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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...

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Livsun

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25,988 просмотров • 2 месяцев назад •via X (Twitter)

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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 просмотров • 2 месяцев назад

Joe Blitzstein, Harvard statistics professor: "Jane Street pays $600K for people who can do one thing in their head: compute the expected value of a bet before they take it. I teach it free at Harvard. skip it and you're the casino's favorite customer." this free lecture holds the entire "edge" every casino and every fund quietly runs on, and the man teaching it put his whole Harvard probability course online for free, where millions have watched it. at the board it's simple. expected value is every outcome times its probability, added up into one number that tells you what a bet is worth on average. the roulette wheel sits at minus 5.26 percent, so every dollar you push across the table is worth about 94 cents back to you and the rest to the house. it never changes and it never has to. that's the whole "edge", minus the marketing. expectation goes back to Pascal, Fermat and Huygens in the 1650s. it has been free for almost 400 years. same point as my casino piece above: the house wins because its number is positive and yours is negative, trade after trade. the calculation is free and every fund runs it on every position. what they cannot sell you is the honesty to compute your real edge instead of the one you wish you had. that is the number that actually decides who goes broke, and no course can make you face it.
50:22

Joe Blitzstein, Harvard statistics professor: "Jane Street pays $600K for people who can do one thing in their head: compute the expected value of a bet before they take it. I teach it free at Harvard. skip it and you're the casino's favorite customer." this free lecture holds the entire "edge" every casino and every fund quietly runs on, and the man teaching it put his whole Harvard probability course online for free, where millions have watched it. at the board it's simple. expected value is every outcome times its probability, added up into one number that tells you what a bet is worth on average. the roulette wheel sits at minus 5.26 percent, so every dollar you push across the table is worth about 94 cents back to you and the rest to the house. it never changes and it never has to. that's the whole "edge", minus the marketing. expectation goes back to Pascal, Fermat and Huygens in the 1650s. it has been free for almost 400 years. same point as my casino piece above: the house wins because its number is positive and yours is negative, trade after trade. the calculation is free and every fund runs it on every position. what they cannot sell you is the honesty to compute your real edge instead of the one you wish you had. that is the number that actually decides who goes broke, and no course can make you face it.

Voltex

386,423 просмотров • 1 месяц назад

nobody making $380k at a quant desk has any reason to write you a roadmap that's not cynicism - that's incentive structure wall street banks have PR teams pushing the "break into finance" pipeline. quant firms don't recruit that way, don't advertise, don't need to the filter has nothing to do with finance knowledge - it's 6 categories: probability, statistics, mental math, game theory, coding, market microstructure zero of those show up in a finance degree interview structure has been roughly the same for over a decade problems are documented, prep path is public, nobody stitched it into a guide for you most applicants wash out in round 1 and assume they weren't the right kind of smart a state school CS grad who grinds the right 6 categories for 6 months outearns a target-school banking analyst within 2 years $140k on the banking path vs $430k on the quant path same city, completely different question set Bookmark this information to close that gap has existed for years you just had to know you were supposed to be looking
0:57

nobody making $380k at a quant desk has any reason to write you a roadmap that's not cynicism - that's incentive structure wall street banks have PR teams pushing the "break into finance" pipeline. quant firms don't recruit that way, don't advertise, don't need to the filter has nothing to do with finance knowledge - it's 6 categories: probability, statistics, mental math, game theory, coding, market microstructure zero of those show up in a finance degree interview structure has been roughly the same for over a decade problems are documented, prep path is public, nobody stitched it into a guide for you most applicants wash out in round 1 and assume they weren't the right kind of smart a state school CS grad who grinds the right 6 categories for 6 months outearns a target-school banking analyst within 2 years $140k on the banking path vs $430k on the quant path same city, completely different question set Bookmark this information to close that gap has existed for years you just had to know you were supposed to be looking

Livsun

20,826 просмотров • 2 месяцев назад

renaissance technologies hasn't had a losing year since 1988 not because they're smarter or have better data they're asking a completely different question than everyone else while retail asks "will it go up?" quant desks ask something else entirely which state is the market in right now, and with what probability does it transition to the next tool for that: markov chains markets cycle through states - trending, ranging, volatile, reversing each transition has a historical probability you can calculate trending -> stays trending: 48% trending -> ranging: 34% trending -> reversing: 18% you don't pick direction. you calculate expected value across every possible transition enter only when the math is positive that's it. that's the entire edge the framework is 119 years old - andrei markov published it in 1906 it's in every statistics textbook on earth Bookmark it or lose last chance to be quant nobody in your trading group ever mentioned it
2:02

renaissance technologies hasn't had a losing year since 1988 not because they're smarter or have better data they're asking a completely different question than everyone else while retail asks "will it go up?" quant desks ask something else entirely which state is the market in right now, and with what probability does it transition to the next tool for that: markov chains markets cycle through states - trending, ranging, volatile, reversing each transition has a historical probability you can calculate trending -> stays trending: 48% trending -> ranging: 34% trending -> reversing: 18% you don't pick direction. you calculate expected value across every possible transition enter only when the math is positive that's it. that's the entire edge the framework is 119 years old - andrei markov published it in 1906 it's in every statistics textbook on earth Bookmark it or lose last chance to be quant nobody in your trading group ever mentioned it

Livsun

51,500 просмотров • 3 месяцев назад

jane street paid a 23yo $250k last year didn't come from a target school didn't have a finance degree knew exactly what the interview was going to test while his classmates were grinding campus recruiting, he was drilling probability problems at 2am that's what nobody tells you about quant finance - the interview has nothing to do with traditional finance citadel wants to know how fast your brain handles expected value under pressure. they don't care about your DCF model 6 categories: >probability, >mental math, >statistics, >logic puzzles, >coding, >market microstructure zero to interview-ready in under a year if you actually know what to study most people apply without knowing any of this. screened out in round 1, assume they weren't smart enough the firms aren't looking for the smartest Bookmark it or will lose all potential they're looking for the most prepared
1:28

jane street paid a 23yo $250k last year didn't come from a target school didn't have a finance degree knew exactly what the interview was going to test while his classmates were grinding campus recruiting, he was drilling probability problems at 2am that's what nobody tells you about quant finance - the interview has nothing to do with traditional finance citadel wants to know how fast your brain handles expected value under pressure. they don't care about your DCF model 6 categories: >probability, >mental math, >statistics, >logic puzzles, >coding, >market microstructure zero to interview-ready in under a year if you actually know what to study most people apply without knowing any of this. screened out in round 1, assume they weren't smart enough the firms aren't looking for the smartest Bookmark it or will lose all potential they're looking for the most prepared

Livsun

39,087 просмотров • 3 месяцев назад

a quant at a prop firm showed me a 5x5 grid on a napkin said: > this is our entire edge. we don't predict price. we predict which box the market is in and where that box historically leads i didn't understand it for weeks. then it clicked never looked at a chart the same way since grid is called a Markov Chain transition matrix. the math is from 1906, it's in every probability textbook on earth and hedge funds use it because it asks a completely different question than retail traders ever ask retail: will this go up or down quant: what state is this market in, and where does this state typically go every market lives in one of maybe 5-6 states at any given moment tight range, volatility compression, trending with momentum, post-spike reversal, pre-breakout coil not random labels - clusters you identify from actual data using volatility, volume, and momentum readings stacked together once you have the states, you build the matrix: P(state 2 -> state 4) = 73% P(state 4 -> state 1) = 61% P(state 1 -> state 3) = 68% each cell is a historical probability. now when the market is in state 2, you're not guessing you're betting on 73% historical completion. you size it with Kelly. you take the trade when the math says to, not when it feels right i built this on BTC using 2 years of 4-hour data. identified 5 states one i labeled "volatility compression below 20-day mean for 6+ consecutive candles" transitioned to a directional move above 1.8 ATR in 71% of cases average reward/risk on those trades: 5.4 that's not prediction. that's reading a probability table the market keeps filling in for you every single day the part that should bother you: the data to build this is free. the framework is in any quant textbook python to implement it is maybe 200 lines what Renaissance Technologies has that you don't isn't secret data or proprietary signals it's this framework applied to higher-resolution data with more sophisticated state definitions you're not missing information you're asking the wrong question every single time you open a chart
0:26

a quant at a prop firm showed me a 5x5 grid on a napkin said: > this is our entire edge. we don't predict price. we predict which box the market is in and where that box historically leads i didn't understand it for weeks. then it clicked never looked at a chart the same way since grid is called a Markov Chain transition matrix. the math is from 1906, it's in every probability textbook on earth and hedge funds use it because it asks a completely different question than retail traders ever ask retail: will this go up or down quant: what state is this market in, and where does this state typically go every market lives in one of maybe 5-6 states at any given moment tight range, volatility compression, trending with momentum, post-spike reversal, pre-breakout coil not random labels - clusters you identify from actual data using volatility, volume, and momentum readings stacked together once you have the states, you build the matrix: P(state 2 -> state 4) = 73% P(state 4 -> state 1) = 61% P(state 1 -> state 3) = 68% each cell is a historical probability. now when the market is in state 2, you're not guessing you're betting on 73% historical completion. you size it with Kelly. you take the trade when the math says to, not when it feels right i built this on BTC using 2 years of 4-hour data. identified 5 states one i labeled "volatility compression below 20-day mean for 6+ consecutive candles" transitioned to a directional move above 1.8 ATR in 71% of cases average reward/risk on those trades: 5.4 that's not prediction. that's reading a probability table the market keeps filling in for you every single day the part that should bother you: the data to build this is free. the framework is in any quant textbook python to implement it is maybe 200 lines what Renaissance Technologies has that you don't isn't secret data or proprietary signals it's this framework applied to higher-resolution data with more sophisticated state definitions you're not missing information you're asking the wrong question every single time you open a chart

Livsun

188,928 просмотров • 3 месяцев назад

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 просмотров • 2 месяцев назад

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

13,315 просмотров • 2 месяцев назад

83% of options expire worthless wall street has known this since 1973, it's literally in the original black-scholes derivation they just never told retail which side of that number to stand on implied volatility - what options cost - chronically overstates what volatility actually delivers. not sometimes. structurally. for 40 years straight the gap has a name: volatility risk premium IV runs at 22%. actual realized vol delivers 14%. you pocketed 8 vol points without predicting a single direction, a single earnings move, a single fed decision quant vol desks have been harvesting this gap the whole time: > pull 30-day realized vol from price history (free on any data terminal) > compare to current IV on the options chain (visible on any broker platform) > enter when the spread sits 1.5 standard deviations above its own historical mean > kelly-size based on the 83% historical close rate the spread always closes. vol reverts. that's not a prediction - it's what 40 years of options data shows without a single exception insurance companies don't predict which house burns. they write enough policies that 83% probability compounds into something inevitable every single quarter quant desks run the exact same logic on options markets retail keeps buying calls hoping to be directionally right once. the firm on the other side doesn't care which way price goes the data to build this yourself has been public since 1973. the math is 4 lines of python. every derivatives textbook covers it in chapter 2 Bookmark this they've been on the other side of your trades the whole time
0:30

83% of options expire worthless wall street has known this since 1973, it's literally in the original black-scholes derivation they just never told retail which side of that number to stand on implied volatility - what options cost - chronically overstates what volatility actually delivers. not sometimes. structurally. for 40 years straight the gap has a name: volatility risk premium IV runs at 22%. actual realized vol delivers 14%. you pocketed 8 vol points without predicting a single direction, a single earnings move, a single fed decision quant vol desks have been harvesting this gap the whole time: > pull 30-day realized vol from price history (free on any data terminal) > compare to current IV on the options chain (visible on any broker platform) > enter when the spread sits 1.5 standard deviations above its own historical mean > kelly-size based on the 83% historical close rate the spread always closes. vol reverts. that's not a prediction - it's what 40 years of options data shows without a single exception insurance companies don't predict which house burns. they write enough policies that 83% probability compounds into something inevitable every single quarter quant desks run the exact same logic on options markets retail keeps buying calls hoping to be directionally right once. the firm on the other side doesn't care which way price goes the data to build this yourself has been public since 1973. the math is 4 lines of python. every derivatives textbook covers it in chapter 2 Bookmark this they've been on the other side of your trades the whole time

Livsun

79,393 просмотров • 6 дней назад

hedge funds spent $2M to build what you could replicate in a weekend they just knew it was possible first - that was their only advantage a researcher at d.e. shaw handed me a paper in 2023 two years of quant labor, internal "discovery" - original source was an MIT lecture from 1998, public, free, searchable mechanism is cointegration two assets drift independently, but their spread behaves like a rubber band - stationary, mean-reverting, measurable retail asks: will this go up quant asks: is this spread 2.3 standard deviations outside its 5-year mean - and what's historical reversion rate from here completely different question. second one has an answer run it on GLD/GDX, SPY/QQQ, XLE/XOM - data goes back 20 years, free on yahoo finance spread reverts within 8 trading days 71% of the time when it crosses 2 sigma size with Kelly on that 71%, take the trade when math clears, exit at mean paper they spent two years on is engle & granger, 1987 - won a nobel prize Bookmark before it deleted it's in every econometrics textbook on earth, chapter 4 they didn't hide the math from you they just never told you it existed
1:02

hedge funds spent $2M to build what you could replicate in a weekend they just knew it was possible first - that was their only advantage a researcher at d.e. shaw handed me a paper in 2023 two years of quant labor, internal "discovery" - original source was an MIT lecture from 1998, public, free, searchable mechanism is cointegration two assets drift independently, but their spread behaves like a rubber band - stationary, mean-reverting, measurable retail asks: will this go up quant asks: is this spread 2.3 standard deviations outside its 5-year mean - and what's historical reversion rate from here completely different question. second one has an answer run it on GLD/GDX, SPY/QQQ, XLE/XOM - data goes back 20 years, free on yahoo finance spread reverts within 8 trading days 71% of the time when it crosses 2 sigma size with Kelly on that 71%, take the trade when math clears, exit at mean paper they spent two years on is engle & granger, 1987 - won a nobel prize Bookmark before it deleted it's in every econometrics textbook on earth, chapter 4 they didn't hide the math from you they just never told you it existed

Livsun

26,816 просмотров • 2 месяцев назад

Alan Oppenheim, MIT professor: "Jump Trading pays quants $650K to pull one real signal out of market noise. I wrote the textbook on that exact math and taught it for free." this free MIT lecture is the math the whole "quant edge" is built on, and almost nobody selling you a course will name it. the man teaching it wrote the signal-processing textbook every engineer on earth learned from, and he put the lectures online for nothing. here is the idea. a price chart is a signal buried in noise. most of what you see is random static, and hidden inside is a faint, repeating pattern with real mathematics for pulling it out. that is not a metaphor. jim simons was a codebreaker who did exactly this to enemy transmissions, then pointed the same math at the market and made a hundred billion dollars. none of it is new. oppenheim taught this in 1987, the math is older, and every lecture is free. same story i told in the article above. the edge was never a secret formula. it was knowing a market is a noisy signal, and having the tools to read it. here is what the gurus skip. the math to separate signal from noise is free and everyone has it. the edge is cleaner data than the next desk, and the discipline to trust a faint signal across thousands of trades instead of chasing the static. the filter is free. the patience to use it is not.
29:44

Alan Oppenheim, MIT professor: "Jump Trading pays quants $650K to pull one real signal out of market noise. I wrote the textbook on that exact math and taught it for free." this free MIT lecture is the math the whole "quant edge" is built on, and almost nobody selling you a course will name it. the man teaching it wrote the signal-processing textbook every engineer on earth learned from, and he put the lectures online for nothing. here is the idea. a price chart is a signal buried in noise. most of what you see is random static, and hidden inside is a faint, repeating pattern with real mathematics for pulling it out. that is not a metaphor. jim simons was a codebreaker who did exactly this to enemy transmissions, then pointed the same math at the market and made a hundred billion dollars. none of it is new. oppenheim taught this in 1987, the math is older, and every lecture is free. same story i told in the article above. the edge was never a secret formula. it was knowing a market is a noisy signal, and having the tools to read it. here is what the gurus skip. the math to separate signal from noise is free and everyone has it. the edge is cleaner data than the next desk, and the discipline to trust a faint signal across thousands of trades instead of chasing the static. the filter is free. the patience to use it is not.

Rossst.03

281,905 просмотров • 1 месяц назад

Gilbert Strang, a quiet MIT professor, picked up a piece of chalk and explained the one skill standing between you and every account you have ever blown. MIT put the entire course online. it has been free for over twenty years. almost nobody finishes it. His point: the market never pays in certainties. it pays in probabilities, and a human gut can barely hold one straight. the math that holds ten thousand at once, weighs them, and collapses them into a single call is linear algebra. it is the branch Citadel and OpenAI both pay $400K to hire for. he is not a trading guru. his 18.06 lectures are the most watched math class on the planet. Skip to where he turns a wall of numbers into one vector. a single object carrying a thousand odds at once, in notation a beginner can follow. No slides. no jargon. one piece of chalk. a quant I know rewatches lecture one every year before he sizes a single position. You do not have a strategy problem. you have a math problem. the fix has been free for two decades, and you are still guessing.
39:31

Gilbert Strang, a quiet MIT professor, picked up a piece of chalk and explained the one skill standing between you and every account you have ever blown. MIT put the entire course online. it has been free for over twenty years. almost nobody finishes it. His point: the market never pays in certainties. it pays in probabilities, and a human gut can barely hold one straight. the math that holds ten thousand at once, weighs them, and collapses them into a single call is linear algebra. it is the branch Citadel and OpenAI both pay $400K to hire for. he is not a trading guru. his 18.06 lectures are the most watched math class on the planet. Skip to where he turns a wall of numbers into one vector. a single object carrying a thousand odds at once, in notation a beginner can follow. No slides. no jargon. one piece of chalk. a quant I know rewatches lecture one every year before he sizes a single position. You do not have a strategy problem. you have a math problem. the fix has been free for two decades, and you are still guessing.

Loran

47,412 просмотров • 25 дней назад

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

1,284,610 просмотров • 3 месяцев назад

In 2008 a Stanford professor stood at a podium and proved, for free, that information and money grow by the exact same equation. it was filmed. it has been free for 18 years. 7,000 people watched it. Thomas Cover died four years later and took the fame with him. his life's work: Shannon's information theory and Kelly's betting formula are one thing. how much to bet is just how much you know. every quant sizing a trade in 2026 is standing on Cover, whether they know the name or not. he wrote the book every information theorist trains on. the lecture that ties it all to money sits on YouTube with fewer views than a cat clip. skip to where he turns a horse race into a stock market. same math. a bit of information, a fraction of your bankroll, one growth rate. no fund. no signal. one professor and one equation. a quant I know says this hour is the missing chapter behind every book on position sizing he owns. you are 18 years late. it is still free.
57:26

In 2008 a Stanford professor stood at a podium and proved, for free, that information and money grow by the exact same equation. it was filmed. it has been free for 18 years. 7,000 people watched it. Thomas Cover died four years later and took the fame with him. his life's work: Shannon's information theory and Kelly's betting formula are one thing. how much to bet is just how much you know. every quant sizing a trade in 2026 is standing on Cover, whether they know the name or not. he wrote the book every information theorist trains on. the lecture that ties it all to money sits on YouTube with fewer views than a cat clip. skip to where he turns a horse race into a stock market. same math. a bit of information, a fraction of your bankroll, one growth rate. no fund. no signal. one professor and one equation. a quant I know says this hour is the missing chapter behind every book on position sizing he owns. you are 18 years late. it is still free.

Loran

33,640 просмотров • 20 дней назад

a free government report tells you exactly what hedge funds, asset managers and leveraged traders are holding across every futures market on earth it's published every friday at 3:30pm funds have been running systematic strategies on it since 1993, and you've had exact same access the entire time it's the Commitment of Traders report - CFTC website, no login, free signal that runs on it: when leveraged funds flip net short after 4+ consecutive weeks net long, go long monday's open historical win rate on ES futures: 67%, tested back to 2006 not because report predicts direction. because positioning unwind creates mechanical buying pressure - 40,000 contracts covering short, someone absorbs that demand, and price follows average hold is 4 days, reward/risk 2.1 to 1 - Kelly says 15% position size 52 opportunities a year - drift is one-directional congress mandated this transparency in 1936. original intent: so retail could see what big money was doing Dm "Quant" for full quant course with all knowledge bookmark this before it buried retail never looked. quant desks built whole books around it data is free, backtest goes to 1993, implementation is a weekend of python they kept you staring at RSI and candle patterns while running probability tables on data congress was legally required to hand you for free
1:07

a free government report tells you exactly what hedge funds, asset managers and leveraged traders are holding across every futures market on earth it's published every friday at 3:30pm funds have been running systematic strategies on it since 1993, and you've had exact same access the entire time it's the Commitment of Traders report - CFTC website, no login, free signal that runs on it: when leveraged funds flip net short after 4+ consecutive weeks net long, go long monday's open historical win rate on ES futures: 67%, tested back to 2006 not because report predicts direction. because positioning unwind creates mechanical buying pressure - 40,000 contracts covering short, someone absorbs that demand, and price follows average hold is 4 days, reward/risk 2.1 to 1 - Kelly says 15% position size 52 opportunities a year - drift is one-directional congress mandated this transparency in 1936. original intent: so retail could see what big money was doing Dm "Quant" for full quant course with all knowledge bookmark this before it buried retail never looked. quant desks built whole books around it data is free, backtest goes to 1993, implementation is a weekend of python they kept you staring at RSI and candle patterns while running probability tables on data congress was legally required to hand you for free

Livsun

18,895 просмотров • 1 месяц назад

Citadel hired this Quant analyst at $450,000-$700,000/year after this interview In 23-min, you’ll learn what a Tier-1 fund interview is like Here are some of the questions they ask: 1. What is the probability of randomly selecting three acceptable pairs of shoes (differing by at most one size) - from six shoes (two each of sizes 4, 5, and 6) without replacement? 2. What is the probability that none of the three baskets will remain empty if K ball - which are given when the first heads appears on the Kth flip of a fair coin - are distributed among them completely at random? 3. What does the resulting value of 9/10 represent? 4. Could you briefly explain your solution methodology? Quant intern answered all these questions in the video __ Bookmark & watch - you'll learn how to answer questions in a hedge fund interview
23:22

Citadel hired this Quant analyst at $450,000-$700,000/year after this interview In 23-min, you’ll learn what a Tier-1 fund interview is like Here are some of the questions they ask: 1. What is the probability of randomly selecting three acceptable pairs of shoes (differing by at most one size) - from six shoes (two each of sizes 4, 5, and 6) without replacement? 2. What is the probability that none of the three baskets will remain empty if K ball - which are given when the first heads appears on the Kth flip of a fair coin - are distributed among them completely at random? 3. What does the resulting value of 9/10 represent? 4. Could you briefly explain your solution methodology? Quant intern answered all these questions in the video __ Bookmark & watch - you'll learn how to answer questions in a hedge fund interview

bodila

459,209 просмотров • 3 месяцев назад

a quant who started as a technical trader explained what changed everything for him he stopped trying to predict where price goes he started measuring the probability of each market state and betting only when the math was asymmetric that shift took him from drawing trendlines to writing models at 22 the realization was simple but brutal: technical analysis gives you a narrative quantitative analysis gives you a number one feels right the other is testable, repeatable, and either works or doesn't across 10,000 trades the setup on his desk tells the whole story no TradingView. no candlestick charts. just code, data, and a terminal he didn't go to MIT he didn't intern at Goldman he learned Python, statistics, and probability theory on his own and built something that actually worked > the math: free in any stats textbook > the data: free on Yahoo Finance, FRED, exchange APIs > the code: Python, 200 lines, running on a laptop > the barrier: not intelligence. just knowing this path exists most people spend years staring at indicators from the 1970s wondering why they can't find consistency the answer was never a better indicator it was a completely different framework one that treats trading as a math problem, not a prediction game full breakdown in the video below
4:36

a quant who started as a technical trader explained what changed everything for him he stopped trying to predict where price goes he started measuring the probability of each market state and betting only when the math was asymmetric that shift took him from drawing trendlines to writing models at 22 the realization was simple but brutal: technical analysis gives you a narrative quantitative analysis gives you a number one feels right the other is testable, repeatable, and either works or doesn't across 10,000 trades the setup on his desk tells the whole story no TradingView. no candlestick charts. just code, data, and a terminal he didn't go to MIT he didn't intern at Goldman he learned Python, statistics, and probability theory on his own and built something that actually worked > the math: free in any stats textbook > the data: free on Yahoo Finance, FRED, exchange APIs > the code: Python, 200 lines, running on a laptop > the barrier: not intelligence. just knowing this path exists most people spend years staring at indicators from the 1970s wondering why they can't find consistency the answer was never a better indicator it was a completely different framework one that treats trading as a math problem, not a prediction game full breakdown in the video below

delost

112,271 просмотров • 2 месяцев назад

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 просмотров • 1 месяц назад

quant at D.E. Shaw told me his best strategy loses 6 out of every 10 trades showed me the ledger. didn't believe him until i saw the equity curve retail optimizes for win rate. quants optimize for expected value - completely different targets that gap is why hedge funds extract money from retail every single day math is literally 4th grade: EV = (win% x avg win) - (loss% x avg loss) a 39% win rate at 3:1 reward/risk beats an 80% win rate at 1:2, every single time retail either doesn't know this, or ignores it because losing 61% of trades feels wrong so they cut winners early to "lock in gains" and hold losers because "it'll come back" both moves destroy expected value. both are emotional, not mathematical citadel calls systematic position sizing its edge. retail calls it going with your gut Kelly Criterion fixes it - bet (EV / odds) of your capital per trade, sized from your own historical data ran 2 years of backtests on a 40% win-rate strategy: > Kelly-sized positions: +340% > same signals, gut-sized: +71% same market, same entries and exits only variable: math decided how much to bet Bookmark this before you open your next position they kept you counting candles while they ran expected value tables Reply below what you think
0:51

quant at D.E. Shaw told me his best strategy loses 6 out of every 10 trades showed me the ledger. didn't believe him until i saw the equity curve retail optimizes for win rate. quants optimize for expected value - completely different targets that gap is why hedge funds extract money from retail every single day math is literally 4th grade: EV = (win% x avg win) - (loss% x avg loss) a 39% win rate at 3:1 reward/risk beats an 80% win rate at 1:2, every single time retail either doesn't know this, or ignores it because losing 61% of trades feels wrong so they cut winners early to "lock in gains" and hold losers because "it'll come back" both moves destroy expected value. both are emotional, not mathematical citadel calls systematic position sizing its edge. retail calls it going with your gut Kelly Criterion fixes it - bet (EV / odds) of your capital per trade, sized from your own historical data ran 2 years of backtests on a 40% win-rate strategy: > Kelly-sized positions: +340% > same signals, gut-sized: +71% same market, same entries and exits only variable: math decided how much to bet Bookmark this before you open your next position they kept you counting candles while they ran expected value tables Reply below what you think

Livsun

14,773 просмотров • 1 месяц назад

a hotel front desk clerk in nashville figured out why markets move exactly when they do not direction, not news - the actual mechanism of why a move happens at all he works overnight shift, 11pm to 7am. lobby goes quiet after midnight, nothing but a monitor and a wifi connection question that started it: why does volatility cluster he'd read it in passing - options dealers cause price moves they didn't intend spent 6 hours across two nights searching, wrote everything into a google doc called "options thing" here's what he found when you buy a call option from a dealer, dealer has a new problem. they sold you the right to buy shares at a certain price if stock moves up, your option gains value and dealer owes you money. to protect themselves they have to buy shares immediately - no discretion, no delay amount they have to buy at every price level is published every second for free - it's open interest on the options chain. every brokerage shows it he built a spreadsheet every morning at 9:29am, one minute before open, he pulled SPY's options chain and calculated where dealers were most exposed marked strikes with heaviest call open interest. watched what happened in first 30 minutes of trading day 12 he stopped breathing for a second price moved to the strike with heaviest dealer exposure 73% of the time in the first 45 minutes not because of a chart pattern, not because of any signal because 400 dealers ran the same hedge calculation at open, and all of them had to buy the same shares at the same time he started calling it gravity price pulls toward certain strikes when dealer positioning is heavy enough - not prediction, mechanics math has a name: gamma exposure, or GEX SpotGamma built a whole company surfacing it. Squeeze Metrics published an academic paper on mechanics in 2018 python implementation is around 400 lines, nothing but the options chain you already have he built it in google colab over 3 weekends, free, working only on nights the lobby was empty tracked it against 60 days of live SPY data on negative GEX days - dealers short gamma, forced to amplify moves - average daily range expanded 2.8x on positive GEX days, 63% of sessions closed within half a percent of open this is not a signal. it's a regime classifier negative GEX: something moves big today, whichever direction gets started. buy straddles, size up, let dealers carry it positive GEX: nothing moves today. dealers kill every attempt before it gets 2 points sell premium, collect theta, sleep at month 4 he went live. $4,200 account, pure options, no directional bet six months later: $4,200 became $19,800 he still works overnight shift. told me about it in the lobby at 3am when i asked what he was typing google doc still says "options thing" - he never renamed it i asked why he never shared this. he looked at the lobby doors and said "who would believe a hotel clerk" data is free, formula is public, wall street has run this since 2017 they assumed retail would never think to read options flow as a mechanical map of where price has to go they were right about retail. they weren't right about him bookmark this and go build it market tells you exactly where it's going. you just have to stop reading the wrong layer Write your thought below
0:39

a hotel front desk clerk in nashville figured out why markets move exactly when they do not direction, not news - the actual mechanism of why a move happens at all he works overnight shift, 11pm to 7am. lobby goes quiet after midnight, nothing but a monitor and a wifi connection question that started it: why does volatility cluster he'd read it in passing - options dealers cause price moves they didn't intend spent 6 hours across two nights searching, wrote everything into a google doc called "options thing" here's what he found when you buy a call option from a dealer, dealer has a new problem. they sold you the right to buy shares at a certain price if stock moves up, your option gains value and dealer owes you money. to protect themselves they have to buy shares immediately - no discretion, no delay amount they have to buy at every price level is published every second for free - it's open interest on the options chain. every brokerage shows it he built a spreadsheet every morning at 9:29am, one minute before open, he pulled SPY's options chain and calculated where dealers were most exposed marked strikes with heaviest call open interest. watched what happened in first 30 minutes of trading day 12 he stopped breathing for a second price moved to the strike with heaviest dealer exposure 73% of the time in the first 45 minutes not because of a chart pattern, not because of any signal because 400 dealers ran the same hedge calculation at open, and all of them had to buy the same shares at the same time he started calling it gravity price pulls toward certain strikes when dealer positioning is heavy enough - not prediction, mechanics math has a name: gamma exposure, or GEX SpotGamma built a whole company surfacing it. Squeeze Metrics published an academic paper on mechanics in 2018 python implementation is around 400 lines, nothing but the options chain you already have he built it in google colab over 3 weekends, free, working only on nights the lobby was empty tracked it against 60 days of live SPY data on negative GEX days - dealers short gamma, forced to amplify moves - average daily range expanded 2.8x on positive GEX days, 63% of sessions closed within half a percent of open this is not a signal. it's a regime classifier negative GEX: something moves big today, whichever direction gets started. buy straddles, size up, let dealers carry it positive GEX: nothing moves today. dealers kill every attempt before it gets 2 points sell premium, collect theta, sleep at month 4 he went live. $4,200 account, pure options, no directional bet six months later: $4,200 became $19,800 he still works overnight shift. told me about it in the lobby at 3am when i asked what he was typing google doc still says "options thing" - he never renamed it i asked why he never shared this. he looked at the lobby doors and said "who would believe a hotel clerk" data is free, formula is public, wall street has run this since 2017 they assumed retail would never think to read options flow as a mechanical map of where price has to go they were right about retail. they weren't right about him bookmark this and go build it market tells you exactly where it's going. you just have to stop reading the wrong layer Write your thought below

Livsun

200,378 просмотров • 1 месяц назад