<filename>datacamp-master/22-introduction-to-time-series-analysis-in-python/02-some-simple-time-series/02-are-we-confident-this-stock-is-mean-reverting.py
'''
Are We Confident This Stock is Mean Reverting?

In the last chapter, you saw that the autocorelation of MSFT's weekly stock returns was -0.16. That autocorrelation seems large, but is it statistically significant? In other words, can you say that there is less than a 5% chance that we would observe such a large negative autocorrelation if the true autocorrelation were really zero? And are there any autocorrelations at other lags that are significantly different from zero?

Even if the true autocorrelations were zero at all lags, in a finite sample of returns you won't see the estimate of the autocorrelations exactly zero. In fact, the standard deviation of the sample autocorrelation is 1/N‾‾√
1
/
N
 where N
N
 is the number of observations, so if N=100
N
=
100
, for example, the standard deviation of the ACF is 0.1, and since 95% of a normal curve is between +1.96 and -1.96 standard deviations from the mean, the 95% confidence interval is ±1.96/N‾‾√
±
1.96
/
N
. This approximation only holds when the true autocorrelations are all zero.

You will compute the actual and approximate confidence interval for the ACF, and compare it to the lag-one autocorrelation of -0.16 from the last chapter. The weekly returns of Microsodt is pre-loaded in a DataFrame called returns.

INSTRUCTIONS
100XP
INSTRUCTIONS
100XP
Recompute the autocorrelation of weekly returns in the Series 'Adj Close' in the returns DataFrame.
Find the number of observations in the returns DataFrame using the len() function.
Approximate the 95% confidence interval of the estimated autocorrelation. The math function sqrt() has been imported and can be used.
Plot the autocorrelation function of returns using plot_acf that was imported from statsmodels. Set alpha=0.05 for the confidence intervals (that's the default) and lags=20.
'''
# Import the plot_acf module from statsmodels and sqrt from math
from statsmodels.graphics.tsaplots import plot_acf
from math import sqrt

# Compute and print the autocorrelation of MSFT weekly returns
autocorrelation = returns['Adj Close'].autocorr()
print("The autocorrelation of weekly MSFT returns is %4.2f" %(autocorrelation))

# Find the number of observations by taking the length of the returns DataFrame
nobs = len(returns)

# Compute the approximate confidence interval
conf = 1.96/sqrt(nobs)
print("The approximate confidence interval is +/- %4.2f" %(conf))

# Plot the autocorrelation function with 95% confidence intervals and 20 lags using plot_acf
plot_acf(returns, alpha=0.05, lags=20)
plt.show()
<filename>datacamp-master/22-introduction-to-time-series-analysis-in-python/02-some-simple-time-series/02-are-we-confident-this-stock-is-mean-reverting.py
'''
Are We Confident This Stock is Mean Reverting?

In the last chapter, you saw that the autocorelation of MSFT's weekly stock returns was -0.16. That autocorrelation seems large, but is it statistically significant? In other words, can you say that there is less than a 5% chance that we would observe such a large negative autocorrelation if the true autocorrelation were really zero? And are there any autocorrelations at other lags that are significantly different from zero?

Even if the true autocorrelations were zero at all lags, in a finite sample of returns you won't see the estimate of the autocorrelations exactly zero. In fact, the standard deviation of the sample autocorrelation is 1/N‾‾√
1
/
N
 where N
N
 is the number of observations, so if N=100
N
=
100
, for example, the standard deviation of the ACF is 0.1, and since 95% of a normal curve is between +1.96 and -1.96 standard deviations from the mean, the 95% confidence interval is ±1.96/N‾‾√
±
1.96
/
N
. This approximation only holds when the true autocorrelations are all zero.

You will compute the actual and approximate confidence interval for the ACF, and compare it to the lag-one autocorrelation of -0.16 from the last chapter. The weekly returns of Microsodt is pre-loaded in a DataFrame called returns.

INSTRUCTIONS
100XP
INSTRUCTIONS
100XP
Recompute the autocorrelation of weekly returns in the Series 'Adj Close' in the returns DataFrame.
Find the number of observations in the returns DataFrame using the len() function.
Approximate the 95% confidence interval of the estimated autocorrelation. The math function sqrt() has been imported and can be used.
Plot the autocorrelation function of returns using plot_acf that was imported from statsmodels. Set alpha=0.05 for the confidence intervals (that's the default) and lags=20.
'''
# Import the plot_acf module from statsmodels and sqrt from math
from statsmodels.graphics.tsaplots import plot_acf
from math import sqrt

# Compute and print the autocorrelation of MSFT weekly returns
autocorrelation = returns['Adj Close'].autocorr()
print("The autocorrelation of weekly MSFT returns is %4.2f" %(autocorrelation))

# Find the number of observations by taking the length of the returns DataFrame
nobs = len(returns)

# Compute the approximate confidence interval
conf = 1.96/sqrt(nobs)
print("The approximate confidence interval is +/- %4.2f" %(conf))

# Plot the autocorrelation function with 95% confidence intervals and 20 lags using plot_acf
plot_acf(returns, alpha=0.05, lags=20)
plt.show()
Are We Confident This Stock is Mean Reverting?  In the last chapter, you saw that the autocorelation of MSFT's weekly stock returns was -0.16. That autocorrelation seems large, but is it statistically significant? In other words, can you say that there is less than a 5% chance that we would observe such a large negative autocorrelation if the true autocorrelation were really zero? And are there any autocorrelations at other lags that are significantly different from zero?  Even if the true autocorrelations were zero at all lags, in a finite sample of returns you won't see the estimate of the autocorrelations exactly zero. In fact, the standard deviation of the sample autocorrelation is 1/N‾‾√ 1 / N  where N N  is the number of observations, so if N=100 N = 100 , for example, the standard deviation of the ACF is 0.1, and since 95% of a normal curve is between +1.96 and -1.96 standard deviations from the mean, the 95% confidence interval is ±1.96/N‾‾√ ± 1.96 / N . This approximation only holds when the true autocorrelations are all zero.  You will compute the actual and approximate confidence interval for the ACF, and compare it to the lag-one autocorrelation of -0.16 from the last chapter. The weekly returns of Microsodt is pre-loaded in a DataFrame called returns.  INSTRUCTIONS 100XP INSTRUCTIONS 100XP Recompute the autocorrelation of weekly returns in the Series 'Adj Close' in the returns DataFrame. Find the number of observations in the returns DataFrame using the len() function. Approximate the 95% confidence interval of the estimated autocorrelation. The math function sqrt() has been imported and can be used. Plot the autocorrelation function of returns using plot_acf that was imported from statsmodels. Set alpha=0.05 for the confidence intervals (that's the default) and lags=20. # Import the plot_acf module from statsmodels and sqrt from math # Compute and print the autocorrelation of MSFT weekly returns # Find the number of observations by taking the length of the returns DataFrame # Compute the approximate confidence interval # Plot the autocorrelation function with 95% confidence intervals and 20 lags using plot_acf