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 75% 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 learning path is about taking the first step in leveling the playing field for retail equity investors. It provides the concepts, process, and technological tools for developing algorithmic trading strategies. Note that live trading is out of scope for this learning path.

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