Stockit - AI-Powered Stock Analysis Platform

A comprehensive stock analysis platform powered by real-time data, AI-driven sentiment analysis, and intelligent trading strategies. Built with a modern frontend and scalable Flask backend.

📋 Table of Contents

Overview
Features
Tech Stack
Project Structure
Setup & Installation
API Endpoints
Environment Variables
Usage Guide
Docker Deployment
Troubleshooting

🎯 Overview

Stockit is an intelligent stock analysis platform that combines:

Real-time Stock Data: Integration with NSE India (National Stock Exchange) and Yahoo Finance
AI Sentiment Analysis: FinBERT-powered analysis of financial news for sentiment detection
Smart Trading Strategies: Automated generation of short-term, swing, and long-term trading suggestions
Interactive Dashboard: Modern, responsive UI for comprehensive stock analysis
Technical Analysis: Price charts, volume analysis, and historical data visualization

Perfect for investors, traders, and financial analysts who want data-driven insights at their fingertips.

✨ Features

Frontend (Client)

🔍 Real-time Stock Search: Instant search across Nifty 50 companies
📊 Interactive Price Charts: Line and candlestick charts with multiple timeframes (1M, 3M, 6M, 1Y)
📰 News & Sentiment Analysis: Real-time news feed with AI-powered sentiment scoring
💹 Financial Metrics: Comprehensive display of Revenue, Net Profit, EBITDA, and more
📈 Trading Strategies: AI-generated trading recommendations based on technical and sentiment analysis
📊 Volume Analysis: Identify volume spikes and trading patterns
🎨 Modern UI: Clean, professional design inspired by Screener.in

Backend (Server)

🏦 Multi-Source Data Integration: NSE, Yahoo Finance, and custom Google Sheets data
🤖 FinBERT Sentiment Analysis: State-of-the-art NLP for financial news classification
📈 Historical Data: Complete OCHLV (Open, Close, High, Low, Volume) data
🔐 CORS Support: Secure cross-origin requests
📡 RESTful API: Well-documented endpoints for all data needs
⚡ Caching & Optimization: Efficient data fetching and processing

🛠 Tech Stack

Frontend

HTML5 - Semantic markup
CSS3 - Responsive design with modern features
JavaScript (ES6+) - Modular, component-based architecture
Chart.js - Interactive data visualization
Fetch API - Modern HTTP client

Backend

Python 3.10 - Core programming language
Flask - Lightweight web framework
Flask-CORS - Cross-origin request handling
yfinance - Yahoo Finance data integration
nsepython - NSE India API client
pandas - Data manipulation and analysis
requests - HTTP client for API calls
gradio_client - FinBERT sentiment analysis client
python-dotenv - Environment variable management

AI/ML

FinBERT - Hugging Face's financial BERT model for sentiment analysis
Gradio - Model serving and API wrapper

DevOps

Docker - Containerization
Docker Compose - Multi-container orchestration

📁 Project Structure

Stockit/
├── README.md                           # This file
├── docker-compose.yml                  # Docker compose configuration
├── Dockerfile                          # Root Dockerfile for unified build
│
├── client/                             # Frontend application
│   ├── index.html                      # Main dashboard UI
│   ├── README.md                       # Frontend documentation
│   ├── Dockerfile                      # Frontend container setup
│   ├── css/
│   │   └── style.css                   # Styling and responsive design
│   ├── js/
│   │   ├── app.js                      # Application logic (479 lines)
│   │   └── api.js                      # API client (71 lines)
│   └── assets/                         # Static images/icons
│
├── backend/                            # Flask API server
│   ├── app.py                          # Main Flask application (318 lines)
│   ├── functions.py                    # Core analysis functions (230 lines)
│   ├── requirements.txt                # Python dependencies
│   ├── Dockerfile                      # Backend container setup
│   ├── .env                            # Environment variables (git-ignored)
│   └── __pycache__/                    # Python cache
│
├── FinBERT_FinancialSentimentAnalysis/ # Sentiment analysis module
│   ├── app.py                          # Gradio app for FinBERT
│   ├── README.md                       # FinBERT documentation
│   └── requirements.txt                # FinBERT dependencies
│
├── MVP/                                # Prototype versions
│   └── Stockit_MVP.html                # Original MVP interface
│
└── frontend/                           # Alternative frontend folder (deprecated)
    └── [legacy files]

Key Files Description

File	Purpose
`backend/app.py`	Flask server with 10+ API endpoints
`backend/functions.py`	Core functions for data fetching & analysis
`client/js/app.js`	Main application logic & UI rendering
`client/js/api.js`	API wrapper with error handling
`client/index.html`	Responsive HTML dashboard
`docker-compose.yml`	Orchestrates backend & frontend services

🚀 Setup & Installation

Prerequisites

Python 3.10+
Node.js (optional, for frontend serving)
Docker & Docker Compose (for containerized deployment)
Modern web browser (Chrome, Firefox, Safari, Edge)

Option 1: Local Setup (Development)

Step 1: Clone & Navigate

cd "My BACKUP/Consultancy Project/Stockit"

Step 2: Install Backend Dependencies

cd backend
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

Step 3: Setup Environment Variables

Create .env file in backend/ directory:

NEWS_API_KEY=your_newsapi_key_here
GOOGLE_SHEETS_URL=your_google_sheets_api_url

Step 4: Start Backend Server

cd backend
python app.py

Backend will run on: http://localhost:5000

Step 5: Serve Frontend

# In a new terminal
cd client
# Using Python 3:
python -m http.server 8000

Frontend will be available at: http://localhost:8000

Option 2: Docker Deployment (Production)

Using Docker Compose (Recommended)

# Build and start all services
docker-compose up --build

# Frontend:  http://localhost:10000
# Backend:   http://localhost:5000

Using Dockerfile (Unified)

# Build the image
docker build -t stockit:latest .

# Run the container
docker run -p 10000:10000 -e FLASK_ENV=production stockit:latest

Environment Variables for Docker

Create .env file in project root:

FLASK_ENV=production
GOOGLE_SHEETS_URL=your_google_sheets_api_url
NEWS_API_KEY=your_newsapi_key_here

📡 API Endpoints

Base URL

http://localhost:5000/api

Endpoints

1. Nifty 50 Companies

GET /api/nifty50

Description: Get list of all Nifty 50 companies
Response:

[
    {
        "Symbol": "ADANIPORTS",
        "Company Name": "Adani Ports and Special Economic Zone",
        "Speciality": "Ports & Logistics"
    }
]

2. Company Information

GET /api/company/info?symbol=TCS

Description: Get company details and current price
Parameters:

symbol (required): Stock ticker (e.g., TCS, RELIANCE, INFY)

Response:

{
    "Company Name": "Tata Consultancy Services Limited",
    "Symbol": "TCS.NS",
    "Price": 4085.55,
    "Short Summary": "TCS is a leading global IT services & consulting..."
}

3. Stock History (OCHLV)

GET /api/company/history?symbol=RELIANCE&start=2024-01-01&end=2024-12-31

Description: Get historical stock data
Parameters:

symbol (required): Stock ticker
start (required): Start date (YYYY-MM-DD)
end (required): End date (YYYY-MM-DD)

Response:

[
    {
        "Date": "2024-01-01",
        "Open": 2885.50,
        "Close": 2910.25,
        "High": 2920.00,
        "Low": 2880.00,
        "Volume": 15432100
    }
]

4. Financial Metrics

GET /api/company/financials?symbol=INFY

Description: Get financial metrics (Revenue, Net Profit, EBITDA, etc.)
Parameters:

symbol (required): Stock ticker

Response:

{
    "Revenue": 21156000000,
    "Net Profit": 4156000000,
    "EBITDA Margin": 21.5,
    "P/E Ratio": 18.5,
    "Market Cap": 650000000000
}

5. Company Search (with News & Sentiment)

GET /api/company/search?symbol=RELIANCE

Description: Search company and get news with AI sentiment analysis
Parameters:

symbol (required): Stock ticker

Response:

{
    "data": [
        {
            "title": "Reliance Industries Announces Q4 Results",
            "sentiment": "Positive",
            "confidence": 0.87,
            "pubdate": "2024-01-15T10:30:00Z",
            "summary": "Strong Q4 performance with 15% YoY growth...",
            "symbol": "RELIANCE"
        }
    ]
}

6. Sentiment Analysis (Full News Analysis)

GET /api/news/analyze-full?symbol=TCS

Description: Comprehensive news sentiment analysis using FinBERT
Parameters:

symbol (required): Stock ticker

Response: Same as Company Search endpoint

🔐 Environment Variables

Backend Environment Variables

Variable	Description	Example
`NEWS_API_KEY`	API key for news source	`abc123xyz`
`GOOGLE_SHEETS_URL`	Google Sheets query endpoint	`https://script.google.com/macros/...`
`FLASK_ENV`	Environment mode	`production` or `development`
`PORT`	Port number (default: 5000)	`5000`

Setting Environment Variables

Local (.env file)

# backend/.env
NEWS_API_KEY=your_api_key
GOOGLE_SHEETS_URL=your_sheets_url

Docker (docker-compose.yml)

environment:
  - FLASK_ENV=production
  - NEWS_API_KEY=${NEWS_API_KEY}
  - GOOGLE_SHEETS_URL=${GOOGLE_SHEETS_URL}

💡 Usage Guide

For End Users

Search for a Stock
- Enter company name or ticker in search box
- Select from Nifty 50 suggestions
View Analytics
- Check real-time price and key metrics
- View company overview and financials
- Analyze multi-timeframe price charts
Read Sentiment
- Look at overall sentiment (Positive/Neutral/Negative)
- Check individual news articles with confidence scores
- Make informed trading decisions
Follow Trading Strategies
- Review AI-generated short-term strategies
- Check swing trading recommendations
- Evaluate long-term investment outlook

For Developers

Adding New Data Sources

Create function in backend/functions.py
Add Flask route in backend/app.py
Call from frontend client/js/api.js
Render in client/js/app.js

Customizing Frontend

Styling: Edit client/css/style.css
Logic: Modify client/js/app.js
API calls: Update client/js/api.js

Debugging

Backend logs: Check terminal output when running python app.py
Frontend logs: Open browser DevTools (F12 → Console)
API responses: Use browser Network tab to inspect requests/responses

🐳 Docker Deployment

Prerequisites

Docker installed and running
Docker Compose (comes with Docker Desktop)

Deploy with Docker Compose

# Build images
docker-compose build

# Start services
docker-compose up

# In background
docker-compose up -d

# View logs
docker-compose logs -f

# Stop services
docker-compose down

Docker Compose Services

Service	Port	Purpose
`backend`	5000	Flask API server
`frontend`	10000	Frontend web interface

Custom Docker Build

# Build
docker build -t stockit:latest .

# Run
docker run -p 10000:10000 \
  -e FLASK_ENV=production \
  -e GOOGLE_SHEETS_URL="your_url" \
  stockit:latest

🐛 Troubleshooting

Frontend Issues

Issue: "Cannot read properties of undefined"

Solution: Ensure backend is running on http://localhost:5000
Check: Browser Console (F12) for detailed error messages

Issue: Charts not displaying

Solution: Verify chart.js is loaded in index.html
Check: Network tab for failed resource loads

Issue: News showing "No title"

Solution: API response might have spaced keys (e.g., "title ")
Check: Browser Network tab → inspect API response

Backend Issues

Issue: "ModuleNotFoundError"

Solution: Install dependencies: pip install -r requirements.txt
Verify: pip list | grep -E "flask|yfinance|pandas"

Issue: Port 5000 already in use

Solution:

# Kill process on port 5000
lsof -ti:5000 | xargs kill -9  # macOS/Linux
netstat -ano | findstr :5000   # Windows (then taskkill)

Issue: FinBERT sentiment returns "Neutral" for all

Solution: Verify Gradio client is installed: pip install gradio_client

API Issues

Issue: 404 errors for /api/* endpoints

Solution: Verify static_folder='../client' in app.py
Check: Backend is serving frontend files correctly

Issue: CORS errors

Solution: Flask-CORS is properly configured
Check: CORS(app) is called in app.py

📚 Additional Resources

Documentation Files

Frontend Details: See client/README.md
FinBERT Setup: See FinBERT_FinancialSentimentAnalysis/README.md

External APIs & Libraries

Useful Commands

# Backend
cd backend && python app.py              # Start backend
pip install -r requirements.txt          # Install dependencies
python -m pip freeze > requirements.txt  # Update requirements

# Frontend
cd client && python -m http.server 8000  # Serve frontend
npm install                              # If using npm

# Docker
docker-compose up --build                # Build and start
docker-compose ps                        # View running services
docker logs stockit_backend_1            # View backend logs
docker-compose down                      # Stop all services

# Development
python -m pytest backend/tests/          # Run tests (if available)
python app.py --debug                    # Run in debug mode

🔮 Future Enhancements

📄 License

This project is provided as-is for educational and commercial use.

👥 Support

For issues, questions, or contributions:

Check the Troubleshooting section
Review API endpoint documentation
Check browser console for detailed errors
Verify all dependencies are installed

Built with ❤️ for investors and traders.
Last Updated: February 2026 cd frontend python server.py

The frontend will open on `http://localhost:8000`

#### Option B: Direct Browser Access
If you have a web server running, simply navigate to:

http://localhost:8000/index.html


## API Endpoints Available

### Company Data
- `GET /api/nifty50` - Get Nifty 50 companies list
- `GET /api/company/info?symbol=TCS` - Get company information
- `GET /api/company/history?symbol=RELIANCE&start=2026-01-01&end=2026-02-01` - Get historical OHLCV data
- `GET /api/company/financials?symbol=INFY` - Get financial metrics

### News & Sentiment Analysis
- `GET /api/news/analyze-full?symbol=TCS` - Get news and FinBERT sentiment analysis
- `POST /api/news/analyze` - Analyze news sentiment (JSON: `{"text": "..."}`)

## Frontend Features

### 1. Price Data Analysis
- **Input**: Stock symbol (e.g., TCS, RELIANCE) or CSV price data
- **Output**: 
  - Price trend (uptrend/downtrend/sideways)
  - Period returns, average volume, volatility
  - Interactive price charts with volume analysis
  - Candlestick patterns

### 2. News Sentiment Analysis
- **Input**: Stock symbol or news headlines
- **Output**:
  - Overall sentiment (positive/negative/neutral)
  - Sentiment breakdown with confidence scores
  - Keyword extraction
  - Headlines analysis with sentiment labels

### 3. Financial Data Parsing
- **Input**: Financial metrics in text format
- **Output**: Organized financial table with metrics and values

### 4. Trading Strategy Generator
- **Input**: Price trend + News sentiment
- **Output**:
  - Market outlook probability (bullish/bearish/consolidation)
  - Short-term trader strategies
  - Swing trader recommendations
  - Long-term investor guidance
  - Key insights and alerts

## Usage Examples

### Example 1: Analyze a Stock by Symbol
1. Open the frontend (http://localhost:8000)
2. Enter `TCS` in the Price Data input
3. Click "Analyze Price Data"
4. Enter `TCS` in the News input
5. Click "Analyze Sentiment"
6. Click "Generate Strategy" to see recommendations

### Example 2: Paste Custom Data
1. Copy OHLCV data (Date, Open, Close, High, Low, Volume)
2. Paste into Price Data field
3. Click "Analyze Price Data"

## Troubleshooting

### Backend not connecting
- Ensure Flask is running on port 5000
- Check firewall settings
- Verify all dependencies are installed

### No news data found
- Check if the stock symbol is valid (NSE symbol format)
- Try adding `.NS` suffix (e.g., TCS.NS)

### FinBERT not working
- Ensure `gradio_client` is installed
- Check internet connection (Gradio API requires external connection)

### CORS errors
- CORS is enabled in Flask (`flask-cors` installed)
- If issues persist, check browser console for specific errors

## Technical Details

### Frontend Technologies
- HTML5
- Vanilla JavaScript (no dependencies)
- CSS3 with variables and gradients
- Responsive design
- Dark theme with accent colors

### Backend Technologies
- Python Flask
- yfinance for stock data
- nsepython for NSE integration
- FinBERT (Gradio API) for sentiment analysis
- NewsAPI integration
- CORS enabled for cross-origin requests

### Data Flow

User Input ↓ Frontend (JavaScript) ↓ Backend API (Flask) ↓ Data Sources (yfinance, NSE, NewsAPI, FinBERT) ↓ Processed Results ↓ Frontend Display (Charts, Tables, Analysis)


## Future Enhancements
- Real-time data streaming
- Advanced technical indicators
- Portfolio backtesting
- Machine learning price predictions
- User authentication and saved analyses
- Mobile responsive optimization
- Export to PDF/CSV

## Support
For issues or questions, check the error console (F12 in browser) and backend terminal output for detailed error messages.

Name		Name	Last commit message	Last commit date
Latest commit History 16 Commits
FinBERT_FinancialSentimentAnalysis		FinBERT_FinancialSentimentAnalysis
MVP		MVP
backend		backend
client		client
.dockerignore		.dockerignore
.gitignore		.gitignore
Dockerfile		Dockerfile
README.md		README.md
docker-compose.yml		docker-compose.yml

SABARNA6/Stockit

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Stockit - AI-Powered Stock Analysis Platform

📋 Table of Contents

🎯 Overview

✨ Features

Frontend (Client)

Backend (Server)

🛠 Tech Stack

Frontend

Backend

AI/ML

DevOps

📁 Project Structure

Key Files Description

🚀 Setup & Installation

Prerequisites

Option 1: Local Setup (Development)

Step 1: Clone & Navigate

Step 2: Install Backend Dependencies

Step 3: Setup Environment Variables

Step 4: Start Backend Server

Step 5: Serve Frontend

Option 2: Docker Deployment (Production)

Using Docker Compose (Recommended)

Using Dockerfile (Unified)

Environment Variables for Docker

📡 API Endpoints

Base URL

Endpoints

1. Nifty 50 Companies

2. Company Information

3. Stock History (OCHLV)

4. Financial Metrics

5. Company Search (with News & Sentiment)

6. Sentiment Analysis (Full News Analysis)

🔐 Environment Variables

Backend Environment Variables

Setting Environment Variables

Local (.env file)

Docker (docker-compose.yml)

💡 Usage Guide

For End Users

For Developers

Adding New Data Sources

Customizing Frontend

Debugging

🐳 Docker Deployment

Prerequisites

Deploy with Docker Compose

Docker Compose Services

Custom Docker Build

🐛 Troubleshooting

Frontend Issues

Backend Issues

API Issues

📚 Additional Resources

Documentation Files

External APIs & Libraries

Useful Commands

🔮 Future Enhancements

📄 License

👥 Support

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