Ethereum RSI Calculation Problem: A Solution

The problem you are facing is common when working with financial data in Python and NumPy. In this article, we will review the error message and provide an updated solution to calculate the Relative Strength Index (RSI) using the Simple Moving Average (SMA).

Problem: AttributeError: Object 'numpy.float64' does not have the 'rolling' attribute

When importing the numpy library, a series of numeric arrays are returned as objects. However, in some cases, the internal implementation of NumPy may not support certain operations on these objects, causing errors like this:

import numpy as np









Create an array with a float64 value
arr = np.float64(10)

The error message indicates that numpy.float64 does not have the rolling attribute. This is because the rolling method is actually part of the Pandas library, which we will import later.

The solution: Use Pandas to calculate the RSI

To calculate the RSI using SMA and process it correctly, you need to use the Pandas library. Here is an updated code snippet showing how to calculate RSI:

import pandas as pd
import numpy as np

Function to calculate RSI with SMA
def calculate_rsi(data, short_window=14, long_window=26):

Calculate the simple moving average (SMA)
data['sma'] = data['Close'].rolling(window=short_window).mean()

Calculate the relative strength index (RSI)
data['rsi'] = 100.0 - (100.0 / (1 + data['sma'].pct_change().dropna()) ** long_window)
return data

Load closing prices into a Pandas DataFrame
df = pd.read_csv('stock_prices.csv', index_col='Data', parse_dates=['Data'])

Calculate RSI with SMA
rsi_df = calculate_rsi(df)

Print the first rows of the calculated RSI
print(rsi_df.head())

Explanation

In this updated code, we define a function calculate_rsi that takes a Pandas DataFrame (data) and two parameters for the short and long moving averages (short_window and long_window). We then calculate the SMA using the rolling method, followed by calculating the RSI using the pct_change method. Finally, we return the calculated RSI as a new column in our Pandas DataFrame.

Example Use Case

To use this function with your own data, simply replace the placeholders `stock_prices.csv' and `Date' with your desired file path and date range. You can then use the calculate_rsi function to calculate the RSI of your stock prices.

I hope this helps! If you have any other questions or problems, feel free to contact me.