mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-02 21:57:46 +00:00
Mark Aron Szulyovszky
58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
from data_loader.types import ReturnSeries, ForwardReturnSeries
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from ..types import EventLabeller, EventsDataFrame
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import pandas as pd
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from .utils import create_forward_returns
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class FixedTimeHorionThreeClassBalancedEventLabeller(EventLabeller):
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time_horizon: int
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def __init__(self, time_horizon: int):
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self.time_horizon = time_horizon
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def label_events(
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self, event_start_times: pd.DatetimeIndex, returns: ReturnSeries
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) -> tuple[EventsDataFrame, ForwardReturnSeries]:
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forward_returns = create_forward_returns(returns, self.time_horizon)
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cutoff_point = returns.index[-self.time_horizon]
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event_start_times[event_start_times < cutoff_point]
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event_candidates = forward_returns[event_start_times]
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def get_bins_threeway(x):
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bins = pd.qcut(event_candidates, 3, retbins=True, duplicates="drop")[1]
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if len(bins) != 4:
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# if we don't have enough data for the quantiles, we'll need to add hard-coded values
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lower_bound = bins[0]
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upper_bound = bins[-1]
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bins = [lower_bound] + [-0.02, 0.02] + [upper_bound]
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return bins
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bins = get_bins_threeway(event_candidates)
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def map_class_threeway(current_value):
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lower_threshold = bins[1]
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upper_threshold = bins[2]
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if current_value <= lower_threshold:
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return -1
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elif current_value > lower_threshold and current_value < upper_threshold:
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return 0
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else:
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return 1
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labels = event_candidates.map(map_class_threeway)
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return (
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pd.DataFrame(
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{
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"start": event_start_times,
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"end": event_start_times + pd.Timedelta(days=self.time_horizon),
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"label": labels,
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"returns": forward_returns[event_start_times],
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}
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),
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forward_returns[event_start_times],
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)
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