mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-15 17:18:05 +00:00
updates from mac
This commit is contained in:
Miha Kralj
@@ -1,49 +1,49 @@
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using System;
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using Xunit;
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using QuanTAlib;
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namespace QuanTAlib.Tests
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{
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public class TSeriesTests
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{
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[Fact]
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public void Add_NewValue_IncreasesCount()
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{
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var series = new TSeries();
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long time = DateTime.UtcNow.Ticks;
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series.Add(time, 10.0, isNew: true);
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Assert.Single(series);
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Assert.Equal(10.0, series.Last.Value);
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}
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[Fact]
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public void Add_UpdateValue_DoesNotIncreaseCount()
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{
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var series = new TSeries();
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long time = DateTime.UtcNow.Ticks;
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series.Add(time, 10.0, isNew: true);
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series.Add(time, 11.0, isNew: false);
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Assert.Single(series);
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Assert.Equal(11.0, series.Last.Value);
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}
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[Fact]
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public void Add_MultipleValues_MaintainsOrder()
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{
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var series = new TSeries();
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long t0 = DateTime.UtcNow.Ticks;
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long t1 = t0 + TimeSpan.TicksPerMinute;
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series.Add(t0, 10.0, isNew: true);
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series.Add(t1, 20.0, isNew: true);
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Assert.Equal(2, series.Count);
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Assert.Equal(10.0, series[0].Value);
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Assert.Equal(20.0, series[1].Value);
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}
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}
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}
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using System;
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using Xunit;
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using QuanTAlib;
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namespace QuanTAlib.Tests
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{
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public class TSeriesTests
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{
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[Fact]
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public void Add_NewValue_IncreasesCount()
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{
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var series = new TSeries();
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long time = DateTime.UtcNow.Ticks;
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series.Add(time, 10.0, isNew: true);
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Assert.Single(series);
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Assert.Equal(10.0, series.Last.Value);
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}
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[Fact]
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public void Add_UpdateValue_DoesNotIncreaseCount()
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{
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var series = new TSeries();
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long time = DateTime.UtcNow.Ticks;
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series.Add(time, 10.0, isNew: true);
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series.Add(time, 11.0, isNew: false);
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Assert.Single(series);
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Assert.Equal(11.0, series.Last.Value);
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}
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[Fact]
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public void Add_MultipleValues_MaintainsOrder()
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{
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var series = new TSeries();
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long t0 = DateTime.UtcNow.Ticks;
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long t1 = t0 + TimeSpan.TicksPerMinute;
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series.Add(t0, 10.0, isNew: true);
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series.Add(t1, 20.0, isNew: true);
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Assert.Equal(2, series.Count);
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Assert.Equal(10.0, series[0].Value);
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Assert.Equal(20.0, series[1].Value);
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}
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}
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}
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+56
-56
@@ -1,56 +1,56 @@
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# TSeries: Time Series Data
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## Overview
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`TSeries` is a high-performance container for time-series data. Unlike a standard `List<TValue>`, it uses a **Structure of Arrays (SoA)** layout internally. This means it stores timestamps and values in separate contiguous arrays (`List<long>` and `List<double>`).
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This layout is critical for performance because it allows:
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1. **SIMD Optimization**: The `Values` property returns a `ReadOnlySpan<double>` that can be directly processed by CPU vector instructions (AVX/SSE).
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2. **Cache Locality**: Iterating over values doesn't load timestamps into the CPU cache, and vice versa.
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## Structure
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```csharp
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public class TSeries : IReadOnlyList<TValue>
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{
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// Internal SoA storage
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protected readonly List<long> _t;
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protected readonly List<double> _v;
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// Public accessors
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public ReadOnlySpan<double> Values => ...; // Zero-copy access
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public ReadOnlySpan<long> Times => ...; // Zero-copy access
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public TValue Last { get; }
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public int Count { get; }
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}
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```
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## Key Features
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* **SoA Layout**: Optimized for numerical computing and SIMD.
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* **Zero-Copy Access**: `Values` and `Times` properties expose internal storage as Spans without copying.
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* **Streaming Support**: The `Add` method supports `isNew` parameter to handle intra-bar updates (replacing the last value instead of appending).
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* **Event Publishing**: Optional `Pub` event for reactive pipelines.
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## Usage
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### Creating and Adding Data
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```csharp
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var series = new TSeries();
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series.Add(DateTime.Now, 100.0); // isNew=true by default
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```
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### Streaming Updates
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```csharp
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// New bar
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series.Add(time, 100.0, isNew: true);
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// Update current bar (e.g. price change within same minute)
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series.Add(time, 101.0, isNew: false);
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```
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### SIMD Processing
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```csharp
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// Calculate average using SIMD
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double avg = series.Values.AverageSIMD();
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# TSeries: Time Series Data
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## Overview
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`TSeries` is a high-performance container for time-series data. Unlike a standard `List<TValue>`, it uses a **Structure of Arrays (SoA)** layout internally. This means it stores timestamps and values in separate contiguous arrays (`List<long>` and `List<double>`).
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This layout is critical for performance because it allows:
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1. **SIMD Optimization**: The `Values` property returns a `ReadOnlySpan<double>` that can be directly processed by CPU vector instructions (AVX/SSE).
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2. **Cache Locality**: Iterating over values doesn't load timestamps into the CPU cache, and vice versa.
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## Structure
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```csharp
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public class TSeries : IReadOnlyList<TValue>
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{
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// Internal SoA storage
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protected readonly List<long> _t;
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protected readonly List<double> _v;
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// Public accessors
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public ReadOnlySpan<double> Values => ...; // Zero-copy access
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public ReadOnlySpan<long> Times => ...; // Zero-copy access
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public TValue Last { get; }
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public int Count { get; }
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}
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```
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## Key Features
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* **SoA Layout**: Optimized for numerical computing and SIMD.
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* **Zero-Copy Access**: `Values` and `Times` properties expose internal storage as Spans without copying.
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* **Streaming Support**: The `Add` method supports `isNew` parameter to handle intra-bar updates (replacing the last value instead of appending).
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* **Event Publishing**: Optional `Pub` event for reactive pipelines.
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## Usage
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### Creating and Adding Data
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```csharp
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var series = new TSeries();
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series.Add(DateTime.Now, 100.0); // isNew=true by default
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```
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### Streaming Updates
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```csharp
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// New bar
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series.Add(time, 100.0, isNew: true);
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// Update current bar (e.g. price change within same minute)
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series.Add(time, 101.0, isNew: false);
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```
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### SIMD Processing
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```csharp
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// Calculate average using SIMD
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double avg = series.Values.AverageSIMD();
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+146
-146
@@ -1,146 +1,146 @@
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using System.Collections;
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using System.Runtime.CompilerServices;
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using System.Runtime.InteropServices;
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namespace QuanTAlib;
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/// <summary>
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/// A high-performance time series implementation using Structure of Arrays (SoA) layout.
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/// Stores Time (long) and Value (double) in separate contiguous arrays for SIMD efficiency.
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/// Supports "New Bar" vs "Update Last" streaming semantics.
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/// </summary>
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public class TSeries : IReadOnlyList<TValue>
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{
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// Internal storage: SoA layout
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// We use List<T> for dynamic sizing but access internal arrays via CollectionsMarshal for speed
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protected readonly List<long> _t;
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protected readonly List<double> _v;
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public string Name { get; set; } = "Data";
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// Event optimization: Use Action<TValue> to avoid EventArgs allocation
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// Note: Events are generally discouraged in the hot path of this high-perf design,
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// but kept for compatibility/chaining.
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public event Action<TValue>? Pub;
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public TSeries()
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{
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_t = new List<long>();
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_v = new List<double>();
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}
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/// <summary>
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/// Constructor with capacity hint to avoid List growth overhead.
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/// </summary>
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public TSeries(int capacity)
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{
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_t = new List<long>(capacity);
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_v = new List<double>(capacity);
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}
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/// <summary>
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/// Constructor for wrapping existing lists (e.g. from TBarSeries).
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/// </summary>
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public TSeries(List<long> time, List<double> values)
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{
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_t = time;
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_v = values;
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}
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public int Count
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{
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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get => _v.Count;
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}
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public TValue this[int index]
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{
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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get => new(_t[index], _v[index]);
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}
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public TValue Last
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{
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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get => _v.Count > 0 ? new(_t[^1], _v[^1]) : default;
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}
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public double LastValue
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{
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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get => _v.Count > 0 ? _v[^1] : double.NaN;
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}
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public long LastTime
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{
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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get => _t.Count > 0 ? _t[^1] : 0;
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}
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/// <summary>
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/// Direct access to the underlying Value array as a Span for SIMD operations.
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/// </summary>
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public ReadOnlySpan<double> Values
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{
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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get => CollectionsMarshal.AsSpan(_v);
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}
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/// <summary>
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/// Direct access to the underlying Time array as a Span.
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/// </summary>
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public ReadOnlySpan<long> Times
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{
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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get => CollectionsMarshal.AsSpan(_t);
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public virtual void Add(TValue value, bool isNew)
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{
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if (isNew || _v.Count == 0)
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{
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_t.Add(value.Time);
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_v.Add(value.Value);
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}
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else
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{
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// Update last bar
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int lastIdx = _v.Count - 1;
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_t[lastIdx] = value.Time;
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_v[lastIdx] = value.Value;
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}
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Pub?.Invoke(value);
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}
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// Overload for backward compatibility (assumes isNew=true)
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public virtual void Add(TValue value) => Add(value, true);
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public void Add(long time, double value, bool isNew = true) => Add(new TValue(time, value), isNew);
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public void Add(DateTime time, double value, bool isNew = true) => Add(new TValue(time.Ticks, value), isNew);
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public void Add(IEnumerable<double> values)
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{
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long t = DateTime.UtcNow.Ticks;
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foreach (var v in values)
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{
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Add(new TValue(t, v), isNew: true);
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t += TimeSpan.TicksPerMinute; // Dummy time increment
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}
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}
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// IEnumerable implementation
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public IEnumerator<TValue> GetEnumerator()
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{
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for (int i = 0; i < _v.Count; i++)
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{
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yield return new TValue(_t[i], _v[i]);
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}
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}
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IEnumerator IEnumerable.GetEnumerator() => GetEnumerator();
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}
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using System.Collections;
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using System.Runtime.CompilerServices;
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using System.Runtime.InteropServices;
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namespace QuanTAlib;
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/// <summary>
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/// A high-performance time series implementation using Structure of Arrays (SoA) layout.
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/// Stores Time (long) and Value (double) in separate contiguous arrays for SIMD efficiency.
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/// Supports "New Bar" vs "Update Last" streaming semantics.
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/// </summary>
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public class TSeries : IReadOnlyList<TValue>
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{
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// Internal storage: SoA layout
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// We use List<T> for dynamic sizing but access internal arrays via CollectionsMarshal for speed
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protected readonly List<long> _t;
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protected readonly List<double> _v;
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public string Name { get; set; } = "Data";
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// Event optimization: Use Action<TValue> to avoid EventArgs allocation
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// Note: Events are generally discouraged in the hot path of this high-perf design,
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// but kept for compatibility/chaining.
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public event Action<TValue>? Pub;
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public TSeries()
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{
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_t = new List<long>();
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_v = new List<double>();
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}
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/// <summary>
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/// Constructor with capacity hint to avoid List growth overhead.
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/// </summary>
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public TSeries(int capacity)
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{
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_t = new List<long>(capacity);
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_v = new List<double>(capacity);
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}
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/// <summary>
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/// Constructor for wrapping existing lists (e.g. from TBarSeries).
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/// </summary>
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public TSeries(List<long> time, List<double> values)
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{
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_t = time;
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_v = values;
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}
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public int Count
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{
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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get => _v.Count;
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}
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public TValue this[int index]
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{
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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get => new(_t[index], _v[index]);
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}
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public TValue Last
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{
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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get => _v.Count > 0 ? new(_t[^1], _v[^1]) : default;
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}
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public double LastValue
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{
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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get => _v.Count > 0 ? _v[^1] : double.NaN;
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}
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public long LastTime
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{
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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get => _t.Count > 0 ? _t[^1] : 0;
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}
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|
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/// <summary>
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/// Direct access to the underlying Value array as a Span for SIMD operations.
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/// </summary>
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public ReadOnlySpan<double> Values
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{
|
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
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get => CollectionsMarshal.AsSpan(_v);
|
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}
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|
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/// <summary>
|
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/// Direct access to the underlying Time array as a Span.
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/// </summary>
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public ReadOnlySpan<long> Times
|
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{
|
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
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get => CollectionsMarshal.AsSpan(_t);
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}
|
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|
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public virtual void Add(TValue value, bool isNew)
|
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{
|
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if (isNew || _v.Count == 0)
|
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{
|
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_t.Add(value.Time);
|
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_v.Add(value.Value);
|
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}
|
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else
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{
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// Update last bar
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int lastIdx = _v.Count - 1;
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_t[lastIdx] = value.Time;
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_v[lastIdx] = value.Value;
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}
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Pub?.Invoke(value);
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}
|
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|
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// Overload for backward compatibility (assumes isNew=true)
|
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public virtual void Add(TValue value) => Add(value, true);
|
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|
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
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public void Add(long time, double value, bool isNew = true) => Add(new TValue(time, value), isNew);
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public void Add(DateTime time, double value, bool isNew = true) => Add(new TValue(time.Ticks, value), isNew);
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|
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public void Add(IEnumerable<double> values)
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{
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long t = DateTime.UtcNow.Ticks;
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foreach (var v in values)
|
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{
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Add(new TValue(t, v), isNew: true);
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t += TimeSpan.TicksPerMinute; // Dummy time increment
|
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}
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}
|
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|
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// IEnumerable implementation
|
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public IEnumerator<TValue> GetEnumerator()
|
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{
|
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for (int i = 0; i < _v.Count; i++)
|
||||
{
|
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yield return new TValue(_t[i], _v[i]);
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}
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||||
}
|
||||
|
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IEnumerator IEnumerable.GetEnumerator() => GetEnumerator();
|
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}
|
||||
|
||||
Reference in New Issue
Block a user