Real-time Trading Research Articles

Online Optimization for Real-Time Peer-to-Peer Electricity Market Mechanisms

Owing to the fast deployment of distributed energy resources (DERs) and the further development of demand-side management, small agents in electricity markets are becoming more proactive. This may boost the development of peer-to-peer (P2P) market mechanisms. Meanwhile, since actual load and power generation may substantially deviate from schedules obtained at the day-ahead (forward) market stage, it is needed to rapidly reschedule the trades among agents to maintain power balance through a real-time market mechanism, also in a P2P framework. However, it is technically challenging to develop and operate such P2P market mechanisms in real-time, since they most often involve a heavy computational burden (e.g., based on iterative distributed optimization approaches), while real-time trading demands fast calculation. Our core contribution is hence to describe and analyze a novel online optimization framework to enable the real-time P2P market. It relies on online social welfare maximization using a novel online consensus alternating direction method of multipliers (OC-ADMM) algorithm. The computational complexity is then heavily reduced since only one iteration is performed for each agent at every time step in order to satisfy real-time requirements. We derive a sublinear non-stationary regret upper bound for our algorithm, which implies that social welfare will be maximized in the long run. Simulations based on a number of case studies show that our algorithm has good convergence performance, tracking ability, and high computational efficiency.

Global cryptocurrency trend prediction using social media

This paper aims to investigate the global crypto-currency price movement trends with respect to the social media communication data. The idea is to analyze the topical trends in the online communities and social media platforms to understand and extract insights that could be used to predict the price fluctuations in crypto-currencies. The hypothesis rests in finding the empirical evidence to exploit the relationship between price variations and social media activities. Such models and insights will help us better understand the crypto currency ecosystems in context of social media behavior which can be used for real-time trading systems.

An Energy Internet for India: are we ready for a technology leapfrogging?

Abstract Globally, national electricity systems are amid aggressive transitions. The shift from large fossil-fuel based power systems to many small-scale distributed renewable energy systems is a favourable paradigm for a transition to the Energy Internet. Relying on wide spectrum of emerging technologies such as information & communication technologies, the Energy Internet facilitates real-time trade of power through bidirectional flows of electricity, communication, and money. Energy transitions in India are at a fast pace and further transformation to an Energy Internet is an achievable dream. Current structure of expedited energy transition is experienced in the form of transformation in business models of energy utility service companies and energy markets. The objective of this paper is to systematically assess the ongoing transitions, the challenges and the opportunities and examine whether India is ready to leapfrog into a new way of energy transactions which will gradually transform the present electricity system to an Energy Internet. Our findings elaborate a reality check on India’s current transition pathways and answer the question: can an Energy Internet become real in near future?

Online Learning Approach for Predictive Real-Time Energy Trading in Cloud-RANs.

Constantly changing electricity demand has made variability and uncertainty inherent characteristics of both electric generation and cellular communication systems. This paper develops an online learning algorithm as a prescheduling mechanism to manage the variability and uncertainty to maintain cost-aware and reliable operation in cloud radio access networks (Cloud-RANs). The proposed algorithm employs a combinatorial multi-armed bandit model and minimizes the long-term energy cost at remote radio heads. The algorithm preschedules a set of cost-efficient energy packages to be purchased from an ancillary energy market for the future time slots by learning both from cooperative energy trading at previous time slots and by exploring new energy scheduling strategies at the current time slot. The simulation results confirm a significant performance gain of the proposed scheme in controlling the available power budgets and minimizing the overall energy cost compared with recently proposed approaches for real-time energy resources and energy trading in Cloud-RANs.

Design of renewable energy consumption market system based on the interactive transaction of source-grid-load-storage

Renewable energy has the characteristics of clean, safe and low variable cost. High proportion of renewable energy access is of great significance for improving energy structure and improving power economy. However, with the continuous increase of renewable energy access ratio, in the case of full guaranteed acquisition, the peak load regulation capacity of power generation side will be insufficient, which can’t meet the requirements of full purchase of renewable energy, resulting in the phenomenon of power abandonment. It is urgent to stimulate the interactive ability of all users to participate in the consumption of renewable energy. Based on this, this paper puts forward a renewable energy consumption market system which includes medium and long-term, day ahead and real-time trading mode to promote the interaction between source network and load storage. Through the design of the mechanism, we can give full play to the regulatory potential of the user side and let the more efficient renewable energy units obtain more auxiliary service resources.

Gambling and Online Trading Emerging Risks of Real-Time Stock and Cryptocurrency Trading Platforms

Aims: Online platforms enable real-time trading activities that are similar to those of gambling. This study aimed to investigate the associations of traditional investing, real-time stock trading, and cryptocurrency trading with excessive behavior and mental wellbeing.Design: Cross-sectional population survey.Setting: Population-based survey in Finland.Participants: Finnish people aged 18–75 years (N = 1530, 50.33% male).Measurements: Survey asked about monthly regular investing, real-time stock-trading platform use, and cryptocurrency trading. The study had measures for excessive behavior: gambling (Problem Gambling Severity Index, PGSI), gaming (Internet Gaming Disorder Test, IGDT), internet use (Compulsive Internet Use Scale, CIUS), and alcohol use (AUDIT-C). Psychological distress (Mental Health Inventory, MHI-5), perceived stress (Perceived Stress Scale, PSS), COVID-19 anxiety, and perceived loneliness were also measured. Background factors included socio-demographic variables, instant loan taking, and involvement in social media identity bubbles (Identity Bubble Reinforcement Scale, IBRS-9)Findings: Within the sample, 22.18% were categorized into monthly regular investors only, 3.00% were investors using real-time stock-trading platforms, and 3.48% were crypto-market traders. Real-time stock-trading platform use and cryptocurrency trading were associated with younger age and male gender. Real-time platform users were more involved in social media identity bubbles than non-investors. Cryptomarket traders were more likely to have an immigrant background and have taken instant loans. Both real-time stock-trading platform use and cryptomarket trading were associated with excessive forms of gambling, gaming, and internet use. Cryptomarket traders had more issues with mental wellbeing, manifested in higher psychological distress, perceived stress, and loneliness.Conclusions: Regular investing is not a risk factor for excessive behavior. However, rapid online trading platforms and applications were significantly more commonly used by participants reporting excessive behavior and mental health issues. The strong association between cryptomarket trading and excessive behavior in particular underlines the need for harm prevention policies in this area of trading practices.Funding: The study was funded by the Finnish Foundation for Alcohol Studies (Gambling in the Digital Age Project, 2021, PI: A. Oksanen). Ilkka Vuorinen was supported by grant from the Jenny and Antti Wihuri Foundation.Declaration of Interest: None of the authors have a conflict of interest to declare.Ethical Approval: The study was approved by the Academic Ethics Committee of Tampere region inFinland in March 2021.

A rolling horizon approach for real-time trading and portfolio optimization of end-user flexibilities

Flexible energy consumption patterns represent a significant potential for end-users to reduce their energy costs due to the increasing price volatility in the electricity spot markets. However, a simple and exhaustive description of flexibilities is needed to efficiently coordinate multiple flexible components. In this paper, we propose a simple and complete method to describe the end-user flexibility potential. The proposed method describes flexibilities of the end-user technologies as a combination of inflexible loads and virtual batteries with variable capacities. This technique allows to describe the flexible components (such as heat pumps and boilers) with the exclusive use of linear relationships which can be implemented in mixed integer optimization problems. Furthermore, a rolling horizon optimization framework is used to define the operating strategy of the end-user components and the trades at the Day-Ahead and the Intraday spot markets. Moreover, we apply our proposed methods to a real-life use case in Austria with measured data to prove their effectiveness, validity and reliability. The results show that trading energy in the Day-Ahead and the Intraday spot markets can lead the end-user to a reduction of the energy procurement costs of 8%, but at the same time increases its energy consumption by 3.21%.

Meet me halfway

From Industry 4.0-driven factories to real-time trading algorithms, businesses depend on analytics on high-velocity real-time data. Often these analytics are performed not in dedicated stream processing engines but on views within a general-purpose database to combine current with historical data. However, traditional view maintenance algorithms are not designed with both the volume and velocity of data streams in mind. In this paper, we propose a new type of view specialized for queries involving high-velocity inputs, called continuous view. The key component of continuous views is a novel maintenance strategy, splitting the work between inserts and queries. By performing initial parts of the view's query for each insert and the remainder at query time, we achieve both high input rates and low query latency. Further, we keep the memory overhead of our views small, independent of input velocity. To demonstrate the practicality of this strategy, we integrate continuous views into our Umbra database system. We show that split maintenance can outperform even dedicated stream processing engines on analytical workloads, all while still offering similar insert rates. Compared to modern materialized view maintenance approaches, such as deferred and incremental view maintenance, that often need to materialize expensive deltas, we achieve up to an order of magnitude higher insert throughput.

On the different impacts of fixed versus floating bid-ask spreads on an automated intraday stock trading

Trading or transaction costs are one of the most important attributes of any trading system and can be divided into two major groups: explicit (visible) and implicit (hidden). In this paper, we investigate the impact of the bid-ask spreads, a form of hidden cost, on the results of backtesting (and, therefore, the potential impact on real-time trading) of an automated trading system based on genetic programming. We concentrate on the nature (fixed or floating) of bid-ask spreads (hereafter ‘spread’) and demonstrate that the effectiveness of an automated trading system more significantly degrades in the case of floating spreads compared to fixed spreads. We investigate four fixed spreads (one, two, five and ten pips) and a floating spread with a median value of two pips and demonstrate that the floating spread with a mean value of 0.02 USD results in significantly worse performance than a fixed spread of 0.1 USD. ‘Floating spreads’ in this paper is a term used for market-determined continuously changing bid-ask spreads.

Assessing the financial value of real-time energy trading services for privately owned non-commercial electric vehicles

Real-time energy trading services for privately owned non-commercial electric vehicles are characterized by an e-vehicle provider, by a provider of energy trading skills and technology, and by the fact that the latter manages (dis-)charging of the e-vehicle of the former with real-time energy prices. We conduct a simulation study to present a comprehensive assessment of the financial value of such services. Such an assessment is required in order to provide policymakers with guidance on if and how real-time trading services can serve as a tool to incentivize e-vehicle ownership. We propose a fully reproducible simulation model of the value creation process of real-time trading services, and use the model to assess services with a range of e-vehicle provider characteristics as well as with a range of technology setups. Our empirical results show that all considered real-time trading services are able to create significant energy cost savings, and that overall cost savings strongly depend on technology characteristics, surcharge rate, as well as on the e-vehicle provider's commute, household size, and office hours. We show that services including solar energy generation have the largest economic potential but do not necessarily maximize renewable energy deployment with residential households. We conclude with recommendations for policymakers on how to tap the full economic potential of real-time trading services for stimulating the adoption of e-vehicles.

Prediction accuracy improvement for Bitcoin market prices based on symmetric volatility information using artificial neural network approach

Generally, information is the fundamental driver of assets pricing volatility in the financial market. This information can enter into the market either symmetrically or asymmetrically. The financial literature shows that Bitcoin market volatility is symmetrically informative and has a long memory to persist in the future. Additionally, the symmetricity of volatility has been revealed to be of greater sensitivity to its past values compared to the new shock of the market values. This study therefore applied the symmetric volatility structure of Bitcoin currency which can be measured through four input attributes such as open price (OP), high price (HP), low price (LP), and close price (CP) for predicting its price future trend. The study uses Rapid-Miner programme based on artificial neural network (ANN) algorithm. The optimal model employs a multilayer neural network (NN) along with an “optimised operator” with the ability to locate the optimal factor loading of the applied algorithm. The findings indicate that ANN is an effective and adequate model for correctly predicting Bitcoin market prices using symmetric volatility attributes with accuracy level of 92.15% against the actual price, whereas the low price attribute is found to be the major promoter for Bitcoin price trend with percentage of 63%. This is followed by close price, high price, and open price with percentages of 49%, 46%, and 37%, respectively. The findings of the study therefore would be a valuable and significant input for commercial purposes among the cryptocurrency market players. In other worlds, based on these outcomes investors will proactively predicate the Bitcoin price trend and make the right investment decision either to buy, hold, or sale to gain up normal market return. This is considered a pioneering study that predicates the Bitcoin price trend based on its symmetric volatility structure. As these replication findings demonstrate, the proposed model is highly promising and applicable in a real-time trading system for predicting Bitcoin price future trend and maximising investment profits in Cryptocurrency markets.

Deep Q-Network Based Energy Scheduling in Retail Energy Market

As a significant component of electric energy trades, retail electric market (REM) can effectively alleviate the pressure of load demand from the power grid. However, the load demand uncertainty of customers becomes a nodus because retail electricity providers (REPs) should predict the load demand when trading with wholesaler electricity provider (WEP) based on the interaction. Therefore, in this paper, we propose an optimal energy scheduling scheme in REM with consideration of the influence of decisions made in pre-purchase stages to situations in real-time stages. Firstly, we present a trading framework to analyze the strategies of REPs in REM, in which REPs conduct both pre-purchase trading with WEP, and real-time trading with customers. Then, to solve the scheduling problem caused by the demand uncertainty of customers, we design a power allocation mechanism based on the charging demand degree, by which REPs can minimize the operating cost while ensuring that each electric vehicle can be charged with sufficient energy. Next, to minimize the cost of REPs in the pre-purchase stage, we adopt deep Q-network (DQN) algorithm to implement the pre-purchase schedule. The charging station adjusts the pre-purchased schedule for each period through Q-learning and utilizes the optimal strategy to design the electricity schedule. Finally, simulation experiments show that the proposal can obtain the optimal strategy to significantly reduce the operating costs of REP.

Evaluation of Level and Growth Factors of Mining Companies

The article reflects some aspects of the authors’ research on issues related to the growth of mining companies. The authors analyzed the main approaches to assessing growth, revealed correlation dependencies between them, as well as factors of the industry and macroeconomic level. The study allows concluding that not all proposed models show the same dynamic trends, as a result, the authors proposed a reasonable choice of one of the models. The authors found that the important factors the development of which is necessary for the growth of companies and the mining industry as a whole are the restructuring of the business model, the transition to more active use of digital technologies and overcoming the effects of the pandemic. In addition, it is important for companies to maintain demand for their products, the achievement of which feasible through access to e- commerce sites and real-time trading.

Research on key technologies of deduction of multinational power trading in the context of Global Energy Interconnection

When transnationalized electricity trade is conducted in the context of Global Energy Interconnection (GEI), the transaction settlement usually has a long cycle and high cost and is influenced by the volatility of the exchange rate. It is thus necessary to overcome the problems associated with the transaction settlement, change in the trading model data, and trading strategy in the transnational transaction deduction. To overcome the problem of trade settlement, this paper proposes the use of a digital currency (energy currency) for the cross-border electricity trading settlement based on the special drawing rights of the International Monetary Fund, which is controlled by the Global Energy Interconnection Development and Cooperation Organization (GEIDCO), to enable the proposed currency to become a stable digital currency. The traders can use the energy coins as a unit of currency for quotes, combined with the data pertaining to the changes in the energy information obtained from the GEI framework and data regarding the optimally extrapolated reference trading indicators. To realize the implementation of the multi-trader concurrent transaction deduction using a microservice architecture, this paper proposes a method of computing the microservice and synchronous interaction among the traders, based on the database table data, because the large amount of computation is required to be accomplished asynchronously with a single process. The key technology behind these cross-national electricity trading simulations can not only enable the GEI transnational traders to performed daily real-time trading, but it also demonstrates the advantages of the rapid settlement of the energy currency and the realization of a stable payment in the global energy interconnection cross-border electricity trading.

A local adaptive learning system for online portfolio selection

Online portfolio selection is an important problem in financial trading which has attracted increasing interests from the machine learning and data mining community. Most existing state-of-the-art systems, however, rely on the defensive strategies, which lack adaptivity to some extent and may lose investment opportunities in the real financial market. In this paper, we propose a novel local adaptive learning model which seamlessly integrates both defensive and aggressive strategies to enhance the adaptivity and profitability of the whole portfolio system. Different from some popular portfolio selection systems that assume a predefined price tendency, we set up an evaluation function to predict the tendency and activate the corresponding selection strategies. The total capital is allocated according to the value expectations of different assets. Through taking the bests of the complementary strategies, it can make a good balance between wealth returns and risks. In addition, our system is capable of dealing with financial data in linear time, which is suitable for real-time trading applications. Experimental results on several benchmark datasets show that our model outperforms some state-of-the-art ones both in effectiveness and efficiency.

Real-time autonomous trading system

In this research we implemented a real-time trading system for trading the fluctuations in price of stocks in the stock market by using the technical analysis. The system uses trading strategies written in Python programming language to gauge the current sentiment of the stock market by various technical analysis methodologies. The developed trading system uses state-of-the art technologies like Web Sockets, PostgreSQL, Ruby on Rails Framework , Redis etc. for achieving low latency ∼ 90ms. The implemented trading system is very flexible to work with multiple and independently programmed strategies by extending a secure RESTful Application Programming Interface.

Pattern Matching Trading System Based on the Dynamic Time Warping Algorithm

The futures market plays a significant role in hedging and speculating by investors. Although various models and instruments are developed for real-time trading, it is difficult to realize profit by processing and trading a vast amount of real-time data. This study proposes a real-time index futures trading strategy that uses the KOSPI 200 index futures time series data. We construct a pattern matching trading system (PMTS) based on a dynamic time warping algorithm that recognizes patterns of market data movement in the morning and determines the afternoon’s clearing strategy. We adopt 13 and 27 representative patterns and conduct simulations with various ranges of parameters to find optimal ones. Our experimental results show that the PMTS provides stable and effective trading strategies with relatively low trading frequencies. Financial market investors are able to make more efficient investment strategies by using the PMTS. In this sense, the system developed in this paper contributes the efficiency of the financial markets and helps to achieve sustained economic growth.

Ornstein-Uhlenbeck-Lévy Electricity Portfolios with Wind Energy Contracting

To leverage the potential of integrating renewable sources into electricity portfolios the risk and cost trade-off of intermittency needs to be assessed. From the perspective of a Load Serving Entity (LSE), this work present the theoretical implications of energy allocation from two type of markets: bilateral long-term contracts and real-time trading. The purchasing of energy on both markets and from two different sources: wind energy and conventional generation is formulated with a stochastic procurement model (SPM). The unexpected jumps of spot market prices are modeled with a mean-reverting Levy process. The wind energy availability is modeled with multiplicative Brownian motion transformed to a Rayleigh probability density function. The risk assessment is defined by the efficient frontier and a user defined risk level. The SPM is tested numerically. The contracted share of wind power is found to range between 8% and 16%. Moreover, the analysis shows the convergence of SPM to an optimal portfolio irrespectively of the wind farm autocorrelation decay rate.

Executive Functions in Naive Middle-age Patients with Uncomplicated Essential Arterial Hypertension

Online platforms enable real-time trading activities that are similar to those of gambling. This study aimed to investigate the associations of traditional investing, real-time stock trading, and cryptocurrency trading with excessive behavior and mental health problems.This was a cross-sectional population-based survey.The participants were Finnish people aged 18–75 years (N = 1530, 50.33% male). Survey asked about monthly regular investing, real-time stock-trading platform use, and cryptocurrency trading. The study had measures for excessive behavior: gambling (Problem Gambling Severity Index), gaming (Internet Gaming Disorder Test), internet use (Compulsive Internet Use Scale), and alcohol use (Alcohol Use Disorders Identification Test). Psychological distress (Mental Health Inventory), perceived stress (Perceived Stress Scale), COVID-19 anxiety, and perceived loneliness were also measured. Background factors included sociodemographic variables, instant loan taking, and involvement in social media identity bubbles (Identity Bubble Reinforcement Scale). Multivariate analyses were conducted with regression analysis.Within the sample, 22.29% were categorized into monthly regular investors only, 3.01% were investors using real-time stock-trading platforms, and 3.59% were cryptomarket traders. Real-time stock-trading platform use and cryptocurrency trading were associated with younger age and male gender. Cryptomarket traders were more likely to have an immigrant background and have taken instant loans. Both real-time stock-trading platform use and cryptomarket trading were associated with higher excessive behavior. Cryptomarket traders especially reported higher excessive gambling, gaming, and internet use than others. Cryptomarket traders reported also higher psychological distress, perceived stress, and loneliness.Regular investing is not a risk factor for excessive behavior. However, rapid online trading platforms and applications were significantly more commonly used by participants reporting excessive behavior and mental health problems. The strong association between cryptomarket trading and excessive behavior in particular underlines the need to acknowledge the potential risks related to real-time trading platforms.

A hierarchical framework for ad inventory allocation in programmatic advertising markets

Abstract Enabled by the big data driven user profiling and precision bidding techniques, online programmatic advertising markets have evolved from the traditional website-buying or ad-slot-buying models to a fine-grained and real-time trading model at the level of ad impressions (i.e., ad inventory). As a result, Web publishers are now facing a challenging decision of allocating the ad inventory across multiple advertising models, which has a direct and important influence on both their individual revenues, and the market-wide supply-demand balance. In this paper, we propose a novel hierarchical ad inventory allocation framework (AIAF), taking into consideration the possible scenarios of ad inventory allocation in programmatic advertising markets. AIAF explicitly captures the specific features of ad inventory allocation in each of three levels (i.e., channel level, market level and platform level), and also their influence-feedback effects. We present the general solution process for solving this model on the basis of its property analysis. An illustrative instantiation of our AIAF model is formulated to demonstrate its applications in supporting publishers’ decision-making on the ad inventory allocation. We also conduct experiments based on empirical data so as to validate the model and analysis. Our research findings indicate that 1) our AIAF model outperforms other single-level and two-level allocation strategies; 2) the fine-grained optimization is superior to that of the coarse-grained level; 3) allocation decisions should be made on the basis of the comparative marginal revenue instead of the absolute marginal revenue.