Trading Framework Research Articles

The influence of codex guidelines on international trade: An analysis focused on Kimchi

The global food trade, with an estimated value of approximately USD 2,395 billion in 2022, encounters various obstacles pertaining to consumer health and the preservation of species. To mitigate these, governments enact legislation and regulations, potentially hindering international food trade. The FAO and WHO established the Codex Alimentarius Committee in 1963 with the goal of harmonizing international food standards, guidelines, and moral behavior for consumer health and fair trade. This paper analyzes the economic impact of Codex certification on Kimchi trade in Korea. Before Codex, various non-standardized versions of kimchi were marketed globally. The implementation codex standards serve to mitigate these disparities by establishing a standardized global norm. The examination of multivariate import and export demand functions investigates the impact of the codex on the trade dynamics of kimchi. The findings indicate a notable rise in kimchi exports to Japan and an increase in imports from China, resulting in benefits for multiple nations. The analysis employs regression models with variables like export prices, GDP per capita, financial crisis dummies, and Codex accreditation. Findings show Codex significantly increases Korean Kimchi exports by 7,096 metric tonnes annually and Chinese Kimchi imports by 99,628 metric tonnes yearly. This highlights Codex's potential to foster international trade and enhance consumer trust. In conclusion, Codex standards offer a robust framework for safe international trade while ensuring consumer safety and promoting market growth. The study provides insights into the economic implications of Codex standards for global food trade.

Research on the influence factors and improvement path of rural e-commerce development based on neural network

Abstract This paper uses big data evaluation models to predict market trends with the goal of promoting rural e-commerce development. A closely integrated algorithmic trading framework is constructed, and strategic evaluation indicators are proposed in traditional indicators. By analyzing the current situation and problems of online marketing of agricultural products, a series of development strategies and safeguards are formulated, and suggestions are made to establish QR code traceability and highlight personalized product development. The results show that age has a greater influence on the development of agribusiness. 65.73% of the 20-35 year olds choose to be willing to buy and do so through the Internet, which is 53.40% higher than the average, and 59.09% of the under-20 year olds choose to be unclear but willing to know, which is 28.64% higher than the average. The data studied and analyzed in this paper are useful for predicting the sales trend of the rural e-commerce market and promoting the development of rural e-commerce development.

An innovative high-frequency statistical arbitrage in Chinese futures market

The primary use of futures is hedging risk. Traders in the spot market can hedge certain risks through the futures market. With the development of the futures market, the arbitrage transactions around futures have attracted increasingly attention. The aim of this paper is to establish an innovative and unique pairs trading framework, and use it to test the effectiveness of China's futures market. The framework for pairs trading is based on cointegration test, Kalman filter and Hurst index filtering. We use the data of 47 commodities with relatively good liquidity in the Chinese commodity futures market. We apply the representative index of China's commodity futures market, "Wenhua Commodity Index" as the benchmark, to evaluate the performance of the strategy and compare it with the benchmark model. This study found that, according to the pairs trading framework, after considering transaction costs, the cumulative return in the sample reached 81%, the cumulative return out of the sample was 21%. It is worth noting that the out-of-sample maximum drawdown achieved excellent results of no more than 1%. In the same period, trading the Wenhua Commodity Index with a "buy and hold" strategy achieved a gain of 31%, but with maximum drawdown reached 15%. The values of our paper are, first it proves that Chinese commodity futures market is not a weak-form efficient market, because technical analysis based on machine learning could obtain excess returns. Second, this research combines the theory and practice of statistical arbitrage, which also provides guiding significance for investment practice.

Consortium Blockchain-Based Spectrum Trading for Network Slicing in 5G RAN: A Multi-Agent Deep Reinforcement Learning Approach

Network slicing (NS) is envisioned as an emerging paradigm for accommodating different virtual networks on a common physical infrastructure. Considering the integration of blockchain and NS, a secure decentralized spectrum trading platform can be established for autonomous radio access network (RAN) slicing. Moreover, the realization of proper incentive mechanisms for fair spectrum trading is crucial for effective RAN slicing. This paper proposes a novel hierarchical framework for blockchain-empowered spectrum trading for NS in RAN. Specifically, we deploy a consortium blockchain platform for spectrum trading among spectrum providers and buyers for slice creation, and autonomous slice adjustment. For slice creation, the spectrum providers are infrastructure providers (InPs) and buyers are mobile virtual network operators (MVNOs). Then, underloaded MVNOs with extra spectrum to spare, trade with overloaded MVNOs, for slice spectrum adjustment. For proper incentive maximization, we propose a three-stage Stackelberg game framework among InPs, seller MVNOs, and buyer MVNOs, for joint optimal pricing and demand prediction strategies. Then, a multi-agent deep reinforcement learning (MADRL) method is designed to achieve a Stackelberg equilibrium (SE). Security assessment and extensive simulation results confirm the security and efficacy of our proposed method in terms of players’ utility maximization and fairness, compared with other baselines.

Fruit Flies: Challenges and Opportunities to Stem the Tide of Global Invasions.

Global trade in fresh fruit and vegetables, intensification of human mobility, and climate change facilitate fruit fly (Diptera: Tephritidae) invasions. Life-history traits, environmental stress response, dispersal stress, and novel genetic admixtures contribute to their establishment and spread. Tephritids are among the most frequently intercepted taxa at ports of entry. In some countries, supported by the rules-based trade framework, a remarkable amount of biosecurity effort is being arrayed against the range expansion of tephritids. Despite this effort, fruit flies continue to arrive in new jurisdictions, sometimes triggering expensive eradication responses. Surprisingly, scant attention has been paid to biosecurity in the recent discourse about new multilateral trade agreements. Much of the available literature on managing tephritid invasions is focused on a limited number of charismatic (historically high-profile) species, and the generality of many patterns remains speculative.

P2P Energy Trading through Prospect Theory, Differential Evolution, and Reinforcement Learning

Peer-to-peer (P2P) energy tradingis a decentralized energy market where local energyprosumersact as peers, trading energy among each other. Existing works in this area largely overlook the importance of user behavioral modeling and assume users’ sustained active participation and full compliance in the decision-making process. To overcome these unrealistic assumptions, and their deleterious consequences, in this article, we propose anautomatedP2P energy-trading framework that specifically considers the users’ perception by exploitingprospect theory. We formalize an optimization problem that maximizes the buyers’ perceived utility while matching energy production and demand. We prove that the problem is NP-hard and we propose a Differential Evolution-based Algorithm for Trading Energy (DEbATE) heuristic. Additionally, we propose two automated pricing solutions to improve the sellers’ profit based on reinforcement learning. The first solution, named Pricing mechanism with Q-learning and Risk-sensitivity (PQR), is based on Q-learning. Additionally, given the scalability issues ofPQR, we propose a Deep Q-Network-based algorithm calledProDQNthat exploits deep learning and a novel loss function rooted in prospect theory. Results based on real traces of energy consumption and production, as well as realistic prospect theory functions, show that our approaches achieve 26% higher perceived value for buyers and generate 7% more reward for sellers, compared to recent state-of-the-art approaches.

CompCube: A Space-Time-Request Resource Trading Framework for Edge-Cloud Service Market

As the footing stone of artificial intelligence (AI), ubiquitous computing resource is beginning to receive interest. With this trend, a new form of edge-cloud service market dedicated to collecting, trading, and scheduling computing resources is rising. The computing participants in the service market, as providers and demanders of computing resources, are becoming more diversified and open. As such, the intriguing economic phenomenon and the circulation mechanism have emerged. These bring inherent challenges, such as a volatile market, the ossification of pricing, isolation, and inefficiency. In this paper, we propose a novel space-time-request trading framework for the edge-cloud service market, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CompCube</i> . To ensure stability, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CompCube</i> adopts the dual-circulation futures-spot trading method, including space-time dynamic pricing in the macro-cycle, request intention conversion, and resource matching in the micro-cycle. Among this, an incomplete information game model is designed to determine the long-term trading pricing in the macro-cycle. Then, to tackle the indicator isolation problem due to the inconsistency between the user's requests and the computing-power provider's (CPP's) resources, we focus on minimizing the rental cost of computing resources while satisfying diverse service level agreements (SLA) of users. To address this problem, a spatiotemporal scale Lyapunov optimization and an alternating actor-critic algorithm, A2SC, are developed. Besides, in the micro-cycle, a discriminatory double auction helps to determine the computing resource matching results efficiently and impersonally. We evaluate the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CompCube</i> of the A2SC algorithm with realistic datasets. Compared to other baselines, i.e., DYRECEIVE, Price Preferred, and Random, A2SC reduces the average rental cost by 30.45%, 5.74%, and 17.57%, respectively. Furthermore, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CompCube</i> can improve SLA satisfaction, as well as promote resource efficiency and social welfare compared with the traditional methods.

On-Demand Self-Media Data Trading in Heterogeneous Vehicular Networks

With the rapid development of autonomous driving and heterogeneous vehicular networks (HetVNets), vehicles can collect data and generate valuable information to obtain profits, thus forming a new vehicular self-media paradigm. However, with potential security risks, the current data trading schemes in the HetVNets lack the comprehensive consideration of the requirements of the participants including media data producers (MDPs), media data sellers (MDSs), and media data buyers (MDBs) to design a trading framework that integrates data caching services and data selling services. To this end, we propose an on-demand self-media data trading scheme in HetVNets. Specifically, based on blockchain and smart contracts, we first design an on-demand self-media trading architecture that jointly considers the data caching services and the data selling services to provide the participants with a secure transaction environment. In the data caching phase, by considering the value of media data generated by the MDPs and the sales capabilities of different MDSs, we model the requirements of the MDPs and the MDSs as utility functions and formulate the interactions between them as a Stackelberg game, where an iterative algorithm is designed for the MDPs and the MDSs to obtain their optimal strategies. In the data selling phase, we consider the diversified requirements of the MDBs and propose an iteration-based mechanism to help the MDSs dynamically determine the data selling strategies. Compared with the traditional schemes, the simulation results show that our scheme can obtain the optimal strategies for the MDPs and the MDSs and bring them the highest utilities.

Pathways from the (semi) Periphery: Early Assessment of EU Mercosur Trade Agreement in Principle

In the current international and domestic context, imagining a more complex bilateral trade agreement is more accessible than between the European Union (E.U.) and Mercosur (the South American International Trading Bloc). The tensions between these major trading blocs only aggravate international trade and economic relations. Consequently, the World Trade Organization (WTO) is bracing for its most significant challenge since its establishment in 1994. Between the E.U. and Mercosur's complex trade agreement and the WTO's challenge, the post-COVID-19 international legal environment has become even more complicated. &#x0D; The aims of this paper are: (1) to analyze the importance of the E.U.–Mercosur agreement within the current institutional trade framework; (2) to present some of the most controversial dilemmas posed by the present agreement; and (3) to evaluate the extent to which the agreement may support or hinder more sustainable and inclusive development among both trade partners. This study will focus on Brazil due to its geography, population size, and historical efforts to bring the nation closer to the international periphery and its radical turn to authoritarian populism. Additionally, Brazil's Amazon Rainforest's global necessity is essential for this study.

Human Trafficking and Piracy in Early Modern East Asia: Maritime Challenges to the Ming Dynasty Economy, 1370–1565

AbstractThis study asks how human trafficking in Ming China (1368–1644) became enveloped in the emerging global economy of the sixteenth century. Utilizing theoretical insights from the model of “slaving zones,” the essay examines recorded incidents of human trafficking along China’s littoral from 1370 to 1565 and contends that its presence and persistence were intertwined with the Ming court’s economic policies and problems. Here the history of human trafficking in early-to-mid-Ming China is viewed from the perspective of a series of challenges to the country’s economic well-being but also to its power to govern according to its own laws and norms. These challenges include the Ming regime’s efforts: to eradicate piracy and smuggling through their integration into the lawful framework of tribute trade; to support provincial requests for extra revenue to promote military security; to acquire Japanese silver but deny Japan mercantile access to China; to profit from Portugal’s Southeast Asian and Japanese commercial networks. This study argues that the increasing prevalence of human trafficking along China’s coastline was the result of competing forces anxious for power and riches that fused into the thrust of sixteenth-century China’s expanding economy, as well as the adaptability of those in authority to ignore the consequences of allowing safe havens for persons bartering and selling human beings. These factors turned the status of Ming China’s littoral from a “no slaving zone” into an “imperfect no slaving zone.”

Next-power: Next-generation framework for secure and sustainable energy trading in the metaverse

The rapid emergence of the Metaverse as a leading virtual digital technology is driving innovation in power generation. The increasing demand for sustainable energy and the rise of the Metaverse has provided an opportunity to develop a secure and sustainable energy trading system using Smart Grid (SG), Virtual Power Plants (VPP), Digital Twins (DT), and Blockchain. Integrating VPP with SG can optimize the utilization of distributed energy resources and improve the stability and reliability of the energy supply in the metaverse. Meanwhile, the use of DT can facilitate the efficient management of VPPs by simulating their behavior and performance to identify potential issues and test different scenarios for improvement. Blockchain is a rising technology that enables decentralized and trustworthy applications, making it a promising tool for creating decentralized energy trading systems in the Metaverse. In this context, we propose Next-Power, a next-generation framework for secure and sustainable energy trading in the Metaverse. Next-Power consists of: (1) an automated blockchain-based fair payment of energy traded between consumers and prosumers in the Metaverse, it uses Ethereum’s smart contracts to determine the price at which energy is traded using a cooperative game theory (i.e., reverse auction); (2) an access token-based blockchain system that ensures a secure mutual authentication between consumers and prosumers; and (3) a lightweight on-chain collaborative reputation scheme that keeps track of prosumers reputation as well as consumers credibility in the Metaverse in a fully decentralized manner. The system of Next-Power is implemented, tested, and deployed on the Ethereum official test network. Next-Power is a promising framework for the future of energy trading in the Metaverse, as demonstrated by its successful experimental results in terms of flexibility, efficiency, security, and cost-effectiveness.

Developing an Ethical Framework for Responsible Artificial Intelligence (AI) and Machine Learning (ML) Applications in Cryptocurrency Trading: A Consequentialism Ethics Analysis

The rise in artificial intelligence (AI) and machine learning (ML) in cryptocurrency trading has precipitated complex ethical considerations, demanding a thorough exploration of responsible regulatory approaches. This research expands upon this need by employing a consequentialist theoretical framework, emphasizing the outcomes of AI and ML’s deployment within the sector and its effects on stakeholders. Drawing on critical case studies, such as SBF and FTX, and conducting an extensive review of relevant literature, this study explores the ethical implications of AI and ML in the context of cryptocurrency trading. It investigates the necessity for novel regulatory methods that address the unique characteristics of digital assets alongside existing legalities, such as those about fraud and insider trading. The author proposes a typology framework for AI and ML trading by comparing consequentialism to other ethical theories applicable to AI and ML use in cryptocurrency trading. By applying a consequentialist lens, this study underscores the significance of balancing AI and ML’s transformative potential with ethical considerations to ensure market integrity, investor protection, and overall well-being in cryptocurrency trading.

Reliable and Secure X2V Energy Trading Framework for Highly Dynamic Connected Electric Vehicles

Vehicle-to-vehicle energy trading has become one of the most popular charge-sharing systems nowadays. It allows energy transfer between electric vehicles without being necessarily relied on infrastructure-based charging stations. However, the demands-offers matching and energy transportation between the vehicles remain challenging issues while considering vehicles' space and temporal location, their dynamicity, availability, and reliability. This paper addresses these issues by proposing a framework of energy trading based on blockchain and smart contracts. The energy transfer between vehicles is performed via a distributed coalition of unmanned aerial vehicles transporting the electric energy from selected sellers to a needy requester vehicle. The selection mechanism of sellers aims to maximize the service availability and fault-tolerance and minimize both the energy transportation latency and overhead. We modeled the selection process by a 0-1 knapsack problem, which we relaxed using a dynamic protocol of energy negotiation, and then developed a linear approach for its resolution. The seller reliability assessment is addressed by the proposition of a trust management approach, which evaluates over time the quality of participants regarding their history of transactions. We conducted intensive simulations with a comparison to the exact solution of resolution. The obtained results show a reduction of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$42\%$</tex-math></inline-formula> of charging latency, an improvement of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$24\%$</tex-math></inline-formula> of service availability, a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$96\%$</tex-math></inline-formula> of approximation from the exact resolution, and an increase of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$62\%$</tex-math></inline-formula> of robustness against unfulfilled commitments.

An Efficient and Secure Trading Framework for Shared Charging Service Based on Multiple Consortium Blockchains

While electric vehicles are proliferating rapidly, the number of charging piles is only 1/16 of the number of electric vehicles, showing big tension between small supply of public charging piles and intense charging demand of electric vehicles. To alleviate the tension, it is highly desired to incorporate existing idle private charging piles into a shared charging service. However, existing solutions do not well implement the system due to some non-negligible issues, like poor consensus performance or lack of verification for outputs from blockchain networks. In the paper, we propose an efficient and secure trading framework built atop multiple consortium blockchains. It devises a novel node voting mechanism so as to improve consensus efficiency. Meanwhile, it employs a publicly verifiable design based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(t,n)$</tex-math></inline-formula> Boneh-Lynn-Shacham (BLS) threshold signature technique, so as to enable entities outside the blockchain to verify the consistency between the output results with the valid consensus. Besides these novel system-level designs, it also introduces a fair and robust trading strategy running by smart contracts on the platform. Furthermore, we implemented a proof-of-concept system and conducted extensive experiments. The results show that the overall latency across chains is reduced by about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$26\%$</tex-math></inline-formula> with the node voting mechanism and the transactions fees are comparable to recent popular cross-chain applications.

A Cooperative P2P Trading Framework: Developed and Validated Through Hardware-in-Loop

In the past five years, the importance of peer-to-peer (P2P) trading for promoting the use of renewable energy has been demonstrated in many scientific literature and pilot projects. However, a large-scale demonstration of P2P trading in today’s electricity market is yet to be implemented. The reason for this can be partially attributed to two factors. First, most existing pilot projects focus on the aspects of communications between different participants and settlements of energy trading parameters between them without giving considerable attention to the impact of such trading on the distribution network. Second, scientific studies that have rigorously investigated the impact on distribution networks mostly validated their frameworks using numerical simulations without substantiating the findings through hardware implementation. In this paper, this gap is addressed by proposing a ready-to-implement canonical coalition game-based P2P energy trading scheme for electricity customers within a local distribution network. It is shown that the proposed scheme not only guarantees stable and fair outcomes for all participants, but also satisfies all mandatory power network constraints during the trading process. Importantly, the proposed framework is implemented in an RSCAD-RTDS (real-time digital simulator) platform and the readiness of the scheme for a real-field trial is demonstrated through validation in software-in-loop and hardware-in-loop setups. The usefulness and suitability of the proposed scheme to be implemented in today’s electricity network are established via both software-in-loop and hardware-in-loop case studies.

AI-Bazaar: A Cloud-Edge Computing Power Trading Framework for Ubiquitous AI Services

Driven by the burgeoning growth of the Internet of Everything and the substantial breakthroughs in deep learning (DL) algorithms, a booming of artificial intelligence (AI) applications keep emerging. Meanwhile, the advance in existing computing paradigms, i.e., cloud computing and edge computing, provide assorted computing solutions to satisfy the increasingly high requirements for ubiquitous AI services. Nevertheless, there are some non-trivial issues in the computing frameworks, including the underutilization of computing power, the self-interest of computing-power trading mechanism, and the inefficiency of AI services management. To tackle the above issues, we propose a computing-power trading framework based on blockchain, also named AI-Bazaar. In AI-Bazaar, the AI consumers play multiple roles and feel free to contribute the computing power rented from the computing-power provider (CPP) for blockchain mining and AI services. Accordingly, we formulate the computing trading problem as a Stackelberg game. Based on the win or learn fast principle (WoLF), we design a profit-balanced multi-agent reinforcement learning (PB-MARL) algorithm to search the AI-Bazaar equilibrium, while finding the balanced profits for AI consumers and CPP. Numerical simulations are carried out to demonstrate the satisfactory performance and effectiveness of the proposed framework.

Sino-US Conflict in Hybrid World War

This article offers a strategy that explores the idea of a Hybrid World War and how it can be applied to the current military struggle between China and the US. The global capitalist system, which arose as a worldwide framework for production, trade, and finance between the 16th and 18th centuries, is factored into the Hybrid World War concept in a way that the new-fangled Cold War was not. In addition to analyzing China's ascent and its global repercussions, this research work also focuses on how the current crisis in the unipolar global order has paved the way for strategic competition between China and US. This theoretical stance provides a new lens through which the US-China rivalry can be examined from several angles beyond conventional rivalry. We can understand the complexities of the ongoing battle between these two global powers by using the Hybrid World War paradigm, which investigates these dimensions.

Enhancing resilience by reducing critical load loss via an emergent trading framework considering possible resources isolation under typhoon

AbstractLeveraging distributed resources to enhance distribution network (DN) resilience is an effective measure in response to natural disasters. However, the willingness and economy of distributed resources are typically ignored. To address this issue, this paper proposes an emergent trading framework that uses parking lots (PLs) as resources to provide power support to critical loads (CLs) in a blackout due to typhoons. In this trading framework, an evolutionary Stackelberg game‐based trading model is established to consider maximizing all stakeholders’ economic benefits, considering possible resources isolation under typical fault scenarios caused by typhoons, and a benefit allocation mechanism is proposed for all stakeholders to motivate all stakeholders to participate in the trading. This framework allows that critical loads could reduce their load loss, parking lots could receive adequate compensation to stimulate them to participate in the trading, and distribution utility could ensure its economic benefits. Furthermore, an iterative evolutionary‐Stackelberg solution set‐up is applied to obtain the equilibria of the proposed framework. Simulation results on the modified IEEE 69‐bus test system and IEEE 123‐bus test system reveal the validity of the proposed method.

Peer-to-peer electricity trading considering voltage-constrained adjustment and loss allocation in blockchain-enabled distribution network

Interest in peer-to-peer (P2P) electricity trading has been growing, especially with respect to distributed energy resources (DERs) accommodation in distribution networks, which utilizes P2P trading mechanism to realize the decentralized operation of DERs. A key technology underpinning P2P electricity trading is the blockchain, which ensures that P2P trading is efficient and transparent. However, due to the limited calculation performance of blockchain, the blockchain-based P2P trading process has to be simplified, which makes on-chain grid security-constrained trading adjustment and on-chain power loss allocation become difficult to be solved. To address this challenge, a new on-chain P2P electricity trading framework with easy implementation is proposed. First, an on-chain linear power flow (OC-LPF) model is developed to reduce the difficulty of on-chain power flow calculation. Second, a novel self-adjustment loss allocation model with low complexity is proposed, which integrates the power loss into OC-LPF-based virtual power variable, and enables power flow calculation and power loss allocation to be performed synchronously. Third, a distributed market clearing algorithm with accelerated convergence based on the OC-LPF model is designed to improve the efficiency of on-chain convergence while protecting the privacy of participants. In addition, the effectiveness of the proposed mechanism has been verified with simulation studies on Ethereum.

Hierarchical Transactive Energy Scheduling of Electric Vehicle Charging Stations in Constrained Power Distribution and Transportation Networks

There is a growing opportunity to explore transactive energy potentials among electric vehicle charging station (EVCS) systems as the ongoing trend is toward the deployment of more distributed energy resources such as solar PV and energy storage units at EVCS premises. In this paper, we propose a multi-agent hierarchical framework for energy scheduling and trading of EVCSs considering the mobility constraints in the transportation network (TN) and the operational constraints in the power distribution network (PDN). Modeled as independent profit-driven entities, each EVCS optimally schedules its operation based on a multi-period traffic assignment problem (TAP) solved by the traffic operator (TO) agent. A modified single-sided auction mechanism with limited shared information is used to clear the electricity market based on submitted EVCS bids and offers. The resulting trading operations are shared with the PDN operator to guarantee a reliable network operation. A trading adjustment signal is sent back to market participants (i.e., EVCSs) in case of any PDN violations. We use a realistic three-phase unbalanced representation formulated as a MISOCP problem to model the PDN operation. A four-stage solution method is proposed to solve the proposed EVCS energy scheduling and trading problem. Numerical simulations performed on a modified IEEE 33-bus test system and 12-node benchmark TN prove the effectiveness of the proposed multi-agent-based hierarchical transactive market model and its solution approach for EVCSs.