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Ethereum’s RSI calculation problem: A solution
The problem you encounter is a regular one when working with financial data in Python and Number. In this article, we will overcome what the error message tells us and provide an updated solution to calculate the relative resistance index (RSI) using a simple movement (SMA).
** problem: attributeerror: 'numpy.float64' the object has no attribute 'rolling'`
When importing the "Number" library, it returns a number of numerical paintings as objects. However, in some cases, the internal implementation of Number may not support certain operations on these objects, which leads to errors like this:
Python
Imports Number as NP
Create a painting with a float value64
arr = np.float64 (10)
The error message indicates thatnumpy.float64 not has a rolling attribute. This is because the « Rolling » method is part of the Pandas Library, which we import later.
Solution: Use Pandas to calculate RSI
To calculate RSI using SMA and process it correctly, you will need to use the Pandas library. Here is an updated code fragment that demonstrates how to calculate the RSI:
`Python
imports the panda as pd
Imports Number as NP
Function to calculate RSI with SMA
Def calculated_rsi (data, short_window = 14, long_window = 26):
Calculate the simple movement media (SMA)
data ['sma'] = data ['close']. Rolling (Window = short_window). Mean ()
Calculate the relative resistance index (RSI)
data ['rsi'] = 100.0 - (100.0 / (1 + data ['sm']. pct_change (). dropna ()) ** long_window)
return data
Load the closing prices in a Pandas Datoframe
DF = pd.read_csv ('stock_price.csv', index_col = 'data', pars_dates = ['data'])
Calculate RSI with SMA
RSI_DF = calculated_rsi (DF)
Print the first rows of Calculated RSI
Print (RSI_DF.Head ())
Explanation
In this updated code, we define acalculated_rsifunction that takes on a Pandas Datoframe ( data) and two parameters for short and long moving media (short_window and long_window). Then we calculate the SMA using the Rolling method, followed by the calculation of the RSI using the PCT_Change method. Finally, we return RSI calculated as a new column in our Pandas data data.
Example of case use
To use this feature with your own data, simply replace ‘stock_price.csv’and' '' a placehers with the desired file and database. Then you can use thecalculated_rsi` function to calculate the Action Prices.
I hope this will help! Announce it if you still have any questions or problems.