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 the bottom of this page.
Topics of discussion:
- Dave explains why he couldn’t “escape randomness” in the beginning, how he landed a position in a HFT firm, and why he became more data-driven as a trader.
- The reasons why Dave learned how to program (in multiple languages) and how it’s comparable to having “superpowers”, plus a few tips for learning the basics.
- Should data-mining be avoided? Dave shares a high-level overview for how he finds an edge by mining data and the measures he takes to reduce curve fitting.
- Dave’s “three trading laws” for strategy development, the benefits of variance testing, and how Monte Carlo analysis can help to set realistic expectations.
Links and resources mentioned:
- Evidence-Based Technical Analysis, by David Aronson
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