meridianalgo 2.2.0

Advanced Algorithmic Trading and Statistical Analysis Library

MeridianAlgo

Advanced Algorithmic Trading and Statistical Analysis Library

Overview

MeridianAlgo is a comprehensive Python library designed for quantitative finance, algorithmic trading, and statistical analysis. It provides powerful tools for portfolio optimization, time series analysis, statistical arbitrage, and machine learning for financial markets.

Features

• Portfolio Optimization: Modern portfolio theory implementation with efficient frontier calculation
• Statistical Analysis: Advanced statistical methods including cointegration, volatility modeling, and risk metrics
• Machine Learning: Feature engineering and model evaluation for financial time series prediction
• Data Processing: Efficient tools for handling and preprocessing financial data
• Risk Management: Value at Risk (VaR), Expected Shortfall (CVaR), and other risk metrics

Installation

pip install meridianalgo

Quick Start

import meridianalgo as ma
import yfinance as yf

# Fetch market data
data = yf.download(['AAPL', 'MSFT', 'GOOGL'], start='2020-01-01')['Adj Close']

# Calculate returns
returns = data.pct_change().dropna()

# Portfolio optimization
optimizer = ma.PortfolioOptimizer(returns)
efficient_frontier = optimizer.calculate_efficient_frontier()

# Statistical analysis
analyzer = ma.StatisticalArbitrage(data)
correlation = analyzer.calculate_rolling_correlation(window=21)

# Calculate risk metrics
var = ma.calculate_value_at_risk(returns['AAPL'])
es = ma.calculate_expected_shortfall(returns['AAPL'])

Documentation

Core Modules

PortfolioOptimizer

Optimize portfolio allocation using modern portfolio theory.

optimizer = ma.PortfolioOptimizer(returns)
frontier = optimizer.calculate_efficient_frontier()

StatisticalArbitrage

Statistical arbitrage and cointegration analysis.

arbitrage = ma.StatisticalArbitrage(data)
cointegration_test = arbitrage.test_cointegration(data['AAPL'], data['MSFT'])

TimeSeriesAnalyzer

Time series analysis and technical indicators.

analyzer = ma.TimeSeriesAnalyzer(data['AAPL'])
volatility = analyzer.calculate_volatility(window=21)

Risk Metrics

• calculate_value_at_risk(returns, confidence_level=0.95)
• calculate_expected_shortfall(returns, confidence_level=0.95)
• calculate_max_drawdown(returns)
• hurst_exponent(time_series, max_lag=20)

Machine Learning

Feature Engineering

engineer = ma.FeatureEngineer()
features = engineer.create_features(data['AAPL'])

Model Evaluation

metrics = ma.ModelEvaluator.calculate_metrics(y_true, y_pred)
cv_results = ma.ModelEvaluator.time_series_cv(model, X, y)

Examples

See the examples/ directory for complete usage examples:

1. Portfolio Optimization
2. Statistical Arbitrage
3. Time Series Prediction

Requirements

• Python 3.7+
• numpy
• pandas
• scipy
• scikit-learn
• yfinance
• torch (for deep learning features)

Contributing

Contributions are welcome! Please read our Contributing Guidelines for details on how to submit pull requests, report issues, or suggest features.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For support, please open an issue on GitHub or contact [e-mail]

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MeridianAlgo is developed and maintained by the Meridian Algorithmic Research Team.