Tutorials on algorithmic trading#

Here is a collection of blog posts and tutorials that are relevant for algorithmic trading in Python.

Financial Models and Numerical Methods#

A collection of Jupyter notebooks based on different topics in the area of quantitative finance.

Read more.

Teddy Koker’s blog#

Articles on trading, gambling and machine learning.

Read blog.

Backtesting option strategy with Backtrader#

An example tutorial.

Read post.

ML Algotrading Wiki#

A wiki website with research and various news sources.

MLTraders’ Algotrading and Machine Learning work for everybody..

Pair Trading: A market-neutral trading strategy with integrated Machine Learning#

The primary goal in an investment endeavor is the implementation of strategies that minimize the risk while also maximizing the financial gain or return from the said investment. While there have been many popular strategies and techniques developed over the years that point towards the same goal, the ‘Pairs-Trading’ strategy is one that has been used to great extent in modern hedge-funds, for its simplicity and inherent market-neutral qualities.

Read post.

Dead Simple 2-Asset Portfolio that Crushes the S&P500#

This is an update to the original blog series that explored a simple strategy of being long UPRO and TMF in equal weight, inverse volatility and inverse-inverse volatility. This strategy crushed the cumulative and risk-adjusted returns of the benchmark SPY etf.

Read post.

Didact AI#

The anatomy of an ML-powered stock picking engine.

Read post.

Trend following regime filter with HMM#

In this article the Hidden Markov Model will be utilised within the QSTrader framework as a risk-managing market regime filter. It will disallow trades when higher volatility regimes are predicted. The hope is that by doing so it will eliminate unprofitable trades and possibly remove volatility from the strategy, thus increasing its Sharpe ratio.

Read post.