From Raw Data to Rich Predictions: Machine Learning Strategies With COT Reports in Finance

Creating a Forecasting Algorithm on the COT Report

Sofien Kaabar, CFA

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In this article, we delve into the realm of predictive analytics, exploring how the rich insights provided by the COT report can be harnessed to enhance the capabilities of machine learning models. By deciphering the patterns within trader positions, we unlock the potential for more informed and data-driven decision-making in the complex world of finance.

Introduction to the KNN Algorithm and the COT Report

K-Nearest Neighbors (KNN) is a simple and intuitive machine learning algorithm used for both classification and regression tasks. But first, what does classification and regression mean?

  • Classification is a type of supervised learning where the goal is to categorize data points into predefined classes or labels. In classification, the model learns to assign a class or category to an input based on its features. The output in classification is discrete and represents a category or class label. For example, classifying emails as spam or not spam.
  • Regression, on the other hand, is a type of supervised learning where the goal is to predict a continuous numeric value. In regression, the model learns to establish a relationship between the input features and the output, which is a real-valued number. The output in regression is a continuous range of values, and it represents a quantity or a numerical value. For example, predicting the price of a house based on its features and forecasting stock prices.

KNN is a type of lazy learning, which means that it doesn’t build a model during training but instead memorizes the entire training dataset and makes predictions based on the similarity between new data points and the existing data points.

So, the central idea behind KNN is that objects (data points) with similar characteristics are close to each other in the feature space. It makes predictions by finding the K training examples that are closest to a given test data point in the feature space and then assigns a label or value to the test point based on the labels or values of its nearest neighbors. This means…

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