# 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:

python
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Data Exploration:
Explore the fetched data to understand their structure and contents:

python
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print("Nifty 50 Data:")

print("\nS&P 500 Data:")
Basic Statistics:
Calculate basic statistics for both indices, such as mean, standard deviation, minimum, and maximum prices:

python
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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:

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

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=(128))
array([<matplotlib.axes._subplots.AxesSubplot object at 0x7ff8f1855910>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7ff8f07cc710>],
dtype=object)
nsei mean standard deviation

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=(128))
array([<matplotlib.axes._subplots.AxesSubplot object at 0x7ff8f065ef50>],
dtype=object)
Nsei log return

0s
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?

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
start='1985-01-01',
end='2021-08-22',
progress=False)
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(12, figsize=(168))
# 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(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')]
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='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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