QuantriX 1.0.5

This library provides tools for quantitative analysis in finance.

QA

Last update

Stable function for Brownion Bridge simulation is added to the 1.0.4 version.

Description

The QuantriX library is aimed to provide essential tools for quantitative finance analysis. It is a copyrighted software available only for direct use.

Available tools

The first version allows to build binomial model and random walk, brownion motion and brownion bridge.

Instalation

For installing, run these code in terminal:

pip install QuantriX

Usage

Available function can be used in the following ways:

1. Import necessary functions:

from quantrix.modules import bin_model_simulation

or

from quantrix.modules import *

In the code, just use the function:

paths = bin_model_simulation(N, n, S0, q, u, d)

2. Import all existing modules:

from quantrix import modules

Use in the code in the following way:

paths = modules.bin_model_simulation(N, n, S0, q, u, d)

Or use the following importing path:

import quantrix.modules as qx

And then use in code as

qx.bin_model_simulation(N, n, S0, q, u, d)

3. Available functions:

• bin_model_simulation(n_paths: int, max_time: int, S0: int, q: float, u: float, d: float) -> list[np.array]

This function simulates binomial model for n_paths number of paths for max_time periods. It requiers initial stock value - S0, probability of going up and up and down factors.

• rw_simulations(n_paths: int, max_time: int, p: float) -> list[np.array]

The function simulates random walk: n_paths for max_time with probability p with step +1 or -1.

• bm_simulations(n_paths: int, granularity: int, max_time: int) -> list[np.array]

This function simulates brownion motion: n_paths number of paths for max_time periods with granularity scale.

• bb_simulations(n_paths: int, granularity: int, max_time: int , T: int = 1) -> list[np.array]

This function simulates brownion bridge: it returns n_paths which follows Gaussian process s.t. B(t) = W(t) - t/T*W(T), where W(t) is a Winer process. By defolt, T is fixed and equals to 1. Function uses bm_simulation in it.