## Inspiration:

Many people may find it difficult to understand the stock market, a complex system where ordinary people can take part in a company's success. This applies to those newly entering the adult world, as well as many others that haven't had the opportunity to learn. We want not only to introduce people to the importance of the stock market, but also to teach them the importance of saving money. When we heard that 44% of Americans have fewer than $400 in emergency savings, we felt compelled to take this mission to heart, with the increasing volatility of the world and of our environment today.

## What it does

With Prophet Profit, ordinary people can invest easily in the stock market. There's only one step - to input the amount of money you wish to invest. Using data and rankings provided by Goldman Sachs, we automatically invest the user's money for them. Users can track their investments in relation to market indicators such as the S&P 500, as well as see their progress toward different goals with physical value, such as being able to purchase an electric generator for times of emergency need.

## How we built it

Our front end is entirely built on HTML and CSS. This is a neat one-page scroller that allows the user to navigate by simply scrolling or using the navigation bar at the top. Our back end is written in JavaScript, integrating many APIs and services.

APIs that we used:
-Goldman Sachs Marquee
-IEX Cloud

Additional Resources:
-Yahoo Finance

## Challenges we ran into

The biggest challenge was the limited scope of the Goldman Sachs Marquee GIR Factor Profile Percentiles Mini API that we wanted to use. Although the data provided was high quality and useful, we had difficulties trying to put together a portfolio with the small amount of data provided. For many of us, it was also our first times using many of the tools and technologies that we employed in our project.

## Accomplishments that we're proud of

We're really, really proud that we were able to finish on time to the best of our abilities!

## What we learned

Through exploring financial APIs deeply, we not only learned about using the APIs, but also more about the financial world as a whole. We're glad to have had this opportunity to learn skills and gain knowledge outside the fields we typically work in.

## What's next for Prophet Profit

We'd love to use data for the entire stock market with present-day numbers instead of the historical data that we were limited to. This would improve our analyses and allow us to make suggestions to users in real-time. If this product were to realize, we'd need the ability to handle and trade with large amounts of money as well.
## 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

Prolonged covid restrictions have caused immense damage to the economy and local markets alike. Shifts in this economic landscape have led to many individuals seeking alternate sources of income to account for the losses imparted by lack of work or general opportunity. One major sector that has seen a boom, despite local market downturns, is investment in the stock market. While stock market trends at first glance, seem to be logical, and fluid, they're in fact the opposite. Beat earning expectation? New products on the market? *It doesn't matter!*, because at the end of the day, a stock's value is inflated by speculation and **hype**. Many see the allure of rapidly increasing ticker charts, booming social media trends, and hear talk of town saying how someone made millions in a matter of a day *cough* **GameStop** *cough* , but more often then not, individual investors lose money when market trends spiral. It is *nearly* impossible to time the market. Our team sees the challenges and wanted to create a platform which can account for social media trends which may be indicative of early market changes so that small time investors can make smart decisions ahead of the curve.

## What it does

McTavish St. Bets is a platform that aims to help small time investors gain insight on when to buy, sell, or hold a particular stock on the DOW 30 index. The platform uses the recent history of stock data along with tweets in the same time period in order to estimate the future value of the stock. We assume there is a correlation between tweet sentiment towards a company, and it's future evaluation.

## How we built it

The platform was build using a client-server architcture and is hosted on a remote computer made available to the team. The front-end was developed using react.js and bootstrap for quick and efficient styling, while the backend was written in python with flask. The dataset was constructed by the team using a mix of tweets and article headers. The public Twitter API was used to scrape tweets according to popularity and were ranked against one another using an engagement scoring function. Tweets were processed using a natural language processing module with BERT embeddings which was trained for sentiment analysis. Time series prediction was accomplished through the use of a neural stochastic differential equation which incorporated text information as well. In order to incorporate this text data, the latent representations were combined based on the aforementioned scoring function. This representation is then fed directly to the network for each timepoint in the series estimation in an attempt to guide model predictions.

## Challenges we ran into

Obtaining data to train the neural SDE proved difficult. The free Twitter API only provides high engagement tweets for the last seven days. Obtaining older tweets requires an enterprise account costing thousands of dollars per month. Unfortunately, we didn’t feel that we had the data to train an end-to-end model to learn a single representation for each day’s tweets. Instead, we use a weighted average tweet representation, weighing each tweet by its importance computed as a function of its retweets and likes. This lack of data extends to the validation side too, with us only able to validate our model’s buy/sell/hold prediction on this Friday's stock price.
Finally, without more historical data, we can only model the characteristics of the market this week, which has been fairly uncharacteristic of normal market conditions. Adding additional data for the trajectory modeling would have been invaluable.

## Accomplishments that we're proud of

* We used several API to put together a dataset, trained a model, and deployed it within a web application.
* We put together several animations introduced in the latest CSS revision.
* We commissioned McGill-themed banner in keeping with the /r/wallstreetbets culture. Credit to Jillian Cardinell for the help!
* Some jank nlp

## What we learned

Learned to use several new APIs, including Twitter and Web Scrapers.

## What's next for McTavish St. Bets

Obtaining much more historical data by building up a dataset over several months (using Twitters 7-day API). We would have also liked to scale the framework to be reinforcement based which is data hungry.