Calculate ratio compared with the previous period for continuous intervals of grouped subsets

Task:Calculate the longest consecutive rising days of each stock.

Python

1	import pandas as pd
2	def con_rise(stock:pd.DataFrame):
3	rise_day_list = []
4	rise_num = 0
5	shift_1 = stock['CL']>stock['CL'].shift(1)
6	for bl in shift_1:
7	if bl == False:
8	rise_num = 0
9	else:
10	rise_num+=1
11	rise_day_list.append(rise_num)
12	return max(rise_day_list)
13	stock_file = "E:\\txt\\StockRecords.txt"
14	stock_info = pd.read_csv(stock_file,sep="\t")
15	stock_info.sort_values(by='DT',inplace=True)
16	stock_group = stock_info.groupby(by='CODE')
17	max_rise_list = []
18	for index,stock_g in stock_group:
19	code = stock_g.iloc[0]['CODE']
20	max_rise_list.append([code,con_rise(stock_g)])
21	max_rise_df = pd.DataFrame(max_rise_list,columns=['CODE','con_rise'])
22	print(max_rise_df)

esProc

	A	B
1	E:\\txt\\StockRecords.txt
2	=file(A1).import@t()
3	=A2.sort(DT)
4	=A3.group(CODE)
5	=A4.new(CODE,func(A6,~):con_rise)
6	func
7		=(num=0,A6.max(num=if(CL>CL[-1],if(#==1,0,num+1),0)))

The idea of comparing with last period for continuous periods of a single stock: if it is higher than the stock price of the previous day, add 1; if it is not greater than, set 0; finally, check the maximum value in the sequence. The calculation method of a single stock is written as a function, and the table of each stock is passed in as a parameter. esProc can easily call the function in the loop function to get the result. With the same idea, pandas code looks much more complex.