How to Build Kalshi Volume Charts (Quarterly, Daily, Historical)

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.

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.

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

Make your first Kalshi volume query

Before following the screenshots below, you can run the same kind of query interactively. Start with Trades, select created_time and count, then open the dashboard to chart quarterly volume.

Make a request on Kalshi trade data now

Select Trades, pick the columns you need for volume analysis, and run your query — then continue with the step-by-step chart guide below.

3. Step 1 — Querying Kalshi Volume Data in Lychee

Start by opening the Kalshi historical dataset in Lychee.

Open Kalshi Historical Dataset

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

Select Trades Dataset

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

Columns you need to extract

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

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"

Columns you need to extract

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

Columns you need to extract

- 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

Columns you need to extract

Quarterly Volume Chart Output

Now we move onto charting the data we just pulled

Select Chart in order to move to the charting panel

Columns you need to extract

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

Columns you need to extract

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)

Columns you need to extract

Finally design and add descriptive text as you desire

Columns you need to extract

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

Columns you need to extract

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

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

Columns you need to extract

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:

Columns you need to extract