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fix(Pipeline): remove PCA step that introduced clear lookahead bias (#164)

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This commit is contained in:
Mark Aron Szulyovszky
2022-01-13 12:11:38 +01:00
committed by GitHub
parent 3c2a0d4247
commit 4aefba33ea
4 changed files with 10 additions and 19 deletions
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run_pipeline.py
+3 -3
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@@ -56,13 +56,13 @@ def __run_training(model_config:dict, training_config:dict, data_config:dict):
# 1. Load data, check for validity and process data (feature selection, dimensionality reduction, etc.)
X, y, target_returns = load_data(**configs['data_config'])
if check_data(X, y, training_config) is False: continue
X, original_X, X_pca = process_data(X, y, configs)
X, original_X = process_data(X, y, configs)
# 2. Train a Primary model with optional metalabeling for each asset
training_step_primary, current_predictions = primary_step(X, y, original_X, X_pca, asset, target_returns, configs, reporting)
training_step_primary, current_predictions = primary_step(X, y, original_X, asset, target_returns, configs, reporting)
# 3. Train an Ensemble model with optional metalabeling for each asset
training_step_secondary = secondary_step(X, y, original_X, X_pca, current_predictions, asset, target_returns, configs, reporting)
training_step_secondary = secondary_step(X, y, original_X, current_predictions, asset, target_returns, configs, reporting)
# 4. Save the models
reporting.all_assets.append(Reporting.Asset(ticker=asset[1], primary=training_step_primary, secondary=training_step_secondary))