Time Series Analysis in Automatic Trade Execution

The aim of this, and the other workshops in this series (DS305ff) is to redeploy existing knowledge (subject-matter domain and technical) to devise solutions collaboratively. Optimum class size for this and subsequent workshops is 12-16, to allow for groups of 3 and 4 to solve programming and design challenges as they arise. The workshops are less deterministically structured than the webinars, reflecting the somewhat unpredictable nature of real-world projects and the need to flex accordingly.


Tech Analysts, S&T Analysts (optionally)

Learning Outcomes

By the end of the course, participants will be able to:rn• Implement several time series analysis methods in Python,rn• Write a backtest to compare a strategy to its underlying benchmarkrn• Code a simplified automated trading system.

Course Content

• Recap of relevant tools in Python: pandas, Numpy and rolling() to generate signalsrn• Introduction to Automated Trade Execution methodsrn• Design: What are the prerequisites of a simple Automated Trading system?rn• Implement a predictive model using a machine learning or statistical method covered in precedingrn• Validate using a backtester (place trades on the validation dataset: do we make money / beat the market?)rn• Present results to class and use implementations as a forum to suggest further developments / optimisations.


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