QuanTAlib/lib/averages/tema/Tema.Notebook.dib

#!meta

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#!markdown

# Triple Exponential Moving Average (TEMA) 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.

For detailed documentation on the TEMA indicator, including mathematical formulas and interpretation, please refer to [Tema.md](Tema.md).

The **Triple Exponential Moving Average (TEMA)** is a technical indicator designed to reduce the lag associated with traditional moving averages even further than DEMA. It combines single, double, and triple EMAs to achieve superior responsiveness.

This notebook demonstrates:
1.  **Manual Data Processing**: Understanding Batch vs. Streaming modes.
2.  **Streaming with `isNew`**: Handling intra-bar updates.
3.  **Large Dataset Processing**: Using Geometric Brownian Motion (GBM) generated data.
4.  **Handling Invalid Values**: Last-value substitution for NaN/Infinity.

#!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 to clearly see how Batch and Streaming operations work.

### Batch Processing
Batch processing calculates the TEMA for the entire dataset at once. This is efficient for historical analysis.

#!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 TEMA (Period 3) ---");
var temaBatch = new Tema(3);
var resultBatch = temaBatch.Update(manualData);

PrintSeries(resultBatch, 5);

#!markdown

### Streaming Processing
Streaming processing updates the TEMA one data point at a time. This is essential for real-time trading systems where data arrives sequentially.

#!csharp

Console.WriteLine("\n--- Streaming TEMA (Period 3) ---");
var temaStream = new Tema(3);

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

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

#!markdown

## 2. Streaming with `isNew` (Intra-bar Updates)

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.
*   `isNew = true`: The input is a new bar (advances time).
*   `isNew = false`: The input is an update to the current bar (recalculates without advancing).

#!csharp

Console.WriteLine("\n--- Streaming with Intra-bar Updates ---");
var temaIntra = new Tema(3);

// 1. Process the first 4 bars normally
for (int i = 0; i < 4; i++)
{
    temaIntra.Update(manualData[i]);
}
Console.WriteLine($"After 4th bar: {temaIntra.Value.Value:F2}");

// 2. Simulate intra-bar updates for the 5th bar (Final value is 105.0)
// Update 1: Price moves to 104.0
var update1 = new TValue(manualData[4].Time, 104.0);
temaIntra.Update(update1, isNew: true); // First update for this bar is "New"
Console.WriteLine($"Update 1 (104.0): {temaIntra.Value.Value:F2}");

// Update 2: Price moves to 106.0 (Same time, same bar)
var update2 = new TValue(manualData[4].Time, 106.0);
temaIntra.Update(update2, isNew: false); // Not new, just an update
Console.WriteLine($"Update 2 (106.0): {temaIntra.Value.Value:F2}");

// Update 3: Final Close at 105.0
var update3 = manualData[4];
temaIntra.Update(update3, isNew: false); // Final update
Console.WriteLine($"Update 3 (105.0): {temaIntra.Value.Value:F2}");

// Verify match with batch result
Console.WriteLine($"Match with Batch: {Math.Abs(temaIntra.Value.Value - batchLast) < 1e-10}");

#!markdown

## 3. 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($"Generated {closeSeries.Count} bars of GBM data.");
Console.WriteLine($"First 5 values: {string.Join(", ", closeSeries.Take(5).Select(x => x.Value.ToString("F2")))}");

#!markdown

### Batch vs. Streaming Performance on Large Data

#!csharp

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

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

#!markdown

## 4. Handling Invalid Values (NaN/Infinity)

`Tema` 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.

#!csharp

Console.WriteLine("\n--- Handling Invalid Values ---");

// Single TEMA
var temaNaN = new Tema(10);

// Feed valid values first
temaNaN.Update(new TValue(DateTime.Now, 100.0));
temaNaN.Update(new TValue(DateTime.Now.AddMinutes(1), 110.0));
Console.WriteLine($"After valid values: {temaNaN.Value.Value:F2}");

// Feed NaN - should use last valid value (110)
var resultAfterNaN = temaNaN.Update(new TValue(DateTime.Now.AddMinutes(2), double.NaN));
Console.WriteLine($"After NaN input: {resultAfterNaN.Value:F2} (IsFinite: {double.IsFinite(resultAfterNaN.Value)})");

// Feed Infinity - should use last valid value (110)
var resultAfterInf = temaNaN.Update(new TValue(DateTime.Now.AddMinutes(3), double.PositiveInfinity));
Console.WriteLine($"After Infinity input: {resultAfterInf.Value:F2} (IsFinite: {double.IsFinite(resultAfterInf.Value)})");

// Continue with valid value
var resultAfterValid = temaNaN.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 temaBatchNaN = new Tema(3);
var resultsWithNaN = temaBatchNaN.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)})");
}