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Refactor and optimize various components of QuanTAlib

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- Removed WmaVector class to streamline weighted moving average calculations.
- Simplified RingBuffer implementation by removing unnecessary comments and improving clarity.
- Enhanced SIMD extensions for better performance and readability.
- Updated TBar and TBarSeries classes to improve property calculations and reduce overhead.
- Cleaned up TValue struct by removing redundant comments.
- Added comprehensive unit tests for IndicatorExtensions and TrimaIndicator to ensure functionality and correctness.
This commit is contained in:
Miha Kralj
2025-12-04 13:49:05 -08:00
parent 3ed35322a5
commit 967096d4f5
27 changed files with 387 additions and 3367 deletions
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lib/averages/wma/Wma.Notebook.dib
+5 -59
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@@ -21,7 +21,7 @@ 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. **Vectorized Operations**: Calculating multiple WMAs simultaneously.
4. **Handling Invalid Values**: Last-value substitution for NaN/Infinity.
5. **WMA vs SMA vs EMA**: Comparing different moving averages.
#!csharp
@@ -190,63 +190,9 @@ Console.WriteLine($"Match: {Math.Abs(batchLargeResult.Last().Value - lastStreamV
#!markdown
## 4. Vectorized WMA (Multiple Periods)
## 4. Handling Invalid Values (NaN/Infinity)
`WmaVector` allows calculating multiple WMAs (e.g., 5, 10, 20) simultaneously. This is useful for comparing different timeframes.
### Vectorized Batch
#!csharp
int[] periods = { 5, 10, 20 };
Console.WriteLine($"\n--- Vectorized Batch WMA (Periods: {string.Join(", ", periods)}) ---");
var wmaVectorBatch = new WmaVector(periods);
var vectorBatchResults = wmaVectorBatch.Calculate(closeSeries);
for (int i = 0; i < periods.Length; i++)
{
Console.WriteLine($"WMA({periods[i]}) Last Value: {vectorBatchResults[i].Last().Value:F4}");
}
#!markdown
### Vectorized Streaming
#!csharp
Console.WriteLine($"\n--- Vectorized Streaming WMA (Periods: {string.Join(", ", periods)}) ---");
var wmaVectorStream = new WmaVector(periods);
TValue[] lastVectorVal = null;
foreach(var item in closeSeries)
{
lastVectorVal = wmaVectorStream.Update(item);
}
for (int i = 0; i < periods.Length; i++)
{
Console.WriteLine($"WMA({periods[i]}) Last Value: {lastVectorVal[i].Value:F4}");
}
// Verification
bool allMatch = true;
for (int i = 0; i < periods.Length; i++)
{
if (Math.Abs(vectorBatchResults[i].Last().Value - lastVectorVal[i].Value) > 1e-10)
{
allMatch = false;
break;
}
}
Console.WriteLine($"\nAll Vectorized Stream/Batch values match: {allMatch}");
#!markdown
## 5. Handling Invalid Values (NaN/Infinity)
Both `Wma` and `WmaVector` use **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.
`Wma` 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
@@ -299,7 +245,7 @@ for (int i = 0; i < seriesWithNaN.Count; i++)
#!markdown
## 6. WMA vs SMA vs EMA Comparison
## 5. WMA vs SMA vs EMA Comparison
The WMA, SMA, and EMA are all trend-following indicators, but they weight data differently:
@@ -346,7 +292,7 @@ Console.WriteLine("- WMA provides a balance between SMA's stability and EMA's re
#!markdown
## 7. WMA Weights More Recent Values
## 6. WMA Weights More Recent Values
This example demonstrates how WMA weights more recent values compared to SMA.