Permutation tests for correlations
How does the volatility of Bitcoin compare to the volatility of the S&P 500?
You previously computed volatility as the percent daily change, which has been stored for you in the Pct_Daily_Change_BTC and Pct_Daily_Change_SP500 columns in your data. The question you want to answer is the extent to which these two values correlate. One way to answer this is through a permutation test. By randomly shuffling values between the S&P 500 and BTC you are able to see what a random outcome would like like, and then compare this to the observed values.
A DataFrame of S&P 500 and Bitcoin prices (btc_sp_df) has been loaded for you, as have the packages pandas as pd, NumPy as np, and stats from SciPy.
本练习是课程的一部分
Foundations of Inference in Python
练习说明
- Define a
statistic()function which returns just the Pearson R value between two vectors. - Set your
dataequal to a tuple containing the volatility ofBTCandSP500. - Conduct a permutation test with this data, statistic, 1000 resamples, and with an alternative hypothesis of greater volatility with Bitcoin.
- Print if the p-value is significant at 5%.
交互式实操练习
通过完成这段示例代码来试试这个练习。
# Define a function which returns the Pearson R value
def statistic(x, y):
____
# Define the data as the percent daily change from each asset
data = ____
# Compute a permutation test for the percent daily change of each asset
res = ____(____, ____,
n_resamples=____,
vectorized=____,
alternative='____')
# Print if the p-value is significant at 5%
print(res.pvalue < 0.05)