The Probability Feed: Using Kalshi as a Real-Time Signal

Introduction

In markets, price is the ultimate truth machine. Every tick distills millions of beliefs about value, risk, and expectation into a single number. But what if prices existed not just for stocks, bonds, and tokens, but for events themselves? 

That’s the premise behind prediction markets, platforms where users can trade on the outcomes of real-world events. On Kalshi, a CFTC-regulated exchange, you can buy “Yes” shares on whether the Federal Reserve will cut rates at its next meeting, or take the “No” side on whether the Cowboys will win on Monday Night Football. Each contract settles at $1 if true, $0 if false. 

This data is invaluable for researchers and investors, as it represents a new frontier in market-implied data. Exchanges like Kalshi display information about what people think will happen, making it just as measurable as what is currently happening. At Artemis Analytics, where we quantify on-chain fundamentals and digital-asset activity, markets like Kalshi offer a parallel dataset: a real-time feed of collective belief.

In this piece, we’ll explore how Kalshi bridges traditional finance and crypto-native data, and how prediction markets can help illuminate the behavioral forces behind digital-asset fundamentals.

I. What Kalshi Is and How It Works

At its core, Kalshi is a federally regulated exchange for event contracts, i.e., markets where users trade outcomes of real-world events. Each contract trades between $0 and $1, with price representing the implied probability of that event occurring.

For example, consider the contract “Will Google’s Gemini be the top-ranked LLM on October 31st, 2025?” At the time of writing, it trades around $0.89 on the bid, and $0.92 on the ask. In market terms, that means the most recent bid - what someone is immediately willing to pay - reflects an 89% implied probability that the event will occur. Some may conceptualize implied probability as the midpoint, or the average of these two prices, a middle ground for the contract.

You can think of the other side of the trade in two ways. Buying “No” and selling “Yes” are economically equivalent positions, even though they are operationally different actions in the interface. Kalshi’s matching engine simply pairs “Yes” and “No” orders symmetrically.  Every “Yes” buyer is matched with an equivalent “No” buyer (or, equivalently, a “Yes” seller).

• Buying “Yes” at $0.92 = you pay $0.92 now, get $1 if Google wins → profit $0.08.

• Buying “No” at $0.11 = you pay $0.11 now, get $1 if Google loses → profit $0.89.

Note that even if you bought both outcomes, you will only get $1 back upon resolution despite having paid $1.03 – this difference is the bid/ask spread.

This price is determined by buyers and sellers interacting directly on the exchange. However, things work a bit differently than on the stock market, where traders exchange existing assets whose value fluctuates indefinitely. On Kalshi, each contract has a binary payoff and a fixed lifespan, settling at $1 if the event occurs, $0 if it doesn’t. 

There’s no concept of partial ownership or long-term holding; the value of a contract converges toward either extreme as the event date approaches. The most either side can lose is the amount they wagered upfront. When you short a stock, in theory you are exposed to unlimited losses if revenue or earnings multiples enter a bubble. Kalshi on the other hand reflects a forecast of reality, not an opinion about valuation.

Every market Kalshi lists must be reviewed and approved by the CFTC. Users technically trade through KalshiEX LLC, a regulated entity that holds all customer funds in segregated accounts. An objective data source must determine the outcome (for this market, it’s the LM Arena Leaderboard) to ensure that every outcome can be objectively verified. 

In rare cases where a data source is delayed, revised, or ambiguous, Kalshi follows a strict internal Market Resolution Policy:

1. They may delay settlement temporarily while verifying the ruling data.

2. If ambiguity persists, Kalshi’s Market Supervisory Committee can issue a ruling, documented publicly for transparency.

3. Because it’s under CFTC oversight, any user can appeal through formal regulatory channels, which is the same process available for futures-market disputes.

This differs from DeFi-native services like Polymarket, which follow a decentralized resolution process (in Polymarket’s case, UMA’s Optimistic Oracle). These services lack legal oversight, which is partly why Polymarket is currently a zero-fee platform, but can be susceptible to oracle manipulation risk or low participation bias. 

This vulnerability surfaced earlier this year in the market titled “Ukraine Agrees to Trump Mineral Deal Before April?” The contract ultimately resolved to “Yes,” even though “Yes” shares were trading near 9.4% and most participants interpreted the event’s criteria as unmet. The resolution hinged on a technical interpretation of ambiguous wording, and because few users challenged it within the oracle’s dispute window, the “Yes” outcome stood.

Kalshi charges trading fees for orders that are immediately matched with orders sitting on the order book. Fees fluctuate with the price of the contracts, and are either 1 cent or 2 cents per contract, with discounts on size for larger lots. There are also debit and wire deposit fees, given Kalshi is utilizing traditional payment rails. Lastly, Kalshi partners with third parties like Robinhood and IBKR, which may charge their own fees as well.

The philosophical contrast between Kalshi and Polymarket is summarized in the table below:

II. Market-Based Data as a Signal 

Each contract price represents the community’s collective probability estimate of a future event, continuously updated in real time as traders process new information. This dynamic is often described as the “wisdom of the crowd.” When diverse participants each bring their own information, biases, and priors into a market, the aggregation of their views through price discovery tends to outperform most individual forecasts. A 2019 paper from Stanford confirmed this phenomenon in one of the most comprehensive studies to date, with more than 500,000 responses to 1,000 questions across 50 topical areas.

Kalshi’s contracts stand in contrast to sportsbooks, where prices are deliberately skewed by the vig (or house edge). A sportsbook might quote both sides of an evenly matched contest at -110 American odds, implying a combined probability above 100%. The spread between those two odds is the bookmaker’s margin, a built-in distortion that ensures the house profits regardless of outcome. Kalshi’s exchange model removes that bias entirely: because users trade directly with one another and pay only a small transaction fee, prices reflect true market consensus rather than bookmaker-adjusted odds. The result is a cleaner, more interpretable dataset, one where a $0.92 “Yes” price genuinely represents a 92% implied probability.

Kalshi’s typical trading fee is 1-2% when transacting directly on the platform and not through a third-party exchange which typically overlays its own fees. In contrast, the online sports betting “take rate” (sportsbook revenue as a percentage of total handle) is substantially higher. DraftKings, the second largest U.S. online sportsbook, has seen its take rate trend up from 2.5% in Q1 2022 to 8.7% in its latest fiscal quarter. Note that a large share of recent Kalshi volume is currently linked to sports-based contracts.

Another aspect of Kalshi’s design is its order book, which functions similarly to those found in equities or futures markets. Every bid and offer reflects a trader’s willingness to buy or sell probability at a specific price. A deep order book with narrow spreads indicates confidence and liquidity, while a thin one with wide gaps suggests uncertainty or low conviction. For researchers, this microstructure data exposes the distribution of belief within the market. The distance between the best bid and offer becomes a measure of informational clarity, and sudden shifts in depth or spread often precede news events or sentiment changes.

An illiquid event order book is not much different than an illiquid order book for a stock. Prices become less reliable, and even small trades can move the market disproportionately. Making a market in such a book, by standing as the only price taker on the ask side, could generate high alpha, albeit with more risk given you are essentially “becoming” the consensus. For these reasons, both Kalshi and Polymarket offer liquidity incentives via complex algorithms to facilitate efficient trade. In Kalshi’s case, these incentives are through fee rebates and market-maker programs, while for Polymarket it is through USDC-denominated rewards (as the platform lacks a native token).

A novel concept in the market is “holding rewards”, or an interest rate designed to stabilize long-term markets. Introduced by Polymarket in September 2025, this initiative allows traders to earn up to 4% annualized interest, paid daily on open positions in select high-volume event markets. The goal is to encourage liquidity and position stability in markets that often experience speculative churn or last-minute volatility. In traditional prediction markets, frequent position flipping can distort the implied probabilities, especially when liquidity thins as events approach settlement.

By rewarding users for holding through volatility, Polymarket hopes to smooth its price curves, preserve informational accuracy, and discourage last-minute manipulation before resolution. Note that Kalshi does not offer holding rewards. As a CFTC-registered exchange, it cannot pay or advertise a yield on open positions, or operate any incentive programs tied to holding duration. Instead, it maintains market stability through structural mechanisms and standardized market design.

III. Integrating Prediction Markets with On-Chain Fundamentals

Activity on Kalshi has climbed sharply over the past three months, as illustrated by the chart below. Open interest has surged from roughly $87.1M to $189.7M (30-day moving average), while daily trade volume has expanded even faster, from 107.3 thousand to 364.9 thousand. This steady rise in participation signals growing investor attention to event-driven trading and the maturation of prediction markets as legitimate data sources.

For researchers, integrating Kalshi’s event-level data with Artemis’ on-chain fundamentals opens new avenues for analysis. For monetary policy expectations, a rising probability of a Fed rate cut on Kalshi could precede increases in stablecoin issuance or DeFi borrowing, as lower-rate expectations ease risk aversion. For political and regulatory outcomes, shifts in election or legislation markets might lead movements in token classes exposed to U.S. regulation. For liquidity and sentiment, expanding prediction market open interest often coincides with higher gas fees and DEX throughput, reflecting a broader risk-on environment.

As it relates to “frontrunning” monetary policy, total stablecoin supply calculated by Artemis has nearly doubled to $300B from $160B at the beginning of the Fed’s cutting cycle, which began in September 2024. Supply actually declined between 2022 and 2023 as both the federal funds rate and inflation surged in the U.S.

By observing both datasets in tandem, analysts can trace how expectations propagate through capital flows. Over time, these linkages could help quantify how much of crypto’s volatility is driven by macroeconomic belief versus endogenous fundamentals.

Unlike social sentiment or survey data, Kalshi’s prices are self-verifying: traders who misjudge the future lose money, and prices converge toward the collective best guess. Coupled with on-chain measures of wallet activity, liquidity depth, and token velocity, this framework allows Artemis to model the feedback loop between belief and behavior.

Ultimately, prediction-market data provides a bridge between the informational world of expectations and the transactional world of blockchain activity. Kalshi captures what investors think will happen; on-chain data records what they do about it. Bringing these together transforms market analysis from a static snapshot into a dynamic system, one where sentiment, liquidity, and fundamentals continuously inform one another.

IV. The Future of Market-Implied Data

Taken together, Kalshi and other prediction markets illustrate a powerful shift in how information becomes measurable. By distilling expectations into a single probability, collective belief becomes quantitative, and more importantly, democratic. Traditional derivatives markets are institutional by nature, requiring brokerage accounts, margin, and familiarity with metrics like contango and backwardation. 

Many futures markets require six figures or more to even trade a single lot. Kalshi’s contracts are simple, fully collateralized, and inexpensive; any user can express a macro view without leverage or complex instruments. For many retail traders and researchers, this accessibility turns macro forecasting into something observable and testable, rather than abstract and inferred.

Kalshi lists event contracts for CPI releases, employment reports, and individual FOMC meetings, giving traders a precise way to express expectations. Each event includes multiple contracts covering a wide range of possible outcomes. For fed funds rates, arguably the most topical macro market in the U.S., Kalshi’s structure provides a clearer, more intuitive view of policy expectations than the implied probabilities derived from CME futures. 

Even the Bloomberg Terminal, long the gold standard for institutional market data, now references event-market prices for everything from FOMC probabilities to election forecasting. Prediction markets are becoming an integral layer of the information stack, transforming the abstract notion of expectation into something that can be tracked, modeled, and verified in real time. 

Looking ahead, this convergence between prediction markets and traditional finance will only deepen. As event-market data gains credibility, it’s likely to be incorporated directly into economic dashboards, forecasting models, and algorithmic trading systems. Just as volatility indices like the VIX became institutional benchmarks, event-based probability curves could evolve into standard reference rates for uncertainty.