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Ω-HMM. Enhanced Hidden Markov Model 3D probability surface. April 20, 2007. S&P closed +2.6% the red peak on that heatmap is where the model said probability was concentrated the x marks where price actually landed they matched Markov's math from 1906 predicting Wall Street 101 years later hidden markov...

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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 views • 3 months ago

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 views • 5 days ago

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 views • 2 months ago

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 views • 3 months ago

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

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

Livsun

30,300 views • 2 months ago

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 views • 2 months ago

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 views • 2 months ago

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 views • 3 months ago

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 views • 1 month ago

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 views • 2 months ago

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 views • 3 months ago

this model doesn't predict where the S&P 500 will go it predicts when the market's own velocity is about to collapse Z(x,y) = F(β, α, τ, ∇τ; x, y, t) one equation. four parameters. a 3D surface that maps how price momentum evolves across time and space β is drift velocity: μ/σ, the ratio of expected return to volatility when β is high the market is moving with conviction when β decays toward zero the trend is losing energy before the chart shows any sign of it α measures how fast that velocity is changing τ is the time structure of the regime ∇τ is the gradient, the rate at which the regime itself is shifting the 3D surface on screen is not decoration the red peaks are where momentum is concentrated and unstable the blue basin is where the system is calm and mean-reverting the yellow marker is where the S&P sat at the moment of the snapshot the model's output: position sizing optimization not buy or sell. how much exposure to hold given the current position on that surface the bottom chart shows it: blue line is the model, white line is buy & hold same asset, same period, different sizing at every point based on where the dynamics equation said the market was > time dynamics models: rooted in physics, applied to finance since the 1990s > drift-to-volatility ratio: standard risk metric at Two Sigma, AQR, Man Group > this exact framework: free, public, 60 seconds in this video > what retail uses instead: lagging indicators that measure the past, not the velocity of the present retail asks "is the market going up or down?" this model asks "how fast is the market's own energy decaying and where on the surface are we right now?" completely different question. completely different result full breakdown in the video below
1:01

this model doesn't predict where the S&P 500 will go it predicts when the market's own velocity is about to collapse Z(x,y) = F(β, α, τ, ∇τ; x, y, t) one equation. four parameters. a 3D surface that maps how price momentum evolves across time and space β is drift velocity: μ/σ, the ratio of expected return to volatility when β is high the market is moving with conviction when β decays toward zero the trend is losing energy before the chart shows any sign of it α measures how fast that velocity is changing τ is the time structure of the regime ∇τ is the gradient, the rate at which the regime itself is shifting the 3D surface on screen is not decoration the red peaks are where momentum is concentrated and unstable the blue basin is where the system is calm and mean-reverting the yellow marker is where the S&P sat at the moment of the snapshot the model's output: position sizing optimization not buy or sell. how much exposure to hold given the current position on that surface the bottom chart shows it: blue line is the model, white line is buy & hold same asset, same period, different sizing at every point based on where the dynamics equation said the market was > time dynamics models: rooted in physics, applied to finance since the 1990s > drift-to-volatility ratio: standard risk metric at Two Sigma, AQR, Man Group > this exact framework: free, public, 60 seconds in this video > what retail uses instead: lagging indicators that measure the past, not the velocity of the present retail asks "is the market going up or down?" this model asks "how fast is the market's own energy decaying and where on the surface are we right now?" completely different question. completely different result full breakdown in the video below

delost

72,275 views • 2 months ago

the entire math roadmap for quant trading fits on a single page and this video breaks it down in 60 seconds probability. linear algebra. calculus. statistics four subjects. every quant strategy ever built sits on top of these four probability comes first because everything else depends on it conditional probability, Bayes' theorem, expected value this is the actual language trading models are written in linear algebra is how you stop thinking about one stock and start thinking about 5,000 at once factor models, PCA, portfolio optimization Bridgewater, AQR, Two Sigma all run on matrix math calculus is derivatives pricing. Black-Scholes is a PDE if you trade options without understanding the equation underneath them, you're reading the answer key without knowing the question statistics is the filter regression, hypothesis testing, distribution fitting this is how Renaissance decides if a signal is real or if a backtest got lucky here's what nobody tells retail: every indicator you've ever used is a dumbed-down version of one of these four subjects RSI is momentum statistics. Bollinger Bands are standard deviation. MACD is signal processing they took the math, removed the part that makes it useful, and sold you the wrapper > this roadmap: free, 60 seconds, in this video > same four subjects tested at every quant interview since the 1990s > time to learn properly: 6-8 months with free resources > what they unlock: the actual language Wall Street runs on the information was never gated it was just never packaged for the people who needed it most full breakdown in the video below
1:04

the entire math roadmap for quant trading fits on a single page and this video breaks it down in 60 seconds probability. linear algebra. calculus. statistics four subjects. every quant strategy ever built sits on top of these four probability comes first because everything else depends on it conditional probability, Bayes' theorem, expected value this is the actual language trading models are written in linear algebra is how you stop thinking about one stock and start thinking about 5,000 at once factor models, PCA, portfolio optimization Bridgewater, AQR, Two Sigma all run on matrix math calculus is derivatives pricing. Black-Scholes is a PDE if you trade options without understanding the equation underneath them, you're reading the answer key without knowing the question statistics is the filter regression, hypothesis testing, distribution fitting this is how Renaissance decides if a signal is real or if a backtest got lucky here's what nobody tells retail: every indicator you've ever used is a dumbed-down version of one of these four subjects RSI is momentum statistics. Bollinger Bands are standard deviation. MACD is signal processing they took the math, removed the part that makes it useful, and sold you the wrapper > this roadmap: free, 60 seconds, in this video > same four subjects tested at every quant interview since the 1990s > time to learn properly: 6-8 months with free resources > what they unlock: the actual language Wall Street runs on the information was never gated it was just never packaged for the people who needed it most full breakdown in the video below

delost

34,703 views • 2 months ago

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 views • 5 days ago

In 1905, Russian Mathematician Andrey Andreyevich Markov asked a heretic question for the time: if randomness is allowed to remember something, do averages still behave or does probability theory fall apart? His answer was a very specific kind of memory. The next step only depends on the present, P(Xₙ₊₁=j | Xₙ=i, Xₙ₋₁, …) = Pᵢⱼ, and yet the law-of-large-numbers stability survives. The bead jitters forever, but long-run occupation settles. Time-averaged state frequencies converge to a fixed profile π satisfying π = πP. Fast-forward to 1931, another Russian Andrey Nikolaevich Kolmogorov, takes the same Markov mechanism and turns it into dynamics. Instead of only asking where does the chain spend its time?, you watch the whole distribution move in real time through the Kolmogorov forward (master) equation dp/dt = pQ, where Q is the generator of the continuous-time chain. That’s exactly what the render is showing as the same mechanism wearing two different lenses. The fog is p(t) spreading through the labyrinth, the flux layer is the net current pushed through corridors and the portal, and the particles are just sample paths driven by the same generator. One Markov engine...either you look at the evolving law, or you watch trajectories and let ergodic averages do the estimating. That’s also why Markov’s "memory without collapse" became a workhorse. MCMC engineers a chain whose stationary distribution is the target, then uses time-averages to estimate things you can’t integrate directly (posteriors, partition functions, constrained geometries). The same skeleton appears in hidden Markov models for time series, in biophysics as channels switching between states, and in control/RL through Markov decision processes. #ProbabilityTheory #MarkovChains #ContinuousTimeMarkovChains #KolmogorovForwardEquation #StochasticProcesses #Kolmogorov #Markov #MCMC
0:59

In 1905, Russian Mathematician Andrey Andreyevich Markov asked a heretic question for the time: if randomness is allowed to remember something, do averages still behave or does probability theory fall apart? His answer was a very specific kind of memory. The next step only depends on the present, P(Xₙ₊₁=j | Xₙ=i, Xₙ₋₁, …) = Pᵢⱼ, and yet the law-of-large-numbers stability survives. The bead jitters forever, but long-run occupation settles. Time-averaged state frequencies converge to a fixed profile π satisfying π = πP. Fast-forward to 1931, another Russian Andrey Nikolaevich Kolmogorov, takes the same Markov mechanism and turns it into dynamics. Instead of only asking where does the chain spend its time?, you watch the whole distribution move in real time through the Kolmogorov forward (master) equation dp/dt = pQ, where Q is the generator of the continuous-time chain. That’s exactly what the render is showing as the same mechanism wearing two different lenses. The fog is p(t) spreading through the labyrinth, the flux layer is the net current pushed through corridors and the portal, and the particles are just sample paths driven by the same generator. One Markov engine...either you look at the evolving law, or you watch trajectories and let ergodic averages do the estimating. That’s also why Markov’s "memory without collapse" became a workhorse. MCMC engineers a chain whose stationary distribution is the target, then uses time-averages to estimate things you can’t integrate directly (posteriors, partition functions, constrained geometries). The same skeleton appears in hidden Markov models for time series, in biophysics as channels switching between states, and in control/RL through Markov decision processes. #ProbabilityTheory #MarkovChains #ContinuousTimeMarkovChains #KolmogorovForwardEquation #StochasticProcesses #Kolmogorov #Markov #MCMC

Mathelirium

96,415 views • 7 months ago

This Russian mathematician from 1906 just dropped the cheat code that’s printing millions on Polymarket right now. Markov Chains aren’t some dusty theory - they’re exactly how the market actually works. Current price = the only state that matters. Past is already priced in. No ego, no anchoring, no career risk. Just pure, lightning-fast updates as new info hits. The smartest traders stopped "guessing" years ago. They read states and size up on the highest-probability outcomes. Exhibit A: [bobe2] -> one of the OGs on Polymarket (joined April 2023). > 1,317 predictions. > $7,500,000 positions. > $1,619,000+ all-time profit. He doesn’t chase moonshots. He stacks $7-8M on 97-99¢ obvious plays (FOMC, Biden term, Trump picks, Zelensky, BTC reserve). Classic Markov execution: the current state already contains everything. He just capitalizes. His profile -> This is how the real quants own Polymarket. Want the exact same edge without staring at charts 24/7? Copy him + the rest of the top Markov whales automatically with Just add his wallet [0xed107a85a4585a381e48c7f7ca4144909e7dd2e5] here and track/copy him. Stop guessing the future. Start copying the states that are already winning. Save him and this alpha at all.
0:23

This Russian mathematician from 1906 just dropped the cheat code that’s printing millions on Polymarket right now. Markov Chains aren’t some dusty theory - they’re exactly how the market actually works. Current price = the only state that matters. Past is already priced in. No ego, no anchoring, no career risk. Just pure, lightning-fast updates as new info hits. The smartest traders stopped "guessing" years ago. They read states and size up on the highest-probability outcomes. Exhibit A: [bobe2] -> one of the OGs on Polymarket (joined April 2023). > 1,317 predictions. > $7,500,000 positions. > $1,619,000+ all-time profit. He doesn’t chase moonshots. He stacks $7-8M on 97-99¢ obvious plays (FOMC, Biden term, Trump picks, Zelensky, BTC reserve). Classic Markov execution: the current state already contains everything. He just capitalizes. His profile -> This is how the real quants own Polymarket. Want the exact same edge without staring at charts 24/7? Copy him + the rest of the top Markov whales automatically with Just add his wallet [0xed107a85a4585a381e48c7f7ca4144909e7dd2e5] here and track/copy him. Stop guessing the future. Start copying the states that are already winning. Save him and this alpha at all.

slash1s

54,119 views • 4 months ago

Wall Street doesn't predict prices - it prices the randomness in them. That is where the billions are, and it runs on one equation. The model trading desks actually use is Geometric Brownian Motion: dS = μS·dt + σS·dW μ (drift) is the slow expected trend. σ·dW is the random shock - a coin flip scaled by volatility. It is the engine under Black-Scholes, the formula that prices trillions in options. The idea is 125 years old. Louis Bachelier modeled prices as a random walk in 1900 - five years before Einstein used the same math for particles. His verdict: "the mathematical expectation of the speculator is zero." Sixty years later Eugene Fama won a Nobel for the same finding: prices are "no more predictable than the path of a series of cumulated random numbers." And the math is brutal. Drift grows with time (t); noise grows only with its square root (√t). Over one day the shock buries the trend - so daily direction is a coin flip, and traders invent reasons after the candle prints. The trap hides in one assumption: that returns are "normal." Real markets have fat tails. Black Monday 1987 (-22.6% in a day) was a 20-sigma event under the bell curve - impossible in billions of years. Nassim Taleb calls that bell curve "the great intellectual fraud." The equation that prices everything is blind to the one day it matters most. the edge was never the trend inside the wiggle. it is knowing how much of the wiggle is nothing. the full quant build is in the article below.
0:48

Wall Street doesn't predict prices - it prices the randomness in them. That is where the billions are, and it runs on one equation. The model trading desks actually use is Geometric Brownian Motion: dS = μS·dt + σS·dW μ (drift) is the slow expected trend. σ·dW is the random shock - a coin flip scaled by volatility. It is the engine under Black-Scholes, the formula that prices trillions in options. The idea is 125 years old. Louis Bachelier modeled prices as a random walk in 1900 - five years before Einstein used the same math for particles. His verdict: "the mathematical expectation of the speculator is zero." Sixty years later Eugene Fama won a Nobel for the same finding: prices are "no more predictable than the path of a series of cumulated random numbers." And the math is brutal. Drift grows with time (t); noise grows only with its square root (√t). Over one day the shock buries the trend - so daily direction is a coin flip, and traders invent reasons after the candle prints. The trap hides in one assumption: that returns are "normal." Real markets have fat tails. Black Monday 1987 (-22.6% in a day) was a 20-sigma event under the bell curve - impossible in billions of years. Nassim Taleb calls that bell curve "the great intellectual fraud." The equation that prices everything is blind to the one day it matters most. the edge was never the trend inside the wiggle. it is knowing how much of the wiggle is nothing. the full quant build is in the article below.

Di Krass

251,980 views • 3 days ago

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

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

Livsun

25,988 views • 2 months ago

a $40/month server beat a room full of analysts to the same trade by five and a half hours market opens at 9:30. his position was already in at 4am the system is a neural net trained on 11 years of tick data. it flagged the setup before the candle that "confirmed" it had even started forming this is the part retail misunderstands about ML in markets it isn't prediction in the mystical sense. it's pattern classification at a speed and scale human eyes physically cannot match the mechanics: 847,000 labeled historical setups as training data 4,200 data points per second ingested live each new state scored against every pattern the net has ever seen, in milliseconds the model isn't asking "where is price going" it's asking "how closely does the current microstructure match the conditions that preceded a move in my training set" that's a classification problem, and classification is what neural nets do better than anything else output: 3-4 candidate trades a day. he takes the top 2 by confidence score last 90 days: 71% win rate at 2.3 average risk-reward the edge isn't the architecture. the architecture is public pytorch is free, the papers are on arxiv, the network is a few hundred lines the edge is the labeling. what you feed it and how you tag the setups is the entire game retail feeds a model price and time and gets noise a desk feeds it order flow, volatility state, cross-asset context, each example hand-labeled by outcome same network. different training data. that's the whole difference retail watches the news at the open and reacts this system scored every pattern before sunrise and already decided you're not losing because your analysis is wrong you're losing to something that doesn't sleep, doesn't panic, and doesn't second-guess a probability it already computed the dataset was free. the framework was free. the compute was $40 a month the edge was never behind a paywall. it was sitting in a format almost nobody bothered to train on full breakdown in the article below
0:32

a $40/month server beat a room full of analysts to the same trade by five and a half hours market opens at 9:30. his position was already in at 4am the system is a neural net trained on 11 years of tick data. it flagged the setup before the candle that "confirmed" it had even started forming this is the part retail misunderstands about ML in markets it isn't prediction in the mystical sense. it's pattern classification at a speed and scale human eyes physically cannot match the mechanics: 847,000 labeled historical setups as training data 4,200 data points per second ingested live each new state scored against every pattern the net has ever seen, in milliseconds the model isn't asking "where is price going" it's asking "how closely does the current microstructure match the conditions that preceded a move in my training set" that's a classification problem, and classification is what neural nets do better than anything else output: 3-4 candidate trades a day. he takes the top 2 by confidence score last 90 days: 71% win rate at 2.3 average risk-reward the edge isn't the architecture. the architecture is public pytorch is free, the papers are on arxiv, the network is a few hundred lines the edge is the labeling. what you feed it and how you tag the setups is the entire game retail feeds a model price and time and gets noise a desk feeds it order flow, volatility state, cross-asset context, each example hand-labeled by outcome same network. different training data. that's the whole difference retail watches the news at the open and reacts this system scored every pattern before sunrise and already decided you're not losing because your analysis is wrong you're losing to something that doesn't sleep, doesn't panic, and doesn't second-guess a probability it already computed the dataset was free. the framework was free. the compute was $40 a month the edge was never behind a paywall. it was sitting in a format almost nobody bothered to train on full breakdown in the article below

delost

20,942 views • 1 month ago

JIM SIMONS NEVER HAD A LOSING YEAR IN 34 ATTEMPTS. HERE IS THE 5 LAYER PIPELINE BEHIND IT. Spring 1988. A Cold War mathematician fired every trader and replaced them with signal detectors. $60 billion in performance fees. 39% annual returns after a 44% fee. Zero losing years. Layer 1 : Hurst exponent. Below 0.45 fades work. Above 0.55 momentum works. Medallion switches strategy automatically based on regime. Layer 2 : Factor decomposition. Renaissance runs 200+ factors privately. High alpha plus low R-squared means your edge is unexplained by known factors. Layer 3 : Markov Chain regime modeling. 76% Bear probability over 5 periods means no long positions. The model decides before you do. Layer 4 : Neural net. Three outputs : long, short, flat. Not predicting price. Predicting direction probability. 52% accuracy Kelly-sized correctly still prints money. Layer 5 : Execution. Half-Kelly sizing. Hard 2% risk cap. Edge must exceed execution cost by 1.5x minimum. This is where 80% of quants bleed out. 400 people. One shared model. The edge was never a single formula. It was always the pipeline.
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JIM SIMONS NEVER HAD A LOSING YEAR IN 34 ATTEMPTS. HERE IS THE 5 LAYER PIPELINE BEHIND IT. Spring 1988. A Cold War mathematician fired every trader and replaced them with signal detectors. $60 billion in performance fees. 39% annual returns after a 44% fee. Zero losing years. Layer 1 : Hurst exponent. Below 0.45 fades work. Above 0.55 momentum works. Medallion switches strategy automatically based on regime. Layer 2 : Factor decomposition. Renaissance runs 200+ factors privately. High alpha plus low R-squared means your edge is unexplained by known factors. Layer 3 : Markov Chain regime modeling. 76% Bear probability over 5 periods means no long positions. The model decides before you do. Layer 4 : Neural net. Three outputs : long, short, flat. Not predicting price. Predicting direction probability. 52% accuracy Kelly-sized correctly still prints money. Layer 5 : Execution. Half-Kelly sizing. Hard 2% risk cap. Edge must exceed execution cost by 1.5x minimum. This is where 80% of quants bleed out. 400 people. One shared model. The edge was never a single formula. It was always the pipeline.

Lummox

24,134 views • 2 months ago