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quant desks run Apollo navigation math on their trading terminals and nobody told you Rudolf Kalman published the equation in 1960 NASA used it 9 years later to guide three astronauts through 240,000 miles of space same filter. different problem for NASA: strip sensor noise in real time to...

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Livsun

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17,724 views • 1 month ago •via X (Twitter)

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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 views • 2 months ago

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 views • 27 days ago

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 views • 2 months ago

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 views • 1 month ago

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 views • 2 months ago

a prop desk cleared $480M last year - four people, one strategy they run cointegration find two assets that historically move together, bet when they drift apart, exit when they close back math behind it has been in academic journals since 1987 - no secret edge, no proprietary data in practice: SPY and IVV track the same index. when their spread hits 1.8 standard deviations from its 18-month mean, it reverts 79% of the time average hold: 4 hours, no direction call - just a statistical relationship that hasn't broken in 14 years of backtesting data is free. cointegration is in every econometrics textbook, has been since the 80s reason retail never runs this: payoff per trade is 0.3%. too small to feel like an edge quant desks run it 3,000 times a year Bookmark this knowledge you're not missing information you're solving the wrong problem they kept you hunting for a 10% winner. 3,000 small ones is the actual game
0:56

a prop desk cleared $480M last year - four people, one strategy they run cointegration find two assets that historically move together, bet when they drift apart, exit when they close back math behind it has been in academic journals since 1987 - no secret edge, no proprietary data in practice: SPY and IVV track the same index. when their spread hits 1.8 standard deviations from its 18-month mean, it reverts 79% of the time average hold: 4 hours, no direction call - just a statistical relationship that hasn't broken in 14 years of backtesting data is free. cointegration is in every econometrics textbook, has been since the 80s reason retail never runs this: payoff per trade is 0.3%. too small to feel like an edge quant desks run it 3,000 times a year Bookmark this knowledge you're not missing information you're solving the wrong problem they kept you hunting for a 10% winner. 3,000 small ones is the actual game

Livsun

14,040 views • 1 month ago

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

81,293 views • 13 days ago

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 views • 2 months ago

Citadel quant quit and walked out with one thing fund never lets leave the building: 120-year-old math that sees a market crash before the chart does. Every big desk runs it. None of them explain it. He turned it into an HMM bot running BTC 5min markets and made $570,000. It switches strategy the second the regime flips and cuts everything the moment panic spikes. His wallet: Full breakdown below. Here's what they actually see. You look at a Bitcoin chart and see a price. A quant sees three states: bull, bear, sideways. The market's always in one. The only question that pays is which one comes next. That's the math. It's called a Markov chain, and it runs on one rule: where price goes next depends only on where it is now. You count how often each state flips to another and drop it into a grid. That grid is a map of the market. Now the part that feels illegal. Want the odds of a crash twelve steps out? You multiply that grid by itself twelve times. One line of math, and you've got the probability of every future state. Then it acts: run the trend when calm, flip to mean reversion in chop, cut everything when panic odds spike. It doesn't predict the future. It gives you the odds of every version of it, then sizes the bet to match. That's the edge. Renaissance ran 66% a year for 30 years on math like this. You can build the basic version in a weekend. Bookmark & read this before it gets buried.
0:42

Citadel quant quit and walked out with one thing fund never lets leave the building: 120-year-old math that sees a market crash before the chart does. Every big desk runs it. None of them explain it. He turned it into an HMM bot running BTC 5min markets and made $570,000. It switches strategy the second the regime flips and cuts everything the moment panic spikes. His wallet: Full breakdown below. Here's what they actually see. You look at a Bitcoin chart and see a price. A quant sees three states: bull, bear, sideways. The market's always in one. The only question that pays is which one comes next. That's the math. It's called a Markov chain, and it runs on one rule: where price goes next depends only on where it is now. You count how often each state flips to another and drop it into a grid. That grid is a map of the market. Now the part that feels illegal. Want the odds of a crash twelve steps out? You multiply that grid by itself twelve times. One line of math, and you've got the probability of every future state. Then it acts: run the trend when calm, flip to mean reversion in chop, cut everything when panic odds spike. It doesn't predict the future. It gives you the odds of every version of it, then sizes the bet to match. That's the edge. Renaissance ran 66% a year for 30 years on math like this. You can build the basic version in a weekend. Bookmark & read this before it gets buried.

cvxv666

93,596 views • 1 month ago

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 views • 2 months ago

In 2013 a Wall Street risk boss walked up to an MIT chalkboard and gave away the exact math desks charge $400,000 a year to run. MIT filmed it. it has been free for 13 years. 7.9 million people pressed play and almost none of them changed a thing. his point: the Wall Street edge was never a secret. it is a first lecture. options, risk, how a payout is actually built. every prediction-market trader hunting an edge in 2026 is chasing what Jake Xia put on a board in hour one of a free class. he ran risk for a living, not a channel. the video outdrew most blockbusters and still moved almost no one, because watching is not doing. skip to where he prices risk. he takes the thing quant threads sell for $500 and builds it from zero, slow enough for a beginner to follow. no paywall. no NDA. one board and a marker. a quant I know sends this link to every junior who whines the math is too hard. you are 13 years late. it is still free.
1:00:07

In 2013 a Wall Street risk boss walked up to an MIT chalkboard and gave away the exact math desks charge $400,000 a year to run. MIT filmed it. it has been free for 13 years. 7.9 million people pressed play and almost none of them changed a thing. his point: the Wall Street edge was never a secret. it is a first lecture. options, risk, how a payout is actually built. every prediction-market trader hunting an edge in 2026 is chasing what Jake Xia put on a board in hour one of a free class. he ran risk for a living, not a channel. the video outdrew most blockbusters and still moved almost no one, because watching is not doing. skip to where he prices risk. he takes the thing quant threads sell for $500 and builds it from zero, slow enough for a beginner to follow. no paywall. no NDA. one board and a marker. a quant I know sends this link to every junior who whines the math is too hard. you are 13 years late. it is still free.

Loran

151,219 views • 28 days ago

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 views • 3 months ago

quant at aqr sat next to me at a conference and said something that rewired how i think about markets "we don't analyze 500 stocks. we run 5 factors that explain all 500 at once" i asked him to draw it out take your entire stock universe - 500 names, 3 years of daily returns - and run PCA the 500 collapse into 5 factor 1: broad market momentum - explains 58% of all price movement by itself factor 2: sector rotation - where institutional money is actually flowing factor 3: rate sensitivity - duration risk bleeding across the equity curve factor 4: momentum tilt - which names are accelerating vs mean-reverting factor 5: credit spread stress - signal hiding inside equity vol if you know these 5 numbers, you know what 500 stocks are about to do retail watches 500 charts and sees noise. quant desks watch 5 eigenvalues and see structure math is called Principal Component Analysis, in every intro stats course on earth data to run it is free. python implementation is ~120 lines renaissance, two sigma, aqr - running this since before most retail traders could name a stock reason nobody told you isn't that it's proprietary it's that telling you costs them their edge Bookmark this market was never 500 moving parts it was always 5 - hiding inside 500
0:49

quant at aqr sat next to me at a conference and said something that rewired how i think about markets "we don't analyze 500 stocks. we run 5 factors that explain all 500 at once" i asked him to draw it out take your entire stock universe - 500 names, 3 years of daily returns - and run PCA the 500 collapse into 5 factor 1: broad market momentum - explains 58% of all price movement by itself factor 2: sector rotation - where institutional money is actually flowing factor 3: rate sensitivity - duration risk bleeding across the equity curve factor 4: momentum tilt - which names are accelerating vs mean-reverting factor 5: credit spread stress - signal hiding inside equity vol if you know these 5 numbers, you know what 500 stocks are about to do retail watches 500 charts and sees noise. quant desks watch 5 eigenvalues and see structure math is called Principal Component Analysis, in every intro stats course on earth data to run it is free. python implementation is ~120 lines renaissance, two sigma, aqr - running this since before most retail traders could name a stock reason nobody told you isn't that it's proprietary it's that telling you costs them their edge Bookmark this market was never 500 moving parts it was always 5 - hiding inside 500

Livsun

22,073 views • 1 month ago

Quant funds don’t go live after a lucky streak. They go live after their strategy survives thousands of simulations. You? You probably went live after 2–3 green trades in a row. That’s not a discipline issue. That’s an access issue. Before a single dollar touches the market, real quant desks run: → Multi-asset robustness checks across timeframes → Monte Carlo simulations across thousands of market conditions → Stress tests to expose exactly where the strategy breaks This used to require $25K/year terminals and entire research teams. Now it doesn’t. Horizon put that entire workflow into a single chat interface. Describe your strategy in plain English. It builds it. Tests it. Breaks it. Shows you where it survives — and where it doesn’t. Before you ever go live. The edge was never intelligence. It was access. Now you have it. 👇 Join early. Get on the waitlist. Stay ahead. Save this.
0:23

Quant funds don’t go live after a lucky streak. They go live after their strategy survives thousands of simulations. You? You probably went live after 2–3 green trades in a row. That’s not a discipline issue. That’s an access issue. Before a single dollar touches the market, real quant desks run: → Multi-asset robustness checks across timeframes → Monte Carlo simulations across thousands of market conditions → Stress tests to expose exactly where the strategy breaks This used to require $25K/year terminals and entire research teams. Now it doesn’t. Horizon put that entire workflow into a single chat interface. Describe your strategy in plain English. It builds it. Tests it. Breaks it. Shows you where it survives — and where it doesn’t. Before you ever go live. The edge was never intelligence. It was access. Now you have it. 👇 Join early. Get on the waitlist. Stay ahead. Save this.

Marry Evan

56,965 views • 4 months ago

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 views • 1 month ago

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 views • 2 months ago

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

34,703 views • 2 months ago

robert engle won the nobel prize in economics for proving something hedge funds already knew they never bothered to mention it to retail price direction is mostly noise. but volatility? predictable. mathematically, provably, across every liquid market ever studied it clusters - that's not a pattern someone found, it's a structural law. high vol today predicts high vol tomorrow with 70%+ historical accuracy the model is called GARCH. published in every econometrics textbook on earth, chapter 4, about 60 lines of python to run quant desks at citadel and D.E. Shaw don't ask "will it go up?" - they ask "will the next move be large or small?" because sizing correctly inside a vol regime is worth more than being right on direction a trader right 48% of the time who sizes with vol awareness beats someone right 62% of the time sizing blindly - every time, over any long enough sample run it on 10 years of SPY data: > low vol state -> 74% chance next session stays low vol > vol spike -> 81% chance next session is also elevated now you're not predicting markets. you're reading a state machine the market keeps filling in for you every single session with real data the predictable part of markets was never price direction it was the distribution of price. the size of the moves. which regime you're currently inside math is free, data is free, implementation is free you were just told to stare at candlesticks instead Bookmark this before the feed buries it
0:30

robert engle won the nobel prize in economics for proving something hedge funds already knew they never bothered to mention it to retail price direction is mostly noise. but volatility? predictable. mathematically, provably, across every liquid market ever studied it clusters - that's not a pattern someone found, it's a structural law. high vol today predicts high vol tomorrow with 70%+ historical accuracy the model is called GARCH. published in every econometrics textbook on earth, chapter 4, about 60 lines of python to run quant desks at citadel and D.E. Shaw don't ask "will it go up?" - they ask "will the next move be large or small?" because sizing correctly inside a vol regime is worth more than being right on direction a trader right 48% of the time who sizes with vol awareness beats someone right 62% of the time sizing blindly - every time, over any long enough sample run it on 10 years of SPY data: > low vol state -> 74% chance next session stays low vol > vol spike -> 81% chance next session is also elevated now you're not predicting markets. you're reading a state machine the market keeps filling in for you every single session with real data the predictable part of markets was never price direction it was the distribution of price. the size of the moves. which regime you're currently inside math is free, data is free, implementation is free you were just told to stare at candlesticks instead Bookmark this before the feed buries it

Hrundel75 🐷

803,763 views • 6 days ago

an ex-Goldman banker said the key to understanding markets is reading 3 WSJ articles a day a quant at Renaissance would tell you that's the exact wrong input here's why the Wall Street Journal publishes after the move by the time you read "tech stocks fall on rate fears" the move already happened, the positioning already shifted, and every algorithm already repriced news explains what happened. it doesn't predict what's next quant desks don't read news to form trade ideas they measure whether news has a statistically significant effect on price after controlling for everything else the answer, across decades of academic research: individual news articles explain less than 0.5% of daily price variance the other 99.5% is flow, positioning, regime, volatility structure, and cross-asset correlation none of which shows up in a WSJ headline this is the Goldman mindset vs the quant mindset Goldman: read narratives, form opinions, act on conviction Renaissance: measure everything, strip out noise, trade only what survives statistical testing one produced traders who sound smart at dinner parties the other produced $100 billion in profit over 30 years > WSJ subscription: $468/year > SEC EDGAR data: free > FRED economic data: free > exchange-level order flow: free > academic papers on news impact studies: free on ArXiv since the 2000s the banker tells you to read the news the quant tells you to measure whether the news matters those are not the same instruction full breakdown in the video below
0:37

an ex-Goldman banker said the key to understanding markets is reading 3 WSJ articles a day a quant at Renaissance would tell you that's the exact wrong input here's why the Wall Street Journal publishes after the move by the time you read "tech stocks fall on rate fears" the move already happened, the positioning already shifted, and every algorithm already repriced news explains what happened. it doesn't predict what's next quant desks don't read news to form trade ideas they measure whether news has a statistically significant effect on price after controlling for everything else the answer, across decades of academic research: individual news articles explain less than 0.5% of daily price variance the other 99.5% is flow, positioning, regime, volatility structure, and cross-asset correlation none of which shows up in a WSJ headline this is the Goldman mindset vs the quant mindset Goldman: read narratives, form opinions, act on conviction Renaissance: measure everything, strip out noise, trade only what survives statistical testing one produced traders who sound smart at dinner parties the other produced $100 billion in profit over 30 years > WSJ subscription: $468/year > SEC EDGAR data: free > FRED economic data: free > exchange-level order flow: free > academic papers on news impact studies: free on ArXiv since the 2000s the banker tells you to read the news the quant tells you to measure whether the news matters those are not the same instruction full breakdown in the video below

delost

15,317 views • 2 months ago

A lot of people still don’t understand how ruthless systematic trading can be. This is exactly why I run a quant fund alongside everything else I do. The quant fund is built to measure momentum on the 4 hour timeframe. Nothing more. Nothing less. No opinions. No emotions. No discretion. It tracks 15 core stocks and follows the same rule set every single time. When the 4 hour signal line turns green and increases, it buys. When it rolls over and turns red, it exits. Period. This fund does not care what I think. It does not care what I want. It does not care about headlines, FOMC, earnings or narratives. It follows the signal to a T. That’s what makes it so powerful. This is completely separate from my main fund, which is longer term positioning based on monthly and weekly signal lines across sectors. It’s also completely separate from my options trades, which are built around a 2–3 month outlook and larger macro structure. Three different approaches. Three different time horizons. Three different objectives. The quant fund exists for one reason. To systematically capture short term momentum while removing every human weakness that causes traders to fail. The part most people can’t handle? They want to intervene. They want to override. They want to “feel” their way through it. Quant trading doesn’t allow that and that’s why it works. If you want discretion, opinions and emotion… this isn’t for you. If you want structure, probability and discipline… this is how accounts actually compound. That’s everything you need to know about my quant fund and the 4 hour signal line that drives it. THANK YOU FOR YOUR ATTENTION TO THIS MATTER! — TJ #SP500 #SPY #QQQ #TSLA #PLTR #NVDA #AAPL #Bitcoin #Crypto #StockMarket
16:50

A lot of people still don’t understand how ruthless systematic trading can be. This is exactly why I run a quant fund alongside everything else I do. The quant fund is built to measure momentum on the 4 hour timeframe. Nothing more. Nothing less. No opinions. No emotions. No discretion. It tracks 15 core stocks and follows the same rule set every single time. When the 4 hour signal line turns green and increases, it buys. When it rolls over and turns red, it exits. Period. This fund does not care what I think. It does not care what I want. It does not care about headlines, FOMC, earnings or narratives. It follows the signal to a T. That’s what makes it so powerful. This is completely separate from my main fund, which is longer term positioning based on monthly and weekly signal lines across sectors. It’s also completely separate from my options trades, which are built around a 2–3 month outlook and larger macro structure. Three different approaches. Three different time horizons. Three different objectives. The quant fund exists for one reason. To systematically capture short term momentum while removing every human weakness that causes traders to fail. The part most people can’t handle? They want to intervene. They want to override. They want to “feel” their way through it. Quant trading doesn’t allow that and that’s why it works. If you want discretion, opinions and emotion… this isn’t for you. If you want structure, probability and discipline… this is how accounts actually compound. That’s everything you need to know about my quant fund and the 4 hour signal line that drives it. THANK YOU FOR YOUR ATTENTION TO THIS MATTER! — TJ #SP500 #SPY #QQQ #TSLA #PLTR #NVDA #AAPL #Bitcoin #Crypto #StockMarket

TraderJonesy

43,814 views • 6 months ago