A multi-agent platform for auction-based allocation of loads in transportation logistics

This paper describes an agent-based platform for the allocation of loads in distributed transportation logistics, developed as a collaboration between CWI, Dutch National Center for Mathematics and Computer Science, Amsterdam and Vos Logistics Organizing, Nijmegen, The Netherlands. The platform follows a real business scenario proposed by Vos, and it involves a set of agents bidding for transportation loads to be distributed from a central depot in the Netherlands to different locations across Germany. The platform supports both human agents (i.e. transportation planners), who can bid through specialized planning and bidding interfaces, as well as automated, software agents. We exemplify how the proposed platform can be used to test both the bidding behaviour of human logistics planners, as well as the performance of automated auction bidding strategies, developed for such settings. The paper first introduces the business problem setting and then describes the architecture and main characteristics of our auction platform. We conclude with a preliminary discussion of our experience from a human bidding experiment, involving Vos planners competing for orders both against each other and against some (simple) automated strategies

Hybridizing data stream mining and technical indicators in automated trading systems

Automated trading systems for financial markets can use data mining techniques for future price movement prediction. However, classifier accuracy is only one important component in such a system: the other is a decision procedure utilizing the prediction in order to be long, short or out of the market. In this paper, we investigate the use of technical indicators as a means of deciding when to trade in the direction of a classifier’s prediction. We compare this “hybrid” technical/data stream mining-based system with a naive system that always trades in the direction of predicted price movement. We are able to show via evaluations across five financial market datasets that our novel hybrid technique frequently outperforms the naive system. To strengthen our conclusions, we also include in our evaluation several “simple” trading strategies without any data mining component that provide a much stronger baseline for comparison than traditional buy-and-hold or sell-and-hold strategies

Where do electronic markets come from? Regulation and the transformation of financial exchanges

The practices of high-frequency trading (HFT) are dependent on automated financial markets, especially those produced by securities exchanges electronically interconnected with competing exchanges. How did this infrastructural and organizational state of affairs come to be? Employing the conceptual distinction between fixed-role and switch-role markets, we analyse the discourse surrounding the design and eventual approval of the Securities and Exchange Commission’s Regulation of Exchanges and Alternative Trading Systems (Reg ATS). We find that the disruption of the exchange industry at the hands of automated markets was produced through an interweaving of both technological and political change. This processual redefinition of the ‘exchange’, in addition, may provide a suggestive precedent for understanding contemporary regulatory crises generated by other digital marketplace platforms

GPS Spoofing and the Financial Sector

All global financial exchanges, including the New York Stock Exchange (NYSE) and the Nasdaq, have gone digital. Large data centers hold the exchanges’ matching engines—the modern-day equivalent of the historic trading floor—in racks of interconnected servers. The Department of Homeland Security considers these data centers critical national infrastructure. Private security personnel, tall fences, and the best network security money can buy protect the integrity of the thousands of high-stakes trades executed every second within these data centers.Aerospace Engineering and Engineering Mechanic