218 lines
7.9 KiB
Markdown
218 lines
7.9 KiB
Markdown
# Nifty50 AI Trading Dashboard
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A modern, feature-rich frontend for the TradingAgents multi-agent AI stock analysis system. This dashboard provides real-time AI-powered recommendations for all 50 stocks in the Nifty 50 index, with full visibility into the analysis pipeline, agent reports, and debate processes.
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## Features Overview
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### Dashboard - Main View
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The main dashboard displays AI recommendations for all 50 Nifty stocks with:
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- **Summary Statistics**: Quick view of Buy/Hold/Sell distribution
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- **Top Picks**: Highlighted stocks with the strongest buy signals
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- **Stocks to Avoid**: High-confidence sell recommendations
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- **Analyze All**: One-click bulk analysis of all stocks
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- **Filter & Search**: Filter by recommendation type or search by symbol
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### Dark Mode Support
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Full dark mode support with automatic system theme detection:
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### Settings Panel
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Configure the AI analysis system directly from the browser:
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- **LLM Provider Selection**: Choose between Claude Subscription or Anthropic API
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- **API Key Management**: Securely store API keys in browser localStorage
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- **Model Selection**: Configure Deep Think (Opus) and Quick Think (Sonnet/Haiku) models
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- **Analysis Settings**: Adjust max debate rounds for thoroughness vs speed
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### Stock Detail View
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Detailed analysis view for individual stocks with:
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- **Price Chart**: Interactive price history with buy/sell/hold signal markers
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- **Recommendation Details**: Decision, confidence level, and risk assessment
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- **Recommendation History**: Historical AI decisions for the stock
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- **AI Analysis Summary**: Expandable detailed analysis sections
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### Analysis Pipeline Visualization
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See exactly how the AI reached its decision with the full analysis pipeline:
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- **9-Step Pipeline**: Track progress through data collection, analysis, debates, and final decision
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- **Agent Reports**: View individual reports from Market, News, Social Media, and Fundamentals analysts
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- **Real-time Status**: See which steps are completed, running, or pending
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### Investment Debates
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The AI uses a debate system where Bull and Bear analysts argue their cases:
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- **Bull vs Bear**: Opposing viewpoints with detailed arguments
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- **Research Manager Decision**: Final judgment weighing both sides
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- **Full Debate History**: Complete transcript of the debate rounds
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#### Expanded Debate View
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Full debate content with Bull and Bear arguments:
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### Data Sources Tracking
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View all raw data sources used for analysis:
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- **Source Types**: Market data, news, fundamentals, social media
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- **Fetch Status**: Success/failure indicators for each data source
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- **Data Preview**: Expandable view of fetched data
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### How It Works Page
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Educational content explaining the multi-agent AI system:
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- **Multi-Agent Architecture**: Overview of the specialized AI agents
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- **Analysis Process**: Step-by-step breakdown of the pipeline
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- **Agent Profiles**: Details about each analyst type
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- **Debate Process**: Explanation of how consensus is reached
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### Historical Analysis & Backtesting
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Track AI performance over time with comprehensive analytics:
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- **Prediction Accuracy**: Overall and per-recommendation-type accuracy
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- **Accuracy Trend**: Visualize accuracy over time
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- **Risk Metrics**: Sharpe ratio, max drawdown, win rate
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- **Portfolio Simulator**: Test different investment amounts with Zerodha-accurate brokerage charges
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- **AI vs Nifty50**: Compare AI strategy performance against the index
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- **Return Distribution**: Histogram of hold-period returns
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- **Date Backtest Runner**: Run AI analysis for any date directly from the History page
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- **Cancel Support**: Cancel in-progress bulk analysis
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#### Date Selection & Stock List
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Select any date to view all 50 ranked stocks with decisions, hold periods, and returns:
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## Tech Stack
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- **Frontend**: React 18 + TypeScript + Vite
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- **Styling**: Tailwind CSS with dark mode support
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- **Charts**: Recharts for interactive visualizations
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- **Icons**: Lucide React
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- **State Management**: React Context API
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- **Backend**: FastAPI (Python) with SQLite database
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## Getting Started
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### Prerequisites
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- Node.js 18+
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- Python 3.10+
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- npm or yarn
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### Installation
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1. **Install frontend dependencies:**
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```bash
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cd frontend
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npm install
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```
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2. **Install backend dependencies:**
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```bash
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cd frontend/backend
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pip install -r requirements.txt
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```
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### Running the Application
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1. **Start the backend server:**
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```bash
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cd frontend/backend
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python server.py
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```
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The backend runs on `http://localhost:8001`
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2. **Start the frontend development server:**
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```bash
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cd frontend
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npm run dev
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```
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The frontend runs on `http://localhost:5173`
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## Project Structure
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```
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frontend/
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├── src/
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│ ├── components/
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│ │ ├── pipeline/ # Pipeline visualization components
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│ │ │ ├── PipelineOverview.tsx
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│ │ │ ├── AgentReportCard.tsx
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│ │ │ ├── DebateViewer.tsx
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│ │ │ ├── RiskDebateViewer.tsx
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│ │ │ └── DataSourcesPanel.tsx
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│ │ ├── Header.tsx
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│ │ ├── SettingsModal.tsx
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│ │ └── ...
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│ ├── contexts/
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│ │ └── SettingsContext.tsx # Settings state management
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│ ├── pages/
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│ │ ├── Dashboard.tsx
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│ │ ├── StockDetail.tsx
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│ │ ├── History.tsx
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│ │ └── About.tsx
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│ ├── services/
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│ │ └── api.ts # API client
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│ ├── types/
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│ │ └── pipeline.ts # TypeScript types for pipeline data
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│ └── App.tsx
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├── backend/
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│ ├── server.py # FastAPI server
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│ ├── database.py # SQLite database operations
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│ └── recommendations.db # SQLite database
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└── docs/
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└── screenshots/ # Feature screenshots
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```
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## API Endpoints
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### Recommendations
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- `GET /recommendations/{date}` - Get all recommendations for a date
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- `GET /recommendations/{date}/{symbol}` - Get recommendation for a specific stock
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- `POST /recommendations` - Save new recommendations
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### Pipeline Data
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- `GET /recommendations/{date}/{symbol}/pipeline` - Get full pipeline data
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- `GET /recommendations/{date}/{symbol}/agents` - Get agent reports
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- `GET /recommendations/{date}/{symbol}/debates` - Get debate history
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- `GET /recommendations/{date}/{symbol}/data-sources` - Get data source logs
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### Analysis
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- `POST /analyze/{symbol}` - Run analysis for a single stock
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- `POST /analyze-bulk` - Run analysis for multiple stocks
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## Configuration
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Settings are stored in browser localStorage and include:
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- `deepThinkModel`: Model for complex analysis (opus/sonnet/haiku)
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- `quickThinkModel`: Model for fast operations (opus/sonnet/haiku)
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- `provider`: LLM provider (claude_subscription/anthropic_api)
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- `anthropicApiKey`: API key for Anthropic API provider
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- `maxDebateRounds`: Number of debate rounds (1-5)
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## Contributing
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1. Fork the repository
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2. Create a feature branch
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3. Make your changes
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4. Run tests and linting
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5. Submit a pull request
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## License
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This project is part of the TradingAgents research project.
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## Disclaimer
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AI-generated recommendations are for educational and informational purposes only. These do not constitute financial advice. Always conduct your own research and consult with a qualified financial advisor before making investment decisions.
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