mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-29 03:37:45 +00:00
Mark Aron Szulyovszky
b5ddee8dce
* feat(HPO): added `run_hpo` script * fix(Linter): ran * feat(HPO): removed any reference to sweep (superseeded by optuna) * fix(HPO): optimize for sharpe * fix(Config): removed glassnode data, save trials from hpo * feat(Labelling): added three-balanced method works again * fix(BetSizing): set the correct class labels * fix(HPO): powerset should return what's expected, added two new normalization methods * fix(Linter): ran * fix(DataLoader): sort the dataframe when fetching data * fix(Config): only take z-score of other assets
110 lines
3.6 KiB
Python
110 lines
3.6 KiB
Python
from data_loader.types import ForwardReturnSeries, XDataFrame, ySeries
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from utils.evaluate import evaluate_predictions
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from utils.helpers import equal_except_nan
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from .train_model import train_model
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import pandas as pd
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from models.base import Model
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from typing import Optional
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from config.types import Config
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from .types import (
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BetSizingWithMetaOutcome,
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ModelOverTime,
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TransformationsOverTime,
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)
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from training.walk_forward import walk_forward_process_transformations
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from transformations.base import Transformation
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from labeling.labellers.utils import (
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discretize_binary_zero_one,
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discretize_threeway_threshold,
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)
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import pprint
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def bet_sizing_with_meta_model(
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X: XDataFrame,
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input_predictions: pd.Series,
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y: ySeries,
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forward_returns: ForwardReturnSeries,
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model: Model,
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transformations: list[Transformation],
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config: Config,
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from_index: Optional[pd.Timestamp],
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transformations_over_time: Optional[TransformationsOverTime] = None,
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preloaded_models: Optional[ModelOverTime] = None,
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) -> BetSizingWithMetaOutcome:
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input_predictions.name = "model_predictions"
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discretized_predictions = input_predictions.apply(
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discretize_threeway_threshold(0.33)
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)
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discretized_predictions.name = "model_discretized_predictions"
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meta_y: pd.Series = pd.concat([discretized_predictions, y], axis=1).apply(
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equal_except_nan, axis=1
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)
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meta_X = pd.concat([X, input_predictions, discretized_predictions], axis=1)
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if transformations_over_time is None:
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print("Preprocess transformations")
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transformations_over_time = walk_forward_process_transformations(
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X=meta_X,
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y=meta_y,
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forward_returns=forward_returns,
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window_size=config.initial_window_size,
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retrain_every=config.retrain_every,
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from_index=from_index,
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transformations=transformations,
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)
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meta_outcome = train_model(
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ticker_to_predict="prediction_correct",
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X=meta_X,
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y=meta_y,
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forward_returns=forward_returns,
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model=model,
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initial_window_size=config.initial_window_size,
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retrain_every=config.retrain_every,
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from_index=from_index,
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level="meta",
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class_labels=[0, 1],
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transformations_over_time=transformations_over_time,
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model_over_time=preloaded_models,
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)
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meta_predictions = meta_outcome.predictions
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bet_size = meta_outcome.probabilities.iloc[:, 1]
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avg_predictions_with_sizing = input_predictions * meta_predictions * bet_size
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if config.mode == "training":
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pp = pprint.PrettyPrinter(depth=2)
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meta_stats = evaluate_predictions(
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forward_returns=forward_returns,
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y_pred=meta_outcome.predictions,
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y_true=meta_y,
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discretize_func=discretize_binary_zero_one,
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labels=[0, 1],
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transaction_costs=config.transaction_costs,
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)
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pp.pprint(meta_stats)
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stats = evaluate_predictions(
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forward_returns=forward_returns,
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y_pred=avg_predictions_with_sizing,
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y_true=y,
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discretize_func=config.labeling.get_discretize_function(),
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labels=config.labeling.get_labels(),
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transaction_costs=config.transaction_costs,
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)
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pp.pprint(stats)
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else:
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stats = None
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model_id = "model_" + config.target_asset.file_name + "_meta"
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outcome_dict = vars(meta_outcome)
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outcome_dict["model_id"] = model_id
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return BetSizingWithMetaOutcome(
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**outcome_dict,
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transformations=transformations_over_time,
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weights=avg_predictions_with_sizing,
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stats=stats,
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)
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