Bitcoin price history -- most profitable and bloodiest months, days

[tranthidung]

Notes

• Bitcoin 2017 - 2020. Better price not yet to come. Be careful ^read
• It is summarized stats. There are winners and losers in market, over days, months despite of how market moves.
• It seems more outliers in second half of each year
• For months, p is median of % of difference between open and close price over days in a month, then I takes median of each month stats (from month 1 to month 12, according to ordinal numbers). There are 4 months have median >= 30%: January, March, May and November. When talking about median (p50), please look at the interquartile range (p25 to p75).
• 3 months with extremely spikes are December, November and somewhat March, October (look at max values).
• You can expand the analysis with stat for open and end days of each months (I might or might not do it later)
• Data source: https://coinmarketcap.com/currencies/bitcoin/historical-data/
• Months: I will update with the difference between open price at the first day of every month and close price at the last day of every month. This idea comes from that topic Bitcoin pumps/ dumps and altcoin price actions (2013 - 2020) but unfortunately I have never done it.

Months

Code:

Summary for variables: p
     by categories of: m (Months)

       m |         N      mean        sd       p50       p25       p75       min       max
---------+--------------------------------------------------------------------------------
       1 |       7.0      43.2      23.9      36.1      23.6      73.4      18.2      76.9
       2 |       7.0      31.0      19.5      20.1      18.6      54.6      18.5      64.0
       3 |       7.0      39.1      28.8      36.0       9.2      68.0       8.6      83.5
       4 |       8.0      27.1      15.1      29.0      15.2      39.2       4.0      46.1
       5 |       8.0      37.3      22.7      37.3      18.7      54.2       6.1      71.9
       6 |       8.0      31.3      18.5      26.8      18.0      39.7      12.4      69.3
       7 |       8.0      31.1      16.0      28.7      18.2      42.1      12.6      57.8
       8 |       8.0      30.0      19.7      27.4      17.1      33.6      10.7      73.5
       9 |       8.0      23.2      15.5      18.2      12.8      30.7       7.3      55.1
      10 |       8.0      35.6      25.0      29.0      21.1      45.6       6.3      87.2
      11 |       8.0      93.0     145.5      39.2      31.2      72.4       9.1     449.0
      12 |       8.0      41.2      38.4      29.3      18.3      53.9       6.1     120.2
---------+--------------------------------------------------------------------------------
   Total |      93.0      38.6      48.5      30.0      18.2      45.4       4.0     449.0
------------------------------------------------------------------------------------------

]

Most profitable days (between open and close prices)

Code:

      +------------------------------------------+
      |    open     close       p_oc        date |
      |------------------------------------------|
   1. |  496.58    703.56    41.6811   18nov2013 |
   2. |  519.06    691.96   33.31021   19dec2013 |
   3. | 14266.1   17899.7   25.47017   07dec2017 |
   4. | 2269.89    2817.6   24.12936   20jul2017 |
   5. |  594.32    722.43   21.55573   21nov2013 |
   6. | 11923.4   14291.5   19.86095   06dec2017 |
   7. |  562.56    667.76   18.70023   03mar2014 |
   8. |   176.9    209.84   18.62069   15jan2015 |
   9. | 5245.42   6191.19    18.0304   19mar2020 |
  10. | 4156.92   4879.88   17.39172   02apr2019 |
  11. |  363.71    420.95   15.73781   11apr2014 |
  12. |  7490.7    8660.7   15.61937   25oct2019 |
  13. | 1932.62   2228.41   15.30513   17jul2017 |
  14. |  805.73     928.1   15.18747   26nov2013 |
  15. |  367.98    423.56   15.10408   12nov2014 |
  16. |  3166.3   3637.52   14.88235   15sep2017 |
  17. | 14036.6   16099.8   14.69872   26dec2017 |
  18. |    98.1     112.5    14.6789   04may2013 |
  19. |  697.31    795.87   14.13432   08dec2013 |
  20. |  297.85    338.11   13.51687   08nov2013 |
  21. | 6955.38   7889.25   13.42659   12apr2018 |
  22. |  261.68    296.41   13.27194   07nov2013 |
  23. | 3591.09    4065.2    13.2024   18sep2017 |
  24. |   76.72     86.76   13.08655   10jul2013 |
  25. | 6379.67   7204.77   12.93327   11may2019 |
  26. |  360.97    407.37   12.85425   13nov2013 |
  27. | 7267.96   8197.69   12.79217   19may2019 |
  28. | 4829.58   5446.91   12.78227   12oct2017 |
  29. | 7806.71   8801.04   12.73686   29apr2020 |
  30. | 15477.2   17429.5   12.61404   05jan2018 |
      +------------------------------------------+

Most profitable days (between close and low prices)

Code:

      +------------------------------------------+
      |   close       low       p_cl        date |
      |------------------------------------------|
   1. |  703.56    494.94   42.15056   18nov2013 |
   2. |  691.96    502.89   37.59669   19dec2013 |
   3. | 5563.71   4106.98   35.46962   13mar2020 |
   4. |  590.83    448.45   31.74936   20nov2013 |
   5. |    7754   6048.26   28.20216   06feb2018 |
   6. |  538.71    420.41    28.1392   25feb2014 |
   7. |  584.61    456.39   28.09439   19nov2013 |
   8. | 17899.7   14057.3   27.33384   07dec2017 |
   9. |  722.43    577.29   25.14161   21nov2013 |
  10. |   522.7    420.51   24.30144   18dec2013 |
  11. |  2817.6   2269.89   24.12936   20jul2017 |
  12. |  681.03     550.5   23.71117   10feb2014 |
  13. |   97.75      79.1   23.57775   03may2013 |
  14. | 3637.52   2946.62    23.4472   15sep2017 |
  15. |  661.99    541.04   22.35509   14feb2014 |
  16. |   112.5      92.5   21.62162   04may2013 |
  17. | 14291.5   11923.4   19.86095   06dec2017 |
  18. |  420.95    351.27   19.83659   11apr2014 |
  19. |  955.85    801.82   19.21005   01dec2013 |
  20. |  667.76    560.52   19.13223   03mar2014 |
  21. | 11188.6   9402.29   18.99867   17jan2018 |
  22. |  795.87    670.88   18.63075   08dec2013 |
  23. |  209.84     176.9   18.62069   15jan2015 |
  24. | 6191.19   5236.97   18.22084   19mar2020 |
  25. |  326.62    277.24   17.81128   10nov2013 |
  26. |  198.23    168.52   17.62996   24oct2013 |
  27. | 4879.88   4155.32   17.43692   02apr2019 |
  28. | 13831.8     11833   16.89174   22dec2017 |
  29. | 15455.4   13226.6   16.85089   10dec2017 |
  30. | 1045.11    897.11   16.49742   05dec2013 |
      +------------------------------------------+

Most profitable days (between open and high prices)

Code:

      +--------------------------------------------+
      |     open       high       p_oh        date |
      |--------------------------------------------|
   1. |   496.58     703.78    41.7254   18nov2013 |
   2. |   519.06     707.23   36.25207   19dec2013 |
   3. |    176.9     229.07   29.49124   15jan2015 |
   4. |  2269.89     2900.7   27.79033   20jul2017 |
   5. |  14266.1    17899.7   25.47017   07dec2017 |
   6. |   562.56     702.91   24.94845   03mar2014 |
   7. |   594.32      733.4   23.40154   21nov2013 |
   8. |   403.66     495.56   22.76669   04nov2015 |
   9. |   254.08     309.38    21.7648   26jan2015 |
  10. |  5245.42    6329.74   20.67175   19mar2020 |
  11. |  11923.4    14369.1   20.51177   06dec2017 |
  12. |   363.71     429.77   18.16282   11apr2014 |
  13. |  4156.92    4905.95   18.01887   02apr2019 |
  14. |   3166.3    3733.45   17.91207   15sep2017 |
  15. |  14036.6    16461.2   17.27341   26dec2017 |
  16. |     98.1        115   17.22732   04may2013 |
  17. | 11778.58   13796.49   17.13203   26jun2019 |
  18. |    88.98        104    16.8802   12jul2013 |
  19. |   367.98     429.72   16.77809   12nov2014 |
  20. |  5017.83    5838.11   16.34731   13mar2020 |
  21. |   261.68     304.17   16.23739   07nov2013 |
  22. |    793.8     921.93   16.14135   09dec2013 |
  23. |   7490.7    8691.54   16.03108   25oct2019 |
  24. |  8667.58   10021.74   15.62328   26oct2019 |
  25. |   361.87      417.9   15.48346   03nov2015 |
  26. |  6971.18    8047.41   15.43828   13may2019 |
  27. |  1932.62    2230.49   15.41276   17jul2017 |
  28. |   805.73     928.54   15.24208   26nov2013 |
  29. |   697.31     802.51   15.08655   08dec2013 |
  30. |   601.17     691.72   15.06229   14feb2014 |
      +--------------------------------------------+

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[tranthidung]

Bloodiest days (between open and close prices)

I listed to top 50 bloodiest days only and pay attention on days in December over years. We are in the December, calendar day.

I am not here to give you any financial advice to buy or sell, long or short bitcoin. It is stats and data interpretation is for your side. Please use it with risk, and verify my information (if you can), don't trust me.  

Code:

      +---------------------------------------------+
      |     open      close        p_oc        date |
      |---------------------------------------------|
   1. |  7913.62    4970.79    -37.1869   12mar2020 |
   2. |    678.2      522.7   -22.92834   18dec2013 |
   3. |   223.89      178.1   -20.45201   14jan2015 |
   4. |  1042.38     829.45   -20.42729   06dec2013 |
   5. |   880.33     705.97   -19.80621   16dec2013 |
   6. |   580.26     471.24   -18.78813   27mar2014 |
   7. |  3875.37    3154.95   -18.58971   14sep2017 |
   8. |   257.93     211.08   -18.16384   18aug2015 |
   9. |   712.76     584.61    -17.9794   19nov2013 |
  10. |   442.26     365.18   -17.42866   10apr2014 |
  11. |  13836.1    11490.5   -16.95275   16jan2018 |
  12. |   835.32     698.23   -16.41167   07dec2013 |
  13. |  8270.54    6955.27   -15.90307   05feb2018 |
  14. |      139     116.99   -15.83453   01may2013 |
  15. |   267.39     225.86   -15.53162   13jan2015 |
  16. |  1128.92     955.85   -15.33058   01dec2013 |
  17. |   430.26     364.33   -15.32329   15jan2016 |
  18. |   946.49        802   -15.26588   07jan2014 |
  19. |    79.99      68.43   -14.45181   05jul2013 |
  20. |   908.11     777.76   -14.35399   11jan2017 |
  21. |     90.4      77.53   -14.23673   03jul2013 |
  22. | 13017.12   11182.81   -14.09152   27jun2019 |
  23. |   132.05     114.13   -13.57062   02oct2013 |
  24. |  5620.78    4871.49   -13.33071   19nov2018 |
  25. | 10896.65    9477.64   -13.02244   16jul2019 |
  26. |    15898    13831.8    -12.9966   22dec2017 |
  27. |    884.6     771.39   -12.79787   27jan2014 |
  28. |  1156.73    1013.38   -12.39269   05jan2017 |
  29. |  14681.9    12952.2   -11.78117   30dec2017 |
  30. |  1099.69     973.82   -11.44595   18mar2017 |
      +---------------------------------------------+

[tranthidung]

Most profitable and bloodiest days in 2020 (I sorted them out chronologically and here I use cut-offs at 5% or -5%)

Profitable

Code:

      +---------------------------------------------------+
      | year        date       open      close       p_oc |
      |---------------------------------------------------|
2441. | 2020   03jan2020    6984.43    7344.88   5.160765 |
2445. | 2020   07jan2020    7768.68    8163.69   5.084648 |
2452. | 2020   14jan2020    8140.93    8827.76   8.436751 |
2466. | 2020   28jan2020    8912.52    9358.59   5.004982 |
2511. | 2020   13mar2020    5017.83    5563.71   10.87881 |
2517. | 2020   19mar2020    5245.42    6191.19    18.0304 |
2521. | 2020   23mar2020    5831.37    6416.31   10.03092 |
2528. | 2020   30mar2020    5925.54    6429.84   8.510616 |
2535. | 2020   06apr2020    6788.05    7271.78     7.1262 |
2545. | 2020   16apr2020    6640.45     7116.8    7.17346 |
2558. | 2020   29apr2020    7806.71    8801.04   12.73686 |
2566. | 2020   07may2020     9261.9    9951.52   7.445773 |
2572. | 2020   13may2020    8805.39    9269.99   5.276314 |
2591. | 2020   01jun2020    9463.61   10167.27   7.435429 |
2647. | 2020   27jul2020    9905.22   10990.87   10.96038 |
2706. | 2020   24sep2020   10227.48   10745.55   5.065471 |
2733. | 2020   21oct2020   11913.08   12823.69   7.643783 |
2748. | 2020   05nov2020   14133.73   15579.85   10.23169 |
2760. | 2020   17nov2020   16685.69   17645.41   5.751755 |
2773. | 2020   30nov2020   18178.32   19625.84   7.962892 |
      +---------------------------------------------------+

Bloodiest

Code:

      +----------------------------------------------------+
      | year        date       open      close        p_oc |
      |----------------------------------------------------|
2488. | 2020   19feb2020    10143.8    9633.39   -5.031744 |
2495. | 2020   26feb2020    9338.29    8820.52   -5.544591 |
2506. | 2020   08mar2020    8908.21    8108.12    -8.98149 |
2510. | 2020   12mar2020    7913.62    4970.79    -37.1869 |
2512. | 2020   14mar2020    5573.08    5200.37   -6.687685 |
2514. | 2020   16mar2020    5385.23    5014.48   -6.884572 |
2520. | 2020   22mar2020    6185.56    5830.25   -5.744185 |
2527. | 2020   29mar2020    6245.62    5922.04   -5.180911 |
2539. | 2020   10apr2020    7303.82    6865.49    -6.00138 |
2569. | 2020   10may2020    9591.17    8756.43   -8.703214 |
2592. | 2020   02jun2020   10162.97     9529.8   -6.230167 |
2601. | 2020   11jun2020    9870.08    9321.78   -5.555173 |
2653. | 2020   02aug2020   11758.76   11053.61   -5.996806 |
2685. | 2020   03sep2020   11407.19    10245.3   -10.18559 |
2769. | 2020   26nov2020   18729.84   17150.62   -8.431572 |
      +----------------------------------------------------+

[fillippone]

Something is not clear to me.
Why you sort the "most profitable" days with Open prices higher than Close  and "Bloodiest" the day where Close is Higher than Open?
I guess you made the assumption of a long investor, so I think you should reverse the title of the table (most probably), or the title of the column, as it is not very clear by now.

[tranthidung]

Something is not clear to me.
Why you sort the "most profitable" days with Open prices higher than Close  and "Bloodiest" the day where Close is Higher than Open?
I guess you made the assumption of a long investor, so I think you should reverse the title of the table (most probably), or the title of the column, as it is not very clear by now.

Let me check my code, @fil and I will give you updates soon.  
Update:
I mis-calculated it. Initially, my formula is:

Code:

p_oc = (open - close)/open*100

It should be as of now

Code:

p_oc = (close - open)/open*100

Thank you for your head up. OP will be updated soon.

[veznata]

interesting stats. we all know past numbers can not predict future outcome. what we can say with big enough percentage is that it is trends that matter not exact numbers.....BTC price rises more and more after 4 years and it rises dramatically. i can not recall an investition in my lifetime with such a big ROI and that could ne boght so easily and cashed out so easily (at least for many of the developed countries).

[tranthidung]

Updates

With plots (period from 2013 to 2020; and in 2020 only)
Periods:

• 29/4/2013 - 9/12/2020
• In 2020 only (01/1/2020 - 09/12/2020)

This part is important. As I promised in OP, here is results when I calculated the difference in a different method

• p_oc = [ close ( of end date of every month) - open (of start date of every month) ] / open (of start date of every month) *100
• open_s: open of start date of every month
• close_e: close of end date of every month

Results are clear so you are free to interpret them by yourself.

Plots

Summary

Code:

Summary for variables: p_oc
     by categories of: m (m)

       m |         N      mean        sd       p50       p25       p75       min       max
---------+--------------------------------------------------------------------------------
       1 |       7.0      -5.9      21.7      -7.7     -27.6       9.9     -32.1      30.0
       2 |       7.0       4.1      19.7      11.4      -8.0      18.5     -33.7      21.5
       3 |       7.0     -12.3      13.5      -9.2     -25.1      -4.0     -32.9       6.5
       4 |       7.0      17.8      16.6      25.8      -2.0      31.9      -3.3      34.5
       5 |       8.0      21.0      32.4      13.8      -4.8      49.8     -19.0      69.6
       6 |       8.0       4.4      18.4       5.5      -9.0      20.2     -25.0      26.8
       7 |       8.0       6.9      13.0       8.4      -6.8      18.4      -8.6      23.8
       8 |       8.0       4.4      28.2      -6.2     -14.0      15.3     -19.2      63.8
       9 |       8.0      -5.9       8.2      -6.8     -10.8       0.5     -19.0       5.9
      10 |       8.0      21.5      23.8      21.4       3.1      41.1     -12.7      53.8
      11 |       8.0      67.4     159.1      15.7      -5.7      50.6     -36.4     453.9
      12 |       7.0       3.1      25.5      -5.0     -15.4      29.2     -33.2      38.8
---------+--------------------------------------------------------------------------------
   Total |      91.0      11.0      52.4       2.6      -8.0      21.5     -36.4     453.9
------------------------------------------------------------------------------------------

Raw data

Code:

     +----------------------------------------------------------------+
     |   month       start     open_s         end    close_e     p_oc |
     |----------------------------------------------------------------|
  1. |  2013m5   01may2013        139   31may2013        129    -7.19 |
  2. |  2013m6   01jun2013     128.82   30jun2013      96.61      -25 |
  3. |  2013m7   01jul2013      97.51   31jul2013     106.09      8.8 |
  4. |  2013m8   01aug2013     106.21   31aug2013     135.35    27.44 |
  5. |  2013m9   01sep2013     135.14   30sep2013        133    -1.58 |
  6. | 2013m10   01oct2013     132.68   31oct2013        204    53.75 |
  7. | 2013m11   01nov2013      203.9   30nov2013    1129.43   453.91 |
  8. | 2013m12   01dec2013    1128.92   31dec2013     754.01   -33.21 |
     |----------------------------------------------------------------|
  9. |  2014m1   01jan2014     754.97   31jan2014     829.92     9.93 |
 10. |  2014m2   01feb2014     828.61   28feb2014     549.26   -33.71 |
 11. |  2014m3   01mar2014     549.92   31mar2014        457    -16.9 |
 12. |  2014m4   01apr2014        457   30apr2014     447.64    -2.05 |
 13. |  2014m5   01may2014     447.63   31may2014     623.68    39.33 |
 14. |  2014m6   01jun2014     623.69   30jun2014      639.8     2.58 |
 15. |  2014m7   01jul2014     641.39   31jul2014     586.23     -8.6 |
 16. |  2014m8   01aug2014      586.2   31aug2014     477.76    -18.5 |
 17. |  2014m9   01sep2014     477.79   30sep2014     386.94   -19.01 |
 18. | 2014m10   01oct2014     387.43   31oct2014     338.32   -12.68 |
 19. | 2014m11   01nov2014     338.65   30nov2014     378.05    11.63 |
 20. | 2014m12   01dec2014     378.25   31dec2014     320.19   -15.35 |
     |----------------------------------------------------------------|
 21. |  2015m1   01jan2015     320.43   31jan2015     217.46   -32.13 |
 22. |  2015m2   01feb2015     216.87   28feb2015     254.26    17.24 |
 23. |  2015m3   01mar2015     254.28   31mar2015     244.22    -3.96 |
 24. |  2015m4   01apr2015     244.22   30apr2015     236.15     -3.3 |
 25. |  2015m5   01may2015     235.94   31may2015     230.19    -2.44 |
 26. |  2015m6   01jun2015     230.23   30jun2015     263.07    14.26 |
 27. |  2015m7   01jul2015     263.35   31jul2015     284.65     8.09 |
 28. |  2015m8   01aug2015     284.69   31aug2015     230.06   -19.19 |
 29. |  2015m9   01sep2015     230.26   30sep2015     236.06     2.52 |
 30. | 2015m10   01oct2015        236   31oct2015     314.17    33.12 |
 31. | 2015m11   01nov2015     315.01   30nov2015     377.32    19.78 |
 32. | 2015m12   01dec2015     377.41   31dec2015     430.57    14.09 |
     |----------------------------------------------------------------|
 33. |  2016m1   01jan2016     430.72   31jan2016     368.77   -14.38 |
 34. |  2016m2   01feb2016     369.35   29feb2016      437.7    18.51 |
 35. |  2016m3   01mar2016     437.92   31mar2016     416.73    -4.84 |
 36. |  2016m4   01apr2016     416.76   30apr2016     448.32     7.57 |
 37. |  2016m5   01may2016     448.48   31may2016     531.39    18.49 |
 38. |  2016m6   01jun2016     531.11   30jun2016     673.34    26.78 |
 39. |  2016m7   01jul2016     672.52   31jul2016     624.68    -7.11 |
 40. |  2016m8   01aug2016      624.6   31aug2016     575.47    -7.87 |
 41. |  2016m9   01sep2016     575.55   30sep2016     609.73     5.94 |
 42. | 2016m10   01oct2016     609.93   31oct2016     700.97    14.93 |
 43. | 2016m11   01nov2016     701.34   30nov2016     745.69     6.32 |
 44. | 2016m12   01dec2016     746.05   31dec2016     963.74    29.18 |
     |----------------------------------------------------------------|
 45. |  2017m1   01jan2017     963.66   31jan2017      970.4       .7 |
 46. |  2017m2   01feb2017     970.94   28feb2017    1179.97    21.53 |
 47. |  2017m3   01mar2017    1180.04   31mar2017    1071.79    -9.17 |
 48. |  2017m4   01apr2017    1071.71   30apr2017    1347.89    25.77 |
 49. |  2017m5   01may2017     1348.3   31may2017    2286.41    69.58 |
 50. |  2017m6   01jun2017    2288.33   30jun2017    2480.84     8.41 |
 51. |  2017m7   01jul2017     2492.6   31jul2017    2875.34    15.36 |
 52. |  2017m8   01aug2017     2871.3   31aug2017    4703.39    63.81 |
 53. |  2017m9   01sep2017    4701.76   30sep2017    4338.71    -7.72 |
 54. | 2017m10   01oct2017    4341.05   31oct2017     6468.4    49.01 |
 55. | 2017m11   01nov2017    6440.97   30nov2017    10233.6    58.88 |
 56. | 2017m12   01dec2017    10198.6   31dec2017    14156.4    38.81 |
     |----------------------------------------------------------------|
 57. |  2018m1   01jan2018    14112.2   31jan2018    10221.1   -27.57 |
 58. |  2018m2   01feb2018    10237.3   28feb2018    10397.9     1.57 |
 59. |  2018m3   01mar2018      10385   31mar2018    6973.53   -32.85 |
 60. |  2018m4   01apr2018    7003.06   30apr2018    9240.55    31.95 |
 61. |  2018m5   01may2018    9251.47   31may2018    7494.17   -18.99 |
 62. |  2018m6   01jun2018     7500.7   30jun2018       6404   -14.62 |
 63. |  2018m7   01jul2018    6411.68   31jul2018    7780.44    21.35 |
 64. |  2018m8   01aug2018    7769.04   31aug2018    7037.58    -9.42 |
 65. |  2018m9   01sep2018    7044.81   30sep2018    6625.56    -5.95 |
 66. | 2018m10   01oct2018    6619.85   31oct2018    6317.61    -4.57 |
 67. | 2018m11   01nov2018    6318.14   30nov2018    4017.27   -36.42 |
 68. | 2018m12   01dec2018    4024.46   31dec2018     3742.7       -7 |
     |----------------------------------------------------------------|
 69. |  2019m1   01jan2019    3746.71   31jan2019    3457.79    -7.71 |
 70. |  2019m2   01feb2019    3460.55   28feb2019    3854.79    11.39 |
 71. |  2019m3   01mar2019    3853.76   31mar2019     4105.4     6.53 |
 72. |  2019m4   01apr2019    4105.36   30apr2019    5350.73    30.34 |
 73. |  2019m5   01may2019    5350.91   31may2019     8574.5    60.24 |
 74. |  2019m6   01jun2019    8573.84   30jun2019   10817.16    26.16 |
 75. |  2019m7   01jul2019   10796.93   31jul2019   10085.63    -6.59 |
 76. |  2019m8   01aug2019   10077.44   31aug2019    9630.66    -4.43 |
 77. |  2019m9   01sep2019    9630.59   30sep2019    8293.87   -13.88 |
 78. | 2019m10   01oct2019    8299.72   31oct2019    9199.58    10.84 |
 79. | 2019m11   01nov2019    9193.99   30nov2019    7569.63   -17.67 |
 80. | 2019m12   01dec2019    7571.62   31dec2019     7193.6    -4.99 |
     |----------------------------------------------------------------|
 81. |  2020m1   01jan2020    7194.89   31jan2020    9350.53    29.96 |
 82. |  2020m2   01feb2020    9346.36   29feb2020    8599.51    -7.99 |
 83. |  2020m3   01mar2020    8599.76   31mar2020    6438.64   -25.13 |
 84. |  2020m4   01apr2020    6437.32   30apr2020    8658.55    34.51 |
 85. |  2020m5   01may2020    8672.78   31may2020    9461.06     9.09 |
 86. |  2020m6   01jun2020    9463.61   30jun2020    9137.99    -3.44 |
 87. |  2020m7   01jul2020    9145.99   31jul2020   11323.47    23.81 |
 88. |  2020m8   01aug2020   11322.57   31aug2020   11680.82     3.16 |
 89. |  2020m9   01sep2020   11679.32   30sep2020   10787.62    -7.63 |
 90. | 2020m10   01oct2020   10785.01   31oct2020   13780.99    27.78 |
 91. | 2020m11   01nov2020   13780.99   30nov2020   19625.84    42.41 |
 92. | 2020m12   01dec2020   19633.77   31dec2020          .        . |
     +----------------------------------------------------------------+

[tranthidung]

There are 6 Decembered-days in the most bloodiest days in bitcoin history. Again, this thread and this post are not my financial advice for you to Short bitcoin it can be a trap for you to put you into a liquidation traps. In contrast, if you have good profit enough, it is the right time for you to protect your capital. Not to be greedy and hope for more profits.

Profit only become real profit if you take it. If you only watch your profit on screen, it might be stolen. Move your stop loss to your entry price, just in case.

Stop the profit steal and stay safe!

Remember there are other indicators (technical) but I don't bring them here because I don't give you financial advice and this thread is all about historic price of bitcoin (most profitable and bloodiest days).

Code:

      +---------------------------------------------+
      |     open      close        p_oc        date |
      |---------------------------------------------|
   2. |    678.2      522.7   -22.92834   18dec2013 |
   4. |  1042.38     829.45   -20.42729   06dec2013 |
   5. |   880.33     705.97   -19.80621   16dec2013 |
  12. |   835.32     698.23   -16.41167   07dec2013 |
  16. |  1128.92     955.85   -15.33058   01dec2013 |
  26. |    15898    13831.8    -12.9966   22dec2017 |
  29. |  14681.9    12952.2   -11.78117   30dec2017 |
      +---------------------------------------------+

Altcoins? Please check out with Bitcoin pumps/ dumps and altcoin price actions (2013 - 2020). You know what to do.

[Wind_FURY]

Bloodiest days (between open and close prices)

I listed to top 50 bloodiest days only and pay attention on days in December over years. We are in the December, calendar day.

I am not here to give you any financial advice to buy or sell, long or short bitcoin. It is stats and data interpretation is for your side. Please use it with risk, and verify my information (if you can), don't trust me.  

Code:

      +---------------------------------------------+
      |     open      close        p_oc        date |
      |---------------------------------------------|
   1. |  7913.62    4970.79    -37.1869   12mar2020 |
   2. |    678.2      522.7   -22.92834   18dec2013 |
   3. |   223.89      178.1   -20.45201   14jan2015 |
   4. |  1042.38     829.45   -20.42729   06dec2013 |
   5. |   880.33     705.97   -19.80621   16dec2013 |
   6. |   580.26     471.24   -18.78813   27mar2014 |
   7. |  3875.37    3154.95   -18.58971   14sep2017 |
   8. |   257.93     211.08   -18.16384   18aug2015 |
   9. |   712.76     584.61    -17.9794   19nov2013 |
  10. |   442.26     365.18   -17.42866   10apr2014 |
  11. |  13836.1    11490.5   -16.95275   16jan2018 |
  12. |   835.32     698.23   -16.41167   07dec2013 |
  13. |  8270.54    6955.27   -15.90307   05feb2018 |
  14. |      139     116.99   -15.83453   01may2013 |
  15. |   267.39     225.86   -15.53162   13jan2015 |
  16. |  1128.92     955.85   -15.33058   01dec2013 |
  17. |   430.26     364.33   -15.32329   15jan2016 |
  18. |   946.49        802   -15.26588   07jan2014 |
  19. |    79.99      68.43   -14.45181   05jul2013 |
  20. |   908.11     777.76   -14.35399   11jan2017 |
  21. |     90.4      77.53   -14.23673   03jul2013 |
  22. | 13017.12   11182.81   -14.09152   27jun2019 |
  23. |   132.05     114.13   -13.57062   02oct2013 |
  24. |  5620.78    4871.49   -13.33071   19nov2018 |
  25. | 10896.65    9477.64   -13.02244   16jul2019 |
  26. |    15898    13831.8    -12.9966   22dec2017 |
  27. |    884.6     771.39   -12.79787   27jan2014 |
  28. |  1156.73    1013.38   -12.39269   05jan2017 |
  29. |  14681.9    12952.2   -11.78117   30dec2017 |
  30. |  1099.69     973.82   -11.44595   18mar2017 |
      +---------------------------------------------+

The people who bought during 2017's ATH, and didn't sell truly deserve an award. They're the only group of people who should be called the "HODLers". The relief from their stress just arrived last month.

[tranthidung]

The people who bought during 2017's ATH, and didn't sell truly deserve an award. They're the only group of people who should be called the "HODLers". The relief from their stress just arrived last month.

They chased the market and they got lessons from it. I don't do this. My trades are based on my calculations (of course can be correct or inaccurate) with my formula. I take profit at the price I calculate and if the price moves upward more intensively and reach a higher price, I skip it. Stay aside and watch the market goes, I don't chase it. When pull backs, corrections or turning point is found, I join the market again.

Safety is my first priority, not profit. I don't say I make right calculations and right decisions in my all trading-life but I try to reduce or minimize risks for my trades. My trades should be good from my own side, not from the support or luck is given by the market.

[examplens]

Is it possible to give us something more. For example, the most profitable days but here that we consider all Mondays, Tuesdays, Wednesdays etc... Or weeks in the year for last 5-6 yrs.
It can be interesting how is important what time of year it is or what day it is.

[Darker45]

The people who bought during 2017's ATH, and didn't sell truly deserve an award. They're the only group of people who should be called the "HODLers". The relief from their stress just arrived last month.

Indeed! They're the ones who made it after going through the hardest of obstacles, the ones forged by fire. And I doubt there's many of them. In the first place, many of those people who buy while the rally has already been going on for days and weeks are probably those who have weak hands. They're the sheep. They couldn't muster enough courage to buy at the bloodiest of days. They have no guts. They're probably the ones who are also panic selling when long red candles take over the greens.

[tranthidung]

Is it possible to give us something more. For example, the most profitable days but here that we consider all Mondays, Tuesdays, Wednesdays etc... Or weeks in the year for last 5-6 yrs.
It can be interesting how is important what time of year it is or what day it is.

Here you go

• Monday, Thursday and Wednesday probably best days to trade every week.
• Friday, Saturday and Sunday are less volatile days and this fact somewhat is reflected with lower transaction fees on the network. As I pointed out the weekend effects
• Those statements are true for ranks based on either median or interquartile range (iqr) -- on plots, iqr is represented by the Green box.
• Pay attention on whiskers (outside boxes). Monday and Thursday have biggest gaps between whiskers. If you are good at tradings, you can take advantage of such
• Of course, bullish and bearish market can have a bit difference and don't let you are obsessed with statistics.

Details
All years

Code:

* Ranks are based on median
     +-------------------------------------------------------------------------------+
     |      dofw     median        iqr         p25        p75         min        max |
     |-------------------------------------------------------------------------------|
  1. |    Monday    .255949   3.592846    -1.27665   2.316196   -19.80621    41.6811 |
  2. |  Saturday    .251333   2.399184   -.8964984   1.502686   -16.41167    14.6789 |
  3. |    Friday   .1802792   3.059987   -1.235891   1.824096   -20.42729   15.73781 |
  4. |   Tuesday    .122944   3.142102   -1.203938   1.938165   -18.16384   17.39172 |
  5. |  Thursday    .082507   3.592934    -1.51409   2.078844    -37.1869   33.31021 |
  6. | Wednesday   .0794501   3.242431   -1.553851   1.688581   -22.92834   19.86095 |
  7. |    Sunday   .0693629   2.536824   -1.040577   1.496247   -15.33058   14.13432 |
     +-------------------------------------------------------------------------------+

* Ranks are based on interquartile range (iqr = p75 - p25)
     +-------------------------------------------------------------------------------+
     |      dofw        iqr         p25        p75     median         min        max |
     |-------------------------------------------------------------------------------|
  1. |  Thursday   3.592934    -1.51409   2.078844    .082507    -37.1869   33.31021 |
  2. |    Monday   3.592846    -1.27665   2.316196    .255949   -19.80621    41.6811 |
  3. | Wednesday   3.242431   -1.553851   1.688581   .0794501   -22.92834   19.86095 |
  4. |   Tuesday   3.142102   -1.203938   1.938165    .122944   -18.16384   17.39172 |
  5. |    Friday   3.059987   -1.235891   1.824096   .1802792   -20.42729   15.73781 |
  6. |    Sunday   2.536824   -1.040577   1.496247   .0693629   -15.33058   14.13432 |
  7. |  Saturday   2.399184   -.8964984   1.502686    .251333   -16.41167    14.6789 |
     +-------------------------------------------------------------------------------+

In 2020 only

Code:

* Ranks are based on median
     +--------------------------------------------------------------------------------+
     |      dofw      median        iqr         p25        p75         min        max |
     |--------------------------------------------------------------------------------|
  1. | Wednesday    .8378724   2.618876   -.7242507   1.894625   -5.544591   12.73686 |
  2. |    Monday    .5560157   3.891994    -.556548   3.335446   -6.884572   10.96038 |
  3. |  Saturday    .2637979   1.688826   -.4005385   1.288288   -6.687685   3.859725 |
  4. |  Thursday    .2544709   3.238406   -1.167028   2.071378    -37.1869    18.0304 |
  5. |   Tuesday    .0488164   3.354167   -1.036243   2.317924   -6.230167   8.436751 |
  6. |    Sunday    .0117402   2.106423   -.9847237     1.1217    -8.98149    4.35372 |
  7. |    Friday   -.0321932   2.067744   -.9416836    1.12606    -6.00138   10.87881 |
     +--------------------------------------------------------------------------------+

* Ranks are based on interquartile range (iqr = p75 - p25)
     +--------------------------------------------------------------------------------+
     |      dofw        iqr         p25        p75      median         min        max |
     |--------------------------------------------------------------------------------|
  1. |    Monday   3.891994    -.556548   3.335446    .5560157   -6.884572   10.96038 |
  2. |   Tuesday   3.354167   -1.036243   2.317924    .0488164   -6.230167   8.436751 |
  3. |  Thursday   3.238406   -1.167028   2.071378    .2544709    -37.1869    18.0304 |
  4. | Wednesday   2.618876   -.7242507   1.894625    .8378724   -5.544591   12.73686 |
  5. |    Sunday   2.106423   -.9847237     1.1217    .0117402    -8.98149    4.35372 |
  6. |    Friday   2.067744   -.9416836    1.12606   -.0321932    -6.00138   10.87881 |
  7. |  Saturday   1.688826   -.4005385   1.288288    .2637979   -6.687685   3.859725 |
     +--------------------------------------------------------------------------------+

[Wind_FURY]

The people who bought during 2017's ATH, and didn't sell truly deserve an award. They're the only group of people who should be called the "HODLers". The relief from their stress just arrived last month.

They chased the market and they got lessons from it. I don't do this. My trades are based on my calculations (of course can be correct or inaccurate) with my formula. I take profit at the price I calculate and if the price moves upward more intensively and reach a higher price, I skip it. Stay aside and watch the market goes, I don't chase it. When pull backs, corrections or turning point is found, I join the market again.

Safety is my first priority, not profit. I don't say I make right calculations and right decisions in my all trading-life but I try to reduce or minimize risks for my trades. My trades should be good from my own side, not from the support or luck is given by the market.

I wish I had your trading-skills. I was a more active "trader", or tried to be, before I accepted my pleb status in the community. It all worked for me because, buying the dip, and HODL was easier for my sanity. Hahaha.

The people who bought during 2017's ATH, and didn't sell truly deserve an award. They're the only group of people who should be called the "HODLers". The relief from their stress just arrived last month.

Indeed! They're the ones who made it after going through the hardest of obstacles, the ones forged by fire. And I doubt there's many of them. In the first place, many of those people who buy while the rally has already been going on for days and weeks are probably those who have weak hands. They're the sheep. They couldn't muster enough courage to buy at the bloodiest of days. They have no guts. They're probably the ones who are also panic selling when long red candles take over the greens.

Or the whale-cumulators. They buy to make the market rally, they sell to make it crash.

[fillippone]

• Friday, Saturday and Sunday are less volatile days and this fact somewhat is reflected with lower transaction fees on the network. As I pointed out the weekend effects

This effect always strikes me a little bit. I guess this was less prominent when bitcoin was a “nerd money”, as they didn’t care about weekends.
Now bitcoin is more institutionalised money, so I guess there’s a correlation with inflows/outflows from traditional banking systems, working only on workdays. But I am wondering about the future: how to match a 24/7 market with a 9:00 AM /5:00 PM workday only traditional system? I guess this is only an incident in history.
I think I made a similar post on the linked threads, but this aspect puzzles me a lot.

EDIT: Edit for Clarity

[tranthidung]

This effect always strikes me a little bit. I guess this was less prominent when bitcoin was a “nerd money”, as they didn’t care about weekends.
Now bitcoin is more institutionalised money, so I guess there’s a correlation with  inflows/outflows from traditional banking systems, working only on workdays. But I am wondering about the future: how to match a 24/7 market with a 9/17 workday only traditional system? I guess this is only an incident in history.
I think I made a similar post on the linked threads, but this aspect puzzles me a lot.

It is hard to say because I don't have enough evidence (data) to prove it. What I get here is very raw picture but it give us something. Personally, I thought that the workday effects on price (volatility) and weekend effects on transaction fees can be explained as simply as this:

• Whales can be a single person, a group or institutes. Of course, to manipulate price they need to work as a group (there are potential conflict of interests between groups). They are rich so they don't have dedication to trade and play with the market in weekends that are time for them to enjoy entertainment in live.
• The market is actively trading 24/7 but the game is in the hands of whales, not all traders. Without big market makers, the market is less volatile.

In Interquartile range ranks, there is no changes for all-year period or for 2020-only period. Let's see whether it will be change next 1, 2 or 5 years (after the next halving).
Ooops! Let's me check the result again. It seems I mistakenly copied and pasted the result. Confirmed it !

All years

Code:

* Ranks are based on interquartile range (iqr = p75 - p25)
     +-------------------------------------------------------------------------------+
     |      dofw        iqr         p25        p75     median         min        max |
     |-------------------------------------------------------------------------------|
  1. |  Thursday   3.592934    -1.51409   2.078844    .082507    -37.1869   33.31021 |
  2. |    Monday   3.592846    -1.27665   2.316196    .255949   -19.80621    41.6811 |
  3. | Wednesday   3.242431   -1.553851   1.688581   .0794501   -22.92834   19.86095 |
  4. |   Tuesday   3.142102   -1.203938   1.938165    .122944   -18.16384   17.39172 |
  5. |    Friday   3.059987   -1.235891   1.824096   .1802792   -20.42729   15.73781 |
  6. |    Sunday   2.536824   -1.040577   1.496247   .0693629   -15.33058   14.13432 |
  7. |  Saturday   2.399184   -.8964984   1.502686    .251333   -16.41167    14.6789 |
     +-------------------------------------------------------------------------------+

In 2020 only

Code:

* Ranks are based on interquartile range (iqr = p75 - p25)
     +--------------------------------------------------------------------------------+
     |      dofw        iqr         p25        p75      median         min        max |
     |--------------------------------------------------------------------------------|
  1. |    Monday   3.891994    -.556548   3.335446    .5560157   -6.884572   10.96038 |
  2. |   Tuesday   3.354167   -1.036243   2.317924    .0488164   -6.230167   8.436751 |
  3. |  Thursday   3.238406   -1.167028   2.071378    .2544709    -37.1869    18.0304 |
  4. | Wednesday   2.618876   -.7242507   1.894625    .8378724   -5.544591   12.73686 |
  5. |    Sunday   2.106423   -.9847237     1.1217    .0117402    -8.98149    4.35372 |
  6. |    Friday   2.067744   -.9416836    1.12606   -.0321932    -6.00138   10.87881 |
  7. |  Saturday   1.688826   -.4005385   1.288288    .2637979   -6.687685   3.859725 |
     +--------------------------------------------------------------------------------+

I don't get what you mean about 9/17 workday. Could you explain it, please.  

Differences between median and IQR of 2020-only and all-year.

• median_diff = median20 - median
• iqr_diff = iqr20 - iqr
• median_diff2 = median20 - median20ex (ex means excluded)
• iqr_diff2 = iqr20 - iqr20ex
• In difference of median, values of 2020 tend to higher than all-year period in workdays. Good and likely logic for all days, except Tuesday. I don't know how to explain the strange day.
• Differences look bigger for 2020 and non-2020 period (see at the bottom)

Code:

* Median
     +-----------------------------------------------------------+
     |      dofw   median_diff    median20     median        iqr |
     |-----------------------------------------------------------|
  1. | Wednesday      .7584223    .8378724   .0794501   3.242431 |
  2. |    Monday      .3000666    .5560157    .255949   3.592846 |
  3. |  Thursday      .1719639    .2544709    .082507   3.592934 |
  4. |  Saturday      .0124649    .2637979    .251333   2.399184 |
  5. |    Sunday     -.0576228    .0117402   .0693629   2.536824 |
  6. |   Tuesday     -.0741276    .0488164    .122944   3.142102 |
  7. |    Friday     -.2124724   -.0321932   .1802792   3.059987 |
     +-----------------------------------------------------------+

Interquartile range

Code:

     +--------------------------------------------------------+
     |      dofw    iqr_diff      iqr20        iqr     median |
     |--------------------------------------------------------|
  1. |    Monday    .2991476   3.891994   3.592846    .255949 |
  2. |   Tuesday    .2120643   3.354167   3.142102    .122944 |
  3. |  Thursday   -.3545282   3.238406   3.592934    .082507 |
  4. |    Sunday   -.4304004   2.106423   2.536824   .0693629 |
  5. | Wednesday   -.6235559   2.618876   3.242431   .0794501 |
  6. |  Saturday   -.7103577   1.688826   2.399184    .251333 |
  7. |    Friday   -.9922428   2.067744   3.059987   .1802792 |
     +--------------------------------------------------------+

2020 and non-2020 periods.
Median

Code:

     +---------------------------------------------------+
     |      dofw   median_diff2    median20   median20ex |
     |---------------------------------------------------|
  1. | Wednesday       .8084463    .8378724     .0294261 |
  2. |    Monday       .3298792    .5560157     .2261365 |
  3. |  Thursday       .1885768    .2544709     .0658942 |
  4. |  Saturday       .0259284    .2637979     .2378695 |
  5. |    Sunday      -.0646415    .0117402     .0763816 |
  6. |   Tuesday      -.0895118    .0488164     .1383282 |
  7. |    Friday      -.2644089   -.0321932     .2322156 |
     +---------------------------------------------------+

Interquartile range

Code:

     +---------------------------------------------+
     |      dofw   iqr_diff2      iqr20    iqr20ex |
     |---------------------------------------------|
  1. |    Monday     .444531   3.891994   3.447463 |
  2. |   Tuesday    .1343999   3.354167   3.219767 |
  3. |  Thursday   -.3918223   3.238406   3.630229 |
  4. | Wednesday   -.5871975   2.618876   3.206073 |
  5. |    Sunday   -.6207919   2.106423   2.727215 |
  6. |  Saturday    -.918386   1.688826   2.607212 |
  7. |    Friday   -1.213345   2.067744   3.281089 |
     +---------------------------------------------+

[tranthidung]

how to match a 24/7 market with a 9:00 AM /5:00 PM workday only traditional system?

I don't know about the future, but for now we can look at the price chart and available data retrospectively. It can not be done with data I used in this thread (from coinmarketcap.com) as it is for daily price data.

Fortunately, it can be done with data from Trading View. I've never tried this but a quick search and a small trial shed me a light that I can do this analysis.

• Export 1-hour data sheet.
• Run the analysis and compare the difference between different time windows in workdays. Similar to what I did in this thread, just focuces on workdays and stratify it into worktime and non-worktime.
• Problems
  
  • I don't know how to download all-time (at least in 2020) data from Trading view with 1-hr chart. The guide shows I only be able to download data in the chart I see. I have to scroll left to download past data, and so on. I tried to scroll, then downloaded data, it works but I don't want to waste my time like that.
  • https://www.tradingview.com/blog/en/export-chart-data-in-csv-14395/
  • Do you know how to set up the wanted window and click to download all data? Please help
  • Which time zone to use for this analysis? UTC (maybe). From the UNIX timestamp, I have to choose a specific time zone to convert.

[fillippone]

I didn’t mean to actually do this analysis with such details. I am only interested to the weekends, as I think in the future the institutional investors will have to adapt to the continuous market, rather than the other way round as it is currently happening.

[tranthidung]

It's not a bump, not an update but it is another point that explains why December usually is bad for cryptocurrency market.

Congress killed a landmark wildlife bill to preserve a massive crypto tax loophole

People can do wash sales in order to get a negative net figure for their taxation documents and avoid paying money for tax.

In other markets like stock, there are legislation about it but in cryptocurrency market, we have yet had a similar Act.

How the crypto wash-sale loophole works

To understand why the loophole is so egregious, consider this hypothetical we previously put forth:

Let’s say you bought one bitcoin for $40,000 at the beginning of this year. Maybe you watched the Super Bowl ads, and came away thinking fortune favors the brave. And it sure felt that way until mid-year, when bitcoin began falling sharply; it now trades around $17,000. But you think bitcoin still has a bright future, so you commit to maintaining the position long-term.

Here’s the totally legal tax move: Rather than just holding onto your bitcoin for dear life, you sell it at $17,000 and then immediately buy it back. You have realized a $23,000 loss, which can be used to offset other income and lower your taxes. And yet you haven’t lost anything at all, really: You still own one bitcoin, and you can enjoy any future gains from the investment. Meanwhile, you get to pay less in tax and invest the savings.

[fillippone]

With this method you realise 23,000 dollar less today, but you increase your future tax burden of an equal amount.
So this is not exactely a loophole, but rather an “optimisation”, as we say in Italy:”to die, and to pay taxes, it’s never too early!”.