Posted on

[podcast] Creating the ultimate algorithm: How Numerai gets experts to build successful trading strategies

automating Wall Street, fintech, and layoffs

We’ve spent the past couple of weeks of this show talking to leading early-stage investors in financial technologies — guys like Charles Moldow of Foundation Capital and Caribou Honig of QED Investors.

These are the same guys that have invested in some of the biggest names in the financial technology space. Companies like Lending Club, Credit Karma, Motif Investing, OnDeck Capital, Prosper, and Zopa. What makes many of these companies different from their incumbent competitors is that they’re bringing technology solutions to financial problems.

Richard Craib, Numerai
Richard Craib, Numerai

That’s not to say incumbent financial institutions don’t use technology — they certainly do, a whole lot of it. It’s just that this generation of fintech company is exactly that fin – tech: they’re technology companies at their core and their solutions to financial problems — whether lending to people without credit history or remitting money more cheaply and quicker across global banking borders. They’re technological in nature.

Some of the most interesting things we’ll see in the future is machine learning applied to the stock market. We know human beings’ biases when it comes to investing and even the world’s best investors fall prey to our own shortcomings, our own humanity. It’s entirely possible machines can do it better. Our next guest, Richard Craib, is working on a machine learning solution to the stock market much in the same way Netflix turned to top external algorithm developers to improve its own recommendation engine — by offering prizes.

Richard’s company, Numerai is an amazingly ambitious undertaking. He’s offering top investing systems developers cash prizes in return for using his firm’s encrypted data set to build alpha-producing strategies. Sitting on top of it all is Numerai with its own big data solution that’s investing its own nostro account, trying to optimize allocating capital between strategies. It’s a black box of black boxes but crowdsourced from some of the world’s smartest data scientists.

As I talked with Richard, I kept thinking to myself, what does this spell for the future of investing? People like Warren Buffett and Peter Lynch — two of my favorite all-time investors — seem so antiquated when you start thinking about AI’s potential to participate in investing. Will future episodes on CNBC profile — not today’s celebrity cowboy stock picker — but tomorrow’s PhD-touting data scientist who builds software for a living? Interesting.

Listen to the FULL episode

In this episode, you’ll hear about

  • how data science contests, like the Netflix Prize, are successful in getting scientists to work on innovative solutions to problems
  • how the stock market is also an interesting problem to solve for scientists
  • the encrypted data set Richard’s firm, Numerai has built as a toolset for trading system developers
  • Numerai’s challenge of optimizing its own allocation of capital across the models on the platform
  • the type of people, quants, students, and professors working on Numerai tournaments and what motivates them
  • Numerai’s business model and how Richard’s background in pure mathematics
  • How he used Reddit to get the word out to the machine learning community

More resources

EVEN MORE RESOURCES

 

Photo credit: spenceyc via Visualhunt.com / CC BY-ND

Posted on

A screener of screeners, Wootrader is creating affordable predictive analytics models for all kinds of investors

online investing

Atanas Stoyanov is the CEO of Wootrader.

What is Wootrader?

Atanas Stoyanov, CEO of Wootrader
Atanas Stoyanov, CEO of Wootrader

Wootrader is the only complete system that uses technical analysis, company fundamentals, analysts estimates, options volatility, guru strategies, stock sentiment and more to generate a ranking for each stock.

What makes Wootrader so radically different is that it adapts to the dynamically changing stock markets by using weighted predictive analytics models that have been used for years in other industries, such as military, to predict the trajectories of missiles, business modeling to predict sales of new products, or even in city planning.  The end-user does not have to manually configure screeners into an investing strategy – Wootrader selects and weights every day the screeners that are outperforming the S&P500 Index in the current market conditions. During some periods we have markets driven by fundamentals, other times by technicals, analysts estimates etc. –  this is what Wootrader accounts for.

Do we really need another screener? Don’t most people not understand how to use them effectively? How is it different than other screeners on the market? What’s the use case?

The currently available screeners require a high level of sophistication from end-users. Most screeners focus on technical, fundamentals, or a small combination of the two and the more data points are available, the more complicated those screeners are…to the point of becoming unusable.

By automatically selecting  the best performing screeners, Wootrader allows even beginner investors to use advanced data such as options volatility and analytics, social sentiments, and guru strategies,. on top of technical analysis and company fundamentals.

Additionally, Wootrader daily optimizes the models to use only the screeners that are outperforming the S&P500 depending on the current markets, while the other existing screeners are ’static’ – once the user has configured (and eventually backtested) them – they do not change/evolve and at some point in time, when the markets have changed, the screeners/strategies stop performing.

Enough has been said about the abysmal performance of Mutual Funds, and expert money managers using those same kind of screeners and strategies – over 80% are underperforming the markets exactly for using such ’static’ strategies that work only for short periods of time and need to be constantly maintained.

We see the following as use cases for Wootrader:

  • A complete beginner can log into Wootrader and start investing within 5 minutes using a basic model that has no timeframe constraints
  • More advanced users can use models with specific time/number of stocks constraints. For example, these users would select (using a wizard)  the model that works best for a one-month investing period for a portfolio of 10 stocks.
  • Professional investors can create custom models from scratch by selecting the data points that they believe are the most relevant and Wootrader, under the hood, will use its predictive analytics engine to automatically assign weights based on the current market performance. For example, a user can select some screeners like the P/E Ratio, MACD, 10 Day Options Volatility and Wootrader automatically assigns a weight to each one of them on a daily basis.
  • Financial institutions can integrate the Wootrader models and rankings into their own systems using our REST API

What kind of development went into building a screener of screeners?

Several years went into developing the predictive analytics engine of Wootrader. Currently, our platform is cloud hosted and gets new data every day from Zacks, CSI, Quandl, Orats, PsychSignal, Quantcha and soon, TipRanks (performance weighted analysts ratings and price targets). After the new data is downloaded, Wootrader builds the models and generates the rankings.

My background is in software development and optimization – my previous company (a former Inc 500 firm that I sold in 2007) AutomatedQA/Smartbear.com develops software optimization and test automation tools. Optimizing software and optimizing the stock markets have quite a few surprising similarities.

You’ve integrated your screeners into a few of the online brokers — can you describe how that works from a user’s point of view? Were there technical challenges that you faced?

Actually the brokers are integrated into Wootrader – allowing the user to place trades and retrieve accounts and positions. Wootrader analyzes a user’s existing equity positions and recommends which ones should be sold/bought over the short term. The challenges are mostly due to various implementations of the brokerages APIs, so each integration has to be custom-coded and extensively tested. However there are several new Broker ‘aggregators’  (think of these like portals to other brokers, allowing the use of a unified API to access the different brokers) that we are in the process of integrating – trade.it and tradable.com are two examples.

Another challenge is that not all information is readily available yet through brokerages APIs – for the future, we are working on accessing portfolio transactions such as dividend distributions/reinvestments, interest, splits, and other events like those.

What does 2016 have in store for Wootrader?

The main features coming in 2016 are related to portfolio analysis, as well as tax harvesting and optimizations. We want to make Wootrader a one-stop easy solution for everyday budding investors who desire better-than-market returns while saving on the fees associated with money managers and robo-advisors. We don’t pay our accountants a percentage of our earnings and I have a really hard time accepting fees based on a percentage (small or high) of the assets being managed. We are also constantly adding new great data from various sources.

Photo credit: Dennis Wong / VisualHunt.com / CC BY

Posted on

Investing with more return and less risk – with Lee Hull

As an investment advisor, Lee Hull’s clients can’t afford to have down years.

They’re retirees looking for steady income — no matter what Mr. Market has to say about it. Where traditional advisors are willing to ride the market’s ups-and-downs, Hull uses a different approach.written by Lee Hull

His returns have trounced the markets while he puts less of his clients’ capital at risk. Over the past 10 years where the markets have literally gone nowhere, Hull’s investment firm has averaged over 8% per year (net of inflation!).

That’s pretty darn good but when you see that he was “only down” 9% in his largest losing year during the same time period, that should make you sit up and listen (if you’re not already).

On today’s episode of Tradestreaming Radio., Hull shares much of his research and methodology with us.  He’s the author of Less Risk, More Return: A Proven Blueprint for Retirement Plan Investing.

Look below to access Hull’s 10 Tips to Improve Investing Returns and Lower Risk.
Continue reading “Investing with more return and less risk – with Lee Hull”

Posted on

Busting jackass myths to find more profitable investment strategies – with Mike Dever

finding a job in finance

I’ve been a fan of Mike Dever and his firm, Brandywine Asset Management since I first began learning about mutual funds 20 years ago. Mike joins us today on Tradestreaming Radio to discuss his new book, Jackass Investing— a book with 30 years of hard-earned investment experience. Mike describes multiple myths investors (and the media!) have about investing — and then proceeds to bust them. In this podcast, you’ll learn: