## Inspiration

In today's fast-paced world, the average person often finds it challenging to keep up with the constant flow of news and financial updates. With demanding schedules and numerous responsibilities, many individuals simply don't have the time to sift through countless news articles and financial reports to stay informed about stock market trends. Despite this, they still desire a way to quickly grasp which stocks are performing well and make informed investment decisions.

Moreover, the sheer volume of news articles, financial analyses and market updates is overwhelming. For most people finding the time to read through and interpret this information is not feasible. Recognizing this challenge, there is a growing need for solutions that distill complex financial information into actionable insights. Our solution addresses this need by leveraging advanced technology to provide streamlined financial insights. Through web scraping, sentiment analysis, and intelligent data processing we can condense vast amounts of news data into key metrics and trends to deliver a clear picture of which stocks are performing well.

Traditional financial systems often exclude marginalized communities due to barriers such as lack of information. We envision a solution that bridges this gap by integrating advanced technologies with a deep commitment to inclusivity.

## What it does

This website automatically scrapes news articles from the domain of the user's choosing to gather the latests updates and reports on various companies. It scans the collected articles to identify mentions of the top 100 companies. This allows users to focus on high-profile stocks that are relevant to major market indices. Each article or sentence mentioning a company is analyzed for sentiment using advanced sentiment analysis tools. This determines whether the sentiment is positive, negative, or neutral. Based on the sentiment scores, the platform generates recommendations for potential stock actions such as buying, selling, or holding.

## How we built it

Our platform was developed using a combination of robust technologies and tools. Express served as the backbone of our backend server. Next.js was used to enable server-side rendering and routing. We used React to build the dynamic frontend. Our scraping was done with beautiful-soup. For our sentiment analysis we used TensorFlow, Pandas and NumPy.

## Challenges we ran into

The original dataset we intended to use for training our model was too small to provide meaningful results so we had to pivot and search for a more substantial alternative. However, the different formats of available datasets made this adjustment more complex. Also, designing a user interface that was aesthetically pleasing proved to be challenging and we worked diligently to refine the design, balancing usability with visual appeal.

## Accomplishments that we're proud of

We are proud to have successfully developed and deployed a project that leverages web scrapping and sentiment analysis to provide real-time, actionable insights into stock performances. Our solution simplifies complex financial data, making it accessible to users with varying levels of expertise. We are proud to offer a solution that delivers real-time insights and empowers users to stay informed and make confident investment decisions.

We are also proud to have designed an intuitive and user-friendly interface that caters to busy individuals. It was our team's first time training a model and performing sentiment analysis and we are satisfied with the result. As a team of 3, we are pleased to have developed our project in just 32 hours.

## What we learned

We learned how to effectively integrate various technologies and acquired skills in applying machine learning techniques, specifically sentiment analysis. We also honed our ability to develop and deploy a functional platform quickly.

## What's next for MoneyMoves

As we continue to enhance our financial tech platform, we're focusing on several key improvements. First, we plan to introduce an account system that will allow users to create personal accounts, view their past searches, and cache frequently visited websites. Second, we aim to integrate our platform with a stock trading API to enable users to buy stocks directly through the interface. This integration will facilitate real-time stock transactions and allow users to act on insights and make transactions in one unified platform. Finally, we plan to incorporate educational components into our platform which could include interactive tutorials, and accessible resources.
## ✨ Inspiration

Driven by the goal of more accessible and transformative education, our group set out to find a viable solution. Stocks are very rarely taught in school and in 3rd world countries even less, though if used right, it can help many people go above the poverty line. We seek to help students and adults learn more about stocks and what drives companies to gain or lower their stock value and use that information to make more informed decisions.

## 🚀 What it does

Users are guided to a search bar where they can search a company stock for example "APPL" and almost instantly they can see the stock price over the last two years as a graph, with green and red dots spread out on the line graph. When they hover over the dots, the green dots explain why there is a general increasing trend in the stock and a news article to back it up, along with the price change from the previous day and what it is predicted to be from. An image shows up on the side of the graph showing the company image as well.

## 🔧 How we built it

When a user writes a stock name, it accesses yahooFinance API and gets the data on stock price from the last 3 years. It takes the data and converts to a JSON File on local host 5000. Then using flask it is converted to our own API that populates the ChartsJS API with the data on the stock. Using a Matlab Server, we then take that data to find areas of most significance (the absolute value of slope is over a certain threshold). Those data points are set as green if it is positive or red if it is negative. Those specific dates in our data points are fed back to Gemini and asks it why it is thinking the stock shifted as it did and the price changed on the day as well. The Gemini at the same time also takes another request for a phrase that easy for the json search API to find a photo for about that company and then shows it on the screen.

## 🤯 Challenges we ran into

Using the amount of API's we used and using them properly was VERY Hard especially making our own API and incorporating Flask. As well, getting Stock data to a MATLAB Server took a lot of time as it was all of our first time using it. Using POST and Fetch commands were new for us and took us a lot of time for us to get used too.

## 🏆 Accomplishments that we're proud of

Connecting a prompt to a well-crafted stocks portfolio.
learning MATLAB in a time crunch.
connecting all of our API's successfully
making a website that we believe has serious positive implications for this world

## 🧠 What we learned

MATLAB integration
Flask Integration
Gemini API

## 🚀What's next for StockSee

* Incorporating it on different mediums such as VR so users can see in real-time how stocks shift in from on them in an interactive way.
* Making a small questionnaire on different parts of the stocks to ask whether if it good to buy it at the time
* Use (MPT) and other common stock buying algorthimns to see how much money you would have made using it.
## Inspiration

There should be an effective way to evaluate company value by examining the individual values of those that make up the company.

## What it does

Simplifies the research process of examining a company by showing it in a dynamic web design that is free-flowing and easy to follow.

## How we built it

It was originally built using a web scraper that scraped from LinkedIn which was written in python. The web visualizer was built using javascript and the VisJS library to have a dynamic view and aesthetically pleasing physics. In order to have a clean display, web components were used.

## Challenges we ran into

Gathering and scraping the data was a big obstacle, had to pattern match using LinkedIn's data

## Accomplishments that we're proud of

It works!!!

## What we learned

Learning to use various libraries and how to setup a website

## What's next for Yeevaluation

Finetuning and reimplementing dynamic node graph, history. Revamping project, considering it was only made in 24 hours.