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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 Alpha Vantage (historical market data API) Credit Suisse ("Technical analysis - Explained") (list of NYSE traded stocks) Finbox screener (data universe screener) Mplfinance Newsapi Quandl (market data sets) Time Series analysis (TSA)

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