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Merge branch 'develop' into align_userdata

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Matthias
2019-08-18 15:00:12 +02:00
parent 2c5a499a8b 9005447590
commit 0a478bc0dc
63 changed files with 1517 additions and 941 deletions
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78
freqtrade/optimize/hyperopt.py
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@@ -7,17 +7,22 @@ This module contains the hyperopt logic
import logging
import sys
from collections import OrderedDict
from operator import itemgetter
from pathlib import Path
from pprint import pprint
from typing import Any, Dict, List, Optional
import rapidjson
from colorama import init as colorama_init
from colorama import Fore, Style
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.configuration import Arguments
from freqtrade.configuration import TimeRange
from freqtrade.data.history import load_data, get_timeframe
from freqtrade.optimize.backtesting import Backtesting
# Import IHyperOptLoss to allow users import from this file
@@ -136,30 +141,61 @@ class Hyperopt(Backtesting):
results = sorted(self.trials, key=itemgetter('loss'))
best_result = results[0]
params = best_result['params']
log_str = self.format_results_logstring(best_result)
print(f"\nBest result:\n\n{log_str}\n")
if self.has_space('buy'):
print('Buy hyperspace params:')
pprint({p.name: params.get(p.name) for p in self.hyperopt_space('buy')},
indent=4)
if self.has_space('sell'):
print('Sell hyperspace params:')
pprint({p.name: params.get(p.name) for p in self.hyperopt_space('sell')},
indent=4)
if self.has_space('roi'):
print("ROI table:")
pprint(self.custom_hyperopt.generate_roi_table(params), indent=4)
if self.has_space('stoploss'):
print(f"Stoploss: {params.get('stoploss')}")
if self.config.get('print_json'):
result_dict: Dict = {}
if self.has_space('buy') or self.has_space('sell'):
result_dict['params'] = {}
if self.has_space('buy'):
result_dict['params'].update({p.name: params.get(p.name)
for p in self.hyperopt_space('buy')})
if self.has_space('sell'):
result_dict['params'].update({p.name: params.get(p.name)
for p in self.hyperopt_space('sell')})
if self.has_space('roi'):
# Convert keys in min_roi dict to strings because
# rapidjson cannot dump dicts with integer keys...
# OrderedDict is used to keep the numeric order of the items
# in the dict.
result_dict['minimal_roi'] = OrderedDict(
(str(k), v) for k, v in self.custom_hyperopt.generate_roi_table(params).items()
)
if self.has_space('stoploss'):
result_dict['stoploss'] = params.get('stoploss')
print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
else:
if self.has_space('buy'):
print('Buy hyperspace params:')
pprint({p.name: params.get(p.name) for p in self.hyperopt_space('buy')},
indent=4)
if self.has_space('sell'):
print('Sell hyperspace params:')
pprint({p.name: params.get(p.name) for p in self.hyperopt_space('sell')},
indent=4)
if self.has_space('roi'):
print("ROI table:")
pprint(self.custom_hyperopt.generate_roi_table(params), indent=4)
if self.has_space('stoploss'):
print(f"Stoploss: {params.get('stoploss')}")
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:
is_best_loss = results['loss'] < self.current_best_loss
if print_all or is_best_loss:
if is_best_loss:
self.current_best_loss = results['loss']
log_str = self.format_results_logstring(results)
# Colorize output
if self.config.get('print_colorized', False):
if results['total_profit'] > 0:
log_str = Fore.GREEN + log_str
if print_all and is_best_loss:
log_str = Style.BRIGHT + log_str
if print_all:
print(log_str)
else:
@@ -174,7 +210,6 @@ class Hyperopt(Backtesting):
total = self.total_epochs
res = results['results_explanation']
loss = results['loss']
self.current_best_loss = results['loss']
log_str = f'{current:5d}/{total}: {res} Objective: {loss:.5f}'
log_str = f'*{log_str}' if results['is_initial_point'] else f' {log_str}'
return log_str
@@ -242,6 +277,7 @@ class Hyperopt(Backtesting):
results_explanation = self.format_results(results)
trade_count = len(results.index)
total_profit = results.profit_abs.sum()
# If this evaluation contains too short amount of trades to be
# interesting -- consider it as 'bad' (assigned max. loss value)
@@ -252,6 +288,7 @@ class Hyperopt(Backtesting):
'loss': MAX_LOSS,
'params': params,
'results_explanation': results_explanation,
'total_profit': total_profit,
}
loss = self.calculate_loss(results=results, trade_count=trade_count,
@@ -261,6 +298,7 @@ class Hyperopt(Backtesting):
'loss': loss,
'params': params,
'results_explanation': results_explanation,
'total_profit': total_profit,
}
def format_results(self, results: DataFrame) -> str:
@@ -302,7 +340,7 @@ class Hyperopt(Backtesting):
)
def start(self) -> None:
timerange = Arguments.parse_timerange(None if self.config.get(
timerange = TimeRange.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,
@@ -344,6 +382,10 @@ class Hyperopt(Backtesting):
logger.info(f'Number of parallel jobs set as: {config_jobs}')
opt = self.get_optimizer(config_jobs)
if self.config.get('print_colorized', False):
colorama_init(autoreset=True)
try:
with Parallel(n_jobs=config_jobs) as parallel:
jobs = parallel._effective_n_jobs()