mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-02 21:57:46 +00:00
Mark Aron Szulyovszky
3eb3ea94e3
* refactor(Training): added InferenceResult & TrainedModel types * refactor(Pipeline): introduced TrainingOutcome, BetSizingWithMetaOutcome, etc. * fix(Pipeline): getting it to compile * refactor(WalkForward): separate preprocessing step * feat(Pipeline): separate out transformations processing step * refactor(Pipeline): use the Directional model terminology, put bet_sizing into pipeline instead of hiding it in a step * refactor(WalkForward): moved functions to separate folder * fix(WalkForward): use sparse array to store models, process transformations in parallel (lot faster) * fix(Tests): and evaluation * fix(Tests): for realz * fix(Inference): preloading everything now, renamed primary models to directional models * fix(BetSizing): was running transformations on the wrong data, oops * fix(BetSizing): concatenated on the wrong axis accidentally * fix(Reporting): able to use the new Stats type * fix(BetSizing): renamed int column names * fix(Portfolio): name the column properly * fix(Reporting): rename the correct Series, lol * fix(Inference): walk_forwad_inference() can deal with models not being aligned with the starting index * fix(WalkForward): accidentally using the wrong index * fix(WalkForward): use the correct indicies to fetch last model/transformations * fix(CI): changed the name of the results
80 lines
3.6 KiB
Python
80 lines
3.6 KiB
Python
from typing import Optional
|
|
|
|
from config.types import Config, RawConfig
|
|
from config.preprocess import preprocess_config, validate_config
|
|
from config.presets import get_default_ensemble_config, get_lightweight_ensemble_config
|
|
|
|
from data_loader.load import load_data
|
|
from data_loader.process import check_data
|
|
|
|
from labeling.process import label_data
|
|
|
|
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.types import PipelineOutcome
|
|
|
|
import ray
|
|
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)
|
|
save_models(pipeline_outcome, config)
|
|
return pipeline_outcome, config
|
|
|
|
|
|
def __setup_config(project_name:str, with_wandb: bool, sweep: bool, raw_config: RawConfig) -> tuple[Optional[object], Config]:
|
|
wandb = None
|
|
if with_wandb:
|
|
wandb = launch_wandb(project_name=project_name, default_config=raw_config, sweep=sweep)
|
|
raw_config = override_config_with_wandb_values(wandb, raw_config)
|
|
config = preprocess_config(raw_config)
|
|
|
|
return wandb, config
|
|
|
|
|
|
|
|
def __run_training(config: Config) -> PipelineOutcome:
|
|
|
|
validate_config(config)
|
|
|
|
# 1. Load data, check for validity
|
|
X, returns, forward_returns = load_data(
|
|
assets = config.assets,
|
|
other_assets = config.other_assets,
|
|
exogenous_data = config.exogenous_data,
|
|
target_asset = config.target_asset,
|
|
load_non_target_asset = config.load_non_target_asset,
|
|
own_features = config.own_features,
|
|
other_features = config.other_features,
|
|
exogenous_features = config.exogenous_features,
|
|
)
|
|
|
|
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)
|
|
|
|
# 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)
|
|
|
|
# 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]
|
|
|
|
# 4. Ensemble weights
|
|
ensemble_outcome = ensemble_weights([o.weights for o in bet_sizing_outcomes], forward_returns, y, config.no_of_classes)
|
|
|
|
# 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)
|
|
|
|
if __name__ == '__main__':
|
|
run_pipeline(project_name='price-prediction', with_wandb = False, sweep = False, raw_config=get_default_ensemble_config()) |