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

Original Article Author: The Smart Ape, Partner at LBank
Original Article Translation: Saoirse, Foresight News

This is an exceptional case study that demonstrates the importance of "learning to code" — with programming, you can increase $6,800 to $1.5 million on the Hyperliquid cryptocurrency exchange platform in just two weeks.

Recently, a Hyperliquid trader accomplished this.

Even more astonishing is that the trader took on almost no risk. They did not speculate on the market direction or chase hype around popular assets, relying solely on a sophisticated market-making strategy — the core logic revolved around "maker rebate" and was combined with automated operations and strict risk management.

Market-Making Mechanism on the Hyperliquid Platform

Before delving into the strategy, we first need to understand the market-making logic of the Hyperliquid platform. Hyperliquid is an order book model exchange where users can place two types of orders:

· Buy Order: Known as a "buy order" (e.g., "I want to buy SOL token at a price of $100")

· Sell Order: Known as a "sell order" (e.g., "I want to sell SOL token at a price of $101")

These outstanding orders collectively form the "order book." The traders placing buy or sell orders are referred to as "makers."

· The core role of makers is to "provide liquidity": by placing limit orders in advance, they add tradable order volume to the market.

· In contrast, there are "takers": these traders directly execute against existing orders in the order book (e.g., "market buy" a token at the current best ask price).

Makers are crucial to the market: it is because of them providing liquidity that the market's bid-ask spread can be maintained at a lower level; without makers, traders may face issues such as "unreasonable pricing" and "significant slippage losses."

Key Point: Liquidity Provider Rebate

The core of an exchange is "liquidity" — to incentivize users to become liquidity providers and enhance market liquidity, Hyperliquid offers a "trade rebate" to liquidity providers: whenever a liquidity provider's order is executed, the platform will return a small rebate.

On the Hyperliquid platform, the rebate percentage for each trade is approximately 0.0030% — in other words, for every $1000 traded, you would receive a $0.03 rebate.

And it is this seemingly small rebate that allowed a trader to achieve a leap from $6800 to $1.5 million. His strategy's core was "one-sided quoting": placing limit orders only on one side of the order book (either only placing buy orders or sell orders); once the market price changes, he quickly cancels the original order or switches to the other side of quoting.

In simple terms, his operational logic was: providing liquidity on one side only to earn rebates, while using bots to adjust order direction in real time, avoiding risks due to exposure to positions. Ultimately, relying on the huge trading volume brought by "automated high-frequency trading," the meager single trade rebate accumulated into substantial profit.

Core Pain Point of Traditional Liquidity Providers

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

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

If both orders are filled, you have earned a $1 spread profit through "buying low and selling high."

However, this model has a key issue: position risk.

- If the buy order is filled but the sell order is not: you will passively hold SOL tokens;

- If the sell order is filled but the buy order is not: you will passively hold a stablecoin (such as USDT).

Once the market price moves against you, these passively held assets will face significant losses.

This is also why the Hyperliquid trader opted for "one-sided quoting": through one-sided orders, he could more strictly control his positions, avoiding holding unnecessary assets passively. However, this model faces the cost of higher "arbitrage trap" risk.

What Does "Being Front-Run" Mean?

Let's walk through a specific scenario: You place a "buy SOL at $100" order on the order book. Suddenly, a negative news event causes the price of SOL to instantly drop to $90.

· Your "buy at $100" order is still on the order book unredeemed;

· A faster trader immediately sells SOL to you at the price of $100 (your buy order gets executed);

· End result: You end up paying a 10% premium to buy SOL, and even with platform rebates, you would still incur significant losses.

This situation is known as "adverse selection," which is commonly referred to as "being front-run."

Therefore, when employing a "one-sided quote" strategy, "accuracy" and "speed" are crucial for success—the effectiveness of the entire strategy depends entirely on the robot's responsiveness and operational precision.

High-Frequency Trading Infrastructure

To avoid "being front-run," the trader has set up a "ultra-high-speed execution system," the core of which includes:

· Custody Service: Physically deploying trading servers close to the Hyperliquid platform servers to minimize network latency;

· Automated Operations: The robot can adjust quotes thousands of times per second, achieving "real-time tracking of prices";

· Real-time Risk Control: Automatically liquidating positions or adjusting positions before the position risk gets out of control.

Building such infrastructure requires not only high costs but also extremely high technical complexity—this is why only a few professional market makers can deploy such systems.

From a technical perspective, his trading robot is most likely written in C++ or Rust (both languages known for "fast execution speed" and "low latency"); the servers are hosted near the Hyperliquid "order matching engine" to ensure that his orders are prioritized for matching.

The robot receives real-time order book data through WebSocket or gRPC protocols, completing "order placement - cancellation - bidirectional quote switching" operations in milliseconds—ensuring continuous rebates while avoiding orders becoming "invalid" due to price changes.

How to Maintain "Delta Neutrality"?

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

How did he achieve this?

1. A bot tracked the real-time changes in the holding amount of the SOL token;

2. Strict risk limits were set (net position risk never exceeded $100,000);

3. Once the position risk approached the limit, the bot immediately halted trading on the current side and switched to the other side quoting, achieving position rebalancing through a reverse trade.

He did not adopt the "Spot and Futures Arbitrage" model but operated entirely in the "Perpetual Contract" market — since all trades were executed in the same market, position hedging and risk control were simpler.

However, this strategy demands high levels of "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 quite clear:

· Within two weeks, the trader's total trading volume reached $1.4 billion;

· The market maker rebate rate is 0.003% per trade;

· Profit obtained solely through rebates = $1.4 billion × 0.003% ≈ $420,000.

Furthermore, he employed a "Profit Reinvestment" strategy — immediately reinvesting each rebate into trading, amplifying profits through the "compounding effect." In the end, the total profit reached $1.5 million.

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

Why You Can't Simply Copy This Strategy?

You might think: "If that's the case, can't I just replicate his trades and earn the same amount of money?" However, the reality is that this strategy is almost impossible to replicate, with the core reasons being:

1. You don't have his level of "execution speed": the combination of professional hosting servers + low-latency code is beyond the reach of ordinary traders;

2. You don't have his level of "capital scale": although the initial funds were only $6,800, with profit compounding, the later trading scale has reached a professional level;

3. You don't have "Precise Code and a Bot": His bot, after repeated debugging, can adapt to every tiny fluctuation in the order book, making it difficult for ordinary developers to replicate;

4. You don't have "24/7 Infrastructure and Monitoring": The cryptocurrency market trades 24/7, requiring real-time monitoring systems to address sudden risks.

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

The Potential Risks of This Strategy

Even for this highly sophisticated bot, there are still risks that cannot be ignored:

1. Server Failure: If the server crashes, it may cause the bot to fail to cancel orders in time, resulting in passively holding a large risk position;

2. Exchange Downtime: Although rare, if the Hyperliquid platform experiences downtime or malfunctions, it could disrupt the bot's trading logic within seconds;

3. Extreme Market Volatility: Violent market movements can break the balance of "one-sided quoting," causing the strategy to fail and incur losses;

4. Fee Structure Changes: If Hyperliquid adjusts the market maker rebate rate or trading fees, it could immediately significantly reduce the profitability of this strategy.

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

Conclusion

Increasing $6,800 to $1.5 million in two weeks may sound like "getting lucky with a meme coin," but in reality, it is based on solid technical expertise, strict discipline, and precise system design.

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

The core insight from this case is: trading is not just about "predicting prices." Sometimes, the most profitable strategy is to fully understand market structure rules and build a system that can create value in the "overlooked corners" of the market.

Original Article Link

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