Files
QuanTAlib/lib/averages/ema/Ema.Tests.cs
T
Miha Kralj 74b49d2bb4 Add TBar, TBarSeries, TSeries, TValue, and IFeed implementations with comprehensive documentation and examples
- Introduced TBar struct for efficient OHLCV data representation.
- Implemented TBarSeries class for high-performance collection of TBar instances using Structure of Arrays (SoA) layout.
- Added TSeries class for time-series data management with zero-copy access.
- Created TValue struct for time-value pairs with implicit conversions.
- Defined IFeed interface for consistent data feed implementations.
- Developed CsvFeed class for loading historical OHLCV data from CSV files.
- Implemented GBM class for generating synthetic financial data using Geometric Brownian Motion.
- Added Quantower project files for Averages indicator with necessary dependencies and configurations.
- Included extensive usage examples and notebooks for TBar, TBarSeries, TSeries, TValue, and feed implementations.
2025-11-27 19:51:43 -08:00

229 lines
6.5 KiB
C#

using System;
using Xunit;
using QuanTAlib;
namespace QuanTAlib.Tests;
public class EmaTests
{
[Fact]
public void Ema_Constructor_Period_ValidatesInput()
{
Assert.Throws<ArgumentException>(() => new Ema(0));
Assert.Throws<ArgumentException>(() => new Ema(-1));
var ema = new Ema(10);
Assert.NotNull(ema);
}
[Fact]
public void Ema_Constructor_Alpha_ValidatesInput()
{
Assert.Throws<ArgumentException>(() => new Ema(0.0));
Assert.Throws<ArgumentException>(() => new Ema(-0.1));
Assert.Throws<ArgumentException>(() => new Ema(1.1));
var ema = new Ema(0.5);
Assert.NotNull(ema);
}
[Fact]
public void Ema_Calc_ReturnsValue()
{
var ema = new Ema(10);
Assert.Equal(0, ema.Value.Value);
TValue result = ema.Update(new TValue(DateTime.Now, 100));
Assert.True(result.Value > 0);
Assert.Equal(result.Value, ema.Value.Value);
}
[Fact]
public void Ema_Calc_IsNew_AcceptsParameter()
{
var ema = new Ema(10);
ema.Update(new TValue(DateTime.Now, 100), isNew: true);
double value1 = ema.Value;
ema.Update(new TValue(DateTime.Now, 105), isNew: true);
double value2 = ema.Value;
// Values should change with new bars
Assert.NotEqual(value1, value2);
}
[Fact]
public void Ema_Calc_IsNew_False_UpdatesValue()
{
var ema = new Ema(10);
ema.Update(new TValue(DateTime.Now, 100));
ema.Update(new TValue(DateTime.Now, 110), isNew: true);
double beforeUpdate = ema.Value;
ema.Update(new TValue(DateTime.Now, 120), isNew: false);
double afterUpdate = ema.Value;
// Update should change the value
Assert.NotEqual(beforeUpdate, afterUpdate);
}
[Fact]
public void Ema_Reset_ClearsState()
{
var ema = new Ema(10);
ema.Update(new TValue(DateTime.Now, 100));
ema.Update(new TValue(DateTime.Now, 105));
double valueBefore = ema.Value;
ema.Reset();
Assert.Equal(0, ema.Value.Value);
// After reset, should accept new values
ema.Update(new TValue(DateTime.Now, 50));
Assert.NotEqual(0, ema.Value.Value);
Assert.NotEqual(valueBefore, ema.Value.Value);
}
[Fact]
public void Ema_Properties_Accessible()
{
var ema = new Ema(10);
Assert.Equal(0, ema.Value.Value);
Assert.False(ema.IsHot);
ema.Update(new TValue(DateTime.Now, 100));
Assert.NotEqual(0, ema.Value.Value);
}
[Fact]
public void Ema_IsHot_BecomesTrueAfterWarmup()
{
var ema = new Ema(10);
// Initially IsHot should be false
Assert.False(ema.IsHot);
// Feed values until it warms up
// Warmup condition is state.E <= 1e-10
// state.E starts at 1.0 and decays by (1 - alpha) each step
// alpha = 2 / (10 + 1) = 2/11 ~= 0.1818
// (1 - alpha) ~= 0.8181
// 1.0 * (0.8181)^n <= 1e-10
// n * log(0.8181) <= log(1e-10)
// n * -0.200 <= -23.02
// n >= 115 steps roughly
int steps = 0;
while (!ema.IsHot && steps < 1000)
{
ema.Update(new TValue(DateTime.Now, 100));
steps++;
}
Assert.True(ema.IsHot);
Assert.True(steps > 0); // Should take some steps
}
[Fact]
public void Ema_PeriodEquivalence_BothConstructorsWork()
{
int period = 20;
double alpha = 2.0 / (period + 1);
var emaPeriod = new Ema(period);
var emaAlpha = new Ema(alpha);
// Both should accept Calc calls and produce same result
TValue result1 = emaPeriod.Update(new TValue(DateTime.Now, 100));
TValue result2 = emaAlpha.Update(new TValue(DateTime.Now, 100));
Assert.Equal(result1.Value, result2.Value, 1e-10);
}
[Fact]
public void Ema_IterativeCorrections_RestoreToOriginalState()
{
var ema = new Ema(10);
var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.1);
// Feed 10 new values
TValue tenthInput = default;
for (int i = 0; i < 10; i++)
{
var bar = gbm.Next(isNew: true);
tenthInput = new TValue(bar.Time, bar.Close);
ema.Update(tenthInput, isNew: true);
}
// Remember EMA state after 10 values
double emaAfterTen = ema.Value;
// Generate 9 corrections with isNew=false (different values)
for (int i = 0; i < 9; i++)
{
var bar = gbm.Next(isNew: false);
ema.Update(new TValue(bar.Time, bar.Close), isNew: false);
}
// Feed the remembered 10th input again with isNew=false
TValue finalEma = ema.Update(tenthInput, isNew: false);
// EMA should match the original state after 10 values
Assert.Equal(emaAfterTen, finalEma.Value, 1e-10);
}
[Fact]
public void Ema_BatchCalc_MatchesIterativeCalc()
{
var emaIterative = new Ema(10);
var emaBatch = new Ema(10);
var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.1);
// Generate data
var series = new TSeries();
for (int i = 0; i < 100; i++)
{
var bar = gbm.Next(isNew: true);
series.Add(bar.Time, bar.Close);
}
// Calculate iteratively
var iterativeResults = new TSeries();
foreach (var item in series)
{
iterativeResults.Add(emaIterative.Update(item));
}
// Calculate batch
var batchResults = emaBatch.Update(series);
// Compare
Assert.Equal(iterativeResults.Count, batchResults.Count);
for (int i = 0; i < iterativeResults.Count; i++)
{
Assert.Equal(iterativeResults[i].Value, batchResults[i].Value, 1e-10);
Assert.Equal(iterativeResults[i].Time, batchResults[i].Time);
}
}
[Fact]
public void Ema_Result_ImplicitConversionToDouble()
{
var ema = new Ema(10);
ema.Update(new TValue(DateTime.Now, 100));
// This should compile and work because TValue has implicit conversion to double
double result = ema.Value;
Assert.Equal(100.0, result, 1e-10);
}
}