kingchenc
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* feat(bindings): expose name() on every indicator in Node, WASM, and Python Surface the core Indicator::name() / BarBuilder::name() accessor through the three native bindings so every indicator reports its canonical name at runtime, matching the existing reset/isReady/warmupPeriod surface. - Node (napi): name(): string on all 514 classes (regenerated index.d.ts) - WASM (wasm-bindgen): name(): string on all 514 classes - Python (pyo3): name() -> str on all classes * feat(bindings): expose name() across the C ABI and C/C++/Go/C#/Java/R Regenerate the C ABI and the four generated language bindings from the updated ScriptHelpers generators so every indicator and bar builder reports its canonical name at runtime, completing name() coverage across all 10 languages. - C ABI (bindings/c): wickra_<ind>_name() -> *const c_char for all 514, cached in a per-function OnceLock<CString> with ind.name() as the source of truth; cbindgen header regenerated and vendored into bindings/go/include. - Go: Name() string; C#: string Name(); Java: String name(); R: name() S3 generic over the wk_<ind>_name C glue (methods.R + NAMESPACE). The Java regeneration also restores two fixes that had drifted out of the generator (bool* arrays via boolSegment; uint8_t ctor args cast to byte) and C# re-emits '#nullable enable'; these are no-op vs the previous committed output apart from the new name() accessors. * test(golden): pin canonical name() across all 10 language bindings Add a cross-language name() consistency check: every indicator must report the exact core Indicator::name() (which can differ from the registered class name, e.g. ChaikinMoneyFlow -> "CMF", Donchian -> "DonchianChannels"). The 514 core names are committed as testdata/golden/names.json (keyed by Rust canonical) and asserted by each binding's golden replay, which already reconstructs the whole catalogue: - node / wasm: assert against names.json in the existing golden test - python: new test_golden_names.py over the shared node manifest - go / csharp / java / c+c++ / r: the golden-test generators load names.json and emit a name assertion per indicator (regenerated test artifacts committed) All 10 bindings return identical names by construction (each delegates to core), so this pins that contract and guards against a future binding breaking the passthrough. * docs(changelog): record name() across all 10 bindings under Unreleased * fix(r): restore bool* flag marshalling in the regenerated C glue The name() regeneration had reverted the cross-section bool fix: the R glue emitted (bool *)REAL(x) for const bool* inputs, reinterpreting 8-byte doubles as 1-byte bools so every flag read as false (PercentAboveMa, NewHighsNewLows, HighLowIndex, BullishPercentIndex returned 0 instead of the breadth value). The wk_bool_vec() helper is restored in the generator and the glue routes bool arrays through it again.
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 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 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, 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.