535 lines
19 KiB
Python
535 lines
19 KiB
Python
# pragma pylint: disable=missing-docstring,W0212,W0603
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import json
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import logging
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import os
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import pickle
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import signal
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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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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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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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# Remove noisy log messages
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logging.getLogger('hyperopt.mongoexp').setLevel(logging.WARNING)
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logging.getLogger('hyperopt.tpe').setLevel(logging.WARNING)
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logger = logging.getLogger(__name__)
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# set TARGET_TRADES to suit your number concurrent trades so its realistic to 20days of data
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TARGET_TRADES = 600
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TOTAL_TRIES = 0
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_CURRENT_TRIES = 0
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CURRENT_BEST_LOSS = 100
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# max average trade duration in minutes
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# if eval ends with higher value, we consider it a failed eval
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MAX_ACCEPTED_TRADE_DURATION = 300
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# this is expexted avg profit * expected trade count
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# for example 3.5%, 1100 trades, EXPECTED_MAX_PROFIT = 3.85
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# check that the reported Σ% values do not exceed this!
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EXPECTED_MAX_PROFIT = 3.0
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# Configuration and data used by hyperopt
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PROCESSED = None # optimize.preprocess(optimize.load_data())
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OPTIMIZE_CONFIG = hyperopt_optimize_conf()
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# Hyperopt Trials
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TRIALS_FILE = os.path.join('user_data', 'hyperopt_trials.pickle')
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TRIALS = Trials()
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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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pickle.dump(trials, open(trials_path, 'wb'))
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def read_trials(trials_path=TRIALS_FILE):
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"""Read hyperopt trials file"""
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logger.info('Reading Trials from \'{}\''.format(trials_path))
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trials = pickle.load(open(trials_path, 'rb'))
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os.remove(trials_path)
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return trials
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def log_trials_result(trials):
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vals = json.dumps(trials.best_trial['misc']['vals'], indent=4)
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results = trials.best_trial['result']['result']
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logger.info('Best result:\n%s\nwith values:\n%s', results, vals)
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def log_results(results):
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""" log results if it is better than any previous evaluation """
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global CURRENT_BEST_LOSS
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if results['loss'] < CURRENT_BEST_LOSS:
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CURRENT_BEST_LOSS = results['loss']
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logger.info('{:5d}/{}: {}. Loss {:.5f}'.format(
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results['current_tries'],
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results['total_tries'],
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results['result'],
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results['loss']))
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else:
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print('.', end='')
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sys.stdout.flush()
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def calculate_loss(total_profit: float, trade_count: int, trade_duration: float):
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""" objective function, returns smaller number for more optimal results """
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trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
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profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
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duration_loss = 0.7 + 0.3 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
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return trade_loss + profit_loss + duration_loss
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def generate_roi_table(params):
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roi_table = {}
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roi_table["0"] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
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roi_table[str(params['roi_t3'])] = params['roi_p1'] + params['roi_p2']
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roi_table[str(params['roi_t3'] + params['roi_t2'])] = params['roi_p1']
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roi_table[str(params['roi_t3'] + params['roi_t2'] + params['roi_t1'])] = 0
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return roi_table
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def roi_space() -> List[Dict]:
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return {
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'roi_t1': hp.quniform('roi_t1', 10, 220, 10),
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'roi_t2': hp.quniform('roi_t2', 10, 120, 10),
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'roi_t3': hp.quniform('roi_t3', 10, 120, 10),
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'roi_p1': hp.quniform('roi_p1', 1, 5, 1),
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'roi_p2': hp.quniform('roi_p2', 1, 5, 1),
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'roi_p3': hp.quniform('roi_p3', 1, 10, 1),
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}
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def stoploss_space() -> Dict:
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return {
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'stoploss': hp.uniform('stoploss', -0.5, -0.02),
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}
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def indicator_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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return {
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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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}
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def hyperopt_space() -> List[Dict]:
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return {**indicator_space(), **roi_space(), **stoploss_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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if 'roi_t1' in params:
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strategy = Strategy()
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strategy.minimal_roi = generate_roi_table(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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'stoploss': params['stoploss']})
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result_explanation = format_results(results)
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total_profit = results.profit_percent.sum()
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trade_count = len(results.index)
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trade_duration = results.duration.mean() * 5
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if trade_count == 0 or trade_duration > MAX_ACCEPTED_TRADE_DURATION:
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print('.', end='')
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return {
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'status': STATUS_FAIL,
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'loss': float('inf')
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}
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loss = calculate_loss(total_profit, trade_count, trade_duration)
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_CURRENT_TRIES += 1
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log_results({
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'loss': loss,
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'current_tries': _CURRENT_TRIES,
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'total_tries': TOTAL_TRIES,
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'result': result_explanation,
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})
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return {
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'loss': loss,
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'status': STATUS_OK,
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'result': result_explanation,
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}
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def format_results(results: DataFrame):
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return ('{:6d} trades. Avg profit {: 5.2f}%. '
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'Total profit {: 11.8f} BTC ({:.4f}Σ%). Avg duration {:5.1f} mins.').format(
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len(results.index),
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results.profit_percent.mean() * 100.0,
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results.profit_BTC.sum(),
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results.profit_percent.sum(),
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results.duration.mean() * 5,
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)
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|
|
|
|
|
def start(args):
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global TOTAL_TRIES, PROCESSED, TRIALS, _CURRENT_TRIES
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|
|
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TOTAL_TRIES = args.epochs
|
|
|
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exchange._API = Bittrex({'key': '', 'secret': ''})
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|
|
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# Initialize logger
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logging.basicConfig(
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|
level=args.loglevel,
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format='\n%(message)s',
|
|
)
|
|
|
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logger.info('Using config: %s ...', args.config)
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config = load_config(args.config)
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pairs = config['exchange']['pair_whitelist']
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|
|
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# init the strategy to use
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|
config.update({'strategy': args.strategy})
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|
strategy = Strategy()
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|
strategy.init(config)
|
|
|
|
timerange = misc.parse_timerange(args.timerange)
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|
data = optimize.load_data(args.datadir, pairs=pairs,
|
|
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)
|
|
|
|
if args.mongodb:
|
|
logger.info('Using mongodb ...')
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|
logger.info('Start scripts/start-mongodb.sh and start-hyperopt-worker.sh manually!')
|
|
|
|
db_name = 'freqtrade_hyperopt'
|
|
TRIALS = MongoTrials('mongo://127.0.0.1:1234/{}/jobs'.format(db_name), exp_key='exp1')
|
|
else:
|
|
logger.info('Preparing Trials..')
|
|
signal.signal(signal.SIGINT, signal_handler)
|
|
# read trials file if we have one
|
|
if os.path.exists(TRIALS_FILE):
|
|
TRIALS = read_trials()
|
|
|
|
_CURRENT_TRIES = len(TRIALS.results)
|
|
TOTAL_TRIES = TOTAL_TRIES + _CURRENT_TRIES
|
|
logger.info(
|
|
'Continuing with trials. Current: {}, Total: {}'
|
|
.format(_CURRENT_TRIES, TOTAL_TRIES))
|
|
|
|
try:
|
|
best_parameters = fmin(
|
|
fn=optimizer,
|
|
space=hyperopt_space(),
|
|
algo=tpe.suggest,
|
|
max_evals=TOTAL_TRIES,
|
|
trials=TRIALS
|
|
)
|
|
|
|
results = sorted(TRIALS.results, key=itemgetter('loss'))
|
|
best_result = results[0]['result']
|
|
|
|
except ValueError:
|
|
best_parameters = {}
|
|
best_result = 'Sorry, Hyperopt was not able to find good parameters. Please ' \
|
|
'try with more epochs (param: -e).'
|
|
|
|
# Improve best parameter logging display
|
|
if best_parameters:
|
|
best_parameters = space_eval(
|
|
hyperopt_space(),
|
|
best_parameters
|
|
)
|
|
|
|
logger.info('Best parameters:\n%s', json.dumps(best_parameters, indent=4))
|
|
if 'roi_t1' in best_parameters:
|
|
logger.info('ROI table:\n%s', generate_roi_table(best_parameters))
|
|
logger.info('Best Result:\n%s', best_result)
|
|
|
|
# Store trials result to file to resume next time
|
|
save_trials(TRIALS)
|
|
|
|
|
|
def signal_handler(sig, frame):
|
|
"""Hyperopt SIGINT handler"""
|
|
logger.info('Hyperopt received {}'.format(signal.Signals(sig).name))
|
|
|
|
save_trials(TRIALS)
|
|
log_trials_result(TRIALS)
|
|
sys.exit(0)
|