This website uses cookies to collect usage information in order to offer a better browsing experience. By browsing this site or by clicking on the "ACCEPT COOKIES" button you accept our Cookie Policy.

Trading Using Machine Learning In Python – Part I

QuantInsti

Contributor:
QuantInsti
Visit: QuantInsti

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.

Most wanted 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.

Disclosure: Interactive Brokers

Information posted on IBKR Traders’ Insight that is provided by third-parties and not by Interactive Brokers does NOT constitute a recommendation by Interactive Brokers that you should contract for the services of that third party. Third-party participants who contribute to IBKR Traders’ Insight are independent of Interactive Brokers and Interactive Brokers does not make any representations or warranties concerning the services offered, their past or future performance, or the accuracy of the information provided by the third party. Past performance is no guarantee of future results.

This material is from QuantInsti and is being posted with permission from QuantInsti. The views expressed in this material are solely those of the author and/or QuantInsti and IBKR is not endorsing or recommending any investment or trading discussed in the material. This material is not and should not be construed as an offer to sell or the solicitation of an offer to buy any security. To the extent that this material discusses general market activity, industry or sector trends or other broad based economic or political conditions, it should not be construed as research or investment advice. To the extent that it includes references to specific securities, commodities, currencies, or other instruments, those references do not constitute a recommendation to buy, sell or hold such security. This material does not and is not intended to take into account the particular financial conditions, investment objectives or requirements of individual customers. Before acting on this material, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice.

trading top