Algorithmic Trading with Python
Algorithmic Trading with Python, Real world Quantitative Trading with Python – Momentum and Mean Reversion models – Jupyter Notebooks included.
Course Description
In the algorithmic trading course you will learn how to build momentum, mean reversion and factor models. This course is different from other algorithmic trading courses where you only learn to code some basic technical indicators such as MACD or Bollinger Bands. Here you will learn succesful real world trading models. You need to know Python. If you are a trader at an institution or an independent investor and you are quantitative oriented, this course is for you. Click on the enroll button and start learning.
If you want to understand the curriculum, the course is divided in 6 different topics:
1. Introduction: You will understand the concept of market efficiency and also why markets don’t always behave that way. One of the main causes is related to behavioral biases. You will learn some of them. You will also learn a simple test to see if a market is efficient or not.
2. First Momentum Model – Alexander’s filter: This is a trend following system that was proposed by a professor at MIT and was later refined by professional traders. We will explore different variants of the models. Here you will learn how to run optimizations on python to find the best parameters for the model.
3. Second Momentum Model – Break out model: This is a trend following system inspired in a trading rule that was very profitable in commodities markets. The rule was implemented by a group of traders known as the turtle traders. In this model you will learn how to run a logistic regression to predict the direction of a trend.
4. Mean Reversion Model – Pairs Trading: This is a mean reverting strategy using a pair of assets that are fundamentally related.
5. Factor Model: In this model you will learn how to select explanatory variables that can be used to build a successful trading strategy. This kind of models are used in high frequency trading strategies.
6. Final remarks: We will explain how to select optimal trading size based on the Kelly Criteria. We will also revisit backtesting methods.
