Singapore
Joined November 2018
I ran 16.5 million filter tests on NASDAQ. One condition beat every other oscillator, trend indicator, volatility filter, and volume condition. The winner: a simple CCI correction filter. Exact rule for long entries: Lowest(CCI(27), 18 bars) < –100 In plain English: Only take the long breakout if, somewhere in the last 18 bars, CCI(27) dipped below –100. (i.e. the market has already had a real correction) Results across 1,000 strategy variations: > Net profit improved on 69% of strategies > Average trade improved by 22% on more than half > Bounce index: 57.8% (turned that many losing variations profitable) On the base NASDAQ breakout: > Profit/drawdown ratio: 7.53 → 8.6 > Average trade: $116 → $149 Breakouts taken after a genuine washout have room to run. Breakouts taken into already-extended strength tend to fade. This wasn’t tested on one strategy. It was stress-tested across 1,000 different ones.
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My full conversation with @based16z 0:19 the Hyperliquid call 5:01 almost losing his $HYPE airdrop 8:27 life after life changing money 10:12 dating a billionaire's daughter 14:37 from online poker to Crypto 16:40 two trades made his year 20:56 the Snapchat trade 27:43 Sydney Sweeney $AEO trade 31:14 posters move markets 41:42 loot and rob 44:23 advice for a 20 year old 47:23 math lore 51:49 why he has no social media
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I’m a huge fan of @rajivpoc @arthur0x @0xsmac , all exceptional thinkers with skin in the game. Selfishly I was just genuinely excited to spend 30mins with them chatting markets. Enjoy!
NEW POD WE COVER 🔸 Where crypto x AI works 🔸 Building size in illiquid tokens 🔸 Dispersion beats levered beta 🔸 Does $HYPE stay consensus winner? 🔸 Seed vs. growth next cycle @fejau_inc @arthur0x @rajivpoc @0xsmac TIMESTAMPS: 00:00 Intro 01:21 Where Is Crypto AI’s Edge? 07:10 Ads (Fidelity Crypto) 07:43 Do AI Agents Need Blockchains? 10:16 How Crypto Funds Allocate Capital 16:33 Ads (Fidelity Crypto) 17:13 Finding Alpha In A Crowded Market 23:37 Longs, Shorts, And Beating Bitcoin 26:34 Seed Or Growth: Where’s The Opportunity?
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A trader who’s compounded at 60% a year with a Sortino over 2 on why he refuses to compete with the pods on their timeframe: Joe Walsh is the Founding Partner of hedge fund Code Partners. Joe Walsh explains: “The first thing I think about is time horizon.” “I don’t want to be valuing a security on a three to six month timeframe where I’m competing against a very well-resourced, motivated person inside one of these firms with way better corporate access, way better short-term information, proprietary data sets.” “You’ve got to pick your time horizons to be avoiding very good players. You’ve got to pick your sectors where all the pods who used to do that have been shut down and all the investors have given up.” “The game selection aspect is running away from competition, and that’s where greater inefficiency by definition lives. There are huge pockets of that in the equity market today.” “If you think you’re going to have a huge edge trading European industrials on a three-month timeframe, I would politely disagree.” “We have huge edge in timeframes greater than 12 months, in sectors and markets that are just not as competitive. It’s not worth their time, when they’re running $100 billion levered multiple times, to muck around in names with $10, 20, 30 million a day of liquidity.” “The capacity I could run money with is very attractive, to single digit billions, and still have a huge edge and still remain very liquid.”
"Take advantage of volatility. Don't get taken advantage of by it. The last thing you want is to be the guy stopped out at the low." Joe Walsh is the Founding Partner of hedge fund Code Partners. Personal track record: 60% annualized with a Sortino over 2. "The expected value of buying a stock at 100 on a certain day and 100 on a different day, they're not the same." We cover: - Pods run equity books at up to 8x GMV, credit at 10–15x, vol-targeting with centralized stops. "Volatility targeting is not profit maximizing," and their forced selling is his entry - Why he runs no portfolio leverage in normal times and preserves the balance sheet for when vol erupts, then puts 20–25% of the book in a truly exceptional situation - The right side of the V: buying at 100 on the way down and at 100 after the bottom are not the same trade, because only one has a defined stop - Liberation Day: financial conditions worse than the 2011 Eurozone crisis, PM sentiment worse than 2008, and why that read as a bottom - Poker and trading are both games of missing information, and the winning players who never make it have the wins but not the bankroll management - Game selection: you have to increase your edge faster than the market becomes efficient, which means running away from competition - Time horizon is game selection: don't price a stock on 3–6 months against a PM with better corporate access. His edge is 12+ months in sectors where the pods got shut down - Passive is a price-indiscriminate buyer, and market-cap weighting makes it a momentum trade: from 6% of flows to over half - Why two investors who both made 100% on the same stock took wildly different risk. The early alpha is the best alpha (bought Palantir at a $20B EV, sold too early) - The AI trade: 40% of S&P earnings, data-center landlords at teens yield-on-cost trading at single-digit cap rates, and gigawatt delivery missing by 25% as the risk to consensus - Industry structure as the biggest edge: power-shell players at $1M/MW that went to $7–8M, and the offshore-wind value chain where one installation-vessel company became the monopolist of the profit pool Highlights: 00:00 Intro 01:10 What poker teaches you about trading: missing information and bankroll management 06:04 Game selection: avoiding passive flows and the pods 13:34 De-grossing events: SVB, the yen carry blowout, Liberation Day 21:01 Dry powder: how much to deploy when volatility erupts 27:32 Reflexivity: why a windfall tax belongs in your bull case 35:08 Underwrite the downside first, then size the upside 41:53 The right side of the V: why 100 is not always 100 48:39 When to put 25% of the book in one name 51:18 Why shorts get hard limits and far smaller sizing 56:24 New sectors nobody knows how to value: space and SpaceX 01:01:42 Tesla, Palantir, and why early alpha is the best alpha 01:11:24 The AI trade: hyperscaler CapEx, power constraints, where the asymmetry is 01:21:40 Industry structure: mapping profit pools in offshore wind and AI power shells
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Let's talk a little bit about vanna, vomma and zomma, and their effects on the distribution. Vanna is the risk reversal/skew channel linking the spot and vol hedges. If dealers are long vanna, a fall in spot makes their vega shorter. Buying vol to restore that vega pushes IV higher. Higher IV then makes delta longer through the same vanna exposure, requiring underlying sales. This creates a loop between falling spot, vol buying and further spot selling. (Long gamma supplies an opposing flow because the spot decline itself calls for underlying purchases. The full shadow book determines which response wins.) MM short vanna is the inverse of this spot/vol response, thats when customers are basically short skew... Vomma changes how much further vol buying that loop requires. With dealers long vomma, rising IV adds vega back to the book, partly replacing the vega lost as spot fell. The need to keep buying vol therefore diminishes. With dealers short vomma, rising IV removes more vega, so restoring the hedge can require additional vol buying providing even more convexity. This is how vol convexity can compress or amplify the IV response. In my tests, the same 10 bp sell impulse in a simulated book, long vomma reduced the IV rise by about 40% relative to zero vomma; short vomma increased it by about 51%. That can alter the fourth moment when the wings are damped or amplified differently from the centre. Zomma feeds that IV change back into gamma. In a long-zomma zone, rising IV contributes to making gamma longer or less short. If the full shadow hedge becomes stabilising, the dealer buys into the decline and absorbs part of the selling, this is why I often call it as "synthetic circuit breaker" (although its strength depends on the hedge flow relative to incoming selling and the available market depth...) Through that feedback, zomma directly influences the IV/RV relationship, which matters particularly to vol traders. Remember, that IV is the price you pay and RV is the movement subsequently delivered for that price. For a delta hedged long vol position, that movement generates gamma-scalping gains against the option’s carry. The relevant realised value depends on when and where the movement occurs, because your gamma changes along the path. Take the long-vanna setup for example... Spot falls, dealer vega becomes shorter, so they are buying vol to restore the hedge pushes IV higher. Higher IV also makes dealer delta longer through vanna, requiring underlying sales. Initially, the two hedges can reinforce the decline. But if spot enters a sufficiently long zomma zone, the IV rise also makes gamma longer. If that turns the full shadow hedge stabilising, subsequent declines attract dealer buying. The same IV rise that accompanied the selloff now strengthens the flow absorbing it... This is a bearish long-vol trap. Traders pay higher IV for further downside and continued movement, while the evolving dealer book increasingly suppresses that continuation. IV can remain bid while subsequent local RV falls short of what the premium requires. Negative spot/vol correlation can persist throughout. A directional put can still profit from the initial decline, and a long vol position can benefit from further IV appreciation, but buyers arriving after the repricing may receive too little subsequent movement for the price paid. Also I must mention speed... Speed is the gamma-to-spot fuction. When dealers are short speed, falling spot makes their gamma longer. If the full shadow gamma is positive, each further decline can require more underlying purchases. The dealer contribution to liquidity therefore becomes more supportive as spot moves lower. If gamma remains negative, the selling can weaken while dealers continue to sell into declines. This is how speed can affect both local RV and the asymmetry of the realised return distribution. Stronger absorption on the downside can thin the left-tail, while weaker absorption on the upside allows the right-tail to stretch. Overall dispersion can remain almost unchanged even though the distribution becomes positively skewed. Vanna describes the spot/IV hedge connection while speed describes how gamma changes across spot levels. Their dominant horizon depends on the expiry mix, exposure concentration and hedge frequency. Speed shapes the dealer liquidity response across prices, which can influence where intraday volume accumulates. ☝️But the chicken still comes before the egg. It is not the tail that wags the dog. The relationship is reflexive, because customers go long liquidity where their calculations suggest liquidity could increase. These expectations, which I call the “implied liquidity structure”, can be read from positioning data. We can even calculate the implied distribution and its associated probabilities, which is particularly useful for developing strategies, sizing positions and interpreting the volume profile. Where customers repeatedly sell downside options, dealers become short speed. If that region also carries long dealer zomma, rising IV adds another contribution towards longer gamma. Long vomma dampens the accompanying vega-hedging feedback. Together, these exposures build the supportive profile behind the “synthetic circuit breaker”, provided the hedge demand is strong enough relative to incoming selling and market depth. This is what we keep seeing, and that I keep flagging when I foreshadowed upside grind and supported downside while most ppl where bear-panicking... So, ▫️Long speed shifts realised skew negative and short speed shifts it positive, acting immediately on spot moves and persisting across intraday, weekly or longer horizons according to the expiry mix. Through this channel, one side of the distribution becomes more compressed while the other becomes more extended. Speed therefore describes how effective hedging liquidity changes as price travels through different levels. ▫️Vanna reshapes realised skew through state-dependent spot/vol feedback, with the direction determined by the full exposure surface and effects ranging from intraday adjustments to persistent monthly or quarterly positioning. It changes the distribution’s left–right asymmetry and therefore its skewness. A symmetric response can change dispersion without changing skewness. Vanna consequently helps determine which price regions become easier or harder to traverse as volatility changes. ▫️Long dealer vomma acts as a brake on the spot/vol feedback loop, reducing the additional hedging pressure that would otherwise amplify a move. This can suppress flow-driven acceleration and reduce kurtosis when extreme moves are damped disproportionately relative to ordinary moves. The braking comes from weaker feedback; uniformly compressing returns reduces their dispersion while leaving standardised kurtosis and return autocorrelation unchanged. ▫️With IV rising on sell-offs and falling on rallies, long dealer zomma strengthens downside gamma damping and weakens upside damping, compressing the left side of the realised distribution relative to the right and shifting skewness positively, while short dealer zomma reverses these effects.
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Market Context This is the most important foundation of my trading: @Trader_XO was the first person to bring this to my attention and to this day his teachings still have a tremendous impact on my trading / process. Market context should be one of the foundations of your trading. It helps you identify which trades you should be focusing on, from a more directional point of view. Patterns don’t mean shit unless you are trading them in the right context, this goes for double tops / bottoms and even compression setups. The daily / weekly 12/25 EMAs (TraderXO Macro Trend Scanner) is one of the simplest yet most efficient way to ensure your always on the right side of the trade. In an uptrend, the daily bands will be green and price comfortably above. In a downtrend, the daily bands will be red, and they will act as resistance when price trades into them from the underside. In rotational (Range) environments you will see the daily bands more frequently cross both bullish & bearish. My favourite tip for identifying a rangebound environment after an uptrend, is when we have the first bearish cross on the bands. Vis versa, in a downtrend you can wait for the first bullish cross to anticipate a rotational environment. I urge you all to implement market context into your trading process and have clear rules defining how you identify each environment.
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Recommended reading for the weekend: 1. @a16z — State of Markets II 2. @GoldmanSachs — AI Is Becoming a Stock Picker’s Market 3. @FundaAI — Optics: FCC Transceiver Rules Might Be Better Than Feared, and Better for Top Chinese Players Than You'd Think 4. @abcampbell — There Are Bonds in the Singularity 5. @federalreserve — An Update on AI and the Economy Have a great weekend everyone!
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I reverse-engineered every buyable bottom on SPY. Here is what actually flagged them. Instead of debating which indicator predicts bottoms, I worked backwards. I took 2 years of SPY daily data (501 trading days), found every pivot low that bounced 5% or more within 10 days (7 of them) and every pivot high that dropped 5% or more (3 of them), then measured what 13 technical studies were printing at each exact turning point versus ordinary days. The winner surprised me. Bollinger %B under 0.15 caught 86% of the buyable bottoms. 6 out of 7 bottoms printed %B below 0.15 at the pivot close. Zero of the tops did. Only 7% of ordinary days did. Quick refresher: %B measures where price sits inside the Bollinger Bands. 1.0 is the upper band, 0 is the lower band, below 0 means price closed under the lower band. How the studies ranked (median at bottoms vs ordinary days): • MACD histogram: -2.80 vs +0.42 (catches everything, but noisy, 43% false positives) • Bollinger %B: 0.11 vs 0.67 (the clean trigger) • Stochastic %K: 16 vs 83 • Stochastic %D: 21 vs 83 • RSI(14): 39.6 vs 55.3 The RSI surprise: median RSI at real bottoms was 39.6, not 30. The classic "buy when RSI goes under 30" rule caught only 29% of actual bottoms. Waiting for deep oversold meant missing most of the bounces. Three rules worth forward-testing: 1. %B under 0.15 alone: 86% of bottoms, 7% false positive rate. Best single filter in the study. 2. %B under 0.15 AND price under the 50-day average: keeps most bottoms, trims the noise further. 3. RSI under 35 AND %B under 0.15: only caught 29% of bottoms, but zero false flags anywhere. Rare, high conviction when it fires. Recent flags for reference: Jul 29 2026 (%B -0.16, then +6.1%), Jun 26 2026 (%B 0.14, then +5.0%), Mar 30 2026 (%B -0.02, then +7.6%). The honest part, because small samples lie: 7 bottoms is thin, the window was mostly bull market, and tops had no clean signal at all. In September the flag tripped 4 times without a full 5% bounce following, so treat %B under 0.15 as "conditions are washed out," not "bottom confirmed." I am setting up automations to flag for these triggers live. When the next %B washout flags it will auto post in my slack. Was only able to pull for a couple years but would like to see a larger sample set
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sleepandsleep retweeted
stop paying for paid groups this caught $QNT 6x and $ZEC $NEAR $ENA 2x all before they ran gurus charge $1,000/month for this but im giving it away for free read it or don't, choice is yours
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sleepandsleep retweeted
BREAKING NEWS 🚨: ChatGPT can now redesign any room like a $150 per hour interior designer. Upload one photo of your room and use these 7 prompts:
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Buy and Hold good coins, my thesis for the cycle. How I'm picking cryptomoneys and money making protocols. +plus my detailed thoughts on Ethereum & Solana 0:25 the dot-com crash 3:52 the '21 cycle vs now 6:13 this is a selective market 10:52 crypto money vs everything else 24:05 coins that make money 26:37 buy the good projects and wait
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A humble attempt to est. the infra required to serve 100M DAU @Muse Rough conclusion is: 1 GW of power to serve 100M DAU in the base case, of which only ~0.1 GW comes from the CPU/VM layer. Depending on the # of reasoning-equivalent model calls one Muse DAU generates per day, 3-4GW is entirely plausible. Maybe that’s why @Meta is rumored to be adding 7-10GW of compute next year. The sandbox layer = sub $1B of CPU content and ~$2B of DRAM content, which is much smaller than many expected. Lot of moving assumptions. Welcome all feedbacks/ pushbacks. ------ Two very different pieces of infrastructure behind Muse. 1. Muse VM / sandbox infrastructure 2 vCPUs, ~8 GB of RAM and ~100 GB of persistent logical storage per user. starkinsider.com/2026/09/met… 2. Muse Spark inference Model inference goes out through Meta's external inference infrastructure. research.meta.ai/blog/securi… ------ 1/ Sandbox infrastructure A. CPU The first mistake is assuming that 100M DAU means 100M VMs are actively consuming compute at the same time. Suppose the average Muse DAU has an agent actively working for two hours per day. 100M users * 2 hours / 24 hours = ~8M average simultaneous active VMs Meta obviously cannot provision only for the daily average. Usage will be concentrated during waking hours and bursty. Assume a 2.5x peak-to-average ratio: 8M * 2.5 = ~20M peak active VMs Then add roughly 20% capacity headroom: ~25M provisioned live VMs. So the base assumption is effectively that Meta needs enough infrastructure to support roughly 25% of DAU being live simultaneously. The next important distinction is between virtual CPU allocation and physical CPU demand. Agent sandboxes are particularly well suited to CPU oversubscription. They spend a lot of time waiting. During those periods, the VM may still be alive, but it is barely using CPU. DeepSeek’s recently published DSec infrastructure provides a useful benchmark. Its production agent sandbox platform runs approximately 30,000 physical CPU cores and 250TB of DRAM across ~160 nodes, with peak concurrency above 380,000 sandboxes. arxiv.org/abs/2609.22978 DSec also demonstrates stable operation at around: 800 microVMs per node. With roughly 188 physical cores per node: 188 physical cores / 800 microVMs = ~0.23 physical cores per live VM. DeepSeek is obviously the King of efficiency. The number for Muse might be at 0.3-0.75 physical cores per live VM, or assume 0.5 physical cores per live VM as the base case. That is equivalent to roughly two simultaneously live Muse VMs per physical CPU core. Using the base assumptions: 25M live VMs * 0.5 physical cores per VM = 12.5M physical CPU cores. On a 256-core CPU: 12.5M cores / 256 cores per CPU = ~50K CPUs; Or on a 192-core CPU that would be 65K CPUs. At the current public pricing, that is ~$800M. B. DRAM CPU can be aggressively oversubscribed because a VM that is waiting may consume almost no CPU. Memory is harder to oversubscribe because a live VM still needs to retain its working state. Muse exposes roughly 8GB of RAM to the user environment, but one observed instance was actually using only around 3GB at the time of measurement. 25M live VMs * 3GB = 75PB of physical DRAM, call it ~75-100PB of physical DRAM feels like a reasonable base range. At the current public pricing, that is ~$2B. C. Sandbox power ~0.1 GW for the entire Muse sandbox / VM layer at 100M DAU. ------ 2/ Inference Muse’s personal computer executes tools and stores state locally, but the actual model runs on separate inference infrastructure. Meta’s Muse architecture Energy per inference event Microsoft’s 2026 study estimates that optimized frontier-scale inference consumes a median of approximately: 0.31Wh per normal query But a long reasoning query with roughly 15x the token count consumes approximately 13x as much energy, or around: 4Wh per long reasoning query The study specifically highlights reasoning and agentic workloads as significantly more energy intensive. microsoft.com/en-us/research… Sensitivity analysis on # reasoning-equivalent events per DAU per day Suppose each active @Muse user generates the equivalent of 50 heavy inference events per day. At 5Wh each: 100M users * 50 events/day * 5Wh = 25GWh/day 25GWh/day / 24 hours = ~1.0GW average power So inference alone could require: ~1-2GW of average power A 3-4GW Muse is entirely plausible. Maybe that’s why @Meta is rumored to be adding 7-10GW of compute next year. ------ The popular framing around Muse is that giving every user 2 vCPUs and 8GB of RAM creates an enormous CPU requirement. But the naive calculation materially exaggerates the CPU requirement because it treats logical VM allocation as dedicated physical infrastructure. The more interesting conclusion is: Consumer agents may be a meaningful new demand driver for CPUs and conventional DRAM, but inference remains the real compute bottleneck. And as agents do more work, run longer trajectories and increasingly spawn other agents, inference demand can scale much faster than the number of users itself. +++ Calling my peer review group: @bubbleboi @damnang2 @Midnight_Captl @FundaAI @fi56622380 . Feedback/ Pushbacks pls :). ++ Better formatted: robonomics.substack.com/p/ag…
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William Cooper said the “elite” are following a script… He lost his life to expose it! RIP WILLIAM COOPER ❤️💫⚖️🙏🫡
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sleepandsleep retweeted
TOKENS WITH BUYBACKS, ACCORDING TO @DefiLlama: HYPE, LIT, AERO, PUMP, DRV, KNTQ, SYRUP, ENA, ETHFI, RAY, JTO, RAY, AAVE, LDO, STONK, ASTER, SKY, NEAR, DBR, PONS, PENDLE, CC, CARDS, AAVE, MET, RLB, JTO, META, KMNO Welcome to the next stage of the revenue meta, lads.
WHY: None of these onchain business tokens are securities. We did not know this until now. This clarity makes this massively bullish. We heard about a “DeFi carveout” for cash-flowing assets in the past, but this the nail in the coffin for onchain finance bears. The floodgates are about to open in a massive way. Trillions are coming onchain. Be more bullish.
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🚨 ملياردير جلس لمدة 42 دقيقة فقط وشرح كيف يعمل الاقتصاد العالمي بالكامل.. أفضل وأبسط من برامج MBA تكلفتها 200,000 دولار! 🤯💰 ​بدون فلسفة أكاديمية معقدة ولا معادلات مملة.. قواعد اللعبة المالية الحقيقية وكيف تتحرك الثروات والديون في العالم كما لم يشرحها لك أحد من قبل! 📉⚡️ ​42 دقيقة ستغير طريقة فهمك للمال إلى الأبد.. ​احفظ التغريدة وشاهد الفيديو التاريخي فوراً! 🎬👇
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00:10 FVG (Fair Value GAP) 03:05 IFVG (Inversion Fair Value GAP) 05:20 LS (Liquidity SWEEP) 07:18 MB (Mitigation Block) 10:21 BB (Breaker Block) 11:55 BB + FVG 13:30 PO3 (Power Of Three) 15:00 PO3 + FVG 16:30 OB (OrderBlock) 17:57 OB + Sweep 19:28 QML (Quasimodo LEVEL)
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LISTEN FROM A MAN WHO BEAT S&P500 A SON OF WILLIAM O'NEIL. Topic: How Leading Stocks Act in A Bear Market
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