StockSentimentAnalysis
HACK TCNJ
Garrett Beatty, Taylor He, and Kwong Lee
OBJECTIVE:
Make some kind of software that predicts aggregate stock data by analyzing political news sources and their sentiments.
HOW IT WORKS:
1) Mined twitter posts from @realDonaldTrump using twitter’s API
2) Mined Bloomberg’s stock market API to get S&P500 data
3) Used NLP - TextBlob API - to generate a sentiment from text
4) Factor retweets and favorites to generate weighted sentiments
5) Used a LinearRegression model to predict the effect of trump’s next statement
6) Used the past 7 days’ tweets as an indicator of prediction:
over 100000 simulations, 69.4% accuracy for predicting 7 days
OUTLINE:
Software:
Use Bloomberg’s API to get stock data from a time range
Use IBM’s AlchemyData News API to get news from a time range
Connect stock data with political news with python
Use ML algorithm [Linear Regression] to predict stock behavior based on news articles
Electrical:
Design LED structure that displays predicted stock behavior (positive or negative)
Use of Processing 3.3 to read data and transfer to Arduino
Programmable scrolling LCD screen that displays stock stats using Arduino Uno
The LEDs are LIT.
THINGS WE DID WELL:
Identified good APIs to use
Quick decision making
Acceptable accuracy
Work ethic in the first 5 hours
Fast at gathering and organizing data from API
Mutual agreement of laziness
Kwong learned a lot (False)
CHALLENGES:
Importing files to the Arduino
Getting both stock and csv data to line up
Too much salt in chinese food (More than rassbaby for comparison)
Doing the project
Consistently doing work
Lack of motivation
Finding enough relevant memes
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