# 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, Any, Callable 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 the number of days 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 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 = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1) return trade_loss + profit_loss + duration_loss def generate_roi_table(params) -> Dict[int, float]: roi_table = {} roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3'] roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2'] roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1'] roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0 return roi_table def has_space(spaces, space): if space in spaces or 'all' in spaces: return True return False def hyperopt_space(selected_spaces: str, strategy) -> Dict[str, Any]: spaces = {} if has_space(selected_spaces, 'buy'): spaces = {**spaces, **strategy.indicator_space()} if has_space(selected_spaces, 'roi'): spaces = {**spaces, **strategy.roi_space()} if has_space(selected_spaces, 'stoploss'): spaces = {**spaces, **strategy.stoploss_space()} return spaces def generate_optimizer(args, strategy): def optimizer(params): global _CURRENT_TRIES if has_space(args.spaces, 'roi'): strategy.minimal_roi = generate_roi_table(params) if has_space(args.spaces, 'buy'): backtesting.populate_buy_trend = strategy.buy_strategy_generator(params) if has_space(args.spaces, 'stoploss'): strategy.stoploss = params['stoploss'] results = backtest({'stake_amount': OPTIMIZE_CONFIG['stake_amount'], 'processed': PROCESSED, 'realistic': args.realistic_simulation, }) result_explanation = format_results(results) total_profit = results.profit_percent.sum() trade_count = len(results.index) trade_duration = results.duration.mean() 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, } return optimizer 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(), ) 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'] # If -i/--ticker-interval is use we override the configuration parameter # (that will override the strategy configuration) if args.ticker_interval: config.update({'ticker_interval': args.ticker_interval}) # 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=strategy.ticker_interval, timerange=timerange) if has_space(args.spaces, 'buy'): optimize.populate_indicators = strategy.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=generate_optimizer(args, strategy), space=hyperopt_space(args.spaces, strategy), 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(args.spaces, strategy), 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)