In [4]:

!pip install yfinance

Collecting yfinance
  Downloading yfinance-0.1.70-py2.py3-none-any.whl (26 kB)
Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.3.5)
Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.21.6)
Collecting lxml>=4.5.1
  Downloading lxml-4.8.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (6.4 MB)
     |████████████████████████████████| 6.4 MB 12.9 MB/s 
Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.10)
Collecting requests>=2.26
  Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB)
     |████████████████████████████████| 63 kB 1.5 MB/s 
Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2022.1)
Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2.8.2)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24.0->yfinance) (1.15.0)
Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.10)
Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.0.12)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (1.24.3)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2021.10.8)
Installing collected packages: requests, lxml, yfinance
  Attempting uninstall: requests
    Found existing installation: requests 2.23.0
    Uninstalling requests-2.23.0:
      Successfully uninstalled requests-2.23.0
  Attempting uninstall: lxml
    Found existing installation: lxml 4.2.6
    Uninstalling lxml-4.2.6:
      Successfully uninstalled lxml-4.2.6
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.
datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.
Successfully installed lxml-4.8.0 requests-2.27.1 yfinance-0.1.70

In [5]:

# Importing the modules
import yfinance as yf
import pandas as pd
import numpy as np 
import math
import matplotlib.pyplot as plt

In [8]:

dataset = yf.download("TSLA", start="2017-01-01", end="2022-05-19")
dataset

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Out[8]:

	Open	High	Low	Close	Adj Close	Volume
Date
2017-01-03	42.972000	44.066002	42.192001	43.397999	43.397999	29616500
2017-01-04	42.950001	45.599998	42.862000	45.397999	45.397999	56067500
2017-01-05	45.284000	45.495998	44.389999	45.349998	45.349998	29558500
2017-01-06	45.386002	46.062000	45.090000	45.801998	45.801998	27639500
2017-01-09	45.793999	46.383999	45.599998	46.256001	46.256001	19897500
...	...	...	...	...	...	...
2022-05-12	701.000000	759.659973	680.000000	728.000000	728.000000	46771000
2022-05-13	773.479980	787.349976	751.570007	769.590027	769.590027	30651800
2022-05-16	767.159973	769.760010	719.090027	724.369995	724.369995	28699500
2022-05-17	747.359985	764.479980	728.849976	761.609985	761.609985	26745400
2022-05-18	744.520020	760.500000	700.809998	709.809998	709.809998	29270600

1354 rows × 6 columns

In [9]:

dataset.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1354 entries, 2017-01-03 to 2022-05-18
Data columns (total 6 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   Open       1354 non-null   float64
 1   High       1354 non-null   float64
 2   Low        1354 non-null   float64
 3   Close      1354 non-null   float64
 4   Adj Close  1354 non-null   float64
 5   Volume     1354 non-null   int64  
dtypes: float64(5), int64(1)
memory usage: 74.0 KB

In [11]:

(dataset['Adj Close'] / dataset['Adj Close'].iloc[0] * 100).plot(figsize = (15, 6));
plt.show()

Let's do the multiple stocks.¶

In [17]:

tickers = ['SBUX', 'WMT', 'AMZN', 'HD']
mydata = pd.DataFrame()
for t in tickers:
    mydata[t] = yf.download(t, start="2000-01-01", end="2022-05-31")['Adj Close']

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In [18]:

mydata.head()

Out[18]:

	SBUX	WMT	AMZN	HD
Date
2000-01-03	2.503720	44.580296	89.3750	41.701786
2000-01-04	2.424388	42.912197	81.9375	39.502750
2000-01-05	2.456121	42.036438	69.7500	40.302402
2000-01-06	2.544973	42.495159	65.5625	38.383224
2000-01-07	2.532281	45.706284	69.5625	40.622269

In [ ]:

In [19]:

mydata.plot(figsize=(10, 12), subplots=True);  

In [20]:

mydata.describe().round(2)  

Out[20]:

	SBUX	WMT	AMZN	HD
count	5632.00	5632.00	5632.00	5632.00
mean	30.75	59.33	642.96	84.04
std	30.04	32.15	965.07	88.83
min	2.42	28.96	5.97	13.01
25%	7.52	36.35	43.15	24.42
50%	15.18	42.04	179.33	32.53
75%	50.42	66.65	769.87	118.19
max	123.53	159.29	3731.41	413.68

In [27]:

mydata.pct_change().describe().round(4) 

Out[27]:

	SBUX	WMT	AMZN	HD
count	5631.0000	5631.0000	5631.0000	5631.0000
mean	0.0008	0.0003	0.0011	0.0005
std	0.0213	0.0150	0.0320	0.0194
min	-0.1842	-0.1138	-0.2477	-0.2874
25%	-0.0090	-0.0067	-0.0119	-0.0081
50%	0.0003	0.0002	0.0005	0.0005
75%	0.0100	0.0070	0.0135	0.0092
max	0.1838	0.1171	0.3447	0.1407

In [28]:

mydata.pct_change().mean().plot(kind='bar', figsize=(10, 6));  

In [29]:

mydata.pct_change().std().plot(kind='bar', figsize=(10, 6));  

In [33]:

ret = np.log(mydata / mydata.shift(1))  
ret.describe()

Out[33]:

	SBUX	WMT	AMZN	HD
count	5631.000000	5631.000000	5631.000000	5631.000000
mean	0.000596	0.000174	0.000564	0.000343
std	0.021199	0.014969	0.031585	0.019546
min	-0.203626	-0.120765	-0.284568	-0.338773
25%	-0.009081	-0.006747	-0.012017	-0.008143
50%	0.000303	0.000241	0.000462	0.000536
75%	0.009962	0.006937	0.013455	0.009195
max	0.168728	0.110723	0.296181	0.131612

In [34]:

ret.mean().plot(kind='bar', figsize=(10, 6));  

In [35]:

ret.std().plot(kind='bar', figsize=(10, 6));  

In [36]:

ret.cumsum().apply(np.exp).plot(figsize=(10, 6));  

In [37]:

ret.dropna(inplace=True)

In [39]:

ret.plot(subplots=True, figsize=(10, 12));

In [41]:

ret.plot(subplots=False, figsize=(15, 10));

In [ ]:

Oops! AMZN was not a public firm in 1995. Let's choose another one instead.¶

In [42]:

tickers = ['SBUX', 'WMT', 'ORCL', 'HD']
mydata = pd.DataFrame()
for t in tickers:
    mydata[t] = yf.download(t, start="1995-01-01", end="2020-12-31")['Adj Close']

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In [43]:

mydata

Out[43]:

	SBUX	WMT	ORCL	HD
Date
1995-01-03	0.679083	6.736091	1.778506	6.333531
1995-01-04	0.666390	6.978107	1.794061	6.298632
1995-01-05	0.648936	7.058782	1.757766	6.263737
1995-01-06	0.628311	7.099112	1.778506	6.246289
1995-01-09	0.647349	7.058782	1.830358	6.333531
...	...	...	...	...
2020-12-23	99.195160	139.926331	63.821392	262.703430
2020-12-24	99.146561	140.199890	63.489101	263.784241
2020-12-28	101.411163	141.880325	63.401131	262.158234
2020-12-29	102.664948	140.981476	63.000408	259.178802
2020-12-30	102.995415	140.864227	62.941772	258.273346

6546 rows × 4 columns

What if we invest $1 in 1995?¶

In [44]:

(mydata / mydata.iloc[0] * 1)

Out[44]:

	SBUX	WMT	ORCL	HD
Date
1995-01-03	1.000000	1.000000	1.000000	1.000000
1995-01-04	0.981308	1.035928	1.008746	0.994490
1995-01-05	0.955607	1.047905	0.988338	0.988980
1995-01-06	0.925234	1.053892	1.000000	0.986225
1995-01-09	0.953270	1.047905	1.029155	1.000000
...	...	...	...	...
2020-12-23	146.072220	20.772630	35.884833	41.478195
2020-12-24	146.000654	20.813241	35.697996	41.648844
2020-12-28	149.335449	21.062709	35.648533	41.392114
2020-12-29	151.181739	20.929271	35.423219	40.921692
2020-12-30	151.668377	20.911865	35.390249	40.778730

6546 rows × 4 columns

What if we invest $100?¶

In [45]:

(mydata / mydata.iloc[0] * 100).plot(figsize = (15, 6));
plt.show()

In [ ]:

In [46]:

tickers = ['SBUX', 'WMT', 'ORCL', 'HD']
mydata = pd.DataFrame()
for t in tickers:
    mydata[t] = yf.download(t, start="2000-01-01", end="2020-12-31", interval="1wk")['Adj Close']

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In [47]:

mydata["HD"].pct_change().plot.hist(alpha=0.9, bins=50)

Out[47]:

<matplotlib.axes._subplots.AxesSubplot at 0x7fb75299e710>

In [ ]:

In [48]:

tickers = ['SBUX', 'WMT', 'ORCL', 'HD']
mydata = pd.DataFrame()
for t in tickers:
    mydata[t] = yf.download(t, start="2000-01-01", end="2020-12-31", interval="1d")['Adj Close']

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In [49]:

mydata

Out[49]:

	SBUX	WMT	ORCL	HD
Date
2000-01-03	2.503720	44.580292	24.806074	41.701782
2000-01-04	2.424388	42.912189	22.614220	39.502758
2000-01-05	2.456121	42.036449	21.419847	40.302418
2000-01-06	2.544973	42.495148	20.159863	38.383232
2000-01-07	2.532281	45.706284	21.708597	40.622265
...	...	...	...	...
2020-12-23	99.195160	139.926346	63.821404	262.703430
2020-12-24	99.146568	140.199890	63.489098	263.784210
2020-12-28	101.411163	141.880325	63.401127	262.158203
2020-12-29	102.664940	140.981476	63.000412	259.178802
2020-12-30	102.995407	140.864227	62.941772	258.273315

5283 rows × 4 columns

resmaple of Pandas¶

https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases

In [50]:

mydata_m=mydata.resample('BM').last()
mydata_m

Out[50]:

	SBUX	WMT	ORCL	HD
Date
2000-01-31	3.249441	36.531647	20.980167	36.224167
2000-02-29	3.566770	32.528210	31.184801	36.783936
2000-03-31	4.550488	37.746773	32.786026	41.292625
2000-04-28	3.070152	36.995167	33.573517	36.171055
2000-05-31	3.452532	38.498367	30.187281	31.369606
...	...	...	...	...
2020-08-31	81.713600	135.162766	55.698818	274.579224
2020-09-30	83.116272	136.194595	58.112888	268.928955
2020-10-30	84.122337	135.065430	54.839489	258.276794
2020-11-30	95.268570	148.732544	56.413033	268.638489
2020-12-31	102.995407	140.864227	62.941772	258.273315

252 rows × 4 columns

In [51]:

returns=mydata_m.pct_change()

In [52]:

returns

Out[52]:

	SBUX	WMT	ORCL	HD
Date
2000-01-31	NaN	NaN	NaN	NaN
2000-02-29	0.097656	-0.109588	0.486394	0.015453
2000-03-31	0.275801	0.160432	0.051346	0.122572
2000-04-28	-0.325314	-0.019912	0.024019	-0.124031
2000-05-31	0.124548	0.040632	-0.100860	-0.132743
...	...	...	...	...
2020-08-31	0.109755	0.077441	0.031921	0.073637
2020-09-30	0.017166	0.007634	0.043341	-0.020578
2020-10-30	0.012104	-0.008291	-0.056328	-0.039610
2020-11-30	0.132500	0.101189	0.028694	0.040119
2020-12-31	0.081106	-0.052902	0.115731	-0.038584

252 rows × 4 columns

In [53]:

returns2=returns.dropna()

In [54]:

returns2

Out[54]:

	SBUX	WMT	ORCL	HD
Date
2000-02-29	0.097656	-0.109588	0.486394	0.015453
2000-03-31	0.275801	0.160432	0.051346	0.122572
2000-04-28	-0.325314	-0.019912	0.024019	-0.124031
2000-05-31	0.124548	0.040632	-0.100860	-0.132743
2000-06-30	0.123162	0.001092	0.169565	0.020020
...	...	...	...	...
2020-08-31	0.109755	0.077441	0.031921	0.073637
2020-09-30	0.017166	0.007634	0.043341	-0.020578
2020-10-30	0.012104	-0.008291	-0.056328	-0.039610
2020-11-30	0.132500	0.101189	0.028694	0.040119
2020-12-31	0.081106	-0.052902	0.115731	-0.038584

251 rows × 4 columns

In [55]:

sbux_stock = returns2["SBUX"]

In [56]:

sbux_stock

Out[56]:

Date
2000-02-29    0.097656
2000-03-31    0.275801
2000-04-28   -0.325314
2000-05-31    0.124548
2000-06-30    0.123162
                ...   
2020-08-31    0.109755
2020-09-30    0.017166
2020-10-30    0.012104
2020-11-30    0.132500
2020-12-31    0.081106
Freq: BM, Name: SBUX, Length: 251, dtype: float64

In [57]:

plt.hist(sbux_stock,bins=25)

Out[57]:

(array([ 2.,  0.,  0.,  0.,  2.,  0.,  2.,  4.,  5., 13., 16., 26., 27.,
        40., 34., 28., 17., 15.,  9.,  5.,  1.,  1.,  1.,  2.,  1.]),
 array([-0.3253137 , -0.30023997, -0.27516623, -0.25009249, -0.22501875,
        -0.19994501, -0.17487127, -0.14979753, -0.12472379, -0.09965006,
        -0.07457632, -0.04950258, -0.02442884,  0.0006449 ,  0.02571864,
         0.05079238,  0.07586612,  0.10093986,  0.12601359,  0.15108733,
         0.17616107,  0.20123481,  0.22630855,  0.25138229,  0.27645603,
         0.30152977]),
 <a list of 25 Patch objects>)

In [58]:

p=np.percentile(sbux_stock, 50)
p

Out[58]:

0.020890229606218558

In [59]:

sbux_stock.median()

Out[59]:

0.020890229606218558

In [60]:

p=np.percentile(sbux_stock, 95)
p

Out[60]:

0.14582886300958497

In [ ]: