mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-31 20:57:51 +00:00
Mark Aron Szulyovszky
9d47ee942d
* 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
88 lines
3.8 KiB
Python
88 lines
3.8 KiB
Python
from data_loader.types import ForwardReturnSeries, XDataFrame, ySeries
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from utils.evaluate import discretize_threeway_threshold, 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 models.model_map import default_feature_selector_classification
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from typing import Optional
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from config.types import Config
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from .types import BetSizingWithMetaOutcome, ModelOverTime, TransformationsOverTime
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from training.walk_forward import walk_forward_process_transformations
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from transformations.scaler import get_scaler
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from transformations.rfe import RFETransformation
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from transformations.pca import PCATransformation
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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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config: Config,
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model_suffix: str,
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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(discretize_threeway_threshold(0.33))
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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(equal_except_nan, axis = 1)
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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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expanding_window = config.expanding_window_meta,
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window_size = config.sliding_window_size_meta,
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retrain_every = config.retrain_every,
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from_index = from_index,
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transformations= [
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get_scaler(config.scaler),
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PCATransformation(ratio_components_to_keep=0.5, sliding_window_size=config.sliding_window_size_meta),
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RFETransformation(n_feature_to_select=40, model=default_feature_selector_classification)
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],
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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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expanding_window = config.expanding_window_meta,
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sliding_window_size = config.sliding_window_size_meta,
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retrain_every = config.retrain_every,
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from_index = from_index,
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no_of_classes = 'two',
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level = 'meta',
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output_stats = config.mode == 'training',
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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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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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no_of_classes = 'three-balanced',
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discretize=False
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)
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print(stats)
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else:
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stats = None
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model_id = "model_" + config.target_asset[1] + "_" + model_suffix
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return BetSizingWithMetaOutcome(model_id, meta_outcome, transformations_over_time, avg_predictions_with_sizing, stats)
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