From asset entry to strategy market, the TradFi competition of exchanges has entered the next stage

As stocks, ETFs, and commodities enter crypto trading platforms, the accessibility of TradFi assets is rapidly converging. How to discover, validate, and amplify high-quality strategies is becoming the next competitive direction.

Asset access is transitioning from a scarce capability to a fundamental capability

TradFi perpetuals are one of the fastest-growing exchange product lines this year. TokenInsight's second-quarter report shows that the monthly trading volume of this market grew from $52 billion in January to $268 billion in June, expanding more than five times within six months.

Product expansion is also accelerating. Since the beginning of this year, the speed of launching stocks, ETFs, and commodity perpetuals has significantly increased, and it is no longer uncommon to see multiple TradFi contracts launched at once. Meanwhile, the forms of TradFi products accessed by various platforms are also diversifying. Native perpetuals on exchanges typically follow existing derivatives account and margin systems, while some platforms provide CFDs through licensed entities or MT5-like terminals, and tokenized stocks offer exposure to traditional asset prices or rights through blockchain. Currently, some platforms are simultaneously laying out more than two of these forms.

Stocks, ETFs, and commodities are becoming universally expanded trading categories on crypto trading platforms, making it increasingly difficult to establish differentiation solely through asset access.

Beyond the underlying assets, exchanges are beginning to compete for strategy supply

Whether a market can form sustained trading depends not only on how much capital enters but also on how that capital continuously generates trading decisions. Listing new underlying assets solves the range of tradable options, while strategy supply further influences how capital participates in trading.

Copy trading has already demonstrated this mechanism. An active strategy provider can simultaneously influence a large number of follower accounts, and a single rebalancing can translate into a large volume of trades. The profit-sharing ratio of copy traders, the exposure position of strategy rankings, the API permissions of quantitative teams, and fee discounts are essentially all competing for stable strategy supply.

This also adds a layer to the matching objects on the platform. Traditional exchanges primarily connect assets with users, while the strategy market further connects verified trading strategies with capital willing to follow those strategies. The challenge lies in how to record long-term performance, present risks, and determine whether strategies remain effective after capital scales up. These capabilities cannot be obtained merely by increasing the number of contracts.

TradFi increases the weight of strategy selection

Once TradFi assets enter crypto accounts, users need to face more market variables simultaneously. Earnings reports may cause fluctuations in U.S. stock prices, macro data such as non-farm payrolls and CPI can significantly impact gold in a short time, and perpetual contracts also carry funding rates, margin usage, and liquidation risks.

When stocks, ETFs, gold, and digital assets simultaneously enter the trading range, users must judge not only what underlying assets to trade but also which strategies are suitable for the current environment. The performance of trend, macro, event-driven, and volatility strategies varies across different market cycles; the richer the asset selection, the higher the importance of strategy selection.

AI is entering two different levels of trading

After AI enters exchanges, two gradually overlapping capability layers can currently be observed.

The first layer is trading interaction and execution. Some platforms have already opened market data, accounts, and trading capabilities to AI agents through standardized interfaces and established independent trading sub-accounts for the agents. Users set trading goals and trigger conditions in natural language, and the system continuously monitors and executes the corresponding operations. Here, AI primarily changes the way users invoke trading functions.

The second layer begins to delve into the strategies themselves. Strategies are organized into products that can run continuously, be compared, and subscribed to; beyond execution, platforms must also handle performance records, risk presentations, and user selections for strategies. This year, some platforms have launched browsing, configuration, and subscription forms for structured strategies, extending from trading interaction to the strategy layer.

These two directions are converging. Natural language and agents lower the barriers to trading operations, while the strategy market addresses the question of which strategies to run. The latter presents another set of requirements for platforms, as strategy quality cannot be judged solely by model capabilities but also relies on long-term operational data, risk control, and stable strategy supply.

A sample that places the strategy market at its core

In terms of productizing strategies, OneBullEx provides a relatively complete sample. Its 300 Spartan robots adopt a subscription-based automated strategy market model, where strategy providers submit strategies that go live after approval. Users choose based on publicly available performance and risk data, and the system automatically executes according to preset rules, continuously accumulating actual operational records. Compared to traditional copy trading products, it further integrates strategy selection, performance display, and automatic execution into the same framework, placing the strategy market at the core of the platform's product sequence.

Recently, the platform has also integrated contract trading for stocks, ETFs, and gold, providing new space for the strategy market to cover more assets in the future. Rather than simply increasing the number of TradFi contracts, it is more worth observing whether these new markets can further translate into new strategy supply and accumulate long-term cross-market operational records.

According to information provided by OneBullEx, beyond user subscriptions, the platform will also consider the historical performance, risk indicators, and actual execution data of strategies for capital allocation, allowing the scale of capital that strategies can accommodate to be more closely linked to verifiable actual performance rather than solely depending on the strategy provider's own capital or number of subscribers.

The barriers of the strategy market come from long-term accumulation

This model has the potential to form network effects. More strategy supply can expand user choices, and more participating capital can enhance the commercialization space for strategy providers. As the operational time extends, platforms will also accumulate richer actual performance data, providing a basis for strategy selection and capital allocation.

Compared to asset listings, long-term strategy data and the network of strategy providers require a longer time to accumulate. Exchanges can launch a batch of stock perpetuals in a relatively short time but cannot obtain long-term strategy operational records at the same speed, nor can they quickly establish a stable strategy supply network.

However, for this network effect to truly take shape, the strategy market still needs to address several key issues. The first is strategy capacity. An increase in capital does not linearly amplify strategy returns; a set of rules effective at a smaller capital scale may face higher slippage and different execution quality as the managed scale expands. For high turnover or liquidity-limited strategies, the capital scale itself can become a variable affecting performance.

Historical performance also requires more comprehensive explanations. Compared to a single return curve, longer actual operational records, the market environments experienced, and performance during drawdowns all need to become important bases for judging strategies. Issues like backtest overfitting, sample interval selection, and survivor bias will not automatically disappear just because strategies enter exchanges.

As platforms further engage in capital allocation, the transparency of rules and risk information is equally important. Profit-sharing, capital allocation, and withdrawal conditions, as well as the drawdowns, volatility, and applicable market environments of strategies, need to be clearly presented. As operational time extends, data accumulation, strategy selection, and capital allocation capabilities will gradually widen the gap between platforms.

Conclusion

After TradFi enters crypto exchanges, asset access is increasingly approaching a fundamental capability. The next layer of differentiation is more likely to come from whether platforms can continuously discover high-quality strategies, establish credible operational records, and allow capital to allocate to these strategies according to clear rules. In the future, what may become more scarce is not just more trading underlyings, but those high-quality strategies that have been long-validated, whose risks can be explained, and that can continuously operate under real capital scales.