Inspiration
We realized that accessible end-to-end financial backtesters do not exist. We wanted to make an accessible and useful backtester and thus, Alpha Trade was born.
What it does
Our product provides a backtesting framework which allows users to test different trading strategies over historical data. When the user inputs a trade strategy, the first step is interfacing with our financial backtester. Our financial backtester (written in python) supports both website and json input format. After the financial backtester receives the data, it performs calculations using rolling standard deviations and rolling means in order to calculate the profit and loss (in percentage). From there, the financial backtester sends data to be graphed.
How we built it
We compiled datasets during our preprocessing. From there, we went through the dataset based on the trade strategy.
Challenges we ran into
We ran into challenges on both the backend and the frontend. Our overall solution was to focus on modularization, which allowed us to test one bug at a time. Additionally, we focused on following naming conventions and having an architecture of our data.
Accomplishments that we're proud of
We are proud of creating a full stack, functional website that implements a proper backend. We believe that we have created a genuine product that can be used by hundreds of retail traders and will allow them to tackle the complex issues of financial data analysis.
What we learned
We learned a lot about development practices, time management, and user interfacing.
What's next for AlphaTrade
We want to continue to hone the trading features in AlphaTrade. We also want to incorporate a feature which uses machine learning to hone trade suggestions by the users.
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