Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

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 belowshow more

delost

5,534 subscribers

113,459 views • 4 months ago •via X (Twitter)

Education Science & Technology Finance

Anya Rossi• Live Now

Private livecam show

23 Comments

Donovan Miller4 months ago

I’m all on quantitative, but FYI and fwiw, competent TAs don’t treat trading as a prediction game. If he was using TA to predict price he wasn’t using it correctly or was not competent in it in the first place. TA is simply the study of behaviour. And behaviour is probabilistic.

攞錢 lo2cin44 months ago

Clickbait article titles that hype up the most basic trading concepts as the 'Holy Grail' and promise massive profits always make me cringe.

Nahid4 months ago

trading as a math problem is so eye-opening

SCALP4 months ago

J'ai vu beaucoup de traders passer de la prédiction à l'analyse de probabilité, mais rares sont ceux qui mettent en pratique la discipline requise pour bettre seulement sur des asymétries mathématiques. Le reste, c'est du bruit.

SLOT.WIN4 months ago

i saw the same truth in baccarat tables before i built my first algorithm the house doesn't predict it calculates

stockxian4 months ago

My eureka moment was when I realised I am not meant for intraday trading. Switched to Higher timeframe and i am killing it

Kazurai4 months ago

a framework beats a forecast every time

Gipp 🦅4 months ago

but a lot has really changed

Miya4 months ago

This shift hits hard—TA narratives vs real probability edges. Math over charts all day, that's how you actually win.

アキラ4 months ago

Predicting price movements is futile. Focus on probabilities for success. #TradeSmart

Khadarba4 months ago

The shift in trading strategy that took him from drawing trendlines to betting on market states with asymmetric probabilities shifted his win rate from 51% to $100 billion.

testorosso4 months ago

Risks must be managed, but we should not be afraid of risk.

Laken ❤️4 months ago

A fading memory hides silent wishes as another message never

0xExpl0rer4 months ago

Polymarket has whales. They move before news hits. You can see it happen live. @Polydata_pro

Pham Khanh4 months ago

Great way to explain it.

beamnxw ./4 months ago

this is the way. hard to argue with math

Pham Khanh4 months ago

Nice point.

Đào tạo DiHotel4 months ago

200 dòng code? Thật lẽ quá đơn giản!

rari🈀4 months ago

python stats and clean data can take you surprisingly far

0xSlyth4 months ago

math doesn't lie, charts do

Mont Pelerin4 months ago

Keep it mechanical.

Tô Thị Ngọc Hân4 months ago

Interesting shift there. Feels kinda like I'm still chasing trends while the math nerds are quietly taking over. But can you really trust a model over gut? Sometimes I think I miss that chaos, but hey, maybe I'm just burnt out and overthinking it all

Parsa4 months ago

Such a great post. I really liked it.

Related Videos

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 views • 4 months ago

you're not bad at trading. the game is just designed so you never find out what the actual game is retail gets candlestick patterns and RSI. the kids at citadel get probability theory, transition matrices, and kelly criterion from day one they literally filter recruits who don't already know this. they don't teach it to you, they find people who found it themselves the math that runs renaissance technologies has been sitting in free statistics textbooks since the 1950s markov chains, autocorrelation, state detection - none of it is secret, none of it is locked behind a paywall the information gap isn't about money or access it's about the fact that the finance industry makes more selling you indicators and courses than it ever loses to you trading "retail investor" is industry language for "someone we extract from" meanwhile a 24 year old with a stats degree and 6 months of python experience is clearing $400k at a prop desk same markets. completely different question being asked you ask: is it going up or down they ask: what state is the market in and what does history say happens next one of those questions has an answer. the other one is just noise dressed up as analysis the textbook is free. the math is learnable. the only thing that wasn't free was someone telling you it existed

you're not bad at trading. the game is just designed so you never find out what the actual game is retail gets candlestick patterns and RSI. the kids at citadel get probability theory, transition matrices, and kelly criterion from day one they literally filter recruits who don't already know this. they don't teach it to you, they find people who found it themselves the math that runs renaissance technologies has been sitting in free statistics textbooks since the 1950s markov chains, autocorrelation, state detection - none of it is secret, none of it is locked behind a paywall the information gap isn't about money or access it's about the fact that the finance industry makes more selling you indicators and courses than it ever loses to you trading "retail investor" is industry language for "someone we extract from" meanwhile a 24 year old with a stats degree and 6 months of python experience is clearing $400k at a prop desk same markets. completely different question being asked you ask: is it going up or down they ask: what state is the market in and what does history say happens next one of those questions has an answer. the other one is just noise dressed up as analysis the textbook is free. the math is learnable. the only thing that wasn't free was someone telling you it existed

Livsun

22,621 views • 1 month 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

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

13,367 views • 5 months ago

He was just a janitor at MIT. One day, while mopping the floor, he glanced at a math problem on the blackboard. He had no idea it was one of the most advanced, difficult equations in the world, yet he solved it effortlessly,this gained him a lot of attention.

He was just a janitor at MIT. One day, while mopping the floor, he glanced at a math problem on the blackboard. He had no idea it was one of the most advanced, difficult equations in the world, yet he solved it effortlessly,this gained him a lot of attention.

Ola

63,466 views • 7 months ago

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

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

Livsun

32,241 views • 2 months ago

Ray Dalio, billionaire founder of Bridgewater ($150B+): "In 1982 I was totally certain the economy would collapse. I bet everything on it, and I was so wrong I nearly went bankrupt. That's when I learned confidence is not skill." the article above is about telling a real edge from luck. dalio built the biggest hedge fund on earth only after learning the hardest way that being certain and being right are not the same thing. he had every reason to feel like a genius. he'd been right before, his conviction was total. then the market wiped him out, and all that confidence turned out to be worth exactly nothing. it didn't care how sure he was. so he made one change that saved his career: he stopped asking "am I right" and started asking "how do I know I'm right." he treated his own certainty as the enemy. that shift is the entire gap between the fool he was in 1982 and the investor he became. he tells the whole story in this free TED talk. here is the trap. a trader on a winning streak feels the exact certainty dalio felt in 1982. it feels like skill. usually it is just the market not having punished you yet. the edge is doubting yourself hardest when you feel surest, stress-testing the idea you love most, and sizing so that being wrong can never end you. the math is free. treating your own confidence as a warning sign is the discipline almost no one has.

Ray Dalio, billionaire founder of Bridgewater ($150B+): "In 1982 I was totally certain the economy would collapse. I bet everything on it, and I was so wrong I nearly went bankrupt. That's when I learned confidence is not skill." the article above is about telling a real edge from luck. dalio built the biggest hedge fund on earth only after learning the hardest way that being certain and being right are not the same thing. he had every reason to feel like a genius. he'd been right before, his conviction was total. then the market wiped him out, and all that confidence turned out to be worth exactly nothing. it didn't care how sure he was. so he made one change that saved his career: he stopped asking "am I right" and started asking "how do I know I'm right." he treated his own certainty as the enemy. that shift is the entire gap between the fool he was in 1982 and the investor he became. he tells the whole story in this free TED talk. here is the trap. a trader on a winning streak feels the exact certainty dalio felt in 1982. it feels like skill. usually it is just the market not having punished you yet. the edge is doubting yourself hardest when you feel surest, stress-testing the idea you love most, and sizing so that being wrong can never end you. the math is free. treating your own confidence as a warning sign is the discipline almost no one has.

Rossst.03

17,408 views • 3 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

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 • 4 months ago

IN 1986 MIT FILMED THE LECTURE WHERE CODE STOPPED BEING CODE AND BECAME DATA 43 minutes from Gerald Sussman, in the most legendary intro programming course ever recorded. -> The idea that lands: to a program, your code is just data it can read and rewrite. He builds a program that does real calculus -- not by crunching numbers, but by reading the math as a list and transforming it piece by piece. Then he shows the trick hiding under all of it: in this language, code and data are the same material. Forty years later that is exactly what an AI coding agent is -- a program that reads your code as data, rewrites it, and hands it back. Sussman drew the whole idea on a blackboard in 1986. Writing code was never the deepest skill -- understanding that code itself is data a program can manipulate is. This is where you learn it. Most people think AI writing code is brand new. The ones who watch this saw the blueprint 40 years ago. Bookmark & Watch it. This one's a legend ↓

slash1s

22,107 views • 4 months ago

THIS GUY CONNECTED CLAUDE TO TRADINGVIEW AND CUT 200+ HOURS OF MANUAL CHART WORK 40 trades a week used to mean the same loop: open the chart, check RSI, compare EMA, write notes and wait for the next setup now Claude reads TradingView 24/7, tracks volume, indicators and price conditions while he is not even at the desk this is not a $20k trading terminal and not a quant fund setup, just one laptop, one chart and one strategy written in plain English the crazy part is not that Claude predicts the market, it is that a regular trader can remove the boring human loop from every setup ↓

THIS GUY CONNECTED CLAUDE TO TRADINGVIEW AND CUT 200+ HOURS OF MANUAL CHART WORK 40 trades a week used to mean the same loop: open the chart, check RSI, compare EMA, write notes and wait for the next setup now Claude reads TradingView 24/7, tracks volume, indicators and price conditions while he is not even at the desk this is not a $20k trading terminal and not a quant fund setup, just one laptop, one chart and one strategy written in plain English the crazy part is not that Claude predicts the market, it is that a regular trader can remove the boring human loop from every setup ↓

Gipp 🦅

55,825 views • 5 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

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

189,575 views • 5 months ago

a prop trader showed me one number last year and it broke my entire framework H = 0.61 that single value told him the market was in trending mode before he placed a single trade not a chart pattern, not a moving average cross. one number from a rolling calculation most traders spend years asking "is this breakout real?" he already had the answer before the candle closed number is called the Hurst exponent. Harold Hurst invented it in the 1950s - british hydrologist, was studying nile river flood cycles, had nothing to do with finance quant funds found the paper in the 80s and built regime detection systems around it. never mentioned it to retail here's the framework: > H below 0.5: market is mean-reverting, every breakout is a trap, fades work > H above 0.5: momentum is real, pullbacks are entries, trends carry hard > H at exactly 0.5: pure random walk, no edge exists, sit on your hands most traders run a trend strategy on an H = 0.38 market for six months and assume their system is broken it's not broken. they never checked what mode the market was actually in he runs it on a rolling 60-day window, recalculates daily, 4 lines of python when H flips from 0.46 to 0.59 on SPY, he doesn't change the stock - he switches the entire approach to it Bookmark this math was published in 1951 in a hydrology journal. sits in every statistical physics textbook. price data to run it is free

a prop trader showed me one number last year and it broke my entire framework H = 0.61 that single value told him the market was in trending mode before he placed a single trade not a chart pattern, not a moving average cross. one number from a rolling calculation most traders spend years asking "is this breakout real?" he already had the answer before the candle closed number is called the Hurst exponent. Harold Hurst invented it in the 1950s - british hydrologist, was studying nile river flood cycles, had nothing to do with finance quant funds found the paper in the 80s and built regime detection systems around it. never mentioned it to retail here's the framework: > H below 0.5: market is mean-reverting, every breakout is a trap, fades work > H above 0.5: momentum is real, pullbacks are entries, trends carry hard > H at exactly 0.5: pure random walk, no edge exists, sit on your hands most traders run a trend strategy on an H = 0.38 market for six months and assume their system is broken it's not broken. they never checked what mode the market was actually in he runs it on a rolling 60-day window, recalculates daily, 4 lines of python when H flips from 0.46 to 0.59 on SPY, he doesn't change the stock - he switches the entire approach to it Bookmark this math was published in 1951 in a hydrology journal. sits in every statistical physics textbook. price data to run it is free

Livsun

62,957 views • 2 months ago

In 1988, Jim Simons flew to Berkeley to beg a math professor to fix his hedge fund. The professor had never traded a stock. He had spent his career on coding theory and mathematical board games. He agreed to help on the condition he could leave when he wanted. He delivered 55 percent net in his full year running it. Then he handed the whole thing back and went home to teach undergraduates. His name was Elwyn Berlekamp. He is one of two people who ever ran what would become the most profitable trading operation in history. Simons was the other one. MIT math PhD, 1964. Berlekamp wrote foundational papers in coding theory that still run every CD, DVD, satellite link, and QR code on Earth. The Berlekamp-Massey algorithm, published 1968, is why every scratched CD you owned still played through to the end. He also co-wrote "Winning Ways for Your Mathematical Plays" with John Conway and Richard Guy. Four volumes. It became the foundational text of combinatorial game theory. Berlekamp thought about board games the way most mathematicians think about theorems. He proved endgame results in Go that professional masters had assumed were unprovable. His 1994 book "Mathematical Go" reduced the last moves of a Go game to a formula. Top-ranked professionals started studying it. Simons had a problem in the late 1980s. His trading partnership was falling apart. The fund was losing money. He flew west to see the game theorist. Berlekamp bought a controlling stake, cut what was not working, and rebuilt the trading logic from combinatorial game theory principles. The fund returned 55 percent net after fees in his full year running it. In December 1990, Berlekamp sold his stake back to Simons and walked out. He wanted to go back to Berkeley. In interviews he said the same thing many times, in different words: Berkeley was where he belonged. Simons kept building on the system Berlekamp rebuilt. It became the Medallion Fund. Over the next 30 years, Medallion compounded at roughly 66 percent gross per year. It is the most profitable trading strategy in the history of finance. Berlekamp took his cut in 1990 and never went back. He spent the rest of his life at UC Berkeley. He gave a lecture called "Mathematics and Go" that is on YouTube. He died in 2019, aged 78. The paradox is not that Berlekamp made a fortune. It is that he had the door to the biggest fortune in trading history held open for him and walked out. The math was fun. The billions were not.

In 1988, Jim Simons flew to Berkeley to beg a math professor to fix his hedge fund. The professor had never traded a stock. He had spent his career on coding theory and mathematical board games. He agreed to help on the condition he could leave when he wanted. He delivered 55 percent net in his full year running it. Then he handed the whole thing back and went home to teach undergraduates. His name was Elwyn Berlekamp. He is one of two people who ever ran what would become the most profitable trading operation in history. Simons was the other one. MIT math PhD, 1964. Berlekamp wrote foundational papers in coding theory that still run every CD, DVD, satellite link, and QR code on Earth. The Berlekamp-Massey algorithm, published 1968, is why every scratched CD you owned still played through to the end. He also co-wrote "Winning Ways for Your Mathematical Plays" with John Conway and Richard Guy. Four volumes. It became the foundational text of combinatorial game theory. Berlekamp thought about board games the way most mathematicians think about theorems. He proved endgame results in Go that professional masters had assumed were unprovable. His 1994 book "Mathematical Go" reduced the last moves of a Go game to a formula. Top-ranked professionals started studying it. Simons had a problem in the late 1980s. His trading partnership was falling apart. The fund was losing money. He flew west to see the game theorist. Berlekamp bought a controlling stake, cut what was not working, and rebuilt the trading logic from combinatorial game theory principles. The fund returned 55 percent net after fees in his full year running it. In December 1990, Berlekamp sold his stake back to Simons and walked out. He wanted to go back to Berkeley. In interviews he said the same thing many times, in different words: Berkeley was where he belonged. Simons kept building on the system Berlekamp rebuilt. It became the Medallion Fund. Over the next 30 years, Medallion compounded at roughly 66 percent gross per year. It is the most profitable trading strategy in the history of finance. Berlekamp took his cut in 1990 and never went back. He spent the rest of his life at UC Berkeley. He gave a lecture called "Mathematics and Go" that is on YouTube. He died in 2019, aged 78. The paradox is not that Berlekamp made a fortune. It is that he had the door to the biggest fortune in trading history held open for him and walked out. The math was fun. The billions were not.

Veles

73,226 views • 3 months ago

Every gambler who ever muttered "I'm due for a win" was one theorem away from understanding why he was about to lose. an Oxford professor teaches that exact theorem for free. Vegas does not mind. the people who need these 50 minutes most are the ones feeding the slot machines instead. his subject is conditional probability. the wheel has no memory. ten reds in a row change nothing. the next spin carries the same edge against you, every single time. that is the whole casino in one idea. and the post above argues the whole market runs on it too. Crawford teaches it to Oxford first-years. no system. no strategy. no hot streak. just the proof that the house edge does not care what already happened. skip to conditional probability. watch him kill "I'm due" in one line. every bet independent, every one bleeding the same tiny percentage, the crowd paying it a million times over. no memory. no streaks. one edge, forever. a trader I know says the day he understood independence was the day he stopped revenge trading. the math has been free the whole time. the house is betting you never learn it.

Every gambler who ever muttered "I'm due for a win" was one theorem away from understanding why he was about to lose. an Oxford professor teaches that exact theorem for free. Vegas does not mind. the people who need these 50 minutes most are the ones feeding the slot machines instead. his subject is conditional probability. the wheel has no memory. ten reds in a row change nothing. the next spin carries the same edge against you, every single time. that is the whole casino in one idea. and the post above argues the whole market runs on it too. Crawford teaches it to Oxford first-years. no system. no strategy. no hot streak. just the proof that the house edge does not care what already happened. skip to conditional probability. watch him kill "I'm due" in one line. every bet independent, every one bleeding the same tiny percentage, the crowd paying it a million times over. no memory. no streaks. one edge, forever. a trader I know says the day he understood independence was the day he stopped revenge trading. the math has been free the whole time. the house is betting you never learn it.

Rossst.03

10,472 views • 3 months ago

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 views • 4 months ago

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 views • 4 months ago

In 1950 a scientist at Bell Labs admitted, out loud, that he was envious of the geniuses in the offices around him. He was sharing an attic with Claude Shannon. So instead of resenting them, he spent the rest of his career studying exactly what separated the great ones from everyone else, and near the end of his life he laid the whole answer out in a single talk. His name was Richard Hamming. He was honest about where he started. At Los Alamos, he called himself a janitor of science, one of the people who kept things running while the important decisions happened without him. He was, in his own words, plain envious. And rather than swallow it, he turned it into a question. What actually is the difference between the first-class people and the rest of us? His first answer is uncomfortable, because it removes every excuse. It is not luck, though luck plays a part. It is not raw IQ, since plenty of the greats did not test especially well, and Einstein spent seven years in a patent office before anyone noticed him. What it comes down to, again and again, is that the able people simply work on important problems, and they work on them relentlessly, while everyone else stays busy with whatever happens to be in front of them. Then he tells the story that stays with you. For years he ate lunch with the physicists, the Nobel winners, and learned how they thought. One day he turned to a chemist at the table and asked, if what you are working on is not important, and not likely to lead to anything important, why are you working on it? The man was offended. But months later he stopped Hamming in the hallway and admitted the question had gotten under his skin. That chemist went on to run his department and join the National Academy. Of everyone else at that table, Hamming says, he never heard of a single one again. He noticed the same pattern in something as small as an office door. The people who worked with their doors closed got more done in the moment, but ten years later they no longer knew what was worth doing. The ones who left their doors open stayed connected to what actually mattered, even at the cost of constant interruption. And underneath all of it was a habit most people never build. He set aside Friday afternoons, every week, for nothing but great thoughts. No task, no email, just the question of what the important problems in his field really were, and whether he was working on any of them. He was not claiming to be the smartest man in the building. He knew he was not. That was the entire point. He watched the smartest men closely, took what he could use, and made himself into someone who did work that lasted. As he put it near the end, no one ever told him these things. He had to find them alone. You have no such excuse.

In 1950 a scientist at Bell Labs admitted, out loud, that he was envious of the geniuses in the offices around him. He was sharing an attic with Claude Shannon. So instead of resenting them, he spent the rest of his career studying exactly what separated the great ones from everyone else, and near the end of his life he laid the whole answer out in a single talk. His name was Richard Hamming. He was honest about where he started. At Los Alamos, he called himself a janitor of science, one of the people who kept things running while the important decisions happened without him. He was, in his own words, plain envious. And rather than swallow it, he turned it into a question. What actually is the difference between the first-class people and the rest of us? His first answer is uncomfortable, because it removes every excuse. It is not luck, though luck plays a part. It is not raw IQ, since plenty of the greats did not test especially well, and Einstein spent seven years in a patent office before anyone noticed him. What it comes down to, again and again, is that the able people simply work on important problems, and they work on them relentlessly, while everyone else stays busy with whatever happens to be in front of them. Then he tells the story that stays with you. For years he ate lunch with the physicists, the Nobel winners, and learned how they thought. One day he turned to a chemist at the table and asked, if what you are working on is not important, and not likely to lead to anything important, why are you working on it? The man was offended. But months later he stopped Hamming in the hallway and admitted the question had gotten under his skin. That chemist went on to run his department and join the National Academy. Of everyone else at that table, Hamming says, he never heard of a single one again. He noticed the same pattern in something as small as an office door. The people who worked with their doors closed got more done in the moment, but ten years later they no longer knew what was worth doing. The ones who left their doors open stayed connected to what actually mattered, even at the cost of constant interruption. And underneath all of it was a habit most people never build. He set aside Friday afternoons, every week, for nothing but great thoughts. No task, no email, just the question of what the important problems in his field really were, and whether he was working on any of them. He was not claiming to be the smartest man in the building. He knew he was not. That was the entire point. He watched the smartest men closely, took what he could use, and made himself into someone who did work that lasted. As he put it near the end, no one ever told him these things. He had to find them alone. You have no such excuse.

Zyron

16,163 views • 3 months ago

In 1984 a guy got kicked out of every casino in Vegas for counting cards. So he flew to Hong Kong with $150,000 and started betting on horses instead. He walked away with almost a billion dollars. It's Bill Benter. He figured horse racing was just another counting problem. Same math, more moving parts. He and a partner showed up with $150k and a computer. Benter spent years teaching that computer to guess one thing: the real chance each horse had to win. If his number was better than the odds the bookies gave, he bet. If not, he skipped it. That's the whole trick. Expected value: EV = p · b - (1 - p) Only bet when your win chance p, at odds b, is worth more than your chance of losing. That's it. None of this was secret. What Benter used is the first probability lecture at MIT - the clip here, Professor Tsitsiklis writing the basic rules on the board. Freshman stuff. Now people call it a "one-person hedge fund" and sell you a bot to run it. Same edge. It was always just the math nobody bothered to learn.

In 1984 a guy got kicked out of every casino in Vegas for counting cards. So he flew to Hong Kong with $150,000 and started betting on horses instead. He walked away with almost a billion dollars. It's Bill Benter. He figured horse racing was just another counting problem. Same math, more moving parts. He and a partner showed up with $150k and a computer. Benter spent years teaching that computer to guess one thing: the real chance each horse had to win. If his number was better than the odds the bookies gave, he bet. If not, he skipped it. That's the whole trick. Expected value: EV = p · b - (1 - p) Only bet when your win chance p, at odds b, is worth more than your chance of losing. That's it. None of this was secret. What Benter used is the first probability lecture at MIT - the clip here, Professor Tsitsiklis writing the basic rules on the board. Freshman stuff. Now people call it a "one-person hedge fund" and sell you a bot to run it. Same edge. It was always just the math nobody bothered to learn.

Veles

500,816 views • 2 months ago

Kevin O'Leary reveals the cruelest sentence of his life was the one that saved it. Kevin O'Leary said he has always wanted to be a photographer. He was already working as a cameraman, ready to bet his life on a lens. His stepfather sat him down and said: "I'm sorry, but you're just not good enough. You don't have what it takes to survive against the level of quality that's out there." Kevin calls it the harshest sentence anyone ever delivered to him. He also calls it the most important one. He went back to school. Got a business degree. Started a production company called Special Event Television out of spite — but he didn't put himself behind the camera. He hired a real cameraman. He learned to edit. He sold the company. That was his first deal. The thing he took from that day became his operating system: knowing what you're bad at matters more than knowing what you're good at. Logistics bore him, so he gives up equity to anyone who can do it. He stays in marketing because that's the only seat where he compounds. Then he stopped pretending. The cruelest sentence of his life was the one that saved it. P.S. Pull the thread on any story like this and you'll find the hidden incentive at the other end. As Munger said: "Show me the incentive and I'll show you the outcome." So I wrote a short book on how to spot them and design your own. Comment "INCENTIVES" and I'll send you the details. If you're new here, follow GeniusThinking for content on the greatest minds in economics, psychology, and history. — Kevin O'Leary ( Kevin O'Leary aka Mr. Wonderful ), chairman of O'Leary Ventures and Shark Tank investor, on Graham Stephan's ( Graham Stephan ) and Jack Selby's ( Jack Selby ) Iced Coffee Hour podcast

Kevin O'Leary reveals the cruelest sentence of his life was the one that saved it. Kevin O'Leary said he has always wanted to be a photographer. He was already working as a cameraman, ready to bet his life on a lens. His stepfather sat him down and said: "I'm sorry, but you're just not good enough. You don't have what it takes to survive against the level of quality that's out there." Kevin calls it the harshest sentence anyone ever delivered to him. He also calls it the most important one. He went back to school. Got a business degree. Started a production company called Special Event Television out of spite — but he didn't put himself behind the camera. He hired a real cameraman. He learned to edit. He sold the company. That was his first deal. The thing he took from that day became his operating system: knowing what you're bad at matters more than knowing what you're good at. Logistics bore him, so he gives up equity to anyone who can do it. He stays in marketing because that's the only seat where he compounds. Then he stopped pretending. The cruelest sentence of his life was the one that saved it. P.S. Pull the thread on any story like this and you'll find the hidden incentive at the other end. As Munger said: "Show me the incentive and I'll show you the outcome." So I wrote a short book on how to spot them and design your own. Comment "INCENTIVES" and I'll send you the details. If you're new here, follow GeniusThinking for content on the greatest minds in economics, psychology, and history. — Kevin O'Leary ( Kevin O'Leary aka Mr. Wonderful ), chairman of O'Leary Ventures and Shark Tank investor, on Graham Stephan's ( Graham Stephan ) and Jack Selby's ( Jack Selby ) Iced Coffee Hour podcast

GeniusThinking

25,562 views • 5 months ago

The man who invented modern fantasy didn't publish his first novel until he was forty-five. By that age, J.R.R. Tolkien had already built a respectable life. He was an Oxford professor, an expert in ancient languages, with a wife and four children and a settled academic career. He was exactly the kind of man who might reasonably have decided that the shape of his life was already fixed. The work he would be remembered for, he had not yet even begun... The story, which Tolkien told himself, is that one summer he was grading examination papers, when he turned a page and found that a student had left it blank. Without quite knowing why, he wrote a single sentence on it: "In a hole in the ground there lived a hobbit." He did not know what a hobbit was. He had spent years inventing languages and mythologies as a private passion, and telling stories to his own children, never imagining any of it would reach the world. But that one line began to grow. It became a story, and then a book, and in 1937, at the age of forty-five, Tolkien published The Hobbit. It was a success, and his publisher asked for a sequel. Tolkien warned them it might take some time. It took 17 years... He wrote it in the margins of a demanding full-time job, revising endlessly, doubting it often. When The Lord of the Rings was finally published, in 1954 and 1955, he was in his early sixties. That book, begun as a middle-aged professor's private side project, went on to sell well over a hundred million copies, to invent modern fantasy as we know it, and to reshape the imagination of the entire world. Tolkien already had a full and respectable life behind him. And still, the thing he is remembered for, the thing that outlived him and reached hundreds of millions of people, was something he began at forty-five, at an age when it would have been the easiest thing in the world to tell himself he had already missed his chance. He didn't. It's never as late as it feels.

The man who invented modern fantasy didn't publish his first novel until he was forty-five. By that age, J.R.R. Tolkien had already built a respectable life. He was an Oxford professor, an expert in ancient languages, with a wife and four children and a settled academic career. He was exactly the kind of man who might reasonably have decided that the shape of his life was already fixed. The work he would be remembered for, he had not yet even begun... The story, which Tolkien told himself, is that one summer he was grading examination papers, when he turned a page and found that a student had left it blank. Without quite knowing why, he wrote a single sentence on it: "In a hole in the ground there lived a hobbit." He did not know what a hobbit was. He had spent years inventing languages and mythologies as a private passion, and telling stories to his own children, never imagining any of it would reach the world. But that one line began to grow. It became a story, and then a book, and in 1937, at the age of forty-five, Tolkien published The Hobbit. It was a success, and his publisher asked for a sequel. Tolkien warned them it might take some time. It took 17 years... He wrote it in the margins of a demanding full-time job, revising endlessly, doubting it often. When The Lord of the Rings was finally published, in 1954 and 1955, he was in his early sixties. That book, begun as a middle-aged professor's private side project, went on to sell well over a hundred million copies, to invent modern fantasy as we know it, and to reshape the imagination of the entire world. Tolkien already had a full and respectable life behind him. And still, the thing he is remembered for, the thing that outlived him and reached hundreds of millions of people, was something he began at forty-five, at an age when it would have been the easiest thing in the world to tell himself he had already missed his chance. He didn't. It's never as late as it feels.

James Lucas

94,784 views • 4 months ago

A 26-year-old guy from China runs an agency that gains over 1,000,000 followers a year, and not a single one of his streams is actually live. he started as a real streamer, on camera every night, and made almost nothing. the moment he stopped, the caps stopped. a shop that closes when you sleep isn't a business, it's a shift. so he stopped going live. he built a rig that only looks live: > two vertical 4K monitors looping a clean pre-recorded product demo > phones on tripods filming the screens through anti-moiré glass > TikTok Shop links wired to every "stream" > one operator minding a rack of them instead of a camera the trick was never the tech. it was the reframe: a live-commerce stream never had to be live. it had to sell. a good demo, looped clean, closed the same at 4am as it did at noon. then he ran it for other people. brands handed him their catalog, he spun up always-on storefronts across a dozen accounts, and took a cut of every sale the loops closed. the $100,000 a year didn't come from being on camera. they came the night he realized the camera was the only part that needed a human, so he removed it.

A 26-year-old guy from China runs an agency that gains over 1,000,000 followers a year, and not a single one of his streams is actually live. he started as a real streamer, on camera every night, and made almost nothing. the moment he stopped, the caps stopped. a shop that closes when you sleep isn't a business, it's a shift. so he stopped going live. he built a rig that only looks live: > two vertical 4K monitors looping a clean pre-recorded product demo > phones on tripods filming the screens through anti-moiré glass > TikTok Shop links wired to every "stream" > one operator minding a rack of them instead of a camera the trick was never the tech. it was the reframe: a live-commerce stream never had to be live. it had to sell. a good demo, looped clean, closed the same at 4am as it did at noon. then he ran it for other people. brands handed him their catalog, he spun up always-on storefronts across a dozen accounts, and took a cut of every sale the loops closed. the $100,000 a year didn't come from being on camera. they came the night he realized the camera was the only part that needed a human, so he removed it.

Nekt0

21,700 views • 4 months ago