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. And even before that - they haven't even properly assessed whether AI is needed in their business. Remember, AI is a tool, not the goal.
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:
Monica Rogati via hackernoon
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.
Slower sometimes means faster.
Onwards.
Fueling Alpha: Data In The New World
Data has long been a precious commodity - yet it’s only getting more valuable.
At the beginning of 2020, the number of bytes in the digital universe was already 40X greater than the number of stars in the observable universe.
Data is today’s “wild west” and the battlefield of today’s tech titans; with AI, The IoT, the Blockchain, and analytics leading the forefront.
Alphabet, Amazon, Apple, Facebook, and Microsoft all have an unprecedented amount of data (and power).
Rapid growth means little time to create adequate rules. Everyone’s jumping to own more data than the next and to protect their data from prying eyes.
The trading industry has undoubtedly experienced the rapid growth of data, but it’s pervasive in every industry – and in our personal lives as well.
Having basic data and basic analytics used to be enough, but the game is changing. Traders used to focus on price data, but now you’re seeing an influx of firms using alternative data sets to find an edge. If you’re using the same data sources as your competitors and competing on the same set of beliefs, it’s hard to find a sustainable advantage. Understanding the game they’re playing (and the strategies and rules they follow are important), but now that’s just the table stakes.
Figuring out where you can find extra insight, or make the invisible visible, creates a moat between you and your competition – and lets you play your own game.
I shot a video in 2019 about Data as fuel for your business. Check it out.
It is interesting to think about what’s driving the new world (of trading, technology, AI, etc.), and that often involves identifying what drove the old world. History has a way of repeating itself.
Before e-mails, fax machines were essential. Before cars, people were pleased with a horse and buggy.
These comparisons contextualize the importance of data in today’s new world of economics and commerce.
via gapingvoid
Data as the New Oil
While not the best comparison, people often default to comparing data to oil.
Since the Industrial Revolution, petroleum has played a pivotal role in human advancement. It fueled (and still fuels) our creativity, technology advancements, and a variety of derivative products. There are direct competitors to fossil fuels that are gaining steam, but it’s more interesting to compare petroleum to data due to their parallels in effect on innovation.
The process of pumping crude oil out of the ground and transforming it into a finished product is far from simple, but anyone can understand the process at a high level. You have to locate a reservoir, drill, capture the resource, and then refine it for the desired product (e.g., heating oil, gasoline, asphalt, plastics, etc.). Then, you have to move it to where it’s needed.
The same is true for data.
Data can be seen as the fuel to the information economy and oil to the industrial economy. The amount of power someone has often correlates to their control of and access to resources ... and diminishing or diluting those resources can lead to extreme consequences.
Why Data Is Better Than Oil
The analogy works, but it’s just that, an analogy. The more you analyze it, the more it falls apart. Unlike a finite resource (like oil), Data is all around us and increasing at an exponential rate. So the game is a little different:
On Alternative Data
In the past, investors relied on information and experience from their real lives, from counterparties, and from fastidious attention to CNBC and stock tickers.
However, fundamental discretionary traders account for just 10% of today’s trading volume. Quantitative investing based on machine intelligence and algorithms is the new normal.
While the games, the rules, and the players have all changed, the goal hasn’t... more alpha ... more money ... more reliably.
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.
It also doesn’t always have to be confidential or challenging to find information. Traders now have access to vast amounts of structured and unstructured data. An important source that many overlook is the data produced through their own process or the metadata from their own trades or transactions.
In a word of caution, there are two common mistakes people make when making data-driven decisions. First, people often become slaves to the data, losing focus on the bigger picture. Second, even the most insightful data can’t predict black swans. It’s important to exercise caution.
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 does (or by doing what 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.
The future of Data is bright, but it’s also littered with potential challenges. Privacy concerns and misuse of data have been hot button topics, as have fake news and the ability of systems to generate misleading data. In addition, as we gain access to more data, our ability to separate signal from noise becomes more important.
The question becomes, how do you capitalize on data, without becoming a victim to it?
Food for thought!
Posted at 05:01 PM in Business, Current Affairs, Gadgets, Ideas, Market Commentary, Science, Trading, Trading Tools, Web/Tech | Permalink | Comments (0)
Reblog (0)