Support/Resistance DBSCAN

Hello, my friends. This is a new version of the support and resistance indicator implemented by the fast clustering algorithm DBSCAN

(1) Indicator description
  • The indicator clusters key top and bottom points in the historical K-line to find support and resistance areas with a high probability of occurrence
  • The clustering algorithm used for this indicator is the density-based fast clustering algorithm DBSCAN
  • The minimum unit of support and resistance found by this indicator is the core region, i.e., the key top and bottom points that frequently occur within a certain price range
  • Core regions may be superimposed on the chart. The more they are superimposed, the stronger possibility of support and resistance
  • The clustering algorithm does not work for all markets, so you need to adjust the parameters to suit different markets and timeframe

(2) Key parameters
- Support/Resistance Clustering
  • Pivot Lookback Period: Number of K-lines to look back left/right from the pivot top/bottom
  • Max of Lookback Forward: The maximum number of historical K-lines
  • Min Strength of Clustering Core: Minimum strength of the clustered core region, the higher the strength, the smaller the core region
  • Min Points of Clustering Core: Minimum number of clustering points in the core region of clustering

(3) Script description
  • Due to some circumstances that I don't want to see, subsequent scripts will not be open source, but you can still use the script for free. Thanks for your understanding and support!
  • If you have any suggestions or comments about the script, please feel free to leave your comments!

Happy trading, and enjoy your life!



(1) 指标说明
  • 该指标通过对历史K线中的关键顶底点进行聚类,查找大概率出现的支撑和压力区间
  • 该指标采用的聚类算法为基于密度的快速聚类算法 DBSCAN
  • 该指标找到的支撑压力的最小单位为核心区间,即在一定价格范围内频繁出现的关键顶底点
  • 核心区间可能会在图表上叠加,叠加越多,支持和压力的可能性越强
  • 聚类算法不适用于所有的市场,因此需要您调整参数以适应不同的市场和时间周期

(2) 关键参数
- Support/Resistance Clustering
  • Pivot Lookback Period: 枢纽顶/底点往左/右回顾的 K线 数量
  • Max of Lookback Forward: 回顾历史 K线 的最大数量
  • Min Strength of Clustering Core: 聚类核心区间的最小强度,强度越大,区间越小
  • Min Points of Clustering Core: 聚类核心区间的最小聚类点数量

(3) 脚本说明
  • 因为出现了一些我不希望看到的情况,后续的脚本将不再开源代码,但是您依然可以免费使用该脚本,感谢理解和支持!
  • 如果您存在对于该脚本的使用建议或者意见,欢迎各位留言!