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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...

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Livsun

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25,048 просмотров • 1 месяц назад •via X (Twitter)

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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

376,195 просмотров • 2 месяцев назад

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 просмотров • 1 месяц назад

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 просмотров • 2 месяцев назад

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

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

Livsun

30,217 просмотров • 1 месяц назад

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

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

Livsun

266,884 просмотров • 2 месяцев назад

renaissance technologies hasn't had a losing year since 1988 not because they're smarter or have better data they're asking a completely different question than everyone else while retail asks "will it go up?" quant desks ask something else entirely which state is the market in right now, and with what probability does it transition to the next tool for that: markov chains markets cycle through states - trending, ranging, volatile, reversing each transition has a historical probability you can calculate trending -> stays trending: 48% trending -> ranging: 34% trending -> reversing: 18% you don't pick direction. you calculate expected value across every possible transition enter only when the math is positive that's it. that's the entire edge the framework is 119 years old - andrei markov published it in 1906 it's in every statistics textbook on earth Bookmark it or lose last chance to be quant nobody in your trading group ever mentioned it
2:02

renaissance technologies hasn't had a losing year since 1988 not because they're smarter or have better data they're asking a completely different question than everyone else while retail asks "will it go up?" quant desks ask something else entirely which state is the market in right now, and with what probability does it transition to the next tool for that: markov chains markets cycle through states - trending, ranging, volatile, reversing each transition has a historical probability you can calculate trending -> stays trending: 48% trending -> ranging: 34% trending -> reversing: 18% you don't pick direction. you calculate expected value across every possible transition enter only when the math is positive that's it. that's the entire edge the framework is 119 years old - andrei markov published it in 1906 it's in every statistics textbook on earth Bookmark it or lose last chance to be quant nobody in your trading group ever mentioned it

Livsun

51,500 просмотров • 2 месяцев назад

a prop trader from chicago made $847k in 180 days just by asking one question every single morning that 99% of traders never ask he didn't build a new model didn't touch machine learning just opened excel and spent 8 minutes on one calculationthe question: what state is the market in right now, and where does it statistically go next most traders ask "will this go up or down". that's 50/50 he started asking "is the market trending, ranging, or reversing" and then looked at the historical probability of each transitionturns out markets don't flip randomly they cycle through states. each state has a fixed probability of shifting to the next onehe built a 5x5 grid on a napkin: trending up -> 68% stays trending, 21% flips to range, 11% reverses ranging -> 54% stays range, 28% breaks up, 18% breaks down trending down -> 61% stays falling, 24% to range, 15% reverses he didn't predict direction he just calculated which state had the highest expected value and sized the position with kelly criterion that's the entire edgethe framework is from 1906 - andrei markov. free in every probability textbook on earthrenaissance technologies has been running this since 1988 37 years of 66% annual returnsdata costs nothing - yahoo finance, federal reserve, any broker implementation is 200 lines of python what separates him from the retail traders losing money isn't intelligence or capital or luck it's that he was willing to think differently about the same data everyone else sees every single day they kept you staring at candles while the people who got it were reading transition matrices bookmark this - you're either asking the wrong question or you're not asking it at all
0:58

a prop trader from chicago made $847k in 180 days just by asking one question every single morning that 99% of traders never ask he didn't build a new model didn't touch machine learning just opened excel and spent 8 minutes on one calculationthe question: what state is the market in right now, and where does it statistically go next most traders ask "will this go up or down". that's 50/50 he started asking "is the market trending, ranging, or reversing" and then looked at the historical probability of each transitionturns out markets don't flip randomly they cycle through states. each state has a fixed probability of shifting to the next onehe built a 5x5 grid on a napkin: trending up -> 68% stays trending, 21% flips to range, 11% reverses ranging -> 54% stays range, 28% breaks up, 18% breaks down trending down -> 61% stays falling, 24% to range, 15% reverses he didn't predict direction he just calculated which state had the highest expected value and sized the position with kelly criterion that's the entire edgethe framework is from 1906 - andrei markov. free in every probability textbook on earthrenaissance technologies has been running this since 1988 37 years of 66% annual returnsdata costs nothing - yahoo finance, federal reserve, any broker implementation is 200 lines of python what separates him from the retail traders losing money isn't intelligence or capital or luck it's that he was willing to think differently about the same data everyone else sees every single day they kept you staring at candles while the people who got it were reading transition matrices bookmark this - you're either asking the wrong question or you're not asking it at all

Livsun

41,634 просмотров • 24 дней назад

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 просмотров • 1 месяц назад

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

112,161 просмотров • 1 месяц назад

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 просмотров • 2 месяцев назад

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

269,381 просмотров • 23 дней назад

market opens at 9:30am his trade was already in at 4am neural net trained on 11 years of tick data called that setup 5 hours before candle even formed he's not smarter than the market. just built something that reads patterns human eyes can't process fast enough 4,200 data points per second, 847,000 labeled historical setups, running on a $40/mo server surfaces 3-4 trades a day. he takes top 2 last 90 days: 71% win rate, 2.3 avg risk/reward while retail traders watch news at open, this system already decided before sunrise you're not losing because your analysis is wrong you're losing because you're competing with something that doesn't sleep, panic or second-guess itself most people think this requires a PhD and a $2M quant desk he built his whole setup for under $500 using a free dataset and 3 weeks of evenings Bookmark this setup edge was never hidden behind a paywall or locked inside a fund it was sitting in a format nobody bothered to train on
0:32

market opens at 9:30am his trade was already in at 4am neural net trained on 11 years of tick data called that setup 5 hours before candle even formed he's not smarter than the market. just built something that reads patterns human eyes can't process fast enough 4,200 data points per second, 847,000 labeled historical setups, running on a $40/mo server surfaces 3-4 trades a day. he takes top 2 last 90 days: 71% win rate, 2.3 avg risk/reward while retail traders watch news at open, this system already decided before sunrise you're not losing because your analysis is wrong you're losing because you're competing with something that doesn't sleep, panic or second-guess itself most people think this requires a PhD and a $2M quant desk he built his whole setup for under $500 using a free dataset and 3 weeks of evenings Bookmark this setup edge was never hidden behind a paywall or locked inside a fund it was sitting in a format nobody bothered to train on

Livsun

70,370 просмотров • 1 месяц назад

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 просмотров • 1 месяц назад

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 просмотров • 1 месяц назад

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 просмотров • 28 дней назад

option pricing models used to cost $50k a year in bloomberg fees. they're free in 2026 and almost nobody's noticed yet black-scholes, binomial trees, full vol surfaces - hedge funds paid terminal licensing to keep this math away from retail for 30 years moat was never the math. black-scholes published in 1973, free in every university library moat was the terminal. $2,000/month just to run the calculation pause at 0:23 - below the title on the chalkboard there's a partial formula. that formula costs $40k/year to access inside bloomberg it's been free since 1973 real edge was never black-scholes itself - it was the vol surface. implied volatility across every strike and expiry, showing you what the market thinks the actual distribution of outcomes is retail never saw it. terminal users built strategies around it every morning now yfinance pulls live option chains for free. scipy builds the full surface in under 60 seconds what took a prop desk analyst his whole morning takes a laptop and one lunch break access gap closed. knowledge gap didn't, and that's where the actual edge lives now bookmark this
1:29

option pricing models used to cost $50k a year in bloomberg fees. they're free in 2026 and almost nobody's noticed yet black-scholes, binomial trees, full vol surfaces - hedge funds paid terminal licensing to keep this math away from retail for 30 years moat was never the math. black-scholes published in 1973, free in every university library moat was the terminal. $2,000/month just to run the calculation pause at 0:23 - below the title on the chalkboard there's a partial formula. that formula costs $40k/year to access inside bloomberg it's been free since 1973 real edge was never black-scholes itself - it was the vol surface. implied volatility across every strike and expiry, showing you what the market thinks the actual distribution of outcomes is retail never saw it. terminal users built strategies around it every morning now yfinance pulls live option chains for free. scipy builds the full surface in under 60 seconds what took a prop desk analyst his whole morning takes a laptop and one lunch break access gap closed. knowledge gap didn't, and that's where the actual edge lives now bookmark this

Livsun

14,225 просмотров • 1 месяц назад

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

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

cvxv666

93,073 просмотров • 24 дней назад

a hotel front desk clerk in nashville figured out why markets move exactly when they do not direction, not news - the actual mechanism of why a move happens at all he works overnight shift, 11pm to 7am. lobby goes quiet after midnight, nothing but a monitor and a wifi connection question that started it: why does volatility cluster he'd read it in passing - options dealers cause price moves they didn't intend spent 6 hours across two nights searching, wrote everything into a google doc called "options thing" here's what he found when you buy a call option from a dealer, dealer has a new problem. they sold you the right to buy shares at a certain price if stock moves up, your option gains value and dealer owes you money. to protect themselves they have to buy shares immediately - no discretion, no delay amount they have to buy at every price level is published every second for free - it's open interest on the options chain. every brokerage shows it he built a spreadsheet every morning at 9:29am, one minute before open, he pulled SPY's options chain and calculated where dealers were most exposed marked strikes with heaviest call open interest. watched what happened in first 30 minutes of trading day 12 he stopped breathing for a second price moved to the strike with heaviest dealer exposure 73% of the time in the first 45 minutes not because of a chart pattern, not because of any signal because 400 dealers ran the same hedge calculation at open, and all of them had to buy the same shares at the same time he started calling it gravity price pulls toward certain strikes when dealer positioning is heavy enough - not prediction, mechanics math has a name: gamma exposure, or GEX SpotGamma built a whole company surfacing it. Squeeze Metrics published an academic paper on mechanics in 2018 python implementation is around 400 lines, nothing but the options chain you already have he built it in google colab over 3 weekends, free, working only on nights the lobby was empty tracked it against 60 days of live SPY data on negative GEX days - dealers short gamma, forced to amplify moves - average daily range expanded 2.8x on positive GEX days, 63% of sessions closed within half a percent of open this is not a signal. it's a regime classifier negative GEX: something moves big today, whichever direction gets started. buy straddles, size up, let dealers carry it positive GEX: nothing moves today. dealers kill every attempt before it gets 2 points sell premium, collect theta, sleep at month 4 he went live. $4,200 account, pure options, no directional bet six months later: $4,200 became $19,800 he still works overnight shift. told me about it in the lobby at 3am when i asked what he was typing google doc still says "options thing" - he never renamed it i asked why he never shared this. he looked at the lobby doors and said "who would believe a hotel clerk" data is free, formula is public, wall street has run this since 2017 they assumed retail would never think to read options flow as a mechanical map of where price has to go they were right about retail. they weren't right about him bookmark this and go build it market tells you exactly where it's going. you just have to stop reading the wrong layer Write your thought below
0:39

a hotel front desk clerk in nashville figured out why markets move exactly when they do not direction, not news - the actual mechanism of why a move happens at all he works overnight shift, 11pm to 7am. lobby goes quiet after midnight, nothing but a monitor and a wifi connection question that started it: why does volatility cluster he'd read it in passing - options dealers cause price moves they didn't intend spent 6 hours across two nights searching, wrote everything into a google doc called "options thing" here's what he found when you buy a call option from a dealer, dealer has a new problem. they sold you the right to buy shares at a certain price if stock moves up, your option gains value and dealer owes you money. to protect themselves they have to buy shares immediately - no discretion, no delay amount they have to buy at every price level is published every second for free - it's open interest on the options chain. every brokerage shows it he built a spreadsheet every morning at 9:29am, one minute before open, he pulled SPY's options chain and calculated where dealers were most exposed marked strikes with heaviest call open interest. watched what happened in first 30 minutes of trading day 12 he stopped breathing for a second price moved to the strike with heaviest dealer exposure 73% of the time in the first 45 minutes not because of a chart pattern, not because of any signal because 400 dealers ran the same hedge calculation at open, and all of them had to buy the same shares at the same time he started calling it gravity price pulls toward certain strikes when dealer positioning is heavy enough - not prediction, mechanics math has a name: gamma exposure, or GEX SpotGamma built a whole company surfacing it. Squeeze Metrics published an academic paper on mechanics in 2018 python implementation is around 400 lines, nothing but the options chain you already have he built it in google colab over 3 weekends, free, working only on nights the lobby was empty tracked it against 60 days of live SPY data on negative GEX days - dealers short gamma, forced to amplify moves - average daily range expanded 2.8x on positive GEX days, 63% of sessions closed within half a percent of open this is not a signal. it's a regime classifier negative GEX: something moves big today, whichever direction gets started. buy straddles, size up, let dealers carry it positive GEX: nothing moves today. dealers kill every attempt before it gets 2 points sell premium, collect theta, sleep at month 4 he went live. $4,200 account, pure options, no directional bet six months later: $4,200 became $19,800 he still works overnight shift. told me about it in the lobby at 3am when i asked what he was typing google doc still says "options thing" - he never renamed it i asked why he never shared this. he looked at the lobby doors and said "who would believe a hotel clerk" data is free, formula is public, wall street has run this since 2017 they assumed retail would never think to read options flow as a mechanical map of where price has to go they were right about retail. they weren't right about him bookmark this and go build it market tells you exactly where it's going. you just have to stop reading the wrong layer Write your thought below

Livsun

200,125 просмотров • 21 дней назад

A billionaire trader has spent 40 years trying to delete a one-hour documentary. It shows him making $100 million in a single afternoon. He predicted the crash that made it possible three months in advance. He has never explained why he wants the film gone. His name is Paul Tudor Jones. The film is on YouTube. The documentary is called "Trader." PBS filmed it in 1987, three months before Black Monday. Jones was 32 years old, working from a small New York office, wearing shorts and a t-shirt, yelling at his phones, throwing paper across the room, and sleeping under his desk. The film captures him and his research partner Peter Borish overlaying a chart of the 1929 market on 1987, month by month. The two charts tracked within one percent. Borish said this is exactly what happened in 1929. Jones said if the analog holds, October is when it breaks. On October 19, 1987, the Dow fell 22.6 percent in a single day. It remains the largest one-day percentage loss in stock market history. That afternoon, Tudor Jones covered his shorts and made roughly $100 million. He was 33 years old. He was one of the very few traders on the street who came out ahead. He tried to bury the tape because it made him look reckless in a professional world that punished swagger. Twenty years of legal effort did not delete it. Someone kept a copy. It is on YouTube. It has fewer views than most makeup tutorials. The film is not really about a crash. It is about a specific philosophy of trading. Jones is shown building conviction slowly, sizing carefully, then striking hard when the setup arrives. He is never once shown making a random bet. He is shown doing the same thing five times a day, every day, for three months. His signature line, repeated across a 45-year career: "The most important rule of trading is to play great defense, not great offense." He does not try to be right. He tries not to lose. He sets stops tight, cuts positions fast, and never averages down on a loser. Every trade in the film follows this template. Tudor Investment Corp, the fund he founded in 1980, has compounded at roughly 19 percent a year for 45 years. He is 71 years old and still trading. His method has not changed since the film. The lesson: greatness in markets is a refusal, not a talent. Refusal to be reckless. Refusal to be certain. Refusal to average down. Refusal to trust yourself in a drawdown. Tudor Jones has refused those refusals for 45 years. The tape is free. The philosophy is repeated in every trade. Most traders will never watch it.
54:09

A billionaire trader has spent 40 years trying to delete a one-hour documentary. It shows him making $100 million in a single afternoon. He predicted the crash that made it possible three months in advance. He has never explained why he wants the film gone. His name is Paul Tudor Jones. The film is on YouTube. The documentary is called "Trader." PBS filmed it in 1987, three months before Black Monday. Jones was 32 years old, working from a small New York office, wearing shorts and a t-shirt, yelling at his phones, throwing paper across the room, and sleeping under his desk. The film captures him and his research partner Peter Borish overlaying a chart of the 1929 market on 1987, month by month. The two charts tracked within one percent. Borish said this is exactly what happened in 1929. Jones said if the analog holds, October is when it breaks. On October 19, 1987, the Dow fell 22.6 percent in a single day. It remains the largest one-day percentage loss in stock market history. That afternoon, Tudor Jones covered his shorts and made roughly $100 million. He was 33 years old. He was one of the very few traders on the street who came out ahead. He tried to bury the tape because it made him look reckless in a professional world that punished swagger. Twenty years of legal effort did not delete it. Someone kept a copy. It is on YouTube. It has fewer views than most makeup tutorials. The film is not really about a crash. It is about a specific philosophy of trading. Jones is shown building conviction slowly, sizing carefully, then striking hard when the setup arrives. He is never once shown making a random bet. He is shown doing the same thing five times a day, every day, for three months. His signature line, repeated across a 45-year career: "The most important rule of trading is to play great defense, not great offense." He does not try to be right. He tries not to lose. He sets stops tight, cuts positions fast, and never averages down on a loser. Every trade in the film follows this template. Tudor Investment Corp, the fund he founded in 1980, has compounded at roughly 19 percent a year for 45 years. He is 71 years old and still trading. His method has not changed since the film. The lesson: greatness in markets is a refusal, not a talent. Refusal to be reckless. Refusal to be certain. Refusal to average down. Refusal to trust yourself in a drawdown. Tudor Jones has refused those refusals for 45 years. The tape is free. The philosophy is repeated in every trade. Most traders will never watch it.

veles

3,577,996 просмотров • 10 дней назад

The CEO of the biggest bank in America just dropped a warning that nobody on Wall Street wanted to hear. The market is overheating. His proof? ONE stock that doubled from $500 billion to $1 trillion in 48 trading days. The fastest doubling in market history. Here's what happened and what it means for your investments: May 29, 2026. Jamie Dimon walks on stage at the Reagan National Economic Forum. CNBC's cameras are rolling. JPMorgan runs $4 trillion in assets. When he talks, every desk on Wall Street listens. He looks at the interviewer and says the market is exuberant. Then he does something he almost never does. He names a stock. Micron. The memory chipmaker most people couldn't pick out of a lineup. The stock that just doubled from $500 billion to $1 trillion in 48 trading days. The fastest doubling in market history. It beat Samsung's record from six weeks earlier. Samsung did it in 82 days. It beat Tesla's record by 182 days. Tesla took 230. Micron did it in 48. Now look at the rest of the chart. 850% return in 12 months. A UBS analyst tripled his price target in a single note. From $535 to $1,625. Bloomberg called it the most aggressive analyst revision in recent semiconductor history. The stock jumped 19% the same day. SK Hynix did the same move 13 days later in South Korea. This is what the top of a cycle looks like. And Dimon knows it because he's seen it before. Here's the part that should concern you. Dimon didn't stop at "exuberant." He said credit spreads are at historic lows. He said interest rates are gravity to asset prices. He said inflation could easily hit 4% this year. Then he closed with this: "If something goes wrong, those asset prices can come down." That's not bearish commentary. That's a structural risk warning from the man who runs the biggest balance sheet in American finance. He's not predicting a crash. He's flagging what every cycle looks like before one happens. And retail just walked straight into the trade. A viral wallstreetbets post flashed a +6,476% gain on Micron LEAPS this week. Retail loaded up at the top. Same way they did in 2021. Same way they did in 2000. The story always sounds different. The behavior never is. Here's the math nobody's running. Micron's earnings ARE growing. Wall Street expects 121% annual growth for the next five years. The PEG ratio is 0.07. By traditional value metrics, the stock isn't even expensive. That's not the problem. The problem is concentration. Retail isn't buying Micron because they ran the math. They're buying because the chart looks like a moonshot. They're buying because a UBS analyst tripled his price target. They're buying because the stock is trending on every finance account on X. They're buying narrative. Not inputs. And the thing about narrative-driven trades is they work right up until they don't. This is the pattern. Every cycle. No exceptions. In 2000, the story was the internet. In 2007, the story was housing. In 2021, the story was meme stocks and SPACs. The narrative was always real. The companies were real. The growth was real. The problem was retail showing up at the end and paying institutional prices on the way out. Dimon has watched this movie three times in his career. He's seen 1996. He's seen 2007. He's seen 2020. He knows exuberance can run for years before it breaks. He also knows nobody rings a bell at the top. Here's what most investors miss about a warning like this. Dimon isn't telling you to sell. He's telling you the math is getting fragile. That when the rotation comes, the stocks that doubled in 48 trading days don't slowly drift lower. They get repriced violently. And the people who get hurt the worst are the ones who showed up last and held the longest. That's the retail trade. Every cycle. The institutions that ran Micron from $500 billion to $1 trillion already have rules for when to trim. Most retail investors don't have any of that. They have conviction. They have hope. They have a Discord screenshot from someone up 6,000% on LEAPS. That's not a strategy. That's a position waiting to get unwound. The investors who survive this cycle aren't the ones who can call the top. They're the ones whose system already decided when to rotate before the headlines forced the decision. Automated. Rules-based. No emotion. No guessing. That's exactly what Surmount was built for. Rules-based strategies that execute on inputs, not vibes. That trim into strength. That don't need Jamie Dimon to flash a warning before they react. When the next "obvious winner" gets repriced, you're not the one scrambling. You're already positioned:
3:56

The CEO of the biggest bank in America just dropped a warning that nobody on Wall Street wanted to hear. The market is overheating. His proof? ONE stock that doubled from $500 billion to $1 trillion in 48 trading days. The fastest doubling in market history. Here's what happened and what it means for your investments: May 29, 2026. Jamie Dimon walks on stage at the Reagan National Economic Forum. CNBC's cameras are rolling. JPMorgan runs $4 trillion in assets. When he talks, every desk on Wall Street listens. He looks at the interviewer and says the market is exuberant. Then he does something he almost never does. He names a stock. Micron. The memory chipmaker most people couldn't pick out of a lineup. The stock that just doubled from $500 billion to $1 trillion in 48 trading days. The fastest doubling in market history. It beat Samsung's record from six weeks earlier. Samsung did it in 82 days. It beat Tesla's record by 182 days. Tesla took 230. Micron did it in 48. Now look at the rest of the chart. 850% return in 12 months. A UBS analyst tripled his price target in a single note. From $535 to $1,625. Bloomberg called it the most aggressive analyst revision in recent semiconductor history. The stock jumped 19% the same day. SK Hynix did the same move 13 days later in South Korea. This is what the top of a cycle looks like. And Dimon knows it because he's seen it before. Here's the part that should concern you. Dimon didn't stop at "exuberant." He said credit spreads are at historic lows. He said interest rates are gravity to asset prices. He said inflation could easily hit 4% this year. Then he closed with this: "If something goes wrong, those asset prices can come down." That's not bearish commentary. That's a structural risk warning from the man who runs the biggest balance sheet in American finance. He's not predicting a crash. He's flagging what every cycle looks like before one happens. And retail just walked straight into the trade. A viral wallstreetbets post flashed a +6,476% gain on Micron LEAPS this week. Retail loaded up at the top. Same way they did in 2021. Same way they did in 2000. The story always sounds different. The behavior never is. Here's the math nobody's running. Micron's earnings ARE growing. Wall Street expects 121% annual growth for the next five years. The PEG ratio is 0.07. By traditional value metrics, the stock isn't even expensive. That's not the problem. The problem is concentration. Retail isn't buying Micron because they ran the math. They're buying because the chart looks like a moonshot. They're buying because a UBS analyst tripled his price target. They're buying because the stock is trending on every finance account on X. They're buying narrative. Not inputs. And the thing about narrative-driven trades is they work right up until they don't. This is the pattern. Every cycle. No exceptions. In 2000, the story was the internet. In 2007, the story was housing. In 2021, the story was meme stocks and SPACs. The narrative was always real. The companies were real. The growth was real. The problem was retail showing up at the end and paying institutional prices on the way out. Dimon has watched this movie three times in his career. He's seen 1996. He's seen 2007. He's seen 2020. He knows exuberance can run for years before it breaks. He also knows nobody rings a bell at the top. Here's what most investors miss about a warning like this. Dimon isn't telling you to sell. He's telling you the math is getting fragile. That when the rotation comes, the stocks that doubled in 48 trading days don't slowly drift lower. They get repriced violently. And the people who get hurt the worst are the ones who showed up last and held the longest. That's the retail trade. Every cycle. The institutions that ran Micron from $500 billion to $1 trillion already have rules for when to trim. Most retail investors don't have any of that. They have conviction. They have hope. They have a Discord screenshot from someone up 6,000% on LEAPS. That's not a strategy. That's a position waiting to get unwound. The investors who survive this cycle aren't the ones who can call the top. They're the ones whose system already decided when to rotate before the headlines forced the decision. Automated. Rules-based. No emotion. No guessing. That's exactly what Surmount was built for. Rules-based strategies that execute on inputs, not vibes. That trim into strength. That don't need Jamie Dimon to flash a warning before they react. When the next "obvious winner" gets repriced, you're not the one scrambling. You're already positioned:

Surmount

98,529 просмотров • 1 месяц назад