Add institutional trading and market microstructure analysis toolkit

[Copilot]

Implements a comprehensive Python toolkit for quantitative finance, institutional trading, and market microstructure analysis. The repository previously contained only a README.

Core Modules

Market Data - Real-time quotes, historical OHLCV, tick data, and order book snapshots with extensible data source architecture

Order Book - Limit order book representation with spread calculations, depth analysis, imbalance metrics, and market impact estimation

Execution - Order management system supporting Market/Limit/Stop orders with full lifecycle tracking and position calculation

Microstructure Metrics - Quoted/effective/realized spreads, Kyle's lambda, Amihud illiquidity, PIN, Roll's measure, price impact

Technical Indicators - RSI, MACD, Bollinger Bands, ATR, Stochastic, ADX, OBV, VWAP, CCI, momentum indicators

Risk Management - VaR/CVaR, Sharpe/Sortino ratios, maximum drawdown, position sizing, Kelly criterion, beta calculation, portfolio metrics

Example Usage

from src import MarketDataFetcher, OrderBook, OrderManager, RiskCalculator

# Fetch and analyze order book
fetcher = MarketDataFetcher(data_source="simulated")
ob_data = fetcher.get_order_book_snapshot("AAPL", depth=10)

order_book = OrderBook("AAPL")
order_book.update(ob_data['bids'], ob_data['asks'])

print(f"Spread: {order_book.get_spread():.4f}")
print(f"Imbalance: {order_book.get_imbalance():.4f}")
print(f"Market Impact (1000 shares): {order_book.get_market_impact('buy', 1000):.2f} bps")

# Risk calculations
risk_calc = RiskCalculator()
var_95 = risk_calc.calculate_var(returns, 0.95)
position_size = risk_calc.calculate_position_size(100000, 0.02, 150.00, 145.00)

Implementation Notes

• Simulated data provider with realistic market characteristics (hash-based deterministic pricing)
• Proper error handling for division by zero in indicators and metrics (numpy error states)
• Trading days constant (252) extracted for maintainability
• Input validation with descriptive exceptions on invalid order types
• 5 example scripts demonstrating all features
• Zero security vulnerabilities (CodeQL verified)

Structure

src/
├── market_data/       # Data fetching and management
├── order_book/        # Order book analysis
├── execution/         # Order lifecycle management  
├── microstructure/    # Market microstructure metrics
├── indicators/        # Technical analysis
├── risk_management/   # Risk calculations
└── utils/            # Configuration management

examples/             # 5 working examples

Dependencies: numpy, pandas, requests, websocket-client, python-dateutil, pytz

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Original prompt

https://chatgpt.com/g/g-iR15hX15H-finance-economics-stock-crypto-trade-invest/c/69781042-8c34-8329-8abd-ae3dfdb189b3

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