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fix(MetaLabeling): previously misinterpreted meta-labeling, now also multiplying base model's prediction with the meta model's prediction (#193)

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* fix(MetaLabeling): previously misinterpreted meta-labeling, now also multiplying base model's prediction with the meta model's prediction

* fix(Evaluate): print results

* fix(Evaluate): make sure we have numerical stability in returns

* fix(Inference): only output and print stats in training mode

* fix(Evaluate): don't add miniscule amount to result
This commit is contained in:
Mark Aron Szulyovszky
2022-02-01 13:09:00 +01:00
committed by GitHub
parent 3eb3ea94e3
commit f85ee6bb9c
11 changed files with 61 additions and 46 deletions
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run_inference.py
+4 -3
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@@ -19,6 +19,7 @@ def run_inference(preload_models:bool, fallback_raw_config: RawConfig):
else:
pipeline_outcome, config = run_pipeline(project_name='price-prediction', with_wandb = False, sweep = False, raw_config=fallback_raw_config)
config.mode = 'inference'
__inference(config, pipeline_outcome)
@@ -39,9 +40,9 @@ def __inference(config: Config, pipeline_outcome: PipelineOutcome):
events, X, y, forward_returns = label_data(config.event_filter, config.labeling, X, returns, forward_returns)
inference_from: pd.Timestamp = X.index[len(X.index) - 2]
inference_from: pd.Timestamp = X.index[len(X.index) - 1]
# 2. Filter for significant events when we want to trade, and label data
# 2. Filter for significant events when we want to trade, and label data
events, X, y, forward_returns = label_data(config.event_filter, config.labeling, X, returns, forward_returns)
# 3. Train directional models
@@ -51,7 +52,7 @@ def __inference(config: Config, pipeline_outcome: PipelineOutcome):
bet_sizing_outcomes = [bet_sizing_with_meta_models(X, training_outcome.predictions, y, forward_returns, config.meta_models, config, 'meta', from_index = inference_from, transformations_over_time = preloaded_outcome.meta_transformations, preloaded_models = [b.model_over_time for b in preloaded_outcome.meta_training]) for training_outcome, preloaded_outcome in zip(directional_training_outcome.training, pipeline_outcome.bet_sizing)]
# 4. Ensemble weights
ensemble_outcome = ensemble_weights([o.weights for o in bet_sizing_outcomes], forward_returns, y, config.no_of_classes)
ensemble_outcome = ensemble_weights([o.weights for o in bet_sizing_outcomes], forward_returns, y, config.no_of_classes, config.mode == 'training')
# 5. (Optional) Additional bet sizing on top of the ensembled weights
ensemble_bet_sizing_outcome = bet_sizing_with_meta_models(X, ensemble_outcome.weights, y, forward_returns, config.meta_models, config, 'ensemble', from_index = inference_from, transformations_over_time = pipeline_outcome.secondary_bet_sizing.meta_transformations, preloaded_models= [b.model_over_time for b in pipeline_outcome.secondary_bet_sizing.meta_training]) if len(config.meta_models) > 0 else None