How to Build a Kalshi Volume Chart (Quarterly, Daily, Historical Guide)

This guide is a direct extension of our main breakdown:

👉 Kalshi Volume Explained: Quarterly Trends and Market Activity

That page explains what Kalshi volume is and why it matters.

This one shows you something more important:

How to actually build those volume charts yourself using historical data in Lychee

No code. No API stitching. Just structured analysis.

And more importantly:

You’re working with the exact same dataset behind the charts you see in the main Kalshi volume analysis.

No approximations. No scraped snapshots. Full trade-level history.

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1. Why Kalshi Volume Charts Matter

Kalshi volume only becomes meaningful when you can see it over time.

Without visualization, you're just looking at raw trade activity.

With charts, you unlock:

• quarterly growth trends
• daily spikes during events
• long-term structural shifts
• baseline vs event-driven activity

This is the foundation of every analysis in the Kalshi Volume cluster.

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2. The Dataset Behind Kalshi Volume (Lychee Historical Data)

All charts in this guide are built using Lychee’s Kalshi historical dataset.

Key scale:

• 7.68 million markets
• 72.1 million trades

This is a complete historical dataset — not sampled data or partial API responses. The largest publicly available dataset of Polymarket and Kalshi market and trade data.

This matters because:

Kalshi volume is not a single metric — it is reconstructed from trade-level activity.

Each trade contributes to total volume, which can then be aggregated into:

• daily volume
• quarterly volume
• yearly volume

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3. Step 1 — Querying Kalshi Volume Data in Lychee

• Start by opening the Kalshi historical dataset in Lychee.

• Select "Trades" as your Data Source. Here each row of data represents a single trade execution

What you need to extract:

• created_time: when the trade occurred
• count: the number of contracts traded
• ticker: (optional) Market ticker this trade belongs to

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4. Step 2 — Building a Quarterly Volume Chart

This is the core chart used in the main Kalshi Volume article.

You are aggregating:

• Total contracts traded (volume)
• Grouped by quarter
• Across the full history of Kalshi trades

sum(count) grouped by quarter = sum(the number of contracts traded over all trades since Kalshi conception) grouped by quarter

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Working with aggregations No-Code

• Select a Date/time shape under your created_time field: Lychee provides some very useful aggregation helpers which lets you bucket dates by days, weeks, months, quarters and years.
• In this case lets select Quarters
• Let's also rename out Column "Quarter"

• Select Summarize and SUM in the "count" column selection: we need to aggregate all the traded contracts within a given time bucket using the SUM function just like we would do in regular SQL, Python or any Data Science tool

- Lychee tooling already handles the date formatting and chronological adjustment, which normally is a pain to deal with in any other platform.

• Hit Run Request to run your query across all Kalshi Trade history since the beginning of time

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Quarterly Volume Chart Output

Now we move onto charting the data we just pulled

• Select Chart in order to move to the charting panel

• Select Bar chart: we select from line, area, bar, pie, radial, heat maps, live charts, etc. In this case line and bar charts make the most sense. Lets go with Bar charts

• Select Date for the x-axis and volume for the y-axis, and your chart will be instantly rendered (this will reflect the name you chose for your columns in the Data Sheets section)

• Finally design and add descriptive text as you desire

• Hit Publish to Embed in order to get a sharable and embeddable version of your interactive chart. Or you can download a static png, jpg, etc

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At this point, you’ve already recreated the core Kalshi volume chart using raw trade data.

If you want to explore this dataset yourself:

→ Filter by specific events
→ Drill into individual markets
→ Export full datasets instantly

Start exploring in Lychee

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5. Step 3 — Building Daily and Yearly Volume Trends

Once quarterly structure is built, you can zoom in or out:

Daily Volume

• captures volatility spikes
• highlights event-driven trading behavior

Yearly Volume

• smooths short-term noise
• shows structural adoption curve

Analyzing different timeframes is very simple:

Monthly:

• Group by month instead of quarter
• Sum volume in the same way

• Select "Create another request" if you want to work on the same project. And create a new sheet when prompted

• Select Date/time shape as Bucket by month:

• You will now have 2 sheets (Quarterly and Monthly)

• Chart using chart functionality

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Yearly:

• Yearly analysis is the same simple approach
• Group by year (YYYY)
• Sum volume per year

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6. Step 4 — Cleaning Volume Data for Accurate Charts

Before visualizing, apply filters to remove noise.

This step is optional — but critical if you want cleaner, more reliable volume charts.

Without filtering: → low-quality markets can distort trends → inactive markets add noise → unresolved data can skew totals

Recommended filters:

• exclude markets with < $100 notional volume
• exclude voided / delisted markets
• optionally include only resolved markets

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7. Step 5 — Exporting Volume Data

Once your dataset is structured:

You can export or visualize directly inside Lychee.

Export options:

• CSV
• Excel
• JSON
• Dashboard view

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8. How These Charts Are Used in Practice

These volume charts are used for:

• research dashboards
• market structure analysis
• event impact studies
• historical trend comparison
• content and reporting layers

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9. Relationship to the Main Kalshi Volume Article

This guide is the execution layer of:

Kalshi Volume Explained

That page explains:

• what volume means
• why it matters
• how it behaves

This page shows:

• how to build it
• how to visualize it
• how to reproduce it

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10. Next Step

Once you can build Kalshi volume charts, you can extend this workflow into:

• volatility analysis
• category breakdowns
• event-driven spikes
• cross-market comparisons

Each of these will be broken out into dedicated deep-dive guides:

→ Volume by category
→ Event-driven spike analysis
→ Market-level volume breakdowns

(coming next in this series)

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Explore more:

• Kalshi Volume Explained
• Kalshi Historical Data Guide
• Lychee Homepage

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Final Note

Kalshi volume is only as useful as your ability to structure and visualize it properly.

This is exactly what Lychee is built for:

turn raw prediction market data into structured, queryable, visual insights — instantly.

You just built a full Kalshi volume chart from raw trade data.

That’s the difference:

→ reading about markets
→ vs actually working with them

Lychee is built to close that gap.