kingchenc
cb6da4d737
* feat(data-layer): Resampler (candle resampling) in all 10 languages Second data-layer feature (F3): resample candles into a higher timeframe. - Native (Node.js/WASM): new Resampler(timeframe) -> update(o,h,l,c,v,ts): Candle|null + flush(): Candle|null. Python the same -> tuple|None. - C ABI: wickra_resampler_new/update/flush/free (update has the multi-output shape so the generators auto-emit it; flush is bespoke). Go Update -> (Candle, bool) + Flush; C# Candle? Update/Flush; Java Candle update/flush; R update() generic + a flush() S3 method (extends base::flush); C/C++ direct. - Cross-language golden (testdata/golden/data_resampled.csv): the shared input candles resampled into 5-unit buckets, the final partial bucket via flush, pinned bit-for-bit across every binding. Verified locally in all 10 (3 candles for the 5-unit smoke; 16 for the golden). The WickraCandle output record is shared with the tick aggregator (deduped). * test(node): exclude data-layer types from the indicator completeness contract The Resampler exposes update(), so the completeness test flagged it as an indicator and required batch/reset/isReady/warmupPeriod, which a data-layer type does not have. Exclude TickAggregator and Resampler like the bar builders.
81 lines
2.5 KiB
R
81 lines
2.5 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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test_that("resampler 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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do.call(rbind, lapply(lines, function(l) as.numeric(strsplit(l, ",")[[1]])))
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}
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input <- read_mat("input") # open,high,low,close,volume (timestamp = row index)
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r <- Resampler(5)
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got <- matrix(numeric(0), 0, 6)
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for (i in seq_len(nrow(input))) {
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c <- update(r, input[i, 1], input[i, 2], input[i, 3], input[i, 4], input[i, 5], i - 1)
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if (!is.na(c[1])) {
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got <- rbind(got, unname(c))
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}
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}
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f <- flush(r)
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if (!is.null(f)) {
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got <- rbind(got, unname(f))
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}
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want <- unname(read_mat("data_resampled"))
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expect_equal(nrow(got), nrow(want))
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expect_equal(got, want, tolerance = 1e-9)
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})
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