Thank you, Jet Cash for creating the topic. The video in the OP is great, I have not watched all the video, but I will do watch all of it. Your topic also raised a ideas in my mind. You will know what it is soon. By the way, have a nice weekend, all bitcointalkers.  |
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You acted so fast, LoyceV. Pumperboy, the name of one of these three accounts make sense. It is likely created for pumping services, I believe. Additionally, the answer of OP is ridiculous. We use the logo only to attract attention on the pilot site
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It is interesting to see such speedy listings for Grin Coin and amazing supports from former biggest exchanges, Poloniex, then Bittrex.
They all actually realised the big potential of Grin coin.
By saying about potential of Grin for exchanges, I implied about the coming trading fees from Grin coin.
The coin has high demand from community, so in long term it will contribute significant trading fees for exchanges on which it got listed.
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Recently, I made abstract for both intra-day and intra-week merits thereYou can see it on the link above. In short:It seems that the merit circulation in the forum has been slown down, one more time. |
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ABSTRACT
Intra-day merits:
Notes:
- The part of the asbstract describes figures of intraday merits over the period from 19/2/2018 to 11/02/2019 (truncated dataset);
- Days from 24/1/2018 to 18/2/2018 truncated due to highly potential outliers; and days after 04/02/2019 truncated as well due to incomplete week (the seventh week of 2019, 2019w7);
- Statistics presented in the post are for truncated dataset.
(1) Potential outliers are days that have intraday total merits beyond 141 or 1145;
(2) Median of intraday merits over the period is 620;
(3) 50% of observed days have their intra-day merits range from 517 to 768 (the interquartile range);
(4) Friday [in GTM time] is the day over weeks has lowest intraday merits in terms of both median and mean, at 548, and 616, respectively.
(5) Monday [in GMT time] is the day over weeks has highest intraday merits in terms of both median and mean, at 674, and 747, respectively.
(6) There are 22 potential outliers in total, and there is only one potential outlier day happened in early weeks of 2019, on 09 Jan. 2019, at 1161.
(7) Minimum and maximum of intraday merits (full dataset) are 312 and 13018, on 11/2/2019 and 24/1/2018 respectively.
Intra-week merits:
Notes:
The part of the abstract use full dataset, only dropped last two days due to incomple week (2019w7).
(1) The median of intra-week merits is 4474;
(2) 50% of observed weeks (55 weeeks in total), have total merits in the range from 3800 to 5630 (the interquaritle range of intra-week merits);
(3) Minimum and maximum of intraweek merits are 3065 and 30949, in 2018w35, and 2018w4, respectively;
(4) Six potential outliers [beyond 1055 or 8375], all of them occurred in the year 2018.
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Poloniex will come back. In fact, Poloniex has performed well by improving their supports, user interface, security methods, and their strategy to list new coins. Polo isn't as big as it used to be.
Yeap, Binance is a new Poloniex in both sides. First, they have become big, and the biggest exchange for now. Second, they will deflate later months, or years. At that time, might be the chance for old exchanges, like Poloniex or Bittrex to come back and take over Binance. Binance is the new poloniex. After so many KYC enforcements and locked accounts, many people left poloniex.
It is still better than nothing though.
Everthing change so fast in crypto, and investors, traders simply choose the best one for them at specific period. |
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Congratulations, m2017 got promotion to be a Full Member! | Rank | User name | BPIP profile | BPIP Merit RECEIVED | BPIP Merit SEND | Trust | Status | Personal comment about yourself | |  | m2017 | m2017 | m2017 | m2017 | 0: -0 / +0 | active | Information expected |
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Update for the week:Both median and mean of after period (cut-off day is 09/1/2019, the Default Trust Change) is higher than the before period. However, those percent of changes decreased significantly from the first week later to five weeks later. It seems that the assumption on effects of additional merit sources and re-allocation of sMerits to active merit sources have their impacts on merit circulation in the forum is wrong. I tend to fall into the assumption that those sudden rise of merit circulation around 09/1/2019 due to abundant topics on Default Trust Change, that in turn caused more appearance of high quality posts. More higher quality posts, more merit circulation within 1 or 2 weeks after the Default Trust Change. 5 weeks later update:
Median: +6.2%
Mean: +6.4%
Update on intra-week merits (from 24/1/2018 to 11/2/2019)< ... > Time series plotBasic statistics:- 50% of observed weeks have total intra-week merits above 4474, whilst the rest 50% of them have total intra-week merits below 4474. 4474 is the median. - 50% of observed weeks have total intra-week merits fluctuated in the range from 3800 to 5630 (the interquartile range, from p25 to p75, in raw statistics below). - Min - max: 3065 - 30949. For more details, please visit original analyses with links above.
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5 weeks later update:Median: +6.2%Mean: +6.4%
ABSTRACT(1) Both median and mean of five-week-later period are higher then the before period, with cut-off day is 09/01/2019, at 6.2% and 6.4%, respectively. (2) 50 percent of days in the five weeks later period have intraday merits in the range from 595 to 813 (the interquartile range), whilst the figures of the before period are 511 to 767.
Box plots:Outliers displayed with red circles Outliers, non-displayed Basic statistics:. tabstat before090119 wkslater_2 wkslater_3 wkslater_4 wkslater_5, s(n mean sd p50 p25 p75 min max) format(%9.1f) c(s)
variable | N mean sd p50 p25 p75 min max
-------------+--------------------------------------------------------------------------------
before090119 | 324.0 677.0 263.0 616.5 510.5 766.5 312.0 2463.0
wkslater_2 | 14.0 840.4 191.4 845.5 658.0 987.0 611.0 1161.0
wkslater_3 | 21.0 781.9 179.5 715.0 643.0 880.0 587.0 1161.0
wkslater_4 | 28.0 752.0 183.4 713.0 613.5 879.0 450.0 1161.0
wkslater_5 | 35.0 719.5 176.9 655.0 595.0 813.0 450.0 1161.0
----------------------------------------------------------------------------------------------
. * For Means
. di (720-677)*100/677
6.351551
. * For Medians
. di (655-617)*100/617
6.1588331
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Update on intra-week merits (from 24/1/2018 to 11/2/2019)Converted dataset:. list merit week
+-----------------+
| merit week |
|-----------------|
1. | 30949 2018w4 |
2. | 19958 2018w5 |
3. | 13304 2018w6 |
4. | 11722 2018w7 |
5. | 8758 2018w8 |
|-----------------|
6. | 8806 2018w9 |
7. | 7253 2018w10 |
8. | 7309 2018w11 |
9. | 6941 2018w12 |
10. | 6707 2018w13 |
|-----------------|
11. | 6415 2018w14 |
12. | 5487 2018w15 |
13. | 4631 2018w16 |
14. | 4585 2018w17 |
15. | 4953 2018w18 |
|-----------------|
16. | 4753 2018w19 |
17. | 4346 2018w20 |
18. | 3854 2018w21 |
19. | 4183 2018w22 |
20. | 4527 2018w23 |
|-----------------|
21. | 3818 2018w24 |
22. | 4921 2018w25 |
23. | 4457 2018w26 |
24. | 4253 2018w27 |
25. | 4239 2018w28 |
|-----------------|
26. | 4159 2018w29 |
27. | 3652 2018w30 |
28. | 3798 2018w31 |
29. | 3994 2018w32 |
30. | 3618 2018w33 |
|-----------------|
31. | 3789 2018w34 |
32. | 3065 2018w35 |
33. | 3574 2018w36 |
34. | 5630 2018w37 |
35. | 7825 2018w38 |
|-----------------|
36. | 4388 2018w39 |
37. | 4271 2018w40 |
38. | 3800 2018w41 |
39. | 4821 2018w42 |
40. | 3945 2018w43 |
|-----------------|
41. | 3339 2018w44 |
42. | 4513 2018w45 |
43. | 3722 2018w46 |
44. | 4558 2018w47 |
45. | 3750 2018w48 |
|-----------------|
46. | 3560 2018w49 |
47. | 3782 2018w50 |
48. | 3753 2018w51 |
49. | 3278 2018w52 |
50. | 4793 2019w1 |
|-----------------|
51. | 6624 2019w2 |
52. | 5306 2019w3 |
53. | 4659 2019w4 |
54. | 4474 2019w5 |
55. | 4318 2019w6 |
+-----------------+
- 50% of observed weeks have total intra-week merits above 4474, whilst the rest 50% of them have total intra-week merits below 4474. 4474 is the median. - 50% of observed weeks have total intra-week merits fluctuated in the range from 3800 to 5630 (the interquartile range, from p25 to p75, in raw statistics below). - Min - max: 3065 - 30949. . tabstat merit, s(n mean sd p50 p25 p75 min max)
variable | N mean sd p50 p25 p75 min max
-------------+--------------------------------------------------------------------------------
merit | 55 5816.127 4456.532 4474 3800 5630 3065 30949
----------------------------------------------------------------------------------------------
Potential outliers: . di 5630-3800
1830
. di 1830*1.5
2745
. di 5630+2745
8375
. di 3800-2745
1055
It means that potential outliers are weeks that have intra-week merits beyond 1055 or 8375. How many weeks are potential outliers? . count if (merit >= 8375| merit < 1055) & merit != .
6
6 weeks are outliers, in total. List of those six weeks: . list merit week if merit >=8375 | merit <=1055
+----------------+
| merit week |
|----------------|
1. | 30949 2018w4 |
2. | 19958 2018w5 |
3. | 13304 2018w6 |
4. | 11722 2018w7 |
5. | 8758 2018w8 |
|----------------|
6. | 8806 2018w9 |
+----------------+
All of them occured in the year 2018.
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List of the top 50-highest day in terms of intra-day merits: . list merit id date dofw day month2 year week month
+-------------------------------------------------------------------------------+
| merit id date dofw day month2 year week month |
|-------------------------------------------------------------------------------|
1. | 13018 1 24jan2018 Wednesday 24 1 2018 2018w4 2018m1 |
2. | 6761 2 25jan2018 Thursday 25 1 2018 2018w4 2018m1 |
3. | 4493 3 26jan2018 Friday 26 1 2018 2018w4 2018m1 |
4. | 4192 7 30jan2018 Tuesday 30 1 2018 2018w5 2018m1 |
5. | 3799 6 29jan2018 Monday 29 1 2018 2018w5 2018m1 |
|-------------------------------------------------------------------------------|
6. | 3489 4 27jan2018 Saturday 27 1 2018 2018w4 2018m1 |
7. | 3188 5 28jan2018 Sunday 28 1 2018 2018w4 2018m1 |
8. | 2820 8 31jan2018 Wednesday 31 1 2018 2018w5 2018m1 |
9. | 2568 10 02feb2018 Friday 2 2 2018 2018w5 2018m2 |
10. | 2545 9 01feb2018 Thursday 1 2 2018 2018w5 2018m2 |
|-------------------------------------------------------------------------------|
11. | 2513 22 14feb2018 Wednesday 14 2 2018 2018w7 2018m2 |
12. | 2463 236 16sep2018 Sunday 16 9 2018 2018w37 2018m9 |
13. | 2308 14 06feb2018 Tuesday 6 2 2018 2018w6 2018m2 |
14. | 2167 12 04feb2018 Sunday 4 2 2018 2018w5 2018m2 |
15. | 2141 15 07feb2018 Wednesday 7 2 2018 2018w6 2018m2 |
|-------------------------------------------------------------------------------|
16. | 2141 16 08feb2018 Thursday 8 2 2018 2018w6 2018m2 |
17. | 2077 13 05feb2018 Monday 5 2 2018 2018w6 2018m2 |
18. | 1991 23 15feb2018 Thursday 15 2 2018 2018w7 2018m2 |
19. | 1867 11 03feb2018 Saturday 3 2 2018 2018w5 2018m2 |
20. | 1862 237 17sep2018 Monday 17 9 2018 2018w38 2018m9 |
|-------------------------------------------------------------------------------|
21. | 1747 18 10feb2018 Saturday 10 2 2018 2018w6 2018m2 |
22. | 1696 38 02mar2018 Friday 2 3 2018 2018w9 2018m3 |
23. | 1608 25 17feb2018 Saturday 17 2 2018 2018w7 2018m2 |
24. | 1579 21 13feb2018 Tuesday 13 2 2018 2018w7 2018m2 |
25. | 1448 17 09feb2018 Friday 9 2 2018 2018w6 2018m2 |
|-------------------------------------------------------------------------------|
26. | 1442 19 11feb2018 Sunday 11 2 2018 2018w6 2018m2 |
27. | 1411 24 16feb2018 Friday 16 2 2018 2018w7 2018m2 |
28. | 1409 32 24feb2018 Saturday 24 2 2018 2018w8 2018m2 |
29. | 1403 27 19feb2018 Monday 19 2 2018 2018w8 2018m2 |
30. | 1382 34 26feb2018 Monday 26 2 2018 2018w9 2018m2 |
|-------------------------------------------------------------------------------|
31. | 1354 48 12mar2018 Monday 12 3 2018 2018w11 2018m3 |
32. | 1333 37 01mar2018 Thursday 1 3 2018 2018w9 2018m3 |
33. | 1331 20 12feb2018 Monday 12 2 2018 2018w7 2018m2 |
34. | 1326 35 27feb2018 Tuesday 27 2 2018 2018w9 2018m2 |
35. | 1322 56 20mar2018 Tuesday 20 3 2018 2018w12 2018m3 |
|-------------------------------------------------------------------------------|
36. | 1294 238 18sep2018 Tuesday 18 9 2018 2018w38 2018m9 |
37. | 1289 26 18feb2018 Sunday 18 2 2018 2018w7 2018m2 |
38. | 1279 30 22feb2018 Thursday 22 2 2018 2018w8 2018m2 |
39. | 1268 239 19sep2018 Wednesday 19 9 2018 2018w38 2018m9 |
40. | 1266 29 21feb2018 Wednesday 21 2 2018 2018w8 2018m2 |
|-------------------------------------------------------------------------------|
41. | 1245 41 05mar2018 Monday 5 3 2018 2018w10 2018m3 |
42. | 1233 68 01apr2018 Sunday 1 4 2018 2018w13 2018m4 |
43. | 1227 57 21mar2018 Wednesday 21 3 2018 2018w12 2018m3 |
44. | 1186 33 25feb2018 Sunday 25 2 2018 2018w8 2018m2 |
45. | 1169 28 20feb2018 Tuesday 20 2 2018 2018w8 2018m2 |
|-------------------------------------------------------------------------------|
46. | 1161 351 09jan2019 Wednesday 9 1 2019 2019w2 2019m1 |
47. | 1159 50 14mar2018 Wednesday 14 3 2018 2018w11 2018m3 |
48. | 1146 69 02apr2018 Monday 2 4 2018 2018w14 2018m4 |
49. | 1138 153 25jun2018 Monday 25 6 2018 2018w26 2018m6 |
50. | 1130 51 15mar2018 Thursday 15 3 2018 2018w11 2018m3 |
|-------------------------------------------------------------------------------|
List of the top 50-lowest days in terms of intra-day merits: . list merit id date dofw day month2 year week month
+-------------------------------------------------------------------------------+
| merit id date dofw day month2 year week month |
|-------------------------------------------------------------------------------|
1. | 312 335 24dec2018 Monday 24 12 2018 2018w52 2018m12 |
2. | 316 333 22dec2018 Saturday 22 12 2018 2018w51 2018m12 |
3. | 325 340 29dec2018 Saturday 29 12 2018 2018w52 2018m12 |
4. | 347 298 17nov2018 Saturday 17 11 2018 2018w46 2018m11 |
5. | 347 338 27dec2018 Thursday 27 12 2018 2018w52 2018m12 |
|-------------------------------------------------------------------------------|
6. | 348 304 23nov2018 Friday 23 11 2018 2018w47 2018m11 |
7. | 370 122 25may2018 Friday 25 5 2018 2018w21 2018m5 |
8. | 376 191 02aug2018 Thursday 2 8 2018 2018w31 2018m8 |
9. | 376 342 31dec2018 Monday 31 12 2018 2018w52 2018m12 |
10. | 377 326 15dec2018 Saturday 15 12 2018 2018w50 2018m12 |
|-------------------------------------------------------------------------------|
11. | 379 220 31aug2018 Friday 31 8 2018 2018w35 2018m8 |
12. | 383 217 28aug2018 Tuesday 28 8 2018 2018w35 2018m8 |
13. | 385 214 25aug2018 Saturday 25 8 2018 2018w34 2018m8 |
14. | 386 339 28dec2018 Friday 28 12 2018 2018w52 2018m12 |
15. | 389 341 30dec2018 Sunday 30 12 2018 2018w52 2018m12 |
|-------------------------------------------------------------------------------|
16. | 394 345 03jan2019 Thursday 3 1 2019 2019w1 2019m1 |
17. | 395 228 08sep2018 Saturday 8 9 2018 2018w36 2018m9 |
18. | 397 320 09dec2018 Sunday 9 12 2018 2018w49 2018m12 |
19. | 399 262 12oct2018 Friday 12 10 2018 2018w41 2018m10 |
20. | 402 329 18dec2018 Tuesday 18 12 2018 2018w51 2018m12 |
|-------------------------------------------------------------------------------|
21. | 405 287 06nov2018 Tuesday 6 11 2018 2018w45 2018m11 |
22. | 412 222 02sep2018 Sunday 2 9 2018 2018w35 2018m9 |
23. | 415 109 12may2018 Saturday 12 5 2018 2018w19 2018m5 |
24. | 415 278 28oct2018 Sunday 28 10 2018 2018w43 2018m10 |
25. | 418 186 28jul2018 Saturday 28 7 2018 2018w30 2018m7 |
|-------------------------------------------------------------------------------|
26. | 420 187 29jul2018 Sunday 29 7 2018 2018w30 2018m7 |
27. | 421 192 03aug2018 Friday 3 8 2018 2018w31 2018m8 |
28. | 422 140 12jun2018 Tuesday 12 6 2018 2018w24 2018m6 |
29. | 424 313 02dec2018 Sunday 2 12 2018 2018w48 2018m12 |
30. | 424 276 26oct2018 Friday 26 10 2018 2018w43 2018m10 |
|-------------------------------------------------------------------------------|
31. | 426 277 27oct2018 Saturday 27 10 2018 2018w43 2018m10 |
32. | 430 264 14oct2018 Sunday 14 10 2018 2018w41 2018m10 |
33. | 430 284 03nov2018 Saturday 3 11 2018 2018w44 2018m11 |
34. | 432 208 19aug2018 Sunday 19 8 2018 2018w33 2018m8 |
35. | 432 221 01sep2018 Saturday 1 9 2018 2018w35 2018m9 |
|-------------------------------------------------------------------------------|
36. | 433 282 01nov2018 Thursday 1 11 2018 2018w44 2018m11 |
37. | 435 154 26jun2018 Tuesday 26 6 2018 2018w26 2018m6 |
38. | 435 190 01aug2018 Wednesday 1 8 2018 2018w31 2018m8 |
39. | 444 182 24jul2018 Tuesday 24 7 2018 2018w30 2018m7 |
40. | 445 143 15jun2018 Friday 15 6 2018 2018w24 2018m6 |
|-------------------------------------------------------------------------------|
41. | 450 373 31jan2019 Thursday 31 1 2019 2019w5 2019m1 |
42. | 451 206 17aug2018 Friday 17 8 2018 2018w33 2018m8 |
43. | 454 283 02nov2018 Friday 2 11 2018 2018w44 2018m11 |
44. | 455 229 09sep2018 Sunday 9 9 2018 2018w36 2018m9 |
45. | 455 167 09jul2018 Monday 9 7 2018 2018w28 2018m7 |
|-------------------------------------------------------------------------------|
46. | 457 216 27aug2018 Monday 27 8 2018 2018w35 2018m8 |
47. | 458 324 13dec2018 Thursday 13 12 2018 2018w50 2018m12 |
48. | 458 227 07sep2018 Friday 7 9 2018 2018w36 2018m9 |
49. | 460 263 13oct2018 Saturday 13 10 2018 2018w41 2018m10 |
50. | 461 130 02jun2018 Saturday 2 6 2018 2018w22 2018m6 |
|-------------------------------------------------------------------------------|
During the period from 24/1/2018 to 11/2/2019, the minimum and maximum of intra-day merits are 312 and 13018 , on 24/12/2018 and 24/1/2018, respectively. |
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Medians and means of intra-day merits over days of weeks.- In median, the highest days are Monday, Wednesday, and Thursday at 674, 652, and 640, respectively; whislt the lowest days are Friday, Saturday, and Sunday at 548, 601, and 610, respectively. - In means, the highest days are Monday, Wednesday, and Sunday at 747, 716, and 700, respectively; whilst the lowest days are Friday, Saturday, and Thursday at 616, 629, and 673, respectively. - In both medians and means, Monday is the highest day, whilst the lowest day is Friday. Calendar day is in GMT time.To take away all doubt: the first Merit was this one: 1516831941 1 2818066.msg28853325 35 877396 Use EpochConverter to convert 1516831941 (Unix Time) to GMT: Wednesday 24 January 2018 22:12:21. . tabstat merit, s(n mean sd p50 p25 p75 min max) format(%9.1f) by(dofw)
Summary for variables: merit
by categories of: dofw
dofw | N mean sd p50 p25 p75 min max
----------+--------------------------------------------------------------------------------
Sunday | 50.0 699.9 336.0 610.0 476.0 804.0 389.0 2463.0
Monday | 51.0 746.6 301.8 674.0 536.0 822.0 312.0 1862.0
Tuesday | 51.0 697.2 230.1 626.0 578.0 768.0 383.0 1326.0
Wednesday | 51.0 715.2 221.7 652.0 558.0 761.0 435.0 1268.0
Thursday | 50.0 673.0 225.7 639.5 509.0 774.0 347.0 1333.0
Friday | 50.0 616.0 232.1 548.0 475.0 698.0 348.0 1696.0
Saturday | 50.0 628.4 222.5 600.5 461.0 695.0 316.0 1409.0
----------+--------------------------------------------------------------------------------
Total | 353.0 682.6 257.9 620.0 517.0 773.0 312.0 2463.0
-------------------------------------------------------------------------------------------
Outliers displayed as red circles. Outliers non-displayed.
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Update on intra-day merit(from 24/1/2018 to 13/2/2019) Time series plots:Full datasetTruncated dataset:
Basic statistics:Full dataset. tabstat merit, s(n mean sd p50 p25 p75 min max) format(%9.1f)
variable | N mean sd p50 p25 p75 min max
-------------+--------------------------------------------------------------------------------
merit | 382.0 785.6 523.7 636.0 522.0 822.0 312.0 4493.0
----------------------------------------------------------------------------------------------
Potential outliers: . di 822-522
300
. di 300*1.5
450
. di 822+450
1272
. di 522-450
72
How many outliers identified? . count if (merit >= 1272 | merit <= 72) & merit != .
36 36 days in total are extremely potential outliers. List of 36 potential outliers, none of them occured in 2019. . list id merit date if (merit >= 1272 | merit <= 72) & merit != .
+-------------------------+
| id merit date |
|-------------------------|
1. | 3 4493 26jan2018 |
2. | 4 3489 27jan2018 |
3. | 5 3188 28jan2018 |
4. | 6 3799 29jan2018 |
5. | 7 4192 30jan2018 |
|-------------------------|
6. | 8 2820 31jan2018 |
7. | 9 2545 01feb2018 |
8. | 10 2568 02feb2018 |
9. | 11 1867 03feb2018 |
10. | 12 2167 04feb2018 |
|-------------------------|
11. | 13 2077 05feb2018 |
12. | 14 2308 06feb2018 |
13. | 15 2141 07feb2018 |
14. | 16 2141 08feb2018 |
15. | 17 1448 09feb2018 |
|-------------------------|
16. | 18 1747 10feb2018 |
17. | 19 1442 11feb2018 |
18. | 20 1331 12feb2018 |
19. | 21 1579 13feb2018 |
20. | 22 2513 14feb2018 |
|-------------------------|
21. | 23 1991 15feb2018 |
22. | 24 1411 16feb2018 |
23. | 25 1608 17feb2018 |
24. | 26 1289 18feb2018 |
25. | 27 1403 19feb2018 |
|-------------------------|
28. | 30 1279 22feb2018 |
30. | 32 1409 24feb2018 |
32. | 34 1382 26feb2018 |
33. | 35 1326 27feb2018 |
35. | 37 1333 01mar2018 |
|-------------------------|
36. | 38 1696 02mar2018 |
46. | 48 1354 12mar2018 |
54. | 56 1322 20mar2018 |
234. | 236 2463 16sep2018 |
235. | 237 1862 17sep2018 |
|-------------------------|
236. | 238 1294 18sep2018 |
+-------------------------+
. tabstat merit, s(n mean sd p50 p25 p75 min max) format(%9.1f)
variable | N mean sd p50 p25 p75 min max
-------------+--------------------------------------------------------------------------------
merit | 358.0 681.4 256.3 619.5 517.0 768.0 312.0 2463.0
---------------------------------------------------------------------------------------------- Potential outliers: . di 768-517
251
. di 1.5*251
376.5
. di 768+376.6
1144.6
. di 517-376.5
140.5
How many potential outliers identified in truncated dataset? . count if (merit >= 1145 | merit <= 141) & merit != .
22
List of those 22 days . list id merit date if (merit >= 1145 | merit <= 141) & merit != .
+-------------------------+
| id merit date |
|-------------------------|
1. | 27 1403 19feb2018 |
2. | 28 1169 20feb2018 |
3. | 29 1266 21feb2018 |
4. | 30 1279 22feb2018 |
6. | 32 1409 24feb2018 |
|-------------------------|
7. | 33 1186 25feb2018 |
8. | 34 1382 26feb2018 |
9. | 35 1326 27feb2018 |
11. | 37 1333 01mar2018 |
12. | 38 1696 02mar2018 |
|-------------------------|
15. | 41 1245 05mar2018 |
22. | 48 1354 12mar2018 |
24. | 50 1159 14mar2018 |
30. | 56 1322 20mar2018 |
31. | 57 1227 21mar2018 |
|-------------------------|
42. | 68 1233 01apr2018 |
43. | 69 1146 02apr2018 |
210. | 236 2463 16sep2018 |
211. | 237 1862 17sep2018 |
212. | 238 1294 18sep2018 |
|-------------------------|
213. | 239 1268 19sep2018 |
325. | 351 1161 09jan2019 |
+-------------------------+
Only one of them occured in 2019, on 09/1/2019.
Notes:
Update more later
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Thanks LoyceV, for another data dump in my topic.  Converted intra-day merits for days in 2019. . list id merit date day month2 year week month dofw if year == 2019
+-----------------------------------------------------------------------------+
| id merit date day month2 year week month dofw |
|-----------------------------------------------------------------------------|
343. | 343 603 01jan2019 1 1 2019 2019w1 2019m1 Tuesday |
344. | 344 526 02jan2019 2 1 2019 2019w1 2019m1 Wednesday |
345. | 345 394 03jan2019 3 1 2019 2019w1 2019m1 Thursday |
346. | 346 1082 04jan2019 4 1 2019 2019w1 2019m1 Friday |
347. | 347 835 05jan2019 5 1 2019 2019w1 2019m1 Saturday |
|-----------------------------------------------------------------------------|
348. | 348 783 06jan2019 6 1 2019 2019w1 2019m1 Sunday |
349. | 349 570 07jan2019 7 1 2019 2019w1 2019m1 Monday |
350. | 350 782 08jan2019 8 1 2019 2019w2 2019m1 Tuesday |
351. | 351 1161 09jan2019 9 1 2019 2019w2 2019m1 Wednesday |
352. | 352 987 10jan2019 10 1 2019 2019w2 2019m1 Thursday |
|-----------------------------------------------------------------------------|
353. | 353 878 11jan2019 11 1 2019 2019w2 2019m1 Friday |
354. | 354 711 12jan2019 12 1 2019 2019w2 2019m1 Saturday |
355. | 355 978 13jan2019 13 1 2019 2019w2 2019m1 Sunday |
356. | 356 1127 14jan2019 14 1 2019 2019w2 2019m1 Monday |
357. | 357 813 15jan2019 15 1 2019 2019w3 2019m1 Tuesday |
|-----------------------------------------------------------------------------|
358. | 358 880 16jan2019 16 1 2019 2019w3 2019m1 Wednesday |
359. | 359 1018 17jan2019 17 1 2019 2019w3 2019m1 Thursday |
360. | 360 611 18jan2019 18 1 2019 2019w3 2019m1 Friday |
361. | 361 643 19jan2019 19 1 2019 2019w3 2019m1 Saturday |
362. | 362 658 20jan2019 20 1 2019 2019w3 2019m1 Sunday |
|-----------------------------------------------------------------------------|
363. | 363 683 21jan2019 21 1 2019 2019w3 2019m1 Monday |
364. | 364 618 22jan2019 22 1 2019 2019w4 2019m1 Tuesday |
365. | 365 735 23jan2019 23 1 2019 2019w4 2019m1 Wednesday |
366. | 366 715 24jan2019 24 1 2019 2019w4 2019m1 Thursday |
367. | 367 615 25jan2019 25 1 2019 2019w4 2019m1 Friday |
|-----------------------------------------------------------------------------|
368. | 368 587 26jan2019 26 1 2019 2019w4 2019m1 Saturday |
369. | 369 655 27jan2019 27 1 2019 2019w4 2019m1 Sunday |
370. | 370 734 28jan2019 28 1 2019 2019w4 2019m1 Monday |
371. | 371 612 29jan2019 29 1 2019 2019w5 2019m1 Tuesday |
372. | 372 510 30jan2019 30 1 2019 2019w5 2019m1 Wednesday |
|-----------------------------------------------------------------------------|
373. | 373 450 31jan2019 31 1 2019 2019w5 2019m1 Thursday |
374. | 374 595 01feb2019 1 2 2019 2019w5 2019m2 Friday |
375. | 375 940 02feb2019 2 2 2019 2019w5 2019m2 Saturday |
376. | 376 571 03feb2019 3 2 2019 2019w5 2019m2 Sunday |
377. | 377 796 04feb2019 4 2 2019 2019w5 2019m2 Monday |
|-----------------------------------------------------------------------------|
378. | 378 776 05feb2019 5 2 2019 2019w6 2019m2 Tuesday |
379. | 379 559 06feb2019 6 2 2019 2019w6 2019m2 Wednesday |
380. | 380 548 07feb2019 7 2 2019 2019w6 2019m2 Thursday |
381. | 381 611 08feb2019 8 2 2019 2019w6 2019m2 Friday |
382. | 382 623 09feb2019 9 2 2019 2019w6 2019m2 Saturday |
|-----------------------------------------------------------------------------|
383. | 383 559 10feb2019 10 2 2019 2019w6 2019m2 Sunday |
384. | 384 642 11feb2019 11 2 2019 2019w6 2019m2 Monday |
385. | 385 585 12feb2019 12 2 2019 2019w7 2019m2 Tuesday |
386. | 386 671 13feb2019 13 2 2019 2019w7 2019m2 Wednesday |
+-----------------------------------------------------------------------------+
For days in 2018, please get it there |
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I have my private keys, so it will be good for me and my BLOC coins. You need to use private key to import your wallet and everything will be ok.
By the way, I want to ask you one more thing relate to the restore of my coins from private key. What will happen if: - I setup password for my old wallet; - Then, I forgot the password; - Will I got my coins in un-locked/ un-encrypted wallet after restore with private key? Thank you, BLOC. |
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Account register required to be added into WaitList for later opportunities to earn ZCash, not now. Anyway, Coinbase is reliable, so it's safe to have accounts their for future ZCash's airdrops (something like airdrops). Do you ready to earn ZCash? If you do want to join it, please move to the next step. Next steps, please discover by yourself. |
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Our development team prefers to stay anonymous, in full accordance with the principles promoted by Monero itself.
By now, except bitcoin, when it created by Satoshi Nakamoto. No one know who is Satoshi Nakamoto, even a group, a team, an organization. It is too bad ideas to keep the team anonymous in modern day, especially after massively scamming projects in 2017 and 2018. I believe that investors will mostly stay away from projects started and managed by anonymous teams. Best regards for Monero Rings and the team strategy to stay as anonymous as possible. |
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Me too. I used Crypto Bridge to buy shit coins when they dip, and take profits later. cryptobridge for shitcoins to btc
What's happened with you and Coinbase, Kraken? Did you get their bad supports to claim fork coins, or anything else? I got fucked by Coinbase during the bullrun and same with kraken so they can go to hell in my book.
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It is normal, because ETH ecosystem has expanded well over last two years. By now, ETH ecosystem has become the second largest one, only behind Bitcoin ecosystem. Of course, WAVES is a good project, and has its good ecosystem, that has potentially bright future in aspect of expansion. Let's see the next expansion of WAVES in years to come. many many exchanges are set up tp list ETH tokens while very few list waves assets. This is the biggest roadblock, although the best perform currency on the planet is a waves asset (WAU). If more exchanges were encouraged to use waves assets there could be more successful tokens and coins on wavesplatform
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