test: golden-pin the four de-duplicated indicators across all bindings (#305)
* 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
This commit is contained in:
kingchenc
@@ -0,0 +1,70 @@
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"""Generic golden replay for the alt-chart bar builders and the footprint.
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Each builder turns one candle into 0..n completed bars, so the fixture stores
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one CSV line per input candle holding every bar flattened (an empty line means
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no bar closed on that candle). Close-driven builders (Renko, Kagi, P&F, Range,
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Three-Line-Break) receive a flat candle, mirroring the binding's `update(close)`.
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Values are checked bit-for-bit against the 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 _bar_rows(name):
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# Keep blank lines: one row per input candle, blank == no bar closed.
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with open(os.path.join(GOLDEN, name + ".csv")) as f:
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lines = f.read().splitlines()[1:]
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return [[] if not ln.strip() else [_cell(x) for x in ln.split(",")] for ln in lines]
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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, "bars_manifest.json")) as _mf:
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MANIFEST = json.load(_mf)
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ROWS = _input()
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def _flatten(bars):
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out = []
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for bar in bars:
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out.extend(float(x) for x in bar)
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return out
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@pytest.mark.parametrize("spec", MANIFEST, ids=[m["canonical"] for m in MANIFEST])
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def test_bars_match_golden(spec):
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ind = getattr(ta, spec["native"])(*spec["params"])
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expected = _bar_rows("g_" + spec["canonical"])
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feed = spec["feed"]
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assert len(expected) == len(ROWS), f"{spec['canonical']}: {len(expected)} rows vs {len(ROWS)} inputs"
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for i, (o, h, l, c, v) in enumerate(ROWS):
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if feed == "close":
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produced = ind.update(c)
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elif feed == "candle4":
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produced = ind.update(o, h, l, c)
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elif feed == "candle5":
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produced = ind.update(o, h, l, c, v)
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else: # trade footprint
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produced = ind.update(c, v, c >= o)
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got = _flatten(produced)
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want = expected[i]
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assert len(got) == len(want), f"{spec['canonical']} row {i}: arity {len(got)} vs {len(want)}"
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for gv, w in zip(got, 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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@@ -0,0 +1,128 @@
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"""Generic golden replay for the exotic-input families: DerivativesTick,
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CrossSection, Trade, TradeQuote and OrderBook indicators.
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Each family feeds a synthetic stream deterministically derived from the shared
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`testdata/golden/input.csv` OHLCV rows — the exact same construction the Rust
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`gen_golden` binary uses — and every value is checked bit-for-bit against the
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Rust-generated `g_<Canonical>.csv`. This pins the Python FFI for indicators
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whose inputs cannot be expressed as a plain close/candle/pair stream.
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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, "exotic_manifest.json")) as _mf:
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MANIFEST = json.load(_mf)
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ROWS = _input()
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def _deriv_fields(o, h, l, c, v):
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return {
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"funding_rate": (c - o) / c * 0.01,
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"mark_price": c,
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"index_price": c - 0.5,
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"futures_price": c + 1.0,
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"open_interest": v * 10.0,
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"long_size": v * 0.6,
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"short_size": v * 0.4,
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"taker_buy_volume": v * 0.55,
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"taker_sell_volume": v * 0.45,
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"long_liquidation": h - c,
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"short_liquidation": c - l,
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}
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def _cross_lists(o, h, l, c, v):
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change = [(c - o) + j for j in range(5)]
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volume = [v + j * 10.0 for j in range(5)]
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new_high = [j % 2 == 0 for j in range(5)]
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new_low = [j % 3 == 0 for j in range(5)]
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above_ma = [j % 2 == 0 for j in range(5)]
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on_buy_signal = [j % 3 == 0 for j in range(5)]
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return change, volume, new_high, new_low, above_ma, on_buy_signal
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def _ob_lists(o, h, l, c, v):
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bid_px = [c - 0.1 * (k + 1) for k in range(5)]
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bid_sz = [v / (k + 1) for k in range(5)]
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ask_px = [c + 0.1 * (k + 1) for k in range(5)]
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ask_sz = [v * 0.9 / (k + 1) for k in range(5)]
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return bid_px, bid_sz, ask_px, ask_sz
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def _assert_scalar(got, want, canonical, i):
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got = math.nan if got is None else got
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if math.isnan(want):
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assert math.isnan(got), f"{canonical} row {i}: want NaN got {got}"
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elif math.isinf(want):
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assert math.isinf(got) and (got > 0) == (want > 0), f"{canonical} row {i}: got {got} want {want}"
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else:
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assert abs(got - want) <= 1e-6 * max(1.0, abs(want)), f"{canonical} row {i}: got {got} want {want}"
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def _specs(family):
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return [(family, s) for s in MANIFEST[family]]
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@pytest.mark.parametrize(
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"family,spec",
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_specs("deriv") + _specs("cross") + _specs("trade") + _specs("trademid") + _specs("ob"),
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ids=[s["canonical"] for fam in ("deriv", "cross", "trade", "trademid", "ob") for s in MANIFEST[fam]],
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)
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def test_exotic_matches_golden(family, 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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n = spec.get("n")
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for i, (o, h, l, c, v) in enumerate(ROWS):
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if family == "deriv":
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f = _deriv_fields(o, h, l, c, v)
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got = ind.update(*[f[a] for a in spec["args"]])
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elif family == "cross":
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change, volume, nh, nl, above_ma, on_buy = _cross_lists(o, h, l, c, v)
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extra = spec.get("extra")
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if extra == "above_ma":
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got = ind.update(change, volume, nh, nl, above_ma)
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elif extra == "on_buy_signal":
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got = ind.update(change, volume, nh, nl, on_buy)
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else:
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got = ind.update(change, volume, nh, nl)
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elif family == "trade":
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got = ind.update(c, v, c >= o)
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elif family == "trademid":
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got = ind.update(c, v, c >= o, (h + l) / 2.0)
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else: # ob
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bid_px, bid_sz, ask_px, ask_sz = _ob_lists(o, h, l, c, v)
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got = ind.update(bid_px, bid_sz, ask_px, ask_sz)
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want = expected[i]
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if n: # multi-output (LiquidationFeatures)
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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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vals = list(got)
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assert len(vals) == len(want), f"{spec['canonical']} row {i}: arity {len(vals)} vs {len(want)}"
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for gv, w in zip(vals, want):
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_assert_scalar(gv, w, spec["canonical"], i)
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else:
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_assert_scalar(got, want[0], spec["canonical"], i)
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@@ -0,0 +1,60 @@
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"""Generic golden replay for multi-output indicators: each entry in
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multi_manifest.json is reconstructed by its native name and every output field
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is checked against the Rust-generated g_<Canonical>.csv (one column per field).
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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, "multi_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_multi_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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inp = spec["input"]
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for i, (o, h, l, c, v) in enumerate(ROWS):
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if inp == "f64":
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got = ind.update(c)
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elif inp == "Candle":
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got = ind.update((o, h, l, c, v, i))
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else:
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got = ind.update(c, o)
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want = expected[i]
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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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vals = list(got)
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assert len(vals) == len(want), f"{spec['canonical']} row {i}: arity {len(vals)} vs {len(want)}"
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for gv, w in zip(vals, want):
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gv = math.nan if gv is None else gv
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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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elif math.isinf(w):
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assert math.isinf(gv) and (gv > 0) == (w > 0)
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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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@@ -0,0 +1,61 @@
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"""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):
|
||||
if math.isnan(w):
|
||||
assert math.isnan(gv), f"{spec['canonical']} row {i}: want NaN got {gv}"
|
||||
else:
|
||||
assert abs(gv - w) <= 1e-6 * max(1.0, abs(w)), f"{spec['canonical']} row {i}: got {gv} want {w}"
|
||||
@@ -0,0 +1,67 @@
|
||||
"""Generic golden replay: every scalar indicator in scalar_manifest.json is
|
||||
constructed by its native name with the recorded params, fed the shared golden
|
||||
input, and checked bit-for-bit against the Rust-generated g_<Canonical>.csv.
|
||||
|
||||
This ties the Python binding to the Rust reference for the whole scalar-output
|
||||
tranche (not just the seven archetype representatives). Fixtures + manifest are
|
||||
produced by `cargo run -p wickra-examples --bin gen_golden`.
|
||||
"""
|
||||
import json
|
||||
import math
|
||||
import os
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
import wickra as ta
|
||||
|
||||
HERE = os.path.dirname(__file__)
|
||||
GOLDEN = os.path.normpath(os.path.join(HERE, "..", "..", "..", "testdata", "golden"))
|
||||
|
||||
|
||||
def _cell(s):
|
||||
return math.nan if s == "nan" else float(s)
|
||||
|
||||
|
||||
def _load(name):
|
||||
with open(os.path.join(GOLDEN, name + ".csv")) as f:
|
||||
return [_cell(line.strip()) for line in f.read().splitlines()[1:] if line.strip()]
|
||||
|
||||
|
||||
def _input():
|
||||
rows = []
|
||||
with open(os.path.join(GOLDEN, "input.csv")) as f:
|
||||
for line in f.read().splitlines()[1:]:
|
||||
if line.strip():
|
||||
rows.append([float(x) for x in line.split(",")])
|
||||
return rows
|
||||
|
||||
|
||||
with open(os.path.join(GOLDEN, "scalar_manifest.json")) as _mf:
|
||||
MANIFEST = json.load(_mf)
|
||||
ROWS = _input()
|
||||
|
||||
|
||||
@pytest.mark.parametrize("spec", MANIFEST, ids=[m["canonical"] for m in MANIFEST])
|
||||
def test_scalar_matches_golden(spec):
|
||||
cls = getattr(ta, spec["native"])
|
||||
ind = cls(*spec["params"])
|
||||
expected = _load("g_" + spec["canonical"])
|
||||
inp = spec["input"]
|
||||
for i, (o, h, l, c, v) in enumerate(ROWS):
|
||||
if inp == "f64":
|
||||
got = ind.update(c)
|
||||
elif inp == "Candle":
|
||||
got = ind.update((o, h, l, c, v, i))
|
||||
else: # pairwise (f64, f64): generator fed (close, open)
|
||||
got = ind.update(c, o)
|
||||
want = expected[i]
|
||||
got = math.nan if got is None else got
|
||||
if math.isnan(want):
|
||||
assert math.isnan(got), f"{spec['canonical']} row {i}: want NaN got {got}"
|
||||
elif math.isinf(want):
|
||||
assert math.isinf(got) and (got > 0) == (want > 0), (
|
||||
f"{spec['canonical']} row {i}: got {got} want {want}"
|
||||
)
|
||||
else:
|
||||
tol = 1e-6 * max(1.0, abs(want))
|
||||
assert abs(got - want) <= tol, f"{spec['canonical']} row {i}: got {got} want {want}"
|
||||
Reference in New Issue
Block a user