I'm sure we'll talk more about Bitcoin … but, more importantly, Blockchain (which is the technology that Bitcoin and other cryptocurrencies were built upon).
I am currently investigating and planning how we'll use Blockchain.
You don't have to be a rocket scientist to understand this quote from Einstein.
"When you are courting a nice girl, an hour seems like a second. When you sit on a red-hot cinder, a second seems like an hour. That's relativity."
It is about more than perception.
Here is something that highlights the relative value of time.
The Value Of Time:
To understand the value of a year, talk to a student who has failed an important exam.
To understand the value of a month, talk to a mother who has given birth to a baby a month prematurely.
To understand the value of a week, talk to the publisher of a weekly newspaper.
To understand the value of an hour, talk to a couple in love who are separated and want only to be together again.
To understand the value of a minute, talk to someone who has just missed their train or plane flight.
To understand the value of a second, talk to someone who has lost a loved one in an accident.
And to understand the value of a millisecond, talk to someone who won the silver medal at the Olympic Games.
Time waits for no one. So it is important to remember to make the best use of the time you have.
That Doesn't Mean Time Is Scarce Or Has To Be A Constraint:
Time is often thought of as a constraint or a scarce resource. There are lots of phrases that highlight this type of thinking. For example: I don't have enough time; I'm running late; I'm up against a deadline; There are only 24-hours in a day; or, I’m going as fast as I can. As you might guess, that list goes on further. Yet, time does not have to be that way … it can be a tool instead.
So, I started to think about how I used time.
Was I making the most of it … or taking it for granted? It didn’t take much introspection to notice a few of the ruts I fell into. I'm going to talk about one of them, here, because a small shift can have a massive impact.
To start, let's talk about pace.
A Change of Pace:
When I jog, the beginning and the end are the hardest for me. Yet, after I find that initial pace and I settle into a comfortable rhythm, the majority of the run is relatively painless. My mind and body switch to a nearly automatic mode and I have time to think about many things.
Work is similar in many respects. Once a team gets into a rhythm, work and progress are somewhat automatic. Breaking inertia is a challenge; but, people recognize that it's a challenge. The more insidious problem is to fail to recognize that the work rhythm that's comfortable, and which produces progress, is still a rut. It doesn't stretch and challenge the team to strive for more. Yet, this stretching is what drives innovation. It's the thought we haven't had yet … and a new perspective that changes everything.
Changing your pace can be an incredible catalyst to make that happen for you. For example, imagine that we put together a new portfolio in two weeks, on a wholly new tech platform, with new markets, and using new techniques. Then we tested, re-balanced and rebuilt that portfolio in one week. What we did, or the time in which we did it, wasn’t important. The important part is that it caused the team to work at a radically different pace than before. It was a sprint.
Moreover, this sprint caused us to re-think what we do, and more importantly, how we do it. Many of the innovations and new distinctions that we discovered through this process will work their way into other areas of our work (and will act as a catalyst for us to re-evaluate the way we do things).
A Challenge For You:
I challenge you to consciously change the pace of something that you are already comfortable doing a certain way. The pace can be faster, or the pace can be slower … it doesn't matter. Then notice what comes up for you, and what new opportunities and possibilities you discover.
Time is a valuable resource. Take this opportunity to re-examine how you can best view and use time to make the most of it.
We often talk about Artificial Intelligence's applications – meaning, what we use it for – but we often forget to talk about a more crucial question:
How do we use AI effectively?
Many people misuse AI. They think they can simply plug in a dataset, press a button, and poof! Magically, an edge appears.
Most commonly, people lack the infrastructure (or the data literacy) to properly handle even the most basic algorithms and operations.
That doesn't even touch machine learning or deep learning (where you have to understand math and statistics to make sure you use the right tools for the right jobs).
Even though this is the golden age of AI … we are just at the beginning. Awareness leads to focus, which leads to experimentation, which leads to finer distinctions, which leads to wisdom.
Do you remember Maslow's Hierarchy of Needs? Ultimately, self-actualization is the goal … but before you can focus on that, you need food, water, shelter, etc.
In other words, you most likely have to crawl before you can walk, and you have to be able to survive before you can thrive.
Artificial Intelligence and Data Science follow a similar model. Here it is:
First, there's data collection. Do you have the right dataset? Is it complete?
Then, data flow. How is the data going to move through your systems?
Once your data is accessible and manageable you can begin to explore and transform it.
Exploring and transforming is a crucial stage that's often neglected.
One of the biggest challenges we had to overcome at Capitalogix was handling real-time market data.
The data stream from exchanges isn't perfect.
Consequently, using real-time market data as an input for AI is challenging. We have to identify, fix, and re-publish bad ticks or missing ticks as quickly as possible. Think of this like trying to drink muddy stream water (without a filtration process, it isn't always safe).
Once your data is clean, you can then define which metrics you care about, how they all rank in the grand scheme of things … and then begin to train your data.
Compared to just plugging in a data set, there are a lot more steps; but, the results are worth it.
That's the foundation to allow you to start model creation and optimization.
The point is, ultimately, it's more efficient and effective to spend the time on the infrastructure and methodology of your project (rather than to rush the process and get poor results).
If you put garbage into a system, most likely you'll get garbage out.
There are a lot of commonalities between successful tech companies. Pretty much all of them attempt to leverage software to do jobs that would be too expensive, too boring, or virtually impossible to do otherwise.
Obviously, technology is a huge force factor impacting the success of a company. As a result, there is an arms race to get to "next" …
Research and Development has always been a key in growing companies. Now, that is truer than ever. As tech improves faster and more dramatically, technologies become relevant and irrelevant faster than ever.
Challenging … sure, but a great problem to have.
What a great time to be an entrepreneur!
Good luck with your 2018 plans. I'd love to hear what you are focusing on next year.
Today's investors have access to data and information that would have been unheard of 10 years ago … and unfathomable 20 years ago. In the past, investors relied on information and experience from their real lives, from counterparties, and from fastidious attention to CNBC and stock tickers.
While the games, the rules, and the players have all changed, the goal hasn't … more alpha … more money … more reliably.
What's Changed?
Algorithmic trading isn't new, but there is a shift in who's making the algorithms. For example, you can crowdsource development through Quantopian … or let machines do the heavy lifting through A.I.-based firms like Sentient.
But I'd argue that's only true if you look at the same data, the same way.
The Future of Trading
One of the reasons A.I. is a great option for trading is that it takes away the human element of fear, greed, and discretionary mistakes.
Sentient's founder says:
"For me, it's scarier to be relying on those human-based intuitions and justifications than relying on purely what the data and statistics are telling you." – Babak Hodjat
In addition, people tend to get similar results because they do things similarly. As A.I. matures (and more researchers become better versed in what's possible) solutions will evolve.
It won't be a Ph.d. writing an algorithm … it will be machines and code trying unthinkable combinations and finding edges that otherwise would remain invisible and unused.
Currently, most people train their algorithms on markets, or with human intervention, but there are more data sets that can be used to build more robust models.
Alternative Data
Alternative data, to most, means tracking Twitter and Facebook sentiment, but confining your definition to that limits potential alpha.
New sources of data are being mined everywhere, and are letting investors understand trends "before they happen".
For example, mobile devices, low-cost sensors, and a host of new technologies have led to an explosion of new potential data sources to use directly for predictive insight or indirectly to help improve models.
In addition, private company performance, logistics data, and satellite imagery are becoming popular data sets in a data scientist's alpha creation toolbox.
There are often concerns about the cost and completeness of these datasets, but as we get better at creating and using them, both will improve.
Finding more ways to train algorithms on new data can help traders once again find an edge on their competition.
The thing about "sustainable alpha" is that while one might be able to achieve it, you can't expect to have it doing the same thing everyone else, or that you've always done.
Markets change, and what worked yesterday won't necessarily work today or tomorrow. Trading is a zero-sum game, and as we move toward the future, this only gets more apparent.
Behavioral Game Theory shows that human choices don't necessarily reflect the benefits they expect to receive. That's no longer the case with algorithms.
For more on Big Data and its potential, here's access to the full panel discussion I participated in recently at The Trading Show in New York.
In many senses time is relative. 
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