kingchenc
f7f0bfbc48
Maintenance release — supply-chain and CI housekeeping only. No library code or public API changes. ### Security - Triaged the pyo3 advisories RUSTSEC-2026-0176 / RUSTSEC-2026-0177 as not affecting Wickra (vulnerable APIs unreachable from the binding; fix blocked upstream by rust-numpy pinning pyo3 `^0.28`). Recorded in `deny.toml` and `osv-scanner.toml`. ### Changed - Java binding: `central-publishing-maven-plugin` 0.5.0 → 0.10.0. - CI GitHub Actions bumped to latest (checkout, setup-go, setup-java, codeql-action, taiki-e/install-action). - Added a Maven ecosystem to Dependabot. Version bumped across all manifests/lockfiles via `bump_version.py`; Cargo.lock refreshed. CHANGELOG `[0.8.9]` filled. Tag/publish to follow on explicit GO.
Wickra — R 
Streaming-first technical indicators for R, over the Wickra C ABI hub via .Call.
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 and historical backtests share the exact same
implementation. This package is the R binding; it reaches the C ABI hub through
R's native .Call interface and exposes all 514 indicators as constructors that
return a lightweight wickra_indicator object.
Install
The package compiles a thin C glue layer (.Call) against the prebuilt Wickra
C ABI library, so a C toolchain (Rtools on Windows) is required, plus the C ABI
header and library. Build the library from the workspace, then install the
package pointing at it:
cargo build -p wickra-c --release
WICKRA_INCLUDE_DIR="$PWD/bindings/c/include" \
WICKRA_LIB_DIR="$PWD/target/release" \
R CMD INSTALL bindings/r
On Windows the C ABI DLL is bundled into the package and put on the load path automatically; on Linux and macOS the library path is baked in via rpath.
Quick start
library(wickra)
# Batch: run an indicator over a whole series (NaN at warmup positions).
prices <- 100 + (0:999) * 0.1
sma <- Sma(20)
values <- batch(sma, prices)
# Streaming: the same indicator, fed one observation at a time in O(1).
rsi <- Rsi(14)
for (price in prices) {
v <- update(rsi, price) # NaN during warmup
if (!is.na(v) && v > 70) message("overbought")
}
# Multi-output indicators return a named vector (NA while warming up).
macd <- MacdIndicator(12, 26, 9)
update(macd, 42) # c(macd = NA, signal = NA, histogram = NA)
batch(ind, prices) and feeding the same prices through update() produce
identical values — the equivalence is enforced by the test suite. Candle-input
indicators take the OHLCV fields plus a timestamp, e.g.
update(atr, open, high, low, close, volume, timestamp). The native handle is
freed automatically when the object is garbage-collected.
Benchmark
benchmarks/throughput.R 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.
Rscript benchmarks/throughput.R
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/r/
Wickra ships native bindings for Python, Node.js, WebAssembly 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.
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.