Cherry
Cherry is an all-in-one investment tracking application designed to help users monitor portfolios, understand risk, and make better long-term investment decisions through clean analytics and AI-assisted insights.
The goal of Cherry is not to encourage trading or speculation, but to give investors clarity — a clean, centralized view of their portfolio combined with educational insights that help them make better long-term decisions.
Why I Built Cherry
Most investors rely on a mix of tools:
- Spreadsheets
- Investment Platforms
- Financial Advisors
- YouTube, Reddit, or X for explantions
This creates fragmented understanding. You might know what you own, but not why your portfolio behaves the way it does.
I built Cherry to solve that problem. I wanted a single place where an investor can:
- See their full portfolio clearly
- Understand performance and allocation
- Learn what’s happening in plain language
What Cherry Does
Cherry focuses on three core ideas:
- Centralized visibility
- Clarity over complexity
- Education first
All portfolio data is presented in a clean, distraction-free interface.
Instead of overwhelming users with numbers, Cherry emphasizes understandable insights.
AI is used to explain concepts, risks, and strategies — not to give financial advice.
Core Features
- Portfolio tracking across assets
- Performance visualization over time
- Asset allocation and diversification breakdown
- AI-powered explanations for stocks and options
- Simple UI designed to reduce cognitive overload
Some features are actively being built, while others are planned and documented ahead of time.
Architecture & Tech Stack
Cherry is designed with separation of concerns and scalability in mind.
- Frontend
- Next.js
- Tailwind CSS
- Backend
- Next.js (TypeScript)
- RESTful API architecture
- AI
- OpenAI API for financial explanations
- Prompting designed to focus on education, not predictions
- Data
- Supabase (PostgreSQL)
- External market data APIs
- Authentication
- Supabase Authentication
Key Design Decisions
I chose Next.js backend with TypeScript for its type safety, seamless integration with the frontend, and strong developer experience. AI services are kept separate from core data logic to avoid tight coupling. Explanations are prioritized over recommendations to avoid regulatory and ethical issues. The UI is intentionally minimal — investing is already stressful, the interface shouldn't add to it.
Challenges & Tradeoffs
Building Cherry came with real tradeoffs:
- Financial APIs vary in structure and reliability.
- AI responses must be carefully constrained to avoid sounding like financial advice.
- Real-time data needs to be balanced against API limits and cost.
- Designing for beginners while still being useful to advanced users is difficult.
These constraints shaped many of the architectural and product decisions.
What I'd Improve Next
With more time and resources, I would focus on:
- Deeper portfolio risk analysis
- Backtesting tools for long-term strategies
- User-defined investment goals and projections
- More transparency around options strategies and Greeks
- Better caching and cost optimization across services
Final Thoughts
Cherry is both a product and a learning project.
It reflects how I approach software engineering: build intentionally, understand tradeoffs, and design systems that help people think more clearly — not react emotionally.
This project is still evolving, and that's intentional. If you would like to see or learn more about the project, you can check out the GitHub repository above and read the README.md file.