066: Dan Shapiro – Blocking Excess Noise, Establishing Your Process and Getting Screen Time

066: Dan Shapiro – Blocking Excess Noise, Establishing Your Process and Getting Screen Time

This week we have returning guest, Dan Shapiro, who was first on episode 32. And after receiving a lot of really great feedback from that interview, I knew I would have to have him back on, so here we are… Dan works on an intraday timeframe and predominately trades high beta stocks. He’s been trading for 16 years, and during this time has experienced the extreme highs and lows that this business can throw at you. For a good part of his career, Dan was heavily involved in the prop world, but now trades independently from New York. Over the next 60 minutes you’ll gain some insight to Dan’s methodology, thoughts on managing risk, an outsiders view on the current state of prop trading, and more… This interview will leave you with plenty to think about, and may even help you to adjust your mindset – if that’s something which is holding you back. Learn more about your ad choices. Visit megaphone.fm/adchoices

Episoder(322)

109: Edward Thorp – The Man Who Beat the Dealer, and Later, Beat the Market

109: Edward Thorp – The Man Who Beat the Dealer, and Later, Beat the Market

I’m not sure how to best say this, but Edward Thorp, is kind of a big deal… Not only in the world of financial markets, but he’s also a household name amongst the gambling scene. He’s the man who beat...

26 Jan 20171h 4min

Q6: Delaney Mackenzie – Your Quantitative Trading Questions Answered

Q6: Delaney Mackenzie – Your Quantitative Trading Questions Answered

Throughout this series, which has been a window into the workflow of professional quant trading firms, we’ve encouraged you to submit questions and requests for further clarification. So, in this epis...

23 Jan 20171h 15min

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

John Netto, a former U.S. marine, describes himself as a high velocity, cross-asset class trader. He connects the ability to be versatile, adaptable, and interpret large amounts of information to be h...

19 Jan 201754min

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’...

12 Jan 20171h 49min

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 Jan 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 Des 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 Des 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 Des 201656min

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