feat(Project): use SKLearn models directly, removed custom ensembling, use 5 minute data, batch inference, numba cusum filter (#192)

* feat(Project): use 5 minute data, running training in parallel, sped up cusum filter by 10x with numba

* fix(WalkForward): inference mini-batch parallelization

* fix(WalkForward): don't use the parallel version of any of the functions

* feat(CI): download the data required

* fix(Project): 5min_crypto folder added

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

* feat(Models): use SKLearn models directly to enable composability

* feat(Inference): batched inference now working, added forecasting_horizon

* fix(Inference): works again

* fix(Inference)

* chore(Models): remove unused Ensemble model

* fix(Labeller): don't just forward shift returns, also take the sum of the data happened until then

* Update test.yml

run_pipeline.py

@@ -1,7 +1,7 @@
 from typing import Optional
 
 from config.types import Config, RawConfig
-from config.preprocess import preprocess_config, validate_config
+from config.preprocess import preprocess_config
 from config.presets import get_default_ensemble_config, get_lightweight_ensemble_config
 
 from data_loader.load import load_data
@@ -13,9 +13,8 @@ from reporting.wandb import launch_wandb, override_config_with_wandb_values
 from reporting.reporting import report_results
 from reporting.saving import save_models
 
-from training.directional_training import train_directional_models
-from training.bet_sizing import bet_sizing_with_meta_models
-from training.ensemble import ensemble_weights
+from training.directional_training import train_directional_model
+from training.bet_sizing import bet_sizing_with_meta_model
 from training.types import PipelineOutcome
 
 import ray
@@ -25,7 +24,7 @@ ray.init()
 def run_pipeline(project_name:str, with_wandb: bool, sweep: bool, raw_config: RawConfig) -> tuple[PipelineOutcome, Config]:
     wandb, config = __setup_config(project_name, with_wandb, sweep, raw_config)
     pipeline_outcome = __run_training(config) 
-    report_results([s.stats for s in pipeline_outcome.directional_training.training], pipeline_outcome.get_output_stats(), pipeline_outcome.get_output_weights(), config, wandb, sweep)
+    report_results(pipeline_outcome.directional_training.training.stats, pipeline_outcome.get_output_stats(), pipeline_outcome.get_output_weights(), config, wandb, sweep)
     save_models(pipeline_outcome, config)
     return pipeline_outcome, config
     
@@ -42,11 +41,9 @@ def __setup_config(project_name:str, with_wandb: bool, sweep: bool, raw_config:
 
 
 def __run_training(config: Config) -> PipelineOutcome:
-
-    validate_config(config)
     
-    # 1. Load data, check for validity
-    X, returns, forward_returns = load_data(
+    print("---> Load data, check for validity")
+    X, returns = load_data(
         assets = config.assets,
         other_assets = config.other_assets,
         exogenous_data = config.exogenous_data,
@@ -59,22 +56,16 @@ def __run_training(config: Config) -> PipelineOutcome:
 
     assert check_data(X, config) == True, "Data is not valid." 
 
-    # 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)
+    print("---> 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)
 
-    # 3. Train directional models
-    directional_training_outcome = train_directional_models(X, y, forward_returns, config, config.directional_models, from_index = None, preloaded_training_step = None)
+    print("---> Train directional models")
+    directional_training_outcome = train_directional_model(X, y, forward_returns, config, config.directional_model, from_index = None, preloaded_training_step = None)
     
-    # 4. Run bet sizing on primary model's output
-    bet_sizing_outcomes = [bet_sizing_with_meta_models(X, outcome.predictions, y, forward_returns, config.meta_models, config, 'meta', None, None, None) for outcome in directional_training_outcome.training]
+    print("---> Run bet sizing on directional model's output")
+    bet_sizing_outcomes = bet_sizing_with_meta_model(X, directional_training_outcome.training.predictions, y, forward_returns, config.meta_model, config, 'meta', None, None, None)
 
-    # 4. Ensemble weights
-    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', None, None, None) if len(config.meta_models) > 0 else None
-    
-    return PipelineOutcome(directional_training_outcome, bet_sizing_outcomes, ensemble_outcome, ensemble_bet_sizing_outcome)
+    return PipelineOutcome(directional_training_outcome, bet_sizing_outcomes)
     
 if __name__ == '__main__':
-    run_pipeline(project_name='price-prediction', with_wandb = False, sweep = False, raw_config=get_default_ensemble_config())
+    run_pipeline(project_name='price-prediction', with_wandb = False, sweep = False, raw_config=get_lightweight_ensemble_config())