Ethereum RSI Calculation Issue: Solution
The issue you are facing is common when working with financial data in Python and NumPy. In this article, we will look into the information provided by the error message and provide an updated solution for calculating the Relative Strength Index (RSI) using a Simple Moving Average (SMA).
Problem: ‘AttributeError: ‘numpy.float64’ object has no ‘rolling’ attribute
When you import the library ‘numpy’, it returns a series of numeric arrays as objects. However, in some cases, NumPy’s internal implementation may not support certain operations on these objects, resulting in the following errors:
import numpy as np
Create an array with a float64 valuearr = np.float64(10)
The error message indicates that the attribute 'numpy.float64' does not have a 'rolling' attribute. This is because the scroll method is actually part of the Pandas library, which we will 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 snippet showing how to calculate RSI:
import pandas as pd
import numpy as np
Function to calculate RSI with SMAdef calculate_rsi(data, short_window=14, long_window=26):
Calculate Simple Moving Average (SMA)data['sma'] = data['Close'].rolling(window=short_window).mean();
Calculate Relative Strength Index (RSI)data['rsi'] = 100.0 - (100.0/(1 + data['sma'].pct_change().drop()) **long_window);
return data
Load closing prices into a Pandas DataFramedf = pd.read_csv('stock_prices.csv', index_col='Date', parse_dates=['Date']);
Calculate RSI using SMArsi_df = calculate_rsi(df)
Print the first few rows of the calculated RSIprint ( rsi_df . head ( ) )
Explanation
In this updated code, we define a function calculate_rsithat takes a Pandas DataFrame (data) and two parameters for the short and long moving averages (short_windowandlong_window). We then calculate the SMA using thescrollmethod, followed by calculating the RSI using thepct_changemethod. Finally, we return the calculated RSI as a new column in our Pandas DataFrame.
Use Case Example
To use this function with your own data, simply replace the placeholdersstock_prices.csvandDatewith the desired file path and date range. You can then use thecalculate_rsi` function to calculate the RSI for your stock prices.
I hope this helps! If you have any further questions or problems, please contact me.
