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MeridianAlgo Documentation

This directory contains the documentation for the MeridianAlgo package.

Contents

  • README.md - This file
  • api/ - API reference documentation
  • examples/ - Usage examples and tutorials
  • guides/ - User guides and best practices

Quick Start

import meridianalgo as ma

# Get market data
data = ma.get_market_data(['AAPL', 'MSFT'], start_date='2023-01-01')

# Analyze time series
analyzer = ma.TimeSeriesAnalyzer(data['AAPL'])
returns = analyzer.calculate_returns()
volatility = analyzer.calculate_volatility()

# Calculate risk metrics
var = ma.calculate_value_at_risk(returns)
es = ma.calculate_expected_shortfall(returns)

Features

  • Portfolio Optimization: Modern portfolio theory and efficient frontier calculation
  • Time Series Analysis: Returns, volatility, and technical indicators
  • Risk Management: VaR, Expected Shortfall, and other risk metrics
  • Statistical Arbitrage: Cointegration testing and correlation analysis
  • Machine Learning: LSTM models for time series prediction
  • Feature Engineering: Technical indicators and feature creation

Installation

pip install meridianalgo

Requirements

  • Python 3.7+
  • NumPy, Pandas, SciPy
  • Scikit-learn
  • PyTorch (for ML features)
  • yfinance (for market data)