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Two Sigma runs 500 stocks through one formula and looks at 5 numbers math has been public since 1901 PCA - principal component analysis - run it on the S&P and entire market compresses 87% of all price movement. 5 hidden variables not sectors, not earnings - 5 eigenvectors....

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

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

Livsun

22,073 görüntüleme • 1 ay önce

you're not trading 500 stocks you're trading 5 hidden forces, recycled on repeat quants cracked this decades ago using principal component analysis strip correlations across every ticker and what's left isn't 500 independent bets it's 5 latent signals: > broad market direction > growth vs value tilt > rate sensitivity > size premium > volatility regime every stock is just a weighted combo of those 5 so when a citadel desk builds a "diversified" book, they're not thinking in tickers they're thinking in factor exposures - "how much rate risk am I carrying, how much of my book is a value bet" Bookmark asap before it buried retail manages 500 variables. quants manage 5 PCA lives in every stats textbook, scipy builds it in 3 lines of code, data is public and always has been they kept you counting tickers while they were reading 5 forces underneath them
0:29

you're not trading 500 stocks you're trading 5 hidden forces, recycled on repeat quants cracked this decades ago using principal component analysis strip correlations across every ticker and what's left isn't 500 independent bets it's 5 latent signals: > broad market direction > growth vs value tilt > rate sensitivity > size premium > volatility regime every stock is just a weighted combo of those 5 so when a citadel desk builds a "diversified" book, they're not thinking in tickers they're thinking in factor exposures - "how much rate risk am I carrying, how much of my book is a value bet" Bookmark asap before it buried retail manages 500 variables. quants manage 5 PCA lives in every stats textbook, scipy builds it in 3 lines of code, data is public and always has been they kept you counting tickers while they were reading 5 forces underneath them

Livsun

23,865 görüntüleme • 1 ay önce

citadel doesn't analyze 500 stocks they compress them to 5 hidden forces - then trade the forces most people saw the math behind this in a stats class and scrolled past it. nobody told them it was worth $30 billion a year technique is called PCA - principal component analysis Karl Pearson published it in 1901 in a free journal. it's in every stats textbook on earth here's what it does: you feed it 10 years of daily returns across 500 stocks it ignores earnings reports, management teams, every narrative retail obsesses over it finds underlying forces moving groups of stocks together without anyone naming them what comes out: > component 1 - broad market direction (~45% of all movement) > 2 - growth vs value tilt (~12%) > 3 - sector rotation (~8%) > 4 - volatility regime (~5%) > 5 - liquidity premium (~3%) five numbers explain 73% of everything moving in markets a quant desk doesn't ask "will NVDA go up tomorrow" it asks: which regime are we in, and where does this regime historically lead portfolio built around forces, not tickers if component 3 signals sector rotation at a historical inflection, they rotate without reading a single 10-K Two Sigma runs this across equities, bonds, commodities and currencies simultaneously Save this before someone turns it into a $2,000 data to build this is free - Ken French's factor library, CRSP, any linear algebra textbook 150 lines of python is the whole engine what Renaissance has isn't secret data or better intel
0:56

citadel doesn't analyze 500 stocks they compress them to 5 hidden forces - then trade the forces most people saw the math behind this in a stats class and scrolled past it. nobody told them it was worth $30 billion a year technique is called PCA - principal component analysis Karl Pearson published it in 1901 in a free journal. it's in every stats textbook on earth here's what it does: you feed it 10 years of daily returns across 500 stocks it ignores earnings reports, management teams, every narrative retail obsesses over it finds underlying forces moving groups of stocks together without anyone naming them what comes out: > component 1 - broad market direction (~45% of all movement) > 2 - growth vs value tilt (~12%) > 3 - sector rotation (~8%) > 4 - volatility regime (~5%) > 5 - liquidity premium (~3%) five numbers explain 73% of everything moving in markets a quant desk doesn't ask "will NVDA go up tomorrow" it asks: which regime are we in, and where does this regime historically lead portfolio built around forces, not tickers if component 3 signals sector rotation at a historical inflection, they rotate without reading a single 10-K Two Sigma runs this across equities, bonds, commodities and currencies simultaneously Save this before someone turns it into a $2,000 data to build this is free - Ken French's factor library, CRSP, any linear algebra textbook 150 lines of python is the whole engine what Renaissance has isn't secret data or better intel

Livsun

21,476 görüntüleme • 1 ay önce

Bridgewater doesn't have 500 positions they have 5 exposures that explain 78% of everything those 500 positions do this video shows what that looks like in 3D PCA Risk-Mode Trajectory. three axes: PC1, PC2, PC3 each one is a principal component extracted from the return matrix of the entire market PC1 is broad market direction. it alone explains roughly 40% of all stock movement PC2 separates growth from value. another 15% PC3 captures rate sensitivity. another 10% three invisible forces. 65% of the variance in every stock you've ever traded the dot moving through that 3D space is where the market is right now when the dot drifts toward a corner, the market is concentrating into one regime when it sits in the center, forces are balanced and directional trades are noise quant desks don't watch that dot for fun. they use its position to size every trade in the book if PC1 is dominating, stock picking is pointless because everything moves together if PC2 is leading, the value vs growth rotation is the only trade that matters if PC3 spikes, interest rate sensitivity is driving everything and your "stock pick" is really a rate bet you didn't know you made > PCA: Karl Pearson, 1901 > used at Bridgewater, AQR, Two Sigma for factor decomposition since the 1990s > this visualization: free, public, 29 seconds > scipy builds PCA in 3 lines of Python retail analyzes 500 tickers and thinks they're making 500 decisions quant desks decompose the same 500 into 5 forces and make 5 decisions one is complexity. the other is the math that simplifies it full breakdown in the video below
0:29

Bridgewater doesn't have 500 positions they have 5 exposures that explain 78% of everything those 500 positions do this video shows what that looks like in 3D PCA Risk-Mode Trajectory. three axes: PC1, PC2, PC3 each one is a principal component extracted from the return matrix of the entire market PC1 is broad market direction. it alone explains roughly 40% of all stock movement PC2 separates growth from value. another 15% PC3 captures rate sensitivity. another 10% three invisible forces. 65% of the variance in every stock you've ever traded the dot moving through that 3D space is where the market is right now when the dot drifts toward a corner, the market is concentrating into one regime when it sits in the center, forces are balanced and directional trades are noise quant desks don't watch that dot for fun. they use its position to size every trade in the book if PC1 is dominating, stock picking is pointless because everything moves together if PC2 is leading, the value vs growth rotation is the only trade that matters if PC3 spikes, interest rate sensitivity is driving everything and your "stock pick" is really a rate bet you didn't know you made > PCA: Karl Pearson, 1901 > used at Bridgewater, AQR, Two Sigma for factor decomposition since the 1990s > this visualization: free, public, 29 seconds > scipy builds PCA in 3 lines of Python retail analyzes 500 tickers and thinks they're making 500 decisions quant desks decompose the same 500 into 5 forces and make 5 decisions one is complexity. the other is the math that simplifies it full breakdown in the video below

delost

49,014 görüntüleme • 1 ay önce

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

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

Livsun

188,258 görüntüleme • 2 ay önce

a quant at Two Sigma told me something at a bar i can't stop thinking about "retail looks at price. we look at the autocorrelation of price changes - completely different signal" i asked him to explain it like i was 12 he drew on a napkin: if today's move predicts tomorrow's move - even slightly, 53% of the time - that's an edge that edge, sized with Kelly, compounds into something insane data is free - Bloomberg and the Fed publish all of it. math takes a weekend to learn reason retail loses isn't intelligence. it's that they're reading the wrong representation of the same data "a chart hides serial correlation. a time series shows it naked" went home, ran autocorrelation tests on 3 years of SPY data found 4 patterns - statistically significant, all exploitable on a 5-day window signals aren't perfect, right 58% of the time. Kelly says that's enough data was free the whole time, framework sitting in every stats textbook. nobody pointed retail toward it they kept you staring at candles
0:29

a quant at Two Sigma told me something at a bar i can't stop thinking about "retail looks at price. we look at the autocorrelation of price changes - completely different signal" i asked him to explain it like i was 12 he drew on a napkin: if today's move predicts tomorrow's move - even slightly, 53% of the time - that's an edge that edge, sized with Kelly, compounds into something insane data is free - Bloomberg and the Fed publish all of it. math takes a weekend to learn reason retail loses isn't intelligence. it's that they're reading the wrong representation of the same data "a chart hides serial correlation. a time series shows it naked" went home, ran autocorrelation tests on 3 years of SPY data found 4 patterns - statistically significant, all exploitable on a 5-day window signals aren't perfect, right 58% of the time. Kelly says that's enough data was free the whole time, framework sitting in every stats textbook. nobody pointed retail toward it they kept you staring at candles

Livsun

149,183 görüntüleme • 2 ay önce

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 görüntüleme • 2 ay önce

a researcher at a systematic fund showed me a 6x6 matrix on a whiteboard said: > this is our whole system. we don't forecast price. we classify which cell the market occupies and where that cell historically transitions i stared at it for days before it made sense. then everything changed never interpreted a chart the same way again the matrix is called a Markov Chain transition table. the theory dates back to 1913, it's in every introductory statistics textbook ever written and systematic funds rely on it because it poses a fundamentally different question than what retail traders ever think to ask retail: is this going higher or lower systematic: what regime is this market in, and where does this regime historically resolve every market sits in one of roughly 4-6 regimes at any point in time narrow consolidation, expanding volatility, trending with acceleration, post-reversal drift, pre-expansion squeeze not arbitrary categories - clusters you extract from real data using volatility, volume, and trend strength layered together once you define the regimes, you construct the table: P(regime 3 → regime 5) = 71% P(regime 1 → regime 3) = 64% P(regime 2 → regime 4) = 69% each cell is a historical frequency. now when the market sits in regime 3, you're not speculating you're trading on 71% historical resolution. you scale it with Kelly. you execute when the math confirms, not when it feels convincing i constructed this on ETH using 3 years of 1-hour data. isolated 6 regimes one i named "volatility squeeze below 15-day average for 8+ consecutive bars" transitioned to a directional breakout exceeding 2.2 ATR in 74% of cases average reward/risk on those setups: 4.8 that's not forecasting. that's reading a probability matrix the market populates for you every single session the part that should concern you: the data to construct this is free. the methodology is in any quantitative finance textbook python to build it is maybe 180 lines what Citadel has that you don't isn't classified data or secret algorithms it's this methodology applied to tick-level data with more granular regime definitions you're not lacking information you're framing the wrong question every single time you open a chart
0:26

a researcher at a systematic fund showed me a 6x6 matrix on a whiteboard said: > this is our whole system. we don't forecast price. we classify which cell the market occupies and where that cell historically transitions i stared at it for days before it made sense. then everything changed never interpreted a chart the same way again the matrix is called a Markov Chain transition table. the theory dates back to 1913, it's in every introductory statistics textbook ever written and systematic funds rely on it because it poses a fundamentally different question than what retail traders ever think to ask retail: is this going higher or lower systematic: what regime is this market in, and where does this regime historically resolve every market sits in one of roughly 4-6 regimes at any point in time narrow consolidation, expanding volatility, trending with acceleration, post-reversal drift, pre-expansion squeeze not arbitrary categories - clusters you extract from real data using volatility, volume, and trend strength layered together once you define the regimes, you construct the table: P(regime 3 → regime 5) = 71% P(regime 1 → regime 3) = 64% P(regime 2 → regime 4) = 69% each cell is a historical frequency. now when the market sits in regime 3, you're not speculating you're trading on 71% historical resolution. you scale it with Kelly. you execute when the math confirms, not when it feels convincing i constructed this on ETH using 3 years of 1-hour data. isolated 6 regimes one i named "volatility squeeze below 15-day average for 8+ consecutive bars" transitioned to a directional breakout exceeding 2.2 ATR in 74% of cases average reward/risk on those setups: 4.8 that's not forecasting. that's reading a probability matrix the market populates for you every single session the part that should concern you: the data to construct this is free. the methodology is in any quantitative finance textbook python to build it is maybe 180 lines what Citadel has that you don't isn't classified data or secret algorithms it's this methodology applied to tick-level data with more granular regime definitions you're not lacking information you're framing the wrong question every single time you open a chart

Hrundel75 🐷

12,088 görüntüleme • 26 gün önce

most powerful market regime tool on earth was invented to study nile river floods harold hurst published it in 1951. wall street found it decades later, buried it, never told retail the hurst exponent: one number between 0 and 1 below 0.5 means reversion, above 0.5 means market has memory and trends, at 0.5 you're flipping coins run it on SPY over a 90-day rolling window and you instantly know which playbook to use when it reads 0.38 you fade every breakout, when it reads 0.61 you buy every pullback i ran this on 4 years of daily closes - 40 lines of python, free data momentum in a trending regime: 64% win rate mean-reversion in a reverting regime: 71% when exponent sits between 0.45 and 0.55 - both strategies lose, that's the chop zone nobody tells you to sit on your hands there most retail traders aren't wrong about direction. they're wrong about regime bookmark this before your trading group figures it out edge isn't knowing where price is going - it's knowing what kind of market you're already in
0:30

most powerful market regime tool on earth was invented to study nile river floods harold hurst published it in 1951. wall street found it decades later, buried it, never told retail the hurst exponent: one number between 0 and 1 below 0.5 means reversion, above 0.5 means market has memory and trends, at 0.5 you're flipping coins run it on SPY over a 90-day rolling window and you instantly know which playbook to use when it reads 0.38 you fade every breakout, when it reads 0.61 you buy every pullback i ran this on 4 years of daily closes - 40 lines of python, free data momentum in a trending regime: 64% win rate mean-reversion in a reverting regime: 71% when exponent sits between 0.45 and 0.55 - both strategies lose, that's the chop zone nobody tells you to sit on your hands there most retail traders aren't wrong about direction. they're wrong about regime bookmark this before your trading group figures it out edge isn't knowing where price is going - it's knowing what kind of market you're already in

Livsun

15,785 görüntüleme • 13 gün önce

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

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

Livsun

13,963 görüntüleme • 2 ay önce

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

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

Livsun

18,906 görüntüleme • 1 ay önce

a risk analyst at two sigma told me something over drinks i still think about "our models are right 54% of the time. retail models can be right 60% and still blow up" i asked him why "you trade every day. we don't" two sigma sits completely flat roughly 40% of the year - not because signals are missing, because regime filter says math doesn't work in that environment trending regime eats mean reversion alive - ranging regime eats momentum most retail accounts blow up not from bad signals but from running the right signal in the wrong regime framework is called regime detection - published academic work, in every quant textbook since the 1990s 3 inputs to identify it: realized vol vs 30-day mean, autocorrelation of daily returns, volume profile vs 20-day average vol elevated + autocorrelation negative = mean-reversion regime - momentum will destroy you here autocorrelation positive + vol compressing = trending regime - reversion will destroy you here wrong regime turns a 60% signal into a 40% signal - you're paying the house right regime turns a 54% signal into a 65% edge - kelly compounds it into something insane bookmark this before you open another chart Dm me "Quant" for full quant course data is free, formula is public, 150 lines of python to run the whole thing they didn't hide the signals from you they hid the question you were supposed to ask first
0:58

a risk analyst at two sigma told me something over drinks i still think about "our models are right 54% of the time. retail models can be right 60% and still blow up" i asked him why "you trade every day. we don't" two sigma sits completely flat roughly 40% of the year - not because signals are missing, because regime filter says math doesn't work in that environment trending regime eats mean reversion alive - ranging regime eats momentum most retail accounts blow up not from bad signals but from running the right signal in the wrong regime framework is called regime detection - published academic work, in every quant textbook since the 1990s 3 inputs to identify it: realized vol vs 30-day mean, autocorrelation of daily returns, volume profile vs 20-day average vol elevated + autocorrelation negative = mean-reversion regime - momentum will destroy you here autocorrelation positive + vol compressing = trending regime - reversion will destroy you here wrong regime turns a 60% signal into a 40% signal - you're paying the house right regime turns a 54% signal into a 65% edge - kelly compounds it into something insane bookmark this before you open another chart Dm me "Quant" for full quant course data is free, formula is public, 150 lines of python to run the whole thing they didn't hide the signals from you they hid the question you were supposed to ask first

Livsun

120,252 görüntüleme • 1 ay önce

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 görüntüleme • 8 gün önce

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

12,857 görüntüleme • 12 gün önce

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

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

Livsun

13,335 görüntüleme • 2 ay önce

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

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

Livsun

25,988 görüntüleme • 2 ay önce

A 91-year-old professor is why Nvidia is worth $4 trillion. His name is Gilbert Strang. He teaches linear algebra at MIT. Every AI model on Earth runs on his course. The course has been free on YouTube since 2005. The videos have earned him nothing. MIT 18.06 opens with "The Geometry of Linear Equations." No advanced math. Strang takes a system of two equations, draws it two ways, and shows the class that a matrix is a picture, not an abstraction. The row picture is two lines that cross. The column picture is two arrows that sum to a target. Every neural network on Earth operates on the column picture. Strang first taught linear algebra at MIT in 1962. He wrote the textbook in 1976. It is on every serious engineer's shelf. Every quant fund, every ML lab, every rendering engine at Pixar is running his math. His central insight is that most people are taught matrices as bookkeeping. That is the first thing to unlearn. A matrix is a linear transformation. A linear transformation is a way of moving space. Once you see the space move, the math stops being algebra and becomes geometry. The Kalman filter is a linear system. PCA is a linear system. Every gradient step in a neural net is a matrix-vector product. GPT is a stack of matrix-vector products, each one a scene from MIT 18.06 running on a Blackwell GPU. He retired in 2023 after 61 years at MIT. The course is still up. Watched tens of millions of times. The chip is $40,000. Strang never asked for a royalty.
37:24

A 91-year-old professor is why Nvidia is worth $4 trillion. His name is Gilbert Strang. He teaches linear algebra at MIT. Every AI model on Earth runs on his course. The course has been free on YouTube since 2005. The videos have earned him nothing. MIT 18.06 opens with "The Geometry of Linear Equations." No advanced math. Strang takes a system of two equations, draws it two ways, and shows the class that a matrix is a picture, not an abstraction. The row picture is two lines that cross. The column picture is two arrows that sum to a target. Every neural network on Earth operates on the column picture. Strang first taught linear algebra at MIT in 1962. He wrote the textbook in 1976. It is on every serious engineer's shelf. Every quant fund, every ML lab, every rendering engine at Pixar is running his math. His central insight is that most people are taught matrices as bookkeeping. That is the first thing to unlearn. A matrix is a linear transformation. A linear transformation is a way of moving space. Once you see the space move, the math stops being algebra and becomes geometry. The Kalman filter is a linear system. PCA is a linear system. Every gradient step in a neural net is a matrix-vector product. GPT is a stack of matrix-vector products, each one a scene from MIT 18.06 running on a Blackwell GPU. He retired in 2023 after 61 years at MIT. The course is still up. Watched tens of millions of times. The chip is $40,000. Strang never asked for a royalty.

Ochob

127,409 görüntüleme • 13 gün önce

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

25,048 görüntüleme • 1 ay önce

My ex laughed when she saw me writing Python scripts instead of going to interviews. "You're calculating… weather probabilities?" I was. Every market on Polymarket runs on one equation: C(q) = b · ln Σ e^(qi/b) It's called LMSR. The same math that GPT uses to pick the next word - Polymarket uses to price beliefs. 93% of traders don't know this formula exists. They look at 40¢ and think "cheap." I look at 40¢ and calculate the theoretical price is 60¢. That's a 20-cent edge per share. One position: market at 0.35, my model said 0.55. Bought in. Market resolved. +$4,200. But the real edge isn't the formula. It's what happens after. I'm use for copytrade bots: Kelly criterion says bet 20% of your bankroll. Every pro I've studied bets 5%. Base rates, not gut feelings. Bayesian updates, not Twitter threads. 93% of Polymarket wallets lose money. They trade on vibes while the math is sitting on Wikipedia. My roommate just asked me to explain the formula to him. I wrote an entire breakdown - LMSR, expected value, Kelly sizing, the 5 cognitive traps that blow up accounts.
0:25

My ex laughed when she saw me writing Python scripts instead of going to interviews. "You're calculating… weather probabilities?" I was. Every market on Polymarket runs on one equation: C(q) = b · ln Σ e^(qi/b) It's called LMSR. The same math that GPT uses to pick the next word - Polymarket uses to price beliefs. 93% of traders don't know this formula exists. They look at 40¢ and think "cheap." I look at 40¢ and calculate the theoretical price is 60¢. That's a 20-cent edge per share. One position: market at 0.35, my model said 0.55. Bought in. Market resolved. +$4,200. But the real edge isn't the formula. It's what happens after. I'm use for copytrade bots: Kelly criterion says bet 20% of your bankroll. Every pro I've studied bets 5%. Base rates, not gut feelings. Bayesian updates, not Twitter threads. 93% of Polymarket wallets lose money. They trade on vibes while the math is sitting on Wikipedia. My roommate just asked me to explain the formula to him. I wrote an entire breakdown - LMSR, expected value, Kelly sizing, the 5 cognitive traps that blow up accounts.

Lunar

192,972 görüntüleme • 5 ay önce

🚨 SOMETHING VERY STRANGE IS HAPPENING The stock market keeps pushing to new all-time highs. But nobody is paying attention to what’s actually happening. Semiconductor stocks are now worth $13.4T. That’s 19.7% of the entire S&P 500. 4x growth in just five years. And all of that growth depends on one trade: AI. Numbers do not lie: - AI chips generate 50% of all semiconductor revenue - They represent less than 0.2% of total chip shipments - A small group of companies is carrying the entire market Nvidia. Broadcom. TSMC. The same companies every major institution already owns. Here’s how the bubble feeds itself: - Big players fund each other - Partnerships create paper revenue - Money circulates inside the same system We have seen this before: 2000: - A few tech companies carried the entire market - Massive valuations - Narratives driving everything Then reality hit. The S&P 500 collapsed 50%. Now we’re watching the same cycle again. Less than 0.2% of chip volumes are now holding up trillions in market value. And one cut in AI spending is all it takes to break the entire market. Remember, I’ve predicted all the market tops and bottoms for the last 15 years, including the exact Bitcoin bottom at $16,000 three years ago and the top at $126,000 in October. If you missed those calls, don’t worry. I’ll call the next one too. Turn notifications on. If you’re not following yet, you’ll understand why that was a mistake later.
0:33

🚨 SOMETHING VERY STRANGE IS HAPPENING The stock market keeps pushing to new all-time highs. But nobody is paying attention to what’s actually happening. Semiconductor stocks are now worth $13.4T. That’s 19.7% of the entire S&P 500. 4x growth in just five years. And all of that growth depends on one trade: AI. Numbers do not lie: - AI chips generate 50% of all semiconductor revenue - They represent less than 0.2% of total chip shipments - A small group of companies is carrying the entire market Nvidia. Broadcom. TSMC. The same companies every major institution already owns. Here’s how the bubble feeds itself: - Big players fund each other - Partnerships create paper revenue - Money circulates inside the same system We have seen this before: 2000: - A few tech companies carried the entire market - Massive valuations - Narratives driving everything Then reality hit. The S&P 500 collapsed 50%. Now we’re watching the same cycle again. Less than 0.2% of chip volumes are now holding up trillions in market value. And one cut in AI spending is all it takes to break the entire market. Remember, I’ve predicted all the market tops and bottoms for the last 15 years, including the exact Bitcoin bottom at $16,000 three years ago and the top at $126,000 in October. If you missed those calls, don’t worry. I’ll call the next one too. Turn notifications on. If you’re not following yet, you’ll understand why that was a mistake later.

Alex Mason 👁△

227,729 görüntüleme • 1 ay önce