For mongodb

hyperopy-mongodb.py

@@ -0,0 +1,615 @@
+# pragma pylint: disable=too-many-instance-attributes, pointless-string-statement
+
+"""
+This module contains the hyperopt logic
+"""
+
+import json
+import logging
+import os
+import pickle
+import signal
+import sys
+from argparse import Namespace
+from functools import reduce
+from math import exp
+from operator import itemgetter
+from typing import Dict, Any, Callable
+
+import numpy
+import talib.abstract as ta
+from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, hp, space_eval, tpe
+from hyperopt.mongoexp import MongoTrials
+from pandas import DataFrame
+
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+from freqtrade.arguments import Arguments
+from freqtrade.configuration import Configuration
+from freqtrade.logger import Logger
+from freqtrade.optimize import load_data
+from freqtrade.optimize.backtesting import Backtesting
+from user_data.hyperopt_conf import hyperopt_optimize_conf
+
+
+
+class Testz():
+    db_name = 'freqtrade_hyperopt'
+    MongoTrials(
+        arg='mongo://127.0.0.1:1234/{}/jobs'.format(db_name),
+        exp_key='exp1'
+        )
+
+
+class Hyperopt(Backtesting):
+    """
+    Hyperopt class, this class contains all the logic to run a hyperopt simulation
+
+    To run a backtest:
+    hyperopt = Hyperopt(config)
+    hyperopt.start()
+    """
+    def __init__(self, config: Dict[str, Any]) -> None:
+
+        super().__init__(config)
+
+        # Rename the logging to display Hyperopt file instead of Backtesting
+        self.logging = Logger(name=__name__, level=config['loglevel'])
+        self.logger = self.logging.get_logger()
+
+        # set TARGET_TRADES to suit your number concurrent trades so its realistic
+        # to the number of days
+        self.target_trades = 600
+        self.total_tries = config.get('epochs', 0)
+        self.current_tries = 0
+        self.current_best_loss = 100
+
+        # max average trade duration in minutes
+        # if eval ends with higher value, we consider it a failed eval
+        self.max_accepted_trade_duration = 300
+
+        # this is expexted avg profit * expected trade count
+        # for example 3.5%, 1100 trades, self.expected_max_profit = 3.85
+        # check that the reported Σ% values do not exceed this!
+        self.expected_max_profit = 3.0
+
+        # Configuration and data used by hyperopt
+        self.processed = None
+
+        # Hyperopt Trials
+        self.trials_file = os.path.join('user_data', 'hyperopt_trials.pickle')
+        self.trials = Testz()
+
+    @staticmethod
+    def populate_indicators(dataframe: DataFrame) -> DataFrame:
+        """
+        Adds several different TA indicators to the given DataFrame
+        """
+        dataframe['adx'] = ta.ADX(dataframe)
+        dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
+        dataframe['cci'] = ta.CCI(dataframe)
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['macdhist'] = macd['macdhist']
+        dataframe['mfi'] = ta.MFI(dataframe)
+        dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
+        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+        dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
+        dataframe['plus_di'] = ta.PLUS_DI(dataframe)
+        dataframe['roc'] = ta.ROC(dataframe)
+        dataframe['rsi'] = ta.RSI(dataframe)
+        # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
+        rsi = 0.1 * (dataframe['rsi'] - 50)
+        dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
+        # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
+        dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
+        # Stoch
+        stoch = ta.STOCH(dataframe)
+        dataframe['slowd'] = stoch['slowd']
+        dataframe['slowk'] = stoch['slowk']
+        # Stoch fast
+        stoch_fast = ta.STOCHF(dataframe)
+        dataframe['fastd'] = stoch_fast['fastd']
+        dataframe['fastk'] = stoch_fast['fastk']
+        # Stoch RSI
+        stoch_rsi = ta.STOCHRSI(dataframe)
+        dataframe['fastd_rsi'] = stoch_rsi['fastd']
+        dataframe['fastk_rsi'] = stoch_rsi['fastk']
+        # Bollinger bands
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        dataframe['bb_lowerband'] = bollinger['lower']
+        dataframe['bb_middleband'] = bollinger['mid']
+        dataframe['bb_upperband'] = bollinger['upper']
+        # EMA - Exponential Moving Average
+        dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
+        dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
+        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
+        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
+        dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
+        # SAR Parabolic
+        dataframe['sar'] = ta.SAR(dataframe)
+        # SMA - Simple Moving Average
+        dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
+        # TEMA - Triple Exponential Moving Average
+        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
+        # Hilbert Transform Indicator - SineWave
+        hilbert = ta.HT_SINE(dataframe)
+        dataframe['htsine'] = hilbert['sine']
+        dataframe['htleadsine'] = hilbert['leadsine']
+
+        # Pattern Recognition - Bullish candlestick patterns
+        # ------------------------------------
+        """
+        # Hammer: values [0, 100]
+        dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
+        # Inverted Hammer: values [0, 100]
+        dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
+        # Dragonfly Doji: values [0, 100]
+        dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
+        # Piercing Line: values [0, 100]
+        dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
+        # Morningstar: values [0, 100]
+        dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
+        # Three White Soldiers: values [0, 100]
+        dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
+        """
+
+        # Pattern Recognition - Bearish candlestick patterns
+        # ------------------------------------
+        """
+        # Hanging Man: values [0, 100]
+        dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
+        # Shooting Star: values [0, 100]
+        dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
+        # Gravestone Doji: values [0, 100]
+        dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
+        # Dark Cloud Cover: values [0, 100]
+        dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
+        # Evening Doji Star: values [0, 100]
+        dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
+        # Evening Star: values [0, 100]
+        dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
+        """
+
+        # Pattern Recognition - Bullish/Bearish candlestick patterns
+        # ------------------------------------
+        """
+        # Three Line Strike: values [0, -100, 100]
+        dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
+        # Spinning Top: values [0, -100, 100]
+        dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
+        # Engulfing: values [0, -100, 100]
+        dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
+        # Harami: values [0, -100, 100]
+        dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
+        # Three Outside Up/Down: values [0, -100, 100]
+        dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
+        # Three Inside Up/Down: values [0, -100, 100]
+        dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
+        """
+
+        # Chart type
+        # ------------------------------------
+        # Heikinashi stategy
+        heikinashi = qtpylib.heikinashi(dataframe)
+        dataframe['ha_open'] = heikinashi['open']
+        dataframe['ha_close'] = heikinashi['close']
+        dataframe['ha_high'] = heikinashi['high']
+        dataframe['ha_low'] = heikinashi['low']
+
+        return dataframe
+
+    def save_trials(self) -> None:
+        """
+        Save hyperopt trials to file
+        """
+        self.logger.info('Saving Trials to \'%s\'', self.trials_file)
+        pickle.dump(self.trials, open(self.trials_file, 'wb'))
+
+    def read_trials(self) -> Trials:
+        """
+        Read hyperopt trials file
+        """
+        self.logger.info('Reading Trials from \'%s\'', self.trials_file)
+        trials = pickle.load(open(self.trials_file, 'rb'))
+        os.remove(self.trials_file)
+        return trials
+
+    def log_trials_result(self) -> None:
+        """
+        Display Best hyperopt result
+        """
+        vals = json.dumps(self.trials.best_trial['misc']['vals'], indent=4)
+        results = self.trials.best_trial['result']['result']
+        self.logger.info('Best result:\n%s\nwith values:\n%s', results, vals)
+
+    def log_results(self, results) -> None:
+        """
+        Log results if it is better than any previous evaluation
+        """
+        if results['loss'] < self.current_best_loss:
+            self.current_best_loss = results['loss']
+            log_msg = '{:5d}/{}: {}. Loss {:.5f}'.format(
+                results['current_tries'],
+                results['total_tries'],
+                results['result'],
+                results['loss']
+            )
+            self.logger.info(log_msg)
+        else:
+            print('.', end='')
+            sys.stdout.flush()
+
+    def calculate_loss(self, total_profit: float, trade_count: int, trade_duration: float) -> float:
+        """
+        Objective function, returns smaller number for more optimal results
+        """
+        trade_loss = 1 - 0.25 * exp(-(trade_count - self.target_trades) ** 2 / 10 ** 5.8)
+        profit_loss = max(0, 1 - total_profit / self.expected_max_profit)
+        duration_loss = 0.4 * min(trade_duration / self.max_accepted_trade_duration, 1)
+        return trade_loss + profit_loss + duration_loss
+
+    @staticmethod
+    def generate_roi_table(params: Dict) -> Dict[int, float]:
+        """
+        Generate the ROI table thqt will be used by Hyperopt
+        """
+        roi_table = {}
+        roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
+        roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
+        roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
+        roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
+
+        return roi_table
+
+    @staticmethod
+    def roi_space() -> Dict[str, Any]:
+        """
+        Values to search for each ROI steps
+        """
+        return {
+            'roi_t1': hp.quniform('roi_t1', 10, 120, 20),
+            'roi_t2': hp.quniform('roi_t2', 10, 60, 15),
+            'roi_t3': hp.quniform('roi_t3', 10, 40, 10),
+            'roi_p1': hp.quniform('roi_p1', 0.01, 0.04, 0.01),
+            'roi_p2': hp.quniform('roi_p2', 0.01, 0.07, 0.01),
+            'roi_p3': hp.quniform('roi_p3', 0.01, 0.20, 0.01),
+        }
+
+    @staticmethod
+    def stoploss_space() -> Dict[str, Any]:
+        """
+        Stoploss Value to search
+        """
+        return {
+            'stoploss': hp.quniform('stoploss', -0.5, -0.02, 0.02),
+        }
+
+    @staticmethod
+    def indicator_space() -> Dict[str, Any]:
+        """
+        Define your Hyperopt space for searching strategy parameters
+        """
+        return {
+            'macd_below_zero': hp.choice('macd_below_zero', [
+                {'enabled': False},
+                {'enabled': True}
+            ]),
+            'mfi': hp.choice('mfi', [
+                {'enabled': False},
+                {'enabled': True, 'value': hp.quniform('mfi-value', 10, 25, 5)}
+            ]),
+            'fastd': hp.choice('fastd', [
+                {'enabled': False},
+                {'enabled': True, 'value': hp.quniform('fastd-value', 15, 45, 5)}
+            ]),
+            'adx': hp.choice('adx', [
+                {'enabled': False},
+                {'enabled': True, 'value': hp.quniform('adx-value', 20, 50, 5)}
+            ]),
+            'rsi': hp.choice('rsi', [
+                {'enabled': False},
+                {'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 5)}
+            ]),
+            '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': 'lower_bb_tema'},
+                {'type': 'faststoch10'},
+                {'type': 'ao_cross_zero'},
+                {'type': 'ema3_cross_ema10'},
+                {'type': 'macd_cross_signal'},
+                {'type': 'sar_reversal'},
+                {'type': 'ht_sine'},
+                {'type': 'heiken_reversal_bull'},
+                {'type': 'di_cross'},
+            ]),
+        }
+
+    def has_space(self, space: str) -> bool:
+        """
+        Tell if a space value is contained in the configuration
+        """
+        if space in self.config['spaces'] or 'all' in self.config['spaces']:
+            return True
+        return False
+
+    def hyperopt_space(self) -> Dict[str, Any]:
+        """
+        Return the space to use during Hyperopt
+        """
+        spaces = {}
+        if self.has_space('buy'):
+            spaces = {**spaces, **Hyperopt.indicator_space()}
+        if self.has_space('roi'):
+            spaces = {**spaces, **Hyperopt.roi_space()}
+        if self.has_space('stoploss'):
+            spaces = {**spaces, **Hyperopt.stoploss_space()}
+        return spaces
+
+    @staticmethod
+    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the buy strategy parameters to be used by hyperopt
+        """
+        def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
+            """
+            Buy strategy Hyperopt will build and use
+            """
+            conditions = []
+            # GUARDS AND TRENDS
+            if 'uptrend_long_ema' in params and params['uptrend_long_ema']['enabled']:
+                conditions.append(dataframe['ema50'] > dataframe['ema100'])
+            if 'macd_below_zero' in params and params['macd_below_zero']['enabled']:
+                conditions.append(dataframe['macd'] < 0)
+            if 'uptrend_short_ema' in params and params['uptrend_short_ema']['enabled']:
+                conditions.append(dataframe['ema5'] > dataframe['ema10'])
+            if 'mfi' in params and params['mfi']['enabled']:
+                conditions.append(dataframe['mfi'] < params['mfi']['value'])
+            if 'fastd' in params and params['fastd']['enabled']:
+                conditions.append(dataframe['fastd'] < params['fastd']['value'])
+            if 'adx' in params and params['adx']['enabled']:
+                conditions.append(dataframe['adx'] > params['adx']['value'])
+            if 'rsi' in params and params['rsi']['enabled']:
+                conditions.append(dataframe['rsi'] < params['rsi']['value'])
+            if 'over_sar' in params and params['over_sar']['enabled']:
+                conditions.append(dataframe['close'] > dataframe['sar'])
+            if 'green_candle' in params and params['green_candle']['enabled']:
+                conditions.append(dataframe['close'] > dataframe['open'])
+            if 'uptrend_sma' in params and params['uptrend_sma']['enabled']:
+                prevsma = dataframe['sma'].shift(1)
+                conditions.append(dataframe['sma'] > prevsma)
+
+            # TRIGGERS
+            triggers = {
+                'lower_bb': (
+                    dataframe['close'] < dataframe['bb_lowerband']
+                ),
+                'lower_bb_tema': (
+                    dataframe['tema'] < dataframe['bb_lowerband']
+                ),
+                'faststoch10': (qtpylib.crossed_above(
+                    dataframe['fastd'], 10.0
+                )),
+                'ao_cross_zero': (qtpylib.crossed_above(
+                    dataframe['ao'], 0.0
+                )),
+                'ema3_cross_ema10': (qtpylib.crossed_above(
+                    dataframe['ema3'], dataframe['ema10']
+                )),
+                'macd_cross_signal': (qtpylib.crossed_above(
+                    dataframe['macd'], dataframe['macdsignal']
+                )),
+                'sar_reversal': (qtpylib.crossed_above(
+                    dataframe['close'], dataframe['sar']
+                )),
+                'ht_sine': (qtpylib.crossed_above(
+                    dataframe['htleadsine'], dataframe['htsine']
+                )),
+                'heiken_reversal_bull': (
+                    (qtpylib.crossed_above(dataframe['ha_close'], dataframe['ha_open'])) &
+                    (dataframe['ha_low'] == dataframe['ha_open'])
+                ),
+                'di_cross': (qtpylib.crossed_above(
+                    dataframe['plus_di'], dataframe['minus_di']
+                )),
+            }
+            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 generate_optimizer(self, params: Dict) -> Dict:
+        if self.has_space('roi'):
+            self.analyze.strategy.minimal_roi = self.generate_roi_table(params)
+
+        if self.has_space('buy'):
+            self.populate_buy_trend = self.buy_strategy_generator(params)
+
+        if self.has_space('stoploss'):
+            self.analyze.strategy.stoploss = params['stoploss']
+
+        results = self.backtest(
+            {
+                'stake_amount': self.config['stake_amount'],
+                'processed': self.processed,
+                'realistic': self.config.get('realistic_simulation', False),
+            }
+        )
+        result_explanation = self.format_results(results)
+
+        total_profit = results.profit_percent.sum()
+        trade_count = len(results.index)
+        trade_duration = results.duration.mean()
+
+        if trade_count == 0 or trade_duration > self.max_accepted_trade_duration:
+            print('.', end='')
+            return {
+                'status': STATUS_FAIL,
+                'loss': float('inf')
+            }
+
+        loss = self.calculate_loss(total_profit, trade_count, trade_duration)
+
+        self.current_tries += 1
+
+        self.log_results(
+            {
+                'loss': loss,
+                'current_tries': self.current_tries,
+                'total_tries': self.total_tries,
+                'result': result_explanation,
+            }
+        )
+
+        return {
+            'loss': loss,
+            'status': STATUS_OK,
+            'result': result_explanation,
+        }
+
+    @staticmethod
+    def format_results(results: DataFrame) -> str:
+        """
+        Return the format result in a string
+        """
+        return ('{:6d} trades. Avg profit {: 5.2f}%. '
+                'Total profit {: 11.8f} BTC ({:.4f}Σ%). Avg duration {:5.1f} mins.').format(
+                    len(results.index),
+                    results.profit_percent.mean() * 100.0,
+                    results.profit_BTC.sum(),
+                    results.profit_percent.sum(),
+                    results.duration.mean(),
+                )
+
+    def start(self) -> None:
+        timerange = Arguments.parse_timerange(self.config.get('timerange'))
+        data = load_data(
+            datadir=self.config.get('datadir'),
+            pairs=self.config['exchange']['pair_whitelist'],
+            ticker_interval=self.ticker_interval,
+            timerange=timerange
+        )
+
+        if self.has_space('buy'):
+            self.analyze.populate_indicators = Hyperopt.populate_indicators
+        self.processed = self.tickerdata_to_dataframe(data)
+
+        if self.config.get('mongodb'):
+            self.logger.info('Using mongodb ...')
+            self.logger.info(
+                'Start scripts/start-mongodb.sh and start-hyperopt-worker.sh manually!'
+            )
+        else:
+            self.logger.info('Preparing Trials..')
+            signal.signal(signal.SIGINT, self.signal_handler)
+            # read trials file if we have one
+            if os.path.exists(self.trials_file) and os.path.getsize(self.trials_file) > 0:
+                self.trials = self.read_trials()
+
+                self.current_tries = len(self.trials.results)
+                self.total_tries += self.current_tries
+                self.logger.info(
+                    'Continuing with trials. Current: %d, Total: %d',
+                    self.current_tries,
+                    self.total_tries
+                )
+        try:
+            best_parameters = fmin(
+                fn=self.generate_optimizer,
+                space=self.hyperopt_space(),
+                algo=tpe.suggest,
+                max_evals=self.total_tries,
+                trials=MongoTrials(arg='mongo://127.0.0.1:1234/freqtrade_hyperopt/jobs', exp_key='exp1')
+            )
+            # change the Logging format
+            self.logging.set_format('\n%(message)s')
+            results = sorted(self.trials.results, key=itemgetter('loss'))
+            best_result = results[0]['result']
+        # Improve best parameter logging display
+        except ValueError:
+            best_parameters = {}
+            best_result = 'Sorry, Hyperopt was not able to find good parameters. Please ' \
+                          'try with more epochs (param: -e).'
+        if best_parameters:
+            best_parameters = space_eval(
+                self.hyperopt_space(),
+                best_parameters
+            )
+
+        self.logger.info('Best parameters:\n%s', json.dumps(best_parameters, indent=4))
+        if 'roi_t1' in best_parameters:
+            self.logger.info('ROI table:\n%s', self.generate_roi_table(best_parameters))
+
+        self.logger.info('Best Result:\n%s', best_result)
+
+        # Store trials result to file to resume next time
+        self.save_trials()
+
+    def signal_handler(self, sig, frame) -> None:
+        """
+        Hyperopt SIGINT handler
+        """
+        self.logger.info(
+            'Hyperopt received %s',
+            signal.Signals(sig).name
+        )
+
+        self.save_trials()
+        self.log_trials_result()
+        sys.exit(0)
+
+
+
+def start(args: Namespace) -> None:
+    """
+    Start Backtesting script
+    :param args: Cli args from Arguments()
+    :return: None
+    """
+
+    # Remove noisy log messages
+    logging.getLogger('hyperopt.mongoexp').setLevel(logging.WARNING)
+    logging.getLogger('hyperopt.tpe').setLevel(logging.WARNING)
+
+    # Initialize logger
+    logger = Logger(name=__name__).get_logger()
+    logger.info('Starting freqtrade in Hyperopt mode')
+
+    # Initialize configuration
+    # Monkey patch the configuration with hyperopt_conf.py
+    configuration = Configuration(args)
+    optimize_config = hyperopt_optimize_conf()
+    config = configuration._load_common_config(optimize_config)
+    config = configuration._load_backtesting_config(config)
+    config = configuration._load_hyperopt_config(config)
+    config['exchange']['key'] = ''
+    config['exchange']['secret'] = ''
+
+    # Initialize backtesting object
+    hyperopt = Hyperopt(config)
+    hyperopt.start()