@integerQuant Those Yahoo futures prices are unadjusted which can lead to some surprises later on :)
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TheCTAFan
258 posts
TheCTAFan
@TheCTAFan
📈Systematic Investing & Quant | 🚀Tech & Eng. Leader | 💼Exited Startup Vet | 🏦Investment Bank Alum | 💡Tweets with positive skew
Katılım Şubat 2022
399 Takip Edilen130 Takipçiler
Replication of the Squid program looking good!
Couldn't get ES historical data easily so I'm using Yahoo finance ES=F generic, still very close tho
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@PauloMacro The first paragraph just makes me think of the wreck fishing bubble of late 1600s 😅
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Don’t even know what to say
Naval@naval
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@__paleologo I wonder if using LLMs to make investing decisions will just reinforce the existing risk-premia, given how LLMs average out humans in many ways.
For quant research I don’t know — feels like infra should be sped up, research still feels prone even simple errors and oversights.
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My current predictions for the next 5 years are:
1. No significant HC reduction. A 10% in tech HC is possible. But the heavy-tailification of HF size distribution will continue, with the largest ones growing in size.
2. No alpha compression. Alpha moves. It “compresses” for the losers.
Saying 1. because a) five years is a short time, and b) the financial industry is abnormally innovation-averse, much more than what outsiders surmise.
Beyond five years, I can imagine a drastic, *drastic* change in the role of the PM. But it will be resisted tooth and nails because it will be very painful. It’s still vague in my head, and if it were well defined I would not tweet how anyway. But I look forward to it. I might be the rare Greek who fights on the side of the Barbarians.
Prepare the 🍿, Brett. In some form you’ll be very impacted too. Maybe for the better.
Brett Caughran@FundamentEdge
In the 1995-2010 era, it took a lot of bodies to run a scaled Tiger-Cub strategy. 100+ headcount was common across research, back office & trading (starting in '08, I was one of a large research team) Largely due to technology, that headcount requirement declined dramatically: what once had to be built in-house was increasingly offered in a more effective and cost efficient matter on an outsourced basis with technology. Traders, in particularly, deeply understand the impact of technology from 1995-2015 on their role. You no longer needed a full time person sourcing short borrow or a full time person doing forensic accounting. This happened both on front office & back office roles: the idea that you need seven investors on a consumer team to cover that space seems anachronistic even before LLMs (I was one of seven). You see similar approaches now operated with ~15 people across front & back office. This efficiency benefit fed back into alpha compression...as headcount barriers to entry dissipated, traditional alpha in Tiger style investment approaches compressed as it was easier to practice that particular approach to investing. Today, the multi-strategy approach to long/short investing is ascendant. And it has been a very headcount intensive approach, with the large firms employing 3,000-5,000+ bodies. Is the technology impact to Tiger style investing a relevant prior for the next 5 years? With AI, can the multi-manager firm of the future operate with 500 bodies instead of 5,000? And what does that mean for alpha pools & the "peak pod" debate, talent demand and the future of fundamental investing?
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@Robot_Wealth Assuming LLMs have similar biases as humans, they might actually reinforce the existing risk premia, which are often behavioral. Could be a good thing!
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@ScottPh77711570 @Grantblocmates My daily driver. The secret ingredient on top of this one is cocoa nibs!
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@Grantblocmates I do protein on oats with a banana on top and its pretty great!
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Is anyone aware of software being built internally at a non-software or IT company specifically meant to replace existing purchased software?
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@phoenixstealthy Good approach -- was thinking about trying it for new stuff as well, and migrating if it's reasonably seamless / stable.
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@TheCTAFan We use for new ones not migrated all existing yet, but as we use standard cell stuff not fancy extension i guess the mig will be boilerplate here ^^ GL
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I guess if you use AI every day, this is the way to go for notebooks now (don't struggle with all the noise of ipynb formats).
marimo.io
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@Invested_Medici Coding works pretty well. Vision is basically solved for practical purposes. NLP style problems are now a breeze. These are still narrow use-cases, but get us much closer to human-in-the-loop style automated systems.
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GPT 5.1 - Useless
Gemini - As useless
Grok - Don't get me started
What are we even doing with this. And companies using it? Come. On.
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There are always exceptions, but I'm increasingly of the mind that, on average, real estate isn't a very good investment. You buy a house and sell it for multiples a decade or three later which looks great but often the CAGR winds up being 3 to 5% and that's before you consider all the costs of the house (roof, siding, windows, hvac, plumbing, appliances, landscaping, etc.) and then you have to consider the hundreds or thousands of hours that you spend corralling contractors, etc. And then there is the usual interest expense on a mortgage which ends up being about the cost of the house. Unless you buy something that goes up fast with low money down, sell, and repeat, it is hard to see how you'd be worse off renting and investing all those interest payments and other expenditures in the stock market while freeing up thousands of hours to either earn money or do something enjoyable. What am I missing?
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*RUBIO SUGGESTS OBESITY AS REASON TO DENY VISAS: POLITICO
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@MichaelAArouet Yes -- except for the Nov-Feb darkness 🌧️ A different location is required for that part of the year.
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@egr_investor This resonates, and is also less of an issue in Europe, where you have multiple (3+) relevant parties to choose from. The 2-party political systems tend to simplify this a lot.
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How does classical liberalism fit into modern American politics?
Classical liberalism’s emphasis on individual freedoms, limited government, and free markets aligns with aspects of both major American political ideologies, though it doesn’t fully fit into either.
On the right, classical liberalism shares common ground with libertarian and some conservative beliefs, especially regarding limited government and economic freedom. However, classical liberals differ from conservatives on issues where the right supports government intervention in social or cultural matters.
On the left, classical liberalism resonates with progressive ideals of individual rights, personal freedoms, and equality before the law. Yet, classical liberals tend to oppose the extent of government intervention in the economy and social programs typically favored by progressives.
As a result, classical liberals often find themselves caught between parties, sharing values with both but not fully aligning with either.
Do you identify with classical liberal ideology?
#Politics #Economics #FreeMarkets #Election2024
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@TheStalwart @skooookum @cursor_ai It does not. Larger/legacy codebases can be tricky for AI to grasp. In smaller ones it often works like a charm. Industry might shift to smaller depending on the max context, rewrite on a whim. The gains are real and here to stay.
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@skooookum @cursor_ai Does that jibe with your experience/intuition
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I was talking a software developer today. And I asked: “Does @cursor_ai enhance your productivity?” And he said yes.
But then I asked “are you certain about your answer?” And he said no.
(Incidentally a second person I talked to was unambiguous about it).
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@egr_investor Very nice! I looked through the repo and it’s pretty clean with some useful abstractions. How long did this take you? 😅
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Here’s the system architecture behind the managed-futures trend repo at a glance.
Data → ingest/validate → research-ready prices → Signal library (momentum/MA/breakout) → Risk & allocation → Backtest loop + execution/costs → Analytics (NAV, exposures, attribution) → HTML charts.
Modular CLI/API; swap in live data sources.
#ManagedFutures #Quant #Trading #Finance #Investing #TrendFollowing
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Built a fun hobby project (with a little Codex help): systematic trend-following for managed futures in Python.
- Modular pipeline: Signals → sizing → portfolio → risk
- Backtests & chart viz
- Yahoo Finance integration (using synthetic data for now)
- Easily swap in other live sources
PRs, critiques, and benchmarks welcome. Check it out!
#Quant #ManagedFutures #TrendFollowing #CTA #OpenSource #Python #Investing #DataScience #Finance
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@quantian1 Sounds like more potential for mispricings. I'd bet more edge would be coming from flows or alt data.
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@AgustinLebron3 Oh yes please — no video content would be amazing. I’ve been googling for this a few times already.
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Anyone have a plugin/hack/etc that removes video-containing tweets from the timeline?
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@CliffordAsness how am i supposed to interview antti about this series on a podcast next week when he keeps dropping new papers on me like this
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The Antti papers keep coming! Part 7 (yes, 7) of Antti‘s series on investor expectations (finally, after Parts 5-6 on historical returns and objective expected returns) drills into subjective expected returns on the S&P500. While objective expectations are often inferred from market yields, subjective expectations are best “heard directly from the horse’s mouth“ using survey data. Ever-growing academic research reveals overly extrapolative expectations among retail investors and equity analysts (while institutional investors and rates analysts are more contrarian), and more so at short than long horizons. Links below.
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