macrocephalopod

More normally though, we're doing riskier trades that we expect to work out on average. We're serially looking to buy cheap and sell high based usually on simple relative-value models. The assumption is made that deviations from (relative) fair value will converge...

So models are less about "predicting the future" (as per 2) and more about "extrapolating the present" (Q vs P). "But you're *predicting* convergence to your model of fair value you're using to quantify cheap/expensive?" Yeah, exactly.

Advantages of these trades are: - they're easy to understand and economically simple - they converge fast to expected pnl. You know quickly when your model is out or you don't have edge anymore. They fit nicely with @KrisAbdelmessih's "measurement and normalization" paradigm

Disadvantages are that they are capital constrained (you can only eat what you are fed) and require significant investment in infrastructure and staff. Whilst this area isn't practical for "home gamers", the lessons here are crucial for a good understanding of the market.

Takeaways for "home gamers" - Do more of 1) and less of 2) - Dig into 3) to understand and appreciate "the terrifying efficiency of the markets"

For 1) isn't that just being invested? Either in equity risk prem, or potentially something like risk parity, and maybe short vol.

Also, I would think that the retail player, trading a smaller AUM can capitalize on lower liquidity pockets for 2) providing additional edge. It's harder to argue that this provides them additional edge in 1) (in fact it's harder for them to diversify so they arguably do worse)

Yeah, you're absolutely right. If you have the skill, tech, and discipline to take advantage of ultra-low capacity wrinkles, that's a large edge. My caution would be that most people don't. And even if you do, you still want some higher probability less skill-dependent bets on