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Ethereum: Python EMA calculation using talib and pandas ewn different from tradingview
Understanding the difference in ema calculation between Talib and tradingview with pandas
As a developer, it’s not uncommon to encounter differences in library usage between various tools, such as trading platforms, data sources, or third-party libraries. In this article, we’ll explore why your code might be displaying different results for calculating exponential moving average (EMAS) using Talib and tradingview with pandas.
The issue: Different EMA Calculation Methods
Talib is a widely-used library developed by cognizant, while tradingview provides its own implementation of emas. Although both libraries use the same mathematical formula for calculating ema, there may be subtle differences in their implementations:
- Mathematical Formula : While the underlying mathematics remains the same, slight variations can occur between the two libraries.
- Implementation Details : The code snippets used by Talib and Tradingview Might Differences in Coding Style or Implementation Choices.
Pandas Ema Calculation vs. Talib and Tradingview
When using pandas with talib, you’ll notice a different approach:
- using
EWM 'FUNCTION FROM PANDAS:
`python
Import pandas as pd
Def calculate_ema (data, period):
Return data.ewm (span = period, Adjust = False) .mean ()
In this example, the ewm function calculates the ema using a specifiedspan (number of periods) and return the result.
When Comparing this with Tradingview’s Implementation:
`python
Import pandas as pd
Def calculate_ema (data, period):
Return data ['close']. EWM (Span = Period, Adjust = False) .mean ()
Notice how we’re accessing the closing prices directly (Data ['Close']) Instead of Creating a New Column. This is likelya due to differences in library-specific implementation.
WHY TRADINGVIEW AND TAIL MIGHT SEEM DIFFERENT
Tradingview’s EMA calculation uses a slightly different approach:
- using
PlotFunction from Talib : When you call theplotfunction on a dataframe, it applies various technical indicators, including ema calculations.
- Different Data Structures
: Tradingview’s Implementation Likty Stores Prices in a pandas series instead of using the
EwumMethod.
Conclusion
To ensure ema calculation using both talib and tradingview with pandas:
- Verify Library Versions
: Double-check that you are using compatible libraries for your platform.
- Inspect code differences : carfully review the implementation details to identify any discrepancies.
- Test with identical data sets : verify that your results match when calculating ema using both libraries and tradingview.
By understanding these potential differences, you’ll be better equipped to tackle similar tasks on various platforms or in different scenarios.