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This comprehensive guide serves as an essential resource for navigating the complex and interdisciplinary field of token engineering, grounded in systems design and complex systems engineering. This article outlines the critical phases — System Requirements Analysis, System Analysis, and System Design — of developing decentralized crypto-economic systems. The guide also aligns these phases with established frameworks in both systems engineering and token-based ecosystem creation. Additionally, it provides a detailed breakdown of tasks, skills, and deliverables for each phase. Readers will not only gain a deep understanding of why token engineering is vital for sustainable digital economies but also practical insights into the process, tools, and experts shaping this dynamic field. Content Overview Introduction What is Token Engineering? Token Engineering Process System Requirements Analysis System Analysis System Design Summary Companies to Watch Online Communities Tools GitHub Courses Additional Resources Introduction In 2022, I started my journey in token engineering, with a background in mechatronics and robotics engineering. Over the past two years, I’ve gathered resources that I believe can guide others in navigating this emerging domain. This research has been significantly influenced by several key figures in the field. The conceptual framework for understanding draws heavily from ’s groundbreaking contributions, which define token engineering as both a Systems Design and a Complex Systems Engineering discipline. has played a key role in advancing the field of token engineering through TE Academy Platform and her initiatives, which include organizing research sessions, events like EthCC Barcamp, study groups, courses, and grants, while also assembling a global community of experts and enthusiasts in this field. , , and are other key figures whose valuable research has played a pivotal role in defining and establishing this emerging domain by emphasizing the centrality of token engineering to innovation and development within web3 ecosystems. Recognizing the intricate and interdisciplinary nature of this domain and the complex and diverse nature of topics, I’ve included a variety of resource links throughout the article to offer readers a deeper understanding of specific concepts. crypto-economic systems Michael Zargham Angela Kreitenweis Krzysztof Paruch Trent McConaghy Dr. Achim Struve What is Token Engineering? Definition Token engineering is the design, verification, and optimization of token-based complex economic systems Why Token Engineering Matters Token Engineering is not just about creating ; it’s a rigorous discipline that demands a comprehensive approach to designing decentralized systems. Just as traditional systems require meticulous planning, analysis, and design, token ecosystems too demand a rigorous process for their conception and realization. Whether you're gathering requirements or deploying each phase plays a critical role in ensuring that the decentralized system is not only technically sound but also economically viable and socially impactful. As we delve into the nuances of each phase, you'll see that Token Engineering is not merely a technical endeavor but a comprehensive approach to creating sustainable digital economies. digital assets smart contracts, In the contemporary landscape of blockchain-enabled decentralized and distributed economic systems, we’ll delve into the field of token engineering as an important part of the development of decentralized ecosystems, viewing it through the framework of systems design and situating it as a specialized subset of complex systems engineering. Token Engineering Process In this article, we’ll delineate the scope of the token engineering process within the framework of the three standard phases of the product development lifecycle. This token engineering process aligns closely with established frameworks in systems engineering, and also with Outlier Ventures’ , which includes the Discovery, Design, and Deployment phases. We’ll outline the requisite skills, the expected deliverables, and the essential tools for each phase to offer a comprehensive understanding of the field. structured framework of token-based ecosystems creation System Requirements Analysis System Analysis System Design System Requirements Analysis System requirements analysis is the first phase where we document the system requirements. Here, we define what the system is supposed to achieve. Stakeholders generally agree on these requirements before moving forward. This sets the stage for subsequent analysis, design, and development phases and serves as a point of reference for all stakeholders. The System Requirement Analysis phase is further divided into the following sub-steps/phases: System Requirements Gathering Requirements Analysis System Requirements Gathering In this step, high-level needs and constraints are collected from stakeholders (documented / verbal). The focus is on understanding what the stakeholders expect the system to achieve. This could include both functional and non-functional requirements like features, performance, security, and compliance. Process Primarily focused on identifying and detailing the features and functionalities the system must have. This phase often involves stakeholder interviews, use-case definitions, and the documentation of both functional and non-functional requirements. It answers questions like “What should the system do?” and “What are the constraints?” Conduct stakeholder interviews to gather needs and expectations. Document user stories or use cases. Identify functional and non-functional requirements (e.g., security, scalability). Components Functional Requirements (Features the system must have) Non-functional requirements (Performance, security, compliance with legal regulations such as anti-money laundering (AML) laws, etc.) Performance Requirements System Technical Requirements Specifications Use cases or user stories (Scenarios describing how the system will be used) Requirements Analysis After the initial gathering of system requirements, the second sub-step/phase of requirements analysis focuses specifically on examining and refining the project’s requirements by dissecting, validating, and prioritizing these requirements while documenting the functional and non-functional requirements of a system. The aim is to ensure that the requirements are specific, measurable, achievable, relevant, and time-bound (SMART). This involves clarification, prioritization, and validation activities. The refined requirements then become the basis for system analysis and system design. This step ensures that the requirements are unambiguous, complete, and aligned with the project’s goals. It ensures that all stakeholders have a mutual understanding of what the system is supposed to achieve. Requirements Analysis might answer: “What kind of staking rewards will incentivize network participation?” Components While system requirements analysis often starts with the gathering of initial requirements from stakeholders, the requirements analysis part dives deeper to scrutinize those requirements for clarity, completeness, feasibility, and relevance by: Ensuring that the gathered requirements are aligned with the project’s goals and stakeholders’ needs. Validation: : Deciding which requirements are must-haves versus nice-to-haves. Prioritization Breaking down high-level requirements into more detailed specifications. Clarification: Identifying and resolving conflicting requirements from different stakeholders. Conflict Resolution: The output of the requirements analysis process is often an updated and more detailed version of the Requirements Specification Document, which now includes prioritized, clarified, and validated requirements. Skills / Techniques Requirements Engineering Use-case modeling, user story mapping, and feature prioritization Mechanism Design and Game Theory Communication and Stakeholder Management Basic understanding of blockchain and Web3 concepts In the context of token ecosystems or blockchain projects, phase could involve mechanism design and game theory. These methods help in designing incentive structures, governance models, and other features that are crucial for decentralized systems. The focus here is on what mechanisms should be in place for the system to function as desired. Output/Deliverable A detailed document outlining what the system should do, often including mockups or wireframes. Requirements Specification Document: It includes both the initial requirements gathering and the more detailed Requirements Analysis. It outlines what the system is expected to do. The Requirements Specification Document focuses on the ‘what’ — what the system should achieve without detailing how it will do so. Example Let’s assume we’re building a Metaverse protocol that allows users to own, trade, and interact with digital assets in a virtual world. The requirements might include: User-owned digital assets (NFTs). A marketplace for trading assets. Virtual spaces for social interaction. Governance mechanisms for community decisions. Interoperability with other Metaverse protocols. Low latency and high performance. In system requirements analysis, we document these features and criteria that the Metaverse protocol must fulfill and duly scrutinize and refine these requirements. This stage sets the foundation and serves as a guideline for what the system is supposed to achieve System Analysis Once we know what the system is supposed to do, we proceed to analyze how it can be done and what challenges might arise. This step involves feasibility studies, risk management, stress testing, economic modeling, and sometimes, initial prototypes to validate key assumptions. The findings from this stage can refine or even alter the original requirements. System analysis also involves evaluating the existing similar systems and understanding various facets like user requirements, system limitations, and potential bottlenecks. It often includes analyzing the feasibility of the project in terms of technology and economics. It involves examining the implications of integrating with existing ecosystems or the potential for creating new systems. This phase, especially in complex systems like token ecosystems, involves to validate the feasibility and robustness of the mechanisms designed in the requirements analysis phase to validate assumptions about the system’s behavior, user incentives, and economic viability. These methods aim to foresee how the system will behave under different conditions. mathematical specification, differential specifications, state space representation, agent-based modeling and system dynamics modelling System Analysis might answer: “Will the staking mechanism stand up to extreme market volatility, and how will agents behave in such conditions?” System analysis aims to understand how to best implement the requirements and technical challenges associated with implementing those features and functionalities. This phase involves simulations, modeling, and other forms of validation to understand how the system can meet the defined requirements. It answers questions like “Is this technically feasible?” and “What are the potential risks?” Tasks Feasibility studies: Technical, economic, and operational viability. Preliminary modeling to validate assumptions or to understand system behavior. Risk assessment: Identifying potential bottlenecks, limitations or security risks. Modeling and Simulations: For behavioral modelling and user incentives design, to validate assumptions and understand system behavior. Skills / Techniques Mathematical Modelling (Mathematical Specifications, ) Differential Specifications ns State Space Representatio Statistical Modeling System Dynamics and Agent-Based Simulations Output/Deliverable Feasibility Report: Documenting the viability and risks associated with the project Updated Requirements: Modifications or refinements to the initial requirements based on analysis Mathematical and Differential Specifications of Mechanisms and System Mathematical (State Space Representations)and Statistical Models Simulations (Agent Based and System Dynamics) Example In this stage, we assess the feasibility and implications of the requirements. For our Metaverse example, this could include: Feasibility: Can current blockchain technology support the level of performance we need? Economic Model: How will the marketplace function economically? Are there any game-theoretical considerations for trading? User Behavior: How are users likely to interact with assets and each other? Stress Testing: How the agents will have behave in extreme conditions? Boundary Conditions: What worst can happen and how to avoid it? Security: What are the potential vulnerabilities that actors can exploit? System Design After understanding the requirements and completing the analysis, we move on to designing the architecture and components of the system. This stage results in a blueprint for building the actual system, including technology stack decisions, data models, and workflows. In the context of a decentralized system or a token ecosystem, System Design serves as the architectural blueprint that dictates how the system will be built and how its components will interact with each other. This phase comes after System Requirements Analysis and System Analysis have established what the system should do and verified that it’s feasible. Tasks Architectural design: Laying out the system’s high-level structure. Component design: Detailed design of each component, like smart contracts or APIs, Integrations. Data modeling: Deciding how data will be stored, accessed, and managed. Token Models, Strategies, KPIs, Skills Required Software Architecture Smart Contract Development Systems Engineering Verification and Optimization User Experience (UX) Design Output/Deliverable System Design Document: A comprehensive blueprint of the system architecture, components, data models, and interaction flows. The System Specification Document focuses on the ‘how’ — providing a blueprint for building the system. This document is more technical and is usually the output of the System Design phase. It outlines how the system will meet the requirements set forth in the Requirements Specification Document. The System Specification Document may include: Example After requirements are set and analyzed, we move on to system design to create the architecture that will satisfy these requirements. For our Metaverse protocol, this could involve: Blockchain Layer: Choosing between a layer 1 or layer 2 solution based on scalability needs. Smart Contracts: Designing the contracts that will handle asset ownership, trading, and governance. Tokenomics: Defining the role of tokens in incentivizing participation and governance. If we’ve determined in the Requirements Analysis that our token ecosystem needs a governance token, and the System Analysis has confirmed that such a mechanism is feasible, the System Design phase will specify: The smart contract code for minting and distributing governance tokens. Governance Structure, Quorum, Quorum Threshold, Voting Power etc. Summary These steps of product development are sequential but not strictly linear; they are often iterative and may loop back on each other. For example: Feedback Loops: During system analysis, you might discover that some requirements are not feasible or could be optimized, leading to a revision in the system requirements. Iterative Refinement: As the design progresses, new insights could necessitate revisiting the analysis or requirements stages. Agile Development: In agile frameworks, these steps might happen in smaller, iterative cycles rather than one large sequence, allowing for continual refinement. These phases are iterative and often loop back for refinements. For instance, during system design, you might realize a need to update requirements or re-analyze certain aspects, triggering a return to earlier phases. Each phase has its own set of specialized skills, but all contribute to the project’s ultimate goal: building a functional and effective Web3 system. Companies to Watch BlockScience Gauntlet TE Academy TE Labs Outlier Ventures TE Commons BlockApex Labs Tokenomia Pro Tokenomics DAO Economics Design CryptoEconLab Here is a list of companies indirectly contributing to the field of token engineering through valuable research: ConsenSys a16z Crypto Ocean Protocol Paradigm ChainLink Online Communities TE Academy Discord TE Labs Discord TE Academy Research Sessions TE Academy Study Groups Outlier Venture’s Discord Commons Stack Discord cadCAD Discord Twitter List Bonding Curve Research Group Tools The following is a list of tools commonly used in this domain, complete with links for further learning and understanding. Notable individuals working on these tools are also highlighted. Michael Zargham, , , , , , , , cadCAD study Group on TE Academy discord) cadCAD by BlockScience — Zanecstarr ( Introduction to cadCAD by Micheal Zargham Youtube Tutorials Model Walkthrough Model Structure Overview Study Group Modelling Process Community Forum — Machinations Curiousrabbit Eth radCAD by CADLabs , TokenSpice by Ocean Protocol Team — Trent McConaghy ( Verification of Token Based Systems Tools of verification ) QTM by Outlier Venture’s — Dr. Achim Struve QTM radCAD Integration (Ongoing) — Dr. Achim Struve (Ongoing) — Bonding curve Research Curiousrabbit Eth AI-powered Token Engineering (Ongoing) — Rohan Mehta GitHub Beyond the GitHub repositories for tools and models cited in this article, the following additional GitHub repositories are worth exploring. https://github.com/CADLabs https://github.com/BlockScience https://github.com/bonding-curves https://github.com/Jeiwan/uniswapv3-code/tree/main https://github.com/backstop-protocol/whitepaper/blob/master/Risk Analysis Framework.pdf https://github.com/A-Hitchhiker-s/Guide-to-Token-Engineering/tree/master Courses TE Academy Course Software Architecture & Design of Modern Large Scale Systems cadCAD Masterclass: Ethereum Validator Economics Additional Resources In addition to the numerous resources referenced throughout this article, the following additional materials can further deepen your understanding of this complex field. — Compiled Notes Token Engineering 101 Economics and Math of Token Engineering and Defi Tokenomics and blockchain tokens: A design-oriented morphological framework Token Ecosystem Creation Framework by Outlier Ventures g by Micheal Zargham Complex Systems Engineerin Also published . here

Token Engineering: A Blueprint for Building Sustainable Decentralized Economies

টোকেন ইঞ্জিনিয়ারিং—টেকসই বিকেন্দ্রীভূত অর্থনীতি গড়ে তোলার জন্য একটি ব্লুপ্রিন্ট

Token Engineering – Eine Blaupause für den Aufbau nachhaltiger dezentraler Volkswirtschaften

Ingeniería de tokens: un plan para construir economías descentralizadas sostenibles

Ingénierie des jetons : un modèle pour construire des économies décentralisées durables

टोकन इंजीनियरिंग- टिकाऊ विकेंद्रीकृत अर्थव्यवस्थाओं के निर्माण के लिए एक खाका

トークンエンジニアリング—持続可能な分散型経済を構築するための青写真

토큰 엔지니어링 - 지속 가능한 분산 경제 구축을 위한 청사진

Engenharia de Tokens – Um Projeto para Construir Economias Descentralizadas Sustentáveis

Разработка токенов — план построения устойчивой децентрализованной экономики

Token Mühendisliği—Sürdürülebilir Merkezi Olmayan Ekonomiler Oluşturmak için Bir Taslak

Kỹ thuật mã thông báo—Kế hoạch chi tiết để xây dựng nền kinh tế phi tập trung bền vững

代币工程——构建可持续去中心化经济的蓝图

Liquidity provisioning in Uniswap V3 presents a stochastic optimal control problem with a well-defined utility function to maximize. This article introduces an innovative framework for intelligent liquidity provisioning, utilizing a combination of agent-based modeling and reinforcement learning. Our framework provides a robust and adaptive solution for optimizing liquidity provisioning strategies. The Uniswap V3 model mimics real-world market conditions, while the agent-based model (ABM) creates an environment for simulating agent interactions with Uniswap V3 pools. The reinforcement learning agent, trained using deep deterministic policy gradients (DDPG), learns optimal strategies, showcasing the potential of machine learning in enhancing DeFi participation. This approach aims to improve liquidity providers’ profitability and understanding of CFMM markets. Content Overview Introduction Intelligent liquidity provisioning framework Components of Intelligent Liquidity Provisioning Framework Agent-based simulator Reinforcement Learning Model Limitations Future Work Conclusion Resources References Introduction In my previous article on market making we explored the mechanics and strategies of market making in traditional financial markets. Building upon those insights, this article introduces an innovative framework for intelligent liquidity provisioning in the context of Uniswap V3. As mentioned in our prior research, our goal was to extend our understanding of market dynamics and liquidity management in decentralized finance ( , specifically through the development of the Intelligent Liquidity Provisioning Framework. [Market Making Mechanics and Strategies], DeFi) Decentralized finance (DeFi) has undergone remarkable growth, introducing innovative financial products and services accessible to a global audience. Uniswap V3, at the forefront of this innovation, has revolutionized liquidity provisioning with its concentrated liquidity feature. However, this advancement brings forth complex decision-making challenges for liquidity providers. This article introduces a comprehensive framework designed to address these challenges, offering a simulated environment for studying and optimizing liquidity provisioning strategies. Our framework comprises three key components: the Uniswap V3 model, an agent-based model (ABM), and a reinforcement learning agent. The Uniswap V3 model provides a representation of the pool, enabling the deployment and interaction with tokens and pools. The ABM introduces complexity by simulating agent interactions and market dynamics, creating a rich environment for strategy evaluation. The reinforcement learning agent, operating within this environment, adopts a deep deterministic policy gradient approach to learn and adapt strategies, aiming for optimal performance in liquidity provisioning. This research aims to develop an intelligent liquidity provisioning (ILP) mechanism using reinforcement learning (RL) to autonomously manage and optimize liquidity within the Uniswap V3 environment. The mechanism seeks to maximize the utility function, considering fees earned, impermanent loss, and other metrics based on liquidity providers’ preferences while adapting to the complex dynamics of the CFMM market. Intelligent Liquidity Provisioning Framework In the RL framework, the liquidity provisioning problem is formulated as a Markov Decision Process (MDP). The MDP consists of states, actions, and rewards. States represent the current market conditions, including asset prices, trading volumes, and other relevant variables. States: Actions correspond to the decisions made by the liquidity provider, such as adjusting liquidity allocations, rebalancing portfolios, etc. Actions: Rewards quantify the desirability of the outcomes based on the liquidity provider’s objective function, preferences, and constraints. The rewards can be positive for desirable outcomes (e.g., high returns) and negative for undesirable outcomes (e.g., high risk or underperformance). Rewards: The objective function represents the liquidity provider’s desired outcome, which can be a combination of factors like maximizing returns, minimizing risks, or achieving a specific trade-off between the two. Constraints can include limitations on liquidity allocations, capital utilization, risk tolerance levels, or other restrictions defined by the liquidity provider. Objective Function: RL training is an iterative process where the agent continuously updates its policy based on feedback. The agent learns from its experiences and refines its decision-making over time, gradually converging to more optimal liquidity provisioning strategies. Once the RL agent has been trained, it can be tested and evaluated using historical data or simulated environments to assess its performance against the liquidity provider’s objective function and constraints. The agent’s performance can be measured using metrics like returns, risk measures, or other relevant performance indicators. By applying RL algorithm, the liquidity provisioning mechanism can learn and adapt to changing market conditions, identify optimal liquidity provision strategies, and balance constraints and preferences specified by the liquidity provider. RL enables the mechanism to find solutions that maximize the liquidity provider’s objective function, considering various trade-offs and constraints autonomously and dynamically. Components of Intelligent Liquidity Provisioning Framework The framework comprises three major components: UniswapV3 Model The Uniswap V3 model implemented in Python offers a detailed and functional simulation of the Uniswap V3 protocol, capturing its nuanced mechanics and providing users with a comprehensive toolset for interacting with the protocol. The UniswapV3_Model class handles the deployment of tokens and pools, initializes pools, and provides an interface for pool actions and pool state retrieval. Overview The Uniswap Model serves as the foundation of the Intelligent Liquidity Provisioning Framework, encapsulating the core mechanics of Uniswap V3. It leverages compiled smart contracts from Uniswap’s V3-Core, deployed in a local Ganache environment using brownie, to create a realistic and interactive simulation. Contract Compilation and Deployment The framework integrates with Brownie, a Python-based development and testing framework for smart contracts, to compile and deploy the Uniswap V3 smart contracts. These contracts are then deployed to a local Ganache environment, providing a sandbox for testing and development. This setup ensures that users can interact with the Uniswap environment without the need for real assets or network transactions, fostering a safe and controlled experimentation space. Agent-Based Simulator is used to simulate the Uniswap V3 environment, agent policies are defined to incorporate the dynamics of Uniswap market participants. Different types of agents are used to simulate the dynamic Uniswap environment Tokenspice agent-based simulator Introduction simulates the actions and interactions of individual agents within the Uniswap V3 ecosystem. By modeling the complex behaviors of different participants, the ABM provides a comprehensive interface of Uniswap V3 dynamic environment, enabling the analysis and optimization of liquidity provisioning strategies. Tokenspice Agent-Based Model (ABM) Agent Types and Behaviors The ABM includes various agent types, each representing a specific role within the Uniswap V3 ecosystem. The two main agents are the Liquidity Provider Agent and the Swapper Agent, which interact with the Uniswap pools to provide liquidity and perform token swaps, respectively. The behavior of these agents is dictated by policies defined in the file, ensuring that their actions are aligned with real-world strategies and market conditions. agents_policies.py This agent adds and removes liquidity from the Uniswap pools. It follows a set of policies that dictate its actions based on the current state of the market and the agent’s preferences. Liquidity Provider Agent: The Swapper Agent performs token swaps within the Uniswap pools, taking advantage of price discrepancies and arbitrage opportunities. Its behavior is guided by policies that assess the potential profitability of trades, considering transaction fees and slippage. Swapper Agent: Simulation Configuration and Execution The file is central to the ABM, configuring how agents interact with each other and with the Uniswap pools. It defines the relationships between agents, policies, and the simulation environment. netlist.py The , , and modules provide the foundational elements for running simulations. The SimEngine orchestrates the simulation, managing the flow of time and the execution of agent actions. The SimStateBase maintains the current state of the simulation, storing data on agent holdings, pool states, and other relevant variables. The SimStrategyBase defines the overarching strategies that guide agent behavior throughout the simulation. SimEngine.py SimStateBase.py SimStrategyBase.py Reinforcement Learning Model Introduction The Reinforcement Learning (RL) Agent is a pivotal component of the Intelligent Liquidity Provisioning Framework, designed to interact with the Uniswap V3 ecosystem through Uniswap Model an agent-based model. This section delves into the RL Agent, its environment, and the DDPG (Deep Deterministic Policy Gradient) algorithm used for training. RL Agent Environment The RL Agent operates in a custom environment, , which interfaces with the Uniswap model and the agent-based model to simulate the DeFi market. This environment facilitates the agent’s interaction with Uniswap pools, allowing it to add and remove liquidity, and observe the consequences of its actions. The RL Agent interacts with the Uniswap model and ABM to simulate real-world liquidity provisioning in Uniswap V3. It chooses actions that result in adding or removing liquidity, with policies and simulation configuration defined in the ABM, ensuring realistic interactions. DiscreteSimpleEnv The environment’s state space includes various market indicators such as the current price, liquidity, and fee growth. These parameters are normalized and provided to the agent at each timestep. State Space: The agent’s action space consists of continuous values representing the price bounds for adding liquidity to a Uniswap pool. These actions are translated into interactions with the Uniswap pools, affecting the state of the environment. Action Space: The reward function is crucial for training the RL Agent. It takes into account the fee income, impermanent loss, portfolio value, and potential penalties, providing a scalar reward signal to guide the agent’s learning process. Reward Function: DDPG Agent The DDPG Agent is a model-free, off-policy actor-critic algorithm using deep function approximators. It can handle high-dimensional state spaces and continuous action spaces, making it well-suited for our Uniswap V3 environment. This network is responsible for providing the best-believed action given a state. It has a sigmoid output layer, outputting relative values for and which are then scaled to the desired range in agent env, representing the price bounds for adding liquidity. Actor Network: price_lower price_upper This network evaluates the action value function, estimating the expected return of taking an action in a given state. Critic Network: DDPG employs target networks for both the actor and critic, which are slowly updated to stabilize training. Target Networks: This technique is used to store a replay buffer of past experiences, allowing the agent to learn from a diverse set of samples, breaking correlations in observations, and smoothing out learning. Experience Replay: Interaction with Uniswap Model and ABM The RL Agent leverages the Uniswap model and agent-based model to simulate real-world liquidity provisioning in Uniswap V3. It interacts with the Uniswap pools through the , performing actions that result in adding or removing liquidity. The agent’s policies and the simulation configuration are defined in the ABM component, ensuring a realistic and coherent dynamic environment. DiscreteSimpleEnv The agent is trained over a series of episodes, each representing a different market scenario (different pool). The agent’s performance is evaluated based on its ability to maximize returns while minimizing risks associated with liquidity provisioning. The effectiveness of the Intelligent Liquidity Provisioning Framework is assessed through the evaluation of the reinforcement learning (RL) agent’s performance. Train and Evaluate Agent: To evaluate the RL agent, we set up a specialized evaluation environment, , which extends the base environment, . This environment is tailored for the evaluation of agent policies. Environment Setup: DiscreteSimpleEnvEval DiscreteSimpleEnv In our evaluation setup, we compare the RL agent’s performance against that of a baseline agent. The baseline agent’s actions are determined by a baseline policy that relies on the current state of the liquidity pool. This agent aims to provide a reference point for evaluating the RL agent’s performance. Baseline Agent: Results Training Evaluation Limitations Currently, the framework does not fully capture the real-time synchronization of pools, which can lead to discrepancies in modeling real Uniswap V3 dynamics. Future work should focus on incorporating mechanisms for better pool synchronization, potentially utilizing tick/position data or events to enhance realism. Pools Synchronization: The agent policies employed in the current framework are relatively simple and naive. To achieve more accurate simulations, future iterations should aim to define more comprehensive agent policies. These policies could model various types of Uniswap agents, such as noise traders, informed traders, retail liquidity providers, and institutional liquidity providers. Alternatively, statistical models trained on historical pool data can inform agent policies for more realistic behavior. Naive Agent Policies: The observation space provided to the agents lacks comprehensive information about the state of the pool. To improve decision-making capabilities, future enhancements should include tick and position data, along with engineered features that offer agents a more comprehensive understanding of the pool’s status. Sparse Observation Space: The action space for agents is currently constrained, with fixed liquidity amounts and restricted price range bounds. Expanding the action space to allow for more flexibility in liquidity provision, as well as considering multiple positions per step, can enhance the fidelity of the simulations. Limited Action Space: Future Work Agent Environment: Implement mechanisms to synchronize pools, possibly using tick/position data or events, to create more realistic dynamics in the Uniswap V3 environment. Synced Pools: Actor/Critic Network Architecture, alpha, beta, tau, batch size, steps, episodes, scaling parameters (rewards, actions, observation space) Hyperparameter Tuning: Define more sophisticated analytical policies that accurately model various Uniswap agents or utilize statistical models trained on historical pool data to inform agent behavior. Comprehensive Agent Policies: Enhance the observation space by including tick and position data, and engineer features that provide agents with a comprehensive view of the pool’s state. Informative Observation Space: Develop an improved reward function that accounts for a wider range of factors, leading to more effective agent training. Improved Reward Function: Instead of one position with a fixed budget at each timestep, implement a more comprehensive mechanism in which the agent is allocated a budget once at the start of the simulation and then learns to use this budget optimally in subsequent steps. Multiple Positions: Define more comprehensive baseline policies to evaluate the performance of the RL agent Baseline Policies: Agent Algorithm Further refine and optimize the hyperparameters of the reinforcement learning agent for better training performance. Hyperparameter Tuning: Explore alternative RL agent models, such as Proximal Policy Optimization (PPO) or Soft Actor-Critic (SAC), to determine if they offer advantages in specific scenarios. Experimentation with Other RL Agents: Investigate the application of multi-agent reinforcement learning techniques, which can be beneficial for modeling interactions among multiple liquidity providers and swappers. Multi-Agent RL (MARL): Implement online learning strategies that allow agents to adapt to changing market conditions in real time, providing a more dynamic and adaptive liquidity provisioning solution. Online Learning: Conclusion In the rapidly evolving landscape of decentralized finance (DeFi), liquidity provisioning plays a pivotal role in enabling efficient and secure trading. Uniswap V3, with its innovative concentrated liquidity feature, has pushed the boundaries of what is possible in DeFi liquidity management. However, the complexities of optimizing liquidity provisioning strategies within this dynamic ecosystem require innovative solutions. Our Intelligent Liquidity Provisioning Framework represents a significant step forward in addressing these challenges. By combining agent-based modeling and reinforcement learning, we have created a powerful toolkit for liquidity providers and market participants. This framework offers a robust and adaptive solution for optimizing liquidity provisioning strategies, with a focus on maximizing utility functions that encompass fees earned, impermanent loss mitigation, and other metrics tailored to individual preferences. Resources Github: https://github.com/idrees535/Intelligent-Liquidity-Provisioning-Framework-V1 References https://kth.diva-portal.org/smash/get/diva2:1695877/FULLTEXT01.pdf https://arxiv.org/pdf/2305.15821.pdf https://github.com/KodAgge/Reinforcement-Learning-for-Market-Making/tree/main https://arxiv.org/ftp/arxiv/papers/2211/2211.01346.pdf https://arxiv.org/pdf/2004.06985.pdf https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9682687 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0277042 https://deliverypdf.ssrn.com/delivery.php?ID=104119098102026014120072084014107007042068069003049020126088025087121115103007084028042013055035009000054122074096068089064070102052026003014069082076098016080066026088066039027093020006122067093104092065070020126069068106118079127088008098077106031120&EXT=pdf&INDEX=TRUE https://medium.com/blockapex/market-making-mechanics-and-strategies-4daf2122121c https://www.gauntlet.xyz/resources/uniswap-user-cohort-analysis https://gov.uniswap.org/t/uniswap-incentive-design-analysis/21662 https://arxiv.org/pdf/2108.07806.pdf https://www.researchgate.net/publication/341848292_Market_makers_activity_behavioural_and_agent_based_approach https://fruct.org/publications/volume-29/fruct29/files/Struc.pdf https://www.arxiv-vanity.com/papers/1911.03380/ https://insights.glassnode.com/the-week-onchain-week-31-2023/ Also published . here

A Detailed Framework for Intelligent Liquidity Provisioning in Uniswap V3 

Uniswap V3-এ বুদ্ধিমান তারল্য বিধানের জন্য একটি বিস্তারিত কাঠামো

Ein detailliertes Framework für die intelligente Liquiditätsbereitstellung in Uniswap V3

Un marco detallado para el aprovisionamiento inteligente de liquidez en Uniswap V3

Un cadre détaillé pour l'approvisionnement intelligent en liquidités dans Uniswap V3

Uniswap V3 में बुद्धिमान तरलता प्रावधान के लिए एक विस्तृत रूपरेखा

Uniswap V3 におけるインテリジェントな流動性プロビジョニングの詳細なフレームワーク

Uniswap V3의 지능형 유동성 프로비저닝을 위한 세부 프레임워크

Uma estrutura detalhada para provisionamento inteligente de liquidez no Uniswap V3

Подробная структура интеллектуального предоставления ликвидности в Uniswap V3

Uniswap V3'te Akıllı Likidite Tedariği için Ayrıntılı Bir Çerçeve

Khung chi tiết để cung cấp thanh khoản thông minh trong Uniswap V3

Uniswap V3 智能流动性配置的详细框架

A Floating City and The Blockade Runners by Jules Verne is part of the HackerNoon Books Series. Read this book online for free on HackerNoon! A Floating City and The Blockade Runners Title: Jules Verne Author: April 14, 2022 [eBook #67829] Release Date: English Language: TABLE OF LINKS Part 1: A Floating City Chapter I. Chapter II. Chapter III. Chapter IV. Chapter V. Chapter VI. Chapter VII. Chapter VIII. Chapter IX. Chapter X. Chapter XI. Chapter XII. Chapter XIII. Chapter XIV. Chapter XV. Chapter XVI. Chapter XVII. Chapter XVIII. Chapter XIX. Chapter XX. Chapter XXI. Chapter XXII. Chapter XXIII. Chapter XXIV. Chapter XXV. Chapter XXVI. Chapter XXVII. Chapter XXVIII. Chapter XXIX. Chapter XXX. Chapter XXXI. Chapter XXXII. Chapter XXXIII. Chapter XXXIV. Chapter XXXV. Chapter XXXVI. Chapter XXXVII. Chapter XXXVIII. Chapter XXXIX. Part 2: The Blockade Runners Chapter I. The “Dolphin.” Chapter II. Getting under sail. Chapter III. Chapter IV. Crockston’s trick. Chapter V. The shot from the “Iroquois,” and miss Jenny’s arguments. Chapter VI. Sullivan Island channel. Chapter VII. A southern general. Chapter VIII. The escape. Chapter IX. Between two fires. Chapter X. St. Mungo. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Jules Verne (2022). A Floating City and The Blockade Runners. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/files/67829/67829-h/67829-h.htm This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

A Floating City and The Blockade Runners by Jules Verne - Table of Links

Few months ago Canada's Federal Court issued the country's first pirate 'site' blocking order. The order was requested by Bell, Rogers, and Groupe TVA, who recently asked the court to amend the order to ban additional domains that provide access the pirate IPTV service GoldTV. This request was granted. Despite attempts from the rightsholders to keep the update quiet, the new domains have been revealed as well. Last November Canada’s Federal Court approved the first in the country. piracy blockade Following a complaint from major media companies Rogers, Bell and TVA, the Court ordered several major ISPs to block access to domains and IP-addresses of the pirate IPTV service GoldTV. A few days after the order was issued the first blockades were active. These prevented GoldTV customers from accessing the IPTV portal directly, as intended, but it didn’t take long before several popped up. alternative domains These new domains are managed by GoldTV or its resellers and point to the access portal, allowing subscribers an unblocked route to access the IPTV service. This wasn’t entirely unexpected. While IPTV blockades are relatively rare, we have seen similar ‘proxy’ workarounds in the past when traditional pirate sites were blocked in other counties. Having learned from this experience, the Canadian court order specifically allowed Bell and the other companies to expand the blocklist. Specifically, they can amend the original blocklist with any “domain, subdomain or IP address that has as its sole or predominant purpose to enable or facilitate access to the Target Websites,” provided that the IP-address is “not associated with any other active domain.” Such an update was requested early last month and two weeks later the Canadian Federal Court approved it. An overview of the new blocking requirements was published this week by Andy Kaplan-Myrth, TekSavvy’s vice-president of regulatory affairs. This shows that, after some IP-addresses and a domain name were previously removed, several new ones were added with the latest order. The newly added addresses include gold.myiptvplanet.com, live4k.online, and several pctvhd.net and beex.me subdomains. When we checked these, all indeed redirected to the GoldTV access portal. According to reports we received, the new domains have been added to the blocklist of several Canadian ISPs, as expected. Avvidavids reveal that the new domains were tracked down by posing as a customer or reseller of the GoldTV service. Interestingly, the rightsholders asked to keep the names of the new domains secret until the order was granted. The Wire Report that they sent a to the ISP asking them to “refrain from publicizing” the new domains until the Court made a decision. notes letter (pdf) Keeping possible updates out of the public eye is in the interest of the copyright holders, as it prevents GoldTV from anticipating new blocks. However, it raises concerns among some legal experts who believe that information in a public case should be out in the open. If not, that should be up to a court to decide. That said, the Canadian procedure is much more transparent than in other countries such as the UK, where new blocklist updates aren’t published at all, making it impossible for the public to check for potential overblocking. While the expanded blocks are certainly frustrating for GoldTV customers, there will likely be new domains to replace them, continuing the whack-a-mole. The downside for the copyright holders is that there’s a significant delay in the process. Bell and the others first have to file for an amended order, which then has to be approved by the court. After that, it can take up to two weeks before ISPs implement the blockade. This whole process can take more than a month. In this timeframe, new domain names may have already been put into use. While website blocks are far from perfect, the continued frustration of switching to new domains may be enough for some pirates to throw in the towel. Or they may switch to more permanent circumvention alternatives, such as VPNs. Meanwhile, the bigger blocking battle continues as well. Internet provider TekSavvy has and hopes to have it overturned. It clearly violates network neutrality and undermines the open Internet, the ISP previously said. appealed the blocking order Originally published as “ Canadian Pirate Site Blockade Expands With New Domains ” with the Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0) license

Canadian Pirate Site Blockade Expands With New Domains

Russia is planning action against apps that facilitate access to pirated content. If app developers don't quickly respond to a request from authorities to cease distribution of their software, telecoms watchdog Roscomnadzor will demand action against platforms such as Google Play and Apple's App Store. Failure to respond could result in ISP blockades. Site-blocking is one of the entertainment industries’ preferred tools for dealing with the spread of unlicensed content. Operated in dozens of countries around the world and currently as an effective anti-piracy solution for the United States, site-blocking is not only but also struggles to keep up with piracy innovations. being championed controversial When file-sharing hit the mainstream, it was via software clients such as Napster, Kazaa and LimeWire. After traversing through torrent clients it then hit the web, with people more likely to stream or download from a site using a browser. Over the past few years, however, part of the market came full circle, with dedicated piracy apps gaining in popularity once again. While these pieces of software are usually downloaded from websites, they are effectively aggregators pulling content from a wide range of third-party platforms. It is technically possible to block all of those platforms to prevent apps from working but Russia now wants to deny access to the apps themselves. According to draft amendments to copyright law reviewed by , the plan is for telecoms watchdog Roscomnadzor to contact app developers/owners with a request for them to cease distribution of their infringing tools. They will be expected to respond positively and quickly, within a day. Kommersant In the event that fails, Roscomnadzor will then turn to the entity that “ensures the placement of the application”, i.e websites, distribution platforms etc. If no action is taken there, they could find themselves subjected to ISP blocking instead. According to Karen Ghazaryan, leading analyst at the Russian Association for Electronic Communications (RAEC), services such as Google Play and Apple’s App Store will be covered by the legislation. While some will view blocking of legitimate platforms as draconian, there doesn’t appear to be any desire to block entire sites after a failure to restrict access to infringing apps. Instead, the proposed blocking is at least intended to be precise, targeting the specific locations where the apps can be found. Whether that will be possible or whether over-blocking will raise its head again is unclear but Ghazaryn is concerned by the development and questions whether it is needed at all. Faced with copyright or trademark complaints, official platforms already remove applications from their stores. Nevertheless, Alexei Byrdin of anti-piracy group Internet Video Association suggests that such distributors should also face sanctions for failing to respond. “It’s worth expanding the ways of influencing aggregators so that they really respond, for example, provide for significant fines – such as those in place for search engines for not deleting links to pirated resources,” he told Kommersant. It’s certainly possible the government is hoping to tackle a broader range of distribution mechanisms other than Google Play or the App Store, however. When applications are denied distribution on the larger services, they tend to get pushed out to file-hosting platforms and third-party websites, which can also be blocked. That being said, pirate apps are typically quite small so they can also be distributed via email or chat applications. Those mechanisms are hard or even impossible to block but restricting access to the masses is probably the pragmatic goal of the legislation, not eradicating the apps completely. Originally published as “ Russia Plans to Block Access to ‘Pirate’ Apps on Google Play and Apple’s App Store ” with the Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0) license

Russian Watchdog to Place a Blockade on Pirate Apps

1. Major UAE Bank Employs Blockchain To Tackle Cheque Fraud The National Bank of Dubai (NBD), one of the largest banking groups in the Middle East in terms of asset volumes, is . The successful project, called Cheque Chain, made NBD the first bank in the region to adopt this technique. A QR code is printed on each cheque, and then recorded on blockchain. The new system improves security standards and cheque authenticity. A pilot was previously launched, under the supervision of Emirates NBD Future Lab. Almost a million chequebook pages have been registered with this system. The option of is also under consideration currently. The news comes following the . countering cheque fraud by using blockchain in its cheque-issuance system using the blockchain platform to collaborate with other UAE financial institutions launch of ‘UAE Blockchain Strategy 2021’ 2. Taiwan To Implement Crypto Regulations By November Qiu Taisan, Taiwan’s Minister of Justice, , in order to fight money laundering. Institutions such as the Ministry of Interior, the Central Bank and the Bureau of Investigation will all be involved in the drafting of these rules. Taisan shared the plan during the Financial Industry Money Laundering Prevention conference hosted by the Taiwan Financial Services Coalition. Two virtual currency operators have previously been invited by the government Legal Department to discuss virtual currency operations. The latter plans to learn about a ‘control mechanism’ by the end of November, when the Taiwan-based Asia Pacific Anti-Money Laundering Organization will take place. wants to implement a set of regulations for cryptocurrencies by November 3. Messaging Provider Line To Promote dApps Through Own Blockchain Japanese messaging giant Line wants to . During the 2018 DEV WEEK, CTO Euivin Park outlined the company’s efforts to create a token economy with dApps which are based on ‘incentive-type information services’. The company will also promote the development of dApp services which don’t take place on Line’s platform, as well as the future creation of its own blockchain mainnet. In order to achieve its plan, Line is increasing the number of developers. It was also previously the opening of a South Korea-based blockchain subsidiary called Unblock. promote the creation of dApps through its own blockchain platform reported 4. Dispute Over Ban on Banking Services for Crypto Companies in India We previously on many waiting for the Indian Supreme Court’s ruling on cryptocurrencies. A lack of clarity from the Reserve Bank of India (RBI), the country’s central bank, was also brought up. RBI has now been accused of breaching the constitution, by . This caused the RBI, the Centre, and the Goods and Services Tax (GST) Council to receive a notice by the Delhi High Court. A reply is expected by May 24. The issue began when Kali Digital Ecosystems Pvt Ltd wanted to launch a crypto exchange in the Indian territory, and its relationship with RBI broke down because of the ban, which has a 3-month implementation period. Not only are there calls for the ban to be nullified, but also for the GST Council to create regulations, which would reduce the current uncertainty. reported stopping financial entities from offering their services to anyone dealing with virtual currencies This news roundup was brought to you by . BlockEx To receive our daily news roundup in your mailbox, sign up here: http://bit.ly/BlockExNewsRoundup-Updates

23/04/2018: Biggest Stories in the Cryptosphere by BlockEx

From OpenAI to Closed AI: Custom Chips Are Closing The Doors—What’s Next? 

Ад OpenAI да закрытага AI: Карыстальніцкія мікрасхемы зачыняюць дзверы - што далей? 

De l'OpenAI a la IA tancada: els xips personalitzats estan tancant les portes: què passa? 

Von OpenAI zu Closed AI: Kundenspezifische Chips schließen die Türen – was kommt als Nächstes?

Από το OpenAI στο Closed AI: Τα προσαρμοσμένα τσιπ κλείνουν τις πόρτες—Τι ακολουθεί; 

De la IA abierta a la IA cerrada: los chips personalizados están cerrando las puertas. ¿Qué sigue?

Az OpenAI-tól a zárt mesterséges intelligenciaig: Az egyedi chipek bezárják az ajtókat – mi a következő lépés? 

Dari OpenAI ke AI Tertutup: Chip Kustom Menutup Pintu—Apa Selanjutnya?

OpenAI からクローズド AI へ: カスタム チップが扉を閉ざす — 次は何が起こるのか?

Kobanda na OpenAI tii na AI oyo ekangami: Ba puces personnalisées ezali kokanga ba portes —What’s Next? 

له OpenAI څخه تړل شوي AI ته: دودیز چپس دروازې بندوي — بیا څه دي؟ 

От открытого ИИ к закрытому ИИ: кастомные чипы закрывают двери — что дальше?

Від відкритого штучного інтелекту до закритого штучного інтелекту: спеціальні мікросхеми закривають двері — що далі? 

Financial Censorship: 5 Global Cases and How Crypto Can Help You Avoid It

الرقابة المالية: 5 حالات عالمية وكيف يمكن للعملات المشفرة أن تساعدك على تجنبها

আর্থিক সেন্সরশিপ: 5টি গ্লোবাল কেস এবং কীভাবে ক্রিপ্টো আপনাকে এটি এড়াতে সহায়তা করতে পারে

Censura financiera: cinco casos globales y cómo las criptomonedas pueden ayudarte a evitarla

Censure financière : 5 cas mondiaux et comment les crypto-monnaies peuvent vous aider à les éviter

צנזורה פיננסית: 5 מקרים גלובליים וכיצד קריפטו יכול לעזור לך להימנע מכך

Ֆինանսական գրաքննություն. 5 գլոբալ դեպքեր և ինչպես կրիպտոն կարող է օգնել ձեզ խուսափել դրանից

金融検閲: 世界的な事例 5 つと、暗号通貨でどのように回避できるか

Финансылык цензура: 5 глобалдык учур жана крипто кантип андан качууга жардам берет

Financiële censuur: 5 wereldwijde gevallen en hoe crypto u kan helpen dit te voorkomen

Kwemari Censorship: 5 Global Mhosva uye Sei Crypto Inogona Kukubatsira Kuidzivirira

Финансијска цензура: 5 глобалних случајева и како вам крипто може помоћи да је избегнете

مالیاتی سنسرشپ: 5 عالمی معاملات اور کس طرح کرپٹو آپ کو اس سے بچنے میں مدد کر سکتا ہے۔

Can Anything Stop the Death of the Token Airdrop? No, Bring It On.

የማስመሰያ የአየር ጠብታ ሞትን የሚያቆመው ነገር አለ? አይ፣ አምጣው

¿Kunas Token Airdrop jiwataparux jark’aspa? Janiwa, Apanipxam.

Qualsevol cosa pot aturar la mort del token Airdrop? No, porta-ho.

¿Puede algo detener la muerte del airdrop de tokens? No, adelante.

Edozerk gelditu al dezake Token Airdrop-aren heriotza? Ez, Ekarri.

آیا چیزی می تواند مرگ توکن ایردراپ را متوقف کند؟ نه، آن را روشن کن.

Algo pode deter a morte do token Airdrop? Non, trae.

トークンエアドロップの終焉を阻止できるものはあるか？いいえ、やって来い。

Misy afaka manakana ny fahafatesan'ny Token Airdrop ve? Tsia, Ampidiro.

Token howa gämisiniň ölümini duruzyp biljek zat barmy? .Ok, getir.

May Mapapahinto ba ang Kamatayan ng Token Airdrop? Hindi, Dalhin Ito.

Чи може щось зупинити смерть Token Airdrop? Ні, принеси це.

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