Google stockprice analysis in python

What are alternative sources to get financial data?

 Google is an American multinational company specializing in Internet services.

Analyzing Google's stock price using Python involves collecting historical price data, performing data analysis, and creating visualizations to gain insights into the company's stock performance. Here's a step-by-step guide on how to conduct Google stock price analysis in Python:

Import Libraries:

Start by importing the necessary Python libraries for data manipulation, analysis, and visualization. Commonly used libraries include pandas, numpy, matplotlib, and yfinance to fetch historical data:

python

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import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import yfinance as yf

Data Retrieval:

Use the yfinance library or other financial data sources to fetch historical data for Google's stock. Specify the start and end dates for the data you want to analyze:

python

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google = yf.download('GOOGL', start='2020-01-01', end='2021-12-31')

Data Exploration:

Explore the fetched data to understand its structure and contents. Use functions like head(), tail(), describe(), and info() to inspect the dataset:

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print(google.head())

Data Visualization:

Create visualizations to analyze the historical performance of Google's stock. Common visualizations include line charts to visualize price movements:

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plt.figure(figsize=(12, 6))

plt.plot(google['Adj Close'], label='Google')

plt.title('Google Stock Price')

plt.xlabel('Date')

plt.ylabel('Price')

plt.legend()

plt.show()

Technical Analysis (Optional):

Perform technical analysis by calculating and visualizing technical indicators like moving averages, relative strength index (RSI), and MACD. Libraries like ta-lib can be used for these calculations.

Statistical Analysis (Optional):

Conduct statistical analysis to calculate summary statistics, volatility measures, and correlations with other assets. numpy and pandas are useful for these calculations.

Sentiment Analysis (Optional):

Consider incorporating sentiment analysis of news articles or social media data related to Google to understand market sentiment's impact on the stock price.

Fundamental Analysis (Optional):

Analyze fundamental factors affecting Google, such as earnings reports, revenue growth, and market share, which can influence the stock's performance.

Prediction and Forecasting (Optional):

You can use time series forecasting techniques like ARIMA or machine learning models to make predictions about future Google stock price movements.

Risk Management and Decision Making:

Based on your analysis, formulate investment strategies, set risk management parameters, and make informed investment decisions regarding Google's stock.

Regular Updates:

Keep your analysis up to date with the latest data to adapt to changing market conditions and make timely decisions.

Remember that investing in stocks carries risks, and it's crucial to do thorough research, consider factors like company news and market trends, and potentially consult with financial experts before making investment decisions based on your analysis of Google's stock price or any other stock.

What are alternative sources to get financial data?

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

How to get financial 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 describe the 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 the 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's 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's absolute 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>

New Updates of Google Stock Price Analysis