How can I optimize my pinescript tradingview code for faster backtesting of cryptocurrency trading strategies?

3 answers

• Peeyush kumar YadavOct 26, 2020 · 5 years ago
  One way to optimize your pinescript tradingview code for faster backtesting of cryptocurrency trading strategies is to minimize the use of unnecessary calculations and indicators. Focus on the essential indicators and variables that directly impact your trading strategy. Additionally, consider using vectorized calculations and avoiding loops whenever possible to improve performance. Another optimization technique is to use barssince() and highest() or lowest() functions instead of for loops to reduce the number of iterations. Lastly, make sure to test your code on smaller data sets before running it on larger datasets to identify any potential bottlenecks and optimize accordingly.
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• m8tenJul 10, 2024 · a year ago
  Hey there! If you want to speed up your backtesting of cryptocurrency trading strategies on TradingView using pinescript, here are a few tips for you. Firstly, try to avoid using too many unnecessary calculations and indicators in your code. Stick to the ones that are essential for your strategy. Secondly, consider using vectorized calculations instead of loops whenever possible. This can significantly improve the performance of your code. Lastly, make sure to test your code on smaller datasets before running it on larger ones to identify any potential issues and optimize accordingly. Happy backtesting!
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• Patryk PersakMay 24, 2025 · 2 months ago
  Optimizing your pinescript tradingview code for faster backtesting of cryptocurrency trading strategies can be achieved by following a few key steps. Firstly, ensure that you are only using the necessary indicators and calculations in your code. Removing any unnecessary elements can significantly improve the speed of your backtesting. Secondly, consider using vectorized calculations instead of loops, as this can greatly enhance the performance of your code. Lastly, make sure to test your code on smaller datasets before running it on larger ones to identify any potential bottlenecks and optimize accordingly. Good luck with your backtesting!
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