CryptoSight

Inspiration

All of us have keen interests in the computer science and finance sector. Since some of us are avid investors, we decided to make a website that gives the user insight into a specific cryptocurrency performance.

What it does

Cryptosight runs market simulations of various cryptocurrencies using Machine Learning based algorithms, taking into consideration users' investing goals. We use QuantConnect to run our custom-made, optimal algorithm and get information such as Compounding Annual Return, Win Percentage, Probabilistic Sharpe Ratio, Sharpe Ratio, Alpha, Beta, and a benchmark comparison to the S&P 500. Our goal is to help traders meet their goals with the use of algorithmic trading.

How We built it

We created multiple machine-learning algorithms and stored them on QuantConnect to be run. We also used reactjs, chartjs, and animejs, to make a crypto and goal selection page, a loading page, and also a dashboard page where we display all the information and graphs that are relevant to the optimal algorithm.

Challenges We ran into

Some challenges we ran into were using chartjs to create the s&p 500 benchmark, and also styling all the graphs. Additionally, we had some trouble with getting the QuantConnect API to work.

Accomplishments that We're proud of

We're proud of the website we made, and also for fixing all the issues our project faced, getting QuantConnect, Flask, chartjs, and also animejs to work. Also we're proud of our tool that can be very useful to new traders.