Add golden-output regression test for feature engineering - #194
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… golden snapshot) Agent-Logs-Url: https://github.com/ch55secake/hyperion/sessions/ee73702d-5f4c-49dc-b185-845c802d3c72 Co-authored-by: ch55secake <87881861+ch55secake@users.noreply.github.com>
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@copilot run ruff format . |
Agent-Logs-Url: https://github.com/ch55secake/hyperion/sessions/9acac620-170c-4a5e-b3c3-2711703b0835 Co-authored-by: ch55secake <87881861+ch55secake@users.noreply.github.com>
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Done — |
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Description
Existing feature tests only validated shape and column presence — silent value regressions (wrong window, off-by-one, incorrect column wiring) would pass undetected. This adds three complementary test classes that assert on computed values directly.
Changes in this pull request
TestKnownInputSanitytotest_feature_engineering.py: constant-price OHLCV input (all prices = 100) where all SMA/EMA/WMA/HMA ≈ 100, ATR = 0, and Momentum_n = 0TestCrossIndicatorConsistencytotest_feature_regression.py: verify FE output matchesti.xxx().shift(1)directly for SMA_10, ATR, RSI, and Momentum_5TestGoldenOutputtotest_feature_regression.py: hard-coded last-5-row snapshot for 10 key features (seed=42, n=50) to catch silent value regressionsruff format .to ensure consistent code style