Chris Conlan

Financial Data Scientist

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Pulling All Sorts of Financial Data in Python [Updated for 2021]

December 13, 2020 By Chris Conlan Leave a Comment

Contributed so kindly by Joe Wojniak It may seem obvious, but financial research requires data — and a lot of it. If financial research isn’t your day job, it can be surprisingly difficult to come by. Here are some suggestions for acquiring data to use in your financial research project. Data Sources Every project has […]

Filed Under: Automated Trading, Programming with Python

When to Use Heap Sort to Speed Up your Python Code

June 10, 2020 By Chris Conlan 1 Comment

If you have ever read an algorithms textbook, you know about the handful of sorting algorithms that run in O(n*log(n)) time. These include quicksort, heapsort, and mergesort. Under the hood, Python’s List.sort function uses yet another one called Timsort. That’s not the point of this post. The point of this post is show you how […]

Filed Under: Programming with Python, Snippets

Fastest Way to Flatten a List in Python

June 5, 2020 By Chris Conlan 23 Comments

In my latest book, Fast Python, I bust a lot of speed myths in Python. Some of the most basic advice people give about optimizing Python code is wrong, likely because no one ever bothered to test it. In this book, I go back to the basics and verify (or bust) all of the advice […]

Filed Under: Programming with Python, Snippets

70+ Code Profiles of Common Python Algorithms

June 1, 2020 By Chris Conlan Leave a Comment

I just released my latest book, Fast Python: Master the Basics to Write Faster Code. In it, you will find a blend of academic discussion of algorithms and a pragmatic optimizations of computation time. Whether or not you are interested in buying the book, I wanted to share some free resources from the accompanying GitHub […]

Filed Under: Programming with Python

Multicore Repeated K-Fold Classifier

April 12, 2020 By Chris Conlan Leave a Comment

The following snippet is a reusable multicore K-Fold classifier for scikit-learn models. The return value is an array of cross validation scores of length N_SPLITS * N_REPEATS. The sklearn library already provides a simple interface for multicore cross validation through the cross_val_score function, but it does not provide a facility for repeating the cross validation […]

Filed Under: Programming with Python, Snippets

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Latest Release: The Financial Data Playbook

The Financial Data Playbook

Available for purchase at Amazon.com.

Algorithmic Trading

Calculating Triple Barrier Labels from Advances in Financial Machine Learning

Calculating Financial Performance Metrics in Pandas

Moving from Single-Asset to Multi-Asset Algorithmic Trading

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