Should on-chain DEX traders be worried about front-running?

Original Author: sysls

Original Compilation: AididiaoJP, Foresight News

Introduction

I have recently been pondering how to execute large-scale portfolios on decentralized exchanges like Hyperliquid.

Theoretically, if:

You can achieve excess returns.
Your positions and orders are public and transparent, just like on a decentralized exchange like Hyperliquid.

Then:

You should expect that a class of traders will attempt to front-run you and grab your excess returns.
They will do this by executing the positions you want before you do.

The end result is that, due to being front-run, the execution cost you incur (slippage) will be higher.

Imagine you want to buy $1 million worth of Bitcoin at $100,000. Just then, someone listed a $1 million sell order at $100,000. A front-runner sees your intention, jumps in before you, fills that sell order, and then sells that $1 million Bitcoin to you for $100,100. The additional $100 is slippage that could have been avoided had your intentions been hidden.

Two Extremes of Front-Running

Theoretically, if this situation is pushed to its "inevitable conclusion," then almost any form of "serious trading" on decentralized exchanges would be suppressed.

However, we know that this is not the case. Many highly professional players engage in professional trading on Hyperliquid with excess returns. Thus, it is clear that the conclusion "players with excess returns should not trade on decentralized exchanges" is not so absolute.

Can we derive an intuitive boundary for the limits of being front-run based on fundamental principles and existing evidence?

Clearly, if your size is very small and you are trading on a highly opaque exchange like Binance, your likelihood of being front-run is almost zero. A small size means your trading footprint (volume) is negligible relative to the market, making you almost invisible; and even if your actions are entirely predictable, no one can tie your specific trading activities (orders placed and executed) back to you.

On the other hand, on Hyperliquid, the most typical example of a large and highly transparent wallet is the HLP treasury itself—the publicly listed market-making treasury that provides liquidity to other traders on Hyperliquid. I am quite sure that there are strategies specifically designed to front-run HLP, and this ongoing pressure has effectively compressed market-making alpha to nearly zero.

HLP represents quite an extreme case. First, it possesses both "extremely large size" and "extremely high transparency." It is characterized as "extremely large" because its trading footprint in those illiquid long-tail assets is enormous (for instance, its trading volume represents a large proportion of the daily average volume).

Furthermore, it is "extremely high transparency," because it primarily acts as a market maker, attempting to fulfill the explicit goal of unwinding existing inventory by providing liquidity at a premium. This means that whenever a "large" position appears on HLP, you know it will inevitably need to be unwound. Worse yet, you can see every position and order from HLP. This allows you to adjust your portfolio to sell to HLP more cheaply whenever you see it needs to buy to close a short position, and vice versa.

All these characteristics make HLP particularly attractive for front-running, much like exchange-traded funds are front-run due to their strict adherence to index rebalancing. In hedge fund circles, if you use the term "front-running," compliance departments will definitely flag you across various dimensions; the insider jargon suggests that index rebalancing teams are very adept at providing a service that "captures premiums by anticipating liquidity needs."

How Does Front-Running Occur?

In the classical sense of front-running, one market participant knows in advance what another market participant intends to do, and then takes a series of actions to profit from this information.

For example (illegal): If I am an insurance agent and I know that my very wealthy client plans to buy $1 billion of a poorly liquid stock throughout the trading session today, then at the market open, I would place a market buy order for $1 million and simultaneously place a market sell order for the same number of shares at the close.

By knowing my client's intent and action, I am able to execute before them, allowing their buying activity to drive up the stock price, and then I profit from the price difference. This is highly illegal because I:

Acted on insider information,
Violated my fiduciary duty,
Profited at the expense of my client's interests.

However, this is a good example because it clearly shows that my ability to profit is solely due to my knowledge of another market participant's intentions and actions and my ability to estimate the outcomes of these actions, thus positioning myself favorably.

Every day, front-running occurs on smaller scales and with less illegality. Trading algorithms can approximate intentions without having to be informed, utilizing publicly available information that everyone can access (orders, executions, positions). They then estimate the market behaviors resulting from these approximated intentions and decide whether to act based on the expected value of "front-running."

From this, we can deduce that the transparency and leakage of your "intent" are the primary determining factors for whether you will be easily front-run.

Gradient Distribution of Front-Running

Now that we know, if you are small and trading on an opaque platform, you need not worry about being front-run, as no one can judge your intentions. Similarly, if you are large, trading on a transparent platform, and your intentions are very clear (like HLP), then you are destined to be front-run with no way to resist.

However, these extreme cases are of little reference value for the vast majority of traders. What we are more interested in are those "grey areas." As mentioned above, what ultimately determines your tendency to be front-run is how transparent your intentions are.

Even if you are large and trade on an opaque exchange, it is not easy for others to front-run you. Your orders are part of the daily average volume, represented as a "large order footprint," but attributing all orders to a "single entity" is not straightforward unless your trading methods are exceedingly transparent—for instance, if you have no randomization in your operations, trade with fixed lot sizes or fixed nominal amounts in split orders, or send split orders in a very deterministic pattern (like every 30 seconds).

If you can hide your intentions—for example, by randomizing your trade sizes, randomizing the timing of your split orders, and avoiding placing orders that are too large relative to the daily average volume or order book orders—then it becomes difficult for others to attribute your orders to one person. The market may perceive a significant amount of buying interest overall, but may not be able to assign this buying interest to a party holding alpha returns, and thus may not price liquidity accordingly.

Fortunately, we can actually generalize this point to transparent exchanges. Although there are many treasuries on Hyperliquid and Lighter and their operations are relatively transparent, front-running these treasuries is actually not straightforward.

The conclusion is: Unless your size is quite large (like a treasury managing hundreds of millions in assets), you probably don't need to worry about being front-run.

Limitations of Front-Running

Attempting to gain alpha returns through front-running without breaking the law is, in itself, a practice of an alpha strategy. You are modeling intentions based on publicly available information (orders, executions, positions), which inherently carries model risk.

While orders, executions, and positions may be visible, intentions are not. An order sitting there may represent alpha returns, inventory management, or hedging. Models that assume that every order has alpha returns will be gradually depleted by countless misjudgments.

Furthermore, even if we assume you can extract intentions relatively accurately. Even so, alpha returns are not "omnipotent." All alpha returns carry a degree of statistical noise, and your portfolio is not only exposed to the statistical noise of alpha returns but also bears additional model risk stemming from misunderstanding certain behaviors as alpha returns.

You might say, if I blindly 1:1 copy the target's actions, then I can surely capture all the alpha returns—but the problem is, this will actually expose you to the risk of being exploited. If you always place the same buy order when the target acts, then when the target wants to sell, they can first place a limit buy order, watch you place the same buy order, then immediately cancel it and sell back to you. So you see, thoughtlessly front-running can also create vulnerabilities.

It should also be remembered and recognized that alpha returns have a time span. Some alpha returns may be fleeting, and attackers may not be able to exploit them themselves (for example, the alpha from high-frequency trading of eating orders); while others may last a very long time, and attackers may choose to forgo because they are unwilling to take on such long-term risks (for example, rebalancing trades over several days or weeks).

Ultimately, even if you have a highly sophisticated front-runner watching your back, the actual impact it manifests is merely a few basis points. If you truly possess sustained alpha returns, many strategies can easily absorb those additional costs of a few basis points.

How Not to Become an Easy Target

Even knowing that it is not that simple, as a smart market participant who can generate alpha returns, your task remains to hide your intentions and make it as difficult as possible for attackers to front-run you.

You can do many things, with varying complexity and effectiveness. The first thing you should do is to persistently collect telemetry data and logs, so you can quantify the specific "degree" to which you have been front-run (if it indeed exists). You can achieve this by analyzing large sample orders and executed prices, slippages, and impact costs.

Then, once you have the data, you can take a series of defensive measures. A common thread among these measures is: you should make whether "you want to buy or sell," "how much you actually want to buy or sell," "how urgently you want to buy or sell," and "whether you are trading an alpha position or a hedging position" less obvious.

Some simple methods to obscure your intentions include: placing simultaneous bids on both sides, using random sizes, and operating at non-deterministic time intervals.

One (high-level, complex) method to effectively obscure your positions is to split your portfolio across multiple wallets, each maintaining basic long and short neutrality internally while being "efficient" in margin. Inside each wallet, you hold positions that generate alpha returns as well as hedging positions. Some wallets hold 80% alpha positions and 20% hedging positions; others might have 80% hedging positions and 20% alpha positions. Over time, you rotate each wallet's "type" and randomly introduce new wallets while phasing out old ones.

This means that if an attacker is only tracking one wallet, they might end up tracking a wallet primarily set up for hedging, thereby getting caught in those loss positions designed for hedging purposes. If they track all wallets, you can further obscure your true intentions through a series of contradictory operations. What this具体 will look like is left to the reader's imagination!

Finally, there are existing (external) solutions on the market that address this problem. I personally have not used them, but theoretically, they tackle privacy issues in one of the following two ways:

Pooling orders together for internal matching first, then executing the remaining portion on decentralized exchanges, and finally attributing the positions back to you—this is not much different from the practice in hedge funds of central liquidity books aggregating orders from various strategy teams and then redistributing positions back.

Splitting your orders with those of other users in the scheme into multiple wallets, executing them on decentralized exchanges, and then attributing the positions back to you.

Conclusion

If you are a retail trader with a small trading scale, even trading on transparent decentralized exchanges, you probably have little to worry about. Front-running has its own limitations, making it difficult for others to truly profit at your expense.

That said, as your trading size gradually increases and the quality of alpha returns improves, this will naturally incentivize front-runners to keep an eye on you. By that time, you should invest more resources to obscure your intentions, making their lives as difficult as possible.

This problem is by no means "resolved," and for any institution or trader conducting large-scale trading in open, decentralized, and transparent liquidity venues, it will remain a continuous "cat and mouse game."

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