As is often the case, Machine Learning (ML) techniques outperform traditional ones when allocating weights to different assets. The idea of this project "Portfolio Assets Allocation: A practical and scalable framework for Machine Learning Development" is to design a market neutral (long/short) portfolio of assets to be rebalanced periodically choosing different assets during every rebalance and evaluate different portfolio techniques such as: This article is the final project submitted by the author as a part of his coursework in the Executive Programme in Algorithmic Trading (EPAT) at QuantInsti. Do check our Projects page and have a look at what our students are building. Raimondo Marino is a professional freelance working as an Artificial intelligence Engineer for Italian Small and Medium Companies. Through AI applications, he comes up with end to end solutions (from Development to Production using cloud services) for different corporate functions within a company: Marketing, HR, Sales, Production, etc.

Recent research has shown that a single arbitrarily efficient solver can be significantly outperformed by a portfolio of possibly slower on-average solvers. The solver selection is usually done by means of (un)supervised learning techniques which exploit features extracted from the problem specification. In this paper we present an useful and flexible framework that is able to extract an extensive set of features from a Constraint (Satisfaction/Optimization) Problem defined in possibly different modeling languages: MiniZinc, FlatZinc or XCSP. We also report some empirical results showing that the performances that can be obtained using these features are effective and competitive with state of the art CSP portfolio techniques.