kingchenc
4f708d410d
* test: golden-pin the four de-duplicated indicators across all C-ABI bindings Extend gen_golden to emit reference fixtures for AdOscillator (ADOSC), IntradayIntensity, AwesomeOscillatorHistogram and AverageDrawdown, and replay them through the Go / C# / Java / R golden harnesses so their corrected definitions stay bit-identical to the Rust core in every binding. Go suite verified locally (gcc 13 + cgo): all 9 golden tests pass; C#/Java/R use the same fixtures and harness pattern (CI-verified). First step of extending the golden coverage beyond the seven archetype representatives. * test: golden-pin the scalar-output tranche (308 indicators) against Rust Extend gen_golden with a generated emit_scalar that writes reference fixtures for every single-f64-output indicator (scalar / candle / pairwise input) using valid constructor params, and add a manifest-driven generic Python golden replay that reconstructs each by its native name and checks it bit-for-bit against the Rust output. 308 indicators now value-tied to the Rust core in Python (pytest: 308/308). Takes golden coverage from the 7 archetype representatives to 308+ of the catalogue. 22 scalar indicators with non-default constructor constraints are skipped by gen_golden for now (logged), as are non-f64-output ones; multi-output, exotic inputs and the per-indicator arg arities of the C-ABI/Node replays follow. Generated + verified locally with the full toolchain. * test: golden-pin the multi-output tranche (70 indicators) in Python Add a generated emit_multi to gen_golden (per-indicator Output-field access, one CSV column per field) and a manifest-driven generic Python replay that checks every field of each multi-output indicator against the Rust reference. 70 multi-output indicators now value-tied to Rust in Python; combined with the scalar tranche, 378 indicators are golden-pinned. 8 multi with non-default param constraints and 5 with non-f64 Output fields (Option/Vec/i64) are deferred. pytest green. * test(golden): add 30 constraint-tuned indicators to scalar/multi golden suite Emit golden fixtures for 22 scalar-output and 8 multi-output indicators whose constructors need non-default parameters (Alma, Jma, Psar, T3, Mama, DoubleBollinger, ZigZag, ...). All 408 fixtures replay bit-for-bit through the Python binding. * test(golden): cover 36 missed scalar/multi indicators Add 26 single-output (LinearRegression family, HT cycle, Candle volatility estimators, DrawdownDuration) and 10 multi-output (BollingerBands, MACD/MACDEXT/MACDFIX, Camarilla, VWAP bands, ...) indicators to the golden suite. 444 fixtures replay bit-for-bit through the Python binding. * test(golden): cover 50 exotic-input indicators Add deterministic synthetic feeders for the DerivativesTick (17), CrossSection (15), Trade (8), TradeQuote (3) and OrderBook (7) families, derived from the shared OHLCV input series in both gen_golden and a new Python replay harness (test_golden_exotic). All 494 fixtures replay bit-for-bit through the Python binding. * test(golden): complete 514-indicator golden coverage Add the final tranches: 3 mixed multi-output indicators (Ichimoku, WilliamsFractals, LeadLagCrossCorrelation), 6 histogram profiles (time/volume seasonality + TPO/volume price profiles), 10 alt-chart bar builders and the footprint. Every one of the 514 distinct indicators now has a Rust-generated g_<Canonical>.csv fixture and a generic Python replay (scalar/multi/exotic/profile/bars), all passing bit-for-bit. * test(golden): add generic Node replay for all 514 indicators A manifest-driven node:test harness reconstructs every indicator by its native class, feeds the same synthetic stream derived from the shared golden input, and checks output bit-for-bit against the Rust reference fixtures (scalar/multi/exotic/profile/bars). node_manifest.json is generated from index.d.ts plus the Python-side manifests. 514/514 pass. * test(golden): add generated Go replay for all 514 indicators golden_all_test.go (generated by gen_golden_test.py) reconstructs every Go indicator, feeds the shared synthetic stream and checks output bit-for-bit against the Rust reference fixtures. A reflection-based comparator flattens multi-output structs, profiles and bar slices so one path covers all archetypes. This is the first C-ABI binding verified across the full catalogue. 514/514 pass. * test(golden): add generated C# replay for all 514 indicators GoldenAllTests.g.cs (generated by gen_golden_test.py) reconstructs every C# indicator, feeds the shared synthetic stream and checks output bit-for-bit against the Rust reference fixtures via a reflection-based flatten covering scalar/multi/profile/bar archetypes. 514/514 pass. Also add the '#nullable enable' directive the compiler requires to the generated Indicators.g.cs, clearing the four CS8669 warnings on the nullable double[] profile return types. * fix(java): marshal C ABI bool params correctly; add 514 golden replay The Java FFM binding marshalled the cross-section state flags (newHigh, newLow, aboveMa, onBuySignal) as JAVA_DOUBLE arrays, but the C ABI takes them as const bool* (one byte each), so the native side read the low byte of each 8-byte double and saw every flag as false. Add WickraNative. boolSegment and use it across the 15 cross-section indicators. Also pass the MacdExt MaType arguments as byte to match the uint8_t downcall descriptor (was int, throwing WrongMethodTypeException). Add GoldenAllTest.java (generated by gen_golden_test.py): a reflection runner replaying all 514 indicators against the Rust reference fixtures. The bugs above were found by this test; 514/514 now pass. * fix(r): marshal C ABI bool flags correctly; add 514 golden replay The R wrapper passed the cross-section state flags as (bool *)REAL(x), reinterpreting the 8-byte doubles as 1-byte bools so the native side read every flag as false. Add wk_bool_vec to convert each flag vector into a real C bool buffer and use it for all 15 cross-section update wrappers. Add test-golden-all.R + generated golden_specs.R: a reflective runner replaying all 514 indicators against the Rust reference fixtures. The bug above was found by this test; verified 514/514 pass locally. * test(golden): add WASM replay for all 514 indicators A manifest-driven node:test harness loads the nodejs-target wasm-pack build, reconstructs every indicator by its JS class, feeds the shared synthetic stream and checks output bit-for-bit against the Rust reference fixtures. wasm_manifest.json is generated from the wasm .d.ts plus the shared manifests; a recursive flattener covers scalar, multi (Reflect objects), profile and bar shapes. 514/514 pass locally (wasm-pack build --target nodejs, then node --test). * test(golden): add C and C++ replay for all 514 indicators golden_test.c (generated by gen_golden_test.py) drives every indicator through the C ABI (wickra.h) and checks output bit-for-bit against the Rust reference fixtures. golden_test.cpp #includes the same source so the identical runner is compiled and run under both gcc (C) and g++ (C++) via the CMake targets golden_test / golden_test_cpp — proving the extern "C" header is consumable from each language. Both 514/514 (verified via ctest). * test(golden): gofmt the generated Go golden replay * test(golden): make the Node fixture reader CRLF-safe and pin fixtures to LF
62 lines
2.3 KiB
Python
62 lines
2.3 KiB
Python
"""Generic golden replay for the profile family: time/volume seasonality
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histograms (`bins`) and price-binned market profiles (`price_low, price_high,
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bins`). Each profile emits a fixed-width row once warm and `NaN`s during warmup.
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The shared `testdata/golden/input.csv` candle series is replayed through the
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Python FFI and the flattened histogram is checked bit-for-bit against the
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Rust-generated `g_<Canonical>.csv`.
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"""
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import json
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import math
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import os
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import pytest
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import wickra as ta
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HERE = os.path.dirname(__file__)
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GOLDEN = os.path.normpath(os.path.join(HERE, "..", "..", "..", "testdata", "golden"))
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def _cell(s):
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return math.nan if s == "nan" else float(s)
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def _rows(name):
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with open(os.path.join(GOLDEN, name + ".csv")) as f:
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return [[_cell(x) for x in line.split(",")] for line in f.read().splitlines()[1:] if line.strip()]
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def _input():
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with open(os.path.join(GOLDEN, "input.csv")) as f:
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return [[float(x) for x in line.split(",")] for line in f.read().splitlines()[1:] if line.strip()]
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with open(os.path.join(GOLDEN, "profile_manifest.json")) as _mf:
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MANIFEST = json.load(_mf)
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ROWS = _input()
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@pytest.mark.parametrize("spec", MANIFEST, ids=[m["canonical"] for m in MANIFEST])
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def test_profile_matches_golden(spec):
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ind = getattr(ta, spec["native"])(*spec["params"])
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expected = _rows("g_" + spec["canonical"])
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width = spec["width"]
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for i, (o, h, l, c, v) in enumerate(ROWS):
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got = ind.update((o, h, l, c, v, i))
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want = expected[i]
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assert len(want) == width, f"{spec['canonical']} row {i}: fixture width {len(want)} != {width}"
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if got is None:
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assert all(math.isnan(w) for w in want), f"{spec['canonical']} row {i}: want {want} got None"
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continue
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if spec["kind"] == "pricebins":
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price_low, price_high, bins = got
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vals = [price_low, price_high, *list(bins)]
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else:
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vals = list(got)
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assert len(vals) == width, f"{spec['canonical']} row {i}: arity {len(vals)} != {width}"
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for gv, w in zip(vals, want):
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if math.isnan(w):
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assert math.isnan(gv), f"{spec['canonical']} row {i}: want NaN got {gv}"
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else:
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assert abs(gv - w) <= 1e-6 * max(1.0, abs(w)), f"{spec['canonical']} row {i}: got {gv} want {w}"
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