This tutorial is based on last tutorial, modified the program to show a list of stock closing prices that higher or lower than its own 20-day simple moving average (SMA), then sort the list.
These 2 programs are some easy warmup exercises, but not part of my trading system. It is good to know what are the strongest and weakest stocks in the list. Again, this is assume user already download the historical stock closing price in your pc. Otherwise, please refer to this tutorial to get the stock prices first.
YouTube video here when it is ready.
Python program source code:
'''
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]
dayfaraway = int(input("Enter 1 for current date, or any number for day that away from current date: "))
column_names = ["stockname", "date", "price"]
dfnew = pd.DataFrame(columns=column_names)
print("")
i = 0
for stock_num in stock_list:
stockname = 'HK' + stock_num[0:4]
filepath = 'C:/pythonprojects/stocks/' + stockname + '.csv'
df = pd.read_csv(filepath, parse_dates=["Date"])
# df = idf.loc['2014-09-02':'2021-09-01', :]
df['SMA_10'] = df['Close'].rolling(10).mean()
df['SMA_20'] = df['Close'].rolling(20).mean()
df['SMA_50'] = df['Close'].rolling(50).mean()
# print(df)
totalrows = len(df.index)
row1 = totalrows - dayfaraway
date1 = (df['Date'][row1]).strftime('%Y-%m-%d')
close1 = df['Close'][row1]
dfnew.loc[i, "stockname"] = stockname
dfnew.loc[i, "date"] = date1
dfnew.loc[i, "price"] = close1
dfnew.loc[i, "SMA_10"] = df.loc[row1, "SMA_10"]
dfnew.loc[i, "SMA_20"] = df.loc[row1, "SMA_20"]
dfnew.loc[i, "SMA_50"] = df.loc[row1, "SMA_50"]
diff_perc = (dfnew.loc[i, 'price'] - df.loc[row1, 'SMA_20']) * 100 / df.loc[row1, 'SMA_20']
dfnew.loc[i, "diff_perc"] = diff_perc
i += 1
dfnew2 = dfnew.sort_values(by=['diff_perc'], ascending=True)
pd.set_option('display.max_rows', None)
print(dfnew2)
filename = input("Input file name for saving details, or empty input to skip saving: ")
if filename:
writefilepath = 'C:/pythonprojects/stocks/' + filename + '.csv'
dfnew2.to_csv(writefilepath)
- Line 25, input a stock code number in console, or empty input to continue.
- Line 25, 39, 40, if stock code is like “GOOG”, comment line 39 and change “stockname” on line 40 to “stock_num”.
- Line 30, input a number in console. e.g. 1, if using latest closing price; 10, if using closing price 10 days ago.
- Line 42, read csv file (historical stock price) here.
- Line 44-46, create 10-day, 20-day and 50-day simple moving average (SMA) column here. Modified the number if user wanted. e.g. df[‘SMA_5’] = df[‘Close’].rolling(5).mean() for 5-day SMA.
- Line 56-61, create a table row and copy the row details.
- Line 63-64, create new column, calculate the difference between the closing price and its own 20-day sma in percentage. User can choose 10-day or 50-day if wanted.
- Line 66, repeat with next row, until end of stock list.
- Line 69, table created and sort on the column “diff_perc”
- Line 74-77, input file name in console, to save the table into csv file. Modified the file path for your own pc.
Here is a sample result:
Enter 1 stock code, e.g. 0323, or empty for all stocks:
Enter 1 for current date, or any number for day that away from current date: 1
stockname date price SMA_10 SMA_20 SMA_50 diff_perc
45 HK0960 2022-10-31 10.0 16.1360 18.72300 23.01820 -46.589756
99 HK3900 2022-10-31 7.47 10.9320 12.89600 14.59880 -42.075062
79 HK2331 2022-10-31 40.6 47.6300 54.36750 61.90800 -25.323033
26 HK0688 2022-10-31 14.98 18.0300 19.43500 20.63000 -22.922562
84 HK2380 2022-10-31 2.27 2.7820 2.91050 3.63760 -22.006528
73 HK2208 2022-10-31 6.81 8.3550 8.65200 10.15260 -21.289875
14 HK0323 2022-10-31 1.26 1.5660 1.60000 1.78660 -21.250000
100 HK8083 2022-10-31 0.07 0.0787 0.08820 0.10466 -20.634921
7 HK0200 2022-10-31 4.1 4.3650 5.13600 5.28380 -20.171340
4 HK0168 2022-10-31 55.0 63.0650 68.27250 71.31300 -19.440477
82 HK2343 2022-10-31 1.9 2.1780 2.35650 2.60700 -19.371950
89 HK2688 2022-10-31 78.05 91.8000 96.43750 105.06500 -19.066753
94 HK3323 2022-10-31 4.56 5.2930 5.63350 6.55140 -19.055649
53 HK1109 2022-10-31 24.6 28.5350 30.06000 31.29100 -18.163673
15 HK0347 2022-10-31 1.63 1.9000 1.97800 2.19940 -17.593529
17 HK0384 2022-10-31 6.96 8.0230 8.42550 9.84180 -17.393626
18 HK0388 2022-10-31 209.0 235.4000 251.96000 282.72400 -17.050325
42 HK0914 2022-10-31 20.2 22.9050 24.22500 27.05500 -16.615067
55 HK1171 2022-10-31 22.1 24.9650 26.49000 29.37300 -16.572291
30 HK0732 2022-10-31 0.96 1.0660 1.13300 1.34800 -15.269197
77 HK2318 2022-10-31 31.4 34.6800 36.87000 41.29100 -14.835910
28 HK0700 2022-10-31 205.6 220.7400 240.66000 277.80800 -14.568271
78 HK2319 2022-10-31 25.15 27.8950 29.43500 31.77900 -14.557500
2 HK0135 2022-10-31 4.69 5.3190 5.48850 5.99860 -14.548602
58 HK1208 2022-10-31 1.55 1.6560 1.80800 1.96000 -14.269912
36 HK0838 2022-10-31 1.03 1.1460 1.20000 1.38420 -14.166667
5 HK0175 2022-10-31 8.44 9.1180 9.79150 12.37340 -13.802788
57 HK1193 2022-10-31 20.1 22.3350 23.31000 26.63200 -13.770914
71 HK2020 2022-10-31 69.0 74.5300 79.85000 86.44100 -13.587977
3 HK0148 2022-10-31 19.36 21.8210 22.09050 23.25620 -12.360517
74 HK2238 2022-10-31 4.78 5.3450 5.44050 6.08200 -12.140428
87 HK2600 2022-10-31 2.24 2.4770 2.54950 2.77260 -12.139635
47 HK1053 2022-10-31 0.61 0.6800 0.69300 0.76680 -11.976912
68 HK1999 2022-10-31 4.38 4.8180 4.96850 5.45260 -11.844621
8 HK0257 2022-10-31 2.61 2.8450 2.95950 3.33080 -11.809427
60 HK1308 2022-10-31 12.86 14.2160 14.55300 16.74480 -11.633340
35 HK0836 2022-10-31 11.42 13.0740 12.91700 14.24960 -11.589378
22 HK0489 2022-10-31 3.55 3.8270 4.00600 4.49140 -11.382926
12 HK0316 2022-10-31 114.7 125.9600 129.31500 152.32600 -11.301860
39 HK0868 2022-10-31 10.1 11.0220 11.34700 12.82800 -10.989689
19 HK0392 2022-10-31 19.92 21.9920 22.34850 23.46140 -10.866501
61 HK1378 2022-10-31 5.57 5.9100 6.20800 6.98220 -10.277062
63 HK1600 2022-10-31 2.81 3.0470 3.12950 3.45860 -10.209299
75 HK2313 2022-10-31 54.4 58.6300 60.58000 68.92500 -10.201387
88 HK2678 2022-10-31 5.06 5.6300 5.62250 6.20960 -10.004446
51 HK1083 2022-10-31 2.75 3.0590 3.04400 3.17860 -9.658344
33 HK0823 2022-10-31 46.4 48.9600 51.21500 56.98200 -9.401543
72 HK2128 2022-10-31 6.37 6.8280 6.97350 8.11780 -8.654191
59 HK1211 2022-10-31 175.7 187.4200 191.58000 215.27600 -8.288965
6 HK0187 2022-10-31 2.44 2.6030 2.64650 3.12860 -7.802758
38 HK0867 2022-10-31 8.57 9.2420 9.28350 10.18340 -7.685679
27 HK0698 2022-10-31 0.074 0.0759 0.08005 0.10022 -7.557776
23 HK0512 2022-10-31 3.36 3.6720 3.62600 3.84700 -7.335907
85 HK2382 2022-10-31 68.05 71.8000 73.37750 88.07800 -7.260400
37 HK0855 2022-10-31 5.56 5.8620 5.98550 6.53520 -7.108846
1 HK0116 2022-10-31 7.24 7.5360 7.79150 8.21200 -7.078226
46 HK1044 2022-10-31 30.45 32.0350 32.75250 35.01200 -7.029998
93 HK3311 2022-10-31 7.06 7.7060 7.59250 8.39560 -7.013500
40 HK0880 2022-10-31 2.45 2.4860 2.63400 2.71900 -6.985573
13 HK0322 2022-10-31 12.28 12.9220 13.16000 13.47720 -6.686930
44 HK0939 2022-10-31 4.17 4.4090 4.45950 4.62360 -6.491759
67 HK1919 2022-10-31 8.47 8.9320 9.04750 9.83860 -6.382979
29 HK0716 2022-10-31 0.6 0.6420 0.63850 0.68080 -6.029757
20 HK0398 2022-10-31 3.36 3.4250 3.57500 4.06200 -6.013986
31 HK0746 2022-10-31 4.98 5.2260 5.29550 5.94940 -5.957889
24 HK0590 2022-10-31 17.06 17.7480 18.09900 19.23700 -5.740649
76 HK2314 2022-10-31 2.38 2.4830 2.52350 2.66640 -5.686546
98 HK3899 2022-10-31 7.65 7.9400 8.07050 8.46460 -5.210334
86 HK2388 2022-10-31 24.4 25.3100 25.72500 26.34700 -5.150632
11 HK0303 2022-10-31 41.75 43.6800 43.94250 48.35500 -4.989475
16 HK0358 2022-10-31 8.58 8.9230 9.02450 9.40520 -4.925481
49 HK1066 2022-10-31 10.82 11.9280 11.37900 10.65920 -4.912558
48 HK1057 2022-10-31 1.31 1.3720 1.37100 1.50740 -4.449307
41 HK0883 2022-10-31 9.36 9.7750 9.74500 9.97260 -3.950744
92 HK2899 2022-10-31 7.5 7.6910 7.79250 8.31580 -3.753609
25 HK0669 2022-10-31 74.35 76.3300 77.20000 85.21400 -3.691710
56 HK1177 2022-10-31 3.81 4.0840 3.93600 3.93000 -3.201220
95 HK3339 2022-10-31 1.08 1.1040 1.11000 1.20360 -2.702703
90 HK2689 2022-10-31 4.65 4.8090 4.77000 5.53960 -2.515723
43 HK0921 2022-10-31 6.5 6.5600 6.65800 7.66640 -2.373085
32 HK0763 2022-10-31 14.0 14.3860 14.29100 15.33680 -2.036247
96 HK3808 2022-10-31 7.0 7.2810 7.11050 7.32580 -1.554040
69 HK2002 2022-10-31 1.63 1.6390 1.65400 1.74520 -1.451028
81 HK2338 2022-10-31 7.52 7.6180 7.56600 8.84520 -0.607983
54 HK1133 2022-10-31 2.65 2.7480 2.65700 2.58000 -0.263455
62 HK1478 2022-10-31 3.1 3.1060 3.10700 3.68440 -0.225298
52 HK1093 2022-10-31 8.07 8.3250 8.03100 7.82320 0.485618
64 HK1812 2022-10-31 2.22 2.2040 2.20000 2.35340 0.909091
66 HK1882 2022-10-31 15.72 15.6060 15.17300 16.44200 3.605088
50 HK1072 2022-10-31 11.84 11.7880 11.28100 11.47960 4.955234
34 HK0826 2022-10-31 2.18 2.0760 2.06300 2.29100 5.671352
91 HK2883 2022-10-31 8.84 8.5310 8.30500 8.22200 6.441902
83 HK2357 2022-10-31 3.44 3.4070 3.23050 3.46680 6.485064
65 HK1818 2022-10-31 6.48 6.2800 6.04600 6.07200 7.178300
80 HK2333 2022-10-31 8.57 7.9240 7.93950 9.79700 7.941306
0 HK0038 2022-10-31 3.16 2.9820 2.90650 3.04480 8.721830
97 HK3888 2022-10-31 23.75 22.0450 20.83300 22.15320 14.001824
70 HK2018 2022-10-31 14.38 12.7260 12.35500 13.34080 16.390125
10 HK0285 2022-10-31 22.8 20.3290 19.32800 20.79400 17.963576
9 HK0268 2022-10-31 12.84 11.2240 10.50950 12.22380 22.175175
Input file name for saving details, or empty input to skip saving:
Process finished with exit code 0
- It can be drawn the conclusion that most Hong Kong stocks are far below 20-day simple moving average.