In-depth interpretation of Quantlytica: What sparks will the combination of AI and asset management collide?

Preface

Blockchain is a great invention, which has brought about a transformation of certain production relations, allowing the precious thing of 'trust' to be partially resolved. But in the world of encryption, there has always been a saying, 'Blockchain is a huge dark forest, and you must always be vigilant to avoid losing all your property'.

The black incident library organized by the slow mist technology has recorded 1572 security incidents, which have caused a terrifying $32.7B in losses, including various reasons such as private key theft, contract vulnerabilities, project rug, etc. It can be seen from this that asset management is particularly important in the encrypted world, whether for individuals or institutional organizations.

To do asset management well, the most important thing is to do risk control well. Here, risk control can be to check whether various values are correct during the trading process and set your estimated stop-loss and take-profit points; it can also be to remain vigilant when receiving unknown links, conduct sufficient research before deciding whether to click to view; it can also be to check every time the signature and other related information pop up during on-chain interaction to avoid granting important permissions to malicious parties. From this, we can see that doing risk control well is an extremely difficult thing, requiring us to remain vigilant about anything at all times, which is very draining of people's energy and attention.

Since OpenAI has driven a huge wave of AI, the AI track in the encrypted world has also begun to stir, gradually emerging various AI applications. From personalized AI Agents to decentralized AI computing power markets, all are current hot directions in the field. So, if AI is combined with risk control to reduce frequent manual operations, can it help encrypted users better survive in this dark forest without constantly worrying about observing their assets? This is the problem that Quantlytica wants to solve.

This article will elaborate on the application and integration of AI in the asset management field from three aspects: the asset management track, the Quantlytica project analysis, and the project status.

Overview of Asset Management Track

2019-2022 is the first year of DeFi, when thousands of DeFi products emerged, and sub-tracks that we now know well also emerged: DEX, Lending, Derivatives, Staking/Yield Farming, etc. Apart from these categories, most other types of DeFi projects are lukewarm, such as asset management.

One of the core reasons is that the market costs are different. We can observe from some new Layer1/Layer2 projects that they usually configure multiple protocols (DEX, Lending, Derivatives, Staking/Yield Farming) to meet users' basic needs, which means that users inevitably need to use these types of protocols in this Layer1/Layer2. Therefore, the market promotion costs of these protocols are mostly compensated by the market influence of Layer1/Layer2, generally only needing to bear the market costs within the Layer1/Layer2 competitive environment. Other types of protocols, which do not address users' basic needs, also means that these protocols have additional market education costs. Similarly, in the asset management field, many needs still require market education and validation.

What is asset management?

First of all, we need to be clear about how to define on-chain asset management. **In TradFi, asset management refers to the act of the settlor handing over his assets to the trustee, and the trustee provides financial services for the settlor. On the on-chain, this "trustee" is reduced to a smart contract, which should eliminate the need for centralized trust assumptions. **Broadly speaking, the asset management track can be dismantled according to two categories:

1. Passive Asset Management: Users do not need to frequently change their positions, and the logic executed by the target smart contract is relatively fixed. Common sub-tracks include Yield Farming, Indexes, and Staking.
2. Active Asset Management: Users may need to change positions frequently, and the logic executed by the target smart contract is strongly related to user intent. Common sub-tracks include Fund Tokenization, which can contain a variety of strategies such as Swap, Lending, etc.

Therefore, from a broad perspective, almost all hot projects involve Yield Farming/Staking, such as Lido. Therefore, the analysis of the asset management track should not be limited to the general, which may cause distortion.

Narrowly speaking, when we mention the asset management track, we should focus on the combination of Indexes and Fund Tokenization.

• Indexes: The protocol tracks and tokenizes portfolios of fixed strategies in some way. For example, it tokenizes and tracks the performance of the BTC 2x leverage strategy.
• Optimisation: This protocol provides more flexible strategy selection and position adjustment possibilities based on Indexes. For example, further adjustments and leverage of the above BTC double leverage product.

Its core participants are the fund manager (Manager), the fund manager (Trader), and the investor (Investor). The product lifecycle of asset management is roughly as follows:

1. The fund manager creates a fund, in addition to setting basic parameters (management fee, strategy, target, etc.), can also choose passive management/active management. Smart contracts receive the request and generate a contract account, and can receive deposits from investors according to the settings of the fund manager (mostly USDT).
2. If in the case of active management, the fund manager sets an allowlist for a certain address as the fund manager, this role has the right to make strategy changes, adjust positions, and other operations for the future of the fund.
3. Subsequently, the fund can officially accept user deposits. Users invest according to the fund's accepted deposit targets and then receive Token certificates.
4. According to the exit clause, users can return the fund's shares with vouchers within the specified period, and return the investment funds based on the profit and loss during the investment period.
5. When the fund manager does not want to continue operating the fund, he can choose to suspend the strategy and calculate the final profit and loss situation of all users. Afterwards, users can redeem their investment amount with token certificates at any time after the fund is closed.

The core process in the above process is how to track or tokenize these investment portfolios. From the perspective of current conventional protocols, there are two core components used:

1. Token Voucher: After users stake their assets, they can obtain a voucher representing the asset portfolio. In Ethereum, the common asset management token voucher is ERC-20, and the supply of this token will be adjusted based on AUM (Asset Under Management) through Mint&Burn.
2. Contract Account: The funds staked by the user will be deposited into the contract account, and the fund manager can perform proxy operations based on active/passive fund categories. During the process, the funds cannot be withdrawn to the fund manager's own account. To ensure the safety of funds, most protocols restrict the protocols (i.e. strategies) that can interact with this contract account.

Problems in the Asset Management Industry

Currently, common asset management protocols are structured in a platform-style manner, inevitably requiring the maintenance of supply and demand relationships, which means that there must be fund managers as well as investors. Based on an investigation of conventional asset management protocols currently available on the market, including but not limited to Enzyme Finance, dHEDGE, and Symmetry, I have summarized the impossible trilemma in the asset management field.

1. High PNL: Refers to whether the potential profit and loss ratio of users under this asset management method is higher than the benchmark (usually referring to the return of blue-chip coins).
2. Simplicity: Refers to whether users can invest in the portfolio without having to perform complex operations. For example, most asset management platforms now offer one-click investment products, which require no user operations.
3. **Transparency（透明性）：**Refers to whether users under this asset management method can clearly understand the flow of funds and the reasons for strategy changes. For example, although users do not have direct control in structured products, they also have a clear understanding of the flow of funds. In active strategies, users generally need to be informed of the flow of funds only after the fund manager makes strategy changes, so their transparency is lower than that of structured products.

Although the problem of the three difficulties is common, there are still more problems in conventional Optimization and Indexes products:

1. Strategy Replicability: Currently, the investment portfolios and strategies provided by funds are very simple and do not have barriers. For example, Bitcoin 3x leverage, popular Solana token combinations, etc. Users can directly purchase assets based on these combinations. In traditional finance, there are capital thresholds (minimum transaction amount) for purchasing stocks. When there are many types of intended stocks, users may not have enough funds to allocate to multiple stocks. Therefore, funds can lower the participation threshold. However, in the on-chain world, this situation hardly exists. Therefore, for relatively simple strategies, most users can execute them by themselves instead of relying on funds.
2. Investment targets/strategies are restricted: Currently, asset management protocols only support interaction with blue-chip protocols (such as Aave). The core consideration is to protect investors from the risks brought by long-tail protocols and investment targets. This also directly leads to the fact that the majority of users purchasing asset management products are DAOs, institutions, and basically belong to the B2B business model.
3. Fund Product Qualifications: At present, there are basically no threshold restrictions on fund managers in the asset management agreement, which further leads to uneven product returns. Especially in the Optimization, due to the frequent operations by fund managers, the potential profit and loss ratio of the product fluctuates more.

Quantlytica Solutions

Introduction

**Quantlytica is an innovative Cross-Chain Asset Management infrastructure that integrates multiple CEX platforms and DeFi protocols into a unified platform, aiming to provide a secure, efficient, and user-friendly solution for users to easily access and manage assets across multiple blockchains. Quantlytica has combined AI and asset management to launch a series of innovative asset management products, including Smart DCA, AI-driven grid trading strategies, and AI-supported risk monitoring and simulation tools. These tools not only enhance the intelligence of investment strategies but also provide users with deeper market insights and risk management capabilities.

At the same time, Quantlytica will also release the Quantlytica Fund SDK and Risk Management Framework, providing developers, fund managers, and other market participants with the ability to build and extend customized investment strategies. Quantlytica will use these tools to simplify the participation process of DeFi, while improving the profitability and security of the strategies, providing users with a more comprehensive, efficient, and secure asset management service.

The Quantlytica team is composed of talents with deep financial and technical backgrounds. The CEO has ten years of experience in the financial industry, having developed AI investment advisor prototypes at Grab Invest and driving the development of structured financial instruments in the Singapore banking industry, making significant contributions to the architecture design of Murex. As a CFA charterholder, the CPO has not only achieved outstanding performance in the traditional finance field but also successfully led and co-founded innovative DeFi projects, attracting the attention and investment of industry giants.

The development of a successful asset management tool requires a deep understanding of financial instruments and the encryption market, a profound technical background, and accurate grasp of user needs. Therefore, we believe that Quantlytica has already possessed these key capabilities with its professional background and extensive experience.

Core Components

The core components mainly consist of In House Product Line, Quantlytica Fund SDK, and Risk Management.

In-House Product Line

The In House Product Line consists mainly of Asset Management and Index (i.e., Strategy and Assets).

Currently, Asset Management has three strategies in total: DCA, Smart DCA, AI Grid Trading.

1. DCA (Dollar Cost Averaging) is a product that reduces the risk of making large investments in a single transaction by dividing the total investment amount into regular purchases of target assets, commonly known as Auto-Invest. The form of Auto-Invest can spread funds into different time periods to reduce market timing risks. At the same time, the operation of the product is relatively simple and easy for users to understand. It is mainly targeted at investors with a lower risk appetite who focus on long-term asset growth. Users can set the investment chain, amount, frequency, and asset type according to their preferences, including single tokens, custom indices, or Quantlytica index assets. Once set up, Quantlytica will only deduct the investment amount from the user's account at the predetermined occurrence and transaction time after being signed and approved, without the need to transfer USDT to the vault or smart contracts in advance.
2. Smart DCA (Smart Dollar-Cost Average) is Quantlytica's intelligent Auto-Invest product, which optimizes traditional investment strategies through AI technology. Unlike traditional DCA, Smart DCA does not simply invest a fixed amount, but dynamically adjusts the quantity of tokens purchased based on the current market conditions, and will also sell some tokens when the price reaches its peak. Smart DCA dynamically adjusts the buying and selling strategy based on real-time market conditions to achieve optimized returns. In terms of asset selection, due to the introduction of AI to optimize investment returns, users can only choose single assets or index assets designed by Quantlytica for various scenarios. Other interactions are basically the same as DCA.
3. AI Grid Trading is an intelligent grid trading strategy offered by the Quantlytica platform, designed to optimize trading in volatile or sideways markets through automation. This strategy captures profits by setting multiple buy and sell orders within a predetermined price range and taking advantage of small market fluctuations, reducing the dependence on market timing for each trade. It is suitable for investors who wish to reduce manual operations and utilize AI technology for trading. Based on periodic market analysis and quantitative factor analysis, AI evaluates and selects the tokens that may perform the best under current market conditions and presents relevant trading strategies to users. Users can easily set up AI Grid Trading according to their preferences, choose investment strategies, view backtest results, and determine investment amounts and leverage levels. Once set up, Quantlytica will take over automatically and execute trades using the predefined parameters and AI grid trading strategy.

Quantlytica Index currently offers a product: Q3TV. Q3TV consists of the top three currency pairs with the highest trading volume, with equal weight for each currency pair, and the coins will be reselected periodically. It is worth noting that the construction of the Quantlytica Index strictly follows the process of traditional quantification: selection and preprocessing of the data set, Index construction factors, and Index fitting process. In the selection and preprocessing of the data set, the product uses 1-hour perpetual contract data, which is more sensitive to market price fluctuations. In the Index construction factors, the product combines fundamental and quantitative factors to select its component coins. The model will be constructed using the top 10 cryptocurrencies selected from the whitelist. The whitelist selection is based on the trading volume of tokens on CEX and DEX, and only tokens with relatively large trading volume will be included in the whitelist. Therefore, prioritizing trading volume can make the index more accurately reflect the actual capital flow and investment trends in the market. In the fitting process, the model adopts an equal weight allocation strategy. This weight allocation not only simplifies the model, but also ensures that the overall index better reflects the comprehensive market trends. The component coins of the Index are reselected every 30 days. Due to the relatively stable composition of the current index, the possibility of major adjustments is small.

The goal of the Quantlytica Index is to build an index that is both robust and accurate, and can truly reflect the current state of the cryptocurrency market. When selecting the index components for Q3TV, the team used quantitative factors to select the top 3 currencies with the highest trading volume in the allowlist, and combined their price changes in an equal-weighted manner to ensure that the contribution of each component in the index is relatively balanced. These risk control strategies not only help protect investors from unnecessary risks, but also provide them with a reliable and reference-worthy market indicator. In this way, the project can provide stable and trustworthy investment choices for investors in the dynamic and ever-changing cryptocurrency market. It is evident that the uniqueness of this index lies in its combination of fundamentals and quantitative factors, aiming to provide investors with a more comprehensive, dynamic, and competitive investment vehicle. Indexes can be traded in combination with Quantlytica Asset Management's strategies, or independently as investment targets, and there will be more indexes with different components launched in the future, further expanding the flexibility of trading while ensuring professionalism.

Quantlytica Fund SDK

In the next phase, Quantlytica will also launch the highly anticipated Quantlytica Fund SDK. The Quantlytica Fund SDK will integrate a wealth of tools into a user-friendly interface, making it easy for both novice and professional investors to create, test, and deploy strategies. Here are the key features of Quantlytica Fund SDK:

• Custom Strategy Building: Provides flexibility to create and refine strategies using templates or starting from scratch.
• AI Support: Provides AI-driven insights during construction and testing to enhance strategy effectiveness.
• Comprehensive Backtesting: Allows users to evaluate strategies under various conditions and receive AI improvement suggestions.
• Public Strategy Management: Allows users, especially professional fund managers, to share their strategies, attract followers, and monetize their strategies.
• DAO Approved Standards: Ensure all public policies meet high performance and reliability standards.
• DeFi Incentives: Encourage DeFi projects to reward users with QTLX tokens to increase participation

Risk Management

Taking reference from Murex's design, Quantlytica also combines AI in its risk management framework to ensure the security and profitability of user investments. This framework identifies, assesses, and prioritizes risks, and then deploys strategies to mitigate these risks and maximize opportunities. The following are the characteristics of Quantlytica's risk management framework:

1. Data Source Support and Custom Data Training

• Integrated Data Sources: Users can integrate their own data sources to create personalized data models tailored to specific market conditions and requirements.
  • Custom data training: Ensure that the risk management strategy is consistent with the unique needs of each user and provide customized risk handling methods.

2. Customizable Risk Parameters
   • Flexible Risk Management: Users can define and adjust risk parameters according to their personal investment strategies and risk tolerance, achieving a personalized risk management approach.

• Dynamic Adjustment: With the change of market conditions, these parameters can be modified to ensure that risk management is always effective and responsive.

3. AI-driven risk monitoring and simulation
   • Continuous Monitoring: Advanced AI algorithms continuously monitor risk factors, providing real-time insights into potential risks.
   • Real-time simulation: Users can run simulations to see the potential impact of different risk scenarios, effectively predict and mitigate risks.

• Data-driven insights: AI-powered methods improve the accuracy of market insights, helping to make better decision-making.

4. Comprehensive on-chain and off-chain support
   • Comprehensive Risk Management: The platform supports risk management across a variety of activities, including investment, yield farming, TVL acceleration, and instant automation.

• Unified approach: Ensure that both on-chain and off-chain encryption activities are covered, providing a seamless Risk Management experience.

5. Real-time Simulation

• Impact on visualization: Users can test and visualize in real time the impact of different risk scenarios, helping them make informed decisions.
• Mitigation Strategy: Simulate insights into potential outcomes, enabling users to devise effective mitigation strategies.

6. SDK and API Documentation

• Comprehensive Guide: Quantlytica will provide detailed documentation to help users implement and optimize their risk management strategies using the SDK.
  • Easy to integrate: Ensure smooth integration process, Risk Management can operate efficiently in the custom capital flow of each user.

So when we break down the above product structure, Quantlytica's solution seeks to address the existing problems in the industry as much as possible:

1. In-House Product Line: Bring more self-developed products to investors for a more professional and difficult-to-replicate strategy experience.
2. Quantlytica Fund SDK: Improve strategy diversity and autonomy through pre-configured strategy templates and AI assistance.
3. Risk Management: Improve product qualifications through more risk control solutions.

Quantlytica's Smart DCA and AI Grid Trading strategies combined with AI can achieve dynamic adjustment of investment decisions. Compared with existing asset management platforms on the market, I believe that Quantlytica's investment strategy is more diverse and professional. As a truly professional asset management platform, Quantlytica can enable users to enjoy the advantages of fund products in the traditional financial market, including lowering the participation threshold, risk diversification, and professional management, while achieving asset preservation and appreciation in the cryptocurrency market.

Tokenomics

The total supply of project token QTLX is 100,000,000, and the distribution and emission plan of the token are as follows:

Image source: Similar to CRV, QTLX has also launched veQTLX token as a core component of Quantlytica DAO to reward liquidity contributors and attract long-term supporters to participate in Quantlytica governance. Users can receive 1 veQTLX by depositing 1 QTLX, which is non-transferable and non-tradeable. The utility of QTLX and veQTLX tokens is as follows:

1. Fee Distribution: veQTLX holders are entitled to receive up to 50% of the platform's revenue share.
2. Governance Participation: QTLX and veQTLX holders have governance rights, influencing the development of platform functionality and making key decisions.
3. Exclusive Access: QTLX and veQTLX holders have access to advanced features, including custom strategy design and advanced functionality.
4. Discount Service Fee: Users need to purchase CREDIT to use the platform's features. Using QTLX to purchase CREDIT is more favorable than using USDT.
5. API Usage: Quantlytica's API is open to third parties, allowing them to access and use all of Quantlytica's data without registration. The price for purchasing request counts using QTLX is more favorable than using USDT.
6. Fund Manager Rewards: In order to increase the TVL or protocol usage of DeFi projects, QTLX tokens must be provided as rewards to incentivize user participation.
7. Repurchase and Burn: In order to ensure the stability and empowerment of the QTLX token, Quantlytica commits to publicly and transparently repurchasing and burning 20% of the monthly income in QTLX tokens, and regularly updating the progress to the community. This will systematically reduce the supply of QTLX tokens to increase their scarcity and value.

After the subsequent launch of the Quantlytica Fund SDK module and Risk Management module, the utility of the Token will also include:

1. Discount Insurance: QTLX Token offers the option to purchase loss of income insurance at a more favorable price than traditional USDT payment.
2. Data Analysis Services: QTLX will also be accepted as a payment method for proprietary market analysis, data insights, and push notifications for institutions and individuals.

Product-related activities and market strategies

Currently, Quantlytica has launched two incentive activities: Earn Season and Community Quontos.

1. Earn Season

The event starts on May 27th and is divided into testnet and mainnet activities. The testnet activities mainly focus on product experiences such as DCA, Smart DCA, and Q3TV. Participants can earn Operation EXP on the testnet and increase their rewards by staking assets on the mainnet. The total reward pool is 3,000,000 USD QTLX and 100% USD BTR airdrop.

2. Community Quontos

Users can earn points on TaskOn and convert them into Quantlytica tokens (QTLX) according to Quantlytica's rules after TGE.

Summary

As the compliance process of the cryptocurrency market accelerates, we are witnessing a continuous expansion of the cryptocurrency user base. This trend indicates an increasing demand for cryptocurrency asset management tools. However, there are some obvious problems with existing asset management solutions: many funds offer portfolios and strategies that are too basic and lack competitiveness; investment choices are limited, and most asset management protocols only support interactions with a few mainstream protocols; in addition, there is a lack of qualifications for fund managers, resulting in inconsistent product returns quality.

In this context, Eureka Partners is confident in the potential and prospects of Quantlytica. We believe that with Quantlytica's advanced technology, especially its integrated artificial intelligence capabilities, and a professional team composed of senior financial experts and technical personnel, Quantlytica can provide an innovative and efficient asset management solution. This solution can not only meet the growing market demand for complex trading strategies and precise risk management tools, but also bring unprecedented professional and customized asset management experience to investors through AI-driven personalized services.

Meanwhile, Eureka Partners also emphasized that although Quantlytica provides powerful tools and support, investors should remain cautious about various asset management tools on the market, including Quantlytica, and have a full understanding and preparation for market risks while enjoying the convenience. No tool can completely eliminate investment risks. Users should make wise decisions when using asset management tools like Quantlytica, taking into account their investment goals and risk tolerance.

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