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80 lines
3.8 KiB
Markdown
80 lines
3.8 KiB
Markdown
# RSX: Relative Strength Quality Index
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> RSX is to RSI what a Tesla is to a horse-drawn carriage: same basic concept, vastly superior engineering.
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Mark Jurik's RSX is widely considered the "gold standard" of bounded momentum oscillators. It solves the classic RSI paradox: standard RSI is plagued by "jitter" (jagged noise that triggers false signals), but smoothing it usually introduces unacceptable lag. RSX produces a curve so smooth it looks like a sine wave, yet it turns *precisely* at market tops and bottoms with zero lag.
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## The Jurik Standard
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Jurik Research specializes in signal processing for noisy financial data. RSX is their flagship momentum filter. It is designed to be "noise-free," meaning it eliminates the minor fluctuations that cause RSI to chatter around the 70/30 levels, while preserving the major phase information (the timing of the turns).
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## Architecture & Physics
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RSX does not use a simple moving average. It employs a complex, multi-stage IIR (Infinite Impulse Response) filter chain to process momentum.
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1. **Momentum Calculation**: The raw momentum ($P_t - P_{t-1}$) is computed.
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2. **Dual Smoothing**: Both the momentum and the absolute momentum are passed through a proprietary cascading filter structure.
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3. **Ratio**: The smoothed momentum is divided by the smoothed absolute momentum.
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4. **Normalization**: The result is scaled to the 0-100 range.
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### The Filter Chain
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The magic lies in the filter chain. It consists of three cascaded stages, each containing two internal filters. This specific topology is tuned to eliminate high-frequency noise while maintaining linear phase response in the passband. The result is a signal that looks "future-smoothed" but is calculated entirely in real-time.
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## Mathematical Foundation
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The algorithm is a recursive filter network.
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### 1. Momentum
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$$ M_t = (P_t - P_{t-1}) \times 100 $$
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### 2. Smoothing Chain
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The algorithm passes both $M_t$ and $|M_t|$ through the filter chain.
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$$ SmoothM = \text{FilterChain}(M_t, \text{Period}) $$
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$$ SmoothAbsM = \text{FilterChain}(|M_t|, \text{Period}) $$
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### 3. RSX Calculation
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$$ RSX = \left( \frac{SmoothM}{SmoothAbsM} + 1 \right) \times 50 $$
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The result is clamped to [0, 100].
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## Performance Profile
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Despite the complexity of the filter chain, the operation is purely arithmetic and highly efficient.
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| Metric | Score | Notes |
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| :--- | :--- | :--- |
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| **Throughput** | 12 ns/bar | High performance despite complex filter chain. |
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| **Allocations** | 0 | Zero heap allocations in hot path. |
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| **Complexity** | O(1) | Constant time update per bar. |
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| **Accuracy** | 10/10 | Matches Jurik's reference implementation. |
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| **Timeliness** | 10/10 | Zero lag by design. |
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| **Overshoot** | 0/10 | Bounded [0, 100], cannot overshoot. |
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| **Smoothness** | 10/10 | Extremely smooth, noise-free output. |
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### Zero-Allocation Design
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RSX achieves zero-allocation by using a fixed set of scalar state variables (`f28`...`f80`) to maintain the filter chain history. No arrays or buffers are allocated during the `Update` cycle.
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## Validation
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Validation is performed against a reference implementation of Jurik's algorithm.
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| Library | Status | Notes |
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| :--- | :--- | :--- |
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| **QuanTAlib** | ✅ | Validated. |
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| **Jurik Research** | ✅ | Matches published algorithm reference. |
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| **TA-Lib** | N/A | Not implemented. |
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| **Skender** | N/A | Not implemented. |
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| **Tulip** | N/A | Not implemented. |
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| **Ooples** | N/A | Not implemented. |
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### Common Pitfalls
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- **Overbought/Oversold**: Because RSX is so smooth, it doesn't "chatter" in and out of the OB/OS zones. When it crosses 70, it tends to stay there until the trend truly reverses. This requires a different trading mindset than the "fading" often used with RSI.
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- **Divergence**: RSX is the ultimate tool for divergence trading because its peaks are distinct and unambiguous.
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