Google stockprice analysis in python

What is alternative sources to get financial data?

 Google is an american multinational company specialised in internet services.

What is alternative sources to get financial data?
There are number of alternative sources like Quandal,intrinio,google others.

How to get Financianl data from google in Python?

!pip install yfinance
import pandas as pd
import numpy as np
import yfinance as yf
df = yf.download('GOOG',
 start='2020-01-01',
 end='2021-08-28',
 progress=False)
df.tail(9)

OpenHighLowCloseAdj CloseVolume
Date
2021-08-172763.8200682774.3701172735.7500002746.0100102746.0100101063600
2021-08-182742.3100592765.8798832728.4199222731.3999022731.399902746700
2021-08-192709.3500982749.0400392707.1201172738.2700202738.270020914800
2021-08-202741.6599122772.2900392729.3400882768.7399902768.739990778200
2021-08-232779.9699712843.5400392774.9589842821.9899902821.9899901054500
2021-08-242830.8701172860.1499022827.0700682847.9699712847.969971756300
2021-08-252857.6599122866.2600102848.7900392859.0000002859.000000641900
2021-08-262852.3701172862.6960452841.8300782842.4599612842.45996174610
import matplotlib.pyplot as plt

How to plot google close price?

df['Close'].plot(figsize=(12,8))
<matplotlib.axes._subplots.AxesSubplot at 0x7fb1102efb90>
google stock close price

How to plot google close price and volume?

df[['Close''Volume']].plot(subplots=True, style='b',
figsize=(128))
array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fb1101eed90>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7fb10fd55d90>],
      dtype=object)
google stock price and volume

How to discribe mean median and other statistics?

df.describe()
df['simple_rtn'] = df.Close.pct_change()
df['log_rtn'] = np.log(df.Close/df.Close.shift(1))
df['log_rtn'].plot(subplots=True, style='b',
figsize=(128))
array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fb10fc48150>],
      dtype=object)
google closing stock price

How to convert Stock prices  into  log and simple returns in python?

df['log_rtn'].tail(12)
Date 2021-08-12 0.005071 2021-08-13 0.000119 2021-08-16 0.003678 2021-08-17 -0.011698 2021-08-18 -0.005335 2021-08-19 0.002512 2021-08-20 0.011066 2021-08-23 0.019050 2021-08-24 0.009164 2021-08-25 0.003865 2021-08-26 -0.005802 2021-08-27 0.016936 Name: log_rtn, dtype: float64
import cufflinks as cf
from plotly.offline import iplot, init_notebook_mode
init_notebook_mode()
df_rolling = df[['simple_rtn']].rolling(window=21) \
.agg(['mean''std'])
df_rolling.columns = df_rolling.columns.droplevel()
df_outliers = df.join(df_rolling)
import pandas as pd
import numpy as np
import yfinance as yf
import seaborn as sns
import scipy.stats as scs
import statsmodels.api as sm
import statsmodels.tsa.api as smt
df = yf.download(['GOOG''^VIX'],
start='1985-01-01',
end='2021-08-28',
progress=False)
df = df[['Adj Close']]
df.columns = df.columns.droplevel(0)
df = df.rename(columns={'GOOG''goog''^VIX''vix'})
df.tail()
df['log_rtn'] = np.log(df.goog / df.goog.shift(1))
df['vol_rtn'] = np.log(df.vix / df.vix.shift(1))
df.dropna(how='any', axis=0, inplace=True)
corr_coeff = df.log_rtn.corr(df.vol_rtn)
corr_coeff = df.log_rtn.corr(df.vol_rtn)
ax = sns.regplot(x='log_rtn', y='vol_rtn', data=df,
line_kws={'color''red'})
ax.set(title=f'GOOG vs. VIX ($\\rho$ = {corr_coeff:.2f})',
ylabel='VIX log returns',
xlabel='GOOG log returns')
[Text(0, 0.5, 'VIX log returns'),
 Text(0.5, 0, 'GOOG log returns'),
 Text(0.5, 1.0, 'GOOG vs. VIX ($\\rho$ = -0.48)')]

How to compare stock price of google and vix?

google and vix stock price
r_range = np.linspace(min(df.log_rtn), max(df.log_rtn), num=1000)
mu = df.log_rtn.mean()
sigma = df.log_rtn.std()
norm_pdf = scs.norm.pdf(r_range, loc=mu, scale=sigma)
fig, ax = plt.subplots(12, figsize=(168))
# histogram
sns.distplot(df.log_rtn, kde=False, norm_hist=True, ax=ax[0])
ax[0].set_title('Distribution of GOOG returns', fontsize=16)
ax[0].plot(r_range, norm_pdf, 'g', lw=2,
label=f'N({mu:.2f}{sigma**2:.4f})')
ax[0].legend(loc='upper left');
# Q-Q plot
qq = sm.qqplot(df.log_rtn.values, line='s', ax=ax[1])
ax[1].set_title('Q-Q plot', fontsize = 16)
distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).

How to plot google distribution of stock price and Q-Q plot?

Text(0.5, 1.0, 'Q-Q plot')
google stock price distribution and qq plot return

How to plot google daily stock return?

df.log_rtn.plot(title='Daily GOOG returns')
<matplotlib.axes._subplots.AxesSubplot at 0x7fb0ecf04dd0>
google stock price daily return
N_LAGS = 50
SIGNIFICANCE_LEVEL = 0.05
acf = smt.graphics.plot_acf(df.log_rtn,
lags=N_LAGS,
alpha=SIGNIFICANCE_LEVEL
)

How to plot google absoult return and autocorrelation?


fig, ax = plt.subplots(21, figsize=(1210))
smt.graphics.plot_acf(df.log_rtn ** 2, lags=N_LAGS,
alpha=SIGNIFICANCE_LEVEL, ax = ax[0])
ax[0].set(title='Autocorrelation Plots',
ylabel='Squared Returns')
smt.graphics.plot_acf(np.abs(df.log_rtn), lags=N_LAGS,
alpha=SIGNIFICANCE_LEVEL, ax = ax[1])
ax[1].set(ylabel='Absolute Returns',
xlabel='Lag')
[Text(0, 0.5, 'Absolute Returns'), Text(0.5, 0, 'Lag')]
google stock price absolute return

How to plot google 252 days and 21 days moving volatility?

df['moving_std_252'] = df[['log_rtn']].rolling(window=252).std()
df['moving_std_21'] = df[['log_rtn']].rolling(window=21).std()
fig, ax = plt.subplots(31, figsize=(1815),
sharex=True)
df.plot(ax=ax[0])
ax[0].set(title='GOOG time series',
ylabel='Stock price ($)')
df.log_rtn.plot(ax=ax[1])
ax[1].set(ylabel='Log returns (%)')
df.moving_std_252.plot(ax=ax[2], color='r',
label='Moving Volatility 252d')
df.moving_std_21.plot(ax=ax[2], color='g',
label='Moving Volatility 21d')
ax[2].set(ylabel='Moving Volatility',
xlabel='Date')
ax[2].legend()
<matplotlib.legend.Legend at 0x7fb0e4997e50>
google stock price volatility



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