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December 2016
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Syndication

Brendan Poots is the founder of sports betting hedge fund, The Priomha Group, who mainly bet on football, cricket, golf and also horse racing and tennis.

Priomha setup shop in Melbourne (Australia) in 2010, and more recently, have expanded with a second location in Gibraltar (Europe). From inception up to the release of this episode, Priomha’s Cloney fund has returned a little over 220%.

From listening to our discussion, you’ll gain great insight to how Brendan runs his operation—from getting investors to buy in, to controlling risk and minimizing the volatility of returns, and how the fund makes money trading sports games.


Alex (@AT09_Trader) is a 22-year old, discretionary day trader, who’s seen great results in the few years he’s been grinding away at this. He trades small caps, and he trades aggressively—as you’ll soon hear, Alex is far from conservative.

This conversation was recorded on the 16th of November (2016), right around the time when the madness in the shipping sector was unfolding. So we got talking about the ticker DRYS and how Alex racked up a $40k loss a day or two before our interview (although, he had his first +$100k day shortly after too).

Also we spoke about how Alex got started, the types of trade scenarios that he looks for, areas that he’s working on to improve, and also his venture into real estate—where he flipped a foreclosure property for a tidy profit, plus much more.

Note: Nothing you hear on this podcast is financial advice. You’re entirely responsible for your own trading decisions.


Machine learning is a hot topic right now, with a lot of people wondering how it could be used in finance and trading. Used naively, machine learning poses a great deal of risk. We’ll discuss why that’s the case and also some good ways to use it carefully.

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On this episode, I’m joined by a quant trader who works at a high frequency trading firm—though you might be surprised to hear, he started out on the same path that many retail traders do—his name is; Dave Bergstrom.

The thing that makes Dave unique from most traders who’ve been on this podcast previously, is how he uses data-mining techniques to develop trading strategies. Though data-mining, in trading, often has a negative connotation attached to it, Dave believes this stems from bad practices and poor evaluation of methods.

In addition to the above and ways to reduce curve-fitting, we talk about escaping randomness, learning to write code, Dave’s three laws for strategy development, setting expectations and plenty more.

Q+A: Got a question for Dave? Write in the comments area at chatwithtraders.com/103.


When one has a price model that they think will work well for forecasting returns, the next step is to actually trade it. This isn’t that simple for a variety of reasons. For one thing, you need to define how much risk you’re okay with taking on in a portfolio, and then try to maximize your returns while staying within those boundaries. This is the foundation of modern portfolio theory—we’ll discuss some real life issues with this.

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Eugene Soltes is an author and finance professor at Harvard Business School.

Over the past eight years, give or take, he’s spent a lot of time with many big-time executives and professionals who have been convicted of major financial crimes, such as; cooking the books, fraud, Ponzi schemes, and insider trading.

What initially began as nothing other than self-interest has materialized into a 464-page hardcover book, which was released in October this year (2016). The book is titled, Why They Do It: Inside the Mind of the White-Collar Criminal.

Intrigued by the subject matter, I invited Eugene onto the podcast and we got talking about; how Bernie Madoff became the mastermind behind the biggest fraudulent scheme in US history—sucking billions of dollars from unsuspecting investors, some of the notorious insider trading cases, and ultimately, why they do it.


In practice, no one trading model will ever be that good on its own. Luckily statistics has come up with a lot of theory about how you can combine weaker models to create better overall predictions. We’ll discuss how to combine many different trading signals into overall models and some of the practical considerations in doing so.

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