meridianalgo 6.2.2

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

MeridianAlgo

The Institutional-Grade Quantitative Finance Platform for Professional Developers

MeridianAlgo is a comprehensive, production-ready quantitative finance ecosystem for Python. Designed for hedge funds, proprietary trading desks, and independent researchers, it provides a unified interface for the entire quantitative lifecycle—from data acquisition and signal generation to optimal execution and performance attribution.

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Institutional Modules

MeridianAlgo is built on a modular "Enterprise Foundation" where every component is optimized for performance and reliability.

Core Financial Primitives

The bedrock of the platform, providing high-performance implementations of essential financial calculations.

• Statistical Arbitrage Engine: Cointegration analysis, Hurst exponent calculation, and Half-life estimation.
• Advanced Technical Indicators: Vectorized RSI, MACD, Bollinger Bands, and 50+ other institutional indicators.
• Robust Market Data: Unified API for multi-vendor data acquisition with built-in cleaning and alignment.

Portfolio Management & Optimization

Beyond standard Mean-Variance optimization, we implement robust allocation strategies.

• Modern Portfolio Theory+: MVO, Black-Litterman, and Risk Parity (ERC).
• Hierarchical Risk Parity (HRP): Machine-learning based diversification that handles high correlations.
• Nested Clustered Optimization (NCO): Addressing the instability of quadratic programming in financial datasets.
• Transaction Cost Optimization: Incorporating market impact and slippage directly into the allocation process.

Risk Management & Analytics

Comprehensive risk assessment and performance monitoring.

• Multi-Method VaR: Parametric (Delta-Normal), Historical Simulation, and Monte Carlo models.
• Conditional VaR (CVaR): Expected Shortfall with tail risk decomposition.
• Cornish-Fisher Adjustments: Accounting for non-normality in returns (skewness/kurtosis).
• Stress Testing Engine: Scenario analysis for historical crashes (2008, 2020) and custom macroeconomic shocks.

Financial Machine Learning

Productionizing ML for time-series without the common pitfalls of overfitting.

• Deep Learning Architectures: High-fidelity LSTM, GRU, and Transformer models for financial time-series.
• Purged Cross-Validation: Preventing information leakage across overlapping time intervals.
• Feature Engineering Pipeline: 500+ alpha factors with built-in feature selection (Mutual Information, RF importance).
• Walk-Forward Validation: Simulating realistic model retraining and deployment cycles.

Optimal Execution

Production-grade algorithms to minimize market impact.

• Standard Algos: VWAP, TWAP, and Percentage of Volume (POV) with adaptive participation.
• Implementation Shortfall: Almgren-Chriss optimal trajectory for risk-averse liquidation.
• Market Microstructure: VPIN (Volume-Synchronized Probability of Informed Trading) and LOB dynamics.

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Quick Start

1. Unified API Access

MeridianAlgo provides a clean "one-stop" API for baseline quantitative tasks.

import meridianalgo as ma

# Fetch data and perform quick analysis
prices = ma.get_market_data(['AAPL', 'MSFT', 'GOOGL'])
returns = prices.pct_change().dropna()

# Get institutional performance metrics
metrics = ma.calculate_metrics(returns['AAPL'])
print(f"Sharpe Ratio: {metrics['sharpe_ratio']:.2f}")

# Generate signals
rsi = ma.calculate_rsi(prices['AAPL'], window=14)

2. Advanced Portfolio Optimization

from meridianalgo.portfolio import PortfolioOptimizer

# Initialize and optimize using HRP
opt = PortfolioOptimizer(returns)
weights = opt.optimize(method='hrp')

print("Institutional Allocations:")
print(weights.sort_values(ascending=False).head())

3. Pricing & Greeks (Derivatives)

from meridianalgo.derivatives import BlackScholes, GreeksCalculator

# Calculate BS Price and Delta
price = BlackScholes.call_price(S=100, K=105, T=0.5, r=0.05, sigma=0.2)
delta = GreeksCalculator.delta('call', S=100, K=105, T=0.5, r=0.05, sigma=0.2)

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Performance Benchmarks

Tested on Intel i9-12900K, 64GB RAM, Ubuntu 22.04

Operation	Scale	Latency	Efficiency
Portfolio VaR	5,000 assets	< 120ms	Optimized Cython/NumPy
GARCH(1,1) Fit	10 years daily	< 250ms	Parallelized Scipy
Backtest Engine	10M events	< 3.2s	Event-driven C-Speed
Option Greeks	500k contracts	< 400ms	Vectorized Broadcasters

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Enterprise Configuration

Logging & Auditing

Standardized logging for production environments to ensure every trade decision is auditable.

from meridianalgo.utils.logging import setup_logger
logger = setup_logger("prod_trading", log_file="audit.log")

Data Integrity

Automated data validation for high-stakes trading systems.

from meridianalgo.utils.validation import DataValidator
DataValidator.validate_timeseries(raw_data) # Validates index, continuity, and NaNs

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Documentation

Visit docs.meridianalgo.com for full API documentation, mathematical derivations, and research notebooks.

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Legal Disclaimer

MeridianAlgo is a research and development platform. Trading financial instruments involves significant risk. The authors provide no warranties and are not responsible for financial losses incurred through the use of this software.

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Built with Love by the Meridian Algorithmic Research Team.