mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 11:17:47 +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
19 lines
734 B
Python
19 lines
734 B
Python
from .types import EventFilter, EventLabeller, EventsDataFrame
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from data_loader.types import ForwardReturnSeries, XDataFrame, ReturnSeries, ySeries
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def label_data(
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event_filter: EventFilter,
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event_labeller: EventLabeller,
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X: XDataFrame,
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returns: ReturnSeries) -> tuple[EventsDataFrame, XDataFrame, ySeries, ForwardReturnSeries]:
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event_start_times = event_filter.get_event_start_times(returns)
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print("| Filtered out ", (1 - (len(event_start_times) / len(returns))) * 100, "% of timestamps" )
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events, forward_returns = event_labeller.label_events(event_start_times, returns)
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X = X.filter(items = events.index, axis = 0)
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y = events['label']
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forward_returns = events['returns']
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return events, X, y, forward_returns |