## Inspiration

We want people to make right decision at right time so that they can get returns on their investments and trading in stock market.

## What it does

It will enable to invest and trade in the stock by using HFT and technical analysis techniques. The product will have large scale applications in situations where the investor or trader does not has much time for individual analysis and for professionals that would like to deploy their money on auto pilot.

## How we built it

We have only the idea :)

## Challenges we ran into

How to implement the web app and mobile app for auto trading.

## What's next for TradeGO

We want to implement the app so that it can be used for auto trading.
## 💡 Inspiration 💡

Mental health is a growing concern in today's population, especially in 2023 as we're all adjusting back to civilization again as COVID-19 measures are largely lifted. With Cohere as one of our UofT Hacks X sponsors this weekend, we want to explore the growing application of natural language processing and artificial intelligence to help make mental health services more accessible. One of the main barriers for potential patients seeking mental health services is the negative stigma around therapy -- in particular, admitting our weaknesses, overcoming learned helplessness, and fearing judgement from others. Patients may also find it inconvenient to seek out therapy -- either because appointment waitlists can last several months long, therapy clinics can be quite far, or appointment times may not fit the patient's schedule. By providing an online AI consultant, we can allow users to briefly experience the process of therapy to overcome their aversion in the comfort of their own homes and under complete privacy. We are hoping that after becoming comfortable with the experience, users in need will be encouraged to actively seek mental health services!

## ❓ What it does ❓

This app is a therapy AI that generates reactive responses to the user and remembers previous information not just from the current conversation, but also past conversations with the user. Our AI allows for real-time conversation by using speech-to-text processing technology and then uses text-to-speech technology for a fluent human-like response. At the end of each conversation, the AI therapist generates an appropriate image summarizing the sentiment of the conversation to give users a method to better remember their discussion.

## 🏗️ How we built it 🏗️

We used Flask to make the API endpoints in the back-end to connect with the front-end and also save information for the current user's session, such as username and past conversations, which were stored in a SQL database. We first convert the user's speech to text and then send it to the back-end to process it using Cohere's API, which as been trained by our custom data and the user's past conversations and then sent back. We then use our text-to-speech algorithm for the AI to 'speak' to the user. Once the conversations is done, we use Cohere's API to summarize it into a suitable prompt for the DallE text-to-image API to generate an image summarizing the user's conversation for them to look back at when they want to.

## 🚧 Challenges we ran into 🚧

We faced an issue with implementing a connection from the front-end to back-end since we were facing a CORS error while transmitting the data so we had to properly validate it. Additionally, incorporating the speech-to-text technology was challenging since we had little prior experience so we had to spend development time to learn how to implement it and also format the responses properly. Lastly, it was a challenge to train the cohere response AI properly since we wanted to verify our training data was free of bias or negativity, and that we were using the results of the Cohere AI model responsibly so that our users will feel safe using our AI therapist application.

## ✅ Accomplishments that we're proud of ✅

We were able to create an AI therapist by creating a self-teaching AI using the Cohere API to train an AI model that integrates seamlessly into our application. It delivers more personalized responses to the user by allowing it to adapt its current responses to users based on the user's conversation history and
making conversations accessible only to that user. We were able to effectively delegate team roles and seamlessly integrate the Cohere model into our application. It was lots of fun combining our existing web development experience with venturing out to a new domain like machine learning to approach a mental health issue using the latest advances in AI technology.

## 🙋‍♂️ What we learned 🙋‍♂️

We learned how to be more resourceful when we encountered debugging issues, while balancing the need to make progress on our hackathon project. By exploring every possible solution and documenting our findings clearly and exhaustively, we either increased the chances of solving the issue ourselves, or obtained more targeted help from one of the UofT Hacks X mentors via Discord. Our goal is to learn how to become more independent problem solvers. Initially, our team had trouble deciding on an appropriately scoped, sufficiently original project idea. We learned that our project should be both challenging enough but also buildable within 36 hours, but we did not force ourselves to make our project fit into a particular prize category -- and instead letting our project idea guide which prize category to aim for. Delegating our tasks based on teammates' strengths and choosing teammates with complementary skills was essential for working efficiently.

## 💭 What's next? 💭

To improve our project, we could allow users to customize their AI therapist, such as its accent and pitch or the chat website's color theme to make the AI therapist feel more like a personalized consultant to users. Adding a login page, registration page, password reset page, and enabling user authentication would also enhance the chatbot's security. Next, we could improve our website's user interface and user experience by switching to Material UI to make our website look more modern and professional.
## Inspiration

Think about the most intimidating thing you can... I can guarantee it's less intimidating than starting to look and plan for your finances, specifically how you want to start investing. We all know of the stock market and investments, but how many of us actually believe anything there is ever going to be applicable to us, especially as young students? The thing is investing is actually something everyone can and should do! The aim of our project is to help people over that first hump and show them that planning, investing, and starting to become finically literate will not only lead to independence but will help make and save you money in the long term. We also want to make investing an engaging topic to learn about, and provide a little entertainment on a pretty stressful journey, but one we should all take.

## What it does

The web app has a few sections, the main section comes in the form of a webpage where you get a lot of initial information about the facts that we found were most necessary to know when you're starting to invest. From this page you're able to download the body of our game, where you meet two characters ready and willing to teach you about investing, as best as they can! To make things more engaging, you then fill out a little quiz just to see if you've retained some information from the website and mini lesson. But what's the point of education without action? Which is why our game also provides the user with current data for whichever stock they're interested in, combined with a nice line of best fit so they can start to recognize patterns in close prices.

## How we built it

The website was built with html and css, and the game was built fully with a python library called pygame, that specializes primarily in UI additions to most python programs. The characters were also drawn by our team.

## Challenges we ran into

Originally, we had planned on participating in Hack Western as a team of three, unfortunately we ended up competing as a team of two. Not only was the very unexpected but the difference between three people and two people is nothing to brush off. Regardless of our lack of members, we were genuinely able to build something we're proud of and in the end had a great time hacking!
In terms of technical challenges, not only is pygame a little outdated but it's also not suited for user input, so we struggled to properly obtain user input for several components of the project. Additionally, the combination of all the elements proved to be a lot harder than we thought and it took us several attempts to fit each component together.

## Accomplishments that we're proud of

This is our very first python graphical user interface, which was out of our comfort zone in the sense that there was a fair amount of learning new content and applying our experience from other graphical programs. Additionally, we were able to work with lots of fun designs and animations that made things not only more fun for the user and us!

## What we learned

Like financial literacy, the creation of a stock program takes patience and practice! In all seriousness, not only did we get a crash course in time and workload management with only two people but we learned vital skills in presenting educational content. We also expanded our knowledge about workflow, simultaneous processes in python, and honed our graphic design skills.

## What's next for Stock Overflow

We are aiming to expand our "action" component, currently we provide interesting statistics but no next steps for our user. In the future we want to expand how people can take action with their personal banks. We also wanted to implement a budgeting component, as investing and budgeting are two sides of the same coin. Financial literacy can be improved in countless different ways, so there's so much potential for expansion!