Data: 75% of Traders on Hyperliquid Are Losing Money. What Are the Profitable Ones Using?

marsbitОпубліковано о 2026-05-20Востаннє оновлено о 2026-05-20

Анотація

On Hyperliquid, approximately 75% of addresses are losing money, indicating that manual traders are increasingly competing against automated systems. Profitable traders primarily employ one of three approaches: 1) Running systematic, high-frequency strategies (e.g., one address executed 261k trades with a 64.75% win rate), 2) Placing high-conviction, asymmetric bets with large positions (e.g., 50 trades yielding $4.48M at a 28% win rate), or 3) Using algorithms for execution while making manual macro judgments. The article argues that traditional methods like chart patterns or reacting to social media news are often already priced in by bots, making such traders the "exit liquidity" for systematic players. Success now depends on unique narrative timing, structural insights, or conviction during market capitulation.

Author: Stacy Muur

Compiled by: Deep Tide TechFlow

Deep Tide Guide: Three-quarters of addresses on Hyperliquid are losing money. It's not bad luck; the rules of the game have changed. While you're still looking at candlestick patterns for formations, arbitrage bots have already made their profit. Data shows that truly profitable traders either use algorithmic systematic execution or place large bets on asymmetric opportunities with high conviction. Manual retail traders watching screens are becoming the exit liquidity for others.

About 75% of addresses on Hyperliquid are in a loss-making position.

The reality is that people trading manually on Hyperliquid are competing against systems that never rest. The trading opportunity you just discovered has likely already been priced in by the system.

When you see a certain pattern, chances are it has already been arbitraged.

Extreme funding rates are balanced in an instant.

Technical patterns have already appeared across more than 50 order books.

News headlines are priced in as soon as they appear.

What the most profitable traders are doing:

→1. Running systematic strategies (the second most profitable address executed 261,000 trades this month with a 64.75% win rate)

→ 2. Holding high-conviction positions, betting on asymmetric returns (a wallet made $4.48 million with just 50 trades and a 28% win rate)

→ 3. Using algorithms as execution tools while making macro judgments manually

If you have timing in narratives, structural insight, or conviction when everyone else capitulates, you can still make profitable trades.

But if your trades are based on chart patterns or news you saw on X, you are likely just exit liquidity.

Пов'язані питання

QAccording to the article, what percentage of addresses on Hyperliquid are losing money, and what is presented as the primary reason?

AApproximately 75% of addresses on Hyperliquid are losing money. The primary reason is that manual traders are competing against automated, systemized strategies that execute trades faster and more efficiently, often pricing in opportunities before a human trader can act.

QWhat are the two main strategies used by the most profitable traders on Hyperliquid, as described in the article?

AThe two main strategies are: 1. Running systematic, automated strategies (one example executed 261,000 trades with a 64.75% win rate). 2. Holding high-conviction positions to bet on asymmetric opportunities (one wallet made $4.48M with only 50 trades, despite a 28% win rate).

QThe article suggests that several common trading signals are often ineffective. Name at least three of these signals.

AThree common but often ineffective signals mentioned are: 1. Technical chart patterns (which appear across many order books). 2. Extreme funding rates (which get arbitraged instantly). 3. News headlines (which are priced in immediately upon release).

QWhat role does the article suggest algorithms can play for a trader who still wants to make manual, high-level decisions?

AThe article suggests using algorithms as an execution tool. A trader can make macro or high-level directional judgments manually, and then use an algorithm to handle the rapid, systematic execution of trades based on that judgment.

QAccording to the article's conclusion, what kind of manual trader is most likely to succeed, and what kind is most likely to fail?

AA manual trader who succeeds is likely one acting on deep narrative timing, unique structural insights, or having strong conviction when others are capitulating. A manual trader who fails is likely one whose decisions are based on commonly seen chart patterns or news headlines found on social media (like X), as they become 'exit liquidity' for others.

Пов'язані матеріали

From Farm to Entrepreneur: After Building Flying Cars, He Bet on the Robot Sector and Created a $39 Billion Giant

From Farm to Tech Tycoon: Brett Adcock's Journey to a $39B Robot Giant Brett Adcock, a serial entrepreneur from an Illinois farm, has built his third major venture, humanoid robotics company Figure AI, into a $39 billion behemoth backed by NVIDIA, Intel, and others. His path began with the sale of his recruiting platform Vettery for $110 million in 2018. He then co-founded and took electric air taxi company Archer Aviation public in 2021 before departing over strategic differences. Adcock founded Figure AI in 2022 with a 30-year vision to create general-purpose humanoid robots that can work in human-designed environments, aiming to address labor shortages in manufacturing, logistics, and retail. The company gained attention through live-streamed robot sorting challenges and a viral demo, though it faces skepticism over its high valuation versus early commercial progress. A key moment was Figure's split from OpenAI in 2024 after a brief collaboration, with Adcock claiming OpenAI provided limited value and deciding to develop AI models internally—a move OpenAI contested. Adcock's pattern is tackling capital-intensive, long-term tech frontiers, moving from software to aviation to robotics, betting on AI and automation as the future of labor.

marsbit6 хв тому

From Farm to Entrepreneur: After Building Flying Cars, He Bet on the Robot Sector and Created a $39 Billion Giant

marsbit6 хв тому

How to Become a Pro Claude User in 30 Days?

"How to Become a Claude Power User in 30 Days" outlines a structured month-long program to transform from a casual user into someone who leverages Claude as a core productivity system. The first week focuses on mastering prompt structure (Role, Context, Task, Format, Constraints), understanding context windows, and setting up foundational Projects and Memory for personalized context. The second week involves building reusable workflows for research, writing (using a two-step outline-first process), and decision-making. The third week shifts to automation: using Claude Cowork for autonomous file tasks, connecting tools like Google Drive and Slack, and setting scheduled automation. The final week is for compounding gains: optimizing all workflows based on feedback, building a personal knowledge repository, teaching the system to a colleague, and designing a complete, ideal "Claude Operating System." By day 31, the user operates with an autonomous assistant that handles routine tasks with deep context, freeing them for high-value creative and strategic work. The core message is that consistent, systematic configuration over 30 days creates a powerful, personalized productivity advantage.

marsbit48 хв тому

How to Become a Pro Claude User in 30 Days?

marsbit48 хв тому

Five Core Forms of AI Agent in YC's Eyes

The article outlines five core architectural patterns for effective AI Agents, emerging from tools like Codex and Claude, that move beyond simple prompts towards reusable, process-based capabilities. 1. **Skills**: Reusable, parameterized workflows that function like method calls, allowing a single process (e.g., "/investigate") to handle various tasks based on input parameters. 2. **Thin Harness**: A lightweight execution framework (~200 lines) that manages the AI model's "hands and feet"—handling loops, file I/O, and context—without becoming bloated. 3. **Resolvers**: Routing tables that map tasks to specific Skills, preventing "context corruption" when managing dozens of Skills and ensuring outputs go to the correct locations. 4. **Latent vs. Deterministic Layer**: A critical separation where LLMs handle judgment, synthesis, and pattern recognition, while deterministic code handles tasks requiring precision, consistency, and low cost (like calculations). 5. **Memory**: A persistent, accumulating knowledge base (e.g., a markdown folder) with a "current trusted conclusion" section and an append-only timeline, enabling the system to learn and retain context over time. Together, these patterns create a "process power"—a durable competitive advantage. Unlike one-off prompt-based applications whose value quickly commoditizes, a well-designed AI Agent system encodes experience into reusable, parameterized workflows, offloads stable rules to code, and continuously learns through memory. This creates a structured, hard-to-replicate capability that can provide sustained value for individuals or businesses, such as an accountant automating client reviews while preserving privacy and accumulating expertise.

marsbit1 год тому

Five Core Forms of AI Agent in YC's Eyes

marsbit1 год тому

Tiger Research: On-Chain Risk Operators, The Market Cap Gap Between 147 Trillion and 70 Billion

This report by Tiger Research examines the evolution of risk management in decentralized finance (DeFi) lending. It highlights a power shift from protocol developers to specialized professional risk operators who manage on-chain capital. The era of protocols and community governance solely dictating DeFi lending is ending. A new professional asset management layer has emerged. While the sector is nascent, capital and distribution channels are rapidly consolidating around top risk operator teams, whose past performance is now a key criterion for institutional entry. The industry's development, accelerated by modular infrastructures like Morpho, has led to a clear division of labor mirroring traditional finance: distribution channels (e.g., exchanges), strategy/risk management (the risk operators), and product infrastructure/asset custody (smart contract protocols). This structure lowers the entry barrier for traditional institutions. Currently, the total value managed by risk operators is approximately $70 billion, dominated by a few leading teams like Steakhouse (RWA focus), Sentora (AI models), and Gauntlet (crisis management). Competition now centers on collateral standards, distribution access, and crisis response capabilities. The report outlines three primary entry paths for institutions: 1) **Distribution Model**: Leveraging external risk operators as backend service providers (common for exchanges). 2) **Asset Supply Model**: Onboarding real-world assets to DeFi as collateral. 3) **Independent Operator Model**: Building an in-house team to become a risk operator (e.g., Bitwise). The core opportunity lies in the strategy/risk management layer, where traditional financial institutions can leverage their existing expertise in due diligence and risk assessment without deep technical development. A vast opportunity gap exists: the global traditional asset management industry manages ~$147 trillion, while the entire DeFi sector is only ~$800 billion, with the risk operator niche at ~$70 billion. This disparity signifies immense growth potential. Once robust risk frameworks and clearer regulations are established, even a minor allocation from traditional markets could trigger exponential DeFi growth. Early movers who help build these foundational systems will gain significant rule-setting influence and first-mover advantages.

marsbit1 год тому

Tiger Research: On-Chain Risk Operators, The Market Cap Gap Between 147 Trillion and 70 Billion

marsbit1 год тому

Interview with Circle's Chief Economist: USDC's Entry into Hyperliquid Benefits Circle and HYPE, Stablecoins Are Becoming Marginal Buyers of U.S. Treasuries

In an interview with Circle's Chief Economist Gordon Liao, the conversation covers the strategic significance of USDC replacing USDH as the reference asset on the decentralized perpetual exchange Hyperliquid. This shift, facilitated by Coinbase as the reserve manager and Circle providing technical infrastructure, aims to capture net interest income for the platform, with 90% of reserve earnings directed back to Hyperliquid for HYPE token buybacks. Liao discusses how stablecoins like USDC, with their substantial on-chain settlement volumes (e.g., $21 trillion in Q1 2026), are emerging as marginal buyers of U.S. Treasuries, concentrating on short-term debt and effectively reducing the weighted duration of the market, which may provide underlying support for long-term rates. The dialogue also explores the evolving nature of stablecoins as both a medium of exchange and a vehicle for capital and collateral liquidity. Additionally, the panel touches on the CLARITY Act's legislative progress, noting compromises around "activity-based rewards" and remaining hurdles like ethics concerns. On AI, there's debate over value capture, with predictions that distribution and application layers, rather than foundational model companies like OpenAI, will accrue most value. Regarding the bond market, Liao attributes the rise in 30-year yields primarily to an increased term premium (around 80 bps) driven by supply-demand dynamics, including fiscal expansion and changing investor demand, rather than expectations of Fed rate hikes.

marsbit1 год тому

Interview with Circle's Chief Economist: USDC's Entry into Hyperliquid Benefits Circle and HYPE, Stablecoins Are Becoming Marginal Buyers of U.S. Treasuries

marsbit1 год тому

Торгівля

Спот

Ф'ючерси