Upon a Message-Oriented Trading API

In this paper, we introduce the premises for a trading system application-programming interface (API) based on a message-oriented middleware (MOM), and present the results of our research regarding the design and the implementation of a simulation-trading system employing a service-oriented architecture (SOA) and messaging. Our research has been conducted with the aim of creating a simulation-trading platform, within the academic environment, that will provide both the foundation for future experiments with trading systems architectures, components, APIs, and the framework for research on trading strategies, trading algorithm design, and equity markets analysis tools. Mathematics Subject Classification: 68M14 (distributed systems).Trading System API, Straight-Through Processing, Distributed Computing, Service-Oriented Architecture (SOA), Message-Oriented Middleware (MOM), Java Message Service (JMS), OpenMQ

The Informatics of the Equity Markets - A Collaborative Approach

This paper aims to provide a high-level overview upon the information technology that supports the electronic transactions performed on the equity markets. It is meant to offer a succinct introduction to the various technologies tailored to tackle the data transfer between the participants on an equity market, the architectural approaches regarding trading system design, and the communication in a collaborative distributed computing environment. Our intention here is not to provide solutions, or to propose definitive designs, merely to scratch the surface of this vast domain, and open the path for subsequent researches.securities exchange, stock order flow, trading system architecture, distributed computing, middleware, collaborative system, order-matching algorithm

Software Architecture Coupling Metric for Assessing Operational Responsiveness of Trading Systems

The empirical observation that motivates our research relies on the difficulty to assess the performance of a trading architecture beyond a few synthetic indicators like response time, system latency, availability or volume capacity. Trading systems involve complex software architectures of distributed resources. However, in the context of a large brokerage firm, which offers a global coverage from both, market and client perspectives, the term distributed gains a critical significance indeed. Offering a low latency ordering system by nowadays standards is relatively easily achievable, but integrating it in a flexible manner within the broader information system architecture of a broker/dealer requires operational aspects to be factored in. We propose a metric for measuring the coupling level within software architecture, and employ it to identify architectural designs that can offer a higher level of operational responsiveness, which ultimately would raise the overall real-world performance of a trading system

ASETS – An Academic Trading Simulation Platform

This paper is intended to present the results of our academic research upon a distributed computing environment dedicated to trading simulation. Our research has been conducted with the aim of creating a trading simulation platform, that would provide both the foundation for future experiments with trading systems architectures, components, APIs, and the framework for research on trading strategies, trading algorithms design, and equity markets analysis tools.Trading Systems, Simulation, Distributed Computing, Service-Oriented Architecture (SOA), Message-Oriented Middleware (MOM), Java Message Service (JMS)

An Algoritm for the Alocation Optimization of Trading Executions

n this paper, I wish to propose the Integer Allocation employing Tabu Search in conjunction with Simulated Annealing Heuristics for optimizing the distribution of trading executions in investors’ accounts. There is no polynomial algorithm discovered for Integer Linear Programming (a problem which is NP-complete). Generally, the practical experience shows that large-scale integer linear programs seem as yet practically unsolvable or extremely time-consuming. The algorithm described herein proposes an alternative approach to the problem. The algorithm consists of three steps: allocate the total executed quantity proportionally on the accounts, based on the allocation instructions (pro-rata basis); construct an initial solution, distributing the executed prices; improve the solution iteratively, employing Tabu Search in conjunction with Simulated Annealing heuristics

A GCM Solution for Leveraging Server-side JMS Functionality to Android-based Trading Application

The paper presents our solution for a message oriented communication mechanism, employing Google Cloud Messaging (GCM) on the client-side, and Java Message Service (JMS) on the server-side, in order to leverage JMS functionality to Android-based trading application. Our ongoing research has been focused upon conceiving a way to expose the trading services offered by our academic trading system ASETS to a mobile trading application based on Android platform. ASETS trading platform is a distributed SOA implementation, with an original API based on JMS. In order to design and implement an Android based client, able to inter-communicate with the server-side components of ASETS, in a manner consistent with publisher/subscriber JMS communication model, there was particularly necessary to have object embedded messages, produced by various ASETS services, pushed to the client application. While point-to-point communication model could be resolved on the client-side by employing synchronous HTTP socket connections over TCP/IP, the asynchronously generated messages from the server-side had to reach the client application in a push manner

Upon a Home Assistant Solution Based on Raspberry Pi Platform

Our ongoing research on Internet of Things (IoT) has been focused on a project aiming to creating a proof of concept for a distributed system capable of controlling common devices found in a house such as TVs, air conditioning units, and other electrical devices. In order to automate these devices, the system integrates various sensors and actuators and, depending of user’s needs and creativity in conceiving and implementing new commands, the system is able to take care and execute the respective commands in a safe and secure manner. This paper presents our current research results upon a personal home assistant solution designed and built around Raspberry Pi V3 platform. The distributed, client-server approach enables users to control home electric and electronic devices from an Android based mobile application

A Volatility Estimator of Stock Market Indices Based on the Intrinsic Entropy Model

Grasping the historical volatility of stock market indices and accurately estimating are two of the major focuses of those involved in the financial securities industry and derivative instruments pricing. This paper presents the results of employing the intrinsic entropy model as a substitute for estimating the volatility of stock market indices. Diverging from the widely used volatility models that take into account only the elements related to the traded prices, namely the open, high, low, and close prices of a trading day (OHLC), the intrinsic entropy model takes into account the traded volumes during the considered time frame as well. We adjust the intraday intrinsic entropy model that we introduced earlier for exchange-traded securities in order to connect daily OHLC prices with the ratio of the corresponding daily volume to the overall volume traded in the considered period. The intrinsic entropy model conceptualizes this ratio as entropic probability or market credence assigned to the corresponding price level. The intrinsic entropy is computed using historical daily data for traded market indices (S&P 500, Dow 30, NYSE Composite, NASDAQ Composite, Nikkei 225, and Hang Seng Index). We compare the results produced by the intrinsic entropy model with the volatility estimates obtained for the same data sets using widely employed industry volatility estimators. The intrinsic entropy model proves to consistently deliver reliable estimates for various time frames while showing peculiarly high values for the coefficient of variation, with the estimates falling in a significantly lower interval range compared with those provided by the other advanced volatility estimators

An Intrinsic Entropy Model for Exchange-Traded Securities

This article introduces an intrinsic entropy model that can be used as an indicator to gauge investor interest in a given exchange-traded security, along with the state of the general market corroborated by individual security trade data. Although the syntagma of intrinsic entropy might sound somehow pleonastic, since entropy itself characterizes the fundamentals of a system, we would like to make a clear distinction between entropy models based on the values that a random variable may take and the model that we propose, which employs actual stock exchange trading data. The model we propose for intrinsic entropy does not include any exogenous factor that could influence the level of entropy. The intrinsic entropy signals whether the market is inclined to buy the security or rather to sell it. We further explore the usage of the intrinsic entropy model for algorithmic trading, in order to demonstrate the value of our model in assisting investors in the selection of the intraday stock portfolio, along with timely generated signals to support the buy / sell decision making process. The test results provide empirical evidence that the proposed intrinsic entropy model can be used as an indicator to evaluate the direction and intensity of intraday trading activity of an exchange-traded security. The data used for the test consisted of historical intraday transactions executed on The Bucharest Stock Exchange (BVB)

An Intrinsic Entropy Model for Exchange-Traded Securities.

This paper introduces an intrinsic entropy model which can be employed as an indicator for gauging investors’ interest in a given exchange-traded security, along with the state of the overall market corroborated by individual security trading data. Although the syntagma of intrinsic entropy might sound somehow pleonastic, since entropy itself characterizes the fundamentals of a system, we would like to make a clear distinction between entropy models based on the values that a random variable may take, and the model that we propose, which employs actual stock exchange trading data. The model that we propose for the intrinsic entropy does not include any exogenous factor that could influence the level of entropy. The intrinsic entropy signals if the market is either inclined to buy the security or rather to sell it. We further explore the usage of the intrinsic entropy model for algorithmic trading, in order to demonstrate the value of our model in assisting investors’ intraday stock portfolio selection, along with timely generated signals for supporting the buy/sell decision-making process. The test results provide empirical evidence that the proposed intrinsic entropy model can be used as an indicator for evaluating the direction and the intensity of intraday trading activity of an exchange-traded security. The data employed for testing consisted of historical intraday transactions executed on The Bucharest Stock Exchange (BVB).<br