ex macro quantitative analysis / private equity / investment banking
asuncion
Joined May 2026
- Tweets73
- Following37
- Followers93
- Likes3
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
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.
quant_rick retweeted
Bill Ackman recently delivered a 44-minute masterclass on wealth creation, investing, and capital allocation—offering insights that many would argue are more practical and valuable than what is taught in most business schools.
quant_rick retweeted
The only edge in markets that never gets arbitraged away — from a crypto quant running Sharpe ~4:
Leigh Drogen (@LDrogen), CIO of Starkiller Capital. Sharpe ~4. Started his career as a quant at a NY equity hedge fund.
Drogen:
"If the only thing you ever did was put a 50-day moving average on Bitcoin — long when above, out when below — you would massively outperform everything."
"You would stay out of the massive puke-inducing crashes."
"If you knew nothing — couldn't tell Ethereum from the Nth lending protocol — and just bought the top 20th percentile by 30-day return and shorted the bottom 20th, you'd make a whole bunch of money."
"What is the intrinsic value of Dogecoin? Nothing. You could make the case Bitcoin has no intrinsic value either."
"The vast majority of assets in digital asset space have no intrinsic value — which is why cross-sectional momentum works so well."
"Momentum is the only persistent alpha in all markets. All other alpha gets arbitraged away. Human behavior never will."
A 4-Sharpe crypto fund. 27 of 28 months positive.
Leigh Drogen (@LDrogen) is CIO of Starkiller Capital, a crypto quant fund running momentum and market-neutral crypto strategies.
His edge is diligence — and a "never lose more than 1%" position rule.
We cover:
- Why block space is worthless — and the fiber-optics-in-1999 analogy that explains every L1 collapse since blobs launched on Ethereum
- Starkiller's "never lose more than 1%" position sizing rule (how it compounds into 27 of 28 positive months)
- The Ripple / RLUSD / USCC trade — borrowing at 2.5% against a 5-8% yielding tokenized basis fund, hidden in plain sight on Aave Horizon
- How Starkiller dodged the Kelp DAO hack, lending USDC at 17-18% APR while the rest of DeFi was on fire
- Why momentum is the only actual persistent alpha (it's the only persistent behavioral characteristic of humans)
- "F*ckery risk" on the short book — why Drogen runs a more diversified short book than long book, even when his thesis screams short
- Why "sales is way overcompensated" relative to the difficulty of the job — and what that means for ambitious young quants
- The 2019 DM from a 21-year-old that became Drogen's biggest career miss — and how Polymarket's Shane Coplin (@shayne_coplan) actually solved the SEC problem ("USDC and VPNs")
Highlights:
00:00 Intro
01:09 Mechanics of a 4 Sharpe market neutral DeFi strategy
03:24 Quantifying protocol risk and code provenance
06:40 Case study: Exploiting incentivized spreads in carry trades
10:53 Three primary sources of alpha in liquid crypto markets
14:28 Capacity constraints and institutional yield compression
18:54 Position sizing via the 1% max loss rule
21:38 Pro-cyclical returns and the risk modulation framework
26:44 Compounding capital through trend following and cross-sectional momentum
33:35 Why momentum is the only persistent behavioral alpha
48:39 Extracting alpha from token unlock schedules and market structure
51:20 Lessons from building Estimize and the SEC/ForceRank fight
55:00 The Polymarket origin story: Arbitraging regulatory hurdles
01:01:45 Career risk premia and the value of "eating sh*t"
01:05:34 Table selection: Positioning your career on the right macro curve
quant_rick retweeted
Study past big winners. That’s how you train your eyes to spot future winners. 📈
The biggest stocks in history all showed the same characteristics before their huge moves.
Here’s what we look for 👇
1. Massive Relative Strength: The best stocks outperform the market long before everyone notices them. They resist pullbacks and keep making higher highs.
2. Big Catalyst: Strong EPS growth, huge sales growth. Big winners always have a story that attracts institutional money.
3. Clean Patterns: Tight consolidations, VCPs, flat bases, clean breakouts. The chart tells you when institutions are accumulating shares.
4. Strong Market Timing: The biggest winners often emerge right after market corrections. That’s when fresh leadership starts to separate itself from weak stocks.
5. Volume Clues: Big up-days on 2x volume are not random. That’s often funds building positions aggressively.
6. Repeating Behavior: These patterns repeated in the past. They repeat today. And they will repeat in the future because human behavior never changes.
Most traders study indicators.
I study past monster winners.
That completely changed my trading.
The goal is not to predict.
The goal is to recognize.
Learn the patterns.
Practice them.
Build your pattern recognition.
I’ve studied thousands of past winners over the years.
That’s why new leaders often look familiar to me before they make their biggest moves.
These patterns repeat.
I’ve taught this to thousands of traders.
You can learn this too.
quant_rick retweeted
Sweden is choosing capitalism.
Social spending as percentage of GDP is now 24%, lower than most of Northern Europe.
Sweden has surpassed the US in billionaires per capita.
School choice is universal, one in ten teens goes to a school operated by a company listed on the Stockholm stock exchange.
Taxes have been cut three years in a row.
Sweden has seen "more than 500 initial public offerings over the 10 years through 2024, more than Germany, France, the Netherlands and Spain combined"
Nearly half of primary healthcare clinics are now privately owned.
The result? Same as always. Sweden is projected to grow 2% a year through 2030, which is the same as the US and double France and Germany.
How many times does this have to keep happening across the world? How many times does free market capitalism have to prove itself superior to socialism before the world accepts the truth?
quant_rick retweeted
OOS performance, for me, is one of the most important validation metrics. If used correctly, it is one of the closest things we have to an unbiased estimate of future strategy performance.
But most people use OOS incorrectly. I used to as well, until I came across an interview with Timothy Masters where he explained how to properly think about it.
Here is the main idea:
Your OOS is only truly valid ONCE. The moment you look at your OOS results, your development process should be done. You should not go back and make more changes. If you do, your OOS is compromised and you can no longer fully trust it as an unbiased estimate.
You also should not compare two or more strategies based on their OOS performance and then “choose the best one.” For example, you cannot say:
“Strategy X had better OOS performance than Strategy Y, so I will choose Strategy X.”
That creates optimistic bias because you are now selecting based on the OOS result itself. In other words, you are likely overestimating future performance.
If you are building one strategy idea, only test it on OOS when you are certain you are done tweaking. Once development is finished, run it on your OOS. If the OOS performance is good, great. Move it to live testing. If the OOS performance is bad, that idea failed. Do not go back to the build phase, change parameters, and test on OOS again. COMPROMISED!
If you are data mining strategies, do not take 6, 7 or 8 candidates, test all of them on the final OOS and then choose the best OOS result. Again, COMPROMISED!
Do your analysis before the final OOS. Look at robustness, logic, parameter stability, drawdown profile and whatever else you use to judge strategy quality.
Then choose the one strategy you believe makes the most sense. Test that one on OOS. If it passes, good. It can move to live testing. If it fails, it failed. Do not go back and pick another strategy to test on the same OOS. That is how you introduce optimistic bias. If you take this approach, your OOS becomes a much cleaner estimate of future performance.
But “unbiased” here does not mean future performance will look exactly like the OOS. It also does not mean you cannot overestimate or underestimate future performance.
Random variance still exists. Unbiased simply means there is no inherent reason for the result to be systematically too optimistic or too pessimistic. In other words, we are trying to create a process where the OOS result is just as likely to overestimate future performance as it is to underestimate it.
This is a lesson I learned from Timothy Masters and it changed how I approach OOS testing. I do not see enough people talk about this, so I wanted to share it.
