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 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. Learn more about your ad choices. Visit megaphone.fm/adchoices
22 Dec 201659min
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’s the case and also some good ways to use it carefully. -- Sponsored by DataCamp.com – Learning to code can be challenging, but with A-grade courses at your fingertips, things are a whole lot easier. Visit DataCamp start today—sign up for free! Learn more about your ad choices. Visit megaphone.fm/adchoices
20 Dec 201656min
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; 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. Learn more about your ad choices. Visit megaphone.fm/adchoices
15 Dec 201658min
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 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. -- Sponsored by DataCamp.com – Wanna learn how to code? Then you best visit DataCamp. They’ve got a full suite of data science courses that’ll give you the skill-sets necessary of a quant. Learn more about your ad choices. Visit megaphone.fm/adchoices
12 Dec 20161h 11min
102: Eugene Soltes – Unraveling a $65-Billion Ponzi Scheme and Notorious Cases of Insider Trading
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. Learn more about your ad choices. Visit megaphone.fm/adchoices
7 Dec 20161h 29min
Q3: Jonathan Larkin – Seeking Alpha? Try MORE Alpha Factors
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. -- Sponsored by DataCamp.com – For new and intermediate programmers, DataCamp have an evergrowing library of interactive courses that’ll help you to become a data science wizard. Learn more about your ad choices. Visit megaphone.fm/adchoices
5 Dec 201651min
101: Siam Kidd – Latching Onto Trends, Strategic Business Moves, and Breaking the Norm
My guest for episode 1-0-1 is Siam Kidd, from Norwich in the UK. He’s a former-air force pilot, turned retail trader. He’s also a serial entrepreneur, and on a quest to dramatically improve the schooling system. We got to chat about his shaky beginnings as a trader, his rock bottom moment, and how he trades currency pairs—using a technical-driven approach, with the goal of catching major trends when they happen. During the later half, I ask Siam about why he got into business, how he’s found the ability to “think big” and how he’s gotten to a point where he now owns fifteen businesses. Then we also talk about flaws in the schooling system, Siam’s grand scheme, and what the future has in store for us. -- Please spare two-minutes to take the anonymous Chat With Traders survey: click here Learn more about your ad choices. Visit megaphone.fm/adchoices
1 Dec 20161h 55min
Q2: Jessica Stauth – Seeking Alpha? Try Alpha Factors
Factors are at the core of a modern quant equity workflow. This episode introduces the notion of alpha and risk factors at a high level, and delves into some of the use cases which include: understanding how the market is moving, understanding how a portfolio is exposed to sources of risk, and turning ideas for price forecasting into encapsulated alpha factors. -- Sponsored by DataCamp.com – DataCamp is the ultimate go-to for learning data science and stats, using programming languages Python and R. Start any online course today—for FREE! Learn more about your ad choices. Visit megaphone.fm/adchoices
28 Nov 20161h 7min
