Exporting Market Data to Excel

Why Traders Export Prediction Market Data to Excel

Exporting market data to Excel is the fastest way to build your own audit trail on Kalshi and Polymarket positions without depending on a platform's native dashboard. Native interfaces on both exchanges are built for order entry, not analysis — they don't let you overlay volume history against your entry price, tag trades by pillar thesis, or run a rolling win-rate calculation across a season. Once you pull raw contract data into a spreadsheet, you control the columns, the formulas, and the version history.

This matters more in prediction markets than in traditional equities because contract prices move on discrete news events, not continuous price discovery. A spreadsheet lets you timestamp every price snapshot next to the event that caused it — a Fed statement, an injury report, a polling update — so you can later reconstruct exactly why a market moved and whether your read on it was justified. That reconstruction is the raw material for improving your process, not just your P&L.

What Kalshi and Polymarket Export Tools Actually Give You

Kalshi's account activity page lets you download a CSV of your fills, orders, and settlements. Polymarket's approach is different — it's a decentralized exchange on Polygon, so your full trade history technically lives on-chain, and most traders pull it through a block explorer or a portfolio tracker that reads wallet activity. Neither export gives you live order book depth, historical implied-probability curves, or volume-weighted average price by contract — you get transaction records, not market structure.

That's an important distinction if you're trying to build anything beyond a tax ledger. A CSV of your own fills tells you what you did. It doesn't tell you what the market was doing around your position — how quickly liquidity thinned out, whether the spread widened before a move, or how implied probability compared to a sportsbook line at the same timestamp. If you're serious about post-trade review, you need to pair your fills export with independent market snapshots, which usually means pulling data through an API or a third-party aggregator rather than relying on the exchange's own download button.

Building a Clean Excel Template for Market Data Export

Structure matters more than volume here. A workable template needs, at minimum: contract ticker, market question, entry timestamp, entry price (as implied probability), exit timestamp, exit price, position size, platform (Kalshi or Polymarket), category, and a free-text thesis field. Add a settlement column that records the actual outcome and the settlement price so you can calculate realized variance between your implied probability and the true outcome — that's the number that tells you whether you're systematically over- or under-pricing certain categories.

Keep raw imports on a separate sheet from your analysis sheet. Pulling a fresh CSV every week and pasting it directly over formulas is the single most common way traders corrupt their own tracking spreadsheets. Use a dedicated "Raw_Import" tab, then reference it with formulas on an "Analysis" tab so refreshing data never breaks your calculations. If you're weighing which exchange to route capital through in the first place, Kalshi vs Polymarket 2026 breaks down the structural differences in liquidity and settlement that affect how clean your exported data will be.

Formulas Worth Building In

• Brier score per trade (squared error between your implied probability and the binary outcome) to measure calibration over time
• Rolling 30-trade win rate by category, so you can see whether your edge concentrates in politics, sports, or economic-data markets
• Implied-probability delta versus a reference line (sportsbook odds, polling average) pulled in as a second data column
• Realized P&L versus theoretical P&L at your original target exit, to separate execution slippage from thesis error

Automating the Export with the Kalshi and Polymarket APIs

Manual CSV downloads work for casual tracking, but if you're running more than a handful of positions a week, automate the pull. Kalshi's REST API exposes market data, order history, and fills endpoints that you can hit on a schedule and append to a running spreadsheet or database using a simple script — Python's `requests` library plus `openpyxl` or `pandas.to_excel()` covers most of it. Polymarket's data is queryable through its subgraph and public APIs, which return trade-level data indexed by wallet address and market ID.

The practical benefit of automating this isn't convenience — it's data integrity. A scheduled pull captures market state at consistent intervals, which is what you need if you're trying to build a time series of implied probability rather than a handful of manually-grabbed snapshots. If you're new to how Kalshi's contracts and settlement actually function before you build automation around them, How Kalshi Works covers the mechanics you need to model correctly in a script.

Reading Exported Odds Data Correctly Before You Analyze It

The most common analysis error isn't a broken formula — it's misreading what the exported price column actually represents. Kalshi and Polymarket both quote contracts on a 0-100 cent scale that maps directly to implied probability, which is not the same convention as American or decimal odds from a sportsbook export. If you're merging prediction market data with sportsbook lines in the same spreadsheet to compare pricing, convert everything to a single implied-probability format first, or every downstream calculation — Brier scores, edge estimates, expected value — will be wrong. How to Read Prediction Market Odds walks through the conversion math if you're combining formats.

Also check timestamp handling on export. Kalshi timestamps in UTC by default; if your spreadsheet's date functions assume local time, your intraday volatility calculations will be silently off by however many hours separate you from UTC. This is an easy thing to miss because the spreadsheet won't throw an error — it just quietly produces wrong charts.

How PillarLab AI Fits Into This

PillarLab AI exists for the analysis Excel can't automate on its own. Rather than exporting raw fills and building formulas from scratch, PillarLab runs a structured 9-pillar analysis on live Kalshi and Polymarket data in real time — pulling order book depth, volume trends, cross-platform pricing gaps, and news-driven catalysts into a single read on where a market's edge actually sits. Each pillar scores a distinct input (liquidity, momentum, sentiment, historical base rates, and more), so instead of manually reconstructing why a contract moved after the fact, you get that structure applied while the market is still live.

That doesn't replace your spreadsheet — it feeds it. You can still export your own fills and outcomes to track calibration over time, but the heavy lifting of scanning both exchanges for mispriced contracts, watching for cross-platform arbitrage, and flagging when implied probability diverges from underlying fundamentals happens continuously inside PillarLab rather than in a formula you have to maintain. If you're deciding which exchange or tool stack to build your process around, Best Prediction Market 2026 and Best AI for Sports Betting lay out how PillarLab's approach compares to manual tracking and to other analysis tools on the market.

Frequently Asked Questions

Can you export Kalshi trade history directly to Excel?

Yes. Kalshi's account activity page provides a CSV download of fills, orders, and settlements that opens natively in Excel or Google Sheets without conversion.

Does Polymarket have a built-in Excel export button?

No. Polymarket is on-chain, so trade history is pulled via its subgraph, a block explorer, or a wallet-tracking tool, then imported into Excel manually or via script.

What columns should a prediction market tracking spreadsheet include?

At minimum: ticker, question, entry/exit timestamps and prices, position size, platform, category, thesis notes, and settlement outcome for calibration scoring.

Why does my exported price data look different from sportsbook odds?

Kalshi and Polymarket quote contracts as implied probability on a 0-100 scale, not American or decimal odds — convert formats before comparing or merging data.

Can PillarLab AI replace manual spreadsheet tracking?

PillarLab automates real-time edge detection across both exchanges, but most traders still export settled trades to a spreadsheet separately to track personal calibration over time.

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