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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...

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Livsun

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30,815 次观看 • 2 天前 •via X (Twitter)

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

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

Livsun

188,928 次观看 • 3 个月前

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 次观看 • 2 个月前

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

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

Livsun

26,816 次观看 • 2 个月前

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 次观看 • 2 个月前

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

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

Livsun

14,040 次观看 • 2 个月前

a russian mathematician solved trading in 1906 wall street found the paper 70 years later, built billion-dollar funds around it, never mentioned it to retail andrei markov proved markets aren't random - they're state machines trending, ranging, reversing - each state has a fixed historical probability of shifting to the next build a transition matrix from real 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 identify your state, size with kelly, take the trade when math says yes that's the edge. the whole edge renaissance has run this since 1988. 37 years of 66% annual returns paper is free, data is free, implementation is 200 lines of python Bookmark and use in your own strategy they kept you staring at candles while they ran probability tables
2:30

a russian mathematician solved trading in 1906 wall street found the paper 70 years later, built billion-dollar funds around it, never mentioned it to retail andrei markov proved markets aren't random - they're state machines trending, ranging, reversing - each state has a fixed historical probability of shifting to the next build a transition matrix from real 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 identify your state, size with kelly, take the trade when math says yes that's the edge. the whole edge renaissance has run this since 1988. 37 years of 66% annual returns paper is free, data is free, implementation is 200 lines of python Bookmark and use in your own strategy they kept you staring at candles while they ran probability tables

Livsun

376,877 次观看 • 3 个月前

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 次观看 • 1 个月前

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 次观看 • 2 个月前

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

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

Livsun

25,085 次观看 • 2 个月前

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 次观看 • 1 个月前

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

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

Livsun

11,425 次观看 • 3 个月前

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 次观看 • 3 个月前

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. the hidden skeleton underneath price > market pressure - a tide moving every name in sync > growth vs value rotation > size divergence, mega cap vs small cap > momentum regime - trending or mean reverting > volatility clustering when eigenvector 4 flips, every momentum strategy on earth breaks at the same time same force rotating under all 500 names at once - not coincidence, that's structure retail reads 500 charts. quant desks read 5 transitions data: free. code: 300 lines of python. framework: 1901 been sitting in every linear algebra textbook for 120 years they just never pointed you at the right chapter
2:14

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. the hidden skeleton underneath price > market pressure - a tide moving every name in sync > growth vs value rotation > size divergence, mega cap vs small cap > momentum regime - trending or mean reverting > volatility clustering when eigenvector 4 flips, every momentum strategy on earth breaks at the same time same force rotating under all 500 names at once - not coincidence, that's structure retail reads 500 charts. quant desks read 5 transitions data: free. code: 300 lines of python. framework: 1901 been sitting in every linear algebra textbook for 120 years they just never pointed you at the right chapter

Livsun

13,957 次观看 • 2 个月前

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

70,716 次观看 • 2 个月前

Wall Street pays you to be calm. Then kills you for it a vol trader in chicago told me quietest money on street and the most brutal "retail buys insurance. we sell it. every single day. and we get paid to" i asked why that isn't suicide "because market is calm 80% of the time. calm is default. fear is exception" here's the part that should make you uncomfortable VIX futures curve is normally in contango near-term vol is cheap, far-term is expensive every day curve rolls down toward spot and whoever is short that curve collects roll. automatically it's: > called volatility risk premium > been paying out for decades > market's fee for letting you sleep but here's twist they don't put on retail: curve rolling down isn't luck, it's structural when front-month VIX is below the 2nd month by more than ~1 point, shorting the roll earns ~1-2% a month, nearly risk-free catch is the 20% of the time it's not calm: that's when one week wipes out two years free data. VIX futures term structure is published by CBOE every morning save this post before next vol spike they kept you buying the dip. the desk was selling the calm
0:51

Wall Street pays you to be calm. Then kills you for it a vol trader in chicago told me quietest money on street and the most brutal "retail buys insurance. we sell it. every single day. and we get paid to" i asked why that isn't suicide "because market is calm 80% of the time. calm is default. fear is exception" here's the part that should make you uncomfortable VIX futures curve is normally in contango near-term vol is cheap, far-term is expensive every day curve rolls down toward spot and whoever is short that curve collects roll. automatically it's: > called volatility risk premium > been paying out for decades > market's fee for letting you sleep but here's twist they don't put on retail: curve rolling down isn't luck, it's structural when front-month VIX is below the 2nd month by more than ~1 point, shorting the roll earns ~1-2% a month, nearly risk-free catch is the 20% of the time it's not calm: that's when one week wipes out two years free data. VIX futures term structure is published by CBOE every morning save this post before next vol spike they kept you buying the dip. the desk was selling the calm

Livsun

26,734 次观看 • 1 个月前

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

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

Livsun

14,773 次观看 • 1 个月前

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 次观看 • 1 个月前

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 次观看 • 2 个月前

a market maker in singapore told me the most accurate sentiment gauge on earth is free and updates every 8 hours "you're reading charts. i'm reading leverage" he told about funding rate every perpetual swap charges longs or shorts a fee every 8 hours to keep price pinned to spot positive funding: longs pay shorts. negative: shorts pay longs the number tells you who's overcrowded he pulled 2 years of data: > funding > +0.05% per 8h: longs maxed, mean reversion within 24h hits 68% > funding funding near zero: nobody committed, stay out, range bleeds you the play: crowded longs + parabolic price = the top is closer than it looks crowded shorts + capitulation = the bottom is in you don't predict. you read the leverage and fade the crowd binance publishes funding for free. every exchange does. one API call no indicator. no signal service. just the cost of being wrong, printed every 8 hours they don't sell you this because it's already on your screen bookmark it and check funding before your next trade the crowd always tells you where it's trapped. you just have to read the bill
0:20

a market maker in singapore told me the most accurate sentiment gauge on earth is free and updates every 8 hours "you're reading charts. i'm reading leverage" he told about funding rate every perpetual swap charges longs or shorts a fee every 8 hours to keep price pinned to spot positive funding: longs pay shorts. negative: shorts pay longs the number tells you who's overcrowded he pulled 2 years of data: > funding > +0.05% per 8h: longs maxed, mean reversion within 24h hits 68% > funding funding near zero: nobody committed, stay out, range bleeds you the play: crowded longs + parabolic price = the top is closer than it looks crowded shorts + capitulation = the bottom is in you don't predict. you read the leverage and fade the crowd binance publishes funding for free. every exchange does. one API call no indicator. no signal service. just the cost of being wrong, printed every 8 hours they don't sell you this because it's already on your screen bookmark it and check funding before your next trade the crowd always tells you where it's trapped. you just have to read the bill

Livsun

19,380 次观看 • 1 个月前

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

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

delost

112,271 次观看 • 2 个月前