Opportunities and challenges coexist at the forefront of Hong Kong’s Web3 industry. Individuals are often without trustworthy information sources and guidance for informed decision-making. Enterprises unfamiliar with Web3 seek expertise in adopting relevant business models. Moreover, regulatory bodies necessitate advanced technology and tools to maintain a clean market environment.
Taking into account the industry pain points of Web3, combined with the research accumulation of academic tokenomics and industry data analysis experience, we provide one-stop services from data infrastructure construction to data analysis reports. Everyone can become a participant in Web3 and benefit from technological development.
- Blockchain solutions, including design of tokenomics model and development of smart contract;
- On-chain data analysis, modeling and predicting user behavior based on on-chain data
- Media analysis and large language model, extensively collect internet information from various channels, use large language model for processing, and provide question and answer services;
- Trading and finance, including trading strategy and comprehensive financial tools.
1. Tokenomics – Full Lifecycle Modeling of Tokenomics
Tokenomics, a unique feature of Web3, encompasses the entire journey of a project from inception to maturity. It involves a series of activities, including planning, design, implementation, and maintenance. Tokenomics lifecycle modeling provides a framework for project design tailored to the anticipated lifespan of the project. It encompasses factors such as token types and supplies (utility tokens, governance tokens, NFTs), distribution methods (allocation to founding teams, investors, and community users), rewards structure, and unlock periods. For instance, in the context of a Play-to-Earn game, our team will adopt a three-token model and utilize mathematical modeling based on game data to predict the game’s lifecycle precisely.
2. Digital Forensics – Tracking the Flow of Cryptocurrency
Cryptocurrency tracking is a complex process that involves blockchain technology, cybersecurity, and investigations related to financial crimes. The primary objective of this process is to trace and understand the transactions and flow of cryptocurrency. It is a highly technical endeavor requiring a deep understanding of blockchain technology and expertise in data analysis and cybersecurity. Our team possesses robust blockchain data analysis capabilities, enabling us to analyze the flow of currency transactions within the blockchain accurately. Additionally, by leveraging shared on-chain address labels with SlowMist Technology, our team can access additional information unavailable in on-chain data, integrating on-chain and off-chain data for in-depth and comprehensive analysis of blockchain data dynamics, thereby providing more precise market forecasts.
3. Large Language Models – Sentiment Analysis and Conversational Chatbots
Using large language models and traditional machine learning methods, we extract industry insights and conduct in-depth observations from the vast amount of data, addressing the problem of Web3 information being unreliable, incomplete, and fragmented. We provide decision analysis for predicting short-term price movements (such as which token may become the next “moon rocket”) and industry development trends (such as the next killer project).
We have also developed an industry assistant chatbot based on large models. The chatbot not only engages in conversational interactions with users but also accesses our backend data to provide users with real-time and in-depth analysis of information.
4. Behavioral Psychology – Modeling the Behavior of Game Players
In the realm of blockchain gaming, we have uncovered a remarkable phenomenon. The “play-to-earn” model, unique to this domain, attracts a substantial player base by allowing them to earn income while enjoying the game. This is further enhanced by the concept of digital ownership, enabling players to freely trade in-game assets and characters, heightening the immersion factor. We propose several strategies to address the common issue of rapid user attrition: precise control over in-game item production, balancing game costs and returns, reducing wealth disparities within the gaming community, and accommodating blockchain limitations in game design.
5. Behavioral Psychology – Modeling the Behavior of DAO Users
The service is dedicated to comprehending and forecasting user actions within Decentralized Autonomous Organizations (DAOs) through data and algorithms. Given the distinctive nature of DAOs, our primary focus lies on modeling voting behavior (timing, methods, frequency, tendencies, and influencing factors), economic activities (token transactions, token holding periods, distribution patterns, and allocation methods), and community engagement (proposals, communication patterns, and influence). By meticulously modeling these behaviors, we enhance our understanding of user dynamics within DAOs, predict future actions, and craft more effective incentive mechanisms.
6. Financial Audit – Financial and Risk Analysis Based on On-Chain Data
Classic on-chain data analysis dimensions include project activity, user continuity, “whales”, “smart money” trends, and other dimensions. Based on scientific research results, we further strengthen the analysis of these dimensions and construct more than 50 basic indicators to characterize user behavior, and formulate universal financial indicators for decentralized projects to provide a more in-depth characterization of project health. At the same time, we also examine the transaction network among users in the entire market from the perspective of network analysis, and identify various key roles such as core players, intermediaries, and “behind-the-scenes manipulators” in the market.
In addition, we have also constructed a risk spill-over model that measures the risk of DeFi protocols that have been spread by users, providing a more profound perspective on measuring the risk of the DeFi market.
7. Quantitative Trading – Strategies Based on Big Data and Artificial Intelligence
After extensively exploring various factors, we have embarked on a journey to employ straightforward algorithms, such as LSTM, to forecast cryptocurrency price trends and implement basic trading strategies for holding positions. The backtesting results have been highly promising, showcasing exceptional profit and loss (PNL) outcomes as well as remarkable performance in terms of maximum drawdown. We plan to utilize deep learning techniques to identify stable trading strategies, followed by small-scale live testing in the short term.