How is it calculated?
This filter identifies significant price points within a specified dynamic range by applying linear regression to the absolute deviation of the range, smoothing out fluctuations, and determining the trend direction. The algorithm then normalizes the data and searches for extreme points.
How is it calculated?
Once the external filters are selected and enabled within the settings panel, our AI function is applied to enhance the filter's ability to execute trades, even when the set conditions of the filter are not met. For instance, if a trader wants to take trades only when the price is above a moving average, the AI filter can actually execute trades even if the price is below the moving average.
The filter works by combining k-nearest Neighbors (KNN) with Geometric Brownian Motion (GBM) involves first using GBM to model the historical price trends of an asset, identifying patterns of drift and volatility. KNN is then applied to compare the current market conditions with historical instances, identifying the closest matches based on similar market behaviors. By examining the drift values of these nearest historical neighbors, KNN predicts the current trend's direction.
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Only users authorized by the author have access to this script, and this usually requires payment. You can add the script to your favorites, but you will only be able to use it after requesting permission and obtaining it from its author — learn more here. For more details, follow the author's instructions below or contact Zeiierman directly.
除非您完全信任其作者并了解脚本的工作原理,否則TradingView不建议您付费或使用脚本。您还可以在我们的社区脚本中找到免费的开源替代方案。
提醒:在请求访问权限之前,请阅读仅限邀请脚本指南。