I just finished writing my latest book, Algorithmic Trading with Python. When writing the chapter on performance metrics, I was consistently surprised with the simplicity of the pandas code. If you, as a developer, resolve to only work with datetime-indexed pd.Series objects, the resulting code is really clean and easy. Simulating Data For those unfamiliar […]
Moving from Single-Asset to Multi-Asset Algorithmic Trading
In my latest book, Algorithmic Trading with Python (2020), readers work through the process of developing a trading strategy, simulator, and optimizer against a portfolio of 100 assets. Each asset has 10 years of end-of-day data, creating about 2,500 data points per asset, totaling 250,000 data points. A lot of similar work in this field […]
Activate Verbose Logging Output in Django
When you are developing Django, you likely want the most verbose debugging output possible. Django uses the logging levels defined by Python logging module, and defines the logging style in a Python dictionary in settings.py. Read more about Django logging. Example Logging Configuration My favorite logging configuration is to dump the most verbose output possible […]
The Haskell Stack Ecosystem in 30 Seconds Or Less
While I am not an avid Haskell developer, there are some useful software suites (e.g. Pandoc) that are built on it. When managing dependencies for a distributed cloud environment, it is important to have a secure and replicable means of installing Haskell packages with tons of dependencies. So, I went about investigating the relationships between […]
Using Anaconda Python from Powershell
Recently, I found the need to use Anaconda Python from a more sophisticated shell than CMD.exe. If for no other reason than getting a shell that was easier on the eyes, I offered $100 to /r/PowerShell to get it done. The community followed through and we built a pretty good replica of Anaconda’s activate.bat. Our […]
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