meridianalgo 7.2.1

MeridianAlgo Quant Packages - The Complete Quantitative Finance Platform for Professional Developers

MeridianAlgo

MeridianAlgo is a Python library for quantitative finance and algorithmic trading. It covers portfolio optimization, risk management, derivatives pricing, backtesting, machine learning, execution algorithms, and more, all in one library.

Installation

pip install meridianalgo

Optional extras add heavier capabilities on demand.

pip install "meridianalgo[ml]"            # scikit-learn, torch, statsmodels, hmmlearn
pip install "meridianalgo[optimization]"  # cvxpy, cvxopt
pip install "meridianalgo[volatility]"    # arch (GARCH family)
pip install "meridianalgo[all]"           # everything

Requires Python 3.10 or newer. The core install runs on numpy, pandas, and scipy alone. Modules that need an optional dependency stay unavailable until the matching extra is installed.

Quick start

import meridianalgo as ma

# Top level convenience metrics on a return series
sharpe = ma.calculate_sharpe_ratio(returns)
max_dd = ma.calculate_max_drawdown(returns)
cvar_95 = ma.calculate_expected_shortfall(returns)

# One call summary of around 28 metrics plus a formatted text report
stats = ma.summary_stats(returns)
print(ma.tearsheet(returns))

# Technical indicators, base install, no extras
rsi = ma.RSI(prices, period=14)
upper, mid, lower = ma.BollingerBands(prices, period=20)

Portfolio optimization takes annualized expected returns as a pandas Series and a covariance matrix as a pandas DataFrame.

from meridianalgo import MeanVariance

expected_returns = returns.mean() * 252
covariance = returns.cov() * 252
result = MeanVariance().optimize(expected_returns, covariance, objective="max_sharpe")
print(result.weights, result.sharpe_ratio)

What is inside

Domain	Highlights
Portfolio	Mean variance, HRP, Black Litterman, risk parity, Kelly, CPPI
Risk	VaR, CVaR, stress testing, scenario analysis, risk budgeting
Derivatives	Black Scholes, greeks, implied vol, exotics
Volatility	GARCH and the GARCH family, realized vol estimators, regimes
Monte Carlo	GBM, Heston, jump diffusion, CIR, variance reduction
Credit	Merton model, CDS pricing, Z spread, expected loss
Fixed income	Bond pricing, duration, convexity, yield curves
Backtesting	Event driven engine, order management, slippage
Machine learning	LSTM models, walk forward CV, feature engineering
Execution	VWAP, TWAP, POV, implementation shortfall
Signals	More than forty technical indicators, functional and OOP APIs

Links

• Documentation at https://meridianalgo.readthedocs.io
• Source and issues at https://github.com/MeridianAlgo/Python-Packages
• Changelog at https://github.com/MeridianAlgo/Python-Packages/blob/main/CHANGELOG.md

License

MIT License. For research and educational use. Trading involves substantial risk of loss, and past performance does not guarantee future results.