## Inspiration

Our project aims to democratize algorithmic trading and the data associated with it to capture a 150 billion dollar a month trade volume and aim it towards transparency and purpose. Our project is a culmination of the technical depth and breadth of a new step forward in technology. We really wanted to open up the centralized and ever exclusive profession of algorithmic trading, giving the average retail trader the same tools and data as billion dollar companies. Empowering curiosity and innovation through open sourced data and tools.

## What it does

Our project is an brokerage platform that hosts compute, data APIs, processing tools, algorithms, and data-stream. A mix of the usability of Robinhood with the decentralized community building of technical people like Kaggle. We transform strategies and ideas that require a huge amount of capital and expertise, and hand it to the every-day retail investor. When customers upload code, our platform allows them to test their ideas out on paper trades, and when they are confident enough, we host and execute with a small fee on our infrastructure.

There are two distinct properties of our project that stand out as unique:

1. **Algorithm Marketplace**: Anyone can create an algorithm and earn commission by allowing others to run the algorithm. This means investors can invest in unorthodox and unique public algorithms, which removes all financial and technological barriers young analysts and programmers without any capital might face. By doing so, the project opens up the investment community to a new diverse and complex set of financial products.
2. **Collaborative Data Streams**: All users are encouraged to connect their individual data streams, growing a community that helps each other innovate and develop refined and reliable sources of data. This can serve as both a gateway to accessibility and a lens of transparency, enabling users to track and encourage responsible investments, allowing users to monitor and invest in entities that emphasize certain causes such as sustainability or other social movements.

## How we built it

Our project was specifically made in two stages, the framework and then the use cases. We used our combined experience at Bloomberg and Amazon working with this type of data to create a framework that is both highly optimized and easy to use. Below, we highlight three use case examples that were developed on our platform.

## Use Case 1: Technical Analysis Using Independent Component Analysis (ICA)

1. **Traditional Stock Analysis Using ICA**:

	* We utilize **Independent Component Analysis (ICA)** to decompose a data matrix 𝑋 (observations) into independent components. The goal is to estimate 𝐴 (mixing matrix) and 𝑆 (source signals), assuming 𝑆 contains statistically independent components.
	* ICA maximizes non-Gaussianity (e.g., using kurtosis or negentropy) to ensure independence, allowing us to identify independent forces or components in mixed signals that contribute to changes in the overall system.
2. **Cosine Similarity Between Stocks**:

	* By analyzing the independent components driving the stock prices, we perform **cosine similarity** between them. This generates a value within the range of [-1, 1], representing how much any two stocks share these independent components.
3. **Dynamic Graph Representation**:

	* We build an **updating graph** based on the relationships derived from the cosine similarity, providing real-time insight into how stocks are interrelated through their independent components.

## Use Case 2: Prediction Algorithm

* Our second use case involves a **prediction algorithm** that tracks stock movement and applies trend-based estimation across various stocks.
* This demonstrates a **low latency real-time application**, emphasizing the capability of **SingleStore** for handling real-time database operations, and showing how the platform can support high-speed, real-time financial data processing.

## Challenges we ran into

We encountered several challenges, including latency issues, high costs, and difficulties with integrating real-time data processing due to rate limits and expenses. Another hurdle was selecting the right source for real-time stock data, as both maintaining the database and processing the data were costly, with the data stream alone costing nearly $60.

## Accomplishments that we're proud of

We collectively managed to create a framework that is impressive on a technical scale and scalable as we look into the future of this project.

## What we learned

We gained experience with data normalization techniques for stock data and learned how to sync time series datasets with missing information. We also had to think deeply about the scalability of our platform and the frictionless experience we wanted to present.

## What's next for The OpenTradeLab

We have several initiatives we're excited to work on:

1. **Growing Communities Around Social Investments**:

	* We aim to explore sustainable ways to build and foster communities focused on investment in social causes.
2. **Direct Exchange Connectivity**:

	* We're looking into the possibility of connecting directly to an exchange to enable real-time trade routing.
3. **Optimized Code Conversion**:

	* We plan to develop an API and library that converts Python code into optimized C++ code for enhanced performance.
4. **Investment Safeguards**:

	* Implementing safeguards to promote responsible and secure investment practices is another key area of focus.
## Inspiration

The World of High frequency trading and its possibilities as well as the rise in the popularity of wall street bets

## What it does

Our program scraps reddit data using the praw algorithm, based on a certain key word, passing that data to a sentiment analysis algorithm, this algorithm then passes that information into out trading algorithm which uses the number calculated to as a weight as too if to buy and sell a particular stock

## How we built it

We build it initially we jupyter notebook then build it further from there using QuantConnect to create a trading algorithm, which determines if to buy or sell based on both information from QuantConnect past data, as well as a confidence interval calculated by the jupyter notebook

## Challenges we ran into

Getting the Quantconnects algorithms to work locally on our computers but to the high degree of specificity and optimization needed to run Quantconnect, we also faced difficulty getting both the backend and front end to communicate.

## Accomplishments that we're proud of

we got Quantconnect to work locally on our computers, we were able to create a sentiment analysis bot which makes use of reddit scrappers, we learned new technologies.

## What we learned

How software development truly works

## What's next for 2MenOneCode

improve our algorithm, and get the software to run smoothly within a single application
## Inspiration

After being overwhelmed by the volume of financial educational tools available, we discovered how the majority of products are focused for institutions or expensive. We decided there needs to be an easy approach to learning about stocks in a more casual environment. Interested in the simplicity of Tinder’s yes or no swiping mechanics, we decided to combine the 2 ideas to create Tickr!

## What it does

Tickr is a stock screening tool designed to help beginner retail investors discover their next trade! Using an intuitive yes or no discovery system through swiping mechanics, Tickr the next Tinder for stocks. For a more in depth video demo, see our [original screen recorded demo video!](https://youtu.be/dU6rF8vymKE)

## How we built it

Our team created this web app using a Node and Express back end paired with a React front end. The back end of our project used 3 linked Supabase tables to host authenticated user information, static information about stocks from the New York stock exchange and NASDAQ. We also used the [Finnhub API](https://finnhub.io/) to get real time metrics about the stocks we were showing our users.

## Challenges we ran into

Our biggest challenge was setting the scope into something that our team could complete in a weekend. We hadn't used Node and Express in a long time, so getting comfortable with our stack again took more time than we thought.

We were also completely new to Supabase and decided to try it out because it sounded really interesting. While Supabase turned out to be incredibly useful and userfriendly, the learning curve for it also took a bit more time than we thought.

## Accomplishments that we're proud of

The two accomplishments we are most proud of are our finished UI and successful integration of the Finnhub API. Drawing inspiration from Tinder, we were able to recreate a similar UI/UX design with minimal help from pre-existing libraries. Further, we were able to design our backend to make seamless API calls to fetch relevant data for our application.

## What we learned

During this project we learned a lot about the power of friendship and anime. Some of us learned what a market cap was and how to write a viable business proposal while others learned more about full stack development and how to host a database on Supabase.

Overall it was a very fun project and we're really glad we were able to get our MVP done 😁✌️

## What's next for Tickr

Our next goal for Tickr is to finish off the aggregate news feed function. This would entail a news feed of all stocks swiped on and provide notification. This would help improve our north star metric of time spent on platform and daily active users!