kingchenc
91f6f67257
The first language stecker on the C ABI hub: a .NET binding exposing all 514 indicators as idiomatic `IDisposable` classes, generated from `wickra.h`. ## What's here - **`bindings/csharp/`** — the `Wickra` .NET 8 package. `[LibraryImport]` source-generated P/Invoke (`NativeMethods.g.cs`) plus idiomatic wrappers (`Indicators.g.cs`), both generated from the committed `bindings/c/include/wickra.h`. The binding owns no indicator maths — it only marshals types across the C ABI. - **Marshalling, verified end-to-end against the native library.** Opaque handles cross as `nint` kept alive per call via a `SafeHandle`; `bool` as `[MarshalAs(U1)]` (Rust `bool` is one byte); a self-correcting `DllImportResolver` validates the loaded library actually exports the Wickra ABI. Tests cover one representative per FFI archetype (scalar, candle, pairwise, multi-output, bars, profile, values-profile, order-book / array-input) plus exact Sma reference values. - **NuGet packaging** — `dotnet pack` produces `Wickra.<version>.nupkg`; the release pipeline stages prebuilt native libraries under `runtimes/<rid>/native/` for six target triples (win/linux/osx × x64/arm64). - **`examples/csharp/`** — nine examples mirroring `examples/c/`: streaming, backtest, multi_timeframe, parallel_assets, three strategies, and fetch_btcusdt + live_binance. - **CI** — a `csharp` job on the three OSes builds the C ABI, tests the binding, and runs the offline examples. **Release** — a gated `csharp-publish` job packs and pushes to NuGet (gated on `NUGET_API_KEY`, independent of the GitHub-release job so a C# hiccup never blocks the C/C++ asset release). - **Docs consistency wave** — README, CONTRIBUTING, CHANGELOG, examples/README, the issue / PR templates, `sync-about.yml`, and `.gitattributes`. The native Python / Node / WASM bindings and the C ABI are untouched; this is additive. Publishing to NuGet stays gated behind the release tag and the secret.
48 lines
1.2 KiB
C#
48 lines
1.2 KiB
C#
using System.Diagnostics;
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using Wickra;
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using Wickra.Examples;
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// Run SMA(20) batch over a panel of assets, serial vs Parallel.For, and report the speedup.
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var assets = args.Length > 0 ? int.Parse(args[0]) : 500;
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var bars = args.Length > 1 ? int.Parse(args[1]) : 20_000;
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var panel = new double[assets][];
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for (var a = 0; a < assets; a++)
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{
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panel[a] = MarketData.SyntheticPrices(bars, start: 50.0 + a * 0.1);
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}
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// Warm up the JIT and thread pool so the comparison is fair.
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using (var warm = new Sma(20))
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{
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warm.Batch(panel[0]);
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}
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var sink = 0.0;
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var sw = Stopwatch.StartNew();
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for (var a = 0; a < assets; a++)
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{
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using var sma = new Sma(20);
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var result = sma.Batch(panel[a]);
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sink += result[^1];
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}
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sw.Stop();
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var serialMs = sw.Elapsed.TotalMilliseconds;
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var lasts = new double[assets];
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sw.Restart();
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Parallel.For(0, assets, a =>
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{
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using var sma = new Sma(20);
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var result = sma.Batch(panel[a]);
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lasts[a] = result[^1];
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});
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sw.Stop();
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var parallelMs = sw.Elapsed.TotalMilliseconds;
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Console.WriteLine($"{assets} assets x {bars} bars, SMA(20) batch:");
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Console.WriteLine($" serial {serialMs,8:F1} ms");
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Console.WriteLine($" parallel {parallelMs,8:F1} ms ({serialMs / Math.Max(parallelMs, 1e-9):F1}x speedup)");
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GC.KeepAlive(sink);
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