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How to support different timescale ? #583
Description
For example, I have 1 minute scale tick data, and I'd like add 5 minute/ 15 minute feature upon .
I wrote a function
def apply_over(func, arr, stride):
n = len(arr)
s = np.empty(n).reshape(-1, stride)
for i in range(stride):
s[:,i] = func(arr[i::stride])
return s.reshape(n,)
This generate 5 minute SMA features for each 1 minute tick .
arr = np.arange(n).astype(float)
sma_5_5 = apply_over(lambda a: talib.SMA(a, 5) , arr, 5)
I'd like adapt above code to every talib function, though there are many Indicator need multi input.
So wonder is there any suggestion way to wrap talib function with my apply_over fucntion ?
Or I have to rewrite all the functions ...
Activity
mrjbq7 commentedon Mar 14, 2023
eromoe commentedon Mar 14, 2023
No, arr is input .
you can think
arr = np.arange(1000).astype(float)as a stock close price at minute level .It is a 1000 time tick collection.
On every tick , I need calculate
sma5ontalib.SMA(arr, 5))apply_over(lambda a: talib.SMA(a, 5) , arr, 5))apply_over(lambda a: talib.SMA(a, 5) , arr, 15))For example:
A series
...,500, 501, 502, 503, 504 , 505,....At 505,
Using
pandas resmple(freq='5min').first()would make gaps , shrinkarrlength from1000to200. You need do it 5 times with each shift [0,1,2,3,4] and applySMAto make sure every tick have 5 minute feature, , like what I do inapply_overeromoe commentedon Mar 14, 2023
I am looking for a way, either inherit a interface or a wrap to achieve my goal efficiently.
Avoid writing too much replicated codes.
Marc-Bogonovich commentedon Sep 23, 2023
Hello @eromoe , I'm curious if you've had any progress on this issue. I am currently working on similar code myself.