Green Thumb

We use AI, NLP, and sentiment analysis to simplify clean energy stock research, providing easy, data-driven insights & personalized strategies—empowering all investors, not just Wall Street.

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Inspiration

The transition to clean energy is critical for a sustainable future, yet retail investors often lack access to the data and tools necessary to confidently invest in this space. Institutional investors dominate financial markets with access to advanced analytics, while retail investors face barriers such as complex financial data, misinformation, and limited awareness of clean energy opportunities. Green Thumb was created to bridge this gap by leveraging AI, sentiment analysis, and financial modeling to make clean energy investing accessible, transparent, and data-driven.

What it does

Green Thumb is an AI-powered investment platform that provides retail investors with actionable insights into the clean energy sector. It:

  • Scrapes macroeconomic and regulatory news using NLP models.
  • Conducts sentiment analysis with FinBERT to assess market sentiment.
  • Aggregates financial and technical data from Yahoo Finance and Polygon.
  • Ranks clean energy stocks based on sentiment, EBITDA, revenue growth, valuation, and risk-adjusted returns.
  • Generates personalized investment strategies based on investor risk tolerance.

How we built it

Green Thumb integrates multiple technologies and methodologies:

  • Data Collection: Web scraping macroeconomic and regulatory news using NLP.
  • Sentiment Analysis: Using FinBERT to evaluate investor sentiment in financial news.
  • Financial Modeling: Pulling stock data from Yahoo Finance and Polygon to assess market health.
  • Ranking Algorithm: A mathematical model incorporating sentiment, financial metrics, and Sharpe Ratio.
  • AI-Generated Strategies: OpenAI generates personalized investment strategies based on user preferences.

Challenges we ran into

  • Data Overload: Processing and filtering high volumes of financial news while maintaining relevance.
  • Sentiment Bias: FinBERT works well but still struggles with nuanced financial language.
  • Feature Engineering: Determining which financial metrics were most impactful in ranking stocks.
  • Personalization: Tailoring investment strategies for different risk tolerances while maintaining accuracy.

Accomplishments that we're proud of

  • Successfully implemented an AI-driven investment ranking model for clean energy stocks.
  • Developed a robust pipeline for collecting and analyzing financial sentiment data.
  • Created an intuitive user experience that simplifies complex financial analysis.
  • Designed a personalized investment strategy generator that aligns portfolios with sustainability goals.

What we learned

  • The importance of real-time sentiment analysis in financial decision-making.
  • How AI can bridge the knowledge gap in finance, making investing more accessible.
  • The challenges of integrating financial data, NLP, and machine learning into a seamless workflow.
  • The impact of clean energy policies on investment trends and market sentiment.

What's next for Green Thumb

  • Deeper AI Integration: Enhancing our ranking model with machine learning techniques.
  • Expanded Coverage: Including more clean energy sectors and global markets.
  • Interactive Dashboard: A real-time visualization platform for investors.
  • User Customization: More granular portfolio recommendations based on individual preferences.
  • Community-Driven Insights: Leveraging social sentiment analysis and investor discussions.
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