Introduction The aim of this write-up is to investigate what fundamental features can be seen as the driver of corn calendar spreads.
For each calendar spread we start out with a random forest model that tries to forecast the value of the spread with input features consisting of the stock-to-usage numbers of
Argentina Brazil China Russia Ukraine United States World World without China as well as the number of days the front month contract has to expiry.
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.
Introduction In this write-up, we explore how the front month price and time to expiry of the front-month contract can be used to model the C UZ spread.
Seasonalality Using a similar methodology to the Calendar Spread Seasonal Entries and Exits post, we study the roll adjusted seaonal behaviour of the C UZ spread.
The plot below shows the continuous and roll adjusted C UZ spreads since 2000.
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