Add the data-layer CSV candle reader to every binding so loading OHLCV
candles from a CSV no longer needs a per-language CSV/dataframe dependency.
- C ABI: wickra_candle_reader_new(bytes, len) / _count / _read / _free over
an opaque CandleReader handle (parse the whole buffer up front, then drain).
- Native: Node/WASM CandleReader.read() -> Candle[], Python read() -> list[tuple].
- C-ABI languages: Go Read() []Candle, C# Candle[] Read(), Java Candle[] read(),
R read() S3 generic (n x 6 matrix); C / C++ call the C ABI directly.
- Cross-language golden testdata/golden/data_csv*.csv pins the parsed candles
bit-for-bit across every binding.
Verified locally across Rust (test+clippy+fmt), Node, WASM, Python, C#, Go,
Java, R, and the C/C++ cmake parity suite.
* 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.
Introduces a BarBuilder trait for price-driven chart constructors that emit a variable number of bars per candle (deliberately not Indicator). Adds Renko (box-size bricks, 2-box reversal), Kagi (reversal-amount segments) and Point & Figure (box-size X/O columns, N-box reversal) in a new Alt-Chart Bars family, with custom Python/Node/WASM bindings, a dedicated fuzz target, tests and docs. Indicator count 292 -> 295.
* examples(node): add RSI mean-reversion strategy
Node counterpart of strategy_rsi_mean_reversion.{py,rs}: RSI(14) < 30 long,
> 70 exit, 0.1% fees, hourly BTCUSDT. Output verified byte-identical to the
Rust reference (37 trades W24/L13, -17.84% return, 46.89% max drawdown).
* examples(node): add MACD + ADX trend-filter strategy
Node counterpart of strategy_macd_adx.{py,rs}: MACD(12,26,9) histogram
crossover entries gated by ADX(14) > 20, hourly BTCUSDT, 0.1% fees. Output
verified byte-identical to the Rust reference (246 trades W90/L156, -47.19%
return, 53.75% max drawdown).
* examples(node): add Bollinger-squeeze breakout strategy
Node counterpart of strategy_bollinger_squeeze.{py,rs}: enter on a fresh
180-bar Bollinger-bandwidth low + close above the upper band, exit on a
2*ATR(14) stop or upper-band collapse, daily BTCUSDT, 0.1% fees. Output
verified byte-identical to the Rust reference (1 trade, -7.82% return,
13.01% max drawdown).
* examples(wasm): add RSI mean-reversion strategy demo
Browser counterpart of strategy_rsi_mean_reversion.{py,js,rs}: RSI(14) < 30
long, > 70 exit, 0.1% fees, summary table. Same signal/fill/PnL/equity loop as
the runtime-verified Node example; loads via the established wickra_wasm.js
init + fetch-CSV pattern. (wasm32 build runs in CI.)
* examples(wasm): add MACD + ADX trend-filter strategy demo
Browser counterpart of strategy_macd_adx.{py,js,rs}: MACD(12,26,9) histogram
crossover gated by ADX(14) > 20, hourly BTCUSDT, 0.1% fees. Logic identical to
the runtime-verified Node example; standard wickra_wasm.js init + fetch-CSV
loader. (wasm32 build runs in CI.)
* examples(wasm): add Bollinger-squeeze breakout strategy demo
Browser counterpart of strategy_bollinger_squeeze.{py,js,rs}: fresh 180-bar
Bollinger-bandwidth low + upper-band breakout, 2*ATR(14) stop, daily BTCUSDT,
0.1% fees. Logic identical to the runtime-verified Node example; standard
wickra_wasm.js init + fetch-CSV loader. (wasm32 build runs in CI.)
* ci: add examples syntax-smoke job (P2.3)
The Rust examples are built by 'cargo build -p wickra-examples --bins'; the
Node, browser-WASM and Python examples had no build gate. New job parse-checks
every examples/{node,wasm}/*.js, extracts and node --checks each WASM .html
module script, and python -m py_compile's every examples/python/*.py — so a
broken example edit fails CI instead of landing silently.
* docs(examples): list the new Node + WASM strategy examples
Add the three Node strategy scripts and three WASM strategy demos to the
examples README tables, bringing Node and WASM to parity with the existing
Rust and Python strategy rows.
* chore(examples): refresh examples/node lockfile for the linked wickra binding
npm install rewrote the file: dependency snapshot of the local wickra binding
that the examples link against (version 0.1.4 -> 0.3.1, license + engines
fields), which had gone stale in the committed lockfile.
* test(node): add input-validation suite
Node counterpart of bindings/python/tests/test_input_validation.py: invalid
constructor parameters (ATR zero period, MACD non-increasing fast/slow,
BollingerBands negative multiplier, PSAR step > max, ValueArea period/pct,
InitialBalance/OpeningRange zero period, Ichimoku non-increasing periods,
Ehlers-family ordering) and unequal-length candle/ValueArea batch inputs all
throw a JS Error. Validated against the built binding.
* test(node): add indicator completeness contract
Introspects every exported indicator class and asserts the full interface
(update / batch / reset / isReady / warmupPeriod) plus the pre-warmup contract
for zero-arg indicators, and guards that the full catalogue (>= 200 classes)
is exported. Catches a new indicator wired without the standard methods, or a
stale/partial native build dropping exports, with no per-indicator boilerplate.
* test(wasm): broaden scalar streaming-vs-batch coverage
Extend the inline wasm-bindgen-test suite with a streaming==batch check across
~70 scalar indicators spanning moving averages, momentum, volatility,
statistics/regression, Ehlers/cycle and risk/performance families (previously
only EMA + the candle-input group were covered per-indicator), plus four more
invalid-constructor assertions. Constructor args mirror the CI-passing Node
factories. Host-compiles (cargo test -p wickra-wasm --no-run); executed in CI
via wasm-pack test --node.
* bench(node): add indicator throughput benchmark
Node counterpart of the Rust criterion benches / Python compare_libraries:
measures streaming (per-tick update) and batch throughput in Mupd/s across a
representative indicator set over a synthetic OHLCV series (--bars, default
200k). Dependency-free; wired as 'npm run bench'.
* docs(wasm): list strategy demos + document the benchmark story
Add the three new strategy demos to the WASM examples table and a Performance
section: parallel_assets.html is the in-browser benchmark, with raw throughput
covered by the Rust criterion / Python / Node benchmarks (the WASM engine is the
same core compiled to wasm32).
kingchenc