511 lines
22 KiB
Python
Executable File
511 lines
22 KiB
Python
Executable File
import io
|
|
import logging
|
|
from copy import deepcopy
|
|
from datetime import datetime, timezone
|
|
from pathlib import Path
|
|
from typing import Any, Dict, Iterator, List, Optional, Tuple
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
import rapidjson
|
|
import tabulate
|
|
from colorama import Fore, Style
|
|
from pandas import isna, json_normalize
|
|
|
|
from freqtrade.constants import FTHYPT_FILEVERSION, USERPATH_STRATEGIES, Config
|
|
from freqtrade.enums import HyperoptState
|
|
from freqtrade.exceptions import OperationalException
|
|
from freqtrade.misc import deep_merge_dicts, round_coin_value, round_dict, safe_value_fallback2
|
|
from freqtrade.optimize.hyperopt_epoch_filters import hyperopt_filter_epochs
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
NON_OPT_PARAM_APPENDIX = " # value loaded from strategy"
|
|
|
|
|
|
def hyperopt_serializer(x):
|
|
if isinstance(x, np.integer):
|
|
return int(x)
|
|
if isinstance(x, np.bool_):
|
|
return bool(x)
|
|
|
|
return str(x)
|
|
|
|
|
|
class HyperoptStateContainer():
|
|
""" Singleton class to track state of hyperopt"""
|
|
state: HyperoptState = HyperoptState.OPTIMIZE
|
|
|
|
@classmethod
|
|
def set_state(cls, value: HyperoptState):
|
|
cls.state = value
|
|
|
|
|
|
class HyperoptTools():
|
|
|
|
@staticmethod
|
|
def get_strategy_filename(config: Config, strategy_name: str) -> Optional[Path]:
|
|
"""
|
|
Get Strategy-location (filename) from strategy_name
|
|
"""
|
|
from freqtrade.resolvers.strategy_resolver import StrategyResolver
|
|
directory = Path(config.get('strategy_path', config['user_data_dir'] / USERPATH_STRATEGIES))
|
|
strategy_objs = StrategyResolver.search_all_objects(
|
|
directory, False, config.get('recursive_strategy_search', False))
|
|
strategies = [s for s in strategy_objs if s['name'] == strategy_name]
|
|
if strategies:
|
|
strategy = strategies[0]
|
|
|
|
return Path(strategy['location'])
|
|
return None
|
|
|
|
@staticmethod
|
|
def export_params(params, strategy_name: str, filename: Path):
|
|
"""
|
|
Generate files
|
|
"""
|
|
final_params = deepcopy(params['params_not_optimized'])
|
|
final_params = deep_merge_dicts(params['params_details'], final_params)
|
|
final_params = {
|
|
'strategy_name': strategy_name,
|
|
'params': final_params,
|
|
'ft_stratparam_v': 1,
|
|
'export_time': datetime.now(timezone.utc),
|
|
}
|
|
logger.info(f"Dumping parameters to {filename}")
|
|
with filename.open('w') as f:
|
|
rapidjson.dump(final_params, f, indent=2,
|
|
default=hyperopt_serializer,
|
|
number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN
|
|
)
|
|
|
|
@staticmethod
|
|
def try_export_params(config: Dict[str, Any], strategy_name: str, params: Dict):
|
|
if params.get(FTHYPT_FILEVERSION, 1) >= 2 and not config.get('disableparamexport', False):
|
|
# Export parameters ...
|
|
fn = HyperoptTools.get_strategy_filename(config, strategy_name)
|
|
if fn:
|
|
HyperoptTools.export_params(params, strategy_name, fn.with_suffix('.json'))
|
|
else:
|
|
logger.warning("Strategy not found, not exporting parameter file.")
|
|
|
|
@staticmethod
|
|
def has_space(config: Dict[str, Any], space: str) -> bool:
|
|
"""
|
|
Tell if the space value is contained in the configuration
|
|
"""
|
|
# 'trailing' and 'protection spaces are not included in the 'default' set of spaces
|
|
if space in ('trailing', 'protection'):
|
|
return any(s in config['spaces'] for s in [space, 'all'])
|
|
else:
|
|
return any(s in config['spaces'] for s in [space, 'all', 'default'])
|
|
|
|
@staticmethod
|
|
def _read_results(results_file: Path, batch_size: int = 10) -> Iterator[List[Any]]:
|
|
"""
|
|
Stream hyperopt results from file
|
|
"""
|
|
import rapidjson
|
|
logger.info(f"Reading epochs from '{results_file}'")
|
|
with results_file.open('r') as f:
|
|
data = []
|
|
for line in f:
|
|
data += [rapidjson.loads(line)]
|
|
if len(data) >= batch_size:
|
|
yield data
|
|
data = []
|
|
yield data
|
|
|
|
@staticmethod
|
|
def _test_hyperopt_results_exist(results_file) -> bool:
|
|
if results_file.is_file() and results_file.stat().st_size > 0:
|
|
if results_file.suffix == '.pickle':
|
|
raise OperationalException(
|
|
"Legacy hyperopt results are no longer supported."
|
|
"Please rerun hyperopt or use an older version to load this file."
|
|
)
|
|
return True
|
|
else:
|
|
# No file found.
|
|
return False
|
|
|
|
@staticmethod
|
|
def load_filtered_results(results_file: Path, config: Dict[str, Any]) -> Tuple[List, int]:
|
|
filteroptions = {
|
|
'only_best': config.get('hyperopt_list_best', False),
|
|
'only_profitable': config.get('hyperopt_list_profitable', False),
|
|
'filter_min_trades': config.get('hyperopt_list_min_trades', 0),
|
|
'filter_max_trades': config.get('hyperopt_list_max_trades', 0),
|
|
'filter_min_avg_time': config.get('hyperopt_list_min_avg_time'),
|
|
'filter_max_avg_time': config.get('hyperopt_list_max_avg_time'),
|
|
'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit'),
|
|
'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit'),
|
|
'filter_min_total_profit': config.get('hyperopt_list_min_total_profit'),
|
|
'filter_max_total_profit': config.get('hyperopt_list_max_total_profit'),
|
|
'filter_min_objective': config.get('hyperopt_list_min_objective'),
|
|
'filter_max_objective': config.get('hyperopt_list_max_objective'),
|
|
}
|
|
if not HyperoptTools._test_hyperopt_results_exist(results_file):
|
|
# No file found.
|
|
logger.warning(f"Hyperopt file {results_file} not found.")
|
|
return [], 0
|
|
|
|
epochs = []
|
|
total_epochs = 0
|
|
for epochs_tmp in HyperoptTools._read_results(results_file):
|
|
if total_epochs == 0 and epochs_tmp[0].get('is_best') is None:
|
|
raise OperationalException(
|
|
"The file with HyperoptTools results is incompatible with this version "
|
|
"of Freqtrade and cannot be loaded.")
|
|
total_epochs += len(epochs_tmp)
|
|
epochs += hyperopt_filter_epochs(epochs_tmp, filteroptions, log=False)
|
|
|
|
logger.info(f"Loaded {total_epochs} previous evaluations from disk.")
|
|
|
|
# Final filter run ...
|
|
epochs = hyperopt_filter_epochs(epochs, filteroptions, log=True)
|
|
|
|
return epochs, total_epochs
|
|
|
|
@staticmethod
|
|
def show_epoch_details(results, total_epochs: int, print_json: bool,
|
|
no_header: bool = False, header_str: str = None) -> None:
|
|
"""
|
|
Display details of the hyperopt result
|
|
"""
|
|
params = results.get('params_details', {})
|
|
non_optimized = results.get('params_not_optimized', {})
|
|
|
|
# Default header string
|
|
if header_str is None:
|
|
header_str = "Best result"
|
|
|
|
if not no_header:
|
|
explanation_str = HyperoptTools._format_explanation_string(results, total_epochs)
|
|
print(f"\n{header_str}:\n\n{explanation_str}\n")
|
|
|
|
if print_json:
|
|
result_dict: Dict = {}
|
|
for s in ['buy', 'sell', 'protection', 'roi', 'stoploss', 'trailing']:
|
|
HyperoptTools._params_update_for_json(result_dict, params, non_optimized, s)
|
|
print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
|
|
|
|
else:
|
|
HyperoptTools._params_pretty_print(params, 'buy', "Buy hyperspace params:",
|
|
non_optimized)
|
|
HyperoptTools._params_pretty_print(params, 'sell', "Sell hyperspace params:",
|
|
non_optimized)
|
|
HyperoptTools._params_pretty_print(params, 'protection',
|
|
"Protection hyperspace params:", non_optimized)
|
|
HyperoptTools._params_pretty_print(params, 'roi', "ROI table:", non_optimized)
|
|
HyperoptTools._params_pretty_print(params, 'stoploss', "Stoploss:", non_optimized)
|
|
HyperoptTools._params_pretty_print(params, 'trailing', "Trailing stop:", non_optimized)
|
|
|
|
@staticmethod
|
|
def _params_update_for_json(result_dict, params, non_optimized, space: str) -> None:
|
|
if (space in params) or (space in non_optimized):
|
|
space_params = HyperoptTools._space_params(params, space)
|
|
space_non_optimized = HyperoptTools._space_params(non_optimized, space)
|
|
all_space_params = space_params
|
|
|
|
# Merge non optimized params if there are any
|
|
if len(space_non_optimized) > 0:
|
|
all_space_params = {**space_params, **space_non_optimized}
|
|
|
|
if space in ['buy', 'sell']:
|
|
result_dict.setdefault('params', {}).update(all_space_params)
|
|
elif space == 'roi':
|
|
# Convert keys in min_roi dict to strings because
|
|
# rapidjson cannot dump dicts with integer keys...
|
|
result_dict['minimal_roi'] = {str(k): v for k, v in all_space_params.items()}
|
|
else: # 'stoploss', 'trailing'
|
|
result_dict.update(all_space_params)
|
|
|
|
@staticmethod
|
|
def _params_pretty_print(params, space: str, header: str, non_optimized={}) -> None:
|
|
if space in params or space in non_optimized:
|
|
space_params = HyperoptTools._space_params(params, space, 5)
|
|
no_params = HyperoptTools._space_params(non_optimized, space, 5)
|
|
appendix = ''
|
|
if not space_params and not no_params:
|
|
# No parameters - don't print
|
|
return
|
|
if not space_params:
|
|
# Not optimized parameters - append string
|
|
appendix = NON_OPT_PARAM_APPENDIX
|
|
|
|
result = f"\n# {header}\n"
|
|
if space == "stoploss":
|
|
stoploss = safe_value_fallback2(space_params, no_params, space, space)
|
|
result += (f"stoploss = {stoploss}{appendix}")
|
|
|
|
elif space == "roi":
|
|
result = result[:-1] + f'{appendix}\n'
|
|
minimal_roi_result = rapidjson.dumps({
|
|
str(k): v for k, v in (space_params or no_params).items()
|
|
}, default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
|
|
result += f"minimal_roi = {minimal_roi_result}"
|
|
elif space == "trailing":
|
|
for k, v in (space_params or no_params).items():
|
|
result += f"{k} = {v}{appendix}\n"
|
|
|
|
else:
|
|
# Buy / sell parameters
|
|
|
|
result += f"{space}_params = {HyperoptTools._pprint_dict(space_params, no_params)}"
|
|
|
|
result = result.replace("\n", "\n ")
|
|
print(result)
|
|
|
|
@staticmethod
|
|
def _space_params(params, space: str, r: int = None) -> Dict:
|
|
d = params.get(space)
|
|
if d:
|
|
# Round floats to `r` digits after the decimal point if requested
|
|
return round_dict(d, r) if r else d
|
|
return {}
|
|
|
|
@staticmethod
|
|
def _pprint_dict(params, non_optimized, indent: int = 4):
|
|
"""
|
|
Pretty-print hyperopt results (based on 2 dicts - with add. comment)
|
|
"""
|
|
p = params.copy()
|
|
p.update(non_optimized)
|
|
result = '{\n'
|
|
|
|
for k, param in p.items():
|
|
result += " " * indent + f'"{k}": '
|
|
result += f'"{param}",' if isinstance(param, str) else f'{param},'
|
|
if k in non_optimized:
|
|
result += NON_OPT_PARAM_APPENDIX
|
|
result += "\n"
|
|
result += '}'
|
|
return result
|
|
|
|
@staticmethod
|
|
def is_best_loss(results, current_best_loss: float) -> bool:
|
|
return bool(results['loss'] < current_best_loss)
|
|
|
|
@staticmethod
|
|
def format_results_explanation_string(results_metrics: Dict, stake_currency: str) -> str:
|
|
"""
|
|
Return the formatted results explanation in a string
|
|
"""
|
|
return (f"{results_metrics['total_trades']:6d} trades. "
|
|
f"{results_metrics['wins']}/{results_metrics['draws']}"
|
|
f"/{results_metrics['losses']} Wins/Draws/Losses. "
|
|
f"Avg profit {results_metrics['profit_mean']:7.2%}. "
|
|
f"Median profit {results_metrics['profit_median']:7.2%}. "
|
|
f"Total profit {results_metrics['profit_total_abs']:11.8f} {stake_currency} "
|
|
f"({results_metrics['profit_total']:8.2%}). "
|
|
f"Avg duration {results_metrics['holding_avg']} min."
|
|
)
|
|
|
|
@staticmethod
|
|
def _format_explanation_string(results, total_epochs) -> str:
|
|
return (("*" if results['is_initial_point'] else " ") +
|
|
f"{results['current_epoch']:5d}/{total_epochs}: " +
|
|
f"{results['results_explanation']} " +
|
|
f"Objective: {results['loss']:.5f}")
|
|
|
|
@staticmethod
|
|
def prepare_trials_columns(trials: pd.DataFrame, has_drawdown: bool) -> pd.DataFrame:
|
|
trials['Best'] = ''
|
|
|
|
if 'results_metrics.winsdrawslosses' not in trials.columns:
|
|
# Ensure compatibility with older versions of hyperopt results
|
|
trials['results_metrics.winsdrawslosses'] = 'N/A'
|
|
|
|
if not has_drawdown:
|
|
# Ensure compatibility with older versions of hyperopt results
|
|
trials['results_metrics.max_drawdown_account'] = None
|
|
if 'is_random' not in trials.columns:
|
|
trials['is_random'] = False
|
|
|
|
# New mode, using backtest result for metrics
|
|
trials['results_metrics.winsdrawslosses'] = trials.apply(
|
|
lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} "
|
|
f"{x['results_metrics.losses']:>4}", axis=1)
|
|
|
|
trials = trials[['Best', 'current_epoch', 'results_metrics.total_trades',
|
|
'results_metrics.winsdrawslosses',
|
|
'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
|
|
'results_metrics.profit_total', 'results_metrics.holding_avg',
|
|
'results_metrics.max_drawdown',
|
|
'results_metrics.max_drawdown_account', 'results_metrics.max_drawdown_abs',
|
|
'loss', 'is_initial_point', 'is_random', 'is_best']]
|
|
|
|
trials.columns = [
|
|
'Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
|
|
'Total profit', 'Profit', 'Avg duration', 'max_drawdown', 'max_drawdown_account',
|
|
'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_random', 'is_best'
|
|
]
|
|
|
|
return trials
|
|
|
|
@staticmethod
|
|
def get_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
|
|
print_colorized: bool, remove_header: int) -> str:
|
|
"""
|
|
Log result table
|
|
"""
|
|
if not results:
|
|
return ''
|
|
|
|
tabulate.PRESERVE_WHITESPACE = True
|
|
trials = json_normalize(results, max_level=1)
|
|
|
|
has_account_drawdown = 'results_metrics.max_drawdown_account' in trials.columns
|
|
|
|
trials = HyperoptTools.prepare_trials_columns(trials, has_account_drawdown)
|
|
|
|
trials['is_profit'] = False
|
|
trials.loc[trials['is_initial_point'] | trials['is_random'], 'Best'] = '* '
|
|
trials.loc[trials['is_best'], 'Best'] = 'Best'
|
|
trials.loc[
|
|
(trials['is_initial_point'] | trials['is_random']) & trials['is_best'],
|
|
'Best'] = '* Best'
|
|
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
|
|
trials['Trades'] = trials['Trades'].astype(str)
|
|
# perc_multi = 1 if legacy_mode else 100
|
|
trials['Epoch'] = trials['Epoch'].apply(
|
|
lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
|
|
)
|
|
trials['Avg profit'] = trials['Avg profit'].apply(
|
|
lambda x: f'{x:,.2%}'.rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
|
|
)
|
|
trials['Avg duration'] = trials['Avg duration'].apply(
|
|
lambda x: f'{x:,.1f} m'.rjust(7, ' ') if isinstance(x, float) else f"{x}"
|
|
if not isna(x) else "--".rjust(7, ' ')
|
|
)
|
|
trials['Objective'] = trials['Objective'].apply(
|
|
lambda x: f'{x:,.5f}'.rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
|
|
)
|
|
|
|
stake_currency = config['stake_currency']
|
|
|
|
trials[f"Max Drawdown{' (Acct)' if has_account_drawdown else ''}"] = trials.apply(
|
|
lambda x: "{} {}".format(
|
|
round_coin_value(x['max_drawdown_abs'], stake_currency, keep_trailing_zeros=True),
|
|
(f"({x['max_drawdown_account']:,.2%})"
|
|
if has_account_drawdown
|
|
else f"({x['max_drawdown']:,.2%})"
|
|
).rjust(10, ' ')
|
|
).rjust(25 + len(stake_currency))
|
|
if x['max_drawdown'] != 0.0 or x['max_drawdown_account'] != 0.0
|
|
else '--'.rjust(25 + len(stake_currency)),
|
|
axis=1
|
|
)
|
|
|
|
trials = trials.drop(columns=['max_drawdown_abs', 'max_drawdown', 'max_drawdown_account'])
|
|
|
|
trials['Profit'] = trials.apply(
|
|
lambda x: '{} {}'.format(
|
|
round_coin_value(x['Total profit'], stake_currency, keep_trailing_zeros=True),
|
|
f"({x['Profit']:,.2%})".rjust(10, ' ')
|
|
).rjust(25 + len(stake_currency))
|
|
if x['Total profit'] != 0.0 else '--'.rjust(25 + len(stake_currency)),
|
|
axis=1
|
|
)
|
|
trials = trials.drop(columns=['Total profit'])
|
|
|
|
if print_colorized:
|
|
for i in range(len(trials)):
|
|
if trials.loc[i]['is_profit']:
|
|
for j in range(len(trials.loc[i]) - 3):
|
|
trials.iat[i, j] = "{}{}{}".format(Fore.GREEN,
|
|
str(trials.loc[i][j]), Fore.RESET)
|
|
if trials.loc[i]['is_best'] and highlight_best:
|
|
for j in range(len(trials.loc[i]) - 3):
|
|
trials.iat[i, j] = "{}{}{}".format(Style.BRIGHT,
|
|
str(trials.loc[i][j]), Style.RESET_ALL)
|
|
|
|
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit', 'is_random'])
|
|
if remove_header > 0:
|
|
table = tabulate.tabulate(
|
|
trials.to_dict(orient='list'), tablefmt='orgtbl',
|
|
headers='keys', stralign="right"
|
|
)
|
|
|
|
table = table.split("\n", remove_header)[remove_header]
|
|
elif remove_header < 0:
|
|
table = tabulate.tabulate(
|
|
trials.to_dict(orient='list'), tablefmt='psql',
|
|
headers='keys', stralign="right"
|
|
)
|
|
table = "\n".join(table.split("\n")[0:remove_header])
|
|
else:
|
|
table = tabulate.tabulate(
|
|
trials.to_dict(orient='list'), tablefmt='psql',
|
|
headers='keys', stralign="right"
|
|
)
|
|
return table
|
|
|
|
@staticmethod
|
|
def export_csv_file(config: dict, results: list, csv_file: str) -> None:
|
|
"""
|
|
Log result to csv-file
|
|
"""
|
|
if not results:
|
|
return
|
|
|
|
# Verification for overwrite
|
|
if Path(csv_file).is_file():
|
|
logger.error(f"CSV file already exists: {csv_file}")
|
|
return
|
|
|
|
try:
|
|
io.open(csv_file, 'w+').close()
|
|
except IOError:
|
|
logger.error(f"Failed to create CSV file: {csv_file}")
|
|
return
|
|
|
|
trials = json_normalize(results, max_level=1)
|
|
trials['Best'] = ''
|
|
trials['Stake currency'] = config['stake_currency']
|
|
|
|
base_metrics = ['Best', 'current_epoch', 'results_metrics.total_trades',
|
|
'results_metrics.profit_mean', 'results_metrics.profit_median',
|
|
'results_metrics.profit_total',
|
|
'Stake currency',
|
|
'results_metrics.profit_total_abs', 'results_metrics.holding_avg',
|
|
'loss', 'is_initial_point', 'is_best']
|
|
perc_multi = 100
|
|
|
|
param_metrics = [("params_dict." + param) for param in results[0]['params_dict'].keys()]
|
|
trials = trials[base_metrics + param_metrics]
|
|
|
|
base_columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Median profit', 'Total profit',
|
|
'Stake currency', 'Profit', 'Avg duration', 'Objective',
|
|
'is_initial_point', 'is_best']
|
|
param_columns = list(results[0]['params_dict'].keys())
|
|
trials.columns = base_columns + param_columns
|
|
|
|
trials['is_profit'] = False
|
|
trials.loc[trials['is_initial_point'], 'Best'] = '*'
|
|
trials.loc[trials['is_best'], 'Best'] = 'Best'
|
|
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
|
|
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
|
|
trials['Epoch'] = trials['Epoch'].astype(str)
|
|
trials['Trades'] = trials['Trades'].astype(str)
|
|
trials['Median profit'] = trials['Median profit'] * perc_multi
|
|
|
|
trials['Total profit'] = trials['Total profit'].apply(
|
|
lambda x: f'{x:,.8f}' if x != 0.0 else ""
|
|
)
|
|
trials['Profit'] = trials['Profit'].apply(
|
|
lambda x: f'{x:,.2f}' if not isna(x) else ""
|
|
)
|
|
trials['Avg profit'] = trials['Avg profit'].apply(
|
|
lambda x: f'{x * perc_multi:,.2f}%' if not isna(x) else ""
|
|
)
|
|
trials['Objective'] = trials['Objective'].apply(
|
|
lambda x: f'{x:,.5f}' if x != 100000 else ""
|
|
)
|
|
|
|
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
|
|
trials.to_csv(csv_file, index=False, header=True, mode='w', encoding='UTF-8')
|
|
logger.info(f"CSV file created: {csv_file}")
|