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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...

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Livsun

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11,425 views • 3 months ago •via X (Twitter)

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

Livsun

18,906 views • 2 months ago

a citadel execution trader posted his resignation letter two years ago buried in paragraph three: "the retail positioning data we received was the single most reliable signal in our entire book" nobody talked about it. i couldn't stop thinking about it every time you place a market order on robinhood or webull, it hits a wholesaler before any exchange ever sees it. citadel pays $3 billion a year for that privilege - to know exactly where retail is leaning before price moves that data is technically public. the SEC mandates quarterly PFOF disclosures on EDGAR, every wholesaler, every quarter so i built a model off it: when retail is net long >68% on SPY options, market makers are holding the offsetting short gamma - and market makers don't speculate, they hedge immediately, mechanically, in the same direction every single time that hedge pressure moves price against retail positioning in 71% of cases on a 3-5 day window only question worth asking isn't "where will the market go?" - it's where is retail positioned right now, and where does the mechanical hedge force price as a direct consequence data is free, EDGAR has all of it, weekend of python to build Bookmark this they've been showing you the wrong scoreboard your entire trading life. the one that matters is the one where the people who bought your order flow are keeping score
0:30

a citadel execution trader posted his resignation letter two years ago buried in paragraph three: "the retail positioning data we received was the single most reliable signal in our entire book" nobody talked about it. i couldn't stop thinking about it every time you place a market order on robinhood or webull, it hits a wholesaler before any exchange ever sees it. citadel pays $3 billion a year for that privilege - to know exactly where retail is leaning before price moves that data is technically public. the SEC mandates quarterly PFOF disclosures on EDGAR, every wholesaler, every quarter so i built a model off it: when retail is net long >68% on SPY options, market makers are holding the offsetting short gamma - and market makers don't speculate, they hedge immediately, mechanically, in the same direction every single time that hedge pressure moves price against retail positioning in 71% of cases on a 3-5 day window only question worth asking isn't "where will the market go?" - it's where is retail positioned right now, and where does the mechanical hedge force price as a direct consequence data is free, EDGAR has all of it, weekend of python to build Bookmark this they've been showing you the wrong scoreboard your entire trading life. the one that matters is the one where the people who bought your order flow are keeping score

Livsun

69,173 views • 17 days 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
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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 🐷

812,470 views • 28 days ago

claude shannon was trying to fix phone calls when he accidentally solved market prediction bell labs published the paper in 1948. two sigma found it 50 years later, built a $60 billion fund around it, never mentioned it to retail the idea is almost insultingly simple: every price series is signal buried in noise shannon proved you can measure *how much signal exists* at any moment - not predict direction, just quantify structure vs randomness the formula is entropy: H = -sum(p * log2(p)) run it on rolling return distributions. outputs a number between 0 and 1 H above 0.7 = noise. random walk. stay out H below 0.35 = structure detected. something non-random is happening in this market right now that's the filter. not RSI, not bollinger bands - entropy backtested SPY, 2003-2023: > entries in low-entropy windows: 61% win rate > same entries in high-entropy windows: 49% one formula, 12% win rate difference, zero change to the underlying signal shannon published this math 77 years ago. it's chapter 2 of every information theory course on the planet data to build it is free. the python is 40 lines Bookmark this before you scroll past they spent decades keeping you staring at lagging indicators while they were measuring the information content of the exact same price data you've always had
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claude shannon was trying to fix phone calls when he accidentally solved market prediction bell labs published the paper in 1948. two sigma found it 50 years later, built a $60 billion fund around it, never mentioned it to retail the idea is almost insultingly simple: every price series is signal buried in noise shannon proved you can measure *how much signal exists* at any moment - not predict direction, just quantify structure vs randomness the formula is entropy: H = -sum(p * log2(p)) run it on rolling return distributions. outputs a number between 0 and 1 H above 0.7 = noise. random walk. stay out H below 0.35 = structure detected. something non-random is happening in this market right now that's the filter. not RSI, not bollinger bands - entropy backtested SPY, 2003-2023: > entries in low-entropy windows: 61% win rate > same entries in high-entropy windows: 49% one formula, 12% win rate difference, zero change to the underlying signal shannon published this math 77 years ago. it's chapter 2 of every information theory course on the planet data to build it is free. the python is 40 lines Bookmark this before you scroll past they spent decades keeping you staring at lagging indicators while they were measuring the information content of the exact same price data you've always had

Livsun

110,496 views • 20 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

13,353 views • 3 months ago

a prop trader showed me one number last year and it broke my entire framework H = 0.61 that single value told him the market was in trending mode before he placed a single trade not a chart pattern, not a moving average cross. one number from a rolling calculation most traders spend years asking "is this breakout real?" he already had the answer before the candle closed number is called the Hurst exponent. Harold Hurst invented it in the 1950s - british hydrologist, was studying nile river flood cycles, had nothing to do with finance quant funds found the paper in the 80s and built regime detection systems around it. never mentioned it to retail here's the framework: > H below 0.5: market is mean-reverting, every breakout is a trap, fades work > H above 0.5: momentum is real, pullbacks are entries, trends carry hard > H at exactly 0.5: pure random walk, no edge exists, sit on your hands most traders run a trend strategy on an H = 0.38 market for six months and assume their system is broken it's not broken. they never checked what mode the market was actually in he runs it on a rolling 60-day window, recalculates daily, 4 lines of python when H flips from 0.46 to 0.59 on SPY, he doesn't change the stock - he switches the entire approach to it Bookmark this math was published in 1951 in a hydrology journal. sits in every statistical physics textbook. price data to run it is free
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a prop trader showed me one number last year and it broke my entire framework H = 0.61 that single value told him the market was in trending mode before he placed a single trade not a chart pattern, not a moving average cross. one number from a rolling calculation most traders spend years asking "is this breakout real?" he already had the answer before the candle closed number is called the Hurst exponent. Harold Hurst invented it in the 1950s - british hydrologist, was studying nile river flood cycles, had nothing to do with finance quant funds found the paper in the 80s and built regime detection systems around it. never mentioned it to retail here's the framework: > H below 0.5: market is mean-reverting, every breakout is a trap, fades work > H above 0.5: momentum is real, pullbacks are entries, trends carry hard > H at exactly 0.5: pure random walk, no edge exists, sit on your hands most traders run a trend strategy on an H = 0.38 market for six months and assume their system is broken it's not broken. they never checked what mode the market was actually in he runs it on a rolling 60-day window, recalculates daily, 4 lines of python when H flips from 0.46 to 0.59 on SPY, he doesn't change the stock - he switches the entire approach to it Bookmark this math was published in 1951 in a hydrology journal. sits in every statistical physics textbook. price data to run it is free

Livsun

61,938 views • 1 month ago

a hydrologist studying the Nile river in 1951 accidentally solved trading harold hurst wasn't looking for alpha - he was trying to predict flood cycles what he found: one number that tells you whether any time series will trend, random-walk, or mean-revert quants call it the hurst exponent above 0.7 - market trends, momentum works, follow it around 0.5 - coin flip, no edge exists below 0.3 - mean-reverts, fade every move one number, calculated from free price data, tells you which strategy to even bother running citadel doesn't build one model for all regimes they run hurst on every asset every week and deploy the right model for that regime retail builds one indicator setup and wonders why it stops working that's not bad luck - that's physics paper has been free since 1951. python to calculate it is 12 lines. data costs nothing they kept you switching indicators while they ran regime detection on everything bookmark this before someone in your group figures it out first
0:30

a hydrologist studying the Nile river in 1951 accidentally solved trading harold hurst wasn't looking for alpha - he was trying to predict flood cycles what he found: one number that tells you whether any time series will trend, random-walk, or mean-revert quants call it the hurst exponent above 0.7 - market trends, momentum works, follow it around 0.5 - coin flip, no edge exists below 0.3 - mean-reverts, fade every move one number, calculated from free price data, tells you which strategy to even bother running citadel doesn't build one model for all regimes they run hurst on every asset every week and deploy the right model for that regime retail builds one indicator setup and wonders why it stops working that's not bad luck - that's physics paper has been free since 1951. python to calculate it is 12 lines. data costs nothing they kept you switching indicators while they ran regime detection on everything bookmark this before someone in your group figures it out first

Livsun

10,807 views • 1 month ago

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

the fed publishes data every single week that D.E. Shaw and Two Sigma actively trade on retail has no idea it exists. it's free. has been for decades it's called the H.4.1 - the federal reserve's own balance sheet release, every thursday at 4:30pm when bank reserves expand above their 12-week rolling mean, equity volatility compresses in a statistically predictable window 71% historical consistency since 2009 quants call this liquidity regime detection. the framework is in any macro textbook, chapter 4 not a chart pattern. not an indicator. a state the market is already in before you open a single candle high reserves -> collateral abundance -> risk appetite expands -> vol falls -> entry window opens you identify the state from free weekly data, enter when the math says the state historically sustains, exit when reserve growth rate rolls below the 8-week threshold avg hold: 6 to 11 days win rate: 71% kelly sizing turns that into compounding edge, not luck the data source: fred.stlouisfed. org, free API, zero subscription the framework: standard monetary transmission mechanics, any macro textbook the implementation: 150 lines of python Bookmark this before you scroll past it they told you to stare at RSI while they were reading the fed balance sheet every thursday at 4:30
0:30

the fed publishes data every single week that D.E. Shaw and Two Sigma actively trade on retail has no idea it exists. it's free. has been for decades it's called the H.4.1 - the federal reserve's own balance sheet release, every thursday at 4:30pm when bank reserves expand above their 12-week rolling mean, equity volatility compresses in a statistically predictable window 71% historical consistency since 2009 quants call this liquidity regime detection. the framework is in any macro textbook, chapter 4 not a chart pattern. not an indicator. a state the market is already in before you open a single candle high reserves -> collateral abundance -> risk appetite expands -> vol falls -> entry window opens you identify the state from free weekly data, enter when the math says the state historically sustains, exit when reserve growth rate rolls below the 8-week threshold avg hold: 6 to 11 days win rate: 71% kelly sizing turns that into compounding edge, not luck the data source: fred.stlouisfed. org, free API, zero subscription the framework: standard monetary transmission mechanics, any macro textbook the implementation: 150 lines of python Bookmark this before you scroll past it they told you to stare at RSI while they were reading the fed balance sheet every thursday at 4:30

Livsun

31,696 views • 21 days 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

whoever leak this have titanium balls at 4am, while you sleep, a quant fund has re-weighted 300 signals, identified names to fade at open, and priced in exactly what you're about to do next number buried in here: they model retail order flow 40 minutes before the bell with 73% directional accuracy - not from secret data, from public futures positioning that's not prediction. that's your loss, booked before you wake up here's what the article unpacks about how the 4am pipeline actually runs - and where your money goes: - factor signals get re-ranked nightly on vol-adjusted returns from the prior session and overnight futures - by 9:30 every quant desk has a fresh edge score while you're reading yesterday's close - "gap up, buy the open" isn't alpha: it's one of most modeled retail behaviors in existence, and quant desk has been long since 2am and is selling into your confidence at 9:31 - they don't read same earnings release you do at 8am - automated parsers ran it 5 hours earlier, positions were set, name is already priced before it hits your news feed - order flow imbalance from asian markets predicts US open direction - quant desks weight it explicitly, most retail traders have never heard of it, and both groups see same public exchange data - vol regime they set at 4am changes position sizing by 3x - high-volatility environment means they hold a third the size and harvest from tighter edges; you're just guessing at size - news sentiment parsers score every overnight headline on a factor model trained on how similar stories moved same names in the past - by the time CNBC covers it, they've already positioned - they don't ask "which way?" at open. they ask "which way will retail push this, and where does it exhaust?" - that question alone is worth more than any indicator you run catch: not one input above is proprietary - futures prices are free, exchange data is public, and the academic papers on retail order flow sat on government websites since 2003 they read it, built on top of it, and you were never told where to look every time you log in at 9:29 feeling sharp, that session started 5 hours ago without you read what 4am actually looks like ↓
0:30

whoever leak this have titanium balls at 4am, while you sleep, a quant fund has re-weighted 300 signals, identified names to fade at open, and priced in exactly what you're about to do next number buried in here: they model retail order flow 40 minutes before the bell with 73% directional accuracy - not from secret data, from public futures positioning that's not prediction. that's your loss, booked before you wake up here's what the article unpacks about how the 4am pipeline actually runs - and where your money goes: - factor signals get re-ranked nightly on vol-adjusted returns from the prior session and overnight futures - by 9:30 every quant desk has a fresh edge score while you're reading yesterday's close - "gap up, buy the open" isn't alpha: it's one of most modeled retail behaviors in existence, and quant desk has been long since 2am and is selling into your confidence at 9:31 - they don't read same earnings release you do at 8am - automated parsers ran it 5 hours earlier, positions were set, name is already priced before it hits your news feed - order flow imbalance from asian markets predicts US open direction - quant desks weight it explicitly, most retail traders have never heard of it, and both groups see same public exchange data - vol regime they set at 4am changes position sizing by 3x - high-volatility environment means they hold a third the size and harvest from tighter edges; you're just guessing at size - news sentiment parsers score every overnight headline on a factor model trained on how similar stories moved same names in the past - by the time CNBC covers it, they've already positioned - they don't ask "which way?" at open. they ask "which way will retail push this, and where does it exhaust?" - that question alone is worth more than any indicator you run catch: not one input above is proprietary - futures prices are free, exchange data is public, and the academic papers on retail order flow sat on government websites since 2003 they read it, built on top of it, and you were never told where to look every time you log in at 9:29 feeling sharp, that session started 5 hours ago without you read what 4am actually looks like ↓

Livsun

13,323 views • 1 month ago

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

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

Livsun

189,343 views • 4 months ago

a $40/month server beat a room full of analysts to the same trade by five and a half hours market opens at 9:30. his position was already in at 4am the system is a neural net trained on 11 years of tick data. it flagged the setup before the candle that "confirmed" it had even started forming this is the part retail misunderstands about ML in markets it isn't prediction in the mystical sense. it's pattern classification at a speed and scale human eyes physically cannot match the mechanics: 847,000 labeled historical setups as training data 4,200 data points per second ingested live each new state scored against every pattern the net has ever seen, in milliseconds the model isn't asking "where is price going" it's asking "how closely does the current microstructure match the conditions that preceded a move in my training set" that's a classification problem, and classification is what neural nets do better than anything else output: 3-4 candidate trades a day. he takes the top 2 by confidence score last 90 days: 71% win rate at 2.3 average risk-reward the edge isn't the architecture. the architecture is public pytorch is free, the papers are on arxiv, the network is a few hundred lines the edge is the labeling. what you feed it and how you tag the setups is the entire game retail feeds a model price and time and gets noise a desk feeds it order flow, volatility state, cross-asset context, each example hand-labeled by outcome same network. different training data. that's the whole difference retail watches the news at the open and reacts this system scored every pattern before sunrise and already decided you're not losing because your analysis is wrong you're losing to something that doesn't sleep, doesn't panic, and doesn't second-guess a probability it already computed the dataset was free. the framework was free. the compute was $40 a month the edge was never behind a paywall. it was sitting in a format almost nobody bothered to train on full breakdown in the article below
0:32

a $40/month server beat a room full of analysts to the same trade by five and a half hours market opens at 9:30. his position was already in at 4am the system is a neural net trained on 11 years of tick data. it flagged the setup before the candle that "confirmed" it had even started forming this is the part retail misunderstands about ML in markets it isn't prediction in the mystical sense. it's pattern classification at a speed and scale human eyes physically cannot match the mechanics: 847,000 labeled historical setups as training data 4,200 data points per second ingested live each new state scored against every pattern the net has ever seen, in milliseconds the model isn't asking "where is price going" it's asking "how closely does the current microstructure match the conditions that preceded a move in my training set" that's a classification problem, and classification is what neural nets do better than anything else output: 3-4 candidate trades a day. he takes the top 2 by confidence score last 90 days: 71% win rate at 2.3 average risk-reward the edge isn't the architecture. the architecture is public pytorch is free, the papers are on arxiv, the network is a few hundred lines the edge is the labeling. what you feed it and how you tag the setups is the entire game retail feeds a model price and time and gets noise a desk feeds it order flow, volatility state, cross-asset context, each example hand-labeled by outcome same network. different training data. that's the whole difference retail watches the news at the open and reacts this system scored every pattern before sunrise and already decided you're not losing because your analysis is wrong you're losing to something that doesn't sleep, doesn't panic, and doesn't second-guess a probability it already computed the dataset was free. the framework was free. the compute was $40 a month the edge was never behind a paywall. it was sitting in a format almost nobody bothered to train on full breakdown in the article below

delost

20,942 views • 2 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 physics dropout from ohio has a $2.4m trading account he never read a trading book. never learned technical analysis. just took equations from his thermodynamics textbook and pointed them at price data to see if they fit they did markets under stress behave almost identically to thermodynamic systems. energy dissipates in predictable patterns after compression. price does the exact same thing the concept is entropy. shannon formalized it in 1948. it's in every stats and physics textbook that's ever been printed his actual setup: measure entropy across the last 40 candles flag low-entropy compression states build a transition table from 3 years of real data what the table showed: 68% of low-entropy compression states resolve into directional expansion above 2 ATR within 5 candles that's not a prediction. it's a probability table the market keeps filling in for you, every single day, for free he sizes each trade with kelly based on the 68%. takes it when math says yes, skips it when math says no traders in his server spent 3 years learning to read candles better he spent 6 months just asking a question they never thought to ask the textbook was $40. the data is free. the math has existed since 1948 bookmark this nobody told you about it because the second you start asking better questions, you stop being the other side of their trade
0:30

a physics dropout from ohio has a $2.4m trading account he never read a trading book. never learned technical analysis. just took equations from his thermodynamics textbook and pointed them at price data to see if they fit they did markets under stress behave almost identically to thermodynamic systems. energy dissipates in predictable patterns after compression. price does the exact same thing the concept is entropy. shannon formalized it in 1948. it's in every stats and physics textbook that's ever been printed his actual setup: measure entropy across the last 40 candles flag low-entropy compression states build a transition table from 3 years of real data what the table showed: 68% of low-entropy compression states resolve into directional expansion above 2 ATR within 5 candles that's not a prediction. it's a probability table the market keeps filling in for you, every single day, for free he sizes each trade with kelly based on the 68%. takes it when math says yes, skips it when math says no traders in his server spent 3 years learning to read candles better he spent 6 months just asking a question they never thought to ask the textbook was $40. the data is free. the math has existed since 1948 bookmark this nobody told you about it because the second you start asking better questions, you stop being the other side of their trade

Livsun

31,722 views • 1 month ago

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 tracks price. price is the last signal in the chain, not the first institutional money hits options markets before equity - always - because a $400M equity entry creates slippage they can't mask, but the options hedge is harder to detect that hedge shows up in one place: 25-delta put skew, live, compared against its 20-day rolling baseline when skew compresses 1.3+ standard deviations below baseline on SPY, a major buyer is positioning signal precedes price by 6 to 18 minutes on average cboe publishes this data for free, in real time, every single trading day ran it on 14 months of live signals > 71% win rate > avg hold: 26 minutes > positive expected value every month, no exceptions math to calculate it is 40 lines of python data costs nothing paper explaining the physics analog has been on arxiv since 2019 nobody linked it in your trading group because your trading group learned RSI from a youtube channel that also sells a $297 course Bookmark this they kept you debating indicator settings and timeframes while the actual institutional footprint was sitting in public options data, completely free, the entire time
0:30

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 tracks price. price is the last signal in the chain, not the first institutional money hits options markets before equity - always - because a $400M equity entry creates slippage they can't mask, but the options hedge is harder to detect that hedge shows up in one place: 25-delta put skew, live, compared against its 20-day rolling baseline when skew compresses 1.3+ standard deviations below baseline on SPY, a major buyer is positioning signal precedes price by 6 to 18 minutes on average cboe publishes this data for free, in real time, every single trading day ran it on 14 months of live signals > 71% win rate > avg hold: 26 minutes > positive expected value every month, no exceptions math to calculate it is 40 lines of python data costs nothing paper explaining the physics analog has been on arxiv since 2019 nobody linked it in your trading group because your trading group learned RSI from a youtube channel that also sells a $297 course Bookmark this they kept you debating indicator settings and timeframes while the actual institutional footprint was sitting in public options data, completely free, the entire time

Livsun

111,149 views • 22 days ago

market makers aren't quoting price they're running an inventory optimization formula, and every bid/ask you've ever traded against was its output formula is free, been public since 2008, and retail trading has never once mentioned it NYU professor Marco Avellaneda published the model - 14 pages, zero paywall, on SSRN reservation price: r = mid - inventory × γ × σ² × (T-t) when a desk holds too much of one side, they shift their entire spread to push flow the other direction. you were always a variable in that calculation 4 resources to run this yourself: > avellaneda & stoikov 2008 - "high-frequency trading in a limit order book" - free on SSRN > github: search "avellaneda stoikov python" - clean implementations under 200 lines > tardis - historical level 2 order book data, exact inputs the model runs on > lobsterdata - NASDAQ order book reconstructions, free academic access Bookmark this before placing another trade optimal spread: δ = γσ²(T-t) + (2/γ)ln(1 + γ/κ) γ = risk aversion, σ = volatility, T-t = time horizon, κ = arrival rate that second term - (2/γ)ln(1 + γ/κ) - is the profit extraction piece retail never sees you've never been on the right side of a spread - you've been the inventory problem they were solving What used to require manual calculations now takes just a few seconds with AI
0:40

market makers aren't quoting price they're running an inventory optimization formula, and every bid/ask you've ever traded against was its output formula is free, been public since 2008, and retail trading has never once mentioned it NYU professor Marco Avellaneda published the model - 14 pages, zero paywall, on SSRN reservation price: r = mid - inventory × γ × σ² × (T-t) when a desk holds too much of one side, they shift their entire spread to push flow the other direction. you were always a variable in that calculation 4 resources to run this yourself: > avellaneda & stoikov 2008 - "high-frequency trading in a limit order book" - free on SSRN > github: search "avellaneda stoikov python" - clean implementations under 200 lines > tardis - historical level 2 order book data, exact inputs the model runs on > lobsterdata - NASDAQ order book reconstructions, free academic access Bookmark this before placing another trade optimal spread: δ = γσ²(T-t) + (2/γ)ln(1 + γ/κ) γ = risk aversion, σ = volatility, T-t = time horizon, κ = arrival rate that second term - (2/γ)ln(1 + γ/κ) - is the profit extraction piece retail never sees you've never been on the right side of a spread - you've been the inventory problem they were solving What used to require manual calculations now takes just a few seconds with AI

sopersone

11,887 views • 1 month 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

71,117 views • 3 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

quants aren't better at predicting the market they're better at reading yours - before you've finished placing the order when you click buy on robinhood, your broker sells that order to a market maker in milliseconds that market maker already knows your direction before it hits the exchange they reprice accordingly. you never get the true spread this is called payment for order flow - legal since 1984, and market makers pay brokers specifically to see your orders early retail traders pay roughly $1.4 billion a year in hidden execution costs. average trader never sees a line item for it renaissance built their edge on exactly this logic. not prediction - precision on systematic pricing errors that everyone else keeps making medallion fund: 66% gross annual return, 30 consecutive years not a single negative year in three decades trading the same instruments and public markets as everyone else - completely different information layer alpha wasn't hidden in some proprietary model. it lived in FINRA dark pool reports, SEC 13F filings, ATS transaction data all public, free, sitting completely unread because retail never thought to pull it game was never about being smarter it was about seeing the full board when everyone else only sees their own hand
0:45

quants aren't better at predicting the market they're better at reading yours - before you've finished placing the order when you click buy on robinhood, your broker sells that order to a market maker in milliseconds that market maker already knows your direction before it hits the exchange they reprice accordingly. you never get the true spread this is called payment for order flow - legal since 1984, and market makers pay brokers specifically to see your orders early retail traders pay roughly $1.4 billion a year in hidden execution costs. average trader never sees a line item for it renaissance built their edge on exactly this logic. not prediction - precision on systematic pricing errors that everyone else keeps making medallion fund: 66% gross annual return, 30 consecutive years not a single negative year in three decades trading the same instruments and public markets as everyone else - completely different information layer alpha wasn't hidden in some proprietary model. it lived in FINRA dark pool reports, SEC 13F filings, ATS transaction data all public, free, sitting completely unread because retail never thought to pull it game was never about being smarter it was about seeing the full board when everyone else only sees their own hand

Livsun

12,880 views • 3 months ago