MathWorks: building an automated trader using reinforcement learning [video]

Reinforcement learning is a type of machine learning in which a computer learns to perform a task through repeated interactions with a dynamic environment. This trial-and-error learning approach enables the computer to make a series of decisions without human intervention and without being explicitly programmed to perform the task. One famous example of reinforcement learning in action is AlphaGo, the first computer program to defeat a world champion at the game of Go.

Reinforcement learning works with data from a dynamic environment—in other words, with data that changes based on external conditions, such as weather or traffic flow. The goal of a reinforcement learning algorithm is to find a strategy that will generate the optimal outcome. The way reinforcement learning achieves this goal is by allowing a piece of software called an agent to explore, interact with, and learn from the environment.

In a real-world scenario, where transaction costs exist, it becomes critical, while observing the market, to know when to hedge during the life of the option to have this tradeoff between trading costs and hedging risk. This video shows how to apply reinforcement learning in hedging by building an automated trader that decides when to hedge a European call option contract to have a trade-off between transaction costs and hedging risk.

Watch the video

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