QuanTAlib/lib/averages/trima/Trima.Notebook.dib

#!meta

{"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"}]}}

#!markdown

# Triangular Moving Average (TRIMA) Examples

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.

The **Triangular Moving Average (TRIMA)** is a weighted moving average where the weights increase linearly to the middle of the period and then decrease. It is equivalent to a double-smoothed SMA (SMA of an SMA).

**Key characteristics:**
- Triangular weighting (emphasis on middle values)
- Smoother than SMA
- Higher lag than SMA
- O(1) update complexity

#!csharp

// Reference the library
#r "..\..\bin\QuanTAlib.dll"

using System;
using System.Linq;
using QuanTAlib;

// Helper to print TSeries
void PrintSeries(TSeries series, int count = 5)
{
    Console.WriteLine($"Series Length: {series.Count}");
    foreach (var item in series.Take(count))
    {
        Console.WriteLine($"Time: {item.Time:HH:mm:ss}, Value: {item.Value:F2}");
    }
    if (series.Count > count) Console.WriteLine("...");
}

#!markdown

## 1. Manual Data: Batch vs. Streaming

We'll start with a small, manually created dataset.

#!csharp

// Create a small manual dataset
var manualData = new TSeries();
manualData.Add(DateTime.Now, 100.0);
manualData.Add(DateTime.Now.AddMinutes(1), 102.0);
manualData.Add(DateTime.Now.AddMinutes(2), 101.0);
manualData.Add(DateTime.Now.AddMinutes(3), 103.0);
manualData.Add(DateTime.Now.AddMinutes(4), 105.0);

Console.WriteLine("--- Input Data ---");
PrintSeries(manualData, 5);

// Batch Calculation
Console.WriteLine("\n--- Batch TRIMA (Period 3) ---");
var trimaBatch = new Trima(3);
var resultBatch = trimaBatch.Update(manualData);

PrintSeries(resultBatch, 5);

#!markdown

### Streaming Processing

#!csharp

Console.WriteLine("\n--- Streaming TRIMA (Period 3) ---");
var trimaStream = new Trima(3);

foreach (var item in manualData)
{
    var result = trimaStream.Update(item);
    Console.WriteLine($"Time: {item.Time:HH:mm:ss}, Input: {item.Value:F2}, TRIMA: {result.Value:F2}, IsHot: {trimaStream.IsHot}");
}

// Verify that the last values match
var batchLast = resultBatch.Last().Value;
var streamLast = trimaStream.Value.Value;
Console.WriteLine($"\nMatch: {Math.Abs(batchLast - streamLast) < 1e-10} (Batch: {batchLast:F2}, Stream: {streamLast:F2})");

#!markdown

## 2. Large Dataset: Geometric Brownian Motion (GBM)

We'll generate a larger dataset (1000 bars) using a Geometric Brownian Motion generator to simulate realistic market data.

#!csharp

// Generate 1000 bars of data
var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.2);
var gbmData = gbm.Fetch(1000, DateTime.Now.Ticks, TimeSpan.FromMinutes(1));
var closeSeries = gbmData.Close;

Console.WriteLine($"Generated {closeSeries.Count} bars of GBM data.");

#!markdown

### Batch vs. Streaming Performance on Large Data

#!csharp

// Batch
var trimaLargeBatch = new Trima(20);
var batchLargeResult = trimaLargeBatch.Update(closeSeries);
Console.WriteLine($"Batch Last Value: {batchLargeResult.Last().Value:F2}");

// Streaming
var trimaLargeStream = new Trima(20);
TValue lastStreamVal = default;
foreach(var item in closeSeries)
{
    lastStreamVal = trimaLargeStream.Update(item);
}
Console.WriteLine($"Streaming Last Value: {lastStreamVal.Value:F2}");

// Verify match
Console.WriteLine($"Match: {Math.Abs(batchLargeResult.Last().Value - lastStreamVal.Value) < 1e-10}");

#!markdown

## 3. TRIMA vs SMA Comparison

TRIMA is smoother than SMA but has more lag. Let's compare them on a volatile dataset.

#!csharp

Console.WriteLine("\n--- TRIMA vs SMA 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 trimaCompare = new Trima(10);
var smaCompare = new Sma(10);

Console.WriteLine("Position | Input  |  TRIMA  |   SMA   | Difference");
Console.WriteLine("---------+--------+---------+---------+-----------");

for (int i = 0; i < compareData.Count; i++)
{
    var trimaVal = trimaCompare.Update(compareData[i]);
    var smaVal = smaCompare.Update(compareData[i]);
    var input = compareData[i].Value;
    var diff = trimaVal.Value - smaVal.Value;
    
    Console.WriteLine($"   {i,2}    | {input,6:F0} | {trimaVal.Value,7:F2} | {smaVal.Value,7:F2} | {diff,+9:F2}");
}

Console.WriteLine("\nNote how TRIMA reacts more gradually to the spike compared to SMA.");