R simple project
The project is designed to integrate what you learned in class to construct a big data alpha model. You will identify a portfolio of stocks (of your own interest); retrieve various data that you think will help you predict the future price of these stocks; process the big data and construct factors accordingly; implement the big data alpha model (follow the R sample codes discussed in class) using R; examine and report its performance.
What to do
Specifically, your project should at least consist of these steps:
- Choose a portfolio of securities
- Can be stocks, cryptocurrencies (tiker:^XBTS, BTC-USD, ETH-USD etc.), commodities or ETF. You decide on the portfolio size, minimum two
- For every stock, retrieve at least a couple of years’ data
- retrieve the fundamental data you deem relevant
- e.g., Financial statement data (quarterly at least), macroeconomics data (monthly or quarterly)
- retrieval the daily trading data
- Extract the technical analysis indicators
- Retrieve data from social media (e.g. twitter or reddit or other social media sources)
- Analyze the data to calculate media attention level, sentiment etc.
- Hopefully every team can think of at least one thing innovative in big data analytics, e.g. wallstreetbets factor, create TA for sentiments, geographic location analytics and follower-followee social network etc.
- retrieve the fundamental data you deem relevant
3. Identify good factors from the data
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- Evaluate which factors predict the next period (e.g. week, month, year) return best
- Execute the big data alpha model and report the performance
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