How machine learning in Python gained popularity
Machine learning packages/libraries are developed in-house by firms for their proprietary use or by third parties who make it freely available to the user community. In recent years, the number of machine learning packages has increased substantially which has helped the developer community in accessing various machine learning techniques and applying the same to their trading needs.
There are hundreds of ML algorithms which can be classified into different types depending on how these work. For example, machine learning regression algorithms are used to model the relationship between variables; decision tree algorithms construct a model of decisions and are used in classification or regression problems. Of these, some algorithms have become popular among quants. Some of these include:
- Linear Regression
- Logistic Regression
- Random Forests (RM)
- Support Vector Machine (SVM)
- k-Nearest Neighbor (kNN)
- Classification and Regression Tree (CART)
- Deep learning
But Why Machine Learning in Python?
Over the years, we have realised that Python is becoming a popular language for programmers with that, a generally active and enthusiastic community who are always there to support each other. In fact, as stated in our introductory blog on Python, according to the Developer Survey Results 2019 at stackoverflow, Python is the fastest-growing programming language.
It was also found that among the languages the people were most interested to learn, Python was the most desired programming language.
Python trading has gained traction in the quant finance community as it makes it easy to build intricate statistical models due to the availability of sufficient scientific libraries like Pandas, NumPy, PyAlgoTrade, Pybacktest and more. First updates to Python trading libraries are a regular occurrence in the developer community. In fact, Scikit-learn is a Python package developed specifically for machine learning which features various classification, regression and clustering algorithms.
Before we go any further, let me state that this code is written in Python 2.7.
Stay tuned for the next installment in this series. Varun will discuss Python packages and libraries.
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