Why AI trading is rapidly concentrating in the futures market

On March 3, Michael Selig, chairman of the U.S. Commodity Futures Trading Commission (CFTC), stated at the Milken Institute's "Future of Finance" conference that the CFTC will launch a regulatory framework for cryptocurrency perpetual contracts within weeks, aiming to gradually bring this trading product, which has been almost entirely dominated by offshore exchanges, back to the U.S. domestic market. This statement continues the trend of the U.S. market advancing relevant layouts over the past year. In July 2025, Coinbase launched CFTC-regulated perpetual futures products for U.S. retail users; in December 2025, Cboe listed continuous futures products for Bitcoin and Ethereum; by March 2026, Coinbase further expanded its product line for non-U.S. users, launching stock perpetual futures. It can be seen that perpetual futures are gradually becoming the core infrastructure for derivative trading execution, and the U.S. is accelerating its efforts to catch up in this area.

AI trading is often packaged as a smarter way to trade cryptocurrencies. However, when focusing on practical applications, it is actually better suited for the futures market. Futures contracts inherently possess standardized, margin-driven, daily mark-to-market, and a more symmetrical structure for both long and short positions, making systematic execution easier to implement than in the spot market. The logic of spot trading often gets entangled with a series of non-trading operational issues such as custody, settlement, and borrowing mechanisms that vary significantly between platforms (if you want to short). Futures eliminate these burdens. Automated trading capital and strategies are increasingly concentrated in the derivatives market, with perpetual contracts accounting for the vast majority of trading volume in crypto derivatives, a trend that is not surprising.

Retail investors are accelerating their shift from following trades and copying signals to automated execution. Those who used to copy calls in Telegram groups are now starting to subscribe to trading bots, and some have even begun to build systematic strategies themselves. The built-in margin mechanism and standardized contracts in the futures market make this transition easier to implement.

What the Futures Market Offers That the Spot Market Cannot

Spot trading means directly holding assets. Even in an exchange with clear matching rules and price-time priority, the algorithms have to deal with mixed issues of custody, settlement, and borrowing mechanisms that can vary greatly between platforms (if you want to short).

Futures contracts separate these processes from the trading logic. Based on margin, daily mark-to-market, and natural symmetry between long and short positions, the same strategy can express views in both directions. Position size becomes an adjustable parameter linked to margin, and risk limits directly correspond to margin thresholds. The model's adjustments in risk control and position management are more granular, and the parameters are clearer.

For automated strategies, this difference directly changes the way risk management, position calculation, and execution are handled. The regulatory framework views margin and daily mark-to-market as fundamental mechanisms of the futures market, manifested in standardized terms, centralized clearing, margin as performance guarantees, and daily settlement. These mechanisms provide the futures market with liquidity and scalability, making it easier to transform into a rules-based trading system.

Perpetual contracts have no expiration date. The funding rate (usually settled every eight hours) serves an anchoring function, pulling the perpetual contract price back near the spot price. The calculation of the rate is based on the recent price difference between futures and spot. For systematic strategies, the funding rate is an additional state variable. It reflects in real-time the position bias and leverage distribution of both long and short sides. This signal cannot be obtained in the spot market.

Signals Unique to the Derivatives Market

The data layer generated by the futures market is absent from the spot order book. This is the most underestimated reason why automated trading leans towards derivatives.

Basis (the price difference between spot and futures) and funding rates (the cash flows periodically paid by both sides in perpetual contracts) are important signals for assessing the degree of deviation and leverage direction in the derivatives market. They inform models how far derivatives deviate from the underlying asset and which direction leverage is tilted. Models can treat this deviation as feature input, risk control signals, or both.

Open interest provides a second layer of market intent information. When perpetual contracts dominate both the trading volume and open interest in Bitcoin futures, the embedded position information in derivatives is the densest in the entire market. Microstructure patterns, clearing cascades, and sentiment proxy indicators often first emerge in the futures market because participants express their judgments through leveraged funds in futures. For models, the densest signals are often the most valuable learning opportunities.

The same applies to the execution layer. The standardized contract specifications of the futures order book, clear matching rules, and granular order book data are naturally suitable for machine learning. Execution optimization and order book modeling are applications of machine learning that coexist with market structure in the derivatives market. In the spot framework, they resemble capabilities added later.

Why Price Discovery Matters for Automated Trading

Another often underestimated advantage is that futures typically dominate price discovery.

Research on the dynamics between spot and futures prices repeatedly shows that, under normal market conditions, futures contribute the majority of price discovery. When arbitrage signals appear, this proportion expands further. In the cryptocurrency market, standard price discovery indicators point to futures as the leader. Deviations between futures and spot can predict subsequent movements in the spot market, but the reverse does not hold. Information typically reflects first in futures before transmitting to spot, with a time lag in between.

The foreign exchange market provides a useful reference. During periods of lower transparency in the spot market, futures exhibited disproportionately high information content, sometimes leading spot by several minutes. After the transparency of the spot market improved, the information share gradually flowed back to spot, with market design and transparency determining where informed capital concentrates. Futures trading venues, as centralized, rules-driven bidding environments, possess machine-readable transparency, naturally attracting such capital. For systematic models, the mapping relationship between market states and trading actions is cleaner when learned in areas with concentrated signals.

Better for AI Does Not Mean Safer for Everyone

Futures compress time. Leverage amplifies both gains and losses. Margin serves as a performance guarantee, and when an account falls below the maintenance margin level, traders must add variation margin. In crypto perpetual contracts, the contract itself is a high-leverage tool, and the details of order protection (for example, when the latest contract price deviates from a reasonable benchmark price beyond a threshold, stop-loss and take-profit orders will be rejected) directly affect the execution results of any robots operating in that venue.

Several aspects are non-negotiable for automated systems. Assumptions about slippage must be conservative, operational monitoring must be continuous, and perceptions of the margin model must be clear. A position may be liquidated even if there are funds elsewhere on the platform, depending on whether isolated or cross-margin is used at the time. These risks do not disappear just because the executor is an algorithm. Systems designed around them can contain risks. Systems that ignore them will ultimately be bitten by amplified risks.

What AI truly needs is structure; predictive capability is just one part of it. The so-called structure is knowing how it will operate even when the market is disordered.

What This Means

The structural fit between automated strategies and the futures market is giving rise to a new class of native futures trading platforms. These platforms are built around derivatives infrastructure from the start, with automation capabilities embedded in the trading architecture.

OneBullEx is an example of this approach. Its 300 SPARTANS run directly on proprietary futures infrastructure, with net value and historical performance being traceable and auditable. OneALPHA transforms natural language inputs into deployable futures strategies, allowing non-coding users to enter systematic trading. If the market itself has already provided the standardization, signals, and risk architecture needed for systematic strategies, then the platform should be built around this structure from day one.
More important than any single platform is the overall trend. AI-native trading is most likely to mature first in the futures market because futures are inherently built for structured execution.

AI will continue to evolve, but the discipline it truly needs is not a new invention. The futures market is precisely born for this discipline.