Vietlot data

Data crawling from https://vietlott.vn/, results for products:

• 6/55
• 6/45
• Keno
• Max 3D
• Max 3D Pro

Table of content

• Vietlot data
  • Predictions (just for testing, not a financial advice)
    • random
  • raw details 6/55
  • stats 6/55 all time
  • stats 6/55 -15d
  • stats 6/55 -30d
  • stats 6/55 -60d
  • stats 6/55 -90d
• Install
  • via pip
  • cli
    • crawl
    • Backfill missing data

Predictions (just for testing, not a financial advice)

These are backtest results for the strategies I have tested (just the abstract method at the moment, you can't predict lotery lol)

random strategy

predicted: 20 / day (20 tickets perday or 200,000 vnd) predicted corrected:

date	result	predicted

raw details 6/55 last 10 days

date	id	result	page	process_time
2025-03-01	01158	[15, 17, 34, 37, 39, 45, 41]	0	2025-03-01 19:00:19.333042
2025-02-27	01157	[5, 9, 21, 31, 43, 53, 11]	0	2025-02-27 22:55:20.424084
2025-02-25	01156	[1, 7, 11, 24, 29, 30, 48]	0	2025-02-25 20:58:27.007819
2025-02-22	01155	[1, 2, 7, 22, 23, 46, 50]	0	2025-02-22 19:00:38.011886
2025-02-20	01154	[13, 17, 20, 27, 36, 54, 47]	0	2025-02-20 19:00:12.840993
2025-02-18	01153	[12, 13, 30, 38, 40, 47, 42]	0	2025-02-19 19:00:18.391949
2025-02-15	01152	[22, 37, 38, 47, 51, 55, 31]	0	2025-02-15 19:00:15.907100
2025-02-13	01151	[2, 8, 23, 26, 42, 47, 7]	0	2025-02-13 19:00:13.398152
2025-02-11	01150	[1, 9, 18, 21, 35, 40, 44]	0	2025-02-11 19:00:16.958421
2025-02-08	01149	[11, 22, 28, 44, 48, 49, 23]	0	2025-02-08 19:00:21.621172

stats 6/55 all time

result	count	%	-	result	count	%	-	result	count	%
1	162	2		21	142	1.75		41	169	2.09
2	135	1.67		22	170	2.1		42	146	1.8
3	157	1.94		23	161	1.99		43	161	1.99
4	129	1.59		24	146	1.8		44	152	1.88
5	147	1.81		25	135	1.67		45	142	1.75
6	126	1.55		26	136	1.68		46	156	1.92
7	128	1.58		27	134	1.65		47	149	1.84
8	155	1.91		28	131	1.62		48	156	1.92
9	162	2		29	153	1.89		49	153	1.89
10	138	1.7		30	127	1.57		50	147	1.81
11	156	1.92		31	154	1.9		51	171	2.11
12	158	1.95		32	153	1.89		52	152	1.88
13	140	1.73		33	149	1.84		53	156	1.92
14	141	1.74		34	162	2		54	142	1.75
15	134	1.65		35	150	1.85		55	146	1.8
16	136	1.68		36	139	1.71
17	135	1.67		37	133	1.64
18	151	1.86		38	140	1.73
19	144	1.78		39	139	1.71
20	159	1.96		40	160	1.97

stats 6/55 -15d

result	count	%	-	result	count	%	-	result	count	%
1	5	5.1		28	2	2.04		50	1	1.02
2	2	2.04		29	2	2.04		51	2	2.04
5	2	2.04		30	2	2.04		52	2	2.04
7	4	4.08		31	3	3.06		53	1	1.02
8	2	2.04		34	3	3.06		54	2	2.04
9	2	2.04		35	1	1.02		55	2	2.04
11	4	4.08		36	1	1.02
12	1	1.02		37	2	2.04
13	2	2.04		38	3	3.06
15	1	1.02		39	2	2.04
17	3	3.06		40	2	2.04
18	1	1.02		41	2	2.04
19	1	1.02		42	2	2.04
20	2	2.04		43	2	2.04
21	2	2.04		44	2	2.04
22	3	3.06		45	2	2.04
23	3	3.06		46	1	1.02
24	2	2.04		47	5	5.1
26	1	1.02		48	3	3.06
27	1	1.02		49	2	2.04

stats 6/55 -30d

result	count	%	-	result	count	%	-	result	count	%
1	5	5.1		28	2	2.04		50	1	1.02
2	2	2.04		29	2	2.04		51	2	2.04
5	2	2.04		30	2	2.04		52	2	2.04
7	4	4.08		31	3	3.06		53	1	1.02
8	2	2.04		34	3	3.06		54	2	2.04
9	2	2.04		35	1	1.02		55	2	2.04
11	4	4.08		36	1	1.02
12	1	1.02		37	2	2.04
13	2	2.04		38	3	3.06
15	1	1.02		39	2	2.04
17	3	3.06		40	2	2.04
18	1	1.02		41	2	2.04
19	1	1.02		42	2	2.04
20	2	2.04		43	2	2.04
21	2	2.04		44	2	2.04
22	3	3.06		45	2	2.04
23	3	3.06		46	1	1.02
24	2	2.04		47	5	5.1
26	1	1.02		48	3	3.06
27	1	1.02		49	2	2.04

stats 6/55 -60d

result	count	%	-	result	count	%	-	result	count	%
1	6	3.3		22	7	3.85		43	3	1.65
2	2	1.1		23	4	2.2		44	2	1.1
3	2	1.1		24	3	1.65		45	3	1.65
4	3	1.65		25	1	0.55		46	3	1.65
5	3	1.65		26	3	1.65		47	6	3.3
7	4	2.2		27	1	0.55		48	4	2.2
8	4	2.2		28	4	2.2		49	3	1.65
9	3	1.65		29	4	2.2		50	5	2.75
10	3	1.65		30	4	2.2		51	7	3.85
11	7	3.85		31	5	2.75		52	3	1.65
12	2	1.1		33	1	0.55		53	4	2.2
13	4	2.2		34	4	2.2		54	2	1.1
14	2	1.1		35	1	0.55		55	2	1.1
15	1	0.55		36	3	1.65
16	2	1.1		37	6	3.3
17	3	1.65		38	3	1.65
18	5	2.75		39	4	2.2
19	2	1.1		40	5	2.75
20	2	1.1		41	4	2.2
21	4	2.2		42	4	2.2

stats 6/55 -90d

result	count	%	-	result	count	%	-	result	count	%
1	7	2.63		21	6	2.26		41	6	2.26
2	3	1.13		22	8	3.01		42	6	2.26
3	3	1.13		23	4	1.5		43	3	1.13
4	4	1.5		24	5	1.88		44	4	1.5
5	3	1.13		25	1	0.38		45	4	1.5
6	2	0.75		26	4	1.5		46	3	1.13
7	5	1.88		27	3	1.13		47	8	3.01
8	4	1.5		28	4	1.5		48	6	2.26
9	5	1.88		29	7	2.63		49	4	1.5
10	5	1.88		30	6	2.26		50	5	1.88
11	10	3.76		31	5	1.88		51	10	3.76
12	3	1.13		32	2	0.75		52	4	1.5
13	5	1.88		33	4	1.5		53	6	2.26
14	3	1.13		34	4	1.5		54	5	1.88
15	2	0.75		35	2	0.75		55	4	1.5
16	9	3.38		36	5	1.88
17	5	1.88		37	8	3.01
18	7	2.63		38	4	1.5
19	5	1.88		39	7	2.63
20	4	1.5		40	5	1.88

How project works

Since there are many people asked, I write this section.

Schedule

The project is schedule automatically via Github Actions, run a script, fetch data and auto commit to Github. No server is required, I don't need to do anything. Details in workflow file

How crawling works

I just inspected network packages sent between browser and server to find out how data is fetched and replicated that in Python code.

Install

via pip

pip install -i https://test.pypi.org/simple/ vietlott-data==0.1.3

cli

project provides two cli

crawl

Usage: vietlott-crawl [OPTIONS] PRODUCT

  crawl a product with a given run date or from/to index page 

Options:
  --run-date TEXT
  --index_from INTEGER  page index from run since we crawl by pagination the
                        pages
  --index_to INTEGER    page index from run since we crawl by pagination the
                        pages
  --help                Show this message and exit.

Backfill missing data

Usage: vietlott-missing [OPTIONS] PRODUCT

  detect_missing_data and run if needed :param ctx: context :param product:
  product to run :param limit: number of pages to run :return:

Options:
  --limit INTEGER
  --help           Show this message and exit.