Welcome to AWT-Quant’s Documentation!¶

AWT-Quant is an advanced quantitative finance library specializing in:

Stochastic modeling & forecasting (SPDE, GARCH, Jump Diffusion, Monte Carlo).
Multi-factor analysis (MFA) for financial markets.
Portfolio optimization (Mean-Variance, Black-Litterman, Monte Carlo).
Risk management & stress testing.

🚀 GitHub Repository: AWT-Quant 📦 PyPI Package: awt-quant on PyPI 📖 Full Documentation: ReadTheDocs

Contents:

Getting Started¶

If you’re new to AWT-Quant, start with:

Installation Guide → installation
Quick Start Tutorials → quickstart
API Reference & Modules → api_reference

Core Features¶

📊 Stochastic PDE Forecasting¶

Supports Geometric Brownian Motion (GBM), Heston, CIR, OU, Jump Diffusion models.
Monte Carlo simulations & likelihood estimation.

from awt_quant.forecast.stochastic.run_simulations import SPDEMCSimulator

# Initialize stochastic simulator with Heston model
sim = SPDEMCSimulator(
    symbol='AAPL',
    start_date='2022-01-01',
    end_date='2022-03-01',
    dt=1,
    num_paths=1000,
    eq='heston'
)

sim.download_data()
sim.set_parameters()
sim.simulate()
sim.plot_simulation()

📈 Multi-Factor Analysis (MFA)¶

Constructs factors from macro & historical data.
Uses ML-based feature selection (Random Forest, PCA).
K-Means clustering & stress testing.

from awt_quant.portfolio.multi_factor_analysis.main import MultiFactorAnalysis

# Run factor analysis with clustering and stress testing
mfa = MultiFactorAnalysis(
    assets=["AAPL", "MSFT", "TSLA", "AMZN", "GOOG"],
    factors=["Market", "Size", "Value", "Momentum", "Quality"]
)

mfa.collect_data()
mfa.construct_factors()
mfa.run_clustering()
mfa.run_stress_sensitivity()
mfa.plot_factor_attribution()

💰 Portfolio Optimization¶

Mean-Variance Optimization (MPT) & Efficient Frontier.
Black-Litterman Bayesian Portfolio Optimization.
Monte Carlo Portfolio Simulations.

from awt_quant.portfolio.optimization.optimize import PortfolioOptimizer

# Set up portfolio optimizer with constraints
optimizer = PortfolioOptimizer(
    assets=["AAPL", "MSFT", "AMZN", "TSLA", "BND"],
    objective="sharpe",
    constraints={
        "max_volatility": 0.15,
        "max_per_asset": 0.25,
        "min_per_asset": 0.05
    }
)

weights = optimizer.optimize()
optimizer.plot_efficient_frontier()
optimizer.plot_allocation()

📉 Risk Management & Stress Testing¶

VaR, CVaR, Sharpe Ratio, Maximum Drawdown.
Factor exposure analysis & custom performance tear sheets.

from awt_quant.risk.tearsheet import RiskTearsheet

# Generate comprehensive risk report
tearsheet = RiskTearsheet(
    assets=["AAPL", "MSFT", "TSLA", "AMZN", "GOOG"],
    weights=[0.2, 0.2, 0.2, 0.2, 0.2],
    start_date="2020-01-01",
    benchmark="SPY"
)

tearsheet.generate(
    include_drawdowns=True,
    include_stress_tests=True,
    include_factor_attribution=True
)

Installation¶

To install AWT-Quant:

# Using pip
pip install awt-quant

# Using Poetry
poetry add awt-quant

# For CUDA support
poetry install --with cuda