Continuous Double Auction Research Articles

Conflicting versus reinforcing private information, information aggregation, and the time series properties of asset prices

We study how the relationship between independent private information signals affects information aggregation in laboratory asset markets. We employ two mechanisms, a continuous double auction and a prediction market. Under both mechanisms, when information is reinforcing, partial information aggregation occurs. When information is in conflict, information aggregation lessens and attempts to profit from private information frequently harm informational efficiency. In both mechanisms, results become stronger with experience in previous experimental sessions, and provide a private information benchmark for studies of the implications of conflicting public information. Under reasonable assumptions, our results are consistent with both momentum effects and weak reversals.

The impact of a preopening session on subsequent trading: An experimental analysis

AbstractIn this study, we examine the influence of a preopening call market (CM) session on the subsequent continuous double auction (CDA) trading phase within a combined market structure (CMDA). We propose that the introduction of a CM phase before the CDA could potentially mitigate price volatility during the CDA by facilitating the disclosure of agents' private information through the preopening pricing phase. Our findings reveal a positive relationship between the preopening price and the prices traded during the subsequent phase, alongside a decreased deviation of prices from their fundamental value when high‐quality information is available. These results suggest that a well‐defined preopening phase has the potential to improve market efficiency.

Data-Driven Knowledge-Aware Inference of Private Information in Continuous Double Auctions

Inferring the private information of humans from their strategic behavioral data is crucial and challenging. The main approach is first obtaining human behavior functions (which map public information and human private information to behavior), enabling subsequent inference of private information from observed behavior. Most existing studies rely on strong equilibrium assumptions to obtain behavior functions. Our work focuses on continuous double auctions, where multiple traders with heterogeneous rationalities and beliefs dynamically trade commodities and deriving equilibria is generally intractable. We develop a knowledge-aware machine learning-based framework to infer each trader's private cost vectors for producing different units of its commodity. Our key idea is to learn behavior functions by incorporating the statistical knowledge about private costs given the observed trader asking behavior across the population. Specifically, we first use a neural network to characterize each trader's behavior function. Second, we leverage the statistical knowledge to derive the posterior distribution of each trader's private costs given its observed asks. Third, through designing a novel loss function, we utilize the knowledge-based posterior distributions to guide the learning of the neural network. We conduct extensive experiments on a large experimental dataset, and demonstrate the superior performance of our framework over baselines in inferring the private information of humans.

IEL-CDA model: A more accurate theory of behavior in continuous double auctions

The continuous double auction (CDA) is a well-studied and widely used trading institution. However, there is no universally accepted theory regarding the dynamics of price formation, especially within the first period, that has endured experimental testing. In this paper, we introduce a behavioral model called IEL-CDA, which builds upon the Individual Evolutionary Learning (IEL) model of Arifovic et al. (2022). We enhance IEL by (a) incorporating Marshallian Selection, favoring traders with a higher expected surplus in making offers, and (b) allowing a trader's hypothetical reasoning to depend on the history of transactions. Using new experimental data, we test the hypothesis that efficiencies and average prices observed in the experiments follow the same distribution as those produced by simulations with various models. The hypothesis is rejected for both the Zero Intelligent theory of Gode and Sunder (1993) and IEL; however, it is not rejected for IEL-CDA. Therefore, IEL-CDA emerges as a more accurate theory of behavior in the continuous double auction.

A real time peer-to-peer energy trading for prosumers utilizing time-varying building virtual energy storage

With the increasing penetration of distributed energy resources (DER) in the electric power system, Peer-to-Peer (P2P) energy trading has become a promising paradigm for future electric power systems. Building thermal load, which is an important demand side resource, should be considered carefully in the design of a P2P trading method. In this paper, we investigate the application of building thermal energy storage capability in P2P energy trading. We aggregate and model building thermal loads as a virtual energy storage and derive a time-varying virtual energy storage system (T-VESS) model to quantify the flexibility of a building. Key parameters of T-VESS, including charging/discharging rate, energy capacity, and state of charge (SOC), are analyzed so that T-VESS can be embedded in the building prosumer model to participate effectively in electricity-oriented P2P energy trading. We propose a real time P2P energy trading method of prosumers based on model predictive control (MPC). This method consists of an energy supply and demand quantification stage and a transaction price optimization stage, which can effectively capture the time-varying nature of T-VESS. To preserve the trading preference for prosumers, we propose a distributed P2P energy trading actions implementation method based on the continuous double auction (CDA) considering multiple trading preference grades. Numerical results demonstrate that the proposed P2P energy trading method considering T-VESS can reduce the operational cost of prosumers by 3.7%, while also promoting the local integration of renewable energy by 3.1%. Furthermore, compared to existing P2P trading methods considering single preference grade, the proposed method greatly enhances the utilities of both individual prosumers and the overall community.

Mean-Field Multi-Agent Reinforcement Learning for Peer-to-Peer Multi-Energy Trading

With increasing numbers of prosumers employed with multi-energy systems (MES) towards higher energy utilization efficiency, an advanced energy management scheme is becoming increasingly important. The incorporation of MES into the existential energy market holds promise for future power systems. The continuous double auction (CDA) market, in a decentralized manner, makes it ideal for enabling peer-to-peer (P2P) energy trading due to its high transparency and efficiency. However, the CDA market is difficult to model when considering the highly stochastic and dynamic behaviors of market participants. For this reason, we formulate this task as a Decentralized Partially Observed Markov Decision Process and propose a novel multi-agent reinforcement learning method that allows each prosumer agent to stabilize the training performance with mean-field approximation and also to maintain the scalability and privacy with market public information. Case studies constructed on a real-world scenario of 100 prosumers show that our method captures the economic benefits of the P2P energy trading paradigm without violating the prosumers' privacy, and outperforms the state-of-the-art methods in terms of policy performance, scalability, and computational performance.

A decentralized peer-to-peer energy trading strategy considering flexible resource involvement and renewable energy uncertainty

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.

Parameterised response zero intelligence traders

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]\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$[-1.0, +1.0]$$\\end{document} that determines the strategy for that trader. When s=0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$s=0$$\\end{document}, a PRZI trader behaves identically to the ZIC strategy, with a uniform PMF; but when s≈±1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$s \\approx \\pm 1$$\\end{document} 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.

To Subsidize Or Not to Subsidize: A Comparison of Market Scoring Rules and Continuous Double Auctions for Price Discovery

To Subsidize Or Not to Subsidize: A Comparison of Market Scoring Rules and Continuous Double Auctions for Price Discovery

An Efficient Data-Independent Priority Queue and its Application to Dark Pools

We introduce a secure data-independent priority queue which supports polylogarithmic-time insertion operations and constant-time deletions and read-front (aka peek) operations as opposed to the originally introduced queue by Toft (PODC '11). Moreover, we minimize the number of comparisons required to perform different operations on Toft's priority queue. Data-independent data structures—first identified explicitly by Toft, and further elaborated by Mitchell and Zimmerman (STACS '14)—serve the purpose of computing on encrypted data without executing branching code which can be used to avoid prohibitively expensive operations in secure computation applications. Focusing on the costly sorting operations, we show significant asymptotic improvements over prior privacy preserving dark pool applications. Dark pools are securities-trading venues which attain ad-hoc order privacy, by matching orders outside of publicly visible exchanges via the so-called dark pool operators. In this paper, we describe an efficient and secure dark pool (implementing a full continuous double auction) based on our new priority queue. Our construction's security guarantees are cryptographic based on secure multiparty computation (MPC), and do not require that the dark pool operators are trusted. Our construction improves upon the asymptotic efficiency attained by previous efforts. Existing cryptographic dark pools process new orders in time which grows linearly in the size of the standing order book; ours does so in polylogarithmic time. We describe a concrete implementation of our MPC protocol with malicious security in the honest majority setting. We also report benchmarks of our implementation and compare them to prior works. Our protocol reduces the total running time by several orders of magnitude over prior secure dark pool solutions.

Integrated operation of gas and power system through the P2P market mechanism

AbstractPeer‐to‐peer (P2P) energy trading is a new technology for integrating distributed energy resources (DERs) into the power system. A P2P market allows direct energy trading between end‐users, enables local power and energy equilibrium and supports power grid operations. As a common DER, gas‐fired power plants are employed to deal with the intermittency of the power system due to their flexible characteristics. Therefore, the intermittency in the power system transmits to the gas system through the gas‐fired power plants, which makes the operation of these systems even more interdependent and cost‐effective. This paper proposes a market‐based two‐stage framework for the integrated operation of power and natural gas grids taking into account demand response and both network constraints. In the first stage (scheduling stage), the MINLP‐based optimisation approach is used for the optimal scheduling of two energy carriers considering AC power flow and gas hydraulic calculations for the next 24 h. Then, in the second stage, the continuous double auction (CDA)‐based P2P energy trading approach is used for enabling customers to trade energy with each other. To simulate human trader behaviour and maximises the benefits of customers, the authors considered the optimum bidding strategy through the zero intelligent plus trader model. The simulations executed on a 33‐bus power distribution grid and a 33‐node gas network indicate that the proposed framework can dramatically reduce the total operational cost and improve the performance of both networks. Using only the MINLP optimisation problem, first stage, the total operational cost of both networks is reduced by 15.58%, while the voltage profile at the end of the power grid is improved by about 7%. In the next stage, the total operating cost of both networks is further decreased by 29.31% via implementing the P2P energy trading mechanism.

COMPETITIVE MARKET BEHAVIOR: CONVERGENCE AND ASYMMETRY IN THE EXPERIMENTAL DOUBLE AUCTION

AbstractWe conducted a large number of controlled continuous double auction experiments to reproduce and stress‐test the phenomenon of convergence to competitive equilibrium under private information with decentralized trading feedback. Our main finding is that across a total of 104 markets (involving over 1,700 subjects), convergence occurs after a handful of trading periods. Initially, however, there is an inherent asymmetry that favors buyers, typically resulting in prices below equilibrium levels. Analysis of over 80,000 observations of individual bids and asks helps identify empirical ingredients contributing to the observed phenomena including higher levels of aggressiveness initially among buyers than sellers.

Insights on the Statistics and Market Behavior of Frequent Batch Auctions

This paper extends previous research performed with the SHIFT financial market simulation platform. In our previous work, we show how this order-driven, distributed asynchronous, and multi-asset simulated environment is capable of reproducing known stylized facts of real continuous double auction financial markets. Using the platform, we study a pricing mechanism based on frequent batch auctions (FBA) proposed by a group of researchers from University of Chicago. We demonstrate our simulator’s capability as an environment to experiment with potential rule changes. We present the first side-by-side comparison of frequent batch auctions with a continuous double auction. We show that FBA is superior in terms of market quality measures but we also discover a potential problem in the technical implementation of FBA.

Day‐ahead continuous double auction‐based peer‐to‐peer energy trading platform incorporating trading losses and network utilisation fee

AbstractIntegration of distributed energy resources, such as photovoltaic solar (PV), introduces new opportunities to establish local energy market frameworks to improve renewable energy utilisation in residential sectors. Such peer‐to‐peer (P2P) energy trading refers to a local market structure where customers (and prosumers) interact to share excess PV generation to enhance the individual and community social welfare. In this work, a day‐ahead continuous double auction (CDA)‐based P2P market structure considering network losses and network utilisation fees was designed. Day‐ahead PV energy is modelled using fractional integral polynomials and the output is forecasted using an autoregressive integrated moving average model for each market interval. Based on the customer load and excess PV energy, the CDA market is cleared using a bid/ask matching mechanism. The performance of the P2P market was evaluated by computing different welfare metrics while analysing the effect of network constraints. The results show that the designed CDA‐based P2P market structure increases the social welfare of all participants by an average of 17.75% compared to the baseline for the presented cases. Moreover, the impact of the forecasting error between the day‐ahead and real‐time market was also quantified.

A privacy-preserving trading strategy for blockchain-based P2P electricity transactions

With the rising demand for energy in distribution networks and the growing development of communication technology, peer-to-peer (P2P) electricity trading realized by blockchain technology not only promotes the participation of distributed generation resources in the energy supply but also enhances the fairness of P2P electricity trading through the decentralized and transparent characteristics of the blockchain. However, owing to the openness and transparency of the on-chain data in the blockchain, the order information submitted by P2P market participants to the blockchain may encounter threats of information disclosure. To address this challenge, we have designed a novel three-layer architecture for P2P electricity trading, whose primary objective is to protect the private information submitted to the blockchain through a new privacy-preserving trading strategy. The proposed three-layer architecture is composed of a physical layer, information layer and market layer. An on-chain data protection method with homomorphic encryption and secure multi-party computing (SMPC) is used to sort and clear encrypted orders in the information layer, and the trading checking and safe settlement process of encrypted orders in continuous double auction (CDA) market are realized through grid security-constrained market mechanism in the market layer. In addition, the effectiveness of the proposed method is verified by case studies running on the IBM Hyperledger Fabric.

Automated Market Maker vs Continuous Double Auction: An Asset Market Experiment

Automated Market Maker vs Continuous Double Auction: An Asset Market Experiment

Strategic Peer-to-peer Energy Trading Framework Considering Distribution Network Constraints

With the development of smart home energy management technology, prosumers are endowed with increased initiative in peer-to-peer (P2P) transactions, bringing new potential for cost savings. In this study, a novel strategic P2P energy trading framework is proposed considering the impact of network constraints on personal transaction strategies. Prosumers can estimate the allowed power injection before engaging in the P2P energy trading, which is solved in a distributed manner based on the sharing form alternating direction method of multipliers (ADMM) algorithm. To quantify the network usage cost for each prosumer and promote local transactions among prosumers at the same bus, a modified continuous double auction (CDA) matching algorithm is proposed including a transaction fee. An adaptive aggressiveness-based bidding strategy is generated considering the risk of uncertainty in real-time energy delivery amount under the limitations of the distribution network. The proposed strategic P2P energy trading framework is tested with the IEEE 37-bus distribution network and it is effective in creating profits for prosumers and supporting distribution network operations.

A Multi-Stage Information Protection Scheme for CDA-Based Energy Trading Market in Smart Grids

Building a reliable information exchange scheme is the prerequisite for Peer-to-Peer (P2P) energy trading in the smart grid. This paper proposes a multi-stage information protection scheme to meet the data security requirements including personal privacy-preserving, message authentication and confidentiality. In this study, the adversary is supposed to attack the continuous double auction (CDA)-based energy trading system in both the bidding period and implementation period. To prevent the smart grid from all potential cyber-attacks, our protection scheme is proposed to deal with personal privacy leakage, malicious data injection and impersonation attack. Before the trading period, in the home energy data collection stage, we propose a dual privacy-preserving scheme based on the short key homomorphic encryption and the decentralized framework. In the trading period, an encryption-signature mixed (E-S) communication model is proposed to achieve the secure transmission of trading messages (e.g., energy bidding from prosumer). At last, in the implementation period, a decentralized field measurement monitoring scheme is proposed to detect the node compromise attack to the power measurements. In addition, the computational feasibility, time cost and communication cost are evaluated with the home-use PC. The anti-attack performance of the proposed scheme is verified on the IEEE 39-bus distribution network with the CDA-based P2P energy trading market.

Comments on “the role of information in a continuous double auction: An experiment and learning model” by Mikhail Anufriev, Jasmina Arifovic, John Ledyard and Valentyn Panchenko

Anufriev et al. study whether more detailed order book information is always better for market efficiency in a modified continuous double auction market. This paper belongs to a broad research agenda on the impact of information transparency and trading speed on market stability. This comment discusses the implication of this research agenda on the design of market institutions and regulatory policies with the rise of mobile trading platforms and algorithm trading.

The role of information in a continuous double auction: An experiment and learning model

We analyze trading in a modified continuous double auction market. We study how more or less information about trading in a prior round affects allocative and informational efficiency. We find that more information reduces allocative efficiency in early rounds relative to less information but that the difference disappears in later rounds. Informational efficiency is not affected by the information differences. We complement the experiment with simulations of the Individual Evolutionary Learning model which, after modifications to account for the CDA, seems to fit the data reasonably well.