FinStoch 0.6.3

A Python library for simulating stochastic processes in finance.

What is it?

FinStoch is a Python library for simulating stochastic processes commonly used in quantitative finance. It provides clean, consistent interfaces for Monte Carlo path simulation using Euler-Maruyama discretization, with built-in analytics, seed control for reproducibility, and pandas integration.

• Source code: https://github.com/Yosri-Ben-Halima/FinStoch
• Bug reports: https://github.com/Yosri-Ben-Halima/FinStoch/issues
• PyPI: https://pypi.org/project/FinStoch/

Table of Contents

• Main Features
• Supported Processes
• Where to Get It
• Dependencies
• Quick Start
• Analytics
• Development
• License

Main Features

• Six stochastic process models covering equity prices, interest rates, and stochastic volatility
• Reproducible simulations via seed control on all processes
• Flexible time grids with configurable granularity (daily, hourly, minute-level) and business day support
• Built-in analytics including VaR, CVaR, max drawdown, confidence bands, and summary statistics
• pandas integration with to_dataframe() for seamless downstream analysis
• Consistent API across all models: every process exposes simulate(), plot(), and the full analytics suite

Supported Processes

Model	Class	SDE
Geometric Brownian Motion	GeometricBrownianMotion	$dS = \mu S, dt + \sigma S, dW$
Merton Jump Diffusion	MertonJumpDiffusion	$dS = (\mu - \lambda k) S, dt + \sigma S, dW + JS, dN$
Ornstein-Uhlenbeck	OrnsteinUhlenbeck	$dS = \theta(\mu - S), dt + \sigma, dW$
Cox-Ingersoll-Ross	CoxIngersollRoss	$dS = \theta(\mu - S), dt + \sigma\sqrt{S}, dW$
Constant Elasticity of Variance	ConstantElasticityOfVariance	$dS = \mu S, dt + \sigma S^\gamma, dW$
Heston Stochastic Volatility	HestonModel	$dS = \mu S, dt + \sqrt{v} S, dW_S$, $dv = \kappa(\theta - v), dt + \sigma\sqrt{v}, dW_v$

All processes are discretized using the Euler-Maruyama scheme and return NumPy arrays of shape (num_paths, num_steps). The Heston model returns a tuple (S, v) of price and variance paths.

Where to Get It

# PyPI
pip install FinStoch

Dependencies

Package	Minimum Version	Purpose
NumPy	1.23	Array operations and random number generation
pandas	2.0	Time grid generation and DataFrame conversion
matplotlib	3.7	Path visualization
SciPy	1.9	Statistical functions for analytics
python-dateutil	2.9	Date range duration calculation

Quick Start

Simulate and plot

from FinStoch import GeometricBrownianMotion

gbm = GeometricBrownianMotion(
    S0=100, mu=0.05, sigma=0.2,
    num_paths=10,
    start_date='2023-09-01',
    end_date='2024-09-01',
    granularity='D',
)

# Reproducible simulation
paths = gbm.simulate(seed=42)
gbm.plot(paths=paths, title='GBM Simulation', ylabel='Price')

Convert to DataFrame

df = gbm.to_dataframe(paths)
# DataFrame with DatetimeIndex columns, one row per path

Heston model (stochastic volatility)

from FinStoch import HestonModel

heston = HestonModel(
    S0=100, v0=0.04, mu=0.05, sigma=0.3,
    theta=0.04, kappa=2.0, rho=-0.7,
    num_paths=10,
    start_date='2023-09-01',
    end_date='2024-09-01',
    granularity='D',
)

prices, variance = heston.simulate(seed=42)

# Convert either component to DataFrame
df_prices = heston.to_dataframe((prices, variance), variance=False)
df_var = heston.to_dataframe((prices, variance), variance=True)

Analytics

All processes inherit a suite of analytics methods from the base class:

paths = gbm.simulate(seed=42)

# Descriptive statistics at each time step
stats = gbm.summary_statistics(paths)  # dict: mean, std, skew, kurtosis, min, max

# Central tendency
mean_path = gbm.expected_path(paths)     # mean across paths
median = gbm.median_path(paths)          # median across paths

# Uncertainty
lower, upper = gbm.confidence_bands(paths, level=0.95)

# Risk measures (computed at terminal time step)
gbm.var(paths, alpha=0.05)    # Value at Risk
gbm.cvar(paths, alpha=0.05)   # Conditional VaR (Expected Shortfall)

# Drawdown analysis
drawdowns = gbm.max_drawdown(paths)  # max peak-to-trough per path

# Distribution visualization
gbm.terminal_distribution(paths, bins=50)  # histogram + fitted normal

Development

# Install in editable mode with dev dependencies
pip install -e ".[dev]"

# Run tests
python -m unittest discover -s tests -p "*_test.py"

# Format
ruff format

# Lint
flake8 --max-line-length 127

# Type check
mypy . --exclude venv --exclude build --ignore-missing-imports

License

MIT