Synopsis- A small crypto trader transformed ₹5.95 lakh into ₹13 crore in two weeks using automated, delta-neutral market-making, leveraging rebates, risk controls, and technical efficiency with no speculation involved.
Something extraordinary has reshaped the crypto trading world in just two weeks. A little-known trader used technical mastery and discipline not luck or hype to turn just ₹5.95 lakh into a staggering ₹13.14 crore. This trader didn’t bet big on meme coins or ride the drama of ETF launches. Instead, they harnessed a sophisticated, high-frequency, delta-neutral market-making strategy that stands out for its efficiency and innovation. This success signals a changing landscape, making skilled infrastructure, rather than market speculation, the new hallmark of crypto trading greatness.
“Liquidity Ghost”
By June 2025, the decentralized perpetuals exchange Hyperliquid emerged as a proving ground for elite trading. Through the wallet address “0x6f90…336a,” the trader began with just under ₹1.75 crore in early 2024, most notably focusing on Solana perpetual futures. Their disciplined approach rapidly caught the attention of on-chain analysts. By mid-year, this wallet had moved more than ₹1,80,476.6 crore in trading volume, representing over 3% of all maker-side flow on Hyperliquid.
Interest in this account spread across platforms like Hypurrscan.io and sparked lively debate on X, with users dubbing the trader a “liquidity ghost.” Notably, the maximum active exposure never exceeded ₹87 lakh, and the trader regularly withdrew profits. The secret? Outsize returns from careful risk management, automation, and pure infrastructure play.
How a Market-Maker Beat the Odds
At the heart of this story lies a powerful trio: precision execution, tight risk limits, and a system that earned from volatility rather than predicting price direction. Instead of mimicking classic market makers, the bot posted only bids or asks, not both. This one-sided quoting system sharply cut inventory risk, making the strategy lean and efficient.
The true engine here was maker rebates just 0.0030% per fill. That’s ₹2.63 for every ₹87,600 traded. Yet, when applied to crores in volume, these tiny earnings snowballed. Across two weeks, the bot moved about ₹12,265.4 crore, cycling through hundreds of trades per day. This level of turnover required bots running on exchange-connected, colocated servers, synchronizing orders with split-second accuracy.
Remarkably, the strategy’s drawdowns reached only 6.48%, a testament to disciplined exposure limits. The trader steered clear of guessing games, sticking strictly to perpetual futures and ignoring spot trading or staking mismatches. In doing so, every move remained delta-neutral, leveraging volatility and liquidity mechanics with near surgical precision.
What Makes This Strategy Stand Apart?
Unlike traditional market makers who place both bids and asks, this method focused on just one side at a time. Flipping between the two with algorithmic speed, the trader reduced inventory risk and harvested rebates at unprecedented scale. Profits didn’t come from wild price swings or bullish predictions, but rather from systematic, repeated gains on every transaction.
To operate at such volume, the process had to be fully automated bots connected directly to dashboards like Hypurrscan.io, executing trades at a pace no human could match. The barriers to entry here are steep: speed, development chops, access to exchange internals, and the technical know-how to calibrate risk in real time. Most retail traders can’t simply mimic this model.
Traditional strategies, such as yield farming or staking, seldom return more than 10x in such a short period. In stark contrast, this approach generated a jaw-dropping 220x return with almost no speculative guesswork. Instead, it was engineered arbitrage, exploiting small inefficiencies invisible to slower players.
The Risks and Rewards of Engineering Advantage
While robust, this strategy isn’t risk-free. The very infrastructure that fuels high-speed trades also creates fragility. If a server crashes, or an exchange suffers downtime, open trades could suddenly face losses. Sharp market moves can expose one-sided quote systems, and changes to exchange rules or blockchain mechanics can put even the best strategies at risk.
Perhaps most importantly, not everyone can replicate these results. Millisecond latency, backend access, working capital, and intricate coding are all required to perform at this level. Regulatory shifts could also rapidly alter the field, alongside risks unique to the world of decentralized exchanges. Although risky, success in this arena is not a lottery win; it’s a reward for methodical engineering and real-time precision. This story shows that in 2025, the game belongs to those with the technical skills to build, not just bet.
Written By Fazal Ul Vahab C H
