stable/freqtrade/optimize/hyperopt.py

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# pragma pylint: disable=missing-docstring,W0212
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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
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from freqtrade.exchange import Bittrex
from freqtrade.misc import load_config
from freqtrade.optimize.backtesting import backtest
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from freqtrade.optimize.hyperopt_conf import hyperopt_optimize_conf
from freqtrade.vendor.qtpylib.indicators import crossed_above
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# Remove noisy log messages
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__)
# 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
AVG_PROFIT_TO_BEAT = 0
AVG_DURATION_TO_BEAT = 100
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# this is expexted avg profit * expected trade count
# for example 3.5%, 1100 trades, EXPECTED_MAX_PROFIT = 3.85
EXPECTED_MAX_PROFIT = 3.85
# Configuration and data used by hyperopt
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PROCESSED = optimize.preprocess(optimize.load_data())
OPTIMIZE_CONFIG = hyperopt_optimize_conf()
# Monkey patch config
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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'},
]),
}
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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']
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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()
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def optimizer(params):
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global _CURRENT_TRIES
from freqtrade.optimize import backtesting
backtesting.populate_buy_trend = buy_strategy_generator(params)
results = backtest(OPTIMIZE_CONFIG['stake_amount'], PROCESSED)
result = format_results(results)
total_profit = results.profit_percent.sum()
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 / EXPECTED_MAX_PROFIT)
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_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
}
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 {: 11.8f} BTC. 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'])
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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'])
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if params['rsi']['enabled']:
conditions.append(dataframe['rsi'] < params['rsi']['value'])
if params['over_sar']['enabled']:
conditions.append(dataframe['close'] > dataframe['sar'])
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if params['green_candle']['enabled']:
conditions.append(dataframe['close'] > dataframe['open'])
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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'])),
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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'])),
}
conditions.append(triggers.get(params['trigger']['type']))
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
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def start(args):
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
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:
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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'])