price-process 1.0

Library for generating various stochastic price sequences

Price process generator

This library provides various generators of stochastic processes modelling real price processes. The generated synthetic price data can be used in Monte Carlo simulations (or similar statistical experiments) and potentially even to train ML algorithms.

Currently the implemented/planned generators are

• Geometric Wiener motion
• Geometric Lévy flights
• Ising (TODO)

Installation

pip install price_process to get the current PyPi version or clone this repo and run pip install . in the directory for most recent version.

Basic usage

The standard model of stochastic price dynamics is the SDE

with solution the geometric Brownian motion

In order to display, say 10 samples of a 1000 point process, one would run

from price_process.process import *
price_proc = Gaussian([1000, 10]).to_geometric(0, 0.04)
price_proc.plot()

The np.ndarray output is accessed through price_proc.process

Custom process

Custom generators can be implemented by subclassing Process. Here is how one might implement the gamma process for instance

from price_process.process import *
from scipy.stats import gamma
import numpy as np

class Gamma(Process):
    def __init__(self, alpha, beta, size, initial=0, T=1):
        super().__init__(size, initial=initial, T=T)
        self.alpha, self.beta = alpha, beta
        self.rvs = gamma.rvs(alpha, size=self.size, scale=1/self.beta)
        self.process = np.cumsum(self.rvs, axis=0)

See this for a more advanced use case.