Wickra Feature Store — R

Turn OHLCV and microstructure event streams into ML-ready feature matrices over 497 O(1) streaming indicators — for R. install.packages("wickrafeaturestore", repos = "https://wickra-lib.r-universe.dev") — over the C ABI via .Call, prebuilt library fetched on install.

R bindings for the Wickra feature-matrix core over its C ABI hub, via .Call. A store is built from a spec JSON and driven over a JSON boundary, so the result is byte-identical to every other Wickra Feature Store binding.

Install

From r-universe:

install.packages("wickrafeaturestore", repos = "https://wickra-lib.r-universe.dev")

The package's configure downloads the prebuilt C ABI library for this exact version from the GitHub release and bundles it, so an ordinary install needs nothing but a C toolchain (Rtools on Windows) for the thin .Call glue layer. To build against a local checkout instead, point it at the header and library with the environment variables below.

Building from this repository (contributors)

The C ABI header and shared library are provided out-of-tree through two environment variables (set by CI / the installer):

export WKFEATURESTORE_INC=/path/to/bindings/c/include   # the header dir
export WKFEATURESTORE_LIB=/path/to/target/release       # the library dir
R CMD INSTALL bindings/r
Rscript bindings/r/tests/run_tests.R

At run time the loader must find the shared library on LD_LIBRARY_PATH (Linux), DYLD_LIBRARY_PATH (macOS) or PATH (Windows).

Quick start

library(wickrafeaturestore)

spec <- paste0(
  '{"universe":["AAA"],',
  '"features":[{"kind":"indicator","name":"Sma","params":[2]},{"kind":"price","field":"close"}],',
  '"labels":[{"kind":"forward_return","horizon":1}]}'
)

store <- wkfeaturestore_new(spec)
response <- wkfeaturestore_command(
  store,
  '{"cmd":"build_batch","data":{"AAA":[{"ts":0,"open":100,"high":100,"low":100,"close":100,"volume":1}]}}'
)
cat(response)

Surface

wkfeaturestore_new(spec_json) — build a feature store from a spec JSON (an external pointer; freed by a finalizer). Raises an R error if the spec is invalid.
wkfeaturestore_command(store, cmd_json) — run a command envelope ({"cmd":"...", ...}) and return the response JSON. Commands: set_spec, push, push_batch, build, build_batch, labels, reset, version.
wkfeaturestore_version() — the crate version.

Domain errors (a bad command, an unknown command name) come back as an {"ok": false, "error": ...} response, not as an R error. Arrow / Parquet output is a binary file format and is not available over this JSON surface; use the wickra-feature-store CLI for columnar output.

Determinism

The response bytes are identical across languages and between the parallel and sequential build paths, because the whole feature fold lives once in the Rust core and this binding forwards its JSON verbatim.

Benchmark

Every binding forwards to the same data-driven Rust core, so what this one adds is the call overhead of R's native .Call interface over the C ABI, not a different result. The core's throughput is measured by the repository's benchmark suite and the nightly bench.yml run; the numbers, the machine and how to reproduce them are in the repository BENCHMARKS.md.

Documentation

The full guide, the spec reference and the API documentation live in the main repository and the documentation site:

Repository: https://github.com/wickra-lib/wickra-feature-store
Docs (guides, spec reference, cookbook): https://feature-store.wickra.org
Runnable example: examples/r/
The main project: https://github.com/wickra-lib/wickra-feature-store
Documentation: https://wickra.org

Wickra Feature Store 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 forwarding to the same data-driven, unsafe-forbidden Rust core.

Security

Found a security issue? Please don't open a public issue. Report it privately via the 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-feature-store/blob/main/SECURITY.md.

Disclaimer

wickra-feature-store is research and engineering tooling, not financial advice. A feature matrix describes historical data under the spec you provide; it makes no claim about the profitability or future performance of any model trained on it. Trading carries risk; you are responsible for your own decisions.

License

Licensed under either of Apache-2.0 or MIT at your option.