Introduction In Precio-Temporal Spread Model we investigated the seasonality and front month dependence of calendar spreads. Subsequently we have build models for all of the calendar spreads we are interested in that take as input features the value of the front month and days until expiry of the near dated contract. The plot below shows an example of one of these models for the C UZ spread.
Introduction This write-up tries to address the question of how we can increase the synergy and collaboration between quantitative and fundamental aspects of commodity investing. As a first step we need to define what we mean by quantitative and fundamental commodities research or investing and what we hope to achieve with each of these methods.
A clash of quant and fundamental points of view Broadly, in the commodities space, fundamental analysis can be thought of as the study of the balance sheets of different commodities of the major producing an consuming nations.
1 Introduction 2 Supervised Learning 2.1 General Idea 2.2 Calendar Spread Regression Example 2.3 Using classification to help determine bet sizing 3 Unsupervised Learning 4 Feature Importance 5 Remarks 1 Introduction The aim of this document is to give a very broad overview of artificial intelligence or machine learning from the point of view of a commodity centric hedge fund. We will start off by looking at the two main branches of machine learning