mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-07-29 10:07:43 +00:00
Miha Kralj
74b49d2bb4
- Introduced TBar struct for efficient OHLCV data representation. - Implemented TBarSeries class for high-performance collection of TBar instances using Structure of Arrays (SoA) layout. - Added TSeries class for time-series data management with zero-copy access. - Created TValue struct for time-value pairs with implicit conversions. - Defined IFeed interface for consistent data feed implementations. - Developed CsvFeed class for loading historical OHLCV data from CSV files. - Implemented GBM class for generating synthetic financial data using Geometric Brownian Motion. - Added Quantower project files for Averages indicator with necessary dependencies and configurations. - Included extensive usage examples and notebooks for TBar, TBarSeries, TSeries, TValue, and feed implementations.
92 lines
2.9 KiB
Plaintext
92 lines
2.9 KiB
Plaintext
#!meta
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#!csharp
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// Reference the library
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#r "..\..\bin\QuanTAlib.dll"
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using System;
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using System.Linq;
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using System.Numerics;
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using QuanTAlib;
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// 1. Check Hardware Support
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Console.WriteLine($"SIMD Hardware Acceleration: {Vector.IsHardwareAccelerated}");
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Console.WriteLine($"Vector<double> Count: {Vector<double>.Count}");
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#!csharp
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// 2. Basic Operations
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// Demonstrate Sum, Min, Max, Average using SIMD extensions
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// Define data locally in this cell
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double[] data = new double[1000];
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for (int i = 0; i < data.Length; i++) data[i] = i;
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// We use explicit static method calls with .AsSpan() to ensure correct overload resolution
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// and avoid creating top-level ReadOnlySpan variables (which causes CS8345).
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double sum = SimdExtensions.SumSIMD(data.AsSpan());
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double minVal = SimdExtensions.MinSIMD(data.AsSpan());
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double maxVal = SimdExtensions.MaxSIMD(data.AsSpan());
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double avg = SimdExtensions.AverageSIMD(data.AsSpan());
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Console.WriteLine($"Sum: {sum}");
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Console.WriteLine($"Min: {minVal}");
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Console.WriteLine($"Max: {maxVal}");
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Console.WriteLine($"Average: {avg}");
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#!csharp
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// 3. Advanced Statistics
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double[] dataStats = new double[1000];
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for (int i = 0; i < dataStats.Length; i++) dataStats[i] = i;
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double variance = SimdExtensions.VarianceSIMD(dataStats.AsSpan());
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double stdDev = SimdExtensions.StdDevSIMD(dataStats.AsSpan());
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Console.WriteLine($"Variance: {variance:F4}");
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Console.WriteLine($"Standard Deviation: {stdDev:F4}");
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#!csharp
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// 4. Combined Operations
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double[] dataComb = new double[1000];
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for (int i = 0; i < dataComb.Length; i++) dataComb[i] = i;
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var (min, max) = SimdExtensions.MinMaxSIMD(dataComb.AsSpan());
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Console.WriteLine($"Min: {min}, Max: {max}");
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#!csharp
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// 5. Performance Comparison (Simple Benchmark)
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int size = 1_000_000;
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double[] largeData = new double[size];
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Random rnd = new Random(42);
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for (int i = 0; i < size; i++) largeData[i] = rnd.NextDouble();
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// Warmup
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SimdExtensions.SumSIMD(largeData.AsSpan());
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// Measure SIMD
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long start = DateTime.UtcNow.Ticks;
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double sumSimd = SimdExtensions.SumSIMD(largeData.AsSpan());
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long end = DateTime.UtcNow.Ticks;
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double simdTime = (end - start) / 10000.0; // ms
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// Measure Scalar (LINQ Sum as proxy for scalar loop)
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start = DateTime.UtcNow.Ticks;
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double sumScalar = largeData.Sum();
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end = DateTime.UtcNow.Ticks;
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double scalarTime = (end - start) / 10000.0; // ms
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Console.WriteLine($"Array Size: {size:N0}");
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Console.WriteLine($"SIMD Time: {simdTime:F4} ms");
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Console.WriteLine($"Scalar Time: {scalarTime:F4} ms");
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Console.WriteLine($"Speedup: {scalarTime / simdTime:F2}x");
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