# pragma pylint: disable=missing-docstring,W0212,W0603 import json import logging import sys import pickle import signal import os from functools import reduce from math import exp from operator import itemgetter from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, hp, space_eval, tpe from hyperopt.mongoexp import MongoTrials from pandas import DataFrame from freqtrade import main # noqa from freqtrade import exchange, optimize from freqtrade.exchange import Bittrex from freqtrade.misc import load_config from freqtrade.optimize.backtesting import backtest from freqtrade.optimize.hyperopt_conf import hyperopt_optimize_conf from freqtrade.vendor.qtpylib.indicators import crossed_above # 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('freqtrade', 'optimize', 'hyperopt_trials.pickle') TRIALS = Trials() # Monkey patch config from freqtrade import main # noqa main._CONF = OPTIMIZE_CONFIG SPACE = { 'mfi': hp.choice('mfi', [ {'enabled': False}, {'enabled': True, 'value': hp.quniform('mfi-value', 5, 25, 1)} ]), 'fastd': hp.choice('fastd', [ {'enabled': False}, {'enabled': True, 'value': hp.quniform('fastd-value', 10, 50, 1)} ]), 'adx': hp.choice('adx', [ {'enabled': False}, {'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)} ]), 'rsi': hp.choice('rsi', [ {'enabled': False}, {'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)} ]), 'uptrend_long_ema': hp.choice('uptrend_long_ema', [ {'enabled': False}, {'enabled': True} ]), 'uptrend_short_ema': hp.choice('uptrend_short_ema', [ {'enabled': False}, {'enabled': True} ]), 'over_sar': hp.choice('over_sar', [ {'enabled': False}, {'enabled': True} ]), 'green_candle': hp.choice('green_candle', [ {'enabled': False}, {'enabled': True} ]), 'uptrend_sma': hp.choice('uptrend_sma', [ {'enabled': False}, {'enabled': True} ]), 'trigger': hp.choice('trigger', [ {'type': 'lower_bb'}, {'type': 'faststoch10'}, {'type': 'ao_cross_zero'}, {'type': 'ema5_cross_ema10'}, {'type': 'macd_cross_signal'}, {'type': 'sar_reversal'}, {'type': 'stochf_cross'}, {'type': 'ht_sine'}, ]), 'stoploss': hp.uniform('stoploss', -0.5, -0.02), } 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 - 0.35 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.2) profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT) duration_loss = min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1) return trade_loss + profit_loss + duration_loss def optimizer(params): global _CURRENT_TRIES from freqtrade.optimize import backtesting backtesting.populate_buy_trend = buy_strategy_generator(params) results = backtest(OPTIMIZE_CONFIG['stake_amount'], 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 buy_strategy_generator(params): def populate_buy_trend(dataframe: DataFrame) -> DataFrame: conditions = [] # GUARDS AND TRENDS if params['uptrend_long_ema']['enabled']: conditions.append(dataframe['ema50'] > dataframe['ema100']) if params['uptrend_short_ema']['enabled']: conditions.append(dataframe['ema5'] > dataframe['ema10']) if params['mfi']['enabled']: conditions.append(dataframe['mfi'] < params['mfi']['value']) if params['fastd']['enabled']: conditions.append(dataframe['fastd'] < params['fastd']['value']) if params['adx']['enabled']: conditions.append(dataframe['adx'] > params['adx']['value']) if params['rsi']['enabled']: conditions.append(dataframe['rsi'] < params['rsi']['value']) if params['over_sar']['enabled']: conditions.append(dataframe['close'] > dataframe['sar']) if params['green_candle']['enabled']: conditions.append(dataframe['close'] > dataframe['open']) if params['uptrend_sma']['enabled']: prevsma = dataframe['sma'].shift(1) conditions.append(dataframe['sma'] > prevsma) # TRIGGERS triggers = { 'lower_bb': dataframe['tema'] <= dataframe['blower'], 'faststoch10': (crossed_above(dataframe['fastd'], 10.0)), 'ao_cross_zero': (crossed_above(dataframe['ao'], 0.0)), 'ema5_cross_ema10': (crossed_above(dataframe['ema5'], dataframe['ema10'])), 'macd_cross_signal': (crossed_above(dataframe['macd'], dataframe['macdsignal'])), 'sar_reversal': (crossed_above(dataframe['close'], dataframe['sar'])), 'stochf_cross': (crossed_above(dataframe['fastk'], dataframe['fastd'])), 'ht_sine': (crossed_above(dataframe['htleadsine'], dataframe['htsine'])), } conditions.append(triggers.get(params['trigger']['type'])) dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'buy'] = 1 return dataframe return populate_buy_trend def start(args): global TOTAL_TRIES, PROCESSED, SPACE, 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'] PROCESSED = optimize.preprocess(optimize.load_data( args.datadir, pairs=pairs, ticker_interval=args.ticker_interval)) 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=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(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)