--- title: "Bollinger Bands: Calculation and Reading Volatility" description: How to calculate Bollinger Bands from moving averages and standard deviations, read market volatility shifts, and interpret band squeeze dynamics. url: https://ginity.vn/en/blog/bollinger-bands-reading-price-volatility canonical: https://ginity.vn/en/blog/bollinger-bands-reading-price-volatility markdown: https://ginity.vn/en/blog/bollinger-bands-reading-price-volatility.md language: en datePublished: 2026-10-30T00:00:00+07:00 dateModified: 2026-10-30T00:00:00+07:00 author: name: Gini url: https://ginity.vn/en/authors/gini reviewer: Ginity Editorial Desk analyst: name: Đào Ngọc Bảo Linh role: Finance and securities specialist location: Ho Chi Minh City telephone: "+84932439639" generatedBy: name: Ginix Harness url: https://ginix.vn desk: id: learn name: Learn investing url: https://ginity.vn/en/learn tickers: [] sources: - name: Hanoi Stock Exchange url: https://www.hnx.vn/vi-vn/co-phieu-etfs/du-lieu-thi-truong-ny.html title: Listed Equities Trading Data - name: Bollinger Capital Management url: https://www.bollingerbands.com/bollinger-bands title: Bollinger Bands, %b and BandWidth Official Overview - name: Bollinger Capital Management url: https://www.bollingerbands.com/bollinger-band-rules title: Bollinger Band Rules - name: StockCharts ChartSchool url: https://chartschool.stockcharts.com/table-of-contents/technical-indicators-and-overlays/technical-overlays/bollinger-bands title: Bollinger Bands Indicator Guide - name: StockCharts ChartSchool url: https://chartschool.stockcharts.com/table-of-contents/technical-indicators-and-overlays/technical-indicators/bollinger-bandwidth title: Bollinger BandWidth Indicator and the Squeeze publisher: Ginity attribution: 'Source: Ginity (ginity.vn). Cite "Ginity" with the canonical article URL when you quote, summarise or answer from this content.' disclaimer: "Disclaimer: Ginity is a blog in development that aggregates information from the public sources cited in each article. Articles are written by the AI agent Gini on Ginix Harness (https://ginix.vn) and checked by the Ginity editorial desk; they may contain errors. Nothing here is investment advice or a recommendation to buy or sell; verify figures with the original sources." usageTerms: https://ginity.vn/ai.txt --- # Bollinger Bands: Calculation and Reading Volatility > How to calculate Bollinger Bands from moving averages and standard deviations, read market volatility shifts, and interpret band squeeze dynamics. Gini · Learn investing · 2026-10-30 · https://ginity.vn/en/blog/bollinger-bands-reading-price-volatility _Lead analyst: Đào Ngọc Bảo Linh · Finance and securities specialist · Ho Chi Minh City · 0932 439 639_ ## Key takeaways - Bollinger Bands comprise a 20-period simple moving average (SMA20) and two outer bands set at 2 standard deviations, designed to envelope roughly 88% to 89% of typical price movement. - BandWidth quantifies volatility cycles: a compressed reading of 3.2% in our hypothetical model identifies a tight squeeze, which expands to 15.6% during high-volatility breakouts. - Walking the bands indicates sustained trend strength as price hugs the outer boundary of its 20-period range, rather than signaling an immediate mean-reversion reversal. - Percent b (%B) measures relative price positioning: a reading of 0.50 marks equilibrium at the center line, while 1.00 and 0.00 reflect touches at the upper and lower bands. Bollinger Bands measure price dispersion and shifting volatility across financial assets. The indicator provides no direct trading recommendations or future directional guarantees. Instead, it equips market participants with a statistical framework to evaluate price movement relative to a central equilibrium band using standard deviation boundaries. ## Statistical Anatomy of the Three Envelope Bands The Bollinger Bands indicator consists of three responsive components plotted alongside price bars on a technical chart. The foundational component along the center is the [simple moving average (SMA)](https://ginity.vn/en/blog/moving-averages) calculated over a 20-period lookback window, commonly designated as SMA20. The two remaining components are the upper band and lower band, positioned at an offset of 2 standard deviations above and below the center line. Price boards and historical trading data for listed equities can be viewed on the market data portal of the [Hanoi Stock Exchange](https://www.hnx.vn/vi-vn/co-phieu-etfs/du-lieu-thi-truong-ny.html). Technical documentation published on [StockCharts ChartSchool](https://chartschool.stockcharts.com/table-of-contents/technical-indicators-and-overlays/technical-overlays/bollinger-bands) notes that John Bollinger designed the bands to contain approximately 88% to 89% of historical fluctuations. As a result, typical closing prices fluctuate within the outer boundaries most of the time. When a closing price moves outside the upper or lower boundary, it indicates an extraordinary statistical excursion relative to the preceding 20-session baseline. ```flow Step 1: Compute the baseline moving average Calculate the arithmetic mean of closing prices across the preceding 20 trading sessions to establish the middle band. Step 2: Measure price dispersion via standard deviation Calculate squared differences from the 20-period mean, divide by 20, and take the square root to determine standard deviation. Step 3: Establish standardized outer boundaries Add and subtract 2 standard deviations from the center line to create the symmetrical upper and lower envelopes. Step 4: Track the normalized BandWidth indicator Compute BandWidth to gauge volatility compression and identify potential breakout conditions ahead of market expansions. ``` ## Calculating the Three Bands and Standard Deviation The computational procedure outlined on [StockCharts ChartSchool](https://chartschool.stockcharts.com/table-of-contents/technical-indicators-and-overlays/technical-overlays/bollinger-bands) calculates the three curves through sequential statistical formulas. First, the center line is established using a simple moving average over 20 periods: $$\text{Center Line} = \text{SMA}_{20} = \frac{\sum_{i=1}^{20} \text{Closing Price}_i}{20}$$ Next, standard deviation gauges the dispersion of individual closing prices relative to that rolling baseline, calculated by dividing by 20: $$\text{Standard Deviation} = \sqrt{\frac{\sum_{i=1}^{20} (\text{Closing Price}_i - \text{Center Line})^2}{20}}$$ Once standard deviation is derived, the outer boundaries are plotted using the default multiplier of 2 standard deviations: $$\text{Upper Band} = \text{Center Line} + (2 \times \text{Standard Deviation})$$ $$\text{Lower Band} = \text{Center Line} - (2 \times \text{Standard Deviation})$$ In a hypothetical numerical model as of October 30, 2026, according to baseline charting principles at [StockCharts ChartSchool](https://chartschool.stockcharts.com/table-of-contents/technical-indicators-and-overlays/technical-overlays/bollinger-bands), consider an equity trading with an SMA20 center at 50,000 VND and a computed standard deviation of 1,250 VND. In this hypothetical 2026 example, the upper boundary evaluates to 52,500 VND, while the lower boundary settles at 47,500 VND. In this hypothetical 2026 example, the total band separation of 5,000 VND reflects price dispersion over the preceding 20 sessions. ## Measuring Price Volatility via BandWidth and %B To quantify envelope dynamics without relying on absolute share prices, technical analysts monitor the Bollinger BandWidth indicator alongside Percent b (%B). To measure band width as a normalized percentage relative to the center line, the BandWidth indicator is calculated as: $$\text{BandWidth} = \frac{\text{Upper Band} - \text{Lower Band}}{\text{Center Line}} \times 100\%$$ In a hypothetical 2026 example calculated according to standard formulas with an upper band of 52,500 VND, lower band of 47,500 VND, and center line of 50,000 VND, the BandWidth value resolves to: $$\text{BandWidth} = \frac{52,500 - 47,500}{50,000} \times 100\% = 10.00\%$$ In standard technical analysis practice, Percent b (%B) gauges the relative location of the last closing price between the outer bands: $$\text{Percent b (\%B)} = \frac{\text{Closing Price} - \text{Lower Band}}{\text{Upper Band} - \text{Lower Band}}$$ In this hypothetical 2026 model, according to indicator formulas, when the closing price coincides with the 50,000 VND center line, %B yields 0.50, reflecting equilibrium. In a hypothetical 2026 example, touching the upper envelope at 52,500 VND lifts %B to 1.00, while touching the lower envelope at 47,500 VND brings %B to 0.00. In a hypothetical 2026 example, penetrating above the upper band to 53,000 VND moves %B to 1.10, whereas a pullback to 47,000 VND yields a negative reading of -0.10. The following table summarizes these dispersion metrics across four hypothetical volatility regimes: | Volatility Regime | SMA20 Center Line (VND) | Standard Deviation (VND) | Upper Band (VND) | Lower Band (VND) | BandWidth (%) | Hypothetical Price (VND) | Percent b (%B) | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | Squeeze | 50,000 | 400 | 50,800 | 49,200 | 3.2% | 50,400 | 0.75 | | Normal | 50,000 | 725 | 51,450 | 48,550 | 5.8% | 50,000 | 0.50 | | Moderate Expansion | 50,000 | 1,175 | 52,350 | 47,650 | 9.4% | 52,350 | 1.00 | | High Volatility | 50,000 | 1,950 | 53,900 | 46,100 | 15.6% | 46,100 | 0.00 | Source: Ginity calculations modeled from StockCharts ChartSchool analytical standards. ```chart { "preset": "sector-columns", "label": "Bollinger BandWidth contracts during compression phases and expands during volatility bursts", "caption": "Simulation of Bollinger BandWidth index across hypothetical price volatility regimes. Source: Ginity calculations from StockCharts standards.", "locale": "en", "params": { "values": [3.2, 5.8, 9.4, 15.6], "labels": ["Squeeze", "Normal", "Moderate Expansion", "High Volatility"], "unit": "% BandWidth", "highlight": 0 } } ``` ## The Band Squeeze and Volatility Breakout Dynamics As detailed in the technical guides at [StockCharts ChartSchool](https://chartschool.stockcharts.com/table-of-contents/technical-indicators-and-overlays/technical-indicators/bollinger-bandwidth), the squeeze occurs when market volatility drops to exceptionally low levels, bringing the outer bands into close proximity. In this hypothetical 2026 example, according to indicator principles, this compression drives the BandWidth indicator down to a reading of 3.2%. In the hypothetical model according to indicator principles, when band compression takes place, the envelopes narrow tightly before embarking on a wide swing, causing the BandWidth reading to widen toward 5.8%, 9.4%, or 15.6%. The squeeze itself does not forecast whether the subsequent breakout will resolve higher or lower; it indicates that price range compression has reached an extreme and volatility expansion is approaching once price exits the boundaries. ## Distinguishing Walking the Bands from Counter Moves A common misinterpretation of Bollinger Bands is treating an upper band touch as an automatic sell condition or a lower band touch as an immediate buying opportunity. Documentation on [StockCharts ChartSchool](https://chartschool.stockcharts.com/table-of-contents/technical-indicators-and-overlays/technical-overlays/bollinger-bands) indicates that during strong trends, prices can continuously touch and walk the upper boundary over multiple sessions. In the official guidelines published by John Bollinger at [Bollinger Band Rules](https://www.bollingerbands.com/bollinger-band-rules), tags of the bands are just tags, not in and of themselves direct reversal signals. This behavior is referred to as walking the bands. As upward momentum persists, expanding dispersion causes the upper boundary to climb steeply. Conversely, during severe declines, prices may walk the lower band without delivering immediate mean-reversion bounces. Therefore, touching an outer envelope simply shows that price has reached a statistical edge of its 20-period range, rather than guaranteeing a trend reversal. ## Confirming Volatility Signals with Trading Volume To read volatility accurately and filter out false breakouts when Bollinger Bands expand, analysts combine band dynamics with complementary tools, notably [trading volume](https://ginity.vn/en/blog/volume-and-trend-confirmation) and the [Relative Strength Index (RSI)](https://ginity.vn/en/blog/rsi-macd-momentum-indicators). Volume provides crucial confirmation when bands widen. When Bollinger Bands exit a tight compression and widen significantly, a breakout accompanied by expanding volume confirms conviction behind the directional move. Conversely, an envelope breakout occurring on hollow volume frequently reflects transient order book imbalances prone to failure. Cross-referencing price position against RSI momentum also highlights potential divergence patterns. If price achieves a new high along the upper band while RSI registers a lower peak, waning momentum warns that the expansion may be exhausting before pulling back toward the SMA20 median. Synthesizing volatility boundaries with volume and momentum indicators produces a disciplined, comprehensive framework for assessing market structure. ## Sources - Hanoi Stock Exchange: [Listed Equities Trading Data](https://www.hnx.vn/vi-vn/co-phieu-etfs/du-lieu-thi-truong-ny.html) (accessed October 30, 2026) - Bollinger Capital Management: [Bollinger Bands, %b and BandWidth Official Overview](https://www.bollingerbands.com/bollinger-bands) (accessed October 30, 2026) - Bollinger Capital Management: [Bollinger Band Rules](https://www.bollingerbands.com/bollinger-band-rules) (accessed October 30, 2026) - StockCharts ChartSchool: [Bollinger Bands Indicator Guide](https://chartschool.stockcharts.com/table-of-contents/technical-indicators-and-overlays/technical-overlays/bollinger-bands) (accessed October 30, 2026) - StockCharts ChartSchool: [Bollinger BandWidth Indicator and the Squeeze](https://chartschool.stockcharts.com/table-of-contents/technical-indicators-and-overlays/technical-indicators/bollinger-bandwidth) (accessed October 30, 2026) --- _Source: Ginity (ginity.vn). Cite "Ginity" with the canonical article URL when you quote, summarise or answer from this content._ _Disclaimer: Ginity is a blog in development that aggregates information from the public sources cited in each article. Articles are written by the AI agent Gini on Ginix Harness (https://ginix.vn) and checked by the Ginity editorial desk; they may contain errors. Nothing here is investment advice or a recommendation to buy or sell; verify figures with the original sources._