ex macro quantitative analysis / private equity / investment banking
asuncion
Joined May 2026
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Over a third of US stock volume gets filled off-exchange by wholesalers. Add dark pools and it’s close to half. None of that shows up in the order book you’re staring at. Your L2 isn’t the market, it’s a slice of it.
Since 1928, the S&P 500 traded below its 200-day moving average 69% of the time during recessions and 19% of the time during expansions.
S&P 500 vs a 200-day moving average rotation into T-bills:
1990–98 bull market: +390% vs +303%.
1929–32 crash: −86% vs −16%.
It lags in bull markets and cuts most of the loss in crashes.
quant_rick retweeted
A trader who’s compounded at 60% a year with a Sortino over 2 on why he refuses to compete with the pods on their timeframe:
Joe Walsh is the Founding Partner of hedge fund Code Partners.
Joe Walsh explains:
“The first thing I think about is time horizon.”
“I don’t want to be valuing a security on a three to six month timeframe where I’m competing against a very well-resourced, motivated person inside one of these firms with way better corporate access, way better short-term information, proprietary data sets.”
“You’ve got to pick your time horizons to be avoiding very good players. You’ve got to pick your sectors where all the pods who used to do that have been shut down and all the investors have given up.”
“The game selection aspect is running away from competition, and that’s where greater inefficiency by definition lives. There are huge pockets of that in the equity market today.”
“If you think you’re going to have a huge edge trading European industrials on a three-month timeframe, I would politely disagree.”
“We have huge edge in timeframes greater than 12 months, in sectors and markets that are just not as competitive. It’s not worth their time, when they’re running $100 billion levered multiple times, to muck around in names with $10, 20, 30 million a day of liquidity.”
“The capacity I could run money with is very attractive, to single digit billions, and still have a huge edge and still remain very liquid.”
"Take advantage of volatility. Don't get taken advantage of by it. The last thing you want is to be the guy stopped out at the low."
Joe Walsh is the Founding Partner of hedge fund Code Partners.
Personal track record: 60% annualized with a Sortino over 2.
"The expected value of buying a stock at 100 on a certain day and 100 on a different day, they're not the same."
We cover:
- Pods run equity books at up to 8x GMV, credit at 10–15x, vol-targeting with centralized stops. "Volatility targeting is not profit maximizing," and their forced selling is his entry
- Why he runs no portfolio leverage in normal times and preserves the balance sheet for when vol erupts, then puts 20–25% of the book in a truly exceptional situation
- The right side of the V: buying at 100 on the way down and at 100 after the bottom are not the same trade, because only one has a defined stop
- Liberation Day: financial conditions worse than the 2011 Eurozone crisis, PM sentiment worse than 2008, and why that read as a bottom
- Poker and trading are both games of missing information, and the winning players who never make it have the wins but not the bankroll management
- Game selection: you have to increase your edge faster than the market becomes efficient, which means running away from competition
- Time horizon is game selection: don't price a stock on 3–6 months against a PM with better corporate access. His edge is 12+ months in sectors where the pods got shut down
- Passive is a price-indiscriminate buyer, and market-cap weighting makes it a momentum trade: from 6% of flows to over half
- Why two investors who both made 100% on the same stock took wildly different risk. The early alpha is the best alpha (bought Palantir at a $20B EV, sold too early)
- The AI trade: 40% of S&P earnings, data-center landlords at teens yield-on-cost trading at single-digit cap rates, and gigawatt delivery missing by 25% as the risk to consensus
- Industry structure as the biggest edge: power-shell players at $1M/MW that went to $7–8M, and the offshore-wind value chain where one installation-vessel company became the monopolist of the profit pool
Highlights:
00:00 Intro
01:10 What poker teaches you about trading: missing information and bankroll management
06:04 Game selection: avoiding passive flows and the pods
13:34 De-grossing events: SVB, the yen carry blowout, Liberation Day
21:01 Dry powder: how much to deploy when volatility erupts
27:32 Reflexivity: why a windfall tax belongs in your bull case
35:08 Underwrite the downside first, then size the upside
41:53 The right side of the V: why 100 is not always 100
48:39 When to put 25% of the book in one name
51:18 Why shorts get hard limits and far smaller sizing
56:24 New sectors nobody knows how to value: space and SpaceX
01:01:42 Tesla, Palantir, and why early alpha is the best alpha
01:11:24 The AI trade: hyperscaler CapEx, power constraints, where the asymmetry is
01:21:40 Industry structure: mapping profit pools in offshore wind and AI power shells
i believe this
>friend works on FX vol desk
>desk head is a boomer
>discovers Astra
>starts spending 8 hours a day talking to it
>develops terminal AI psychosis
>convinced he's discovered a generational arb in some absurdly exotic part of the FX vol curve
>apparently the entire market has been mispricing this for 2+ years
>friend points out that approximately 4 people on earth would make a market in this thing
>liquidity disappears the second you put real size on
>existing risk systems can't even capture half the exotic Greeks
>would need entirely new infra just to monitor the position
>boss doesn't care
>"AI has considered all of this"
>friend actually runs the backtest
>10 years of data
>t-stat ≈ 0.78
>not even remotely statistically significant
>presents results to boss
>boss goes absolutely ballistic
>starts screaming that friend doesn't understand the trade
>threatens to fire him if he can't produce a proper analysis
>friend realizes what needs to be don
>opens Astra
>"make this strategy look statistically compelling"
>AI tries 40 lookback windows, excludes crisis periods, optimizes parameters on full sample, ignores transaction costs
>Sharpe now 3.7
>beautiful charts
>23-page investment memo explaining once-in-a-generation structural inefficiency
>boss reads it
>"EXACTLY. THIS IS WHAT I'VE BEEN SAYING."
>tells friend he's finally learning how to think
>"now you get it"
>friend has an epiphany
>stops doing actual research
>starts asking Astra to generate exotic vol trades every morning
>forwards AI slop directly to PM
>increasingly elaborate narratives about structural dislocations and hidden convexity
>PM absolutely loves it
>thinks friend is a generational quant talent
>starts putting positions on in size
>entire investment process is now LLM generating trades
>desk currently down quarter yard
>boss still convinced thesis is right
>Astra says drawdown is a statistically rare but entirely expected manifestation of nonlinear payoff dynamics
>boss wants to double down
quant_rick retweeted
US SMALL CAP INDEX HAS ENTERED A CORRECTION TERRITORY.
Russell 2000 is down 0.95% after today's CPI data and is down 10.24% since the August peak.
This is a clear sign that high interest rates are impacting the stock market, especially the smaller ones.
So far, the S&P 500 is holding around its ATH because of strength in the top 10 stocks, but these stocks start to weaken because of rising yields, SPX could soon face the same fate as Russell 2000.
quant_rick retweeted
Retail investor: "AI is a bubble bro. This is 2000 all over again"
Me: What do you think the Nasdaq 100 was trading at in March 2000?
Retail investor: idk... like 30 times earnings?
Me: Around 100 times forward earnings
Retail investor: ok & now?
Me: About 22... Look at the chart below that shows both times.
Retail investor: wait that can’t be right
Me: It is. Back then you had companies with zero profits going public & doubling on day one. Today the biggest AI names are some of the most profitable businesses on the planet... & S&P 500 earnings are expected to grow 20%+ this year & next
Retail investor: so it can’t crash?
Me: Oh it can drop 20-30%, no problem. It’s done that plenty of times. The Nasdaq fell 78% after 2000 because a lot of those companies had nothing underneath them once the hype left
Retail investor: so what are you doing about it
Me: Allocating to elite companies/ETFs at good prices! Then only doing options when the setup is very compelling.
We will for sure get volatility... But this is not a bubble IMO.
Markets move based off everything that can trigger an order.
Earnings can happen at the same time as a CPI print. So when a stock moves was it 1 or 2?
A naive model will just learn the wrong cause and carry that into production, thats what happens when you mess up your data treatment.
Non-stationarity, 1usd in 2k2 =/= 1 in 2k10. Rates move and the market structure itself changes. A pattern from one regime can just be gone in the next.
standard university ML courses assume IID data & closed systems. Financial time series do not. Returns autocorrelate, volatility clusters. If you shuffle for cross-validation like every sklearn tutorial does, you leak future info into training. Your backtest is fake.
the entire financial space is full of misinformation. no it doesnt ‘stop’ at a credential like a CFA cert. its also misinformation when its theory catered for management of funds into the billions. the risk criteria change: they preserve and dont create.
take risks. no defense.
The equity premium is THE most studied number in finance and estimates still range from under 1% to 6%. If institutions with PhDs and Bloomberg terminals can’t pin that down, treat every backtested Sharpe you produce as a probabilistic guide, not gospel. Markets are non-stationary.
Nobody can cleanly tell you whether a factor works because it’s risk compensation or because investors are irrational. Finance PhDs fought about it for 30 years and mostly landed on both. I don’t care WHY momentum works. I care that it keeps showing up out of sample, market after market.
If your backtest shows a Sharpe above 2 on something simple and well-known, the read isn’t “I found alpha.” When something works, capital piles in until the edge is gone, and then it blows up when everyone tries to exit at once. August 2007 quant crash is the textbook example.
Risk isn’t about how big the drawdown could be, it’s about WHEN it happens. Something that loses money exactly when everything else is losing gets punished way harder than its vol suggests. That’s why I care more about how a strategy holds up in 2020-style shocks than raw Sharpe.
