-
Notifications
You must be signed in to change notification settings - Fork 0
/
capm_functions.py
36 lines (30 loc) · 1.18 KB
/
capm_functions.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
import plotly.express as px
import numpy as np
# Function to plot interactive plotly chart
def interactive_plot(df):
fig = px.line()
for i in df.columns[1:]:
fig.add_scatter(x = df['Date'], y = df[i], name = i)
fig.update_layout(width = 450, margin = dict(l=20, r=20, t=20, b=20), legend = dict(orientation = 'h', yanchor = 'bottom',
y = 1.02, xanchor = 'right', x = 1,))
return fig
# Function to normalize the prices based on the initial price
def normalize(df_2):
df = df_2.copy()
for i in df.columns[1:]:
df[i] = df[i]/df[i][0]
return df
# Function to calculate daily returns
# (current value - previous close price) / previous close price
def daily_return(df):
df_daily_return = df.copy()
for i in df.columns[1:]:
for j in range(1,len(df)):
df_daily_return[i][j] = (df[i][j]-df[i][j-1])/df[i][j-1]*100
df_daily_return[i][0] = 0
return df_daily_return
# Function to calculate beta
def calculate_beta(stocks_daily_return, stock):
rm = stocks_daily_return['sp500'].mean()*252
b, a = np.polyfit(stocks_daily_return['sp500'], stocks_daily_return[stock],1)
return b,a