Ethereum: Python EMA calculation using talib and pandas ewn different from tradingview

Understanding the Difference Between Talib and TradingView EMA Calculation with Pandas

As a developer, it is not uncommon to encounter differences in library usage between different tools, such as trading platforms, data sources, or third-party libraries. In this article, we will explore why your code may show different results when calculating exponential moving averages (EMAs) using Talib and TradingView with Pandas.

Problem: Different EMA calculation methods

Talib is a widely used library developed by Cognizant, while TradingView offers its own EMA implementation. Although both libraries use the same mathematical formula to calculate EMA, there may be subtle differences in their implementations:

• Mathematical Formula: While the underlying mathematics remains the same, there may be minor differences between the two libraries.

• Implementation Details: The code snippets used by Talib and TradingView may vary due to differences in coding styles or implementation options.

Pandas EMA Calculation vs. Talib and TradingView

When using Panda with Talib, you will notice a different approach:

• Using panda’s ewm function:

import pandas in pd format
def calculate_ma(data, period):
return data.ewm(span=period, adjust=False).mean()

In this example, the “ewm” function calculates the EMA using the specified “span” (number of periods) and returns the result.

When comparing this to the TradingView implementation:

import pandas in pd format
def calculate_ma(data, period):
return data['Close'].ewm(span=period, custom=False).mean()

Notice how we use the closing prices directly (data['Close']) instead of creating a new column. This is likely due to library-specific implementation differences.

Why TradingView and Talib may look different

TradingView uses a slightly different approach to calculating the EMA:

• Using Talib’s plot function

  : When you call the plot function on a DataFrame, it uses different technical indicators, including EMA calculations.

• Different data structures: TradingView’s implementation probably stores prices in a pandas array instead of the “ewm” method.

Conclusion

To ensure accurate EMA calculation with both Talib and TradingView pandas:

• Check library versions: Make sure you are using libraries that are compatible with your platform.

• Check code differences: Carefully review the implementation details to find differences.

• Test with identical data sets: Make sure the results match when calculating the EMA using both libraries and TradingView.

By understanding these potential differences, you will be better able to handle similar tasks on different platforms or in different scenarios.