quantfinlib 0.0.4

Fundamental package for quantitative finance with Python.

QuantFinLib

QuantFinLib is a comprehensive Python library designed for quantitative finance. It offers a wide range of tools and algorithms that cater to various domains within quantitative finance, including machine learning, asset management, portfolio optimization, time series transformations, indicators, labeling, feature engineering, stochastic simulation, randomization tests, and backtesting. This library aims to provide robust and efficient solutions for financial data analysis, modeling, and trading strategy development.

Time Series Simulation

Historical Sampling : Returns are randomly sampled from a historical time-series. The generated random simulation has the same return distribution as the historical return distribution, but any autocorrelation or dependencies over time is broken. This simulation method is used as a benchmark for generating time series that have no structural patterns, only random patterns.

Brownian Motion : Classical random price model without memory. Price changes are Normal distributed, the price can potentially go below zero.

Model	Properties
Historical Sampling	Returns have the same distributions as in the past
Brownian Motion	Classical random price model without memory
Geometric Brownian Motion	Classical random price model without memory, and where prices are postive
Ornstein Uhlenbeck	Mean reverting model
Garch	Dynamical volatility
Dynamic Conditional Correlation GARCH	Dynamical volatility and correlations
Markov Chains	Discrete state transitioning model
Phase Randomisation	Randomization that preserves return distributions and autocorrelations
Fractal Brownian Motion	Brownian motion with trends or mean reversion