A machine learning approach to stock screening with fundamental analysis

We present HPC.FASSR, a High-Performance Computation Fundamental Analysis Stock Screener and Ranker to compare many ML models and the criteria of famous Benjamin Graham for stock investing using fundamental data. FASSR is distributed so it can explore massive amounts of models in short time

A machine learning approach to stock screening with fundamental analysis

We present HPC.FASSR, a High-Performance Computation Fundamental Analysis Stock Screener and Ranker to compare many ML models and the criteria of famous Benjamin Graham for stock investing using fundamental data. FASSR is distributed so it can explore massive amounts of models in short time

pyProCT optimization

PyProCT és una eina Python de clustering de propòsit general. PyCOMPSs és un entorn de treball que facilita el desenvolupament de d'aplicacions paral·leles en Python. L'objectiu d'aquest projecte és modificar pyProCT per a que usi pyCOMPSs per a millorar el seu rendiment.PyProCT is a general purpose clustering analysis software. PyCOMPSs is a parallelization framework that facilitates the development of parallel computational workflows in Python. This project will refactor pyProCT with the pyCOMPSs framework to increase its performance

A machine learning approach to stock screening with fundamental analysis

We present HPC.FASSR, a High-Performance Computation Fundamental Analysis Stock Screener and Ranker to compare many ML models and the criteria of famous Benjamin Graham for stock investing using fundamental data. FASSR is distributed so it can explore massive amounts of models in short time

pyProCT optimization

PyProCT és una eina Python de clustering de propòsit general. PyCOMPSs és un entorn de treball que facilita el desenvolupament de d'aplicacions paral·leles en Python. L'objectiu d'aquest projecte és modificar pyProCT per a que usi pyCOMPSs per a millorar el seu rendiment.PyProCT is a general purpose clustering analysis software. PyCOMPSs is a parallelization framework that facilitates the development of parallel computational workflows in Python. This project will refactor pyProCT with the pyCOMPSs framework to increase its performance

A survey on the distributed computing stack

In this paper, we review the background and the state of the art of the Distributed Computing software stack. We aim to provide the readers with a comprehensive overview of this area by supplying a detailed big-picture of the latest technologies. First, we introduce the general background of Distributed Computing and propose a layered top–bottom classification of the latest available software. Next, we focus on each abstraction layer, i.e. Application Development (including Task-based Workflows, Dataflows, and Graph Processing), Platform (including Data Sharing and Resource Management), Communication (including Remote Invocation, Message Passing, and Message Queuing), and Infrastructure (including Batch and Interactive systems). For each layer, we give a general background, discuss its technical challenges, review the latest programming languages, programming models, frameworks, libraries, and tools, and provide a summary table comparing the features of each alternative. Finally, we conclude this survey with a discussion of open problems and future directions.This work is partly supported by the Spanish Ministry of Science, Innovation, and Universities through the Severo Ochoa Program (SEV-2015-0493) and the TIN2015-65316-P project. It is also supported by the Generalitat de Catalunya, Spain under contracts 2014-SGR-1051 and 2014-SGR-1272. Cristian Ramon-Cortes pre-doctoral contract is financed by the Spanish Ministry of Science, Innovation, and Universities under the contract BES-2016-076791.Peer ReviewedPostprint (author's final draft