Cindicator and the Holy Grail of Big Data
It’s 5 am. My partner is getting up to head out to an early class. I should, by all rights, be turning over to try and get another three or four hours of sleep. But there’s something I need to do first…
“Hey, toss my phone on the bed, will you?” I say groggily. Five minutes later, there’s a thud in the darkness. I pick up my phone, engage the screen, and open up the Cindicator app. I’ve got predicting to do, and I’m in deep.
I started my odyssey into the crypto trading universe in November 2017. After a severe accident, I was left badly injured and spent many months recovering. I had a little bit of money, a ton of time on my hands, and a computer. It took a call from an old friend to put all the pieces together. He gave me a few places to start gathering information, and I began to make my first tentative investments. 100 IOTA, 500 Bitshares, dipping my toes in as it were. I got my first break with the big Verge pump in December 2017. I had bought in cheap and plopped down a few hundred dollars. Now, as the Wraith countdown ticked away on the Verge home page and the price escalated, I made a decision I do not regret and sold my holdings.
I had made a tidy profit, turned two hundred into two thousand dollars, and thought I pretty much had it figured out. The once mysterious interfaces of Binance and Bittrex became second nature over time. I planned, researched, and fanned that two thousand USD of Bitcoin out into myriad other crypto investments. Some did well, others didn’t, but overall I was coming out okay. I’ve heard it said that beginner’s luck is terrible luck for a trader. In that case, a lot of the people who began trading in the Q4 of 2017 are unlucky, as the majority started trading at a point when you could throw a dart at the top 100 list, put in one thousand dollars USD, and expect massive gains. I began to experiment with high risk/low market cap coins on low volume exchanges. I knew better than to extend myself the way I was, but it seemed like I couldn’t lose.
Then January came, and The Bear awoke from his slumber. The fallout from the plummeting Bitcoin price devastated the altcoin holdings I had accumulated up to that point. So now, with a sprawling portfolio of small holdings (some purchased at or near all-time high) I had a decision to make: Liquidate what I had before it lost more value — in a market awash in the blood of lambo kids and crypto old-timers alike — or hold on and resume trading when I was no longer completely underwater. Luckily, I had followed the golden rule repeated so many times to others during the December craze: Don’t invest more than you’re willing to lose.
So I decided to wait. It’s what I was doing anyway. Recovery from major injury takes time, but trading and learning about crypto gave me an important focus. Something to learn about and master, something with the possibility of helping me achieve financial autonomy. I began to scan my holdings for tokens and coins with interesting use cases, trying to get into the communities of the currencies I was holding to see which services were available to me. There was one in particular that stood out: Cindicator.
I picked up the tokens early on, after reviewing the website and whitepaper. Immediately I saw the potential of machine learning and predictive technology, especially in the crypto environment. I was even more pleased to see their technology picked up on some ideas that had been gestating in my mind for many years, specifically since reading The Foundation Series by Isaac Asimov as a teenager. In these books, society has developed a predictive method they call “psychohistory,” about which Wikipedia says:
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Psychohistory is a fictional science in Isaac Asimov’s Foundation universe that combines history, sociology, and mathematical statistics to make general predictions about the future behavior of vast groups of people, such as the Galactic Empire.
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Psychohistory is a systematized science combining big data collected on every aspect of human reality smashed into a psychic interface allowing trained users to tweak and run algorithmic models on the development of a large group of people. Summarized, it is a useful machine learning interface — This idea, combined with the idea of “the wisdom of crowds,” the idea that with a sufficiently broad sample group with a reasonable knowledge of a subject, you can average their predictions to achieve a correct answer. These notions led me to place an urgent call to a programmer friend one rainy evening in San Francisco and request an immediate meeting at a local bar. I asked him if he could design a “generalized prediction platform that would allow people everywhere to enter their predictions, post their questions, and vote on the most relevant ones.” He answered that he could, but he couldn’t understand why I would want to do such a thing.
Clearly, someone does understand, and not only that, but decided to début this idea in the crypto world, one that financially incentivizes those who participate in it to learn more about it. The more you know, the higher your potential for success, and the more you know, the better predictor you will be. As I read more and understood more, I began to get the feeling one gets when you brush up against an idea that is bigger than your current understanding will permit — like trying to swim against the pressure of currents from a passing whale in the ocean.
I took the plunge and bought five thousand tokens to use the beginner bot, which is their entry-level trading product. I downloaded the phone app, then began predicting — and learning. I did not feel comfortable selling my holdings, all of which were worth less than I had paid for them. I lacked more money to play with, so I began reading up on technical analysis and trying to understand the Cindicator prediction system.
It was rough at first. I had to educate myself on technical jargon to grasp the questions thoroughly. I began to read everything I could find on trading in general and crypto markets specifically. I lived on coinmarketcap.com and cryptopanic.com, and the gamer in me began to come out — I wanted to climb the Cindicator ranking, yet still, I remained in the lower five to ten thousand predictors, sometimes even lower. I didn’t understand anything about the traditional markets, I tried to guess far too wide on price spreads, and I made predictions based on emotion instead of knowledge. My score continues to reflect this reality. Through the Cindicator prediction feedback, I began to become more aware of the limits of my knowledge of trading without staking any of my own money, but also where I do well.
I had to admit to myself my romantic tendency to become emotionally involved in my investments, to root for an investment like a team. It’s easy to do and one of the worst ways to interact with politics, investments, religions, or anything that affects the world — also, an excellent way to lose points on the platform quickly.
You have to separate yourself from what you hold and focus on what market movements appear to be likely, given the data. All of this has made me a much better trader. For whatever reason, I’ve found that percent change questions don’t frame as well in my mind, I do much better charting specific price points and interpreting the chart for daily and weekly price movements, so I focused there. Now I maintain a score in the top thousand of both the traditional and crypto prediction rankings and an accuracy rating in the seventy to seventy-five percent range most days of the week.
Cindicator itself is one of the only projects in the crypto world with a practical application, a working product, a reason for the token to be held and go up in value, and a development team that meets its goals. These reasons alone make it a must hold for any crypto investor. As far as fundamentals, there aren’t that many other projects that stack up — with both a strong product and team that consistently hits its’ goals. Providing the combined possibility of learning and improving your trading skills while earning extra Ethereum and the fact that as the machine learning and crowd indicators get better, the tokens will be in high demand makes it an easy pick.
But more than that, there is a community. Highly active developers who answer questions at any time and a push to involve anyone who truly wants to help shape the direction of the platform. The team deals with community suggestions seriously, and the discussion doesn’t disintegrate into coin price, ‘moonshots,’ etc. These are people genuinely interested in the future of prediction technology.
All of this has done wonders for understanding which kind of trades I would like to make, keeping my eye on which markets are moving, and keeping up my overall engagement in the crypto realm. If you aren’t going to be engaged, you will get passed by, and end up with nothing but wasted fiat and out of date wallets attempting to sync to chains that no longer exist. Participating in the Cindicator platform as an analyst has improved my trading in almost every way — but it is the broader context on which I would now like to focus.
It’s 5 am, my partner has left for class, and I’m reading the charts. Getting in all my price predictions for the day, but why this dedication? Outside of my financial self-interest, which is considerable, there is a larger drive at play here. All of us have bought into the promise of big data, whether we like it or not. But what is this promise? Has it ever clearly been stated? What is the end game of all this documentation and intrusion into the minutiae of daily life? It has been used to sell advertising opportunities, for political control, for situational awareness in relation to control through force… But it could be so much more than that. It could help predict the next flu pandemic, the next reservoir that will run dry, leaving a region devastated and the world reeling from migration. To identify and reverse trends leading towards the next World War. But whose decision is it?
Every time we use our phones, tablets, and computers, we aggregate data — but to what end, and for whom? In the past few weeks, Facebook and its’ privacy abuses have become standard fare on the nightly news around the world. Yet, U.S. senators serve up softball questions, and most demonstrate a complete lack of understanding of the platforms they intend to regulate. Our methods of societal control have been outpaced by the technical innovation of our corporate entities. I would assert that the responsibility falls on the User, the individual, to engage in arrangements where their data is being used in a way that suits theirs, and hopefully, the greater needs of society as a whole.
Users have a choice, and ultimately it is our engagement that drives which platforms succeed and fail. We have experimented with private platforms for corporate financial gain, and it has gotten us to this place where the scanners are for certain seeing darkly. The road ahead, forging our way out to clarity, will be rocky as pioneers carve out new niches on the internet that reward interaction and transparency, and provide users with control over the flow of data to and from the systems. Cindicator, Bitshares, Steemit, and other DAO organizations are all excellent examples of these principles in application. Ultimately, it is up to us to decide, with our engagement, what we want our personal data experience to be going forward. There are many options. I hope the model of pure corporate exploitation is abandoned as better technology moves to the forefront.
I am optimistic for the future, and to me, the Cindicator platform is a harbinger of things to come — a future where data is shared communally for common goals. A future with platforms that incentivize producers and users. One in which they have a say in how their data is used. Not the lifeless siphoning to opaque corporate databases that happens today. These are the reasons why, every day at 5 am, I get up to make my predictions. If I could pose a question to the platform, it would be — What is the percent likelihood that the future holds for humanity a variety of experiences, and expansion of freedom hitherto unknown to us as a species?
I give it an eighty percent currently — as I said, I am optimistic. I wonder what the machine learning algorithm would say.
Find me on twitter — https://twitter.com/MithSeraphi
Find me on steemit — https://steemit.com/@seraphismith
Find me on twitter — https://twitter.com/MithSeraphi
Find me on steemit — https://steemit.com/@seraphismith
