Brain trading algorithms

Brain develops algorithms for trading strategies and investment decisions

read more


Brain develops algorithms for trading strategies and investment decisions.

Brain also supports investment companies in transforming their ideas in proprietary models and in testing and optimizing their investment algorithms.

Our workflow is supported by robust software development competences to develop and deploy our solution on the most suitable IT infrastructure.

We base our approach on dynamic stochastic models, as well as on Machine Learning (ML) techniques.

We have developed a proprietary backtesting method to assess investment strategies, either proprietary or third parties.

Brain has also developed BSI, a proprietary “Sentiment” indicator, based on enhanced Bag of Words and on Natural Language Processing algorithms that process in real time the news flow from a multitude of providers.

The sentiment indicator is available on a dedicated dashboard.

Our competences on Machine Learning algorithms enable us to build predictive models for various asset classes. ML models can be used as inputs for investment strategies or combined in a portfolio approach.

find out more

Sentiment Analysis

Lorem Ipsum In today's world the constant news streams continually changes an investor knowledge and undestanding of the market and influences investor sentiment. Recent advances in big-data technologies, computational linguistics and machine learning models have now made possible an automatic analysis of the financial news flow to compute sentiment indicators.

How to use sentiment analysis

As discussed in literature value investors might use the sentiment analysis to establish what percentage of a company current value is caused by sentiment, and how much is based on real elements about the business itself. This may be of use to find companies that are cheap mainly due to very poor sentiment, or to exit companies that are becoming overpriced due to sentiment. Other investors may find it useful to track the sentiment of companies they follow as indicator that could be correlated to the price in the short term.

Brain Sentiment Indicator

Brain has developed a proprietary sentiment indicator (BSI) based on mixture of bag of words techniques together with natural language processing approaches to collect and classify the company news from a series of providers. The data are collected using data mining and filtering techniques to assess the pertinence of news to a specific company. Also the similarity and repetition of news on the same topic is taken into account and weighted in the final calculation of the sentiment.

Brain sentiment indicator can be accessed in real time through a dedicated dashboard.

start free trial

white paper


Matteo Campellone   Linkedin

Executive Chairman

Matteo Campellone holds a Ph.D. in Physics and a Master in Business Administration. In the past Matteo's activities included Corporate Risk and Value Based Management for industrial companies as well as Financial Modelling and Risk Management for financial institutions. As a Theoretical Physicist he worked in the field of statistical mechanics of complex systems and of non-linear stochastic equations. Amongst other results, he put forward some new solutions for the finite size corrections to an universality class of Spin Glass models, and developed an approximation method to approach some non-linear stochastic equations.

Francesco Cricchio   Linkedin

Chief Executive Officer & CTO

Francesco obtained his Ph.D. in Computational Physics applied to Quantum Physics from Uppsala University in 2010. He is the author of several scientific publications on the prediction of material properties from computer simulations with focus on superconductors and magnetic compounds. In 2009 one of his publications has been awarded the cover of Physical Review Letters. He focused his career in solving complex computational problems in different sectors using a wide range of techniques, from density functional theory in the domain of solid state physics to the application of machine learning methods and non-relational databases in the industrial domain.

Simone Conradi   Linkedin


Simone Conradi obtained a Ph.D. in Theoretical Physics focusing his research activities on Lattice Quantum Chromodynamics using methods of Computational Physics. He specialized in statistical physics and in thermodynamics of quantum field theories applied to the fundamental matter, achieving new insights about the confining properties of quarks and gluons at finite temperature and density. Moreover he got a ten years long career in the railway industry, focusing in the development of human safety relevant systems and in the management of trains diagnostic data, from cloud architecture design to predictive models development.

Alessandro Sellerio   Linkedin


Alessandro Sellerio obtained a Ph.D. in Physics focusing on jamming and vitrous phase transition in granular media, using theoretical models, simulations and experiments. He has extensive experience and a ten year long career in the fields of condensed matter physics, statistical physics and complex systems, during which he collaborated with a number of international research groups.

Michael Burnett   Linkedin

Board Member

Michael has an MBA in finance and strategy from London Business School and a Bachelor of Science from the University of Southern California where he attended on academic scholarship.  Michael’s career has spanned technology and finance, working for companies such as Apple, Cisco and Yahoo! and working in investment banking where he closed more than 45 transactions with media and technology companies totaling more than $25 billion. Michael has been invited and guest lectured at New York University (Stern School of Management), SDA Bocconi and Università Cattolica.

Lucia Rota   Linkedin

Board Member

Lucia Rota is a Certificated Public Accountant in Torino, Partner of Studio Rota, President of Board of Director of ACR srl, auditing company, and statutory auditor of Fidersel Spa, Nomen Fiduciaria Spa and Cofin Srl. She holds a MBA degree in SDA Bocconi, Milan (2010). She is also registered as freelance journalist.  She joined the “start up system” creating InnerDesign, an online platform dedicated to the design world. She has been focused on online communication and e-commerce processes, and her research was on online shopper behavior. Her interest continues with collaborations with Italian accelerators and Business Angels as external consultant. IED Moda Professor of Enterpreneurship, Milan. IED Moda Professor in Communication and Marketing for the period 2013 – 2016.