fmarthoz

def prob_default(ticker):
    """ DEFINING THE FUNCTION WE WANT TO OPTIMISE"""
    def fun(x):
        va=x[0]
        siga=x[1]
        d1=(np.log(va/X)+(r+0.5*siga**2)*T)/(siga*np.sqrt(T))
        d2=d1-siga*np.sqrt(T)
        return [va*norm.cdf(d1)-X*np.exp(-r*T)*norm.cdf(d2)-ve,va*norm.cdf(d1)*siga/ve-sige]
    """DATA SELECTION"""
    start_date='2018-01-01'

fmarthoz

scaler = StandardScaler()
df=scaler.fit_transform(df)

kmeans_kwargs = {"init": "random","n_init": 20,"max_iter": 1000,"random_state": 1984}
cut_off=0.5
maxvars=3
kmin=2
kmax=8

cols=list(df.columns)

fmarthoz

from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import random
import seaborn as sns
from sklearn.preprocessing import StandardScaler

# We define a function returning the centroid of a set of points and the distance between two points