ema5'] > dataframe['ema10'])
|
||||
if 'mfi' in params and params['mfi']['enabled']:
|
||||
conditions.append(dataframe['mfi'] < params['mfi']['value'])
|
||||
if 'fastd' in params and params['fastd']['enabled']:
|
||||
conditions.append(dataframe['fastd'] < params['fastd']['value'])
|
||||
if 'adx' in params and params['adx']['enabled']:
|
||||
conditions.append(dataframe['adx'] > params['adx']['value'])
|
||||
if 'rsi' in params and params['rsi']['enabled']:
|
||||
conditions.append(dataframe['rsi'] < params['rsi']['value'])
|
||||
if 'over_sar' in params and params['over_sar']['enabled']:
|
||||
conditions.append(dataframe['close'] > dataframe['sar'])
|
||||
if 'green_candle' in params and params['green_candle']['enabled']:
|
||||
conditions.append(dataframe['close'] > dataframe['open'])
|
||||
if 'uptrend_sma' in params and params['uptrend_sma']['enabled']:
|
||||
prevsma = dataframe['sma'].shift(1)
|
||||
conditions.append(dataframe['sma'] > prevsma)
|
||||
|
||||
# TRIGGERS
|
||||
triggers = {
|
||||
'lower_bb': (
|
||||
dataframe['close'] < dataframe['bb_lowerband']
|
||||
),
|
||||
'lower_bb_tema': (
|
||||
dataframe['tema'] < dataframe['bb_lowerband']
|
||||
),
|
||||
'faststoch10': (qtpylib.crossed_above(
|
||||
dataframe['fastd'], 10.0
|
||||
)),
|
||||
'ao_cross_zero': (qtpylib.crossed_above(
|
||||
dataframe['ao'], 0.0
|
||||
)),
|
||||
'ema3_cross_ema10': (qtpylib.crossed_above(
|
||||
dataframe['ema3'], dataframe['ema10']
|
||||
)),
|
||||
'macd_cross_signal': (qtpylib.crossed_above(
|
||||
dataframe['macd'], dataframe['macdsignal']
|
||||
)),
|
||||
'sar_reversal': (qtpylib.crossed_above(
|
||||
dataframe['close'], dataframe['sar']
|
||||
)),
|
||||
'ht_sine': (qtpylib.crossed_above(
|
||||
dataframe['htleadsine'], dataframe['htsine']
|
||||
)),
|
||||
'heiken_reversal_bull': (
|
||||
(qtpylib.crossed_above(dataframe['ha_close'], dataframe['ha_open'])) &
|
||||
(dataframe['ha_low'] == dataframe['ha_open'])
|
||||
),
|
||||
'di_cross': (qtpylib.crossed_above(
|
||||
dataframe['plus_di'], dataframe['minus_di']
|
||||
)),
|
||||
}
|
||||
conditions.append(triggers.get(params['trigger']['type']))
|
||||
|
||||
dataframe.loc[
|
||||
reduce(lambda x, y: x & y, conditions),
|
||||
'buy'] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
return populate_buy_trend
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user