Intelligent Market Analytics Platform for Real-Time Stock Screening
Built by Wichaya Kanlaya | LinkedIn | Portfolio
🚀 Live Demo | 📹 Video Demo | 📄 Documentation
StockSense Pro is a sophisticated stock screening and technical analysis platform that empowers investors with data-driven insights. The system processes real-time market data, applies advanced technical indicators, and delivers actionable intelligence through an intuitive web interface.
Built a full-stack financial analytics application processing 50+ stocks with 10+ technical indicators in under 30 seconds, deployed on cloud infrastructure.
- Real-time data integration via Yahoo Finance API
- Custom composite scoring algorithm (0-100 scale)
- Multi-factor analysis: Momentum, RSI, Volume, Moving Averages
- Interactive candlestick charts with Plotly
- Multi-indicator overlays (MA20, MA50, MA200, RSI, MACD)
- Volume profile analysis with color-coded bars
- Signal strength classification (Strong/Moderate/Weak)
- Sector-based filtering (Technology, Healthcare, Finance, etc.)
- Performance metrics (1D, 1W, 1M returns)
- Personal watchlist with persistent storage
- Bulk analysis capabilities
- CSV export functionality
| Layer | Technology | Purpose |
|---|---|---|
| Frontend | Streamlit 1.25+ | Interactive web UI |
| Data Processing | Pandas, NumPy | Data manipulation & computation |
| Visualization | Plotly 5.20+ | Interactive charts |
| Data Source | yfinance API | Real-time market data |
| Deployment | Docker, Cloud | Containerized deployment |
┌─────────────────────────────────────────┐
│ Presentation Layer │
│ (Streamlit Web UI) │
└──────────────┬──────────────────────────┘
│
┌──────────────▼──────────────────────────┐
│ Application Layer │
│ • Stock Scanner Engine │
│ • Technical Analysis Processor │
│ • Scoring Algorithm │
└──────────────┬──────────────────────────┘
│
┌──────────────▼──────────────────────────┐
│ Data Layer │
│ • Yahoo Finance API │
│ • Historical Price Data │
│ • Company Fundamentals │
└─────────────────────────────────────────┘
User Input → API Request → Data Cleaning →
Indicator Calculation → Scoring → Visualization →
Interactive Analysis
The proprietary scoring algorithm combines multiple technical factors:
Score = Weighted Sum of:
• 32% - Momentum (21-day price change)
• 20% - Volume Spike (current vs average)
• 18% - RSI Position (relative strength)
• 12% - MA Alignment (trend direction)
• 10% - Higher Lows Pattern (accumulation)
• 8% - Revenue Growth (fundamental)
Result: 0-100 scale
🟢 Strong: Score ≥ 65
🟡 Moderate: 45 ≤ Score < 65
🔴 Weak: Score < 45- Python 3.11+
- pip package manager
# Clone repository
git clone
cd stocksense-pro
# Install dependencies
pip install -r requirements.txt
# Run application
python -m streamlit run streamlit_mcrf_dashboard.py# Build image
docker build -t stocksense-pro .
# Run container
docker run -p 8501:8501 stocksense-proAccess at: http://localhost:8501
| Metric | Value |
|---|---|
| Data Processing Speed | 50 stocks in ~30 seconds |
| Technical Indicators | 10+ calculated per stock |
| Supported Universes | 8 pre-configured lists |
| Chart Types | 3 (Candlestick, Volume, RSI) |
| API Response Time | < 300ms (cached) |
- Implemented caching strategy reducing API calls by 80%
- Parallel processing ready architecture
- Error handling for edge cases (delisted stocks, missing data)
# RSI Calculation (14-period)
delta = df['Close'].diff()
up = delta.clip(lower=0)
down = -1 * delta.clip(upper=0)
roll_up = up.rolling(14).mean()
roll_down = down.rolling(14).mean()
rs = roll_up / (roll_down + 1e-9)
df['RSI14'] = 100 - (100 / (1 + rs))- Multi-subplot charts with synchronized zooming
- Color-coded volume bars (red/green)
- Dynamic legend filtering
- Responsive design for mobile/desktop
- 5-minute cache TTL for live data
- Automatic refresh mechanism
- Last-updated timestamp display
Main scanner interface with universe selection and filtering options
Color-coded signal strength with performance metrics
Interactive candlestick chart with technical indicators
System design and data flow diagram
- ✅ Python Programming (Advanced)
- ✅ Data Engineering & ETL Pipelines
- ✅ API Integration (REST APIs)
- ✅ Data Visualization (Plotly, Streamlit)
- ✅ Financial Data Analysis
- ✅ Docker & Containerization
- ✅ Cloud Deployment
- ✅ Version Control (Git)
- ✅ Problem Solving
- ✅ System Design
- ✅ User Experience (UX) Design
- ✅ Technical Documentation
- ✅ Performance Optimization
stocksense-pro/
├── streamlit_mcrf_dashboard.py # Main application
├── requirements.txt # Python dependencies
├── Dockerfile # Container configuration
├── .streamlit/
│ └── config.toml # Streamlit settings
├── screenshots/ # Portfolio images
├── README.md # This file
└── docs/
├── architecture.md # Technical design docs
└── deployment.md # Deployment guide
- Backtesting Module - Historical strategy performance
- Risk Management Tools - Stop-loss & position sizing
- News Sentiment Analysis - AI-powered news integration
- Multi-Timeframe Analysis - Hourly/Daily/Weekly signals
- Portfolio Tracking - Real-time P&L monitoring
- Mobile App - React Native companion app
Contributions welcome! Please read CONTRIBUTING.md for guidelines.
- Fork the repository
- Create feature branch (
git checkout -b feature/AmazingFeature) - Commit changes (
git commit -m 'Add AmazingFeature') - Push to branch (
git push origin feature/AmazingFeature) - Open Pull Request
This project is licensed under the MIT License - see LICENSE file for details.
Important: StockSense Pro is an educational tool designed for learning purposes only. This application does NOT provide financial advice, investment recommendations, or trading signals.
- ✅ Use for learning technical analysis
- ✅ Use for market research
- ❌ Do NOT use as sole basis for trading
- ❌ Always consult licensed financial advisors
Risk Warning: Trading stocks involves substantial risk. Past performance does not guarantee future results.
Wichaya Kanlaya
Data Engineer | Financial Technology Enthusiast
I'm passionate about building data-driven solutions that make complex financial information accessible. StockSense Pro showcases my skills in full-stack development, data engineering, and financial analytics.
Connect with me:
- Yahoo Finance API for market data
- Streamlit team for the amazing framework
- Plotly for interactive visualization tools
- Open-source community for inspiration
⭐ Star this repo if you find it useful!
Made with ❤️ by Wichaya Kanlaya