Hi Pravin -

What is Kinetic Component Analysis? And why might it work?

Also, since you are using daily data, could your code be written as a pipeline custom factor (so that it could be combined with other factors, in a single algo)?

Thanks,

Grant

Hi Grant,

Here is the paper. It could be written as a pipeline factor but first I want to get it working with a single asset. Mostly predicting futures.

Best regards,
Pravin

In the source code, he initialises the state transition matrix A to be equal to

A=np.array([[1,h,.5*h**2, 1./6.*h**3],  
                 [0,1,h,h**2],  
                 [0,0,1,h],  
                 [0,0,0,1]])  

Can someone give an explanation as to how is this derived?

In the paper, A is taken to be

A = np.array([[1,h,h**2],  
                 [0,1,h],  
                 [0,0,1]])  

Thanks!

Oh dear, I misread 0.5 to be 5. This is straightforward.

I tried to put this in pipeline. pipeline loads for a long time and then errors out on schedule function with
TimeoutException: Too much time spent in handle_data and/or scheduled functions. 50 second limit exceeded.

In general, you should call pipeline_output in before_trading_start, as opposed to a scheduled function or handle_data. The before_trading_start function has a 5 minute limit as opposed to scheduled functions and handle_data which have a 50 second limit. There has been some discussion on another thread about ways that might change at some point in the future, but for now the best solution is to call pipeline_output in before_trading_start, save the result in a context variable, and then reference the context variable in other parts of the algorithm.

Let me know if this helps.