Macro-Ops Podcast: Trading Strategies, Curve Fitting and Over Optimization

Chris and Tyler talk about curve fitting and over optimization of trading strategies.

This episode is a lot shorter than normal, we touch on a single subject vs covering a wide variety of subjects. If you like these short and pointed episodes and want more, let us know! If you prefer long form drop us a line. Tell us what YOU want!

Apple Podcasts

Google Podcasts

Spotify

Stitcher

Overcast

Breaker

Anchor.fm

RadioPublic

Pocket Casts

If you want to play this in a different podcast app, this is the RSS Feed https://anchor.fm/s/870c5d0/podcast/rss

In this episode Chris starts off talking about what he looks for in a trading strategy:

  • Predictability – the ability to predict a trade is forthcoming, not predicting the results.  
  • Repeatable – does this setup repeat over time?
  • Definable – can you clearly define the parameters?

Tyler and Chris then go on to discuss the following:

What does over fitting look like in trading systems?

Why seeking high win rate systems leads us down the path of over fitting.

“Finding” setups in random walk price data. How our brains are easily fooled by randomness.

Testing logic on in-sample and out-of-sample data (training and test data sets). A model that works well on both sets is best.

Why resilience is our true goal for a trading system. If it isn’t fragile, it has more potential to last over time.

How to deal with black swan risk. (Risk that doesn’t show up in any past data sets.)

Why robust systems are uncomfortable to sit through.

Why building a system that fits your temperament is the key to having success.

Finally we hit on why we all need to practice the basics of a system over and over. Get those reps in!

You can reach Tyler at @tylerhkling on Twitter and tyler@macro-ops.com

You can reach Chris at @chrisdmacro on Twitter and chris@macro-ops.com

Systems trader with over 20 years of experience, former marine, government contractor, serial tech entrepreneur, angel investor and lifetime student.