SmartScan is a powerful Python-based technical analysis tool designed specifically for analyzing Nifty 50 stocks. This tutorial will guide you through using and understanding the tool's features.
Technical Analysis (TA) is a method of evaluating investments by analyzing statistical trends from trading activity, such as price movement and volume. Unlike fundamental analysis, which looks at a company's business performance, technical analysis focuses on patterns in market data.
- Automated fetching of Nifty 50 stock data using Yahoo Finance API
- Intelligent caching system with SQLite database
- Fallback mechanisms for different stock symbols (NSE/BSE)
- Automatic data freshness checks
The tool calculates a comprehensive set of technical indicators:
- Simple Moving Averages (SMA 20, 50)
- Exponential Moving Averages (EMA 20, 50)
- Bollinger Bands (Upper, Middle, Lower bands)
- Relative Strength Index (RSI)
- Stochastic Oscillator (K and D lines)
- True Strength Index (TSI)
- MACD (Moving Average Convergence Divergence)
- ADX (Average Directional Index)
- Vortex Indicator
- Support and Resistance Levels
- On Balance Volume (OBV)
- Chaikin Money Flow (CMF)
- Ease of Movement (EOM)
- Volume Moving Average
SmartScan uses a multi-factor approach to generate trading signals:
- Signal Types: Strong Buy, Buy, Hold, Sell, Strong Sell
- Factors Considered:
- Trend Strength (SMAs, EMAs)
- Momentum (RSI, MACD)
- Volume Confirmation
- Support/Resistance Levels
- Volatility (ATR)
- Professional-grade technical analysis charts using mplfinance
- Customized layouts with multiple panels:
- Candlestick charts with volume
- RSI indicator panel
- MACD indicator panel
- Bollinger Bands overlay
- Moving averages overlay
graph LR
A[Yahoo Finance API] --> B[Data Collector]
B --> C[SQLite Database]
C --> D[Analysis Engine]
D --> E[Signal Generator]
E --> F[Visualization]
graph TD
A[Raw Stock Data] --> B[Data Cleaning]
B --> C[Technical Indicators]
C --> D[Signal Generation]
D --> E[Chart Generation]
python -m src.cli.command_line --period 1y --min-strength 1.0 --save-reportOptions:
--period: Data fetch period (e.g., 1d, 1mo, 1y)--min-strength: Minimum signal strength to display--save-report: Save analysis report to file
SmartScan - NSE Market Scanner (2024-01-XX XX:XX:XX)
============================================================
Strong Buy Signals (X stocks):
============================================================
Stock Analysis: SYMBOL
Signal: Strong Buy
Current Price: ₹XXX.XX (+X.XX%)
Key Indicators:
RSI: XX.XX
MACD: X.XXX
Signal Strength: X.XX
Moving Averages:
SMA20: ₹XXX.XX
SMA50: ₹XXX.XX
Volume Analysis:
Volume Trend: High/Normal/Low (Ratio: X.XX)
smartscan/
├── src/
│ ├── analysis/ # Technical analysis logic
│ ├── data_collection/ # Data fetching and caching
│ ├── visualization/ # Chart generation
│ ├── cli/ # Command line interface
│ └── output/ # Report generation
├── docs/ # Documentation
└── data/ # SQLite database
- Python 3.x
- pandas: Data manipulation
- yfinance: Stock data fetching
- ta: Technical analysis indicators
- mplfinance: Chart generation
- SQLite: Data caching
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Data Management
- Always check data freshness before analysis
- Use cached data when available
- Handle missing data gracefully
-
Technical Analysis
- Combine multiple indicators for signals
- Consider volume confirmation
- Account for market volatility
-
Risk Management
- Never rely on a single indicator
- Always verify signals across timeframes
- Consider market conditions and volatility
-
Technical Analysis
-
Python for Finance
-
Indian Stock Market
Feel free to contribute to this project by:
- Reporting bugs
- Suggesting new features
- Adding new technical indicators
- Improving documentation
This project is open-source and available under the MIT License.