Sometimes, stock trader may want to find out the strongest and weakest in a list of stocks. Here is the python source code:
#YouTube video here when it is ready:
'''
Source code is from ATradingSystem, https://atradingsystem.com/
All rights reserved.
'''
import pandas as pd
stock_list = ['0038.HK', '0116.HK', '0135.HK', '0148.HK', '0168.HK', '0175.HK', '0187.HK', '0200.HK',
'0257.HK', '0268.HK', '0285.HK', '0303.HK', '0316.HK', '0322.HK', '0323.HK', '0347.HK',
'0358.HK', '0384.HK', '0388.HK', '0392.HK', '0398.HK', '0451.HK', '0489.HK', '0512.HK',
'0590.HK', '0669.HK', '0688.HK', '0698.HK', '0700.HK', '0716.HK', '0732.HK', '0746.HK',
'0763.HK', '0823.HK', '0826.HK', '0836.HK', '0838.HK', '0855.HK', '0867.HK', '0868.HK',
'0880.HK', '0883.HK', '0914.HK', '0921.HK', '0939.HK', '0960.HK', '1044.HK', '1053.HK',
'1057.HK', '1066.HK', '1072.HK', '1083.HK', '1093.HK', '1109.HK', '1133.HK', '1171.HK',
'1177.HK', '1193.HK', '1208.HK', '1211.HK', '1308.HK', '1378.HK', '1478.HK', '1600.HK',
'1812.HK', '1818.HK', '1882.HK', '1919.HK', '1999.HK', '2002.HK', '2018.HK', '2020.HK',
'2128.HK', '2208.HK', '2238.HK', '2313.HK', '2314.HK', '2318.HK', '2319.HK', '2331.HK',
'2333.HK', '2338.HK', '2343.HK', '2357.HK', '2380.HK', '2382.HK', '2388.HK', '2600.HK',
'2678.HK', '2688.HK', '2689.HK', '2883.HK', '2899.HK', '3311.HK', '3323.HK', '3339.HK',
'3808.HK', '3888.HK', '3899.HK', '3900.HK', '8083.HK']
inputstock = input("Enter 1 stock code, e.g. 0323, or empty for all stocks: ")
if inputstock:
stock_list = [inputstock]
# compare_first = 90 # 90 days ago to compare
# compare_last = 1
a, b = input("Enter day1 and last day to compare, e.g. 90 1: ").split()
compare_first = int(a)
compare_last = int(b)
column_names = ["stockname", "start date", "start price", "end date", "end price", "% changed"]
dfnew = pd.DataFrame(columns=column_names)
print("")
i = 0
posChgPerc = 0
negChgPerc = 0
posChgNum = 0
negChgNum = 0
for stock_num in stock_list:
stockname = 'HK' + stock_num[0:4]
# stockname = 'HKHSI'
# print('--> ' + stockname + ': <--')
filepath = 'C:/pythonprojects/stocks/' + stockname + '.csv'
df = pd.read_csv(filepath) #, index_col=0, parse_dates=True)
# df = df.loc['2014-09-02':'2021-09-01', :]
totalrows = len(df.index)
row1 = totalrows - compare_first
rowx = totalrows - compare_last
# print('total rows = ' + str(totalrows))
date1 = df['Date'][row1]
close1 = df['Close'][row1]
datex = df['Date'][rowx]
closex = df['Close'][rowx]
pricediff = ((closex - close1) / close1) * 100
# print(pricediff)
dfnew.loc[i, "stockname"] = stockname
dfnew.loc[i, "start date"] = date1
dfnew.loc[i, "start price"] = close1
dfnew.loc[i, "end date"] = datex
dfnew.loc[i, "end price"] = closex
dfnew.loc[i, "% changed"] = pricediff
if pricediff > 0:
posChgNum += 1
posChgPerc += pricediff
else:
negChgNum += 1
negChgPerc += pricediff
i += 1
dfnew2 = dfnew.sort_values(by=['% changed'], ascending=False)
pd.set_option('display.max_rows', None)
print(dfnew2)
if posChgNum > 0:
posChgAvg = posChgPerc / posChgNum
else:
posChgAvg = 0
if negChgNum > 0:
negChgAvg = negChgPerc / negChgNum
else:
negChgAvg = 0
print("Number of gain: " + str(posChgNum) + ", average gain %: " + f"{posChgAvg:.2f}")
print("Number of loss: " + str(negChgNum) + ", average loss %: " + f"{negChgAvg:.2f}")
print("Total stocks counted: " + str(len(dfnew2.index)))
print("")
filename = input('Enter file name (without extension) to save file, empty input to skip saving file: ')
if filename:
writefilepath = 'C:/pythonprojects/stocks/' + filename + '.csv'
dfnew2.to_csv(writefilepath)
- Line 10 (and line 47), change and input your own stock file name. This is assume you already have download the historical data in your PC. If not, go back to previous post to download historical data by python.
- Line 24, python program waiting for user to input in console. If using a list of stocks in line 10, simply press enter key for empty input.
- Line 32, input 2 numbers in console. First one is x days ago, second one is latest day to compare. If using today latest closing price, second input is 1. The second number should be smaller than the first number, but larger than 0.
- Line 47, change the stock code name for your own. E.g. if stock file name is “AAPL”, simply remove “HK” or remove this line.
- Line 51, change to your own file path location.
- Line 104, input file name to save if necessary. Enter empty input to skip saving file and end this python program.
The input and result should be similar to the following:
Enter 1 stock code, e.g. 0323, or empty for all stocks:
Enter day1 and last day to compare, e.g. 90 1: 191 1
stockname start date start price end date end price % changed
55 HK1171 2022-01-03 16.26 2022-10-12 27.45 68.819188
99 HK3900 2022-01-03 11.88 2022-10-12 14.4 21.212121
41 HK0883 2022-01-03 8.09 2022-10-12 9.58 18.4178
91 HK2883 2022-01-03 6.84 2022-10-12 7.84 14.619883
26 HK0688 2022-01-03 18.42 2022-10-12 20.45 11.02063
49 HK1066 2022-01-03 9.91 2022-10-12 10.58 6.760848
4 HK0168 2022-01-03 70.849998 2022-10-12 70.65 -0.282284
86 HK2388 2022-01-03 25.950001 2022-10-12 25.8 -0.578039
53 HK1109 2022-01-03 32.599998 2022-10-12 30.55 -6.288338
82 HK2343 2022-01-03 3.0 2022-10-12 2.7 -10.0
52 HK1093 2022-01-03 8.47 2022-10-12 7.59 -10.38961
65 HK1818 2022-01-03 6.56 2022-10-12 5.81 -11.432927
19 HK0392 2022-01-03 27.0 2022-10-12 22.85 -15.37037
92 HK2899 2022-01-03 9.28 2022-10-12 7.79 -16.056034
13 HK0322 2022-01-03 15.8 2022-10-12 13.18 -16.582278
24 HK0590 2022-01-03 21.25 2022-10-12 17.58 -17.270588
44 HK0939 2022-01-03 5.44 2022-10-12 4.46 -18.014706
58 HK1208 2022-01-03 2.55 2022-10-12 2.04 -20.0
46 HK1044 2022-01-03 40.400002 2022-10-12 32.3 -20.049509
50 HK1072 2022-01-03 13.92 2022-10-12 10.8 -22.413793
61 HK1378 2022-01-03 8.47 2022-10-12 6.55 -22.668241
33 HK0823 2022-01-03 68.650002 2022-10-12 52.25 -23.889296
20 HK0398 2021-12-31 4.88 2022-10-12 3.63 -25.614754
2 HK0135 2022-01-03 7.41 2022-10-12 5.44 -26.585695
59 HK1211 2021-12-31 266.600006 2022-10-12 195.0 -26.856716
71 HK2020 2022-01-03 114.699997 2022-10-12 82.9 -27.724497
43 HK0921 2022-01-03 9.0 2022-10-12 6.48 -28.0
69 HK2002 2022-01-03 2.28 2022-10-12 1.64 -28.070175
93 HK3311 2022-01-03 9.81 2022-10-12 7.05 -28.134557
1 HK0116 2022-01-03 11.1 2022-10-12 7.96 -28.288288
16 HK0358 2022-01-03 12.7 2022-10-12 9.08 -28.503937
0 HK0038 2022-01-03 4.01 2022-10-12 2.85 -28.927681
6 HK0187 2022-01-03 3.84 2022-10-12 2.71 -29.427083
11 HK0303 2022-01-03 61.650002 2022-10-12 43.2 -29.92701
77 HK2318 2022-01-03 55.75 2022-10-12 38.85 -30.313901
74 HK2238 2022-01-03 7.83 2022-10-12 5.45 -30.395913
79 HK2331 2022-01-03 85.5 2022-10-12 59.1 -30.877193
78 HK2319 2022-01-03 44.849998 2022-10-12 30.2 -32.664434
54 HK1133 2022-01-03 3.82 2022-10-12 2.55 -33.246073
66 HK1882 2022-01-03 21.799999 2022-10-12 14.5 -33.486235
38 HK0867 2022-01-03 13.64 2022-10-12 8.97 -34.237537
56 HK1177 2022-01-03 5.62 2022-10-12 3.69 -34.341637
89 HK2688 2022-01-03 150.800003 2022-10-12 98.0 -35.013264
32 HK0763 2022-01-03 21.5 2022-10-12 13.96 -35.069767
12 HK0316 2022-01-03 201.0 2022-10-12 129.2 -35.721393
98 HK3899 2022-01-03 12.26 2022-10-12 7.85 -35.970636
10 HK0285 2022-01-03 28.1 2022-10-12 17.64 -37.224199
42 HK0914 2022-01-03 39.450001 2022-10-12 24.65 -37.515844
22 HK0489 2022-01-03 6.62 2022-10-12 4.11 -37.915408
36 HK0838 2022-01-03 1.96 2022-10-12 1.2 -38.77551
31 HK0746 2022-01-03 8.59 2022-10-12 5.25 -38.882421
72 HK2128 2022-01-03 11.3 2022-10-12 6.82 -39.646018
18 HK0388 2022-01-03 454.600006 2022-10-12 270.2 -40.563133
39 HK0868 2022-01-03 19.34 2022-10-12 11.4 -41.054809
7 HK0200 2022-01-03 9.47 2022-10-12 5.58 -41.077086
87 HK2600 2022-01-03 4.41 2022-10-12 2.59 -41.269841
48 HK1057 2022-01-03 2.45 2022-10-12 1.43 -41.632653
96 HK3808 2022-01-03 11.9 2022-10-12 6.94 -41.680672
84 HK2380 2021-12-31 5.25 2022-10-12 3.06 -41.714286
94 HK3323 2022-01-03 9.61 2022-10-12 5.6 -41.727367
15 HK0347 2022-01-03 3.5 2022-10-12 2.02 -42.285714
14 HK0323 2021-11-26 2.82 2022-10-12 1.61 -42.907801
3 HK0148 2022-01-03 38.450001 2022-10-12 21.8 -43.302992
67 HK1919 2022-01-03 16.08 2022-10-12 9.04 -43.781095
73 HK2208 2022-01-03 15.86 2022-10-12 8.87 -44.07314
64 HK1812 2022-01-03 3.9 2022-10-12 2.18 -44.102564
28 HK0700 2022-01-03 453.799988 2022-10-12 253.6 -44.116349
90 HK2689 2022-01-03 8.41 2022-10-12 4.66 -44.589774
45 HK0960 2022-01-03 36.099998 2022-10-12 19.88 -44.930745
23 HK0512 2022-01-03 6.55 2022-10-12 3.59 -45.19084
88 HK2678 2022-01-03 10.22 2022-10-12 5.57 -45.499022
83 HK2357 2022-01-03 5.53 2022-10-12 3.0 -45.750452
47 HK1053 2022-01-03 1.3 2022-10-12 0.7 -46.153846
97 HK3888 2022-01-03 34.650002 2022-10-12 18.44 -46.78211
57 HK1193 2022-01-03 44.099998 2022-10-12 23.2 -47.392288
37 HK0855 2022-01-03 11.44 2022-10-12 5.92 -48.251748
17 HK0384 2021-12-31 16.200001 2022-10-12 8.36 -48.395065
95 HK3339 2022-01-03 2.19 2022-10-12 1.12 -48.858447
60 HK1308 2022-01-03 29.65 2022-10-12 14.98 -49.477234
40 HK0880 2022-01-03 5.11 2022-10-12 2.57 -49.706458
29 HK0716 2022-01-03 1.26 2022-10-12 0.62 -50.793651
25 HK0669 2022-01-03 157.600006 2022-10-12 76.9 -51.205586
5 HK0175 2022-01-03 21.299999 2022-10-12 10.18 -52.206571
76 HK2314 2022-01-03 5.44 2022-10-12 2.58 -52.573529
35 HK0836 2022-01-03 27.450001 2022-10-12 12.8 -53.369765
81 HK2338 2022-01-03 15.62 2022-10-12 7.28 -53.393086
8 HK0257 2022-01-03 6.63 2022-10-12 2.99 -54.901961
68 HK1999 2022-01-03 11.54 2022-10-12 5.2 -54.939341
75 HK2313 2022-01-03 138.199997 2022-10-12 61.25 -55.680173
34 HK0826 2022-01-03 4.7 2022-10-12 2.0 -57.446809
51 HK1083 2022-01-03 6.93 2022-10-12 2.94 -57.575758
9 HK0268 2022-01-03 23.9 2022-10-12 9.18 -61.589958
70 HK2018 2022-01-03 30.75 2022-10-12 11.56 -62.406504
63 HK1600 2022-01-03 8.24 2022-10-12 3.0 -63.592233
27 HK0698 2022-01-03 0.255 2022-10-12 0.081 -68.235294
30 HK0732 2022-01-03 3.56 2022-10-12 1.13 -68.258427
62 HK1478 2022-01-03 9.94 2022-10-12 3.02 -69.617706
85 HK2382 2022-01-03 250.0 2022-10-12 73.7 -70.52
80 HK2333 2022-01-03 26.799999 2022-10-12 7.67 -71.380596
100 HK8083 2022-01-03 0.51 2022-10-12 0.097 -80.980392
21 HK0451 2022-01-03 0.249 2022-10-12 0.037 -85.140562
Number of gain: 6, average gain %: 23.48
Number of loss: 95, average loss %: -39.38
Total stocks counted: 101
Enter file name (without extension) to save file, empty input to skip saving file:
Process finished with exit code 0
– Line 1, enter empty input, to use the list of stocks on line 10.
– Line 2, enter input 191 1. This is 191 days ago, and today latest price to compare.
– Line 110, enter empty input to skip saving file. This python program will end here.
– It can be draw conclusion that most Hong Kong stocks are getting lower and lower (hit more than 25-year low).
Bonus Reminder:
Sometimes, investor may wish to find out what stocks go up more, before or after a holiday/event. E.g. 1st trading day after new year – simply run this program with parameters:
First parameter: empty input to use all stocks
Second parameter: x x-1 , where x is the first day and only 1 day after new year. E..g: 150 149
Third parameter: file name to save, optional. Empty input to skip saving file.
Repeat the above steps for past several years to find out what stocks get high possibility to go up after new year!