Eikon Data API - Python Quants Tutorial 8 - Financial Time Series Prediction using Machine Learning

Speaker: Dr. Yves Hilpisch of The Python Quants                   Length: 21 mins

It is easy to retrieve historical intraday pricing data via the Eikon Data API, with Plotly and Cufflinks making the data visualization convenient, and Machine Learning (ML) techniques easily applied by using Python. We then show how easy it is to implement an intraday algorithmic trading strategy using a lag and pattern based approach and a support vector machine (SVM). We then use vectorized backtesting to analyse the effectiveness of the strategy.

  • Retrieving historical intraday price data and preparing lagged data
  • Working with such data using pandas, Plotly and Cufflinks
  • Applying Machine Learning (ML) techniques for time series prediction
  • Implementing a support vector machine (SVM) that learns from historical patterns in the lagged data
  • Generating a strategy that predicts an upwards or downwards movement in price based on the SVM model
  • Testing this ML-based trading strategy using vectorized backtesting as well as out-of-sample testing to build a more realistic picture

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