def test_params_print(capsys):
params = {
"buy": {
"buy_rsi": 30
},
"sell": {
"sell_rsi": 70
},
}
non_optimized = {
"buy": {
"buy_adx": 44
},
"sell": {
"sell_adx": 65
},
"stoploss": {
"stoploss": -0.05,
},
"roi": {
"0": 0.05,
"20": 0.01,
},
"trailing": {
"trailing_stop": False,
"trailing_stop_positive": 0.05,
"trailing_stop_positive_offset": 0.1,
"trailing_only_offset_is_reached": True
},
}
HyperoptTools._params_pretty_print(params, 'buy', 'No header', non_optimized)
captured = capsys.readouterr()
assert re.search("# No header", captured.out)
assert re.search('"buy_rsi": 30,\n', captured.out)
assert re.search('"buy_adx": 44, # value loaded.*\n', captured.out)
assert not re.search("sell", captured.out)
HyperoptTools._params_pretty_print(params, 'sell', 'Sell Header', non_optimized)
captured = capsys.readouterr()
assert re.search("# Sell Header", captured.out)
assert re.search('"sell_rsi": 70,\n', captured.out)
assert re.search('"sell_adx": 65, # value loaded.*\n', captured.out)
HyperoptTools._params_pretty_print(params, 'roi', 'ROI Table:', non_optimized)
captured = capsys.readouterr()
assert re.search("# ROI Table: # value loaded.*\n", captured.out)
assert re.search('minimal_roi = {\n', captured.out)
assert re.search('"20": 0.01\n', captured.out)
HyperoptTools._params_pretty_print(params, 'trailing', 'Trailing stop:', non_optimized)
captured = capsys.readouterr()
assert re.search("# Trailing stop:", captured.out)
assert re.search('trailing_stop = False # value loaded.*\n', captured.out)
assert re.search('trailing_stop_positive = 0.05 # value loaded.*\n', captured.out)
assert re.search('trailing_stop_positive_offset = 0.1 # value loaded.*\n', captured.out)
assert re.search('trailing_only_offset_is_reached = True # value loaded.*\n', captured.out)
def _load_params(self,
params: Dict,
space: str,
hyperopt: bool = False) -> None:
"""
Set optimizable parameter values.
:param params: Dictionary with new parameter values.
"""
if not params:
logger.info(f"No params for {space} found, using default values.")
param_container: List[BaseParameter] = getattr(self,
f"ft_{space}_params")
for attr_name, attr in self.detect_parameters(space):
attr.name = attr_name
attr.in_space = hyperopt and HyperoptTools.has_space(
self.config, space)
if not attr.category:
attr.category = space
param_container.append(attr)
if params and attr_name in params:
if attr.load:
attr.value = params[attr_name]
logger.info(
f'Strategy Parameter: {attr_name} = {attr.value}')
else:
logger.warning(
f'Parameter "{attr_name}" exists, but is disabled. '
f'Default value "{attr.value}" used.')
else:
logger.info(
f'Strategy Parameter(default): {attr_name} = {attr.value}')
def __init__(self, config: Dict[str, Any]) -> None:
self.buy_space: List[Dimension] = []
self.sell_space: List[Dimension] = []
self.protection_space: List[Dimension] = []
self.roi_space: List[Dimension] = []
self.stoploss_space: List[Dimension] = []
self.trailing_space: List[Dimension] = []
self.dimensions: List[Dimension] = []
self.config = config
self.backtesting = Backtesting(self.config)
self.pairlist = self.backtesting.pairlists.whitelist
if not self.config.get('hyperopt'):
self.custom_hyperopt = HyperOptAuto(self.config)
else:
raise OperationalException(
"Using separate Hyperopt files has been removed in 2021.9. Please convert "
"your existing Hyperopt file to the new Hyperoptable strategy interface")
self.backtesting._set_strategy(self.backtesting.strategylist[0])
self.custom_hyperopt.strategy = self.backtesting.strategy
self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(self.config)
self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
time_now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
strategy = str(self.config['strategy'])
self.results_file: Path = (self.config['user_data_dir'] / 'hyperopt_results' /
f'strategy_{strategy}_{time_now}.fthypt')
self.data_pickle_file = (self.config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_tickerdata.pkl')
self.total_epochs = config.get('epochs', 0)
self.current_best_loss = 100
self.clean_hyperopt()
self.num_epochs_saved = 0
self.current_best_epoch: Optional[Dict[str, Any]] = None
# Use max_open_trades for hyperopt as well, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
self.max_open_trades = self.config['max_open_trades']
else:
logger.debug('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
self.max_open_trades = 0
self.position_stacking = self.config.get('position_stacking', False)
if HyperoptTools.has_space(self.config, 'sell'):
# Make sure use_sell_signal is enabled
if 'ask_strategy' not in self.config:
self.config['ask_strategy'] = {}
self.config['ask_strategy']['use_sell_signal'] = True
self.print_all = self.config.get('print_all', False)
self.hyperopt_table_header = 0
self.print_colorized = self.config.get('print_colorized', False)
self.print_json = self.config.get('print_json', False)
def test_read_results_returns_epochs(mocker, hyperopt, testdatadir, caplog) -> None:
epochs = create_results(mocker, hyperopt, testdatadir)
mock_load = mocker.patch('freqtrade.optimize.hyperopt_tools.load', return_value=epochs)
results_file = testdatadir / 'optimize' / 'ut_results.pickle'
hyperopt_epochs = HyperoptTools._read_results(results_file)
assert log_has(f"Reading epochs from '{results_file}'", caplog)
assert hyperopt_epochs == epochs
mock_load.assert_called_once()
def test_load_previous_results(testdatadir, caplog) -> None:
results_file = testdatadir / 'hyperopt_results_SampleStrategy.pickle'
hyperopt_epochs = HyperoptTools.load_previous_results(results_file)
assert len(hyperopt_epochs) == 5
assert log_has_re(r"Reading pickled epochs from .*", caplog)
caplog.clear()
# Modern version
results_file = testdatadir / 'strategy_SampleStrategy.fthypt'
hyperopt_epochs = HyperoptTools.load_previous_results(results_file)
assert len(hyperopt_epochs) == 5
assert log_has_re(r"Reading epochs from .*", caplog)
def _get_no_optimize_details(self) -> Dict[str, Any]:
"""
Get non-optimized parameters
"""
result: Dict[str, Any] = {}
strategy = self.backtesting.strategy
if not HyperoptTools.has_space(self.config, 'roi'):
result['roi'] = {str(k): v for k, v in strategy.minimal_roi.items()}
if not HyperoptTools.has_space(self.config, 'stoploss'):
result['stoploss'] = {'stoploss': strategy.stoploss}
if not HyperoptTools.has_space(self.config, 'trailing'):
result['trailing'] = {
'trailing_stop': strategy.trailing_stop,
'trailing_stop_positive': strategy.trailing_stop_positive,
'trailing_stop_positive_offset': strategy.trailing_stop_positive_offset,
'trailing_only_offset_is_reached': strategy.trailing_only_offset_is_reached,
}
return result
def test_try_export_params(default_conf, tmpdir, caplog, mocker):
default_conf['disableparamexport'] = False
export_mock = mocker.patch("freqtrade.optimize.hyperopt_tools.HyperoptTools.export_params")
filename = Path(tmpdir) / f"{CURRENT_TEST_STRATEGY}.json"
assert not filename.is_file()
params = {
"params_details": {
"buy": {
"buy_rsi": 30
},
"sell": {
"sell_rsi": 70
},
"roi": {
"0": 0.528,
"346": 0.08499,
"507": 0.049,
"1595": 0
}
},
"params_not_optimized": {
"stoploss": -0.05,
"trailing": {
"trailing_stop": False,
"trailing_stop_positive": 0.05,
"trailing_stop_positive_offset": 0.1,
"trailing_only_offset_is_reached": True
},
},
FTHYPT_FILEVERSION: 2,
}
HyperoptTools.try_export_params(default_conf, "StrategyTestVXXX", params)
assert log_has("Strategy not found, not exporting parameter file.", caplog)
assert export_mock.call_count == 0
caplog.clear()
HyperoptTools.try_export_params(default_conf, CURRENT_TEST_STRATEGY, params)
assert export_mock.call_count == 1
assert export_mock.call_args_list[0][0][1] == CURRENT_TEST_STRATEGY
assert export_mock.call_args_list[0][0][2].name == 'strategy_test_v3.json'
def test_export_params(tmpdir):
filename = Path(tmpdir) / f"{CURRENT_TEST_STRATEGY}.json"
assert not filename.is_file()
params = {
"params_details": {
"buy": {
"buy_rsi": 30
},
"sell": {
"sell_rsi": 70
},
"roi": {
"0": 0.528,
"346": 0.08499,
"507": 0.049,
"1595": 0
}
},
"params_not_optimized": {
"stoploss": -0.05,
"trailing": {
"trailing_stop": False,
"trailing_stop_positive": 0.05,
"trailing_stop_positive_offset": 0.1,
"trailing_only_offset_is_reached": True
},
}
}
HyperoptTools.export_params(params, CURRENT_TEST_STRATEGY, filename)
assert filename.is_file()
with filename.open('r') as f:
content = rapidjson.load(f)
assert content['strategy_name'] == CURRENT_TEST_STRATEGY
assert 'params' in content
assert "buy" in content["params"]
assert "sell" in content["params"]
assert "roi" in content["params"]
assert "stoploss" in content["params"]
assert "trailing" in content["params"]
def test_load_previous_results(mocker, hyperopt, testdatadir, caplog) -> None:
epochs = create_results(mocker, hyperopt, testdatadir)
mock_load = mocker.patch('joblib.load', return_value=epochs)
mocker.patch.object(Path, 'is_file', MagicMock(return_value=True))
statmock = MagicMock()
statmock.st_size = 5
# mocker.patch.object(Path, 'stat', MagicMock(return_value=statmock))
results_file = testdatadir / 'optimize' / 'ut_results.pickle'
hyperopt_epochs = HyperoptTools.load_previous_results(results_file)
assert hyperopt_epochs == epochs
mock_load.assert_called_once()
del epochs[0]['is_best']
mock_load = mocker.patch('joblib.load', return_value=epochs)
with pytest.raises(OperationalException):
HyperoptTools.load_previous_results(results_file)
def test__pprint_dict():
params = {
'buy_std': 1.2,
'buy_rsi': 31,
'buy_enable': True,
'buy_what': 'asdf'
}
non_params = {'buy_notoptimied': 55}
x = HyperoptTools._pprint_dict(params, non_params)
assert x == """{
def test_format_results(hyperopt):
# Test with BTC as stake_currency
trades = [
('ETH/BTC', 2, 2, 123),
('LTC/BTC', 1, 1, 123),
('XPR/BTC', -1, -2, -246)
]
labels = ['currency', 'profit_ratio', 'profit_abs', 'trade_duration']
df = pd.DataFrame.from_records(trades, columns=labels)
results_metrics = hyperopt._calculate_results_metrics(df)
results_explanation = hyperopt._format_results_explanation_string(results_metrics)
total_profit = results_metrics['total_profit']
results = {
'loss': 0.0,
'params_dict': None,
'params_details': None,
'results_metrics': results_metrics,
'results_explanation': results_explanation,
'total_profit': total_profit,
'current_epoch': 1,
'is_initial_point': True,
}
result = HyperoptTools._format_explanation_string(results, 1)
assert result.find(' 66.67%')
assert result.find('Total profit 1.00000000 BTC')
assert result.find('2.0000Σ %')
# Test with EUR as stake_currency
trades = [
('ETH/EUR', 2, 2, 123),
('LTC/EUR', 1, 1, 123),
('XPR/EUR', -1, -2, -246)
]
df = pd.DataFrame.from_records(trades, columns=labels)
results_metrics = hyperopt._calculate_results_metrics(df)
results['total_profit'] = results_metrics['total_profit']
result = HyperoptTools._format_explanation_string(results, 1)
assert result.find('Total profit 1.00000000 EUR')
def init_spaces(self):
"""
Assign the dimensions in the hyperoptimization space.
"""
if HyperoptTools.has_space(self.config, 'protection'):
# Protections can only be optimized when using the Parameter interface
logger.debug("Hyperopt has 'protection' space")
# Enable Protections if protection space is selected.
self.config['enable_protections'] = True
self.protection_space = self.custom_hyperopt.protection_space()
if HyperoptTools.has_space(self.config, 'buy'):
logger.debug("Hyperopt has 'buy' space")
self.buy_space = self.custom_hyperopt.buy_indicator_space()
if HyperoptTools.has_space(self.config, 'sell'):
logger.debug("Hyperopt has 'sell' space")
self.sell_space = self.custom_hyperopt.sell_indicator_space()
if HyperoptTools.has_space(self.config, 'roi'):
logger.debug("Hyperopt has 'roi' space")
self.roi_space = self.custom_hyperopt.roi_space()
if HyperoptTools.has_space(self.config, 'stoploss'):
logger.debug("Hyperopt has 'stoploss' space")
self.stoploss_space = self.custom_hyperopt.stoploss_space()
if HyperoptTools.has_space(self.config, 'trailing'):
logger.debug("Hyperopt has 'trailing' space")
self.trailing_space = self.custom_hyperopt.trailing_space()
self.dimensions = (self.buy_space + self.sell_space +
self.protection_space + self.roi_space +
self.stoploss_space + self.trailing_space)
def start_hyperopt_list(args: Dict[str, Any]) -> None:
"""
List hyperopt epochs previously evaluated
"""
from freqtrade.optimize.hyperopt_tools import HyperoptTools
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
print_colorized = config.get('print_colorized', False)
print_json = config.get('print_json', False)
export_csv = config.get('export_csv', None)
no_details = config.get('hyperopt_list_no_details', False)
no_header = False
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', None),
'filter_max_avg_time': config.get('hyperopt_list_max_avg_time', None),
'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit', None),
'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit', None),
'filter_min_total_profit': config.get('hyperopt_list_min_total_profit', None),
'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None),
'filter_min_objective': config.get('hyperopt_list_min_objective', None),
'filter_max_objective': config.get('hyperopt_list_max_objective', None),
}
results_file = get_latest_hyperopt_file(
config['user_data_dir'] / 'hyperopt_results',
config.get('hyperoptexportfilename'))
# Previous evaluations
epochs = HyperoptTools.load_previous_results(results_file)
total_epochs = len(epochs)
epochs = hyperopt_filter_epochs(epochs, filteroptions)
if print_colorized:
colorama_init(autoreset=True)
if not export_csv:
try:
print(HyperoptTools.get_result_table(config, epochs, total_epochs,
not filteroptions['only_best'],
print_colorized, 0))
except KeyboardInterrupt:
print('User interrupted..')
if epochs and not no_details:
sorted_epochs = sorted(epochs, key=itemgetter('loss'))
results = sorted_epochs[0]
HyperoptTools.show_epoch_details(results, total_epochs, print_json, no_header)
if epochs and export_csv:
HyperoptTools.export_csv_file(
config, epochs, total_epochs, not filteroptions['only_best'], export_csv
)
def test_print_epoch_details(capsys):
test_result = {
'params_details': {
'trailing': {
'trailing_stop': True,
'trailing_stop_positive': 0.02,
'trailing_stop_positive_offset': 0.04,
'trailing_only_offset_is_reached': True
},
'roi': {
0: 0.18,
90: 0.14,
225: 0.05,
430: 0
},
},
'results_explanation': 'foo result',
'is_initial_point': False,
'total_profit': 0,
'current_epoch': 2, # This starts from 1 (in a human-friendly manner)
'is_best': True
}
HyperoptTools.print_epoch_details(test_result, 5, False, no_header=True)
captured = capsys.readouterr()
assert '# Trailing stop:' in captured.out
# re.match(r"Pairs for .*", captured.out)
assert re.search(r'^\s+trailing_stop = True$', captured.out, re.MULTILINE)
assert re.search(r'^\s+trailing_stop_positive = 0.02$', captured.out,
re.MULTILINE)
assert re.search(r'^\s+trailing_stop_positive_offset = 0.04$',
captured.out, re.MULTILINE)
assert re.search(r'^\s+trailing_only_offset_is_reached = True$',
captured.out, re.MULTILINE)
assert '# ROI table:' in captured.out
assert re.search(r'^\s+minimal_roi = \{$', captured.out, re.MULTILINE)
assert re.search(r'^\s+\"90\"\:\s0.14,\s*$', captured.out, re.MULTILINE)
def test_save_results_saves_epochs(hyperopt, tmpdir, caplog) -> None:
hyperopt.results_file = Path(tmpdir / 'ut_results.fthypt')
hyperopt_epochs = HyperoptTools.load_filtered_results(
hyperopt.results_file, {})
assert log_has_re("Hyperopt file .* not found.", caplog)
assert hyperopt_epochs == ([], 0)
# Test writing to temp dir and reading again
epochs = create_results()
caplog.set_level(logging.DEBUG)
for epoch in epochs:
hyperopt._save_result(epoch)
assert log_has(f"1 epoch saved to '{hyperopt.results_file}'.", caplog)
hyperopt._save_result(epochs[0])
assert log_has(f"2 epochs saved to '{hyperopt.results_file}'.", caplog)
hyperopt_epochs = HyperoptTools.load_filtered_results(
hyperopt.results_file, {})
assert len(hyperopt_epochs) == 2
assert hyperopt_epochs[1] == 2
assert len(hyperopt_epochs[0]) == 2
result_gen = HyperoptTools._read_results(hyperopt.results_file, 1)
epoch = next(result_gen)
assert len(epoch) == 1
assert epoch[0] == epochs[0]
epoch = next(result_gen)
assert len(epoch) == 1
epoch = next(result_gen)
assert len(epoch) == 0
with pytest.raises(StopIteration):
next(result_gen)
def print_results(self, results) -> None:
"""
Log results if it is better than any previous evaluation
TODO: this should be moved to HyperoptTools too
"""
is_best = results['is_best']
if self.print_all or is_best:
print(
HyperoptTools.get_result_table(self.config, results,
self.total_epochs,
self.print_all,
self.print_colorized,
self.hyperopt_table_header))
self.hyperopt_table_header = 2
def test_save_results_saves_epochs(hyperopt, tmpdir, caplog) -> None:
# Test writing to temp dir and reading again
epochs = create_results()
hyperopt.results_file = Path(tmpdir / 'ut_results.fthypt')
caplog.set_level(logging.DEBUG)
for epoch in epochs:
hyperopt._save_result(epoch)
assert log_has(f"1 epoch saved to '{hyperopt.results_file}'.", caplog)
hyperopt._save_result(epochs[0])
assert log_has(f"2 epochs saved to '{hyperopt.results_file}'.", caplog)
hyperopt_epochs = HyperoptTools.load_previous_results(hyperopt.results_file)
assert len(hyperopt_epochs) == 2
def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
"""
Used Optimize function.
Called once per epoch to optimize whatever is configured.
Keep this function as optimized as possible!
"""
backtest_start_time = datetime.now(timezone.utc)
params_dict = self._get_params_dict(self.dimensions, raw_params)
# Apply parameters
if HyperoptTools.has_space(self.config, 'buy'):
self.assign_params(params_dict, 'buy')
if HyperoptTools.has_space(self.config, 'sell'):
self.assign_params(params_dict, 'sell')
if HyperoptTools.has_space(self.config, 'protection'):
self.assign_params(params_dict, 'protection')
if HyperoptTools.has_space(self.config, 'roi'):
self.backtesting.strategy.minimal_roi = ( # type: ignore
self.custom_hyperopt.generate_roi_table(params_dict))
if HyperoptTools.has_space(self.config, 'stoploss'):
self.backtesting.strategy.stoploss = params_dict['stoploss']
if HyperoptTools.has_space(self.config, 'trailing'):
d = self.custom_hyperopt.generate_trailing_params(params_dict)
self.backtesting.strategy.trailing_stop = d['trailing_stop']
self.backtesting.strategy.trailing_stop_positive = d['trailing_stop_positive']
self.backtesting.strategy.trailing_stop_positive_offset = \
d['trailing_stop_positive_offset']
self.backtesting.strategy.trailing_only_offset_is_reached = \
d['trailing_only_offset_is_reached']
with self.data_pickle_file.open('rb') as f:
processed = load(f, mmap_mode='r')
bt_results = self.backtesting.backtest(
processed=processed,
start_date=self.min_date,
end_date=self.max_date,
max_open_trades=self.max_open_trades,
position_stacking=self.position_stacking,
enable_protections=self.config.get('enable_protections', False),
)
backtest_end_time = datetime.now(timezone.utc)
bt_results.update({
'backtest_start_time': int(backtest_start_time.timestamp()),
'backtest_end_time': int(backtest_end_time.timestamp()),
})
return self._get_results_dict(bt_results, self.min_date, self.max_date,
params_dict,
processed=processed)
def _get_results_dict(self, backtesting_results, min_date, max_date,
params_dict,
processed: Dict[str, DataFrame]) -> Dict[str, Any]:
params_details = self._get_params_details(params_dict)
strat_stats = generate_strategy_stats(
processed,
self.backtesting.strategy.get_strategy_name(),
backtesting_results,
min_date,
max_date,
market_change=0)
results_explanation = HyperoptTools.format_results_explanation_string(
strat_stats, self.config['stake_currency'])
not_optimized = self.backtesting.strategy.get_no_optimize_params()
not_optimized = deep_merge_dicts(not_optimized,
self._get_no_optimize_details())
trade_count = strat_stats['total_trades']
total_profit = strat_stats['profit_total']
# 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.
loss: float = MAX_LOSS
if trade_count >= self.config['hyperopt_min_trades']:
loss = self.calculate_loss(results=backtesting_results['results'],
trade_count=trade_count,
min_date=min_date,
max_date=max_date,
config=self.config,
processed=processed,
backtest_stats=strat_stats)
return {
'loss': loss,
'params_dict': params_dict,
'params_details': params_details,
'params_not_optimized': not_optimized,
'results_metrics': strat_stats,
'results_explanation': results_explanation,
'total_profit': total_profit,
}
def _get_params_details(self, params: Dict) -> Dict:
"""
Return the params for each space
"""
result: Dict = {}
if HyperoptTools.has_space(self.config, 'buy'):
result['buy'] = {p.name: params.get(p.name) for p in self.buy_space}
if HyperoptTools.has_space(self.config, 'sell'):
result['sell'] = {p.name: params.get(p.name) for p in self.sell_space}
if HyperoptTools.has_space(self.config, 'protection'):
result['protection'] = {p.name: params.get(p.name) for p in self.protection_space}
if HyperoptTools.has_space(self.config, 'roi'):
result['roi'] = {str(k): v for k, v in
self.custom_hyperopt.generate_roi_table(params).items()}
if HyperoptTools.has_space(self.config, 'stoploss'):
result['stoploss'] = {p.name: params.get(p.name) for p in self.stoploss_space}
if HyperoptTools.has_space(self.config, 'trailing'):
result['trailing'] = self.custom_hyperopt.generate_trailing_params(params)
return result
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}")
self.hyperopt_table_header = -1
# Initialize spaces ...
self.init_spaces()
self.prepare_hyperopt_data()
# We don't need exchange instance anymore while running hyperopt
self.backtesting.exchange.close()
self.backtesting.exchange._api = None # type: ignore
self.backtesting.exchange._api_async = None # type: ignore
# self.backtesting.exchange = None # type: ignore
self.backtesting.pairlists = None # type: ignore
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}')
self.opt = self.get_optimizer(self.dimensions, config_jobs)
if self.print_colorized:
colorama_init(autoreset=True)
try:
with Parallel(n_jobs=config_jobs) as parallel:
jobs = parallel._effective_n_jobs()
logger.info(
f'Effective number of parallel workers used: {jobs}')
# Define progressbar
if self.print_colorized:
widgets = [
' [Epoch ',
progressbar.Counter(),
' of ',
str(self.total_epochs),
' (',
progressbar.Percentage(),
')] ',
progressbar.Bar(marker=progressbar.AnimatedMarker(
fill='\N{FULL BLOCK}',
fill_wrap=Fore.GREEN + '{}' + Fore.RESET,
marker_wrap=Style.BRIGHT + '{}' + Style.RESET_ALL,
)),
' [',
progressbar.ETA(),
', ',
progressbar.Timer(),
']',
]
else:
widgets = [
' [Epoch ',
progressbar.Counter(),
' of ',
str(self.total_epochs),
' (',
progressbar.Percentage(),
')] ',
progressbar.Bar(marker=progressbar.AnimatedMarker(
fill='\N{FULL BLOCK}', )),
' [',
progressbar.ETA(),
', ',
progressbar.Timer(),
']',
]
with progressbar.ProgressBar(max_value=self.total_epochs,
redirect_stdout=False,
redirect_stderr=False,
widgets=widgets) as pbar:
EVALS = ceil(self.total_epochs / jobs)
for i in range(EVALS):
# Correct the number of epochs to be processed for the last
# iteration (should not exceed self.total_epochs in total)
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)
f_val = self.run_optimizer_parallel(parallel, asked, i)
self.opt.tell(asked, [v['loss'] for v in f_val])
# Calculate progressbar outputs
for j, val in enumerate(f_val):
# Use human-friendly indexes here (starting from 1)
current = i * jobs + j + 1
val['current_epoch'] = current
val['is_initial_point'] = current <= INITIAL_POINTS
logger.debug(f"Optimizer epoch evaluated: {val}")
is_best = HyperoptTools.is_best_loss(
val, self.current_best_loss)
# This value is assigned here and not in the optimization method
# to keep proper order in the list of results. That's because
# evaluations can take different time. Here they are aligned in the
# order they will be shown to the user.
val['is_best'] = is_best
self.print_results(val)
if is_best:
self.current_best_loss = val['loss']
self.current_best_epoch = val
self._save_result(val)
pbar.update(current)
except KeyboardInterrupt:
print('User interrupted..')
logger.info(
f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
f"saved to '{self.results_file}'.")
if self.current_best_epoch:
HyperoptTools.try_export_params(
self.config, self.backtesting.strategy.get_strategy_name(),
self.current_best_epoch)
HyperoptTools.show_epoch_details(self.current_best_epoch,
self.total_epochs,
self.print_json)
else:
# This is printed when Ctrl+C is pressed quickly, before first epochs have
# a chance to be evaluated.
print("No epochs evaluated yet, no best result.")
def start_hyperopt_show(args: Dict[str, Any]) -> None:
"""
Show details of a hyperopt epoch previously evaluated
"""
from freqtrade.optimize.hyperopt_tools import HyperoptTools
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
print_json = config.get('print_json', False)
no_header = config.get('hyperopt_show_no_header', False)
results_file = get_latest_hyperopt_file(
config['user_data_dir'] / 'hyperopt_results',
config.get('hyperoptexportfilename'))
n = config.get('hyperopt_show_index', -1)
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', None),
'filter_max_avg_time':
config.get('hyperopt_list_max_avg_time', None),
'filter_min_avg_profit':
config.get('hyperopt_list_min_avg_profit', None),
'filter_max_avg_profit':
config.get('hyperopt_list_max_avg_profit', None),
'filter_min_total_profit':
config.get('hyperopt_list_min_total_profit', None),
'filter_max_total_profit':
config.get('hyperopt_list_max_total_profit', None),
'filter_min_objective':
config.get('hyperopt_list_min_objective', None),
'filter_max_objective':
config.get('hyperopt_list_max_objective', None)
}
# Previous evaluations
epochs = HyperoptTools.load_previous_results(results_file)
total_epochs = len(epochs)
epochs = hyperopt_filter_epochs(epochs, filteroptions)
filtered_epochs = len(epochs)
if n > filtered_epochs:
raise OperationalException(
f"The index of the epoch to show should be less than {filtered_epochs + 1}."
)
if n < -filtered_epochs:
raise OperationalException(
f"The index of the epoch to show should be greater than {-filtered_epochs - 1}."
)
# Translate epoch index from human-readable format to pythonic
if n > 0:
n -= 1
if epochs:
val = epochs[n]
metrics = val['results_metrics']
if 'strategy_name' in metrics:
strategy_name = metrics['strategy_name']
show_backtest_result(strategy_name, metrics,
metrics['stake_currency'])
HyperoptTools.try_export_params(config, strategy_name, val)
HyperoptTools.show_epoch_details(val,
total_epochs,
print_json,
no_header,
header_str="Epoch details")
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}")
self.hyperopt_table_header = -1
data, timerange = self.backtesting.load_bt_data()
logger.info("Dataload complete. Calculating indicators")
preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
preprocessed[pair] = trim_dataframe(
df,
timerange,
startup_candles=self.backtesting.required_startup)
min_date, max_date = get_timerange(preprocessed)
logger.info(
f'Hyperopting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
dump(preprocessed, self.data_pickle_file)
# We don't need exchange instance anymore while running hyperopt
self.backtesting.exchange.close()
self.backtesting.exchange._api = None # type: ignore
self.backtesting.exchange._api_async = None # type: ignore
# self.backtesting.exchange = None # type: ignore
self.backtesting.pairlists = None # type: ignore
self.backtesting.strategy.dp = None # type: ignore
IStrategy.dp = None # type: ignore
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}')
self.dimensions: List[Dimension] = self.hyperopt_space()
self.opt = self.get_optimizer(self.dimensions, config_jobs)
if self.print_colorized:
colorama_init(autoreset=True)
try:
with Parallel(n_jobs=config_jobs) as parallel:
jobs = parallel._effective_n_jobs()
logger.info(
f'Effective number of parallel workers used: {jobs}')
# Define progressbar
if self.print_colorized:
widgets = [
' [Epoch ',
progressbar.Counter(),
' of ',
str(self.total_epochs),
' (',
progressbar.Percentage(),
')] ',
progressbar.Bar(marker=progressbar.AnimatedMarker(
fill='\N{FULL BLOCK}',
fill_wrap=Fore.GREEN + '{}' + Fore.RESET,
marker_wrap=Style.BRIGHT + '{}' + Style.RESET_ALL,
)),
' [',
progressbar.ETA(),
', ',
progressbar.Timer(),
']',
]
else:
widgets = [
' [Epoch ',
progressbar.Counter(),
' of ',
str(self.total_epochs),
' (',
progressbar.Percentage(),
')] ',
progressbar.Bar(marker=progressbar.AnimatedMarker(
fill='\N{FULL BLOCK}', )),
' [',
progressbar.ETA(),
', ',
progressbar.Timer(),
']',
]
with progressbar.ProgressBar(max_value=self.total_epochs,
redirect_stdout=False,
redirect_stderr=False,
widgets=widgets) as pbar:
EVALS = ceil(self.total_epochs / jobs)
for i in range(EVALS):
# Correct the number of epochs to be processed for the last
# iteration (should not exceed self.total_epochs in total)
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)
f_val = self.run_optimizer_parallel(parallel, asked, i)
self.opt.tell(asked, [v['loss'] for v in f_val])
# Calculate progressbar outputs
for j, val in enumerate(f_val):
# Use human-friendly indexes here (starting from 1)
current = i * jobs + j + 1
val['current_epoch'] = current
val['is_initial_point'] = current <= INITIAL_POINTS
logger.debug(f"Optimizer epoch evaluated: {val}")
is_best = HyperoptTools.is_best_loss(
val, self.current_best_loss)
# This value is assigned here and not in the optimization method
# to keep proper order in the list of results. That's because
# evaluations can take different time. Here they are aligned in the
# order they will be shown to the user.
val['is_best'] = is_best
self.print_results(val)
if is_best:
self.current_best_loss = val['loss']
self.epochs.append(val)
# Save results after each best epoch and every 100 epochs
if is_best or current % 100 == 0:
self._save_results()
pbar.update(current)
except KeyboardInterrupt:
print('User interrupted..')
self._save_results()
logger.info(
f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
f"saved to '{self.results_file}'.")
if self.epochs:
sorted_epochs = sorted(self.epochs, key=itemgetter('loss'))
best_epoch = sorted_epochs[0]
HyperoptTools.print_epoch_details(best_epoch, self.total_epochs,
self.print_json)
else:
# This is printed when Ctrl+C is pressed quickly, before first epochs have
# a chance to be evaluated.
print("No epochs evaluated yet, no best result.")
def test_has_space(hyperopt_conf, spaces, expected_results):
for s in ['buy', 'sell', 'roi', 'stoploss', 'trailing']:
hyperopt_conf.update({'spaces': spaces})
assert HyperoptTools.has_space(hyperopt_conf, s) == expected_results[s]
def test_hyperopt_format_results(hyperopt):
bt_result = {
'results':
pd.DataFrame({
"pair":
["UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC"],
"profit_ratio": [0.003312, 0.010801, 0.013803, 0.002780],
"profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
"open_date": [
Arrow(2017, 11, 14, 19, 32, 00).datetime,
Arrow(2017, 11, 14, 21, 36, 00).datetime,
Arrow(2017, 11, 14, 22, 12, 00).datetime,
Arrow(2017, 11, 14, 22, 44, 00).datetime
],
"close_date": [
Arrow(2017, 11, 14, 21, 35, 00).datetime,
Arrow(2017, 11, 14, 22, 10, 00).datetime,
Arrow(2017, 11, 14, 22, 43, 00).datetime,
Arrow(2017, 11, 14, 22, 58, 00).datetime
],
"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
"close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
"trade_duration": [123, 34, 31, 14],
"is_open": [False, False, False, True],
"stake_amount": [0.01, 0.01, 0.01, 0.01],
"sell_reason": [
SellType.ROI, SellType.STOP_LOSS, SellType.ROI,
SellType.FORCE_SELL
]
}),
'config':
hyperopt.config,
'locks': [],
'final_balance':
0.02,
'backtest_start_time':
1619718665,
'backtest_end_time':
1619718665,
}
results_metrics = generate_strategy_stats({'XRP/BTC': None},
'',
bt_result,
Arrow(2017, 11, 14, 19, 32, 00),
Arrow(2017, 12, 14, 19, 32, 00),
market_change=0)
results_explanation = HyperoptTools.format_results_explanation_string(
results_metrics, 'BTC')
total_profit = results_metrics['profit_total_abs']
results = {
'loss': 0.0,
'params_dict': None,
'params_details': None,
'results_metrics': results_metrics,
'results_explanation': results_explanation,
'total_profit': total_profit,
'current_epoch': 1,
'is_initial_point': True,
}
result = HyperoptTools._format_explanation_string(results, 1)
assert ' 0.71%' in result
assert 'Total profit 0.00003100 BTC' in result
assert '0:50:00 min' in result
def test_load_previous_results2(mocker, testdatadir, caplog) -> None:
mocker.patch('freqtrade.optimize.hyperopt_tools.HyperoptTools._read_results_pickle',
return_value=[{'asdf': '222'}])
results_file = testdatadir / 'hyperopt_results_SampleStrategy.pickle'
with pytest.raises(OperationalException, match=r"The file .* incompatible.*"):
HyperoptTools.load_previous_results(results_file)
def __init__(self, config: Dict[str, Any]) -> None:
self.buy_space: List[Dimension] = []
self.sell_space: List[Dimension] = []
self.roi_space: List[Dimension] = []
self.stoploss_space: List[Dimension] = []
self.trailing_space: List[Dimension] = []
self.dimensions: List[Dimension] = []
self.config = config
self.backtesting = Backtesting(self.config)
if not self.config.get('hyperopt'):
self.custom_hyperopt = HyperOptAuto(self.config)
else:
self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
self.custom_hyperopt.strategy = self.backtesting.strategy
self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(
self.config)
self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
time_now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
strategy = str(self.config['strategy'])
self.results_file: Path = (
self.config['user_data_dir'] / 'hyperopt_results' /
f'strategy_{strategy}_hyperopt_results_{time_now}.pickle')
self.data_pickle_file = (self.config['user_data_dir'] /
'hyperopt_results' /
'hyperopt_tickerdata.pkl')
self.total_epochs = config.get('epochs', 0)
self.current_best_loss = 100
self.clean_hyperopt()
self.num_epochs_saved = 0
# Previous evaluations
self.epochs: List = []
# Populate functions here (hasattr is slow so should not be run during "regular" operations)
if hasattr(self.custom_hyperopt, 'populate_indicators'):
self.backtesting.strategy.advise_indicators = ( # type: ignore
self.custom_hyperopt.populate_indicators) # type: ignore
if hasattr(self.custom_hyperopt, 'populate_buy_trend'):
self.backtesting.strategy.advise_buy = ( # type: ignore
self.custom_hyperopt.populate_buy_trend) # type: ignore
if hasattr(self.custom_hyperopt, 'populate_sell_trend'):
self.backtesting.strategy.advise_sell = ( # type: ignore
self.custom_hyperopt.populate_sell_trend) # type: ignore
# Use max_open_trades for hyperopt as well, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
self.max_open_trades = self.config['max_open_trades']
else:
logger.debug(
'Ignoring max_open_trades (--disable-max-market-positions was used) ...'
)
self.max_open_trades = 0
self.position_stacking = self.config.get('position_stacking', False)
if HyperoptTools.has_space(self.config, 'sell'):
# Make sure use_sell_signal is enabled
if 'ask_strategy' not in self.config:
self.config['ask_strategy'] = {}
self.config['ask_strategy']['use_sell_signal'] = True
self.print_all = self.config.get('print_all', False)
self.hyperopt_table_header = 0
self.print_colorized = self.config.get('print_colorized', False)
self.print_json = self.config.get('print_json', False)
def test_load_previous_results2(mocker, testdatadir, caplog) -> None:
results_file = testdatadir / 'hyperopt_results_SampleStrategy.pickle'
with pytest.raises(OperationalException,
match=r"Legacy hyperopt results are no longer supported.*"):
HyperoptTools.load_filtered_results(results_file, {})