In simple terms, you probably don’t.

But try. Grandma didn't grow up with Facebook, but she eventually joined.

Perhaps the fear of being isolated outweighed the discomfort of learning the technology.

Grandmas are Facebook likers, sharers, and commenters.

There’s no stopping her.

Not even NFTs. Web3 is currently very complex.

It's difficult to explain what NFTs are, how they work, and why we might use them.

Three explanations.

1. Everything will be ours to own, both physically and digitally.

Why own something you can't touch? What's the point?

Blockchain technology proves digital ownership.

Untouchables need ownership proof. What?

Digital assets reduce friction, save time, and are better for the environment than physical goods.

Many valuable things are intangible. Feeling like your favorite brands. You'll pay obscene prices for clothing that costs pennies.

Secondly, NFTs Are Contracts. Agreements Have Value.

Blockchain technology will replace all contracts and intermediaries.

Every insurance contract, deed, marriage certificate, work contract, plane ticket, concert ticket, or sports event is likely an NFT.

We all have public wallets, like Grandma's Facebook page.

3. Your NFT Purchases Will Be Visible To Everyone.

Everyone can see your public wallet. What you buy says more about you than what you post online.

NFTs issued double as marketing collateral when seen on social media.

While I doubt Grandma knows who Snoop Dog is, imagine him or another famous person holding your NFT in his public wallet and the attention that could bring to you, your company, or brand.

This Technical Section Is For You

The NFT is a contract; its founders can add value through access, events, tuition, and possibly royalties.

Imagine Elon Musk releasing an NFT to his network. Or yearly business consultations for three years.

Christ-alive.

It's worth millions.

These determine their value.

No unsuspecting schmuck willing to buy your hot potato at zero. That's the trend, though.

Overpriced NFTs for low-effort projects created a bubble that has burst.

During a market bubble, you can make money by buying overvalued assets and selling them later for a profit, according to the Greater Fool Theory.

People are struggling. Some are ruined by collateralized loans and the gold rush.

Finances are ruined.

It's uncomfortable.

The same happened in 2018, during the ICO crash or in 1999/2000 when the dot com bubble burst. But the underlying technology hasn’t gone away.

My nameless, faceless channel case study

The Numbers

I anonymize this YouTube channel.

It's in a trendy, crowded niche. Sharing it publicly will likely enhance competition.

I'll still share my dashboard numbers:

A year ago, the video was released.

What I earned

I'll stop stalling. Here's a screenshot of my YouTube statistics page displaying Adsense profits.

YouTube Adsense made me ZERO dollars.

OMG!

How is this possible?

YouTube Adsense can't monetize my niche. This is typical in faceless niches like TikTok's rain videos. If they were started a while ago, I'm sure certain rain accounts are monetized, but not today.

I actually started a soothing sounds faceless YouTube channel. This was another account of mine.

I looped Pexels films for hours. No background music, just wind, rain, etc.

People could watch these videos to relax or get ready for bed. They're ideal for background noise and relaxation.

They're long-lasting, too. It's easy to make a lot from YouTube Adsense if you insert ads.

Anyway, I tried to monetize it and couldn’t. This was about a year ago. That’s why I doubt new accounts in this genre would be able to get approved for ads.

Back to my faceless channel with 68 million views.

I received nothing from YouTube Adsense, but I made money elsewhere.

Getting paid by the gods of affiliate marketing

Place links in the video and other videos on the channel to get money. Visitors that buy through your affiliate link earn you a commission.

This video earned many clicks on my affiliate links.

I linked to a couple of Amazon products, a YouTube creator tool, my kofi link, and my subscribe link.

Sponsorships

Brands pay you to include ads in your videos.

This video led to many sponsorships.

I've done dozens of sponsorship campaigns that paid $40 to $50 for an end screen to $450 for a preroll ad.

Last word

Overall, I made less than $3,000.

If I had time, I'd be more proactive with sponsorships. You can pitch brand sponsorships. This actually works.

I'd do that if I could rewind time.

I still can, but I think the reaction rate would be higher closer to the viral video's premiere date.

Blockchain validates transactions with consensus algorithms. Bitcoin and Ethereum use Proof-of-Work, while Polkadot and Cardano use Proof-of-Stake.

Other consensus protocols are used to verify transactions besides these two. This post focuses on Proof-of-Time (PoT), used by Analog, and Proof-of-History (PoH), used by Solana as a hybrid consensus protocol.

PoT and PoH may seem similar to users, but they are actually very different protocols.

Proof-of-Time (PoT)

Analog developed Proof-of-Time (PoT) based on Delegated Proof-of-Stake (DPoS). Users select "delegates" to validate the next block in DPoS. PoT uses a ranking system, and validators stake an equal amount of tokens. Validators also "self-select" themselves via a verifiable random function."

The ranking system gives network validators a performance score, with trustworthy validators with a long history getting higher scores. System also considers validator's fixed stake. PoT's ledger is called "Timechain."

Voting on delegates borrows from DPoS, but there are changes. PoT's first voting stage has validators (or "time electors" putting forward a block to be included in the ledger).

Validators are chosen randomly based on their ranking score and fixed stake. One validator is chosen at a time using a Verifiable Delay Function (VDF).

Validators use a verifiable delay function to determine if they'll propose a Timechain block. If chosen, they validate the transaction and generate a VDF proof before submitting both to other Timechain nodes.

This leads to the second process, where the transaction is passed through 1,000 validators selected using the same method. Each validator checks the transaction to ensure it's valid.

If the transaction passes, validators accept the block, and if over 2/3 accept it, it's added to the Timechain.

Proof-of-History (PoH)

Proof-of-History is a consensus algorithm that proves when a transaction occurred. PoH uses a VDF to verify transactions, like Proof-of-Time. Similar to Proof-of-Work, VDFs use a lot of computing power to calculate but little to verify transactions, similar to (PoW).

This shows users and validators how long a transaction took to verify.

PoH uses VDFs to verify event intervals. This process uses cryptography to prevent determining output from input.

The outputs of one transaction are used as inputs for the next. Timestamps record the inputs' order. This checks if data was created before an event.

PoT vs. PoH

PoT and PoH differ in that:

• PoT uses VDFs to select validators (or time electors), while PoH measures time between events.

• PoH uses a VDF to validate transactions, while PoT uses a ranking system.

• PoT's VDF-elected validators verify transactions proposed by a previous validator. PoH uses a VDF to validate transactions and data.

Conclusion

Both Proof-of-Time (PoT) and Proof-of-History (PoH) validate blockchain transactions differently. PoT uses a ranking system to randomly select validators to verify transactions.

PoH uses a Verifiable Delay Function to validate transactions, verify how much time has passed between two events, and allow validators to quickly verify a transaction without malicious actors knowing the input.

DATA MINING WITH SUPERALGORES

Old pots produce the best soup.

Science has always inspired indicator design. From physics to signal processing, many indicators use concepts from mechanical engineering, electronics, and probability. In Superalgos' Data Mining section, we've explored using thermodynamics and information theory to construct indicators and using statistical and probabilistic techniques like reduced normal law to take advantage of low probability events.

An asset's price is like a mechanical object revolving around its moving average. Using this approach, we could design an indicator using the oscillator's Total Energy. An oscillator's energy is finite and constant. Since we don't expect the price to follow the harmonic oscillator, this energy should deviate from the perfect situation, and the maximum of divergence may provide us valuable information on the price's moving average.

Definition of the Harmonic Oscillator in Few Words

Sinusoidal function describes a harmonic oscillator. The time-constant energy equation for a harmonic oscillator is:

With

Time saves energy.

In a mechanical harmonic oscillator, total energy equals kinetic energy plus potential energy. The formula for energy is the same for every kind of harmonic oscillator; only the terms of total energy must be adapted to fit the relevant units. Each oscillator has a velocity component (kinetic energy) and a position to equilibrium component (potential energy).

The Price Oscillator and the Energy Formula

Considering the harmonic oscillator definition, we must specify kinetic and potential components for our price oscillator. We define oscillator velocity as the rate of change and equilibrium position as the price's distance from its moving average.

Price kinetic energy:

It's like:

With

and

L is the number of periods for the rate of change calculation and P for the close price EMA calculation.

Total price oscillator energy =

Given that an asset's price can theoretically vary at a limitless speed and be endlessly far from its moving average, we don't expect this formula's outcome to be constrained. We'll normalize it using Z-Score for convenience of usage and readability, which also allows probabilistic interpretation.

Over 20 periods, we'll calculate E's moving average and standard deviation.

We calculated Z on BTC/USDT with L = 10 and P = 21 using Knime Analytics.

The graph is detrended. We added two horizontal lines at +/- 1.6 to construct a 94.5% probability zone based on reduced normal law tables. Price cycles to its moving average oscillate clearly. Red and green arrows illustrate where the oscillator crosses the top and lower limits, corresponding to the maximum/minimum price oscillation. Since the results seem noisy, we may apply a non-lagging low-pass or multipole filter like Butterworth or Laguerre filters and employ dynamic bands at a multiple of Z's standard deviation instead of fixed levels.

Kinetic Detrender Implementation in Superalgos

The Superalgos Kinetic detrender features fixed upper and lower levels and dynamic volatility bands.

The code is pretty basic and does not require a huge amount of code lines.

It starts with the standard definitions of the candle pointer and the constant declaration :

let candle = record.current
let len = 10
let P = 21
let T = 20
let up = 1.6
let low = 1.6

Upper and lower dynamic volatility band constants are up and low.

We proceed to the initialization of the previous value for EMA :

if (variable.prevEMA === undefined) {
    variable.prevEMA = candle.close
}

And the calculation of EMA with a function (it is worth noticing the function is declared at the end of the code snippet in Superalgos) :

variable.ema = calculateEMA(P, candle.close, variable.prevEMA)
//EMA calculation
function calculateEMA(periods, price, previousEMA) {
    let k = 2 / (periods + 1)
    return price * k + previousEMA * (1 - k)
}

The rate of change is calculated by first storing the right amount of close price values and proceeding to the calculation by dividing the current close price by the first member of the close price array:

variable.allClose.push(candle.close)
if (variable.allClose.length > len) {
    variable.allClose.splice(0, 1)
}
if (variable.allClose.length === len) {
    variable.roc = candle.close / variable.allClose[0]
} else {
    variable.roc = 1
}

Finally, we get energy with a single line:

variable.E = 1 / 2 * len * variable.roc + 1 / 2 * P * candle.close / variable.ema

The Z calculation reuses code from Z-Normalization-based indicators:

variable.allE.push(variable.E)
if (variable.allE.length > T) {
    variable.allE.splice(0, 1)
}
variable.sum = 0
variable.SQ = 0
if (variable.allE.length === T) {
    for (var i = 0; i < T; i++) {
        variable.sum += variable.allE[i]
    }
    variable.MA = variable.sum / T
for (var i = 0; i < T; i++) {
        variable.SQ += Math.pow(variable.allE[i] - variable.MA, 2)
    }
    variable.sigma = Math.sqrt(variable.SQ / T)
variable.Z = (variable.E - variable.MA) / variable.sigma
} else {
    variable.Z = 0
}
variable.allZ.push(variable.Z)
if (variable.allZ.length > T) {
    variable.allZ.splice(0, 1)
}
variable.sum = 0
variable.SQ = 0
if (variable.allZ.length === T) {
    for (var i = 0; i < T; i++) {
        variable.sum += variable.allZ[i]
    }
    variable.MAZ = variable.sum / T
for (var i = 0; i < T; i++) {
        variable.SQ += Math.pow(variable.allZ[i] - variable.MAZ, 2)
    }
    variable.sigZ = Math.sqrt(variable.SQ / T)
} else {
    variable.MAZ = variable.Z
    variable.sigZ = variable.MAZ * 0.02
}
variable.upper = variable.MAZ + up * variable.sigZ
variable.lower = variable.MAZ - low * variable.sigZ

We also update the EMA value.

variable.prevEMA = variable.EMA

Conclusion

We showed how to build a detrended oscillator using simple harmonic oscillator theory. Kinetic detrender's main line oscillates between 2 fixed levels framing 95% of the values and 2 dynamic levels, leading to auto-adaptive mean reversion zones.

Superalgos' Normalized Momentum data mine has the Kinetic detrender indication.

All the material here can be reused and integrated freely by linking to this article and Superalgos.

This post is informative and not financial advice. Seek expert counsel before trading. Risk using this material.