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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

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AI Phd Professor just showed how to build Hidden Markov Models for time-series data. 61-minutes. free. By AI Researcher for Quants. "for stock market prediction is the same idea you want to predict what prices are going to be in the future" here's what they cover: •why the independent data assumption fails •calculating transition and emission distributions •predicting future states step-by-step •extracting hidden sequences with the Viterbi algorithm Bookmark it & watch today. Then read the article below.

AI Phd Professor just showed how to build Hidden Markov Models for time-series data. 61-minutes. free. By AI Researcher for Quants. "for stock market prediction is the same idea you want to predict what prices are going to be in the future" here's what they cover: •why the independent data assumption fails •calculating transition and emission distributions •predicting future states step-by-step •extracting hidden sequences with the Viterbi algorithm Bookmark it & watch today. Then read the article below.

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43,465 просмотров • 1 месяц назад

Jane Street pays $750K/year for quants who master large deviations and tail bounds. 82-minutes. free. By MIT professor. "Markov gives us a really bad bound, Chebyshev is pretty reasonable, Chernoff gives us exponential." here's what they cover: • Markov vs Chebyshev vs Chernoff bounds • bounding tail risk for extreme random variables • why variance alone fails in high-frequency regimes • exponential 10x tighter risk modeling Bookmark it & watch today. Then read the article below.

Jane Street pays $750K/year for quants who master large deviations and tail bounds. 82-minutes. free. By MIT professor. "Markov gives us a really bad bound, Chebyshev is pretty reasonable, Chernoff gives us exponential." here's what they cover: • Markov vs Chebyshev vs Chernoff bounds • bounding tail risk for extreme random variables • why variance alone fails in high-frequency regimes • exponential 10x tighter risk modeling Bookmark it & watch today. Then read the article below.

venus

61,358 просмотров • 21 дней назад

Stanford CS professor just dropped the ultimate framework for building self-learning ai agents. 65-minutes. free. by stanford ai faculty. "reward hacking - your agent is gonna learn to do exactly what it is that you tell him to do" here's what they cover: •solving the credit assignment problem and delayed rewards •exploration vs exploitation trade-offs in high-stakes domains •model-based vs model-free system evaluation •4 foundational pillars of autonomous decision making Bookmark & watch today. Worth more than any $500 machine learning course.

Stanford CS professor just dropped the ultimate framework for building self-learning ai agents. 65-minutes. free. by stanford ai faculty. "reward hacking - your agent is gonna learn to do exactly what it is that you tell him to do" here's what they cover: •solving the credit assignment problem and delayed rewards •exploration vs exploitation trade-offs in high-stakes domains •model-based vs model-free system evaluation •4 foundational pillars of autonomous decision making Bookmark & watch today. Worth more than any $500 machine learning course.

venus

14,835 просмотров • 1 месяц назад

Jane Street pays $400K+ to first-year traders out of college, no PhD required. This 26-minute mock interview with two actual Jane Street traders, Graham and Andrea, will teach you more about passing a quant trader interview than the entire Green Book. They walk through a real probability puzzle - exactly the kind that gets you cut in round 2 - and show you what interviewers actually look for. It's not the right answer. It's how you reason out loud. 26 minutes. Free. Watch it today. Then read the article below.

Jane Street pays $400K+ to first-year traders out of college, no PhD required. This 26-minute mock interview with two actual Jane Street traders, Graham and Andrea, will teach you more about passing a quant trader interview than the entire Green Book. They walk through a real probability puzzle - exactly the kind that gets you cut in round 2 - and show you what interviewers actually look for. It's not the right answer. It's how you reason out loud. 26 minutes. Free. Watch it today. Then read the article below.

zostaff

13,287 просмотров • 2 месяцев назад

the engineer who built Claude Code just dropped a 28-minute video on how to write prompts that actually work I've seen $300 courses that don't cover what he shows in the first 10 minutes CLAUDE.md files, memory shortcuts, parallel sessions, prompting patterns all in one video and completely free works whether you're a developer, a beginner, or someone who's been using Claude for months based on this, I put together 18 things you can copy and use in Claude today full guide in the article below

the engineer who built Claude Code just dropped a 28-minute video on how to write prompts that actually work I've seen $300 courses that don't cover what he shows in the first 10 minutes CLAUDE.md files, memory shortcuts, parallel sessions, prompting patterns all in one video and completely free works whether you're a developer, a beginner, or someone who's been using Claude for months based on this, I put together 18 things you can copy and use in Claude today full guide in the article below

Anatoli Kopadze

6,293,732 просмотров • 2 месяцев назад

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

269,381 просмотров • 19 дней назад

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

112,140 просмотров • 1 месяц назад

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 просмотров • 1 месяц назад

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 просмотров • 1 месяц назад

I asked my bot to read 20 Polymarket quant articles and build me a trading system. One week later: $600 → $20,400. The crazy part? It didn’t just copy the strategies from the articles. It found the edges the authors never mentioned. I gave Claude Opus a simple prompt: “Read these 20 quant articles. Extract every strategy. Then figure out what they’re not saying.” 48 hours later, the bot was live. The articles talked about the usual things: • basic arbitrage • weather markets • simple spreads But the bot looked deeper. It analyzed 847 trades from top wallets, cross-referenced them with article timestamps, and found something interesting: The gap between what quants publish and what they actually trade. Examples: Articles say: wait for 5% mispricing. Top wallets enter at 2.3%. Articles say: avoid volatile markets. Top wallets make 80% of their profits during volatility spikes. Articles say: start small with $100. Successful bots compound 3–6% per trade. So the system ignored the advice. And copied the behavior. Week 1 results • 127 trades executed • 71% win rate • $600 → $20,400 Biggest trade: $4,100 profit on a 15-minute BTC market Funny thing? The articles called those markets “too risky.” The bot saw 18 top wallets entering at the same time. Copytrade → What the bot actually does → scans quant articles for strategy mentions → tracks wallets belonging to the authors → compares what they write vs what they trade → trades the difference The edge isn’t reading quant articles. The edge is watching what happens after they publish them. Most people learn from what traders say. The bot learned from what they hide.

I asked my bot to read 20 Polymarket quant articles and build me a trading system. One week later: $600 → $20,400. The crazy part? It didn’t just copy the strategies from the articles. It found the edges the authors never mentioned. I gave Claude Opus a simple prompt: “Read these 20 quant articles. Extract every strategy. Then figure out what they’re not saying.” 48 hours later, the bot was live. The articles talked about the usual things: • basic arbitrage • weather markets • simple spreads But the bot looked deeper. It analyzed 847 trades from top wallets, cross-referenced them with article timestamps, and found something interesting: The gap between what quants publish and what they actually trade. Examples: Articles say: wait for 5% mispricing. Top wallets enter at 2.3%. Articles say: avoid volatile markets. Top wallets make 80% of their profits during volatility spikes. Articles say: start small with $100. Successful bots compound 3–6% per trade. So the system ignored the advice. And copied the behavior. Week 1 results • 127 trades executed • 71% win rate • $600 → $20,400 Biggest trade: $4,100 profit on a 15-minute BTC market Funny thing? The articles called those markets “too risky.” The bot saw 18 top wallets entering at the same time. Copytrade → What the bot actually does → scans quant articles for strategy mentions → tracks wallets belonging to the authors → compares what they write vs what they trade → trades the difference The edge isn’t reading quant articles. The edge is watching what happens after they publish them. Most people learn from what traders say. The bot learned from what they hide.

Discover

54,245 просмотров • 4 месяцев назад

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

Discover

45,260 просмотров • 4 месяцев назад

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.

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

93,073 просмотров • 21 дней назад

THIS SOLAR-POWERED MESH NODE PACKS A LOCAL LLM AND A 6-WATT PANEL, KILLED $200 CLAUDE CODE MAX AND $200 CHATGPT PRO SUBSCRIPTIONS THE DAY IT WENT LIVE AND RUNS AT $0/MO EVEN UNDER CLOUD COVER 00:22 he taps the case with the red led blinking, "even though we have complete cloud cover today, the sun's not even out and we're still getting a charge, that's absolutely phenomenal" the node runs a rak 4630 lora board with a 6-watt solar panel, a small battery bank inside the ip67 case, and a raspberry pi zero 2w wedged next to it running a quantized 3b model on ollama from the rooftop it services 71 other meshtastic nodes across the neighborhood offline, and the same box also handles ai queries piped in over the mesh from his phone without touching the internet claude code points at the pi over tailscale, the cli behaves identically to the cloud version except every token generates on his own rooftop under the panel that keeps charging through cloud cover $200 chatgpt pro and $200 claude code max used to hit his card monthly, and the whole stack died the day the rooftop went live because ai queries now travel over lora mesh and generate on his own hardware for free bookmark this and read the article below

THIS SOLAR-POWERED MESH NODE PACKS A LOCAL LLM AND A 6-WATT PANEL, KILLED $200 CLAUDE CODE MAX AND $200 CHATGPT PRO SUBSCRIPTIONS THE DAY IT WENT LIVE AND RUNS AT $0/MO EVEN UNDER CLOUD COVER 00:22 he taps the case with the red led blinking, "even though we have complete cloud cover today, the sun's not even out and we're still getting a charge, that's absolutely phenomenal" the node runs a rak 4630 lora board with a 6-watt solar panel, a small battery bank inside the ip67 case, and a raspberry pi zero 2w wedged next to it running a quantized 3b model on ollama from the rooftop it services 71 other meshtastic nodes across the neighborhood offline, and the same box also handles ai queries piped in over the mesh from his phone without touching the internet claude code points at the pi over tailscale, the cli behaves identically to the cloud version except every token generates on his own rooftop under the panel that keeps charging through cloud cover $200 chatgpt pro and $200 claude code max used to hit his card monthly, and the whole stack died the day the rooftop went live because ai queries now travel over lora mesh and generate on his own hardware for free bookmark this and read the article below

starmex

50,227 просмотров • 21 дней назад

Sam Altman, CEO of OpenAI: "GPT 5.6 is 54% more efficient on agentic coding, and it's still the best on the market for half the cost." AI spend has never mattered as much as it does today. As a result the models keep getting better and cheaper at the same time. This is going to widen the gap between the people using AI agents and everyone else, because now it's not just powerful, it's cheap. That's exactly why i wrote a full step by step guide on agents, what they are and how to build your own from scratch. Read it below.

Sam Altman, CEO of OpenAI: "GPT 5.6 is 54% more efficient on agentic coding, and it's still the best on the market for half the cost." AI spend has never mattered as much as it does today. As a result the models keep getting better and cheaper at the same time. This is going to widen the gap between the people using AI agents and everyone else, because now it's not just powerful, it's cheap. That's exactly why i wrote a full step by step guide on agents, what they are and how to build your own from scratch. Read it below.

Anatoli Kopadze

91,447 просмотров • 14 дней назад

Tools from this article earn the creator of this bot $1,000,000. I reverse-engineered it and gave you all the tools completely for free. 28 repositories. 6 layers of execution - from bot reasoning engine all the way to paper-trading strategies. On-chain proof of the $1M wallet: This bot doesn’t predict the future. It exploits inefficiencies. This is literally what separates every single top leaderboard wallet - pure math edge, not crystal ball magic. Bot brain = Claude Squad with multiple Opus agents running in parallel. Trading management = Agency Agents, a pro swarm engine that replaces an entire PhD dev team. Full data collection suite (OpenBB + Binance collector + everything else you need). All the necessary repos for trading intelligence, strategy backtesting and Polymarket API interaction are included. This isn’t some basic script anymore - this is a full quant pipeline that shows you exactly how real trading algorithms are built. Bookmark this post or you’ll lose the article. Or start making money right now - copy trades of this $1M PnL bot using copy-trading terminal:

Tools from this article earn the creator of this bot $1,000,000. I reverse-engineered it and gave you all the tools completely for free. 28 repositories. 6 layers of execution - from bot reasoning engine all the way to paper-trading strategies. On-chain proof of the $1M wallet: This bot doesn’t predict the future. It exploits inefficiencies. This is literally what separates every single top leaderboard wallet - pure math edge, not crystal ball magic. Bot brain = Claude Squad with multiple Opus agents running in parallel. Trading management = Agency Agents, a pro swarm engine that replaces an entire PhD dev team. Full data collection suite (OpenBB + Binance collector + everything else you need). All the necessary repos for trading intelligence, strategy backtesting and Polymarket API interaction are included. This isn’t some basic script anymore - this is a full quant pipeline that shows you exactly how real trading algorithms are built. Bookmark this post or you’ll lose the article. Or start making money right now - copy trades of this $1M PnL bot using copy-trading terminal:

cvxv666

56,293 просмотров • 3 месяцев назад

Leaving Citadel & launching a $1B AI hedge fund — how Renee Yao built NeoIvy Capital from scratch Renee Yao walked away from two of the most elite hedge funds on Wall Street — Citadel & Millennium — and built a quant fund on a fundamentally different model: modern AI instead of human-powered alpha generation. The result: $1B+ in regulatory AUM, uncorrelated returns through COVID, & a fund Business Insider named one of the top transforming investing in North America. We cover: - Why large multi-manager quant firms rely on massive global researcher headcounts — & why Renee saw that as a model worth disrupting - The 3 barriers to entry in AI-driven quant — & why legacy sequential infrastructure can be a disadvantage compared to modern parallel distributed systems - How NeoIvy's self-evolving models adapted in real time during the March 2020 crash — while traditional quant managers had a nightmare month - The difference between beta returns, factor returns & pure alpha — & why size is the enemy of true idiosyncratic returns - Why the "black box" reputation of quant funds has been the #1 fundraising obstacle - How a 4-year-old girl visiting her uncle's room-sized supercomputer in China set the foundation for all of this - The edge/breadth/constraint framework from Grinold & Kahn — & how it shaped Renee's thinking on diversification - Renee's raw advice on staying disciplined when everyone around you is chasing beta in a bull market Transcript: 00:00 Intro 01:14 Renee Yao’s journey to founding Neo Ivy 02:28 Joining Citadel after the financial crisis 04:13 Hedge fund diversification and breadth of edge 04:45 Why Neo Ivy trades with AI strategies 07:50 How self-learning AI adapts to markets 09:40 Causation vs correlation in AI hedge funds 10:33 Barriers to entry for AI hedge funds 14:47 Risks of crowded factor bets explained 16:39 Why big funds struggle with AI talent 17:29 From PM at Citadel to hedge fund founder 18:47 Challenges of launching a quant hedge fund 20:25 Biggest constraint for AI hedge fund startups 22:08 How AI hedge funds adapted during COVID 24:04 Modern AI tools used in quant trading 25:13 Building hedge fund infrastructure from scratch 26:26 Career advice for aspiring quants and traders 28:55 Adapting career goals to changing job markets 31:57 Life lessons from trading and risk management 32:51 Staying disciplined while running a hedge fund 34:38 Obsession and belief in AI hedge funds 35:41 Closing thoughts on hedge funds and life

Leaving Citadel & launching a $1B AI hedge fund — how Renee Yao built NeoIvy Capital from scratch Renee Yao walked away from two of the most elite hedge funds on Wall Street — Citadel & Millennium — and built a quant fund on a fundamentally different model: modern AI instead of human-powered alpha generation. The result: $1B+ in regulatory AUM, uncorrelated returns through COVID, & a fund Business Insider named one of the top transforming investing in North America. We cover: - Why large multi-manager quant firms rely on massive global researcher headcounts — & why Renee saw that as a model worth disrupting - The 3 barriers to entry in AI-driven quant — & why legacy sequential infrastructure can be a disadvantage compared to modern parallel distributed systems - How NeoIvy's self-evolving models adapted in real time during the March 2020 crash — while traditional quant managers had a nightmare month - The difference between beta returns, factor returns & pure alpha — & why size is the enemy of true idiosyncratic returns - Why the "black box" reputation of quant funds has been the #1 fundraising obstacle - How a 4-year-old girl visiting her uncle's room-sized supercomputer in China set the foundation for all of this - The edge/breadth/constraint framework from Grinold & Kahn — & how it shaped Renee's thinking on diversification - Renee's raw advice on staying disciplined when everyone around you is chasing beta in a bull market Transcript: 00:00 Intro 01:14 Renee Yao’s journey to founding Neo Ivy 02:28 Joining Citadel after the financial crisis 04:13 Hedge fund diversification and breadth of edge 04:45 Why Neo Ivy trades with AI strategies 07:50 How self-learning AI adapts to markets 09:40 Causation vs correlation in AI hedge funds 10:33 Barriers to entry for AI hedge funds 14:47 Risks of crowded factor bets explained 16:39 Why big funds struggle with AI talent 17:29 From PM at Citadel to hedge fund founder 18:47 Challenges of launching a quant hedge fund 20:25 Biggest constraint for AI hedge fund startups 22:08 How AI hedge funds adapted during COVID 24:04 Modern AI tools used in quant trading 25:13 Building hedge fund infrastructure from scratch 26:26 Career advice for aspiring quants and traders 28:55 Adapting career goals to changing job markets 31:57 Life lessons from trading and risk management 32:51 Staying disciplined while running a hedge fund 34:38 Obsession and belief in AI hedge funds 35:41 Closing thoughts on hedge funds and life

Ethan Kho

138,224 просмотров • 5 месяцев назад

My friend got laid off from a hedge fund last November He was making $2.1M/year running their quant desk Instead of applying to another fund - he took their entire strategy framework and rebuilt it on Polymarket 1300$ → 187,564$ in 3 weeks Total cost: $200/month for Claude, $50/month VPS He said: "The models I built for the fund work better on Polymarket, because on Wall Street you compete against other quants. On Polymarket you compete against guys who bet based on Twitter polls" He rebuilt a 4-factor model in one weekend Same math his fund ran on $800M AUM: Momentum scoring: track 400+ markets, buy when a contract moves 10¢+ with rising volume Mean reversion: when a contract overshoots 15¢+ in under 2 hours - fade it Volatility regime detection: hidden Markov model classifies low-vol vs high-vol every hour. Switches strategy automatically Correlation breakdown: 200+ market pairs monitored. When two normally correlated contracts decorrelate - one is wrong. Trade the lagging one. All 4 run simultaneously COPY HIM: Portfolio optimizer combines the scores Fractional Kelly sizing, max 5% per market, 15 concurrent positions His fund's flagship returned 19% last year on $800M His bot returned 144,000% in 3 weeks on $1300 He doesn't need to work anymore

My friend got laid off from a hedge fund last November He was making $2.1M/year running their quant desk Instead of applying to another fund - he took their entire strategy framework and rebuilt it on Polymarket 1300$ → 187,564$ in 3 weeks Total cost: $200/month for Claude, $50/month VPS He said: "The models I built for the fund work better on Polymarket, because on Wall Street you compete against other quants. On Polymarket you compete against guys who bet based on Twitter polls" He rebuilt a 4-factor model in one weekend Same math his fund ran on $800M AUM: Momentum scoring: track 400+ markets, buy when a contract moves 10¢+ with rising volume Mean reversion: when a contract overshoots 15¢+ in under 2 hours - fade it Volatility regime detection: hidden Markov model classifies low-vol vs high-vol every hour. Switches strategy automatically Correlation breakdown: 200+ market pairs monitored. When two normally correlated contracts decorrelate - one is wrong. Trade the lagging one. All 4 run simultaneously COPY HIM: Portfolio optimizer combines the scores Fractional Kelly sizing, max 5% per market, 15 concurrent positions His fund's flagship returned 19% last year on $800M His bot returned 144,000% in 3 weeks on $1300 He doesn't need to work anymore

Hrundel75 🐷

308,916 просмотров • 3 месяцев назад

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

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

Livsun

188,258 просмотров • 2 месяцев назад

my Claude built me a Hydra Swarm terminal with 512 live agents a month ago i didn't know what mesh topology was now i have a swarm living on my screen that trades for me it started when i fed Claude an article about quant formulas he didn't just read it - he asked: "want to see what this looks like from the inside?" an hour later a web was living on my screen 512 agents. each one drifts. each one decides who to connect with 15,000 threads between them break and reform every second first week i just watched packets flying between nodes the web breathing the screen flickering when agents reach consensus then i turned it on with real contracts: > week 1: swarm caught chatter on iran before CNN ran the headline bought YES on ceasefire at $0.30 by friday the contract was at $0.64 +$2,840 > week 2: two polymarket contracts were linked but prices diverged swarm saw the gap. took both sides. waited convergence by wednesday +$3,190 > week 3: weather contract market gave hurricane landfall 19%. model inside the swarm said 38% bought. confirmed thursday +$3,670 > week 4: fed decision market priced "hold" at 62%. base rate at current unemployment - 74% 12 points of difference isn't an opinion. it's math bought. settled at $0.97 +$2,873 total: $12,573 in the first month i never opened polymarket manually 512 agents did it for me 24 hours a day. 7 days a week no opinions. no emotions. no "i feel like YES is underpriced" the weirdest part - i got used to it i open the terminal every morning like email watch the web breathe and the profit tick copy the bot: i didn't need to become a quant i needed a swarm that thinks for me

my Claude built me a Hydra Swarm terminal with 512 live agents a month ago i didn't know what mesh topology was now i have a swarm living on my screen that trades for me it started when i fed Claude an article about quant formulas he didn't just read it - he asked: "want to see what this looks like from the inside?" an hour later a web was living on my screen 512 agents. each one drifts. each one decides who to connect with 15,000 threads between them break and reform every second first week i just watched packets flying between nodes the web breathing the screen flickering when agents reach consensus then i turned it on with real contracts: > week 1: swarm caught chatter on iran before CNN ran the headline bought YES on ceasefire at $0.30 by friday the contract was at $0.64 +$2,840 > week 2: two polymarket contracts were linked but prices diverged swarm saw the gap. took both sides. waited convergence by wednesday +$3,190 > week 3: weather contract market gave hurricane landfall 19%. model inside the swarm said 38% bought. confirmed thursday +$3,670 > week 4: fed decision market priced "hold" at 62%. base rate at current unemployment - 74% 12 points of difference isn't an opinion. it's math bought. settled at $0.97 +$2,873 total: $12,573 in the first month i never opened polymarket manually 512 agents did it for me 24 hours a day. 7 days a week no opinions. no emotions. no "i feel like YES is underpriced" the weirdest part - i got used to it i open the terminal every morning like email watch the web breathe and the profit tick copy the bot: i didn't need to become a quant i needed a swarm that thinks for me

Hanako

126,945 просмотров • 4 месяцев назад

I made $19,600 in 18 days copy-trading a Chinese quant who turned $200 into $354,000 in 48 hours. Same MiroFish + Claude setup he's used to clear $350K all-time. 7,500% on a single position. I've prepared the exact step-by-step guide to build this BTC simulation engine. Giving this free for 24 hours. To get it: 1. Comment "Claude" + Like and Retweet 2. Follow me Himanshu Kumar (Only then, i can DM you) You only need Claude + a device + 1 hour/day. Used The system below: - Claude = the algorithm's brain - MiroFish = the simulation engine - 10,000 cycles run before every single trade - Closed order book + private OTC desk feeds His wallet hit $350K all-time. Mine hit $19.6K in 18 days copying him. His Polymarket ID: gobblewobble He's not predicting the future. He's running 10,000 versions of every market reaction before the market moves. You Must Follow me Himanshu Kumar, so i can send you DM. You're staring at charts hoping for a setup. He's running Monte Carlo simulations while you sleep.

I made $19,600 in 18 days copy-trading a Chinese quant who turned $200 into $354,000 in 48 hours. Same MiroFish + Claude setup he's used to clear $350K all-time. 7,500% on a single position. I've prepared the exact step-by-step guide to build this BTC simulation engine. Giving this free for 24 hours. To get it: 1. Comment "Claude" + Like and Retweet 2. Follow me Himanshu Kumar (Only then, i can DM you) You only need Claude + a device + 1 hour/day. Used The system below: - Claude = the algorithm's brain - MiroFish = the simulation engine - 10,000 cycles run before every single trade - Closed order book + private OTC desk feeds His wallet hit $350K all-time. Mine hit $19.6K in 18 days copying him. His Polymarket ID: gobblewobble He's not predicting the future. He's running 10,000 versions of every market reaction before the market moves. You Must Follow me Himanshu Kumar, so i can send you DM. You're staring at charts hoping for a setup. He's running Monte Carlo simulations while you sleep.

Himanshu Kumar

129,775 просмотров • 2 месяцев назад