Systems 102: The Pursuit of Alpha
Part 2 of the Systems series. Part 1: Overconfidence
The most important question in investing is also the most humbling: is this already priced in?
If you think gold miners are cheap, the market has had that same thought, processed it, and spit out a price. If you think Hormuz closing will spike LNG demand, every energy desk on the planet is running the same math. The question is never whether you’re right about the world. The question is whether you’re right about something the market hasn’t already figured out.
That gap, between the market’s view and yours, is called alpha. And the pursuit of it is an act of overconfidence, which, if you read yesterday’s piece, is where this whole series begins.
Today we'll define the terms, lay out how quants and discretionary traders each pursue alpha in their own overconfident way, and then get practical about how you can use AI right now to improve your process. The paid section walks through our gold and copper miner screeners as a concrete example. As we go through this series we'll discuss the value (and difficulty) of wiring those screeners up with live data, how to turn them into strategies, how to construct a portfolio comprised of strategies, and finally, how to backtest and risk-control this "book." Over time we'll likely make not only the snapshots and outputs of these systems transparent to paid subscribers, but live versions will go to founding members.
Alpha vs. Beta
Beta is the return you get from holding the market portfolio. Buy the S&P 500, hold it, collect the equity risk premium. It’s there. It’s cheap. Its construction is essentially free.
Alpha is what you earn (or lose) by deviating from that portfolio. Say the market is 60/40 stocks and bonds. You look at it and think: “bonds are going to get crushed, I’m going 90/10.” The difference between your return and the 60/40 return is a rough measure of alpha. You made a choice to be different from the market, and alpha is the scorecard for that choice.
Any deviation from the market portfolio is implicitly a statement that you think you are smarter than the market. The Efficient Market Hypothesis says this is a fool’s errand: the market already incorporates all available information, so any deviation is just noise plus transaction costs. Most of us who trade for a living don’t fully subscribe to the strong form, but the burden of proof is on us, not on the market.
There are numerous technical ways to measure alpha: risk-adjusted, excess, information ratio, Sharpe ratio. All trying to get at the same underlying question: did this person make money because they held a genuinely different portfolio? Or did they just lever up the market?
Quants vs. Discretionary Traders
For a lot of folks on this list, this is novice stuff, so I won’t be offended if you skip it. But for the engineers, the students, the allocators, it helps to define our terms.
A quant uses data to trade markets. The day job of a pure quant is painstakingly acquiring, cleaning, modeling, and testing millions of relationships between some X indicator and some Y market, selecting the strongest, then throwing them in a big witches’ brew with a bunch of other “signals” and torturing that data until they feel comfortable enough to say: give me money to bet on this.
All quants are fundamentally overconfident in data. Which is why they spend just as much time stress testing as signal hunting. “Hold out.” “In sample, out of sample.” “Transaction slippage adjusted.” “Live vs. paper.” These are the mechanisms by which they try to adjust for that overconfidence.
A discretionary trader thinks their own judgment is better than the market. They are fundamentally overconfident in themselves. Which you kind of have to be, just to play the game. EMH be damned, I am going to personally beat the market.
Most discretionary traders can describe their process, but it might not sound that different from what the average retirement investor does. “I read the news, I look at the chart, I punt the stonks” was basically Druckenmiller’s process, and he’s maybe the GOAT.
Which is why track record matters so much in this world. I don’t need to understand your process. I just want to see the outcomes, see when and in what ways you made or lost money, and then underwrite the risk of giving you $200m.
Back to Black Snow
When I was raising for Black Snow, allocators had two boxes. I had bad answers to both.
Are you discretionary? Where is your track record?
Are you a quant? What is your backtest?
I was mildly offended by both questions. Partially because I thought it was a false dichotomy, partially because I was defensive about not having good answers. My track wasn’t portable, my systems were in their infancy. Rather than lie, I made the classic mistake of a first-time entrepreneur: I told the wrong version of the truth.
Investors wanted certainty and transparency. Which is totally fair. I provided neither. They wanted an easy story (private credit, 10%+ returns, CLOs didn’t take a realized loss even in the financial crisis!). I was offering uncertainty dressed as exoticism: “We sit at the border between discretionary and quant investing, we use systems to inform our investment process, but the robot is never in total control. We deploy capital across alpha, beta, and gamma (aaah). Further, if you think there’s not a human sitting behind the robot at every quant shop dialing it up and down, tinkering with ‘the weights,’ you are in for a bloody surprise.”
Again, I never claimed to be a great salesman. But if I had just said “I’m a discretionary manager,” they would have said “ok, where’s the track?!” I didn’t have one I could show. Which led to the word salad above.
What’s interesting is that these two schools pretty nicely cover each other’s biases. Force a discretionary manager to only trade signals from a data-driven algorithm, and you might iron out their behavioral biases. Put a discretionary trader on top of a quant system, and the human can react to conditions that aren’t yet “in the model.” Bridgewater always called this “fundamental and systematic”: write down how the human thinks, then do the years-long data work to build that logic into a machine. As we go through this series, we’ll talk about how AI might actually deliver on that fusion. But as our experiments over the past four years have shown, the machines aren’t quite ready, and the groundwork required is extraordinary.
So today we stay in the world of the discretionary trader and talk about how you can use AI right now to improve your process. As the series goes deeper, we’ll leave this world behind. But it helps to build up from first principles.
Using AI to Trade (Starting Simple)
Most good discretionary traders approach the market with a framework. Before you open a chatbot or look at any asset in particular, it helps to have a thesis. A real one. Something falsifiable, with cause-and-effect linkages:
Hormuz is closed. Energy infrastructure is getting destroyed. US natural gas exporting capacity is near peak utilization. There will be large demand for capital to invest in and expand US capacity to export gas. The reward to capital will be higher than what the market currently discounts, and hence these assets will provide good risk-adjusted returns.
Notice the linkages. Each sentence connects to the next. Each one could, in principle, be wrong. That’s the point. You want something that is both falsifiable and contains more than a pure X→Y relationship. You need to see inside the machine, so that when you are wrong, you can evolve your understanding of why.
What follows is a rough spectrum from pure discretionary trading toward something more systematic.
Level 1: AI as Supercharged Search
“Find me US natural gas exporters and pipeline companies.”
You’re using an LLM as a supercharged version of Google, which is in turn much faster than calling up people in the oil industry (which is what people did in 1970). This is also why allocators like sector-specialist managers: someone who has been trading energy equities for 20 years already has this map in their head. Level 1 just gets you the map faster.
There’s no real data in this process. You aren’t betting on more than one linkage. But if the move is big enough and you are fast enough, there are probably decent returns to be had. Decent, because it’s also a great way to blow up on names you don’t understand. You don’t know anything about profitability, growth, debt, management, corporate actions, competitive landscape, or input costs. And even if you did, you still wouldn’t know how the market is currently discounting them.
Level 2: AI as Analyst
This is where AI genuinely helps. Each of those fundamental variables is knowable, but expensive in terms of time, energy, and money to extract. There’s tradecraft, commercially available data that isn’t cheap, and many sources of increasing returns to scale (which explains 50 years of asset management consolidation).
“Find me the 10 most liquid US natural gas exporters, then fill in this framework with their fundamentals, convert them to z-scores, and rank them.”
Pretty much what we learned on Day 5 on the vol prop desk at Lehman. Though the variables were things like implied minus realized vol, skew, term structure, momentum, and distance from the high/low. And it took Sumit and me about 4 hours to run overnight, every day.
For the average discretionary trader, this is where I’d leave off. Know your linkages, build rankings, visualize the information, and prioritize where you spend your alpha-generating attention.
Level 3: Toward Strategy Construction
The last step is to assemble a portfolio of these ranked assets and look at how they would have performed over time. This is still a long way from a real system. A true systematic approach requires doing this continuously, at some regular cadence, and finding ways to do it that aren’t backtesting theater. Alpha decay notwithstanding, there are real pitfalls in the gap between “this looks good on paper” and “this actually works with real money.”
But as a stepping stone from pure discretionary trading toward something more rigorous, it’s a meaningful upgrade. And it’s where the paid section picks up.
Till next time.
Level 2 in Practice: The Gold Miner Screener
Let’s walk through what this actually looks like. Below is a screener we built for the gold mining universe: 30 names spanning GDX (senior miners) and GDXJ (junior miners), with full fundamentals pulled from yfinance and prices pushed to Rose.
