Support/Resistance DBSCANHello, 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!
————————————————————————————————————————
各位朋友大家好，这是一个全新的基于快速聚类算法DBSCAN的支撑压力位指标
(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！
祝大家交易愉快
