# pragma pylint: disable=missing-docstring,W0212,W0603 import json import logging import os import pickle import signal import sys from functools import reduce from math import exp from operator import itemgetter from typing import Dict, List import numpy import talib.abstract as ta from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, hp, space_eval, tpe from hyperopt.mongoexp import MongoTrials from pandas import DataFrame import freqtrade.vendor.qtpylib.indicators as qtpylib # Monkey patch config from freqtrade import main # noqa; noqa from freqtrade import exchange, misc, optimize from freqtrade.exchange import Bittrex from freqtrade.misc import load_config from freqtrade.optimize import backtesting from freqtrade.optimize.backtesting import backtest from freqtrade.strategy.strategy import Strategy from user_data.hyperopt_conf import hyperopt_optimize_conf # Remove noisy log messages logging.getLogger('hyperopt.mongoexp').setLevel(logging.WARNING) logging.getLogger('hyperopt.tpe').setLevel(logging.WARNING) logger = logging.getLogger(__name__) # set TARGET_TRADES to suit your number concurrent trades so its realistic to 20days of data TARGET_TRADES = 600 TOTAL_TRIES = 0 _CURRENT_TRIES = 0 CURRENT_BEST_LOSS = 100 # max average trade duration in minutes # if eval ends with higher value, we consider it a failed eval MAX_ACCEPTED_TRADE_DURATION = 300 # this is expexted avg profit * expected trade count # for example 3.5%, 1100 trades, EXPECTED_MAX_PROFIT = 3.85 # check that the reported Σ% values do not exceed this! EXPECTED_MAX_PROFIT = 3.0 # Configuration and data used by hyperopt PROCESSED = None # optimize.preprocess(optimize.load_data()) OPTIMIZE_CONFIG = hyperopt_optimize_conf() # Hyperopt Trials TRIALS_FILE = os.path.join('user_data', 'hyperopt_trials.pickle') TRIALS = Trials() main._CONF = OPTIMIZE_CONFIG def populate_indicators(dataframe: DataFrame) -> DataFrame: """ Adds several different TA indicators to the given DataFrame """ dataframe['adx'] = ta.ADX(dataframe) dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) dataframe['cci'] = ta.CCI(dataframe) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] dataframe['mfi'] = ta.MFI(dataframe) dataframe['minus_dm'] = ta.MINUS_DM(dataframe) dataframe['minus_di'] = ta.MINUS_DI(dataframe) dataframe['plus_dm'] = ta.PLUS_DM(dataframe) dataframe['plus_di'] = ta.PLUS_DI(dataframe) dataframe['roc'] = ta.ROC(dataframe) dataframe['rsi'] = ta.RSI(dataframe) # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1) # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy) dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) # Stoch stoch = ta.STOCH(dataframe) dataframe['slowd'] = stoch['slowd'] dataframe['slowk'] = stoch['slowk'] # Stoch fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] # Stoch RSI stoch_rsi = ta.STOCHRSI(dataframe) dataframe['fastd_rsi'] = stoch_rsi['fastd'] dataframe['fastk_rsi'] = stoch_rsi['fastk'] # 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['ema3'] = ta.EMA(dataframe, timeperiod=3) 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 Parabolic 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) # Hilbert Transform Indicator - SineWave hilbert = ta.HT_SINE(dataframe) dataframe['htsine'] = hilbert['sine'] dataframe['htleadsine'] = hilbert['leadsine'] # Pattern Recognition - Bullish candlestick patterns # ------------------------------------ """ # Hammer: values [0, 100] dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) # Inverted Hammer: values [0, 100] dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) # Dragonfly Doji: values [0, 100] dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) # Piercing Line: values [0, 100] dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100] # Morningstar: values [0, 100] dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100] # Three White Soldiers: values [0, 100] dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100] """ # Pattern Recognition - Bearish candlestick patterns # ------------------------------------ """ # Hanging Man: values [0, 100] dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) # Shooting Star: values [0, 100] dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) # Gravestone Doji: values [0, 100] dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) # Dark Cloud Cover: values [0, 100] dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) # Evening Doji Star: values [0, 100] dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) # Evening Star: values [0, 100] dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) """ # Pattern Recognition - Bullish/Bearish candlestick patterns # ------------------------------------ """ # Three Line Strike: values [0, -100, 100] dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) # Spinning Top: values [0, -100, 100] dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100] # Engulfing: values [0, -100, 100] dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100] # Harami: values [0, -100, 100] dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100] # Three Outside Up/Down: values [0, -100, 100] dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100] # Three Inside Up/Down: values [0, -100, 100] dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] """ # Chart type # ------------------------------------ # Heikinashi stategy heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] return dataframe def save_trials(trials, trials_path=TRIALS_FILE): """Save hyperopt trials to file""" logger.info('Saving Trials to \'{}\''.format(trials_path)) pickle.dump(trials, open(trials_path, 'wb')) def read_trials(trials_path=TRIALS_FILE): """Read hyperopt trials file""" logger.info('Reading Trials from \'{}\''.format(trials_path)) trials = pickle.load(open(trials_path, 'rb')) os.remove(trials_path) return trials def log_trials_result(trials): vals = json.dumps(trials.best_trial['misc']['vals'], indent=4) results = trials.best_trial['result']['result'] logger.info('Best result:\n%s\nwith values:\n%s', results, vals) def log_results(results): """ log results if it is better than any previous evaluation """ global CURRENT_BEST_LOSS if results['loss'] < CURRENT_BEST_LOSS: CURRENT_BEST_LOSS = results['loss'] logger.info('{:5d}/{}: {}. Loss {:.5f}'.format( results['current_tries'], results['total_tries'], results['result'], results['loss'])) else: print('.', end='') sys.stdout.flush() def calculate_loss(total_profit: float, trade_count: int, trade_duration: float): """ objective function, returns smaller number for more optimal results """ trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8) profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT) duration_loss = 0.7 + 0.3 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1) return trade_loss + profit_loss + duration_loss def roi_space() -> List[Dict]: return { 'roi_t1': hp.quniform('roi_t1', 10, 220, 10), 'roi_t2': hp.quniform('roi_t2', 10, 120, 10), 'roi_t3': hp.quniform('roi_t3', 10, 120, 10), 'roi_p1': hp.quniform('roi_p1', 1, 5, 1), 'roi_p2': hp.quniform('roi_p2', 1, 5, 1), 'roi_p3': hp.quniform('roi_p3', 1, 10, 1), } def indicator_space() -> List[Dict]: """ Define your Hyperopt space for searching strategy parameters """ return { '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), } def hyperopt_space() -> List[Dict]: return {**indicator_space(), **roi_space()} def buy_strategy_generator(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 def optimizer(params): global _CURRENT_TRIES backtesting.populate_buy_trend = buy_strategy_generator(params) results = backtest({'stake_amount': OPTIMIZE_CONFIG['stake_amount'], 'processed': PROCESSED, 'stoploss': params['stoploss']}) result_explanation = format_results(results) total_profit = results.profit_percent.sum() trade_count = len(results.index) trade_duration = results.duration.mean() * 5 if trade_count == 0 or trade_duration > MAX_ACCEPTED_TRADE_DURATION: print('.', end='') return { 'status': STATUS_FAIL, 'loss': float('inf') } loss = calculate_loss(total_profit, trade_count, trade_duration) _CURRENT_TRIES += 1 log_results({ 'loss': loss, 'current_tries': _CURRENT_TRIES, 'total_tries': TOTAL_TRIES, 'result': result_explanation, }) return { 'loss': loss, 'status': STATUS_OK, 'result': result_explanation, } def format_results(results: DataFrame): return ('{:6d} trades. Avg profit {: 5.2f}%. ' 'Total profit {: 11.8f} BTC ({:.4f}Σ%). Avg duration {:5.1f} mins.').format( len(results.index), results.profit_percent.mean() * 100.0, results.profit_BTC.sum(), results.profit_percent.sum(), results.duration.mean() * 5, ) def start(args): global TOTAL_TRIES, PROCESSED, TRIALS, _CURRENT_TRIES TOTAL_TRIES = args.epochs exchange._API = Bittrex({'key': '', 'secret': ''}) # Initialize logger logging.basicConfig( level=args.loglevel, format='\n%(message)s', ) logger.info('Using config: %s ...', args.config) config = load_config(args.config) pairs = config['exchange']['pair_whitelist'] # init the strategy to use config.update({'strategy': args.strategy}) strategy = Strategy() strategy.init(config) timerange = misc.parse_timerange(args.timerange) data = optimize.load_data(args.datadir, pairs=pairs, ticker_interval=args.ticker_interval, timerange=timerange) optimize.populate_indicators = populate_indicators PROCESSED = optimize.tickerdata_to_dataframe(data) if args.mongodb: logger.info('Using mongodb ...') 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)) 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)