Compare between Nifty Fifty and s&p Five hundred

How to compare nifty50 and S&P 500 in Python?

To describe the Nifty 50 and S&P 500 stock market indices in Python, you can gather key information such as historical price data, statistics, and visualize their performance. Here's a step-by-step guide to describing these indices using Python:

Import Libraries:

Start by importing the necessary Python libraries for data analysis and visualization:

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 to fetch historical price data for both the Nifty 50 and S&P 500 indices. Specify the start and end dates for the data:

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nifty = yf.download('^NSEI', start='2020-01-01', end='2021-12-31')  # Nifty 50

sp500 = yf.download('^GSPC', start='2020-01-01', end='2021-12-31')  # S&P 500

Data Exploration:

Explore the fetched data to understand their structure and contents:

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print("Nifty 50 Data:")

print(nifty.head())

print("\nS&P 500 Data:")

print(sp500.head())

Basic Statistics:

Calculate basic statistics for both indices, such as mean, standard deviation, minimum, and maximum prices:

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nifty_stats = nifty['Adj Close'].describe()

sp500_stats = sp500['Adj Close'].describe()

print("Nifty 50 Statistics:")

print(nifty_stats)

print("\nS&P 500 Statistics:")

print(sp500_stats)

Data Visualization:

Create visualizations to compare the historical performance of Nifty 50 and S&P 500. Common visualizations include line charts to visualize price movements:

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

plt.plot(nifty['Adj Close'], label='Nifty 50', color='blue')

plt.plot(sp500['Adj Close'], label='S&P 500', color='red')

plt.title('Nifty 50 vs. S&P 500')

plt.xlabel('Date')

plt.ylabel('Price')

plt.legend()

plt.show()

Correlation Analysis:

Calculate the correlation coefficient between Nifty 50 and S&P 500 returns to understand their relationship:

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nifty_returns = nifty['Adj Close'].pct_change()

sp500_returns = sp500['Adj Close'].pct_change()

correlation = nifty_returns.corr(sp500_returns)

print(f"Correlation between Nifty 50 and S&P 500: {correlation:.2f}")

This code provides a description of the Nifty 50 and S&P 500 indices, including basic statistics, visualizations of their price movements, and a correlation analysis to assess their relationship. You can further customize the analysis by incorporating additional metrics or visualization techniques based on your specific requirements.

 How to get financial data from Yahoo Finance in Python?

First to analyse nifty 50 data

python code get data;

nsei = yf.download('^NSEI',

 start='2020-01-01',

 end='2021-08-22',

 progress=False)

What are alternative sources to get financial data?

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

NSEI DATA

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

import matplotlib.pyplot as plt

import pandas as pd

import numpy as np

import yfinance as yf

pythone plot code nsei close

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

<matplotlib.axes._subplots.AxesSubplot at 0x7ff8f1e4bc90>

nsei['Volume'].plot(figsize=(12,8))

<matplotlib.axes._subplots.AxesSubplot at 0x7ff8f1892b90>

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

figsize=(12, 8))

array([<matplotlib.axes._subplots.AxesSubplot object at 0x7ff8f1855910>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7ff8f07cc710>],
      dtype=object)

nsei mean standard deviation

nsei.describe()

How to change stock frequency in python?

We can change monthly and weekly erc.

How to visualize time series financial data in python?

nsei['simple_rtn'] = nsei.Close.pct_change()

nsei['log_rtn'] = np.log(nsei.Close/nsei.Close.shift(1))

nsei['log_rtn'].plot(subplots=True, style='b',

figsize=(12, 8))

array([<matplotlib.axes._subplots.AxesSubplot object at 0x7ff8f065ef50>],
      dtype=object)

Nsei log return

0s

nsei['log_rtn'].tail(12)

Date
2021-08-04    0.007907
2021-08-05    0.002199
2021-08-06   -0.003467
2021-08-09    0.001234
2021-08-10    0.001343
2021-08-11    0.000132
2021-08-12    0.005033
2021-08-13    0.010014
2021-08-16    0.002052
2021-08-17    0.003107
2021-08-18   -0.002757
2021-08-20   -0.007169
Name: log_rtn, dtype: float64

How do get identify stock outliers in python?

import cufflinks as cf

from plotly.offline import iplot, init_notebook_mode

import seaborn as sns

init_notebook_mode()

How to find simple and log returns?

df_rolling = nsei[['simple_rtn']].rolling(window=21) \

.agg(['mean', 'std'])

df_rolling.columns = df_rolling.columns.droplevel()

df_outliers = nsei.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(['^NSEI', '^VIX'],

start='1985-01-01',

end='2021-08-22',

progress=False)

df = df[['Adj Close']]

df.columns = df.columns.droplevel(0)

df = df.rename(columns={'^NSEI': 'nsei', '^VIX': 'vix'})

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

ylabel='VIX log returns',

xlabel='NSEI log returns')

[Text(0, 0.5, 'VIX log returns'),
 Text(0.5, 0, 'NSEI log returns'),
 Text(0.5, 1.0, 'NSEI vs. VIX ($\\rho$ = -0.21)')]

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 NSEI 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)

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

How to analyse nsei distribution and QQplot?

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

<matplotlib.axes._subplots.AxesSubplot at 0x7ff8ce380310>

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')]

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='NSEI 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()

What is technical analysis?

The technical analysis is a way to read stock prices open, high, low, and close with volume.

How to plot Nsei time series modeling in stocks? What is volatility?

<matplotlib.legend.Legend at 0x7ff8c5d4e550>

Click ANALYSIS NIFTY50

Price S&P500 Analysis with Python

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