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I introduce parameterised response zero intelligence (PRZI), a new form of zero intelligence (ZI) trader intended for use in simulation studies of the dynamics of continuous double auction markets. Like Gode and Sunder's classic ZIC trader, PRZI generates quote prices from a random distribution over some specified domain of discretely valued allowable quote prices. Unlike ZIC, which uses a uniform distribution to generate prices, the probability distribution in a PRZI trader is parameterised in such a way that its probability mass function (PMF) is determined by a real-valued control variable s in the range [ - 1.0 , + 1.0 ] that determines the strategy for that trader. When s = 0 , a PRZI trader behaves identically to the ZIC strategy, with a uniform PMF; but when s ≈ ± 1 the PRZI trader's PMF becomes maximally skewed to one extreme or the other of the price range, thereby making it more or less "urgent" in the prices that it generates, biasing the quote price distribution towards or away from the trader's limit price. To explore the co-evolutionary dynamics of populations of PRZI traders that dynamically adapt their strategies, I show initial results from long-term market experiments in which each trader uses a simple stochastic hill-climber algorithm to repeatedly evaluate alternative s-values and choose the most profitable at any given time. In these experiments the profitability of any particular s-value may be non-stationary because the profitability of one trader's strategy at any one time can depend on the mix of strategies being played by the other traders at that time, which are each themselves continuously adapting. Results from these market experiments demonstrate that the population of traders' strategies can exhibit rich dynamics, with periods of stability lasting over hundreds of thousands of trader interactions interspersed by occasional periods of change. Python source code for PRZI traders, and for the stochastic hill-climber, have been made publicly available on GitHub. [ABSTRACT FROM AUTHOR]

We investigate which of the two most common prediction market mechanisms – market scoring rules and continuous double auctions – leads to better price discovery. The relative contribution of a particular prediction market to price discovery also depends on the total number of trades observed in that market. We use real-world prediction market price data to estimate price discovery shares of each market and their relationship to the number of trades. We find that when the number of trades is low, prediction markets that use market scoring rules have a higher speed of incorporating information into prices. When the number of trade is high, however, the continuous double auctions have a higher price discovery share. As market scoring rules require a variable cost – a subsidy – to operate, and continuous double auctions are costless; our results provide important implications for the design of prediction markets. By combining the methods for measuring price discovery and information aggregation, we introduce a new data-driven approach that can be used by researchers and practitioners to gain further insight into the exact values of the number of trades favoring the use of market scoring rules instead of continuous double auctions or vice versa. [ABSTRACT FROM AUTHOR]

As there have been no effective and reliable transaction mechanisms to standardize user credibility during distributed electricity transaction, an electricity transaction matching mechanism based on blockchain and mixed continuous double auction were proposed. Credit value was introduced by the mixed continuous double auction into adjustment to prices offered by users. The continuous double auction transaction matching was done through credit value and the weighted mean of offered prices. This type of matching mechanism works effectively in minimizing the effects of malicious node on electricity resource allocation and improving total revenues as well as the transaction enthusiasm of users. To maximize the total revenues, the pricing strategies based on equilibrium value were proposed, and the transactions between user electricity digital certificate and fees were realized trough blockchain. Finally, the transaction mechanisms were verified to be workable through distributed electricity transaction simulation experiments. [ABSTRACT FROM AUTHOR]

Several financial exchanges (e.g., IEX and NYSE American) recently introduced messaging delays to protect ordinary investors from high-frequency traders who exploit stale orders. To capture the impact of such delays, we propose a simple parametric model of the continuous double auction market format. The model examines the dynamics of midpoint pegged order queues and finds their steady states. It shows how messaging delays can protect pegged orders and improve investor welfare, but typically increase queuing costs. Recently available field data show that the empirical distribution of queued pegged orders is highly leptokurtotic and resembles the discrete Laplace distribution predicted by the model. This paper was accepted by Haoxiang Zhu, finance. Funding: Financial support from the European Research Council under the European Union's Horizon 2020 research and innovation programme [Grant 741409] and the Center for Analytical Finance at the University of California Santa Cruz [seed funding of the Experimental High-Frequency Trading project, of which this paper is a part] is gratefully acknowledged. Supplemental Material: The online supplement is available at https://doi.org/10.1287/mnsc.2022.4370. [ABSTRACT FROM AUTHOR]

Abstract Aiming at the data authenticity and storage problems in the current coordinated scheduling of virtual power plants, as well as the opaque information and high transaction costs, a dual blockchains security mechanism is proposed to solve above problems. In the process of security scheduling, a hybrid attribute proxy re-encryption algorithm based on ciphertext strategy is designed. The algorithm is composed of an identity encryption algorithm and an attribute proxy re-encryption algorithm with ciphertext strategy. Combining blockchain, tamper-proof smart metering equipment can effectively solve data authenticity and confidentiality in the information transmission process of distributed energy. In the research process of the trading mechanism, a continuous double auction mechanism based on reputation is proposed. In order to create a favorable trading atmosphere, reputation-based market segmentation mechanisms are integrated, and participants are divided according to reputation value. Depending on the properties of the stored information, they are divided into the private blockchain (with coordination scheduling information) and the consortium blockchain (with transaction information). The system analysis shows the reliability of the dual blockchains architecture. The communication and calculation costs of the proxy re-encryption algorithm verify the practicability of the proposed scheme. The case analysis of the auction mechanism declares that the mechanism can operate effective in the electricity trading market.

The increasing number of residential buildings that are installing distributed energy resources enforces the need for schemes to facilitate a local energy balance. With the continuing evolution of Internet of Things (IoT) technology, Peer-to-Peer (P2P) energy trading is becoming a viable solution to incentivize prosumers and promote efficient energy sharing in a community. This paper develops a model to quantitatively analyze the potential benefits of P2P energy trading for residential buildings that have installed photovoltaic battery systems. The integration of the bidding strategy into a residential energy-management system is feasible to realize cost savings for prosumers. However, the coordination between the bidding strategy and the optimal scheduling of energy has received far too little attention. To better participate in the P2P market, we propose a novel separate bidding energy-management system (SBEMS) that can realize rolling optimal energy scheduling while determining energy bids. The model's effectiveness is verified via case studies of 75 participants in a community. The results indicate that the prosumers can reduce their costs by up to 24% by employing the proposed SBEMS in the P2P market. In addition, the proposed method is found to offer better performance in terms of economic and technical indices. [ABSTRACT FROM AUTHOR]

Aiming at the data authenticity and storage problems in the current coordinated scheduling of virtual power plants, as well as the opaque information and high transaction costs, a dual blockchains security mechanism is proposed to solve above problems. In the process of security scheduling, a hybrid attribute proxy re-encryption algorithm based on ciphertext strategy is designed. The algorithm is composed of an identity encryption algorithm and an attribute proxy re-encryption algorithm with ciphertext strategy. Combining blockchain, tamper-proof smart metering equipment can effectively solve data authenticity and confidentiality in the information transmission process of distributed energy. In the research process of the trading mechanism, a continuous double auction mechanism based on reputation is proposed. In order to create a favorable trading atmosphere, reputation-based market segmentation mechanisms are integrated, and participants are divided according to reputation value. Depending on the properties of the stored information, they are divided into the private blockchain (with coordination scheduling information) and the consortium blockchain (with transaction information). The system analysis shows the reliability of the dual blockchains architecture. The communication and calculation costs of the proxy re-encryption algorithm verify the practicability of the proposed scheme. The case analysis of the auction mechanism declares that the mechanism can operate effective in the electricity trading market. [ABSTRACT FROM AUTHOR]

Infrastructure as a Service (IaaS) is a cloud computing service provided over the internet to facilitate the provisioning of various services such as storage, processes, etc. The provider in the IaaS market may offer some purchasing plans including: reservation, on-demand, and spot plans for its resources. As in real scenarios, demand volume for each plan is assumed to be a random variable with a given probability distribution. The provider maximizes its average revenue in the long run by optimal allocation of its resources among the plans. We formulate an Integer Linear Programming (ILP) model with a stochastic constraint, to determine the number of resources to be allocated for each plan in every time slot in the planning horizon. First, fixed prices are considered for each plan, then two mechanisms of Continuous Double Auction and Second Price Sealed Bid Auction are considered for reservations and spot plans, respectively, to obtain market-driven prices of the services. The Seasonal Weighted Moving Average method is used to predict the amount of demand in every slot. Finally, the proposed mechanisms are evaluated through simulations and the results confirm the effectiveness of the methods in maximizing the revenue and overall utilization of the available IaaS capacity. [ABSTRACT FROM AUTHOR]

In recent years, researchers have introduced many different mechanisms to improve resource allocation in the cloud. One of these resource allocation methods is market-based resource allocation which exploits different models used in exchanging goods and services. In this research, a two-way auction model is used for allocating cloud resources based on the market model. In the case of federated clouds, as the providers may face a shortage of resources during their operation; therefore, the continuous double auction model is suggested to create a cloud federation environment to support a suitable resource allocation among different providers. In our experiment 1, fixed pricing with Reputation-Aware Continuous Double Auction, Continuous Double Auction, and Market-Driven Continuous Double Auction models will be executed for resource allocation. It shows that both the resource efficiency and the income of the providers are improved in the federated clouds using these models. In experiment 2, with changing the type and number of the requested resources by customers and providers, the proposed federated model is also tested. The results of the experiments show that our proposed model for implementing federated clouds based on the continuous double auction model, in terms of successful allocation rates, resource efficiency and provider revenue, is better than other marketoriented models.

The increasing number of residential buildings that are installing distributed energy resources enforces the need for schemes to facilitate a local energy balance. With the continuing evolution of Internet of Things (IoT) technology, Peer-to-Peer (P2P) energy trading is becoming a viable solution to incentivize prosumers and promote efficient energy sharing in a community. This paper develops a model to quantitatively analyze the potential benefits of P2P energy trading for residential buildings that have installed photovoltaic battery systems. The integration of the bidding strategy into a residential energy-management system is feasible to realize cost savings for prosumers. However, the coordination between the bidding strategy and the optimal scheduling of energy has received far too little attention. To better participate in the P2P market, we propose a novel separate bidding energy-management system (SBEMS) that can realize rolling optimal energy scheduling while determining energy bids. The model’s effectiveness is verified via case studies of 75 participants in a community. The results indicate that the prosumers can reduce their costs by up to 24% by employing the proposed SBEMS in the P2P market. In addition, the proposed method is found to offer better performance in terms of economic and technical indices.

We implement a laboratory financial market where traders can access costly technology that reduces communication latency with a remote exchange. In this environment, we conduct a market design study on high-frequency trading: we contrast the performance of the newly proposed frequent batch auction (FBA) against the continuous double auction (CDA), which organizes trades in most exchanges worldwide. Our evidence suggests that, relative to the CDA, the FBA exhibits (1) less predatory trading behavior, (2) lower investments in low-latency communication technology, (3) lower transaction costs, and (4) lower volatility in market spreads and liquidity. We also find that transitory shocks in the environment have substantially greater impact on market dynamics in the CDA than in the FBA. [ABSTRACT FROM AUTHOR]

Leverage trading, which consists of short selling and buying on margin, has been introduced into stock markets in many countries, including China. Ever since, there have been heated debates on how leverage trading influences financial markets. In this paper, an agent-based artificial market model is developed to simulate market behaviors and to analyze the influence of the leverage ratio on liquidity, volatility and price-discovery efficiency. In our artificial market, heterogeneous agents submit limit orders based on the fundamentalist or chartist strategy, and their effective supplies and demands can be increased by short selling or margin trading. Numerical analyses are performed in both one-sided and two-sided markets. We find that in one-sided markets, leverage trading can increase market liquidity and volatility, and decrease price-discovery efficiency. However, in the two-sided market, the increase of liquidity is much smaller, the volatility is decreased, and the price-discovery efficiency is improved. Generally, this model provides some meaningful results, which are supported by many other studies, and these findings underscore the necessity of building up a two-sided market when introducing leverage trading into stock markets. [ABSTRACT FROM AUTHOR]

• A strategy for distributed Peer-to-Peer (P2P) energy trading was proposed. • A quantification strategy for flexible resources was proposed. • A pricing strategy with privacy and intelligence was proposed. • The impact of renewable energy uncertainty on bidding decisions was considered. • The strategy protects privacy, improves benefits, and enhances local energy balance. With the widespread application of distributed power generation, traditional energy consumers are becoming active prosumers. Giving full play to the role of the electricity market in resource allocation, peer-to-peer (P2P) energy trading between multiple prosumers and consumers helps increase the revenue of electricity sales and reduce the cost of electricity consumption. The participation of flexible resources in P2P energy trading achieves local power complementarity, improves benefits, and reduces the operating pressure of lines. To ensure the benefits of prosumers and stimulate their enthusiasm, it is necessary to propose an effective trading strategy to fully allow flexible resources to participate in P2P energy trading. Therefore, this paper establishes a P2P energy market based on the continuous double auction (CDA) mechanism. Based on the model predictive control (MPC) theory, we propose a minimum cost rolling optimization model to optimize the bidding quantity of flexible resources. Moreover, the Automatic Learning (AL) pricing strategy with privacy and learning is proposed. The AL bidding price strategy enables interactive learning among prosumers. In addition, the influence of bidding price on bidding quantity during the trading process is also considered. Furthermore, this paper considers the impact of renewable energy source (RES) uncertainty on the bidding decisions of prosumers. Finally, the effectiveness of the proposed strategy is verified by the simulation results of cases. The simulation results demonstrate that the proposed strategy can improve the benefits for prosumers, enhance the local balance ability, achieve price learning, and increase the transaction rate. [ABSTRACT FROM AUTHOR]

• Continuous double auction is applied to facilitate prosumer-consumer interactions. • A market prediction model is designed to model the market from a data perspective. • The market predictor is successfully integrated into the prosumer operation model. • The proposed model simultaneously improves the prosumer operation and market profit. With increasing prosumers employed with flexible resources, advanced demand-side management has become of great importance. To this end, integrating demand-side flexible resources into electricity markets is a significant trend for smart energy systems. The continuous double auction (CDA) market is viewed as a promising P2P (peer to peer) market mechanism to enable interactions among demand side prosumers and consumers in distribution grids. To achieve optimal operations and maximize profits, prosumers in the electricity market must act as price makers to simultaneously optimize their operations and trading strategies. However, the CDA-based market is difficult to model explicitly because of its information-based clearing mechanism and the stochastic bidding behaviors of its participants. To facilitate prosumers actively participating in the CDA market, this paper proposes a novel prediction-integration strategy optimization (PISO) model. A surrogate market prediction model based on Extreme Learning Machine (ELM) is developed, which learns the interaction relationship between prosumer bidding actions and market responses from historical transaction data. Moreover, the prediction model can be conveniently transformed and integrated into the prosumer operation optimization model in the form of constraints. Therefore, prosumer operations and market trading strategies can be jointly optimized through the proposed approach, facilitating the integration of flexible resources into electricity markets. Numerical studies demonstrate the effectiveness of the proposed model by comparing with existing CDA trading strategies under various market conditions. [ABSTRACT FROM AUTHOR]

Prediction markets are a promising instrument for drawing on the "wisdom of the crowds". For instance, in a corporate context they have been used successfully to forecast sales or project risks by tapping into the heterogeneous information of decentralized actors in and outside of companies. Among the main market mechanisms implemented so far in prediction markets are (1) the continuous double auction and (2) the logarithmic market scoring rule. However, it is not fully understood how this choice affects crucial variables like prediction market accuracy or price variation. Our paper uses an experiment-based and micro validated simulation model to improve the understanding of the mechanism-related effects and to inform further laboratory experiments. The results underline the impact of mechanism selection. Due to the higher number of trades and the lower standard deviation of the price, the logarithmic market scoring rule seems to have a clear advantage at a first glance. This changes when the accuracy level, which is the most important criterion from a practical perspective, is used as an independent variable; the effects become less straightforward and depend on the environment and actors. Besides these contributions, this work provides an example of how experimental data can be used to validate agent strategies on the micro level using statistical methods. [ABSTRACT FROM AUTHOR]

The installed capacity of distributed generation (DG) based on renewable energy sources has increased continuously in power systems, and its market-oriented transaction is imperative. However, traditional transaction management based on centralized organizations has many disadvantages, such as high operation cost, low transparency, and potential risk of transaction data modification. Therefore, a decentralized electricity transaction mode for microgrids is proposed in this study based on blockchain and continuous double auction (CDA) mechanism. A buyer and seller initially complete the transaction matching in the CDA market. In view of the frequent price fluctuation in the CDA market, an adaptive aggressiveness strategy is used to adjust the quotation timely according to market changes. DG and consumer exchange digital certificate of power and expenditure on the blockchain system and the interests of consumers are then guaranteed by multi-signature when DG cannot generate power due to failure or other reasons. The digital certification of electricity assets is replaced by the sequence number with specific tags in the transaction script, and the size of digital certification can be adjusted according to transaction energy quantity. Finally, the feasibility of market mechanism through specific microgrid case and settlement process is also provided. [ABSTRACT FROM AUTHOR]

The newest Distributed Ledger Technology platforms, which delegate the execution of complex tasks in the form of Smart Contracts, make it possible to devise novel local electricity market frameworks, which are performed in a fully automated fashion. This paper proposes a novel fully automated platform for energy and ancillary service markets in distribution networks, able to run in a decentralized fashion, bypassing the need for a physical central authority. The proposed platform, able to perform the role of Virtual Decentralized Market Authority, shows excellent potential applications in the management of local ancillary service markets in local energy communities of various sizes. The proposed Virtual Decentralized Market Authority showed reasonable running costs and comparable technical management capabilities with respect to a physical, centralized managing authority.

Decision-making of microgrids in the condition of a dynamic uncertain bidding environment has always been a significant subject of interest in the context of energy markets. The emerging application of reinforcement learning algorithms in energy markets provides solutions to this problem. In this paper, we investigate the potential of applying a Q-learning algorithm into a continuous double auction mechanism. By choosing a global supply and demand relationship as states and considering both bidding price and quantity as actions, a new Q-learning architecture is proposed to better reflect personalized bidding preferences and response to real-time market conditions. The application of battery energy storage system performs an alternative form of demand response by exerting potential capacity. A Q-cube framework is designed to describe the Q-value distribution iteration. Results from a case study on 14 microgrids in Guizhou Province, China indicate that the proposed Q-cube framework is capable of making rational bidding decisions and raising the microgrids’ profits.

Continuous asynchronous trading activity is a key to understanding real-world market behavior. However, it is not easy to implement an agent-based computational market model because of the ambiguity between time and space. In this study, we use a model of asynchrony in a continuous double auction market in the form of noise and order restrictions to link inside- and outside- uncertainties in the economic system. Our model shows intermittent behavior with a small parameter value, which leads to the misapplication of the price-update rule, and consequently drives burst behavior. The statistical property of time development shows a similar tendency to that in previous empirical studies. Thus, it demonstrates the relationship between the asynchronous property and the complexity of economic systems. [ABSTRACT FROM AUTHOR]

We introduce and treat rigorously a new multi-agent model of the continuous double auction or in other words the order book (OB). It is designed to explain collective behavior of the market when new information affecting the market arrives. The novel feature of the model is two additional slow changing parameters, the so-called sentiment functions. These sentiment functions measure the conception of the fair price of two groups of investors, namely, bulls and bears. Our model specifies differential equations for the time evolution of sentiment functions and constitutes a nonlinear Markov process which exhibits long-term correlations. We explain the intuition behind equations for sentiment functions and present numerical simulations which show that the behavior of our model is similar to the behavior of the real market. We also obtain a diffusion limit of the model, the Ornstein-Uhlenbeck type process with variable volatility. The volatility is proportional to the difference of opinions of bulls and bears about the fair price of a security. [ABSTRACT FROM AUTHOR]

We conduct laboratory experiments incorporating different market structures and insiders who compete with imperfectly informed traders. The insiders possess perfect information regarding the fundamental value of the tradable asset, whereas the imperfectly informed participants receive a noisy signal of fundamental value. In addition to the two trader types, we vary the market design by using either a stand-alone continuous double auction market or a continuous double auction market preceded by either a transparent or a nontransparent opening call auction. The results provide insights into whether and how insiders try to stay undetected, how their profits are accumulated and what market structures are advantageous for insiders. [ABSTRACT FROM AUTHOR]

This paper applies a simple sequential bargaining (SB) as a proxy for the continuous double auction (CDA) to study how the shape of supply and demand curves makes similar outcomes between the two markets populated by zero-intelligence (ZI) agents. At first, we derive analytical expressions for transaction probabilities of ZI pairs in the SB, as well as the three expected outcomes. Those are the allocative efficiency (AE), the average trading price, and the trading volume. Then, we conduct simulations by transferring the identical supply and demand functions from the SB to the CDA because of the complexity of its period. They produce similar statistical results as in the SB although the AE in the CDA is a little higher than that in the SB, while the trading volume is a little smaller than that in the SB. Statistical properties still hold for special shapes that make the long-run outcomes of CDA and the expected outcomes of SB deviate a lot from their corresponding competitive equilibriums. It is concluded that transaction probabilities or frequencies of ZI pairs help to explain how the principle of statistics determines both expected outcomes of a SB and long-run outcomes of a CDA populated by ZI agents. [ABSTRACT FROM AUTHOR]

In this study, we present a simple stochastic order-book model for investors’ swarm behaviors seen in the continuous double auction mechanism, which is employed by major global exchanges. Our study shows a characteristic called “fat tail” seen in the data obtained from our model that incorporates the investors’ swarm behaviors. Our model captures two swarm behaviors: one is investors’ behavior to follow a trend in the historical price movement, and another is investors’ behavior to send orders that contradict a trend in the historical price movement. In order to capture the features of influence by the swarm behaviors, from price data derived from our simulations using these models, we analyzed the price movement range, that is, how much the price is moved when it is continuously moved in a single direction. Depending on the type of swarm behavior, we saw a difference in the cumulative frequency distribution of this price movement range. In particular, for the model of investors who followed a trend in the historical price movement, we saw the power law in the tail of the cumulative frequency distribution of this price movement range. In addition, we analyzed the shape of the tail of the cumulative frequency distribution. The result demonstrated that one of the reasons the trend following of price occurs is that orders temporarily swarm on the order book in accordance with past price trends. [ABSTRACT FROM AUTHOR]

Considering the inconvenients that the assessment of technical indicators may handle in a real stock market, an agent based simulation model is presented in this document as an alternative where two micro aspects are considered for this kind of systems: agents’ decision making mechanisms and the continuous double auction protocol through which transactions are made. In particular, the following indicators are analyzed in an empirical way: Simple, Double and Triple Weighted Moving Averages; Momentum, ROC, MACD and RSI throughout a series of simulations for 280 agents adding also external factors that may affect prices by using a Brownian motion model.