🔖The Tagging System

Precision Strategies and Methodologies for Pinpointing On-Chain Whales

1. Introduction

The transparency of blockchain technology brings a sea of data, but it also presents a core challenge: How can we rapidly identify truly valuable traders among hundreds of millions of addresses?

Traditional analysis tools often stop at displaying basic transaction flows and asset balances. They lack deep extraction and intelligent categorization of address behavioral characteristics, making the search for "Whales" (addresses with significant capital and successful trading experience) feel like searching for a needle in a haystack.

The Hyperbot Trader Tagging System is designed to solve this critical pain point. Based on on-chain historical behavior from the past 90 days, the system utilizes a rigorous, multi-dimensional quantitative algorithmic model to generate a series of highly condensed behavioral tags for every active address. Tags such as "Large Fund," "Stable Profit," and "Long-term" transform complex on-chain actions into intuitive identifiers. This provides users with "Data-Driven Eyes" to quickly insight into a trader's strategy, style, and financial strength, ensuring that true whales have nowhere to hide.

---

2. System Design & Architecture

2.1 Design Philosophy: Objective, Dynamic, and Pragmatic

The system follows three core principles to ensure that the output tags possess high credibility and practical value:

• Data-Driven: All tags are strictly generated based on verifiable on-chain transaction data, eliminating subjective bias and ensuring objective conclusions.

• Dynamic Evolution: All computational data is drawn from a 90-day rolling window, ensuring that tags reflect the trader's latest status rather than stale history.

• User-Controlled: The system interface allows users to freely combine tags for multi-dimensional filtering, granting significant autonomy for exploration.

2.2 Core Dimensions and Tag Definitions

The system profiles addresses across five core dimensions to create a comprehensive evaluation framework. The table below details the definitions and typical tags for each dimension.

Table: Detailed Explanation of the Five Core Dimensions

Dimension	Core Insight	Representative Tags (EN/CN)	Definition Logic
Capital Scale	Evaluates capital strength and regular transaction volume	Large Fund / 大资金	Average single-trade profit > $50,000
Directional Bias	Reveals the trader's natural inclination towards market direction	Bullish / 偏多头	Long position ratio > 70% in the past 90 days
Trading Pace	Characterizes holding periods and transaction frequency	Long-term / 长线	Average holding time > 7 days
Trading Style	Evaluates risk-reward characteristics; key to defining strategy robustness	Stable Profit / 稳定盈利	Must meet strict criteria: Win Rate ≥ 60%, Max Drawdown ≤ 25%, Sharpe Ratio ≥ 1.0, etc.
Profitability Status	Reflects overall PnL performance over the recent period	Sustained Profit / 持续盈利	Consistent positive net growth in PnL over the rolling window

Note: The system enforces a Minimum Activity Threshold. Tags are only generated for addresses with "Total Transactions ≥ 3" and "Average Position Size ≥ $1,000" within the past 90 days, effectively filtering out inactive or experimental accounts.

---

3. Typical Application Scenarios

The tagging system transforms data into actionable insights, providing significant value across various scenarios.

3.1 Whale Discovery and Behavior Tracking

By combining tags such as "Large Fund" + "Stable Profit," users can instantly locate high-net-worth, disciplined whales within a sea of addresses. Users can then track their specific movements, such as large withdrawals from exchanges to cold wallets, which may signal major market trends.

3.2 Strategy Analysis and Mimicry

Users with different risk appetites can find suitable "mentors" based on tags:

• Conservative Users: Focus on "Low Frequency / Robust" traders to learn long-term positioning and risk control.

• Aggressive Users: Study "High Risk / High Return" or "Asymmetric Experts" (low win rate but extremely high risk-reward ratio) to understand the secrets behind capturing alpha.

3.3 Risk Control and Anti-Fraud

The tagging system helps identify potential risks:

• If an address is marked as "Large Fund" but displays a "High Frequency / Aggressive" style with "Volatile Profit," its massive volume itself could be a source of market volatility; following such traders requires extreme caution.

• When evaluating new project teams, if the core team addresses show historical behaviors like "Small Fund," "Short-term," and "Break-even," investors should question their long-term commitment and the project's credibility.