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Merge pull request #6563 from italodamato/opt-ask-force-new-points

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Optimize only new points
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Matthias 2022-04-23 09:43:33 +02:00 committed by GitHub
commit 7328553c0b
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4 changed files with 71 additions and 8 deletions
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50
freqtrade/optimize/hyperopt.py
View File

@ -10,7 +10,7 @@ import warnings
from datetime import datetime, timezone
from math import ceil
from pathlib import Path
from typing import Any, Dict, List, Optional
from typing import Any, Dict, List, Optional, Tuple
import progressbar
import rapidjson
@ -409,6 +409,51 @@ class Hyperopt:
# Store non-trimmed data - will be trimmed after signal generation.
dump(preprocessed, self.data_pickle_file)
def get_asked_points(self, n_points: int) -> Tuple[List[List[Any]], List[bool]]:
'''
Enforce points returned from `self.opt.ask` have not been already evaluated
Steps:
1. Try to get points using `self.opt.ask` first
2. Discard the points that have already been evaluated
3. Retry using `self.opt.ask` up to 3 times
4. If still some points are missing in respect to `n_points`, random sample some points
5. Repeat until at least `n_points` points in the `asked_non_tried` list
6. Return a list with length truncated at `n_points`
'''
def unique_list(a_list):
new_list = []
for item in a_list:
if item not in new_list:
new_list.append(item)
return new_list
i = 0
asked_non_tried: List[List[Any]] = []
is_random: List[bool] = []
while i < 5 and len(asked_non_tried) < n_points:
if i < 3:
self.opt.cache_ = {}
asked = unique_list(self.opt.ask(n_points=n_points * 5))
is_random = [False for _ in range(len(asked))]
else:
asked = unique_list(self.opt.space.rvs(n_samples=n_points * 5))
is_random = [True for _ in range(len(asked))]
is_random += [rand for x, rand in zip(asked, is_random)
if x not in self.opt.Xi
and x not in asked_non_tried]
asked_non_tried += [x for x in asked
if x not in self.opt.Xi
and x not in asked_non_tried]
i += 1
if asked_non_tried:
return (
asked_non_tried[:min(len(asked_non_tried), n_points)],
is_random[:min(len(asked_non_tried), n_points)]
)
else:
return self.opt.ask(n_points=n_points), [False for _ in range(n_points)]
def start(self) -> None:
self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
logger.info(f"Using optimizer random state: {self.random_state}")
@ -473,7 +518,7 @@ class Hyperopt:
n_rest = (i + 1) * jobs - self.total_epochs
current_jobs = jobs - n_rest if n_rest > 0 else jobs
asked = self.opt.ask(n_points=current_jobs)
asked, is_random = self.get_asked_points(n_points=current_jobs)
f_val = self.run_optimizer_parallel(parallel, asked, i)
self.opt.tell(asked, [v['loss'] for v in f_val])
@ -492,6 +537,7 @@ class Hyperopt:
# evaluations can take different time. Here they are aligned in the
# order they will be shown to the user.
val['is_best'] = is_best
val['is_random'] = is_random[j]
self.print_results(val)
if is_best:

14
freqtrade/optimize/hyperopt_tools.py
View File

@ -310,6 +310,8 @@ class HyperoptTools():
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(
@ -322,12 +324,12 @@ class HyperoptTools():
'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_best']]
'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_best'
'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_random', 'is_best'
]
return trials
@ -349,9 +351,11 @@ class HyperoptTools():
trials = HyperoptTools.prepare_trials_columns(trials, has_account_drawdown)
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '* '
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_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
@ -407,7 +411,7 @@ class HyperoptTools():
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'])
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',

13
tests/conftest.py
View File

@ -2672,6 +2672,7 @@ def saved_hyperopt_results():
'total_profit': -0.00125625,
'current_epoch': 1,
'is_initial_point': True,
'is_random': False,
'is_best': True,
}, {
@ -2688,6 +2689,7 @@ def saved_hyperopt_results():
'total_profit': 6.185e-05,
'current_epoch': 2,
'is_initial_point': True,
'is_random': False,
'is_best': False
}, {
'loss': 14.241196856510731,
@ -2698,6 +2700,7 @@ def saved_hyperopt_results():
'total_profit': -0.13639474,
'current_epoch': 3,
'is_initial_point': True,
'is_random': False,
'is_best': False
}, {
'loss': 100000,
@ -2705,7 +2708,7 @@ def saved_hyperopt_results():
'params_details': {'buy': {'mfi-value': 13, 'fastd-value': 35, 'adx-value': 39, 'rsi-value': 29, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 54, 'sell-adx-value': 63, 'sell-rsi-value': 93, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.411946348378729, 215: 0.2052334363683207, 891: 0.06264755784937427, 2293: 0}, 'stoploss': {'stoploss': -0.11818343570194478}}, # noqa: E501
'results_metrics': {'total_trades': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit': 0.0, 'holding_avg': timedelta()}, # noqa: E501
'results_explanation': ' 0 trades. Avg profit nan%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration nan min.', # noqa: E501
'total_profit': 0, 'current_epoch': 4, 'is_initial_point': True, 'is_best': False
'total_profit': 0, 'current_epoch': 4, 'is_initial_point': True, 'is_random': False, 'is_best': False # noqa: E501
}, {
'loss': 0.22195522184191518,
'params_dict': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 1269, 'roi_t2': 601, 'roi_t3': 444, 'roi_p1': 0.07280999507931168, 'roi_p2': 0.08946698095898986, 'roi_p3': 0.1454876733325284, 'stoploss': -0.18181041180901014}, # noqa: E501
@ -2715,6 +2718,7 @@ def saved_hyperopt_results():
'total_profit': -0.002480140000000001,
'current_epoch': 5,
'is_initial_point': True,
'is_random': False,
'is_best': True
}, {
'loss': 0.545315889154162,
@ -2725,6 +2729,7 @@ def saved_hyperopt_results():
'total_profit': -0.0041773,
'current_epoch': 6,
'is_initial_point': True,
'is_random': False,
'is_best': False
}, {
'loss': 4.713497421432944,
@ -2737,6 +2742,7 @@ def saved_hyperopt_results():
'total_profit': -0.06339929,
'current_epoch': 7,
'is_initial_point': True,
'is_random': False,
'is_best': False
}, {
'loss': 20.0, # noqa: E501
@ -2747,6 +2753,7 @@ def saved_hyperopt_results():
'total_profit': 0.0,
'current_epoch': 8,
'is_initial_point': True,
'is_random': False,
'is_best': False
}, {
'loss': 2.4731817780991223,
@ -2757,6 +2764,7 @@ def saved_hyperopt_results():
'total_profit': -0.044050070000000004, # noqa: E501
'current_epoch': 9,
'is_initial_point': True,
'is_random': False,
'is_best': False
}, {
'loss': -0.2604606005845212, # noqa: E501
@ -2767,6 +2775,7 @@ def saved_hyperopt_results():
'total_profit': 0.00021629,
'current_epoch': 10,
'is_initial_point': True,
'is_random': False,
'is_best': True
}, {
'loss': 4.876465945994304, # noqa: E501
@ -2778,6 +2787,7 @@ def saved_hyperopt_results():
'total_profit': -0.07436117,
'current_epoch': 11,
'is_initial_point': True,
'is_random': False,
'is_best': False
}, {
'loss': 100000,
@ -2788,6 +2798,7 @@ def saved_hyperopt_results():
'total_profit': 0,
'current_epoch': 12,
'is_initial_point': True,
'is_random': False,
'is_best': False
}
]

2
tests/optimize/test_hyperopt.py
View File

@ -41,6 +41,7 @@ def generate_result_metrics():
'max_drawdown_abs': 0.001,
'loss': 0.001,
'is_initial_point': 0.001,
'is_random': False,
'is_best': 1,
}
@ -247,6 +248,7 @@ def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
'total_profit': 0,
'current_epoch': 2, # This starts from 1 (in a human-friendly manner)
'is_initial_point': False,
'is_random': False,
'is_best': True
}
)