smart-money-concept 0.1.3

A Python implementation of Smart Money Concepts (SMC) for technical analysis using yfinance data.

Smart Money Concepts (SMC) Python Package

A Python package for performing Smart Money Concepts (SMC) technical analysis using historical stock data from yfinance. This package provides a comprehensive framework for detecting market structures, order blocks, fair value gaps, and other SMC components, with visualization capabilities using matplotlib.

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📌 Features

• Market Structure Analysis: Detects Break of Structure (BOS) and Change of Character (CHoCH) for swing and internal structures.
• Order Blocks: Identifies bullish and bearish order blocks for potential institutional trading zones.
• Fair Value Gaps (FVG): Detects price imbalances with mitigation logic.
• Equal Highs and Lows: Marks potential reversal points with Equal Highs (EQH) and Equal Lows (EQL).
• Premium and Discount Zones: Calculates dynamic zones based on trailing extremes.
• Visualization: Plots candlestick charts with SMC indicators (BOS, CHoCH, order blocks, FVGs, etc.) using matplotlib.
• Real-time Data: Fetches OHLCV data from yfinance for stocks and indices.
• Customizable Settings: Configure analysis parameters like swing length, FVG thresholds, and visualization styles (colored or monochrome).

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⚙️ Installation

From PyPI (when published)

pip install smart-money-concept

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📦 Requirements

• Python 3.8+
• yfinance>=0.2.40
• pandas>=2.0.0
• numpy>=1.24.0
• matplotlib>=3.7.0

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🚀 Usage

The package provides a SmartMoneyConcepts class for analyzing market data and a command-line interface (CLI) for batch processing multiple stocks.

Python Script Example

import asyncio
from typing import List, Dict
from smart_money_concepts import SmartMoneyConcepts

async def main(stock_codes: List[str], period: str = "max", interval: str = "1d", batch_size: int = 10, delay: float = 2.0, visualize: bool = True):
    if not stock_codes:
        stock_codes = ["RELIANCE.NS"]  # Default to NSE-listed Reliance

    for i, stock_code in enumerate(stock_codes):
        print(f"\n==============================")
        print(f"🔍 Analyzing stock: {stock_code}")
        print(f"==============================")

        smc = SmartMoneyConcepts(stock_code=stock_code, period=period, interval=interval)
        
        # Retry logic for fetching data
        max_retries = 3
        for attempt in range(max_retries):
            try:
                success = await smc.fetch_ohlcv()
                if success:
                    smc.prepare_data()
                    smc.run_smc_analysis()
                    if visualize:
                        smc.visualize_smc(bars_to_show=250)
                    else:
                        smc.print_analysis_summary()  # Print summary even if visualization is skipped
                    break
                else:
                    print(f"❌ Analysis failed for {stock_code}!")
                    break
            except Exception as e:
                if "429" in str(e):  # Check for rate limit error
                    print(f"Rate limit hit for {stock_code}. Retrying ({attempt + 1}/{max_retries}) after delay...")
                    await asyncio.sleep(5)  # Wait longer for rate limit errors
                else:
                    print(f"Error for {stock_code}: {e}")
                    break
            if attempt == max_retries - 1:
                print(f"❌ Failed to fetch data for {stock_code} after {max_retries} attempts.")

        # Add delay after every batch_size stocks
        if (i + 1) % batch_size == 0 and i + 1 < len(stock_codes):
            print(f"Pausing for {delay} seconds after processing {batch_size} stocks...")
            await asyncio.sleep(delay)

if __name__ == "__main__":
    # Example stock list (use .NS for NSE-listed stocks, .BO for BSE, or others as needed)

    # Stocks
    stock_codes = ["RELIANCE.NS", "TCS.NS", "INFY.NS", "HDFCBANK.NS", "ICICIBANK.NS"]
    
    # Index
    # stock_codes = ["^NSEI"]
    
    asyncio.run(main(stock_codes, period="1y", interval="1d", batch_size=10, delay=2.0, visualize=True))

CLI Example

python -m smart_money_concepts.cli --stocks ^NSEI RELIANCE.NS --period 1y --interval 1d

CLI Options:

• --stocks: List of stock codes (e.g., ^NSEI, RELIANCE.NS). Default: RELIANCE.NS.
• --period: Data period (e.g., 1d, 1mo, 1y, max). Default: 1y.
• --interval: Data interval (e.g., 1m, 1h, 1d). Default: 1d.
• --batch-size: Number of stocks to process before pausing. Default: 10.
• --delay: Delay (seconds) between batches. Default: 2.0.
• --no-visualize: Disable visualization and print analysis summary only.

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📊 Example Output

The package generates a detailed analysis summary and optional visualizations, including:

• Candlestick charts with swing and internal structure breaks (BOS/CHoCH).
• Order blocks, fair value gaps, and equal highs/lows.
• Premium, equilibrium, and discount zones.

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📂 Project Structure

smart_money_concepts/
├── smart_money_concepts/
│   ├── __init__.py
│   ├── smc.py
│   ├── cli.py
├── README.md
├── pyproject.toml
├── requirements.txt

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🙌 Credits

This project was inspired by and builds upon the following resources:

• Code Tech (YouTube) → This code is Take from YouTube tutorial by Code Tech, which provides a detailed walkthrough of building an SMC indicator in Python
• LuxAlgo → The conceptual framework and feature set are inspired by LuxAlgo's Smart Money Concepts indicator.

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🤝 Contributing

Contributions are welcome! Please submit a pull request or open an issue on the GitHub repository for bug reports, feature requests, or improvements.

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⚠️ Notes

• Rate Limits: The package uses yfinance for data fetching, which may be subject to rate limits. Built-in retry logic handles HTTP 429 errors.
• Internet Connection: Ensure a stable internet connection for fetching market data.
• Disclaimer: This package is for educational purposes and should not be used as financial advice. Trading involves risks, and past performance does not guarantee future results.

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📬 Contact

For questions or support, please open an issue on the GitHub repository.

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