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Miha Kralj
15f4bb90f3
New indicators: - HWC (Holt-Winters Channel) — channels, 27 tests - VWMACD (Volume-Weighted MACD) — momentum, 38 tests - Squeeze Pro — oscillators, 69 tests - BW_MFI (Bill Williams MFI) — oscillators - DSTOCH (Double Stochastic) — oscillators - ATRSTOP (ATR Trailing Stop) — reversals - VSTOP (Volatility Stop) — reversals - Convexity (Beta Convexity) — statistics, 23 tests Integration: - Python bridge: Exports.cs, _bridge.py, wrapper modules - Documentation: _sidebar.md, _index.md pages, SPEC.md - All analyzer warnings fixed (MA0074, xUnit2013, S2699) Build: 0 warnings, 0 errors | Tests: 15,933 passed, 0 failed
108 lines
6.0 KiB
Markdown
108 lines
6.0 KiB
Markdown
# SQUEEZE_PRO: LazyBear's Squeeze Pro
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> *Standard Squeeze uses one Keltner width. Squeeze Pro adds two more — because the market doesn't only compress one way.*
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| Property | Value |
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| ---------------- | -------------------------------- |
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| **Category** | Oscillator |
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| **Inputs** | OHLCV bar (TBar) |
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| **Parameters** | `period` (20), `bbMult` (2.0), `kcMultWide` (2.0), `kcMultNormal` (1.5), `kcMultNarrow` (1.0), `momLength` (12), `momSmooth` (6), `useSma` (true) |
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| **Outputs** | Dual: Momentum (double) + SqueezeLevel (int 0–3) |
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| **Output range** | Momentum: unbounded; SqueezeLevel: {0, 1, 2, 3} |
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| **Warmup** | `max(period, momLength + momSmooth)` bars |
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| **PineScript** | [squeeze_pro.pine](squeeze_pro.pine) |
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- LazyBear's Squeeze Pro enhances the standard TTM Squeeze by replacing the single Keltner Channel width with three graduated Keltner widths (wide, normal, narrow), and substituting MOM+SMA smoothing for linear regression momentum.
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- **Similar:** [SQUEEZE](../squeeze/Squeeze.md), [TTM_SQUEEZE](../../dynamics/ttm_squeeze/TtmSqueeze.md), [BBS](../bbs/Bbs.md) | **Complementary:** ATR, BB | **Trading note:** Level 3 (narrow) = tightest compression, expect explosive breakout. Level 0 = expansion phase.
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- Cross-validated streaming vs batch and across SMA/EMA smoothing modes.
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## Historical Context
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LazyBear's Squeeze Pro appeared on TradingView as an enhanced version of John Carter's TTM Squeeze, addressing a fundamental limitation: the original Squeeze only uses a single Keltner Channel width, providing a binary "squeeze on/off" signal. In practice, volatility compression exists on a spectrum — a market can be lightly compressed (BB barely inside KC) or severely compressed (BB well inside even a narrow KC). The three-level classification captures this gradient: wide squeeze (initial compression), normal squeeze (significant compression), and narrow squeeze (extreme compression that often precedes the largest moves). The momentum component was simplified from Carter's linear regression approach to a straightforward MOM(close, n) smoothed by SMA or EMA, making the indicator more responsive and easier to interpret.
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## Architecture & Physics
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### Computational Stages
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1. **SMA + Standard Deviation** (Bollinger Bands): Circular buffer with running sum and sum-of-squares for O(1) variance computation. BB upper/lower = SMA $\pm$ bbMult $\times$ StdDev.
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2. **EMA + ATR via RMA** (Keltner Channels): A single EMA and ATR computation shared across all three KC widths. Only the multiplier differs:
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- KC Wide: EMA $\pm$ kcMultWide $\times$ ATR
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- KC Normal: EMA $\pm$ kcMultNormal $\times$ ATR
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- KC Narrow: EMA $\pm$ kcMultNarrow $\times$ ATR
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3. **Squeeze classification:** Hierarchical check from tightest to widest:
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- Level 3 (narrow): BB inside KC_narrow
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- Level 2 (normal): BB inside KC_normal but not KC_narrow
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- Level 1 (wide): BB inside KC_wide but not KC_normal
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- Level 0 (off): BB outside KC_wide
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4. **Momentum (MOM):** Simple momentum = close $-$ close\[momLength bars ago\]. Requires a circular buffer of `momLength` close values.
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5. **Smooth MOM:** SMA or EMA of the raw momentum values over `momSmooth` period.
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### Warmup Compensation
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EMA and RMA stages use the $e = \beta^n$ warmup tracking with correction factor $c = 1/(1-e)$ to eliminate initial bias.
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## Mathematical Foundation
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**Bollinger Bands** (SMA + StdDev via running sums):
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$$\mu = \frac{\Sigma x}{n}, \quad \sigma = \sqrt{\frac{\Sigma x^2}{n} - \mu^2}$$
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$$BB_{upper} = \mu + m_{bb} \cdot \sigma, \quad BB_{lower} = \mu - m_{bb} \cdot \sigma$$
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**Keltner Channel** (EMA + ATR):
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$$EMA_t = \frac{\hat{E}_t}{1 - \beta^t}, \quad ATR_t = \frac{\hat{R}_t}{1 - \beta_r^t}$$
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$$KC_{upper}^{(w)} = EMA + m_w \cdot ATR, \quad KC_{lower}^{(w)} = EMA - m_w \cdot ATR$$
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where $w \in \{wide, normal, narrow\}$.
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**Squeeze level:**
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$$SqueezeLevel = \begin{cases} 3 & \text{if } BB \subset KC_{narrow} \\ 2 & \text{if } BB \subset KC_{normal} \setminus KC_{narrow} \\ 1 & \text{if } BB \subset KC_{wide} \setminus KC_{normal} \\ 0 & \text{otherwise (expansion)} \end{cases}$$
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**Momentum:**
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$$MOM_t = close_t - close_{t - momLength}$$
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$$Momentum_t = SMA(MOM, momSmooth) \text{ or } EMA(MOM, momSmooth)$$
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## Performance Profile
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| Operation | Count per bar |
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| --- | --- |
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| ADD/SUB | ~20 |
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| MUL | ~12 |
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| DIV | 4 |
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| CMP | 6 |
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| SQRT | 1 |
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| FMA | 8 |
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Three circular buffers (`period` + `momLength` + `momSmooth`) with snapshot/rollback for bar correction. Memory: $O(period + momLength + momSmooth)$ per instance.
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## Validation
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| Library | Status | Notes |
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| --- | --- | --- |
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| pandas-ta | Algorithm reference | Verified algorithm from source |
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| Self-consistency | ✅ Pass | Streaming = Batch = Eventing |
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| Determinism | ✅ Pass | Same seed → identical output |
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## Common Pitfalls
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1. **KC multiplier ordering:** Ensure kcMultWide > kcMultNormal > kcMultNarrow for meaningful level classification. The algorithm works with any positive values, but inverted ordering produces unintuitive results.
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2. **Momentum warmup:** First `momLength` bars produce MOM = 0 (no lagged close available). Full momentum accuracy requires `momLength + momSmooth` bars.
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3. **SMA vs EMA smoothing:** SMA produces equal-weight smoothing (more stable); EMA gives more weight to recent momentum (more responsive). Both produce valid signals but differ numerically.
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4. **Squeeze level vs squeeze state:** Level 0 doesn't mean "no squeeze ever happened" — it means BB is currently outside KC_wide (expansion phase). The transition from level 3→0 is the breakout signal.
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5. **Memory footprint:** Three circular buffers plus three snapshot arrays. For very large `period`, ArrayPool is used automatically in batch mode.
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## References
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- LazyBear, "Squeeze Momentum Indicator [LazyBear]" (TradingView)
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- pandas-ta `squeeze_pro` implementation (GitHub)
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- John Carter, *Mastering the Trade* (2005) — original TTM Squeeze concept
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