kingchenc
8a103ef920
* feat(data-layer): TickAggregator in Node, WASM, Python + C ABI hub First data-layer feature (F2): roll trade ticks up into fixed-timeframe OHLCV candles, exposed natively and over the C ABI. - wickra-data wired as a binding dependency (workspace dep; its wickra-core dep is default-features=false so it never forces rayon into the rayon-free WASM build — native bindings re-enable parallel through their own dependency). - Node `TickAggregator(bucket, gapFill?)` -> `push(price, size, ts): Candle[]`; WASM the same (array of objects); Python `push(...) -> list[tuple]`. - C ABI: `WickraCandle` struct + `wickra_tick_aggregator_new/push/free` (push writes candles into a caller buffer and returns the count), generated via the capi generator's new DATA_LAYER section; cbindgen now parses wickra-data so `TickAggregator` is a forward-declared opaque; header vendored to bindings/go. Verified bit-identical across Node/WASM/Python/C/C++ (o=100 h=101 l=100 c=101 v=3 ts=0 for the shared 3-tick probe). WIP: Go/C#/Java/R generated bindings and the cross-language golden are still pending. * feat(data-layer): TickAggregator in Go, C#, Java, R (lossless push/drain) Complete F2 across all 10 languages: the C-ABI tick aggregator now uses a two-step push/drain so gap-fill candles are never lost, and the four generated bindings expose it idiomatically. - C ABI redesigned: opaque TickAggregator handle (inner aggregator + pending buffer); push consumes a tick and returns the closed-candle count, drain copies them into a count-sized caller buffer. - Go: NewTickAggregator + Push(price,size,ts) []Candle; C#: TickAggregator + Candle[] Push(...); Java: TickAggregator + Candle[] push(...); R: TickAggregator constructor + push() S3 generic returning an (n x 6) numeric matrix. - Candle output record generated per language from WickraCandle. Verified bit-identical to the native bindings (o=100 h=101 l=100 c=101 v=3 ts=0) in Go, C#, Java, and R at runtime; R passes R CMD check (pre-existing doc warnings only). WIP: cross-language data-layer golden + CHANGELOG still pending. * test(data-layer): cross-language golden for the tick aggregator + CHANGELOG gen_golden emits a deterministic tick stream (testdata/golden/data_ticks.csv) and the reference candle streams with and without gap filling (data_candles.csv, data_candles_gap.csv). Every binding replays the shared ticks through its TickAggregator and checks the candles bit-for-bit (fp tolerance) against the Rust reference: - Node / WASM / Python / Go / C# / Java / R: a dedicated parity test each. - C / C++: data_layer_test.c (compiled as both, run as ctest). The gap-fill fixture closes several candles from a single push, exercising the lossless push/drain path. Records the feature under CHANGELOG [Unreleased]. * fix(examples): rename the CSV-loader candle to WickraBar The example CSV helper (wickra_csv.h) defined its own struct WickraCandle, which now collides with the public C ABI WickraCandle (the tick aggregator output) in any example that includes both headers (backtest, multi_timeframe, the strategy examples). The public type owns the name; rename the example loader's bar to WickraBar. The generated golden_test.c is untouched (its only match was the unrelated WickraCandleVolumeOutput).
53 lines
1.6 KiB
R
53 lines
1.6 KiB
R
# Cross-language data-layer parity: replay the shared golden tick stream through
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# the TickAggregator and check the candles against the Rust reference, with and
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# without gap filling. Fixtures are generated by
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# `cargo run -p wickra-examples --bin gen_golden`.
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find_data_golden_dir <- function() {
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d <- normalizePath(".", mustWork = FALSE)
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repeat {
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g <- file.path(d, "testdata", "golden")
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if (dir.exists(g)) {
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return(g)
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}
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parent <- dirname(d)
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if (identical(parent, d)) {
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return(NULL)
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}
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d <- parent
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}
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}
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test_that("tick aggregator matches the golden candles", {
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gdir <- find_data_golden_dir()
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skip_if(is.null(gdir), "golden fixtures not bundled with the package")
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read_mat <- function(name) {
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lines <- readLines(file.path(gdir, paste0(name, ".csv")))[-1]
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lines <- lines[nzchar(lines)]
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if (length(lines) == 0) {
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return(matrix(numeric(0), 0, 6))
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}
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do.call(rbind, lapply(lines, function(l) as.numeric(strsplit(l, ",")[[1]])))
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}
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ticks <- read_mat("data_ticks")
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specs <- list(
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list(gap = FALSE, fixture = "data_candles"),
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list(gap = TRUE, fixture = "data_candles_gap")
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)
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for (spec in specs) {
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agg <- TickAggregator(1000, spec$gap)
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got <- matrix(numeric(0), 0, 6)
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for (i in seq_len(nrow(ticks))) {
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out <- push(agg, ticks[i, 1], ticks[i, 2], ticks[i, 3])
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if (nrow(out) > 0) {
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got <- rbind(got, unname(out))
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}
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}
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want <- unname(read_mat(spec$fixture))
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expect_equal(nrow(got), nrow(want), info = spec$fixture)
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expect_equal(got, want, tolerance = 1e-9, info = spec$fixture)
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}
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})
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