mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-08 16:47:50 +00:00
Mark Aron Szulyovszky
6ae8acf70e
* feat(Models): added `debug_future_lookahead`, sped up LogisticRegression & DecisionTreeClassifier * feat(Training): added ability to train on expanding_window * feat(Models): tuned some hyperparameters, added expanding_window to sweep config, fixed tests * feat(Models): tune parameters of ensemble models * fix(Config): use window size that works with ensembling
95 lines
4.1 KiB
Python
95 lines
4.1 KiB
Python
from utils.load_data import load_data
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import pandas as pd
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from training.training import run_single_asset_trainig
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from reporting.wandb import launch_wandb, send_report_to_wandb, seperate_configs
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from models.model_map import map_model_name_to_function
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from config import get_default_config, validate_config, get_model_name
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def setup_pipeline(project_name:str, with_wandb: bool, sweep: bool):
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model_config, training_config, data_config = get_default_config()
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wandb = None
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if with_wandb:
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wandb = launch_wandb(project_name=project_name, default_config=dict(**model_config, **training_config, **data_config), sweep=sweep)
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model_config, training_config, data_config = seperate_configs(wandb, model_config, training_config, data_config)
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model_config = map_model_name_to_function(model_config, data_config['method'])
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pipeline(project_name, wandb, sweep, model_config, training_config, data_config)
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def pipeline(project_name:str, wandb, sweep:bool, model_config:dict, training_config:dict, data_config:dict ):
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results = pd.DataFrame()
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validate_config(model_config, training_config, data_config)
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for asset in data_config['all_assets']:
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print('--------\nPredicting: ', asset)
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all_predictions = pd.DataFrame()
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# 1. Load data
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data_params = data_config.copy()
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data_params['target_asset'] = asset
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X, y, target_returns = load_data(**data_params)
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# 2. Train Level-1 models
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current_result, current_predictions = run_single_asset_trainig(
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ticker_to_predict = asset,
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X = X,
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y = y,
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target_returns = target_returns,
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models = model_config['level_1_models'],
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method = data_config['method'],
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expanding_window = training_config['expanding_window'],
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sliding_window_size = training_config['sliding_window_size'],
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retrain_every = training_config['retrain_every'],
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scaler = training_config['scaler'],
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wandb = wandb,
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project_name=project_name,
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sweep=sweep
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)
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results = pd.concat([results, current_result], axis=1)
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all_predictions = pd.concat([all_predictions, current_predictions], axis=1)
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if len(model_config['level_2_models']) > 0:
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# 3. Train Level-2 (Ensemble) model
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ensemble_X = all_predictions
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if training_config['include_original_data_in_ensemble']:
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ensemble_X = pd.concat([ensemble_X, X], axis=1)
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ensemble_result, ensemble_preds = run_single_asset_trainig(
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ticker_to_predict = asset,
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X = ensemble_X,
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y = y,
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target_returns = target_returns,
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models = model_config['level_2_models'],
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method = data_config['method'],
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expanding_window = training_config['expanding_window'],
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sliding_window_size = training_config['sliding_window_size'],
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retrain_every = training_config['retrain_every'],
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scaler = training_config['scaler'],
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wandb = wandb,
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project_name=project_name,
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sweep=sweep
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)
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results = pd.concat([results, ensemble_result], axis=1)
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all_predictions = pd.concat([all_predictions, ensemble_preds], axis=1)
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# 4. Save & report results
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send_report_to_wandb(results, wandb, project_name, get_model_name(model_config))
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results.to_csv('results.csv')
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level1_columns = results[[column for column in results.columns if 'Ensemble' not in column]]
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ensemble_columns = results[[column for column in results.columns if 'Ensemble' in column]]
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print("Mean Sharpe ratio for Level-1 models: ", level1_columns.loc['sharpe'].mean())
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print("Mean Sharpe ratio for Level-2 (Ensemble) models: ", ensemble_columns.loc['sharpe'].mean())
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if sweep:
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if wandb.run is not None:
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wandb.finish()
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if __name__ == '__main__':
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setup_pipeline(project_name='price-prediction', with_wandb = False, sweep = False) |