1,720,976 research outputs found
Insight into trading limits in financial algorithms
No full textIn this project we aimed to create a post-trading day safeguard system that allows for the identification of bugs in the primary and secondary risk control systems at Optiver. These systems are needed to prevent undesirable exposure to the market from happening, and to ensure that they know exactly what this exposure is. The amount of input data for this project, given in the form of log files, equates to roughly 200 GB per trading day, post sanitation. We have developed a program that can simulate an entire trading day and detect if any limits were breached. This program can be run overnight, allowing for a T+1 report in the morning after the respective trading day. The difficulties in this project were in the acquisition of all knowledge concerning the unique traits of various markets around the world, inconsistencies in the data, incomplete documentation, and optimization of the program to run within the required time. Organizationally, the project was executed within an agile workflow, with Kanban as software development methodology. Furthermore, the project is tested extensively to ensure the accuracy and correctness of the program. Concerning the impact of the project, it contributed to the identification and resolution of multiple previously unknown bugs in the control systems at Optiver. Furthermore, our project verified the existence of some previously known issues. In the future, when the software is run to verify all order logs of Optiver, the software will prove its value by either increasing the confidence that there is an absence of bugs in the RiskGuard and autotrading software of Optiver or by identifying breached limits, indicating a bug
Epoch alignment in stateful streams
No full textWhile the amount of data and variability in data produced by numerous systems in a modern company continues to increase, users desire real-time and consistent results from complex analyses across a large variety of event sources. In industry, stream processing systems are emerging to process events with low latency in a scalable and reliable fashion. As more and more stream processing jobs are processing mission critical events, older jobs are subject to maintenance and have to be upgraded or replaced. These upgrade operations include a snapshot-restore operation, where between the snapshot and restore a non-trivial state conversion has to be performed. Such an operation requires a lot of technical expertise and imposes significant down-time on the job itself and all jobs that depend on it. This thesis proposes a mechanism to align the progress of multiple independent jobs sharing common event sources. The mechanism is an extension of the checkpoint protocol proposed by Carbone et al. Not only does this mechanism simplify maintenance of streaming jobs by allowing hot-swap operations with exactly-once processing semantics, but it can also be used to provide consistency of queryable state. By implementing a proof of concept we show that this so called epoch alignment can be achieved with minimal additional costs over exactly-once processing semantics.CodefeedrComputer Scienc
POC High-available Application
No full textProRail uses multiple custom built software applications to control the train infrastructure in the Netherlands. At this moment these applications experience downtime when they are updated. This interrupts processes that are related to controlling the train services. ProRail asked us to redesign an existing application in such a way that updates can be applied with zero downtime.To solve this problem we use redundancy with automatic load balancing. We compared multiple solutions and we chose to use Docker’s swarm functionality. We then redesigned the non-critical application Brugkijker, an application to monitor trains approaching a bridge. In the end we could successfully run multiple instances of Brugkijker in Docker and show how the application can be incrementally updated, inducing no downtime for the service as a whole and without losing data coming from upstream. This proposed approach can be used by ProRail to make other noncritical and critical applications highly available
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Machine learning based aircraft arrival / departure registrations
No full textThe aviation industry is vastly growing, as travelling by air is more common today than it ever was. However due too inefficiency and lack of communication of accurate flight information between airports, congestion and delays are occurring on a daily basis. While Collaborative Decision Making (CDM) is developed by Euro control to address this issue, the problem of transmitting accurate flight information near real time is not yet solved. Adecs Airinfra did a first attempt at automatic landing and departure registration by a fixed rule based algorithm to address this issue. However, this algorithm has limitations that cannot be solved with tweaking and tuning. In this work, we aim to create a replacement based on machine learning models. In this thesis we present the complete process, starting from raw real world data, turning this intolabelled data up to the point where we define a validation method and present the final results. We managed to create a machine learning landing / departure detection system with up to 99% precision and recall for arrivals, and for departures we managed to get a precision of 94% against 98% recall
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