Hands On Algorithmic Trading With Python Elohim – O’reilly

The pace of automation in the investment management industry has become frenetic in the last decade because of algorithmic trading and machine learning technologies. Industry experts estimate that as much as 70% of the daily trading volume in US equity markets is executed algorithmically i.e. by computer programs following a set of pre-defined rules. In the 20th century, algorithmic trading was used by sell-side brokers to get the best execution of large trades for their clients. In the 21st century, algorithms are used in the entire trading process, from idea generation to execution and portfolio management. While all algorithmic trading is executed by computers, the rules for generating trades may be designed by humans or discovered by machine learning algorithms from training data.

Discipline in the face of grueling markets is a key success factor in trading and investing. Emotional irrationality, behavioral biases, inability to multitask effectively and slow execution speeds put manual trading by retail investors at a massive disadvantage. Retail investors are aware of these disadvantages and there is considerable interest in algorithmic trading, especially using Python. This course is about taking the first step in leveling the playing field for retail equity investors. It provides the process and technological tools for developing algorithmic trading strategies. Note that live trading is out of scope for the course.

By the end of this live, hands-on, online course, you’ll understand:

• The advantages and disadvantages of algorithmic trading
• The different types of models used to generate trading and investment strategies
• The process and tools used for researching, designing and developing them
• Pitfalls of backtesting algorithmic strategies
• Risk-adjusted metrics for evaluating their performance
• The paramount importance of risk management and position sizing

And you’ll be able to:

• Use the Pandas library to import, analyze and visualize data from market, fundamental, and alternative sources available for free on the web.
• Design and automate your own specific investment and trading strategies in Python
• Backtest and evaluate the performance of your strategies using vectorized backtesting
• Prepare for competitions by crowd-sourced hedge funds such as Quantopian to fund your algorithmic trading strategies.