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  Stock Snapshot

INVEST.IO - Market-Sentiment-Analysis - Wall Street Made Human

A stock market sentiment analysis that comprises of emotional analyses on S&P 500 companies. Includes a moving average graph derived using data mined from Yahoo Finance. Consumers can utilize this information to make well-informed decisions on which stocks to invest in.

What it does

Our project provides users a sentimental analysis on a specific stock by using the latest API’s to scrape real time internet sources and apply effective emotional classification algorithms. In that way, consumers can better manage risk and determine, using relevant information, which stocks to invest in.

How we built it

Invest.io was built using two main API’s: Parallel Dots and Beautiful Soup. Parallel Dots was used for core emotional analysis, allowing us to measure the general perception of said stock, and Beautiful Soup was used to scrape the actual data that was used to analyze the general emotional analysis.

Challenges we ran into

Some challenges we faced included integrating the graphical interface with the actual emotional analysis and the predictive analysis. We had to find a way to take our data we mined and present that information in an easy to explain and useful way. In addition, we faced challenges of efficiency; the actual data mining takes a decent amount of time, which is something we want to improve on in the future.

Accomplishments that we're proud of

We feel that our back end system, consisting of our webscraping and analysis, and how we managed to integrate everything within one system. We took raw, hard to understand data, and transformed it into an extremely visual presentation of information.

What we learned

We learned how to utilize technologies such as Node.js and Python, and utilize those technologies to analyze and visualize data and statistics. In doing so, we learned core programming skills, such as division of labor and efficient coding.

What's next for Invest.io

In the future, we plan on providing more interesting and relevant visualizations of the data, and possibly devise an algorithm determining whether a consumer should invest. Right now, the consumer interprets the data themselves, and makes a decision based on that, making it slightly more complicated. With an algorithm, we can make the decision more streamlined and more efficient, allowing busy consumers to get an early start on their investing goals.

Built With

• css
• html
• javascript
• python
• shell