This post includes code and notes from python for finance and trading algorithms udemy course and python for finance and trading algorithms udemy course notebooks.

from scipy import stats

# help(stats.linregress)

import pandas as pd

import pandas_datareader as web

start = pd.to_datetime('2020-01-01')
end = pd.to_datetime('today')

FXAIX_stock = web.DataReader('FXAIX', 'yahoo', start, end)
FXAIX_stock.head()

VRTTX_stock = web.DataReader('VRTTX', 'yahoo', start, end)
VRTTX_stock.head()


FNCMX_stock = web.DataReader('FNCMX', 'yahoo', start, end)
FNCMX_stock.head()

FSMAX_stock = web.DataReader('FSMAX', 'yahoo', start, end)
FSMAX_stock.head()
High Low Open Close Volume Adj Close
Date
2020-01-02 65.269997 65.269997 65.269997 65.269997 0.0 65.192108
2020-01-03 65.129997 65.129997 65.129997 65.129997 0.0 65.052277
2020-01-06 65.279999 65.279999 65.279999 65.279999 0.0 65.202103
2020-01-07 65.180000 65.180000 65.180000 65.180000 0.0 65.102219
2020-01-08 65.410004 65.410004 65.410004 65.410004 0.0 65.331947 
import matplotlib.pyplot as plt
%matplotlib inline

stocks
FXAIX_stock VRTTX_stock FNCMX_stock FSMAX_stock
Date
2018-01-02 94.230003 238.889999 91.989998 62.529999
2018-01-03 94.830002 240.289993 92.760002 62.740002
2018-01-04 95.230003 241.199997 92.930000 62.849998
2018-01-05 95.900002 242.750000 93.699997 63.090000
2018-01-08 96.059998 243.199997 93.970001 63.270000
... ... ... ... ...
2021-02-08 135.880005 355.709991 175.610001 93.940002
2021-02-09 135.750000 355.730011 175.839996 94.400002
2021-02-10 135.699997 355.579987 175.380005 94.309998
2021-02-11 135.960007 356.440002 176.100006 94.790001
2021-02-12 136.600006 358.170013 176.979996 95.250000

785 rows × 4 columns

Compare Cumulative Return 

FXAIX_stock['Cumulative'] = FXAIX_stock['Close']/FXAIX_stock['Close'].iloc[0]
VRTTX_stock['Cumulative'] = VRTTX_stock['Close']/VRTTX_stock['Close'].iloc[0]
FNCMX_stock['Cumulative'] = FNCMX_stock['Close']/FNCMX_stock['Close'].iloc[0]
FSMAX_stock['Cumulative'] = FSMAX_stock['Close']/FSMAX_stock['Close'].iloc[0]

FXAIX_stock['Cumulative'].plot(label='FXAIX_stock',figsize=(10,8))
VRTTX_stock['Cumulative'].plot(label='VRTTX_stock',figsize=(10,8))
FNCMX_stock['Cumulative'].plot(label='FNCMX_stock',figsize=(10,8))
FSMAX_stock['Cumulative'].plot(label='FSMAX_stock',figsize=(10,8))
plt.legend()
plt.title('Cumulative Return')

Text(0.5, 1.0, 'Cumulative Return')

Get Daily Return 

FXAIX_stock['Daily Return'] = FXAIX_stock['Close'].pct_change(1)
VRTTX_stock['Daily Return'] = VRTTX_stock['Close'].pct_change(1)

FXAIX_stock['Daily Return']

Date
2020-01-02         NaN
2020-01-03   -0.006992
2020-01-06    0.003565
2020-01-07   -0.002664
2020-01-08    0.004898
                ...   
2021-02-08    0.007414
2021-02-09   -0.000957
2021-02-10   -0.000368
2021-02-11    0.001916
2021-02-12    0.004707
Name: Daily Return, Length: 282, dtype: float64
plt.scatter(FXAIX_stock['Daily Return'],VRTTX_stock['Daily Return'],alpha=0.3)

<matplotlib.collections.PathCollection at 0x7fe6c8aef400>
VRTTX_stock['Daily Return'].hist(bins=100)

<AxesSubplot:>
FXAIX_stock['Daily Return'].hist(bins=100)

<AxesSubplot:>
beta,alpha,r_value,p_value,std_err = stats.linregress(FXAIX_stock['Daily Return'].iloc[1:],VRTTX_stock['Daily Return'].iloc[1:])

beta

1.0124150273238781
alpha

0.00012460564758023734
r_value

0.9973781994620393
FXAIX_stock['Daily Return'].head()

Date
2020-01-02         NaN
2020-01-03   -0.006992
2020-01-06    0.003565
2020-01-07   -0.002664
2020-01-08    0.004898
Name: Daily Return, dtype: float64
import numpy as np

noise = np.random.normal(0,0.001,len(FXAIX_stock['Daily Return'].iloc[1:]))

#noise

FXAIX_stock['Daily Return'].iloc[1:] + noise

Date
2020-01-03   -0.006673
2020-01-06    0.002797
2020-01-07   -0.002088
2020-01-08    0.005773
2020-01-09    0.007326
                ...   
2021-02-08    0.006484
2021-02-09   -0.000506
2021-02-10   -0.000078
2021-02-11    0.001277
2021-02-12    0.005154
Name: Daily Return, Length: 281, dtype: float64
beta,alpha,r_value,p_value,std_err = stats.linregress(FXAIX_stock['Daily Return'].iloc[1:]+noise,FXAIX_stock['Daily Return'].iloc[1:])

beta

0.9959066537237841
alpha

0.00011917125339144037