Preface
Blockchain is a great invention. It has brought about changes in certain production relations and partially solved the precious problem of "trust". But in the crypto world, there is a saying that "blockchain is a huge dark forest. You must always be vigilant to avoid losing all your property."
The hacked archive compiled by SlowMist Technology has recorded 1,572 security incidents, and the amount of losses caused by these security incidents has reached a terrifying $32.7B, including various reasons such as private key theft, contract loopholes, project Rug, etc. This shows that in the crypto world, whether it is individuals or institutional organizations, it is particularly important to do a good job of asset management.
The most important thing to do in asset management is to do a good job of risk control. Risk control here can be to check whether the various values are correct during the transaction and set your own estimated profit and loss points; it can also be to be vigilant when receiving unknown links and decide whether to click to view after sufficient research; it can also be to check the signatures and other related information that pop up every time when interacting on the chain to avoid authorizing important permissions to malicious parties. From this we can see that it is extremely difficult to do a good job of risk control. We need to be vigilant about everything at all times, which consumes a lot of energy and attention.
Since OpenAI has driven a huge wave of AI, the AI track in the crypto world has also begun to move, and a variety of AI applications have gradually emerged. From personalized AI Agents to decentralized AI computing power markets, they are all hot spots in the current track. So if AI is combined with risk control to reduce frequent manual operations, can it help crypto users survive better in this dark forest for a long time without having to constantly observe their assets? This is the problem that Quantlytica wants to solve.
This article will explain the application and integration of AI in the asset management track from three aspects: asset management track, Quantlytica project analysis, and project status.
Overview of the Asset Management Track
2019-2022 is the first year of DeFi, when thousands of DeFi products emerged, and the sub-tracks we are familiar with now were extended: DEX, Lending, Derivatives, Staking/Yield Farming, etc. In addition to the above-mentioned tracks, other types of DeFi projects are mostly tepid, such as asset management.
One of the core reasons is the different market costs. We can observe from some new Layer1/Layer2 projects that they usually configure several protocols (DEX, Lending, Derivatives, Staking/Yield Farming). These protocols meet the basic needs of users, which means that users will inevitably use this type of protocol in this Layer1/Layer2. Therefore, the marketing costs of these protocols will mostly be compensated by the market influence of Layer1/Layer2, and generally only need to bear the market costs of the internal competition environment of Layer1/Layer2. Other types of protocols do not solve the basic needs of users, which also means that these protocols have additional market education costs. Similarly, in the asset management track, many needs still require market education and verification.
What is asset management?
First of all, we need to be clear about how to define on-chain asset management. In traditional finance, asset management refers to the act of the principal handing over his assets to the trustee, and the trustee providing financial services to the principal. On the chain, this "trustee" is simplified into a smart contract, so there should be no need for a centralized trust assumption. In a broad sense, the asset management track can be broken down into two categories:
Passive Asset Management: Users do not need to change their positions frequently, and the logic of the target smart contract execution is relatively fixed. Common sub-tracks include Yield Farming, Indexes and Staking.
Active Asset Management: Users may need to change their positions frequently, and the logic of the target smart contract execution is strongly related to the user's intention. Common sub-tracks include Fund Tokenization, which can include multiple 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 a broad sense, which may lead to distortion.
In a narrow sense, when we talk about the asset management track, we should focus on the combination of Indexes and Fund Tokenization:
Indexes: This protocol tracks and tokenizes asset portfolios of fixed strategies in some way. For example, tokenizing the BTC 2x leverage strategy to track the performance of the product.
Optimisation: This protocol provides more flexible strategy options and position adjustment possibilities based on Indexes. For example, further position adjustment and leverage of the above-mentioned BTC double leveraged product.
The core participants are fund managers, traders, and investors. The product life cycle of asset management is roughly as follows:
When a fund manager creates a fund, in addition to setting basic parameters (management fees, strategies, targets, etc.), he can also select passive management or active management. After receiving the request, the smart contract generates a contract account and can receive deposits from investors (mostly USDT) according to the fund manager's settings.
If in the case of active management, the fund manager sets up a whitelist for a certain address as the fund manager, this role has the right to make future operations such as strategy changes and position adjustments on the fund.
After that, the fund can officially accept deposits from users. Users invest according to the deposit targets accepted by the fund and then receive token certificates.
According to the exit terms, users can return their fund shares with certificates within a specified period of time, and their investment funds will be returned based on the profits and losses during the investment period.
When the fund manager does not want to continue running the fund, he can choose to suspend the strategy and calculate the final profit and loss of all users. Afterwards, users can collect their investment amount with the token certificate at any time after the fund is closed.
The core process of the above process is how to track or tokenize these investment portfolios. From the current conventional protocols, there are two core components:
Token certificate: After users pledge assets, they can obtain a certificate representing the asset portfolio. In Ethereum, the common asset management token certificate is ERC-20, and the token will adjust the supply through Mint&Burn according to AUM (Asset Under Management).
Contract account: The funds pledged by users will be deposited into the contract account. Fund managers can perform proxy operations based on active/passive fund categories. During the process, funds should not be transferred to the fund manager's own account. In order to ensure the safety of funds, most protocols limit the protocols (i.e. strategies) that can interact with the contract account.
Existing problems in the asset management track
Currently, common asset management protocols are all structured in the above-mentioned platform-style, which inevitably requires the maintenance of supply and demand relationships, so there must be both fund managers and investors. The author has summarized the impossible triangle of the asset management track by investigating the conventional asset management protocols currently on the market, including but not limited to Enzyme Finance, dHEDGE, and Symmetry:
High PNL (High Profit and Loss Ratio): refers to whether the user's potential profit and loss ratio under this asset management method is higher than the baseline (usually referring to the returns of blue-chip coins).
Simplicity: refers to whether users do not need to perform complex operations to invest in the portfolio under this asset management method. For example, most asset management platforms now have one-click investment products, and users do not need to perform operations.
Transparency: refers to whether users have priority in understanding the flow of funds and why the strategy is changed under this asset management method. For example, although users do not have direct control rights in structured products, they are also clear about the flow of funds. In active strategies, users generally need to know the flow of funds only after the fund manager makes a strategy change, so its transparency is also lower than that of structured products.
While the trilemma is common, there are many more problems in conventional Optimization and Indexes products:
Strategy replicability: The investment portfolios and strategies currently provided by the fund are very simple and have no barriers, such as Bitcoin 3x leverage, Solana popular token portfolio, etc. Users can directly purchase assets based on the portfolio. In traditional finance, there is a capital threshold (minimum transaction amount) for purchasing stocks. When there are many types of intended stocks, the user's funds may not be enough to allocate multiple stocks, so the participation threshold can be lowered through funds. However, on the chain, this situation almost does not exist, so for more simple strategies, users can mostly execute them themselves instead of through funds.
Limited investment targets/strategies: Current asset management protocols only support interaction with blue chip protocols (such as Aave), and their core consideration is to protect investors from the risks brought by long-tail protocols and targets. This also directly leads to the fact that most of the users who currently purchase asset management products are DAOs and institutions, which basically belong to the B2B business model.
Fund product qualifications: Currently, asset management agreements basically do not impose any threshold restrictions on fund managers, which further leads to uneven product returns. Especially in Optimization, since fund managers can perform frequent operations, the potential profit and loss of the product is more volatile.
Quantlytica’s Solution
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 that allows users to easily access and manage assets across multiple blockchains. Quantlytica combines AI and asset management to launch a series of innovative asset management products, including Smart DCA, AI-driven grid trading strategies, and risk monitoring and simulation tools supported by artificial intelligence. These tools can 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 to provide developers, fund managers and other market participants with the ability to build and expand customized investment strategies. Quantlytica will use these tools to simplify the DeFi participation process, while improving the profitability and security of strategies, and provide users with a more comprehensive, efficient and secure asset management service.
Quantlytica's team is composed of talents with deep financial and technical backgrounds. The CEO has ten years of experience in the financial industry. He has developed an AI investment advisor prototype at Grab Invest and promoted the development of structured financial instruments in the Singapore banking industry, making important contributions to the architectural design of Murex. As a CFA charter holder, the CPO not only has outstanding performance in the traditional financial field, but has also successfully led and co-founded other 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 crypto markets, profound technical accumulation, and a precise grasp of user needs. Therefore, we believe that Quantlytica, with its team’s professional background and rich experience, already possesses these key capabilities.
Core Components
The core components mainly consist of three parts: In House Product Line, Quantlytica Fund SDK and Risk Management.
In-House Product Line
The In House Product Line mainly consists of Asset Management and Index (i.e. strategy and assets).
Currently, Asset Management has three strategies: DCA, Smart DCA, and AI Grid Trading.
DCA (Dollar Cost Averaging) is a product that reduces the risk of investing a large amount in a single transaction by dividing the total investment into regular purchases of target assets. It is commonly known as fixed investment. The form of fixed investment can disperse funds into different time periods to reduce market timing risks. At the same time, the product is relatively simple to operate and easy for users to understand. It is mainly aimed at investors with low risk appetite and 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 the setting is completed, Quantlytica will only deduct the investment amount from the user's account after signature approval at the scheduled occurrence and transaction time, without the need to transfer USDT to the vault or smart contract in advance.
Smart DCA (Smart Dollar-Cost Average) is a smart fixed investment product of the Quantlytica platform, which optimizes traditional fixed investment strategies through AI technology. Unlike traditional DCA, Smart DCA does not simply fix the investment amount, but dynamically adjusts the number of tokens purchased according to the current market conditions, and sells some tokens when the price reaches a peak. Smart DCA will dynamically adjust the buying and selling strategies according to real-time market conditions in order to obtain 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.
AI Grid Trading is an intelligent grid trading strategy provided by the Quantlytica platform, which aims to optimize trading in volatile or sideways markets in an automated manner. This strategy captures profits by setting multiple buy and sell orders within a preset price range, taking advantage of small market fluctuations, and reducing the dependence of a single transaction on market timing. It is suitable for investors who want to reduce manual operations and trade with AI technology. Based on cyclical market analysis and quantitative factor analysis, AI will evaluate and select tokens that may perform best under current market conditions and present 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 the settings are completed, Quantlytica will automatically take over and use the AI grid trading strategy to execute transactions according to predefined parameters.
Quantlytica Index currently offers one product: Q3TV. Q3TV consists of the top three currency pairs with the largest trading volume, with each currency having the same weight, and the currency will be reselected after a period of time. It is worth noting that the process of constructing Quantlytica Index also closely follows the traditional quantitative process: data set selection and preprocessing, 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 fundamentals and quantitative factors to select its constituent currencies. The model is constructed by the top 10 cryptocurrencies selected in the whitelist. The selection of the whitelist is based on the trading volume of the tokens on CEX and DEX, and only token pairs with relatively large trading volumes will be included in the whitelist. Therefore, giving priority to trading volume allows the index to more accurately reflect the actual capital flow and investment trends in the market. In the fitting process, the model adopts an equal weight distribution strategy. This weight distribution not only simplifies the model, but also ensures that the overall index better reflects the overall market trend. The constituent coins of the Index are reselected every 30 days. Since the current index components are relatively stable, the possibility of major adjustments is low.
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 applied quantitative factors, selected the top 3 currencies with the largest trading volume in the whitelist, and combined their rise and fall in an equal-weighted manner to ensure that each component contributes relatively evenly to the index. 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 investors with a stable and reliable investment option in the dynamic and ever-changing cryptocurrency market. It can be seen 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 tool. Indexes can be traded in conjunction with Quantlytica Asset Management's strategies, or invested in as a single target. In the future, more indexes with different components will be launched to further expand trading flexibility while ensuring professionalism.
Quantlytica Fund SDK
In the next phase, Quantlytica will also launch the Quantlytica Fund SDK, which is also very exciting. The Quantlytica Fund SDK will integrate a wealth of tools into a user-friendly interface, allowing both novice and professional investors to easily create, test and deploy strategies. The following are the key features of the Quantlytica Fund SDK:
Custom policy building: Provides the flexibility to create and refine policies using templates or from scratch.
AI-enabled: Enhance your strategy with AI-driven insights during the build and test phases.
Comprehensive backtesting: Allows users to evaluate strategies under various conditions and receive AI suggestions for improvements.
Public Strategy Management: Enable users, especially professional fund managers, to share their strategies, attract followers and monetize their strategies.
DAO-approved standards: Ensure that all public policies meet high performance and reliability standards.
DeFi project incentives: Encourage DeFi projects to reward users with QTLX tokens to increase participation
Risk Management
Following the design of Murex, Quantlytica also incorporates AI into the platform's risk management framework to ensure the safety and profitability of users' investments. The framework identifies, evaluates and prioritizes risks, and then deploys strategies to mitigate these risks and maximize opportunities. The following are the features of Quantlytica's risk management framework:
Data source support and custom data training
Integrate data sources: Users can integrate their own data sources to create personalized data models for specific market conditions and needs.
Customized data training: Ensure that risk management strategies are aligned with the unique needs of each user and provide a customized approach to risk management.
Customizable risk parameters
Flexible risk management: Users can define and adjust risk parameters based on their personal investment strategy and risk tolerance to achieve a personalized risk management approach.
Dynamic Adjustment: As market conditions change, these parameters can be modified, ensuring risk management is always effective and responsive.
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 predicting and mitigating risks.
Data-driven insights: AI-driven approaches improve the accuracy of market insights and facilitate better decision making.
Comprehensive on-chain and off-chain support
Comprehensive risk management: The platform supports risk management across multiple activities, including investing, yield farming, TVL acceleration, and instant automation.
Unified Approach: Ensures both on-chain and off-chain crypto activities are covered, providing a seamless risk management experience.
Real-time simulation
Impact Visualization: Users can test and visualize the impact of different risk scenarios in real time, helping them make informed decisions.
Mitigation Strategies: Simulations provide insight into potential outcomes, enabling users to develop effective mitigation strategies.
SDK and API documentation
Comprehensive Guides: Quantlytica will provide detailed documentation to help users implement and optimize their risk management strategies using the SDK.
Ease of integration: ensuring a smooth integration process and that risk management can operate efficiently within each user’s customized capital flows.
So when we look at the above product structure, Quantlytica's solution makes up for the current problems in the industry as much as possible:
In-House Product Line: Bring investors a more professional and difficult-to-replicate strategy experience through more self-developed products.
Quantlytica Fund SDK: Improves strategy diversity and autonomy through pre-configured strategy solutions and auxiliary AI.
Risk Management: Improve product qualifications through more risk control solutions.
Quantlytica combines AI's Smart DCA and AI Grid Trading strategies to dynamically adjust investment decisions. Compared with existing asset management platforms on the market, I believe that Quantlytica's investment strategies are richer and more professional. As an asset management platform with real professional barriers, Quantlytica enables users to enjoy the advantages provided by fund products in the traditional financial market, including lowering the threshold for participation, risk diversification and professional management, while realizing asset preservation and appreciation in the cryptocurrency market.
Tokenomics
The total supply of the project token QTLX is 100,000,000. The distribution and emission plan of the tokens are as follows:
Image source: https://docs.quantlytica.com/governance/tokenomics
Similar to CRV, QTLX also launched the ve token veQTLX as a core component of Quantlytica DAO to reward liquidity contributors and attract long-term supporters to participate in Quantlytica's governance. Users deposit 1 QTLX to get 1 veQTLX, which is non-transferable and non-tradable. The utility of QTLX and veQTLX tokens is as follows:
Fee Distribution: veQTLX holders are entitled to up to 50% share of the platform revenue.
Governance Participation: QTLX and veQTLX holders gain governance rights to influence the development of platform features and make key decisions.
Exclusive Access: QTLX and veQTLX holders enjoy premium feature access, including custom strategy design and advanced capabilities.
Discounted service fees: Users need to purchase CREDIT to use the platform's functions. The price of using QTLX to purchase CREDIT is more favorable than using USDT.
API Use: Quantlytica's API is open to third parties, allowing them to access and use all of Quantlytica's data without registration. The price of purchasing requests with QTLX is more favorable than using USDT.
Fund Manager Rewards: DeFi projects that aim to increase TVL or protocol usage must offer QTLX tokens as rewards to incentivize user participation.
Buyback and Burn: To ensure the stability of QTLX tokens and enable tokens, Quantlytica commits to use 20% of monthly revenue to buyback and burn QTLX tokens in a transparent manner and regularly update the community on the progress. 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 the Risk Management module, the utility of the token will also include:
Discounted Insurance: QTLX tokens provide the option to purchase income loss insurance at a more favorable price than traditional USDT payments.
Data Analytics Services: QTLX will also be accepted as a payment method for proprietary market analytics, data insights, and push notifications for institutions and individuals.
Product-related activities and marketing strategies
Quantlytica currently offers two incentive campaigns: Earn Season and Community Quontos.
1. Earn Season
The event started on May 27th and is divided into testnet and mainnet activities. The testnet activities are mainly based on product DCA, Smart DCA, and Q3TV product experience. Participants can earn Operation EXP on the Testnet and increase rewards by staking assets on the mainnet. The total reward pool is $3,000,000 QTLX and 100% USD BTR airdrop.
2. Community Quontos
Users can earn points by completing TaskOn, which will be converted into Quantlytica tokens (QTLX) after TGE according to Quantlytica’s rules.
Summarize
As the compliance process of the cryptocurrency market accelerates, we are witnessing the continued expansion of the scale of crypto users. This trend indicates that the demand for crypto asset management tools will continue to grow. 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 options are limited, and most asset management protocols only support interaction with a few mainstream protocols; in addition, there is currently a lack of thresholds for the qualifications of fund managers, resulting in uneven product returns.
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 of senior financial experts and technicians, Quantlytica can provide an innovative and efficient asset management solution. This solution can not only meet the market's growing demand for complex trading strategies and precise risk management tools, but also bring investors an unprecedented professional and customized asset management experience through AI-driven personalized services.
At the same time, Eureka Partners also emphasized that although Quantlytica provides powerful tools and support, investors still need to be cautious about various asset management tools on the market, including Quantlytica, while enjoying the convenience, and have a full understanding and preparation for market risks. No tool can completely eliminate investment risks. When using asset management tools such as Quantlytica, users should make wise decisions based on their own investment goals and risk tolerance.
Reference
https://hacked.slowmist.io/zh/
https://docs.quantlytica.com/
https://enzyme.finance/
https://dhedge.org/
https://app.symmetry.fi/
