Understanding the difference in calculating Emma between Talib and TraringView with Pandas
As a developer, it is not uncommon to find differences in the use of a library between different tools, such as trade platforms, data sources or third -party library. In this article, we will explore why your code can show different results to calculate the exponential moving average (EMA) using Talib and TraringView with pandas.
Question: Different methods of Emma Calculation
Talib is a widely used library developed by Cognizant, while traditionview provides its own EMA implementation. Although both libraries use the same mathematical formula to calculate EMA, there may be subtle differences in their implementation:
- Mathematical formula : Although the basic mathematics remains the same, slight variations between the two libraries may occur.
- Details of implementation : Code clips used by Talib and TraringView may differ due to differences in coding or choice of implementation.
Pandas EMA Calculation opposite talib and traditionview
When using talib pandas, you will notice a different approach:
- Using
EWMFunctions from Pande:
Python
Imports of pandas as pd
Determine Calluce_em (data, period):
return data.ewm (range = period, adjustment = false) .mean ()
`
In this example, the functionEWMcalculates EMA using the said range (number of periods) and restores the result.
When you compare this to the implementation of the traditionview:
`Python
Imports of pandas as pd
Determine Calluce_em (data, period):
Return data [‘Close’]. Ewm (span = period, adjustment = false) .mean ()
`
Notice how we directly access the final prices (data [‘closed’]) instead of creating a new column. This is probably due to the differences in implementation specific to the library.
Why traditionview and talib can seem different
EMA TraringView calculation uses a slightly different approach:
- Using the functionPlot
from Talib: When you call the function Plot
on the Dataframe, it applies different technical indicators, including EMA calculating.
- Different data structures : Implementation of TraringView is likely to store prices in the Pandas series instead of using theEWM` method.
Conclusion
To ensure the exact calculation of EMA using both Talib and TraringView with Pandas:
- Check the library versions
: Twice check that you use compatible libraries for your platform.
- Review the difference in code : Carefully review the implementation details to identify any differences.
- Test with identical data sets : Make sure your results are appropriate when calculating EMA using both libraries and traditionview.
By understanding these potential differences, you will be better equipped to deal with similar tasks on different platforms or in different scenarios.
