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Predicting the direction of volatile assets like Bitcoin is a central challenge in quantitative finance. While daily noise can make short-term predictions resemble random walks, analyzing trends over slightly longer horizons, like a week, might offer more traction. This article details a Python-based approach using a Random Forest classifier and a rolling forecast methodology to predict whether Bitcoin’s price will be higher or lower seven days from the present, leveraging a pre-selected set of technical indicators. We’ll cover the theory, the implementation with code snippets, and how to interpret the results.
1. Theoretical Background
Before diving into the code, let’s understand the core concepts:
a) Random Forest Classifier
- Ensemble Learning: Random Forest is an ensemble machine learning method primarily used for classification and regression. It operates by constructing a multitude of individual decision trees during training.
- How it Works:
- Bagging (Bootstrap Aggregating): It creates multiple…
