laakhay-quantlab 0.1.5

Quant tools built with ♥︎ by Laakhay

Laakhay Quantlab

laakhay-quantlab is a high-performance, backend-agnostic quantitative computation layer designed for simulation-heavy research and production analytics. It provides a unified interface over NumPy, JAX, and PyTorch, enabling seamless switching between CPU and GPU backends without code changes.

Key Features

• Backend Agnostic: Write once, run on NumPy, JAX, or PyTorch.
• Hardware Acceleration: Transparent GPU/TPU support via JAX/Torch backends.
• Vectorized Operations: Optimized ArrayBackend with JIT compilation support.
• Simulation Primitives: Fast Gaussian sampling, Geometric Brownian Motion (GBM), and more.
• Options & Pricing (New): Comprehensive verification and pricing of derivatives using analytical (Black-Scholes) and numerical (Monte Carlo) methods.

Ecosystem

laakhay-quantlab fits into the broader Laakhay quantitative ecosystem:

1. laakhay-data: Market data acquisition and normalization.
2. laakhay-ta: Technical analysis indicators and strategy engine.
3. laakhay-quantlab: Numerical simulation, pricing, and risk modeling.

Installation

pip install laakhay-quantlab
# extensions: [jax, jax-gpu, torch, all]
pip install "laakhay-quantlab[all]"

Quick Start: Options Pricing

The pricing module supports a wide range of exotic and vanilla options, along with Greeks calculation.

from laakhay.quantlab.pricing import (
    EuropeanCall, 
    MarketData, 
    Pricer, 
    PricingMethod
)

# 1. Define Market Conditions
market = MarketData(spot=100.0, rate=0.05, vol=0.2)

# 2. Define Instrument
option = EuropeanCall(strike=100.0, expiry=1.0)

# 3. Price using Black-Scholes (Analytical)
bs_pricer = Pricer(method=PricingMethod.BLACK_SCHOLES)
price, greeks = bs_pricer.price_with_greeks(option, market)

print(f"Price: {price:.4f}")
print(f"Delta: {greeks.delta:.4f}")

# 4. Price using Monte Carlo (Numerical)
mc_pricer = Pricer(method=PricingMethod.MONTE_CARLO)
mc_price = mc_pricer.price(option, market)

print(f"MC Price: {mc_price:.4f}")

Documentation

See the docs/ directory for detailed guides:

• Getting Started: Installation and first steps.
• Pricing: Detailed guide on options, strategies, and pricing models.
• Backends: Configuring specific computation backends.