mirror of
https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
3dd05f23e4
- Updated the name and description of the Hilbert Trendline (HTIT) to "Ehlers Hilbert Transform Instantaneous Trend (HTIT)". - Changed the name and description of the MESA Adaptive Moving Average (MAMA) to "Ehlers MESA Adaptive Moving Average". - Modified the Center of Gravity (CG) indicator to "Ehlers Center of Gravity (CG)". - Renamed the Detrended Synthetic Price (DSP) to "Ehlers Detrended Synthetic Price (DSP)". - Updated the Autocorrelation Periodogram (EACP) to "Ehlers Autocorrelation Periodogram (EACP)". - Changed the Homodyne Discriminator (HOMOD) to "Ehlers Homodyne Discriminator (HOMOD)". - Updated the Hilbert Transform Dominant Cycle Period and Phase indicators to include "Ehlers" in their names. - Renamed the Hilbert Transform Phasor Components to "Ehlers Hilbert Transform Phasor Components (HT_PHASOR)". - Updated the SineWave indicator to "Ehlers Hilbert Transform SineWave (HT_SINE)". - Changed the Phasor Analysis indicator to "Ehlers Hilbert Transform Phasor Components (HT_PHASOR)". - Updated the SSF-Based Detrended Synthetic Price to "Ehlers SSF Detrended Synthetic Price (SSFDSP)". - Renamed the Ultimate Channel to "Ehlers Ultimate Channel (UCHANNEL)". - Added new indicators: Moving Average Variable Period (MAVP), Ehlers Predictive Moving Average (PMA), Ehlers Reverse EMA (REVERSEEMA), and Ehlers Trendflex Indicator (TRENDFLEX). - Updated various SVG badges to reflect changes in classes, comments, source files, lines of code, methods, and public types.
150 lines
4.1 KiB
Markdown
150 lines
4.1 KiB
Markdown
# SSFDSP: Ehlers SSF Detrended Synthetic Price
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> "The Super-Smoother filter provides Butterworth-quality noise rejection—combine two of them and you isolate cycles with surgical precision."
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The SSF-Based Detrended Synthetic Price (SSFDSP) is an advanced oscillator by John Ehlers. It creates a synthetic, detrended price series by subtracting a half-cycle Super-Smoother from a quarter-cycle Super-Smoother, providing superior noise rejection and reduced lag compared to EMA-based DSP.
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## Historical Context
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Ehlers introduced the concept of "Synthetic Price" to remove the DC (trend) component from market data, isolating cyclic energy. While earlier versions used EMAs, the SSF variant exploits the 2-pole Butterworth characteristics of the Super-Smoother Filter to achieve cleaner separation between trend and cycle.
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The SSF provides zero phase lag at the cutoff frequency, making it ideal for cycle isolation in noisy market data.
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## Architecture & Physics
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The indicator computes the difference between two Super-Smoother filters tuned to fractions of the dominant cycle period.
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### 1. Filter Periods
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$$
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P_{fast} = \max(2, \text{round}(P / 4))
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$$
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$$
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P_{slow} = \max(3, \text{round}(P / 2))
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$$
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### 2. Super-Smoother Coefficients
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$$
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\alpha = \frac{\pi\sqrt{2}}{period}
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$$
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$$
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c_2 = 2e^{-\alpha}\cos(\alpha)
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$$
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$$
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c_3 = -e^{-2\alpha}
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$$
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$$
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c_1 = 1 - c_2 - c_3
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$$
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### 3. SSF Recursion
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$$
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SSF_t = c_1 \cdot \frac{P_t + P_{t-1}}{2} + c_2 \cdot SSF_{t-1} + c_3 \cdot SSF_{t-2}
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$$
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### 4. SSFDSP Output
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$$
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SSFDSP = SSF_{fast} - SSF_{slow}
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$$
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## Performance Profile
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### Operation Count (Streaming Mode, per Bar)
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| Operation | Count | Cost (cycles) | Subtotal |
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| :--- | :---: | :---: | :---: |
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| FMA (SSF updates) | 4 | 4 | 16 |
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| MUL (coefficients) | 2 | 3 | 6 |
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| ADD/SUB (input avg, output) | 3 | 1 | 3 |
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| **Total** | **9** | — | **~25 cycles** |
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### Complexity Analysis
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- **Streaming:** O(1) per bar—fixed 2-pole IIR filters
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- **Memory:** O(1)—only filter state variables
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- **Warmup:** ~2 × slow period for convergence
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- **Note:** Recursive dependencies prevent SIMD vectorization
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## Validation
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| Library | Status | Notes |
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| :--- | :---: | :--- |
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| TA-Lib | N/A | Not standard |
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| Skender | N/A | Not standard |
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| PineScript | ✅ | Matches Ehlers' reference logic |
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## Usage & Pitfalls
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- **Oscillates around zero**—positive values indicate bullish cycle phase
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- **Zero crossings** signal cycle phase changes—entry points in direction of cross
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- **Period mismatch** degrades amplitude and phase accuracy
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- **Smoother than EMA-DSP** with sharper turning points
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- **Divergence** (price highs vs DSP highs) indicates trend exhaustion
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- **Pre-smooth input** for extremely noisy data
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## API
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```mermaid
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classDiagram
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class Ssfdsp {
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+int Period
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+double Value
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+bool IsHot
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+Ssfdsp(int period)
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+Ssfdsp(ITValuePublisher source, int period)
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+TValue Update(TValue input, bool isNew)
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+void Reset()
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}
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```
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### Class: `Ssfdsp`
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| Parameter | Type | Default | Range | Description |
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| :--- | :--- | :--- | :--- | :--- |
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| `period` | `int` | `40` | `≥4` | Expected dominant cycle period |
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### Properties
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- `Value` (`double`): The current SSFDSP value (oscillates around 0)
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- `IsHot` (`bool`): Returns `true` when warmup is complete
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### Methods
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- `Update(TValue input, bool isNew)`: Updates the indicator with a new data point
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## C# Example
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```csharp
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using QuanTAlib;
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// Initialize with a 40-bar dominant cycle assumption
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var ssfdsp = new Ssfdsp(period: 40);
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// Update with streaming data
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foreach (var bar in quotes)
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{
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var result = ssfdsp.Update(new TValue(bar.Date, bar.Close));
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if (ssfdsp.IsHot)
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{
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Console.WriteLine($"{bar.Date}: SSF-DSP = {result.Value:F4}");
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// Zero crossing detection
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if (result.Value > 0 && ssfdsp.Previous.Value <= 0)
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Console.WriteLine(" → Bullish cycle phase");
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else if (result.Value < 0 && ssfdsp.Previous.Value >= 0)
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Console.WriteLine(" → Bearish cycle phase");
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}
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}
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// Batch calculation
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var output = Ssfdsp.Calculate(sourceSeries, period: 40);
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```
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