No Magic Formulas: How I Actually Decide What to Trade

Someone recently asked me if I have a checklist for adopting new trading strategies.

You know, a neat little formula like “if backtested Sharpe > 1.8, trade it” or “if drawdown < 15%, green light.”

I get the appeal. We all want clear, objective criteria to make these decisions easier. But strategy adoption just doesn’t work that way.

The reality is messier. More nuanced.

Your decision might differ from mine because:

• We have different objectives and constraints
• Our existing portfolios look different
• We weigh competing evidence differently
• You might be willing to take on operational complexity that I’m not

There’s rarely a binary right/wrong answer – just different paths with different trade-offs.

That said, I can share how I approach this problem and the questions I consider.

Here they are, roughly in order of importance.

My Mental Framework for Strategy Evaluation

Does the effect have a plausible reason behind it?

This is absolutely the most important question. If you can’t explain why an edge exists in plain language, you don’t understand it well enough to trade it.

Trading something you don’t understand – with only your P&L as feedback – is like trying to learn poker by only seeing whether you won or lost each hand, without ever looking at your cards. Good luck figuring out why you’re winning or losing.

How confident am I that this will persist?

This flows directly from the explanation of why the edge exists.

For instance, risk premia tend to be persistent because they’re rooted in human psychology. People will always demand compensation for taking on certain risks.

On the other hand, genuine mispricings or inefficiencies tend to get arbitraged away over time. The more obvious the inefficiency, the faster it disappears.

Have I ruled out non-tradable explanations?

Before you get too excited about a backtested edge, consider whether it might be explained by:

• Data biases or artifacts
• Survivorship bias
• Look-ahead bias
• Transaction cost assumptions
• Liquidity constraints

I’ve seen countless “amazing strategies” evaporate once these factors were properly accounted for.

What does the data analysis say?

Is the effect strong and consistent? Does it show up in the places you’d expect based on your explanation?

If you think your edge is based on sector rotation dynamics, for example, it should show up across different sector ETFs in a way that matches your hypothesis.

What was the after-cost historical performance of harnessing the effect with simple rules?

Notice this isn’t first on my list. Performance metrics matter, but they’re far from the whole story.

What execution considerations exist?

There are almost always execution challenges outside of highly liquid markets.

A strategy that requires trading micro-caps or altcoins is fundamentally different from one trading S&P futures or bitcoin.

How similar is it to strategies I’m already trading?

If a new strategy offers genuine diversification, I might be more willing to adopt it.

How much operational effort is involved?

Be honest about this one. A strategy requiring you to wake up at 3 AM to manually place orders isn’t the same as one you can manage with market-on-close orders once a month.

I’ve abandoned perfectly good strategies because the operational overhead wasn’t worth it for me at the time.

Did I perform statistical tests for randomness?

I don’t put much weight on this, but it can provide another perspective.

The issue with randomness testing is that most strategies we discover have already been “p-hacked” to some degree – either by us or by the natural filters that brought the idea to our attention in the first place.

Also, consider that by the time something is “statistically significant”, everyone else will have noticed it too, and the broader market is almost certainly absorbing it. This is a prime example of why having a reason for the trade to exist is so important. If you can identify edges based on your market understanding, you’ll beat most people to the punch.

The Bottom Line

There’s no magic checklist that eliminates the need for thoughtful analysis. Strategy adoption requires good judgment and clear thinking.

You have to weigh multiple factors, many of which involve subjective assessments.

The good news? Like any skill, this gets easier with practice. Over time, you develop an intuition for what works and what doesn’t – but it’s an intuition built on a foundation of methodical thinking, just like we teach in Trade Like a Quant.