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Scott Hickmann

Scott Hickmann

3 years ago

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Vitalik

Vitalik

3 years ago

Fairness alternatives to selling below market clearing prices (or community sentiment, or fun)

When a seller has a limited supply of an item in high (or uncertain and possibly high) demand, they frequently set a price far below what "the market will bear." As a result, the item sells out quickly, with lucky buyers being those who tried to buy first. This has happened in the Ethereum ecosystem, particularly with NFT sales and token sales/ICOs. But this phenomenon is much older; concerts and restaurants frequently make similar choices, resulting in fast sell-outs or long lines.

Why do sellers do this? Economists have long wondered. A seller should sell at the market-clearing price if the amount buyers are willing to buy exactly equals the amount the seller has to sell. If the seller is unsure of the market-clearing price, they should sell at auction and let the market decide. So, if you want to sell something below market value, don't do it. It will hurt your sales and it will hurt your customers. The competitions created by non-price-based allocation mechanisms can sometimes have negative externalities that harm third parties, as we will see.

However, the prevalence of below-market-clearing pricing suggests that sellers do it for good reason. And indeed, as decades of research into this topic has shown, there often are. So, is it possible to achieve the same goals with less unfairness, inefficiency, and harm?

Selling at below market-clearing prices has large inefficiencies and negative externalities

An item that is sold at market value or at an auction allows someone who really wants it to pay the high price or bid high in the auction. So, if a seller sells an item below market value, some people will get it and others won't. But the mechanism deciding who gets the item isn't random, and it's not always well correlated with participant desire. It's not always about being the fastest at clicking buttons. Sometimes it means waking up at 2 a.m. (but 11 p.m. or even 2 p.m. elsewhere). Sometimes it's just a "auction by other means" that's more chaotic, less efficient, and has far more negative externalities.

There are many examples of this in the Ethereum ecosystem. Let's start with the 2017 ICO craze. For example, an ICO project would set the price of the token and a hard maximum for how many tokens they are willing to sell, and the sale would start automatically at some point in time. The sale ends when the cap is reached.

So what? In practice, these sales often ended in 30 seconds or less. Everyone would start sending transactions in as soon as (or just before) the sale started, offering higher and higher fees to encourage miners to include their transaction first. Instead of the token seller receiving revenue, miners receive it, and the sale prices out all other applications on-chain.

The most expensive transaction in the BAT sale set a fee of 580,000 gwei, paying a fee of $6,600 to get included in the sale.

Many ICOs after that tried various strategies to avoid these gas price auctions; one ICO notably had a smart contract that checked the transaction's gasprice and rejected it if it exceeded 50 gwei. But that didn't solve the issue. Buyers hoping to game the system sent many transactions hoping one would get through. An auction by another name, clogging the chain even more.

ICOs have recently lost popularity, but NFTs and NFT sales have risen in popularity. But the NFT space didn't learn from 2017; they do fixed-quantity sales just like ICOs (eg. see the mint function on lines 97-108 of this contract here). So what?

That's not the worst; some NFT sales have caused gas price spikes of up to 2000 gwei.

High gas prices from users fighting to get in first by sending higher and higher transaction fees. An auction renamed, pricing out all other applications on-chain for 15 minutes.

So why do sellers sometimes sell below market price?

Selling below market value is nothing new, and many articles, papers, and podcasts have written (and sometimes bitterly complained) about the unwillingness to use auctions or set prices to market-clearing levels.

Many of the arguments are the same for both blockchain (NFTs and ICOs) and non-blockchain examples (popular restaurants and concerts). Fairness and the desire not to exclude the poor, lose fans or create tension by being perceived as greedy are major concerns. The 1986 paper by Kahneman, Knetsch, and Thaler explains how fairness and greed can influence these decisions. I recall that the desire to avoid perceptions of greed was also a major factor in discouraging the use of auction-like mechanisms in 2017.

Aside from fairness concerns, there is the argument that selling out and long lines create a sense of popularity and prestige, making the product more appealing to others. Long lines should have the same effect as high prices in a rational actor model, but this is not the case in reality. This applies to ICOs and NFTs as well as restaurants. Aside from increasing marketing value, some people find the game of grabbing a limited set of opportunities first before everyone else is quite entertaining.

But there are some blockchain-specific factors. One argument for selling ICO tokens below market value (and one that persuaded the OmiseGo team to adopt their capped sale strategy) is community dynamics. The first rule of community sentiment management is to encourage price increases. People are happy if they are "in the green." If the price drops below what the community members paid, they are unhappy and start calling you a scammer, possibly causing a social media cascade where everyone calls you a scammer.

This effect can only be avoided by pricing low enough that post-launch market prices will almost certainly be higher. But how do you do this without creating a rush for the gates that leads to an auction?

Interesting solutions

It's 2021. We have a blockchain. The blockchain is home to a powerful decentralized finance ecosystem, as well as a rapidly expanding set of non-financial tools. The blockchain also allows us to reset social norms. Where decades of economists yelling about "efficiency" failed, blockchains may be able to legitimize new uses of mechanism design. If we could use our more advanced tools to create an approach that more directly solves the problems, with fewer side effects, wouldn't that be better than fiddling with a coarse-grained one-dimensional strategy space of selling at market price versus below market price?

Begin with the goals. We'll try to cover ICOs, NFTs, and conference tickets (really a type of NFT) all at the same time.

1. Fairness: don't completely exclude low-income people from participation; give them a chance. The goal of token sales is to avoid high initial wealth concentration and have a larger and more diverse initial token holder community.

2. Don’t create races: Avoid situations where many people rush to do the same thing and only a few get in (this is the type of situation that leads to the horrible auctions-by-another-name that we saw above).

3. Don't require precise market knowledge: the mechanism should work even if the seller has no idea how much demand exists.

4. Fun: The process of participating in the sale should be fun and game-like, but not frustrating.

5. Give buyers positive expected returns: in the case of a token (or an NFT), buyers should expect price increases rather than decreases. This requires selling below market value.
Let's start with (1). From Ethereum's perspective, there is a simple solution. Use a tool designed for the job: proof of personhood protocols! Here's one quick idea:

Mechanism 1 Each participant (verified by ID) can buy up to ‘’X’’ tokens at price P, with the option to buy more at an auction.

With the per-person mechanism, buyers can get positive expected returns for the portion sold through the per-person mechanism, and the auction part does not require sellers to understand demand levels. Is it race-free? The number of participants buying through the per-person pool appears to be high. But what if the per-person pool isn't big enough to accommodate everyone?

Make the per-person allocation amount dynamic.

Mechanism 2 Each participant can deposit up to X tokens into a smart contract to declare interest. Last but not least, each buyer receives min(X, N / buyers) tokens, where N is the total sold through the per-person pool (some other amount can also be sold by auction). The buyer gets their deposit back if it exceeds the amount needed to buy their allocation.
No longer is there a race condition based on the number of buyers per person. No matter how high the demand, it's always better to join sooner rather than later.

Here's another idea if you like clever game mechanics with fancy quadratic formulas.

Mechanism 3 Each participant can buy X units at a price P X 2 up to a maximum of C tokens per buyer. C starts low and gradually increases until enough units are sold.

The quantity allocated to each buyer is theoretically optimal, though post-sale transfers will degrade this optimality over time. Mechanisms 2 and 3 appear to meet all of the above objectives. They're not perfect, but they're good starting points.

One more issue. For fixed and limited supply NFTs, the equilibrium purchased quantity per participant may be fractional (in mechanism 2, number of buyers > N, and in mechanism 3, setting C = 1 may already lead to over-subscription). With fractional sales, you can offer lottery tickets: if there are N items available, you have a chance of N/number of buyers of getting the item, otherwise you get a refund. For a conference, groups could bundle their lottery tickets to guarantee a win or a loss. The certainty of getting the item can be auctioned.

The bottom tier of "sponsorships" can be used to sell conference tickets at market rate. You may end up with a sponsor board full of people's faces, but is that okay? After all, John Lilic was on EthCC's sponsor board!

Simply put, if you want to be reliably fair to people, you need an input that explicitly measures people. Authentication protocols do this (and if desired can be combined with zero knowledge proofs to ensure privacy). So we should combine the efficiency of market and auction-based pricing with the equality of proof of personhood mechanics.

Answers to possible questions

Q: Won't people who don't care about your project buy the item and immediately resell it?

A: Not at first. Meta-games take time to appear in practice. If they do, making them untradeable for a while may help mitigate the damage. Using your face to claim that your previous account was hacked and that your identity, including everything in it, should be moved to another account works because proof-of-personhood identities are untradeable.

Q: What if I want to make my item available to a specific community?

A: Instead of ID, use proof of participation tokens linked to community events. Another option, also serving egalitarian and gamification purposes, is to encrypt items within publicly available puzzle solutions.

Q: How do we know they'll accept? Strange new mechanisms have previously been resisted.

A: Having economists write screeds about how they "should" accept a new mechanism that they find strange is difficult (or even "equity"). However, abrupt changes in context effectively reset people's expectations. So the blockchain space is the best place to try this. You could wait for the "metaverse", but it's possible that the best version will run on Ethereum anyway, so start now.

CNET

CNET

3 years ago

How a $300K Bored Ape Yacht Club NFT was accidentally sold for $3K

The Bored Ape Yacht Club is one of the most prestigious NFT collections in the world. A collection of 10,000 NFTs, each depicting an ape with different traits and visual attributes, Jimmy Fallon, Steph Curry and Post Malone are among their star-studded owners. Right now the price of entry is 52 ether, or $210,000.

Which is why it's so painful to see that someone accidentally sold their Bored Ape NFT for $3,066.

Unusual trades are often a sign of funny business, as in the case of the person who spent $530 million to buy an NFT from themselves. In Saturday's case, the cause was a simple, devastating "fat-finger error." That's when people make a trade online for the wrong thing, or for the wrong amount. Here the owner, real name Max or username maxnaut, meant to list his Bored Ape for 75 ether, or around $300,000. Instead he accidentally listed it for 0.75. One hundredth the intended price.

It was bought instantaneously. The buyer paid an extra $34,000 to speed up the transaction, ensuring no one could snap it up before them. The Bored Ape was then promptly listed for $248,000. The transaction appears to have been done by a bot, which can be coded to immediately buy NFTs listed below a certain price on behalf of their owners in order to take advantage of these exact situations.

"How'd it happen? A lapse of concentration I guess," Max told me. "I list a lot of items every day and just wasn't paying attention properly. I instantly saw the error as my finger clicked the mouse but a bot sent a transaction with over 8 eth [$34,000] of gas fees so it was instantly sniped before I could click cancel, and just like that, $250k was gone."

"And here within the beauty of the Blockchain you can see that it is both honest and unforgiving," he added.

Fat finger trades happen sporadically in traditional finance -- like the Japanese trader who almost bought 57% of Toyota's stock in 2014 -- but most financial institutions will stop those transactions if alerted quickly enough. Since cryptocurrency and NFTs are designed to be decentralized, you essentially have to rely on the goodwill of the buyer to reverse the transaction.

Fat finger errors in cryptocurrency trades have made many a headline over the past few years. Back in 2019, the company behind Tether, a cryptocurrency pegged to the US dollar, nearly doubled its own coin supply when it accidentally created $5 billion-worth of new coins. In March, BlockFi meant to send 700 Gemini Dollars to a set of customers, worth roughly $1 each, but mistakenly sent out millions of dollars worth of bitcoin instead. Last month a company erroneously paid a $24 million fee on a $100,000 transaction.

Similar incidents are increasingly being seen in NFTs, now that many collections have accumulated in market value over the past year. Last month someone tried selling a CryptoPunk NFT for $19 million, but accidentally listed it for $19,000 instead. Back in August, someone fat finger listed their Bored Ape for $26,000, an error that someone else immediately capitalized on. The original owner offered $50,000 to the buyer to return the Bored Ape -- but instead the opportunistic buyer sold it for the then-market price of $150,000.

"The industry is so new, bad things are going to happen whether it's your fault or the tech," Max said. "Once you no longer have control of the outcome, forget and move on."

The Bored Ape Yacht Club launched back in April 2021, with 10,000 NFTs being sold for 0.08 ether each -- about $190 at the time. While NFTs are often associated with individual digital art pieces, collections like the Bored Ape Yacht Club, which allow owners to flaunt their NFTs by using them as profile pictures on social media, are becoming increasingly prevalent. The Bored Ape Yacht Club has since become the second biggest NFT collection in the world, second only to CryptoPunks, which launched in 2017 and is considered the "original" NFT collection.

James Howell

James Howell

3 years ago

Which Metaverse Is Better, Decentraland or Sandbox?

The metaverse is the most commonly used term in current technology discussions. While the entire tech ecosystem awaits the metaverse's full arrival, defining it is difficult. Imagine the internet in the '80s! The metaverse is a three-dimensional virtual world where users can interact with digital solutions and each other as digital avatars.
The metaverse is a three-dimensional virtual world where users can interact with digital solutions and each other as digital avatars.

Among the metaverse hype, the Decentraland vs Sandbox debate has gained traction. Both are decentralized metaverse platforms with no central authority. So, what's the difference and which is better? Let us examine the distinctions between Decentraland and Sandbox.

2 Popular Metaverse Platforms Explained

The first step in comparing sandbox and Decentraland is to outline the definitions. Anyone keeping up with the metaverse news has heard of the two current leaders. Both have many similarities, but also many differences. Let us start with defining both platforms to see if there is a winner.

Decentraland

Decentraland, a fully immersive and engaging 3D metaverse, launched in 2017. It allows players to buy land while exploring the vast virtual universe. Decentraland offers a wide range of activities for its visitors, including games, casinos, galleries, and concerts. It is currently the longest-running metaverse project.

Decentraland began with a $24 million ICO and went public in 2020. The platform's virtual real estate parcels allow users to create a variety of experiences. MANA and LAND are two distinct tokens associated with Decentraland. MANA is the platform's native ERC-20 token, and users can burn MANA to get LAND, which is ERC-721 compliant. The MANA coin can be used to buy avatars, wearables, products, and names on Decentraland.

Sandbox

Sandbox, the next major player, began as a blockchain-based virtual world in 2011 and migrated to a 3D gaming platform in 2017. The virtual world allows users to create, play, own, and monetize their virtual experiences. Sandbox aims to empower artists, creators, and players in the blockchain community to customize the platform. Sandbox gives the ideal means for unleashing creativity in the development of the modern gaming ecosystem.

The project combines NFTs and DAOs to empower a growing community of gamers. A new play-to-earn model helps users grow as gamers and creators. The platform offers a utility token, SAND, which is required for all transactions.

What are the key points from both metaverse definitions to compare Decentraland vs sandbox?

It is ideal for individuals, businesses, and creators seeking new artistic, entertainment, and business opportunities. It is one of the rapidly growing Decentralized Autonomous Organization projects. Holders of MANA tokens also control the Decentraland domain.

Sandbox, on the other hand, is a blockchain-based virtual world that runs on the native token SAND. On the platform, users can create, sell, and buy digital assets and experiences, enabling blockchain-based gaming. Sandbox focuses on user-generated content and building an ecosystem of developers.

Sandbox vs. Decentraland

If you try to find what is better Sandbox or Decentraland, then you might struggle with only the basic definitions. Both are metaverse platforms offering immersive 3D experiences. Users can freely create, buy, sell, and trade digital assets. However, both have significant differences, especially in MANA vs SAND.

For starters, MANA has a market cap of $5,736,097,349 versus $4,528,715,461, giving Decentraland an advantage.
The MANA vs SAND pricing comparison is also noteworthy. A SAND is currently worth $3664, while a MANA is worth $2452.

The value of the native tokens and the market capitalization of the two metaverse platforms are not enough to make a choice. Let us compare Sandbox vs Decentraland based on the following factors.

Workstyle

The way Decentraland and Sandbox work is one of the main comparisons. From a distance, they both appear to work the same way. But there's a lot more to learn about both platforms' workings. Decentraland has 90,601 digital parcels of land.

Individual parcels of virtual real estate or estates with multiple parcels of land are assembled. It also has districts with similar themes and plazas, which are non-tradeable parcels owned by the community. It has three token types: MANA, LAND, and WEAR.

Sandbox has 166,464 plots of virtual land that can be grouped into estates. Estates are owned by one person, while districts are owned by two or more people. The Sandbox metaverse has four token types: SAND, GAMES, LAND, and ASSETS.

Age

The maturity of metaverse projects is also a factor in the debate. Decentraland is clearly the winner in terms of maturity. It was the first solution to create a 3D blockchain metaverse. Decentraland made the first working proof of concept public. However, Sandbox has only made an Alpha version available to the public.

Backing

The MANA vs SAND comparison would also include support for both platforms. Digital Currency Group, FBG Capital, and CoinFund are all supporters of Decentraland. It has also partnered with Polygon, the South Korean government, Cyberpunk, and Samsung.

SoftBank, a Japanese multinational conglomerate focused on investment management, is another major backer. Sandbox has the backing of one of the world's largest investment firms, as well as Slack and Uber.

Compatibility

Wallet compatibility is an important factor in comparing the two metaverse platforms. Decentraland currently has a competitive advantage. How? Both projects' marketplaces accept ERC-20 wallets. However, Decentraland has recently improved by bridging with Walletconnect. So it can let Polygon users join Decentraland.

Scalability

Because Sandbox and Decentraland use the Ethereum blockchain, scalability is an issue. Both platforms' scalability is constrained by volatile tokens and high gas fees. So, scalability issues can hinder large-scale adoption of both metaverse platforms.

Buying Land

Decentraland vs Sandbox comparisons often include virtual real estate. However, the ability to buy virtual land on both platforms defines the user experience and differentiates them. In this case, Sandbox offers better options for users to buy virtual land by combining OpenSea and Sandbox. In fact, Decentraland users can only buy from the MANA marketplace.

Innovation

The rate of development distinguishes Sandbox and Decentraland. Both platforms have been developing rapidly new features. However, Sandbox wins by adopting Polygon NFT layer 2 solutions, which consume almost 100 times less energy than Ethereum.

Collaborations

The platforms' collaborations are the key to determining "which is better Sandbox or Decentraland." Adoption of metaverse platforms like the two in question can be boosted by association with reputable brands. Among the partners are Atari, Cyberpunk, and Polygon. Rather, Sandbox has partnered with well-known brands like OpenSea, CryptoKitties, The Walking Dead, Snoop Dogg, and others.

Platform Adaptivity

Another key feature that distinguishes Sandbox and Decentraland is the ease of use. Sandbox clearly wins in terms of platform access. It allows easy access via social media, email, or a Metamask wallet. However, Decentraland requires a wallet connection.

Prospects

The future development plans also play a big role in defining Sandbox vs Decentraland. Sandbox's future development plans include bringing the platform to mobile devices. This includes consoles like PlayStation and Xbox. By the end of 2023, the platform expects to have around 5000 games.

Decentraland, on the other hand, has no set plan. In fact, the team defines the decisions that appear to have value. They plan to add celebrities, creators, and brands soon, along with NFT ads and drops.

Final Words

The comparison of Decentraland vs Sandbox provides a balanced view of both platforms. You can see how difficult it is to determine which decentralized metaverse is better now. Sandbox is still in Alpha, whereas Decentraland has a working proof of concept.

Sandbox, on the other hand, has better graphics and is backed by some big names. But both have a long way to go in the larger decentralized metaverse. 

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Davlin Knight

Davlin Knight

2 years ago

2 pitfalls to stay away from when launching a YouTube channel

You do not want to miss these

Photo by Souvik Banerjee on Unsplash

Stop! Stop it! Two things to avoid when starting a YouTube channel. Critical. Possible channel-killers Its future revenue.

I'll tell you now, so don't say "I wish I knew."

The Notorious Copyright Allegation

My YouTube channel received a copyright claim before I sold it. This claim was on a one-minute video I thought I'd changed enough to make mine, but the original owner disagreed.

It cost me thousands in ad revenue. Original owner got the profits.

Well, it wasn't your video, you say.

Touché.

I've learned. Sorta

I couldn't stop looking at the video's views. The video got 1,000,000 views without any revenue. I made 4 more similar videos.

If they didn't get copyrighted, I'd be rolling in dough.

You've spent a week editing and are uploading to YouTube. You're thrilled as you stand and stretch your back. You see the video just before publishing.

No way!

The red exclamation point on checks.

Copyright claim!

YouTube lets you publish, but you won't make money.

Sounds fair? Well, it is.

Copyright claims mean you stole someone's work. Song, image, or video clip.

We wouldn't want our content used for money.

The only problem with this is that almost everything belongs to someone else. I doubt some of the biggest creators are sitting down and making their music for their videos. That just seems really excessive when you could make a quick search on YouTube and download a song (I definitely don’t do this because that would be stealing).

So how do you defeat a copyright defense?

Even copyright-free songs on YouTube aren't guaranteed. Some copyrighted songs claim to be free.

Use YouTube's free music library or pay for a subscription to adobe stock, epidemic sound, or artlist.io.

Most of my videos have Nintendo music. Almost all game soundtracks are copyright-free and offer a variety of songs.

Restriction on age

Age restrictions are a must-avoid. A channel dies.

YouTube never suggests age-restricted videos.

Shadow banning means YouTube hides your content from subscribers and non-subscribers.

Keeping your channel family-friendly can help.

I hear you complaining that your channel isn't for kids. I agree. Not everyone has a clean mouth or creates content for minors.

YouTube has changed rapidly in recent years. Focusing on kids. Fewer big creators are using profanity or explicit content in videos. Not YouTube-worthy.

Youtube wants to be family-friendly. A family-friendly movie. It won't promote illegal content. Yes, it allows profanity.

YouTube Policies and Guidelines

Do I recommend avoiding no-no words in videos? Never. Okay. YouTube's policies are shaky. YouTube uses video content to determine ad suitability.

No joke. If you're serious about becoming a content creator, avoid profanity and inappropriate topics.

If your channel covers 18+ topics, like crime or commentary, censor as much as possible.

YouTube can be like walking on eggshells. You never know what is gonna upset the boss. So play it safe and try to avoid getting on their bad side.

Mr. Beast, Dream, Markplier, Faze Rug, and PewDewPie are popular creators. They maintain it family-friendly while entertaining fans.

You got this.

Clive Thompson

Clive Thompson

2 years ago

Small Pieces of Code That Revolutionized the World

Few sentences can have global significance.

Photo by Chris Ried on Unsplash

Ethan Zuckerman invented the pop-up commercial in 1997.

He was working for Tripod.com, an online service that let people make little web pages for free. Tripod offered advertising to make money. Advertisers didn't enjoy seeing their advertising next to filthy content, like a user's anal sex website.

Zuckerman's boss wanted a solution. Wasn't there a way to move the ads away from user-generated content?

When you visited a Tripod page, a pop-up ad page appeared. So, the ad isn't officially tied to any user page. It'd float onscreen.

Here’s the thing, though: Zuckerman’s bit of Javascript, that created the popup ad? It was incredibly short — a single line of code:

window.open('http://tripod.com/navbar.html'
"width=200, height=400, toolbar=no, scrollbars=no, resizable=no, target=_top");

Javascript tells the browser to open a 200-by-400-pixel window on top of any other open web pages, without a scrollbar or toolbar.

Simple yet harmful! Soon, commercial websites mimicked Zuckerman's concept, infesting the Internet with pop-up advertising. In the early 2000s, a coder for a download site told me that most of their revenue came from porn pop-up ads.

Pop-up advertising are everywhere. You despise them. Hopefully, your browser blocks them.

Zuckerman wrote a single line of code that made the world worse.

A photo of the cover of “You Are Not Expected To Understand This”; it is blue and lying on its side, with the spine facing the viewer. The editor’s name, Torie Bosch, is in a green monospaced font; the title is in a white monospaced font

I read Zuckerman's story in How 26 Lines of Code Changed the World. Torie Bosch compiled a humorous anthology of short writings about code that tipped the world.

Most of these samples are quite short. Pop-cultural preconceptions about coding say that important code is vast and expansive. Hollywood depicts programmers as blurs spouting out Niagaras of code. Google's success was formerly attributed to its 2 billion lines of code.

It's usually not true. Google's original breakthrough, the piece of code that propelled Google above its search-engine counterparts, was its PageRank algorithm, which determined a web page's value based on how many other pages connected to it and the quality of those connecting pages. People have written their own Python versions; it's only a few dozen lines.

Google's operations, like any large tech company's, comprise thousands of procedures. So their code base grows. The most impactful code can be brief.

The examples are fascinating and wide-ranging, so read the whole book (or give it to nerds as a present). Charlton McIlwain wrote a chapter on the police beat algorithm developed in the late 1960s to anticipate crime hotspots so law enforcement could dispatch more officers there. It created a racial feedback loop. Since poor Black neighborhoods were already overpoliced compared to white ones, the algorithm directed more policing there, resulting in more arrests, which convinced it to send more police; rinse and repeat.

Kelly Chudler's You Are Not Expected To Understand This depicts the police-beat algorithm.

About 25 lines of code that includes several mathematical formula. Alas, it’s hard to redact it in plain text here, since it uses mathematical notation

Even shorter code changed the world: the tracking pixel.

Lily Hay Newman's chapter on monitoring pixels says you probably interact with this code every day. It's a snippet of HTML that embeds a single tiny pixel in an email. Getting an email with a tracking code spies on me. As follows: My browser requests the single-pixel image as soon as I open the mail. My email sender checks to see if Clives browser has requested that pixel. My email sender can tell when I open it.

Adding a tracking pixel to an email is easy:

<img src="URL LINKING TO THE PIXEL ONLINE" width="0" height="0">

An older example: Ellen R. Stofan and Nick Partridge wrote a chapter on Apollo 11's lunar module bailout code. This bailout code operated on the lunar module's tiny on-board computer and was designed to prioritize: If the computer grew overloaded, it would discard all but the most vital work.

When the lunar module approached the moon, the computer became overloaded. The bailout code shut down anything non-essential to landing the module. It shut down certain lunar module display systems, scaring the astronauts. Module landed safely.

22-line code

POODOO    INHINT
    CA  Q
    TS  ALMCADR

    TC  BANKCALL
    CADR  VAC5STOR  # STORE ERASABLES FOR DEBUGGING PURPOSES.

    INDEX  ALMCADR
    CAF  0
ABORT2    TC  BORTENT

OCT77770  OCT  77770    # DONT MOVE
    CA  V37FLBIT  # IS AVERAGE G ON
    MASK  FLAGWRD7
    CCS  A
    TC  WHIMPER -1  # YES.  DONT DO POODOO.  DO BAILOUT.

    TC  DOWNFLAG
    ADRES  STATEFLG

    TC  DOWNFLAG
    ADRES  REINTFLG

    TC  DOWNFLAG
    ADRES  NODOFLAG

    TC  BANKCALL
    CADR  MR.KLEAN
    TC  WHIMPER

This fun book is worth reading.

I'm a contributor to the New York Times Magazine, Wired, and Mother Jones. I've also written Coders: The Making of a New Tribe and the Remaking of the World and Smarter Than You Think: How Technology is Changing Our Minds. Twitter and Instagram: @pomeranian99; Mastodon: @clive@saturation.social.

Thomas Huault

Thomas Huault

3 years ago

A Mean Reversion Trading Indicator Inspired by Classical Mechanics Is The Kinetic Detrender

DATA MINING WITH SUPERALGORES

Old pots produce the best soup.

Photo by engin akyurt on Unsplash

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
BTD/USDT candle chart at 01-hs timeframe with the Kinetic detrender and its 2 red fixed level and black dynamic levels

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.