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Adds a `throughput` benchmark to every target and closes two small test-coverage documentation/QA gaps. One PR, no merge of binding code beyond the additive benchmarks and one C test. ## 1. Per-binding throughput benchmarks (all 9 targets) Each benchmark feeds a deterministic synthetic OHLCV series through three indicators chosen by **FFI call-signature archetype** (not algorithm — the same Rust core runs underneath all bindings): - `SMA(20)` — 1-in → 1-out (baseline boundary cost) - `ATR(14)` — multi-in → 1-out (input marshalling) - `MACD(12,26,9)` — 1-in → multi-out (output marshalling) Streaming is timed for all three; batch for the single-output SMA and ATR (median of 3 runs, after a warmup pass). New: Python (PyO3), WASM, C (CMake), C# (Stopwatch), Go, Java (FFM), R, and the Rust core baseline (`examples/rust/.../throughput.rs`, **no FFI** — the ceiling the bindings are measured against and the value their batch paths converge towards). Node already had `throughput.js`. **Not a speed claim:** there is no comparable streaming TA library for C, C#, Go, Java, R or WASM to compare against, so these are raw per-binding throughput numbers documenting each language's FFI overhead — see BENCHMARKS.md §3. The "Wickra is fast" claim still lives in §1/§2 (Rust core + the Python/Rust cross-library runs). ## 2. README `## Testing`: C# and C bullets The section listed every layer except C# and C, even though both have suites. Adds the two missing bullets. ## 3. C archetype ctest `examples/c/archetypes.c` drives one indicator per FFI archetype through the real C boundary (scalar + batch==streaming, multi-output, bars, profile, array input) plus reset, invalid-parameter and NULL-safety — the C counterpart of the Go/R/Java archetype suites. Runs on three OSes via the existing CMake/ctest. ## Notes - Benchmarks are not CI-gated (manual-run scripts, like the existing `throughput.js`); no `ci.yml`/`release.yml` changes. - Docs: BENCHMARKS.md §3, a `## Benchmark` section in every binding README, a CHANGELOG entry. - Verified locally by running: Rust, Python, C, C#, Go, Java (real numbers); the C archetype ctest with `-Wall -Wextra -Wpedantic -Werror`. WASM and R are API-correct and syntax-checked but need their own toolchains to run.
97 lines
4.0 KiB
Markdown
97 lines
4.0 KiB
Markdown
# Wickra — R
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[](https://github.com/wickra-lib/wickra/actions/workflows/ci.yml)
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[](https://codecov.io/gh/wickra-lib/wickra)
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[](https://github.com/wickra-lib/wickra#license)
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**Streaming-first technical indicators for R, over the Wickra C ABI hub via `.Call`.**
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Wickra is a multi-language technical-analysis library with a Rust core and
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bindings for Python, Node.js and WebAssembly, plus a C ABI for C/C++, C#, Go, Java, R
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and any other C-capable language. Every indicator is an O(1) streaming state
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machine, so live trading and historical backtests share the exact same
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implementation. This package is the R binding; it reaches the C ABI hub through
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R's native `.Call` interface and exposes all 514 indicators as constructors that
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return a lightweight `wickra_indicator` object.
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## Install
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The package compiles a thin C glue layer (`.Call`) against the prebuilt Wickra
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C ABI library, so a C toolchain (Rtools on Windows) is required, plus the C ABI
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header and library. Build the library from the workspace, then install the
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package pointing at it:
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```bash
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cargo build -p wickra-c --release
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WICKRA_INCLUDE_DIR="$PWD/bindings/c/include" \
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WICKRA_LIB_DIR="$PWD/target/release" \
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R CMD INSTALL bindings/r
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```
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On Windows the C ABI DLL is bundled into the package and put on the load path
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automatically; on Linux and macOS the library path is baked in via rpath.
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## Quick start
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```r
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library(wickra)
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# Batch: run an indicator over a whole series (NaN at warmup positions).
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prices <- 100 + (0:999) * 0.1
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sma <- Sma(20)
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values <- batch(sma, prices)
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# Streaming: the same indicator, fed one observation at a time in O(1).
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rsi <- Rsi(14)
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for (price in prices) {
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v <- update(rsi, price) # NaN during warmup
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if (!is.na(v) && v > 70) message("overbought")
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}
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# Multi-output indicators return a named vector (NA while warming up).
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macd <- MacdIndicator(12, 26, 9)
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update(macd, 42) # c(macd = NA, signal = NA, histogram = NA)
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```
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`batch(ind, prices)` and feeding the same prices through `update()` produce
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identical values — the equivalence is enforced by the test suite. Candle-input
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indicators take the OHLCV fields plus a timestamp, e.g.
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`update(atr, open, high, low, close, volume, timestamp)`. The native handle is
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freed automatically when the object is garbage-collected.
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## Benchmark
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`benchmarks/throughput.R` reports streaming and batch updates-per-second for
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`SMA`, `ATR` and `MACD`. It measures this binding's FFI overhead, not a
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cross-library ratio (the same Rust core runs under every binding) — see the
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repository [BENCHMARKS.md](https://github.com/wickra-lib/wickra/blob/main/BENCHMARKS.md) §3.
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```bash
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Rscript benchmarks/throughput.R
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```
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## Documentation
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The full indicator catalogue, guides, quickstarts, and API reference live in the
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main repository and documentation site:
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- **Repository & full indicator list:** <https://github.com/wickra-lib/wickra>
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- **Docs** (quickstarts, cookbook, TA-Lib migration): <https://docs.wickra.org>
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- **Runnable examples:** [`examples/r/`](https://github.com/wickra-lib/wickra/tree/main/examples/r)
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Wickra ships native bindings for Python, Node.js, WebAssembly and Rust, plus a
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C ABI hub that any C-capable language (C, C++, C#, Go, Java, R) links against —
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all exposing the same indicators from the shared, `unsafe`-forbidden Rust core.
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## Disclaimer
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Wickra is an indicator toolkit, not a trading system. The values it computes are
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deterministic transforms of the input data — they are not financial advice and
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do not predict the market. Any use in a live trading context is at your own risk.
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The library is provided **as is**, without warranty of any kind.
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## License
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Licensed under either of [Apache-2.0](https://github.com/wickra-lib/wickra/blob/main/LICENSE-APACHE)
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or [MIT](https://github.com/wickra-lib/wickra/blob/main/LICENSE-MIT) at your option.
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