Miha Kralj
90d5638008
- LTMA (Linear Trend Moving Average): Introduces a predictive moving average using dual cascaded EMAs for trend estimation. - MCNMA (McNicholl EMA): Implements a zero-lag TEMA using a cascaded EMA structure for enhanced responsiveness. - NLMA (Non-Lag Moving Average): Utilizes a damped cosine kernel to achieve reduced lag in moving averages. - NMA (Natural Moving Average): Adapts smoothing based on volatility profiles using a square-root kernel. - NYQMA (Nyquist Moving Average): Applies the Nyquist-Shannon theorem to prevent aliasing in cascaded moving averages. - RAIN (Rainbow Moving Average): Combines multiple SMA layers with weighted averages for multi-scale smoothing. - TRAMA (Trend Regularity Adaptive Moving Average): Adapts smoothing based on the frequency of new highs and lows in price data.
4.9 KiB
TTM_SQUEEZE: TTM Squeeze
"Volatility compression is the market holding its breath before screaming."
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 |
Pseudo-code
TTM_SQUEEZE(bar, bbLen=20, bbMult=2.0, kcLen=20, kcMult=1.5):
// Bollinger Bands
sma_val = SMA(close, bbLen)
stddev = StdDev(close, bbLen)
bb_upper = sma_val + bbMult * stddev
bb_lower = sma_val - bbMult * stddev
// Keltner Channel
ema_val = EMA(close, kcLen)
atr_val = ATR(bar, kcLen)
kc_upper = ema_val + kcMult * atr_val
kc_lower = ema_val - kcMult * atr_val
// Squeeze state
squeeze_on = (bb_lower > kc_lower) AND (bb_upper < kc_upper)
// Momentum via linear regression of deviation
highest_high = Highest(high, bbLen)
lowest_low = Lowest(low, bbLen)
midline = (highest_high + lowest_low) / 2
delta = close - (midline + sma_val) / 2
momentum = LinReg(delta, bbLen)
// Momentum direction
momentum_rising = momentum > prev_momentum
momentum_positive = momentum > 0
return (momentum, squeeze_on, momentum_rising, momentum_positive)
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.
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.