Here's something that might surprise you:
My engineering degree was a real hindrance to my trading.
I'm speaking from experience here. As an engineer who transitioned to trading, it wasn't until I stopped thinking like an engineer that I started to make progress.
Engineers like me are trained to solve implementation problems. We build bridges based on well-understood physics. The challenge isn't understanding gravity; it's designing an efficient structure given a specific set of constraints.
When I first approached trading, I did what engineers do: I assumed I was looking at an implementation problem, and I jumped straight into solving it.
I built elaborate backtesting frameworks, optimised parameters, and created complicated machine learning systems before I even understood what edge I was trying to capture.
This approach is completely backwards for trading.
Here's the key point: In trading, you're not starting with a well-understood phenomenon like you do in engineering. You're starting with an observation of some market effect. Something you've noticed yourself, read about somewhere, or heard from other traders.
But at the start, you don't know anything about this phenomenon. What causes it? How consistent has it been? How strong is it? Is it very noisy? Under what circumstances might it disappear?
How can you engineer a solution for a problem you don't understand?
You can't. At least, not very effectively.
Instead, you should design small data analysis experiments that gradually reveal clues about the thing you observed. In this respect, trading is more like a science project.
There's a little art in there as well, just as there is in any good science. Creative thinking helps you design good experiments and ask the right questions. But the only engineering is in the implementation of a trading strategy, and that's the last piece of the puzzle.
You wouldn't build a bridge without a deep understanding of structural mechanics. And you shouldn't build a trading strategy without a deep understanding of your edge.
Good trading research looks nothing like what we engineers tend to reach for:
Think about potential causal effects rather than assuming this is settled and well-understood.
Start with simple but maximally data-efficient analysis (think scatter plots and factor plots) rather than complicated simulations of trading rules.
Make small, testable hypotheses and design experiments that gradually reveal the nature of your observed phenomenon.
Ask "What would I see in the data if this were true?" instead of "What trading rules should I design?"
Try to disprove your ideas quickly and be paranoid you're wrong rather than confident you're right.
Another trap we engineers fall into is our attraction to precision and determinism. We often fail to appreciate the probabilistic nature of edge exploitation - that short-term results are dominated by randomness, while long-term results are driven by skill.
We hear about the markets being "low signal to noise" and understand this intellectually, but we don't appreciate what it means for our trading. This leads us to think we can build a perfect system for capturing a single edge to deliver that up-and-to-the-right equity curve.
But it turns out this is a waste of time for indie traders, given the variance of the edges we can realistically compete in.
Instead, embrace the mayhem of any single strategy and focus on the bigger picture: finding additional things to trade, obsessing over your exposures, and looking at how each component contributes to the whole to produce something extraordinary at the portfolio level.
In fact, the ability to trade multiple edges is your biggest edge as a systematic trader.
Summary
If engineers unlearned some of their engineering training, they'd make much better traders:
Research before implementation: Unlike engineering, trading requires scientific exploration of your edge before building any strategy.
Simpler analysis is better: Use direct, data-efficient methods to understand market effects rather than complicated backtests.
Embrace the mayhem: Engineers' desire for precision conflicts with markets' inherently noisy, probabilistic nature.
Portfolio thinking beats obsessing over a single edge: Your greatest advantage is trading multiple uncorrelated strategies.
Disprove quickly: Be paranoid you're wrong rather than confident you're right.
When you first observe a market effect, don't be an engineer. Instead, unleash your inner scientist. Only when you understand the phenomenon and are ready for implementation should you let an engineer loose on the problem.