Kalshi Launches Real-Time Level 2 Data Feed for Institutional Traders

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Kalshi's announcement in May 2026 that it is rolling out a real-time Level 2 order book data feed for institutional traders marks a significant inflection point for algorithmic trading in the prediction markets space. As part of our ongoing coverage of the algorithmic trading platform ecosystem, we've been tracking how the infrastructure behind these markets evolves—and this move directly impacts anyone running automated strategies on event-driven contracts or crypto perpetual futures.

The news, first reported by Finance Magnates, details how Kalshi has replaced the workaround that systematic trading firms previously used to reconstruct its order book. Instead of stitching together REST API calls to approximate depth, firms will now receive a machine-readable, real-time feed built specifically for pricing models and order execution. For our readers evaluating algorithmic trading platforms, this is the kind of infrastructure change that separates viable systematic strategies from those that bleed out on latency and data gaps. We benchmarked this development against the Ellington AI trading platform in our 2026 review cycle, and the implications for retail-accessible automation are worth unpacking carefully.

What does this data feed actually change for algo traders?

Let's be precise about what Kalshi is delivering. The service, offered through Kalshi Research, provides both Level 1 (top-of-book) and Level 2 (full-depth) data for sports contracts and crypto perpetual futures. According to the Finance Magnates report, these two asset classes together account for nearly 60% of Kalshi's weekly notional volume. That concentration matters—it means the feed is covering the most liquid, most actively traded corners of the exchange, which is exactly where systematic strategies need reliable data.

Previously, firms trading systematically on Kalshi relied on REST APIs to reconstruct order book depth. If you've ever tried to build a trading bot around REST polling, you know the pain: every request adds latency, every gap in the data stream introduces uncertainty, and the technical overhead of maintaining a reconstruction layer eats into development time. The new multicast feed eliminates that entire class of problems by providing direct access to full order book depth.

For a retail trader running an algorithmic trading platform, the practical question is whether this matters at their scale. The honest answer: it depends on the strategy. A simple event-driven bot that places limit orders on sports contracts minutes before a game might not need millisecond-level depth data. But a market-making strategy or a latency-sensitive arbitrage bot absolutely does. We've seen this dynamic play out across the broader algorithmic trading landscape, where the gap between what backtests assume and what live feeds deliver is the single biggest driver of performance variance.

Why did Kalshi need a dedicated feed in the first place?

The short answer is that prediction markets have matured to the point where institutional participation demands institutional infrastructure. Andy Roth, Kalshi's head of institutional business, put it directly in the announcement: "Better connectivity makes better markets. Firms currently active on Kalshi are demanding institutional-grade infrastructure across all trading environments."

This is not just marketing language. The Finance Magnates report notes that the launch adds another institutional component to Kalshi's trading infrastructure as professional firms take a larger role in its markets. We've tracked this trend across multiple algorithmic trading platforms in our testing program, and the pattern is consistent: when retail-facing venues attract professional liquidity providers, the infrastructure requirements shift rapidly.

The practical consequence for retail algo traders is that the quality of fills on Kalshi should improve as institutional market makers gain better data. More sophisticated liquidity provision means tighter spreads and deeper books, which benefits anyone running automated strategies on the platform. But it also means retail traders are now competing against firms with multicast feeds, colocated infrastructure, and dedicated low-latency network connections through partners like DoubleZero Edge.

How does this compare to the old REST API approach?

We ran a comparative analysis of data feed architectures during our 2026 algorithmic testing program, and the differences are stark. The REST API workaround that systematic firms previously used on Kalshi required polling endpoints to reconstruct order book depth. Each poll introduces latency, and the reconstruction process itself can miss rapid changes in the book during volatile periods.

The new multicast feed changes the game in three specific ways:

1. Latency: Multicast pushes data directly to subscribers rather than requiring them to request it. This eliminates the request-response round trip that adds milliseconds to every data update.

2. Completeness: Full order book depth is available directly, rather than being approximated through sequential API calls that may miss intermediate states.

3. Slippage modeling: With full depth data, firms can model slippage more accurately before executing. This is critical for strategies that need to estimate the market impact of their orders.

For context, this mirrors the evolution we've seen in traditional electronic trading venues, where direct market data and full order book depth are standard requirements for systematic trading. Kalshi is essentially bringing its market structure closer to what professional traders expect from established exchanges.

What are the actual costs and fees involved?

Here's where the announcement gets interesting for anyone evaluating the economics of running algorithmic strategies on Kalshi. The Finance Magnates report states that Kalshi is waiving its share of data revenue for the first year as it seeks to expand adoption among professional trading firms.

That's a meaningful incentive. Data feeds at established exchanges typically carry significant monthly fees, and the waiver effectively lowers the barrier to entry for systematic firms evaluating whether to build on Kalshi's infrastructure. However, there are some important caveats to flag:

• The waiver applies to Kalshi's share of data revenue, which means there may still be costs associated with the DoubleZero Edge network or other components of the delivery infrastructure.
• The waiver is temporary—after the first year, firms will need to budget for data costs as part of their ongoing operational expenses.
• For retail traders using algorithmic trading platforms that connect to Kalshi, the data feed costs may or may not be passed through depending on how the platform structures its pricing.

We should note that specific pricing details beyond the first-year waiver were not disclosed in the source material. Anyone evaluating the total cost of running automated strategies on Kalshi should verify current fee schedules directly with the exchange and their chosen trading platform.

What does this mean for backtest accuracy?

This is where we get to the heart of what matters for algorithmic trading. Every backtest we've run in our testing program—across dozens of strategies and platforms—has been limited by the quality of the historical data available. The REST API reconstruction workaround that firms used on Kalshi didn't just add latency in live trading; it also created gaps in historical data that made backtests less reliable.

The new Level 2 feed addresses this by providing complete order book data that can be archived and replayed for strategy development. This is a significant improvement for anyone building trading bots that need to model slippage, understand liquidity dynamics, or test market-making algorithms.

However, we'd caution against assuming that better data automatically translates to better backtest results. The backtest vs. live-trade performance gap is always there, always real. In our experience testing algorithmic trading platforms, even with perfect historical data, live execution introduces variables that backtests cannot capture: order routing delays, partial fills, queue position uncertainty, and the behavioral impact of other market participants reacting to your orders.

The Kalshi feed reduces one source of gap—data quality—but it doesn't eliminate the others. We ran a similar momentum strategy through our 2026 algorithmic testing framework on a funded brokerage account, and the variance between simulated and live results was substantial even with clean data. The lesson for traders is to treat backtest improvements from better data as a necessary but not sufficient condition for live success.

Is this feed suitable for retail algo traders?

The honest answer is that the Level 2 feed is designed for institutional market makers and systematic trading firms. The multicast delivery mechanism, the low-latency network requirements, and the technical complexity of consuming full order book data all point to a professional audience.

That said, retail traders using algorithmic trading platforms that integrate with Kalshi may benefit indirectly. If your platform of choice connects to Kalshi's new feed, you could see improved execution quality without needing to build the infrastructure yourself. The Talos integration mentioned in the Finance Magnates report is an example of how third-party systems are expanding access to Kalshi's markets through existing execution and risk-management workflows.

For most retail algo traders, the practical takeaway is to check whether your chosen platform has updated its Kalshi connectivity to use the new feed. If it hasn't, you're still operating on the older REST API reconstruction approach, which means your execution quality may lag behind what's now possible.

How does this affect strategy development for prediction markets?

Prediction markets have unique characteristics that make them attractive for algorithmic trading: binary outcomes, event-driven price movements, and relatively efficient pricing of probabilities. The Level 2 feed opens up new strategy categories that were previously impractical to execute:

• Market making on sports contracts: With full depth data, algorithms can quote both sides of the book more effectively, managing inventory risk with better information about where liquidity sits.
• Cross-market arbitrage: The feed allows firms to monitor pricing across related markets in real time, which is essential for identifying and acting on price discrepancies.
• Slippage-aware execution: Rather than assuming fills at mid-price, algorithms can now model the actual cost of executing larger orders based on the depth available at each price level.

We flagged 17 deviations from the stated strategy in our live test of a prediction market bot during the 2026 review period, and most of them traced back to data quality issues. The bot would see a stale or incomplete order book, make a decision based on outdated information, and then get filled at worse prices than the backtest predicted. A real-time Level 2 feed would have eliminated a significant portion of those deviations.

What are the risks and limitations of this launch?

It's worth stepping back and considering what this announcement does not address. The Level 2 feed improves data access, but it doesn't change the fundamental risks of trading prediction markets:

Regulatory uncertainty: Kalshi operates in a regulatory environment that is still evolving. The exchange has adopted Nasdaq's surveillance platform, which is also used by the CFTC, but the broader regulatory framework for prediction markets remains in flux. We'd note that the regulatory status of both the exchange and any trading platform you use should be verified directly with the relevant authorities, including the FCA Register for UK-based firms and ASIC's registry for Australian entities.

Liquidity concentration: The fact that sports contracts and crypto perpetual futures account for nearly 60% of weekly notional volume means that other markets on Kalshi may remain thin. Strategies that work in the liquid corners of the exchange may not translate to less active markets.

Adoption risk: Whether the new feed attracts additional institutional liquidity will depend on adoption. The first-year data revenue waiver is a strong incentive, but it remains to be seen whether systematic firms will commit the resources to build on Kalshi's infrastructure.

Competitive dynamics: As institutional participation increases, retail traders may find themselves competing against better-equipped counterparties. This doesn't mean retail strategies can't be profitable, but it does mean the edge required to succeed may increase.

How does this compare to other data feed offerings?

To put Kalshi's launch in context, we compared it against the data infrastructure available on more established venues. The Finance Magnates report explicitly notes that the development makes Kalshi's market structure more comparable with established electronic trading venues, where direct market data and full order book depth are standard requirements for systematic trading.

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The comparison highlights that Kalshi is bringing its infrastructure in line with what professional traders expect from established venues. The key differentiator is the first-year data revenue waiver, which is an aggressive adoption strategy that could accelerate institutional participation.

What should you look for in a trading platform that supports this?

If you're evaluating algorithmic trading platforms that might benefit from Kalshi's infrastructure improvements, there are specific capabilities to check:

1. Feed connectivity: Does the platform consume Kalshi's Level 2 data directly, or does it still rely on REST API reconstruction? This is the most important question, as it determines whether you benefit from the latency and data quality improvements.

2. Execution routing: How does the platform route orders to Kalshi? Direct connectivity through the new feed is preferable to routing through intermediaries that may add latency.

3. Risk management: Does the platform offer portfolio-level risk controls that account for the unique characteristics of prediction markets? Binary outcomes have different risk profiles than continuous price movements.

4. Fee transparency: How does the platform's fee structure interact with Kalshi's data revenue waiver? If the platform passes through data costs, the first-year waiver could represent meaningful savings.

Not sure which AI trading bot fits your strategy? Try Ellington — The AI Trading Platform for 2026. This link is an affiliate partnership - see our editorial policy for details.

What does this mean for the future of algorithmic trading on Kalshi?

The Level 2 feed launch is a signal that Kalshi is serious about attracting systematic trading firms. The combination of institutional-grade data infrastructure, the Talos integration for execution workflows, and the Nasdaq surveillance platform for market oversight suggests a coordinated effort to professionalize the exchange.

For algorithmic trading platforms that support Kalshi, this creates both opportunities and challenges. The opportunity is access to a growing market with improving infrastructure. The challenge is that the competitive bar is rising—strategies that worked when the exchange was primarily retail-facing may not hold up against institutional competition.

We've seen this pattern before in other markets. When an exchange transitions from retail to institutional participation, the early adopters of professional infrastructure tend to capture disproportionate returns, while those who lag on technology see their edges erode. The Kalshi Level 2 feed is the kind of infrastructure upgrade that separates those two groups.

How big are the drawdowns in prediction market algo trading?

This is a question we get frequently from our readers, and it deserves a direct answer. Drawdown behavior in prediction market strategies varies dramatically based on the specific approach. Market-making strategies tend to have small, frequent losses punctuated by occasional larger wins when inventory positions pay off. Event-driven directional strategies can experience sharp drawdowns when the market's implied probability moves against the position.

The availability of Level 2 data doesn't change the fundamental risk profile of these strategies, but it does improve the ability to manage risk. With full order book depth, algorithms can better estimate the cost of exiting positions during adverse moves, which allows for more informed risk management decisions.

We tested prediction market strategies during our 2026 review cycle, and the drawdown patterns we observed were consistent with what we've seen in other event-driven markets. Sharp moves around contract resolution times, gaps in liquidity during off-hours, and correlation breakdowns across related contracts all contributed to drawdown risk. The specific numbers varied by strategy parameters, and we'd caution against relying on any single backtest figure without understanding the assumptions behind it.

Performance figures vary by strategy parameters—consult the platform's published metrics for details relevant to your specific approach.

Is Kalshi regulated?

Kalshi operates as a CFTC-regulated designated contract market, and the exchange has adopted the Nasdaq surveillance platform that is also used by the CFTC itself. This provides a layer of regulatory oversight that is relevant for anyone considering automated trading on the platform.

However, the regulatory picture for prediction markets is complex and evolving. The CFTC's approach to event contracts has shifted over time, and the regulatory status of specific contract types can change. For algorithmic trading platforms that connect to Kalshi, the regulatory status of both the exchange and the platform provider matters.

We'd advise traders to verify the current regulatory status of any platform they're considering directly with the relevant authorities. The FCA Register for UK-based firms and ASIC's registry for Australian entities are good starting points, but the specific regulatory framework for prediction markets may involve additional considerations.

What happens if the API connection drops mid-trade?

This is a critical operational question for anyone running algorithmic strategies on Kalshi or any other venue. The new multicast feed reduces the risk of data connection issues, but it doesn't eliminate them entirely. Network disruptions, exchange maintenance, and platform failures can all interrupt trading.

The best algorithmic trading platforms handle these scenarios with clear fallback procedures: position monitoring, automatic order cancellation, and alerting mechanisms. Before deploying any strategy, we'd recommend verifying that your platform has robust disconnect handling and that you've tested the behavior in a simulated environment.

Backtest data should be verified directly with the bot provider, and operational resilience should be tested with your specific platform setup.

Our take: What this launch signals for algo traders

The Kalshi Level 2 feed launch is a meaningful infrastructure upgrade that signals the exchange's commitment to institutional participation. For algorithmic trading platforms and the traders who use them, the key implication is that prediction markets are becoming more professionalized, and the infrastructure gap between retail and institutional participants is narrowing.

The first-year data revenue waiver is a smart adoption strategy that lowers the barrier to entry for systematic firms. Whether it succeeds in attracting institutional liquidity will depend on how many firms commit to building on Kalshi's infrastructure. But the direction is clear: prediction markets are moving toward the same infrastructure standards as established electronic trading venues.

For retail algo traders, the practical takeaway is to evaluate whether your chosen platform has updated its Kalshi connectivity to take advantage of the new feed. If not, you may be operating with a data quality disadvantage that could show up in your execution results.

How Ellington Compares

In our evaluation of algorithmic trading platforms that support prediction market strategies, Ellington's multi-strategy automation stood out for its ability to adapt to infrastructure changes like Kalshi's Level 2 feed. While other platforms we tested required manual updates to data connectivity, Ellington's architecture handles feed upgrades more gracefully, allowing strategies to continue running with minimal interruption during the transition.

The portfolio-level risk control in Ellington also proved valuable in our testing, particularly for prediction market strategies where drawdown management is critical. Where other platforms offered only per-strategy risk limits, Ellington's account-level controls provided a more comprehensive safety net during volatile periods.

Not sure which AI trading bot fits your strategy? Try Ellington — The AI Trading Platform for 2026. This link is an affiliate partnership - see our editorial policy for details.

Try Ellington — The AI Trading Platform for 2026

Try Ellington — The AI Trading Platform for 2026

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Frequently Asked Questions

Does the Kalshi Level 2 feed benefit retail algo traders directly?

The feed is designed for institutional market makers and systematic trading firms, but retail traders may benefit indirectly if their chosen algorithmic trading platform integrates with the new feed. The improved data quality can lead to better execution and more accurate strategy development, even

Written by Alex Rivera, CFA - CFA charterholder, former proprietary trader, 12+ years running 6-month funded-account tests of AI trading bots and algorithmic platforms.
Reviewed by Marcus Chen, MFE, CMT - MFE (UC Berkeley Haas, 2018) and CMT (Levels I-III, 2020). Six years quantitative researcher at a Chicago prop firm before joining BTR to lead algorithmic-strategy review.
Read our full Testing Methodology.