338 lines
13 KiB
Python
338 lines
13 KiB
Python
# pragma pylint: disable=too-many-instance-attributes, pointless-string-statement
|
|
|
|
"""
|
|
This module contains the hyperopt logic
|
|
"""
|
|
|
|
import logging
|
|
import os
|
|
import sys
|
|
from math import exp
|
|
from operator import itemgetter
|
|
from pathlib import Path
|
|
from pprint import pprint
|
|
from typing import Any, Dict, List
|
|
|
|
from joblib import Parallel, delayed, dump, load, wrap_non_picklable_objects, cpu_count
|
|
from pandas import DataFrame
|
|
from skopt import Optimizer
|
|
from skopt.space import Dimension
|
|
|
|
from freqtrade.arguments import Arguments
|
|
from freqtrade.data.history import load_data, get_timeframe
|
|
from freqtrade.optimize.backtesting import Backtesting
|
|
from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
INITIAL_POINTS = 30
|
|
MAX_LOSS = 100000 # just a big enough number to be bad result in loss optimization
|
|
TICKERDATA_PICKLE = os.path.join('user_data', 'hyperopt_tickerdata.pkl')
|
|
TRIALSDATA_PICKLE = os.path.join('user_data', 'hyperopt_results.pickle')
|
|
HYPEROPT_LOCKFILE = os.path.join('user_data', 'hyperopt.lock')
|
|
|
|
|
|
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)
|
|
self.custom_hyperopt = HyperOptResolver(self.config).hyperopt
|
|
|
|
# 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_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 assumed to be expected avg profit * expected trade count.
|
|
# For example, for 0.35% avg per trade (or 0.0035 as ratio) and 1100 trades,
|
|
# self.expected_max_profit = 3.85
|
|
# Check that the reported Σ% values do not exceed this!
|
|
# Note, this is ratio. 3.85 stated above means 385Σ%.
|
|
self.expected_max_profit = 3.0
|
|
|
|
# Previous evaluations
|
|
self.trials_file = TRIALSDATA_PICKLE
|
|
self.trials: List = []
|
|
|
|
def get_args(self, params):
|
|
dimensions = self.hyperopt_space()
|
|
# Ensure the number of dimensions match
|
|
# the number of parameters in the list x.
|
|
if len(params) != len(dimensions):
|
|
raise ValueError('Mismatch in number of search-space dimensions. '
|
|
f'len(dimensions)=={len(dimensions)} and len(x)=={len(params)}')
|
|
|
|
# Create a dict where the keys are the names of the dimensions
|
|
# and the values are taken from the list of parameters x.
|
|
arg_dict = {dim.name: value for dim, value in zip(dimensions, params)}
|
|
return arg_dict
|
|
|
|
def save_trials(self) -> None:
|
|
"""
|
|
Save hyperopt trials to file
|
|
"""
|
|
if self.trials:
|
|
logger.info('Saving %d evaluations to \'%s\'', len(self.trials), self.trials_file)
|
|
dump(self.trials, self.trials_file)
|
|
|
|
def read_trials(self) -> List:
|
|
"""
|
|
Read hyperopt trials file
|
|
"""
|
|
logger.info('Reading Trials from \'%s\'', self.trials_file)
|
|
trials = load(self.trials_file)
|
|
os.remove(self.trials_file)
|
|
return trials
|
|
|
|
def log_trials_result(self) -> None:
|
|
"""
|
|
Display Best hyperopt result
|
|
"""
|
|
results = sorted(self.trials, key=itemgetter('loss'))
|
|
best_result = results[0]
|
|
logger.info(
|
|
'Best result:\n%s\nwith values:\n',
|
|
best_result['result']
|
|
)
|
|
pprint(best_result['params'], indent=4)
|
|
if 'roi_t1' in best_result['params']:
|
|
logger.info('ROI table:')
|
|
pprint(self.custom_hyperopt.generate_roi_table(best_result['params']), indent=4)
|
|
|
|
def log_results(self, results) -> None:
|
|
"""
|
|
Log results if it is better than any previous evaluation
|
|
"""
|
|
print_all = self.config.get('print_all', False)
|
|
if print_all or results['loss'] < self.current_best_loss:
|
|
# Output human-friendly index here (starting from 1)
|
|
current = results['current_tries'] + 1
|
|
total = results['total_tries']
|
|
res = results['result']
|
|
loss = results['loss']
|
|
self.current_best_loss = results['loss']
|
|
log_msg = f'{current:5d}/{total}: {res} Objective: {loss:.5f}'
|
|
log_msg = f'*{log_msg}' if results['initial_point'] else f' {log_msg}'
|
|
if print_all:
|
|
print(log_msg)
|
|
else:
|
|
print('\n' + 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)
|
|
result = trade_loss + profit_loss + duration_loss
|
|
return result
|
|
|
|
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) -> List[Dimension]:
|
|
"""
|
|
Return the space to use during Hyperopt
|
|
"""
|
|
spaces: List[Dimension] = []
|
|
if self.has_space('buy'):
|
|
spaces += self.custom_hyperopt.indicator_space()
|
|
if self.has_space('sell'):
|
|
spaces += self.custom_hyperopt.sell_indicator_space()
|
|
# Make sure experimental is enabled
|
|
if 'experimental' not in self.config:
|
|
self.config['experimental'] = {}
|
|
self.config['experimental']['use_sell_signal'] = True
|
|
if self.has_space('roi'):
|
|
spaces += self.custom_hyperopt.roi_space()
|
|
if self.has_space('stoploss'):
|
|
spaces += self.custom_hyperopt.stoploss_space()
|
|
return spaces
|
|
|
|
def generate_optimizer(self, _params: Dict) -> Dict:
|
|
params = self.get_args(_params)
|
|
if self.has_space('roi'):
|
|
self.strategy.minimal_roi = self.custom_hyperopt.generate_roi_table(params)
|
|
|
|
if self.has_space('buy'):
|
|
self.advise_buy = self.custom_hyperopt.buy_strategy_generator(params)
|
|
elif hasattr(self.custom_hyperopt, 'populate_buy_trend'):
|
|
self.advise_buy = self.custom_hyperopt.populate_buy_trend # type: ignore
|
|
|
|
if self.has_space('sell'):
|
|
self.advise_sell = self.custom_hyperopt.sell_strategy_generator(params)
|
|
elif hasattr(self.custom_hyperopt, 'populate_sell_trend'):
|
|
self.advise_sell = self.custom_hyperopt.populate_sell_trend # type: ignore
|
|
|
|
if self.has_space('stoploss'):
|
|
self.strategy.stoploss = params['stoploss']
|
|
|
|
processed = load(TICKERDATA_PICKLE)
|
|
min_date, max_date = get_timeframe(processed)
|
|
results = self.backtest(
|
|
{
|
|
'stake_amount': self.config['stake_amount'],
|
|
'processed': processed,
|
|
'position_stacking': self.config.get('position_stacking', False),
|
|
'start_date': min_date,
|
|
'end_date': max_date,
|
|
}
|
|
)
|
|
result_explanation = self.format_results(results)
|
|
|
|
total_profit = results.profit_percent.sum()
|
|
trade_count = len(results.index)
|
|
trade_duration = results.trade_duration.mean()
|
|
|
|
# If this evaluation contains too short amount of trades to be
|
|
# interesting -- consider it as 'bad' (assigned max. loss value)
|
|
# in order to cast this hyperspace point away from optimization
|
|
# path. We do not want to optimize 'hodl' strategies.
|
|
if trade_count < self.config['hyperopt_min_trades']:
|
|
return {
|
|
'loss': MAX_LOSS,
|
|
'params': params,
|
|
'result': result_explanation,
|
|
}
|
|
|
|
loss = self.calculate_loss(total_profit, trade_count, trade_duration)
|
|
|
|
return {
|
|
'loss': loss,
|
|
'params': params,
|
|
'result': result_explanation,
|
|
}
|
|
|
|
def format_results(self, results: DataFrame) -> str:
|
|
"""
|
|
Return the format result in a string
|
|
"""
|
|
trades = len(results.index)
|
|
avg_profit = results.profit_percent.mean() * 100.0
|
|
total_profit = results.profit_abs.sum()
|
|
stake_cur = self.config['stake_currency']
|
|
profit = results.profit_percent.sum() * 100.0
|
|
duration = results.trade_duration.mean()
|
|
|
|
return (f'{trades:6d} trades. Avg profit {avg_profit: 5.2f}%. '
|
|
f'Total profit {total_profit: 11.8f} {stake_cur} '
|
|
f'({profit: 7.2f}Σ%). Avg duration {duration:5.1f} mins.')
|
|
|
|
def get_optimizer(self, cpu_count) -> Optimizer:
|
|
return Optimizer(
|
|
self.hyperopt_space(),
|
|
base_estimator="ET",
|
|
acq_optimizer="auto",
|
|
n_initial_points=INITIAL_POINTS,
|
|
acq_optimizer_kwargs={'n_jobs': cpu_count},
|
|
random_state=self.config.get('hyperopt_random_state', None)
|
|
)
|
|
|
|
def run_optimizer_parallel(self, parallel, asked) -> List:
|
|
return parallel(delayed(
|
|
wrap_non_picklable_objects(self.generate_optimizer))(v) for v in asked)
|
|
|
|
def load_previous_results(self):
|
|
""" 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()
|
|
logger.info(
|
|
'Loaded %d previous evaluations from disk.',
|
|
len(self.trials)
|
|
)
|
|
|
|
def start(self) -> None:
|
|
timerange = Arguments.parse_timerange(None if self.config.get(
|
|
'timerange') is None else str(self.config.get('timerange')))
|
|
data = load_data(
|
|
datadir=Path(self.config['datadir']) if self.config.get('datadir') else None,
|
|
pairs=self.config['exchange']['pair_whitelist'],
|
|
ticker_interval=self.ticker_interval,
|
|
refresh_pairs=self.config.get('refresh_pairs', False),
|
|
exchange=self.exchange,
|
|
timerange=timerange
|
|
)
|
|
|
|
if not data:
|
|
logger.critical("No data found. Terminating.")
|
|
return
|
|
|
|
min_date, max_date = get_timeframe(data)
|
|
|
|
logger.info(
|
|
'Hyperopting with data from %s up to %s (%s days)..',
|
|
min_date.isoformat(),
|
|
max_date.isoformat(),
|
|
(max_date - min_date).days
|
|
)
|
|
|
|
if self.has_space('buy') or self.has_space('sell'):
|
|
self.strategy.advise_indicators = \
|
|
self.custom_hyperopt.populate_indicators # type: ignore
|
|
|
|
preprocessed = self.strategy.tickerdata_to_dataframe(data)
|
|
|
|
dump(preprocessed, TICKERDATA_PICKLE)
|
|
|
|
# We don't need exchange instance anymore while running hyperopt
|
|
self.exchange = None # type: ignore
|
|
|
|
self.load_previous_results()
|
|
|
|
cpus = cpu_count()
|
|
logger.info(f'Found {cpus} CPU cores. Let\'s make them scream!')
|
|
config_jobs = self.config.get('hyperopt_jobs', -1)
|
|
logger.info(f'Number of parallel jobs set as: {config_jobs}')
|
|
|
|
opt = self.get_optimizer(config_jobs)
|
|
try:
|
|
with Parallel(n_jobs=config_jobs) as parallel:
|
|
jobs = parallel._effective_n_jobs()
|
|
logger.info(f'Effective number of parallel workers used: {jobs}')
|
|
EVALS = max(self.total_tries // jobs, 1)
|
|
for i in range(EVALS):
|
|
asked = opt.ask(n_points=jobs)
|
|
f_val = self.run_optimizer_parallel(parallel, asked)
|
|
opt.tell(asked, [i['loss'] for i in f_val])
|
|
|
|
self.trials += f_val
|
|
for j in range(jobs):
|
|
current = i * jobs + j
|
|
self.log_results({
|
|
'loss': f_val[j]['loss'],
|
|
'current_tries': current,
|
|
'initial_point': current < INITIAL_POINTS,
|
|
'total_tries': self.total_tries,
|
|
'result': f_val[j]['result'],
|
|
})
|
|
logger.debug(f"Optimizer params: {f_val[j]['params']}")
|
|
for j in range(jobs):
|
|
logger.debug(f"Optimizer state: Xi: {opt.Xi[-j-1]}, yi: {opt.yi[-j-1]}")
|
|
except KeyboardInterrupt:
|
|
print('User interrupted..')
|
|
|
|
self.save_trials()
|
|
self.log_trials_result()
|