Thanks for your great explaination!
But truely to say I find the smooth one ist more reallity than the zick-zack one. I use many other chart tolls and they all show the smooth one.
How to can I get the smooth one in algo?
I find out, the longer I set in hist = data.history(context.spy, 'close', context.ma_length + 40, '1d'), the smoother it will be. But the question is: If the underlying is emited on 2010.01.01 for example and I do the backtesting from 2010.02.01, I can get so many historical datas and there for the WMA will be wrong, right?
Besides, even I use the very long back datas in the hist(), the WMA looks still have some small zick-zack on Dec.19, Sep.19, June 19 etc.(see attached backtesting below). You wrote in your notebook:
The notebook used SPY pricing which was adjusted as of the end_date. The algo used data which was adjusted as of each simulation day (similar to pipeline data).
Is it possible to make the pricing in algo adjusted as of the end_date?