Amazon stock price analysis in python

Amazon(AMZN) focuses on e-commers ,cloud computing etc 

What is alternative sources to get financial data?

There are number of alternative sources like Quandal,intrinio,google others.

!pip install yfinance

import pandas as pd

import numpy as np

import yfinance as yf

How to get  AMZN Financianl data from yahoo finance in Python?

df = yf.download('AMZN',

 start='2020-01-01',

 end='2021-08-28',

 progress=False)

df.tail(9)

details of amazon stock price

import matplotlib.pyplot as plt

How to plot amazon close price?

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

<matplotlib.axes._subplots.AxesSubplot at 0x7f2c61f1cc50>

How to plot amazon close price volume?

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

figsize=(12, 8))

array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f2c61e14c90>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7f2c61e39590>],
      dtype=object)

How to discribe amazon mean median and other statistics?

df.describe()

description on amazon 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 0x7f2c61899a10>],
      dtype=object)

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

df['log_rtn'].tail(12)

Date 2021-08-12 0.003454 2021-08-13 -0.002889 2021-08-16 0.001523 2021-08-17 -0.017438 2021-08-18 -0.012646 2021-08-19 -0.004217 2021-08-20 0.003820 2021-08-23 0.020391 2021-08-24 0.012146 2021-08-25 -0.001999 2021-08-26 0.005085 2021-08-27 0.010091 Name: log_rtn, dtype: float64

import cufflinks as cf

from plotly.offline import iplot, init_notebook_mode

init_notebook_mode()

How to plot log return in python?

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

How to compare stock price and vix?

df = yf.download(['AMZN', '^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={'AMZN': 'amzn', '^VIX': 'vix'})

df.tail()

amazon and vix comparison

df['log_rtn'] = np.log(df.amzn / df.amzn.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'AMZN vs. VIX ($\\rho$ = {corr_coeff:.2f})',

ylabel='VIX log returns',

xlabel='AMZN log returns')

[Text(0, 0.5, 'VIX log returns'),
 Text(0.5, 0, 'AMZN log returns'),
 Text(0.5, 1.0, 'AMZN vs. VIX ($\\rho$ = -0.33)')]

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

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 AMZN 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)

/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning:

`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).

Text(0.5, 1.0, 'Q-Q plot')

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

How to plot daily amazon stock price ?

<matplotlib.axes._subplots.AxesSubplot at 0x7f2c3ecc2e10>

How to plot absoult return and autocorrelation?

N_LAGS = 50

SIGNIFICANCE_LEVEL = 0.05

acf = smt.graphics.plot_acf(df.log_rtn,

lags=N_LAGS,

alpha=SIGNIFICANCE_LEVEL

)

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 AMZN stockprice 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='AMZN 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 0x7f2c36643f50>

NEW AMAZON ANALYSIS