To compute Bitcoin volatility in Python, you can use the pandas library and a dataset with historical Bitcoin prices. Here is an example of how to calculate volatility for Bitcoin using the pandas library:
import pandas as pd # import stock data data = pd.read_csv("stock_data.csv") # calculate daily returns data["returns"] = data["close"].pct_change() # calculate volatility volatility = data["returns"].std() * (252**0.5) print("Volatility: ", volatility)
In this example, we first import the bitcoin data from a CSV file using the pd.read_csv() function. Then we calculate the daily returns of the bitcoin by using the pct_change() function on the Close price column. The volatility is calculated by taking the standard deviation of the daily returns and multiplying it by the square root of 252, which is the number of trading days in a year.
It's worth noting that different sources might have different columns name like "Close" or "close", it's always important to double check the dataset and the columns name before doing any calculation.
You can also use other libraries like ccxt to gather data from different exchange platforms, however it will require more lines of code.
You can read how to get Bitcoin prices from the web here.