Finance Bro - AI Stock Analysis

🚀 Experimental front-end vibe coding project for Vietnamese stock market analysis

This project is an experiment in front-end vibe coding - exploring how intuitive, design-first development approaches can be used to assist analysts' backend work and transform it into a well-rounded full-stack data analysis platform with compelling visuals.

What is Front-End Vibe Coding?

Design-first development approach prioritizing user experience and visual appeal
Rapid prototyping with immediate visual feedback
Intuitive interface design that makes complex data analysis accessible
Focus on creating engaging, interactive data visualizations

Project Goals:

Bridge the gap between backend financial analysis and frontend user experience
Demonstrate how front-end vibe coding can enhance traditional data analysis workflows
Create a comprehensive financial analysis platform that's both powerful and visually appealing
Show how modern web technologies can make complex financial data more accessible

Natural Language Interface: Interact with your financial statements in natural language. Get answers to your questions about any financial statement of listed stocks in Vietnam with a friendly finance bro. He's an introvert so please make your questions short and to the point.

Features

📊 Vietnamese Stock Data - Load financial data using Vnstock (VCI/TCBS sources)
🤖 AI Chat Interface - Natural language queries about financial metrics with PandasAI integration
💬 Interactive Analysis - Real-time conversation with AI analyst including file upload support
📈 Advanced Technical Analysis - Interactive price charts with 75+ QuantStats metrics, professional tearsheets, and comprehensive technical indicators (RSI, MACD, Bollinger Bands, OBV, ADX) with robust error handling
🎯 Intelligent Stock Screener - Multi-criteria filtering with 6 advanced filters (Beta, Alpha, Financial Health, etc.)
💼 Portfolio Optimization - Modern Portfolio Theory, Hierarchical Risk Parity, and risk analysis with riskfolio-lib
🏢 Comprehensive Company Analysis - Ownership structure, management team, subsidiaries, and foreign transaction analysis
📊 Professional Visualizations - Interactive charts with Altair, Bokeh, and custom styling
🔐 Secure API Key Management - Environment-based configuration with UI fallback

Quick Start

Deploy to Render (Recommended)

Try the live demo: https://finance-bro.onrender.com

Deploy Finance Bro to the cloud with one click:

What you need:

A Render account (free tier available)
Your OpenAI API key (get one here)

Deployment steps:

Click the "Deploy to Render" button above
Render will prompt you to connect your GitHub account (if not already connected)
Enter your OPENAI_API_KEY when prompted
Click "Deploy" and wait ~5 minutes for the build to complete
Your app will be live at https://finance-bro-[random-id].onrender.com

Important notes:

Free tier: The app will sleep after 15 minutes of inactivity and may take ~30 seconds to wake up
Environment variables: Only OPENAI_API_KEY is required
Updates: Push changes to your GitHub repo and Render will auto-deploy
Custom domain: Available on paid plans

Local Development

Prerequisites

Python >= 3.10.11
OpenAI API key

Installation

Clone the repository:

git clone https://github.com/yourusername/finance-bro.git
cd finance-bro

Install dependencies using uv (recommended):

uv sync

Or using pip:

pip install -r requirements.txt

Set your OpenAI API key:

export OPENAI_API_KEY="your-openai-api-key"

Run the application:

uv run streamlit run app.py

Or:

streamlit run app.py

Open your browser and navigate to http://localhost:8501

User Flow

Optimal User Experience Flow

Finance Bro is designed with a specific user flow for the best experience. Follow these steps to get the most out of the application:

Step 1: Authentication & Homepage

🔐 Google Login: Start by logging in with your Google account on the authentication page
🏠 Homepage Navigation: After login, you'll be directed to the Finance Bro homepage
📊 Stock Symbol Selection: On the homepage, use the searchable dropdown to select a Vietnamese stock symbol
- Critical Step: This stock symbol selection is essential - it will be shared across all analysis pages
- Search Feature: Type to search through all available Vietnamese stock symbols
- Persistence: Your selected symbol will be available throughout your session

Step 2: Stock Analysis Page (Essential First Stop)

📈 Navigate to Stock Analysis: From the homepage, click the "📊 Stock Analysis" quick navigation button
- Why This Matters: The Stock Analysis page loads and caches essential data for optimal performance
- Data Loading: This page loads all stock symbols and company information for the entire app
- Performance: Visiting this page first ensures faster loading on all other pages
⚙️ Configure Analysis Settings: In the Stock Analysis page:
- Select period (year/quarter)
- Choose data source (VCI/TCBS)
- Click "Analyze Stock" to load financial data
💬 AI Interaction: Once data is loaded:
- Use the chat interface to ask financial questions
- Try sample questions from the sidebar
- Explore AI-generated insights and charts

Step 3: Explore Other Analysis Tools

🚀 Navigate to Other Pages: Now you can efficiently use other analysis tools:
- 📈 Price Analysis: Interactive price charts with 75+ QuantStats metrics and professional tearsheets
- 📊 Technical Analysis: Advanced technical indicators with heating stock discovery and robust error handling
- 🏢 Company Overview: Company profile with ownership structure, management team, subsidiaries, and foreign transaction analysis
- 💼 Portfolio Optimization: Modern Portfolio Theory, Hierarchical Risk Parity, and comprehensive risk analysis
- 🎯 Stock Screener: Multi-criteria stock filtering with advanced metrics (Beta, Alpha, Financial Health) and quick presets

Recommended Setup for Best Experience

🎨 Custom Theme for Best Appearance: This app is designed with a custom color scheme for optimal visual experience.

How to enable custom theme:

Click the hamburger menu (☰) in the top-right corner
Select "Settings"
Under "Theme", choose "Custom"
The app will display with the optimized color scheme and chart styling

Alternative: Light mode is also supported if you prefer standard Streamlit styling.

Why This Flow Matters

🎯 Performance Optimization:

Stock Analysis page loads and caches symbols data once for the entire session
All other pages benefit from this cached data for faster loading
Company names display properly across all pages

📊 Data Consistency:

Your selected stock symbol persists across all pages
Financial data is loaded once and shared across analysis tools
Seamless navigation between different analysis perspectives

🚀 Best User Experience:

No need to re-enter stock symbols on each page
Faster page loads after initial data caching
Professional company name display instead of just stock symbols

Example Questions

"What is the return on invested capital (ROIC) trend?"
"Analyze the dividend yield and payout ratio trends"
"What is the company's debt-to-equity ratio?"
"Compare revenue growth across years"
"What are the key financial strengths and weaknesses?"

📊 Technical Analysis Features

Finance Bro includes a comprehensive Technical Analysis page that automatically discovers and analyzes "heating up" stocks from the Vietnamese market using advanced technical indicators.

Key Features

🔥 Automatic Stock Discovery

Scans entire Vietnamese stock market (HOSE, HNX, UPCOM exchanges)
Identifies stocks with "Overheated in previous trading session" signals
Displays comprehensive market data including industry, market cap, and trading metrics

📈 Advanced Technical Indicators

RSI (Relative Strength Index): Momentum oscillator for overbought/oversold conditions
MACD (Moving Average Convergence Divergence): Trend-following momentum indicator
Bollinger Bands: Volatility bands for price channel analysis
OBV (On-Balance Volume): Volume flow indicator for price movement validation
ADX (Average Directional Index): Trend strength measurement

🎯 Interactive Candlestick Charts

Professional mplfinance integration with custom Finance Bro theme
Multi-panel layouts with synchronized indicators
Configurable time intervals (Daily, Weekly, Monthly)
Earth-toned color scheme for professional analysis

⚙️ Robust Error Handling

Comprehensive technical indicator integration with graceful error recovery
Individual indicator validation with specific failure explanations
Enhanced data validation for optimal ADX calculation (30+ data points required)
Transparent warning system explaining exactly why indicators might fail
Optimized date ranges (90 days for daily analysis) for reliable calculations

Technical Implementation Highlights

Production-Ready Error Handling

Fixed TypeError: 'NoneType' object is not subscriptable from technical indicator None returns
Resolved ValueError: zero-size array to reduction operation maximum in ADX calculations
Safe chart creation with fallback mechanisms for partial indicator availability
Professional user feedback with success/failure indicator reporting

Performance Optimizations

Intelligent data validation before indicator calculation
Graceful degradation when indicators fail while maintaining core functionality
Date range optimization for sufficient technical analysis data
Cached indicator calculations with TTL for better performance

Usage Notes

Data Requirements

Minimum 20 data points required for most indicators
ADX requires 30+ data points for reliable calculation
Daily interval provides 90 days of data for comprehensive analysis
Weekly and monthly intervals provide extended historical coverage

Error Recovery

App continues functioning with available indicators if some calculations fail
Clear warnings explain specific reasons for any indicator failures
Comprehensive fallback system maintains professional user experience
All error handling designed for production stability

⚠️ Critical: Package Compatibility

IMPORTANT: This project uses specific versions of pandas and pandasai that are carefully matched for compatibility. DO NOT upgrade these packages independently without testing.

Why Version Compatibility Matters

pandasai v2.3.0 requires pandas v1.5.3 (not v2.x)
pandasai v3.x (beta) has breaking changes and schema issues
Mismatched versions cause:
- TypeError: sequence item 0: expected str instance, tuple found
- 'DataFrame' object has no attribute 'schema'
- Agent initialization failures

Tested Compatible Versions

✅ WORKING COMBINATION:

pandasai==2.3.0 (stable)
pandas==1.5.3 (exact version required for compatibility)
quantstats==0.0.59 (last version compatible with pandas 1.5.3)
Built-in OpenAI LLM (no separate extension needed)

❌ AVOID:

pandasai>=3.0.0 (beta, unstable)
pandas>=2.0.0 (incompatible with pandasai 2.3.0)
quantstats>=0.0.60 (requires pandas 2.0+ frequency aliases)
pandasai-openai extension (not needed in v2.3.0)

Critical QuantStats Compatibility

NEVER upgrade quantstats independently. The app uses:

QuantStats v0.0.59 - Last version compatible with pandas 1.5.3 legacy frequency aliases (M, Q, A)
Issue with newer versions: QuantStats 0.0.60+ uses pandas 2.0+ frequency aliases (ME, QE, YE) causing "Invalid frequency" errors
Solution: Version pinned in requirements.txt to maintain compatibility

If You Need to Upgrade

Test in a separate environment first
Check pandasai release notes for breaking changes
Update both packages together, not individually
Run the app thoroughly before deploying

Technology Stack

Frontend: Streamlit v1.47.0
AI Engine: PandasAI v2.3.0 (stable) with OpenAI GPT integration
Stock Data: Vnstock v3.2.5 (VCI/TCBS sources for Vietnamese market)
Data Processing: Pandas v1.5.3 (compatible with pandasai 2.3.0)
Financial Analysis: QuantStats v0.0.59 (75+ performance metrics and tearsheets)
Portfolio Optimization: PyPortfolioOpt (Modern Portfolio Theory, Efficient Frontier)
Risk Analysis: riskfolio-lib v5.0.1+ (Hierarchical Risk Parity, advanced risk metrics)
Technical Analysis: Manual implementation for RSI, MACD, Bollinger Bands, OBV with robust error handling
Visualizations: Altair v5.5.0+, Bokeh v2.4.3, mplfinance for interactive charts

Future Refactor: PandasAI 3.x Migration

Target Dependencies (PandasAI 3.x)

For future migration to PandasAI 3.x, the following dependencies will be required:

dependencies = [
    "numpy>=1.26.4",
    "pandasai>=3.0.0b2",
    "pandasai-openai>=0.1.6",
    "streamlit>=1.47.1",
    "vnstock>=3.2.6",
]

Migration Considerations

Breaking Changes: PandasAI 3.x has significant API changes from 2.x
Schema Changes: New dataframe schema handling required
Extension System: Separate pandasai-openai package needed
Testing Required: Full regression testing needed before migration
Compatibility: Verify vnstock integration with new pandas versions

Session State Management

Finance Bro uses Streamlit's st.session_state for comprehensive data sharing and persistence across pages. This ensures a seamless user experience where data, settings, and user interactions are maintained throughout the session.

Global Session State Variables

Authentication & API

api_key - OpenAI API key for AI functionality
stock_symbol - Currently selected stock symbol (shared across all pages)

Data Caching & Performance

stock_symbols_list - Cached list of all available Vietnamese stock symbols
symbols_df - Full DataFrame with stock symbols and company names for performance
last_period - Previously selected period (year/quarter) for change detection

Date Range Management

analysis_start_date - Global start date for data analysis (default: 2024-01-01), shared across all analysis pages
analysis_end_date - Global end date for data analysis (default: today-1), shared across all analysis pages
date_range_changed - Boolean flag to trigger cache invalidation when date range changes

Page-Specific Session State Variables

Stock Analysis Page (bro.py)

Data Storage

dataframes - AI-optimized financial dataframes with Quarter column names for better PandasAI queries
display_dataframes - Original financial dataframes with lengthReport for proper display formatting
uploaded_dataframes - User-uploaded CSV/Excel files for AI analysis
messages - Chat message history for conversation continuity

AI Agent Management

agent - PandasAI agent instance for financial analysis
agent_key - Cache key for intelligent agent recreation when data changes
pending_question - Queued question from sidebar for processing

Stock Price Analysis Page

Price & Returns Data

stock_price_data - Historical stock price data with caching
stock_returns - Calculated stock returns for QuantStats analysis

Portfolio Optimization Page

Portfolio Data

portfolio_returns - Stock returns data shared across all portfolio tabs
weights_max_sharpe - Max Sharpe portfolio weights for cross-tab sharing
weights_min_vol - Min Volatility portfolio weights
weights_max_utility - Max Utility portfolio weights

Portfolio Strategy Selection

portfolio_strategy_choice - Master control for portfolio strategy selection across all tabs (new in v0.2.16)

Screener Page

Screening Data

screener_data - Filtered stock results from screening criteria

Filter Presets (Dynamic Keys)

preset_industries - Selected industries for quick filter presets
preset_market_cap - Market cap filter preset activation
preset_roe - ROE filter preset activation
preset_roa - ROA filter preset activation
preset_dividend - Dividend yield filter preset activation
preset_beta - Beta risk filter preset activation
preset_financial_health - Financial health filter preset activation
preset_stock_rating - Stock rating filter preset activation
auto_run_screener - Automatic screener execution trigger

Session State Architecture Benefits

Data Consistency

Single source of truth for stock symbol selection across all pages
Consistent date ranges: Global start/end dates (today-1 default) shared across Stock Price Analysis and Portfolio Optimization pages
Portfolio strategy selection shared between Dollar Allocation, Report, and Risk Analysis tabs
Financial data loaded once and reused across different analysis tools

Performance Optimization

Stock symbols loaded once in Stock Analysis page and cached for entire session
Financial dataframes cached to avoid repeated API calls
Smart cache invalidation: Automatic cache refresh when date ranges change across pages
Agent recreation only when data actually changes

User Experience

Seamless navigation between pages without data loss
Synchronized date selection: Date changes in one page automatically apply to all analysis pages
Chat history persistence during analysis sessions
Filter presets remember user preferences across screening sessions

Memory Management

Intelligent caching with change detection to prevent unnecessary data reloading
Cleanup mechanisms for dynamic preset keys to prevent memory bloat

Session State Flow

App Entry (app.py): Loads API key, stock symbols list, and handles symbol selection
Stock Analysis (bro.py): Creates comprehensive data cache and AI agent
Other Pages: Access shared data from session state for analysis
Cross-Page Navigation: All data persists seamlessly across page switches

This architecture ensures optimal performance while maintaining a professional user experience comparable to institutional financial analysis tools.

Project Structure

finance-bro/
├── app.py                     # Main entry point with auth & navigation
├── pages/                     # Multi-page Streamlit application
│   ├── bro.py                # Main AI chat interface (Stock Analysis)
│   ├── Stock_Price_Analysis.py  # Price charts & QuantStats tearsheets
│   ├── Technical_Analysis.py # Advanced technical indicators with heating stocks
│   ├── Company_Overview.py   # Company profiles & ownership
│   ├── Portfolio_Optimization.py # Modern Portfolio Theory & HRP
│   └── Screener.py           # Stock screening & filtering
├── static/style.css          # Custom CSS styling
├── .streamlit/               # Streamlit configuration
│   ├── config.toml          # Custom theme & colors
│   └── secrets.example.toml # OAuth configuration template
├── requirements.txt          # Python dependencies
├── pyproject.toml           # Project configuration (Python 3.10.11)
├── Dockerfile               # Docker configuration
├── docker-compose.yml       # Docker Compose configuration
├── .env.example             # Environment variables template
├── CHANGELOG.md             # Version history
├── CLAUDE.md                # AI assistant instructions
└── Reference/               # Legacy code & documentation

Docker Deployment

Using Pre-built Image from GitHub Container Registry

Pull and run the latest image:

macOS/Linux:

docker run -p 8501:8501 \
  -e OPENAI_API_KEY=your_openai_api_key \
  -e VNSTOCK_API_KEY=your_vnstock_api_key \
  -v $(pwd)/exports:/app/exports \
  -v $(pwd)/cache:/app/cache \
  -v $(pwd)/.streamlit:/app/.streamlit \
  ghcr.io/gahoccode/finance-bro:latest

Windows Command Prompt:

docker run -p 8501:8501 -e OPENAI_API_KEY=your_openai_api_key -e VNSTOCK_API_KEY=your_vnstock_api_key -v %cd%/exports:/app/exports -v %cd%/cache:/app/cache -v %cd%/.streamlit:/app/.streamlit ghcr.io/gahoccode/finance-bro:latest

Windows PowerShell:

docker run -p 8501:8501 `
  -e OPENAI_API_KEY=your_openai_api_key `
  -e VNSTOCK_API_KEY=your_vnstock_api_key `
  -v ${PWD}/exports:/app/exports `
  -v ${PWD}/cache:/app/cache `
  -v ${PWD}/.streamlit:/app/.streamlit `
  ghcr.io/gahoccode/finance-bro:latest

Note: The $(pwd), %cd%, and ${PWD} commands automatically get your current working directory. For example, if you're in /home/user/finance-bro, these resolve to that path.

Or use with docker-compose:

services:
  finance-bro:
    image: ghcr.io/gahoccode/finance-bro:latest
    ports:
      - "8501:8501"
    environment:
      - OPENAI_API_KEY=${OPENAI_API_KEY}
      - VNSTOCK_API_KEY=${VNSTOCK_API_KEY}
    volumes:
      - ./exports:/app/exports
      - ./cache:/app/cache
    env_file:
      - .env

Quick Start with Docker

Using Docker Compose (Recommended)

Clone the repository:

git clone https://github.com/yourusername/finance-bro.git
cd finance-bro

Set up environment variables:

cp .env.example .env
# Edit .env and add your OpenAI API key

Build and run with Docker Compose:

docker-compose up --build

Access the application: Open your browser and navigate to http://localhost:8501

Using Docker CLI

Build the Docker image:

docker build -t finance-bro .

Run the container:

docker run -p 8501:8501 \
  -e OPENAI_API_KEY=your_openai_api_key \
  -e VNSTOCK_API_KEY=your_vnstock_api_key \
  -v $(pwd)/exports:/app/exports \
  -v $(pwd)/cache:/app/cache \
  finance-bro

Environment Variables

Create a .env file based on .env.example:

# Copy the template
cp .env.example .env

# Edit .env and add your API keys
# Required - Get from https://platform.openai.com/api-keys
OPENAI_API_KEY=your_openai_api_key_here

# Optional - For higher rate limits and premium features
# Get your API key at vnstocks.com
VNSTOCK_API_KEY=your_vnstock_api_key_here

# Optional - Usually not needed for local development
STREAMLIT_SERVER_PORT=8501
STREAMLIT_SERVER_ADDRESS=0.0.0.0

Important: You must set your OPENAI_API_KEY in the .env file before running Docker. The application won't work without it.

Docker Commands Reference

# Build the image
docker build -t finance-bro .

# Run in development mode
docker-compose up

# Run in background
docker-compose up -d

# View logs
docker-compose logs -f

# Stop containers
docker-compose down

# Remove containers and volumes
docker-compose down -v

Building and Pushing to GHCR

Option 1: Using the helper script (recommended)

# Build and push as 'latest' (with cache)
./build-and-push.sh

# Build and push with version tag
./build-and-push.sh v0.2.36

# Force clean rebuild (no cache) - use when dependencies change
./build-and-push.sh v0.2.36 --no-cache

Option 2: Manual Docker commands

# Build for linux/amd64 platform (required for Render/cloud deployment)
docker buildx build --platform linux/amd64 --load -t finance-bro:latest .

# Build without cache (use when dependencies change)
docker buildx build --platform linux/amd64 --no-cache --load -t finance-bro:latest .

# Tag for GHCR
docker tag finance-bro:latest ghcr.io/gahoccode/finance-bro:latest
docker tag finance-bro:latest ghcr.io/gahoccode/finance-bro:v0.2.36

# Push to GHCR
docker push ghcr.io/gahoccode/finance-bro:latest
docker push ghcr.io/gahoccode/finance-bro:v0.2.36

Note: Use --no-cache when requirements.txt or Dockerfile dependencies change to ensure the new versions are installed.

Volume Mounting

The Docker setup includes volume mounting for:

./exports:/app/exports - Persistent storage for generated charts and exports
./cache:/app/cache - Persistent cache for faster data loading

Production Considerations

For production deployment:

Use a reverse proxy (nginx, traefik) for SSL termination
Set resource limits in docker-compose.yml
Use environment-specific configurations
Monitor container health with the built-in health check

Example production docker-compose.yml additions:

# Add to services.finance-bro in docker-compose.yml
deploy:
  resources:
    limits:
      memory: 1G
      cpus: "1.0"
  restart_policy:
    condition: on-failure
    delay: 5s
    max_attempts: 3

Automated Publishing with GitHub Actions

This project includes automated Docker image publishing to GitHub Container Registry (GHCR) via GitHub Actions.

How It Works

On push to main/master: Builds and publishes latest tag
On version tags (e.g., v1.0.0): Builds and publishes versioned tags
On pull requests: Builds image for testing (doesn't publish)
Multi-platform: Builds for both linux/amd64 and linux/arm64

Available Image Tags

ghcr.io/gahoccode/finance-bro:latest - Latest stable version
ghcr.io/gahoccode/finance-bro:main - Latest from main branch
ghcr.io/gahoccode/finance-bro:v1.0.0 - Specific version tags

Manual Publishing

To manually publish a new version:

# Create and push a version tag
git tag v1.0.0
git push origin v1.0.0

# GitHub Actions will automatically build and publish

Local Development vs Production

For local development:

# Build locally
docker-compose up --build

For production deployment:

# Use pre-built image
docker run -p 8501:8501 -e OPENAI_API_KEY=your_key ghcr.io/gahoccode/finance-bro:latest

Dependencies

Core Dependencies

pandasai==2.3.0 - AI-powered data analysis
pandas==1.5.3 - Data manipulation (exact version for compatibility)
vnstock==3.2.5 - Vietnamese stock data (VCI/TCBS sources)
openai>=1.61.0 - OpenAI API client
streamlit==1.47.0 - Web application framework with OAuth support
python-dotenv==1.0.1 - Environment variable management

Financial Analysis

quantstats==0.0.59 - Performance analytics and tearsheets (75+ metrics)
pyportfolioopt>=1.5.6 - Modern Portfolio Theory optimization
riskfolio-lib>=5.0.1 - Advanced risk analysis and HRP
mplfinance>=0.12.10b0 - Financial data visualization

Visualization & UI

altair>=5.5.0 - Interactive statistical visualizations
bokeh==2.4.3 - Interactive web plots
Authlib>=1.3.2 - Google OAuth authentication

System Dependencies (Docker)

osqp==0.6.2.post8 - Quadratic programming solver (specific version for stability)

Contributing

We welcome contributions! Here are ways you can help improve Finance Bro:

Theme Contributions

🎨 Custom Themes Wanted: We're looking for contributors to help create custom themes for the app. If you have design skills and want to contribute:

Create custom CSS themes (light/dark variants)
Design responsive layouts
Improve accessibility features
Submit theme proposals via pull requests

General Contributions

Fork the repository
Create a feature branch
Make your changes
Add tests if applicable
Submit a pull request

Contribution Guidelines

Follow PEP8 style guidelines
Add appropriate documentation
Test your changes thoroughly
Include screenshots for UI changes
Update README if adding new features

License

This project is open source and available under the MIT License.

Support

If you encounter any issues or have questions, please open an issue on GitHub.

Built with ❤️ using Streamlit, PandasAI, and Vnstock

Name		Name	Last commit message	Last commit date
Latest commit History 169 Commits
.github/workflows		.github/workflows
.streamlit		.streamlit
.windsurf/rules		.windsurf/rules
Reference		Reference
docs		docs
pages		pages
src		src
static		static
tests		tests
.dockerignore		.dockerignore
.env.example		.env.example
.gitignore		.gitignore
.renderignore		.renderignore
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
Dockerfile		Dockerfile
README.md		README.md
app.py		app.py
build-and-push.sh		build-and-push.sh
cleanup_cache.py		cleanup_cache.py
docker-compose.yml		docker-compose.yml
number format.md		number format.md
pyproject.toml		pyproject.toml
quarter ratios.md		quarter ratios.md
quarter statements.md		quarter statements.md
redis.md		redis.md
render.yaml		render.yaml
requirements.txt		requirements.txt

Folders and files

Latest commit

History

Repository files navigation

Finance Bro - AI Stock Analysis

Features

Quick Start

Deploy to Render (Recommended)

Local Development

Prerequisites

Installation

User Flow

Optimal User Experience Flow

Step 1: Authentication & Homepage

Step 2: Stock Analysis Page (Essential First Stop)

Step 3: Explore Other Analysis Tools

Recommended Setup for Best Experience

Why This Flow Matters

Example Questions

📊 Technical Analysis Features

Key Features

Technical Implementation Highlights

Usage Notes

⚠️ Critical: Package Compatibility

Why Version Compatibility Matters

Tested Compatible Versions

Critical QuantStats Compatibility

If You Need to Upgrade

Technology Stack

Future Refactor: PandasAI 3.x Migration

Target Dependencies (PandasAI 3.x)

Migration Considerations

Session State Management

Global Session State Variables

Page-Specific Session State Variables

Stock Analysis Page (bro.py)

Stock Price Analysis Page

Portfolio Optimization Page

Screener Page

Session State Architecture Benefits

Session State Flow

Project Structure

Docker Deployment

Using Pre-built Image from GitHub Container Registry

Quick Start with Docker

Using Docker Compose (Recommended)

Using Docker CLI

Environment Variables

Docker Commands Reference

Building and Pushing to GHCR

Volume Mounting

Production Considerations

Automated Publishing with GitHub Actions

How It Works

Available Image Tags

Manual Publishing

Local Development vs Production

Dependencies

Core Dependencies

Financial Analysis

Visualization & UI

System Dependencies (Docker)

Contributing

Theme Contributions

General Contributions

Contribution Guidelines

License

Support

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