FinSight - Financial Analytics Dashboard

A comprehensive financial analytics dashboard built with Flask, featuring simulated stock market data, technical analysis indicators, portfolio management, and market sentiment scoring.

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Features

• Market Dashboard -- Real-time overview of 22 simulated stocks with top gainers, losers, and sector performance breakdown
• Stock Analysis -- Individual stock pages with OHLCV candlestick-style price charts, volume analysis, and overlaid technical indicators
• Technical Indicators -- Industry-standard calculations:
  • Simple Moving Average (SMA) with configurable periods
  • Exponential Moving Average (EMA) with proper seeding
  • Relative Strength Index (RSI) using Wilder's smoothing
  • MACD with signal line and histogram
  • Bollinger Bands with standard deviation envelope
• Portfolio Management -- Buy/sell stocks, track positions, monitor unrealized and realized P&L, view cost basis and returns
• Portfolio Allocation -- Interactive donut chart showing allocation by stock and by sector with cash position
• Market Sentiment -- Simulated news headlines with bull/bear/neutral scoring, confidence metrics, and aggregated sentiment views
• REST API -- Full JSON API for programmatic access to all data
• Responsive Design -- Dark finance theme with green/red gain/loss coloring

Architecture

finsight/
|-- app.py                     # Flask application factory
|-- config.py                  # Configuration and constants
|-- models/
|   |-- database.py            # SQLAlchemy / SQLite setup
|   |-- schemas.py             # Stock, Portfolio, Transaction, MarketSentiment
|-- routes/
|   |-- api.py                 # REST API endpoints (/api/*)
|   |-- views.py               # HTML template routes
|-- services/
|   |-- market.py              # OHLCV generation, technical indicators
|   |-- portfolio.py           # Portfolio tracking, P&L, allocation
|-- templates/                 # Jinja2 HTML templates
|-- static/
|   |-- css/style.css          # Dark finance theme
|   |-- js/main.js             # Chart.js visualizations
|-- tests/                     # pytest test suite
|-- seed_data/data.json        # Stock metadata

Quick Start

Option 1: Local Python

# Clone the repository
git clone https://github.com/ravikishan/finsight.git
cd finsight

# Run the start script
chmod +x start.sh
./start.sh

Or manually:

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python app.py

Option 2: Docker

docker-compose up --build

The dashboard will be available at http://localhost:8001

API Documentation

Stocks

Method	Endpoint	Description
GET	/api/stocks	List all stocks (optional ?sector=Technology)
GET	/api/stocks/<symbol>	Get stock detail with OHLCV history
GET	/api/stocks/<symbol>/indicators	Get technical indicators (SMA, EMA, RSI, MACD, BB)

Market

Method	Endpoint	Description
GET	/api/market/summary	Market overview: gainers, losers, avg change
GET	/api/market/sectors	Performance breakdown by sector

Portfolio

Method	Endpoint	Description
GET	/api/portfolio	Portfolio summary (cash, initial value)
GET	/api/portfolio/positions	Current holdings with P&L
GET	/api/portfolio/allocation	Allocation by stock and sector
GET	/api/portfolio/performance	Performance metrics and returns
GET	/api/portfolio/transactions	Transaction history
POST	/api/portfolio/buy	Execute buy order ({"symbol": "AAPL", "shares": 10})
POST	/api/portfolio/sell	Execute sell order ({"symbol": "AAPL", "shares": 5})

Sentiment

Method	Endpoint	Description
GET	/api/sentiment	All sentiment entries (optional ?symbol=AAPL)
GET	/api/sentiment/summary	Aggregated sentiment per stock

Example API Calls

# Get all stocks
curl http://localhost:8001/api/stocks

# Get AAPL details with OHLCV data
curl http://localhost:8001/api/stocks/AAPL

# Get technical indicators
curl http://localhost:8001/api/stocks/AAPL/indicators

# Buy 10 shares of MSFT
curl -X POST http://localhost:8001/api/portfolio/buy \
  -H "Content-Type: application/json" \
  -d '{"symbol": "MSFT", "shares": 10}'

# Get portfolio performance
curl http://localhost:8001/api/portfolio/performance

Data Generation

Stock Prices

Stock prices are simulated using Geometric Brownian Motion (GBM):

dS/S = mu * dt + sigma * dW

Where:

• S = stock price
• mu = drift (expected return)
• sigma = volatility
• dW = Wiener process increment

Each stock has unique volatility and drift parameters calibrated to produce realistic price movements over 252 trading days.

Technical Indicators

All indicators are computed using standard financial mathematics:

• SMA: Simple arithmetic mean over N periods
• EMA: Exponentially weighted average with smoothing factor k = 2/(N+1)
• RSI: Relative Strength Index using Wilder's smoothing method
• MACD: Difference between fast and slow EMAs with signal line
• Bollinger Bands: SMA +/- 2 standard deviations

Sentiment

Market sentiment is generated from templated news headlines categorized as bullish, bearish, or neutral, with randomized confidence scores.

Testing

# Run all tests
pytest tests/ -v

# Run with coverage
pytest tests/ -v --cov=. --cov-report=term-missing

# Run specific test file
pytest tests/test_services.py -v

Tech Stack

Component	Technology
Backend	Python 3.11+, Flask 3.0
Database	SQLite with SQLAlchemy ORM
Charts	Chart.js 4.4
Testing	pytest, pytest-cov
Deployment	Docker, Gunicorn
CI/CD	GitHub Actions

Configuration

Configuration is managed through config.py and environment variables:

Variable	Default	Description
FINSIGHT_PORT	8001	Server port
FINSIGHT_DEBUG	false	Debug mode
DATABASE_URL	sqlite:///...	Database connection string
SECRET_KEY	(dev key)	Flask secret key

License

This project is licensed under the MIT License. See LICENSE for details.

Disclaimer

FinSight is a demonstration project. All market data is simulated using mathematical models and does not reflect actual market conditions. This application should not be used for real trading or investment decisions.

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Built with Flask and Chart.js.