# pragma pylint: disable=missing-docstring,W0212 import json import logging import sys from functools import reduce from math import exp from operator import itemgetter from hyperopt import fmin, tpe, hp, Trials, STATUS_OK from hyperopt.mongoexp import MongoTrials from pandas import DataFrame import numpy as np 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 = 1100 TOTAL_TRIES = None _CURRENT_TRIES = 0 TOTAL_PROFIT_TO_BEAT = 0 AVG_PROFIT_TO_BEAT = 0 AVG_DURATION_TO_BEAT = 100 # Configuration and data used by hyperopt PROCESSED = optimize.preprocess(optimize.load_data()) OPTIMIZE_CONFIG = hyperopt_optimize_conf() # 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'}, ]), } def log_results(results): "if results is better than _TO_BEAT show it" current_try = results['current_tries'] total_tries = results['total_tries'] result = results['result'] profit = results['total_profit'] if profit >= TOTAL_PROFIT_TO_BEAT: logger.info('\n{:5d}/{}: {}'.format(current_try, total_tries, result)) else: print('.', end='') sys.stdout.flush() def optimizer(params): global _CURRENT_TRIES from freqtrade.optimize import backtesting backtesting.populate_buy_trend = buy_strategy_generator(params) results = backtest(OPTIMIZE_CONFIG, PROCESSED) result = format_results(results) total_profit = results.profit_percent.sum() * 1000 trade_count = len(results.index) trade_loss = 1 - 0.35 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.2) profit_loss = max(0, 1 - total_profit / 10000) # max profit 10000 _CURRENT_TRIES += 1 result_data = { 'trade_count': trade_count, 'total_profit': total_profit, 'trade_loss': trade_loss, 'profit_loss': profit_loss, 'avg_profit': results.profit_percent.mean() * 100.0, 'avg_duration': results.duration.mean() * 5, 'current_tries': _CURRENT_TRIES, 'total_tries': TOTAL_TRIES, 'result': result, 'results': results } # logger.info('{:5d}/{}: {}'.format(_CURRENT_TRIES, TOTAL_TRIES, result)) log_results(result_data) return { 'loss': trade_loss + profit_loss, 'status': STATUS_OK, 'result': result, 'total_profit': total_profit, 'avg_profit': result_data['avg_profit'], } def format_results(results: DataFrame): return ('Made {:6d} buys. Average profit {: 5.2f}%. ' 'Total profit was {: 7.3f}. Average duration {:5.1f} mins.').format( len(results.index), results.profit_percent.mean() * 100.0, results.profit_BTC.sum(), results.duration.mean() * 5, ) def filter_nan(result, filter_key): return [r for r in result if not np.isnan(r[filter_key])] 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 TOTAL_TRIES = args.epochs exchange._API = Bittrex({'key': '', 'secret': ''}) # Initialize logger logging.basicConfig( level=args.loglevel, format='%(asctime)s - %(name)s - %(levelname)s - %(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( 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: trials = Trials() best = fmin(fn=optimizer, space=SPACE, algo=tpe.suggest, max_evals=TOTAL_TRIES, trials=trials) logger.info('Best parameters:\n%s', json.dumps(best, indent=4)) filt_res = filter_nan(trials.results, 'total_profit') filt_res = filter_nan(filt_res, 'avg_profit') results = sorted(filt_res, key=itemgetter('loss')) logger.info('Best Result:\n%s', results[0]['result'])