stable/freqtrade/optimize/hyperopt.py

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# pragma pylint: disable=too-many-instance-attributes, pointless-string-statement
"""
This module contains the hyperopt logic
"""
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import json
import logging
import os
import pickle
import signal
import sys
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from argparse import Namespace
from functools import reduce
from math import exp
from operator import itemgetter
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from typing import Dict, Any, Callable, Optional
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
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from freqtrade.arguments import Arguments
from freqtrade.configuration import Configuration
from freqtrade.optimize import load_data
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from freqtrade.optimize.backtesting import Backtesting
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from user_data.hyperopt_conf import hyperopt_optimize_conf
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logger = logging.getLogger(__name__)
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)
# 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
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self.processed: Optional[Dict[str, Any]] = None
# Hyperopt Trials
self.trials_file = os.path.join('user_data', 'hyperopt_trials.pickle')
self.trials = Trials()
@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
"""
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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
"""
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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']
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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 = '\n{:5d}/{}: {}. Loss {:.5f}'.format(
results['current_tries'],
results['total_tries'],
results['result'],
results['loss']
)
print(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
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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'},
]),
}
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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
"""
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spaces: Dict = {}
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
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@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:
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"""
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
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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(),
)
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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'):
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self.analyze.populate_indicators = Hyperopt.populate_indicators # type: ignore
self.processed = self.tickerdata_to_dataframe(data)
if self.config.get('mongodb'):
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logger.info('Using mongodb ...')
logger.info(
'Start scripts/start-mongodb.sh and start-hyperopt-worker.sh manually!'
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)
db_name = 'freqtrade_hyperopt'
self.trials = MongoTrials(
arg='mongo://127.0.0.1:1234/{}/jobs'.format(db_name),
exp_key='exp1'
)
else:
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logger.info('Preparing Trials..')
signal.signal(signal.SIGINT, self.signal_handler)
# read trials file if we have one
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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
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logger.info(
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'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=self.trials
)
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results = sorted(self.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).'
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# Improve best parameter logging display
if best_parameters:
best_parameters = space_eval(
self.hyperopt_space(),
best_parameters
)
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logger.info('Best parameters:\n%s', json.dumps(best_parameters, indent=4))
if 'roi_t1' in best_parameters:
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logger.info('ROI table:\n%s', self.generate_roi_table(best_parameters))
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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
self.save_trials()
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def signal_handler(self, sig, frame) -> None:
"""
Hyperopt SIGINT handler
"""
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logger.info(
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'Hyperopt received %s',
signal.Signals(sig).name
)
self.save_trials()
self.log_trials_result()
sys.exit(0)
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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 configuration
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# Monkey patch the configuration with hyperopt_conf.py
configuration = Configuration(args)
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logger.info('Starting freqtrade in Hyperopt mode')
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()