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!”.
Topic: Bitcoin price history -- most profitable and bloodiest months, days (Read 608 times)
December 21, 2022, 03:04:06 AM
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.
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.
Quote
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.
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.
December 24, 2020, 01:10:18 PM
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.
December 24, 2020, 10:09:51 AM
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.
December 24, 2020, 07:25:07 AM
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: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.
- 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
In 2020 only
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 |
+---------------------------------------------+
December 24, 2020, 03:13:54 AM
- 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
December 17, 2020, 01:11:41 AM
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.
December 15, 2020, 09:35:08 PM
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 goIt can be interesting how is important what time of year it is or what day it is.
- 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 |
+--------------------------------------------------------------------------------+
December 15, 2020, 09:28:21 PM
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.
December 15, 2020, 06:45:31 PM
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.
It can be interesting how is important what time of year it is or what day it is.
December 15, 2020, 09:42:16 AM
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.
December 15, 2020, 08:55:53 AM
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.
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.
December 15, 2020, 04:09:09 AM
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).
Altcoins? Please check out with Bitcoin pumps/ dumps and altcoin price actions (2013 - 2020). You know what to do.
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.
December 12, 2020, 11:56:45 PM
Updates
With plots (period from 2013 to 2020; and in 2020 only)
Periods:
This part is important. As I promised in OP, here is results when I calculated the difference in a different method
Plots
Summary
Raw data
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
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 . . |
+----------------------------------------------------------------+
December 11, 2020, 05:12:52 PM
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).
December 11, 2020, 10:15:21 AM
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. 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.
Update:
I mis-calculated it. Initially, my formula is:
Code:
p_oc = (open - close)/open*100
Code:
p_oc = (close - open)/open*100
December 11, 2020, 10:00:40 AM
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.
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.
December 10, 2020, 11:17:20 PM
Most profitable and bloodiest days in 2020 (I sorted them out chronologically and here I use cut-offs at 5% or -5%)
Profitable
Bloodiest
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 |
+----------------------------------------------------+
December 10, 2020, 03:31:48 AM
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.
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 |
+---------------------------------------------+
December 10, 2020, 03:30:55 AM
Notes
Months
]
Most profitable days (between open and close prices)
Most profitable days (between close and low prices)
Most profitable days (between open and high prices)
- 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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