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.

Suitability

Tech Analysts, S&T Analysts (optionally)

Learning Outcomes

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

Course Content

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

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of a cohort was promoted within 2 years after participating in an Alpha course.

96%

of participants felt more confident in their role, following an Alpha commercial leadership course.

Why study with Alpha?

Innovation and creativity born from experience

We are thought leaders in instructional and learning journey design and holistic solution architects. We have extensive finance and investments experience combined with skills application to deliver performance improving results. We develop immersive learning environments that maximize time to productivity, support talent retention and added value to improving quality of hires.

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We are focused on mining the embedded organisational intellectual capital for the benefit of the next generation. We create and curate best in class practice gathered from our experience with the leading financial institutions. We design our programmes with the end in mind – what results are you trying to achieve with this intervention? What metrics will we set ourselves to achieve that?

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