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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...

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Livsun

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30,300 次观看 • 2 个月前 •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

376,877 次观看 • 3 个月前

a quant at citadel told me something that broke my entire trading framework "we don't predict markets. we model the state machine" he explained markov cains 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 - andrei markov published it in 1906 hedge funds use it every day, 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 and use in your own strategy 37 years without a losing year, 66% annual returns paper is free, data is free, implementation is 200 lines of python they kept you staring at candles while they ran probability tables
0:30

a quant at citadel told me something that broke my entire trading framework "we don't predict markets. we model the state machine" he explained markov cains 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 - andrei markov published it in 1906 hedge funds use it every day, 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 and use in your own strategy 37 years without a losing year, 66% annual returns paper is free, data is free, implementation is 200 lines of python they kept you staring at candles while they ran probability tables

Livsun

19,111 次观看 • 5 天前

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

268,026 次观看 • 3 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

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

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 次观看 • 3 个月前

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

41,765 次观看 • 1 个月前

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

73% win rate, $340k in 11 months no indicators, just a 6x6 probability matrix it's called a markov chain - math from 1906 wall street quants used it for decades, retail never heard of it markets cycle between states: trending, ranging, volatile, choppy probability of which state comes next is measurable, stable across decades if market is in "ranging" today: 62% -> trending, 23% -> stays ranging, 15% -> volatile you stop predicting price direction you start predicting market structure one is a coin flip dressed in candlesticks the other is a math problem with an answer Bookmark and add this indicator in your tools RSI: 1978. markov chains: 1906 72 years older and it's eating technical analysis alive
0:58

73% win rate, $340k in 11 months no indicators, just a 6x6 probability matrix it's called a markov chain - math from 1906 wall street quants used it for decades, retail never heard of it markets cycle between states: trending, ranging, volatile, choppy probability of which state comes next is measurable, stable across decades if market is in "ranging" today: 62% -> trending, 23% -> stays ranging, 15% -> volatile you stop predicting price direction you start predicting market structure one is a coin flip dressed in candlesticks the other is a math problem with an answer Bookmark and add this indicator in your tools RSI: 1978. markov chains: 1906 72 years older and it's eating technical analysis alive

Livsun

44,871 次观看 • 2 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

13,315 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

robert engle won the nobel prize in economics for proving something hedge funds already knew they never bothered to mention it to retail price direction is mostly noise. but volatility? predictable. mathematically, provably, across every liquid market ever studied it clusters - that's not a pattern someone found, it's a structural law. high vol today predicts high vol tomorrow with 70%+ historical accuracy the model is called GARCH. published in every econometrics textbook on earth, chapter 4, about 60 lines of python to run quant desks at citadel and D.E. Shaw don't ask "will it go up?" - they ask "will the next move be large or small?" because sizing correctly inside a vol regime is worth more than being right on direction a trader right 48% of the time who sizes with vol awareness beats someone right 62% of the time sizing blindly - every time, over any long enough sample run it on 10 years of SPY data: > low vol state -> 74% chance next session stays low vol > vol spike -> 81% chance next session is also elevated now you're not predicting markets. you're reading a state machine the market keeps filling in for you every single session with real data the predictable part of markets was never price direction it was the distribution of price. the size of the moves. which regime you're currently inside math is free, data is free, implementation is free you were just told to stare at candlesticks instead Bookmark this before the feed buries it
0:30

robert engle won the nobel prize in economics for proving something hedge funds already knew they never bothered to mention it to retail price direction is mostly noise. but volatility? predictable. mathematically, provably, across every liquid market ever studied it clusters - that's not a pattern someone found, it's a structural law. high vol today predicts high vol tomorrow with 70%+ historical accuracy the model is called GARCH. published in every econometrics textbook on earth, chapter 4, about 60 lines of python to run quant desks at citadel and D.E. Shaw don't ask "will it go up?" - they ask "will the next move be large or small?" because sizing correctly inside a vol regime is worth more than being right on direction a trader right 48% of the time who sizes with vol awareness beats someone right 62% of the time sizing blindly - every time, over any long enough sample run it on 10 years of SPY data: > low vol state -> 74% chance next session stays low vol > vol spike -> 81% chance next session is also elevated now you're not predicting markets. you're reading a state machine the market keeps filling in for you every single session with real data the predictable part of markets was never price direction it was the distribution of price. the size of the moves. which regime you're currently inside math is free, data is free, implementation is free you were just told to stare at candlesticks instead Bookmark this before the feed buries it

Hrundel75 🐷

802,471 次观看 • 5 天前

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

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

Livsun

31,722 次观看 • 12 天前

83% of options expire worthless wall street has known this since 1973, it's literally in the original black-scholes derivation they just never told retail which side of that number to stand on implied volatility - what options cost - chronically overstates what volatility actually delivers. not sometimes. structurally. for 40 years straight the gap has a name: volatility risk premium IV runs at 22%. actual realized vol delivers 14%. you pocketed 8 vol points without predicting a single direction, a single earnings move, a single fed decision quant vol desks have been harvesting this gap the whole time: > pull 30-day realized vol from price history (free on any data terminal) > compare to current IV on the options chain (visible on any broker platform) > enter when the spread sits 1.5 standard deviations above its own historical mean > kelly-size based on the 83% historical close rate the spread always closes. vol reverts. that's not a prediction - it's what 40 years of options data shows without a single exception insurance companies don't predict which house burns. they write enough policies that 83% probability compounds into something inevitable every single quarter quant desks run the exact same logic on options markets retail keeps buying calls hoping to be directionally right once. the firm on the other side doesn't care which way price goes the data to build this yourself has been public since 1973. the math is 4 lines of python. every derivatives textbook covers it in chapter 2 Bookmark this they've been on the other side of your trades the whole time
0:30

83% of options expire worthless wall street has known this since 1973, it's literally in the original black-scholes derivation they just never told retail which side of that number to stand on implied volatility - what options cost - chronically overstates what volatility actually delivers. not sometimes. structurally. for 40 years straight the gap has a name: volatility risk premium IV runs at 22%. actual realized vol delivers 14%. you pocketed 8 vol points without predicting a single direction, a single earnings move, a single fed decision quant vol desks have been harvesting this gap the whole time: > pull 30-day realized vol from price history (free on any data terminal) > compare to current IV on the options chain (visible on any broker platform) > enter when the spread sits 1.5 standard deviations above its own historical mean > kelly-size based on the 83% historical close rate the spread always closes. vol reverts. that's not a prediction - it's what 40 years of options data shows without a single exception insurance companies don't predict which house burns. they write enough policies that 83% probability compounds into something inevitable every single quarter quant desks run the exact same logic on options markets retail keeps buying calls hoping to be directionally right once. the firm on the other side doesn't care which way price goes the data to build this yourself has been public since 1973. the math is 4 lines of python. every derivatives textbook covers it in chapter 2 Bookmark this they've been on the other side of your trades the whole time

Livsun

81,220 次观看 • 12 天前

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

Rich Roll on why waiting to "feel like it" is a trap: "You can't think your way into the mood that you seek or the state of mind that you aspire to inhabit. Action is the only thing that can trigger that change." Rich uses running as the perfect illustration of this principle. Imagine you wake up in the morning and you're supposed to do a run because you're training for a race. You don't feel like it. So what do most of us do? "We all resort to that state where we think, 'Well, I don't want to do it right now. I'll just wait until I feel like doing it and then I'll do it then.'" But here's the problem with that logic: "If you're waiting until you feel like doing something, chances are you're probably never going to get to it." The mood you're hoping will arrive on its own? It's not coming. Not without action first. "To take the action despite how you feel about it is the thing that catalyzes the state change." You don't run because you feel motivated. You feel motivated because you ran. He points to what every runner knows from experience: "When they finish the run, they're always glad that they did it. They don't generally regret it. And then they feel better." Notice the sequence. The good feeling comes after the action, not before it. The state change is the reward for showing up, not the prerequisite. And this isn't just about running. As Rich puts it: "That example is applicable to all areas of life." The workout you're avoiding. The conversation you're delaying. The project you're putting off until you're "in the right headspace." You're waiting for a feeling that only exists on the other side of doing the thing.
0:59

Rich Roll on why waiting to "feel like it" is a trap: "You can't think your way into the mood that you seek or the state of mind that you aspire to inhabit. Action is the only thing that can trigger that change." Rich uses running as the perfect illustration of this principle. Imagine you wake up in the morning and you're supposed to do a run because you're training for a race. You don't feel like it. So what do most of us do? "We all resort to that state where we think, 'Well, I don't want to do it right now. I'll just wait until I feel like doing it and then I'll do it then.'" But here's the problem with that logic: "If you're waiting until you feel like doing something, chances are you're probably never going to get to it." The mood you're hoping will arrive on its own? It's not coming. Not without action first. "To take the action despite how you feel about it is the thing that catalyzes the state change." You don't run because you feel motivated. You feel motivated because you ran. He points to what every runner knows from experience: "When they finish the run, they're always glad that they did it. They don't generally regret it. And then they feel better." Notice the sequence. The good feeling comes after the action, not before it. The state change is the reward for showing up, not the prerequisite. And this isn't just about running. As Rich puts it: "That example is applicable to all areas of life." The workout you're avoiding. The conversation you're delaying. The project you're putting off until you're "in the right headspace." You're waiting for a feeling that only exists on the other side of doing the thing.

Kevin Tanaka

10,256 次观看 • 3 个月前

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

93,596 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

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

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

Livsun

10,807 次观看 • 24 天前

Granger and Engle won the nobel prize in economics in 2003 for a formula hedge funds were already running for a decade retail investors still don't know it exists it's called cointegration - and it answers a completely different question than what you're asking you ask "will this stock go up?" pairs desks ask "which two assets have drifted apart and must revert?" if two assets are cointegrated, their spread has a measurable half-life of mean reversion SPY and QQQ: spread half-life of roughly 4 trading days, measured over 12 years of data when that spread hits 2.1 standard deviations, reversion follows within the half-life 76% of the time that's not a hunch. it's conditional probability you can size with kelly and repeat 200 times a year paper is on jstor, data is free, implementation takes a weekend pairs desks have been doing this since the late 80s bookmark this you've been asking the wrong question your whole trading life Reply below what you think
0:47

Granger and Engle won the nobel prize in economics in 2003 for a formula hedge funds were already running for a decade retail investors still don't know it exists it's called cointegration - and it answers a completely different question than what you're asking you ask "will this stock go up?" pairs desks ask "which two assets have drifted apart and must revert?" if two assets are cointegrated, their spread has a measurable half-life of mean reversion SPY and QQQ: spread half-life of roughly 4 trading days, measured over 12 years of data when that spread hits 2.1 standard deviations, reversion follows within the half-life 76% of the time that's not a hunch. it's conditional probability you can size with kelly and repeat 200 times a year paper is on jstor, data is free, implementation takes a weekend pairs desks have been doing this since the late 80s bookmark this you've been asking the wrong question your whole trading life Reply below what you think

Livsun

11,624 次观看 • 1 个月前