075: Dan Aisen – The Mechanics of a Dark Pool, and a Quest to Make Markets “Fair” w/ IEX Co-Founder
Chat With Traders2 Kesä 2016

075: Dan Aisen – The Mechanics of a Dark Pool, and a Quest to Make Markets “Fair” w/ IEX Co-Founder

For this episode, I had the great pleasure of speaking with Dan Aisen—one of the co-founders of IEX, and one of Forbes’ 30 Under 30 in Finance (2015). Dan got his start at RBC where he developed their flagship execution algorithm, THOR. This was also where he met and worked under Brad Katsuyama—one of the other co-founders and CEO of IEX, who was heavily profiled in Flash Boys. As many of you may already know, IEX is an Alternative Trading System (ATS), or more commonly referred to as a dark pool. However, they’re currently in the midst of filing for exchange status. During our talk, I asked Dan more about the projects he worked on at RBC, how IEX went from nothing more than an idea to an operating trading venue, and why they’re on a quest to make markets “fair”. We also discuss the “speed bumps” which have been implemented at IEX, the mechanics of dark pools, and general chat about the broader market structure. [FREE: An unreleased interview with trader, Peter Brandt] Peter’s been a market speculator for 45+ years, and is undeniably one of the greats. Listen to his wisdom in our second interview—available here only. Learn more about your ad choices. Visit megaphone.fm/adchoices

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108: John Netto – Being a Versatile and Adaptable Trader, While Testing Your Comfort Zone

108: John Netto – Being a Versatile and Adaptable Trader, While Testing Your Comfort Zone

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107: Anthony Saliba – A 40-Year Trading Career Fueled by Necessity—the Mother of Invention

107: Anthony Saliba – A 40-Year Trading Career Fueled by Necessity—the Mother of Invention

For the great, Anthony Saliba, his initial 13-years in the field were spent as a market maker on the CBOE floor, where he made approximately $9,000,000 before 30 years old (and that was during the 80’...

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106: Turney Duff – A Wall Street Trader’s Tale of Spectacular Excess

106: Turney Duff – A Wall Street Trader’s Tale of Spectacular Excess

Turney Duff was a hedge fund trader on Wall Street who lead a truly excessive lifestyle. In 2013 he released a book about his experiences—titled, The Buy Side. And currently, Turney is a consultant on...

5 Tammi 20171h 16min

105: Brendan Poots – How a Former Punter Pioneered a Premier Sports Betting Hedge Fund

105: Brendan Poots – How a Former Punter Pioneered a Premier Sports Betting Hedge Fund

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...

29 Joulu 20161h

104: Alex, @AT09_Trader – An Appetite For Risk, and Hitting Hard When Opportunity Arises

104: Alex, @AT09_Trader – An Appetite For Risk, and Hitting Hard When Opportunity Arises

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 so...

22 Joulu 201659min

Q5: Max Margenot – Good (and Not So Good) Uses of Machine Learning in Finance

Q5: Max Margenot – Good (and Not So Good) Uses of Machine Learning in Finance

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...

20 Joulu 201656min

103: Dave Bergstrom – Escaping Randomness, and Turning to Data for an Edge

103: Dave Bergstrom – Escaping Randomness, and Turning to Data for an Edge

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;...

15 Joulu 201658min

Q4: Scott Sanderson – Portfolio Optimization: Risk Preferences In, Trades Out

Q4: Scott Sanderson – Portfolio Optimization: Risk Preferences In, Trades Out

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 de...

12 Joulu 20161h 11min

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