Why is AI trading accelerating its focus on the futures market?

The true advantage of automated trading comes from the market structure itself.

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. The goal is to gradually bring this trading product, which has been almost entirely dominated by offshore exchanges, back to the U.S. domestic market. This statement is a continuation of the U.S. market’s ongoing efforts over the past year. In July 2025, Coinbase launched CFTC-regulated perpetual-like futures products for U.S. retail users; in December 2025, Cboe introduced continuous futures products for Bitcoin and Ethereum; by March 2026, Coinbase further expanded its product line for non-U.S. users by launching stock perpetual futures. It can be seen that perpetual futures are gradually becoming the core infrastructure for executing derivatives trades, and the U.S. is accelerating its efforts in this area.

AI trading is often packaged as a smarter way to trade cryptocurrencies. However, when focusing on practical applications, it is actually more suited to the futures market. Futures contracts inherently possess standardized, margin-driven, daily mark-to-market features, 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-related operational issues such as custody, settlement, and borrowing mechanisms that differ greatly across platforms (if one wishes to short). Futures eliminate these burdens. The capital and strategies of automated trading are increasingly concentrated in the derivatives market, with perpetual contracts accounting for the vast majority of trading volume in crypto derivatives; this trend is not surprising.

Retail traders are accelerating their shift from following signals and copying trades to automated execution. Those who used to copy trades in Telegram groups are now subscribing to trading bots, and some have even started building systematic strategies themselves. The built-in margin mechanism and standardized contracts in the futures market make this transformation easier to realize.

What the futures market offers to machines, the spot market cannot

Spot trading means holding assets directly. Even in an exchange with clear matching rules and price/time priority, the algorithms have to deal with issues mixed in with custody, settlement, and greatly varying borrowing mechanisms (if one wants to short) due to platform differences.

Futures contracts extract these steps from the trading logic. Based on margin, daily mark-to-market, and the natural symmetry of 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 correspond directly to margin thresholds. The granularity of model adjustments in risk control and position management is finer, and the parameters are clearer.

For automated strategies, this difference directly changes the methods of risk management, position calculation, and execution. The regulatory framework views margin and daily mark-to-market as fundamental mechanisms of the futures market, specifically represented by standardized terms, centralized clearing, margins as performance guarantees, and daily settlement. These mechanisms give the futures market liquidity and scalability, while also making it easier to convert into a rules-based trading system.

Perpetual contracts do not have an expiration date. The funding rate (typically settled every eight hours) serves an anchoring function, pulling the price of perpetual contracts back toward 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 parties. This kind of signal is unavailable 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 for automated trading’s preference for derivatives.

Basis (the price difference between spot and futures) and funding rate (the cash flows periodically paid between long and short positions 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 inclined. Models can treat this deviation as feature input, risk control signals, or both.

Open interest provides a second layer of market intention information. When perpetual contracts dominate both the trading volume and open interest in Bitcoin futures, the embedded position information in the derivatives market is the densest across 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 is true on the execution level. The standardized contract specifications of the futures order book, clear matching rules, and granular order book data are inherently suitable for machine learning. Execution optimization and order book modeling are applications of machine learning that coexist with market structure in the derivatives market. When placed in a spot structure, they resemble an added auxiliary capability.

Why price discovery matters for automated trading

Another often underestimated advantage is that futures usually 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 further increases. In the cryptocurrency market, standard price discovery indicators point to futures as the leaders. Deviations between futures and spot can predict subsequent movements in the spot market, but the reverse does not hold true. Information tends to first be reflected in futures and then transmitted 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 exhibit disproportionately high information content, sometimes leading the spot market by several minutes. After transparency in the spot market improves, the information share gradually flows back to the spot, and market design and transparency determine where informed capital is concentrated. Futures trading venues, as centralized, rules-driven auction environments, possess machine-readable transparency, naturally attracting this type of capital. For systematic models, the mapping relationship from market state to trading actions is cleaner to learn in places where signals are concentrated.

Better for AI doesn’t mean safer for everyone

Futures compress time. Leverage amplifies both profits and losses. Margin serves as a performance guarantee; 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 (such as when the latest contract price exceeds the threshold from a reasonable benchmark price, stop-loss and take-profit orders will be rejected) directly impact the execution results of any robot operating in that venue.

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

What AI truly needs is structure; predictive ability is just one part of it. This structure means knowing how it will operate even when the market is in disorder.

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 automated 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 required for systematic strategies, then platforms 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 was born for this discipline.