Add institutional trading and market microstructure analysis toolkit

.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual environments
+venv/
+env/
+ENV/
+
+# IDEs
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# Testing
+.pytest_cache/
+.coverage
+htmlcov/
+
+# Data files
+*.csv
+*.db
+*.sqlite
+data/
+
+# Logs
+*.log
+
+# Environment
+.env

README.md

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-# Institutional-Microstructure-
-My liberty of Code to my Scripts 🗽 
+# Institutional Microstructure Toolkit 🗽
+
+A comprehensive Python toolkit for institutional trading and market microstructure analysis. This library provides tools for market data analysis, order book management, trade execution, microstructure metrics, technical indicators, and risk management.
+
+## Features
+
+### 📊 Market Data
+- Real-time and historical market data fetching
+- Tick-level data support
+- Order book snapshots
+- Multiple data source support (simulated, with extensibility for real APIs)
+
+### 📈 Order Book Analysis
+- Limit order book representation and manipulation
+- Bid-ask spread calculations
+- Market depth analysis
+- Order book imbalance metrics
+- Market impact estimation
+- VWAP calculations
+
+### 🔄 Trade Execution
+- Order creation and management (Market, Limit, Stop orders)
+- Order lifecycle tracking
+- Position calculation
+- Fill management
+- Time-in-force support (GTC, DAY, IOC, FOK)
+
+### 🔬 Market Microstructure Metrics
+- Quoted and relative spread
+- Effective and realized spread
+- Price impact measures
+- Roll's measure
+- Amihud illiquidity ratio
+- Kyle's lambda
+- PIN (Probability of Informed Trading)
+- Market depth metrics
+- Volatility measures
+
+### 📉 Technical Indicators
+- Moving Averages (SMA, EMA)
+- RSI (Relative Strength Index)
+- MACD (Moving Average Convergence Divergence)
+- Bollinger Bands
+- ATR (Average True Range)
+- Stochastic Oscillator
+- ADX (Average Directional Index)
+- OBV (On-Balance Volume)
+- VWAP (Volume Weighted Average Price)
+- CCI (Commodity Channel Index)
+- Momentum and Rate of Change
+
+### ⚠️ Risk Management
+- Value at Risk (VaR)
+- Conditional VaR (CVaR/Expected Shortfall)
+- Sharpe Ratio
+- Sortino Ratio
+- Maximum Drawdown
+- Position sizing algorithms
+- Kelly Criterion
+- Beta calculation
+- Portfolio risk metrics
+
+## Installation
+
+```bash
+# Clone the repository
+git clone https://github.com/Netrade1/Institutional-Microstructure-.git
+cd Institutional-Microstructure-
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Or install in development mode
+pip install -e .
+```
+
+## Requirements
+
+- Python 3.8+
+- numpy >= 1.24.0
+- pandas >= 2.0.0
+- requests >= 2.31.0
+- websocket-client >= 1.6.0
+- python-dateutil >= 2.8.2
+- pytz >= 2023.3
+
+## Quick Start
+
+### Market Data Example
+
+```python
+from src.market_data.data_fetcher import MarketDataFetcher
+from datetime import datetime, timedelta
+
+# Initialize market data fetcher
+fetcher = MarketDataFetcher(data_source="simulated")
+
+# Get real-time quote
+quote = fetcher.get_quote("AAPL")
+print(f"Bid: ${quote['bid']:.2f}, Ask: ${quote['ask']:.2f}")
+
+# Get historical data
+end_date = datetime.now()
+start_date = end_date - timedelta(days=30)
+historical = fetcher.get_historical_data("AAPL", start_date, end_date)
+print(historical.head())
+```
+
+### Order Book Analysis
+
+```python
+from src.market_data.data_fetcher import MarketDataFetcher
+from src.order_book.order_book import OrderBook
+
+# Fetch order book data
+fetcher = MarketDataFetcher()
+ob_data = fetcher.get_order_book_snapshot("MSFT", depth=10)
+
+# Analyze order book
+order_book = OrderBook("MSFT")
+order_book.update(ob_data['bids'], ob_data['asks'])
+
+print(f"Mid Price: ${order_book.get_mid_price():.2f}")
+print(f"Spread: ${order_book.get_spread():.4f}")
+print(f"Imbalance: {order_book.get_imbalance():.4f}")
+```
+
+### Order Management
+
+```python
+from src.execution.order_manager import OrderManager
+
+# Initialize order manager
+order_manager = OrderManager()
+
+# Create a limit order
+order = order_manager.create_order(
+    symbol="AAPL",
+    side="buy",
+    quantity=100,
+    order_type="limit",
+    price=150.50
+)
+
+# Submit the order
+order_manager.submit_order(order.order_id)
+
+# Check position
+position = order_manager.calculate_position("AAPL")
+print(f"Position: {position['quantity']} @ ${position['average_price']:.2f}")
+```
+
+### Technical Indicators
+
+```python
+from src.indicators.technical_indicators import TechnicalIndicators
+import pandas as pd
+
+indicators = TechnicalIndicators()
+
+# Calculate RSI
+rsi = indicators.rsi(price_series, period=14)
+
+# Calculate MACD
+macd_line, signal_line, histogram = indicators.macd(price_series)
+
+# Calculate Bollinger Bands
+upper, middle, lower = indicators.bollinger_bands(price_series)
+```
+
+### Risk Management
+
+```python
+from src.risk_management.risk_calculator import RiskCalculator
+
+risk_calc = RiskCalculator()
+
+# Calculate VaR
+var_95 = risk_calc.calculate_var(returns, confidence_level=0.95)
+
+# Calculate Sharpe Ratio
+sharpe = risk_calc.calculate_sharpe_ratio(returns)
+
+# Position sizing
+position_size = risk_calc.calculate_position_size(
+    account_value=100000,
+    risk_per_trade=0.02,
+    entry_price=150.00,
+    stop_loss_price=145.00
+)
+```
+
+## Examples
+
+The `examples/` directory contains comprehensive examples:
+
+- `example_market_data.py` - Market data fetching and analysis
+- `example_order_book.py` - Order book analysis and microstructure metrics
+- `example_order_management.py` - Order creation and management
+- `example_technical_indicators.py` - Technical indicator calculations
+- `example_risk_management.py` - Risk metrics and position sizing
+
+Run examples:
+
+```bash
+cd examples
+python example_market_data.py
+python example_order_book.py
+python example_order_management.py
+python example_technical_indicators.py
+python example_risk_management.py
+```
+
+## Project Structure
+
+```
+Institutional-Microstructure-/
+├── src/
+│   ├── market_data/          # Market data fetching
+│   │   └── data_fetcher.py
+│   ├── order_book/           # Order book analysis
+│   │   └── order_book.py
+│   ├── execution/            # Order management
+│   │   └── order_manager.py
+│   ├── microstructure/       # Microstructure metrics
+│   │   └── metrics.py
+│   ├── indicators/           # Technical indicators
+│   │   └── technical_indicators.py
+│   ├── risk_management/      # Risk calculations
+│   │   └── risk_calculator.py
+│   └── utils/                # Utility functions
+│       └── config.py
+├── examples/                 # Example scripts
+├── requirements.txt          # Dependencies
+├── setup.py                  # Package setup
+└── README.md                 # This file
+```
+
+## Configuration
+
+The toolkit supports configuration through the `ConfigManager` class:
+
+```python
+from src.utils.config import ConfigManager
+
+config = ConfigManager()
+config.set('market_data.source', 'simulated')
+config.set('risk.max_position_size', 100000)
+config.save()
+```
+
+## Use Cases
+
+This toolkit is designed for:
+
+- **Quantitative Researchers**: Analyze market microstructure and develop trading strategies
+- **Algorithmic Traders**: Build and test trading algorithms with realistic market data
+- **Risk Managers**: Calculate risk metrics and monitor portfolio risk
+- **Market Makers**: Analyze order books and optimize quote placement
+- **Academic Research**: Study market microstructure phenomena
+- **Portfolio Managers**: Track positions and calculate performance metrics
+
+## Extending the Toolkit
+
+### Adding New Data Sources
+
+Extend the `MarketDataFetcher` class to support additional data sources:
+
+```python
+class CustomDataFetcher(MarketDataFetcher):
+    def __init__(self, api_key):
+        super().__init__(api_key=api_key, data_source="custom")
+
+    def get_quote(self, symbol):
+        # Implement custom data source logic
+        pass
+```
+
+### Custom Indicators
+
+Add custom technical indicators by extending the `TechnicalIndicators` class:
+
+```python
+class CustomIndicators(TechnicalIndicators):
+    @staticmethod
+    def my_custom_indicator(prices, period):
+        # Implement custom indicator logic
+        pass
+```
+
+## Contributing
+
+Contributions are welcome! Please feel free to submit issues, fork the repository, and create pull requests.
+
+## License
+
+This project is open source and available under the MIT License.
+
+## Disclaimer
+
+This toolkit is for educational and research purposes. Always perform thorough testing before using in production trading environments. Past performance does not guarantee future results.
+
+## Author
+
+**Netrade1**
+
+## Acknowledgments
+
+Built with passion for quantitative finance and market microstructure analysis. 🗽