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https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
9e152b9027
- Implemented TEMA calculation in QuanTAlib with O(1) update complexity. - Added validation tests for TEMA against Skender, TA-Lib, and Tulip indicators. - Updated documentation for TEMA, including its mathematical foundation and usage examples. - Enhanced existing tests for other indicators (TRIMA, WMA) to generate more records. - Adjusted benchmark tests to include DEMA and TEMA comparisons. - Refactored code for better readability and performance, including zero-allocation Span API.
106 lines
4.9 KiB
Markdown
106 lines
4.9 KiB
Markdown
# TEMA: Triple Exponential Moving Average
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## Overview and Purpose
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The Triple Exponential Moving Average (TEMA) is a technical indicator developed by Patrick Mulloy in 1994, introduced alongside DEMA. It takes the concept of lag reduction even further than DEMA by using a triple smoothing technique. TEMA is designed to be even more responsive to price changes than DEMA or traditional moving averages, effectively eliminating the lag associated with trend-following indicators.
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TEMA is constructed using a combination of single, double, and triple Exponential Moving Averages (EMAs). This unique composition allows it to track price action very closely, making it a favorite among short-term traders and scalpers who require immediate signals.
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## Core Concepts
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* **Maximum Lag Reduction:** TEMA offers superior lag reduction compared to SMA, EMA, and even DEMA.
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* **Triple Smoothing:** It utilizes three layers of EMA calculations to derive its value.
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* **Composite Formula:** The formula cleverly combines $EMA_1$, $EMA_2$, and $EMA_3$ to subtract lag.
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* **Trend Following:** Despite its speed, it remains a trend-following indicator, useful for identifying direction and reversals.
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## Common Settings and Parameters
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| Parameter | Default | Function | When to Adjust |
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|-----------|---------|----------|---------------|
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| Length | 20 | Controls responsiveness/smoothness | Shorter for scalping, longer for trend filtering |
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| Source | Close | Data point used for calculation | Change to HL2 or HLC3 for typical price representation |
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| Alpha | 3/(length+1) | Determines weighting decay | Direct alpha manipulation allows for precise tuning |
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## Calculation and Mathematical Foundation
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**Simplified explanation:**
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TEMA uses a single EMA, a double EMA (EMA of EMA), and a triple EMA (EMA of EMA of EMA). It combines these three components to cancel out the lag inherent in the smoothing process.
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**Technical formula:**
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$$TEMA = 3 \times EMA_1 - 3 \times EMA_2 + EMA_3$$
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Where:
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* $EMA_1 = EMA(Price)$
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* $EMA_2 = EMA(EMA_1)$
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* $EMA_3 = EMA(EMA_2)$
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The formula is derived from the error correction principle, similar to DEMA but extended to a third degree.
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The lag error is estimated and subtracted from the original EMA, resulting in a highly responsive curve that often leads price turns.
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> 🔍 **Technical Note:** The implementation leverages the optimized `Ema` class, which uses **Hunter's bias compensation**. This ensures that all three underlying EMAs are initialized correctly from the very first data point, providing accurate TEMA values immediately without a long warmup period.
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## C# Implementation
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The library provides a high-performance implementation of TEMA that supports both standard period-based initialization and direct alpha specification.
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### Usage Examples
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```csharp
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using QuanTAlib;
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// Initialize with period 14
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var tema = new Tema(14);
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// Or initialize with specific alpha
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var temaAlpha = new Tema(0.15);
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// Streaming update
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TValue result = tema.Update(new TValue(time, price));
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Console.WriteLine($"Current TEMA: {result.Value}");
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// Batch calculation (TSeries API)
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TSeries source = ...;
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TSeries results = Tema.Calculate(source, 14);
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// High-performance Span API (zero allocation)
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double[] prices = new double[10000];
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double[] output = new double[10000];
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Tema.Calculate(prices.AsSpan(), output.AsSpan(), period: 14);
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```
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### Zero-Allocation Span API
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For performance-critical scenarios, the static `Calculate` method uses `ArrayPool` internally to manage the intermediate buffers for the underlying EMAs, ensuring zero heap allocations for the user (beyond the input/output arrays).
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```csharp
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// Allocate buffers once
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double[] source = new double[200000];
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double[] temaOutput = new double[200000];
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// Zero heap allocation during calculation
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Tema.Calculate(source.AsSpan(), temaOutput.AsSpan(), period: 50);
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```
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### Handling Invalid Values
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`Tema` delegates value handling to the underlying `Ema` instances, which use **last-value substitution** for `NaN` or `Infinity`. This ensures continuity and stability in the output series.
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## Interpretation Details
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* **Trend Direction:** Price above TEMA indicates an uptrend; price below indicates a downtrend.
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* **Signal Line:** TEMA is often used as a signal line for other indicators due to its speed.
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* **Crossovers:** TEMA crossovers with price or other averages provide very early entry/exit signals.
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* **Volatility:** Due to its speed, TEMA can be volatile in choppy markets.
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## Limitations and Considerations
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* **Overshoot:** Like DEMA, TEMA can overshoot price action during sudden, sharp reversals.
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* **Noise:** Its extreme responsiveness makes it susceptible to market noise and false signals in sideways markets.
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* **Complexity:** The triple calculation is computationally more expensive than SMA or EMA, though negligible on modern hardware.
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## References
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1. Mulloy, P.G. (1994). "Smoothing Data with Faster Moving Averages." *Technical Analysis of Stocks & Commodities*, 12(1).
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2. Achelis, S.B. (2000). *Technical Analysis from A to Z*. McGraw-Hill.
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