kingchenc
4f708d410d
* test: golden-pin the four de-duplicated indicators across all C-ABI bindings Extend gen_golden to emit reference fixtures for AdOscillator (ADOSC), IntradayIntensity, AwesomeOscillatorHistogram and AverageDrawdown, and replay them through the Go / C# / Java / R golden harnesses so their corrected definitions stay bit-identical to the Rust core in every binding. Go suite verified locally (gcc 13 + cgo): all 9 golden tests pass; C#/Java/R use the same fixtures and harness pattern (CI-verified). First step of extending the golden coverage beyond the seven archetype representatives. * test: golden-pin the scalar-output tranche (308 indicators) against Rust Extend gen_golden with a generated emit_scalar that writes reference fixtures for every single-f64-output indicator (scalar / candle / pairwise input) using valid constructor params, and add a manifest-driven generic Python golden replay that reconstructs each by its native name and checks it bit-for-bit against the Rust output. 308 indicators now value-tied to the Rust core in Python (pytest: 308/308). Takes golden coverage from the 7 archetype representatives to 308+ of the catalogue. 22 scalar indicators with non-default constructor constraints are skipped by gen_golden for now (logged), as are non-f64-output ones; multi-output, exotic inputs and the per-indicator arg arities of the C-ABI/Node replays follow. Generated + verified locally with the full toolchain. * test: golden-pin the multi-output tranche (70 indicators) in Python Add a generated emit_multi to gen_golden (per-indicator Output-field access, one CSV column per field) and a manifest-driven generic Python replay that checks every field of each multi-output indicator against the Rust reference. 70 multi-output indicators now value-tied to Rust in Python; combined with the scalar tranche, 378 indicators are golden-pinned. 8 multi with non-default param constraints and 5 with non-f64 Output fields (Option/Vec/i64) are deferred. pytest green. * test(golden): add 30 constraint-tuned indicators to scalar/multi golden suite Emit golden fixtures for 22 scalar-output and 8 multi-output indicators whose constructors need non-default parameters (Alma, Jma, Psar, T3, Mama, DoubleBollinger, ZigZag, ...). All 408 fixtures replay bit-for-bit through the Python binding. * test(golden): cover 36 missed scalar/multi indicators Add 26 single-output (LinearRegression family, HT cycle, Candle volatility estimators, DrawdownDuration) and 10 multi-output (BollingerBands, MACD/MACDEXT/MACDFIX, Camarilla, VWAP bands, ...) indicators to the golden suite. 444 fixtures replay bit-for-bit through the Python binding. * test(golden): cover 50 exotic-input indicators Add deterministic synthetic feeders for the DerivativesTick (17), CrossSection (15), Trade (8), TradeQuote (3) and OrderBook (7) families, derived from the shared OHLCV input series in both gen_golden and a new Python replay harness (test_golden_exotic). All 494 fixtures replay bit-for-bit through the Python binding. * test(golden): complete 514-indicator golden coverage Add the final tranches: 3 mixed multi-output indicators (Ichimoku, WilliamsFractals, LeadLagCrossCorrelation), 6 histogram profiles (time/volume seasonality + TPO/volume price profiles), 10 alt-chart bar builders and the footprint. Every one of the 514 distinct indicators now has a Rust-generated g_<Canonical>.csv fixture and a generic Python replay (scalar/multi/exotic/profile/bars), all passing bit-for-bit. * test(golden): add generic Node replay for all 514 indicators A manifest-driven node:test harness reconstructs every indicator by its native class, feeds the same synthetic stream derived from the shared golden input, and checks output bit-for-bit against the Rust reference fixtures (scalar/multi/exotic/profile/bars). node_manifest.json is generated from index.d.ts plus the Python-side manifests. 514/514 pass. * test(golden): add generated Go replay for all 514 indicators golden_all_test.go (generated by gen_golden_test.py) reconstructs every Go indicator, feeds the shared synthetic stream and checks output bit-for-bit against the Rust reference fixtures. A reflection-based comparator flattens multi-output structs, profiles and bar slices so one path covers all archetypes. This is the first C-ABI binding verified across the full catalogue. 514/514 pass. * test(golden): add generated C# replay for all 514 indicators GoldenAllTests.g.cs (generated by gen_golden_test.py) reconstructs every C# indicator, feeds the shared synthetic stream and checks output bit-for-bit against the Rust reference fixtures via a reflection-based flatten covering scalar/multi/profile/bar archetypes. 514/514 pass. Also add the '#nullable enable' directive the compiler requires to the generated Indicators.g.cs, clearing the four CS8669 warnings on the nullable double[] profile return types. * fix(java): marshal C ABI bool params correctly; add 514 golden replay The Java FFM binding marshalled the cross-section state flags (newHigh, newLow, aboveMa, onBuySignal) as JAVA_DOUBLE arrays, but the C ABI takes them as const bool* (one byte each), so the native side read the low byte of each 8-byte double and saw every flag as false. Add WickraNative. boolSegment and use it across the 15 cross-section indicators. Also pass the MacdExt MaType arguments as byte to match the uint8_t downcall descriptor (was int, throwing WrongMethodTypeException). Add GoldenAllTest.java (generated by gen_golden_test.py): a reflection runner replaying all 514 indicators against the Rust reference fixtures. The bugs above were found by this test; 514/514 now pass. * fix(r): marshal C ABI bool flags correctly; add 514 golden replay The R wrapper passed the cross-section state flags as (bool *)REAL(x), reinterpreting the 8-byte doubles as 1-byte bools so the native side read every flag as false. Add wk_bool_vec to convert each flag vector into a real C bool buffer and use it for all 15 cross-section update wrappers. Add test-golden-all.R + generated golden_specs.R: a reflective runner replaying all 514 indicators against the Rust reference fixtures. The bug above was found by this test; verified 514/514 pass locally. * test(golden): add WASM replay for all 514 indicators A manifest-driven node:test harness loads the nodejs-target wasm-pack build, reconstructs every indicator by its JS class, feeds the shared synthetic stream and checks output bit-for-bit against the Rust reference fixtures. wasm_manifest.json is generated from the wasm .d.ts plus the shared manifests; a recursive flattener covers scalar, multi (Reflect objects), profile and bar shapes. 514/514 pass locally (wasm-pack build --target nodejs, then node --test). * test(golden): add C and C++ replay for all 514 indicators golden_test.c (generated by gen_golden_test.py) drives every indicator through the C ABI (wickra.h) and checks output bit-for-bit against the Rust reference fixtures. golden_test.cpp #includes the same source so the identical runner is compiled and run under both gcc (C) and g++ (C++) via the CMake targets golden_test / golden_test_cpp — proving the extern "C" header is consumable from each language. Both 514/514 (verified via ctest). * test(golden): gofmt the generated Go golden replay * test(golden): make the Node fixture reader CRLF-safe and pin fixtures to LF
Wickra examples
Runnable examples for every Wickra binding. Rust and Node examples live next
to the code they exercise so the language tooling (cargo run --example,
node) can find them; the Python examples have no crate of their own and
live here under python/.
Rust — examples/rust/
The Rust examples live in the wickra-examples workspace member crate.
| Example | What it does | Run |
|---|---|---|
streaming.rs |
Feed a synthetic price series through SMA / EMA / RSI / MACD tick by tick. | cargo run -p wickra-examples --bin streaming |
backtest.rs |
Compute a basket of indicators over an OHLCV CSV and print a summary. | cargo run -p wickra-examples --bin backtest -- <ohlcv.csv> |
multi_timeframe.rs |
Resample a 1-minute CSV via wickra-data and print indicators per timeframe. | cargo run -p wickra-examples --bin multi_timeframe |
parallel_assets.rs |
Serial vs BatchExt::batch_parallel (rayon) over a synthetic panel, with speedup. |
cargo run --release -p wickra-examples --bin parallel_assets -- --assets 200 --bars 5000 |
fetch_btcusdt.rs |
Download real BTCUSDT klines from the Binance REST API into examples/data/. |
cargo run -p wickra-examples --bin fetch_btcusdt |
live_binance.rs |
Stream live Binance klines through an indicator over a resilient WebSocket. | cargo run -p wickra-examples --bin live_binance |
strategy_rsi_mean_reversion.rs |
Hourly BTCUSDT mean-reversion using RSI(14) thresholds, with PnL / Sharpe / max-DD summary. | cargo run --release -p wickra-examples --bin strategy_rsi_mean_reversion |
strategy_macd_adx.rs |
Hourly BTCUSDT trend-follower: MACD crossover entries gated by ADX(14) > 20. | cargo run --release -p wickra-examples --bin strategy_macd_adx |
strategy_bollinger_squeeze.rs |
Daily BTCUSDT Bollinger-squeeze breakout with ATR(14) trailing stop. | cargo run --release -p wickra-examples --bin strategy_bollinger_squeeze |
C / C++ — examples/c/
Build the library first (cargo build -p wickra-c --release), then build and run
the examples via CMake:
cmake -S examples/c -B examples/c/build -DWICKRA_LIB_DIR="$PWD/target/release" →
cmake --build examples/c/build → ctest --test-dir examples/c/build.
| Example | What it does | CMake target |
|---|---|---|
smoke.c |
Links the generated header + library and asserts SMA streaming / batch values across the boundary. | smoke |
streaming.c |
Feed a synthetic price series through SMA / EMA / RSI / MACD tick by tick. | streaming |
backtest.c |
Basket of indicators over an OHLCV CSV; defaults to the bundled BTCUSDT daily dataset. | backtest |
multi_timeframe.c |
Resample the bundled 1-minute CSV to 5m / 15m / 1h / 4h / 1d and print indicators per timeframe. | multi_timeframe |
parallel_assets.c |
Serial vs OpenMP fan-out over a synthetic panel (one handle per asset), with speedup. | parallel_assets |
strategy_rsi_mean_reversion.c |
Hourly BTCUSDT mean-reversion using RSI(14) thresholds, with PnL / Sharpe / max-DD summary. | strategy_rsi_mean_reversion |
strategy_macd_adx.c |
Hourly BTCUSDT trend-follower: MACD crossover entries gated by ADX(14) > 20. | strategy_macd_adx |
strategy_bollinger_squeeze.c |
Daily BTCUSDT Bollinger-squeeze breakout with ATR(14) stop. | strategy_bollinger_squeeze |
fetch_btcusdt.c |
Download real BTCUSDT klines from the Binance REST API into examples/data/ (shells out to curl). |
fetch_btcusdt |
live_binance.c |
Poll the Binance REST klines endpoint via curl and stream closed candles through RSI(14). |
live_binance |
smoke.cpp |
C++ RAII via wickra::Handle from wickra.hpp: construct, move, auto-free. |
cpp_smoke |
The data-driven examples (backtest, multi_timeframe, parallel_assets, the
three strategy_*) build against the bundled datasets and run under ctest.
fetch_btcusdt and live_binance reach the network, so they are built but not
run in CI; run them by hand. parallel_assets links OpenMP when the toolchain
provides it and falls back to a single-threaded run otherwise.
C# — examples/csharp/
Build the C ABI library first (cargo build -p wickra-c --release), then run any
example with the .NET 8 SDK; the binding resolves the native library automatically.
| Example | What it does | Run |
|---|---|---|
streaming |
Feed a synthetic price series through SMA / EMA / RSI / MACD tick by tick. | dotnet run --project examples/csharp/streaming |
backtest |
Basket of indicators over an OHLCV series (CSV arg or synthetic). | dotnet run --project examples/csharp/backtest -- <ohlcv.csv> |
multi_timeframe |
Resample a 1-minute series to 5m / 15m and print an indicator per timeframe. | dotnet run --project examples/csharp/multi_timeframe |
parallel_assets |
SMA(20) batch over a panel, serial vs Parallel.For, with speedup. |
dotnet run -c Release --project examples/csharp/parallel_assets |
strategy_rsi_mean_reversion |
RSI(14) mean-reversion with PnL / Sharpe / max-DD summary. | dotnet run -c Release --project examples/csharp/strategy_rsi_mean_reversion |
strategy_macd_adx |
Trend-follower: MACD crossover entries gated by ADX(14) > 20. | dotnet run -c Release --project examples/csharp/strategy_macd_adx |
strategy_bollinger_squeeze |
Bollinger-squeeze breakout with an ATR(14) trailing stop. | dotnet run -c Release --project examples/csharp/strategy_bollinger_squeeze |
fetch_btcusdt |
Download real BTCUSDT klines from the Binance REST API into a CSV. | dotnet run --project examples/csharp/fetch_btcusdt |
live_binance |
Stream live Binance klines through EMA(20) over a WebSocket. | dotnet run --project examples/csharp/live_binance |
The offline examples run on deterministic synthetic data (and under CI on all
three OSes); fetch_btcusdt and live_binance reach the network and are built
but not run in CI.
Go — examples/go/
Build the C ABI library first (cargo build -p wickra-c --release) and stage it
under bindings/go/lib/ (see the Go binding README), then run
any example from the examples/go module.
| Example | What it does | Run |
|---|---|---|
streaming |
Feed a synthetic price series through SMA / EMA / RSI / MACD tick by tick. | go run ./streaming |
backtest |
Basket of indicators over an OHLCV series (CSV arg or synthetic). | go run ./backtest <ohlcv.csv> |
multi_timeframe |
Resample a 1-minute series to 5m / 15m and print an indicator per timeframe. | go run ./multi_timeframe |
parallel_assets |
SMA(20) batch over a panel, serial vs goroutine fan-out, with speedup. | go run ./parallel_assets 200 5000 |
strategy_rsi_mean_reversion |
RSI(14) mean-reversion with PnL / Sharpe / max-DD summary. | go run ./strategy_rsi_mean_reversion |
strategy_macd_adx |
Trend-follower: MACD crossover entries gated by ADX(14) > 20. | go run ./strategy_macd_adx |
strategy_bollinger_squeeze |
Bollinger-squeeze breakout with an ATR(14) trailing stop. | go run ./strategy_bollinger_squeeze |
fetch_btcusdt |
Download real BTCUSDT klines from the Binance REST API into a CSV. | go run ./fetch_btcusdt |
live_binance |
Stream live Binance klines through EMA(20) over a WebSocket. | go run ./live_binance |
The offline examples run on deterministic synthetic data (and under CI on all
three OSes); fetch_btcusdt and live_binance reach the network and are built
but not run in CI.
R — examples/r/
Build the C ABI library first (cargo build -p wickra-c --release) and install
the binding (see the R binding README), then run any example
from this directory.
| Example | What it does | Run |
|---|---|---|
streaming.R |
Feed a synthetic price series through SMA / EMA / RSI / MACD tick by tick. | Rscript streaming.R |
backtest.R |
Basket of indicators over an OHLCV series (CSV arg or synthetic). | Rscript backtest.R <ohlcv.csv> |
multi_timeframe.R |
Resample a 1-minute series to 5m / 15m and print an indicator per timeframe. | Rscript multi_timeframe.R |
parallel_assets.R |
SMA(20) batch over a panel, serial vs mclapply, with speedup. |
Rscript parallel_assets.R 200 5000 |
strategy_rsi_mean_reversion.R |
RSI(14) mean-reversion with PnL / Sharpe / max-DD summary. | Rscript strategy_rsi_mean_reversion.R |
strategy_macd_adx.R |
Trend-follower: MACD crossover entries gated by ADX(14) > 20. | Rscript strategy_macd_adx.R |
strategy_bollinger_squeeze.R |
Bollinger-squeeze breakout with an ATR(14) trailing stop. | Rscript strategy_bollinger_squeeze.R |
fetch_btcusdt.R |
Download real BTCUSDT klines from the Binance REST API into a CSV. | Rscript fetch_btcusdt.R |
live_binance.R |
Stream live Binance klines through EMA(20) over a WebSocket. | Rscript live_binance.R |
The offline examples run on deterministic synthetic data (and under CI on all
three OSes); fetch_btcusdt.R and live_binance.R reach the network and are
parse-checked but not run in CI.
Java — examples/java/
Build the C ABI library first (cargo build -p wickra-c --release) and install
the binding (mvn -f bindings/java install -DskipTests), then run any example
from this directory. The exec goal forks a JVM with
--enable-native-access=ALL-UNNAMED.
| Example | What it does | Run |
|---|---|---|
Streaming |
Feed a synthetic price series through SMA / EMA / RSI / MACD tick by tick. | mvn exec:exec -Dexec.mainClass=org.wickra.examples.Streaming |
Backtest |
Basket of indicators over an OHLCV series (CSV arg or synthetic). | mvn exec:exec -Dexec.mainClass=org.wickra.examples.Backtest |
MultiTimeframe |
Resample a 1-minute series to 5m / 15m and print an indicator per timeframe. | mvn exec:exec -Dexec.mainClass=org.wickra.examples.MultiTimeframe |
ParallelAssets |
SMA(20) batch over a panel, serial vs parallel streams, with speedup. | mvn exec:exec -Dexec.mainClass=org.wickra.examples.ParallelAssets |
StrategyRsiMeanReversion |
RSI(14) mean-reversion with PnL / Sharpe / max-DD summary. | mvn exec:exec -Dexec.mainClass=org.wickra.examples.StrategyRsiMeanReversion |
StrategyMacdAdx |
Trend-follower: MACD crossover entries gated by ADX(14) > 20. | mvn exec:exec -Dexec.mainClass=org.wickra.examples.StrategyMacdAdx |
StrategyBollingerSqueeze |
Bollinger-squeeze breakout with an ATR(14) trailing stop. | mvn exec:exec -Dexec.mainClass=org.wickra.examples.StrategyBollingerSqueeze |
FetchBtcusdt |
Download real BTCUSDT klines from the Binance REST API into a CSV. | mvn exec:exec -Dexec.mainClass=org.wickra.examples.FetchBtcusdt |
LiveBinance |
Stream live Binance klines through EMA(20) over a WebSocket. | mvn exec:exec -Dexec.mainClass=org.wickra.examples.LiveBinance |
The offline examples run on deterministic synthetic data (and under CI on all
three OSes); FetchBtcusdt and LiveBinance reach the network and are
build-checked but not run in CI.
Python — examples/python/
| Example | What it does | Run |
|---|---|---|
streaming.py |
Feed a synthetic price series through SMA / EMA / RSI / MACD tick by tick. | python -m examples.python.streaming |
backtest.py |
Basket of indicators over an OHLCV CSV. | python -m examples.python.backtest <ohlcv.csv> |
live_binance.py |
Live Binance feed → RSI / MACD / Bollinger → signals. | python -m examples.python.live_binance --symbol BTCUSDT --interval 1m |
multi_timeframe.py |
Resample a 1-minute CSV to coarser timeframes and compare. | python -m examples.python.multi_timeframe <1m.csv> |
parallel_assets.py |
Process many symbols in parallel — the Rust extension releases the GIL during batch computation. | python -m examples.python.parallel_assets --assets 200 --bars 5000 |
fetch_btcusdt.py |
Download real BTCUSDT klines from the Binance REST API into examples/data/ (urllib + stdlib only). |
python -m examples.python.fetch_btcusdt |
strategy_rsi_mean_reversion.py |
Hourly BTCUSDT mean-reversion using RSI(14) thresholds, with PnL / Sharpe / max-DD summary. | python -m examples.python.strategy_rsi_mean_reversion |
strategy_macd_adx.py |
Hourly BTCUSDT trend-follower: MACD crossover entries gated by ADX(14) > 20. | python -m examples.python.strategy_macd_adx |
strategy_bollinger_squeeze.py |
Daily BTCUSDT Bollinger-squeeze breakout with ATR(14) trailing stop. | python -m examples.python.strategy_bollinger_squeeze |
live_binance.py additionally needs pip install websockets.
Node.js — examples/node/
Build the native binding once, then link it into the examples directory:
cd bindings/node && npm install && npx napi build --platform --release
cd ../../examples/node && npm install # links wickra + installs `ws`
| Example | What it does | Run |
|---|---|---|
streaming.js |
Feed a synthetic price series through several indicators tick by tick. | node streaming.js |
backtest.js |
Basket of indicators over an OHLCV CSV; defaults to the bundled BTCUSDT daily dataset. | node backtest.js [ohlcv.csv] |
multi_timeframe.js |
Roll a 1-minute CSV up to 5m / 15m / 1h / 4h / 1d and print indicators per timeframe. | node multi_timeframe.js [path/to/1m.csv] |
parallel_assets.js |
Serial vs worker_threads pool over a synthetic panel, with speedup. |
node parallel_assets.js --assets 200 --bars 5000 |
live_binance.js |
Live Binance feed → RSI / MACD / Bollinger → signals. | node live_binance.js --symbol BTCUSDT --interval 1m |
fetch_btcusdt.js |
Download real BTCUSDT klines from the Binance REST API into examples/data/ (built-in fetch, Node 18+). |
node fetch_btcusdt.js |
strategy_rsi_mean_reversion.js |
Hourly BTCUSDT mean-reversion using RSI(14) thresholds, with PnL / Sharpe / max-DD summary. | node strategy_rsi_mean_reversion.js |
strategy_macd_adx.js |
Hourly BTCUSDT trend-follower: MACD crossover entries gated by ADX(14) > 20. | node strategy_macd_adx.js |
strategy_bollinger_squeeze.js |
Daily BTCUSDT Bollinger-squeeze breakout with ATR(14) trailing stop. | node strategy_bollinger_squeeze.js |
WASM — examples/wasm/
Build the WASM module first (one-time):
wasm-pack build bindings/wasm --target web --release --features panic-hook
Then serve the repository root (python -m http.server, npx http-server,
…) and open the demo you want in a browser.
| Example | What it does |
|---|---|
index.html |
Streams a synthetic price series through six indicators and draws a live <canvas> chart. |
backtest.html |
Streams a fetched OHLCV CSV through a basket of indicators (SMA, EMA, RSI, MACD, Bollinger, ATR, ADX, OBV) and prints a per-series summary table. |
live_binance.html |
Opens a browser-native WebSocket to Binance, runs RSI / MACD / Bollinger and flags BUY/SELL candidates. |
multi_timeframe.html |
Fetches a 1-minute CSV, rolls it up to 5m / 15m / 1h / 4h / 1d in-page, prints RSI / MACD hist / ADX per timeframe. |
parallel_assets.html |
Spawns a pool of module Workers (each loading its own copy of the WASM module) and reports the speedup over a serial baseline. |
strategy_rsi_mean_reversion.html |
Hourly BTCUSDT RSI(14) mean-reversion (long < 30, exit > 70); prints a PnL / Sharpe / max-DD summary table. |
strategy_macd_adx.html |
Hourly BTCUSDT MACD crossover gated by ADX(14) > 20, with the same summary table. |
strategy_bollinger_squeeze.html |
Daily BTCUSDT Bollinger-squeeze breakout with a 2×ATR(14) stop and summary table. |
Example datasets
examples/data/ holds seven real BTCUSDT OHLCV datasets, one
per timeframe (1m, 5m, 15m, 1h, 12h, 1d, 1month), in the standard
timestamp,open,high,low,close,volume layout. The Rust and Node backtest
examples and the indicator benchmarks run against them. Regenerate them with
the latest market history via cargo run -p wickra-examples --bin fetch_btcusdt.