Someone asked in the comments of edgealchemy.robotwealth… a really good question:

Curious how you actually identify “who is losing and why”? Edge for me is strictly quantified through statistical positive expectancy with a measurable system: if it works consistently, why/how can we verifiably put a ‘reason’ on it?

My answer:

Thanks, and great question. It gets at something I think is really underappreciated.

You mention quantifying edge through statistical positive expectancy. I'd push back a little on that. Or rather, provide a bit more nuance. Yes, a real edge will have the stats on its side. But so will countless non-existent ones, simply because of the low signal-to-noise ratio in financial data and the finite samples we're working with. The stats alone can't tell you which is which. You need the 1-2 combo of a plausible "why" and supportive data.

Neither is sufficient on its own (usually).

So really there are two approaches, and only one of them is something you'd have any confidence building a serious trading operation around:

Approach A (pattern-first): Scan data for patterns with positive expectancy, backtest them, and if the stats look good, trade them. The "why" is optional or reverse-engineered after the fact.

Approach B (mechanism-first): Start with a hypothesis about who is trading at prices that don't reflect expected returns, and why they'll keep doing so. Then look for evidence in the data.

Both can produce systems with positive expectancy. But Approach B is FAR more likely to work out for a few reasons (not necessarily in order of importance):

1. It's a much better filter. Most patterns in financial data are noise. If you start from "I think X is happening because of Y structural reason," you massively reduce the space you're searching. Fewer false positives.

2. It moves you forward. Every time you hypothesise something and test it, you learn something, whether you end up with a trade or not. You often end up with your next lead and your next hypothesis. You don't get that feedback loop if you're just data mining for favourable statistics. There's nothing to link it back to market reality. You never develop the intuition that we call "trader smarts."

3. It tells you when the edge is gone. If you know the mechanism (say, fund managers forced to rebalance at month-end), you can monitor whether that mechanism still exists. If you only know "this pattern had a positive expectancy from 2005-2023," you have no way to distinguish a normal drawdown from an edge that no longer exists.

4. It keeps you in the trade. If you've landed on something without a "why," you'll never really trust it. Deep down you know that price patterns aren't predictive of forward returns (why would they be?), and that will cause you endless anxiety when you try to trade something you don't understand.

I actually have a concrete example. I traded a bond seasonality strategy for years, but the mechanism I had (window dressing) always felt a bit hand-wavy. When it had a rough patch in 2024, I pulled it. It came roaring back in 2025, about 15% at Sharpe 1.8 after costs. I left most of that on the table because I didn't trust the "why." A Pro member who's an ex fixed-income PM recently gave us a much stronger structural explanation, and I now have far more confidence in the trade.

So to answer "how do you identify who is losing and why":

You think about the participants in the market you're trading. What are they forced to do? What are their incentives? What constraints do they face? (This is what I mean by "trader smarts"). Index rebalancing, regulatory requirements, benchmark tracking, liquidity provision, hedging obligations. These create flows that aren't driven by expected returns, and that's where the opportunity is.

This is actually the core of what we teach in Bootcamp - robotwealth.com/trade-l… - building that mechanism-first research process from scratch. If you want the full framework, that's the place to get it all in one place.

It's not that the stats don't matter. They do, a bit, I guess. But the mechanism is what gives you confidence in the stats and tells you what to do when things get rough. I'd 100% trade something based on a clear mechanism that I didn't have enough data to calculate good stats on (have done this a number of times). I'd be much more reluctant to trade something whose stats looked great but whose mechanism didn't make sense (have passed on these in real life too). The point is, there's a hierarchy to all this talk of evidence. And stats definitely ain't at the top.

I've been thinking about writing a proper cornerstone piece on this approach. Your question is a good nudge to actually do it.