Numbers Bars

What it is

Numbers Bars is Sierra Chart's native chart type for rendering footprint charts. Where a standard candlestick chart shows OHLC and total volume per bar, a Numbers Bars chart breaks each bar open into a per-price-level view: at every price the bar touched, you can see how much volume traded at the bid and how much traded at the ask, displayed either as numbers in a grid or as graphical elements.

The result is a chart where each bar is essentially a small two-column table of order flow data. You can read it left-to-right (bar to bar over time) like any chart, but you can also read it top-to-bottom inside any single bar to see how the bar's volume was distributed across price. This is what makes Numbers Bars the foundation for in-bar order flow analysis on Sierra Chart.

The display is extensively configurable. You can show bid and ask volume side-by-side, only delta (the difference), volume only, trade counts, percentage-of-bar imbalances, or any combination. You can color-code cells by magnitude, by imbalance ratio, or by other criteria. The chart type is sometimes informally called the "footprint chart" — Sierra Chart's name for the same concept is Numbers Bars.

Why it matters

Numbers Bars is what makes Sierra Chart a serious order-flow platform. Many competitor platforms either don't support per-price-level data at all, support it only through expensive third-party plugins, or render it in less configurable ways. Sierra Chart bundles Numbers Bars natively and exposes the underlying VAP data to ACSIL for custom analysis.

For traders, Numbers Bars surfaces things that no candlestick chart can show:

• Where the volume actually traded inside each bar — was it concentrated at the high, the low, or spread evenly?
• Bid vs ask imbalances at specific prices — a bar that closed up but had heavy bid-side volume at the high is a different signal from one that closed up with heavy ask-side volume.
• Single prints and zero prints — prices with one-sided volume that frequently get revisited later.
• Stacked imbalances — multiple consecutive prices showing the same-direction imbalance, often associated with strong intent.
• Per-bar delta in context — a bar's delta is one number; the per-price delta distribution that produced it is far more informative.

How it appears on Sierra Chart

You change a chart's type to Numbers Bars via Chart → Chart Settings → Chart Type → Numbers Bars (the exact path is in the chart settings dialog). Once active, an extensive sub-dialog controls every aspect of how cells are rendered: which columns to show, color rules, font, grid spacing, imbalance thresholds, and so on. The same chart can also display a side-attached volume profile, a per-bar delta number, and a session VWAP — combining footprint with profile context.

For ACSIL developers, the per-price data driving Numbers Bars is the VAP container exposed via sc.VolumeAtPriceForBars. Custom studies can read the same data and overlay their own annotations on top.

Common patterns / pitfalls

• Numbers Bars needs per-tick bid/ask data — instruments without bid/ask-tagged trades (some non-futures markets) cannot populate the bid/ask columns meaningfully.
• Performance scales with bar count and price range — Numbers Bars charts with thousands of bars across wide price ranges can be heavy. Most users keep their visible range tight.
• Chart resolution affects readability — small bars on a high-resolution chart make the per-cell numbers tiny. Adjust font size or zoom in.
• Combine with delta and volume profile — Numbers Bars on its own is dense; layered with bar delta and session profile, the structural context becomes much faster to parse.
• Range bars + Numbers Bars — a common combination, but be careful with session handling: Range bars spanning RTH→ETH gaps need explicit session filtering to avoid misleading bar opens.

Related SCS studies

CVD Filled Area, Delta Candle Color, and Single Print and Gap are SCS studies designed to be added on top of Numbers Bars charts, providing per-bar delta-aware visualization (candle coloring), session CVD context, and structural single-print level tracking that complement the raw footprint view.