正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

Citadel & Two Sigma Quant just showed how quants build uncorrelated factor portfolios using PCA. 83-minutes. free. By Harvard PhD at MIT. here's what they cover: • isolating idiosyncratic yield curve factors (Level, Slope, Curvature) • using massive leverage to scale market-neutral portfolios • out-of-sample stability & handling post-COVID... show more

venus

1,782 subscribers

40,185 次观看 • 1 个月前 •via X (Twitter)

科学技术财经教育

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

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 • calculating dynamic rolling betas on log returns • managing independent bets across 200+ parallel tables Bookmark it & watch today. Then read the article below.

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 • calculating dynamic rolling betas on log returns • managing independent bets across 200+ parallel tables Bookmark it & watch today. Then read the article below.

venus

29,687 次观看 • 1 个月前

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.

venus

43,465 次观看 • 2 个月前

Citadel and Jane Street pay quants $62K/month to weaponize order book microstructure. MIT's Dr. Jake Xia just dropped a masterclass breaking down how institutional desks structure risk. 35-minutes. free. By former Morgan Stanley Head Quant. here's the structural logic: • why market makers prioritize flattening out positions over taking directional risk • how macro risk shifts when interest rate curves become inverted • structural execution differences between over-the-counter (OTC) agreements and ECNs • the 3 core divisions of capital routing that drive institutional desks worth more than any $500 vibe-coding course. Bookmark it & watch today. Then read the breakdown article below.

Citadel and Jane Street pay quants $62K/month to weaponize order book microstructure. MIT's Dr. Jake Xia just dropped a masterclass breaking down how institutional desks structure risk. 35-minutes. free. By former Morgan Stanley Head Quant. here's the structural logic: • why market makers prioritize flattening out positions over taking directional risk • how macro risk shifts when interest rate curves become inverted • structural execution differences between over-the-counter (OTC) agreements and ECNs • the 3 core divisions of capital routing that drive institutional desks worth more than any $500 vibe-coding course. Bookmark it & watch today. Then read the breakdown article below.

venus

32,194 次观看 • 2 个月前

quant at aqr sat next to me at a conference and said something that rewired how i think about markets "we don't analyze 500 stocks. we run 5 factors that explain all 500 at once" i asked him to draw it out take your entire stock universe - 500 names, 3 years of daily returns - and run PCA the 500 collapse into 5 factor 1: broad market momentum - explains 58% of all price movement by itself factor 2: sector rotation - where institutional money is actually flowing factor 3: rate sensitivity - duration risk bleeding across the equity curve factor 4: momentum tilt - which names are accelerating vs mean-reverting factor 5: credit spread stress - signal hiding inside equity vol if you know these 5 numbers, you know what 500 stocks are about to do retail watches 500 charts and sees noise. quant desks watch 5 eigenvalues and see structure math is called Principal Component Analysis, in every intro stats course on earth data to run it is free. python implementation is ~120 lines renaissance, two sigma, aqr - running this since before most retail traders could name a stock reason nobody told you isn't that it's proprietary it's that telling you costs them their edge Bookmark this market was never 500 moving parts it was always 5 - hiding inside 500

quant at aqr sat next to me at a conference and said something that rewired how i think about markets "we don't analyze 500 stocks. we run 5 factors that explain all 500 at once" i asked him to draw it out take your entire stock universe - 500 names, 3 years of daily returns - and run PCA the 500 collapse into 5 factor 1: broad market momentum - explains 58% of all price movement by itself factor 2: sector rotation - where institutional money is actually flowing factor 3: rate sensitivity - duration risk bleeding across the equity curve factor 4: momentum tilt - which names are accelerating vs mean-reverting factor 5: credit spread stress - signal hiding inside equity vol if you know these 5 numbers, you know what 500 stocks are about to do retail watches 500 charts and sees noise. quant desks watch 5 eigenvalues and see structure math is called Principal Component Analysis, in every intro stats course on earth data to run it is free. python implementation is ~120 lines renaissance, two sigma, aqr - running this since before most retail traders could name a stock reason nobody told you isn't that it's proprietary it's that telling you costs them their edge Bookmark this market was never 500 moving parts it was always 5 - hiding inside 500

Livsun

22,073 次观看 • 1 个月前

Bridgewater doesn't have 500 positions they have 5 exposures that explain 78% of everything those 500 positions do this video shows what that looks like in 3D PCA Risk-Mode Trajectory. three axes: PC1, PC2, PC3 each one is a principal component extracted from the return matrix of the entire market PC1 is broad market direction. it alone explains roughly 40% of all stock movement PC2 separates growth from value. another 15% PC3 captures rate sensitivity. another 10% three invisible forces. 65% of the variance in every stock you've ever traded the dot moving through that 3D space is where the market is right now when the dot drifts toward a corner, the market is concentrating into one regime when it sits in the center, forces are balanced and directional trades are noise quant desks don't watch that dot for fun. they use its position to size every trade in the book if PC1 is dominating, stock picking is pointless because everything moves together if PC2 is leading, the value vs growth rotation is the only trade that matters if PC3 spikes, interest rate sensitivity is driving everything and your "stock pick" is really a rate bet you didn't know you made > PCA: Karl Pearson, 1901 > used at Bridgewater, AQR, Two Sigma for factor decomposition since the 1990s > this visualization: free, public, 29 seconds > scipy builds PCA in 3 lines of Python retail analyzes 500 tickers and thinks they're making 500 decisions quant desks decompose the same 500 into 5 forces and make 5 decisions one is complexity. the other is the math that simplifies it full breakdown in the video below

Bridgewater doesn't have 500 positions they have 5 exposures that explain 78% of everything those 500 positions do this video shows what that looks like in 3D PCA Risk-Mode Trajectory. three axes: PC1, PC2, PC3 each one is a principal component extracted from the return matrix of the entire market PC1 is broad market direction. it alone explains roughly 40% of all stock movement PC2 separates growth from value. another 15% PC3 captures rate sensitivity. another 10% three invisible forces. 65% of the variance in every stock you've ever traded the dot moving through that 3D space is where the market is right now when the dot drifts toward a corner, the market is concentrating into one regime when it sits in the center, forces are balanced and directional trades are noise quant desks don't watch that dot for fun. they use its position to size every trade in the book if PC1 is dominating, stock picking is pointless because everything moves together if PC2 is leading, the value vs growth rotation is the only trade that matters if PC3 spikes, interest rate sensitivity is driving everything and your "stock pick" is really a rate bet you didn't know you made > PCA: Karl Pearson, 1901 > used at Bridgewater, AQR, Two Sigma for factor decomposition since the 1990s > this visualization: free, public, 29 seconds > scipy builds PCA in 3 lines of Python retail analyzes 500 tickers and thinks they're making 500 decisions quant desks decompose the same 500 into 5 forces and make 5 decisions one is complexity. the other is the math that simplifies it full breakdown in the video below

delost

49,014 次观看 • 1 个月前

citadel doesn't analyze 500 stocks they compress them to 5 hidden forces - then trade the forces most people saw the math behind this in a stats class and scrolled past it. nobody told them it was worth $30 billion a year technique is called PCA - principal component analysis Karl Pearson published it in 1901 in a free journal. it's in every stats textbook on earth here's what it does: you feed it 10 years of daily returns across 500 stocks it ignores earnings reports, management teams, every narrative retail obsesses over it finds underlying forces moving groups of stocks together without anyone naming them what comes out: > component 1 - broad market direction (~45% of all movement) > 2 - growth vs value tilt (~12%) > 3 - sector rotation (~8%) > 4 - volatility regime (~5%) > 5 - liquidity premium (~3%) five numbers explain 73% of everything moving in markets a quant desk doesn't ask "will NVDA go up tomorrow" it asks: which regime are we in, and where does this regime historically lead portfolio built around forces, not tickers if component 3 signals sector rotation at a historical inflection, they rotate without reading a single 10-K Two Sigma runs this across equities, bonds, commodities and currencies simultaneously Save this before someone turns it into a $2,000 data to build this is free - Ken French's factor library, CRSP, any linear algebra textbook 150 lines of python is the whole engine what Renaissance has isn't secret data or better intel

citadel doesn't analyze 500 stocks they compress them to 5 hidden forces - then trade the forces most people saw the math behind this in a stats class and scrolled past it. nobody told them it was worth $30 billion a year technique is called PCA - principal component analysis Karl Pearson published it in 1901 in a free journal. it's in every stats textbook on earth here's what it does: you feed it 10 years of daily returns across 500 stocks it ignores earnings reports, management teams, every narrative retail obsesses over it finds underlying forces moving groups of stocks together without anyone naming them what comes out: > component 1 - broad market direction (~45% of all movement) > 2 - growth vs value tilt (~12%) > 3 - sector rotation (~8%) > 4 - volatility regime (~5%) > 5 - liquidity premium (~3%) five numbers explain 73% of everything moving in markets a quant desk doesn't ask "will NVDA go up tomorrow" it asks: which regime are we in, and where does this regime historically lead portfolio built around forces, not tickers if component 3 signals sector rotation at a historical inflection, they rotate without reading a single 10-K Two Sigma runs this across equities, bonds, commodities and currencies simultaneously Save this before someone turns it into a $2,000 data to build this is free - Ken French's factor library, CRSP, any linear algebra textbook 150 lines of python is the whole engine what Renaissance has isn't secret data or better intel

Livsun

21,476 次观看 • 1 个月前

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 次观看 • 6 个月前

A finance professor manages $200M with AI agents, and he told everyone why: "Large language models are at the level of a fourth-year PhD student in every field" Alejandro Lopez-Lira's AI fund, Autopilot, returned 56% last year. The S&P did 16%. There are 52,000 people with money in it, and most of them just watch the machine work. What he automated is the same six-step loop every fund on earth runs: find an idea, code it, backtest it, deploy it, read the autopsy, learn from it. A quant at Two Sigma runs that loop once a month, and the salary time alone costs around $50,000 per hypothesis. All steps from this loop now fit in AI trading text box. Plain English in, executable strategy out, five-year backtest in 12 seconds, live on a broker 90 seconds after you typed the sentence. He runs $200M with AI. You can run same AI fund in two clicks, free to try: Step 6 on this loop is where everyone is stuck. Your agent has no memory. Every strategy it kills goes into a log nobody reads, and the next one starts from zero. Nobody keeps negative results. Not Citadel, not Man Group, not a single repo on GitHub. Fix that and the agent remembers every hypothesis it killed and the regime it died in. It stops burning cycles on your old mistakes. Jane Street pays 3,500 people to run this cycle and made $39.6 billion doing it. Five sixths of it is now free. Bookmark & read full map of this loop in the article below. Most people still think AI trading is out of reach for them - it isn't. Don't want to spend a dollar for testing this? Kalshi just opened a perps exchange and gives US users $25 free to start ->

A finance professor manages $200M with AI agents, and he told everyone why: "Large language models are at the level of a fourth-year PhD student in every field" Alejandro Lopez-Lira's AI fund, Autopilot, returned 56% last year. The S&P did 16%. There are 52,000 people with money in it, and most of them just watch the machine work. What he automated is the same six-step loop every fund on earth runs: find an idea, code it, backtest it, deploy it, read the autopsy, learn from it. A quant at Two Sigma runs that loop once a month, and the salary time alone costs around $50,000 per hypothesis. All steps from this loop now fit in AI trading text box. Plain English in, executable strategy out, five-year backtest in 12 seconds, live on a broker 90 seconds after you typed the sentence. He runs $200M with AI. You can run same AI fund in two clicks, free to try: Step 6 on this loop is where everyone is stuck. Your agent has no memory. Every strategy it kills goes into a log nobody reads, and the next one starts from zero. Nobody keeps negative results. Not Citadel, not Man Group, not a single repo on GitHub. Fix that and the agent remembers every hypothesis it killed and the regime it died in. It stops burning cycles on your old mistakes. Jane Street pays 3,500 people to run this cycle and made $39.6 billion doing it. Five sixths of it is now free. Bookmark & read full map of this loop in the article below. Most people still think AI trading is out of reach for them - it isn't. Don't want to spend a dollar for testing this? Kalshi just opened a perps exchange and gives US users $25 free to start ->

cvxv666

81,854 次观看 • 11 天前

Ken Griffin, CEO of Citadel, said: "I'm fairly depressed watching AI do a week of PhD work in a few hours" He's worth $51.2 billion and 20% of every stock trade in America goes through his firm. The money isn't what's bothering him. Citadel pays $400,000 to $650,000 a year for that work. New trading AI agent does it now, on a free trial, for people who have never written a line of code. It went live this summer and most of your timeline still hasn't noticed. You type one sentence in plain English. AI agents loop takes it from there: > an AI agent turns your sentence into a real trading strategy > backtests it across 5 years of data in about 12 seconds > scores it 0 to 100 and shows you the exact spot where it leaks > kills the versions that only look good on paper > rebuilds what's left and runs the loop again > stress-tests the survivor on years it has never seen, then puts it live on your exchange in about 90 seconds That list is the entire job description of a junior quant. At a desk, one properly tested idea burns about $87,500 in salary time, and most of what they build is dead by week six. Two clicks and this AI agent is testing your own idea. Costs you nothing and it runs the whole thing by itself: Griffin gets to be depressed about it. He already owns the desk. You just got handed one. Bookmark & read full story of how they got an AI to build trading strategies and kill its own bad ones is in the article below.

Ken Griffin, CEO of Citadel, said: "I'm fairly depressed watching AI do a week of PhD work in a few hours" He's worth $51.2 billion and 20% of every stock trade in America goes through his firm. The money isn't what's bothering him. Citadel pays $400,000 to $650,000 a year for that work. New trading AI agent does it now, on a free trial, for people who have never written a line of code. It went live this summer and most of your timeline still hasn't noticed. You type one sentence in plain English. AI agents loop takes it from there: > an AI agent turns your sentence into a real trading strategy > backtests it across 5 years of data in about 12 seconds > scores it 0 to 100 and shows you the exact spot where it leaks > kills the versions that only look good on paper > rebuilds what's left and runs the loop again > stress-tests the survivor on years it has never seen, then puts it live on your exchange in about 90 seconds That list is the entire job description of a junior quant. At a desk, one properly tested idea burns about $87,500 in salary time, and most of what they build is dead by week six. Two clicks and this AI agent is testing your own idea. Costs you nothing and it runs the whole thing by itself: Griffin gets to be depressed about it. He already owns the desk. You just got handed one. Bookmark & read full story of how they got an AI to build trading strategies and kill its own bad ones is in the article below.

cvxv666

222,320 次观看 • 15 天前

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

93,596 次观看 • 1 个月前

JAPAN'S BOND MARKET IS SENDING A WARNING And most investors have no idea what it means for their portfolio. Let me explain: On Tuesday, Japan's 40-year bond yield smashed through 4% for the first time in history. The 30-year hit 3.7%. The 20-year reached 3.5%. The 10-year touched 2.38% - highest since 1999. The single-day moves on 30 and 40-year JGBs? Over 25 basis points. That's the biggest spike since Trump's Liberation Day tariffs whiplashed global markets last April. But this time, Japan is LEADING the selloff, not following. Here's why this matters for your portfolio: Japan's bond market isn't some isolated backwater. It's the third-largest debt market on the planet at $7.4T. "What happens in Japan does not stay in Japan." Within hours of Japan's meltdown, the US 30-year Treasury yield jumped 9 basis points to 4.93%. The UK, Canada, Germany - all saw yields spike in sympathy. The trigger? Prime Minister Takaichi announced a snap election and proposed tax cuts that spooked bond vigilantes. But here's what the mainstream is missing: This isn't about one election. This is about 3 decades of impossible math catching up to Japan. Japan's debt-to-GDP sits at 235%. Highest of any advanced economy. Higher than Greece at its worst. For decades, the Bank of Japan kept yields near zero by buying any bond that moved. They called it Yield Curve Control. That game is OVER. The BOJ abandoned YCC in March 2024. They ended negative rates. They raised to 0.75% in December - highest since 1995. Governor Ueda just said he'll "keep raising rates." Meanwhile, Japanese inflation has run above the BOJ's 2% target for 43 straight months. When you owe 235% of GDP, every 1% rise in interest rates is an existential threat to your budget. But the contagion risk is what should terrify you... Japan is the largest foreign holder of U.S. Treasuries at $1.2 trillion. Japanese life insurers manage over $2.6 trillion in assets. Much of that is parked in foreign bonds. When Japanese yields rise, the incentive to "reach for yield" overseas disappears. Japanese insurers have "reached a turning point" and are retreating from foreign debt. Remember August 2024? The BOJ raised rates and the Nasdaq crashed 13% in less than a month as the yen carry trade unwound. That was just a taste. The BIS estimates roughly $250B in yen carry trades existed going into that volatility event. Deutsche Bank pegged it closer to $500B. When those trades unwind, investors sell US assets to repay yen loans. The correlation is brutal. And here's the bigger picture: If Japan - the poster child for "debt doesn't matter" - suddenly faces real borrowing costs, what does that signal for every other indebted nation? The US is at 120% debt-to-GDP. Italy, France, the UK - all running massive deficits. Investors are asking: "If Japan pays 4% on 40-year debt, what should the US pay?" That's how a "local" tantrum becomes a global repricing of sovereign risk. We saw this movie in the UK in 2022. Truss announced unfunded tax cuts. Gilt yields exploded. The Bank of England intervened within days. Truss was gone in 44 days. There are striking similarities between Japan and the UK situation. The difference? Japan's debt pile is 2.5x larger relative to GDP. “How did you go bankrupt? Two ways. Gradually, then suddenly.” Here's what I'm doing: - Buying precious metals. Gold just hit $4,800 and silver touched $95 because smart money sees what's coming. - Selling bonds. Most investors will be shocked by how much further yields can rise. This repricing has legs. - Reducing risk in equity portfolios. The S&P 500 trades at a Shiller CAPE near 39 - second highest ever - during a midterm year when markets historically struggle. Add Japan's bond crisis to an already fragile equity market, and the risk/reward for staying fully invested looks terrible. Japan's bond market is the canary in the coal mine for global sovereign debt. That canary just stopped breathing.

JAPAN'S BOND MARKET IS SENDING A WARNING And most investors have no idea what it means for their portfolio. Let me explain: On Tuesday, Japan's 40-year bond yield smashed through 4% for the first time in history. The 30-year hit 3.7%. The 20-year reached 3.5%. The 10-year touched 2.38% - highest since 1999. The single-day moves on 30 and 40-year JGBs? Over 25 basis points. That's the biggest spike since Trump's Liberation Day tariffs whiplashed global markets last April. But this time, Japan is LEADING the selloff, not following. Here's why this matters for your portfolio: Japan's bond market isn't some isolated backwater. It's the third-largest debt market on the planet at $7.4T. "What happens in Japan does not stay in Japan." Within hours of Japan's meltdown, the US 30-year Treasury yield jumped 9 basis points to 4.93%. The UK, Canada, Germany - all saw yields spike in sympathy. The trigger? Prime Minister Takaichi announced a snap election and proposed tax cuts that spooked bond vigilantes. But here's what the mainstream is missing: This isn't about one election. This is about 3 decades of impossible math catching up to Japan. Japan's debt-to-GDP sits at 235%. Highest of any advanced economy. Higher than Greece at its worst. For decades, the Bank of Japan kept yields near zero by buying any bond that moved. They called it Yield Curve Control. That game is OVER. The BOJ abandoned YCC in March 2024. They ended negative rates. They raised to 0.75% in December - highest since 1995. Governor Ueda just said he'll "keep raising rates." Meanwhile, Japanese inflation has run above the BOJ's 2% target for 43 straight months. When you owe 235% of GDP, every 1% rise in interest rates is an existential threat to your budget. But the contagion risk is what should terrify you... Japan is the largest foreign holder of U.S. Treasuries at $1.2 trillion. Japanese life insurers manage over $2.6 trillion in assets. Much of that is parked in foreign bonds. When Japanese yields rise, the incentive to "reach for yield" overseas disappears. Japanese insurers have "reached a turning point" and are retreating from foreign debt. Remember August 2024? The BOJ raised rates and the Nasdaq crashed 13% in less than a month as the yen carry trade unwound. That was just a taste. The BIS estimates roughly $250B in yen carry trades existed going into that volatility event. Deutsche Bank pegged it closer to $500B. When those trades unwind, investors sell US assets to repay yen loans. The correlation is brutal. And here's the bigger picture: If Japan - the poster child for "debt doesn't matter" - suddenly faces real borrowing costs, what does that signal for every other indebted nation? The US is at 120% debt-to-GDP. Italy, France, the UK - all running massive deficits. Investors are asking: "If Japan pays 4% on 40-year debt, what should the US pay?" That's how a "local" tantrum becomes a global repricing of sovereign risk. We saw this movie in the UK in 2022. Truss announced unfunded tax cuts. Gilt yields exploded. The Bank of England intervened within days. Truss was gone in 44 days. There are striking similarities between Japan and the UK situation. The difference? Japan's debt pile is 2.5x larger relative to GDP. “How did you go bankrupt? Two ways. Gradually, then suddenly.” Here's what I'm doing: - Buying precious metals. Gold just hit $4,800 and silver touched $95 because smart money sees what's coming. - Selling bonds. Most investors will be shocked by how much further yields can rise. This repricing has legs. - Reducing risk in equity portfolios. The S&P 500 trades at a Shiller CAPE near 39 - second highest ever - during a midterm year when markets historically struggle. Add Japan's bond crisis to an already fragile equity market, and the risk/reward for staying fully invested looks terrible. Japan's bond market is the canary in the coal mine for global sovereign debt. That canary just stopped breathing.

George Noble

73,242 次观看 • 7 个月前

yesterday someone leaked a full quant trading system on GitHub before they deleted it i forked everything 5,000 lines of code. 7 modules. 25 mathematical factors funds use this system to manage millions i studied it for a week. then pointed it at crypto markets on polymarket here's the full breakdown you can feed this to your claude and build the same thing for just $200 ARCHITECTURE: Python thinks, analyzes, calculates C++ executes orders in 5-10ms data → factors → AI → strategy → risk → execution DATA. 4 streams simultaneously: - Binance WebSocket: prices every second, orderbook at 20 levels - AlphaVantage: news with sentiment score from -1 to +1 -X: mention volume, engagement, influencer activity - On-chain: BTC flows to/from exchanges cache in Redis ( target price) = N(d1) d1 = [ln(current/target) + (σ²/2)T] / (σ√T) then 4 adjustments on top: - momentum: +/-5% - AI sentiment: +/-7% - order flow: +/-2% - historical patterns: +/-8% compare final probability against polymarket price if edge > 10%: enter RISK - Quarter Kelly for position sizing - max 5% bankroll per trade - drawdown 15% = bot stops - VaR < 3% per day - correlation between positions < 0.7 - never take more than 1% of market liquidity key insight is don't hold to expiry. trade the movement, not the outcome cost: → Binance API: free → OpenAI: $50-100/month → AWS EC2: $120/month → monitoring: free - total: $200-300/month - code is open source. formulas above. you already have claude the only thing between you and a working system is one free evening

yesterday someone leaked a full quant trading system on GitHub before they deleted it i forked everything 5,000 lines of code. 7 modules. 25 mathematical factors funds use this system to manage millions i studied it for a week. then pointed it at crypto markets on polymarket here's the full breakdown you can feed this to your claude and build the same thing for just $200 ARCHITECTURE: Python thinks, analyzes, calculates C++ executes orders in 5-10ms data → factors → AI → strategy → risk → execution DATA. 4 streams simultaneously: - Binance WebSocket: prices every second, orderbook at 20 levels - AlphaVantage: news with sentiment score from -1 to +1 -X: mention volume, engagement, influencer activity - On-chain: BTC flows to/from exchanges cache in Redis ( target price) = N(d1) d1 = [ln(current/target) + (σ²/2)T] / (σ√T) then 4 adjustments on top: - momentum: +/-5% - AI sentiment: +/-7% - order flow: +/-2% - historical patterns: +/-8% compare final probability against polymarket price if edge > 10%: enter RISK - Quarter Kelly for position sizing - max 5% bankroll per trade - drawdown 15% = bot stops - VaR < 3% per day - correlation between positions < 0.7 - never take more than 1% of market liquidity key insight is don't hold to expiry. trade the movement, not the outcome cost: → Binance API: free → OpenAI: $50-100/month → AWS EC2: $120/month → monitoring: free - total: $200-300/month - code is open source. formulas above. you already have claude the only thing between you and a working system is one free evening

Archive

249,749 次观看 • 5 个月前

MUST-WATCH: Why SIG Dominated Options Trading — Explained by an 8-Year Insider Kris Abdelmessih (Kris) spent 8 years trading energy derivatives at SIG, then ran options businesses at Parallax & Prime before founding Moontower —one of the world's most popular newsletters on options & volatility trading. "SIG understood there was an abnormal amount of edge in the market. They came from gambling—sports betting, poker—where edge was tiny. A bookie makes 5% margins. But trading a $2.5 call spread for $2.20 when it's worth $2.50? That's a ridiculous amount of edge compared to gambling, with the same risk distribution." We cover: - Why SIG was called "the evil empire" & how they crushed competitors by trading massive size for tighter spreads - The exact structure of prop shop deals: 50/50 splits, escrow accounts, how you get to 60% then 70% payouts - Why markets look efficient from most vantage points & how trading is ultimately about labor—getting your vantage point close enough that it stops looking random - The tyranny of beta: why the best operator in a melting ice cube business will lose to a mediocre performer in a great market - How to escape the "striver" trap & tune out status optimization (hint: find what you got obsessed with before college applications mattered) - Teaching his 12-year-old options market making & involving his 9-year-old in building a trading card game—scattered cards on the bedroom floor that'll become a finished product Thanks to Kris for the masterclass. Highlights: 02:05 How Kris first recognized real trading edge 04:01 How early market structure created easy edge 05:27 Why improvement in trading comes from hindsight 07:08 The core SIG frameworks that shaped his edge 09:37 Why uncovering edge requires labor and precision 11:02 How informed order flow forces trader humility 12:53 What truly differentiated SIG from competitors 13:23 How SIG built a world-class education pipeline 16:30 How SIG captured edge by refusing to hedge 18:11 How centralized risk controlled exposure and variance 19:09 How SIG used size and spreads to dominate markets 23:20 What Kris learned working with Jason McCarthy 25:40 Why elite traders share extreme competitiveness 26:06 How top performers operate across domains and PM roles 28:22 How Kris transitioned from SIG to prop trading 31:56 What shifting into senior roles taught him about trading 33:46 How Kris built training and feedback systems for traders 35:00 How the backer model works inside prop shops 38:41 How escrow capital protects traders from tail events 41:03 How natural gas options trading changed with regime shifts 42:13 How Kris applies trading edge concepts to life decisions 45:46 Why personal alignment beats chasing status in trading 47:13 How status games distort decision-making for young traders 52:23 Why striver behavior is actually risk management 56:27 How Kris teaches opportunity cost through parenting 1:01:28 How exposing kids to decisions builds intuition 1:04:46 How Kris teaches EV using homemade trading games 1:08:05 How iteration and feedback loops shape real learning

MUST-WATCH: Why SIG Dominated Options Trading — Explained by an 8-Year Insider Kris Abdelmessih (Kris) spent 8 years trading energy derivatives at SIG, then ran options businesses at Parallax & Prime before founding Moontower —one of the world's most popular newsletters on options & volatility trading. "SIG understood there was an abnormal amount of edge in the market. They came from gambling—sports betting, poker—where edge was tiny. A bookie makes 5% margins. But trading a $2.5 call spread for $2.20 when it's worth $2.50? That's a ridiculous amount of edge compared to gambling, with the same risk distribution." We cover: - Why SIG was called "the evil empire" & how they crushed competitors by trading massive size for tighter spreads - The exact structure of prop shop deals: 50/50 splits, escrow accounts, how you get to 60% then 70% payouts - Why markets look efficient from most vantage points & how trading is ultimately about labor—getting your vantage point close enough that it stops looking random - The tyranny of beta: why the best operator in a melting ice cube business will lose to a mediocre performer in a great market - How to escape the "striver" trap & tune out status optimization (hint: find what you got obsessed with before college applications mattered) - Teaching his 12-year-old options market making & involving his 9-year-old in building a trading card game—scattered cards on the bedroom floor that'll become a finished product Thanks to Kris for the masterclass. Highlights: 02:05 How Kris first recognized real trading edge 04:01 How early market structure created easy edge 05:27 Why improvement in trading comes from hindsight 07:08 The core SIG frameworks that shaped his edge 09:37 Why uncovering edge requires labor and precision 11:02 How informed order flow forces trader humility 12:53 What truly differentiated SIG from competitors 13:23 How SIG built a world-class education pipeline 16:30 How SIG captured edge by refusing to hedge 18:11 How centralized risk controlled exposure and variance 19:09 How SIG used size and spreads to dominate markets 23:20 What Kris learned working with Jason McCarthy 25:40 Why elite traders share extreme competitiveness 26:06 How top performers operate across domains and PM roles 28:22 How Kris transitioned from SIG to prop trading 31:56 What shifting into senior roles taught him about trading 33:46 How Kris built training and feedback systems for traders 35:00 How the backer model works inside prop shops 38:41 How escrow capital protects traders from tail events 41:03 How natural gas options trading changed with regime shifts 42:13 How Kris applies trading edge concepts to life decisions 45:46 Why personal alignment beats chasing status in trading 47:13 How status games distort decision-making for young traders 52:23 Why striver behavior is actually risk management 56:27 How Kris teaches opportunity cost through parenting 1:01:28 How exposing kids to decisions builds intuition 1:04:46 How Kris teaches EV using homemade trading games 1:08:05 How iteration and feedback loops shape real learning

Ethan Kho

233,306 次观看 • 6 个月前

Microsoft just banned its own engineers from using AI. The tool was literally costing MORE than the humans it was supposed to replace. They lied to you about AI adoption and now the whole narrative is blowing up: Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it. Engineers loved it and adoption exploded. But then the invoices arrived. Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead. The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much. Uber's story is even worse... Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April. Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems. Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session. The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money. Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote: "For my team, the cost of compute is far beyond the costs of the employees." This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans. Think about what this means for the entire AI narrative. Every CEO on every earnings call for the past two years has said the same thing: AI will make us more efficient, reduce headcount, and cut costs. The stock market rewarded every company that said it. Fired workers, stock goes up. Announced AI adoption, stock goes up. But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill. Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools. Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible. Both companies are spending hundreds of billions on AI infrastructure this year alone. And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control. The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP. This is the gap nobody on Wall Street is pricing in. $725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work. What do you think?

Microsoft just banned its own engineers from using AI. The tool was literally costing MORE than the humans it was supposed to replace. They lied to you about AI adoption and now the whole narrative is blowing up: Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it. Engineers loved it and adoption exploded. But then the invoices arrived. Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead. The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much. Uber's story is even worse... Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April. Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems. Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session. The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money. Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote: "For my team, the cost of compute is far beyond the costs of the employees." This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans. Think about what this means for the entire AI narrative. Every CEO on every earnings call for the past two years has said the same thing: AI will make us more efficient, reduce headcount, and cut costs. The stock market rewarded every company that said it. Fired workers, stock goes up. Announced AI adoption, stock goes up. But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill. Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools. Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible. Both companies are spending hundreds of billions on AI infrastructure this year alone. And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control. The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP. This is the gap nobody on Wall Street is pricing in. $725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work. What do you think?

Ricardo

2,970,559 次观看 • 2 个月前

Making Sense Of Strategy What is happening with $MSTR? If you’ve been following me on X for any meaningful length of time, you will know that I have been attempting to calibrate people’s expectations of the stock's performance for the best part of 2025. Here I have synthesised all of my thoughts and distilled them into a single video. If you prefer YouTube, you can watch it here: If you prefer written format, continue reading. The first thing we need to understand is what Strategy is and why people invest in it. Strategy At the highest level, Strategy is leveraged Bitcoin. That’s it. Strategy leverages debt to acquire more Bitcoin. Therefore, the main reason you invest in Strategy is because you want to outperform Bitcoin. The only thing better than Bitcoin is more Bitcoin. The second thing we need to understand is mNAV. mNAV Generally speaking for a pure-play Bitcoin Treasury Company like Strategy, mNAV is a reflection of the market's expectation of future Bitcoin Yield. Bitcoin Yield comes with diminishing returns because each additional Bitcoin purchase contributes less to Bitcoin Per Share. Thus, the larger your Bitcoin stack, the harder it becomes to generate Bitcoin Yield and by extension the harder it becomes to outperform Bitcoin. This is why on a Bitcoin Standard, over a long enough time horizon, mNAV trends towards 1 since the maximum amount of Bitcoin you can own is 21M. With all this in mind, why is Strategy trading where it is and why is it trading at such a low mNAV? There are a few reasons. 1. Strategy Is A Different Company In 2025 Firstly, Strategy is a totally different company in 2025 to the one it was in 2020. For context, believe it or not, the company only introduced Bitcoin Yield and Bitcoin Per Share in the July 2024 Q2 Earnings Call and so it was only after that that they began optimising for those metrics. In my view, that is also when Michael Saylor truly started to understand the opportunity that was in front of him, which is why in October 2024 we saw Strategy announce the 21/21 plan which became the catalyst for the parabolic run we saw in November 2024 where $MSTR went on to briefly hit an all-time-high of around $550. Since people are comparing $MSTR this cycle to the $MSTR of last cycle when it briefly traded at an mNAV of over 8x, it is distorting their expectations. Again, Strategy is a totally different company today with a totally different set of dynamics. 2. New Industry Secondly, we need to recognise that the Bitcoin Treasury Company industry is entirely new which means that the market has been forced to learn and adapt in real-time. With Strategy being the first and by far the largest Bitcoin Treasury Company, it has gained a disproportionate amount of attention and as a result it has attracted a disproportionate amount of speculative capital along the way while everyone has been trying to figure out how to value it. Consequently, in my view, the move we saw in November 2024 was an over-correction to the upside — which by the way coincided with Bitcoin’s parabolic run following Donald Trump’s election win — and what we’re now seeing is an over-correction to the downside. 3. Bitcoin Yield Thirdly, as I mentioned at the beginning, Bitcoin Treasury Companies are currently valued based on how much Bitcoin Yield they are expected to generate in the future. At the time of recording, Strategy currently holds precisely 637,460 Bitcoin — that’s over 3% of the total Bitcoin supply — which means that it is much, much harder to generate meaningful Bitcoin Yield, which again is why we’re seeing the mNAV compress. However, there is a caveat here. There is another metric that Strategy have introduced which is Bitcoin $ Gain. Bitcoin $ Gain is defined as the $ value of newly acquired Bitcoin within any period. Strategy — and I don’t blame them — have been attempting to encourage the market to interpret Bitcoin $ Gain as “earnings” and to value the company based on how much earnings it is expected to generate in the future. For full disclosure, I personally dislike Bitcoin $ Gain as a valuation metric. I think framing it as “earnings” is misleading and disingenuous. I understand why it has been introduced because it speaks the language of Wall Street. However, traditional earnings are final. Bitcoin $ Gain is not because it is forever subject to the price of Bitcoin. Therefore, for Bitcoin $ Gain to be embraced by Wall Street, the market must collectively agree that Bitcoin is going up forever. I remain very sceptical of that happening — especially in the short-to-medium term. However, I am also not attached to my beliefs and so if Wall Street does decide to embrace Bitcoin $ Gain as its primary valuation metric, then $MSTR is likely undervalued by a factor of 5-10x. If not, then $MSTR is likely undervalued by a factor of 1-2x. If you’re not content with the latter being the worst case scenario, then the stock probably isn’t for you. 4. Preferred Products Fourthly, the Strategy thesis right now revolves entirely around the success of its preferred products. Remember, Michael Saylor wants Strategy to become the Amazon of the fixed income market. Thus, we’re not talking about a small innovation here — we are talking about completely transforming global finance. This means that the process of generating awareness and educating the market that will ultimately drive demand for these products is going to take years — not months — which is why you need to have a long time-horizon. Presently, the market is completely discounting the success of Strategy’s preferred products. What it’s not factoring in however is that the capital markets are desperate for yield right now. Thus, when — not if — but when, they eventually wake up to Bitcoin, how do you think they’re going to get that yield? Who is going to be the entity that is offering Bitcoin-backed credit instruments at scale? The answer is obviously Strategy, but again, this is a 5-to-10 year and beyond story. So with all that said, if you’re reading this right now, what should you do? Valuing Strategy There are 3 steps you need to take: 1. Firstly, you need to define your time horizon. In other words, how long do you intend on holding the stock for? 2. Secondly, you need to estimate either — depending on your preferred metric — how much Bitcoin Yield or how much Bitcoin $ Gain you expect Strategy to generate during that period and then calculate how much you expect $MSTR to outperform Bitcoin based on those values. 3. Thirdly, ask yourself whether you’d be satisfied with the level of outperformance you have calculated? In other words, is the trade-off worth it? Or would you be better off investing in either spot Bitcoin, an alternative Bitcoin Treasury Company or a Bitcoin ETF. If you’re satisfied with the level of outperformance that you’ve calculated, then $MSTR it probably a good choice of investment for you. If you're not satisfied, then $MSTR is probably a bad choice of investment for you. I personally believe that $MSTR will outperform Bitcoin by a minimum factor of 1-2x over the next 5/10 years and potentially much more if Bitcoin $ Gain becomes the primary metric by which it is valued, but again, I remain sceptical of that happening. Regardless, the best is yet to come.

Making Sense Of Strategy What is happening with $MSTR? If you’ve been following me on X for any meaningful length of time, you will know that I have been attempting to calibrate people’s expectations of the stock's performance for the best part of 2025. Here I have synthesised all of my thoughts and distilled them into a single video. If you prefer YouTube, you can watch it here: If you prefer written format, continue reading. The first thing we need to understand is what Strategy is and why people invest in it. Strategy At the highest level, Strategy is leveraged Bitcoin. That’s it. Strategy leverages debt to acquire more Bitcoin. Therefore, the main reason you invest in Strategy is because you want to outperform Bitcoin. The only thing better than Bitcoin is more Bitcoin. The second thing we need to understand is mNAV. mNAV Generally speaking for a pure-play Bitcoin Treasury Company like Strategy, mNAV is a reflection of the market's expectation of future Bitcoin Yield. Bitcoin Yield comes with diminishing returns because each additional Bitcoin purchase contributes less to Bitcoin Per Share. Thus, the larger your Bitcoin stack, the harder it becomes to generate Bitcoin Yield and by extension the harder it becomes to outperform Bitcoin. This is why on a Bitcoin Standard, over a long enough time horizon, mNAV trends towards 1 since the maximum amount of Bitcoin you can own is 21M. With all this in mind, why is Strategy trading where it is and why is it trading at such a low mNAV? There are a few reasons. 1. Strategy Is A Different Company In 2025 Firstly, Strategy is a totally different company in 2025 to the one it was in 2020. For context, believe it or not, the company only introduced Bitcoin Yield and Bitcoin Per Share in the July 2024 Q2 Earnings Call and so it was only after that that they began optimising for those metrics. In my view, that is also when Michael Saylor truly started to understand the opportunity that was in front of him, which is why in October 2024 we saw Strategy announce the 21/21 plan which became the catalyst for the parabolic run we saw in November 2024 where $MSTR went on to briefly hit an all-time-high of around $550. Since people are comparing $MSTR this cycle to the $MSTR of last cycle when it briefly traded at an mNAV of over 8x, it is distorting their expectations. Again, Strategy is a totally different company today with a totally different set of dynamics. 2. New Industry Secondly, we need to recognise that the Bitcoin Treasury Company industry is entirely new which means that the market has been forced to learn and adapt in real-time. With Strategy being the first and by far the largest Bitcoin Treasury Company, it has gained a disproportionate amount of attention and as a result it has attracted a disproportionate amount of speculative capital along the way while everyone has been trying to figure out how to value it. Consequently, in my view, the move we saw in November 2024 was an over-correction to the upside — which by the way coincided with Bitcoin’s parabolic run following Donald Trump’s election win — and what we’re now seeing is an over-correction to the downside. 3. Bitcoin Yield Thirdly, as I mentioned at the beginning, Bitcoin Treasury Companies are currently valued based on how much Bitcoin Yield they are expected to generate in the future. At the time of recording, Strategy currently holds precisely 637,460 Bitcoin — that’s over 3% of the total Bitcoin supply — which means that it is much, much harder to generate meaningful Bitcoin Yield, which again is why we’re seeing the mNAV compress. However, there is a caveat here. There is another metric that Strategy have introduced which is Bitcoin $ Gain. Bitcoin $ Gain is defined as the $ value of newly acquired Bitcoin within any period. Strategy — and I don’t blame them — have been attempting to encourage the market to interpret Bitcoin $ Gain as “earnings” and to value the company based on how much earnings it is expected to generate in the future. For full disclosure, I personally dislike Bitcoin $ Gain as a valuation metric. I think framing it as “earnings” is misleading and disingenuous. I understand why it has been introduced because it speaks the language of Wall Street. However, traditional earnings are final. Bitcoin $ Gain is not because it is forever subject to the price of Bitcoin. Therefore, for Bitcoin $ Gain to be embraced by Wall Street, the market must collectively agree that Bitcoin is going up forever. I remain very sceptical of that happening — especially in the short-to-medium term. However, I am also not attached to my beliefs and so if Wall Street does decide to embrace Bitcoin $ Gain as its primary valuation metric, then $MSTR is likely undervalued by a factor of 5-10x. If not, then $MSTR is likely undervalued by a factor of 1-2x. If you’re not content with the latter being the worst case scenario, then the stock probably isn’t for you. 4. Preferred Products Fourthly, the Strategy thesis right now revolves entirely around the success of its preferred products. Remember, Michael Saylor wants Strategy to become the Amazon of the fixed income market. Thus, we’re not talking about a small innovation here — we are talking about completely transforming global finance. This means that the process of generating awareness and educating the market that will ultimately drive demand for these products is going to take years — not months — which is why you need to have a long time-horizon. Presently, the market is completely discounting the success of Strategy’s preferred products. What it’s not factoring in however is that the capital markets are desperate for yield right now. Thus, when — not if — but when, they eventually wake up to Bitcoin, how do you think they’re going to get that yield? Who is going to be the entity that is offering Bitcoin-backed credit instruments at scale? The answer is obviously Strategy, but again, this is a 5-to-10 year and beyond story. So with all that said, if you’re reading this right now, what should you do? Valuing Strategy There are 3 steps you need to take: 1. Firstly, you need to define your time horizon. In other words, how long do you intend on holding the stock for? 2. Secondly, you need to estimate either — depending on your preferred metric — how much Bitcoin Yield or how much Bitcoin $ Gain you expect Strategy to generate during that period and then calculate how much you expect $MSTR to outperform Bitcoin based on those values. 3. Thirdly, ask yourself whether you’d be satisfied with the level of outperformance you have calculated? In other words, is the trade-off worth it? Or would you be better off investing in either spot Bitcoin, an alternative Bitcoin Treasury Company or a Bitcoin ETF. If you’re satisfied with the level of outperformance that you’ve calculated, then $MSTR it probably a good choice of investment for you. If you're not satisfied, then $MSTR is probably a bad choice of investment for you. I personally believe that $MSTR will outperform Bitcoin by a minimum factor of 1-2x over the next 5/10 years and potentially much more if Bitcoin $ Gain becomes the primary metric by which it is valued, but again, I remain sceptical of that happening. Regardless, the best is yet to come.

Chris Millas

36,835 次观看 • 11 个月前

MUST-WATCH: Inside Databento with Christina Qi—from MIT dorm room HFT shop to taking on the data incumbents Christina Qi (Christina Qi) went from running a high-frequency trading fund out of her dorm room at MIT to building Databento (Databento), a market data platform growing ~5x YoY with 16,000+ customers that's landed 8 of the 10 largest options market makers & the biggest AI companies in the world. 50-75 new customers sign up daily with their own credit cards. "The biggest AI company in the world sent us an email: 'We want to buy your most expensive data plan'—and we were like, what?" We cover: - Why the "smart MIT founders" pitch failed when launching a hedge fund & what actually worked with investors - The difference between raising venture capital vs. hedge fund capital (they raised both) - How they compete at 1/1,000th the budget of incumbents by selling bottom-up, not top-down - Why member-of-technical-staff employees have more buying power than anyone realizes - The strategic decision to stay one layer upstream from Bloomberg—not compete directly - Why HFT strategies aren't scalable & the data licensing nightmare that plagued their fund - How to know when it's time to shut down (hint: it's not about performance) - Her mom texted "go beta bento"— even family doesn't always get what you do Thank you Christina Qi for coming on the pod! 00:00 Intro 01:36 Can you start a hedge fund in college? 02:16 Dorm-room origin: MIT/Harvard HFT startup story begins 03:18 Early sacrifices, honeymoon phase, building fast together relentlessly 04:28 Then vs now: launching funds in crowded quant landscape 07:50 Fundraising basics: allocators, fees, lockups, track record 11:31 Why raise VC: real tech beyond strategies only 13:21 2025 reality: easier tools, tougher alpha generation 16:46 Blow-ups and risk management: leverage, slippage, TCA lessons 17:22 Why Databento: licensing solved, API-first market data pipeline 22:06 Upstream of terminals: enable analytics, AI, backtesting workflows 24:42 PLG beats sales: bottom-up, self-serve enterprise adoption 28:32 Case study: AI staffer buys top plan instantly 30:06 AI in finance needs clean tick data pipelines 32:34 Bloomberg dominance, network effects, Refinitiv comparison realities 35:56 Users and roadmap: upvotes, feedback prioritize datasets 39:14 Advance commitments fund new exchange integrations, secure adoption 41:55 Market microstructure matters: better order-book signals, execution 44:13 Career advice: data engineering, portfolio construction, Python 48:06 Macro regimes shift fundraising outcomes for founders 49:46 Personal barometer: pursue work that energizes you 51:26 Closing thoughts and thanks

MUST-WATCH: Inside Databento with Christina Qi—from MIT dorm room HFT shop to taking on the data incumbents Christina Qi (Christina Qi) went from running a high-frequency trading fund out of her dorm room at MIT to building Databento (Databento), a market data platform growing ~5x YoY with 16,000+ customers that's landed 8 of the 10 largest options market makers & the biggest AI companies in the world. 50-75 new customers sign up daily with their own credit cards. "The biggest AI company in the world sent us an email: 'We want to buy your most expensive data plan'—and we were like, what?" We cover: - Why the "smart MIT founders" pitch failed when launching a hedge fund & what actually worked with investors - The difference between raising venture capital vs. hedge fund capital (they raised both) - How they compete at 1/1,000th the budget of incumbents by selling bottom-up, not top-down - Why member-of-technical-staff employees have more buying power than anyone realizes - The strategic decision to stay one layer upstream from Bloomberg—not compete directly - Why HFT strategies aren't scalable & the data licensing nightmare that plagued their fund - How to know when it's time to shut down (hint: it's not about performance) - Her mom texted "go beta bento"— even family doesn't always get what you do Thank you Christina Qi for coming on the pod! 00:00 Intro 01:36 Can you start a hedge fund in college? 02:16 Dorm-room origin: MIT/Harvard HFT startup story begins 03:18 Early sacrifices, honeymoon phase, building fast together relentlessly 04:28 Then vs now: launching funds in crowded quant landscape 07:50 Fundraising basics: allocators, fees, lockups, track record 11:31 Why raise VC: real tech beyond strategies only 13:21 2025 reality: easier tools, tougher alpha generation 16:46 Blow-ups and risk management: leverage, slippage, TCA lessons 17:22 Why Databento: licensing solved, API-first market data pipeline 22:06 Upstream of terminals: enable analytics, AI, backtesting workflows 24:42 PLG beats sales: bottom-up, self-serve enterprise adoption 28:32 Case study: AI staffer buys top plan instantly 30:06 AI in finance needs clean tick data pipelines 32:34 Bloomberg dominance, network effects, Refinitiv comparison realities 35:56 Users and roadmap: upvotes, feedback prioritize datasets 39:14 Advance commitments fund new exchange integrations, secure adoption 41:55 Market microstructure matters: better order-book signals, execution 44:13 Career advice: data engineering, portfolio construction, Python 48:06 Macro regimes shift fundraising outcomes for founders 49:46 Personal barometer: pursue work that energizes you 51:26 Closing thoughts and thanks

Ethan Kho

20,054 次观看 • 6 个月前

+773% Profit. I Didn't Place a Single Trade. Claude Did Everything. While you're losing sleep staring at candles, this bot ran overnight and printed 773%. I've made the exact step-by-step guide to build this Claude Trading Engine from scratch. Free for 24 hours. To get it: 1. Comment "Trade" 2. Like and Retweet this post. 3. Follow me Himanshu Kumar (so i can DM you) You need: Claude + laptop + 1 hour/day. You backtested your strategy once on a green week and thought you figured it out. Then the market flipped and wiped your account. Classic. This guy ran Monte Carlo simulations, stress-tested across thousands of scenarios, and used out-of-sample data to make sure the strategy actually works, not just on paper. The difference between you and him: > He runs thousands of bots across multiple coins > You run one gut-feeling trade and pray > He stress-tests for manipulation and drawdowns > You panic sell at the first red candle > His bot optimizes strategies while he sleeps > You set an alarm for London open and still miss it Your discipline is just gambling with extra steps. His bot doesn't have emotions, doesn't overtrade, doesn't revenge trade. Full video tutorial attached. Keep doing what you're doing or watch this once and never manually trade again. You Must Follow me Himanshu Kumar, so i can send you DM.

+773% Profit. I Didn't Place a Single Trade. Claude Did Everything. While you're losing sleep staring at candles, this bot ran overnight and printed 773%. I've made the exact step-by-step guide to build this Claude Trading Engine from scratch. Free for 24 hours. To get it: 1. Comment "Trade" 2. Like and Retweet this post. 3. Follow me Himanshu Kumar (so i can DM you) You need: Claude + laptop + 1 hour/day. You backtested your strategy once on a green week and thought you figured it out. Then the market flipped and wiped your account. Classic. This guy ran Monte Carlo simulations, stress-tested across thousands of scenarios, and used out-of-sample data to make sure the strategy actually works, not just on paper. The difference between you and him: > He runs thousands of bots across multiple coins > You run one gut-feeling trade and pray > He stress-tests for manipulation and drawdowns > You panic sell at the first red candle > His bot optimizes strategies while he sleeps > You set an alarm for London open and still miss it Your discipline is just gambling with extra steps. His bot doesn't have emotions, doesn't overtrade, doesn't revenge trade. Full video tutorial attached. Keep doing what you're doing or watch this once and never manually trade again. You Must Follow me Himanshu Kumar, so i can send you DM.

Himanshu Kumar

90,963 次观看 • 4 个月前

+773% Profit. I Didn't Place a Single Trade. Claude Did Everything. While you're losing sleep staring at candles, this bot ran overnight and printed 773%. I've made the exact step-by-step guide to build this Claude Trading Engine from scratch. Free for 24 hours. To get it: 1. Comment "Trade" 2. Like and Retweet this post. 3. Follow me Marry Evan (so i can DM you) You need: Claude + laptop + 1 hour/day. You backtested your strategy once on a green week and thought you figured it out. Then the market flipped and wiped your account. Classic. This guy ran Monte Carlo simulations, stress-tested across thousands of scenarios, and used out-of-sample data to make sure the strategy actually works, not just on paper. The difference between you and him: > He runs thousands of bots across multiple coins > You run one gut-feeling trade and pray > He stress-tests for manipulation and drawdowns > You panic sell at the first red candle > His bot optimizes strategies while he sleeps > You set an alarm for London open and still miss it Your discipline is just gambling with extra steps. His bot doesn't have emotions, doesn't overtrade, doesn't revenge trade. Full video tutorial attached. Keep doing what you're doing or watch this once and never manually trade again. You Must Follow me Marry Evan, so i can send you DM.

+773% Profit. I Didn't Place a Single Trade. Claude Did Everything. While you're losing sleep staring at candles, this bot ran overnight and printed 773%. I've made the exact step-by-step guide to build this Claude Trading Engine from scratch. Free for 24 hours. To get it: 1. Comment "Trade" 2. Like and Retweet this post. 3. Follow me Marry Evan (so i can DM you) You need: Claude + laptop + 1 hour/day. You backtested your strategy once on a green week and thought you figured it out. Then the market flipped and wiped your account. Classic. This guy ran Monte Carlo simulations, stress-tested across thousands of scenarios, and used out-of-sample data to make sure the strategy actually works, not just on paper. The difference between you and him: > He runs thousands of bots across multiple coins > You run one gut-feeling trade and pray > He stress-tests for manipulation and drawdowns > You panic sell at the first red candle > His bot optimizes strategies while he sleeps > You set an alarm for London open and still miss it Your discipline is just gambling with extra steps. His bot doesn't have emotions, doesn't overtrade, doesn't revenge trade. Full video tutorial attached. Keep doing what you're doing or watch this once and never manually trade again. You Must Follow me Marry Evan, so i can send you DM.

Marry Evan

27,684 次观看 • 4 个月前

An Anthropic safety researcher closed her laptop when she saw my screen at Philz Coffee. I was running my Polymarket bot from the corner table. She was in line. Looked over my shoulder. Stopped moving. That is not a normal trading app. What model is that running on? I told her. Claude Code. Four repos. $25 a month. She sat down without asking. I work on the alignment team. We test Claude for exactly this kind of autonomous behavior. You are letting it find its own trading signals. Not just signals. Wallets. github/warproxxx/poly_data 86 million trades. Every wallet. Every entry. Every exit. You are feeding Claude raw wallet data and letting it identify which traders consistently win. Then cloning their behavior. She said it slowly. Like she was writing an internal report in her head. Claude Code finds the top wallets. Reverse-engineers their timing. Copies their entries. Then exits before they do. Before they do? My bot cuts at 85% of expected move or on a 3x volume spike. Top wallets exit before resolution 91% of the time. They capture 86% of the move. Losers hold to 58%. She put her coffee down. How did you get Claude to learn exit timing on its own? I showed her the second repo. github/Polymarket/polymarket-cli Three commands. 500+ markets. No API key. Claude scores them in 20 minutes. We have 14 people stress-testing Claude's autonomous capabilities. You are just using them. My setup: Claude API: $20 per month VPS: $5 per month poly_data: free polymarket-cli: free 19 days. 4 agents. 74% win rate. She stared at the screen for a long time. This is literally what our red team simulates. Except you actually deployed it. She emailed me two days later. Our policy team found your post. Please take it down. Too late. I built the entire framework: How to connect Claude Code to poly_data wallet analysis How to configure autonomous exit timing at 85% threshold How to deploy polymarket-cli for market scoring How to run 4 parallel agents on a single VPS How to start with $500 and scale on evidence The system runs 24/7. Finds proven wallets. Copies their timing. Exits before the crowd. No prediction. No guessing. Just wallet cloning. You only need Claude + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word MoneyBot 2. Like and retweet this 3. Follow me Himanshu Kumar so I can DM you Save this post. Deploy the wallet cloning system this week. Start with $500. Scale on evidence.

An Anthropic safety researcher closed her laptop when she saw my screen at Philz Coffee. I was running my Polymarket bot from the corner table. She was in line. Looked over my shoulder. Stopped moving. That is not a normal trading app. What model is that running on? I told her. Claude Code. Four repos. $25 a month. She sat down without asking. I work on the alignment team. We test Claude for exactly this kind of autonomous behavior. You are letting it find its own trading signals. Not just signals. Wallets. github/warproxxx/poly_data 86 million trades. Every wallet. Every entry. Every exit. You are feeding Claude raw wallet data and letting it identify which traders consistently win. Then cloning their behavior. She said it slowly. Like she was writing an internal report in her head. Claude Code finds the top wallets. Reverse-engineers their timing. Copies their entries. Then exits before they do. Before they do? My bot cuts at 85% of expected move or on a 3x volume spike. Top wallets exit before resolution 91% of the time. They capture 86% of the move. Losers hold to 58%. She put her coffee down. How did you get Claude to learn exit timing on its own? I showed her the second repo. github/Polymarket/polymarket-cli Three commands. 500+ markets. No API key. Claude scores them in 20 minutes. We have 14 people stress-testing Claude's autonomous capabilities. You are just using them. My setup: Claude API: $20 per month VPS: $5 per month poly_data: free polymarket-cli: free 19 days. 4 agents. 74% win rate. She stared at the screen for a long time. This is literally what our red team simulates. Except you actually deployed it. She emailed me two days later. Our policy team found your post. Please take it down. Too late. I built the entire framework: How to connect Claude Code to poly_data wallet analysis How to configure autonomous exit timing at 85% threshold How to deploy polymarket-cli for market scoring How to run 4 parallel agents on a single VPS How to start with $500 and scale on evidence The system runs 24/7. Finds proven wallets. Copies their timing. Exits before the crowd. No prediction. No guessing. Just wallet cloning. You only need Claude + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word MoneyBot 2. Like and retweet this 3. Follow me Himanshu Kumar so I can DM you Save this post. Deploy the wallet cloning system this week. Start with $500. Scale on evidence.

Himanshu Kumar

23,136 次观看 • 1 个月前

MUST-WATCH: Inside high-frequency trading & building the future of markets with Annanay Kapila Annanay Kapila (ak0) — former quant trader at Flow Traders & Tower Research, now founder of QFEX (YC-backed). Building the first 24/7 perpetual futures exchange for equities, commodities & FX — bringing crypto-level UX to traditional assets. "My friends at Cambridge have been making $5M+ a year for the last few years. They don't have to work anymore. But no one retires early. Why? Everyone who gets selected has that desire to win that transcends money, even rationality." We cover: - What it's really like inside elite HFT shops — international math Olympiad winners, professional poker players & zero room for error - Why the smartest traders keep working after making generational wealth (hint: it's pure competitive ego) - The two schools of quant trading: Chicago pit traders vs MIT mathematicians — and which strategies they dominate - How his team navigated the FTX collapse in real-time (including friends who escaped via Dogecoin withdrawals) - Building QFEX : 24/7 perpetual futures on equities with crypto-level UX — "We're building FTX without the fraud" - YC insider stories: meeting Sam Altman & Paul Graham, raising from Paul Graham (Paul Graham) personally, why the $125k for 7% is worth it - Culture secrets from Citadel & Tower: when your dev environment pages you at 2am on Saturday - The one interview hack VCs use that most founders miss (interrupt their CV walkthrough relentlessly) - Why doomerism is dead wrong: "My life would've been worse if I was born 10 years ago. I dread to think how much better it would be if I was born 10 years from now" Huge thanks to ak0 for the transparency. Follow his journey building the future of financial markets. 01:12 Inside high frequency trading culture and competition 03:00 What drives top performers in HFT 03:47 Why elite traders never retire early 04:50 How Tower structures quant trading work 06:14 Chicago versus MIT quant trading styles 10:29 How modern market making actually works 12:25 Why prop firms struggle to scale capital 16:36 Annanay's work at Tower and Flow 19:15 Tower's crypto trading and API edge 19:24 How firms handled the FTX collapse 23:42 Inside FTX culture and HK crypto scene 27:11 What QFEX is building for traders 29:37 QFEX beta launch and early rollout 29:56 Why building an exchange is exciting 32:48 QFEX execution edge and team DNA 36:24 Why quants leave HFT for startups 38:22 QFEX culture versus HFT culture 41:46 How meritocracy works inside QFEX 47:52 How QFEX hires true A players 53:05 Backchanneling and evaluating candidates 53:27 What YC actually teaches founders 55:21 Lessons from meeting Paul Graham and Sam Altman 58:41 Why startups fail and how to pivot 1:00:48 Why young people should stay optimistic 1:11:22 Closing remarks and QFEX vision

MUST-WATCH: Inside high-frequency trading & building the future of markets with Annanay Kapila Annanay Kapila (ak0) — former quant trader at Flow Traders & Tower Research, now founder of QFEX (YC-backed). Building the first 24/7 perpetual futures exchange for equities, commodities & FX — bringing crypto-level UX to traditional assets. "My friends at Cambridge have been making $5M+ a year for the last few years. They don't have to work anymore. But no one retires early. Why? Everyone who gets selected has that desire to win that transcends money, even rationality." We cover: - What it's really like inside elite HFT shops — international math Olympiad winners, professional poker players & zero room for error - Why the smartest traders keep working after making generational wealth (hint: it's pure competitive ego) - The two schools of quant trading: Chicago pit traders vs MIT mathematicians — and which strategies they dominate - How his team navigated the FTX collapse in real-time (including friends who escaped via Dogecoin withdrawals) - Building QFEX : 24/7 perpetual futures on equities with crypto-level UX — "We're building FTX without the fraud" - YC insider stories: meeting Sam Altman & Paul Graham, raising from Paul Graham (Paul Graham) personally, why the $125k for 7% is worth it - Culture secrets from Citadel & Tower: when your dev environment pages you at 2am on Saturday - The one interview hack VCs use that most founders miss (interrupt their CV walkthrough relentlessly) - Why doomerism is dead wrong: "My life would've been worse if I was born 10 years ago. I dread to think how much better it would be if I was born 10 years from now" Huge thanks to ak0 for the transparency. Follow his journey building the future of financial markets. 01:12 Inside high frequency trading culture and competition 03:00 What drives top performers in HFT 03:47 Why elite traders never retire early 04:50 How Tower structures quant trading work 06:14 Chicago versus MIT quant trading styles 10:29 How modern market making actually works 12:25 Why prop firms struggle to scale capital 16:36 Annanay's work at Tower and Flow 19:15 Tower's crypto trading and API edge 19:24 How firms handled the FTX collapse 23:42 Inside FTX culture and HK crypto scene 27:11 What QFEX is building for traders 29:37 QFEX beta launch and early rollout 29:56 Why building an exchange is exciting 32:48 QFEX execution edge and team DNA 36:24 Why quants leave HFT for startups 38:22 QFEX culture versus HFT culture 41:46 How meritocracy works inside QFEX 47:52 How QFEX hires true A players 53:05 Backchanneling and evaluating candidates 53:27 What YC actually teaches founders 55:21 Lessons from meeting Paul Graham and Sam Altman 58:41 Why startups fail and how to pivot 1:00:48 Why young people should stay optimistic 1:11:22 Closing remarks and QFEX vision

Ethan Kho

164,024 次观看 • 6 个月前