Get Your Money Up

Get Your Money Up Financial Literacy Investment Tool

Inspiration

The theme of financial literacy highlights the challenges individuals face when starting their financial journeys. Our tool leverages historical bank statements to provide users with personalized insights, helping them understand and assess their financial situations, thereby empowering them to embark on their path to financial literacy.

What It Does

This tool analyzes previous bank statements to generate insights on spending patterns, identify areas for improvement, propose saving strategies, and recommend an investment portfolio. We've built our strategy around the S&P 500, utilizing volatility data for each stock derived from historical data and implied volatility calculated using the Black-Scholes model. We implemented Support Vector Machine (SVM) techniques to classify volatility into low, medium, and high categories. Additionally, we employed genetic algorithms to optimize the stock portfolio and used Deep Deterministic Policy Gradients (DDPG) for dynamic allocation of investment percentages across the selected stocks. A moving average strategy further refines stock screening, allowing us to select stocks that have been back-tested and demonstrated successful annualized returns for further evaluation.

How We Built It

Challenges We Encountered

Parsing large PDF files efficiently.
Managing a complex Git and React project with numerous Node modules.
Initially struggled to utilize everyone's strengths effectively, but this challenge turned into a success as we recognized and leveraged each team member's unique skills to enhance our project.

Accomplishments We’re Proud Of

Successfully implementing data analysis, deep learning models, and machine learning techniques.
After initial attempts with linear regression to predict disposable income, we pivoted to ARIMA, which showed promise, and ultimately adopted the SARIMAX model for accurate predictions.
Creating a well-structured backend that seamlessly integrates the app’s diverse features.
Developing an intuitive frontend complemented by effective client-side scripting.

What We Learned

We gained valuable experience in planning a large-scale project, assigning roles within teams, and adapting to new technologies. Even those lacking expertise in certain areas stepped outside their comfort zones to contribute to the project's success.

What's Next for Get Your Money Up

Get Your Money Up has transformed into a functional application within just 24 hours, incorporating complex features. Moving forward, we aim to refine these implementations to provide more tailored and regulated insights for users.

Built With

3
css
node.js
openai
pandas
postgress
python
react
sqlite
typescript
yfinance

Submitted to

HackNotts 24

Created by

I developed a stock screening strategy to optimise a portfolio by analysing historical data. Using yfinance, I categorised stocks by annualised volatility and applied moving averages, SVM and RSI for entry and exit signals. Performance metrics were evaluated and stored in a database for informed investment decision-making.

Sameer Farooq
I worked on the backend, I built APIs using Python & Django to connect the ML models and database processes to the ReactJS frontend. I also used the backend to perform logic calculations.

Tomi Tokko
I started with a SARIMAX model to predict disposable income, which informs potential investment amounts. Then, I utilized genetic algorithms to optimize a stock portfolio, identifying the best stocks to invest in based on expected returns. Finally, I implemented a Deep Deterministic Policy Gradient (DDPG) model to dynamically allocate investment percentages across selected stocks.

Angelo Rosu
Hamza Wahbi