Predicting Intervals of Volatility and Exploiting Sentiment

An immediate hybrid trading strategy based on sentiment analysis and localized search queries.

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What it does

Extrapolating Sentiment Via r/wallstreetbets

On our trial run of the program, we generated a data set of the 10 most mentioned stock tickers and their associated vernacular. We then utilized Asad70’s application (His Project) to perform sentiment analysis upon the top 5 tickers. This analysis returned values for three possible trends — Bearish, Neutral, and Bullish — ranging from 0 to 1. These values were then compounded into a total trend value for the stock in the range of -1 (most bearish) to 1 (most bullish).

What Can Traders Do With This Information?

Traders can exploit the correlation between number of queries, trading volumes, volatility, daily change in price, and bullish/bearish sentiment to predict future stock market trends.

Thanks to the 1-3 day delay between an increase in queries and an increase in trading volume, investors can use real time query tracking and predictive analysis to forecast the activity of stocks several hours preceding relevant activity.

How We Built It

We combined academic research, existing APIS, and new code to generate a predictive tool and the respective trading strategy for investors.

What we did:

  • Analyzed 5 academic papers exploring the correlations between the number of search engine queries about a stock, its trading volumes, its volatility, and its daily change in price.
  • Created and integrated a personal Reddit Script API
  • Modified Asad70’s Application reddit-sentiment-analysis to encompass our Reddit Script API
  • Tested our output by generating a data set of the 10 most mentioned stock tickers on r/wallstreetbets and performing sentiment analysis on the top 5 picks

Modified Code

Original

import re import en_core_web_sm

nlp = en_core_web_sm.load() Stopwords = nlp.Defaults.stop_words

reddit = praw.Reddit(user_agent="Comment Extraction", client_id="", client_secret="", username="", password="")

Modified

import spacy #import en_core_web_sm

#nlp = en_core_web_sm.load() stopwords = []#nlp.Defaults.stop_words

reddit = praw.Reddit(user_agent="Comment Extraction", client_id="qs4uiqJHU2eiLzYneR####", client_secret="MkWEIZsGtkVKQZyWj29KcEjOBE####",username="Up2Early4this",password="###")

Challenges we ran into

Understanding the role of Client IDs and Client Secrets in Reddit APIs

Our team had never tried to integrate a reddit API with code before, so we had to navigate the registration and authentication portion of integrating the API with the codebase.

NLTK Issues

A large portion of our coding efforts went into preparing and utilizing NLTK’s Vader program locally. We experienced several roadblocks, most notably incorporating a library called en-core-web-sm

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