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📏 Bollinger Bands

Bollinger Bands dynamically measure volatility and draw an adaptive "normality fence" around the price.


💡 Financial Meaning

When the bands are wide, the market is volatile; when they squeeze together, a breakout is imminent. A price touching the upper band signals statistical exuberance; touching the lower band signals an abnormal dip.


🔢 Mathematical Formulas

  1. Middle Band (expected value):

    \[ MB_t = SMA_N(C_t) \]
  2. Standard deviation of prices over the window:

    \[ \sigma_t = \sqrt{\frac{1}{N} \sum_{i=0}^{N-1} (C_{t-i} - MB_t)^2} \]
  3. Upper and Lower Bands:

    \[ Upper_t = MB_t + k \cdot \sigma_t, \qquad Lower_t = MB_t - k \cdot \sigma_t \]

With \(k = 2\), if returns were normally distributed the price would stay inside the bands ~95.4% of the time. In practice, financial returns have fat tails (leptokurtosis), so breaches are more frequent — but still statistically significant.


⚙️ Parameters

Parameter Key Default Description
Period (\(N\)) period 20 SMA window for expected value.
Multiplier (\(k\)) multiplier 2 Number of standard deviations.

🎛️ Signal Processing Equivalent — Adaptive Confidence Interval Tracker

The Middle Band is a FIR (Finite Impulse Response) moving average filter — the simplest low-pass with a rectangular window of length \(N\). The bands add a time-varying envelope at \(\pm k\sigma\), which is essentially a running estimate of the signal's instantaneous variance.

In the language of adaptive filters, this is an expected-value tracker with an adaptive confidence interval. When the variance \(\sigma^2\) drops (the "Bollinger Squeeze"), the system is in a low-entropy state. In chaotic systems like financial markets, low-entropy periods are reliably followed by high-entropy (high-volatility) explosions — making the squeeze one of the most watched setups in technical analysis.

FIR vs IIR

Unlike the EMA (IIR, one pole), the SMA is a FIR filter with a perfectly flat group delay of \((N-1)/2\) samples. It trades off a wider transition band for zero-phase distortion — ideal for centring the confidence envelope.

Bollinger Bands on Wikipedia