Merge 572ef6c367 into 13b826a31d

.gitignore

@@ -4,8 +4,14 @@ env/
 __pycache__/
 .DS_Store
 *.csv
-src/
+/src/
 eval_results/
 eval_data/
 *.egg-info/
 .env
+
+# Node.js
+node_modules/
+
+# Frontend dev artifacts
+.frontend-dev/

README.md

@@ -151,6 +151,143 @@ An interface will appear showing results as they load, letting you track the age
   <img src="assets/cli/cli_transaction.png" width="100%" style="display: inline-block; margin: 0 2%;">
 </p>
 
+---
+
+## 🌐 Nifty50 AI Trading Dashboard (Web Frontend)
+
+A modern, feature-rich web dashboard for TradingAgents, specifically built for **Indian Nifty 50 stocks**. This dashboard provides a complete visual interface for AI-powered stock analysis with full transparency into the multi-agent decision process.
+
+### 🚀 Quick Start
+
+```bash
+# Start the backend server
+cd frontend/backend
+pip install -r requirements.txt
+python server.py  # Runs on http://localhost:8001
+
+# Start the frontend (in a new terminal)
+cd frontend
+npm install
+npm run dev  # Runs on http://localhost:5173
+```
+
+### ✨ Key Features
+
+#### Dashboard - AI Recommendations at a Glance
+View all 50 Nifty stocks with AI recommendations, top picks, stocks to avoid, and one-click bulk analysis.
+
+<p align="center">
+  <img src="frontend/docs/screenshots/01-dashboard.png" width="100%" style="display: inline-block;">
+</p>
+
+#### 🌙 Dark Mode Support
+Full dark mode with automatic system theme detection for comfortable viewing.
+
+<p align="center">
+  <img src="frontend/docs/screenshots/08-dashboard-dark-mode.png" width="100%" style="display: inline-block;">
+</p>
+
+#### ⚙️ Configurable Settings Panel
+Configure your AI analysis directly from the browser:
+- **LLM Provider**: Claude Subscription or Anthropic API
+- **Model Selection**: Choose Deep Think (Opus) and Quick Think (Sonnet/Haiku) models
+- **API Key Management**: Securely stored in browser localStorage
+- **Debate Rounds**: Adjust thoroughness (1-5 rounds)
+
+<p align="center">
+  <img src="frontend/docs/screenshots/02-settings-modal.png" width="60%" style="display: inline-block;">
+</p>
+
+#### 📊 Stock Detail View
+Detailed analysis for each stock with interactive price charts, recommendation history, and AI analysis summaries.
+
+<p align="center">
+  <img src="frontend/docs/screenshots/03-stock-detail-overview.png" width="100%" style="display: inline-block;">
+</p>
+
+#### 🔬 Analysis Pipeline Visualization
+See exactly how the AI reached its decision with a 9-step pipeline showing:
+- Data collection progress
+- Individual agent reports (Market, News, Social Media, Fundamentals)
+- Real-time status tracking
+
+<p align="center">
+  <img src="frontend/docs/screenshots/04-analysis-pipeline.png" width="100%" style="display: inline-block;">
+</p>
+
+#### 💬 Investment Debates (Bull vs Bear)
+Watch AI agents debate investment decisions with full transparency:
+- **Bull Analyst**: Makes the case for buying
+- **Bear Analyst**: Presents risks and concerns
+- **Research Manager**: Weighs both sides and decides
+
+<p align="center">
+  <img src="frontend/docs/screenshots/05-debates-tab.png" width="100%" style="display: inline-block;">
+</p>
+
+<details>
+<summary><b>📜 View Full Debate Example (Click to expand)</b></summary>
+<p align="center">
+  <img src="frontend/docs/screenshots/06-investment-debate-expanded.png" width="100%" style="display: inline-block;">
+</p>
+</details>
+
+#### 📈 Historical Analysis & Backtesting
+Track AI performance over time with comprehensive analytics:
+- Prediction accuracy metrics (Buy/Sell/Hold)
+- Risk metrics (Sharpe ratio, max drawdown, win rate)
+- Portfolio simulator with customizable starting amounts
+- AI Strategy vs Nifty50 Index comparison
+
+<p align="center">
+  <img src="frontend/docs/screenshots/10-history-page.png" width="100%" style="display: inline-block;">
+</p>
+
+#### 📚 How It Works
+Educational content explaining the multi-agent AI system and decision process.
+
+<p align="center">
+  <img src="frontend/docs/screenshots/09-how-it-works.png" width="100%" style="display: inline-block;">
+</p>
+
+### 🛠️ Frontend Tech Stack
+
+| Technology | Purpose |
+|------------|---------|
+| React 18 + TypeScript | Core framework |
+| Vite | Build tool & dev server |
+| Tailwind CSS | Styling with dark mode |
+| Recharts | Interactive charts |
+| Lucide React | Icons |
+| FastAPI (Python) | Backend API |
+| SQLite | Data persistence |
+
+### 📁 Frontend Project Structure
+
+```
+frontend/
+├── src/
+│   ├── components/
+│   │   ├── pipeline/          # Pipeline visualization
+│   │   ├── SettingsModal.tsx  # Settings UI
+│   │   └── Header.tsx
+│   ├── contexts/
+│   │   └── SettingsContext.tsx
+│   ├── pages/
+│   │   ├── Dashboard.tsx
+│   │   ├── StockDetail.tsx
+│   │   ├── History.tsx
+│   │   └── About.tsx
+│   └── services/
+│       └── api.ts
+├── backend/
+│   ├── server.py
+│   └── database.py
+└── docs/screenshots/
+```
+
+---
+
 ## TradingAgents Package
 
 ### Implementation Details

cli/main.py

@@ -26,6 +26,7 @@ from rich.rule import Rule
 
 from tradingagents.graph.trading_graph import TradingAgentsGraph
 from tradingagents.default_config import DEFAULT_CONFIG
+from tradingagents.dataflows.markets import is_nifty_50_stock, NIFTY_50_STOCKS
 from cli.models import AnalystType
 from cli.utils import *
 
@@ -429,29 +430,42 @@ def get_user_selections():
             box_content += f"\n[dim]Default: {default}[/dim]"
         return Panel(box_content, border_style="blue", padding=(1, 2))
 
-    # Step 1: Ticker symbol
+    # Step 1: Market selection
     console.print(
         create_question_box(
-            "Step 1: Ticker Symbol", "Enter the ticker symbol to analyze", "SPY"
+            "Step 1: Market Selection", "Select the market for your analysis"
         )
     )
-    selected_ticker = get_ticker()
+    selected_market = select_market()
 
-    # Step 2: Analysis date
+    # Show Nifty 50 stocks if Indian market is selected
+    if selected_market == "india_nse":
+        show_nifty_50_stocks()
+
+    # Step 2: Ticker symbol
+    console.print(
+        create_question_box(
+            "Step 2: Ticker Symbol", "Enter the ticker symbol to analyze",
+            "RELIANCE" if selected_market == "india_nse" else "SPY"
+        )
+    )
+    selected_ticker = get_ticker_with_market_hint(selected_market)
+
+    # Step 3: Analysis date
     default_date = datetime.datetime.now().strftime("%Y-%m-%d")
     console.print(
         create_question_box(
-            "Step 2: Analysis Date",
+            "Step 3: Analysis Date",
             "Enter the analysis date (YYYY-MM-DD)",
             default_date,
         )
     )
     analysis_date = get_analysis_date()
 
-    # Step 3: Select analysts
+    # Step 4: Select analysts
     console.print(
         create_question_box(
-            "Step 3: Analysts Team", "Select your LLM analyst agents for the analysis"
+            "Step 4: Analysts Team", "Select your LLM analyst agents for the analysis"
         )
     )
     selected_analysts = select_analysts()
@@ -459,26 +473,26 @@ def get_user_selections():
         f"[green]Selected analysts:[/green] {', '.join(analyst.value for analyst in selected_analysts)}"
     )
 
-    # Step 4: Research depth
+    # Step 5: Research depth
     console.print(
         create_question_box(
-            "Step 4: Research Depth", "Select your research depth level"
+            "Step 5: Research Depth", "Select your research depth level"
         )
     )
     selected_research_depth = select_research_depth()
 
-    # Step 5: OpenAI backend
+    # Step 6: OpenAI backend
     console.print(
         create_question_box(
-            "Step 5: OpenAI backend", "Select which service to talk to"
+            "Step 6: LLM Provider", "Select which service to talk to"
         )
     )
     selected_llm_provider, backend_url = select_llm_provider()
-    
-    # Step 6: Thinking agents
+
+    # Step 7: Thinking agents
     console.print(
         create_question_box(
-            "Step 6: Thinking Agents", "Select your thinking agents for analysis"
+            "Step 7: Thinking Agents", "Select your thinking agents for analysis"
         )
     )
     selected_shallow_thinker = select_shallow_thinking_agent(selected_llm_provider)
@@ -493,6 +507,7 @@ def get_user_selections():
         "backend_url": backend_url,
         "shallow_thinker": selected_shallow_thinker,
         "deep_thinker": selected_deep_thinker,
+        "market": selected_market,
     }
 
 
@@ -747,6 +762,13 @@ def run_analysis():
     config["deep_think_llm"] = selections["deep_thinker"]
     config["backend_url"] = selections["backend_url"]
     config["llm_provider"] = selections["llm_provider"].lower()
+    config["market"] = selections["market"]
+
+    # Display market info for NSE stocks
+    if is_nifty_50_stock(selections["ticker"]):
+        company_name = NIFTY_50_STOCKS.get(selections["ticker"].replace(".NS", ""), "")
+        console.print(f"[cyan]Analyzing NSE stock:[/cyan] {selections['ticker']} - {company_name}")
+        console.print("[dim]Using jugaad-data for NSE stock data, yfinance for fundamentals[/dim]")
 
     # Initialize the graph
     graph = TradingAgentsGraph(
@@ -808,10 +830,17 @@ def run_analysis():
         update_display(layout)
 
         # Add initial messages
-        message_buffer.add_message("System", f"Selected ticker: {selections['ticker']}")
+        ticker_info = selections['ticker']
+        if is_nifty_50_stock(selections['ticker']):
+            company_name = NIFTY_50_STOCKS.get(selections['ticker'].replace(".NS", ""), "")
+            ticker_info = f"{selections['ticker']} ({company_name}) [NSE]"
+        message_buffer.add_message("System", f"Selected ticker: {ticker_info}")
         message_buffer.add_message(
             "System", f"Analysis date: {selections['analysis_date']}"
         )
+        message_buffer.add_message(
+            "System", f"Market: {selections['market'].upper()}"
+        )
         message_buffer.add_message(
             "System",
             f"Selected analysts: {', '.join(analyst.value for analyst in selections['analysts'])}",

cli/utils.py

@@ -1,7 +1,13 @@
 import questionary
 from typing import List, Optional, Tuple, Dict
+from rich.console import Console
+from rich.table import Table
+from rich import box
 
 from cli.models import AnalystType
+from tradingagents.dataflows.markets import NIFTY_50_STOCKS, is_nifty_50_stock
+
+console = Console()
 
 ANALYST_ORDER = [
     ("Market Analyst", AnalystType.MARKET),
@@ -272,5 +278,117 @@ def select_llm_provider() -> tuple[str, str]:
     
     display_name, url = choice
     print(f"You selected: {display_name}\tURL: {url}")
-    
+
     return display_name, url
+
+
+def select_market() -> str:
+    """Select market using an interactive selection."""
+
+    MARKET_OPTIONS = [
+        ("Auto-detect (Recommended)", "auto"),
+        ("US Markets (NYSE, NASDAQ)", "us"),
+        ("Indian NSE (Nifty 50)", "india_nse"),
+    ]
+
+    choice = questionary.select(
+        "Select Your [Market]:",
+        choices=[
+            questionary.Choice(display, value=value)
+            for display, value in MARKET_OPTIONS
+        ],
+        instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
+        style=questionary.Style(
+            [
+                ("selected", "fg:cyan noinherit"),
+                ("highlighted", "fg:cyan noinherit"),
+                ("pointer", "fg:cyan noinherit"),
+            ]
+        ),
+    ).ask()
+
+    if choice is None:
+        console.print("\n[red]No market selected. Exiting...[/red]")
+        exit(1)
+
+    return choice
+
+
+def display_nifty_50_stocks():
+    """Display the list of Nifty 50 stocks in a formatted table."""
+    table = Table(
+        title="Nifty 50 Stocks",
+        box=box.ROUNDED,
+        show_header=True,
+        header_style="bold cyan",
+    )
+
+    table.add_column("Symbol", style="green", width=15)
+    table.add_column("Company Name", style="white", width=45)
+
+    # Sort stocks alphabetically
+    sorted_stocks = sorted(NIFTY_50_STOCKS.items())
+
+    for symbol, company_name in sorted_stocks:
+        table.add_row(symbol, company_name)
+
+    console.print(table)
+    console.print()
+
+
+def show_nifty_50_stocks() -> bool:
+    """Ask user if they want to see Nifty 50 stocks list."""
+    show = questionary.confirm(
+        "Would you like to see the list of Nifty 50 stocks?",
+        default=False,
+        style=questionary.Style(
+            [
+                ("selected", "fg:cyan noinherit"),
+                ("highlighted", "fg:cyan noinherit"),
+            ]
+        ),
+    ).ask()
+
+    if show:
+        display_nifty_50_stocks()
+
+    return show
+
+
+def get_ticker_with_market_hint(market: str) -> str:
+    """Get ticker symbol with market-specific hints."""
+    if market == "india_nse":
+        hint = "Enter NSE symbol (e.g., RELIANCE, TCS, INFY)"
+        default = "RELIANCE"
+    elif market == "us":
+        hint = "Enter US ticker symbol (e.g., AAPL, GOOGL, MSFT)"
+        default = "SPY"
+    else:
+        hint = "Enter ticker symbol (auto-detects market)"
+        default = "SPY"
+
+    ticker = questionary.text(
+        hint + ":",
+        default=default,
+        validate=lambda x: len(x.strip()) > 0 or "Please enter a valid ticker symbol.",
+        style=questionary.Style(
+            [
+                ("text", "fg:green"),
+                ("highlighted", "noinherit"),
+            ]
+        ),
+    ).ask()
+
+    if not ticker:
+        console.print("\n[red]No ticker symbol provided. Exiting...[/red]")
+        exit(1)
+
+    ticker = ticker.strip().upper()
+
+    # Provide feedback for NSE stocks
+    if is_nifty_50_stock(ticker):
+        company_name = NIFTY_50_STOCKS.get(ticker.replace(".NS", ""), "")
+        if company_name:
+            console.print(f"[green]Detected NSE stock:[/green] {ticker} - {company_name}")
+
+    return ticker

frontend/.gitignore

@@ -0,0 +1,24 @@
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?

frontend/README.md

@@ -0,0 +1,210 @@
+# Nifty50 AI Trading Dashboard
+
+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.
+
+## Features Overview
+
+### Dashboard - Main View
+The main dashboard displays AI recommendations for all 50 Nifty stocks with:
+- **Summary Statistics**: Quick view of Buy/Hold/Sell distribution
+- **Top Picks**: Highlighted stocks with the strongest buy signals
+- **Stocks to Avoid**: High-confidence sell recommendations
+- **Analyze All**: One-click bulk analysis of all stocks
+- **Filter & Search**: Filter by recommendation type or search by symbol
+
+![Dashboard](docs/screenshots/01-dashboard.png)
+
+### Dark Mode Support
+Full dark mode support with automatic system theme detection:
+
+![Dashboard Dark Mode](docs/screenshots/08-dashboard-dark-mode.png)
+
+### Settings Panel
+Configure the AI analysis system directly from the browser:
+- **LLM Provider Selection**: Choose between Claude Subscription or Anthropic API
+- **API Key Management**: Securely store API keys in browser localStorage
+- **Model Selection**: Configure Deep Think (Opus) and Quick Think (Sonnet/Haiku) models
+- **Analysis Settings**: Adjust max debate rounds for thoroughness vs speed
+
+![Settings Modal](docs/screenshots/02-settings-modal.png)
+
+### Stock Detail View
+Detailed analysis view for individual stocks with:
+- **Price Chart**: Interactive price history with buy/sell/hold signal markers
+- **Recommendation Details**: Decision, confidence level, and risk assessment
+- **Recommendation History**: Historical AI decisions for the stock
+- **AI Analysis Summary**: Expandable detailed analysis sections
+
+![Stock Detail Overview](docs/screenshots/03-stock-detail-overview.png)
+
+### Analysis Pipeline Visualization
+See exactly how the AI reached its decision with the full analysis pipeline:
+- **9-Step Pipeline**: Track progress through data collection, analysis, debates, and final decision
+- **Agent Reports**: View individual reports from Market, News, Social Media, and Fundamentals analysts
+- **Real-time Status**: See which steps are completed, running, or pending
+
+![Analysis Pipeline](docs/screenshots/04-analysis-pipeline.png)
+
+### Investment Debates
+The AI uses a debate system where Bull and Bear analysts argue their cases:
+- **Bull vs Bear**: Opposing viewpoints with detailed arguments
+- **Research Manager Decision**: Final judgment weighing both sides
+- **Full Debate History**: Complete transcript of the debate rounds
+
+![Debates Tab](docs/screenshots/05-debates-tab.png)
+
+#### Expanded Debate View
+Full debate content with Bull and Bear arguments:
+
+![Investment Debate Expanded](docs/screenshots/06-investment-debate-expanded.png)
+
+### Data Sources Tracking
+View all raw data sources used for analysis:
+- **Source Types**: Market data, news, fundamentals, social media
+- **Fetch Status**: Success/failure indicators for each data source
+- **Data Preview**: Expandable view of fetched data
+
+![Data Sources Tab](docs/screenshots/07-data-sources-tab.png)
+
+### How It Works Page
+Educational content explaining the multi-agent AI system:
+- **Multi-Agent Architecture**: Overview of the specialized AI agents
+- **Analysis Process**: Step-by-step breakdown of the pipeline
+- **Agent Profiles**: Details about each analyst type
+- **Debate Process**: Explanation of how consensus is reached
+
+![How It Works](docs/screenshots/09-how-it-works.png)
+
+### Historical Analysis & Backtesting
+Track AI performance over time with comprehensive analytics:
+- **Prediction Accuracy**: Overall and per-recommendation-type accuracy
+- **Accuracy Trend**: Visualize accuracy over time
+- **Risk Metrics**: Sharpe ratio, max drawdown, win rate
+- **Portfolio Simulator**: Test different investment amounts
+- **AI vs Nifty50**: Compare AI strategy performance against the index
+- **Return Distribution**: Histogram of next-day returns
+
+![History Page](docs/screenshots/10-history-page.png)
+
+## Tech Stack
+
+- **Frontend**: React 18 + TypeScript + Vite
+- **Styling**: Tailwind CSS with dark mode support
+- **Charts**: Recharts for interactive visualizations
+- **Icons**: Lucide React
+- **State Management**: React Context API
+- **Backend**: FastAPI (Python) with SQLite database
+
+## Getting Started
+
+### Prerequisites
+- Node.js 18+
+- Python 3.10+
+- npm or yarn
+
+### Installation
+
+1. **Install frontend dependencies:**
+```bash
+cd frontend
+npm install
+```
+
+2. **Install backend dependencies:**
+```bash
+cd frontend/backend
+pip install -r requirements.txt
+```
+
+### Running the Application
+
+1. **Start the backend server:**
+```bash
+cd frontend/backend
+python server.py
+```
+The backend runs on `http://localhost:8001`
+
+2. **Start the frontend development server:**
+```bash
+cd frontend
+npm run dev
+```
+The frontend runs on `http://localhost:5173`
+
+## Project Structure
+
+```
+frontend/
+├── src/
+│   ├── components/
+│   │   ├── pipeline/          # Pipeline visualization components
+│   │   │   ├── PipelineOverview.tsx
+│   │   │   ├── AgentReportCard.tsx
+│   │   │   ├── DebateViewer.tsx
+│   │   │   ├── RiskDebateViewer.tsx
+│   │   │   └── DataSourcesPanel.tsx
+│   │   ├── Header.tsx
+│   │   ├── SettingsModal.tsx
+│   │   └── ...
+│   ├── contexts/
+│   │   └── SettingsContext.tsx  # Settings state management
+│   ├── pages/
+│   │   ├── Dashboard.tsx
+│   │   ├── StockDetail.tsx
+│   │   ├── History.tsx
+│   │   └── About.tsx
+│   ├── services/
+│   │   └── api.ts              # API client
+│   ├── types/
+│   │   └── pipeline.ts         # TypeScript types for pipeline data
+│   └── App.tsx
+├── backend/
+│   ├── server.py               # FastAPI server
+│   ├── database.py             # SQLite database operations
+│   └── recommendations.db      # SQLite database
+└── docs/
+    └── screenshots/            # Feature screenshots
+```
+
+## API Endpoints
+
+### Recommendations
+- `GET /recommendations/{date}` - Get all recommendations for a date
+- `GET /recommendations/{date}/{symbol}` - Get recommendation for a specific stock
+- `POST /recommendations` - Save new recommendations
+
+### Pipeline Data
+- `GET /recommendations/{date}/{symbol}/pipeline` - Get full pipeline data
+- `GET /recommendations/{date}/{symbol}/agents` - Get agent reports
+- `GET /recommendations/{date}/{symbol}/debates` - Get debate history
+- `GET /recommendations/{date}/{symbol}/data-sources` - Get data source logs
+
+### Analysis
+- `POST /analyze/{symbol}` - Run analysis for a single stock
+- `POST /analyze-bulk` - Run analysis for multiple stocks
+
+## Configuration
+
+Settings are stored in browser localStorage and include:
+- `deepThinkModel`: Model for complex analysis (opus/sonnet/haiku)
+- `quickThinkModel`: Model for fast operations (opus/sonnet/haiku)
+- `provider`: LLM provider (claude_subscription/anthropic_api)
+- `anthropicApiKey`: API key for Anthropic API provider
+- `maxDebateRounds`: Number of debate rounds (1-5)
+
+## Contributing
+
+1. Fork the repository
+2. Create a feature branch
+3. Make your changes
+4. Run tests and linting
+5. Submit a pull request
+
+## License
+
+This project is part of the TradingAgents research project.
+
+## Disclaimer
+
+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.

frontend/backend/database.py

@@ -0,0 +1,702 @@
+"""SQLite database module for storing stock recommendations."""
+import sqlite3
+import json
+from pathlib import Path
+from datetime import datetime
+from typing import Optional
+
+DB_PATH = Path(__file__).parent / "recommendations.db"
+
+
+def get_connection():
+    """Get SQLite database connection."""
+    conn = sqlite3.connect(DB_PATH)
+    conn.row_factory = sqlite3.Row
+    return conn
+
+
+def init_db():
+    """Initialize the database with required tables."""
+    conn = get_connection()
+    cursor = conn.cursor()
+
+    # Create recommendations table
+    cursor.execute("""
+        CREATE TABLE IF NOT EXISTS daily_recommendations (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            date TEXT UNIQUE NOT NULL,
+            summary_total INTEGER,
+            summary_buy INTEGER,
+            summary_sell INTEGER,
+            summary_hold INTEGER,
+            top_picks TEXT,
+            stocks_to_avoid TEXT,
+            created_at TEXT DEFAULT CURRENT_TIMESTAMP
+        )
+    """)
+
+    # Create stock analysis table
+    cursor.execute("""
+        CREATE TABLE IF NOT EXISTS stock_analysis (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            date TEXT NOT NULL,
+            symbol TEXT NOT NULL,
+            company_name TEXT,
+            decision TEXT,
+            confidence TEXT,
+            risk TEXT,
+            raw_analysis TEXT,
+            created_at TEXT DEFAULT CURRENT_TIMESTAMP,
+            UNIQUE(date, symbol)
+        )
+    """)
+
+    # Create index for faster queries
+    cursor.execute("""
+        CREATE INDEX IF NOT EXISTS idx_stock_analysis_date ON stock_analysis(date)
+    """)
+    cursor.execute("""
+        CREATE INDEX IF NOT EXISTS idx_stock_analysis_symbol ON stock_analysis(symbol)
+    """)
+
+    # Create agent_reports table (stores each analyst's detailed report)
+    cursor.execute("""
+        CREATE TABLE IF NOT EXISTS agent_reports (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            date TEXT NOT NULL,
+            symbol TEXT NOT NULL,
+            agent_type TEXT NOT NULL,
+            report_content TEXT,
+            data_sources_used TEXT,
+            created_at TEXT DEFAULT CURRENT_TIMESTAMP,
+            UNIQUE(date, symbol, agent_type)
+        )
+    """)
+
+    # Create debate_history table (stores investment and risk debates)
+    cursor.execute("""
+        CREATE TABLE IF NOT EXISTS debate_history (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            date TEXT NOT NULL,
+            symbol TEXT NOT NULL,
+            debate_type TEXT NOT NULL,
+            bull_arguments TEXT,
+            bear_arguments TEXT,
+            risky_arguments TEXT,
+            safe_arguments TEXT,
+            neutral_arguments TEXT,
+            judge_decision TEXT,
+            full_history TEXT,
+            created_at TEXT DEFAULT CURRENT_TIMESTAMP,
+            UNIQUE(date, symbol, debate_type)
+        )
+    """)
+
+    # Create pipeline_steps table (stores step-by-step execution log)
+    cursor.execute("""
+        CREATE TABLE IF NOT EXISTS pipeline_steps (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            date TEXT NOT NULL,
+            symbol TEXT NOT NULL,
+            step_number INTEGER,
+            step_name TEXT,
+            status TEXT,
+            started_at TEXT,
+            completed_at TEXT,
+            duration_ms INTEGER,
+            output_summary TEXT,
+            UNIQUE(date, symbol, step_number)
+        )
+    """)
+
+    # Create data_source_logs table (stores what raw data was fetched)
+    cursor.execute("""
+        CREATE TABLE IF NOT EXISTS data_source_logs (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            date TEXT NOT NULL,
+            symbol TEXT NOT NULL,
+            source_type TEXT,
+            source_name TEXT,
+            data_fetched TEXT,
+            fetch_timestamp TEXT,
+            success INTEGER DEFAULT 1,
+            error_message TEXT
+        )
+    """)
+
+    # Create indexes for new tables
+    cursor.execute("""
+        CREATE INDEX IF NOT EXISTS idx_agent_reports_date_symbol ON agent_reports(date, symbol)
+    """)
+    cursor.execute("""
+        CREATE INDEX IF NOT EXISTS idx_debate_history_date_symbol ON debate_history(date, symbol)
+    """)
+    cursor.execute("""
+        CREATE INDEX IF NOT EXISTS idx_pipeline_steps_date_symbol ON pipeline_steps(date, symbol)
+    """)
+    cursor.execute("""
+        CREATE INDEX IF NOT EXISTS idx_data_source_logs_date_symbol ON data_source_logs(date, symbol)
+    """)
+
+    conn.commit()
+    conn.close()
+
+
+def save_recommendation(date: str, analysis_data: dict, summary: dict,
+                        top_picks: list, stocks_to_avoid: list):
+    """Save a daily recommendation to the database."""
+    conn = get_connection()
+    cursor = conn.cursor()
+
+    try:
+        # Insert or replace daily recommendation
+        cursor.execute("""
+            INSERT OR REPLACE INTO daily_recommendations
+            (date, summary_total, summary_buy, summary_sell, summary_hold, top_picks, stocks_to_avoid)
+            VALUES (?, ?, ?, ?, ?, ?, ?)
+        """, (
+            date,
+            summary.get('total', 0),
+            summary.get('buy', 0),
+            summary.get('sell', 0),
+            summary.get('hold', 0),
+            json.dumps(top_picks),
+            json.dumps(stocks_to_avoid)
+        ))
+
+        # Insert stock analysis for each stock
+        for symbol, analysis in analysis_data.items():
+            cursor.execute("""
+                INSERT OR REPLACE INTO stock_analysis
+                (date, symbol, company_name, decision, confidence, risk, raw_analysis)
+                VALUES (?, ?, ?, ?, ?, ?, ?)
+            """, (
+                date,
+                symbol,
+                analysis.get('company_name', ''),
+                analysis.get('decision'),
+                analysis.get('confidence'),
+                analysis.get('risk'),
+                analysis.get('raw_analysis', '')
+            ))
+
+        conn.commit()
+    finally:
+        conn.close()
+
+
+def get_recommendation_by_date(date: str) -> Optional[dict]:
+    """Get recommendation for a specific date."""
+    conn = get_connection()
+    cursor = conn.cursor()
+
+    try:
+        # Get daily summary
+        cursor.execute("""
+            SELECT * FROM daily_recommendations WHERE date = ?
+        """, (date,))
+        row = cursor.fetchone()
+
+        if not row:
+            return None
+
+        # Get stock analysis for this date
+        cursor.execute("""
+            SELECT * FROM stock_analysis WHERE date = ?
+        """, (date,))
+        analysis_rows = cursor.fetchall()
+
+        analysis = {}
+        for a in analysis_rows:
+            analysis[a['symbol']] = {
+                'symbol': a['symbol'],
+                'company_name': a['company_name'],
+                'decision': a['decision'],
+                'confidence': a['confidence'],
+                'risk': a['risk'],
+                'raw_analysis': a['raw_analysis']
+            }
+
+        return {
+            'date': row['date'],
+            'analysis': analysis,
+            'summary': {
+                'total': row['summary_total'],
+                'buy': row['summary_buy'],
+                'sell': row['summary_sell'],
+                'hold': row['summary_hold']
+            },
+            'top_picks': json.loads(row['top_picks']) if row['top_picks'] else [],
+            'stocks_to_avoid': json.loads(row['stocks_to_avoid']) if row['stocks_to_avoid'] else []
+        }
+    finally:
+        conn.close()
+
+
+def get_latest_recommendation() -> Optional[dict]:
+    """Get the most recent recommendation."""
+    conn = get_connection()
+    cursor = conn.cursor()
+
+    try:
+        cursor.execute("""
+            SELECT date FROM daily_recommendations ORDER BY date DESC LIMIT 1
+        """)
+        row = cursor.fetchone()
+
+        if not row:
+            return None
+
+        return get_recommendation_by_date(row['date'])
+    finally:
+        conn.close()
+
+
+def get_all_dates() -> list:
+    """Get all available dates."""
+    conn = get_connection()
+    cursor = conn.cursor()
+
+    try:
+        cursor.execute("""
+            SELECT date FROM daily_recommendations ORDER BY date DESC
+        """)
+        return [row['date'] for row in cursor.fetchall()]
+    finally:
+        conn.close()
+
+
+def get_stock_history(symbol: str) -> list:
+    """Get historical recommendations for a specific stock."""
+    conn = get_connection()
+    cursor = conn.cursor()
+
+    try:
+        cursor.execute("""
+            SELECT date, decision, confidence, risk
+            FROM stock_analysis
+            WHERE symbol = ?
+            ORDER BY date DESC
+        """, (symbol,))
+
+        return [
+            {
+                'date': row['date'],
+                'decision': row['decision'],
+                'confidence': row['confidence'],
+                'risk': row['risk']
+            }
+            for row in cursor.fetchall()
+        ]
+    finally:
+        conn.close()
+
+
+def get_all_recommendations() -> list:
+    """Get all daily recommendations."""
+    dates = get_all_dates()
+    return [get_recommendation_by_date(date) for date in dates]
+
+
+# ============== Pipeline Data Functions ==============
+
+def save_agent_report(date: str, symbol: str, agent_type: str,
+                      report_content: str, data_sources_used: list = None):
+    """Save an individual agent's report."""
+    conn = get_connection()
+    cursor = conn.cursor()
+
+    try:
+        cursor.execute("""
+            INSERT OR REPLACE INTO agent_reports
+            (date, symbol, agent_type, report_content, data_sources_used)
+            VALUES (?, ?, ?, ?, ?)
+        """, (
+            date, symbol, agent_type, report_content,
+            json.dumps(data_sources_used) if data_sources_used else '[]'
+        ))
+        conn.commit()
+    finally:
+        conn.close()
+
+
+def save_agent_reports_bulk(date: str, symbol: str, reports: dict):
+    """Save all agent reports for a stock at once.
+
+    Args:
+        date: Date string (YYYY-MM-DD)
+        symbol: Stock symbol
+        reports: Dict with keys 'market', 'news', 'social_media', 'fundamentals'
+    """
+    conn = get_connection()
+    cursor = conn.cursor()
+
+    try:
+        for agent_type, report_data in reports.items():
+            if isinstance(report_data, str):
+                report_content = report_data
+                data_sources = []
+            else:
+                report_content = report_data.get('content', '')
+                data_sources = report_data.get('data_sources', [])
+
+            cursor.execute("""
+                INSERT OR REPLACE INTO agent_reports
+                (date, symbol, agent_type, report_content, data_sources_used)
+                VALUES (?, ?, ?, ?, ?)
+            """, (date, symbol, agent_type, report_content, json.dumps(data_sources)))
+
+        conn.commit()
+    finally:
+        conn.close()
+
+
+def get_agent_reports(date: str, symbol: str) -> dict:
+    """Get all agent reports for a stock on a date."""
+    conn = get_connection()
+    cursor = conn.cursor()
+
+    try:
+        cursor.execute("""
+            SELECT agent_type, report_content, data_sources_used, created_at
+            FROM agent_reports
+            WHERE date = ? AND symbol = ?
+        """, (date, symbol))
+
+        reports = {}
+        for row in cursor.fetchall():
+            reports[row['agent_type']] = {
+                'agent_type': row['agent_type'],
+                'report_content': row['report_content'],
+                'data_sources_used': json.loads(row['data_sources_used']) if row['data_sources_used'] else [],
+                'created_at': row['created_at']
+            }
+        return reports
+    finally:
+        conn.close()
+
+
+def save_debate_history(date: str, symbol: str, debate_type: str,
+                        bull_arguments: str = None, bear_arguments: str = None,
+                        risky_arguments: str = None, safe_arguments: str = None,
+                        neutral_arguments: str = None, judge_decision: str = None,
+                        full_history: str = None):
+    """Save debate history for investment or risk debate."""
+    conn = get_connection()
+    cursor = conn.cursor()
+
+    try:
+        cursor.execute("""
+            INSERT OR REPLACE INTO debate_history
+            (date, symbol, debate_type, bull_arguments, bear_arguments,
+             risky_arguments, safe_arguments, neutral_arguments,
+             judge_decision, full_history)
+            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+        """, (
+            date, symbol, debate_type,
+            bull_arguments, bear_arguments,
+            risky_arguments, safe_arguments, neutral_arguments,
+            judge_decision, full_history
+        ))
+        conn.commit()
+    finally:
+        conn.close()
+
+
+def get_debate_history(date: str, symbol: str) -> dict:
+    """Get all debate history for a stock on a date."""
+    conn = get_connection()
+    cursor = conn.cursor()
+
+    try:
+        cursor.execute("""
+            SELECT * FROM debate_history
+            WHERE date = ? AND symbol = ?
+        """, (date, symbol))
+
+        debates = {}
+        for row in cursor.fetchall():
+            debates[row['debate_type']] = {
+                'debate_type': row['debate_type'],
+                'bull_arguments': row['bull_arguments'],
+                'bear_arguments': row['bear_arguments'],
+                'risky_arguments': row['risky_arguments'],
+                'safe_arguments': row['safe_arguments'],
+                'neutral_arguments': row['neutral_arguments'],
+                'judge_decision': row['judge_decision'],
+                'full_history': row['full_history'],
+                'created_at': row['created_at']
+            }
+        return debates
+    finally:
+        conn.close()
+
+
+def save_pipeline_step(date: str, symbol: str, step_number: int, step_name: str,
+                       status: str, started_at: str = None, completed_at: str = None,
+                       duration_ms: int = None, output_summary: str = None):
+    """Save a pipeline step status."""
+    conn = get_connection()
+    cursor = conn.cursor()
+
+    try:
+        cursor.execute("""
+            INSERT OR REPLACE INTO pipeline_steps
+            (date, symbol, step_number, step_name, status,
+             started_at, completed_at, duration_ms, output_summary)
+            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
+        """, (
+            date, symbol, step_number, step_name, status,
+            started_at, completed_at, duration_ms, output_summary
+        ))
+        conn.commit()
+    finally:
+        conn.close()
+
+
+def save_pipeline_steps_bulk(date: str, symbol: str, steps: list):
+    """Save all pipeline steps at once.
+
+    Args:
+        date: Date string
+        symbol: Stock symbol
+        steps: List of step dicts with step_number, step_name, status, etc.
+    """
+    conn = get_connection()
+    cursor = conn.cursor()
+
+    try:
+        for step in steps:
+            cursor.execute("""
+                INSERT OR REPLACE INTO pipeline_steps
+                (date, symbol, step_number, step_name, status,
+                 started_at, completed_at, duration_ms, output_summary)
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
+            """, (
+                date, symbol,
+                step.get('step_number'),
+                step.get('step_name'),
+                step.get('status'),
+                step.get('started_at'),
+                step.get('completed_at'),
+                step.get('duration_ms'),
+                step.get('output_summary')
+            ))
+        conn.commit()
+    finally:
+        conn.close()
+
+
+def get_pipeline_steps(date: str, symbol: str) -> list:
+    """Get all pipeline steps for a stock on a date."""
+    conn = get_connection()
+    cursor = conn.cursor()
+
+    try:
+        cursor.execute("""
+            SELECT * FROM pipeline_steps
+            WHERE date = ? AND symbol = ?
+            ORDER BY step_number
+        """, (date, symbol))
+
+        return [
+            {
+                'step_number': row['step_number'],
+                'step_name': row['step_name'],
+                'status': row['status'],
+                'started_at': row['started_at'],
+                'completed_at': row['completed_at'],
+                'duration_ms': row['duration_ms'],
+                'output_summary': row['output_summary']
+            }
+            for row in cursor.fetchall()
+        ]
+    finally:
+        conn.close()
+
+
+def save_data_source_log(date: str, symbol: str, source_type: str,
+                         source_name: str, data_fetched: dict = None,
+                         fetch_timestamp: str = None, success: bool = True,
+                         error_message: str = None):
+    """Log a data source fetch."""
+    conn = get_connection()
+    cursor = conn.cursor()
+
+    try:
+        cursor.execute("""
+            INSERT INTO data_source_logs
+            (date, symbol, source_type, source_name, data_fetched,
+             fetch_timestamp, success, error_message)
+            VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+        """, (
+            date, symbol, source_type, source_name,
+            json.dumps(data_fetched) if data_fetched else None,
+            fetch_timestamp or datetime.now().isoformat(),
+            1 if success else 0,
+            error_message
+        ))
+        conn.commit()
+    finally:
+        conn.close()
+
+
+def save_data_source_logs_bulk(date: str, symbol: str, logs: list):
+    """Save multiple data source logs at once."""
+    conn = get_connection()
+    cursor = conn.cursor()
+
+    try:
+        for log in logs:
+            cursor.execute("""
+                INSERT INTO data_source_logs
+                (date, symbol, source_type, source_name, data_fetched,
+                 fetch_timestamp, success, error_message)
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+            """, (
+                date, symbol,
+                log.get('source_type'),
+                log.get('source_name'),
+                json.dumps(log.get('data_fetched')) if log.get('data_fetched') else None,
+                log.get('fetch_timestamp') or datetime.now().isoformat(),
+                1 if log.get('success', True) else 0,
+                log.get('error_message')
+            ))
+        conn.commit()
+    finally:
+        conn.close()
+
+
+def get_data_source_logs(date: str, symbol: str) -> list:
+    """Get all data source logs for a stock on a date."""
+    conn = get_connection()
+    cursor = conn.cursor()
+
+    try:
+        cursor.execute("""
+            SELECT * FROM data_source_logs
+            WHERE date = ? AND symbol = ?
+            ORDER BY fetch_timestamp
+        """, (date, symbol))
+
+        return [
+            {
+                'source_type': row['source_type'],
+                'source_name': row['source_name'],
+                'data_fetched': json.loads(row['data_fetched']) if row['data_fetched'] else None,
+                'fetch_timestamp': row['fetch_timestamp'],
+                'success': bool(row['success']),
+                'error_message': row['error_message']
+            }
+            for row in cursor.fetchall()
+        ]
+    finally:
+        conn.close()
+
+
+def get_full_pipeline_data(date: str, symbol: str) -> dict:
+    """Get complete pipeline data for a stock on a date."""
+    return {
+        'date': date,
+        'symbol': symbol,
+        'agent_reports': get_agent_reports(date, symbol),
+        'debates': get_debate_history(date, symbol),
+        'pipeline_steps': get_pipeline_steps(date, symbol),
+        'data_sources': get_data_source_logs(date, symbol)
+    }
+
+
+def save_full_pipeline_data(date: str, symbol: str, pipeline_data: dict):
+    """Save complete pipeline data for a stock.
+
+    Args:
+        date: Date string
+        symbol: Stock symbol
+        pipeline_data: Dict containing agent_reports, debates, pipeline_steps, data_sources
+    """
+    if 'agent_reports' in pipeline_data:
+        save_agent_reports_bulk(date, symbol, pipeline_data['agent_reports'])
+
+    if 'investment_debate' in pipeline_data:
+        debate = pipeline_data['investment_debate']
+        save_debate_history(
+            date, symbol, 'investment',
+            bull_arguments=debate.get('bull_history'),
+            bear_arguments=debate.get('bear_history'),
+            judge_decision=debate.get('judge_decision'),
+            full_history=debate.get('history')
+        )
+
+    if 'risk_debate' in pipeline_data:
+        debate = pipeline_data['risk_debate']
+        save_debate_history(
+            date, symbol, 'risk',
+            risky_arguments=debate.get('risky_history'),
+            safe_arguments=debate.get('safe_history'),
+            neutral_arguments=debate.get('neutral_history'),
+            judge_decision=debate.get('judge_decision'),
+            full_history=debate.get('history')
+        )
+
+    if 'pipeline_steps' in pipeline_data:
+        save_pipeline_steps_bulk(date, symbol, pipeline_data['pipeline_steps'])
+
+    if 'data_sources' in pipeline_data:
+        save_data_source_logs_bulk(date, symbol, pipeline_data['data_sources'])
+
+
+def get_pipeline_summary_for_date(date: str) -> list:
+    """Get pipeline summary for all stocks on a date."""
+    conn = get_connection()
+    cursor = conn.cursor()
+
+    try:
+        # Get all symbols for this date
+        cursor.execute("""
+            SELECT DISTINCT symbol FROM stock_analysis WHERE date = ?
+        """, (date,))
+        symbols = [row['symbol'] for row in cursor.fetchall()]
+
+        # Batch fetch all pipeline steps for the date (avoids N+1)
+        cursor.execute("""
+            SELECT symbol, step_name, status FROM pipeline_steps
+            WHERE date = ?
+            ORDER BY symbol, step_number
+        """, (date,))
+        all_steps = cursor.fetchall()
+        steps_by_symbol = {}
+        for row in all_steps:
+            if row['symbol'] not in steps_by_symbol:
+                steps_by_symbol[row['symbol']] = []
+            steps_by_symbol[row['symbol']].append({'step_name': row['step_name'], 'status': row['status']})
+
+        # Batch fetch agent report counts (avoids N+1)
+        cursor.execute("""
+            SELECT symbol, COUNT(*) as count FROM agent_reports
+            WHERE date = ?
+            GROUP BY symbol
+        """, (date,))
+        agent_counts = {row['symbol']: row['count'] for row in cursor.fetchall()}
+
+        # Batch fetch debates existence (avoids N+1)
+        cursor.execute("""
+            SELECT DISTINCT symbol FROM debate_history WHERE date = ?
+        """, (date,))
+        symbols_with_debates = {row['symbol'] for row in cursor.fetchall()}
+
+        summaries = []
+        for symbol in symbols:
+            summaries.append({
+                'symbol': symbol,
+                'pipeline_steps': steps_by_symbol.get(symbol, []),
+                'agent_reports_count': agent_counts.get(symbol, 0),
+                'has_debates': symbol in symbols_with_debates
+            })
+
+        return summaries
+    finally:
+        conn.close()
+
+
+# Initialize database on module import
+init_db()

frontend/backend/requirements.txt

@@ -0,0 +1,3 @@
+fastapi>=0.109.0
+uvicorn>=0.27.0
+pydantic>=2.0.0

frontend/backend/seed_data.py

@@ -0,0 +1,135 @@
+"""Seed the database with sample data from the Jan 30, 2025 analysis."""
+import database as db
+
+# Sample data from the Jan 30, 2025 analysis
+SAMPLE_DATA = {
+    "date": "2025-01-30",
+    "analysis": {
+        "RELIANCE": {"symbol": "RELIANCE", "company_name": "Reliance Industries Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "TCS": {"symbol": "TCS", "company_name": "Tata Consultancy Services Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "HDFCBANK": {"symbol": "HDFCBANK", "company_name": "HDFC Bank Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "INFY": {"symbol": "INFY", "company_name": "Infosys Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "ICICIBANK": {"symbol": "ICICIBANK", "company_name": "ICICI Bank Ltd", "decision": "BUY", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "HINDUNILVR": {"symbol": "HINDUNILVR", "company_name": "Hindustan Unilever Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "ITC": {"symbol": "ITC", "company_name": "ITC Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "SBIN": {"symbol": "SBIN", "company_name": "State Bank of India", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "BHARTIARTL": {"symbol": "BHARTIARTL", "company_name": "Bharti Airtel Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "KOTAKBANK": {"symbol": "KOTAKBANK", "company_name": "Kotak Mahindra Bank Ltd", "decision": "BUY", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "LT": {"symbol": "LT", "company_name": "Larsen & Toubro Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "AXISBANK": {"symbol": "AXISBANK", "company_name": "Axis Bank Ltd", "decision": "SELL", "confidence": "HIGH", "risk": "HIGH"},
+        "ASIANPAINT": {"symbol": "ASIANPAINT", "company_name": "Asian Paints Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "MARUTI": {"symbol": "MARUTI", "company_name": "Maruti Suzuki India Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "HCLTECH": {"symbol": "HCLTECH", "company_name": "HCL Technologies Ltd", "decision": "SELL", "confidence": "MEDIUM", "risk": "HIGH"},
+        "SUNPHARMA": {"symbol": "SUNPHARMA", "company_name": "Sun Pharmaceutical Industries Ltd", "decision": "SELL", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "TITAN": {"symbol": "TITAN", "company_name": "Titan Company Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "BAJFINANCE": {"symbol": "BAJFINANCE", "company_name": "Bajaj Finance Ltd", "decision": "BUY", "confidence": "HIGH", "risk": "MEDIUM"},
+        "WIPRO": {"symbol": "WIPRO", "company_name": "Wipro Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "ULTRACEMCO": {"symbol": "ULTRACEMCO", "company_name": "UltraTech Cement Ltd", "decision": "BUY", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "NESTLEIND": {"symbol": "NESTLEIND", "company_name": "Nestle India Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "NTPC": {"symbol": "NTPC", "company_name": "NTPC Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "POWERGRID": {"symbol": "POWERGRID", "company_name": "Power Grid Corporation of India Ltd", "decision": "SELL", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "M&M": {"symbol": "M&M", "company_name": "Mahindra & Mahindra Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "TATAMOTORS": {"symbol": "TATAMOTORS", "company_name": "Tata Motors Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "ONGC": {"symbol": "ONGC", "company_name": "Oil & Natural Gas Corporation Ltd", "decision": "SELL", "confidence": "MEDIUM", "risk": "HIGH"},
+        "JSWSTEEL": {"symbol": "JSWSTEEL", "company_name": "JSW Steel Ltd", "decision": "BUY", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "TATASTEEL": {"symbol": "TATASTEEL", "company_name": "Tata Steel Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "ADANIENT": {"symbol": "ADANIENT", "company_name": "Adani Enterprises Ltd", "decision": "HOLD", "confidence": "LOW", "risk": "HIGH"},
+        "ADANIPORTS": {"symbol": "ADANIPORTS", "company_name": "Adani Ports and SEZ Ltd", "decision": "SELL", "confidence": "MEDIUM", "risk": "HIGH"},
+        "COALINDIA": {"symbol": "COALINDIA", "company_name": "Coal India Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "BAJAJFINSV": {"symbol": "BAJAJFINSV", "company_name": "Bajaj Finserv Ltd", "decision": "BUY", "confidence": "HIGH", "risk": "MEDIUM"},
+        "TECHM": {"symbol": "TECHM", "company_name": "Tech Mahindra Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "HDFCLIFE": {"symbol": "HDFCLIFE", "company_name": "HDFC Life Insurance Company Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "SBILIFE": {"symbol": "SBILIFE", "company_name": "SBI Life Insurance Company Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "GRASIM": {"symbol": "GRASIM", "company_name": "Grasim Industries Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "DIVISLAB": {"symbol": "DIVISLAB", "company_name": "Divi's Laboratories Ltd", "decision": "SELL", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "DRREDDY": {"symbol": "DRREDDY", "company_name": "Dr. Reddy's Laboratories Ltd", "decision": "SELL", "confidence": "HIGH", "risk": "HIGH"},
+        "CIPLA": {"symbol": "CIPLA", "company_name": "Cipla Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "BRITANNIA": {"symbol": "BRITANNIA", "company_name": "Britannia Industries Ltd", "decision": "BUY", "confidence": "MEDIUM", "risk": "LOW"},
+        "EICHERMOT": {"symbol": "EICHERMOT", "company_name": "Eicher Motors Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "APOLLOHOSP": {"symbol": "APOLLOHOSP", "company_name": "Apollo Hospitals Enterprise Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "INDUSINDBK": {"symbol": "INDUSINDBK", "company_name": "IndusInd Bank Ltd", "decision": "SELL", "confidence": "HIGH", "risk": "HIGH"},
+        "HEROMOTOCO": {"symbol": "HEROMOTOCO", "company_name": "Hero MotoCorp Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "TATACONSUM": {"symbol": "TATACONSUM", "company_name": "Tata Consumer Products Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "BPCL": {"symbol": "BPCL", "company_name": "Bharat Petroleum Corporation Ltd", "decision": "SELL", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "UPL": {"symbol": "UPL", "company_name": "UPL Ltd", "decision": "HOLD", "confidence": "LOW", "risk": "HIGH"},
+        "HINDALCO": {"symbol": "HINDALCO", "company_name": "Hindalco Industries Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "BAJAJ-AUTO": {"symbol": "BAJAJ-AUTO", "company_name": "Bajaj Auto Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+        "LTIM": {"symbol": "LTIM", "company_name": "LTIMindtree Ltd", "decision": "HOLD", "confidence": "MEDIUM", "risk": "MEDIUM"},
+    },
+    "summary": {
+        "total": 50,
+        "buy": 7,
+        "sell": 10,
+        "hold": 33,
+    },
+    "top_picks": [
+        {
+            "rank": 1,
+            "symbol": "BAJFINANCE",
+            "company_name": "Bajaj Finance Ltd",
+            "decision": "BUY",
+            "reason": "13.7% gain over 30 days (Rs.678 to Rs.771), strongest bullish momentum with robust upward trend.",
+            "risk_level": "MEDIUM",
+        },
+        {
+            "rank": 2,
+            "symbol": "BAJAJFINSV",
+            "company_name": "Bajaj Finserv Ltd",
+            "decision": "BUY",
+            "reason": "14% gain in one month (Rs.1,567 to Rs.1,789) demonstrates clear bullish momentum with sector-wide tailwinds.",
+            "risk_level": "MEDIUM",
+        },
+        {
+            "rank": 3,
+            "symbol": "KOTAKBANK",
+            "company_name": "Kotak Mahindra Bank Ltd",
+            "decision": "BUY",
+            "reason": "Significant breakout on January 20th with 9.2% gain on exceptionally high volume (66.6M shares).",
+            "risk_level": "MEDIUM",
+        },
+    ],
+    "stocks_to_avoid": [
+        {
+            "symbol": "DRREDDY",
+            "company_name": "Dr. Reddy's Laboratories Ltd",
+            "reason": "HIGH CONFIDENCE SELL with 14.9% decline in one month. Severe downtrend with high risk.",
+        },
+        {
+            "symbol": "AXISBANK",
+            "company_name": "Axis Bank Ltd",
+            "reason": "HIGH CONFIDENCE SELL with 10.5% sustained decline. Clear and persistent downtrend.",
+        },
+        {
+            "symbol": "HCLTECH",
+            "company_name": "HCL Technologies Ltd",
+            "reason": "SELL with 9.4% drop from recent highs. High risk rating with continued selling pressure.",
+        },
+        {
+            "symbol": "ADANIPORTS",
+            "company_name": "Adani Ports and SEZ Ltd",
+            "reason": "SELL with 12% monthly decline and consistently lower lows. High risk profile.",
+        },
+    ],
+}
+
+
+def seed_database():
+    """Seed the database with sample data."""
+    print("Seeding database...")
+
+    db.save_recommendation(
+        date=SAMPLE_DATA["date"],
+        analysis_data=SAMPLE_DATA["analysis"],
+        summary=SAMPLE_DATA["summary"],
+        top_picks=SAMPLE_DATA["top_picks"],
+        stocks_to_avoid=SAMPLE_DATA["stocks_to_avoid"],
+    )
+
+    print(f"Saved recommendation for {SAMPLE_DATA['date']}")
+    print(f"  - {len(SAMPLE_DATA['analysis'])} stocks analyzed")
+    print(f"  - Summary: {SAMPLE_DATA['summary']['buy']} BUY, {SAMPLE_DATA['summary']['sell']} SELL, {SAMPLE_DATA['summary']['hold']} HOLD")
+    print("Database seeded successfully!")
+
+
+if __name__ == "__main__":
+    seed_database()

frontend/backend/server.py

@@ -0,0 +1,592 @@
+"""FastAPI server for Nifty50 AI recommendations."""
+from fastapi import FastAPI, HTTPException, BackgroundTasks
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from typing import Optional
+import database as db
+import sys
+import os
+from pathlib import Path
+from datetime import datetime
+import threading
+
+# Add parent directories to path for importing trading agents
+PROJECT_ROOT = Path(__file__).parent.parent.parent
+sys.path.insert(0, str(PROJECT_ROOT))
+
+# Track running analyses
+# NOTE: This is not thread-safe for production multi-worker deployments.
+# For production, use Redis or a database-backed job queue instead.
+running_analyses = {}  # {symbol: {"status": "running", "started_at": datetime, "progress": str}}
+
+app = FastAPI(
+    title="Nifty50 AI API",
+    description="API for Nifty 50 stock recommendations",
+    version="1.0.0"
+)
+
+# Enable CORS for frontend
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # In production, replace with specific origins
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+class StockAnalysis(BaseModel):
+    symbol: str
+    company_name: str
+    decision: Optional[str] = None
+    confidence: Optional[str] = None
+    risk: Optional[str] = None
+    raw_analysis: Optional[str] = None
+
+
+class TopPick(BaseModel):
+    rank: int
+    symbol: str
+    company_name: str
+    decision: str
+    reason: str
+    risk_level: str
+
+
+class StockToAvoid(BaseModel):
+    symbol: str
+    company_name: str
+    reason: str
+
+
+class Summary(BaseModel):
+    total: int
+    buy: int
+    sell: int
+    hold: int
+
+
+class DailyRecommendation(BaseModel):
+    date: str
+    analysis: dict[str, StockAnalysis]
+    summary: Summary
+    top_picks: list[TopPick]
+    stocks_to_avoid: list[StockToAvoid]
+
+
+class SaveRecommendationRequest(BaseModel):
+    date: str
+    analysis: dict
+    summary: dict
+    top_picks: list
+    stocks_to_avoid: list
+
+
+# ============== Pipeline Data Models ==============
+
+class AgentReport(BaseModel):
+    agent_type: str
+    report_content: str
+    data_sources_used: Optional[list] = []
+    created_at: Optional[str] = None
+
+
+class DebateHistory(BaseModel):
+    debate_type: str
+    bull_arguments: Optional[str] = None
+    bear_arguments: Optional[str] = None
+    risky_arguments: Optional[str] = None
+    safe_arguments: Optional[str] = None
+    neutral_arguments: Optional[str] = None
+    judge_decision: Optional[str] = None
+    full_history: Optional[str] = None
+
+
+class PipelineStep(BaseModel):
+    step_number: int
+    step_name: str
+    status: str
+    started_at: Optional[str] = None
+    completed_at: Optional[str] = None
+    duration_ms: Optional[int] = None
+    output_summary: Optional[str] = None
+
+
+class DataSourceLog(BaseModel):
+    source_type: str
+    source_name: str
+    data_fetched: Optional[dict] = None
+    fetch_timestamp: Optional[str] = None
+    success: bool = True
+    error_message: Optional[str] = None
+
+
+class SavePipelineDataRequest(BaseModel):
+    date: str
+    symbol: str
+    agent_reports: Optional[dict] = None
+    investment_debate: Optional[dict] = None
+    risk_debate: Optional[dict] = None
+    pipeline_steps: Optional[list] = None
+    data_sources: Optional[list] = None
+
+
+class AnalysisConfig(BaseModel):
+    deep_think_model: Optional[str] = "opus"
+    quick_think_model: Optional[str] = "sonnet"
+    provider: Optional[str] = "claude_subscription"  # claude_subscription or anthropic_api
+    api_key: Optional[str] = None
+    max_debate_rounds: Optional[int] = 1
+
+
+class RunAnalysisRequest(BaseModel):
+    symbol: str
+    date: Optional[str] = None  # Defaults to today if not provided
+    config: Optional[AnalysisConfig] = None
+
+
+def run_analysis_task(symbol: str, date: str, analysis_config: dict = None):
+    """Background task to run trading analysis for a stock."""
+    global running_analyses
+
+    # Default config values
+    if analysis_config is None:
+        analysis_config = {}
+
+    deep_think_model = analysis_config.get("deep_think_model", "opus")
+    quick_think_model = analysis_config.get("quick_think_model", "sonnet")
+    provider = analysis_config.get("provider", "claude_subscription")
+    api_key = analysis_config.get("api_key")
+    max_debate_rounds = analysis_config.get("max_debate_rounds", 1)
+
+    try:
+        running_analyses[symbol] = {
+            "status": "initializing",
+            "started_at": datetime.now().isoformat(),
+            "progress": "Loading trading agents..."
+        }
+
+        # Import trading agents
+        from tradingagents.graph.trading_graph import TradingAgentsGraph
+        from tradingagents.default_config import DEFAULT_CONFIG
+
+        running_analyses[symbol]["progress"] = "Initializing analysis pipeline..."
+
+        # Create config from user settings
+        config = DEFAULT_CONFIG.copy()
+        config["llm_provider"] = "anthropic"  # Use Claude for all LLM
+        config["deep_think_llm"] = deep_think_model
+        config["quick_think_llm"] = quick_think_model
+        config["max_debate_rounds"] = max_debate_rounds
+
+        # If using API provider and key is provided, set it in environment
+        if provider == "anthropic_api" and api_key:
+            os.environ["ANTHROPIC_API_KEY"] = api_key
+
+        running_analyses[symbol]["status"] = "running"
+        running_analyses[symbol]["progress"] = f"Running market analysis (model: {deep_think_model})..."
+
+        # Initialize and run
+        ta = TradingAgentsGraph(debug=False, config=config)
+
+        running_analyses[symbol]["progress"] = f"Analyzing {symbol}..."
+        final_state, decision = ta.propagate(symbol, date)
+
+        running_analyses[symbol] = {
+            "status": "completed",
+            "completed_at": datetime.now().isoformat(),
+            "progress": f"Analysis complete: {decision}",
+            "decision": decision
+        }
+
+    except Exception as e:
+        error_msg = str(e) if str(e) else f"{type(e).__name__}: No details provided"
+        running_analyses[symbol] = {
+            "status": "error",
+            "error": error_msg,
+            "progress": f"Error: {error_msg[:100]}"
+        }
+        import traceback
+        print(f"Analysis error for {symbol}: {type(e).__name__}: {error_msg}")
+        traceback.print_exc()
+
+
+@app.get("/")
+async def root():
+    """API root endpoint."""
+    return {
+        "name": "Nifty50 AI API",
+        "version": "2.0.0",
+        "endpoints": {
+            "GET /recommendations": "Get all recommendations",
+            "GET /recommendations/latest": "Get latest recommendation",
+            "GET /recommendations/{date}": "Get recommendation by date",
+            "GET /recommendations/{date}/{symbol}/pipeline": "Get full pipeline data for a stock",
+            "GET /recommendations/{date}/{symbol}/agents": "Get agent reports for a stock",
+            "GET /recommendations/{date}/{symbol}/debates": "Get debate history for a stock",
+            "GET /recommendations/{date}/{symbol}/data-sources": "Get data source logs for a stock",
+            "GET /recommendations/{date}/pipeline-summary": "Get pipeline summary for all stocks on a date",
+            "GET /stocks/{symbol}/history": "Get stock history",
+            "GET /dates": "Get all available dates",
+            "POST /recommendations": "Save a new recommendation",
+            "POST /pipeline": "Save pipeline data for a stock"
+        }
+    }
+
+
+@app.get("/recommendations")
+async def get_all_recommendations():
+    """Get all daily recommendations."""
+    recommendations = db.get_all_recommendations()
+    return {"recommendations": recommendations, "count": len(recommendations)}
+
+
+@app.get("/recommendations/latest")
+async def get_latest_recommendation():
+    """Get the most recent recommendation."""
+    recommendation = db.get_latest_recommendation()
+    if not recommendation:
+        raise HTTPException(status_code=404, detail="No recommendations found")
+    return recommendation
+
+
+@app.get("/recommendations/{date}")
+async def get_recommendation_by_date(date: str):
+    """Get recommendation for a specific date (format: YYYY-MM-DD)."""
+    recommendation = db.get_recommendation_by_date(date)
+    if not recommendation:
+        raise HTTPException(status_code=404, detail=f"No recommendation found for {date}")
+    return recommendation
+
+
+@app.get("/stocks/{symbol}/history")
+async def get_stock_history(symbol: str):
+    """Get historical recommendations for a specific stock."""
+    history = db.get_stock_history(symbol.upper())
+    return {"symbol": symbol.upper(), "history": history, "count": len(history)}
+
+
+@app.get("/dates")
+async def get_available_dates():
+    """Get all dates with recommendations."""
+    dates = db.get_all_dates()
+    return {"dates": dates, "count": len(dates)}
+
+
+@app.post("/recommendations")
+async def save_recommendation(request: SaveRecommendationRequest):
+    """Save a new daily recommendation."""
+    try:
+        db.save_recommendation(
+            date=request.date,
+            analysis_data=request.analysis,
+            summary=request.summary,
+            top_picks=request.top_picks,
+            stocks_to_avoid=request.stocks_to_avoid
+        )
+        return {"message": f"Recommendation for {request.date} saved successfully"}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.get("/health")
+async def health_check():
+    """Health check endpoint."""
+    return {"status": "healthy", "database": "connected"}
+
+
+# ============== Pipeline Data Endpoints ==============
+
+@app.get("/recommendations/{date}/{symbol}/pipeline")
+async def get_pipeline_data(date: str, symbol: str):
+    """Get full pipeline data for a stock on a specific date."""
+    pipeline_data = db.get_full_pipeline_data(date, symbol.upper())
+
+    # Check if we have any data
+    has_data = (
+        pipeline_data.get('agent_reports') or
+        pipeline_data.get('debates') or
+        pipeline_data.get('pipeline_steps') or
+        pipeline_data.get('data_sources')
+    )
+
+    if not has_data:
+        # Return empty structure with mock pipeline steps if no data
+        return {
+            "date": date,
+            "symbol": symbol.upper(),
+            "agent_reports": {},
+            "debates": {},
+            "pipeline_steps": [],
+            "data_sources": [],
+            "status": "no_data"
+        }
+
+    return {**pipeline_data, "status": "complete"}
+
+
+@app.get("/recommendations/{date}/{symbol}/agents")
+async def get_agent_reports(date: str, symbol: str):
+    """Get agent reports for a stock on a specific date."""
+    reports = db.get_agent_reports(date, symbol.upper())
+    return {
+        "date": date,
+        "symbol": symbol.upper(),
+        "reports": reports,
+        "count": len(reports)
+    }
+
+
+@app.get("/recommendations/{date}/{symbol}/debates")
+async def get_debate_history(date: str, symbol: str):
+    """Get debate history for a stock on a specific date."""
+    debates = db.get_debate_history(date, symbol.upper())
+    return {
+        "date": date,
+        "symbol": symbol.upper(),
+        "debates": debates
+    }
+
+
+@app.get("/recommendations/{date}/{symbol}/data-sources")
+async def get_data_sources(date: str, symbol: str):
+    """Get data source logs for a stock on a specific date."""
+    logs = db.get_data_source_logs(date, symbol.upper())
+    return {
+        "date": date,
+        "symbol": symbol.upper(),
+        "data_sources": logs,
+        "count": len(logs)
+    }
+
+
+@app.get("/recommendations/{date}/pipeline-summary")
+async def get_pipeline_summary(date: str):
+    """Get pipeline summary for all stocks on a specific date."""
+    summary = db.get_pipeline_summary_for_date(date)
+    return {
+        "date": date,
+        "stocks": summary,
+        "count": len(summary)
+    }
+
+
+@app.post("/pipeline")
+async def save_pipeline_data(request: SavePipelineDataRequest):
+    """Save pipeline data for a stock."""
+    try:
+        db.save_full_pipeline_data(
+            date=request.date,
+            symbol=request.symbol.upper(),
+            pipeline_data={
+                'agent_reports': request.agent_reports,
+                'investment_debate': request.investment_debate,
+                'risk_debate': request.risk_debate,
+                'pipeline_steps': request.pipeline_steps,
+                'data_sources': request.data_sources
+            }
+        )
+        return {"message": f"Pipeline data for {request.symbol} on {request.date} saved successfully"}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+# ============== Analysis Endpoints ==============
+
+# Track bulk analysis state
+bulk_analysis_state = {
+    "status": "idle",  # idle, running, completed, error
+    "total": 0,
+    "completed": 0,
+    "failed": 0,
+    "current_symbol": None,
+    "started_at": None,
+    "completed_at": None,
+    "results": {}
+}
+
+# List of Nifty 50 stocks
+NIFTY_50_SYMBOLS = [
+    "RELIANCE", "TCS", "HDFCBANK", "INFY", "ICICIBANK", "HINDUNILVR", "ITC", "SBIN",
+    "BHARTIARTL", "KOTAKBANK", "LT", "AXISBANK", "ASIANPAINT", "MARUTI", "HCLTECH",
+    "SUNPHARMA", "TITAN", "BAJFINANCE", "WIPRO", "ULTRACEMCO", "NESTLEIND", "NTPC",
+    "POWERGRID", "M&M", "TATAMOTORS", "ONGC", "JSWSTEEL", "TATASTEEL", "ADANIENT",
+    "ADANIPORTS", "COALINDIA", "BAJAJFINSV", "TECHM", "HDFCLIFE", "SBILIFE", "GRASIM",
+    "DIVISLAB", "DRREDDY", "CIPLA", "BRITANNIA", "EICHERMOT", "APOLLOHOSP", "INDUSINDBK",
+    "HEROMOTOCO", "TATACONSUM", "BPCL", "UPL", "HINDALCO", "BAJAJ-AUTO", "LTIM"
+]
+
+
+class BulkAnalysisRequest(BaseModel):
+    deep_think_model: Optional[str] = "opus"
+    quick_think_model: Optional[str] = "sonnet"
+    provider: Optional[str] = "claude_subscription"
+    api_key: Optional[str] = None
+    max_debate_rounds: Optional[int] = 1
+
+
+@app.post("/analyze/all")
+async def run_bulk_analysis(request: Optional[BulkAnalysisRequest] = None, date: Optional[str] = None):
+    """Trigger analysis for all Nifty 50 stocks. Runs in background."""
+    global bulk_analysis_state
+
+    # Check if bulk analysis is already running
+    if bulk_analysis_state.get("status") == "running":
+        return {
+            "message": "Bulk analysis already running",
+            "status": bulk_analysis_state
+        }
+
+    # Use today's date if not provided
+    if not date:
+        date = datetime.now().strftime("%Y-%m-%d")
+
+    # Build analysis config from request
+    analysis_config = {}
+    if request:
+        analysis_config = {
+            "deep_think_model": request.deep_think_model,
+            "quick_think_model": request.quick_think_model,
+            "provider": request.provider,
+            "api_key": request.api_key,
+            "max_debate_rounds": request.max_debate_rounds
+        }
+
+    # Start bulk analysis in background thread
+    def run_bulk():
+        global bulk_analysis_state
+        bulk_analysis_state = {
+            "status": "running",
+            "total": len(NIFTY_50_SYMBOLS),
+            "completed": 0,
+            "failed": 0,
+            "current_symbol": None,
+            "started_at": datetime.now().isoformat(),
+            "completed_at": None,
+            "results": {}
+        }
+
+        for symbol in NIFTY_50_SYMBOLS:
+            try:
+                bulk_analysis_state["current_symbol"] = symbol
+                run_analysis_task(symbol, date, analysis_config)
+
+                # Wait for completion
+                import time
+                while symbol in running_analyses and running_analyses[symbol].get("status") == "running":
+                    time.sleep(2)
+
+                if symbol in running_analyses:
+                    status = running_analyses[symbol].get("status", "unknown")
+                    bulk_analysis_state["results"][symbol] = status
+                    if status == "completed":
+                        bulk_analysis_state["completed"] += 1
+                    else:
+                        bulk_analysis_state["failed"] += 1
+                else:
+                    bulk_analysis_state["results"][symbol] = "unknown"
+                    bulk_analysis_state["failed"] += 1
+
+            except Exception as e:
+                bulk_analysis_state["results"][symbol] = f"error: {str(e)}"
+                bulk_analysis_state["failed"] += 1
+
+        bulk_analysis_state["status"] = "completed"
+        bulk_analysis_state["current_symbol"] = None
+        bulk_analysis_state["completed_at"] = datetime.now().isoformat()
+
+    thread = threading.Thread(target=run_bulk)
+    thread.start()
+
+    return {
+        "message": "Bulk analysis started for all Nifty 50 stocks",
+        "date": date,
+        "total_stocks": len(NIFTY_50_SYMBOLS),
+        "status": "started"
+    }
+
+
+@app.get("/analyze/all/status")
+async def get_bulk_analysis_status():
+    """Get the status of bulk analysis."""
+    return bulk_analysis_state
+
+
+@app.get("/analyze/running")
+async def get_running_analyses():
+    """Get all currently running analyses."""
+    running = {k: v for k, v in running_analyses.items() if v.get("status") == "running"}
+    return {
+        "running": running,
+        "count": len(running)
+    }
+
+
+class SingleAnalysisRequest(BaseModel):
+    deep_think_model: Optional[str] = "opus"
+    quick_think_model: Optional[str] = "sonnet"
+    provider: Optional[str] = "claude_subscription"
+    api_key: Optional[str] = None
+    max_debate_rounds: Optional[int] = 1
+
+
+@app.post("/analyze/{symbol}")
+async def run_analysis(symbol: str, background_tasks: BackgroundTasks, request: Optional[SingleAnalysisRequest] = None, date: Optional[str] = None):
+    """Trigger analysis for a stock. Runs in background."""
+    symbol = symbol.upper()
+
+    # Check if analysis is already running
+    if symbol in running_analyses and running_analyses[symbol].get("status") == "running":
+        return {
+            "message": f"Analysis already running for {symbol}",
+            "status": running_analyses[symbol]
+        }
+
+    # Use today's date if not provided
+    if not date:
+        date = datetime.now().strftime("%Y-%m-%d")
+
+    # Build analysis config from request
+    analysis_config = {}
+    if request:
+        analysis_config = {
+            "deep_think_model": request.deep_think_model,
+            "quick_think_model": request.quick_think_model,
+            "provider": request.provider,
+            "api_key": request.api_key,
+            "max_debate_rounds": request.max_debate_rounds
+        }
+
+    # Start analysis in background thread
+    thread = threading.Thread(target=run_analysis_task, args=(symbol, date, analysis_config))
+    thread.start()
+
+    return {
+        "message": f"Analysis started for {symbol}",
+        "symbol": symbol,
+        "date": date,
+        "status": "started"
+    }
+
+
+@app.get("/analyze/{symbol}/status")
+async def get_analysis_status(symbol: str):
+    """Get the status of a running or completed analysis."""
+    symbol = symbol.upper()
+
+    if symbol not in running_analyses:
+        return {
+            "symbol": symbol,
+            "status": "not_started",
+            "message": "No analysis has been run for this stock"
+        }
+
+    return {
+        "symbol": symbol,
+        **running_analyses[symbol]
+    }
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8001)

frontend/eslint.config.js

@@ -0,0 +1,23 @@
+import js from '@eslint/js'
+import globals from 'globals'
+import reactHooks from 'eslint-plugin-react-hooks'
+import reactRefresh from 'eslint-plugin-react-refresh'
+import tseslint from 'typescript-eslint'
+import { defineConfig, globalIgnores } from 'eslint/config'
+
+export default defineConfig([
+  globalIgnores(['dist']),
+  {
+    files: ['**/*.{ts,tsx}'],
+    extends: [
+      js.configs.recommended,
+      tseslint.configs.recommended,
+      reactHooks.configs.flat.recommended,
+      reactRefresh.configs.vite,
+    ],
+    languageOptions: {
+      ecmaVersion: 2020,
+      globals: globals.browser,
+    },
+  },
+])

frontend/index.html

@@ -0,0 +1,86 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+
+    <!-- Primary Meta Tags -->
+    <title>Nifty50 AI - Daily Stock Recommendations for Indian Markets</title>
+    <meta name="title" content="Nifty50 AI - Daily Stock Recommendations for Indian Markets" />
+    <meta name="description" content="AI-powered daily stock recommendations for all Nifty 50 stocks. Get actionable buy, sell, and hold signals based on technical analysis, fundamentals, and news sentiment." />
+    <meta name="keywords" content="Nifty 50, stock recommendations, AI stock analysis, Indian stock market, NSE, BSE, trading signals, buy sell hold, stock market India" />
+    <meta name="author" content="Nifty50 AI" />
+    <meta name="robots" content="index, follow" />
+
+    <!-- Favicon -->
+    <link rel="icon" type="image/svg+xml" href="/favicon.svg" />
+    <link rel="apple-touch-icon" sizes="180x180" href="/apple-touch-icon.png" />
+
+    <!-- Open Graph / Facebook -->
+    <meta property="og:type" content="website" />
+    <meta property="og:url" content="https://nifty50ai.com/" />
+    <meta property="og:title" content="Nifty50 AI - Daily Stock Recommendations for Indian Markets" />
+    <meta property="og:description" content="AI-powered daily stock recommendations for all Nifty 50 stocks. Get actionable buy, sell, and hold signals." />
+    <meta property="og:image" content="/og-image.png" />
+    <meta property="og:locale" content="en_IN" />
+    <meta property="og:site_name" content="Nifty50 AI" />
+
+    <!-- Twitter -->
+    <meta property="twitter:card" content="summary_large_image" />
+    <meta property="twitter:url" content="https://nifty50ai.com/" />
+    <meta property="twitter:title" content="Nifty50 AI - Daily Stock Recommendations for Indian Markets" />
+    <meta property="twitter:description" content="AI-powered daily stock recommendations for all Nifty 50 stocks. Get actionable buy, sell, and hold signals." />
+    <meta property="twitter:image" content="/og-image.png" />
+
+    <!-- Theme Color -->
+    <meta name="theme-color" content="#0284c7" />
+    <meta name="msapplication-TileColor" content="#0284c7" />
+
+    <!-- Canonical URL -->
+    <link rel="canonical" href="https://nifty50ai.com/" />
+
+    <!-- Google Fonts -->
+    <link rel="preconnect" href="https://fonts.googleapis.com">
+    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+    <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Lexend:wght@400;500;600;700&display=swap" rel="stylesheet">
+
+    <!-- Structured Data (JSON-LD) -->
+    <script type="application/ld+json">
+    {
+      "@context": "https://schema.org",
+      "@type": "WebSite",
+      "name": "Nifty50 AI",
+      "description": "AI-powered daily stock recommendations for all Nifty 50 stocks",
+      "url": "https://nifty50ai.com/",
+      "potentialAction": {
+        "@type": "SearchAction",
+        "target": "https://nifty50ai.com/stock/{search_term_string}",
+        "query-input": "required name=search_term_string"
+      }
+    }
+    </script>
+
+    <script type="application/ld+json">
+    {
+      "@context": "https://schema.org",
+      "@type": "Organization",
+      "name": "Nifty50 AI",
+      "url": "https://nifty50ai.com/",
+      "logo": "https://nifty50ai.com/logo.png",
+      "description": "AI-powered stock analysis and recommendations for Indian markets"
+    }
+    </script>
+  </head>
+  <body>
+    <div id="root"></div>
+    <script type="module" src="/src/main.tsx"></script>
+
+    <!-- Noscript fallback -->
+    <noscript>
+      <div style="padding: 20px; text-align: center; font-family: system-ui, sans-serif;">
+        <h1>Nifty50 AI - Stock Recommendations</h1>
+        <p>Please enable JavaScript to view this website.</p>
+      </div>
+    </noscript>
+  </body>
+</html>

frontend/package.json

@@ -0,0 +1,41 @@
+{
+  "name": "frontend",
+  "private": true,
+  "version": "0.0.0",
+  "type": "module",
+  "scripts": {
+    "dev": "vite",
+    "build": "tsc -b && vite build",
+    "lint": "eslint .",
+    "preview": "vite preview"
+  },
+  "dependencies": {
+    "@tailwindcss/postcss": "^4.1.18",
+    "date-fns": "^4.1.0",
+    "lucide-react": "^0.563.0",
+    "react": "^19.2.0",
+    "react-dom": "^19.2.0",
+    "react-router-dom": "^7.13.0",
+    "recharts": "^3.7.0"
+  },
+  "devDependencies": {
+    "@eslint/js": "^9.39.1",
+    "@tailwindcss/vite": "^4.1.18",
+    "@types/node": "^24.10.1",
+    "@types/react": "^19.2.5",
+    "@types/react-dom": "^19.2.3",
+    "@vitejs/plugin-react": "^5.1.1",
+    "autoprefixer": "^10.4.24",
+    "eslint": "^9.39.1",
+    "eslint-plugin-react-hooks": "^7.0.1",
+    "eslint-plugin-react-refresh": "^0.4.24",
+    "globals": "^16.5.0",
+    "playwright": "^1.58.1",
+    "postcss": "^8.5.6",
+    "puppeteer": "^24.36.1",
+    "tailwindcss": "^4.1.18",
+    "typescript": "~5.9.3",
+    "typescript-eslint": "^8.46.4",
+    "vite": "^7.2.4"
+  }
+}

frontend/postcss.config.js

@@ -0,0 +1,6 @@
+export default {
+  plugins: {
+    '@tailwindcss/postcss': {},
+    autoprefixer: {},
+  },
+}

frontend/public/favicon.svg

@@ -0,0 +1,11 @@
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64">
+  <defs>
+    <linearGradient id="bg" x1="0%" y1="0%" x2="100%" y2="100%">
+      <stop offset="0%" style="stop-color:#0ea5e9"/>
+      <stop offset="100%" style="stop-color:#0369a1"/>
+    </linearGradient>
+  </defs>
+  <rect width="64" height="64" rx="14" fill="url(#bg)"/>
+  <path d="M16 44 L26 28 L36 36 L48 20" stroke="white" stroke-width="4" stroke-linecap="round" stroke-linejoin="round" fill="none"/>
+  <circle cx="48" cy="20" r="4" fill="#22c55e"/>
+</svg>

frontend/public/vite.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="31.88" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 257"><defs><linearGradient id="IconifyId1813088fe1fbc01fb466" x1="-.828%" x2="57.636%" y1="7.652%" y2="78.411%"><stop offset="0%" stop-color="#41D1FF"></stop><stop offset="100%" stop-color="#BD34FE"></stop></linearGradient><linearGradient id="IconifyId1813088fe1fbc01fb467" x1="43.376%" x2="50.316%" y1="2.242%" y2="89.03%"><stop offset="0%" stop-color="#FFEA83"></stop><stop offset="8.333%" stop-color="#FFDD35"></stop><stop offset="100%" stop-color="#FFA800"></stop></linearGradient></defs><path fill="url(#IconifyId1813088fe1fbc01fb466)" d="M255.153 37.938L134.897 252.976c-2.483 4.44-8.862 4.466-11.382.048L.875 37.958c-2.746-4.814 1.371-10.646 6.827-9.67l120.385 21.517a6.537 6.537 0 0 0 2.322-.004l117.867-21.483c5.438-.991 9.574 4.796 6.877 9.62Z"></path><path fill="url(#IconifyId1813088fe1fbc01fb467)" d="M185.432.063L96.44 17.501a3.268 3.268 0 0 0-2.634 3.014l-5.474 92.456a3.268 3.268 0 0 0 3.997 3.378l24.777-5.718c2.318-.535 4.413 1.507 3.936 3.838l-7.361 36.047c-.495 2.426 1.782 4.5 4.151 3.78l15.304-4.649c2.372-.72 4.652 1.36 4.15 3.788l-11.698 56.621c-.732 3.542 3.979 5.473 5.943 2.437l1.313-2.028l72.516-144.72c1.215-2.423-.88-5.186-3.54-4.672l-25.505 4.922c-2.396.462-4.435-1.77-3.759-4.114l16.646-57.705c.677-2.35-1.37-4.583-3.769-4.113Z"></path></svg>

frontend/src/App.css

@@ -0,0 +1,42 @@
+#root {
+  max-width: 1280px;
+  margin: 0 auto;
+  padding: 2rem;
+  text-align: center;
+}
+
+.logo {
+  height: 6em;
+  padding: 1.5em;
+  will-change: filter;
+  transition: filter 300ms;
+}
+.logo:hover {
+  filter: drop-shadow(0 0 2em #646cffaa);
+}
+.logo.react:hover {
+  filter: drop-shadow(0 0 2em #61dafbaa);
+}
+
+@keyframes logo-spin {
+  from {
+    transform: rotate(0deg);
+  }
+  to {
+    transform: rotate(360deg);
+  }
+}
+
+@media (prefers-reduced-motion: no-preference) {
+  a:nth-of-type(2) .logo {
+    animation: logo-spin infinite 20s linear;
+  }
+}
+
+.card {
+  padding: 2em;
+}
+
+.read-the-docs {
+  color: #888;
+}

frontend/src/App.tsx

@@ -0,0 +1,34 @@
+import { Routes, Route } from 'react-router-dom';
+import { ThemeProvider } from './contexts/ThemeContext';
+import { SettingsProvider } from './contexts/SettingsContext';
+import Header from './components/Header';
+import Footer from './components/Footer';
+import SettingsModal from './components/SettingsModal';
+import Dashboard from './pages/Dashboard';
+import History from './pages/History';
+import StockDetail from './pages/StockDetail';
+import About from './pages/About';
+
+function App() {
+  return (
+    <ThemeProvider>
+      <SettingsProvider>
+        <div className="min-h-screen flex flex-col bg-gray-50 dark:bg-slate-900 transition-colors">
+          <Header />
+          <main className="flex-1 max-w-7xl mx-auto w-full px-3 sm:px-4 lg:px-6 py-4">
+            <Routes>
+              <Route path="/" element={<Dashboard />} />
+              <Route path="/history" element={<History />} />
+              <Route path="/stock/:symbol" element={<StockDetail />} />
+              <Route path="/about" element={<About />} />
+            </Routes>
+          </main>
+          <Footer />
+          <SettingsModal />
+        </div>
+      </SettingsProvider>
+    </ThemeProvider>
+  );
+}
+
+export default App;

frontend/src/assets/react.svg

@@ -0,0 +1 @@
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frontend/src/components/AIAnalysisPanel.tsx

@@ -0,0 +1,152 @@
+import { useState } from 'react';
+import { Brain, ChevronDown, ChevronUp, TrendingUp, BarChart2, MessageSquare, AlertTriangle, Target } from 'lucide-react';
+import type { Decision } from '../types';
+
+interface AIAnalysisPanelProps {
+  analysis: string;
+  decision?: Decision | null;
+  defaultExpanded?: boolean;
+}
+
+interface Section {
+  title: string;
+  content: string;
+  icon: typeof Brain;
+}
+
+function parseAnalysis(analysis: string): Section[] {
+  const sections: Section[] = [];
+  const iconMap: Record<string, typeof Brain> = {
+    'Summary': Target,
+    'Technical Analysis': BarChart2,
+    'Fundamental Analysis': TrendingUp,
+    'Sentiment': MessageSquare,
+    'Risks': AlertTriangle,
+  };
+
+  // Split by markdown headers (##)
+  const parts = analysis.split(/^## /gm).filter(Boolean);
+
+  for (const part of parts) {
+    const lines = part.trim().split('\n');
+    const title = lines[0].trim();
+    const content = lines.slice(1).join('\n').trim();
+
+    if (title && content) {
+      sections.push({
+        title,
+        content,
+        icon: iconMap[title] || Brain,
+      });
+    }
+  }
+
+  // If no sections found, treat the whole thing as a summary
+  if (sections.length === 0 && analysis.trim()) {
+    sections.push({
+      title: 'Analysis',
+      content: analysis.trim(),
+      icon: Brain,
+    });
+  }
+
+  return sections;
+}
+
+function AnalysisSection({ section, defaultOpen = true }: { section: Section; defaultOpen?: boolean }) {
+  const [isOpen, setIsOpen] = useState(defaultOpen);
+  const Icon = section.icon;
+
+  return (
+    <div className="border-b border-gray-100 dark:border-slate-700 last:border-0">
+      <button
+        onClick={() => setIsOpen(!isOpen)}
+        className="w-full flex items-center justify-between px-4 py-2.5 text-left hover:bg-gray-50 dark:hover:bg-slate-700/50 transition-colors"
+      >
+        <div className="flex items-center gap-2">
+          <Icon className="w-4 h-4 text-nifty-600 dark:text-nifty-400" />
+          <span className="font-medium text-sm text-gray-900 dark:text-gray-100">{section.title}</span>
+        </div>
+        {isOpen ? (
+          <ChevronUp className="w-4 h-4 text-gray-400" />
+        ) : (
+          <ChevronDown className="w-4 h-4 text-gray-400" />
+        )}
+      </button>
+      {isOpen && (
+        <div className="px-4 pb-3 text-sm text-gray-600 dark:text-gray-300 whitespace-pre-wrap leading-relaxed">
+          {section.content.split('\n').map((line, i) => {
+            // Handle bullet points
+            if (line.trim().startsWith('- ')) {
+              return (
+                <div key={i} className="flex gap-2 mt-1">
+                  <span className="text-nifty-500">•</span>
+                  <span>{line.trim().substring(2)}</span>
+                </div>
+              );
+            }
+            return <p key={i} className={line.trim() ? 'mt-1' : 'mt-2'}>{line}</p>;
+          })}
+        </div>
+      )}
+    </div>
+  );
+}
+
+export default function AIAnalysisPanel({
+  analysis,
+  decision,
+  defaultExpanded = false,
+}: AIAnalysisPanelProps) {
+  const [isExpanded, setIsExpanded] = useState(defaultExpanded);
+  const sections = parseAnalysis(analysis);
+
+  const decisionGradient = {
+    BUY: 'from-green-500 to-emerald-600',
+    SELL: 'from-red-500 to-rose-600',
+    HOLD: 'from-amber-500 to-orange-600',
+  };
+
+  const gradient = decision ? decisionGradient[decision] : 'from-nifty-500 to-nifty-700';
+
+  return (
+    <section className="card overflow-hidden">
+      {/* Header with gradient */}
+      <button
+        onClick={() => setIsExpanded(!isExpanded)}
+        className={`w-full bg-gradient-to-r ${gradient} p-3 text-white flex items-center justify-between`}
+      >
+        <div className="flex items-center gap-2">
+          <Brain className="w-5 h-5" />
+          <span className="font-semibold text-sm">AI Analysis</span>
+          <span className="text-xs bg-white/20 px-2 py-0.5 rounded-full">
+            {sections.length} sections
+          </span>
+        </div>
+        <div className="flex items-center gap-2">
+          <span className="text-xs text-white/80">
+            {isExpanded ? 'Click to collapse' : 'Click to expand'}
+          </span>
+          {isExpanded ? (
+            <ChevronUp className="w-4 h-4" />
+          ) : (
+            <ChevronDown className="w-4 h-4" />
+          )}
+        </div>
+      </button>
+
+      {/* Content */}
+      {isExpanded && (
+        <div className="bg-white dark:bg-slate-800">
+          {sections.map((section, index) => (
+            <AnalysisSection
+              key={index}
+              section={section}
+              defaultOpen={index === 0}
+            />
+          ))}
+        </div>
+      )}
+    </section>
+  );
+}

frontend/src/components/AccuracyBadge.tsx

@@ -0,0 +1,72 @@
+import { Check, X, Minus } from 'lucide-react';
+
+interface AccuracyBadgeProps {
+  correct: boolean | null;
+  returnPercent: number;
+  size?: 'small' | 'default';
+}
+
+export default function AccuracyBadge({
+  correct,
+  returnPercent,
+  size = 'default',
+}: AccuracyBadgeProps) {
+  const isPositiveReturn = returnPercent >= 0;
+  const sizeClasses = size === 'small' ? 'text-xs px-1.5 py-0.5 gap-1' : 'text-sm px-2 py-1 gap-1.5';
+  const iconSize = size === 'small' ? 'w-3 h-3' : 'w-3.5 h-3.5';
+
+  if (correct === null) {
+    return (
+      <span className={`inline-flex items-center rounded-full font-medium bg-gray-100 dark:bg-slate-700 text-gray-500 dark:text-gray-400 ${sizeClasses}`}>
+        <Minus className={iconSize} />
+        <span>Pending</span>
+      </span>
+    );
+  }
+
+  if (correct) {
+    return (
+      <span className={`inline-flex items-center rounded-full font-medium bg-green-100 dark:bg-green-900/30 text-green-700 dark:text-green-400 ${sizeClasses}`}>
+        <Check className={iconSize} />
+        <span className={isPositiveReturn ? '' : 'text-green-600 dark:text-green-400'}>
+          {isPositiveReturn ? '+' : ''}{returnPercent.toFixed(1)}%
+        </span>
+      </span>
+    );
+  }
+
+  return (
+    <span className={`inline-flex items-center rounded-full font-medium bg-red-100 dark:bg-red-900/30 text-red-700 dark:text-red-400 ${sizeClasses}`}>
+      <X className={iconSize} />
+      <span>
+        {isPositiveReturn ? '+' : ''}{returnPercent.toFixed(1)}%
+      </span>
+    </span>
+  );
+}
+
+interface AccuracyRateProps {
+  rate: number;
+  label?: string;
+  size?: 'small' | 'default';
+}
+
+export function AccuracyRate({ rate, label = 'Accuracy', size = 'default' }: AccuracyRateProps) {
+  const percentage = rate * 100;
+  const isGood = percentage >= 60;
+  const isModerate = percentage >= 40 && percentage < 60;
+
+  const sizeClasses = size === 'small' ? 'text-xs' : 'text-sm';
+  const colorClass = isGood
+    ? 'text-green-600 dark:text-green-400'
+    : isModerate
+    ? 'text-amber-600 dark:text-amber-400'
+    : 'text-red-600 dark:text-red-400';
+
+  return (
+    <div className={`flex items-center gap-1.5 ${sizeClasses}`}>
+      <span className="text-gray-500 dark:text-gray-400">{label}:</span>
+      <span className={`font-semibold ${colorClass}`}>{percentage.toFixed(0)}%</span>
+    </div>
+  );
+}

frontend/src/components/AccuracyExplainModal.tsx

@@ -0,0 +1,177 @@
+import { X, HelpCircle, TrendingUp, TrendingDown, Minus, CheckCircle } from 'lucide-react';
+import type { AccuracyMetrics } from '../types';
+
+interface AccuracyExplainModalProps {
+  isOpen: boolean;
+  onClose: () => void;
+  metrics: AccuracyMetrics;
+}
+
+export default function AccuracyExplainModal({ isOpen, onClose, metrics }: AccuracyExplainModalProps) {
+  if (!isOpen) return null;
+
+  const buyCorrect = Math.round(metrics.buy_accuracy * metrics.total_predictions * 0.14); // ~7 buy signals
+  const buyTotal = Math.round(metrics.total_predictions * 0.14);
+  const sellCorrect = Math.round(metrics.sell_accuracy * metrics.total_predictions * 0.2); // ~10 sell signals
+  const sellTotal = Math.round(metrics.total_predictions * 0.2);
+  const holdCorrect = Math.round(metrics.hold_accuracy * metrics.total_predictions * 0.66); // ~33 hold signals
+  const holdTotal = Math.round(metrics.total_predictions * 0.66);
+
+  return (
+    <div className="fixed inset-0 z-50 flex items-center justify-center p-4">
+      {/* Backdrop */}
+      <div
+        className="absolute inset-0 bg-black/50 backdrop-blur-sm"
+        onClick={onClose}
+      />
+
+      {/* Modal */}
+      <div className="relative bg-white dark:bg-slate-800 rounded-xl shadow-xl max-w-lg w-full max-h-[90vh] overflow-y-auto">
+        {/* Header */}
+        <div className="sticky top-0 flex items-center justify-between p-4 border-b border-gray-100 dark:border-slate-700 bg-white dark:bg-slate-800">
+          <div className="flex items-center gap-2">
+            <HelpCircle className="w-5 h-5 text-nifty-600 dark:text-nifty-400" />
+            <h2 className="text-lg font-semibold text-gray-900 dark:text-gray-100">
+              How Accuracy is Calculated
+            </h2>
+          </div>
+          <button
+            onClick={onClose}
+            className="p-1.5 rounded-lg hover:bg-gray-100 dark:hover:bg-slate-700 transition-colors"
+          >
+            <X className="w-5 h-5 text-gray-500 dark:text-gray-400" />
+          </button>
+        </div>
+
+        {/* Content */}
+        <div className="p-4 space-y-5">
+          {/* Overview */}
+          <div className="p-4 rounded-lg bg-nifty-50 dark:bg-nifty-900/20 border border-nifty-100 dark:border-nifty-800">
+            <h3 className="font-semibold text-gray-900 dark:text-gray-100 mb-2">Overall Accuracy</h3>
+            <div className="text-3xl font-bold text-nifty-600 dark:text-nifty-400 mb-1">
+              {(metrics.success_rate * 100).toFixed(1)}%
+            </div>
+            <p className="text-sm text-gray-600 dark:text-gray-400">
+              {metrics.correct_predictions} correct out of {metrics.total_predictions} predictions
+            </p>
+          </div>
+
+          {/* Formula */}
+          <div>
+            <h3 className="font-semibold text-gray-900 dark:text-gray-100 mb-2">Calculation Method</h3>
+            <div className="p-3 rounded-lg bg-gray-50 dark:bg-slate-700/50 font-mono text-sm">
+              <p className="text-gray-700 dark:text-gray-300">
+                Accuracy = (Correct Predictions / Total Predictions) × 100
+              </p>
+              <p className="text-gray-500 dark:text-gray-400 mt-2 text-xs">
+                = ({metrics.correct_predictions} / {metrics.total_predictions}) × 100 = {(metrics.success_rate * 100).toFixed(1)}%
+              </p>
+            </div>
+          </div>
+
+          {/* Decision Type Breakdown */}
+          <div>
+            <h3 className="font-semibold text-gray-900 dark:text-gray-100 mb-3">Breakdown by Decision Type</h3>
+            <div className="space-y-3">
+              {/* BUY */}
+              <div className="p-3 rounded-lg bg-green-50 dark:bg-green-900/20 border border-green-100 dark:border-green-800">
+                <div className="flex items-center justify-between mb-2">
+                  <div className="flex items-center gap-2">
+                    <TrendingUp className="w-4 h-4 text-green-600 dark:text-green-400" />
+                    <span className="font-medium text-green-800 dark:text-green-300">BUY Predictions</span>
+                  </div>
+                  <span className="text-lg font-bold text-green-600 dark:text-green-400">
+                    {(metrics.buy_accuracy * 100).toFixed(0)}%
+                  </span>
+                </div>
+                <p className="text-xs text-green-700 dark:text-green-400">
+                  A BUY prediction is correct if the stock price <strong>increased</strong> after the recommendation
+                </p>
+                <div className="flex items-center gap-2 mt-2 text-xs text-green-600 dark:text-green-500">
+                  <CheckCircle className="w-3 h-3" />
+                  <span>~{buyCorrect} correct / {buyTotal} total BUY signals</span>
+                </div>
+              </div>
+
+              {/* SELL */}
+              <div className="p-3 rounded-lg bg-red-50 dark:bg-red-900/20 border border-red-100 dark:border-red-800">
+                <div className="flex items-center justify-between mb-2">
+                  <div className="flex items-center gap-2">
+                    <TrendingDown className="w-4 h-4 text-red-600 dark:text-red-400" />
+                    <span className="font-medium text-red-800 dark:text-red-300">SELL Predictions</span>
+                  </div>
+                  <span className="text-lg font-bold text-red-600 dark:text-red-400">
+                    {(metrics.sell_accuracy * 100).toFixed(0)}%
+                  </span>
+                </div>
+                <p className="text-xs text-red-700 dark:text-red-400">
+                  A SELL prediction is correct if the stock price <strong>decreased</strong> after the recommendation
+                </p>
+                <div className="flex items-center gap-2 mt-2 text-xs text-red-600 dark:text-red-500">
+                  <CheckCircle className="w-3 h-3" />
+                  <span>~{sellCorrect} correct / {sellTotal} total SELL signals</span>
+                </div>
+              </div>
+
+              {/* HOLD */}
+              <div className="p-3 rounded-lg bg-amber-50 dark:bg-amber-900/20 border border-amber-100 dark:border-amber-800">
+                <div className="flex items-center justify-between mb-2">
+                  <div className="flex items-center gap-2">
+                    <Minus className="w-4 h-4 text-amber-600 dark:text-amber-400" />
+                    <span className="font-medium text-amber-800 dark:text-amber-300">HOLD Predictions</span>
+                  </div>
+                  <span className="text-lg font-bold text-amber-600 dark:text-amber-400">
+                    {(metrics.hold_accuracy * 100).toFixed(0)}%
+                  </span>
+                </div>
+                <p className="text-xs text-amber-700 dark:text-amber-400">
+                  A HOLD prediction is correct if the stock price stayed <strong>relatively stable</strong> (±2% range)
+                </p>
+                <div className="flex items-center gap-2 mt-2 text-xs text-amber-600 dark:text-amber-500">
+                  <CheckCircle className="w-3 h-3" />
+                  <span>~{holdCorrect} correct / {holdTotal} total HOLD signals</span>
+                </div>
+              </div>
+            </div>
+          </div>
+
+          {/* Timeframe */}
+          <div>
+            <h3 className="font-semibold text-gray-900 dark:text-gray-100 mb-2">Evaluation Timeframe</h3>
+            <div className="p-3 rounded-lg bg-gray-50 dark:bg-slate-700/50">
+              <ul className="text-sm text-gray-600 dark:text-gray-400 space-y-1">
+                <li className="flex items-start gap-2">
+                  <span className="text-nifty-600 dark:text-nifty-400">•</span>
+                  <span><strong>1-week return:</strong> Short-term price movement validation</span>
+                </li>
+                <li className="flex items-start gap-2">
+                  <span className="text-nifty-600 dark:text-nifty-400">•</span>
+                  <span><strong>1-month return:</strong> Primary accuracy metric (shown in results)</span>
+                </li>
+              </ul>
+            </div>
+          </div>
+
+          {/* Disclaimer */}
+          <div className="p-3 rounded-lg bg-gray-100 dark:bg-slate-700/30 border border-gray-200 dark:border-slate-600">
+            <p className="text-xs text-gray-500 dark:text-gray-400">
+              <strong>Note:</strong> Past performance does not guarantee future results.
+              Accuracy metrics are based on historical data and are for educational purposes only.
+              Market conditions can change rapidly and predictions may not hold in future periods.
+            </p>
+          </div>
+        </div>
+
+        {/* Footer */}
+        <div className="sticky bottom-0 p-4 border-t border-gray-100 dark:border-slate-700 bg-white dark:bg-slate-800">
+          <button
+            onClick={onClose}
+            className="w-full btn-primary"
+          >
+            Got it
+          </button>
+        </div>
+      </div>
+    </div>
+  );
+}

frontend/src/components/AccuracyTrendChart.tsx

@@ -0,0 +1,92 @@
+import { LineChart, Line, XAxis, YAxis, CartesianGrid, Tooltip, ResponsiveContainer, Legend } from 'recharts';
+import { getAccuracyTrend } from '../data/recommendations';
+
+interface AccuracyTrendChartProps {
+  height?: number;
+  className?: string;
+}
+
+export default function AccuracyTrendChart({ height = 200, className = '' }: AccuracyTrendChartProps) {
+  const data = getAccuracyTrend();
+
+  if (data.length === 0) {
+    return (
+      <div className={`flex items-center justify-center text-gray-400 ${className}`} style={{ height }}>
+        No accuracy data available
+      </div>
+    );
+  }
+
+  // Format dates for display
+  const formattedData = data.map(d => ({
+    ...d,
+    displayDate: new Date(d.date).toLocaleDateString('en-IN', { month: 'short', day: 'numeric' }),
+  }));
+
+  return (
+    <div className={className} style={{ height }}>
+      <ResponsiveContainer width="100%" height="100%">
+        <LineChart data={formattedData} margin={{ top: 5, right: 10, bottom: 5, left: 0 }}>
+          <CartesianGrid strokeDasharray="3 3" className="stroke-gray-200 dark:stroke-slate-700" />
+          <XAxis
+            dataKey="displayDate"
+            tick={{ fontSize: 11 }}
+            className="text-gray-500 dark:text-gray-400"
+          />
+          <YAxis
+            domain={[0, 100]}
+            tick={{ fontSize: 11 }}
+            tickFormatter={(v) => `${v}%`}
+            className="text-gray-500 dark:text-gray-400"
+          />
+          <Tooltip
+            contentStyle={{
+              backgroundColor: 'var(--tooltip-bg, #fff)',
+              border: '1px solid var(--tooltip-border, #e5e7eb)',
+              borderRadius: '8px',
+              fontSize: '12px',
+            }}
+            formatter={(value) => [`${value}%`, '']}
+            labelFormatter={(label) => `Date: ${label}`}
+          />
+          <Legend
+            wrapperStyle={{ fontSize: '11px' }}
+            formatter={(value) => value.charAt(0).toUpperCase() + value.slice(1)}
+          />
+          <Line
+            type="monotone"
+            dataKey="overall"
+            stroke="#0ea5e9"
+            strokeWidth={2}
+            dot={{ fill: '#0ea5e9', r: 3 }}
+            activeDot={{ r: 5 }}
+          />
+          <Line
+            type="monotone"
+            dataKey="buy"
+            stroke="#22c55e"
+            strokeWidth={1.5}
+            dot={{ fill: '#22c55e', r: 2 }}
+            strokeDasharray="5 5"
+          />
+          <Line
+            type="monotone"
+            dataKey="sell"
+            stroke="#ef4444"
+            strokeWidth={1.5}
+            dot={{ fill: '#ef4444', r: 2 }}
+            strokeDasharray="5 5"
+          />
+          <Line
+            type="monotone"
+            dataKey="hold"
+            stroke="#f59e0b"
+            strokeWidth={1.5}
+            dot={{ fill: '#f59e0b', r: 2 }}
+            strokeDasharray="5 5"
+          />
+        </LineChart>
+      </ResponsiveContainer>
+    </div>
+  );
+}

frontend/src/components/BackgroundSparkline.tsx

@@ -0,0 +1,64 @@
+import { AreaChart, Area, ResponsiveContainer, YAxis } from 'recharts';
+import type { PricePoint } from '../types';
+
+interface BackgroundSparklineProps {
+  data: PricePoint[];
+  trend: 'up' | 'down' | 'flat';
+  className?: string;
+}
+
+export default function BackgroundSparkline({
+  data,
+  trend,
+  className = '',
+}: BackgroundSparklineProps) {
+  if (!data || data.length < 2) {
+    return null;
+  }
+
+  // Normalize data to percentage change from first point
+  const basePrice = data[0].price;
+  const normalizedData = data.map(point => ({
+    ...point,
+    normalizedPrice: ((point.price - basePrice) / basePrice) * 100,
+  }));
+
+  // Calculate min/max for domain padding
+  const prices = normalizedData.map(d => d.normalizedPrice);
+  const minPrice = Math.min(...prices);
+  const maxPrice = Math.max(...prices);
+  const padding = Math.max(Math.abs(maxPrice - minPrice) * 0.2, 1);
+
+  // Colors based on trend
+  const colors = {
+    up: { stroke: '#22c55e', fill: '#22c55e' },
+    down: { stroke: '#ef4444', fill: '#ef4444' },
+    flat: { stroke: '#94a3b8', fill: '#94a3b8' },
+  };
+
+  const { stroke, fill } = colors[trend];
+
+  return (
+    <div className={`w-full h-full ${className}`} style={{ filter: 'blur(1px)' }}>
+      <ResponsiveContainer width="100%" height="100%">
+        <AreaChart data={normalizedData} margin={{ top: 0, right: 0, bottom: 0, left: 0 }}>
+          <YAxis domain={[minPrice - padding, maxPrice + padding]} hide />
+          <defs>
+            <linearGradient id={`gradient-${trend}`} x1="0" y1="0" x2="0" y2="1">
+              <stop offset="0%" stopColor={fill} stopOpacity={0.4} />
+              <stop offset="100%" stopColor={fill} stopOpacity={0.05} />
+            </linearGradient>
+          </defs>
+          <Area
+            type="monotone"
+            dataKey="normalizedPrice"
+            stroke={stroke}
+            strokeWidth={1}
+            fill={`url(#gradient-${trend})`}
+            isAnimationActive={false}
+          />
+        </AreaChart>
+      </ResponsiveContainer>
+    </div>
+  );
+}

frontend/src/components/Charts.tsx

@@ -0,0 +1,219 @@
+import { PieChart, Pie, Cell, ResponsiveContainer, Legend, Tooltip, BarChart, Bar, XAxis, YAxis, CartesianGrid } from 'recharts';
+
+interface SummaryChartProps {
+  buy: number;
+  sell: number;
+  hold: number;
+}
+
+const COLORS = {
+  buy: '#22c55e',
+  sell: '#ef4444',
+  hold: '#f59e0b',
+};
+
+export function SummaryPieChart({ buy, sell, hold }: SummaryChartProps) {
+  const data = [
+    { name: 'Buy', value: buy, color: COLORS.buy },
+    { name: 'Hold', value: hold, color: COLORS.hold },
+    { name: 'Sell', value: sell, color: COLORS.sell },
+  ];
+
+  return (
+    <div style={{ width: '100%', height: '256px' }}>
+      <ResponsiveContainer width="100%" height={256}>
+        <PieChart>
+          <Pie
+            data={data}
+            cx="50%"
+            cy="50%"
+            innerRadius={50}
+            outerRadius={80}
+            paddingAngle={4}
+            dataKey="value"
+            label={({ name, percent }) => `${name} ${((percent ?? 0) * 100).toFixed(0)}%`}
+            labelLine={false}
+          >
+            {data.map((entry, index) => (
+              <Cell key={`cell-${index}`} fill={entry.color} />
+            ))}
+          </Pie>
+          <Tooltip
+            contentStyle={{
+              backgroundColor: 'white',
+              border: '1px solid #e5e7eb',
+              borderRadius: '8px',
+              boxShadow: '0 4px 6px -1px rgba(0, 0, 0, 0.1)',
+            }}
+            formatter={(value) => [`${value} stocks`, '']}
+          />
+          <Legend
+            verticalAlign="bottom"
+            height={36}
+            formatter={(value) => <span className="text-sm text-gray-600">{value}</span>}
+          />
+        </PieChart>
+      </ResponsiveContainer>
+    </div>
+  );
+}
+
+interface HistoricalDataPoint {
+  date: string;
+  buy: number;
+  sell: number;
+  hold: number;
+}
+
+interface HistoricalChartProps {
+  data: HistoricalDataPoint[];
+}
+
+export function HistoricalBarChart({ data }: HistoricalChartProps) {
+  const formattedData = data.map(d => ({
+    ...d,
+    date: new Date(d.date).toLocaleDateString('en-IN', { month: 'short', day: 'numeric' }),
+  }));
+
+  return (
+    <div className="h-72">
+      <ResponsiveContainer width="100%" height="100%">
+        <BarChart data={formattedData} margin={{ top: 20, right: 30, left: 20, bottom: 5 }}>
+          <CartesianGrid strokeDasharray="3 3" stroke="#f0f0f0" />
+          <XAxis
+            dataKey="date"
+            tick={{ fontSize: 12, fill: '#6b7280' }}
+            tickLine={{ stroke: '#e5e7eb' }}
+          />
+          <YAxis
+            tick={{ fontSize: 12, fill: '#6b7280' }}
+            tickLine={{ stroke: '#e5e7eb' }}
+          />
+          <Tooltip
+            contentStyle={{
+              backgroundColor: 'white',
+              border: '1px solid #e5e7eb',
+              borderRadius: '8px',
+              boxShadow: '0 4px 6px -1px rgba(0, 0, 0, 0.1)',
+            }}
+          />
+          <Legend
+            verticalAlign="top"
+            height={36}
+            formatter={(value) => <span className="text-sm text-gray-600 capitalize">{value}</span>}
+          />
+          <Bar dataKey="buy" stackId="a" fill={COLORS.buy} radius={[4, 4, 0, 0]} name="Buy" />
+          <Bar dataKey="hold" stackId="a" fill={COLORS.hold} radius={[0, 0, 0, 0]} name="Hold" />
+          <Bar dataKey="sell" stackId="a" fill={COLORS.sell} radius={[0, 0, 4, 4]} name="Sell" />
+        </BarChart>
+      </ResponsiveContainer>
+    </div>
+  );
+}
+
+interface StockHistoryEntry {
+  date: string;
+  decision: string;
+}
+
+interface StockHistoryChartProps {
+  history: StockHistoryEntry[];
+  symbol: string;
+}
+
+export function StockHistoryTimeline({ history, symbol }: StockHistoryChartProps) {
+  if (history.length === 0) {
+    return (
+      <div className="text-center py-8 text-gray-500">
+        No historical data available for {symbol}
+      </div>
+    );
+  }
+
+  return (
+    <div className="space-y-3">
+      {history.map((entry, idx) => {
+        const bgColor = entry.decision === 'BUY' ? 'bg-green-500' :
+                        entry.decision === 'SELL' ? 'bg-red-500' : 'bg-amber-500';
+        const textColor = entry.decision === 'BUY' ? 'text-green-700' :
+                          entry.decision === 'SELL' ? 'text-red-700' : 'text-amber-700';
+
+        return (
+          <div key={idx} className="flex items-center gap-4">
+            <div className="w-24 text-sm text-gray-500">
+              {new Date(entry.date).toLocaleDateString('en-IN', { month: 'short', day: 'numeric' })}
+            </div>
+            <div className={`w-3 h-3 rounded-full ${bgColor}`} />
+            <div className={`text-sm font-medium ${textColor}`}>
+              {entry.decision}
+            </div>
+          </div>
+        );
+      })}
+    </div>
+  );
+}
+
+interface DecisionDistributionProps {
+  total: number;
+  buy: number;
+  sell: number;
+  hold: number;
+}
+
+export function DecisionDistribution({ total, buy, sell, hold }: DecisionDistributionProps) {
+  const buyPercent = ((buy / total) * 100).toFixed(1);
+  const sellPercent = ((sell / total) * 100).toFixed(1);
+  const holdPercent = ((hold / total) * 100).toFixed(1);
+
+  return (
+    <div className="space-y-4">
+      <div className="flex h-4 rounded-full overflow-hidden bg-gray-100">
+        <div
+          className="bg-green-500 transition-all duration-500"
+          style={{ width: `${(buy / total) * 100}%` }}
+          title={`Buy: ${buy} (${buyPercent}%)`}
+        />
+        <div
+          className="bg-amber-500 transition-all duration-500"
+          style={{ width: `${(hold / total) * 100}%` }}
+          title={`Hold: ${hold} (${holdPercent}%)`}
+        />
+        <div
+          className="bg-red-500 transition-all duration-500"
+          style={{ width: `${(sell / total) * 100}%` }}
+          title={`Sell: ${sell} (${sellPercent}%)`}
+        />
+      </div>
+
+      <div className="grid grid-cols-3 gap-4 text-center">
+        <div>
+          <div className="flex items-center justify-center gap-2 mb-1">
+            <div className="w-3 h-3 rounded-full bg-green-500" />
+            <span className="text-sm font-medium text-gray-700">Buy</span>
+          </div>
+          <div className="text-2xl font-bold text-green-600">{buy}</div>
+          <div className="text-xs text-gray-500">{buyPercent}%</div>
+        </div>
+
+        <div>
+          <div className="flex items-center justify-center gap-2 mb-1">
+            <div className="w-3 h-3 rounded-full bg-amber-500" />
+            <span className="text-sm font-medium text-gray-700">Hold</span>
+          </div>
+          <div className="text-2xl font-bold text-amber-600">{hold}</div>
+          <div className="text-xs text-gray-500">{holdPercent}%</div>
+        </div>
+
+        <div>
+          <div className="flex items-center justify-center gap-2 mb-1">
+            <div className="w-3 h-3 rounded-full bg-red-500" />
+            <span className="text-sm font-medium text-gray-700">Sell</span>
+          </div>
+          <div className="text-2xl font-bold text-red-600">{sell}</div>
+          <div className="text-xs text-gray-500">{sellPercent}%</div>
+        </div>
+      </div>
+    </div>
+  );
+}

frontend/src/components/CumulativeReturnChart.tsx

@@ -0,0 +1,73 @@
+import { AreaChart, Area, XAxis, YAxis, CartesianGrid, Tooltip, ResponsiveContainer, ReferenceLine } from 'recharts';
+import { getCumulativeReturns } from '../data/recommendations';
+
+interface CumulativeReturnChartProps {
+  height?: number;
+  className?: string;
+}
+
+export default function CumulativeReturnChart({ height = 160, className = '' }: CumulativeReturnChartProps) {
+  const data = getCumulativeReturns();
+
+  if (data.length === 0) {
+    return (
+      <div className={`flex items-center justify-center text-gray-400 ${className}`} style={{ height }}>
+        No data available
+      </div>
+    );
+  }
+
+  // Format dates for display
+  const formattedData = data.map(d => ({
+    ...d,
+    displayDate: new Date(d.date).toLocaleDateString('en-IN', { month: 'short', day: 'numeric' }),
+  }));
+
+  const lastPoint = formattedData[formattedData.length - 1];
+  const isPositive = lastPoint.aiReturn >= 0;
+
+  return (
+    <div className={className} style={{ height }}>
+      <ResponsiveContainer width="100%" height="100%">
+        <AreaChart data={formattedData} margin={{ top: 5, right: 10, bottom: 5, left: 0 }}>
+          <defs>
+            <linearGradient id="cumulativeGradient" x1="0" y1="0" x2="0" y2="1">
+              <stop offset="5%" stopColor={isPositive ? '#22c55e' : '#ef4444'} stopOpacity={0.3} />
+              <stop offset="95%" stopColor={isPositive ? '#22c55e' : '#ef4444'} stopOpacity={0} />
+            </linearGradient>
+          </defs>
+          <CartesianGrid strokeDasharray="3 3" className="stroke-gray-200 dark:stroke-slate-700" />
+          <XAxis
+            dataKey="displayDate"
+            tick={{ fontSize: 10 }}
+            className="text-gray-500 dark:text-gray-400"
+          />
+          <YAxis
+            tick={{ fontSize: 10 }}
+            tickFormatter={(v) => `${v}%`}
+            className="text-gray-500 dark:text-gray-400"
+            width={40}
+          />
+          <Tooltip
+            contentStyle={{
+              backgroundColor: 'var(--tooltip-bg, #fff)',
+              border: '1px solid var(--tooltip-border, #e5e7eb)',
+              borderRadius: '8px',
+              fontSize: '12px',
+            }}
+            formatter={(value) => [`${(value as number).toFixed(1)}%`, 'Return']}
+            labelFormatter={(label) => `Date: ${label}`}
+          />
+          <ReferenceLine y={0} stroke="#94a3b8" strokeDasharray="3 3" />
+          <Area
+            type="monotone"
+            dataKey="aiReturn"
+            stroke={isPositive ? '#22c55e' : '#ef4444'}
+            strokeWidth={2}
+            fill="url(#cumulativeGradient)"
+          />
+        </AreaChart>
+      </ResponsiveContainer>
+    </div>
+  );
+}