Files
wickra/bindings/java
kingchenc 4f708d410d 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
2026-06-15 04:48:51 +02:00
..
2026-06-14 02:42:45 +02:00
2026-06-14 02:42:45 +02:00

Wickra — Java

CI codecov Maven Central License: MIT OR Apache-2.0

Streaming-first technical indicators for the JVM, on the Java Foreign Function & Memory API — prebuilt native library, no JNI, no system dependencies.

Wickra is a multi-language technical-analysis library with a Rust core and bindings for Python, Node.js and WASM, plus a C ABI for C, C++, C#, Go, Java, R and any other C-capable language. Every indicator is an O(1) streaming state machine, so live trading bots and historical backtests share the exact same implementation. This package is the Java binding; it consumes the C ABI hub through the Panama FFM API (java.lang.foreign) and exposes all 514 streaming-first indicators as idiomatic AutoCloseable classes.

Requirements

  • Java 22 or later (the FFM API is final since Java 22; no preview flag).
  • The FFM API is restricted: pass --enable-native-access=ALL-UNNAMED when you run your application to silence the native-access warning.

Install

Maven:

<dependency>
  <groupId>org.wickra</groupId>
  <artifactId>wickra</artifactId>
  <version>0.9.1</version>
</dependency>

Gradle:

implementation("org.wickra:wickra:0.9.1")

The native library ships prebuilt per platform (Linux, macOS, Windows — x64 and arm64) inside the jar and is extracted automatically on first use. There is nothing to compile.

Quick start

import org.wickra.Ema;
import org.wickra.Rsi;

// Batch: run an indicator over a whole series (NaN at warmup positions).
double[] prices = new double[1000];
for (int i = 0; i < prices.length; i++) {
    prices[i] = 100.0 + i * 0.1;
}
try (Ema ema = new Ema(20)) {
    double[] values = ema.batch(prices);
}

// Streaming: the same indicator, fed tick by tick in O(1).
try (Rsi rsi = new Rsi(14)) {
    for (double price : liveFeed) {
        double value = rsi.update(price); // NaN during warmup, no recomputation
        if (Double.isFinite(value) && value > 70) {
            System.out.println("overbought");
        }
    }
}

batch(prices) and feeding the same prices through update() produce identical values — the equivalence is enforced by the test suite. Multi-output indicators (MACD, Bollinger, ADX, …) return a record, null while warming up. Each indicator owns a native handle freed by a Cleaner; close() releases it eagerly (use try-with-resources).

Benchmark

benchmarks/ reports streaming and batch updates-per-second for SMA, ATR and MACD. It measures this binding's FFI overhead, not a cross-library ratio (the same Rust core runs under every binding) — see the repository BENCHMARKS.md §3.

cargo build -p wickra-c --release
mvn -q install -DskipTests
mvn -q -f benchmarks exec:exec -Dexec.mainClass=org.wickra.benchmarks.Throughput

Documentation

The full indicator catalogue, guides, quickstarts, and API reference live in the main repository and documentation site:

Wickra ships native bindings for Python, Node.js, WASM and Rust, plus a C ABI hub that any C-capable language (C, C++, C#, Go, Java, R) links against — all exposing the same indicators from the shared, unsafe-forbidden Rust core.

Security

Found a security issue? Please don't open a public issue. Report it privately via the affected repository's Security tab ("Report a vulnerability") or email support@wickra.org with a subject line starting [wickra security]. Full policy: https://github.com/wickra-lib/wickra/blob/main/SECURITY.md.

Disclaimer

Wickra is an indicator toolkit, not a trading system. The values it computes are deterministic transforms of the input data — they are not financial advice and do not predict the market. Any use in a live trading context is at your own risk. The library is provided as is, without warranty of any kind.

License

Licensed under either of Apache-2.0 or MIT at your option.