mirror of
https://github.com/freqtrade/freqtrade.git
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251 lines
8.1 KiB
Python
251 lines
8.1 KiB
Python
# pragma pylint: disable=missing-docstring,W0212
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import json
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import logging
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import sys
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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
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from hyperopt.mongoexp import MongoTrials
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from pandas import DataFrame
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import numpy as np
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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 = None
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_CURRENT_TRIES = 0
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TOTAL_PROFIT_TO_BEAT = 0
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AVG_PROFIT_TO_BEAT = 0
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AVG_DURATION_TO_BEAT = 100
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# Configuration and data used by hyperopt
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PROCESSED = optimize.preprocess(optimize.load_data())
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OPTIMIZE_CONFIG = hyperopt_optimize_conf()
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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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}
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def log_results(results):
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"if results is better than _TO_BEAT show it"
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current_try = results['current_tries']
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total_tries = results['total_tries']
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result = results['result']
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profit = results['total_profit']
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if profit >= TOTAL_PROFIT_TO_BEAT:
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logger.info('\n{:5d}/{}: {}'.format(current_try, total_tries, result))
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else:
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print('.', end='')
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sys.stdout.flush()
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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)
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result = format_results(results)
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total_profit = results.profit_percent.sum() * 1000
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trade_count = len(results.index)
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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 / 10000) # max profit 10000
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_CURRENT_TRIES += 1
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result_data = {
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'trade_count': trade_count,
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'total_profit': total_profit,
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'trade_loss': trade_loss,
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'profit_loss': profit_loss,
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'avg_profit': results.profit_percent.mean() * 100.0,
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'avg_duration': results.duration.mean() * 5,
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'current_tries': _CURRENT_TRIES,
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'total_tries': TOTAL_TRIES,
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'result': result,
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'results': results
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}
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# logger.info('{:5d}/{}: {}'.format(_CURRENT_TRIES, TOTAL_TRIES, result))
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log_results(result_data)
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return {
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'loss': trade_loss + profit_loss,
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'status': STATUS_OK,
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'result': result,
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'total_profit': total_profit,
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'avg_profit': result_data['avg_profit'],
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}
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def format_results(results: DataFrame):
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return ('Made {:6d} buys. Average profit {: 5.2f}%. '
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'Total profit was {: 11.8f}. Average 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 filter_nan(result, filter_key):
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return [r for r in result if not np.isnan(r[filter_key])]
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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
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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='%(asctime)s - %(name)s - %(levelname)s - %(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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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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trials = Trials()
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best = fmin(fn=optimizer, space=SPACE, algo=tpe.suggest, max_evals=TOTAL_TRIES, trials=trials)
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logger.info('Best parameters:\n%s', json.dumps(best, indent=4))
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filt_res = filter_nan(trials.results, 'total_profit')
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filt_res = filter_nan(filt_res, 'avg_profit')
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results = sorted(filt_res, key=itemgetter('loss'))
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logger.info('Best Result:\n%s', results[0]['result'])
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