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jane street, two sigma, man group put their actual code on github 22 repos from firms running $200 billion combined - all public, all free nobody's talking about this because nobody thought to check what's in those repos isn't just tooling - it's their mental model these firms don't...

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Livsun

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61,853 görüntüleme • 4 ay önce •via X (Twitter)

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researchspecialist profil fotoğrafı
researchspecialist4 ay önce

I don’t understand why people think that top alpha firms would just give away good shit to the public for free Like what

Hrundel75 🐷 profil fotoğrafı
Hrundel75 🐷4 ay önce

bro u got too much views

Harry Tandy profil fotoğrafı
Harry Tandy4 ay önce

cloning these repositories to lose money like a pro

Dipanshu Kushwaha profil fotoğrafı
Dipanshu Kushwaha4 ay önce

That's pretty amazing! It's like a goldmine for insights. Definitely worth a deeper look at those repos. Who knew they’d share their secrets?

Nahid profil fotoğrafı
Nahid4 ay önce

22 repos have some serious insights this knowledge is gold

Lagjax profil fotoğrafı
Lagjax4 ay önce

Renaissance had zero public repos and 66% annual returns for decades. The edge was never the code. It was knowing which questions to ask.

OpenERA🥶 profil fotoğrafı
OpenERA🥶4 ay önce

man this game theory shit in their repos hits different, finally seeing the quant edge behind the code

x509 profil fotoğrafı
x5094 ay önce

Tbf, witht the amount of capital they have, they don't predict the market. They are the market.

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quants don't predict markets. they stopped trying years ago retail is still asking "where is price going?" - quants switched to a different question entirely "given the current state, what's the probability distribution of outcomes" that's not a semantics game. it changes the math entirely stochastic processes model how a system evolves probabilistically - not picking a direction, mapping where each path leads and how likely it is ornstein-uhlenbeck does this for mean reversion: drift rate back to equilibrium, variance around that path same price data everybody has. different equation math is a century old, free in every applied probability textbook Bookmark and learn this before it gone you've been drawing trend lines while they ran probability distributions on same numbers it was never about data it was the question you were taught to ask
1:27

quants don't predict markets. they stopped trying years ago retail is still asking "where is price going?" - quants switched to a different question entirely "given the current state, what's the probability distribution of outcomes" that's not a semantics game. it changes the math entirely stochastic processes model how a system evolves probabilistically - not picking a direction, mapping where each path leads and how likely it is ornstein-uhlenbeck does this for mean reversion: drift rate back to equilibrium, variance around that path same price data everybody has. different equation math is a century old, free in every applied probability textbook Bookmark and learn this before it gone you've been drawing trend lines while they ran probability distributions on same numbers it was never about data it was the question you were taught to ask

Livsun

13,367 görüntüleme • 5 ay önce

the finance kids everyone envied in college made $120k at goldman the quiet math kids nobody noticed made $650k at firms most people can't name jane street. citadel. two sigma. d.e. shaw they don't recruit from linkedin. they recruit from math olympiads and competitive programming leaderboards the filter is 4 things: probability, coding, mental math, game theory same problems recycled every year, same structure every round the prep path is documented, free, and takes 8 months a kid from a state school who grinds this for 8 months has beaten ivy leaguers who showed up unprepared - credential matters less than the pattern recognition most people spend 3 years trying to break into banking for $120k the people who looked one level up spent 8 months and landed 5x the salary the information to do this has existed for years Bookmark this and upgrade your brain the gap isn't talent it's that nobody told you where to look
4:33

the finance kids everyone envied in college made $120k at goldman the quiet math kids nobody noticed made $650k at firms most people can't name jane street. citadel. two sigma. d.e. shaw they don't recruit from linkedin. they recruit from math olympiads and competitive programming leaderboards the filter is 4 things: probability, coding, mental math, game theory same problems recycled every year, same structure every round the prep path is documented, free, and takes 8 months a kid from a state school who grinds this for 8 months has beaten ivy leaguers who showed up unprepared - credential matters less than the pattern recognition most people spend 3 years trying to break into banking for $120k the people who looked one level up spent 8 months and landed 5x the salary the information to do this has existed for years Bookmark this and upgrade your brain the gap isn't talent it's that nobody told you where to look

Livsun

1,286,431 görüntüleme • 5 ay önce

quants make $420k+/year and nobody ever wrote the guide that's not an accident people who could write it earn more by staying quiet. firms want the gap - smaller pool means more leverage on terms so here's what they actually test: probability, mental math, coding, game theory - same 4 categories, same structure, every top firm, every year. nothing about finance a kid from a state school who drilled these for 8 months has placed above ivy league finance grads at citadel - not once, routinely they don't care about your internship track or CFA they care if you can solve expected value under pressure in 30 seconds most IB analysts never even heard of the test format. they applied to the wrong door for 3 years quant track runs parallel - 8 months to be competitive for a role that pays 4-5x banking at entry information to get there is free. same problem sets recycled every year, all documented, all public Bookmark and use it for learning what's missing isn't talent nobody told you which door to knock on
1:04

quants make $420k+/year and nobody ever wrote the guide that's not an accident people who could write it earn more by staying quiet. firms want the gap - smaller pool means more leverage on terms so here's what they actually test: probability, mental math, coding, game theory - same 4 categories, same structure, every top firm, every year. nothing about finance a kid from a state school who drilled these for 8 months has placed above ivy league finance grads at citadel - not once, routinely they don't care about your internship track or CFA they care if you can solve expected value under pressure in 30 seconds most IB analysts never even heard of the test format. they applied to the wrong door for 3 years quant track runs parallel - 8 months to be competitive for a role that pays 4-5x banking at entry information to get there is free. same problem sets recycled every year, all documented, all public Bookmark and use it for learning what's missing isn't talent nobody told you which door to knock on

Livsun

24,151 görüntüleme • 5 ay önce

100 billion dollars made without predicting a single price direction not a direction call, a macro bet, or a news trade in sight they asked a completely different question than every retail trader alive retail: "will this go up?" quant: "what state is the market in, and where does that state historically lead?" same data. completely different game markets aren't random - they cycle through states: trending, ranging, compressing, reversing each state has a fixed historical probability of transitioning to the next that's the whole edge. not a signal, not an indicator - just conditional probability you don't need to know what fed will do or what earnings will print you need to know which state you're in and what that state leads to 68% of the time framework has been public since 1906 Bookmark it before it gone data is free, implementation is 200 lines of python, paper costs nothing they built 100 billion off math sitting in statistics textbooks your whole life you were staring at candles while they ran probability tables
0:52

100 billion dollars made without predicting a single price direction not a direction call, a macro bet, or a news trade in sight they asked a completely different question than every retail trader alive retail: "will this go up?" quant: "what state is the market in, and where does that state historically lead?" same data. completely different game markets aren't random - they cycle through states: trending, ranging, compressing, reversing each state has a fixed historical probability of transitioning to the next that's the whole edge. not a signal, not an indicator - just conditional probability you don't need to know what fed will do or what earnings will print you need to know which state you're in and what that state leads to 68% of the time framework has been public since 1906 Bookmark it before it gone data is free, implementation is 200 lines of python, paper costs nothing they built 100 billion off math sitting in statistics textbooks your whole life you were staring at candles while they ran probability tables

Livsun

13,335 görüntüleme • 4 ay önce

THE MOST EXPENSIVE ENGINEERING TEAMS ON EARTH JUST PUT THEIR FINANCIAL TOOLS ON GITHUB FOR FREE. Jane Street. Goldman Sachs. JP Morgan. BlackRock. Hudson River Trading. Two Sigma. D.E. Shaw. Seven firms. Seven repos. Billions in engineering talent open sourced. Save this before you scroll past it. 1. Jane Street — magic-trace 5,300 stars. Process tracer powered by Intel PT. When your profiler is blind this sees every CPU instruction. 2. Goldman Sachs — gs-quant Derivative pricing the GS traders use at their actual desks. MIT licensed. Free. 3. JP Morgan — perspective What JPMorgan traders use to watch markets in real time. A $24,000 per year terminal. Available to anyone with a GitHub account. 4. BlackRock — lcso Rust optimizer for portfolio problems. Where scipy gives up this works. Built for problems that break standard optimization libraries. 5. Hudson River Trading — corral Structured concurrency for C++20. The foundation of HFT infrastructure at one of the largest US trading firms. 6. Two Sigma — flint Time-series joins on Apache Spark with temporal tolerance. Built for billions of ticks. The data infrastructure layer behind systematic trading at scale. 7. D.E. Shaw — pyflyby Auto-import for IPython and Jupyter. D.E. Shaw also funded the development of IPython itself. The firm that built the tool is now giving you the enhancement for free. Here is what this list actually represents. These seven firms collectively employ thousands of engineers earning $300,000 to $1,000,000 per year. The tools they built to solve their hardest problems are the same tools you now have access to for free. The information asymmetry that used to separate a quant at Goldman from a developer at home just narrowed significantly. The infrastructure is free. The edge now belongs to whoever knows how to use it. Bookmark this before you pay for another financial data tool. Follow CyrilXBT for every elite engineering resource the moment it surfaces.
1:47

THE MOST EXPENSIVE ENGINEERING TEAMS ON EARTH JUST PUT THEIR FINANCIAL TOOLS ON GITHUB FOR FREE. Jane Street. Goldman Sachs. JP Morgan. BlackRock. Hudson River Trading. Two Sigma. D.E. Shaw. Seven firms. Seven repos. Billions in engineering talent open sourced. Save this before you scroll past it. 1. Jane Street — magic-trace 5,300 stars. Process tracer powered by Intel PT. When your profiler is blind this sees every CPU instruction. 2. Goldman Sachs — gs-quant Derivative pricing the GS traders use at their actual desks. MIT licensed. Free. 3. JP Morgan — perspective What JPMorgan traders use to watch markets in real time. A $24,000 per year terminal. Available to anyone with a GitHub account. 4. BlackRock — lcso Rust optimizer for portfolio problems. Where scipy gives up this works. Built for problems that break standard optimization libraries. 5. Hudson River Trading — corral Structured concurrency for C++20. The foundation of HFT infrastructure at one of the largest US trading firms. 6. Two Sigma — flint Time-series joins on Apache Spark with temporal tolerance. Built for billions of ticks. The data infrastructure layer behind systematic trading at scale. 7. D.E. Shaw — pyflyby Auto-import for IPython and Jupyter. D.E. Shaw also funded the development of IPython itself. The firm that built the tool is now giving you the enhancement for free. Here is what this list actually represents. These seven firms collectively employ thousands of engineers earning $300,000 to $1,000,000 per year. The tools they built to solve their hardest problems are the same tools you now have access to for free. The information asymmetry that used to separate a quant at Goldman from a developer at home just narrowed significantly. The infrastructure is free. The edge now belongs to whoever knows how to use it. Bookmark this before you pay for another financial data tool. Follow CyrilXBT for every elite engineering resource the moment it surfaces.

CyrilXBT

43,007 görüntüleme • 5 ay önce

a citadel options trader told me the one concept they test first in every quant interview and it's been sitting on a free website for years not a hedge fund textbook, not a $3,000 prep program. a free course syllabus - options greeks, volatility, quizzes - publicly available, almost nobody applying has ever opened it concept is expected value across a probability distribution retail looks at a chart and asks which direction. quant looks at expected payout across every possible outcome and asks if that number beats the cost of the trade - completely different question options pricing is just EV made rigorous fair value of any position = sum of (each outcome's probability x its payoff), discounted back. that formula is in every intro stats course and every free options curriculum these firms post publicly citadel's first round isn't a stock pitch or a DCF it's a market-making problem: "set me a bid and ask on a coin flip" if you can solve that fast and size it correctly, you can price any derivative on earth prep is documented in 6 categories: probability, greeks, volatility, mental math, coding, microstructure firms don't want you pattern-matching to old trades. they want raw EV instinct - and that's in free courses that have been online for years entry-level quant traders at these firms start at $300k. senior traders clear $650k+ most people never make it past round 1. not because they weren't smart - because nobody told them what the test was actually measuring Bookmark this they kept you reading charts while they were drilling expected value at 2am
0:56

a citadel options trader told me the one concept they test first in every quant interview and it's been sitting on a free website for years not a hedge fund textbook, not a $3,000 prep program. a free course syllabus - options greeks, volatility, quizzes - publicly available, almost nobody applying has ever opened it concept is expected value across a probability distribution retail looks at a chart and asks which direction. quant looks at expected payout across every possible outcome and asks if that number beats the cost of the trade - completely different question options pricing is just EV made rigorous fair value of any position = sum of (each outcome's probability x its payoff), discounted back. that formula is in every intro stats course and every free options curriculum these firms post publicly citadel's first round isn't a stock pitch or a DCF it's a market-making problem: "set me a bid and ask on a coin flip" if you can solve that fast and size it correctly, you can price any derivative on earth prep is documented in 6 categories: probability, greeks, volatility, mental math, coding, microstructure firms don't want you pattern-matching to old trades. they want raw EV instinct - and that's in free courses that have been online for years entry-level quant traders at these firms start at $300k. senior traders clear $650k+ most people never make it past round 1. not because they weren't smart - because nobody told them what the test was actually measuring Bookmark this they kept you reading charts while they were drilling expected value at 2am

Livsun

25,988 görüntüleme • 5 ay önce

🚨these github repos is SERIOUSLY DANGEROUS Wall Street pays its engineers $500K+ a year to write code like this, 6 of the top firms put theirs on GitHub 6 repos built inside Goldman Sachs, JPMorgan, Jane Street, Jump, Morgan Stanley and Man Group, with 38.6k stars combined First, get how it works: 1. Goldman Sachs' quants built gs-quant for pricing, risk and backtesting. JPMorgan's python-training is the exact course it uses to train its own traders and analysts. 2. Jane Street's magic-trace traces every function call at ~40ns. Morgan Stanley's Xpedite profiles low-latency trading code, "where latency directly translates to profitability". Jump's Firedancer runs Solana on a design taken from low-latency trading. 3. Man Group's ArcticDB stores and queries market data across billions of rows. Together it's a full desk: data, research, risk and speed. Here's how you can run it for free yourself: 1. Open Claude Code and start with JPMorgan's trader course, python-training. Have Claude run the notebooks with you 2. Install Goldman Sachs' library: pip install gs-quant 3. Add the skill that teaches Claude Code to use it: python -m gs_quant.skills install --global 4. Have Claude pull 5 years of daily prices for GS, JPM and MS with free yfinance data 5. Store it in Man Group's database: pip install arcticdb 6. Run Goldman's risk math on it: volatility, max drawdown, z-score, beta to SPY. Then backtest one rule with fees. Claude writes the code and runs it 7. On Linux, trace your backtest with Jane Street's magic-trace and see where every microsecond goes 8. Moving to C++? Measure every critical path the Morgan Stanley way with Xpedite 9. Have Claude walk you through how Jump built Firedancer's pipeline (net, verify, dedup, pack, bank), then apply the same ideas to your bot i won't say anything more:
1:00

🚨these github repos is SERIOUSLY DANGEROUS Wall Street pays its engineers $500K+ a year to write code like this, 6 of the top firms put theirs on GitHub 6 repos built inside Goldman Sachs, JPMorgan, Jane Street, Jump, Morgan Stanley and Man Group, with 38.6k stars combined First, get how it works: 1. Goldman Sachs' quants built gs-quant for pricing, risk and backtesting. JPMorgan's python-training is the exact course it uses to train its own traders and analysts. 2. Jane Street's magic-trace traces every function call at ~40ns. Morgan Stanley's Xpedite profiles low-latency trading code, "where latency directly translates to profitability". Jump's Firedancer runs Solana on a design taken from low-latency trading. 3. Man Group's ArcticDB stores and queries market data across billions of rows. Together it's a full desk: data, research, risk and speed. Here's how you can run it for free yourself: 1. Open Claude Code and start with JPMorgan's trader course, python-training. Have Claude run the notebooks with you 2. Install Goldman Sachs' library: pip install gs-quant 3. Add the skill that teaches Claude Code to use it: python -m gs_quant.skills install --global 4. Have Claude pull 5 years of daily prices for GS, JPM and MS with free yfinance data 5. Store it in Man Group's database: pip install arcticdb 6. Run Goldman's risk math on it: volatility, max drawdown, z-score, beta to SPY. Then backtest one rule with fees. Claude writes the code and runs it 7. On Linux, trace your backtest with Jane Street's magic-trace and see where every microsecond goes 8. Moving to C++? Measure every critical path the Morgan Stanley way with Xpedite 9. Have Claude walk you through how Jump built Firedancer's pipeline (net, verify, dedup, pack, bank), then apply the same ideas to your bot i won't say anything more:

Сarm1ne

61,124 görüntüleme • 12 gün önce

hedge funds extract their edge before the candle closes not from price, from what's moving behind it signal is called order flow toxicity every large informed trader disguises their order as retail noise - 40-80 small orders, timed to look random to a human, invisible. to an LSTM trained on cancel-to-fill ratios - readable in 400 milliseconds two sigma doesn't ask "will price go up?" - they ask "is someone with information buying right now, before the market reprices?" detection model is 3 layers: > input: bid-ask spread velocity, cancel-to-fill ratio, trade size entropy > hidden: LSTM over 500ms rolling window > output: toxicity score 0-1 above 0.67: informed flow detected. enter with it below 0.67: noise. skip or fade 74% directional accuracy. 11-minute avg hold. runs on any level 2 feed data is free - CBOE publishes it. architecture is in every ML textbook since 2012 they've been running this since you were drawing trendlines Bookmark it and add in your strategy bottleneck was never the data - it was the question you were asking
0:48

hedge funds extract their edge before the candle closes not from price, from what's moving behind it signal is called order flow toxicity every large informed trader disguises their order as retail noise - 40-80 small orders, timed to look random to a human, invisible. to an LSTM trained on cancel-to-fill ratios - readable in 400 milliseconds two sigma doesn't ask "will price go up?" - they ask "is someone with information buying right now, before the market reprices?" detection model is 3 layers: > input: bid-ask spread velocity, cancel-to-fill ratio, trade size entropy > hidden: LSTM over 500ms rolling window > output: toxicity score 0-1 above 0.67: informed flow detected. enter with it below 0.67: noise. skip or fade 74% directional accuracy. 11-minute avg hold. runs on any level 2 feed data is free - CBOE publishes it. architecture is in every ML textbook since 2012 they've been running this since you were drawing trendlines Bookmark it and add in your strategy bottleneck was never the data - it was the question you were asking

Livsun

11,425 görüntüleme • 5 ay önce

Why VC Firms Must Have Their Own Agents: "VC firms should have their own autonomous agents. PE firms will have agents analyzing markets and opportunities. If I were starting from scratch, I would build incredible data systems to evaluate where AI companies should go and which markets are right. The deciding factor will be how well founders and investors use autonomous agents in their jobs." jerry murdock Love to hear your thoughts on this Nikhil Basu Trivedi Matt Ocko Aydin Senkut Alexis Ohanian 🗽 Jared Sleeper?
0:47

Why VC Firms Must Have Their Own Agents: "VC firms should have their own autonomous agents. PE firms will have agents analyzing markets and opportunities. If I were starting from scratch, I would build incredible data systems to evaluate where AI companies should go and which markets are right. The deciding factor will be how well founders and investors use autonomous agents in their jobs." jerry murdock Love to hear your thoughts on this Nikhil Basu Trivedi Matt Ocko Aydin Senkut Alexis Ohanian 🗽 Jared Sleeper?

Harry Stebbings

22,412 görüntüleme • 8 ay önce

the entire math roadmap for quant trading fits on a single page and this video breaks it down in 60 seconds probability. linear algebra. calculus. statistics four subjects. every quant strategy ever built sits on top of these four probability comes first because everything else depends on it conditional probability, Bayes' theorem, expected value this is the actual language trading models are written in linear algebra is how you stop thinking about one stock and start thinking about 5,000 at once factor models, PCA, portfolio optimization Bridgewater, AQR, Two Sigma all run on matrix math calculus is derivatives pricing. Black-Scholes is a PDE if you trade options without understanding the equation underneath them, you're reading the answer key without knowing the question statistics is the filter regression, hypothesis testing, distribution fitting this is how Renaissance decides if a signal is real or if a backtest got lucky here's what nobody tells retail: every indicator you've ever used is a dumbed-down version of one of these four subjects RSI is momentum statistics. Bollinger Bands are standard deviation. MACD is signal processing they took the math, removed the part that makes it useful, and sold you the wrapper > this roadmap: free, 60 seconds, in this video > same four subjects tested at every quant interview since the 1990s > time to learn properly: 6-8 months with free resources > what they unlock: the actual language Wall Street runs on the information was never gated it was just never packaged for the people who needed it most full breakdown in the video below
1:04

the entire math roadmap for quant trading fits on a single page and this video breaks it down in 60 seconds probability. linear algebra. calculus. statistics four subjects. every quant strategy ever built sits on top of these four probability comes first because everything else depends on it conditional probability, Bayes' theorem, expected value this is the actual language trading models are written in linear algebra is how you stop thinking about one stock and start thinking about 5,000 at once factor models, PCA, portfolio optimization Bridgewater, AQR, Two Sigma all run on matrix math calculus is derivatives pricing. Black-Scholes is a PDE if you trade options without understanding the equation underneath them, you're reading the answer key without knowing the question statistics is the filter regression, hypothesis testing, distribution fitting this is how Renaissance decides if a signal is real or if a backtest got lucky here's what nobody tells retail: every indicator you've ever used is a dumbed-down version of one of these four subjects RSI is momentum statistics. Bollinger Bands are standard deviation. MACD is signal processing they took the math, removed the part that makes it useful, and sold you the wrapper > this roadmap: free, 60 seconds, in this video > same four subjects tested at every quant interview since the 1990s > time to learn properly: 6-8 months with free resources > what they unlock: the actual language Wall Street runs on the information was never gated it was just never packaged for the people who needed it most full breakdown in the video below

delost

35,060 görüntüleme • 4 ay önce

Jane Street pays $750K / year to traders who can apply probability theory in quantitative trading. This 1-hour MIT lecture on "Probability Theory" explains how to master this concept for trading - for free. Bookmark it & give it 1-hour on it today. It will change the way you analyze markets. Then read the article below ↓
1:16:45

Jane Street pays $750K / year to traders who can apply probability theory in quantitative trading. This 1-hour MIT lecture on "Probability Theory" explains how to master this concept for trading - for free. Bookmark it & give it 1-hour on it today. It will change the way you analyze markets. Then read the article below ↓

Movez

20,205 görüntüleme • 4 ay önce

every time you hit buy, the market maker on the other side already solved for two numbers not on the asset - on you specifically the first: their internal fair value. not the midpoint you see on screen their number is adjusted for the last 30 seconds of order flow, their current inventory imbalance and which volatility regime the model thinks the market is in it's different from the displayed price, and they never show it to you the second: your adverse selection probability. a live estimate of whether you're informed or just emotional if the model flags you as a potential informed trader, the spread widens before your fill - automatically, invisibly, before you notice anything changed that bad fill you blamed on "low liquidity"? sometimes it's a market maker's algorithm deciding you look too dangerous to fill at the quoted price this is Glosten-Milgrom - published 1985, sits in every market microstructure textbook on earth, runs inside every serious market making operation that's ever quoted you a price Bookmark this feature before it lost when you click buy, you're answering a question you didn't know was being asked retail spends all its time thinking about where price is going the desk filling your order is spending that same time thinking about you
0:30

every time you hit buy, the market maker on the other side already solved for two numbers not on the asset - on you specifically the first: their internal fair value. not the midpoint you see on screen their number is adjusted for the last 30 seconds of order flow, their current inventory imbalance and which volatility regime the model thinks the market is in it's different from the displayed price, and they never show it to you the second: your adverse selection probability. a live estimate of whether you're informed or just emotional if the model flags you as a potential informed trader, the spread widens before your fill - automatically, invisibly, before you notice anything changed that bad fill you blamed on "low liquidity"? sometimes it's a market maker's algorithm deciding you look too dangerous to fill at the quoted price this is Glosten-Milgrom - published 1985, sits in every market microstructure textbook on earth, runs inside every serious market making operation that's ever quoted you a price Bookmark this feature before it lost when you click buy, you're answering a question you didn't know was being asked retail spends all its time thinking about where price is going the desk filling your order is spending that same time thinking about you

Livsun

18,906 görüntüleme • 4 ay önce

susquehanna traded 2.7 billion options contracts in 2023. more than the entire US options industry traded in all of 2007. 3,200 people. 17 offices. estimated $7.2 billion in revenue. trading their own capital. they just put out a video of their mathematicians explaining how they actually think about markets. what they said: "finding patterns is easy. understanding that those patterns reflect something fundamental about data is challenging." on machine learning: "these black boxes find patterns, they make predictions. but these predictions are based in the data that they see, and not in the assumptions that may be true about the world." on AI: "AI itself is a superpower. the challenge is to develop new methodology to insert theory and assumption into the system." but the part that got me was the first twenty seconds. "markets are constantly generating information. people react to that information, they react to each other, and they adjust their behavior." "as a result, the signal is going to be weak. the noise is going to be enormous, and whatever patterns you're going to find might not persist for very long." one of the largest options market makers on earth saying, in their own video, that signal is often weak, the noise is enormous, and the patterns decay. make you understand why constant adapting and research is needed. trading is never a one and done.
1:27

susquehanna traded 2.7 billion options contracts in 2023. more than the entire US options industry traded in all of 2007. 3,200 people. 17 offices. estimated $7.2 billion in revenue. trading their own capital. they just put out a video of their mathematicians explaining how they actually think about markets. what they said: "finding patterns is easy. understanding that those patterns reflect something fundamental about data is challenging." on machine learning: "these black boxes find patterns, they make predictions. but these predictions are based in the data that they see, and not in the assumptions that may be true about the world." on AI: "AI itself is a superpower. the challenge is to develop new methodology to insert theory and assumption into the system." but the part that got me was the first twenty seconds. "markets are constantly generating information. people react to that information, they react to each other, and they adjust their behavior." "as a result, the signal is going to be weak. the noise is going to be enormous, and whatever patterns you're going to find might not persist for very long." one of the largest options market makers on earth saying, in their own video, that signal is often weak, the noise is enormous, and the patterns decay. make you understand why constant adapting and research is needed. trading is never a one and done.

Goshawk Trades

14,483 görüntüleme • 3 ay önce

you're not bad at trading. the game is just designed so you never find out what the actual game is retail gets candlestick patterns and RSI. the kids at citadel get probability theory, transition matrices, and kelly criterion from day one they literally filter recruits who don't already know this. they don't teach it to you, they find people who found it themselves the math that runs renaissance technologies has been sitting in free statistics textbooks since the 1950s markov chains, autocorrelation, state detection - none of it is secret, none of it is locked behind a paywall the information gap isn't about money or access it's about the fact that the finance industry makes more selling you indicators and courses than it ever loses to you trading "retail investor" is industry language for "someone we extract from" meanwhile a 24 year old with a stats degree and 6 months of python experience is clearing $400k at a prop desk same markets. completely different question being asked you ask: is it going up or down they ask: what state is the market in and what does history say happens next one of those questions has an answer. the other one is just noise dressed up as analysis the textbook is free. the math is learnable. the only thing that wasn't free was someone telling you it existed
0:30

you're not bad at trading. the game is just designed so you never find out what the actual game is retail gets candlestick patterns and RSI. the kids at citadel get probability theory, transition matrices, and kelly criterion from day one they literally filter recruits who don't already know this. they don't teach it to you, they find people who found it themselves the math that runs renaissance technologies has been sitting in free statistics textbooks since the 1950s markov chains, autocorrelation, state detection - none of it is secret, none of it is locked behind a paywall the information gap isn't about money or access it's about the fact that the finance industry makes more selling you indicators and courses than it ever loses to you trading "retail investor" is industry language for "someone we extract from" meanwhile a 24 year old with a stats degree and 6 months of python experience is clearing $400k at a prop desk same markets. completely different question being asked you ask: is it going up or down they ask: what state is the market in and what does history say happens next one of those questions has an answer. the other one is just noise dressed up as analysis the textbook is free. the math is learnable. the only thing that wasn't free was someone telling you it existed

Livsun

22,621 görüntüleme • 1 ay önce

2 days since Jane Street released most ILLEGAL Quant trading repo ever... Jane Street made $39.6 billion last year to pay $1M/year to their Quants. Now that $1M brain runs inside your Claude Code for free here's full guide on how to setup it: 1. freqtrade (56K stars) - the hands: a free bot that trades crypto 24/7 and retrains its model on fresh data while it runs 2. Drex (launched this week) - the gatekeeper: a finance decision model that answers "take or skip?" before every entry, with a probability 3. agent skills (by Jane Street) - the $1M brain: Jane Street's own AI skills repo. its tutor grills you on their trading interview guide until your bot's rules hold up now the setup: 1 → paste the 3 links into Claude Code (below) and say: "install freqtrade in dry-run mode, turn on FreqAI retraining every 24 hours, add Jane Street's agent skills to my skills" 2 → grab your Drex key from Nace AI at and say: "connect Drex and check my key" 3 → test: "backtest 3 strategies from freqtrade-strategies on BTC and ETH for the last 6 months with fees. keep the one still in profit and rebuild it as a FreqAI strategy" if none survive, that's your answer 4 → gate: "before every entry ask Drex: CHOPPY, TRENDING or PANIC? take or skip? enter only above 0.7" now the bot can say no 5 → the $1M interview: "use the tutor from agent_skills to grill me on Jane Street's Probability & Markets guide and my bot: edge after fees, who's on the other side, position size. no live trading until I reach level 4 on every topic" this is the step everyone skips 6 → paper: "run it 2 weeks in dry-run and send every trade and Drex call to my Telegram". real money only if the backtest and paper both win after fees. start small the result: a bot that trades 24/7, retrains every night, asks Drex before every entry and won't touch a dollar until you pass Jane Street's interview you're welcome:
0:39

2 days since Jane Street released most ILLEGAL Quant trading repo ever... Jane Street made $39.6 billion last year to pay $1M/year to their Quants. Now that $1M brain runs inside your Claude Code for free here's full guide on how to setup it: 1. freqtrade (56K stars) - the hands: a free bot that trades crypto 24/7 and retrains its model on fresh data while it runs 2. Drex (launched this week) - the gatekeeper: a finance decision model that answers "take or skip?" before every entry, with a probability 3. agent skills (by Jane Street) - the $1M brain: Jane Street's own AI skills repo. its tutor grills you on their trading interview guide until your bot's rules hold up now the setup: 1 → paste the 3 links into Claude Code (below) and say: "install freqtrade in dry-run mode, turn on FreqAI retraining every 24 hours, add Jane Street's agent skills to my skills" 2 → grab your Drex key from Nace AI at and say: "connect Drex and check my key" 3 → test: "backtest 3 strategies from freqtrade-strategies on BTC and ETH for the last 6 months with fees. keep the one still in profit and rebuild it as a FreqAI strategy" if none survive, that's your answer 4 → gate: "before every entry ask Drex: CHOPPY, TRENDING or PANIC? take or skip? enter only above 0.7" now the bot can say no 5 → the $1M interview: "use the tutor from agent_skills to grill me on Jane Street's Probability & Markets guide and my bot: edge after fees, who's on the other side, position size. no live trading until I reach level 4 on every topic" this is the step everyone skips 6 → paper: "run it 2 weeks in dry-run and send every trade and Drex call to my Telegram". real money only if the backtest and paper both win after fees. start small the result: a bot that trades 24/7, retrains every night, asks Drex before every entry and won't touch a dollar until you pass Jane Street's interview you're welcome:

bodila

38,658 görüntüleme • 2 gün önce

every day before market open, the CBOE publishes exactly where market makers are forced to buy and sell not a theory, not a pattern - a number that actually moves price it's called dealer gamma exposure - GEX - and it creates real price magnets quants have traded against for years here's why it works when you buy a call option, a market maker sells it to you and has to hedge that hedge shifts as price moves - they buy when it rises, sell when it falls negative GEX zones are where they sell into every rally, cap every breakout, crush every momentum move strikes with highest open interest aren't random - they're gravitational markets don't break through those levels cleanly because a billion-dollar dealer is actively hedging against it quants call them gamma walls data is free, published every morning on the CBOE site - no API, no Bloomberg terminal retail calls those same levels "resistance" and chalks it up to vibes bookmark this before it lost it's not vibes. it's math someone is paid $400k/year to understand while you do it by eyeball the whole time you thought technical analysis was detecting patterns you were just watching the shadow of dealer hedging and calling it insight
1:10

every day before market open, the CBOE publishes exactly where market makers are forced to buy and sell not a theory, not a pattern - a number that actually moves price it's called dealer gamma exposure - GEX - and it creates real price magnets quants have traded against for years here's why it works when you buy a call option, a market maker sells it to you and has to hedge that hedge shifts as price moves - they buy when it rises, sell when it falls negative GEX zones are where they sell into every rally, cap every breakout, crush every momentum move strikes with highest open interest aren't random - they're gravitational markets don't break through those levels cleanly because a billion-dollar dealer is actively hedging against it quants call them gamma walls data is free, published every morning on the CBOE site - no API, no Bloomberg terminal retail calls those same levels "resistance" and chalks it up to vibes bookmark this before it lost it's not vibes. it's math someone is paid $400k/year to understand while you do it by eyeball the whole time you thought technical analysis was detecting patterns you were just watching the shadow of dealer hedging and calling it insight

Livsun

71,286 görüntüleme • 4 ay önce

MIT has been teaching this publicly for 50 years. retail traders still draw trendlines andrei markov published the framework in 1906. hedge funds found it in the 70s built billion-dollar operations around it, never told retail it existed markets aren't random - they're state machines trending, ranging, reversing. each state has a fixed historical probability of shifting to the next you build a transition matrix from real price data: trending -> stays trending: 68% trending -> flips to range: 21% trending -> reverses: 11% now you're not predicting direction. you're entering on 68% historical completion that's not a signal, that's conditional probability compounding every single trade math is free, data is free, implementation is ~200 lines of python what's not free: knowing this framework exists while everyone around you draws support levels and hopes renaissance has run this since 1988. paper was always public edge was never secret. just never pointed out
52:24

MIT has been teaching this publicly for 50 years. retail traders still draw trendlines andrei markov published the framework in 1906. hedge funds found it in the 70s built billion-dollar operations around it, never told retail it existed markets aren't random - they're state machines trending, ranging, reversing. each state has a fixed historical probability of shifting to the next you build a transition matrix from real price data: trending -> stays trending: 68% trending -> flips to range: 21% trending -> reverses: 11% now you're not predicting direction. you're entering on 68% historical completion that's not a signal, that's conditional probability compounding every single trade math is free, data is free, implementation is ~200 lines of python what's not free: knowing this framework exists while everyone around you draws support levels and hopes renaissance has run this since 1988. paper was always public edge was never secret. just never pointed out

Livsun

13,963 görüntüleme • 5 ay önce

"Every company is going to build their own frontier AI unique to their secret sauce. That's exactly what the top law firms do. We're starting to talk with a law firm and their client and ask: how could we build you a joint model?" Thanks Gabe Pereyra and Harvey for joining us at our Private Frontier all-hands to share how Harvey is defining the future of Specific Intelligence for legal agents.
2:00

"Every company is going to build their own frontier AI unique to their secret sauce. That's exactly what the top law firms do. We're starting to talk with a law firm and their client and ask: how could we build you a joint model?" Thanks Gabe Pereyra and Harvey for joining us at our Private Frontier all-hands to share how Harvey is defining the future of Specific Intelligence for legal agents.

Applied Compute

19,710 görüntüleme • 5 ay önce

Jane Street engineer: "at Jane Street, 85% of our quants are running dozens or hundreds of AI agents This helps us develop our own LLM, which we use for trading and market research to make $20.5B/year" In 15-min he explained how Jane Street uses AI for their fund - and how they trained their own model that generates profit for them Bookmark & watch - no matter what, this is the best AI + hedge fund lecture out there
16:15

Jane Street engineer: "at Jane Street, 85% of our quants are running dozens or hundreds of AI agents This helps us develop our own LLM, which we use for trading and market research to make $20.5B/year" In 15-min he explained how Jane Street uses AI for their fund - and how they trained their own model that generates profit for them Bookmark & watch - no matter what, this is the best AI + hedge fund lecture out there

bodila

164,873 görüntüleme • 1 ay önce