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

Original Title: How to Turn $6,800 into $1.5M With a Maker Rebate Bot on HyperLiquid

Original Author: The Smart Ape, LBank Partner
Translated by: Saoirse, Foresight News

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

Not long ago, a Hyperliquid trader achieved exactly that.

Even more astonishing, this trader took on almost no risk. He neither bet on market direction nor chased popular assets. Instead, he relied solely on a sophisticated market-making strategy—centered around "maker rebates"—combined with automation and strict risk control.

Market-Making Mechanism on Hyperliquid

Before diving into the strategy, we need to understand the market-making logic of the Hyperliquid platform. Hyperliquid is an order book 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 role 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 buy" 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 may face "unreasonable pricing" and "large slippage losses."

Core Key: Maker Rebates

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

On Hyperliquid, the rebate rate per trade is about 0.0030%—that is, for every $1,000 traded, you get 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 the huge trading volume generated by "automated high-frequency trading," the small rebates from each trade accumulated into massive profits.

Core Pain Points of 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 to purchase 1 SOL at $100, and a sell order to sell 1 SOL at $101.

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

But this model has a key problem: position risk.

· If the buy order is filled but the sell order is not: you are passively holding SOL tokens;

· If the sell order is filled but the buy order is not: you are passively holding stablecoins (such as USDT).

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

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

What Does "Being Arbitraged" Mean?

Here's a specific scenario: you place a buy order in the order book to "buy SOL at $100." Suddenly, negative news causes the price of SOL 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 end 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 known 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-high-speed execution system," which included:

· Hosting service: physically deploying trading servers close to Hyperliquid's servers to minimize network latency;

· Automation: the bot can adjust quotes thousands of times per second, achieving "real-time price tracking";

· Real-time risk control: automatically closing or adjusting positions before position risk gets out of control.

Building such infrastructure requires high costs and is extremely technically complex—this is why only a handful of professional market makers can deploy such systems.

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

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

How to Maintain "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 do it?

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 contract" 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 of the entire strategy is actually very clear:

· In two weeks, the trader's total trading volume reached $1.4 billions;

· Maker rebate rate is 0.003% per trade;

· Profit from rebates alone = $1.4 billions × 0.003% ≈ $420,000.

On top of that, he also used a "profit reinvestment" strategy—immediately reinvesting each rebate into trading, amplifying returns through "compound interest." In the end, 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 copy, for several core reasons:

1. You don't have his "execution speed": the combination of professional hosting servers + 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 unexpected risks.

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

Potential Risks of This Strategy

Even for such a highly sophisticated bot, there are still significant risks:

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 the Hyperliquid platform goes down or malfunctions, it could disrupt the bot's trading logic within seconds;

3. Extreme market volatility: violent market moves could break the balance of "one-sided quoting," causing the strategy to fail and incur losses;

4. Fee structure changes: if Hyperliquid adjusts the maker rebate rate or trading fees, the profitability of this strategy 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 backed by solid technical skills, strict discipline, and sophisticated system design.

This is an excellent case study showing how to "scale up 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 market structure rules and build a system that creates value in "overlooked corners" of the market.