Top Traders Unplugged19 Feb 2023

SI231: The Perfect Fit for Trend Following ft. Richard Brennan

Richard Brennan joins us to reflect on the learnings so far from the new CTA series. We also discuss how to set your stop loss when entering a new position and why you should not allow your trend models to go "backwards", how to fit your model to price data and why it is important to understand the signal to noise ratio in your trading to avoid overfitting. Additionally, we discuss how random entries can work for your models, but why they are not truely random and the challenge of separating luck from real signal, why it is preferable to have an under-fit model as opposed to an over-fit model when developing a trend following system and how to reduce your chances of having an overfit solution. We round off our conversation discussing why trend following is not as easy as it might seem and much more.

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50 YEARS OF TREND FOLLOWING BOOK AND BEHIND-THE-SCENES VIDEO FOR ACCREDITED INVESTORS - CLICK HERE

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Episode TimeStamps:

03:17 - Reflecting on the CTA Series

09:55 - What happened this week?

12:42 - Q1, Jordan: Regarding setting stop loss when entering a position

15:39 - Q2, Mark: Entry lookback vs exit lookback?

19:23 - Signal to noise masterclass

36:03 - The Tom Basso experiment

46:34 - The challenges of creating models

52:40 - Avoiding overfit - Don't pick the "best" parameters

57:23 - Avoiding overfit - Apply the Golden Rules

01:00:19 - Using visual mapping

01:03:50 - Avoiding overfit - trade multiple markets

01:07:34 - Trend Following is not easy

01:14:04 - The January trend report

01:15:46 - Thanks for listening

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2. Daily Trend Barometer and Market Score

One of the things I’m really proud of, is the fact that I have managed to published the Trend Barometer and Market Score each day for more than a decade...as these tools are really good at describing the environment for trend following managers as well as giving insights into the general positioning of a trend following strategy! Click Here

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Disclaimer

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