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a physics phd accidentally found how Citadel knows when to enter 18 minutes before price moves he was studying particle drift equations. published it on arxiv in 2019. nobody in trading ever noticed the math mapped perfectly onto order book dynamics here's the part that should bother you: retail...

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Livsun

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111,149 views • 22 days ago •via X (Twitter)

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options market makers calculate a number every day that tells them whether to pin price or let it run retail has no idea it exists it's called net dealer gamma exposure. when they're long gamma, they hedge by selling rallies and buying dips - price gets pinned when short gamma, they hedge the opposite way - moves accelerate, break out, trend this isn't hidden data or a proprietary signal it's calculable from public cboe options chains, free, updated every morning crossover - where gamma flips zero - is your volatility regime indicator above it: suppression, below it: acceleration python + cboe feed + 200 lines of code to build it framework in academic papers since 2017, sitting in textbooks the whole time retail has been reading candles while market makers were reading their own hedging book Bookmark and reasearch it yourself same market. completely different game
0:35

options market makers calculate a number every day that tells them whether to pin price or let it run retail has no idea it exists it's called net dealer gamma exposure. when they're long gamma, they hedge by selling rallies and buying dips - price gets pinned when short gamma, they hedge the opposite way - moves accelerate, break out, trend this isn't hidden data or a proprietary signal it's calculable from public cboe options chains, free, updated every morning crossover - where gamma flips zero - is your volatility regime indicator above it: suppression, below it: acceleration python + cboe feed + 200 lines of code to build it framework in academic papers since 2017, sitting in textbooks the whole time retail has been reading candles while market makers were reading their own hedging book Bookmark and reasearch it yourself same market. completely different game

Livsun

59,146 views • 3 months ago

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

202,091 views • 2 months ago

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

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

Livsun

71,012 views • 3 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 • 2 months ago

YOUR STRATEGY DIDN'T STOP WORKING. YOU JUST GOT TOO BIG FOR IT One law prices your own footprint. ΔP/P = Y·σ·√(Q/V) Every order you send moves price against you, and it scales with the square root of size. Double your clip and you pay 1.41x more. Strip the math away. Past a certain size you are not trading the market. You are trading against your own order. Same edge on both lines below. One clips 0.8% of volume, the other 5%. That is the only difference. Minara dragged this off the trading floor. Type it in plain English, it writes the Python and hands you the backtest. One sentence to a live strategy & every number, drawdowns included, sits in the article. Read it ↓
0:26

YOUR STRATEGY DIDN'T STOP WORKING. YOU JUST GOT TOO BIG FOR IT One law prices your own footprint. ΔP/P = Y·σ·√(Q/V) Every order you send moves price against you, and it scales with the square root of size. Double your clip and you pay 1.41x more. Strip the math away. Past a certain size you are not trading the market. You are trading against your own order. Same edge on both lines below. One clips 0.8% of volume, the other 5%. That is the only difference. Minara dragged this off the trading floor. Type it in plain English, it writes the Python and hands you the backtest. One sentence to a live strategy & every number, drawdowns included, sits in the article. Read it ↓

slash1s

10,959 views • 1 month 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

13,353 views • 3 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

34,706 views • 3 months 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 • 3 months 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 • 4 months ago

a citadel quant told me something that broke my entire trading framework "we don't predict markets. we model the state machine" he explained markov chains in 90 seconds the market is never random - it always exists in one of three states trending up, trending down, ranging - each has a fixed probability of shifting to another build the transition matrix from real price data: > trending up -> 68% stays trending, 21% flips to range, 11% reverses > ranging -> 54% stays range, 28% breaks up, 18% breaks down > trending down -> 61% stays falling, 24% flips to range, 15% reverses now you're not guessing, you're playing probability identify current state, enter with the 68% edge, size with kelly criterion based on that probability the formula is public - markov published it in 1906 hedge funds use it, the math costs nothing what costs you is asking the wrong question "where is price going?" is random "what state am I in right now?" has an answer transition matrix built from 10 years of data is your edge Bookmark it not a signal, not an indicator - just conditional probability that compounds every single trade
0:40

a citadel quant told me something that broke my entire trading framework "we don't predict markets. we model the state machine" he explained markov chains in 90 seconds the market is never random - it always exists in one of three states trending up, trending down, ranging - each has a fixed probability of shifting to another build the transition matrix from real price data: > trending up -> 68% stays trending, 21% flips to range, 11% reverses > ranging -> 54% stays range, 28% breaks up, 18% breaks down > trending down -> 61% stays falling, 24% flips to range, 15% reverses now you're not guessing, you're playing probability identify current state, enter with the 68% edge, size with kelly criterion based on that probability the formula is public - markov published it in 1906 hedge funds use it, the math costs nothing what costs you is asking the wrong question "where is price going?" is random "what state am I in right now?" has an answer transition matrix built from 10 years of data is your edge Bookmark it not a signal, not an indicator - just conditional probability that compounds every single trade

Livsun

269,433 views • 4 months 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 • 3 months ago

since 1993, hedge funds over $100M have been legally required to publish every position they hold 4 times a year, exact stocks and exact share counts, all of it free on the SEC website i spent 6 months asking one question with python: what happens when 4 or more separate funds all show up with new positions in the same small-cap stock in the same quarter - before the price has moved? backtested 2,400 signals from 2019 to 2025 67% win rate, 18.4% average gain per trade, 31 day average hold the reason the edge exists is the same reason the funds themselves can't collect it an $8B fund building a position in a $400M company moves the price against itself the second it starts buying so they file 45 days after the fact, the window opens, and nobody's standing in it running it live since february - 11 closed trades, 9 winners, +31% while the s&p returned 8% in the same period setup cost: a free API key from SEC EDGAR and one saturday morning this isn't an edge i found by being smart. it's an edge that only exists because the people publishing it are too large to use it themselves Bookmark before it get losted the filing requirement has been law since the clinton administration, sitting in a public federal database for 30 years most retail traders pay monthly subscriptions to signal services built by people who just learned to read a government XML file the data was never hidden. it just required knowing where to look
0:55

since 1993, hedge funds over $100M have been legally required to publish every position they hold 4 times a year, exact stocks and exact share counts, all of it free on the SEC website i spent 6 months asking one question with python: what happens when 4 or more separate funds all show up with new positions in the same small-cap stock in the same quarter - before the price has moved? backtested 2,400 signals from 2019 to 2025 67% win rate, 18.4% average gain per trade, 31 day average hold the reason the edge exists is the same reason the funds themselves can't collect it an $8B fund building a position in a $400M company moves the price against itself the second it starts buying so they file 45 days after the fact, the window opens, and nobody's standing in it running it live since february - 11 closed trades, 9 winners, +31% while the s&p returned 8% in the same period setup cost: a free API key from SEC EDGAR and one saturday morning this isn't an edge i found by being smart. it's an edge that only exists because the people publishing it are too large to use it themselves Bookmark before it get losted the filing requirement has been law since the clinton administration, sitting in a public federal database for 30 years most retail traders pay monthly subscriptions to signal services built by people who just learned to read a government XML file the data was never hidden. it just required knowing where to look

Livsun

32,072 views • 3 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 • 2 months ago

a trader in Shanghai has been running 71% annual returns since 2019 without ever touching a Western exchange nobody outside Weibo knows his name he doesn't manage outside capital, never went on a podcast, never posted a P&L screenshot pause at 0:34 - look at the monitor behind him on the right that's not a price chart. that's a 6x6 state transition matrix built from 11 years of Chinese A-share data he found something in 2018 that every quant textbook describes but almost nobody applies CSI 300 price-state transitions are predictable at a level that makes S&P pattern noise look clean by comparison he mapped 6 market states: trending-up, trending-down, range-tight, range-wide, vol-compression, spike-decay then calculated every historical transition probability across 11 years of 30-minute bars: trending-up -> stays trending: 63% vol-compression -> spike-decay: 78% range-tight -> breaks directional: 71% now he's not predicting direction. he's entering when math says 71% historical completion, sizing with Kelly, closing in under 30 minutes 28 min avg hold, worst month -3.1%, best year +94% framework is markov's from 1906. A-share data is free on WIND Terminal implementation: roughly 180 lines of python insight was never about math - it was about where to aim it Chinese A-shares have thinner institutional algo penetration than US equities. patterns don't get arbed out as fast statistical edges persist for months longer than they would on SPY retail in Shanghai trades on gut. US quants are chasing S&P microstructure nobody was running transition matrices on Chinese state sequences at any real scale he aimed a 119-year-old framework at a market nobody was watching and held the edge for 6 years bookmark this before it becomes obvious math is free, data costs nothing what took time was realizing the most exploitable market wasn't the most-watched one they kept you watching SPY candles while the cleanest probability table on earth sat untouched in Shanghai
1:30

a trader in Shanghai has been running 71% annual returns since 2019 without ever touching a Western exchange nobody outside Weibo knows his name he doesn't manage outside capital, never went on a podcast, never posted a P&L screenshot pause at 0:34 - look at the monitor behind him on the right that's not a price chart. that's a 6x6 state transition matrix built from 11 years of Chinese A-share data he found something in 2018 that every quant textbook describes but almost nobody applies CSI 300 price-state transitions are predictable at a level that makes S&P pattern noise look clean by comparison he mapped 6 market states: trending-up, trending-down, range-tight, range-wide, vol-compression, spike-decay then calculated every historical transition probability across 11 years of 30-minute bars: trending-up -> stays trending: 63% vol-compression -> spike-decay: 78% range-tight -> breaks directional: 71% now he's not predicting direction. he's entering when math says 71% historical completion, sizing with Kelly, closing in under 30 minutes 28 min avg hold, worst month -3.1%, best year +94% framework is markov's from 1906. A-share data is free on WIND Terminal implementation: roughly 180 lines of python insight was never about math - it was about where to aim it Chinese A-shares have thinner institutional algo penetration than US equities. patterns don't get arbed out as fast statistical edges persist for months longer than they would on SPY retail in Shanghai trades on gut. US quants are chasing S&P microstructure nobody was running transition matrices on Chinese state sequences at any real scale he aimed a 119-year-old framework at a market nobody was watching and held the edge for 6 years bookmark this before it becomes obvious math is free, data costs nothing what took time was realizing the most exploitable market wasn't the most-watched one they kept you watching SPY candles while the cleanest probability table on earth sat untouched in Shanghai

Livsun

25,085 views • 3 months ago

The biggest marketplace on Earth won't be the assets humans own. It'll be the data AI needs. Everything that can be digitised is getting pulled in to train the next models. Every scientific dataset, every piece of research sitting in a university, every sensor reading off every John Deere tractor in a field. All of it feeds the machine, and almost none of it is visible to us. What comes out is an agentic economy running at machine speed. You plug your data into a vault, and agents monetise it on your behalf, trading with other agents in real time, in a marketplace we never see. The company sitting on proprietary data becomes part of it without even knowing. This is what the Economic Singularity actually looks like from the inside. The real economy stops being the one we can see and price, and moves somewhere we can't follow. You own a piece of it, or you get priced out of it. Jordi Visser.
1:27

The biggest marketplace on Earth won't be the assets humans own. It'll be the data AI needs. Everything that can be digitised is getting pulled in to train the next models. Every scientific dataset, every piece of research sitting in a university, every sensor reading off every John Deere tractor in a field. All of it feeds the machine, and almost none of it is visible to us. What comes out is an agentic economy running at machine speed. You plug your data into a vault, and agents monetise it on your behalf, trading with other agents in real time, in a marketplace we never see. The company sitting on proprietary data becomes part of it without even knowing. This is what the Economic Singularity actually looks like from the inside. The real economy stops being the one we can see and price, and moves somewhere we can't follow. You own a piece of it, or you get priced out of it. Jordi Visser.

Raoul Pal

79,047 views • 1 month ago

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

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

Livsun

11,425 views • 3 months ago

implied volatility is a price. realized volatility is what actually happens. the gap between them is one of the most durable edges in markets the chart in that clip is the whole story each dot is a day: what options priced in (IV) versus what the market actually delivered over the next 30 days (RV) the regression line has a negative slope and sits below the diagonal that isn't noise. it's the variance risk premium VRP = IV − RV on average, across decades, implied vol prints higher than the volatility that follows options are, structurally, priced for more movement than actually shows up the reason isn't a mistake. it's insurance whoever sells an option is underwriting risk, the same way an insurer underwrites a house fire they demand a premium above fair value to carry that risk, and buyers pay it because they want protection so the seller of volatility is the insurance company and insurance companies, on average, win. not every policy. on average, over a large book that's the edge retail never sees because retail is almost always the buyer buying calls, buying puts, paying the premium, standing on the losing side of a spread that is baked into the price before the trade even opens on the clip's data: IV was 10.95%, the realized vol that followed was higher on that specific day that's the risk. VRP is positive on average, not always. sometimes RV blows past IV and the seller takes the loss which is exactly why it's a premium and not free money you get paid to hold a risk that occasionally hurts. the edge is that the payment, over enough independent bets, exceeds the damage a desk harvests this systematically: sell the overpriced vol, hedge the direction, collect the spread, size so no single blowup ends the book retail buys the lottery ticket on the other side and wonders why theta bleeds it dry the data is public. IV comes off the options chain, RV is just the standard deviation of returns compute both, subtract, and the premium is right there on a chart you can build in an afternoon the number was never hidden retail was just standing on the paying side of it the whole time full breakdown in the article below
0:42

implied volatility is a price. realized volatility is what actually happens. the gap between them is one of the most durable edges in markets the chart in that clip is the whole story each dot is a day: what options priced in (IV) versus what the market actually delivered over the next 30 days (RV) the regression line has a negative slope and sits below the diagonal that isn't noise. it's the variance risk premium VRP = IV − RV on average, across decades, implied vol prints higher than the volatility that follows options are, structurally, priced for more movement than actually shows up the reason isn't a mistake. it's insurance whoever sells an option is underwriting risk, the same way an insurer underwrites a house fire they demand a premium above fair value to carry that risk, and buyers pay it because they want protection so the seller of volatility is the insurance company and insurance companies, on average, win. not every policy. on average, over a large book that's the edge retail never sees because retail is almost always the buyer buying calls, buying puts, paying the premium, standing on the losing side of a spread that is baked into the price before the trade even opens on the clip's data: IV was 10.95%, the realized vol that followed was higher on that specific day that's the risk. VRP is positive on average, not always. sometimes RV blows past IV and the seller takes the loss which is exactly why it's a premium and not free money you get paid to hold a risk that occasionally hurts. the edge is that the payment, over enough independent bets, exceeds the damage a desk harvests this systematically: sell the overpriced vol, hedge the direction, collect the spread, size so no single blowup ends the book retail buys the lottery ticket on the other side and wonders why theta bleeds it dry the data is public. IV comes off the options chain, RV is just the standard deviation of returns compute both, subtract, and the premium is right there on a chart you can build in an afternoon the number was never hidden retail was just standing on the paying side of it the whole time full breakdown in the article below

delost

23,951 views • 2 months ago

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

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

Livsun

13,963 views • 3 months ago

hedge funds have been running this math for 30 years. retail just discovered fibonacci retracements andrei markov was a russian linguist studying letter sequences in 1906 - had nothing to do with markets renaissance found his paper 70 years later, built medallion around it, told nobody markets don't move randomly - they move in states trending, ranging, reversing - each state has a fixed historical probability of shifting to the next you don't predict direction. you identify which state you're in, then let probability do the rest build a transition matrix from 10 years of price data: trending -> stays trending 68% of the time, flips to range 21%, reverses 11% ranging -> holds 54%, breaks up 28%, breaks down 18% now you're not guessing - you're entering on 68% historical completion size with kelly criterion based on that probability. take the trade when math says yes "where is price going?" -> random "what state am i in right now?" -> has an answer paper is free, data is free, python to build this: 200 lines 37 years, zero losing years - that's what medallion looks like when you ask the right question they kept you drawing fibonacci lines while they ran probability tables Bookmark this and run the matrix yourself
0:55

hedge funds have been running this math for 30 years. retail just discovered fibonacci retracements andrei markov was a russian linguist studying letter sequences in 1906 - had nothing to do with markets renaissance found his paper 70 years later, built medallion around it, told nobody markets don't move randomly - they move in states trending, ranging, reversing - each state has a fixed historical probability of shifting to the next you don't predict direction. you identify which state you're in, then let probability do the rest build a transition matrix from 10 years of price data: trending -> stays trending 68% of the time, flips to range 21%, reverses 11% ranging -> holds 54%, breaks up 28%, breaks down 18% now you're not guessing - you're entering on 68% historical completion size with kelly criterion based on that probability. take the trade when math says yes "where is price going?" -> random "what state am i in right now?" -> has an answer paper is free, data is free, python to build this: 200 lines 37 years, zero losing years - that's what medallion looks like when you ask the right question they kept you drawing fibonacci lines while they ran probability tables Bookmark this and run the matrix yourself

Livsun

30,320 views • 3 months ago