![Tech Diagram](https://i.ibb.co/StWfp3V/Princeton-1.jpg)

## Inspiration

With the rise of non-commission brokerages, more teens are investing their hard-earned money in the stock market. I started using apps such as Robinhood when I was in high school, but I had no idea what stocks to buy. Trading securities has a very steep learning curve for the average joe.

We see that lots of new investors have very little experience in the market. Communities like Wall Street Bets developed which further encourage wild speculation. Many open margin accounts, take out huge loans, and incur more risks without their knowledge. Also, many of these new investors are not financially savvy, nor are they looking forward to learning about finance. We wanted to build an app that helps new investors reduce risk and gain knowledge about what they own without much financial knowledge. Hence, we came up with a Social Media app, Due Diligence!

## What it does

Due Diligence allows the user to take a photo of everyday objects: car, laptop, fridge, calculator, collection of watches, etc... and recommends stocks associated with the images. For instance, if you upload a photo of your car, our app will recommend stocks like Tesla General Motors, and Ford. Our object detection model also has brand name recognition. Taking a photo of a Macbook will lead our app to recommend the APPL stock. By recommending companies that manufacture the products and services Due Diligence users daily use, we believe that our userbase can better evaluate the company, its business model, and its position in the market, and come up with a reasonable and safe decision on whether to buy the stock or not.

Our application also has a chat feature. When a user registers to DueDiligence, we ask them questions about their investment strategy (growth, value) and their investment horizon. After a user got a stock recommendation from our app, the user can choose to chat with another person looking to buy the same stock. We math the user with a partner that has similar investment strategies and investment horizons. They are able to use commands to get more specific information about a stock (get recent news articles, get its price, get its EPS this quarter) and we generate tailored questions for them to talk about based on their investment strategies.

## How we built it

We used React Native for the front-end, Flask for the back-end, and Postman for testing.

**1. Back-End**

The back-end dealt with managing users, saving investment strategies, classifying images to stock tickers, and matching/managing the chat. We used MongoDB Atlas to save all the data from the users and chat, and to update the front-end if necessary. We also used the IEX Cloud API which is an all-around stocks API that gives us the price, news, ticker symbol of a stock.

**2. Front-End**

We used React Native for the front end. We were experienced web developers but had little experience in app development. Being able to use web technologies sped up our development process.

**3. Google Cloud Vision API**

We used the Google Cloud Vision API to detect multiple items and logos in an image. After getting the tag names of the image, we ran it through our classification model to the image into ticker symbols.

**4. Classification Model**

The IEX Cloud API could search stocks based on their sector, so we had to relate products to sectors. This is where Naive Bayes came in. We weren't able to find a dataset, but we created our own data that trained the Bayes net table using the posterior probabilities. We built our dataset by figuring out how products matched with the business sector (mobile phone -> tech, sports car -> automobile industry, etc...)

## Challenges we ran into

The problem of relating products to related companies was a very hard problem. There wasn't an API for it. We resorted to using a machine learning model, but that said, it was very challenging to think of a solution that mapped it in a better way. Also, we were novice app developers. We learned how to use reactive native from scratch which was very time-consuming. Finally, working remotely with everyone was very challenging. We solved this problem by using discord and getting into team calls often.

## Accomplishments that we're proud of

We are proud that we were able to think of new solutions to problems that haven't been addressed. Mapping images to stock recommendations have not been done before, and we had to build our Bayes model from scratch. Also, we are proud that we learned to build a mobile app in less than 48 hours!

## What we learned

We learned new technologies such as React Native and new models like Naive Bayes. Most of all, we learned how to work together in a challenging situation, and come up with a program that we are very proud of.

## What's next for DD - Due Diligence

We ran out of time while finishing up the front-end, back-end integration, so finishing that will be our top priority. Other than that, we think expanding our services beyond NYSE and NASDAQ by integrating foreign exchanges into our app would be useful. Also, we think adding new asset classes such as Bonds and ETFs are the right step forward.
## Inspiration

There is a simple and powerful truth: investing is a skill that has a long lasting impact on our ability to reach the most important goals in our lives, from student loans to buying a house to retirement funds.

However, our education system doesn’t prepare us to make these daunting decisions. When we were teenagers, we felt finance was “complex, another mysterious “adult thing” that remotely relates to us. Higher tuition and less money available for financial aid are pushing more and more young people into debt even after years of working in the industry. According to Harvard Law School, about 110,000 youths under 25 filed for bankruptcy in 2017.

Investing shouldn’t just be a sophisticated tool played by the professionals. Investig should be a mindset that helps anyone to make better decisions in this world.

Let’s democratize finance.

## What it does

We have designed Dr. Trade to simulate an interactive investing game that makes it simple and fun for teenagers to learn the most important skills of an intelligent investor:

* A habit of following world news in the morning. Alarm clock UI design with voice control, seamlessly integrate learning to trade into user’s daily life
* Communication skills are essential for traders. The hands-free UX design allows users to practice putting in orders and discussing news like traders do.
* The best traders are the calm and persistent ones. Users need to wait until market close at 4pm to see daily profits and are encouraged to trade every day with the built-in reward system, including peer rankings and medals.
* Traders learn from their mistakes. At the end of the day, Dr. Trade will reflect with the user what went well and what went wrong based on real market data.
* A curious mind to connect the dots between news and stock performance.
* Making tough decisions:
* Users are only allowed to make one trade per day

## How we built it

* Machine learning with ASR, NLP, TTS (Action Google)
* Interaction design
 > Conversation design (Dialogflow/Java)
 > UI/UX design
* Portfolio analysis (BlackRock Aladdin/Python)
* Live stock market data (Yahoo Finance/Python)
* Speaker (Google Home Mini)

## Accomplishments that we're proud of

We have managed not only to identify a true problem, but also to develop a unique and effective solution to address it. Inbetween, we grew as team players through synergy and delved into the Google Action platform and the ML behind it. Needless to say, that focusing on education, we are empowering the future generation to lead a prosperous life, as they now possess the financial literacy to be independent.

## What we learned

By concentrating our efforts on the educational perspective of the project, our team discovered the inner-motivation and the vision that kept us pushing in the last few days and in the future.

## What's next for Dr. Trade

We are hoping to further develop Dr. Trade and hopefully find product-market fit in the next few months, as it is a project deep embedded in the team’s values for world-change. Here’s what we plan to do in the next month:

* Integrate Blackrock Aladdin with Dialogflow to automate portfolio snapshot
* Install a 5 inch display to our MVP for portfolio snapshot and performance visualization
* User research -- test out our MVP with high school students in the greater Philadelphia community

## Keywords:

Education, Experiential Learning, Machine Learning, Interaction Design, UI/UX Design, Action Google, Blackrock, Portfolio Analysis, Financial Literacy
## Inspiration

1 in 2 Canadians will personally experience a mental health issue by age 40, with minority communities at a greater risk. As the mental health epidemic surges and support at its capacity, we sought to build something to connect trained volunteer companions with people in distress in several ways for convenience.

## What it does

Vulnerable individuals are able to call or text any available trained volunteers during a crisis. If needed, they are also able to schedule an in-person meet-up for additional assistance. A 24/7 chatbot is also available to assist through appropriate conversation. You are able to do this anonymously, anywhere, on any device to increase accessibility and comfort.

## How I built it

Using Figma, we designed the front end and exported the frame into Reacts using Acovode for back end development.

## Challenges I ran into

Setting up the firebase to connect to the front end react app.

## Accomplishments that I'm proud of

Proud of the final look of the app/site with its clean, minimalistic design.

## What I learned

The need for mental health accessibility is essential but unmet still with all the recent efforts. Using Figma, firebase and trying out many open-source platforms to build apps.

## What's next for HearMeOut

We hope to increase chatbot’s support and teach it to diagnose mental disorders using publicly accessible data. We also hope to develop a modeled approach with specific guidelines and rules in a variety of languages.