In my earlier post, I wrote about how pyenv is a great tool for running multiple versions of Python on the same host. It makes it simple to install multiple versions of Python on your workstation or server and control which version executes in a shell. But as a Python developer, the Python version is …
Use pyenv and virtual environments to manage Python complexity Read More »
Python 3.9 just came out recently, and I thought it would make sense to check out some of the new features (dict union operators, string remove prefix and suffix, etc.). Of course, doing this requires a Python 3.9 environment. Since new versions of Python may break existing code, I don’t want to update my entire …
You can easily and sensibly run multiple versions of Python with pyenv Read More »
It’s very common in data science work to have some function or a set of functions that you run in a loop to process or analyze data. When you see a loop that’s performing an expensive operation, you should immediately think of (at least) two ways to speed things up. The first is vectorization, which …
Python: how to use multiprocessing to finish work faster Read More »
Sometimes we want to change the ordering of a DataFrame’s columns. Maybe you are saving your data and require the columns to be in a certain order, or you may have a large number of columns and want a few of them to be visible whenever viewing your DataFrame in an interactive session, so you …
A very common need in working with pandas DataFrames is to rename a column. Maybe the columns were supplied by a data source like a CSV file and they need cleanup. Or maybe you just changed your mind during an interactive session. Let’s look at how you can do this, because there’s more than one …
Pandas is one of my favorite Python libraries, and I use it every day. A very common action is to add a column to a DataFrame. This is a pretty basic task. I’m going to look at a few examples to better show what is happening when we add a column, and how we need …
Basic Pandas: How to add a column to a DataFrame Read More »
When exploring the world of quantitative finance or algorithmic trading, you quickly end up facing a very common issue. Where do you get historical market data? If you have an account with Interactive Brokers, you can download historical data from them using Python. This article will show you how. No matter what sort of analysis …
How to get historical market data from Interactive Brokers using Python Read More »
The discount brokerage space is getting very competitive with commissions going to zero recently at many brokerages, including Interactive Brokers. IB has long been a broker with one of the largest breadth of products and service offerings targeting a professional audience. IB is also a low cost brokerage, so that makes it a good option …
How to connect to Interactive Brokers using Python Read More »
After looking at zipline, another backtesting framework, I thought it would make sense to take a look at some other options in the open source community for backtesting and trading. The next framework to investigate is backtrader, an open source project that aims to provide tooling for backtesting and live trading algorithmic strategies. I’ll use …
Backtrader, a Python backtesting and trading framework Read More »
Developing an algorithmic trading strategy is a challenging process, with many factors to consider. One part of the process in developing a strategy is to measure its performance using historical data in what is called a backtest. It can be very helpful is to have a decent library that abstracts away some of the challenging …
Zipline, an open source backtesting library from Quantopian Read More »