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Adds a Java binding (`bindings/java`) over the C ABI hub — the fourth language stecker after C#, Go and R, reaching the hub through the Java Foreign Function & Memory API (Panama, `java.lang.foreign`, final in Java 22) rather than JNI or jextract. ## What's here - **`bindings/java`** — a Maven module (`org.wickra:wickra`) exposing all 514 indicators as idiomatic `AutoCloseable` classes. The downcall handles (`internal/NativeMethods.java`), the per-indicator wrappers and the output records are generated from `bindings/c/include/wickra.h` (same eight-archetype taxonomy as the C#/Go/R generators: scalar/batch, multi-output, bars, profile, values-profile, array-input). The opaque handle is a `MemorySegment` freed by a registered `java.lang.ref.Cleaner` action; multi-output returns a `record` (`null` at warmup), bars a `record[]`, profiles a record with a trailing `double[]`. The hand-written `WickraNative` resolves the native library (a bundled per-platform copy, or a `target/release` fallback for local development) and validates it against a sentinel symbol. repr(C) struct offsets are computed in the generator so the FFM reads land on the exact bytes. - **`examples/java`** — the full example suite mirroring C/C#/Go/R: streaming, backtest, multi_timeframe, parallel_assets (parallel streams), three strategies, and `FetchBtcusdt`/`LiveBinance`. - **CI** — a `java` job builds the C ABI library, sets up JDK 22 (Temurin, with a CDN-flake retry), runs the archetype test suite and the seven offline examples on Linux, macOS and Windows. - **Release** — a gated `java-publish` job (skipped until the `JAVA_PUBLISH_ENABLED` repository variable is set) stages the native libraries from the `wickra-c-<triple>.tar.gz` assets into the binding's resources and deploys to Maven Central with GPG signing. Independent of the GitHub-release job, like the NuGet job. - **Docs** — Java added to the README languages table, project layout, building/testing and comparison table, CONTRIBUTING, ARCHITECTURE, the examples index, the issue/PR templates, the About-description template, and the other binding READMEs. ## Requirements Java 22+ (the FFM API is final since Java 22). The binding requires `--enable-native-access=ALL-UNNAMED` at runtime; the test and example runners pass it automatically. No Rust crate or `Cargo.toml` change — the Java binding is standalone and additive. The generated `*.java` are committed (like the node `index.js`/`index.d.ts`); the generator stays private.
Wickra — Python
Streaming-first technical indicators for Python. pip install wickra — no
system dependencies, no C build tooling.
Wickra is a multi-language technical-analysis library with a Rust core and bindings for Python, Node.js and WebAssembly, 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 Python binding (PyO3); it exposes 200+ streaming-first indicators across sixteen families.
Install
pip install wickra
Pre-built wheels ship for Linux, macOS, and Windows — there is nothing to compile and no C library to track down.
Quick start
import numpy as np
import wickra as ta
# Batch: classic TA-Lib-style usage over a whole array.
prices = np.linspace(100, 200, 1000)
rsi = ta.RSI(14)
values = rsi.batch(prices) # numpy array, NaN during warmup
# Streaming: the same indicator, fed tick by tick in O(1).
rsi = ta.RSI(14)
for price in live_feed:
value = rsi.update(price) # no recomputation over history
if value is not None and value > 70:
print("overbought")
batch(prices) and feeding the same prices through update() produce
identical values — the equivalence is enforced by the test suite.
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/python/
Wickra ships native bindings for Python, Node.js, WebAssembly and Rust, plus a
C ABI hub that any C-capable language (C, C++, Go, C#, Java, R) links against —
all exposing the same indicators from the shared, unsafe-forbidden Rust core.
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.