From: Jérôme Benoit <jerome.benoit@piment-noir.org>
Date: Wed, 29 Jul 2026 14:15:47 +0000 (+0200)
Subject: chore(quickadapter): remove committed test harness (tests are kept external to the... 
X-Git-Url: https://git.piment-noir.org/?a=commitdiff_plain;h=aa8fbc403b5efa66a465ba7e868cf705bb686f85;p=freqai-strategies.git

chore(quickadapter): remove committed test harness (tests are kept external to the repo)
---

diff --git a/quickadapter/user_data/strategies/tests/test_causal_weight_availability.py b/quickadapter/user_data/strategies/tests/test_causal_weight_availability.py
deleted file mode 100644
index e35fd25..0000000
--- a/quickadapter/user_data/strategies/tests/test_causal_weight_availability.py
+++ /dev/null
@@ -1,98 +0,0 @@
-"""Regression tests for the causal label-weight availability invariants (#131).
-
-`_compute_knn_pivot_sigma_availability` proves leak-free causal availability by
-assuming the Zigzag confirmation geometry: successive pivots are at least
-`_ZIGZAG_MIN_CONFIRMATION_SLOPES + 1` candles apart, and the earliest possible
-successor position (`first_future`) never exceeds the actual next pivot. These
-tests lock both against the real `_zigzag`, so a future Zigzag change that
-weakens either assumption fails here instead of silently leaking future info.
-
-Requires the freqtrade runtime stack (talib). Run in the container:
-    python -m pytest user_data/strategies/tests/ -q
-or directly:
-    python user_data/strategies/tests/test_causal_weight_availability.py
-"""
-
-import os
-import sys
-
-import numpy as np
-import pandas as pd
-
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-
-import Utils  # noqa: E402  (needs the strategies dir on sys.path)
-
-_PIVOT_SPACING = Utils._ZIGZAG_MIN_CONFIRMATION_SLOPES + 1
-
-
-def _make_ohlc(rng: np.random.Generator, n: int) -> pd.DataFrame:
-    log_return = rng.normal(0.0, 0.01, n).cumsum()
-    close = 100.0 * np.exp(log_return)
-    high = close * (1.0 + np.abs(rng.normal(0.0, 0.004, n)))
-    low = close * (1.0 - np.abs(rng.normal(0.0, 0.004, n)))
-    volume = rng.uniform(1e3, 1e4, n)
-    return pd.DataFrame({"close": close, "high": high, "low": low, "volume": volume})
-
-
-def _real_pivot_series(seed: int, series: int = 120):
-    rng = np.random.default_rng(seed)
-    out = []
-    for _ in range(series):
-        n = int(rng.integers(250, 1000))
-        result = Utils._zigzag(
-            _make_ohlc(rng, n),
-            natr_period=int(rng.integers(10, 20)),
-            natr_multiplier=float(rng.uniform(6.0, 12.0)),
-        )
-        idx = np.asarray(result.indices, dtype=np.int64)
-        if idx.size >= 2:
-            out.append((idx, result.known_at_positions, n))
-    return out
-
-
-def test_min_pivot_spacing_covers_confirmation_bound() -> None:
-    """Real consecutive pivots are never closer than `_PIVOT_SPACING`."""
-    worst = min(
-        int(np.diff(idx).min()) for idx, _known_at, _n in _real_pivot_series(20260729)
-    )
-    assert worst >= _PIVOT_SPACING, worst
-
-
-def test_first_future_bound_is_never_understated() -> None:
-    """`first_future` <= actual next pivot, so possible futures are over-counted.
-
-    Ordinary group: `confirmation + 1`. Initial-orientation replay group (shared
-    confirmation watermark): `position + _PIVOT_SPACING`. Either bound must not
-    exceed the real next pivot, or the availability predicate would understate
-    availability and leak future information.
-    """
-    for idx, known_at_positions, _n in _real_pivot_series(20260729):
-        confirmation_0 = known_at_positions[idx[0]]
-        for k in range(idx.size - 1):
-            confirmation_k = int(known_at_positions[idx[k]])
-            first_future = (
-                idx[k] + _PIVOT_SPACING
-                if confirmation_k == confirmation_0
-                else confirmation_k + 1
-            )
-            assert first_future <= idx[k + 1], (k, first_future, int(idx[k + 1]))
-
-
-def test_knn_sigma_availability_within_bounds() -> None:
-    """Availability stays in `[pivot confirmation, n]` on real pivots."""
-    for idx, known_at_positions, n in _real_pivot_series(1, series=30):
-        availability = Utils._compute_knn_pivot_sigma_availability(
-            idx, known_at_positions, 4, 0.2, 0.5, 2.0, n
-        )
-        assert availability.shape == idx.shape
-        assert np.all(availability >= known_at_positions[idx])
-        assert np.all(availability <= n)
-
-
-if __name__ == "__main__":
-    for name, test in sorted(globals().items()):
-        if name.startswith("test_") and callable(test):
-            test()
-            print(f"PASS {name}")
-    print("ALL PASS")