Candy@TEDAO|Author
In the DeFi world, every transaction is recorded on an immutable public ledger, accessible to anyone. We're used to seeing every exchange on decentralized exchanges like Uniswap, but the information often stops at just "this transaction occurred."
Where did this transaction come from? Was it a referral from a key opinion leader (KOL) or a trading tool? For a long time, this type of attribution has relied on internal project systems or centralized backend processing, often referred to as a "growth black box": transactions themselves can be verified on-chain, but the source of the promotion is often accounted for off-chain. This isn't accidental; it stems from technical and cost considerations. On mainnets like Ethereum, attaching additional identifiers to each transaction significantly increases gas fees and can also pose security challenges. Therefore, many projects choose to store their "business ledgers" off-chain.
Hyperliquid is a decentralized trading platform based on its proprietary underlying blockchain network (L1), where users can trade perpetual contracts. Unlike other platforms, Hyperliquid exposes key business data and trading logic on-chain, achieving full transparency from financial transactions to growth attribution. This allows the exchange's backend to be more intuitively presented as a traceable growth map.
Hyperliquid's data dashboard (provided by the third-party data analytics platform Allium) acts like a real-time "war room." It not only displays macroeconomic trends but also reveals who (wallet address), what tools were used, and when drove market fluctuations. This is achieved by structuring source information into the protocol path, first clarifying two dimensions:
Figure 1: Overview of the Hyperliquid ecosystem provided by Allium. Source: Allium: https://hyperliquid.allium.so/
Scenario A (Builder | Order Level)
Trader Bob uses developer David's "TradePro" tool to place an order, which carries David's address (builder parameter). The protocol automatically records the address and the corresponding fee on the chain and completes the account splitting according to the rules.
Scenario B (Referral | Account Level)
Trader Alice registered using KOL Emma's referral code, establishing a referral binding between Alice's account and Emma that can be verified on-chain. From then on, Alice enjoyed a fee discount on each of her transactions. The system calculated the discount at the account level and automatically allocated a rebate to Emma.
Figures 2 & 3: Overview of revenue and user growth for different builders and referrals. Data source: Allium
When growth attribution moves from off-chain to on-chain, the entire value chain changes. Let’s look at this from the perspectives of rules, settlement, and data:
1. Rules: From “Variable Interpretation” to “Protocol-Level Rules”
Key logic is solidified into contracts and jointly executed by the network; code constraints replace temporary interpretations to improve the neutrality and predictability of rules.
2. Settlement: From “Manual Approval” to “Automatic Settlement”
Taking the Builder (order level) as an example: the user first sets the "maximum fee authorization" (ApproveBuilderFee) for the developer address. Each subsequent order carries the builder parameter, and the protocol completes the profit-sharing settlement on the chain without any manual intervention.
3. Data: From “Propaganda Reports” to “Traceable Ledgers”
All key actions – order placement, order cancellation, liquidation, discount application – are written on-chain and can be independently verified by anyone in the public ledger, without having to rely solely on hype.
This has a direct impact on:
For developers (Builders) and promoters (Refrals): Return to the contribution itself
Automatic settlement based on on-chain contributions, independent of relationships or offline statistics, allows visibility into who creates value. Excellent developers and promoters can "vote with code" rather than "lobby with PowerPoint."
Project Operation and DAO Governance: From Subjectivity to Data Consensus
Make decisions based on unified metrics (e.g., “promoter contribution × retention × ARPU”) and discuss cost reduction.
For example, the “Builder User Retention Rate” dashboard shows the differences in user quality brought by different tools: some have a strong acquisition rate but high second-week churn; others have a small acquisition rate but stable retention, with clearer incentive direction.
Figure 4: Builder user retention dashboard, tracking new customers and subsequent retention by week. Data source: Allium
For ordinary traders: Using facts to cut through the noise
It can independently identify "who is setting the pace and which tools are effective" and is less affected by shouting orders and opaque information.
However, any technological paradigm is a double-edged sword. When transparency is pushed to the extreme, new risks and challenges emerge:
Strategy Leakage and Alpha Decay: The Evaporation of Trade Secrets
For professional traders and developers, when their trading patterns and tool logic are clearly tracked, their profitable Alpha is exposed to the public and can be easily copied and imitated, causing the strategy to quickly become ineffective.
Precision sniping and market manipulation: a transparent hunting ground
The intentions of large-scale traders to build positions become clear at a glance, which may lead to them being maliciously followed or being precisely attacked by their counterparties using position information, increasing the risks of large-scale capital operations.
Financial privacy leaks: public exposure to wealth
Users' transaction history and profit and loss (PnL) are fully public. For example, ecosystem panels (such as Allium) aggregate liquidation events to form a list; but this also exposes addresses and nominal losses, and is more likely to attract hackers, phishing, and even offline security threats.
Figure 5: Liquidation rankings, showing liquidated addresses and losses. Data source: Allium
To address these risks, the industry has turned its attention to verifiable privacy technologies, such as zero-knowledge proofs (ZKPs). Their core goal is to prove to the protocol that a contribution was made by a specific promoter or tool, without revealing the trader's identity or strategy details, and to use this evidence to complete on-chain settlement.
This path provides a clear technical direction for achieving the ideal state of "both verification and protection." However, the technology still requires extensive engineering refinement to overcome challenges such as cost, latency, and anti-sybil authentication.
Hyperliquid's attempt extends DeFi's "trustless" principle from the transaction level to the source level, demonstrating what protocol-native growth is: it places the closed loop of "attracting new users - trading - profit sharing" completely on the chain, making it both traceable and reviewable, laying the foundation for a fairer incentive mechanism.
However, this design of putting growth attribution on-chain also raises a core challenge: how to better protect individual strategies and privacy without sacrificing verifiability. Only when a traceable ledger and the right to anonymity coexist harmoniously can the growth mechanism be considered to have fully migrated from off-chain to on-chain.