Example: How to achieve 220 times profit using Hyperliquid market Bots?

Written by: The Smart Ape

Compiled by: Saoirse, Foresight News

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This is a perfect case that illustrates the importance of “learning programming” — with programming, you can increase $6,800 to $1.5 million on the cryptocurrency exchange platform Hyperliquid in just two weeks.

Not long ago, a Hyperliquid trader achieved this.

Even more astonishing is that the trader took almost no risks. He neither bet on market direction nor followed the hype of popular assets, relying solely on a sophisticated market-making strategy — the core logic revolves around “market maker rebates” and combines automated operations with strict risk management.

The market-making mechanism of the Hyperliquid platform

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

Buy Order: This refers to a “buy order” (for example, “I want to buy SOL tokens at a price of 100 dollars”)

Sell order: refers to a “sell order” (for example, “I want to sell SOL tokens at a price of $101”)

These pending orders together form the “order book.” Traders who place buy or sell orders are referred to as “market makers” (Makers).

The core role of market makers is to “provide liquidity”: by placing limit orders in advance, they supplement the volume of tradable orders in the market.

In contrast, there are “Takers”: these traders will directly execute existing orders on the order book (for example, “buying a token at the current best selling price” at market price).

Market makers are crucial to the market: it is because of them providing liquidity that the market bid-ask spreads can be maintained at a lower level; without market makers, traders may face issues such as “unreasonable pricing” and “large slippage losses.”

Key Point: Market Maker Rebate

The core of the exchange is “liquidity” — to encourage users to become market makers and supplement market liquidity, Hyperliquid will provide market makers with “trading rebates”: every time a market maker's order is executed, the platform will return a small rebate.

On the Hyperliquid platform, the rebate rate for each transaction is approximately 0.0030% — which means that for every 1000 dollars traded, you can receive a rebate of 0.03 dollars.

It is precisely this seemingly insignificant rebate that enabled the trader to achieve the leap from “$6800 to $1.5 million”. The core of his strategy is “one-sided quoting”: placing limit orders only on one side of the order book (either only placing buy orders or only placing sell orders); once the market price changes, he quickly cancels the original order or switches to quoting on the other side.

In simple terms, his operating logic is to provide liquidity only on one side to earn rebates, while using robots to adjust order directions in real-time, avoiding risks due to exposed positions. Ultimately, relying on the huge trading volume brought by “automated high-frequency trading,” the small rebates from individual transactions accumulate to become substantial profits.

The core pain points of traditional market makers

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

For example: You place two orders at the same time - a buy order to purchase 1 SOL at 100 dollars and a sell order to sell 1 SOL at 101 dollars.

If both orders can be executed, you will have earned a profit of $1 from the price difference by “buying low and selling high.”

But this model has a key issue: position risk.

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

If the sell order is executed and the buy order is not executed: you will passively hold stablecoins (such as USDT).

Once the market price fluctuates unfavorably for you, these passively held assets will face significant losses.

This is also the reason why that Hyperliquid trader chooses “one-sided quoting”: by placing orders on one side, he can more strictly control his positions and avoid passively holding unnecessary assets. However, the cost of this model is a higher risk of “arbitrage.”

What does “being arbitraged” mean?

Here's a specific scenario: You placed a buy order on the order book to “buy SOL at 100 dollars.” At this moment, sudden bad news causes the SOL price to drop to 90 dollars instantly.

Your “100 USD buy” order is still on the order book and has not been canceled;

Faster traders will immediately sell you SOL at the price of “100 dollars” (i.e., matching your buy order);

Final result: You spent 10% more in costs to buy SOL, and even with platform rebates, you will still incur huge losses.

This situation is referred to as “adverse selection,” which is commonly known as “being arbitraged.”

Therefore, when adopting a “one-sided quoting” strategy, “accuracy” and “speed” are the keys to success — the effectiveness of the entire strategy relies entirely on the robot's response efficiency and operational accuracy.

High-frequency trading infrastructure

To avoid “being arbitraged”, that trader built a “high-speed execution system”, the core of which includes:

Custodial service: Physically deploy the trading servers close to the Hyperliquid platform servers to minimize network latency;

Automated operations: The robot can adjust thousands of quotes per second to achieve “real-time price tracking”;

Real-time risk control: Automatically close positions or adjust positions before the holding risk goes out of control.

The construction of this type of infrastructure requires high costs, and the technical complexity is also very high — this is why only a few specialized market makers can deploy such systems.

From a technical perspective, his trading bot is likely written in C++ or Rust (both of which are known for their “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 obtains real-time order book data through WebSocket or gRPC protocols, completing the operations of “placing orders - canceling orders - switching quote direction” within milliseconds — ensuring continuous rebate earnings while avoiding order “invalidations” due to price fluctuations.

How to maintain “Delta Neutral”?

What is most impressive is that the trader always maintained a “Delta-neutral” position: despite his total trading volume reaching billions of dollars, the net position risk was always controlled within $100,000.

How did he do it?

The robot tracks the changes in the holdings of SOL tokens in real time.

Set a strict risk limit (net position risk must not exceed 100,000 USD);

Once the position risk approaches the upper limit, the robot will immediately stop trading on the current side and switch to the opposite quote, achieving position rebalancing through reverse trading.

He did not adopt the “spot and futures arbitrage” model, but instead operated entirely in the “perpetual contract” market - since all transactions are completed in the same market, position hedging and risk control are simpler.

However, this strategy requires extremely high levels of “discipline” and “precision”: even the smallest operational mistake could lead to huge losses.

The mathematical logic behind it

The logic for calculating the returns of the entire strategy is actually very clear:

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

The market maker rebate rate is 0.003% per transaction;

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

On this basis, he also adopted a “profit reinvestment” strategy - reinvesting each rebate immediately into trading, amplifying returns through the “compound effect”. In the end, the total profit reached $1.5 million.

And the starting point of all this is just an initial trading capital of 6800 dollars.

Why can't you directly copy this strategy?

You might be thinking, “Since that's the case, can't I just copy his trades to make the same amount of money?” But the reality is that this strategy is almost impossible to replicate, and the core reasons include:

You don't have his level of “execution speed”: the combination of professional hosting servers and low-latency code is difficult for ordinary traders to reach.

You don't have the “capital scale” like he does: although the initial capital is only $6,800, the trading scale has reached a professional level with the compounding of profits.

You don't have “precise code and robots”: his robots have been repeatedly debugged to adapt to every tiny fluctuation of the order book, which is difficult for ordinary developers to replicate;

You do not have “24/7 uninterrupted infrastructure and monitoring”: The cryptocurrency market trades 24/7, requiring real-time monitoring systems to respond to 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 such highly sophisticated robots, there are still risks that cannot be ignored:

Server failure: If the server crashes, it may cause the bot to be unable to cancel orders in time, resulting in a passive holding of a large risk position.

Exchange Malfunction: Although rare, if the Hyperliquid platform experiences a crash or fault, it may disrupt the trading logic of the bots within seconds.

Extreme market volatility: Dramatic price movements may disrupt the balance of “one-sided quotes,” leading to strategy failures and losses;

Fee structure changes: If Hyperliquid adjusts the market maker rebate ratio or trading fees, it may immediately lead to a significant decrease in the profitability of that strategy.

This strategy, although ingenious, is not “flawless.”

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 that demonstrates how to “scale the use of market maker rebates,” “maintain delta neutrality,” and minimize “directional risk.”

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