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EPFL Capstone project

Transcript: Nicolas VU HUU EPFL Capstone Project COS Applied Data Science: Machine Learning INTRO Introduction Project background DATA OVERVIEW Feature engineering & EDA Data overview Stock Universe Shortlist Market data Preparation CAR/HTZ ML-friendly IB API Decomposition Alphavantage API ML MODELS Machine Learning models Preparation, training and evaluation Decision Tree Linear regression ARIMA Neural networks CONCLUSION & QUESTIONS Question Time BIBLIOGRAPHY Bibliography Haroon, D. (2017). Python Machine Learning Case Studies; Apress Kristjanpoller, W. D., & Concha, D. (2016). Impact of fuel price fluctuations on airline stock returns. Applied Energy Leshik, E., Cralle, J. (2011), An Introduction to Algorithmic Trading: Basic to Advanced Strategies; Wiley Trading Narang, R. K. (2013), Inside the Black Box: A Simple Guide to Quantitative and High Frequency Trading; Wiley Nielsen, A (2020), Practical Time Series Analysis: Prediction with Statistics & Machine Learning; O'Reilly Nison, S. (1991), Japanese Candlestick Charting Techniques: A Contemporary Guide to the Ancient Investment Techniques of the Far East; New York Institute of Finance Weiming, J. (2019), Mastering Python for Finance; Packt Publishing Web sites ​ Agile Business Consortium https://www.agilebusiness.org Alpha Vantage https://www.alphavantage.co/documentation/ (historical market data API) Credit Suisse https://www.credit-suisse.com/pwp/pb/pb_research/technical_tutorial_de.pdf ("Technical analysis - Explained") Datahub.io (list of NYSE traded stocks) https://datahub.io/core/nyse-other-listings#resource-nyse-listed Finbox screener https://finbox.com/screener (data universe screener) Mplfinance https://github.com/matplotlib/mplfinance Newsapi https://newsapi.org/ Quandl http://www.quandl.com (market data sets) Time Series analysis (TSA) http://www.statsmodels.org/stable/tsa.html

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