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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 tradeshow more

Livsun

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

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 Aufrufe • vor 3 Monaten

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

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 Aufrufe • vor 2 Monaten

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

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 Aufrufe • vor 3 Monaten

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.

Alan Oppenheim, MIT professor: "Jump Trading pays quants $650K to pull one real signal out of market noise. I wrote the textbook on that exact math and taught it for free." this free MIT lecture is the math the whole "quant edge" is built on, and almost nobody selling you a course will name it. the man teaching it wrote the signal-processing textbook every engineer on earth learned from, and he put the lectures online for nothing. here is the idea. a price chart is a signal buried in noise. most of what you see is random static, and hidden inside is a faint, repeating pattern with real mathematics for pulling it out. that is not a metaphor. jim simons was a codebreaker who did exactly this to enemy transmissions, then pointed the same math at the market and made a hundred billion dollars. none of it is new. oppenheim taught this in 1987, the math is older, and every lecture is free. same story i told in the article above. the edge was never a secret formula. it was knowing a market is a noisy signal, and having the tools to read it. here is what the gurus skip. the math to separate signal from noise is free and everyone has it. the edge is cleaner data than the next desk, and the discipline to trust a faint signal across thousands of trades instead of chasing the static. the filter is free. the patience to use it is not.

Rossst.03

281,905 Aufrufe • vor 1 Monat

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

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 Aufrufe • vor 2 Monaten

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

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 Aufrufe • vor 2 Monaten

🚨 ANOTHER MASTERCLASS FROM Grant Sanderson The compressibility of language isn’t just a math curiosity, it’s the hidden engine behind every LLM you use. Grant’s new video reframes Shannon’s entropy through one elegant lens: Prediction IS compression. → The better you predict the next word, the fewer bits you need to store it → Shannon measured English at ~1 bit per character: astonishingly compressible → This is exactly what GPT-style models optimize → Intelligence, in this framing, is compression FUN FACT: Von Neumann told Shannon to name it “entropy” because nobody truly understands it anyway 😄 Decades later, that same concept became the bedrock of modern AI. Deep-dive resources in the 🧵 ↓

🚨 ANOTHER MASTERCLASS FROM Grant Sanderson The compressibility of language isn’t just a math curiosity, it’s the hidden engine behind every LLM you use. Grant’s new video reframes Shannon’s entropy through one elegant lens: Prediction IS compression. → The better you predict the next word, the fewer bits you need to store it → Shannon measured English at ~1 bit per character: astonishingly compressible → This is exactly what GPT-style models optimize → Intelligence, in this framing, is compression FUN FACT: Von Neumann told Shannon to name it “entropy” because nobody truly understands it anyway 😄 Decades later, that same concept became the bedrock of modern AI. Deep-dive resources in the 🧵 ↓

Charly Wargnier

319,799 Aufrufe • vor 2 Monaten

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

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 Aufrufe • vor 2 Monaten

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

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

30,300 Aufrufe • vor 2 Monaten

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

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 Aufrufe • vor 3 Monaten

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

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 Aufrufe • vor 2 Monaten

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

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 Aufrufe • vor 1 Monat

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

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 Aufrufe • vor 3 Monaten

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

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 Aufrufe • vor 2 Monaten

quant funds spend millions hiring people who know a formula you learned in 10th grade and never applied to a price chart y = mx + b. that's it linear regression - same math from high school most traders learned, ignored, and forgot existed what quants actually figured out: asset prices don't move randomly, they drift and revert with measurable slope regression separates signal from noise. slope tracks trend, residuals measure the deviation when deviation gets large enough, that's the trade - prices tend to snap back to the line quants call it "mean reversion." retail calls it "a dip" actual workflow: > pull 5 years of daily returns > run OLS, extract beta and residuals > z-score the residuals > entries at -2 standard deviations, exits at 0 retail draws trend lines by hand and calls it analysis Bookmark this ^ data is public, math is free, approach has been in papers since the 1980s 99% of traders have never built one signal this way it was never an information gap - always been a methodology gap, and that's the kind that compounds

quant funds spend millions hiring people who know a formula you learned in 10th grade and never applied to a price chart y = mx + b. that's it linear regression - same math from high school most traders learned, ignored, and forgot existed what quants actually figured out: asset prices don't move randomly, they drift and revert with measurable slope regression separates signal from noise. slope tracks trend, residuals measure the deviation when deviation gets large enough, that's the trade - prices tend to snap back to the line quants call it "mean reversion." retail calls it "a dip" actual workflow: > pull 5 years of daily returns > run OLS, extract beta and residuals > z-score the residuals > entries at -2 standard deviations, exits at 0 retail draws trend lines by hand and calls it analysis Bookmark this ^ data is public, math is free, approach has been in papers since the 1980s 99% of traders have never built one signal this way it was never an information gap - always been a methodology gap, and that's the kind that compounds

Livsun

28,936 Aufrufe • vor 2 Monaten

Claude Shannon ran his personal stock account at 28% a year for 30 years. He's also the man who mathematically proved the ceiling above every filter the thread above is selling. Both facts have been sitting in the same library since 1948. Shannon didn't beat the market with a smarter moving average. He didn't beat it with a swarm, a neural net, or the sharpest filter of his generation, and he could have built any of them in an afternoon. He beat it because he understood the one thing the industry has been quietly ignoring for 78 years: no filter, no matter how clever, can pull more signal from a channel than the channel actually holds. The market channel is almost pure noise by design. Every real edge gets arbitraged toward zero the moment enough people find it. A better filter doesn't create signal that wasn't there - it rounds noise into a shape that fools you into betting on it. Shannon compounded like that because he was ruthless about which channels still had signal left, and disciplined about how much to bet when they did. The filter came last. It always comes last. Build the filter. It's a fine tool. Just remember there's a ceiling above it no engineering can lift, and the part that finds signal before it decays - that part, you still have to bring yourself. Shannon wrote it down in 1948. It's still free. The lesson has been sitting there for 78 years.

Claude Shannon ran his personal stock account at 28% a year for 30 years. He's also the man who mathematically proved the ceiling above every filter the thread above is selling. Both facts have been sitting in the same library since 1948. Shannon didn't beat the market with a smarter moving average. He didn't beat it with a swarm, a neural net, or the sharpest filter of his generation, and he could have built any of them in an afternoon. He beat it because he understood the one thing the industry has been quietly ignoring for 78 years: no filter, no matter how clever, can pull more signal from a channel than the channel actually holds. The market channel is almost pure noise by design. Every real edge gets arbitraged toward zero the moment enough people find it. A better filter doesn't create signal that wasn't there - it rounds noise into a shape that fools you into betting on it. Shannon compounded like that because he was ruthless about which channels still had signal left, and disciplined about how much to bet when they did. The filter came last. It always comes last. Build the filter. It's a fine tool. Just remember there's a ceiling above it no engineering can lift, and the part that finds signal before it decays - that part, you still have to bring yourself. Shannon wrote it down in 1948. It's still free. The lesson has been sitting there for 78 years.

Veles

207,290 Aufrufe • vor 1 Monat

Gilbert Strang, a quiet MIT professor, picked up a piece of chalk and explained the one skill standing between you and every account you have ever blown. MIT put the entire course online. it has been free for over twenty years. almost nobody finishes it. His point: the market never pays in certainties. it pays in probabilities, and a human gut can barely hold one straight. the math that holds ten thousand at once, weighs them, and collapses them into a single call is linear algebra. it is the branch Citadel and OpenAI both pay $400K to hire for. he is not a trading guru. his 18.06 lectures are the most watched math class on the planet. Skip to where he turns a wall of numbers into one vector. a single object carrying a thousand odds at once, in notation a beginner can follow. No slides. no jargon. one piece of chalk. a quant I know rewatches lecture one every year before he sizes a single position. You do not have a strategy problem. you have a math problem. the fix has been free for two decades, and you are still guessing.

Gilbert Strang, a quiet MIT professor, picked up a piece of chalk and explained the one skill standing between you and every account you have ever blown. MIT put the entire course online. it has been free for over twenty years. almost nobody finishes it. His point: the market never pays in certainties. it pays in probabilities, and a human gut can barely hold one straight. the math that holds ten thousand at once, weighs them, and collapses them into a single call is linear algebra. it is the branch Citadel and OpenAI both pay $400K to hire for. he is not a trading guru. his 18.06 lectures are the most watched math class on the planet. Skip to where he turns a wall of numbers into one vector. a single object carrying a thousand odds at once, in notation a beginner can follow. No slides. no jargon. one piece of chalk. a quant I know rewatches lecture one every year before he sizes a single position. You do not have a strategy problem. you have a math problem. the fix has been free for two decades, and you are still guessing.

Loran

47,494 Aufrufe • vor 1 Monat

A guy was sitting in a trader chat complaining for the 3rd month straight about his results being basically zero Someone dropped him a link to a wallet 1 bot, $47K in a month, 860 trades per week. He asked: is this even legal? They replied: this is public data, you could have found it yourself Here is what is actually happening 93% of Polymarket users trade by hand. The other 7% automated the process, and they are the ones pulling $11K to $120K per month. And 5% of those 7 never wrote a single line of code. They just copy trades from top wallets through on-chain data The blockchain shows every trade from every account The bot does not sleep, does not tilt, and processes data faster than any human: This is not a question of talent. It is a question of tools. And the tools already exist. No need to build anything from scratch That guy from the chat is now in that 7%. All he did was stop clicking by hand

A guy was sitting in a trader chat complaining for the 3rd month straight about his results being basically zero Someone dropped him a link to a wallet 1 bot, $47K in a month, 860 trades per week. He asked: is this even legal? They replied: this is public data, you could have found it yourself Here is what is actually happening 93% of Polymarket users trade by hand. The other 7% automated the process, and they are the ones pulling $11K to $120K per month. And 5% of those 7 never wrote a single line of code. They just copy trades from top wallets through on-chain data The blockchain shows every trade from every account The bot does not sleep, does not tilt, and processes data faster than any human: This is not a question of talent. It is a question of tools. And the tools already exist. No need to build anything from scratch That guy from the chat is now in that 7%. All he did was stop clicking by hand

Blaze

36,941 Aufrufe • vor 5 Monaten

David Tepper on what to do when you have zero edge: "i don't have an opinion on the stock market. i just don't feel it being involved. i'm here on the bond market... i definitely have more of an opinion on this one." most guys feel physically sick if they are not in an active trade. if the market is chopping sideways in a muddy range, they will just force an entry anyway. they sit there clicking buttons and handing their account balance to the algos out of pure boredom Tepper runs a massive fund, and his biggest flex is simply admitting when he has no clue what the stock market is about to do instead of guessing, he takes his money and moves it to a completely different market where the setup is actually clean and the central banks are telling him exactly what they want you do not have to trade every single day. you do not have to trade the same ticker all year. if the chart looks like trash and you have no read on it, sitting in cash is a real position stop forcing plays just to feel something. only step in when the math makes sense bookmark and watch the 2016 archive here

David Tepper on what to do when you have zero edge: "i don't have an opinion on the stock market. i just don't feel it being involved. i'm here on the bond market... i definitely have more of an opinion on this one." most guys feel physically sick if they are not in an active trade. if the market is chopping sideways in a muddy range, they will just force an entry anyway. they sit there clicking buttons and handing their account balance to the algos out of pure boredom Tepper runs a massive fund, and his biggest flex is simply admitting when he has no clue what the stock market is about to do instead of guessing, he takes his money and moves it to a completely different market where the setup is actually clean and the central banks are telling him exactly what they want you do not have to trade every single day. you do not have to trade the same ticker all year. if the chart looks like trash and you have no read on it, sitting in cash is a real position stop forcing plays just to feel something. only step in when the math makes sense bookmark and watch the 2016 archive here

ludoonchart

420,069 Aufrufe • vor 1 Monat

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

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

Livsun

11,425 Aufrufe • vor 2 Monaten