synthetic_data.py

import pandas as pd
import numpy as np
from random import gauss
from itertools import product
import seaborn as sns
sns.set()

def main():
    rPT = rSLm = np.linspace(0, 10, 21)
    count = 0
    for prod_ in product([10, 5, 0, -5, -10], [5, 10, 25, 50, 100]):
        count += 1
        coeffs = {'forecast': prod_[0], 'hl': prod_[1], 'sigma': 1}
        output = batch(coeffs, nIter=1e5, maxHP=100, rPT=rPT, rSLm=rSLm)
    return output

def batch(coeffs, nIter=1e5, maxHP=100, rPT=np.linspace(0.5, 10, 20), rSLm=np.linspace(0.5, 10, 20), seed=0):
    phi = 2 ** (-1.0 / coeffs['hl'])
    output1 = []
    for comb_ in product(rPT, rSLm):
        output2 = []
        for iter_ in range(int(nIter)):
            p, hp, count = seed, 0, 0
            while True:
                p = (1 - phi) * coeffs['forecast'] + phi * p + coeffs['sigma'] * gauss(0, 1)
                cP = p - seed
                hp += 1
                if cP > comb_[0] or cP < -comb_[1] or hp > maxHP:
                    output2.append(cP)
                    break
        mean, std = np.mean(output2), np.std(output2)
        print(comb_[0], comb_[1], mean, std, mean / std)
        output1.append((comb_[0], comb_[1], mean, std, mean / std))
    return output1

def process_batch(coeffs_list, **kwargs):
    out = []
    for coeffs in coeffs_list:
        out.append((coeffs, batch(coeffs, **kwargs)))
    return out