from config.hashing import hash_data_config from data_loader.load_data import load_data import pandas as pd from training.training import run_single_asset_trainig from reporting.wandb import launch_wandb, send_report_to_wandb, register_config_with_wandb from models.model_map import default_feature_selector_regression, default_feature_selector_classification from utils.helpers import get_first_valid_return_index from config.config import get_default_level_1_daily_config, get_default_level_2_daily_config, get_default_level_2_hourly_config from config.preprocess import validate_config, preprocess_config from feature_selection.feature_selection import select_features from feature_selection.dim_reduction import reduce_dimensionality from training.meta_labeling import run_meta_labeling_training from training.averaged import average_and_evaluate_predictions from reporting.reporting import report_results from typing import Callable, Optional import ray ray.init() def run_pipeline(project_name:str, with_wandb: bool, sweep: bool, get_config:object): wandb, model_config, training_config, data_config = __setup_pipeline(project_name, with_wandb, sweep, get_config) results, all_predictions, all_probabilities = __run_training(model_config, training_config, data_config) report_results(results, all_predictions, model_config, wandb, sweep, project_name) def __setup_pipeline(project_name:str, with_wandb: bool, sweep: bool, get_config: Callable) -> tuple[Optional[object], dict, dict, dict]: model_config, training_config, data_config = get_config() wandb = None if with_wandb: wandb = launch_wandb(project_name=project_name, default_config=dict(**model_config, **training_config, **data_config), sweep=sweep) model_config, training_config, data_config = register_config_with_wandb(wandb, model_config, training_config, data_config) model_config, training_config, data_config = preprocess_config(model_config, training_config, data_config) return wandb, model_config, training_config, data_config def __run_training(model_config:dict, training_config:dict, data_config:dict): results = pd.DataFrame() all_predictions = pd.DataFrame() all_probabilities = pd.DataFrame() all_models_for_all_assets = dict() validate_config(model_config, training_config, data_config) for asset in data_config['assets']: print('--------\nPredicting: ', asset[1]) # 1. Load data data_params = data_config.copy() data_params['target_asset'] = asset X, y, target_returns = load_data(**data_params) original_X = X.copy() first_valid_index = get_first_valid_return_index(X.iloc[:,0]) samples_to_train = len(y) - first_valid_index if samples_to_train < training_config['sliding_window_size_level1'] * 3: print("Not enough samples to train") continue # 2a. Dimensionality Reduction (optional) if training_config['dimensionality_reduction']: X_pca = reduce_dimensionality(X, int(len(X.columns) / 2)) X = X_pca.copy() else: X_pca = X.copy() # 2b. Feature Selection print("Feature Selection started") # TODO: this needs to be done per model! backup_model = default_feature_selector_regression if data_config['method'] == 'regression' else default_feature_selector_classification X = select_features(X = X, y = y, model = model_config['level_1_models'][0][1], n_features_to_select = training_config['n_features_to_select'], backup_model = backup_model, scaling = training_config['scaler'], dynamic_feature_selection = training_config['dynamic_feature_selection'], data_config_hash = hash_data_config(data_params)) # 3. Train Level-1 models current_result, current_predictions, current_probabilities, all_models_for_single_asset = run_single_asset_trainig( ticker_to_predict = asset[1], original_X = original_X, X = X, y = y, target_returns = target_returns, models = model_config['level_1_models'], method = data_config['method'], expanding_window = training_config['expanding_window_level1'], sliding_window_size = training_config['sliding_window_size_level1'], retrain_every = training_config['retrain_every'], scaler = training_config['scaler'], no_of_classes = data_config['no_of_classes'], level = 1 ) all_models_for_all_assets[asset[1]] = dict( name=asset[1], models=all_models_for_single_asset) # 4. Train a Meta-Labeling model for each Level-1 model and replace its predictions with the meta-labeling predictions if training_config['meta_labeling_lvl_1'] == True: for model_name in current_result.columns: lvl1_model_predictions = current_predictions[model_name] prev_sharpe = current_result[model_name]['sharpe'] lvl1_meta_result, lvl1_meta_preds, lvl1_meta_probabilities, meta_labeling_models = run_meta_labeling_training( target_asset=asset[1], X_pca = X_pca, input_predictions= lvl1_model_predictions, y = y, target_returns = target_returns, data_config= data_config, model_config= model_config, training_config= training_config ) new_sharpe = lvl1_meta_result['sharpe'] print("Improvement in sharpe for the meta model: ", ((new_sharpe / prev_sharpe) - 1) * 100, "%") current_result[model_name] = lvl1_meta_result current_predictions[model_name] = lvl1_meta_preds all_models_for_all_assets[asset[1]][model_name] = meta_labeling_models results = pd.concat([results, current_result], axis=1) # With static models, because of the lag in the indicator, the first prediction is NA, so we fill it with zero. all_predictions = pd.concat([all_predictions, current_predictions], axis=1).fillna(0.) all_probabilities = pd.concat([all_probabilities, current_probabilities], axis=1).fillna(0.) if model_config['level_2_model'] is not None: # 3. Average the Level-1 model predictions averaged_predictions, averaged_results = average_and_evaluate_predictions(current_predictions, y, target_returns, data_config) # 3. Train a Meta-labeling model on the averaged level-1 model predictions meta_result, avg_predictions_with_sizing, meta_probabilities, meta_labeling_models = run_meta_labeling_training( target_asset=asset[1], X_pca = X_pca, input_predictions= averaged_predictions, y = y, target_returns = target_returns, data_config= data_config, model_config= model_config, training_config= training_config ) results = pd.concat([results, meta_result], axis=1) all_predictions = pd.concat([all_predictions, avg_predictions_with_sizing], axis=1) all_probabilities = pd.concat([all_probabilities, meta_probabilities], axis=1).fillna(0.) return results, all_predictions, all_probabilities if __name__ == '__main__': run_pipeline(project_name='price-prediction', with_wandb = False, sweep = False, get_config=get_default_level_2_daily_config)