Files
QuanTAlib/lib/trends/kama/Kama.Tests.cs
T
Miha Kralj d277e08056 refactoring
2025-12-16 21:16:50 -08:00

281 lines
8.2 KiB
C#

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