Several Python notebooks and algorithms to study the correlation of stock prices and google search terms. Currently the following procedures are implemented:
GoogleTrend.ipynb study the correlations between tech stocks and google search terms. correlation between stocks and terms.png results which shows the correlations backtest.py the algorithm which tests a simple momentum trading strategy during the window from 2010 to 2017 based on the correlation factors identified from the above observation. backtest.png backtest results. Apparently, the strategy's performance easily outruns S&P 500. MultiTimeline.csv several keywords downloaded from Google Trends between 2012-11-04 and 2017-10-22.
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This work is only meant to show the great potential of the proposed method to study the correlations of stock markets and google search terms. After filtering through tons of choices from google dataset and applying machine learning algorithms to stock prices, the proposed method can be quite promising to help portfolio management before massive stock movements.