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🟡 Changes recommended
The new parameters accept invalid values that silently produce behavior outside their documented contracts.
Review effort: Balanced
Findings: 2
Open (2)
What changed in this PR
Adds count-based vertical barriers and influence-aware, two-sided z-score event sampling to the labeling and filtering APIs.
Changes:
- Adds
num_barssupport for vertical barriers. - Extends z-score filtering with influence-weighted event handling.
- Adds synthetic oracle-parity and regression coverage.
| File | Description |
|---|---|
mlfinpy/labeling/labeling.py |
Implements count-based barriers. |
mlfinpy/filters/filters.py |
Implements influence-aware z-score filtering. |
tests/test_parity_2308.py |
Adds parity and edge-case tests. |
tests/test_data/poseidon_parity_2308.json |
Provides synthetic golden fixtures. |
tests/test_filters.py |
Updates market-data expectations. |
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| >= raw_time_series.rolling(window=mean_window).mean() | ||
| + z_score * raw_time_series.rolling(window=std_window).std() | ||
| ].index | ||
| values = raw_time_series.to_numpy() |
Comment on lines
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| if num_bars is not None: | ||
| # Count observations rather than elapsed time; an absent event raises KeyError. | ||
| nearest_index = np.array([close.index.get_loc(event) + num_bars for event in t_events], dtype=int) |
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Un needed