mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-09 06:27:45 +00:00
281 lines
8.2 KiB
C#
281 lines
8.2 KiB
C#
using System;
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using System.Linq;
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using Xunit;
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namespace QuanTAlib.Tests;
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public class KamaTests
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{
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[Fact]
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public void Kama_Constructor_ValidatesInput()
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{
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Assert.Throws<ArgumentException>(() => new Kama(0));
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Assert.Throws<ArgumentException>(() => new Kama(10, fastPeriod: 0));
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Assert.Throws<ArgumentException>(() => new Kama(10, slowPeriod: 0));
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Assert.Throws<ArgumentException>(() => new Kama(10, fastPeriod: 10, slowPeriod: 5));
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var kama = new Kama(10);
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Assert.NotNull(kama);
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}
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[Fact]
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public void Kama_Calc_ReturnsValue()
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{
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var kama = new Kama(10);
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TValue result = kama.Update(new TValue(DateTime.UtcNow, 100));
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Assert.True(result.Value > 0);
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}
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[Fact]
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public void Kama_IsHot_BecomesTrueWhenBufferFull()
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{
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// Buffer size is period + 1
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var kama = new Kama(5);
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Assert.False(kama.IsHot);
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for (int i = 0; i < 5; i++)
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{
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kama.Update(new TValue(DateTime.UtcNow, 100));
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Assert.False(kama.IsHot);
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}
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kama.Update(new TValue(DateTime.UtcNow, 100));
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Assert.True(kama.IsHot);
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}
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[Fact]
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public void Kama_StreamingMatchesBatch()
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{
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var kamaStreaming = new Kama(10);
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var kamaBatch = new Kama(10);
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var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.2, seed: 42);
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var series = new TSeries();
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for (int i = 0; i < 100; i++)
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{
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var bar = gbm.Next(isNew: true);
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series.Add(bar.Time, bar.Close);
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}
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// Streaming
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var streamingResults = new TSeries();
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Assert.True(series.Count > 0);
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foreach (var item in series)
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{
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streamingResults.Add(kamaStreaming.Update(item));
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}
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// Batch
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var batchResults = kamaBatch.Update(series);
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Assert.Equal(streamingResults.Count, batchResults.Count);
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foreach (var (stream, batch) in streamingResults.Zip(batchResults))
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{
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Assert.Equal(stream.Value, batch.Value, 1e-9);
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}
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}
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[Fact]
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public void Kama_StaticCalculate_MatchesInstance()
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{
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var series = new TSeries();
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var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.2, seed: 42);
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for (int i = 0; i < 100; i++)
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{
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var bar = gbm.Next(isNew: true);
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series.Add(bar.Time, bar.Close);
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}
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var instanceResults = new Kama(10).Update(series);
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var staticResults = new double[series.Count];
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Kama.Calculate(series.Values.ToArray().AsSpan(), staticResults.AsSpan(), 10);
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for (int i = 0; i < instanceResults.Count; i++)
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{
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Assert.Equal(instanceResults[i].Value, staticResults[i], 1e-9);
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}
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}
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[Fact]
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public void Kama_Update_IsNewFalse_CorrectsValue()
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{
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var kama = new Kama(10);
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var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.2, seed: 42);
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// Feed initial data
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for (int i = 0; i < 20; i++)
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{
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var bar = gbm.Next(isNew: true);
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kama.Update(new TValue(bar.Time, bar.Close), isNew: true);
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}
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// Update with isNew=false (correction)
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var newBar = gbm.Next(isNew: true);
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kama.Update(new TValue(newBar.Time, newBar.Close), isNew: true);
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double valueAfterCommit = kama.Last.Value;
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// Now update the SAME bar with a different value
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kama.Update(new TValue(newBar.Time, newBar.Close + 10.0), isNew: false);
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double valueAfterCorrection = kama.Last.Value;
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Assert.NotEqual(valueAfterCommit, valueAfterCorrection);
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// Now restore original value
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kama.Update(new TValue(newBar.Time, newBar.Close), isNew: false);
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Assert.Equal(valueAfterCommit, kama.Last.Value, 1e-9);
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}
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[Fact]
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public void Kama_NaN_Input_UsesLastValidValue()
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{
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var kama = new Kama(5);
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kama.Update(new TValue(DateTime.UtcNow, 100));
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kama.Update(new TValue(DateTime.UtcNow, 110));
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var resultAfterNaN = kama.Update(new TValue(DateTime.UtcNow, double.NaN));
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Assert.True(double.IsFinite(resultAfterNaN.Value));
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Assert.NotEqual(0, resultAfterNaN.Value);
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}
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[Fact]
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public void Kama_Reset_ClearsState()
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{
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var kama = new Kama(10);
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kama.Update(new TValue(DateTime.UtcNow, 100));
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kama.Update(new TValue(DateTime.UtcNow, 110));
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Assert.True(kama.Last.Value > 0);
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kama.Reset();
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Assert.Equal(0, kama.Last.Value);
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Assert.False(kama.IsHot);
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}
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[Fact]
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public void Kama_FlatLine_ReturnsSameValue()
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{
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var kama = new Kama(10);
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for (int i = 0; i < 20; i++)
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{
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kama.Update(new TValue(DateTime.UtcNow, 100));
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}
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Assert.Equal(100, kama.Last.Value);
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}
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[Fact]
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public void Kama_Calc_IsNew_AcceptsParameter()
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{
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var kama = new Kama(10);
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kama.Update(new TValue(DateTime.UtcNow, 100), isNew: true);
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Assert.Equal(100, kama.Last.Value);
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}
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[Fact]
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public void Kama_IterativeCorrections_RestoreToOriginalState()
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{
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var kama = new Kama(10);
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var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.1);
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// Feed 20 new values (enough to fill buffer and stabilize)
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TValue lastInput = default;
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for (int i = 0; i < 20; i++)
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{
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var bar = gbm.Next(isNew: true);
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lastInput = new TValue(bar.Time, bar.Close);
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kama.Update(lastInput, isNew: true);
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}
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// Remember state
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double valueAfter = kama.Last.Value;
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// Generate 5 corrections with isNew=false (different values)
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for (int i = 0; i < 5; i++)
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{
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var bar = gbm.Next(isNew: false);
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kama.Update(new TValue(bar.Time, bar.Close), isNew: false);
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}
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// Feed the remembered last input again with isNew=false
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TValue finalValue = kama.Update(lastInput, isNew: false);
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// Should match the original state
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Assert.Equal(valueAfter, finalValue.Value, 1e-9);
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}
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[Fact]
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public void Kama_SpanCalc_ValidatesInput()
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{
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double[] source = [1, 2, 3, 4, 5];
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double[] output = new double[5];
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double[] wrongSizeOutput = new double[3];
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Assert.Throws<ArgumentException>(() => Kama.Calculate(source.AsSpan(), output.AsSpan(), 0));
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Assert.Throws<ArgumentException>(() => Kama.Calculate(source.AsSpan(), wrongSizeOutput.AsSpan(), 3));
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}
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[Fact]
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public void Kama_SpanCalc_HandlesNaN()
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{
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double[] source = [100, 110, double.NaN, 120, 130];
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double[] output = new double[5];
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Kama.Calculate(source.AsSpan(), output.AsSpan(), 3);
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foreach (var val in output)
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{
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Assert.True(double.IsFinite(val));
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}
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}
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[Fact]
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public void Kama_AllModes_ProduceSameResult()
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{
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// Arrange
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int period = 10;
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var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 123);
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var bars = gbm.Fetch(1000, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
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var series = bars.Close;
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// 1. Batch Mode
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var batchSeries = Kama.Batch(series, period);
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double expected = batchSeries.Last.Value;
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// 2. Span Mode
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var tValues = series.Values.ToArray();
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var spanInput = new ReadOnlySpan<double>(tValues);
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var spanOutput = new double[tValues.Length];
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Kama.Calculate(spanInput, spanOutput, period);
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double spanResult = spanOutput[^1];
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// 3. Streaming Mode
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var streamingInd = new Kama(period);
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for (int i = 0; i < series.Count; i++)
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{
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streamingInd.Update(series[i]);
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}
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double streamingResult = streamingInd.Last.Value;
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// 4. Eventing Mode
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var pubSource = new TSeries();
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var eventingInd = new Kama(pubSource, period);
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for (int i = 0; i < series.Count; i++)
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{
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pubSource.Add(series[i]);
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}
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double eventingResult = eventingInd.Last.Value;
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// Assert
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Assert.Equal(expected, spanResult, precision: 9);
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Assert.Equal(expected, streamingResult, precision: 9);
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Assert.Equal(expected, eventingResult, precision: 9);
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}
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}
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