mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +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
72 lines
2.0 KiB
Python
72 lines
2.0 KiB
Python
import pandas as pd
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from typing import Literal, Optional
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from training.walk_forward import (
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walk_forward_train,
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walk_forward_inference,
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walk_forward_inference_batched,
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)
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from models.base import Model
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from .types import (
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ModelOverTime,
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TransformationsOverTime,
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BaseTrainingOutcome,
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)
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def train_model(
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ticker_to_predict: str,
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X: pd.DataFrame,
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y: pd.Series,
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forward_returns: pd.Series,
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model: Model,
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initial_window_size: int,
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retrain_every: int,
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from_index: Optional[pd.Timestamp],
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level: str,
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class_labels: list[int],
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transformations_over_time: TransformationsOverTime,
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model_over_time: Optional[ModelOverTime],
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) -> BaseTrainingOutcome:
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levelname = ("_" + level) if level == "meta" else ""
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model_id = (
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"model_" + model.name + "_" + ticker_to_predict + levelname
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if model_over_time is None
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else model_over_time.name
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)
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if model_over_time is None:
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print("Train model")
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model_over_time = walk_forward_train(
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model=model,
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X=X,
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y=y,
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forward_returns=forward_returns,
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expanding_window=True,
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window_size=initial_window_size,
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retrain_every=retrain_every,
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from_index=from_index,
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transformations_over_time=transformations_over_time,
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)
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inference_function = (
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walk_forward_inference
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if from_index is not None
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else walk_forward_inference_batched
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)
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predictions, probabilities = inference_function(
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model_name=model_id,
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model_over_time=model_over_time,
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transformations_over_time=transformations_over_time,
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X=X,
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expanding_window=True,
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window_size=initial_window_size,
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retrain_every=retrain_every,
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class_labels=class_labels,
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from_index=from_index,
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)
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assert len(predictions) == len(y)
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return BaseTrainingOutcome(model_id, predictions, probabilities, model_over_time)
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