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)

Open	High	Low	Close	Adj Close	Volume
Date
2021-08-17	2763.820068	2774.370117	2735.750000	2746.010010	2746.010010	1063600
2021-08-18	2742.310059	2765.879883	2728.419922	2731.399902	2731.399902	746700
2021-08-19	2709.350098	2749.040039	2707.120117	2738.270020	2738.270020	914800
2021-08-20	2741.659912	2772.290039	2729.340088	2768.739990	2768.739990	778200
2021-08-23	2779.969971	2843.540039	2774.958984	2821.989990	2821.989990	1054500
2021-08-24	2830.870117	2860.149902	2827.070068	2847.969971	2847.969971	756300
2021-08-25	2857.659912	2866.260010	2848.790039	2859.000000	2859.000000	641900
2021-08-26	2852.370117	2862.696045	2841.830078	2842.459961	2842.459961	74610

import matplotlib.pyplot as plt

How to plot google close price?

df['Close'].plot(figsize=(12,8))

<matplotlib.axes._subplots.AxesSubplot at 0x7fb1102efb90>

How to plot google close price and volume?

df[['Close', 'Volume']].plot(subplots=True, style='b',

figsize=(12, 8))

array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fb1101eed90>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7fb10fd55d90>],
      dtype=object)

How to discribe mean median and other statistics?

df.describe()

description of google stock price

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=(12, 8))

array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fb10fc48150>],
      dtype=object)

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()

google and vix comparison

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?

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(1, 2, figsize=(16, 8))

# 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')

How to plot google daily stock return?

df.log_rtn.plot(title='Daily GOOG returns')

<matplotlib.axes._subplots.AxesSubplot at 0x7fb0ecf04dd0>

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(2, 1, figsize=(12, 10))

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')]

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(3, 1, figsize=(18, 15),

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>

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