When it comes to trading market cycles, we are really only focused on the market tops and the market bottoms and everything in between is just time. There are many different methods and strategies that a trader might use to decide where they anticipate the market bottoms to be and additionally where they would like to place their buy orders.
This article is largely instigated by observing many people stating things similar to “Just buy already, we are close enough to the bottom” or “Who cares if price goes lower, it’s already cheap enough”. And I wanted to display why this may not actually be that straightforward for many traders and a lot of it comes down to greed, individual analysis, and tolerance.
Assumptions
I want to use the same example for the entirety of this article. So let’s assume that we are looking at a made up crypto called $TOKEN for this example. Let’s assume that last market cycle, $TOKEN hit $100. And let’s imagine that we are confident $TOKEN will hit $150 next market cycle, but are skeptical that it will make it to $225.
So the key stats summarized:
Previous market cycle top = $100
High confidence target = $150
Low confidence target = $225
The 50%-Less Compounding Scenario
The TLDR here is that every time your buy level lowers, your reward increases significantly more. Let’s demonstrate how this happens.
To keep it consistent, we are going to use intervals of 50% and not deviate from that to make the math easier.
So let’s say our first level to buy is 50% from the previous market cycle top:
50% * $100 = $50 entry level
Since we are planning for $TOKEN to hit $150 next cycle, this is setting us up for a 3x return on investment:
$150/$50 = 3
Entry Level ($)
Take Profit Level ($)
Total Return
Portfolio w/ $1k investment
50
150
3x
$3,000
Now let’s do that again, except this time we will take another 50% off of the entry level:
50% * $50 = $25
This entry level gives us a return of 6x to meet our take profit level:
$150/$25 = 6
And adding this to our table we now have these scenarios:
Entry Level ($)
Take Profit Level ($)
Total Return
Portfolio w/ $1k investment
50
150
3x
$3,000
25
150
6x
$6,000
Okay, the idea we are trying to convey isn’t obvious yet, but it is present here already. Notice that the first entry level is -$50 from the previous market cycle top of $100, and this gave us a 3x return. And the second entry level is -$25 from the first entry level, and gave us an additional 3x return.
Let’s run this idea home by doing this exact scenario 2 more more times, cutting 50% off the entry level each time. I’ll save the math and just drop the updated table here:
Entry Level ($)
Take Profit Level ($)
Total Return
Portfolio w/ $1k investment
50
150
3x
$3,000
25
150
6x
$6,000
12.5
150
12x
$12,000
6.25
150
24x
$24,000
By the time we get to our fourth entry, price is only needing to decrease another $6.25 to provide us with an additional 12x return! To put this back into perspective, the first entry needed an enormous decrease of $50 for us to get a simple 3x return. Now we are getting an extra 12x with only a $6.25 drop.
The returns here are a power of 2 series. So each time 50% is knocked off the entry level, the return expectation is 2^x
The Impact on Profit-Taking Yields
This exact same effect is realized on the profit taking side as well. Quick reminder that the stats we were focused on were the previous market cycle top of $100, the high confidence level of $150, and the low confidence target of $225. This low confidence interval was chosen specifically because it follows the same 50% idea, in that it is 50% higher than the previous target.
$150 * 150% = $225
Let’s look at the exact same table, as above, except now I am adding 4 more rows to the bottom to demonstrate if we took profits at $225 instead of $150.
Entry Level ($)
Take Profit Level ($)
Total Return
Portfolio w/ $1k investment
50
150
3x
$3,000
25
150
6x
$6,000
12.5
150
12x
$12,000
6.25
150
24x
$24,000
50
225
4.5x
$4,500
25
225
9x
$9,000
12.5
225
18x
$18,000
6.25
225
36x
$36,000
What we end up observing on the profit-taking side is slightly different than the entry level side. Here, an increase of 50% on the profit taking level gives you exactly an increase of 50% on the yield. And this should feel super obvious when reading it aloud, right? Because taking profit 50% higher gives you 50% higher profits… it’s obvious.
But the real value here is actually observing how that is not the case on the entry level side of the equation.
Closing Thoughts
There are many ways to view this same argument, and it all depends on your experience and way you visualize things to determine which you prefer or which feels more intuitive. This can be viewed through the lens that the profit-taking levels act as a scalar, whereas the entry-level acts as a power. This can also be viewed in numerous ways using only the arguments of absolute vs relative changes. There are countless ways to visualize it, and I had many other examples drawn out before deciding to leave it with what I have here. I took this is as the simplest to view since it’s just a dataset, and let’s you as the reader observe it through the lens of your choice.
I can’t end this without touching on how this impacts a trader/investor. This scenario causes a real dilemma with traders that pushes their greed and skills to the limit. Traders are faced with a massive incentive to identify as close as possible to the maxima and minima of market cycles. But the problem comes in if they miss the mark, if their greed is too high, if their target levels are never hit, their profit goes from exponential gains to zero. This is why it requires a well-structured and mannered trader to come up with a strategy that fits their tolerances and meets their goals
This is why the simple statement of “Who cares if price goes lower, it’s already cheap enough” is not that straightforward. That may fit one person’s strategy, but does not mean it fits for everyone.
When it comes to trading market cycles, we are really only focused on the market tops and the market bottoms and everything in between is just time. There are many different methods and strategies that a trader might use to decide where they anticipate the market bottoms to be and additionally where they would like to place their buy orders.
This article is largely instigated by observing many people stating things similar to “Just buy already, we are close enough to the bottom” or “Who cares if price goes lower, it’s already cheap enough”. And I wanted to display why this may not actually be that straightforward for many traders and a lot of it comes down to greed, individual analysis, and tolerance.
Assumptions
I want to use the same example for the entirety of this article. So let’s assume that we are looking at a made up crypto called $TOKEN for this example. Let’s assume that last market cycle, $TOKEN hit $100. And let’s imagine that we are confident $TOKEN will hit $150 next market cycle, but are skeptical that it will make it to $225.
So the key stats summarized:
Previous market cycle top = $100
High confidence target = $150
Low confidence target = $225
The 50%-Less Compounding Scenario
The TLDR here is that every time your buy level lowers, your reward increases significantly more. Let’s demonstrate how this happens.
To keep it consistent, we are going to use intervals of 50% and not deviate from that to make the math easier.
So let’s say our first level to buy is 50% from the previous market cycle top:
50% * $100 = $50 entry level
Since we are planning for $TOKEN to hit $150 next cycle, this is setting us up for a 3x return on investment:
$150/$50 = 3
Now let’s do that again, except this time we will take another 50% off of the entry level:
50% * $50 = $25
This entry level gives us a return of 6x to meet our take profit level:
$150/$25 = 6
And adding this to our table we now have these scenarios:
Okay, the idea we are trying to convey isn’t obvious yet, but it is present here already. Notice that the first entry level is -$50 from the previous market cycle top of $100, and this gave us a 3x return. And the second entry level is -$25 from the first entry level, and gave us an additional 3x return.
Let’s run this idea home by doing this exact scenario 2 more more times, cutting 50% off the entry level each time. I’ll save the math and just drop the updated table here:
By the time we get to our fourth entry, price is only needing to decrease another $6.25 to provide us with an additional 12x return! To put this back into perspective, the first entry needed an enormous decrease of $50 for us to get a simple 3x return. Now we are getting an extra 12x with only a $6.25 drop.
The returns here are a power of 2 series. So each time 50% is knocked off the entry level, the return expectation is 2^x
The Impact on Profit-Taking Yields
This exact same effect is realized on the profit taking side as well. Quick reminder that the stats we were focused on were the previous market cycle top of $100, the high confidence level of $150, and the low confidence target of $225. This low confidence interval was chosen specifically because it follows the same 50% idea, in that it is 50% higher than the previous target.
$150 * 150% = $225
Let’s look at the exact same table, as above, except now I am adding 4 more rows to the bottom to demonstrate if we took profits at $225 instead of $150.
What we end up observing on the profit-taking side is slightly different than the entry level side. Here, an increase of 50% on the profit taking level gives you exactly an increase of 50% on the yield. And this should feel super obvious when reading it aloud, right? Because taking profit 50% higher gives you 50% higher profits… it’s obvious.
But the real value here is actually observing how that is not the case on the entry level side of the equation.
Closing Thoughts
There are many ways to view this same argument, and it all depends on your experience and way you visualize things to determine which you prefer or which feels more intuitive. This can be viewed through the lens that the profit-taking levels act as a scalar, whereas the entry-level acts as a power. This can also be viewed in numerous ways using only the arguments of absolute vs relative changes. There are countless ways to visualize it, and I had many other examples drawn out before deciding to leave it with what I have here. I took this is as the simplest to view since it’s just a dataset, and let’s you as the reader observe it through the lens of your choice.
I can’t end this without touching on how this impacts a trader/investor. This scenario causes a real dilemma with traders that pushes their greed and skills to the limit. Traders are faced with a massive incentive to identify as close as possible to the maxima and minima of market cycles. But the problem comes in if they miss the mark, if their greed is too high, if their target levels are never hit, their profit goes from exponential gains to zero. This is why it requires a well-structured and mannered trader to come up with a strategy that fits their tolerances and meets their goals
This is why the simple statement of “Who cares if price goes lower, it’s already cheap enough” is not that straightforward. That may fit one person’s strategy, but does not mean it fits for everyone.