RSI Classic calculationClassic RSI with Moving Average
This script implements the Classic RSI (Relative Strength Index) method with the option to use either an Exponential Moving Average (EMA) or a Simple Moving Average (SMA) for smoothing the gains and losses. This custom implementation primarily aims to resolve a specific issue I encountered when cross-referencing RSI values with Python-based data, which is calculated differently than in Pine Script. However, the methodology here can benefit anyone who needs to align RSI calculations across different programming languages or platforms.
The Problem:
When working with Python for data analysis, the RSI values are calculated differently. The smoothing method, for example, can vary—RMA (Relative Moving Average) may be used instead of SMA or EMA, resulting in discrepancies when comparing RSI values across systems. To solve this problem, this script allows for the same type of smoothing to be applied (EMA or SMA) as used in Python, ensuring consistency in the data.
Why This Implementation:
The main goal of this approach was to align RSI calculations across Python and Pine Script so that I could cross-check the results accurately. By offering both EMA and SMA options, this script bridges the gap between Pine Script and Python, ensuring that the data is comparable and consistent. While this particular issue arose from my work with Python, this solution is valuable for anyone dealing with cross-platform RSI comparisons in different coding languages or systems.
Benefits:
Cross-Platform Consistency: This script ensures that RSI values calculated in Pine Script are directly comparable to those from Python (or any other platform), which is crucial for accurate analysis, especially in automated trading systems.
Flexibility: The ability to choose between EMA and SMA provides flexibility in line with the specific needs of your strategy or data source.
Ease of Use: The RSI is plotted with overbought and oversold levels clearly marked, making it easy to visualize and use in decision-making processes.
Limitations:
Calculation Differences: While this script bridges the gap between Pine Script and Python, if you're working with a different platform or coding language that uses variations like RMA, small discrepancies may still arise.
Sensitivity Trade-Off: The choice between EMA and SMA impacts the sensitivity of the RSI. EMA responds quicker to recent price changes, which could lead to faster signals, while SMA provides a more stable but slower response.
Conclusion:
This Classic RSI script, with its customizable moving average type (EMA or SMA), not only solves the issue I faced with Python-based calculations but also provides a solution for anyone needing consistency across different programming languages and platforms. Whether you're working with Pine Script, Python, or other languages, this script ensures that your RSI values are aligned for more accurate cross-platform analysis. However, always be mindful of the small differences that can arise when different smoothing techniques (like RMA) are used in other systems.
