mirror of
https://github.com/freqtrade/freqtrade.git
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318 lines
10 KiB
Python
318 lines
10 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 sys
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import pickle
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import signal
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import os
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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 hyperopt import fmin, tpe, hp, Trials, STATUS_OK, STATUS_FAIL, space_eval
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from hyperopt.mongoexp import MongoTrials
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from pandas import DataFrame
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from freqtrade import exchange, 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.backtesting import backtest
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from freqtrade.optimize.hyperopt_conf import hyperopt_optimize_conf
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from freqtrade.vendor.qtpylib.indicators import crossed_above
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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 = 1100
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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 = 240
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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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EXPECTED_MAX_PROFIT = 3.85
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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('freqtrade', 'optimize', 'hyperopt_trials.pickle')
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TRIALS = Trials()
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# Monkey patch config
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from freqtrade import main # noqa
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main._CONF = OPTIMIZE_CONFIG
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SPACE = {
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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': 'faststoch10'},
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{'type': 'ao_cross_zero'},
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{'type': 'ema5_cross_ema10'},
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{'type': 'macd_cross_signal'},
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{'type': 'sar_reversal'},
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{'type': 'stochf_cross'},
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{'type': 'ht_sine'},
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]),
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'stoploss': hp.uniform('stoploss', -0.5, -0.02),
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}
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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}/{}: {}'.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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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 - 0.35 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.2)
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profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
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duration_loss = min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
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return trade_loss + profit_loss + duration_loss
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def optimizer(params):
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global _CURRENT_TRIES
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from freqtrade.optimize import backtesting
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backtesting.populate_buy_trend = buy_strategy_generator(params)
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results = backtest(OPTIMIZE_CONFIG['stake_amount'], PROCESSED, 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. 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.duration.mean() * 5,
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)
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def buy_strategy_generator(params):
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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 params['uptrend_long_ema']['enabled']:
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conditions.append(dataframe['ema50'] > dataframe['ema100'])
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if params['uptrend_short_ema']['enabled']:
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conditions.append(dataframe['ema5'] > dataframe['ema10'])
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if params['mfi']['enabled']:
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conditions.append(dataframe['mfi'] < params['mfi']['value'])
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if params['fastd']['enabled']:
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conditions.append(dataframe['fastd'] < params['fastd']['value'])
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if params['adx']['enabled']:
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conditions.append(dataframe['adx'] > params['adx']['value'])
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if params['rsi']['enabled']:
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conditions.append(dataframe['rsi'] < params['rsi']['value'])
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if params['over_sar']['enabled']:
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conditions.append(dataframe['close'] > dataframe['sar'])
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if params['green_candle']['enabled']:
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conditions.append(dataframe['close'] > dataframe['open'])
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if 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': dataframe['tema'] <= dataframe['blower'],
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'faststoch10': (crossed_above(dataframe['fastd'], 10.0)),
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'ao_cross_zero': (crossed_above(dataframe['ao'], 0.0)),
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'ema5_cross_ema10': (crossed_above(dataframe['ema5'], dataframe['ema10'])),
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'macd_cross_signal': (crossed_above(dataframe['macd'], dataframe['macdsignal'])),
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'sar_reversal': (crossed_above(dataframe['close'], dataframe['sar'])),
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'stochf_cross': (crossed_above(dataframe['fastk'], dataframe['fastd'])),
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'ht_sine': (crossed_above(dataframe['htleadsine'], dataframe['htsine'])),
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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 start(args):
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global TOTAL_TRIES, PROCESSED, SPACE, TRIALS, _CURRENT_TRIES
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TOTAL_TRIES = args.epochs
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exchange._API = Bittrex({'key': '', 'secret': ''})
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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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)
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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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PROCESSED = optimize.preprocess(optimize.load_data(
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args.datadir, pairs=pairs, ticker_interval=args.ticker_interval))
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if args.mongodb:
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logger.info('Using mongodb ...')
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logger.info('Start scripts/start-mongodb.sh and start-hyperopt-worker.sh manually!')
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db_name = 'freqtrade_hyperopt'
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TRIALS = MongoTrials('mongo://127.0.0.1:1234/{}/jobs'.format(db_name), exp_key='exp1')
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else:
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logger.info('Preparing Trials..')
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signal.signal(signal.SIGINT, signal_handler)
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# read trials file if we have one
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if os.path.exists(TRIALS_FILE):
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TRIALS = read_trials()
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_CURRENT_TRIES = len(TRIALS.results)
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TOTAL_TRIES = TOTAL_TRIES + _CURRENT_TRIES
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logger.info(
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'Continuing with trials. Current: {}, Total: {}'
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.format(_CURRENT_TRIES, TOTAL_TRIES))
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try:
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best_parameters = fmin(
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fn=optimizer,
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space=SPACE,
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algo=tpe.suggest,
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max_evals=TOTAL_TRIES,
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trials=TRIALS
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)
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results = sorted(TRIALS.results, key=itemgetter('loss'))
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best_result = results[0]['result']
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except ValueError:
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best_parameters = {}
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best_result = 'Sorry, Hyperopt was not able to find good parameters. Please ' \
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'try with more epochs (param: -e).'
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# Improve best parameter logging display
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if best_parameters:
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best_parameters = space_eval(SPACE, best_parameters)
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logger.info('Best parameters:\n%s', json.dumps(best_parameters, indent=4))
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logger.info('Best Result:\n%s', best_result)
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# Store trials result to file to resume next time
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save_trials(TRIALS)
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def signal_handler(sig, frame):
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"""Hyperopt SIGINT handler"""
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logger.info('Hyperopt received {}'.format(signal.Signals(sig).name))
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save_trials(TRIALS)
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log_trials_result(TRIALS)
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sys.exit(0)
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