hugo1005

import requests
import pandas as pd
import time
import json
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm
import cvxopt as opt

hugo1005

import pandas as pd
import pymc3 as pm
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm

model_df = pd.read_csv('./breakpoints.csv')
sectors = model_df['equity_sector'].unique()
industry = model_df['equity_industry'].unique()

hugo1005

import matplotlib.pyplot as plt
import pandas as pd

font_title = {'family': 'roboto',
        'color':  '#0f3567',
        'weight': 'bold',
        'size': 16,
}

font_mini_title = {'family': 'roboto',

hugo1005

from scipy.stats import f_oneway
a = all_metrics[all_metrics['significance'] == 'insignficant']['post_covid_return'].dropna().values
b = all_metrics[all_metrics['significance'] == 'intrinsic_only']['post_covid_return'].dropna().values
c = all_metrics[all_metrics['significance'] == 'sharpe_only']['post_covid_return'].dropna().values
d = all_metrics[all_metrics['significance'] == 'both']['post_covid_return'].dropna().values

import statsmodels
print(f_oneway(a,b,c,d)) # Signficicant Difference between groups

from itertools import combinations, product

hugo1005

library(TSA)
library(forecast)
library(tseries)

garchAutoTryFit = function(
  ll,
  data,
  trace=FALSE,
  forecast.length=1,
  with.forecast=TRUE,