- switch max losses with known bad losses
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orehunt
parent
60e519eac4
commit
29e9faf167
@ -55,7 +55,7 @@ logger = logging.getLogger(__name__)
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NEXT_POINT_METHODS = ["cl_min", "cl_mean", "cl_max"]
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NEXT_POINT_METHODS_LENGTH = 3
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MAX_LOSS = iinfo(int32).max # just a big enough number to be bad result in loss optimization
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VOID_LOSS = iinfo(int32).max # just a big enough number to be bad result in loss optimization
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class Hyperopt:
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@ -99,7 +99,7 @@ class Hyperopt:
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# total number of candles being backtested
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self.n_samples = 0
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self.current_best_loss = MAX_LOSS
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self.current_best_loss = VOID_LOSS
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self.current_best_epoch = 0
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self.epochs_since_last_best: List = []
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self.avg_best_occurrence = 0
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@ -514,7 +514,7 @@ class Hyperopt:
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# interesting -- consider it as 'bad' (assigned max. loss value)
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# in order to cast this hyperspace point away from optimization
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# path. We do not want to optimize 'hodl' strategies.
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loss: float = MAX_LOSS
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loss: float = VOID_LOSS
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if trade_count >= self.config['hyperopt_min_trades']:
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loss = self.calculate_loss(results=backtesting_results, trade_count=trade_count,
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min_date=min_date.datetime, max_date=max_date.datetime)
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@ -548,6 +548,23 @@ class Hyperopt:
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f"Avg duration {results_metrics['duration']:5.1f} min."
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).encode(locale.getpreferredencoding(), 'replace').decode('utf-8')
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@staticmethod
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def filter_void_losses(vals: List, opt: Optimizer) -> List:
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""" remove out of bound losses from the results """
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if opt.void_loss == VOID_LOSS and len(opt.yi) < 1:
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# only exclude results at the beginning when void loss is yet to be set
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void_filtered = list(filter(lambda v: v["loss"] != VOID_LOSS, vals))
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else:
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if opt.void_loss == VOID_LOSS: # set void loss once
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opt.void_loss = max(opt.yi)
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void_filtered = []
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# default bad losses to set void_loss
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for k, v in enumerate(vals):
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if v["loss"] == VOID_LOSS:
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vals[k]["loss"] = opt.void_loss
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void_filtered = vals
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return void_filtered
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def get_next_point_strategy(self):
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""" Choose a strategy randomly among the supported ones, used in multi opt mode
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to increase the diversion of the searches of each optimizer """
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@ -606,16 +623,15 @@ class Hyperopt:
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while not backend.results.empty():
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vals.append(backend.results.get())
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if vals:
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# values with improbable loss scores should not be told to the optimizer
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# to reduce noise
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vals = list(filter(lambda v: v['loss'] != MAX_LOSS, vals))
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# filter losses
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void_filtered = self.filter_void_losses(vals, opt)
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if vals: # again if all are filtered
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opt.tell([list(v['params_dict'].values()) for v in vals],
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opt.tell([list(v['params_dict'].values()) for v in void_filtered],
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[v['loss'] for v in vals],
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fit=fit)
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if fit:
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fit = False
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vals = []
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del vals[:], void_filtered[:]
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if not to_ask:
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opt.update_next()
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@ -666,18 +682,20 @@ class Hyperopt:
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asked = opt.ask(n_points=self.n_points, strategy=self.get_next_point_strategy())
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# run the backtest for each point
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f_val = [self.backtest_params(e) for e in asked]
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# filter losses
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void_filtered = self.filter_void_losses(f_val, opt)
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# tell the optimizer the results
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Xi = [list(v['params_dict'].values()) for v in f_val]
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yi = [v['loss'] for v in f_val]
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opt.tell(Xi, yi, fit=False)
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# update the board with the new results
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results = results_board.get()
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no_max_loss_results = list(filter(lambda v: v["loss"] != MAX_LOSS, f_val))
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results.append([no_max_loss_results, jobs - 1])
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results_board.put(results)
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if opt.void_loss != VOID_LOSS or len(void_filtered) > 0:
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Xi = [list(v['params_dict'].values()) for v in void_filtered]
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yi = [v['loss'] for v in void_filtered]
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opt.tell(Xi, yi, fit=False)
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# update the board with the new results
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results = results_board.get()
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results.append([void_filtered, jobs - 1])
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results_board.put(results)
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# send back the updated optimizer
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optimizers.put(opt)
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return f_val
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return void_filtered
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def parallel_objective(self, asked, results: Queue, n=0):
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""" objective run in single opt mode, run the backtest, store the results into a queue """
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@ -822,10 +840,11 @@ class Hyperopt:
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opt = self.get_optimizer(self.dimensions, self.n_jobs, self.opt_n_initial_points)
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for _ in range(remaining): # generate optimizers
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# random state is preserved
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backend.optimizers.put(
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opt.copy(random_state=opt.rng.randint(0,
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iinfo(int32).max)))
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del opt
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opt_copy = opt.copy(random_state=opt.rng.randint(0,
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iinfo(int32).max))
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opt_copy.void_loss = VOID_LOSS
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backend.optimizers.put(opt_copy)
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del opt, opt_copy
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else:
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# if we have more than 1 optimizer but are using single opt,
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# pick one discard the rest...
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@ -835,6 +854,7 @@ class Hyperopt:
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self.opt = self.get_optimizer(
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self.dimensions, self.n_jobs, self.opt_n_initial_points
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)
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self.opt.void_loss = VOID_LOSS
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del opts[:]
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def start(self) -> None:
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