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 a few weeks, aiming to gradually bring this trading product, which has long been 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 to advance related developments. In July 2025, Coinbase launched CFTC-regulated quasi-perpetual futures products for U.S. retail users; in December 2025, Cboe launched continuous futures products for Bitcoin and Ethereum; by March 2026, Coinbase further expanded its product line to non-U.S. users, introducing 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 in this area.

AI trading is often marketed as a smarter way to trade cryptocurrencies. However, when focusing on practical applications, it is actually more suited to the futures market. Futures contracts naturally possess characteristics such as standardization, margin-driven incentives, daily mark-to-market, and a more symmetric structure for both long and short positions, making systematic execution easier to implement than in the spot market. The logic of spot trading often becomes entangled with custody, settlement, and a series of operational issues unrelated to the trade itself, which futures eliminate. 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 rapidly transitioning from following signals and copying trades to automated execution. Those who used to copy trading calls in Telegram groups are now beginning to subscribe to trading bots, and some even start to build their own systematic strategies. The margin mechanism and standardization of contracts built into the futures market make this transition easier to implement.

What the futures market provides machines that the spot market cannot

Spot trading means holding assets directly. Even in an exchange with clear matching rules, prioritizing price and time, the algorithms still have to deal with custody, settlement, and significant differences in borrowing mechanisms due to different platforms (if you want to short).

Futures contracts separate these elements from the trading logic. Based on margin, daily mark-to-market, and natural symmetry in long and short positions, the same strategy can express opinions in both directions. Position size becomes an adjustable parameter linked to margin, and risk limits correspond directly to margin thresholds. The model can finely adjust in risk control and position management, with clearer parameters.

For automated strategies, this difference directly changes the ways of risk management, position calculation, and execution. The regulatory framework views margin and daily mark-to-market as the foundational mechanisms of the futures market, manifested in standardized terms, centralized clearing, margin as performance guarantees, and daily settlement. These mechanisms provide liquidity and scalability to the futures market, while also making it easier to be transformed into rule-based trading systems.

Perpetual contracts have no expiration date. The funding rate (usually settled every eight hours) serves anchoring functions, pulling perpetual contract prices back toward spot levels. The calculation of the rate is based on the recent price differences between futures and spot. For systematic strategies, the funding rate is an additional state variable that reflects the skew of long and short positions and leverage distribution in real time. This signal is unavailable in the spot market.

Signals only available in the derivatives market

The data layer generated by the futures market is not present in the spot order book. This is the most underrated reason for automated trading's bias towards derivatives.

The basis (the price difference between spot and futures) and the funding rate (the cash flow periodically paid by both sides in perpetual contracts) are important signals for judging the degree of divergence in the derivatives market and the direction of leverage. They inform the model how far the derivatives are from the underlying asset and which direction leverage is tilted. The model 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 account for the majority of trading volume and open interest in Bitcoin futures, the embedded position information in derivatives represents the highest density in the entire market. Microstructural patterns, cascading clearing, sentiment proxy indicators often emerge first in the futures market because participants express judgments through leveraged funds in futures. For the model, the area with the densest signals is often the most valuable area to learn from.

The same applies to the execution layer. The standardization of contract specifications in futures order books, clear matching rules, and the finely-grained order book data is naturally suitable for machine learning. Execution optimization and order book modeling are application scenarios for machine learning that are symbiotically integrated with market structure in the derivatives market. In the context of the spot structure, they seem more like supplementary capabilities added later.

Why price discovery matters for automated trading

Another often underrated advantage is that futures usually dominate price discovery.

Research on the dynamics of 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 future trends in the spot market, while the reverse does not hold. Information often first reflects in futures before transmitting to spot, with a time lag in between.

The foreign exchange market offers a useful reference. During times of lower transparency in the spot market, futures displayed disproportionate information content, sometimes leading spot by several minutes. After transparency improved in the spot market, the information share gradually flowed back to spot, with market design and transparency determining where informed capital concentrates. Futures trading venues, as centralized, rule-driven bidding environments, possess machine-readable transparency, naturally attracting such capital. For systematic models, learning the mapping from market states to trading actions is cleaner in areas where signals are concentrated.

Being better for AI does not equate to being safer for everyone

Futures compress time. Leverage simultaneously amplifies both gains and losses. Margin serves as a performance guarantee; when the account falls below the maintenance margin level, traders must add variation margin. In cryptocurrency perpetual contracts, the contract itself is a high-leverage tool, and the details of order protection (such as when the price difference between the latest contract price and the reasonable benchmark price exceeds a threshold, orders triggered by stop-loss or take-profit will be rejected) directly affect the execution results of any bots operating in that venue.

Several things are uncompromising for automated systems. Assumptions about slippage must be conservative, monitoring must be continuous, and perceptions of margin models must be clear. A position may be force liquidated, even if there are still funds in other places on the platform, depending on whether isolated margin or cross margin is used. 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 harmed by amplified risks.

What AI really needs is structure; predictive capability is just one part of it. The so-called structure means 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 breed of native futures trading platforms. These platforms are designed from the outset around derivatives infrastructure, embedding automated capabilities within the trading architecture.

OneBullEx is an example of this approach. Its 300 SPARTANS run directly on its proprietary futures infrastructure, with net values and historical performances traceable and auditable. OneALPHA transforms natural language input into deployable futures strategies, allowing non-coding users to enter systematic trading. If the market itself provides the standardization, signals, and risk framework 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 naturally 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.