quantlib_lite

A lightweight Python library for quantitative modeling and Monte Carlo pricing of stochastic processes.

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Overview

quantlib_lite provides a modular framework to:

• define stochastic models
• simulate sample paths
• evaluate payoffs
• aggregate results via risk measures
• estimate prices using Monte Carlo methods
• implement a delta hedge for european call option within the Black Scholes framework

The library separates stochastic simulation infrastructure from financial evaluation logic. The simulation logic is encapsulated in the

Model → SimulationEngine → Path

wokflow, while the financial evaluation logic is encapsulated in the Pricer and Hedger workflows. For the Pricer it follows the design:

SimulationEngine → Path → Payoff → RiskMeasure → Pricer

For the Hedger it follows the design:

SimulationEngine → Path → Payoff → DeltaHedgingStrategy → Hedger

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Installation

Clone the repository and install in editable mode:

pip install -e .

Install dependencies:

pip install -r requirements.txt

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Example

from quantlib_lite.stochastic_models.gbm import GBM
from quantlib_lite.payoff.european_call import EuropeanCall
from quantlib_lite.risk_measures.risk_free import RiskFree
from quantlib_lite.pricer import Pricer
from quantlib_lite.hedger import Hedger
from quantlib_lite.SimulationEngine import SimulationEngine

seed = 42
model = GBM(mu=0.05, sigma=0.2)
payoff = EuropeanCall(K=1.0)
risk = RiskFree()
engine = SimulationEngine(model, T=1.0, steps=100, seed=seed)

pricer = Pricer(engine, payoff, risk)

price = pricer.price(samples=1000)

print(price)
r = 0.02
n_paths = 1000
strategy = DeltaHedgingStrategy()

hedger = Hedger(engine, payoff, strategy)
pfs, errors, S_T_array, payouts = hedger.run(r, n_paths)

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Project Structure

quantlib_lite/
├── __init__.py
├── stochastic_models/   # stochastic processes (e.g. GBM, OU)
├-- SimulationEngine.py  # core simulation logic
├── path.py              # path representation
├── payoff/              # payoff definitions (e.g. European, Asian)
├── risk_measures/       # aggregation (mean, entropic risk)
├── pricer/              # Monte Carlo pricing logic
├── hedger/              # delta hedging logic

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Testing

Run tests with:

pytest

Tests are also executed automatically via GitHub Actions on each push.

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Notes

• The library is intentionally minimal and focused on clarity
• Designed for learning, experimentation, and extension
• Easily extendable with new models, payoffs, and risk measures

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License

MIT