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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/sma/Sma.Notebook.dib
+5 -79
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@@ -21,7 +21,8 @@ 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 SMAs simultaneously.
4. **Handling Invalid Values**: Last-value substitution for NaN/Infinity.
5. **SMA vs EMA**: Comparing Simple and Exponential Moving Averages.
#!csharp
@@ -189,63 +190,9 @@ Console.WriteLine($"Match: {Math.Abs(batchLargeResult.Last().Value - lastStreamV
#!markdown
## 4. Vectorized SMA (Multiple Periods)
## 4. Handling Invalid Values (NaN/Infinity)
`SmaVector` allows calculating multiple SMAs (e.g., 5, 10, 20) simultaneously. This is useful for comparing different timeframes.
### Vectorized Batch
#!csharp
int[] periods = { 5, 10, 20 };
Console.WriteLine($"\n--- Vectorized Batch SMA (Periods: {string.Join(", ", periods)}) ---");
var smaVectorBatch = new SmaVector(periods);
var vectorBatchResults = smaVectorBatch.Calculate(closeSeries);
for (int i = 0; i < periods.Length; i++)
{
Console.WriteLine($"SMA({periods[i]}) Last Value: {vectorBatchResults[i].Last().Value:F2}");
}
#!markdown
### Vectorized Streaming
#!csharp
Console.WriteLine($"\n--- Vectorized Streaming SMA (Periods: {string.Join(", ", periods)}) ---");
var smaVectorStream = new SmaVector(periods);
TValue[] lastVectorVal = null;
foreach(var item in closeSeries)
{
lastVectorVal = smaVectorStream.Update(item);
}
for (int i = 0; i < periods.Length; i++)
{
Console.WriteLine($"SMA({periods[i]}) Last Value: {lastVectorVal[i].Value:F2}");
}
// Verification
bool allMatch = true;
for (int i = 0; i < periods.Length; i++)
{
if (Math.Abs(vectorBatchResults[i].Last().Value - lastVectorVal[i].Value) > 1e-10)
{
allMatch = false;
break;
}
}
Console.WriteLine($"\nAll Vectorized Stream/Batch values match: {allMatch}");
#!markdown
## 5. Handling Invalid Values (NaN/Infinity)
Both `Sma` and `SmaVector` 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.
`Sma` 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
@@ -296,30 +243,9 @@ for (int i = 0; i < seriesWithNaN.Count; i++)
Console.WriteLine($" {inputStr,-10} → {output:F2} (IsFinite: {double.IsFinite(output)})");
}
#!csharp
Console.WriteLine("\n--- Vectorized SMA with Invalid Values ---");
int[] periodsNaN = { 5, 10 };
var smaVectorNaN = new SmaVector(periodsNaN);
// Feed values including invalid ones
var inputsNaN = new double[] { 100, 110, double.NaN, 120, double.PositiveInfinity, 130 };
var time = DateTime.Now;
foreach (var val in inputsNaN)
{
var results = smaVectorNaN.Update(new TValue(time, val));
var inputStr = double.IsFinite(val) ? val.ToString("F2") : val.ToString();
Console.WriteLine($"Input: {inputStr,-10} → SMA(5): {results[0].Value:F2}, SMA(10): {results[1].Value:F2}");
time = time.AddMinutes(1);
}
Console.WriteLine("\nAll outputs are finite - invalid inputs were substituted with last valid values.");
#!markdown
## 6. SMA vs EMA Comparison
## 5. SMA vs EMA Comparison
The SMA and EMA are both trend-following indicators, but they weight data differently: