Moving Average of time series using a sliding window over an array - moving-average

Question

I need to write a function below that can compute the moving average of time series using a sliding window over an array. This function should take an array of date strings (say arr_date), an array of numbers (say arr_record), and a sliding window (default value 50). It should:
Return a list of dictionaries for all windows.
Each dictionary should include the date, average value, min, max, standard deviation at each window.
Able to handle missing data in time series by replacing missing data with the most recent available data.
(b) Download SPY daily data (Dec. 31, 2017 to Dec. 31, 2018) from Yahoo! as your test data in a .csv file. Read reading .csv file example and write a test programming for calling your function.
Does anyone have any thoughts? Extremely new to python and struggling.

Answer 1

So something following this logic should probably be a good starting point. Hope this is a helpful start, and welcome to the cs community.
def sliding_window( dates, numbers, sliding_window_value):
# list of dictionaries
return_dicts =[{}]
# if window size is greater than length of dates, there's only one window
if sliding_window_value >= len(dates):
return_dicts += [create_window(dates, numbers)]
return return_dicts
# gather all our windows into one list
for i in range (0, len(dates) - sliding_window_value ):
# get our window subsets
dates_subset = dates[i:(sliding_window_value+1)]
numbers_subset = numbers[i:(sliding_window_value+1)]
# get our window stats dictionary
window_stats = create_window(dates_subset,numbers_subset)
# add these stats to our return list
return_dicts += [window_stats]
return return_dicts
def create_window(dates_subset, numbers_subset):
window_min = 1000000 # some high minimum to start
window_max = -1000000 # some low maximuim to start
window_total = 0
for i in range ( 0, len(dates_subset)):
# calculate total
window_total += numbers_subset[i]
# calculate max
if numbers_subset[i] > window_max:
window_max = numbers_subset[i]
# calculate min
if numbers_subset[i] < window_min:
window_min = numbers_subset[i]
# other calculations....
return_dict = {
"min" : window_min,
"max" : window_max,
"average" : window_total / len(dates_subset),
# other calculations....
}
return return_dict
Good luck bud, the work is worth it.