thinking_about_TA.md

About Technical Indicators

There are three types of technical indicators

1. Time series indicators. Must be sorted by time, and then calculated. It is difficult to handle null in the middle.
2. Cross-sectional indicators. No order requirement, the whole row is calculated, and there may be a null, which needs to be compatible.
3. Single element indicators. Can be single-column, multi-column, and can be calculated in any way.

In terms of calculation, time series indicators have one more dimension than cross-sectional indicators. For example

1. cs_rank is a cross-sectional sort

2. ts_rank is to calculate cs_rank in a rolling time window, take [-1] each time, and then concatenate them

3. cs_zscore first aggregates to calculate mean and std, then broadcast mean and std, and perform a one-dimensional calculation with x

So common technical indicators are generally the flexible application of aggregation and broadcasting

Since ts_ indicators are based on cs_, theoretically rolling can be used directly, but in practice, it is generally not used in this way. cs_ is generally rewritten in cython, numba, etc., and if you mix ts_ and cs_, ts_ will make thousands of cs_ call. So it is common to put rolling operations into cython, numba, etc. as well.

对技术指标的再思考

用使用方式来说，指标分为三种

1. 时序指标。必须按时间排序，然后计算，中段出现null比较难处理
2. 截面指标。对顺序无要求，整行计算即可，数据会有null，需要能兼容
3. 单元素指标。可单列、多列，可任意方式计算

从计算原理上来说，时序指标比截面指标多了一个计算维度。例如

1. cs_rank是横截面排序
2. 而ts_rank是对数据滚动时间窗口计算cs_rank,每次取[-1]，然后拼接起来

又例如

1. cs_zscore先聚合计算mean和std
2. 然后将mean与std广播,与x进行一维计算

所以常见的技术指标一般是聚合与广播的灵活应用

由于ts_指标基于cs_,理论上直接rolling即可，但在实践中一般不这么用，因为为了求快，cs_一般是更底层的语言编写，如cython、numba等， 本来cs_版python与底层只交互一次，而ts_调用cs_版会导致交互千万次，性能极低 一般是把rolling操作也放在底层，在底层循环调用cs_底层版