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Citadel Senior Quant Developer just dropped the complete math framework for scaling a pairs trading desk to 200+ concurrent market positions. 54-minutes. free. By Quant Trader. here's what they cover: • integration orders (I(0) vs I(1)) for asset pricing • using the Augmented Dickey-Fuller test over standard graphs •... show more

venus

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29,843 次观看 • 2 个月前 •via X (Twitter)

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MIT Professor just revealed the exact mathematical framework behind quant trading and zero-sum edge. 80-minutes. free. By Ian Bell. here's what they cover: • how von Neumann proved moving first isn't a disadvantage in zero-sum games • Nash equilibrium vs. security strategies (minimax & maximin algebra) • why chess has an exact objective outcome (1, -1, or 0) • von Neumann poker model - proving bluffing as pure math Bookmark & watch today. Then read the article below.

MIT Professor just revealed the exact mathematical framework behind quant trading and zero-sum edge. 80-minutes. free. By Ian Bell. here's what they cover: • how von Neumann proved moving first isn't a disadvantage in zero-sum games • Nash equilibrium vs. security strategies (minimax & maximin algebra) • why chess has an exact objective outcome (1, -1, or 0) • von Neumann poker model - proving bluffing as pure math Bookmark & watch today. Then read the article below.

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codila

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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

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Over a decade ago, a math professor in Chennai opened his first lecture with the simplest idea in all of math, and it is the exact thing every prediction market runs on today. NPTEL filmed it. it has been free ever since. 759,000 people have watched it, almost none of them trade. his point: before probability, before any odds, there are just sets. a bag of everything that can happen, and the slice you are betting on. every trader pricing a market is doing set theory without knowing the name for it. no Wall Street badge, no trading desk. just a professor at a green board building the whole machine from nothing. skip to the board in the first ten minutes. he takes union, intersection, and the outcomes you throw away, and turns them into the rules every payoff has to obey, in notation a schoolkid can follow. no slides. no app. one green board and a piece of chalk. a quant i know rewatched this exact lecture before rebuilding his risk model. said he had been skipping the part that actually matters. I put the practical aspects of this lecture into practice and developed a strategy that generates income, the results surprised me. 27 days. $200 → $25,400. 74% win rate. Sharpe 2.47. I'm giving away the full configuration of my bot for free. 24 hours only. Here's how: >Comment "SEND" >Like and Follow me I'll send the complete setup in your DMs.

Over a decade ago, a math professor in Chennai opened his first lecture with the simplest idea in all of math, and it is the exact thing every prediction market runs on today. NPTEL filmed it. it has been free ever since. 759,000 people have watched it, almost none of them trade. his point: before probability, before any odds, there are just sets. a bag of everything that can happen, and the slice you are betting on. every trader pricing a market is doing set theory without knowing the name for it. no Wall Street badge, no trading desk. just a professor at a green board building the whole machine from nothing. skip to the board in the first ten minutes. he takes union, intersection, and the outcomes you throw away, and turns them into the rules every payoff has to obey, in notation a schoolkid can follow. no slides. no app. one green board and a piece of chalk. a quant i know rewatched this exact lecture before rebuilding his risk model. said he had been skipping the part that actually matters. I put the practical aspects of this lecture into practice and developed a strategy that generates income, the results surprised me. 27 days. $200 → $25,400. 74% win rate. Sharpe 2.47. I'm giving away the full configuration of my bot for free. 24 hours only. Here's how: >Comment "SEND" >Like and Follow me I'll send the complete setup in your DMs.

Voltex

27,825 次观看 • 19 天前

a quant who started as a technical trader explained what changed everything for him he stopped trying to predict where price goes he started measuring the probability of each market state and betting only when the math was asymmetric that shift took him from drawing trendlines to writing models at 22 the realization was simple but brutal: technical analysis gives you a narrative quantitative analysis gives you a number one feels right the other is testable, repeatable, and either works or doesn't across 10,000 trades the setup on his desk tells the whole story no TradingView. no candlestick charts. just code, data, and a terminal he didn't go to MIT he didn't intern at Goldman he learned Python, statistics, and probability theory on his own and built something that actually worked > the math: free in any stats textbook > the data: free on Yahoo Finance, FRED, exchange APIs > the code: Python, 200 lines, running on a laptop > the barrier: not intelligence. just knowing this path exists most people spend years staring at indicators from the 1970s wondering why they can't find consistency the answer was never a better indicator it was a completely different framework one that treats trading as a math problem, not a prediction game full breakdown in the video below

a quant who started as a technical trader explained what changed everything for him he stopped trying to predict where price goes he started measuring the probability of each market state and betting only when the math was asymmetric that shift took him from drawing trendlines to writing models at 22 the realization was simple but brutal: technical analysis gives you a narrative quantitative analysis gives you a number one feels right the other is testable, repeatable, and either works or doesn't across 10,000 trades the setup on his desk tells the whole story no TradingView. no candlestick charts. just code, data, and a terminal he didn't go to MIT he didn't intern at Goldman he learned Python, statistics, and probability theory on his own and built something that actually worked > the math: free in any stats textbook > the data: free on Yahoo Finance, FRED, exchange APIs > the code: Python, 200 lines, running on a laptop > the barrier: not intelligence. just knowing this path exists most people spend years staring at indicators from the 1970s wondering why they can't find consistency the answer was never a better indicator it was a completely different framework one that treats trading as a math problem, not a prediction game full breakdown in the video below

delost

112,271 次观看 • 2 个月前

an ex-Goldman banker said the key to understanding markets is reading 3 WSJ articles a day a quant at Renaissance would tell you that's the exact wrong input here's why the Wall Street Journal publishes after the move by the time you read "tech stocks fall on rate fears" the move already happened, the positioning already shifted, and every algorithm already repriced news explains what happened. it doesn't predict what's next quant desks don't read news to form trade ideas they measure whether news has a statistically significant effect on price after controlling for everything else the answer, across decades of academic research: individual news articles explain less than 0.5% of daily price variance the other 99.5% is flow, positioning, regime, volatility structure, and cross-asset correlation none of which shows up in a WSJ headline this is the Goldman mindset vs the quant mindset Goldman: read narratives, form opinions, act on conviction Renaissance: measure everything, strip out noise, trade only what survives statistical testing one produced traders who sound smart at dinner parties the other produced $100 billion in profit over 30 years > WSJ subscription: $468/year > SEC EDGAR data: free > FRED economic data: free > exchange-level order flow: free > academic papers on news impact studies: free on ArXiv since the 2000s the banker tells you to read the news the quant tells you to measure whether the news matters those are not the same instruction full breakdown in the video below

an ex-Goldman banker said the key to understanding markets is reading 3 WSJ articles a day a quant at Renaissance would tell you that's the exact wrong input here's why the Wall Street Journal publishes after the move by the time you read "tech stocks fall on rate fears" the move already happened, the positioning already shifted, and every algorithm already repriced news explains what happened. it doesn't predict what's next quant desks don't read news to form trade ideas they measure whether news has a statistically significant effect on price after controlling for everything else the answer, across decades of academic research: individual news articles explain less than 0.5% of daily price variance the other 99.5% is flow, positioning, regime, volatility structure, and cross-asset correlation none of which shows up in a WSJ headline this is the Goldman mindset vs the quant mindset Goldman: read narratives, form opinions, act on conviction Renaissance: measure everything, strip out noise, trade only what survives statistical testing one produced traders who sound smart at dinner parties the other produced $100 billion in profit over 30 years > WSJ subscription: $468/year > SEC EDGAR data: free > FRED economic data: free > exchange-level order flow: free > academic papers on news impact studies: free on ArXiv since the 2000s the banker tells you to read the news the quant tells you to measure whether the news matters those are not the same instruction full breakdown in the video below

delost

15,317 次观看 • 2 个月前

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

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 次观看 • 3 个月前

a quant at citadel told me something that broke my entire trading framework "we don't predict markets. we model the state machine" he explained markov cains in 90 seconds the market is never random - it always exists in one of three states trending up, trending down, ranging - each has a fixed probability of shifting to another build the transition matrix from real price data: trending up -> 68% stays trending, 21% flips to range, 11% reverses ranging -> 54% stays range, 28% breaks up, 18% breaks down trending down -> 61% stays falling, 24% flips to range, 15% reverses now you're not guessing, you're playing probability identify current state, enter with the 68% edge, size with kelly criterion based on that probability the formula is public - andrei markov published it in 1906 hedge funds use it every day, the math costs nothing what costs you is asking the wrong question "where is price going" is random "what state am I in right now" has an answer transition matrix built from 10 years of data is your edge Bookmark and use in your own strategy 37 years without a losing year, 66% annual returns paper is free, data is free, implementation is 200 lines of python they kept you staring at candles while they ran probability tables

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

19,650 次观看 • 20 天前

Paul Tudor Jones asked Stan Druckenmiller what he thought about Ray Dalio, who had said a day earlier that even with perfect research he still wanted 15 equally sized, uncorrelated bets: “I have the polar opposite investment philosophy. Put all your eggs in one basket and watch the basket carefully” this is him explaining how large his positions actually became, the four days in which he lost $1 billion, and what Paul Tudor Jones told him on the phone the morning he finally gave up “I’ve had a 350% long-equivalent position in 10-year bonds. In currencies, I’ve had 200% to 300% of my NAV in a single currency. My track record in those positions is much better than in the others” “I bought the NASDAQ beautifully in 1999, sold it in early 2000, then bought at the exact top. I knew I was trapped. By the time I cleaned up the mess I had created, it had probably cost me 15%” bookmark & watch the full conversation, then read the article below ↓

Paul Tudor Jones asked Stan Druckenmiller what he thought about Ray Dalio, who had said a day earlier that even with perfect research he still wanted 15 equally sized, uncorrelated bets: “I have the polar opposite investment philosophy. Put all your eggs in one basket and watch the basket carefully” this is him explaining how large his positions actually became, the four days in which he lost $1 billion, and what Paul Tudor Jones told him on the phone the morning he finally gave up “I’ve had a 350% long-equivalent position in 10-year bonds. In currencies, I’ve had 200% to 300% of my NAV in a single currency. My track record in those positions is much better than in the others” “I bought the NASDAQ beautifully in 1999, sold it in early 2000, then bought at the exact top. I knew I was trapped. By the time I cleaned up the mess I had created, it had probably cost me 15%” bookmark & watch the full conversation, then read the article below ↓

0xDipper

73,796 次观看 • 1 天前

i gave Claude PhD-level formulas and told it to build a trading terminal no questions. it shipped MiroFish 274 agents. 4 quant models running live doing what most Polymarket traders can’t: every 5 seconds it: scans all contracts updates probabilities with bayesian logic calculates expected value vs market price sizes positions using ¼ Kelly checks cross-market inefficiencies (KL divergence) no gut feelings. no “this looks cheap” just math the same kind funds guard behind NDAs here’s 14 days of output: day 2 bayesian model caught early signals on Iran talks 0.31 → 0.58 probability shift bought YES at $0.33 price hit $0.61 by day 5 $2,180 day 6 inefficiency detected between correlated markets primary win: $0.70 general win: $0.48 historically should be ~62% long general + hedge primary converged by day 9 $3,740 day 9 weather mispricing market: 22% model: 41% strong positive EV position sized at 11% event confirmed $4,890 day 11–14 rates hold contract mispriced market: 65% base rate: 74% (given unemployment <4%) entered at $0.65 settled at $0.98 $4,663 total: +$15,473 in 14 days not predictions just probabilities most traders lose because they follow intuition the edge comes from math Copytrade → MiroFish doesn’t read opinions it processes distributions 274 agents zero эмоций continuous recalculation i just watch the PnL update you don’t need to be a quant you just need quant logic running nonstop

i gave Claude PhD-level formulas and told it to build a trading terminal no questions. it shipped MiroFish 274 agents. 4 quant models running live doing what most Polymarket traders can’t: every 5 seconds it: scans all contracts updates probabilities with bayesian logic calculates expected value vs market price sizes positions using ¼ Kelly checks cross-market inefficiencies (KL divergence) no gut feelings. no “this looks cheap” just math the same kind funds guard behind NDAs here’s 14 days of output: day 2 bayesian model caught early signals on Iran talks 0.31 → 0.58 probability shift bought YES at $0.33 price hit $0.61 by day 5 $2,180 day 6 inefficiency detected between correlated markets primary win: $0.70 general win: $0.48 historically should be ~62% long general + hedge primary converged by day 9 $3,740 day 9 weather mispricing market: 22% model: 41% strong positive EV position sized at 11% event confirmed $4,890 day 11–14 rates hold contract mispriced market: 65% base rate: 74% (given unemployment <4%) entered at $0.65 settled at $0.98 $4,663 total: +$15,473 in 14 days not predictions just probabilities most traders lose because they follow intuition the edge comes from math Copytrade → MiroFish doesn’t read opinions it processes distributions 274 agents zero эмоций continuous recalculation i just watch the PnL update you don’t need to be a quant you just need quant logic running nonstop

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45,309 次观看 • 5 个月前