How to achieve 220x returns with a market-making bot on Hyperliquid?

Trading is not just about "predicting prices." Sometimes, the most profitable strategy is to thoroughly understand the rules of market structure and build a system that creates value in "corners others overlook."

Written by: The Smart Ape

Translated by: Saoirse, Foresight News

This is an excellent case that illustrates the importance of "learning programming"—with programming skills, you can, in just two weeks, grow $6,800 into $1.5 million on the Hyperliquid cryptocurrency exchange platform.

Not long ago, a Hyperliquid trader achieved exactly that.

Even more astonishing, this trader took on almost no risk. He did not bet on market direction or chase popular assets. Instead, he relied solely on a sophisticated market-making strategy—centered around "market maker rebates"—combined with automated operations and strict risk control.

Market-Making Mechanism on Hyperliquid

Before analyzing the strategy in depth, we need to understand Hyperliquid's market-making logic. Hyperliquid is an order book-based exchange where users can place two types of orders:

• Buy order: a "buy order" (for example, "I want to buy SOL tokens at $100")
• Sell order: a "sell order" (for example, "I want to sell SOL tokens at $101")

These pending orders together form the "order book." Traders who place buy or sell orders are called "makers."

• The core function of makers is to "provide liquidity": by placing limit orders in advance, they add tradable volume to the market.
• In contrast are "takers": these traders directly execute against existing orders in the order book (for example, "market buying" a token at the current best ask price).

Makers are crucial to the market: it is their provision of liquidity that keeps bid-ask spreads low; without makers, traders might face "unreasonable pricing" and "high slippage losses."

Core Key: Market Maker Rebates

The core of an exchange is "liquidity"—to encourage users to become makers and add liquidity, Hyperliquid provides "trade rebates" to makers: whenever a maker's order is filled, the platform returns a small rebate.

On Hyperliquid, the rebate per trade is about 0.0030%—that is, for every $1,000 traded, you receive a $0.03 rebate.

It was this seemingly tiny rebate that enabled the trader to leap from $6,800 to $1.5 million. The core of his strategy was "one-sided quoting": only placing limit orders on one side of the order book (either only buy or only sell); once the market price moved, he would quickly cancel the original order or switch to quoting on the other side.

Simply put, his operational logic was: provide liquidity on only one side to earn rebates, while using a bot to adjust order direction in real time, thus avoiding exposure and risk from holding positions. Ultimately, by leveraging "automated high-frequency trading" to generate massive trading volume, the small rebates from each trade accumulated into huge profits.

Core Pain Points for Traditional Market Makers

Most market makers place orders on both the "buy side" and "sell side" of the order book simultaneously.

For example: you place two orders at the same time—a buy order for 1 SOL at $100, and a sell order for 1 SOL at $101.

If both orders are filled, you earn a $1 spread profit by "buying low and selling high."

But this model has a key problem: position risk.

• If the buy order is filled and the sell order is not: you end up passively holding SOL tokens;
• If the sell order is filled and the buy order is not: you end up passively holding stablecoins (such as USDT).

If the market price moves against you, these passively held assets could face significant losses.

This is why the Hyperliquid trader chose "one-sided quoting": by placing orders on only one side, he could strictly control his positions and avoid passively holding unnecessary assets. However, this approach comes at the cost of a higher "being arbitraged" risk.

What Does "Being Arbitraged" Mean?

Here's a specific scenario: you place a buy order for SOL at $100 on the order book. Suddenly, negative news causes SOL's price to plummet to $90.

• Your "$100 buy" order remains in the order book, not yet canceled;
• Faster traders immediately sell SOL to you at $100 (i.e., your buy order is filled);
• The result: you paid 10% more to buy SOL, and even with the platform rebate, you still suffer a huge loss.

This situation is called "adverse selection," commonly referred to as "being arbitraged."

Therefore, when using a "one-sided quoting" strategy, "precision" and "speed" are the keys to success—the effectiveness of the entire strategy depends entirely on the bot's reaction speed and operational accuracy.

High-Frequency Trading Infrastructure

To avoid "being arbitraged," the trader built an "ultra-fast execution system," whose core includes:

• Colocation services: physically deploying trading servers close to Hyperliquid's servers to minimize network latency;
• Automated operations: the bot can adjust quotes thousands of times per second, achieving "real-time price following";
• Real-time risk control: automatically closing or adjusting positions before position risk gets out of control.

Building such infrastructure is both costly and highly complex—this is why only a few professional market makers can deploy such systems.

From a technical perspective, his trading bot was likely written in C++ or Rust (both known for "fast execution" and "low latency"); the servers were colocated near Hyperliquid's "order matching engine" to ensure his orders were prioritized for matching.

The bot used WebSocket or gRPC protocols to obtain real-time order book data, completing "order placement - order cancellation - switching quote direction" in milliseconds—ensuring continuous rebate earnings while avoiding order "invalidity" due to price changes.

How to Stay "Delta Neutral"?

Most impressively, the trader always maintained a "Delta neutral" state: despite a total trading volume of several billions of dollars, his net position risk was always kept below $100,000.

How did he achieve this?

1. The bot tracked changes in SOL token positions in real time;
2. Set strict risk limits (net position risk never exceeding $100,000);
3. Once position risk approached the limit, the bot would immediately stop trading on the current side and switch to quoting on the opposite side, rebalancing the position through reverse trades.

He did not use a "spot and futures arbitrage" model, but operated entirely in the "perpetual contracts" market—since all trades were completed in the same market, position hedging and risk control were simpler.

However, this strategy requires extremely high "discipline" and "precision": even the smallest operational error could result in huge losses.

The Mathematical Logic Behind

The profit calculation logic for the entire strategy is actually very clear:

• Within two weeks, the trader's total trading volume reached $1.4 billion;
• The market maker rebate rate was 0.003% per trade;
• Profit from rebates alone = $1.4 billion × 0.003% ≈ $420,000.

On top of that, he also used a "profit reinvestment" strategy—immediately reinvesting each rebate into trading, amplifying returns through the "compound effect." Ultimately, total profit reached $1.5 million.

And all of this started with just $6,800 in initial trading capital.

Why Can't You Simply Copy This Strategy?

You might think: "If that's the case, can't I just copy his trades and make the same amount of money?" But in reality, this strategy is almost impossible to replicate, for several core reasons:

1. You don't have his "execution speed": the combination of professional colocation servers and low-latency code is out of reach for ordinary traders;
2. You don't have his "capital scale": although the initial capital was only $6,800, with compounding profits, the later trading scale reached a professional level;
3. You don't have "precise code and bots": his bot was repeatedly debugged to adapt to every tiny fluctuation in the order book, which ordinary developers can hardly replicate;
4. You don't have "24/7 infrastructure and monitoring": the crypto market trades 24/7, requiring real-time monitoring systems to handle sudden risks.

In short, this is a "professional-grade high-frequency trading system," not something ordinary retail investors can easily copy.

Potential Risks of This Strategy

Even for such a highly sophisticated bot, there are still significant risks that cannot be ignored:

1. Server failure: if the server crashes, the bot may not be able to cancel orders in time, resulting in passively holding large risk positions;
2. Exchange failure: although rare, if Hyperliquid experiences downtime or malfunctions, it could disrupt the bot's trading logic within seconds;
3. Extreme market volatility: violent market moves could break the "one-sided quoting" balance, causing the strategy to fail and incur losses;
4. Fee structure changes: if Hyperliquid adjusts the maker rebate rate or trading fees, the strategy's profitability could drop sharply.

Although this strategy is ingenious, it is not "invulnerable."

Conclusion

Turning $6,800 into $1.5 million in two weeks may sound like "getting lucky with meme coins," but in reality, it is underpinned by solid technical skills, strict discipline, and precise system design.

This is an excellent case study demonstrating how to "scale up market maker rebates," "maintain Delta neutrality," and minimize "directional risk."

The core takeaway from this case is: trading is not just about "predicting prices." Sometimes, the most profitable strategy is to thoroughly understand the rules of market structure and build a system that creates value in "corners others overlook."