Quantitative Update: Bitcoin vs. The Rest of the World

This post is meant to be an addition to what I said earlier this year. Here we compare, in the same historical period of existence of bitcoin, Bitcoin vs other assets: us stock market indexes, US stocks of different sectors and Gold.

Let’s start with this summary table, who follow me regularly should already know the meaning of Shannon’s probability, RMS, G yield and compounded annual G yield; for all the others I refer you to the end of the article.
The data have been sorted in descending order according to Compounded Yearly Gain  G.

Comparison Bitcoin vs. The rest of the world
July 17, 2010 – Dec 31, 2019

Asset RMS or Volatility Shannon Probability P Daily Gain G Compounded Yearly Gain  G Optimal Fraction of your capital to wage
Bitcoin               0.0567                       0.5219        1.00087 38% 4.4%
MasterCard               0.0155                       0.5265          1.0007 19% 5.3%
Visa               0.0144                       0.5241          1.0006 16% 4.8%
Amazon               0.0193                       0.5196        1.00057 15% 3.9%
Apple               0.0161                       0.5172        1.00042 11% 3.4%
Google               0.0148                       0.5167        1.00038 10% 3.3%
Microsoft               0.0143                       0.5169        1.00038 10% 3.4%
Nasdaq Composite Index               0.0106                       0.5179        1.00032 8% 3.6%
Standard & Poor’s 500 Index               0.0091                       0.5160        1.00025 6% 3.2%
McDonald               0.0098                       0.5119        1.00019 5% 2.4%
Berkshire Hathaway Inc. (W.Buffett)               0.0105                       0.5112        1.00018 5% 2.2%
Pfizer               0.0115                       0.5078        1.00011 3% 1.6%
Facebook               0.0226                       0.5080        1.00010 3% 1.6%
Tesla               0.0318                       0.5096        1.00010 3% 1.9%
JPMorgan               0.0155                       0.5070        1.00010 2% 1.4%
Intel               0.0153                       0.5040        1.00001 0% 0.8%
**Gold (XAUUSD)               0.0094                       0.4951        0.99986 -3% 0%
*Ethereum               0.0634                       0.5138        0.99974 -6% 0%
General Motors               0.0178                       0.4903        0.99950 -12% 0%
General Electric               0.0164                       0.4868        0.99943 -13% 0%

*Ethereum Data since Aug 7, 2015, source coinmarketcap.
**Gold since 1970 has been a bit better with +3% yearly compounded gain.

The first comparison to make is with the main competitors of bitcoin, credit cards. I’m surprised to see how good are quantitative parameters of Mastercard and Visa, on the other hand they are monopolies, perhaps that’s why the CEO of mastercard hates so much Bitcoin, he sees it as a strong threat. Even Amazon has worse parameters compared to Visa and MC.

I included only Ethereum  in the comparison because in terms of market cap is second to Bitcoin, its yearly yield G is negative and i’m not surprised because I remind you that volatility reduces by far the yield G and in the case of all altcoins, not only Ethereum, the volatility reaches very high levels and therefore as an investment vehicle altcoins in general are absolutely not recommended, can eventually be considered as purely speculative assets for short-term trading.

Unfortunately for Mr.P.Schiff, in the last ten years Gold performed badly, for your curiosity i computed Gold parameters using available daily data since January 1970 and its yearly gain G or yield has been +3%, nothing exceptional, basically Gold protected you against inflation in the last fifty years but nothing more then this.

As i said 20 days ago Bitcoin volatility is dropping but it remains very high compared to other assets, despite this Bitcoin yearly compounded gain G is an astonishing +38% and it’s the best investment vehicle of the world.
Compared to other bitcoin price models this value is not much, ten years from now compounding 38% yearly bitcoin should be at around 200k usd while, for example, the stock to flow model has a forecast of 10 millions usd after 2028 halving, this is the equivalent of 144% yearly compounded gain instead of 38%.
Let me know what you think, does the stock to flow model price return appear realistic to you or not? Personally i prefer to rely on numbers and they say a clear “no” to me. This is why i’m a bit skeptic about also the bitcoin price model i developed on tradingview but i’m curious to see how it’ll end in a couple of years.

Tech Addendum

The concept of entropic analysis of equity prices is old and it was first proposed by Louis Bachelier in his “theory of speculation”, this thesis anticipated many of the mathematical discoveries made later by Wiener and Markov underlying the importance of these ideas in today’s financial markets. Then in the mid 1940’s we have had the information theory developed by Claude Shannon , theory that is applicable to the analysis and optimization of speculative endeavors and it is exactly what i’ve done just applied to bitcoin and the other assets considered in the above table, especially using the Shannon Probability or entropy that in terms of information theory, entropy is considered to be a measure of the uncertainty in a message.
To put it intuitively, suppose p=0, at this probability, the event is certain never to occur, and so there is no uncertainty at all, leading to an entropy of 0; at the same time if p=1 the result is again certain, so the entropy is 0 here as well. When p=1/2 or 0.50 the uncertainty is at a maximum or basically there is no information and only noise.

Applying this entropy concept to an equity like a stock or a commodity or even bitcoin itself common values for P are 0.52 that can be interpreted as a slightly persistence or tendency to go up, this means that for example stock markets aren’t totally random and up to some extend they are exploitable, same for btc.
Knowing the entropy level of bitcoin/usd is crucial if we want to compute its main quantitative characteristics, as i explained in the technical background of my blog this process is quickly doable once you have all the formulas, the process is as follows:

To compute the Shannon Probability P you should follow these steps:

  1. compute natural logarithm of data increments (today price / yesterday price)
  2. compute the mean for all data increment computed in step 1
  3. compute RMS (root mean square) of all data increments, squaring each data increment and sum all togheter
  4. Compute price momentum probability with the formula P = (((avg / rms) – (1 / sqrt (n))) + 1) / 2
    where avg = data computed in step 2, rms = data computed in step 3, n = total samples of your dataset. If the resulting probability is above 0.5 then there is positive momentum, otherwise under 0.5 negative momentum

To compute the Gain Factor use the following formula:

G = ((1+RMS)^P*((1-RMS)^(1-P))

To compute the yearly gain G or growth just raise daily gain G to the 365th power for Bitcon or 252 for stocks (252 trading days in a year).

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Coronavirus ebook news roundup

Here are a few coronavirus-related ebook news stories I ran across today.

Library ebook lending service ProQuest has announced that its Ebook Central program has partnered with over 150 publishers to provide unlimited library lending access to their titles for the duration of the virus epidemic.

ProQuest Ebook Central customers impacted by COVID-19 get unlimited access to all owned titles from these publishers through mid-June. This means that all licenses – including single-user and three-user models – have automatically converted to unlimited access for that period, helping librarians bridge the gap for their patrons in this rapidly changing environment. The unlimited access also applies to additional titles purchased through mid-June.

Image-1.jpgUK paper The Sun reports that Apple is providing free ebooks and audiobooks to US and UK residents via the bookstore of Apple’s own Apple Books app. I checked it out myself, and there are indeed a lot of free titles there. I’m sure that many of them were already free as promotions even before the coronavirus epidemic, much the same way that Baen’s Free Library is, but still, any good source of free reading material can only help people who need to find more things to do to fill up their time.

South Korean ebook startup Millie’s Library is offering free access to its 50,000 titles for two months, as a way of giving back to society. The service normally costs $8 per month. It is also working with South Korea’s government to provide access to its service through the government’s smartphone app for residents under quarantine.

Meanwhile, just as crisis situations bring out the best in some, they can bring out the worst in others. Independent reports that Amazon has seen a huge uptick in plagiarized self-published works about the coronavirus epidemic, as unscrupulous operators copy and paste online news stories or make ebooks of short articles on corona-related subjects in order to make a quick buck (or pound) from worried readers.

Not all the new corona-related ebooks are for profiteering. The UK paper Express & Star reports that Cornish mother and children’s book author Ellie Jackson was so worried about the impact of the virus on children that she was moved to write a children’s ebook about it. The book, The Little Corona King, seems to be available on Apple Books and Google Play Books but I don’t see it anywhere else at the moment. (I wonder if any preventative measures Amazon might have imposed to try to stop coronavirus profiteering are keeping it off Amazon.)

Photo by Anna Shvets on Pexels.com


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My Bitcoin Price Model Part II

For those who follow me on twitter know that my bitcoin price model v1.1 that I presented on this blog last September 2019 has been invalidated by the recent low of March 13 at $3850.  I use 95% confidence level bands around my model forecast and that day the lower confidence level has been violated thus invalidating my model.
Since that day I have at various times pondered how to improve my old model and I recycled an idea that came to my mind last year when I presented the first model.
This idea is not to use the time factor to calculate the price of bitcoin but instead use the number of existing bitcoins that as you know grows over time and halves about every 4 years (until now it happened in 2012,2016 and 2020).
In doing so I discovered that there is a fairly strong linear relationship between the logarithm of the bitcoin price and the number of existing bitcoins at that particular moment.

All the important bitcoin bottoms are inside the 95% confidence bands (dotted lines)

With the software i use isn’t complicated to find a formula that approximate all the selected bitcoin bottoms.
This is the dataset used to compute the model:

Date Low Bitcoin Supply
2010/07/17 $0.05 3436900
2010/10/08 $0.06 4205200
2010/12/07 $0.17 4812650
2011/04/04 $0.56 5835300
2011/11/23 $1.99 7686200
2012/06/02 $5.21 9135150
2013/01/08 $13.20 10643750
2015/08/26 $198.19 14536950
2015/09/22 $224.08 14637300
2016/04/17 $414.61 15439525
2016/05/25 $444.63 15582350
2016/10/23 $650.32 15943563
2017/03/25 $889.08 16235100
2019/02/08 $3,350.49 17525700
2018/12/15 $3,124.00 17423175
2019/03/25 $3,855.21 17608213
2020/03/13 $3,850.00 18270000

The Formula is a very simple one, a first order price regression  between log(Low) and Bitcoin supply:

Where:
FPL = expected line where bitcoin is fairly priced
intercept = a costant
c1 = another coefficient that defines the slope of the Bitcoin supply input.

Here’s the resulting model after computing the parameters of the above formula.

This is the new bitcoin price model “FPL Line” v1.3 applied to a monthly bitcoin/usd chart:

Next Step: Computing the formula for the TopLine

The formula for computing the Top is:


Where:
TopLine= is the forecasted price where the next long term top might be.
intercept = a costant
c1 = another coefficient that defines at which pow the bitcoin supply is elevated

This formula is different from the one used to compute the FPL or bottom line. I’ve seen that there is not a strong linear relationship betweel the logarithm of important Bitcoin Tops and the Bitcoin supply, so i decided to switch to the formula used for the old model and it works better.

This is the dataset used to compute the model:

Date Price Bitcoin Supply
2010/07/17  $      0.05 3436900
2011/06/08  $      31.91 6471200
2013/11/30  $      1,163.00 12058375
2013/12/04  $      1,153.27 12076500
2017/12/19  $    19,245.59 16750613

Here’s the resulting model after computing the parameters of the above formula.

This is the new bitcoin price model “Top Line” v1.3 applied to a monthly bitcoin/usd chart:

95% Confidence Error Bands

With the indicator that i give you for TradingView i included also the error bands.
This are the error bands for the TopLine:

And for the bottom line or FPL (FairPriceLine)

It is quite obvious that with fewer points available the error bands for the TopLine are wider and less accurate compared to the FPL error bands where I have more points (17 instead of 5).

TradingView Indicator

I have also included an indicator for TradingView to give you the opportunity to experience the concepts and model illustrated in this update. You can also check the code and/or modify it as you like.

On April 10th, 2020 tradingview staff decided to censor my indicator and threatened to close my account, because of this i publish here the code so you can create your own indicator by yourself.

Bitcoin Model v1.3 Sourcecode:

Code is also available at pastebin

Remember to add a “TAB” key once before stock (line 10 and 13), in the process of copying and pasting data back and forth from tradingview the tab key is gone probably because there is not a tab code in HTML.

//@version=2

study(“Bitcoin Price Model v1.3”, overlay=true)

//stock = security(stock, period, close)
stock = security(“QUANDL:BCHAIN/TOTBC”,’M’, close)

if(isweekly)
//insert “TAB” key before stock
stock = security(“QUANDL:BCHAIN/TOTBC”,’W’, close)
if(isdaily)
//insert “TAB” key before stock
stock = security(“QUANDL:BCHAIN/TOTBC”,’D’, close)

FairPriceLine = exp(-5.48389898381523+stock*0.000000759937156985051)

FairPriceLineLoConfLimit = exp(-5.86270418884089+stock*0.000000759937156985051)
FairPriceLineUpConfLimit = exp(-5.10509377878956+stock*0.000000759937156985051)

FairPriceLineLoConfLimit1 = exp(-5.66669176679684+stock*0.000000759937156985051)
FairPriceLineUpConfLimit1 = exp(-5.30110620083361+stock*0.000000759937156985051)

plot(FairPriceLine, color=gray, title=”FairPriceLine”, linewidth=4)

show_FPLErrorBands = input(true, type=bool, title = “Show Fair Price Line Error Bands 95% Confidence 2St.Dev.”)
plot(show_FPLErrorBands ? FairPriceLineLoConfLimit : na, color=gray, title=”FairPriceLine Lower Limit”, linewidth=2)
plot(show_FPLErrorBands ? FairPriceLineUpConfLimit : na, color=gray, title=”FairPriceLine Upper Limit”, linewidth=2)

show_FPLErrorBands1 = input(false, type=bool, title = “Show Fair Price Line Error Bands 68% Confidence 1St.Dev.”)
plot(show_FPLErrorBands1 ? FairPriceLineLoConfLimit1 : na, color=gray, title=”FairPriceLine Lower Limit”, linewidth=1)
plot(show_FPLErrorBands1 ? FairPriceLineUpConfLimit1 : na, color=gray, title=”FairPriceLine Upper Limit”, linewidth=1)

TopPriceLine = exp(-30.1874869318185+pow(stock,0.221847047326554))
TopPriceLineLoConfLimit = exp(-30.780909776998+pow(stock,0.220955789986605))
TopPriceLineUpConfLimit = exp(-29.5940640866389+pow(stock,0.222738304666504))

TopPriceLineLoConfLimit1 = exp(-30.3683801339907+pow(stock,0.221575365176983))
TopPriceLineUpConfLimit1 = exp(-30.0065937296462+pow(stock,0.222118729476125))

plot(TopPriceLine, color=white, title=”TopPriceLine”, linewidth=2)

show_TOPErrorBands = input(false, type=bool, title = “Show Top Price Line Error Bands 95% Confidence 1St.Dev.”)
plot(show_TOPErrorBands ? TopPriceLineLoConfLimit : na, color=white, title=”TopPriceLine Lower Limit”, linewidth=1)
plot(show_TOPErrorBands ? TopPriceLineUpConfLimit : na, color=white, title=”TopPriceLine Upper Limit”, linewidth=1)

show_TOPErrorBands1 = input(false, type=bool, title = “Show Top Price Line Error Bands 68% Confidence 1St.Dev.”)
plot(show_TOPErrorBands1 ? TopPriceLineLoConfLimit1 : na, color=white, title=”TopPriceLine Lower Limit”, linewidth=1)
plot(show_TOPErrorBands1 ? TopPriceLineUpConfLimit1 : na, color=white, title=”TopPriceLine Upper Limit”, linewidth=1)

Forecast up to 2032

Bitcoin Model 1.3

This is a forecast up to 2032 halving, price will saturate between 27,000$ and 130,000$ with a maximum possible peak at 450,000$ in case of a strong bubble.

Conclusions

This model is clearly experimental, we will see in the future how it will behave. It is probably questionable my choice to use the existing bitcoin supply instead of using time as a main input for the model, I’m curious to know your opinion about it. Thank you.

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The Internet Archive’s ‘National Emergency Library’ expands on Open Library’s copyright violations

A few days ago, the Internet Archive announced a change to its Open Library ebook lending program. Until the announcement, this program had worked by treating treating ebooks as substitutes for the paper books the Archive held, and only lending out one electronic copy for each paper copy it had on hand—a practice called Controlled Digital Lending.

During the coronavirus epidemic crisis, however, the Archive announced it would suspend this limitation. Under the new terms, each user can check out up to ten ebooks at a time, but there is no limitation on how many copies of each ebook can be checked out at once by different users. Calling this the “National Emergency Library,” the Archive explained it was meant to benefit students and the general public during this time of crisis.

This announcement came in for a good amount of uncritical news coverage from sources like Forbes and NPR. However, is this really as praiseworthy as it seems? Ars Technica pays a little more attention to the issues in its own story, pointing out much the same thing that I did several years ago: this “ebook lending library” is cheerfully violating copyrights left and right.

Some publishers and authors are encouraging greater fair use of their works during the corona crisis, but the Internet Archive’s actions here are considerably more extreme. While it might have given a nod to the idea of fair use by only lending one ebook at a time for each paper copy it owns, there is no precedent for fair use rights to permit controlled digital lending of copyrighted content—and the Archive has now discarded even that fig leaf with its new temporary “borrow as much as you want” regime.

Just over two years ago, the Authors Guild finally noticed what was going on and issued warnings and complaints, but didn’t take any further legal action. It has now issued another announcement, viewing this new development with alarm.

IA is using a global crisis to advance a copyright ideology that violates current federal law and hurts most authors. It has misrepresented the nature and legality of the project through a deceptive publicity campaign. Despite giving off the impression that it is expanding access to older and public domain books, a large proportion of the books on Open Library are in fact recent in-copyright books that publishers and authors rely on for critical revenue. Acting as a piracy site—of which there already are too many—the Internet Archive tramples on authors’ rights by giving away their books to the world.

In its National Emergency Library FAQ list, the Archive provides a contact email for authors or publishers to request that their books be removed. It does not address the question of legality. As I noted in my previous piece, Internet Archive founder Brewster Kahle is a long-time copyright reform advocate, and one of the ways such people push for reform is by staking out a position just outside what the law permits and trying to push as far forward as they can.

The odd thing is that in all this time, nobody has yet filed any copyright lawsuits over it. Even the Authors Guild hasn’t gone to court despite knowing about what the IA was doing for at least two years now—and this is the same organization that was so incensed over Google daring to scan copyrighted books for search indexing purposes that it took Google and its partner university all the way to the Supreme Court (and ultimately lost both cases).

But lawsuits do cost a lot of money, and the Authors Guild’s legal fees in the Google dispute couldn’t have been cheap. Is the Authors Guild still stinging over those defeats? Does it think that if it lost a fair use case against a commercial entity like Google, it would have even less hope of prevailing over a nonprofit like the Internet Archive? Or maybe it’s just that there are more pressing matters for the courts right now than copyright lawsuits.

In any case, as The Digital Reader points out, plenty of authors aren’t happy that the Internet Archive is providing access to their works for free when they are being hit hard by the epidemic right along with everybody else. There are many other legitimate methods of obtaining ebooks for free, via library-related services like Overdrive and Hoopla Digital, that will earn the authors and publishers money.


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Top Fitness Affiliate Programs

This week’s niche is focused on getting physically fit! If you have not started any fitness programs yet, you’re not alone (LOL). But if you are an affiliate marketer and you are looking for a new website to create and promote, then it’s about time you check out the top fitness affiliate programs we have for you here.

Let's start by visiting sample websites to get an idea of how to succeed in this particular niche. Like this site, Nerd Fitness. I love the overall look and feel of the site as it clearly spells P-O-S-I-T-I-V-I-T-Y! This site is very well targeted at people who want to get fit and motivated all the time.

Another example of a fitness site is body+soul, which is more generalized than the previous one. This site tackles a variety of health and wellness topics. Nonetheless, it has a dedicated page for fitness topics. Additionally, what I love about this site is the overall magazine-like aesthetic. It appeals to me, which makes sense, because the content and layout are geared towards women.   

What Is Fitness?

Let's Google Define It!

"What is fitness?" This is the exact phrase I Googled, and the snippet of information that Google provided was:

The Definition of Fitness

According to Google, the best definition of the word “fitness” is someone who is not only fit but also healthy. If you take this in context, fitness embodies the physical, mental, and emotional condition of a person.  

In contrast to similar niches like the health or bodybuilding niche, the fitness niche is focused on the development of both physique and health.

Is There Demand Online?

Weighing the Fitness Niche's Popularity

Recent data from Statista indicates that the global fitness and health industry's revenue amounts to around $80 billion USD per year.

In North America (US + Canada) alone, the market was estimated at a whopping $28 billion in 2015.

Google Trends shares quite a positive forecast for the fitness niche. Based on the "interest over time" graph, interest in the keyword “fitness” has been constantly high for the last five years, as shown:

Google Trends Data for "Fitness"

The top 5 countries where the keyword “fitness” is most frequently searched for are (in order from highest to lowest):

  1. Denmark
  2. Australia
  3. The United States
  4. Switzerland
  5. Canada

On social media, BuzzSumo also shows a large number of shares for fitness topics across popular social media sites (Facebook, LinkedIn, Twitter, Pinterest, and Google+). That gives us an idea of how popular this niche is on the Internet!

BuzzSumo Data for "Fitness"

Based on the demand and popularity data presented above, there's no doubt that fitness is a good niche to cover.

Keyword Research

"Build Up" Your Fitness Keywords

Let's “work out” our keywords for this niche. A bit of a disclaimer here before we start: Remember that the keywords we feature on our NOTW posts are NOT the only recommended keywords for the niche. You need to choose your own keywords, meaning pick the keywords that best fit the target audience for your niche site.

Get Your Seed Keywords from Google Search Results

Always start by identifying seed keywords that you can use to find more keywords. Since the fitness niche is very, very broad, I'll check out Google for seed keyword ideas:

Fitness Keywords

I have encircled the keywords that appear on the first page of the search results for the term “fitness,” which I can use to find my seed keywords.

These keywords are shown on the Google SERPs because Google decided that these are the most relevant keywords to the term searched.

Gather More Seed Keywords in 3 Easy Ways Using AffiloTools

Now to collect further seed keywords, I will re-enter the keywords that I have found on Google in the AffiloTools keyword research section. You can easily gather seed keywords using AffiloTools in 3 ways:

1. Use the keyword research tool

For instructions on how to do keyword research using AffiloTools, click here.

Here are the keyword match results for the term "video workouts":

"Video Workouts" Keyword Research in AffiloTools

2. Get more seed keywords in AffiloTools’ related keywords section

If you are not satisfied with the keywords, you may switch tabs to “Related Keywords” to get more keyword ideas, as shown below:

Related Keywords for "Video Workouts" in AffiloTools

3. Type the URL of a similar site into AffiloTools to get the site’s keywords

While still using AffiloTools’ keyword research tool, simply enter the URL of a similar site to get the keywords used for that site:

Finding Competitor Keywords in AffiloTools

Re-run the Keywords in Traffic Travis

We always get these questions in support: “How do you pick the keywords in AffiloTools?” "Do you base the selection on competition?"

Personally, when I am in the process of collecting my seed keywords, I do not check the competition yet. What I do is to gather ALL POSSIBLE keywords for my site. Then, once I am done with my keyword list, I re-run the keywords in Traffic Travis to check out the competitiveness of each keyword.

Another question you may ask is, “Why do you need to re-run your keywords in Traffic Travis when you can get the competition data in AffiloTools?”

If you are doing SEO, this is important. The competition data in AffiloTools is based on paid search, NOT organic search. This is the reason why you need to check the competition in Traffic Travis, since TT supplies you with competition data based on organic search.

For detailed instructions on how to check the competition data in Traffic Travis, please click here.

Affiliate Programs

A Healthy Menu of Affiliate Products

The great thing about this niche is you can set up a website dedicated to digital products only (downloadable content), or you can choose to promote physical products only (goods), or you can promote both digital and physical products. In short, you have tons of product choices to choose from.

Below is a list that I simply got from the top 10 results of Google for various keywords related to fitness. Like I have said, you can explore more, since the fitness niche provides you with a lot of choices for both digital and physical products.   

Natalie Jill Fitness

1. Natalie Jill Fitness

Owned by a single mom named Natalie who has turned her life around and focused on improving herself physically, mentally, and financially. The site promotes fitness, wellness, and motivation and has gained media attention, notably that of Fox, ABC, and NBC.   

How much can I make?
Payment is as high as 30% for every sale.

How do I apply?
You can sign up here.

Ace Fitness

2. Ace Fitness

A well-established site on fitness with a solid mission of educating people to become more conscious of their wellbeing and health and change their lifestyle. They are partnered with several known and established companies in the fitness industry. More than that, Ace Fitness is also proudly accredited with valid certifications, which makes them an industry leader.

How much can I make?
You can earn 8% for every sale.

How do I apply?
You can sign up here.

Workout Anywhere By RundleFit

3. Workout Anywhere By RundleFit

The target market for this site is busy people—professionals, parents, businesspeople, etc.—who have no time to work out on long hours. The site provides fitness training and solutions that are not time-consuming but provide maximum results.

How much can I make?
You can earn as high as 50% for every sale.

How do I apply?
You can sign up here.

Wahoo Fitness

4. Wahoo Fitness

A great fitness site for physical products. The company provides their own list of products for cyclists, athletes, and runners.

How much can I make?
You can earn 10% for every sale.

How do I apply?
You can sign up here.

LifeSpan Fitness

5. LifeSpan Fitness

Another option for those interested in promoting physical products. This company was established way back in 2001. They provide a wide range of fitness equipment, accessories, gadgets, and even apps. You can also choose products that are suitable for the gym, home, or even office.

How much can I make?
You need to contact the vendor for more information.

How do I apply?
You can sign up here.

How to Market the Niche

Get a Fit Link-Building Strategy

Driving traffic to your site is challenging yet essential. You need to get traffic to convert to sales.

There are several ways to drive traffic to your site, both paid and unpaid, so it's easy to get stuck on this part of affiliate marketing. If you want to get going right after you get your site up, then you need to create a link-building strategy in the early stages of building the site.

Some elements you can include in your link-building strategy for the fitness niche are:

Content Strategy

Content strategy is the art of managing the content on your site, from research to publication. There's a lot of information on fitness and wellness that you can publish on your site: proper diet, fitness routines, various workouts, etc. A good content strategy will help you better research and organize your articles and keep your site regularly updated.

Blog and Forum Commenting

There are plenty of fitness forums and blogs you can participate in. The good thing about this niche is that it is closely related to muscle building and weight loss, so you can go to sites in those niches too.

Keep in mind that you're not only building links when you're participating in blogs and forums, you're also building your site's online presence. So instead of simply building links with keyword-rich anchor texts in your blog comments, forum signatures and posts, provide comments that are useful to the readers. Make yourself an authority in your niche.

Link Baiting

There are plenty of authority sites in this niche. Instead of competing with these sites, use them as sources for your articles. Once an article is complete, you can write to the owner of the sites you cited and let them know you've linked to them. Most website owners will link back to your article as long as it's good and useful to their audience.

You don't have to limit yourself to articles. You can create infographics or videos and refer to the source site. The more shareable and useful the content, the more likely website owners will link back to it, so take the time to create high-quality content for link baiting.

Wrap Up!

Fitness Affiliate Programs: How Does the Niche Scale?

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Natalie Jill Fitness (Small) Ace Fitness (Small) Workout Anywhere (Small) Wahoo Fitness (Small) LifeSpan (Small)
30%

Commission
8%

Commission
50%

Commission
10%

Commission
Unspecified

Commission

The fitness niche, just like weight loss and muscle building, is highly lucrative. And just like the weight loss niche, it's something anyone can relate to and be interested in.

But just like any other evergreen niche, there's plenty of competition. Not a bad thing, though! You can always find a workaround for any competitive niche out there. Be sure to do your research, pick out a sub-niche to focus on, and plan a link-building strategy. You will need all of these to make your venture into this niche a profitable one.

 

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