* feat(data-layer): TickAggregator in Node, WASM, Python + C ABI hub First data-layer feature (F2): roll trade ticks up into fixed-timeframe OHLCV candles, exposed natively and over the C ABI. - wickra-data wired as a binding dependency (workspace dep; its wickra-core dep is default-features=false so it never forces rayon into the rayon-free WASM build — native bindings re-enable parallel through their own dependency). - Node `TickAggregator(bucket, gapFill?)` -> `push(price, size, ts): Candle[]`; WASM the same (array of objects); Python `push(...) -> list[tuple]`. - C ABI: `WickraCandle` struct + `wickra_tick_aggregator_new/push/free` (push writes candles into a caller buffer and returns the count), generated via the capi generator's new DATA_LAYER section; cbindgen now parses wickra-data so `TickAggregator` is a forward-declared opaque; header vendored to bindings/go. Verified bit-identical across Node/WASM/Python/C/C++ (o=100 h=101 l=100 c=101 v=3 ts=0 for the shared 3-tick probe). WIP: Go/C#/Java/R generated bindings and the cross-language golden are still pending. * feat(data-layer): TickAggregator in Go, C#, Java, R (lossless push/drain) Complete F2 across all 10 languages: the C-ABI tick aggregator now uses a two-step push/drain so gap-fill candles are never lost, and the four generated bindings expose it idiomatically. - C ABI redesigned: opaque TickAggregator handle (inner aggregator + pending buffer); push consumes a tick and returns the closed-candle count, drain copies them into a count-sized caller buffer. - Go: NewTickAggregator + Push(price,size,ts) []Candle; C#: TickAggregator + Candle[] Push(...); Java: TickAggregator + Candle[] push(...); R: TickAggregator constructor + push() S3 generic returning an (n x 6) numeric matrix. - Candle output record generated per language from WickraCandle. Verified bit-identical to the native bindings (o=100 h=101 l=100 c=101 v=3 ts=0) in Go, C#, Java, and R at runtime; R passes R CMD check (pre-existing doc warnings only). WIP: cross-language data-layer golden + CHANGELOG still pending. * test(data-layer): cross-language golden for the tick aggregator + CHANGELOG gen_golden emits a deterministic tick stream (testdata/golden/data_ticks.csv) and the reference candle streams with and without gap filling (data_candles.csv, data_candles_gap.csv). Every binding replays the shared ticks through its TickAggregator and checks the candles bit-for-bit (fp tolerance) against the Rust reference: - Node / WASM / Python / Go / C# / Java / R: a dedicated parity test each. - C / C++: data_layer_test.c (compiled as both, run as ctest). The gap-fill fixture closes several candles from a single push, exercising the lossless push/drain path. Records the feature under CHANGELOG [Unreleased]. * fix(examples): rename the CSV-loader candle to WickraBar The example CSV helper (wickra_csv.h) defined its own struct WickraCandle, which now collides with the public C ABI WickraCandle (the tick aggregator output) in any example that includes both headers (backtest, multi_timeframe, the strategy examples). The public type owns the name; rename the example loader's bar to WickraBar. The generated golden_test.c is untouched (its only match was the unrelated WickraCandleVolumeOutput).
Wickra — Java
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-UNNAMEDwhen you run your application to silence the native-access warning.
Install
Maven:
<dependency>
<groupId>org.wickra</groupId>
<artifactId>wickra</artifactId>
<version>0.9.2</version>
</dependency>
Gradle:
implementation("org.wickra:wickra:0.9.2")
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:
- Repository & full indicator list: https://github.com/wickra-lib/wickra
- Docs (quickstarts, cookbook, TA-Lib migration): https://docs.wickra.org
- Runnable examples:
examples/java/
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.