Part of the Wickra ecosystem: — for R. install.packages("wickradarwin", repos = "https://wickra-lib.r-universe.dev") — over the C ABI via .Call, prebuilt library fetched on install.
R bindings for the Wickra evolutionary strategy search over its C ABI hub, via
.Call. A search is built from a spec JSON and driven over a JSON boundary, so
the result is byte-identical to every other Wickra Darwin binding.
From r-universe:
install.packages("wickradarwin", 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.
The C ABI header and shared library are provided out-of-tree through two environment variables (set by CI / the installer):
export WKDARWIN_INC=/path/to/bindings/c/include # the header dir
export WKDARWIN_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).
library(wickradarwin)
spec <- paste0(
'{"seed":1,"population":8,"generations":3,',
'"mutation_rate":0.2,"crossover_rate":0.6,"fitness":"sharpe",',
'"search_space":{"indicators":[{"name":"rsi","param_ranges":',
'[{"min":2,"max":30,"step":1}]}],"rules":"single_threshold",',
'"max_conditions":1},"elitism":1,"top":5}'
)
darwin <- wkdarwin_new(spec)
data <- '{"BTCUSDT":[{"time":1700000000,"open":100,"high":101,"low":99,"close":100.5,"volume":10}]}'
response <- wkdarwin_command(darwin, paste0('{"cmd":"evolve","data":', data, "}"))
cat(response)
wkdarwin_new(spec_json) — build a search handle from a spec JSON (an
external pointer; "{}" defers configuration to a later set_spec).wkdarwin_command(darwin, cmd_json) — apply a command envelope
({"cmd":"...", ...}) and return the response JSON. Commands: set_spec,
evolve, best, version.wkdarwin_version() — the library version.The search's PRNG lives only in the Rust core; this binding forwards the command string verbatim, so a given seed produces the byte-identical report here and in every other binding — the exact cross-language golden invariant.
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.
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-darwin
Docs (guides, spec reference, cookbook): https://darwin.wickra.org
Runnable example: examples/r/
The main project: https://github.com/wickra-lib/wickra-darwin
Documentation: https://wickra.org
Wickra Darwin 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.
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-darwin/blob/main/SECURITY.md.
Wickra Darwin is a research tool, provided "as is" without warranty of any kind. Evolutionary search optimises a fitness objective over historical data — a strong in-sample fitness is not evidence of out-of-sample performance, and overfitting is the default outcome, not the exception. Nothing here is financial advice; any strategy you deploy is your responsibility, and trading carries risk of loss.
Licensed under either of Apache-2.0 or MIT at your option.