mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-10 10:21:59 +00:00
Merge pull request #2994 from Fredrik81/hyperopt-table
Added dynamic print table function to hyperopt
This commit is contained in:
hroff-1902
GitHub
commit
57523d58df
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@ -51,7 +51,7 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
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try:
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Hyperopt.print_result_table(config, trials, total_epochs,
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not filteroptions['only_best'], print_colorized)
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not filteroptions['only_best'], print_colorized, 0)
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except KeyboardInterrupt:
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print('User interrupted..')
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@ -22,7 +22,7 @@ from colorama import init as colorama_init
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from joblib import (Parallel, cpu_count, delayed, dump, load,
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wrap_non_picklable_objects)
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from pandas import DataFrame, json_normalize, isna
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from tabulate import tabulate
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import tabulate
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from freqtrade.data.converter import trim_dataframe
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from freqtrade.data.history import get_timerange
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@ -117,6 +117,7 @@ class Hyperopt:
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self.config['ask_strategy']['use_sell_signal'] = True
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self.print_all = self.config.get('print_all', False)
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self.hyperopt_table_header = 0
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self.print_colorized = self.config.get('print_colorized', False)
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self.print_json = self.config.get('print_json', False)
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@ -154,7 +155,7 @@ class Hyperopt:
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"""
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num_trials = len(self.trials)
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if num_trials > self.num_trials_saved:
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logger.info(f"Saving {num_trials} {plural(num_trials, 'epoch')}.")
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logger.debug(f"Saving {num_trials} {plural(num_trials, 'epoch')}.")
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dump(self.trials, self.trials_file)
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self.num_trials_saved = num_trials
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if final:
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@ -273,8 +274,10 @@ class Hyperopt:
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if not self.print_all:
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# Separate the results explanation string from dots
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print("\n")
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self.print_results_explanation(results, self.total_epochs, self.print_all,
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self.print_colorized)
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self.print_result_table(self.config, results, self.total_epochs,
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self.print_all, self.print_colorized,
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self.hyperopt_table_header)
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self.hyperopt_table_header = 2
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@staticmethod
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def print_results_explanation(results, total_epochs, highlight_best: bool,
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@ -300,13 +303,15 @@ class Hyperopt:
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@staticmethod
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def print_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
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print_colorized: bool) -> None:
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print_colorized: bool, remove_header: int) -> None:
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"""
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Log result table
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"""
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if not results:
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return
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tabulate.PRESERVE_WHITESPACE = True
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trials = json_normalize(results, max_level=1)
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trials['Best'] = ''
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trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
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@ -318,35 +323,63 @@ class Hyperopt:
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trials['is_profit'] = False
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trials.loc[trials['is_initial_point'], 'Best'] = '*'
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trials.loc[trials['is_best'], 'Best'] = 'Best'
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trials['Objective'] = trials['Objective'].astype(str)
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trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
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trials['Trades'] = trials['Trades'].astype(str)
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trials['Epoch'] = trials['Epoch'].apply(
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lambda x: "{}/{}".format(x, total_epochs))
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lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
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)
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trials['Avg profit'] = trials['Avg profit'].apply(
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lambda x: '{:,.2f}%'.format(x) if not isna(x) else x)
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trials['Profit'] = trials['Profit'].apply(
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lambda x: '{:,.2f}%'.format(x) if not isna(x) else x)
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trials['Total profit'] = trials['Total profit'].apply(
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lambda x: '{: 11.8f} '.format(x) + config['stake_currency'] if not isna(x) else x)
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lambda x: ('{:,.2f}%'.format(x)).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
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)
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trials['Avg duration'] = trials['Avg duration'].apply(
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lambda x: '{:,.1f}m'.format(x) if not isna(x) else x)
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lambda x: ('{:,.1f} m'.format(x)).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
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)
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trials['Objective'] = trials['Objective'].apply(
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lambda x: '{:,.5f}'.format(x).rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
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)
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trials['Profit'] = trials.apply(
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lambda x: '{:,.8f} {} {}'.format(
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x['Total profit'], config['stake_currency'],
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'({:,.2f}%)'.format(x['Profit']).rjust(10, ' ')
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).rjust(25+len(config['stake_currency']))
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if x['Total profit'] != 0.0 else '--'.rjust(25+len(config['stake_currency'])),
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axis=1
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)
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trials = trials.drop(columns=['Total profit'])
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if print_colorized:
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for i in range(len(trials)):
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if trials.loc[i]['is_profit']:
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for z in range(len(trials.loc[i])-3):
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trials.iat[i, z] = "{}{}{}".format(Fore.GREEN,
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str(trials.loc[i][z]), Fore.RESET)
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for j in range(len(trials.loc[i])-3):
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trials.iat[i, j] = "{}{}{}".format(Fore.GREEN,
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str(trials.loc[i][j]), Fore.RESET)
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if trials.loc[i]['is_best'] and highlight_best:
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for z in range(len(trials.loc[i])-3):
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trials.iat[i, z] = "{}{}{}".format(Style.BRIGHT,
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str(trials.loc[i][z]), Style.RESET_ALL)
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for j in range(len(trials.loc[i])-3):
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trials.iat[i, j] = "{}{}{}".format(Style.BRIGHT,
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str(trials.loc[i][j]), Style.RESET_ALL)
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trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
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if remove_header > 0:
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table = tabulate.tabulate(
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trials.to_dict(orient='list'), tablefmt='orgtbl',
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headers='keys', stralign="right"
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)
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print(tabulate(trials.to_dict(orient='list'), headers='keys', tablefmt='psql',
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stralign="right"))
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table = table.split("\n", remove_header)[remove_header]
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elif remove_header < 0:
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table = tabulate.tabulate(
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trials.to_dict(orient='list'), tablefmt='psql',
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headers='keys', stralign="right"
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)
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table = "\n".join(table.split("\n")[0:remove_header])
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else:
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table = tabulate.tabulate(
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trials.to_dict(orient='list'), tablefmt='psql',
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headers='keys', stralign="right"
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)
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print(table)
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def has_space(self, space: str) -> bool:
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"""
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@ -534,7 +567,7 @@ class Hyperopt:
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def start(self) -> None:
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self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
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logger.info(f"Using optimizer random state: {self.random_state}")
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self.hyperopt_table_header = -1
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data, timerange = self.backtesting.load_bt_data()
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preprocessed = self.backtesting.strategy.tickerdata_to_dataframe(data)
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@ -426,17 +426,27 @@ def test_onlyprofit_loss_prefers_higher_profits(default_conf, hyperopt_results)
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def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
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hyperopt.current_best_loss = 2
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hyperopt.total_epochs = 2
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hyperopt.print_results(
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{
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'is_best': True,
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'loss': 1,
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'results_metrics':
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{
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'trade_count': 1,
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'avg_profit': 0.1,
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'total_profit': 0.001,
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'profit': 1.0,
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'duration': 20.0
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},
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'total_profit': 0,
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'current_epoch': 2, # This starts from 1 (in a human-friendly manner)
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'results_explanation': 'foo.',
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'is_initial_point': False
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'is_initial_point': False,
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'is_best': True
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}
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)
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out, err = capsys.readouterr()
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assert ' 2/2: foo. Objective: 1.00000' in out
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assert all(x in out
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for x in ["Best", "2/2", " 1", "0.10%", "0.00100000 BTC (1.00%)", "20.0 m"])
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def test_no_log_if_loss_does_not_improve(hyperopt, caplog) -> None:
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@ -458,13 +468,11 @@ def test_save_trials_saves_trials(mocker, hyperopt, testdatadir, caplog) -> None
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hyperopt.trials = trials
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hyperopt.save_trials(final=True)
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assert log_has("Saving 1 epoch.", caplog)
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assert log_has(f"1 epoch saved to '{trials_file}'.", caplog)
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mock_dump.assert_called_once()
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hyperopt.trials = trials + trials
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hyperopt.save_trials(final=True)
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assert log_has("Saving 2 epochs.", caplog)
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assert log_has(f"2 epochs saved to '{trials_file}'.", caplog)
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@ -502,8 +510,18 @@ def test_start_calls_optimizer(mocker, default_conf, caplog, capsys) -> None:
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parallel = mocker.patch(
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'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
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MagicMock(return_value=[{'loss': 1, 'results_explanation': 'foo result',
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'params': {'buy': {}, 'sell': {}, 'roi': {}, 'stoploss': 0.0}}])
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MagicMock(return_value=[{
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'loss': 1, 'results_explanation': 'foo result',
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'params': {'buy': {}, 'sell': {}, 'roi': {}, 'stoploss': 0.0},
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'results_metrics':
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{
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'trade_count': 1,
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'avg_profit': 0.1,
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'total_profit': 0.001,
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'profit': 1.0,
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'duration': 20.0
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},
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}])
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)
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patch_exchange(mocker)
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# Co-test loading ticker-interval from strategy
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@ -797,11 +815,23 @@ def test_print_json_spaces_all(mocker, default_conf, caplog, capsys) -> None:
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parallel = mocker.patch(
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'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
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MagicMock(return_value=[{'loss': 1, 'results_explanation': 'foo result', 'params': {},
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'params_details': {'buy': {'mfi-value': None},
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MagicMock(return_value=[{
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'loss': 1, 'results_explanation': 'foo result', 'params': {},
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'params_details': {
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'buy': {'mfi-value': None},
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'sell': {'sell-mfi-value': None},
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'roi': {}, 'stoploss': {'stoploss': None},
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'trailing': {'trailing_stop': None}}}])
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'trailing': {'trailing_stop': None}
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},
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'results_metrics':
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{
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'trade_count': 1,
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'avg_profit': 0.1,
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'total_profit': 0.001,
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'profit': 1.0,
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'duration': 20.0
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}
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}])
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)
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patch_exchange(mocker)
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@ -823,7 +853,11 @@ def test_print_json_spaces_all(mocker, default_conf, caplog, capsys) -> None:
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parallel.assert_called_once()
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out, err = capsys.readouterr()
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assert '{"params":{"mfi-value":null,"sell-mfi-value":null},"minimal_roi":{},"stoploss":null,"trailing_stop":null}' in out # noqa: E501
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result_str = (
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'{"params":{"mfi-value":null,"sell-mfi-value":null},"minimal_roi"'
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':{},"stoploss":null,"trailing_stop":null}'
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)
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assert result_str in out # noqa: E501
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assert dumper.called
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# Should be called twice, once for tickerdata, once to save evaluations
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assert dumper.call_count == 2
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@ -840,10 +874,22 @@ def test_print_json_spaces_default(mocker, default_conf, caplog, capsys) -> None
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parallel = mocker.patch(
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'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
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MagicMock(return_value=[{'loss': 1, 'results_explanation': 'foo result', 'params': {},
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'params_details': {'buy': {'mfi-value': None},
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MagicMock(return_value=[{
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'loss': 1, 'results_explanation': 'foo result', 'params': {},
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'params_details': {
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'buy': {'mfi-value': None},
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'sell': {'sell-mfi-value': None},
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'roi': {}, 'stoploss': {'stoploss': None}}}])
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'roi': {}, 'stoploss': {'stoploss': None}
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},
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'results_metrics':
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{
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'trade_count': 1,
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'avg_profit': 0.1,
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'total_profit': 0.001,
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'profit': 1.0,
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'duration': 20.0
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}
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}])
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)
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patch_exchange(mocker)
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@ -882,8 +928,18 @@ def test_print_json_spaces_roi_stoploss(mocker, default_conf, caplog, capsys) ->
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parallel = mocker.patch(
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'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
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MagicMock(return_value=[{'loss': 1, 'results_explanation': 'foo result', 'params': {},
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'params_details': {'roi': {}, 'stoploss': {'stoploss': None}}}])
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MagicMock(return_value=[{
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'loss': 1, 'results_explanation': 'foo result', 'params': {},
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'params_details': {'roi': {}, 'stoploss': {'stoploss': None}},
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'results_metrics':
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{
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'trade_count': 1,
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'avg_profit': 0.1,
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'total_profit': 0.001,
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'profit': 1.0,
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'duration': 20.0
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}
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}])
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)
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patch_exchange(mocker)
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@ -923,7 +979,16 @@ def test_simplified_interface_roi_stoploss(mocker, default_conf, caplog, capsys)
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parallel = mocker.patch(
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'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
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MagicMock(return_value=[{
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'loss': 1, 'results_explanation': 'foo result', 'params': {'stoploss': 0.0}}])
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'loss': 1, 'results_explanation': 'foo result', 'params': {'stoploss': 0.0},
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'results_metrics':
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{
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'trade_count': 1,
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'avg_profit': 0.1,
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'total_profit': 0.001,
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'profit': 1.0,
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'duration': 20.0
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}
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}])
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)
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patch_exchange(mocker)
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@ -1001,7 +1066,17 @@ def test_simplified_interface_buy(mocker, default_conf, caplog, capsys) -> None:
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parallel = mocker.patch(
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'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
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MagicMock(return_value=[{'loss': 1, 'results_explanation': 'foo result', 'params': {}}])
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MagicMock(return_value=[{
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'loss': 1, 'results_explanation': 'foo result', 'params': {},
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'results_metrics':
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{
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'trade_count': 1,
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'avg_profit': 0.1,
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'total_profit': 0.001,
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'profit': 1.0,
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'duration': 20.0
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}
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}])
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)
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patch_exchange(mocker)
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@ -1048,7 +1123,17 @@ def test_simplified_interface_sell(mocker, default_conf, caplog, capsys) -> None
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parallel = mocker.patch(
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'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
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MagicMock(return_value=[{'loss': 1, 'results_explanation': 'foo result', 'params': {}}])
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MagicMock(return_value=[{
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'loss': 1, 'results_explanation': 'foo result', 'params': {},
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'results_metrics':
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{
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'trade_count': 1,
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'avg_profit': 0.1,
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'total_profit': 0.001,
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'profit': 1.0,
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'duration': 20.0
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}
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}])
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)
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patch_exchange(mocker)
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