Miha Kralj
6.5 KiB
TTM_SQUEEZE: TTM Squeeze
Volatility compression is the market holding its breath before screaming.
| Property | Value |
|---|---|
| Category | Dynamic |
| Inputs | OHLCV bar (TBar) |
| Parameters | bbPeriod (default 20), bbMult (default 2.0), kcPeriod (default 20), kcMult (default 1.5), momPeriod (default 20) |
| Outputs | Single series (TtmSqueeze) |
| Output range | Varies (see docs) |
| Warmup | Math.Max(Math.Max(bbPeriod, kcPeriod), momPeriod) bars |
- John Carter's TTM Squeeze detects low-volatility compression by comparing Bollinger Band width against Keltner Channel width: when BB fits inside K...
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
John Carter's TTM Squeeze detects low-volatility compression by comparing Bollinger Band width against Keltner Channel width: when BB fits inside KC, a "squeeze" is on, signaling imminent breakout. The momentum component uses linear regression of price deviation from the Donchian midline to indicate direction. The indicator outputs a boolean squeeze state plus a continuous momentum histogram, requiring BB(20,2.0) and KC(20,1.5) as default parameters with a combined warmup of 20 bars.
Historical Context
John Carter developed TTM Squeeze as his signature volatility breakout indicator, popularized through Mastering the Trade (2005) and the thinkorswim platform. The core insight combines two independent volatility measures: Bollinger's standard-deviation bands and Keltner's ATR-based channels. When the faster-reacting BB contracts inside the slower KC, it signals unusually low volatility, a condition that reliably precedes explosive directional moves. Carter added a momentum oscillator based on linear regression to provide directional bias during squeeze releases. The indicator became one of the most widely used proprietary tools in retail trading.
Architecture & Physics
1. Bollinger Band Width
\text{BB}_{\text{upper}} = \text{SMA}(C, N_{\text{BB}}) + k_{\text{BB}} \cdot \sigma(C, N_{\text{BB}})
\text{BB}_{\text{lower}} = \text{SMA}(C, N_{\text{BB}}) - k_{\text{BB}} \cdot \sigma(C, N_{\text{BB}})
where N_{\text{BB}} = 20, k_{\text{BB}} = 2.0, and \sigma is population standard deviation.
2. Keltner Channel Width
\text{KC}_{\text{upper}} = \text{EMA}(C, N_{\text{KC}}) + k_{\text{KC}} \cdot \text{ATR}(N_{\text{KC}})
\text{KC}_{\text{lower}} = \text{EMA}(C, N_{\text{KC}}) - k_{\text{KC}} \cdot \text{ATR}(N_{\text{KC}})
where N_{\text{KC}} = 20, k_{\text{KC}} = 1.5.
3. Squeeze Detection
\text{SqueezeOn} = (\text{BB}_{\text{lower}} > \text{KC}_{\text{lower}}) \text{ and } (\text{BB}_{\text{upper}} < \text{KC}_{\text{upper}})
When BB fits entirely inside KC, the squeeze is active. The first bar where squeeze transitions from on to off ("squeeze fires") signals the breakout.
4. Momentum Histogram
\text{midline} = \frac{\text{Highest}(H, N) + \text{Lowest}(L, N)}{2}
\delta_t = C_t - \frac{\text{midline}_t + \text{SMA}(C, N)}{2}
\text{Momentum} = \text{LinReg}(\delta, N)
The linear regression extracts the trend component of the deviation, filtering noise. Momentum sign indicates direction; slope indicates acceleration.
5. Momentum Color States
| Color | Condition |
|---|---|
| Cyan | Momentum > 0 and rising |
| Blue | Momentum > 0 and falling |
| Red | Momentum < 0 and falling |
| Yellow | Momentum < 0 and rising |
6. Complexity
| Metric | Value |
|---|---|
| Time | O(1) per bar (incremental BB, KC, LinReg updates) |
| Space | O(N) for sliding window buffers (SMA, StdDev, ATR, high/low, LinReg) |
| Warmup | N bars (default 20) |
Mathematical Foundation
Parameters
| Parameter | Type | Default | Constraint | Description |
|---|---|---|---|---|
| bbLength | int | 20 | > 1 | Bollinger Band period |
| bbMult | double | 2.0 | > 0 | BB standard deviation multiplier |
| kcLength | int | 20 | > 1 | Keltner Channel period |
| kcMult | double | 1.5 | > 0 | KC ATR multiplier |
Squeeze-Fire Signal
The critical trading signal occurs on the transition bar:
\text{SqueezeFired}_t = \text{SqueezeOn}_{t-1} \text{ and } \neg\text{SqueezeOn}_t
Combined with momentum direction, this yields entry signals: long when squeeze fires with positive rising momentum, short when squeeze fires with negative falling momentum.
Performance Profile
Operation Count (Streaming Mode)
TTM Squeeze detects when Bollinger Bands are inside Keltner Channels (the "squeeze"), and fires momentum via a linear-regression oscillator.
Post-warmup steady state (per bar):
| Operation | Count | Cost (cycles) | Subtotal |
|---|---|---|---|
| SMA update (BB middle) + variance (O(N)) | N+5 | 1 | N+5 |
| SQRT (BB StdDev) | 1 | 20 | 20 |
| ATR update (FMA RMA) | 1 | 4 | 4 |
| BB upper/lower (ADD/SUB × 2) | 2 | 1 | 2 |
| KC upper/lower (EMA + ATR × mul, ADD/SUB × 2) | 4 | 4 | 16 |
| CMP × 2 (BB inside KC?) | 2 | 1 | 2 |
| Linear regression oscillator (O(N)) | ~3N | 3 | ~3N |
| Total | ~4N+35 | — | ~4N+49 |
For default N=20: ~129 cycles per bar. The O(N) variance + O(N) linear regression scan dominate.
Batch Mode (SIMD Analysis)
| Operation | Vectorizable? | Notes |
|---|---|---|
| BB computation (prefix sum variance) | Yes | VADDPD + VMULPD for rolling variance |
| ATR (RMA) | No | Recursive IIR |
| Keltner EMA | No | Recursive IIR |
| Linear regression | Yes | Prefix sums of x×y and x² enable O(1) window regression |
| Squeeze detection | Yes | VCMPPD after bands computed |
Regression can be recast as prefix-sum dot products for SIMD acceleration; ATR/EMA chains remain sequential.
Quality Metrics
| Metric | Score | Notes |
|---|---|---|
| Accuracy | 9/10 | SQRT precision adequate; linear regression high fidelity |
| Timeliness | 5/10 | N-bar windows on all components; squeeze detection has inherent N/2 lag |
| Smoothness | 7/10 | Linear regression oscillator is smooth by construction |
| Noise Rejection | 7/10 | Dual-channel squeeze reduces false momentum triggers |
Resources
- Carter, J. (2005). Mastering the Trade. McGraw-Hill.
- Bollinger, J. (2001). Bollinger on Bollinger Bands. McGraw-Hill.
- Keltner, C. (1960). How to Make Money in Commodities. The Keltner Statistical Service.