Quandl’s Tammer Kamel: ‘Investors are looking for gold nuggets in a sea of data’

Tammer Kamel spent some formative years as a quant. Now a self-declared “Wall Street refugee”, he runs five-year-oldold Quandl, a data outfit tailored for quantitative analysts. Quandl burst on to the scene by making data sets more useful, delivering data the way a modern quant wants data. The company has recently expanded into developing its own alternative data sets that are being used by some of the smartest investors around.

Kamel joins us on the Tearsheet Podcast this week to talk about how the data business is evolving around the changing nature of quantitative investing. We talk about where demand is coming and how his firm is coping with the challenges of building for scale.


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Below are highlights, edited for clarity, from the episode.

As a quant, what were your biggest pain points?
The frustration I had was kind of a universal pain point for quants. Data is never easy to find and it’s never easy to get into the format of the system you want. If you talk to any analyst, they’ll tell you they spend half their lives — maybe more — trying to find, format, validate, organize, merge, and ultimately set up the data for the analysis they want to do. The analysis is often the pleasant and easier part of the task.

The problem is data is spread out across all different systems and protocols. One data set you can get as a .csv and another as an API — still others may be in a PDF and you have to pull the data out of the PDF. You have to remember that many of the incumbents in this space aren’t motivated to let the data they offer flow easily to many places. The whole terminal model is built on this idea that one person will sit at this piece of hardware and use this software. it’s a great business model but particularly incompatible with how quants need to work. They need to pull data from different sources, amalgamate them, run analysis and pump this data through programs they’ve written.  The status quo on Wall Street doesn’t facilitate this.

The move into providing alternative data
With Quandl’s core business, we do really well with niche quants, fintechs, small funds and lots of retail-type customers who need data. But, it’s very difficult for Quandl to displace a large incumbent at a large institution. It’s a hard sell to go into a bank and tell them to rip out Thomson Reuters because we have a better API. The growth market for us is to bring new, powerful data to these institutions. If I go into these institutions and say, here’s a data set that you cannot get this data set on Bloomberg and it makes you better at doing what you do — well, that’s a much easier sell.

More fundamentally, we realized that two things are going on: one one side of us, the global data explosion is happening with satellites, drones, mobile phones, the internet of things, and every company is becoming data-centric at a time when professional investors are very hungry for new ways to generate alpha. There is no more potent alpha source than an information advantage. We are looking for gold nuggets in the sea of data that the world is awash in.

How do you price unique alternative data?
Pricing is tricky because the value of a particular data set depends on the marginal impact it has for a particular customer. I can bring a data set to Customer A and it can be absolutely potent for them because they now have access to something they’ve never had before. I can bring the same data set to Customer B, who has access to other data sources, and ultimately the marginal impact of this data source is less than for Customer A. We price data based on what we believe the full impact of the data set is. We know what it’s worth in an absolute sense because we think like investors. Typically, that looks like $40,000 to $500,000 a year, depending on the data set.

Build trading strategies without the need to code – with Rob Johnson

Have you wanted to test an investing strategy but lack the tools and programming knowledge to sufficiently see if it works?

QuantBlocks was designed for you — build and test trading strategies without programming knowledge. It’s drag-and-drop simple. Founder Rob Johnson joins Tradestreaming Radio to discuss how QuantBlocks scratched his own personal itch and how investors of all types use his technology to beat the market.

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Creating a popular investment strategy marketplace – with Matthew Klein

Today’s Internet means unprecedented accessibility and transparency for investors — a huge opportunity to learn from and invest like some of the smartest and most talented investors.

One place this is clearly manifested is at Collective2.com, a marketplace of trading strategies. Whether you’re a publisher or a consumer, a writer or reader — investors can find thousands of investing systems on the site.

Not only can you follow some of these unknown Buffetts, you can also auto-trade them (program your brokerage account to replicate their every move).

Collective2.com‘s founder, Matthew Klein joins me on this episode of Tradestreaming Radio to discuss the foundation of the site and how some investors are getting rich by following some of the best trading models on the site — while the publishers of those models also make real bank.

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Humans and Machines Both Reign Supreme: Takeaways from the Battle of the Quants

As markets gyrate, investors continuously hear two diverging voices in their heads.

Matt Dillon (voice of Confidence): Yo, Johnny.  My strategy rocks and I’m in it for the long hall.  I’ve looked over my allocations and they make sense.  I mean, I know what I’m doing here and everything is under control.

Woody Allen (voice of Self-Doubt):  When I began implementing my strategy, I was sure.  Oh, was I sure.  But now?  I dunno.  Am I headed in the right direction? Is volatility too much for me?

Well, the investing kings of the universe also suffer from these opposing forces.  Put differently, quants need to balance the confidence needed to put millions of dollars behind an algorithm they’ve designed and think works versus the fear that if things hit the fan, they’re just a congressional meeting away from notoriety.

Luis Lovas had a great article recounting a meeting that occurred last week, The Battle of the Quants.

The afternoon’s main battle was a panel that pitted a quant team dedicated to automated algorithms against a team that (presumably) considers human discretionary decision making as a better tool for alpha. In other words, like the Jeopardy challenge of IBM’s Watson, it was human vs. machine. That particular event I was completely fascinated by.  An interesting pre-game commentary relates the Jeopardy match to the Singularity by author Ray Kurzwell, a convergence of human and machines.  In the battle pitting algo’s against humans the outcome was decided by an audience vote.  The voting was not simply two choices: “for the machines” or “for humans”, but a third choice was offered more aligned with Ray Kurzwell’s Singularity – “a combination of human and machine decision making”.  As you might have guessed, that third choice was the overwhelming favorite. I believe the majority have the confidence to let machines decide many things but are wanting of human intuition or that proverbial finger on the button as a measure of risk control so fear does not overwhelm.

Kinda like me flying in an airplane.  I know the technology has been good enough for decades to replace a human pilot.  But I’m happy that someone is sitting there.  Just in case.

And hopefully it’s Sully.

And hopefully the pilot is sober.

Source

Confidence and Fear: Why Quantitative Models Win (High Frequency Traders)