stable/freqtrade/optimize/hyperopt.py

# pragma pylint: disable=missing-docstring,W0212,W0603


import json
import logging
import sys
import pickle
import signal
import os
from functools import reduce
from math import exp
from operator import itemgetter

from hyperopt import fmin, tpe, hp, Trials, STATUS_OK, STATUS_FAIL, space_eval
from hyperopt.mongoexp import MongoTrials
from pandas import DataFrame

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 = 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 = 240

# 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
PROCESSED = None  # optimize.preprocess(optimize.load_data())
OPTIMIZE_CONFIG = hyperopt_optimize_conf()

# Hyperopt Trials
TRIALS_FILE = os.path.join('freqtrade', 'optimize', 'hyperopt_trials.pickle')
TRIALS = Trials()

# 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'},
    ]),
    'stoploss': hp.uniform('stoploss', -0.5, -0.02),
}


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}/{}: {}'.format(
            results['current_tries'],
            results['total_tries'],
            results['result']))
    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 - 0.35 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.2)
    profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
    duration_loss = min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
    return trade_loss + profit_loss + duration_loss


def optimizer(params):
    global _CURRENT_TRIES

    from freqtrade.optimize import backtesting
    backtesting.populate_buy_trend = buy_strategy_generator(params)

    results = backtest(OPTIMIZE_CONFIG['stake_amount'], PROCESSED, stoploss=params['stoploss'])
    result_explanation = format_results(results)

    total_profit = results.profit_percent.sum()
    trade_count = len(results.index)
    trade_duration = results.duration.mean() * 5

    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,
    }


def format_results(results: DataFrame):
    return ('{:6d} trades. Avg profit {: 5.2f}%. '
            'Total profit {: 11.8f} BTC. Avg duration {:5.1f} mins.').format(
                len(results.index),
                results.profit_percent.mean() * 100.0,
                results.profit_BTC.sum(),
                results.duration.mean() * 5,
            )


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, SPACE, 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']
    PROCESSED = optimize.preprocess(optimize.load_data(
        args.datadir, 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:
        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=optimizer,
            space=SPACE,
            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(SPACE, best_parameters)

    logger.info('Best parameters:\n%s', json.dumps(best_parameters, indent=4))
    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)