mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +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
49 lines
1.9 KiB
Python
49 lines
1.9 KiB
Python
from ..types import EventLabeller, EventsDataFrame, ReturnSeries, ForwardReturnSeries
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import pandas as pd
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from .utils import create_forward_returns
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class FixedTimeHorionThreeClassImbalancedEventLabeller(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(self, event_start_times: pd.DatetimeIndex, returns: ReturnSeries) -> 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, 4, retbins=True, duplicates = 'drop')[1]
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if len(bins) != 5:
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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, 0.0, 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[3]
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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 (pd.DataFrame({
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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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}), forward_returns[event_start_times])
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