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https://github.com/mihakralj/QuanTAlib.git
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Add eventing support to WMA indicator and implement unit tests for various indicators
- Enhanced WMA indicator with event-driven capabilities using ITValuePublisher interface. - Created a new TODO file listing various indicators and their corresponding libraries. - Added unit tests for DEMA, HMA, TEMA, and WMA indicators to ensure proper functionality. - Implemented tests for handling new bars, ticks, and historical data updates across indicators. - Verified that indicators correctly compute values and handle different source types.
This commit is contained in:
Miha Kralj
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using System;
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using Xunit;
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namespace QuanTAlib.Tests;
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public class SmaCoverageTests
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{
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[Fact]
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public void Sma_ResyncLogic_IsTriggeredAndCorrect()
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{
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// ResyncInterval is 1000.
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int count = 2500;
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int period = 10;
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var sma = new Sma(period);
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double constantValue = 100.0;
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for (int i = 0; i < count; i++)
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{
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sma.Update(new TValue(DateTime.UtcNow, constantValue));
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if (i >= period)
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{
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Assert.Equal(constantValue, sma.Last.Value, 1e-9);
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}
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}
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}
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[Fact]
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public void Sma_SpanCalc_LargeDataset_TriggersResync()
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{
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int count = 5000;
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int period = 10;
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double[] source = new double[count];
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double[] output = new double[count];
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for (int i = 0; i < count; i++)
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{
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source[i] = 100.0;
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}
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Sma.Calculate(source.AsSpan(), output.AsSpan(), period);
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for (int i = period; i < count; i++)
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{
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Assert.Equal(100.0, output[i], 1e-9);
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}
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}
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[Fact]
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public void Sma_SpanCalc_SimdThreshold_Boundary()
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{
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// SimdThreshold is 256.
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int[] lengths = { 250, 256, 260 };
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int period = 10;
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foreach (int len in lengths)
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{
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double[] source = new double[len];
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double[] output = new double[len];
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for (int i = 0; i < len; i++) source[i] = 100.0;
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Sma.Calculate(source.AsSpan(), output.AsSpan(), period);
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Assert.Equal(100.0, output[^1], 1e-9);
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}
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}
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[Fact]
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public void Sma_SpanCalc_Simd_WithResync()
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{
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int count = 3000;
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int period = 5;
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double[] source = new double[count];
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double[] output = new double[count];
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// Linear increase: 0, 1, 2, ...
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for (int i = 0; i < count; i++) source[i] = i;
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Sma.Calculate(source.AsSpan(), output.AsSpan(), period);
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// SMA(5) of x-4, x-3, x-2, x-1, x
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// = (5x - 10) / 5 = x - 2
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for (int i = period; i < count; i++)
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{
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double expected = i - 2.0;
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Assert.Equal(expected, output[i], 1e-9);
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}
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}
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[Fact]
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public void Sma_Constructor_ThrowsOnInvalidPeriod()
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{
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Assert.Throws<ArgumentException>(() => new Sma(0));
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Assert.Throws<ArgumentException>(() => new Sma(-1));
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}
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[Fact]
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public void Sma_StaticCalculate_ThrowsOnInvalidArgs()
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{
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double[] source = new double[10];
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double[] output = new double[5]; // Mismatch
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Assert.Throws<ArgumentException>(() => Sma.Calculate(source.AsSpan(), output.AsSpan(), 5));
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double[] output2 = new double[10];
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Assert.Throws<ArgumentException>(() => Sma.Calculate(source.AsSpan(), output2.AsSpan(), 0));
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}
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[Fact]
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public void Sma_Calculate_EmptyInput_DoesNothing()
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{
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Sma.Calculate(ReadOnlySpan<double>.Empty, Span<double>.Empty, 5);
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// Should not throw
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}
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[Fact]
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public void Sma_Update_WithNaN_UsesLastValid()
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{
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var sma = new Sma(5);
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sma.Update(new TValue(DateTime.UtcNow, 1.0));
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sma.Update(new TValue(DateTime.UtcNow, 2.0));
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sma.Update(new TValue(DateTime.UtcNow, double.NaN)); // Should use 2.0
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// Buffer: 1, 2, 2
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// SMA(3) = (1 + 2 + 2) / 3 = 5/3 = 1.666...
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Assert.Equal(5.0/3.0, sma.Last.Value, 1e-9);
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}
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[Fact]
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public void Sma_Update_IsNewFalse_UpdatesLastValue()
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{
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var sma = new Sma(3);
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sma.Update(new TValue(DateTime.UtcNow, 1.0));
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sma.Update(new TValue(DateTime.UtcNow, 2.0));
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// Update existing with 3.0 (replaces 2.0)
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sma.Update(new TValue(DateTime.UtcNow, 3.0), isNew: false);
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// Buffer should be: 1, 3
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// SMA = (1 + 3) / 2 = 2
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Assert.Equal(2.0, sma.Last.Value, 1e-9);
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}
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[Fact]
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public void Sma_TSeries_Empty_ReturnsEmpty()
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{
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var sma = new Sma(5);
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var result = sma.Update(new TSeries());
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Assert.Empty(result);
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}
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[Fact]
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public void Sma_TSeries_WithNaN_RestoresStateCorrectly()
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{
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var sma = new Sma(3);
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var series = new TSeries();
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series.Add(new TValue(DateTime.UtcNow, 1.0));
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series.Add(new TValue(DateTime.UtcNow, 2.0));
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series.Add(new TValue(DateTime.UtcNow, double.NaN));
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series.Add(new TValue(DateTime.UtcNow, 4.0));
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sma.Update(series);
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// Buffer: 2.0, 2.0 (from NaN), 4.0
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// SMA(3) = (2 + 2 + 4) / 3 = 8/3 = 2.666...
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// Let's add one more value to verify state is correct
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sma.Update(new TValue(DateTime.UtcNow, 5.0));
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// Buffer: 2.0, 4.0, 5.0
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// SMA(3) = (2 + 4 + 5) / 3 = 11/3 = 3.666...
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Assert.Equal(11.0/3.0, sma.Last.Value, 1e-9);
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}
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[Fact]
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public void Sma_Reset_ClearsState()
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{
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var sma = new Sma(3);
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sma.Update(new TValue(DateTime.UtcNow, 1.0));
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sma.Update(new TValue(DateTime.UtcNow, 2.0));
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sma.Update(new TValue(DateTime.UtcNow, 3.0));
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sma.Reset();
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Assert.Equal(0, sma.Last.Value);
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// Start fresh
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sma.Update(new TValue(DateTime.UtcNow, 10.0));
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// Buffer: 10
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// SMA = 10
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Assert.Equal(10.0, sma.Last.Value);
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}
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[Fact]
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public void Sma_Calculate_ScalarFallback_WithNaN()
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{
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// Force scalar path by including NaN, even with large dataset
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int count = 1000;
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double[] source = new double[count];
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double[] output = new double[count];
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for (int i = 0; i < count; i++) source[i] = 1.0;
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source[500] = double.NaN; // This should trigger HasNonFiniteValues -> true
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Sma.Calculate(source.AsSpan(), output.AsSpan(), 10);
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// Check around the NaN
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// Index 500 is NaN, so it uses previous valid (1.0)
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// So effectively the stream is all 1.0s
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Assert.Equal(1.0, output[500], 1e-9);
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Assert.Equal(1.0, output[501], 1e-9);
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}
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[Fact]
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public void Sma_Constructor_WithSource_Subscribes()
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{
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var source = new Sma(10); // Just using Sma as a publisher
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var sma = new Sma(source, 5);
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source.Update(new TValue(DateTime.UtcNow, 10.0));
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Assert.Equal(10.0, sma.Last.Value);
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}
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}
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@@ -1,285 +0,0 @@
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#!meta
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{"kernelInfo":{"defaultKernelName":"csharp","items":[{"name":"csharp"},{"name":"fsharp","languageName":"F#","aliases":["f#","fs"]},{"name":"html","languageName":"HTML"},{"name":"http","languageName":"HTTP"},{"name":"javascript","languageName":"JavaScript","aliases":["js"]},{"name":"mermaid","languageName":"Mermaid"},{"name":"pwsh","languageName":"PowerShell","aliases":["powershell"]},{"name":"value"}]}}
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#!markdown
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# Simple Moving Average (SMA) Examples
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This is a **.NET Interactive** notebook. To run it, you need the [Polyglot Notebooks](https://marketplace.visualstudio.com/items?itemName=ms-dotnettools.dotnet-interactive-vscode) extension installed in VS Code.
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The **Simple Moving Average (SMA)** is the most basic form of moving average, calculating the arithmetic mean over a specified period. Unlike the EMA, the SMA assigns equal weight to all data points in the window, making it a good baseline for trend analysis.
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**Key characteristics:**
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- Equal weighting for all values in the period
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- O(1) update complexity using running sum
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- O(1) bar correction using scalar state
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- Smooth output with good noise reduction
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- More lag than EMA due to equal weighting
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This notebook demonstrates:
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1. **Manual Data Processing**: Understanding Batch vs. Streaming modes.
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2. **Streaming with `isNew`**: Handling intra-bar updates.
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3. **Large Dataset Processing**: Using Geometric Brownian Motion (GBM) generated data.
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4. **Handling Invalid Values**: Last-value substitution for NaN/Infinity.
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5. **SMA vs EMA**: Comparing Simple and Exponential Moving Averages.
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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 QuanTAlib;
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// Helper to print TSeries
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void PrintSeries(TSeries series, int count = 5)
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{
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Console.WriteLine($"Series Length: {series.Count}");
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foreach (var item in series.Take(count))
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{
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Console.WriteLine($"Time: {item.Time:HH:mm:ss}, Value: {item.Value:F2}");
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}
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if (series.Count > count) Console.WriteLine("...");
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}
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#!markdown
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## 1. Manual Data: Batch vs. Streaming
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We'll start with a small, manually created dataset to clearly see how Batch and Streaming operations work.
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### Batch Processing
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Batch processing calculates the SMA for the entire dataset at once. This is efficient for historical analysis.
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#!csharp
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// Create a small manual dataset
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var manualData = new TSeries();
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manualData.Add(DateTime.Now, 100.0);
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manualData.Add(DateTime.Now.AddMinutes(1), 102.0);
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manualData.Add(DateTime.Now.AddMinutes(2), 101.0);
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manualData.Add(DateTime.Now.AddMinutes(3), 103.0);
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manualData.Add(DateTime.Now.AddMinutes(4), 105.0);
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Console.WriteLine("--- Input Data ---");
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PrintSeries(manualData, 5);
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// Batch Calculation
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Console.WriteLine("\n--- Batch SMA (Period 3) ---");
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var smaBatch = new Sma(3);
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var resultBatch = smaBatch.Update(manualData);
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PrintSeries(resultBatch, 5);
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// Show the calculation for each step
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Console.WriteLine("\nCalculation breakdown:");
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Console.WriteLine(" SMA[0] = 100 / 1 = 100.00");
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Console.WriteLine(" SMA[1] = (100 + 102) / 2 = 101.00");
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Console.WriteLine(" SMA[2] = (100 + 102 + 101) / 3 = 101.00");
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Console.WriteLine(" SMA[3] = (102 + 101 + 103) / 3 = 102.00");
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Console.WriteLine(" SMA[4] = (101 + 103 + 105) / 3 = 103.00");
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#!markdown
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### Streaming Processing
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Streaming processing updates the SMA one data point at a time. This is essential for real-time trading systems where data arrives sequentially.
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#!csharp
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Console.WriteLine("\n--- Streaming SMA (Period 3) ---");
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var smaStream = new Sma(3);
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foreach (var item in manualData)
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{
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var result = smaStream.Update(item);
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Console.WriteLine($"Time: {item.Time:HH:mm:ss}, Input: {item.Value:F2}, SMA: {result.Value:F2}, IsHot: {smaStream.IsHot}");
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}
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// Verify that the last values match
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var batchLast = resultBatch.Last().Value;
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var streamLast = smaStream.Value.Value;
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Console.WriteLine($"\nMatch: {Math.Abs(batchLast - streamLast) < 1e-10} (Batch: {batchLast:F2}, Stream: {streamLast:F2})");
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// Show SMA properties
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Console.WriteLine($"\nSMA Properties:");
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Console.WriteLine($" Name: {smaStream.Name}");
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Console.WriteLine($" WarmupPeriod: {smaStream.WarmupPeriod}");
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Console.WriteLine($" IsHot: {smaStream.IsHot}");
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#!markdown
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## 2. Streaming with `isNew` (Intra-bar Updates)
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In real-time feeds, you often receive multiple updates for the *same* bar (e.g., price changes within the current minute) before the bar closes.
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* `isNew = true`: The input is a new bar (advances time).
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* `isNew = false`: The input is an update to the current bar (recalculates without advancing).
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**SMA achieves O(1) bar correction** by saving scalar state after each `isNew=true` update.
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#!csharp
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Console.WriteLine("\n--- Streaming with Intra-bar Updates ---");
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var smaIntra = new Sma(3);
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// 1. Process the first 4 bars normally
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for (int i = 0; i < 4; i++)
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{
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smaIntra.Update(manualData[i]);
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}
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Console.WriteLine($"After 4th bar: {smaIntra.Value.Value:F2}");
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// 2. Simulate intra-bar updates for the 5th bar (Final value is 105.0)
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// Update 1: Price moves to 104.0
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var update1 = new TValue(manualData[4].Time, 104.0);
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smaIntra.Update(update1, isNew: true); // First update for this bar is "New"
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Console.WriteLine($"Update 1 (104.0): {smaIntra.Value.Value:F2}");
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// Update 2: Price moves to 106.0 (Same time, same bar)
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var update2 = new TValue(manualData[4].Time, 106.0);
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smaIntra.Update(update2, isNew: false); // Not new, just an update
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Console.WriteLine($"Update 2 (106.0): {smaIntra.Value.Value:F2}");
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// Update 3: Final Close at 105.0
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var update3 = manualData[4];
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smaIntra.Update(update3, isNew: false); // Final update
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Console.WriteLine($"Update 3 (105.0): {smaIntra.Value.Value:F2}");
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// Verify match with batch result
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Console.WriteLine($"Match with Batch: {Math.Abs(smaIntra.Value.Value - batchLast) < 1e-10}");
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#!markdown
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## 3. Large Dataset: Geometric Brownian Motion (GBM)
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We'll generate a larger dataset (1000 bars) using a Geometric Brownian Motion generator to simulate realistic market data.
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#!csharp
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// Generate 1000 bars of data
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var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.2);
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var gbmData = gbm.Fetch(1000, DateTime.Now.Ticks, TimeSpan.FromMinutes(1));
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var closeSeries = gbmData.Close;
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Console.WriteLine($"Generated {closeSeries.Count} bars of GBM data.");
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Console.WriteLine($"First 5 values: {string.Join(", ", closeSeries.Take(5).Select(x => x.Value.ToString("F2")))}");
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#!markdown
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### Batch vs. Streaming Performance on Large Data
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#!csharp
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// Batch
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var smaLargeBatch = new Sma(20);
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var batchLargeResult = smaLargeBatch.Update(closeSeries);
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Console.WriteLine($"Batch Last Value: {batchLargeResult.Last().Value:F2}");
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// Streaming
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var smaLargeStream = new Sma(20);
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TValue lastStreamVal = default;
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foreach(var item in closeSeries)
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{
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lastStreamVal = smaLargeStream.Update(item);
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}
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Console.WriteLine($"Streaming Last Value: {lastStreamVal.Value:F2}");
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// Verify match
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Console.WriteLine($"Match: {Math.Abs(batchLargeResult.Last().Value - lastStreamVal.Value) < 1e-10}");
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#!markdown
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## 4. Handling Invalid Values (NaN/Infinity)
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`Sma` uses **last-value substitution** for invalid inputs. When a non-finite value (NaN, PositiveInfinity, NegativeInfinity) is encountered, it is replaced with the last valid value. This provides output continuity instead of propagating invalid values through the calculation.
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#!csharp
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Console.WriteLine("\n--- Handling Invalid Values ---");
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// Single SMA
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var smaNaN = new Sma(10);
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// Feed valid values first
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smaNaN.Update(new TValue(DateTime.Now, 100.0));
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smaNaN.Update(new TValue(DateTime.Now.AddMinutes(1), 110.0));
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Console.WriteLine($"After valid values: {smaNaN.Value.Value:F2}");
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// Feed NaN - should use last valid value (110)
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var resultAfterNaN = smaNaN.Update(new TValue(DateTime.Now.AddMinutes(2), double.NaN));
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Console.WriteLine($"After NaN input: {resultAfterNaN.Value:F2} (IsFinite: {double.IsFinite(resultAfterNaN.Value)})");
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// Feed Infinity - should use last valid value (110)
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var resultAfterInf = smaNaN.Update(new TValue(DateTime.Now.AddMinutes(3), double.PositiveInfinity));
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Console.WriteLine($"After Infinity input: {resultAfterInf.Value:F2} (IsFinite: {double.IsFinite(resultAfterInf.Value)})");
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// Continue with valid value
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var resultAfterValid = smaNaN.Update(new TValue(DateTime.Now.AddMinutes(4), 120.0));
|
||||
Console.WriteLine($"After valid value (120): {resultAfterValid.Value:F2}");
|
||||
|
||||
#!csharp
|
||||
|
||||
Console.WriteLine("\n--- Batch Processing with Invalid Values ---");
|
||||
|
||||
// Create series with NaN values interspersed
|
||||
var seriesWithNaN = new TSeries();
|
||||
seriesWithNaN.Add(DateTime.Now.Ticks, 100.0);
|
||||
seriesWithNaN.Add(DateTime.Now.Ticks + 1, 110.0);
|
||||
seriesWithNaN.Add(DateTime.Now.Ticks + 2, double.NaN);
|
||||
seriesWithNaN.Add(DateTime.Now.Ticks + 3, 120.0);
|
||||
seriesWithNaN.Add(DateTime.Now.Ticks + 4, double.PositiveInfinity);
|
||||
seriesWithNaN.Add(DateTime.Now.Ticks + 5, 130.0);
|
||||
|
||||
var smaBatchNaN = new Sma(3);
|
||||
var resultsWithNaN = smaBatchNaN.Update(seriesWithNaN);
|
||||
|
||||
Console.WriteLine("Input → Output:");
|
||||
for (int i = 0; i < seriesWithNaN.Count; i++)
|
||||
{
|
||||
var input = seriesWithNaN[i].Value;
|
||||
var output = resultsWithNaN[i].Value;
|
||||
var inputStr = double.IsFinite(input) ? input.ToString("F2") : input.ToString();
|
||||
Console.WriteLine($" {inputStr,-10} → {output:F2} (IsFinite: {double.IsFinite(output)})");
|
||||
}
|
||||
|
||||
#!markdown
|
||||
|
||||
## 5. SMA vs EMA Comparison
|
||||
|
||||
The SMA and EMA are both trend-following indicators, but they weight data differently:
|
||||
|
||||
- **SMA**: Equal weight to all values in the window
|
||||
- **EMA**: More weight to recent values (exponentially decreasing)
|
||||
|
||||
#!csharp
|
||||
|
||||
Console.WriteLine("\n--- SMA vs EMA Comparison (Period 10) ---");
|
||||
|
||||
var compareData = new TSeries();
|
||||
var baseTime = DateTime.Now;
|
||||
for (int i = 0; i < 20; i++)
|
||||
{
|
||||
// Create data with a sudden spike at position 10
|
||||
double value = (i == 10) ? 150.0 : 100.0;
|
||||
compareData.Add(baseTime.AddMinutes(i), value);
|
||||
}
|
||||
|
||||
var smaCompare = new Sma(10);
|
||||
var emaCompare = new Ema(10);
|
||||
|
||||
Console.WriteLine("Position | Input | SMA | EMA | Difference");
|
||||
Console.WriteLine("---------+--------+---------+---------+-----------");
|
||||
|
||||
for (int i = 0; i < compareData.Count; i++)
|
||||
{
|
||||
var smaVal = smaCompare.Update(compareData[i]);
|
||||
var emaVal = emaCompare.Update(compareData[i]);
|
||||
var input = compareData[i].Value;
|
||||
var diff = smaVal.Value - emaVal.Value;
|
||||
|
||||
Console.WriteLine($" {i,2} | {input,6:F0} | {smaVal.Value,7:F2} | {emaVal.Value,7:F2} | {diff,+9:F2}");
|
||||
}
|
||||
|
||||
Console.WriteLine("\nNote: After the spike (position 10), EMA reacts faster due to higher weight on recent values.");
|
||||
Console.WriteLine("SMA takes longer to reflect changes as all values have equal weight.");
|
||||
+35
-30
@@ -108,25 +108,13 @@ public sealed class Sma : ITValuePublisher
|
||||
}
|
||||
|
||||
|
||||
// Removed GetValidValue and UpdateState as they are not used in the new Update logic
|
||||
|
||||
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
||||
public TValue Update(TValue input, bool isNew = true)
|
||||
{
|
||||
if (isNew)
|
||||
{
|
||||
double val = GetValidValue(input.Value);
|
||||
|
||||
double removedValue = _buffer.Count == _buffer.Capacity ? _buffer.Oldest : 0.0;
|
||||
_sum = _sum - removedValue + val;
|
||||
_buffer.Add(val);
|
||||
|
||||
_tickCount++;
|
||||
if (_buffer.IsFull && _tickCount >= ResyncInterval)
|
||||
{
|
||||
_tickCount = 0;
|
||||
_sum = _buffer.Sum();
|
||||
}
|
||||
UpdateState(val);
|
||||
|
||||
_p_sum = _sum;
|
||||
_p_lastInput = val;
|
||||
@@ -136,7 +124,7 @@ public sealed class Sma : ITValuePublisher
|
||||
{
|
||||
_lastValidValue = _p_lastValidValue;
|
||||
double val = GetValidValue(input.Value);
|
||||
|
||||
|
||||
_sum = _p_sum - _p_lastInput + val;
|
||||
_buffer.UpdateNewest(val);
|
||||
}
|
||||
@@ -150,7 +138,7 @@ public sealed class Sma : ITValuePublisher
|
||||
public TSeries Update(TSeries source)
|
||||
{
|
||||
if (source.Count == 0) return new TSeries(new List<long>(), new List<double>());
|
||||
|
||||
|
||||
int len = source.Count;
|
||||
var t = new List<long>(len);
|
||||
var v = new List<double>(len);
|
||||
@@ -159,26 +147,43 @@ public sealed class Sma : ITValuePublisher
|
||||
|
||||
var tSpan = CollectionsMarshal.AsSpan(t);
|
||||
var vSpan = CollectionsMarshal.AsSpan(v);
|
||||
var sourceValues = source.Values;
|
||||
var sourceTimes = source.Times;
|
||||
|
||||
// Reset state for batch calculation
|
||||
Reset();
|
||||
|
||||
// We can optimize this later with specific batch logic, but for now use core loop
|
||||
for(int i=0; i < len; i++)
|
||||
Calculate(source.Values, vSpan, _period);
|
||||
source.Times.CopyTo(tSpan);
|
||||
|
||||
// Restore state
|
||||
int windowSize = Math.Min(len, _period);
|
||||
int startIndex = len - windowSize;
|
||||
|
||||
if (startIndex > 0)
|
||||
{
|
||||
double val = GetValidValue(sourceValues[i]);
|
||||
double removedValue = _buffer.Count == _buffer.Capacity ? _buffer.Oldest : 0.0;
|
||||
_sum = _sum - removedValue + val;
|
||||
_buffer.Add(val);
|
||||
vSpan[i] = _sum / _buffer.Count;
|
||||
for (int i = startIndex - 1; i >= 0; i--)
|
||||
{
|
||||
if (double.IsFinite(source.Values[i]))
|
||||
{
|
||||
_lastValidValue = source.Values[i];
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
_lastValidValue = 0;
|
||||
}
|
||||
|
||||
_buffer.Clear();
|
||||
_sum = 0;
|
||||
_tickCount = 0;
|
||||
|
||||
for (int i = startIndex; i < len; i++)
|
||||
{
|
||||
double val = GetValidValue(source.Values[i]);
|
||||
UpdateState(val);
|
||||
}
|
||||
|
||||
sourceTimes.CopyTo(tSpan);
|
||||
_p_lastValidValue = _lastValidValue;
|
||||
_p_sum = _sum;
|
||||
_p_lastInput = sourceValues[len-1];
|
||||
_p_lastInput = source.Values[len - 1];
|
||||
_p_lastValidValue = _lastValidValue;
|
||||
|
||||
Last = new TValue(tSpan[len - 1], vSpan[len - 1]);
|
||||
return new TSeries(t, v);
|
||||
|
||||
@@ -125,6 +125,34 @@ sma.Update(new TValue(time + 1, 101.2), isNew: true);
|
||||
|
||||
**Implementation detail:** Bar correction is O(1) using scalar state save/restore, not buffer copying.
|
||||
|
||||
### Eventing and Reactive Support
|
||||
|
||||
This indicator implements the `ITValuePublisher` interface, enabling event-driven and reactive workflows.
|
||||
|
||||
* **Subscription:** Can be constructed with an `ITValuePublisher` (e.g., `TSeries`) to automatically update when the source emits a new value.
|
||||
* **Publication:** Emits a `Pub` event with the new `TValue` whenever it is updated.
|
||||
|
||||
```csharp
|
||||
using QuanTAlib;
|
||||
|
||||
// 1. Setup a source (publisher)
|
||||
var source = new TSeries();
|
||||
|
||||
// 2. Create indicator subscribed to source
|
||||
// It waits for events from 'source'
|
||||
var sma = new Sma(source, period: 10);
|
||||
|
||||
// 3. Optional: Subscribe to indicator's output
|
||||
sma.Pub += (item) => Console.WriteLine($"SMA Updated: {item.Value}");
|
||||
|
||||
// 4. Ingest data into source
|
||||
// This triggers the chain: source -> sma -> Console.WriteLine
|
||||
source.Add(new TValue(DateTime.Now, 100));
|
||||
source.Add(new TValue(DateTime.Now, 105));
|
||||
```
|
||||
|
||||
This pattern allows building complex, reactive processing pipelines without manual update loops.
|
||||
|
||||
### Handling Invalid Values (NaN/Infinity)
|
||||
|
||||
`Sma` uses **last-value substitution** for handling invalid inputs:
|
||||
|
||||
Reference in New Issue
Block a user