How to Track Kalshi and Polymarket Positions in One Portfolio

The cleanest way to track Kalshi and Polymarket positions in one portfolio is to normalize every fill into the same ledger, then calculate P&L, capital at risk, fees, Fair Value Spread, and exposure from that shared record.

A combined balance alone is tidy but not especially intelligent. Two traders can show the same profit while taking very different risks. One may hold independent positions; the other may own five versions of the same political opinion wearing different ticker symbols.

To compare the portfolio workflows discussed here with available access, review the plans.

Quick Answer: Build One Position Ledger

Store every fill as a separate record with a consistent set of fields. Keep platform-specific identifiers, but translate prices and outcomes into shared units.

• Identity: platform, market ID, title, category, resolution date, and resolution rules.
• Execution: side, contracts, fill price, timestamp, fees, and order notes.
• Valuation: current price, fair-value estimate, entry FVS, and closing FVS.
• Results: realized P&L, unrealized P&L, return on capital at risk, and settlement status.

Use one row per fill rather than one row per market. Partial fills at different prices change cost basis, and collapsing them too early makes fees and execution quality harder to audit.

Core Portfolio Formulas

Cost basis

For a YES position purchased at 42¢, 100 contracts require $42 before fees. When fills occur at several prices, weight the average entry by contracts.

Unrealized P&L

Mark open positions to a realistic executable price rather than a flattering last trade. Thin books have a talent for making paper profits look healthier than the available exit.

Return on capital at risk

This denominator makes platform and strategy comparisons more useful than raw dollars. A $40 profit on $100 at risk is not the same decision as a $40 profit on $2,000 at risk.

Measure Fair Value Spread

P&L answers whether a position made money. Fair Value Spread answers whether the entry price was favorable relative to your probability estimate.

If your research estimates a 57% probability and you buy YES at 51¢, the entry FVS is +6¢. Record the estimate before trading. Editing fair value after the result becomes obvious is not calibration; it is autobiography.

Track closing FVS too. Comparing your fill with a consistent market benchmark provides repeatable execution evidence, while entry FVS tests your model against the price you actually paid.

Normalize Without Erasing Differences

Kalshi and Polymarket both express binary probabilities through prices, but fees, accounts, market rules, and settlement can differ. Standardize the analysis while preserving original platform fields for reconciliation.

• Store prices internally as decimals from 0 to 1, then display them as cents.
• Save gross and net P&L so fees remain visible.
• Keep each platform's market and transaction IDs for duplicate detection.
• Save exact resolution criteria because similar titles do not guarantee identical contracts.
• Match cross-platform markets only after checking wording, deadline, threshold, and settlement rules.

A 52¢ Kalshi contract and a 55¢ Polymarket contract may appear to disagree by 3¢. If their deadlines or resolution sources differ, the apparent arbitrage may simply be two questions with matching outfits.

Find Hidden Portfolio Risk

A combined tracker becomes valuable when it reveals correlated exposure that platform balances miss.

• Category exposure: capital at risk by politics, economics, sports, crypto, and other themes.
• Event exposure: positions tied to the same election, rate decision, game, or asset price.
• Resolution exposure: capital settling in the same day or week.
• Directional exposure: contracts that benefit from the same underlying narrative.
• Platform exposure: balances and open risk held at each venue.

Review at both market and thesis level. Five contracts can still be one trade when all five lose if the same assumption fails.

A Weekly Review Workflow

• Reconcile imported fills, exits, fees, and settlements.
• Inspect stale prices and changed resolution criteria.
• Rank open positions by dollars at risk, FVS, time to resolution, and correlation.
• Compare realized ROI and average FVS by platform and category.
• Write down what changed in the thesis before revising fair value.

The goal is not to manufacture more trades. It is to identify which parts of the process deserve more capital, less capital, or a dignified retirement.

Spreadsheet or Automatic Sync?

A spreadsheet can work when volume is low and records are entered immediately. It becomes fragile when partial fills, exits, fees, redemptions, and cross-platform positions accumulate.

Automatic sync reduces transcription work, but automation is not the analysis. The useful layer begins after import: normalized cost basis, combined exposure, FVS, ROI segmentation, and a reviewable decision history.

Frequently Asked Questions

Related Guides

• Compare Kalshi and Polymarket probabilities
• Calculate expected value on prediction contracts
• Choose a Polymarket tracker