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The only 15m BTC setup where ALL indicators agree. Polymarket traders get steady profits? Here's a solid 15m BTC scalping setup. Complex but all pieces work together to filter noise. Core indicators (each has a job): • 200 SMA + VWAP - main trend filter. Long only above both...

15,586 次观看 • 7 个月前 •via X (Twitter)

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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

250,694 次观看 • 7 个月前

I built a powerful real-time edge terminal specifically for Polymarket - multi-timeframe dashboard covering all coins! (100% FREE & fully open-source) In one sentence: This Python bot gives you live alpha on Polymarket’s Up/Down crypto binaries by fusing real-time Binance order flow, current Polymarket probabilities, and multi-TF technical analysis. Spot mispricings fast - where the market odds haven’t yet caught up to momentum, aggressive delta, or strong signals. Perfect for lightning-fast 15-minute scalps (markets resolve every 15 min with constant repricing) or cleaner swings on 1h / 4h / daily horizons. Pure decision-support tool - no auto-trading, just sharp, actionable insight delivered straight to your terminal. > Coins covered: BTC, ETH, SOL, XRP > Timeframes: 15m, 1h, 4h, daily - All 16 market combinations are live and heavily traded on Polymarket (especially the 15m contracts - ultra-fast flips) Features at a glance: > Streams live trades + full order book from Binance > Pulls real-time Up/Down prices & depth via Polymarket WebSocket > Computes 11+ indicators on the fly > Rolls everything up into a clear BULLISH / BEARISH / NEUTRAL bias score + probability estimate per timeframe > Displays a clean, colorful, auto-refreshing terminal dashboard Order-book signals: > OBI (imbalance) > visible buy/sell walls > liquidity depth (0.1% / 0.5% / 1.0%) > net flow & volume > CVD (across 1m/3m/5m windows) > 1m delta > Volume Profile + POC Technical indicators: > RSI (14) > MACD (12/26/9) + signal line + histogram > VWAP > EMA 5 / EMA 20 cross >Heikin-Ashi candle streak count Important: this is NOT an auto-trading bot. It highlights where Polymarket odds are lagging real Binance order-flow and multi-timeframe TA - giving you an edge on 15m scalping, 1–4h momentum trades, and daily directional confirmation. Built with: Python (asyncio + Rich/Textual for a slick CLI look) One-line start: python Refreshes every few seconds Completely free & open-source → GitHub repo Want access? Like + RT + drop a reply or quote below - I’ll DM you the GitHub link Don’t miss the edge - especially on those 15-minute markets that flip every quarter hour. Seeing all timeframes at once is real alpha. Here’s to green candles and fat PnL!

st1ne

175,987 次观看 • 8 个月前

BITCOIN: GET READY!!!! (these alts next) 🔥 JOIN BULLMANIA WAITLIST: BULLMANIA ARENA: GET KALSHI DEAL: 0:00 Intro: Bitcoin at $86K, why a close above $82K stays bullish 0:59 Three bullish signals: money line flip, engulfing candle, continuation 1:37 The 200 MA bounce and the buy zone 2:27 Four-year cyclers are coming back and Ben Cowen 3:45 Ben Cowen x BitBoy "mechanical rules" skit 4:39 Showbiz, humor and just following the trend 6:25 Bullmania price increase at $100K Bitcoin and the waitlist 6:46 Free newsletter calls (Aug 4 and Sep 18) 7:45 Printing season: stop-losses and risk management 8:54 Bitcoin spot ETFs see the biggest inflow of 2026 9:14 S&P 500 and Nasdaq near all-time highs 10:11 Oil and Iran: noise vs. the trend 11:15 New coffee cup 12:08 CNBC: Bitcoin set up for a rally 12:51 Altcoins reset after a long bear market 13:32 Cardano flipping bullish 13:56 Pengu 14:24 Pump 14:44 Polkadot: a trade, not a marriage 16:38 Toncoin 16:51 Bitcoin Cash 18:18 Uniswap 19:54 Ethena (ENA) and algorithmic stablecoin risk 20:34 SUI and the banana zone 21:45 Bonk 22:17 HBAR, the boomer coin 23:59 Hyperliquid at all-time highs 24:14 Jupiter vs. Raydium 24:59 Stuck in IMX? How to exit losing trades 28:49 Principal-protected Bitcoin ETF 29:53 Ki Young Ju: 3–5x or 10x this cycle? 31:36 Solana bullish since August 32:10 What if we make a new low now? 33:40 Stocks add $1.2T, and Cathie Wood's ARK underperformance 37:30 Trump buys MicroStrategy, copper and gold 38:13 Pengu as the sleeper meme of the cycle 39:01 Is blockchain gaming a scam? Kaggi responds 41:55 Interior design advice for Kaggi 42:54 Q&A begins 44:01 Is Solidity development worth getting into? 47:02 The developer job market and why you might become a plumber 48:49 Why you need to become an owner of assets 50:08 Optimus robots and disrupting renovation 53:24 Outro

Ivan on Tech 🍳📈💰 Head Trader @ Bullmania

25,603 次观看 • 15 天前

This is where a lot of tech professionals landing 6-figure roles actually get hired from. Not random job boards. Not even the usual “Easy Apply.” Here are 8 platforms that are genuinely high-signal for UX designers and tech professionals looking for fully remote roles. I also broke down what makes each one stand out, and how you should use them properly. 1. Wellfound (AngelList) URL: How to win here: Build a profile like a landing page (2 to 3 outcomes + 1 niche). Filter by salary range + stage. Apply to roles where your portfolio matches the product type. Message founder/recruiter with 2 lines: relevant proof + quick question. You will also need a USD account to receive salary, go to Cleva (YC W24) and open one. Best level: mid to senior (junior can still get something here with strong case studies). 2. Otta URL: How to win here: Set preferences tightly (role, level, industry, remote rules). Treat it like “high quality, low volume”: 3–5 strong apps/week. Tailor your first line of CV to match the job’s problem space. Best level: junior-mid to senior (works for all, but best when your profile is clear). 3. Y Combinator Jobs (Work at a Startup) URL: How to win here: Apply to roles where you can show 0→1 or growth-stage wins. Add a short “Operating style” section in your profile (collaboration, scope). Follow up off-platform (LinkedIn/email) with a 3-sentence note. You may also need a USD account to receive salary, go to Cleva (YC W24) and open one. Best level: mid to senior (but juniors can land roles in smaller teams with strong proof). 4. Himalayas URL: How to win here: Set location/timezone filters correctly. Save searches + alerts for your niche (e.g., B2B SaaS, fintech). Apply within 24 - 48 hours of posting when possible. Best level: all levels. 5. Remotive URL: How to win here: Filter to “worldwide” only if you truly can work globally. Don’t apply without rewriting your top 3 bullets to match role keywords. Pair every application with a short “proof note” (1 case study link + why). Best level: mid-level + seniors, but juniors can win with tailored apps. 6. We Work Remotely URL: How to win here: Apply fast (same day if possible). Use a “1-minute cover letter”: 3 bullets (domain match, proof, link). Only apply when you match 70%+ of requirements. Best level: mid to senior. 7. Remote OK URL: How to win here: Use strict filters (role + seniority + benefits). Ignore anything vague (“rockstar”, no salary, unclear company). Treat it like lead gen: apply + then research and follow up elsewhere. Best level: mid to senior. 8. FlexJobs (paid, but filtered) URL: How to win here: Only pay if you’ll apply consistently for 30 days. Use advanced filters and avoid anything without clear employer info. Cross-check listings on the company’s careers page. Best level: junior to mid (also useful for career switchers). You will need a USD account to receive salary, go to Cleva (YC W24) and open one. If you find breakdowns like this useful, Follow for more, I share more of them here. Don't mention.

designwithkingsley

15,164 次观看 • 7 个月前

If $SPX 0DTE feels impossible, this is for you. This is the first time I’ve publicly broken down the 90% win rate method used by the top 1% of SPX traders. 📼 Video: Attached 📝 Full breakdown: Below If you see value in this I'd appreciate a ❤️ (no pressure) Most traders lose because they: - Oversize - Guess direction - Ignore pivots - Misread momentum - Don’t track internals Your edge comes from structure, not luck. ⸻ The Core Mental Framework WSHDH What Should Happen Doesn’t Happen/What Should Happen Does Happen The most important signal in short term trading When the market refuses to do what it should, something bigger and more surprising will happen. ⸻ How Price Actually Moves Price is a battle between buyers and sellers. Pivots show where one side last won. Every decision starts with one question. What should happen here? ⸻ What A Pivot Really Is A pivot is a level where: - Buyers or sellers won decisively - Trend shifted - Volume expanded - Emotion peaked Levels with memory always matter. ⸻ SPX Example Pivot October 10 High and Low A clear battleground Above the high, buyers control Below the low, sellers control Inside the range, two way action ⸻ Failed Breakdowns A failed breakdown is when: - Price breaks below a pivot - Sellers fail to push it lower - Buyers reclaim the level - The backtest holds This is where the squeeze begins. ⸻ Why Failed Breakdowns Work Failed breakdowns: - Trap shorts - Trigger stops - Flip momentum - Create velocity Dips are fuel for higher. Overtime this is true. ⸻ Reading Momentum Momentum reveals: - Who is in control - If the move is real - If a trend is weakening - If exhaustion is near Strong trends expand. Weak trends stall. ⸻ The Internals That Matter Internals confirm direction: - $VIX - Breadth, $ADD - UVOL and DVOL - $TICK Price is the surface. Internals are the engine. ⸻ Using Internals Correctly Internals answer - Are buyers real - Are sellers exhausted - Is momentum fading - Is the shift beginning They expose the truth of the price action. ⸻ Position Sizing Rules SPX 0DTE is the highest volatility trade. This requires strict rules. Rules: - Never start with SPX - Only trade it with gains - Use small sizing - Keep emotion low ⸻ Zero Risk Structure In 0DTE Trade SPX with profits only. Worst case, lose the profit. Best case, multiply it. This is how you never go red on SPX. ⸻ Taking Profit Take profit in batches: 25%, 50%, 75%, and 100% Scaling removes emotion and builds consistency. ⸻ Rolling Roll gains, not losses. Example: 6700c gains. Roll 30 percent into 6710c. You keep upside open while protecting your day. ⸻ Full Framework The process: 1. Identify the pivot 2. Ask WSHDH 3. Watch momentum 4. Confirm with internals 5. Size appropriately 6. Take profits 7. Roll gains Simple to understand. Hard to master. Worth doing. ⸻ Example Walkthrough October 10 pivot example: - Breakdown fails - Buyers reclaim - Backtest holds - Momentum flips - Internals confirm - Short squeeze triggers This is the model to internalize and implement. ⸻ Final Perspective SPX becomes simple when you: - Understand structure - Respect risk - Follow WSHDH - Trust pivots - Track internals Clarity replaces noise. Structure replaces guessing. This is the blueprint to how SPX finally makes sense. If you like this, then like it ❤️ sm

spacemonkey

170,436 次观看 • 10 个月前

🚨🚨🚨A Stark Warning 🚨🚨🚨 🚨Europe is losing its mind. 🚨Where are we going up against a nuclear power? 🚨Russia could wipe us out in a matter of minutes, they could wipe out all of Europe. 🚨Does anyone think that if Russia is massively attacked within its territory, it will just sit back and do nothing? Obviously not. So, what are we trying to achieve? That they'll get really tired and launch two or three, nuclear bombs on some of the main European capitals? Pedro Baños Retired Spanish Army Colonel: 📑 That Mrs. von der Leyen is going to give Ukraine another 3.3 BILLONES euros another because she says that everything Ukraine has already received isn't enough, right? Because she says that everything Ukraine has already received isn't enough, right? 0:08 Exactly, the problem is that they've made a huge bet on Ukraine, and it has gone badly for them 0:09 a huge bet on Ukraine, and it has gone badly for them 0:12 and now, how do they backtrack, 0:13 how do they admit that this has been a mistake 0:14 from the very start, that they've led an entire people to destruction, 0:17 that they've led an entire people to destruction, 0:18 that already two generations of Ukrainians have died, 0:20 those who haven't fled the country, 0:22 which is a total disaster. 0:23 We only have the casualty exchange numbers between Russia and Ukraine, 0:27 between Russia and Ukraine, and the figures are staggering. 0:30 We can provide them later, right? 0:32 But what's going on? 0:34 As you said, that increase ... 0:36 look, in Spain's case, 0:37 it means multiplying the Defence budget by five, 0:41 Because of course, since the United States first asked for 2%. 0:44 Since we agreed, they said, "Hey, then 3.5%.." 0:47 Since we also agreed, they said, "Well, 5%." 0:49 These people are fools, so let's go, let's ask them for more, right? 0:52 In the end, they are going to buy almost everything from me, 0:54 which I really need. 0:55 But what is the problem, Ambassador? 0:56 When you manufacture weapons, 0:59 there is only one way for that system not to fail you, 1:02 and that is to use the weapons you've produced. 1:05 Because look, 1:06 you manufacture—imagine, it's true, there was a deficit here, 1:08 for example, of artillery shells, 1:10 one hundred fifty-five, or shells for, 1:12 cannons, or, or, or tank shells. 1:15 Fine, that's all well and good, whatever we want. 1:17 You've already produced that one million, five million shells. 1:21 And once you've produced them, what do you do? 1:22 That factory you set up with public money, with subsidies, do you shut it down? 1:28 Or you have the other option, 1:30 which is to go to war to use up that ammunition 1:32 and keep producing more. 1:33 And as it happens, in Europe, 1:35 especially in the main countries, 1:37 the economy is a total mess, 1:39 it's in free fall, 1:43 they desperately need, 1:45 as they can no longer make cars, like in Germany, 1:48 to start making tanks or ammunition instead. 1:51 But France is in the same situation. 1:52 A France that has been expelled from Africa, 1:56 where what the franc meant for them, the CFA. 2:00 And not only that, but also all the resources 2:02 they extracted under more than favorable conditions from these countries, 2:06 starting with uranium from Niger, 2:08 they've run out of it. 2:09 France is having a very hard time, 2:11 with an absolutely brutal social crisis. 2:15 And now we see all this coming together 2:16 with skyrocketing energy prices, 2:19 we can already see that the price of fuels 2:21 in Germany has already surpassed 2.5 euro— 2:24 euros per liter, which is truly outrageous. 2:27 We're talking about, and we saw the news, 2:29 that filling a standard tank 2:32 of an average vehicle now means 200 euros, 2:35 200 euros, which is absolutely insane. 2:38 And what does that mean? 2:39 That they only have one solution, indeed, 2:42 which is to go to war. 2:43 And remember there's something that, 2:45 we studied it in the Command and Staff course 2:47 and when you tell people about it, 2:48 they treat it as little more than a joke too, 2:50 that there is something called the relaxing wars. 2:53 The relaxing wars that have been used throughout 2:55 history 2:57 to get rid of part of the population or to get out of an economic chaos. 3:02 And that's ... 3:03 right now it would be very convenient for them to, 3:04 on one hand, get rid of some of the population, 3:07 that is, get rid of this problem they say they won't be able to pay pensions, 3:10 because all of this increase, besides the cost, don't forget, military, 3:15 means a clear and tangible reduction of the welfare state, 3:20 of the four pillars of the welfare state. 3:22 Some of the more honest politicians, the few there are, 3:25 tell you this openly, others still keep it hidden. 3:28 But, of course, what happens? 3:29 That, in fact, it would be very convenient for them to camouflage the dire situation, 3:33 regrettable, that they have within their own country. 3:35 We see and we can talk about Macron's statements today, 3:39 where he said that we should just about go 3:41 to war because Russia is waging a hybrid war against us 3:43 which is unacceptable and that we have to be prepared. 3:46 Macron said it, as well as some of his top ministers. 3:48 Of course, but the thing is, France is in a very, 3:51 very delicate, extremely delicate situation. 3:53 And also in a France 3:55 where people are not as patient as the Spanish, 3:57 over there, as soon as people get a little heated, 3:59 they take to the streets and burn down half the streets. 4:02 But we've talked about it, the way things are in Germany, 4:04 the way things are in Sweden, the way things are in Finland, 4:07 which, ever since it joined NATO, 4:09 has completely come apart, with unemployment even higher than in Spain, 4:14 a sunken economy. 4:16 And then some only see the way out as the United Kingdom, 4:19 which now even wants to break apart, 4:21 there are already, there are small populations that are asking for independence 4:25 within the United Kingdom, even on the verge, 4:28 as many predict, of an internal civil confrontation. 4:32 And of course, what do they always find? 4:33 Well, we already know that to unite the people, 4:36 to cover up internal chaos, 4:40 you know, create an external enemy 4:42 and declare that you have to go to war against them. 4:43 The problem with all that, Ambassador, 4:44 is that when, and you know this perfectly well, 4:46 you have even much more experience than I do, 4:49 you say, you know that the problem with all this is 4:50 that when you get yourself involved in this war context, 4:53 you are making the population get used to it every day. 4:56 We just have to look at a some newspaper, 4:59 a Spanish one that every day, every single day without exception, 5:02 publishes some story to accuse Russia of anything, 5:05 or to convince the population 5:07 that we must go to war against Russia. 5:09 So, when you already have the, the, the atmosphere, the context, the narrative, 5:13 and you've already created it, all that's left is the incident. 5:15 And we're seeing incidents like these every day. 5:17 Like—now, it's a Danish helicopter, 5:20 or a frigate getting close to a Russian ship, 5:23 or the Russian ship fires a grenade, or some warning flares, 5:27 which is nonsense. 5:28 A flare is—what's even happening— 5:29 what range could it have? 300 meters. 5:31 And that's not going to bring down any helicopter. 5:34 But of course, it must be because the helicopter had come closer precisely 5:38 to the Russian warship in international waters. 5:42 But they're searching, they're only looking for the incident that will make all this blow up completely, 5:46 when, on top of that, they're deceiving us every day with the whole drone issue, 5:50 about Russian drones in the middle of Germany, 5:52 about not knowing, knowing where they came from and they just appear out of nowhere, 5:55 almost as if in, in a space-time shortcut. 5:58 And, and it's like that all the time. And we have loads of information. 6:01 But look, but the thing is there is a fact right now that strikes me as devastating 6:04 to show us how they're speeding this up by the minute, Ambassador, 6:09 Today's news 6:10 that Ukraine has launched drones 6:13 against a Russian nuclear power plant inside Russia. 6:17 Because remember that every so often, he launches strikes on Zaporizhia and 6:19 on top of that, they accuse the Russians of being the ones 6:22 launching drones at their own nuclear plant, 6:24 because they currently have it in their possession. 6:27 No, no, we're talking about inside, inside Russia, 6:31 at a nuclear power plant, Ukraine has launched drones. 6:34 In other words, what they're looking for now is for 6:36 Russia to blow up, 6:38 I mean, for them to say, look, this is where we draw the line, 6:41 I've had enough of you. 6:42 And, and, and on top of that, so that we Europeans can play the victim, see? 6:45 We had already warned you that Russia could attack us at any moment. 6:48 If they're provoking them with the, 6:51 the ones living in the Baltic states, 6:54 whose civil rights have been taken away, 6:55 who have been banned from speaking Russian. 6:57 In other words, they're constantly provoking it so that Russia finally snaps, 7:01 and specifically, 7:02 so that it's the one to take the first step in this war, 7:05 and today's statements from everyone, from Donald Tusk, 7:08 from Macron, from Merz they're honestly chilling. 7:13 That is, they're already openly talking to us about war, 7:16 they're openly talking to us about war. 7:18 And besides, the case of Poland, 7:20 which we'll discuss later too and give the exact data, 7:25 but what's happening with Poland is, is also spectacular. 7:27 And by the way, Trump, 7:30 who used to say he was going to leave Europe, 7:31 and now says no, that he's going to create a massive mega-base in Poland, 7:36 which is a plan that's already been in motion for some time. 7:39 But later, since he said he was going to leave Europe, 7:40 What is going to leave Europe? 7:41 And what we're talking about is quite the opposite, 7:43 about an absolutely warmongering Europe, 7:46 with reckless warmongering. 7:48 Because also, as you were saying, from a military point of view, 7:52 this is madness. 7:53 Where are we going up against a nuclear power? 7:56 They could wipe us out 7:58 in a matter of minutes, 8:00 in a matter of minutes, they could wipe out all of Europe. 8:02 But where are we going? 8:03 Does anyone think 8:05 that if Russia is massively attacked 8:09 within its territory, it will just sit back and do nothing? 8:12 Obviously not. So, what are we trying to achieve? 8:14 That they'll get really tired and launch two or three, 8:18 nuclear bombs on some of the main European capitals? 8:21 What are they trying to do? 8:22 I really don't know. 8:24 And you say, "But let's see." 8:25 Also, here no one, absolutely no one, is talking about diplomacy. 8:29 No one is talking... And you are a diplomat, an ambassador. 8:32 You know how important, vital, essential diplomacy is. 8:35 And let's not forget that the First World War, 8:38 which today is more than proven, was a failure of diplomacy. 8:42 It was a failure of diplomacy in entering the First World War. 8:45 So, what are they trying to do? 8:47 No one, no one speaks of negotiation, no one speaks of, of peace, 8:50 no one speaks of diplomacy, starting with the Kaja Kallas, 8:52 who is the head of European diplomacy. 8:54 So, it's a situation that's not, no, not, truly tremendously delicate, 8:58 and we're not paying enough attention to it. 9:00 But those of us who know something about these issues, 9:02 in your case as a diplomat, in my case as a soldier, 9:05 since we've traveled a lot around the world, 9:07 we know how populations are prepared psychologically for war. 9:12 And that's what they've been preparing us for, for some time now, 9:14 but right now, in an absolutely accelerated way. 9:17 That preparation is blatant now, absolutely blatant, 9:21 how they're preparing us for, 9:23 of course, because you have to convince people how positive war was. 9:27 We've already done many programs on this topic, 9:27 We've already done many programs on this topic, 9:30 about how, yes, how the adversary is demonized 9:33 so that in the end, people are convinced 9:35 that it's even, even good, 9:37 even logical, even "Christian," to, to attack, to attack the enemy 9:42 because he's so bad, so bad, truly evil, that he deserves to be attacked, right? 9:45 Without us realizing, the ones who suffer the consequences are us. 9:49 Mrs. von der Leyen, the president of the Commission, 9:52 has already said that none of her seven children will go to war, 9:54 because they're not prepared for that. 9:56 It will be the children of the poor, right, who go to war, 9:59 they're the ones who get sent. 10:00 So, of course, the, concern is immense, Ambassador. 10:04 But look, today itself, for example, the statements 10:07 from, we can talk about here, 10:10 from Figaro, it says here Macron says, 10:14 he says, "The president of the Republic's alert regarding the Russian threat." 10:19 It would be a mistake by Russia to continue 10:22 acting like this toward Europeans." 10:23 You say, "But look, you're the ones pressuring Russia." 10:26 Or the same thing, here on BFM, 10:29 it says, Macron says: 10:30 "The Russian hybrid threat towards Europeans and France has intensified." 10:36 Well, now they don't even know what to make up. 10:37 Or actually, here too, the EFE News Agency, which says: 10:40 "Macron warns of a shift in Russian aggression and threats in Europe." 10:46 In other words, they are preparing the ground, Ambassador, 10:48 they are preparing the ground. 10:50 When they haven't been able to prove anything 10:52 about these threats, not even the helicopter with the flares, 10:55 nor the drone issue. 10:57 In fact, there's information saying it's all staged. 10:59 There are no videos, no clear images, no remains of those drones. 11:04 And the only piece that exists, one from Poland, 11:06 seems to be all staged, 11:07 it's a shame I can't show the image, 11:10 but we'll talk about it later. 11:11 That is to say, but this really worries me, 11:13 knowing how wars are prepared, 11:15 knowing how populations are prepared, 11:17 specifically so they go to those wars gladly 11:21 and without understanding the goals behind them, 11:23 which certainly have nothing to do 11:25 with the evilness of Russia, 11:26 nor that Russia is out to kill us all, 11:28 they have been feeding us this story, 11:29 since when, Ambassador, for the past four years, 11:32 when they told us that Russia at any moment 11:33 was going to invade all of Europe. 11:35 Anyway, in any case, I find this issue absolutely extremely serious, Ambassador. *Pedro Baños Bajo is a retired Spanish Army colonel, writer, and prominent geopolitical analyst specializing in defense, intelligence, and international relations.

Ignorance, the root and stem of all evil

36,945 次观看 • 19 天前

It's not about what we have or don't have that drives our trading decisions—it's what we're afraid of losing. This fear of loss has often led me down the false path of perfectionism. Yet true mastery and profitability in trading, like in art, comes from embracing the craft's imperfections. ✉️ At the recent Mumbai traders' meetup, Chhirag Kedia spoke a line that has resonated with me all week— वो आदमी सफल होने से कोई रोक नहीं सकता जो अपनी कश्ती जला कर आया हो! It made me reflect on my trading journey and how my risk-taking appetite has evolved over the years. I'm inherently risk-conservative as an individual, though my career decisions and trajectory paint a completely opposite picture. I've never gone bust or even had a significant drawdown in my trading life—initially because I was too cautious, and now because my skills have improved. When I look back at my interactions with other traders and analyse my own performance graph, I am noticing a pattern: traders who started recklessly or faced major drawdowns—but persistently improved their execution—often developed better and faster learning curves than those who began cautiously with small positions. Even Quallamaggie (Q) and Zanger (Z) demonstrated similar patterns—their initial failures didn't reduce their risk appetite or aggression. Rather, they increased their risk appetite as their accounts grew larger. When ordinary traders dismiss Q and Z as exceptions in the trading world, they're likely rationalizing their own fears—fear of bouncing back from setbacks beyond their risk comfort zone, and fear of not having enough skin in the game. After all, as Taleb says, courage is the only virtue you cannot fake. I wonder if I would have been a better trader today had I started more aggressively—even borderline recklessly—and then learned to control that aggression, rather than the other way around. Has my obsession with perfection (or trying to get close to it) actually slowed down my learning curve as a trader? Perfectionism and Self-Abuse In every trade—even with flawless setups and meticulously calculated risks—there are countless ways to feel wrong, whether you make money or not: You buy and it goes down You don't buy and it goes up You buy, it falls, you sell—then it goes up It goes up, you sell, and it keeps going up It goes up, you buy, it goes up further—then drops You buy with half size and it moves up; you pyramid with full size and it goes down You buy with double size and it drops; you buy with half size and it doesn't go up as much . . . and the list can go on But there is only one way you'll feel right: When you buy and it immediately goes up, and when you sell and it immediately goes down. And this is a very very rare instance. But the pursuit of perfect trade—trying to capture both the first and last eighth of every trade—is where much self-belief and confidence is needlessly lost. The search for the perfect chart, perfect market conditions, and perfect mindset was probably the most paralyzing form of self-abuse in trading I had done. It led me to the comfort of inaction rather than risk the ego to scrape the imperfect rewards on offer. Lets take up an example that was discussed in the last Mumbai meetup - PDMJE Paper - Trade Objective An Episodic Pivot setup, gapping out of a big base, to be held as a longer positional play. Entry (Orange lines) 29th October 2024 Entry 113.95, Stop Loss 2% - 111.7 (~Day low) Risk on Trade 0.75% of portfolio, Size - 35% Sells (Blue Lines) 50% Sell at 6R - 128.6 - This was not a planned sell, but I observed weak market depth with sporadic volumes over the next 3-4 days. As a precaution, I reduced size in this illiquid counter. = 3R 50% sell at ~12R - 143 - The swing move had become overextended, moving far from the 10/21 EMA. The position was sold when price broke below the opening range lows in weakness. = 6R Impact of portfolio - 6.75% Analyzing this trade up to this point, it was executed well with little room for improvement—almost perfect. This was also a very obvious EP trade, and many others had executed it similarly. In the group discussion of this trade, even though everyone had profited, regret about the price movements after exit overshadowed the satisfaction from actual gains. If you had missed the pullback entries near the 21 EMA on November 13th (which wasn't actually setup-ready) or the breakout entry that triggered on November 29th (when markets were strong and many stocks were breaking out), you would have likely missed the 80% move that happened in less than a month, which I did. The traders in the group spent much of their emotional energy obsessing over this missed opportunity, ultimately accumulating emotional debt from the markets. The paradox of trading is that while realized losses may dent our account, missing potential gains often dent our confidence. Each time we let the fear of missed opportunities overshadow our actual successes, we unconsciously train ourselves to trade smaller, not bigger - precisely when our proven profitability should be empowering us to scale up. It took me years to understand that successful trading doesn't require feeling happy. I can make sound decisions and evaluate my performance objectively, even when I feel frustrated about missed opportunities. The only true nobility in this business is making money, not chasing dopamine highs. The Adjustment Taking a loss is straightforward—we simply follow our stop loss. The real challenge—and greatest potential for regret—lies in managing profitable positions, particularly when a stock has made big moves in a short period. This is particularly common in magnitude trades like an EP or IPO where our objective is to hold for a longer duration and sell into weakness, but often have moments when the stock is overextended in the short term with a high probability of pulling back. However, we hesitate to sell either because it conflicts with our original trade objective or because we fear missing the chance to buy back during the pullback. This is where many professional traders actively manage their core positions. Rather than passively waiting for a deeper trailing stop loss to trigger during weakness, they sell a portion when the price becomes extended and buy back the same amount at a lower price. This strategy proves more effective than enduring drawdowns while waiting for a formal pullback setup at support levels or moving averages. Let's take a recent example of IGIL (5 min chart) - Trade Objective An early-stage IPO setup displaying a typical volatility contraction pattern (VCP) on intraday charts. The plan is to hold this as a longer-term position, treating it as an All-or-Nothing trade. Entry (Orange line) 24th December 2024 Entry 504, Stop Loss 2% - 493.95 Risk on Trade 0.50% of portfolio, Size - 23% of pf Adjustment Context - 27th December 2024 The stock had surged powerfully over the previous two days, hitting Upper Circuits. On the third day, despite gapping up at open, it immediately broke down during the opening range - like a typical parabolic short setup. This was a point with a high probability of a short-term pullback or consolidation. Sells (Blue Line) 27th December - 585 - 50% size Buyback (Orange line 2) 27th December - 565 - 50% size - Gained 1R Rationale - My anchor bias was for the price to cool off for a bit. - At the 27th open, the price was already at ~8R+ for me. Even if the stock rose further after I sold my partial position, I wouldn't regret it much—I had already secured 4R with half my position still pending, well above my journal averages. This served as an important emotional anchor point for this adjustment. - When buying back, I was simply looking to average down my costs without a specific target or a perfect setup in mind. In this case, I bought back at 565, as the buyback itself presented a good psychological point to cover (10 Rs initial stop loss, 20 Rs points averaged ~1R at half size). It could have been lower too if the breakdown was slower. This was more intuitive and intentionally imperfect. - I close most of these adjustments on the same day since they are just short-term pullbacks and my overall bias remains bullish. A magnitude trade can also be looked at as a combination of several intraday trades around a core position. - This adjustment method applies specifically to magnitude trades like EP and IPO positions, where the trade objective aligns with pyramiding or averaging costs. Caveat You might think this is a cherry-picked example—and you'd be partly right, since it's one of my better and more recent trades (you can see similar patterns in Care or TI). However, I urge you to stay open to the concept. Look back at your previous trades where you held positions too long passively—you'll often find that temporary extensions and pullbacks were easily visible, offering opportunities to capture additional R’s along the way. Traders commonly face similar emotions and dilemmas when deciding how to act in these situations. End Note

Anuragg Venkatakrishnan

24,423 次观看 • 1 年前

Announcing the DVM Terminal Presale! 01/ We are excited to formally announce the next step in our journey: our AI and Signal based trading Terminal. See ALL details on our website, including product, tech, deposit address, and tech documentation: Deposit Address (SOL only): 4pyVRFX56MdqtREcxWnf6XuEGfRNCQaKm1LA4xmHeccv By contributing, you agree to our Terms & Privacy Policy – full docs on site. 02/ We are building ‘DVM Terminal’, a signal and AI powered trading platform for the Solana trenches (initially). The first multi-agent AI trading terminal designed as an institutional-grade dashboard – turning market noise into actionable alpha with agent summaries, live signals, rigid filters, and a full multi-agent system. 03/ The problem. Trench hunting is far too inefficient with real data and insights lacking. - Dashboards are noisy (not even sortable), - No AI agents (in an AI world) - No narratives (a critical component to a thesis), - VERY limited signals (only DB/DS), - No advanced trading (no TP, SL, or VWAP), - No portfolio alert/management system post-trade etc. - The list goes on… Products from major competitors are all just homogeneous, even down to the 3-frame design. We have to piece everything together like broken lego blocks, building a weak matrix from existing platforms, X, FNFs, telegram and discord for little to no alpha. 04/ The solution & moat. We rebuild this from the ground up, leveraging signals and AI. - Clean institutional-like dashboards (we can sort and navigate thru a proper terminal, like Bloomberg or Messari) - AI agents (thank goodness for intelligence, distilling all the important info upfront across 2k+ tokens/day) - A Narrative engine (no need to ask “what is this token about?”; additionally, our engine can identify the newest metas like AI, ICM, Cards, etc.) - 100s of value-add Signals overlaid live on charts (momentum, smart money, sentiment, event data; all of it; tell us what’s happening in real-time) - Advanced trading system (finally, SL, TP, VWAP etc.) - Live portfolio monitoring (AI will give us pertinent live info on our holdings, so we can go live life and not look at screens all day) - All in one place. At a higher-level, our advantage will be managing the massive on/off-chain data pipeline being processed by thousands or millions of context-aware AI agents that recognize patterns, filter noise and deliver only the most actionable insights to a trader with which it can execute a trade effectively. 05/ The opportunity. The Industry leader on Solana makes $600m+ in fees annually, with total industry near $1b on Solana alone, according to Adam. Yet, the entire industry gives us total burnout, fragmented data, either little info or info overload, no real signals, no narratives, no personalized AI-driven strategies, and zero incentives (like buybacks or a flywheel). We’ll flip the script, designing a high-powered scalable signal and AI driven intelligence platform with a flywheel (50-100% fee buy-back & burn). Simply put, we want to be tops. 06/ Development. Our product is MVP. We are building this to scale beyond Solana, into multi-chain. V1 is expected in 4-6 weeks. Our approach to building is an open feedback loop with community members, building to the demands of our users. 07/ Pre-sale terms & Valuation. We are offering 50% public sale, with min $100, no max. Ending valuation is susceptible to change based on amount raised, but will be fixed at 2x raise - i.e. $1m raised=$2m val, $50m raised=$100m val. We are seeking to raise $25m on a $50m valuation, which represents 1% of Solana bot market-share. At TGE event, expect ~65% of our tokens to be floating (or outstanding), with 25% in treasury and 10% of the team allocation locked. Tokens are expected to be distributed just ahead of v1 rollout. Again, find more details on our webpage. 08/ Tailwinds. AI input costs are declining 90%/yr also, so the operational model could become very accretive over time, as we scale our tech to other chains. Solana outputs the most tokens (~35k per day), so we start here, where the challenge is the greatest. 09/ Advisors. Big thanks to our advisors, who’ve been part of this community since inception. Austin Barack, JK 🛡️, cryptic, Tachi, , ZoeyLoo and Chetan Badhe. 10/ The end. Thank you for your consideration; and make sure the SOL address posted here is the same as on our website.

Deep Value Memetics

23,075 次观看 • 11 个月前

It printed. I gave Claude the gold trading strategy from a $250K Polymarket wallet and it rebuilt the entire system. $4,298 profit later I knew I need to share this. Gold and silver prices + UP/DOWN = under radar gem. I've never seen anyone explain this publicly I didn't thought I would post it, but here's the entire breakdown of this strategy for you So you can lock the f in and try to copy it for yourself: Albert1953. Joined June 2022. 2.5K views. $248,326.77 all-time. 3,338 predictions. $35,300 biggest win. Profile → 0x777fae71d2ff9ec48a1213d48ba1d9d91024a1bb I found this wallet at 2AM scrolling pages of Polymarket nobody reads. Opened the positions. Stared at the screen. Not a single standard crypto trade. "Will Silver hit HIGH $120 by end of June?" → bought No at 61.3¢ → now 89.9¢. +46% "Will Gold hit HIGH $5,500 by end of June?" → bought No at 22.1¢ → now 75.7¢. +242% "Will Gold settle above $6,200 in June?" → bought No at 75.4¢ → now 94.2¢. +24% $106,300 still sitting in active positions. All green. All commodities. Before I built my own version I copytraded this wallet for 48 hours to stress-test the logic. $80 in. Didn't touch it. Went to sleep. Woke up to $4,298. I alwas stress-test wallets I find by copy trading here: Here's exactly how to build this with Claude yourself: 1. Commodity ceiling/floor probability mapping Open Claude. Type this: "Analyze 36 months of Gold, Silver and WTI Crude Oil price data. For each asset identify the statistical probability of hitting specific price ceilings and floors within 30, 60 and 90 day windows. Flag every current Polymarket commodity market where the implied probability differs from historical probability by more than 25%." The crowd prices Gold hitting $5,500 by June as a 22% chance. Three years of data says it's closer to 8%. That 14% gap is the entire edge. 2. Mean reversion Kelly sizing "Size every position using fractional Kelly weighted by mean reversion strength. Assets further from their historical range get larger positions. Assets near historical midpoints get smaller positions." f* = (p × b - q) / bThat's why he has $16,244 on Silver NOT hitting $120 but only $2,806 on Silver settling above $115. The math knows which prediction is more extreme. Extreme predictions = fatter edge = bigger Kelly size. 3. Macro correlation filter "Before entering any commodity position check current correlation between: DXY dollar index, 10-year Treasury yield, and the target commodity. If macro conditions are actively moving against the position's thesis - skip the entry entirely." Gold and Silver don't move in isolation. They move with dollar strength and interest rates. The bot only fires when macro confirms the statistical edge, not fights it. 4. Multi-month compounding structure "Prioritize end-of-month and end-of-quarter settlement markets over weekly markets. Longer settlement windows allow mean reversion to play out fully without noise interference." This is why every position is June settlement. Not next week. Not April. June. Long enough for the math to be right even if the market is temporarily wrong. Albert1953 has been running this logic since June 2022. 3,338 predictions. $248,326 profit. $106K still active and green. 2,500 people have seen this wallet in 4 years. You're one of them now. - You found this while it's still quiet. The next commodity wallet I find will be even quieter. FOLLOW before that changes.

Frogify

23,399 次观看 • 6 个月前

I’ve spent 2 hours combing through over 160 charts. Here are 40 stock charts you need to watch in the next 5 days! The market is still consolidating, but the tone shifted a bit last week. SPX failed to break out and closed near the weekly lows. QQQ and semiconductors weakened. Software is trying to stabilize, while earnings from names like $TSLA, $GOOGL, $IBM, and $INTC will likely determine where we go next. Here’s the watchlist and recording (audio cuts out after 20 min): $SPX: SPX attempted to break above both the weekly high and the upper trend line but couldn’t hold it. Buyers ran out of momentum and sellers stepped in, leaving us with a weekly close near the lows. While that’s a short-term negative, the bigger trend hasn’t broken. We’re still trading inside a two-month triangle after a strong advance. 7400 remains the key level I’m watching. Lose that and 7235 becomes a realistic target. Recover 7500 and the 50-day moving average, and I’d start looking for another push higher. $QQQ: Tech had one of the weaker weeks. QQQ is now below the 9, 20 and 50-day moving averages, and those averages are beginning to roll over, which is an early warning sign that momentum is fading. I’d keep a close eye on 685. If that level fails, the next meaningful support doesn’t come in until around 640. $IWM: Small caps continue holding above the 50-day moving average, which is constructive relative to QQQ, but the chart is still trapped inside a broad range. Until we reclaim 300, I don’t see a high-conviction setup here. $IGV: Software has cooled off after being one of the stronger groups a few weeks ago. The ETF remains below the 200-day moving average and continues to struggle there. Some individual software names still look attractive, but I’d like to see IGV reclaim 95-96 before becoming more aggressive. $SMH: Semiconductors spent another week under pressure but did manage to defend the 555 area on Friday. This group is sitting at a very important inflection point. If buyers can build on Friday’s bounce, we could start seeing leadership return. If not, this pullback could continue. $BTC: Bitcoin continues drifting sideways without much conviction. It’s holding the 58K-60K region, but there’s still no catalyst or technical confirmation suggesting buyers are ready to take control. For now, it’s simply range-bound. $AAPL: Apple continues to be one of the strongest charts in the market. Three straight weekly gains have brought it right back to all-time highs after fully recovering from the post-WWDC weakness. It has quietly become one of the market leaders again. Above 335, I’d look for continuation toward 350-360. $MSFT: Microsoft briefly reclaimed the 50-day moving average before giving it back. The chart isn’t broken, but it hasn’t shown the same relative strength as Apple or Meta. 400 remains the level I’d like to see recovered before getting more constructive. $GOOGL: Google remains below its key moving averages after the Gemini-related headlines earlier in the week. Friday was a better session relative to the market, but the chart still needs time to repair itself before offering a clean long setup. $META: Buyers stepped in exactly where they needed to, defending both the 200-day moving average and prior support. That reversal keeps the chart constructive despite the recent volatility. Above 650-652, I think Meta has a good chance of working back toward the highs. $TSLA: Tesla continues to be one of the weaker mega caps heading into earnings. The price action has been choppy, momentum is fading, and the chart lacks a clear trend. Below 368 could accelerate another leg lower. For now, I’d rather wait for earnings than force a trade. $AMZN: Amazon briefly reclaimed the 50-day moving average before giving the move back. It’s another chart that’s trying to stabilize but hasn’t earned my confidence yet. A sustained move back above the 50-day would improve the outlook. $NFLX: Netflix sold off after earnings and is now sitting at an important long-term support area around 70. That’s the level that matters. If buyers can reclaim 70, and especially 75-76, this quickly turns into an attractive failed-breakdown setup with room to recover. $NVDA: Friday looked ugly initially, but buyers defended both the psychological 200 level and the 200-day moving average. That’s exactly where you want institutions stepping in. Above 207, I’d look for a move toward 214-215, and only above there does a run back toward the highs become realistic. $BROS: Quietly building one of the cleaner bull flags on my watchlist. Friday’s strength was encouraging, and above 70 I think this one has room for another continuation move. $BE: After an incredible run, BE has finally started pulling back into support. This isn’t a chart I’d chase, but it’s one I’d monitor closely. If buyers defend 195, it could become another attractive continuation setup. $USO: Energy benefited from renewed geopolitical headlines and has started improving technically. A move above 125.85, along with reclaiming the 50-day moving average, would strengthen the bullish case. $NBIS: One of those AI names that can reverse very quickly once buyers return. Friday’s recovery was encouraging after several weak sessions. It remains firmly on my watchlist. $NET: Software hasn’t completely fallen apart, and NET continues to be one of the stronger names in the group. I’m watching 280-282 closely. If software finds its footing again, this is one of the first names I’d expect to move. $PANW: PANW continues holding up well despite broader market weakness and has respected support remarkably well. Earnings aren’t until August, leaving plenty of room for institutions to accumulate. Above 368, I’d expect momentum to build toward 400. $DELL: Dell continues holding its post-earnings gap extremely well despite weakness across AI infrastructure. That tells me institutions still want exposure. Above 410 would likely restart the uptrend. $LLY: Healthcare remains one of the stronger areas of the market, and Lilly continues showing leadership. Above 1200, I’d expect another leg higher as buyers continue rotating into defensive growth. $CRWD: CrowdStrike has done a great job holding above 200 despite the broader volatility. That’s constructive. Above 210, I’d look for buyers to regain momentum. $BAC: Earnings are behind it, removing one layer of uncertainty. As long as 60 holds, I think another breakout attempt remains very possible. $MU: Memory continues weakening after an exceptional run. Momentum has clearly faded. Below 800, I’d expect another wave of selling before buyers become interested again. $AMD: Despite the recent pullback in semiconductors, AMD continues to hold up better than many peers. The 500 area becomes an important decision point early in the week. $V: Visa printed an inside day after a healthy advance. Those often resolve with expansion. Watching 365 closely. $MA: Very similar setup to Visa. Healthy consolidation after a strong move higher. Worth watching if financials regain momentum. $SNDK: After an incredible run, the correction has been significant. The chart still needs time, but 1275-1300 becomes an important area to watch for signs that sellers are finally exhausting themselves. $ALAB: Another AI leader that’s finally cooling off after months of strength. Nothing wrong with the longer-term story, but technically it needs more time before becoming attractive again. $SPCX: SpaceX continues trading below its IPO price and has steadily deteriorated technically. August earnings become the next meaningful catalyst. Until then, I’d rather let the chart prove itself. $HOOD: Robinhood has now lost both 100 and the 200-day moving average. That’s meaningful technical damage. I’d wait for buyers to reclaim those levels before becoming interested again. $ISRG: One of the cleaner downside setups on my list. A break below Friday’s low around 345 could trigger another leg lower. Overall theme: Last week’s failed breakout shifted the short-term tone more cautious, but the bigger picture hasn’t changed. SPX remains inside a two-month consolidation, and earnings will likely determine whether we finally resolve higher or break lower. Semiconductors are trying to stabilize after a difficult stretch, software is mixed, and Wednesday becomes the biggest day of earnings season so far with reports from $TSLA, $GOOGL, $IBM, and $NOW, followed by $INTC on Thursday. $AAPL, $NVDA, $META, $PANW, $NET, $LLY, and $BROS are some of my favorite charts going into next week. If you like this, then like ❤️ it.

spacemonkey

25,744 次观看 • 2 个月前

Made $313 → $2,382,780 in 4 Days Using a Claude AI Bot on Polymarket. 26,738 trades. 98% win rate. Full blockchain proof. Every single trade verifiable on-chain. I've made the exact step-by-step guide to build this Claude Polymarket bot from scratch. You've been trading for 3 years. Still red. He gave Claude $313. Woke up rich. Free for 24 hours. To get this Setup guide: 1. Comment "Money" 2. Like and Retweet 3. Follow me Himanshu Kumar (so i can DM you) Full 2-hour video tutorial attached. Every single click and command explained. Beginner to running bot. Now let me break down exactly how this works. Save this post. This is the most important trading breakdown you'll ever read. ↓ Let's start with the number that should make you sick. $313. That's what this wallet started with. Not $50,000. Not $10,000. Not even $1,000. $313. Less than your monthly Netflix + Uber Eats + Spotify combined. 4 months later: $2,382,780.80. That's a 7,942x return. While you spent those same 4 months staring at charts, drawing trendlines, panic selling, revenge trading, and ending the month exactly where you started. Minus the $200 you lost on that "sure thing." Same 4 months. Same market. Same opportunities. He had a bot. You had feelings. Guess who won. Save this post right now. What I'm about to explain is the exact mechanism behind every dollar of that $2.38M. Follow Himanshu Kumar so you don't miss the rest. ↓ How Polymarket actually works and why bots print money on it. Polymarket is a prediction market. Will BTC be higher in 15 minutes? Yes or No. Will the Fed raise rates? Yes or No. You buy shares between $0 and $1. If you're right, your share settles at $1. If you're wrong, it settles at $0. Simple. Now here's where it gets interesting. Polymarket updates its prices SLOWER than the real market moves. When BTC drops 0.6% on Binance, Polymarket still shows old odds for about 2.7 seconds. 2.7 seconds. In those 2.7 seconds, the bot already knows the outcome. It's not predicting. It's not guessing. It's reading information that already exists and trading before Polymarket catches up. That's not trading. That's collecting free money with a 2.7 second head start. And you're over there using a 15-indicator TradingView setup trying to "predict" where BTC goes next. The bot doesn't predict anything. It just reads faster than you. That's the entire edge. Save this post because if you understand this one concept you understand how millionaires are being made on Polymarket right now. Follow Himanshu Kumar for more breakdowns like this. ↓ Let me walk you through one single trade. A new 15-minute BTC contract opens on Polymarket. Odds are 50/50. Fair price. 10 minutes in, BTC drops 0.6% on Binance. Hard, fast move. The real probability of BTC being lower at expiry is now about 78%. Polymarket still shows 54/46. The bot sees this instantly. Binance WebSocket feed. Under 50ms latency. The edge is 24 percentage points. On a binary contract, that's basically free money. Bot calculates position size using Kelly Criterion. Executes via Polymarket's API. Done. Within 2-3 seconds, other participants update the odds. 54/46 moves toward 78/22. Bot either exits for immediate profit or holds to resolution. Either way, the trade was entered with near-certainty of a positive outcome. Now repeat this 200-500 times per day. $313 → $2,382,780 in 4 months. Not magic. Not prediction. Not luck. Industrial-scale exploitation of a market inefficiency that still exists today. And you're still placing one manual trade per day and calling yourself a "trader." This is the mechanism behind every single dollar. Bookmark this post so you can study it again. Follow Himanshu Kumar because I'm breaking down each strategy separately. ↓ There are 4 strategies. Not all Claude bots do the same thing. Strategy 1: Latency Arbitrage. Win rate: 85-98%. What 0x8dxd used. Monitor Binance price feeds. When Polymarket odds lag behind reality by 3-5%, buy the correct side before the market corrects. No forecasting. No model. No sentiment analysis. Pure speed. You're not guessing. You're reading an outcome that has already happened. Strategy 2: Oracle Arbitrage. Win rate: 78-85%. Chainlink oracle price feeds occasionally diverge from Polymarket's implied prices. When they do, the settlement direction is known. Fewer opportunities. Higher certainty when they appear. Strategy 3: News-Driven Trading. Win rate: 60-75%. Claude ingests real-time news. Government filings. Central bank statements. On-chain data. Assesses probability impact before retail traders even finish reading the headline. Lower win rate because interpretation introduces uncertainty. But works on ANY market category, not just crypto. Strategy 4: Market Making. Return: 2-5% per month. Place buy and sell orders on both sides. Capture the spread. No prediction required. Most consistent. Hardest to blow up. Compounds aggressively over time. You didn't even know there were 4 strategies. You thought "trading bot" meant one thing. That's how far behind you are. 4 strategies. 4 different risk profiles. 4 ways to make money while you sleep. Save this post. Follow Himanshu Kumar for the deep dive into each one. ↓ The timeline that should haunt you. December 2025: Bot launches with $313. Nobody notices. January 6, 2026: Wallet hits ~$438,000. 140x in 30 days. 6,615 predictions. 98% win rate. Finbold reports it. Crypto Twitter explodes. March 10, 2026: Head-to-head test. Claude bot: $1,000 → $14,216 in 48 hours. +1,322%. OpenClaw bot: fully liquidated. Same market. Same timeframe. Claude won because of better risk management. OpenClaw died because it overleveraged. March 16, 2026: Someone trains a swarm model on 3 years of NBA data. Result: +$1.49M on Polymarket. April 2026: 0x8dxd final verified balance: $2,382,780.80. 26,738 trades. 4 months. This all happened while you were "waiting for the right time to start." The right time was December 2025. The second best time is right now. But you'll probably wait until it's too late. That's what you always do. Every date on this timeline is a day you could have started but didn't. Save this post. Follow Himanshu Kumar so you at least start today. ↓ Why Claude and not ChatGPT? This isn't opinion. It's data. March 2026 head-to-head: Claude bot: +1,322%. OpenClaw (GPT-based): liquidated. Same prompt. Same market. Same conditions. Researchers found Claude's code included: > More defensive edge cases > More conservative default parameters > Better error handling > More legible code for debugging > Proper Kelly Criterion position sizing > Hard drawdown kill switches ChatGPT's code overleveraged into a losing sequence and couldn't recover. Claude's code sized positions conservatively, stopped trading when drawdown thresholds hit, and survived to compound another day. The difference between +1,322% and liquidation wasn't the strategy. It was the risk management. And Claude writes better risk management than ChatGPT. That's not a debate. That's a $15,216 difference in 48 hours. But sure, keep using ChatGPT because "everyone uses it." Everyone's broke too. Coincidence? Stop using the popular tool. Start using the profitable one. Save this post. Follow Himanshu Kumar for more Claude vs ChatGPT comparisons with real data. ↓ Why humans lose to bots. Every single time. Same strategy. Same market. Same period. Bots: ~$206,000 profit. Humans: ~$100,000 profit. 2x gap. Same strategy. Here's why: 1. Late entries. By the time you identify the lag, verify your reasoning, and click buy, the 2.7 second window is gone. The bot executes in under 100ms. You execute in 30 seconds. The opportunity doesn't exist for 30 seconds. 2. Emotional sizing. You oversize when "confident." Undersize when scared. Exact opposite of Kelly math. The bot sizes based on edge. Every time. No feelings. 3. Fatigue. You make worse decisions at hour 6 than at hour 1. The bot makes the same decision at hour 72 that it made at hour 1. 4. Drawdown psychology. After 3 losses you either panic quit or double down trying to recover. Both destroy capital. The bot has a kill switch. It stops. It doesn't feel anything. You're not competing with other humans anymore. You're competing with machines that don't sleep, don't feel, don't flinch. And you're losing. The data doesn't lie. Humans lose to bots 2x on the same strategy. Save this post. Follow Himanshu Kumar for the complete bot setup that removes you from the equation. ↓ What can go wrong. Because I'm not going to lie to you. Most people who build this bot will NOT 7,942x their money. Some will lose their initial capital. Here's what can kill you: Edge compression. The arbitrage window was 12 seconds in 2024. It's 2.7 seconds now. It's shrinking. At some point it hits zero for retail operators. This is a time-limited opportunity. Not a permanent income stream. Rule changes. Polymarket can change contract mechanics, settlement rules, or API terms overnight. What worked yesterday can lose money tomorrow. Risk management bugs. A 98% win rate strategy with broken position sizing will blow up your account on the one losing trade. The March 2026 experiment proved this. Claude survived. OpenClaw got liquidated. Same strategy. Different risk management. That's why the 2-hour video tutorial walks through every single risk parameter. Because the strategy doesn't kill you. Bad risk management kills you. This is the section most "gurus" delete. I'm keeping it because I'd rather you make money safely than blow up and blame me. Save this post. Follow Himanshu Kumar for honest breakdowns, not hype. ↓ The step-by-step to build your own. Step 1: Set up a Polymarket wallet. Fund with USDC via Polygon network. Start with $100-$300 for testing. Step 2: Generate API credentials. CLOB API key from docs.polymarket .com. Store private key in environment variable. Never hardcode it. Never share it. Step 3: Prompt Claude to build the bot. Use Claude Code for best results. It reads your filesystem, executes code, and iterates on errors autonomously. Step 4: Paper trade for at least one week. Minimum 200 completed trades. Win rate must be above 70% before going live. This step is NOT optional. Step 5: Configure risk management. Max single position: 8% of portfolio. Daily loss limit: -20% with auto halt. Kill switch at -40% drawdown. Telegram alerts on every threshold. Step 6: Go live small. $1-5 per trade. Watch every trade for first week. Compare to paper results. Scale only on evidence. Skip steps 4 and 5 and you will lose your money. That's not a warning. That's a guarantee. This is your complete build guide. Save this post. Follow Himanshu Kumar because I'll be posting the exact Claude prompts for each strategy. ↓ The edge exists right now. Not next month. Not "when you're ready." Right now. The arbitrage window is 2.7 seconds. It was 12 seconds in 2024. It's shrinking every week. Every day you wait, more bots enter the space. The window gets smaller. Your potential returns get smaller. The bots already running have a compounding advantage. They're making money today that they'll use to make more money tomorrow. You're reading about it and telling yourself "I'll look into this next weekend." That's what you said last weekend. And the weekend before that. The best time to start was 6 months ago. The second best time is today. But you already know you're going to bookmark this and never open it again. Prove me wrong. ↓ Full 2-hour video tutorial attached. Every single click. Every command. Every parameter. From zero to running bot. Beginner friendly. Nothing skipped. A similar bot has already earned $2,382,780. Full blockchain proof in the article below. The video is free. The tools are free. The edge still exists. The only thing that costs money is another month of doing nothing while bots eat every opportunity you're too slow to catch. Follow Himanshu Kumar for the complete series covering every automated income stream using Claude. Prediction markets are just the beginning. Save this post. Bookmark it. Screenshot it. Whatever you need to do so you actually watch the video and build the bot instead of just reading about people who did. You Must Follow me Himanshu Kumar, so i can send you DM.

Himanshu Kumar

54,045 次观看 • 6 个月前

The 118,000% Alpha: Building a High-Frequency AI Trading Floor with Claude Code if you think claude code is just for writing simple scripts then you are already losing to the bots that are hunting your liquidity right now. most traders are still clicking buttons while i have an ai employee running backtests on twenty eight different data sources simultaneously. i am going to show you how a strategy that returned over four hundred thousand percent was built in minutes using a secret sub agent workflow most people treat ai like a chatbot but i treat it like a quant architect that builds systems better than the devs i used to pay hundreds of thousands of dollars. there is one specific indicator combo that actually survived a stress test across tesla and bitcoin at the same time and i will reveal that logic further down. we have to talk about why your current backtests are probably lying to you before we get into the code my name is moon dev and i truly believe that code is the great equalizer in this world. for years i was the guy getting liquidated and overtrading because i was letting my emotions drive the wheel. i spent an insane amount of money hiring developers to build apps for me because i thought i was not smart enough to code myself. through that pain i realized that if i wanted to win i had to automate everything and learn to do it live on youtube for the world to see the secret to trading with claude code is not asking it for a strategy but using it to build a backtest architect. this sub agent acts as a consistent employee that understands how to test against massive datasets without getting tired. it allows me to iterate through hundreds of ideas in the time it used to take me to write one single line of python. this is how i found the strategy that hit a one hundred and eighteen thousand percent return on a single run there is a massive trap that almost every beginner falls into when they start using ai for trading. they find a strategy that looks amazing on one chart and they think they found the holy grail of wealth. that is usually just a lucky fluke or a curve fit mess that will blow up your account next week. the real secret to staying alive is the multi data testing system that claude built for me today we test every single idea against bitcoin and ethereum and solana but we also throw in apple and tesla and nvidia. if a strategy only works on crypto it is probably just riding a trend that is already over. i want to find the logic that is robust enough to handle the volatility of a meme coin and the steady grind of a blue chip stock. this is the only way to prove that the code actually has an edge in the market before we dive into the kalman filter logic i have to tell you about the dca bot i have running on solana right now. it is called housecoin and the thesis behind it is either going to make me a genius or leave me with nothing. it is buying every time we are under the five minute sma and i have been checking the transactions live. i will explain the risk management behind this "all or nothing" play shortly but first we need to look at the winners the winner of today was the acceleration bands combined with a kalman filter. the kalman filter is incredible because it helps remove the noise and lag that you get with standard moving averages. most indicators repaint which means they change their past values to look better after the price has already moved. the way i have implemented this filter prevents that trap so the results you see in the backtest are actually tradable when we ran the acceleration bands across the hourly nvidia chart it returned over two hundred percent while the underlying asset was down forty percent. that is a massive alpha gap that most people will never see because they are stuck using standard rsi settings. i have found that adding a volatility breakout with atr to this setup helps catch the moves that the banks are trying to hide. the math behind the atr breakout is what kept me from getting chopped up in the sideway ranges you might be wondering why i am giving all this code away for free on github instead of keeping it in a vault. it is because i remember what it felt like to be on the other side of the trade losing money every single day. i want to build a community of quads that are all researching and backtesting together. the goal is to chase the legacy of jim simons who proved that math and code are the only things that matter in the long run the rbi system is the framework that i follow every single day without exception. it stands for research and backtest and implement. most traders skip the middle step because they are too impatient to see the results. they hear a rumor on twitter and they buy the top only to get liquidated when the whales decide to take profits. if you do not backtest your ideas then you are just gambling with your life savings i am spending around forty to one hundred dollars a day on claude opus tokens because it is a drop in the bucket compared to what a developer would charge. this ai does not need a lunch break and it does not get bored when i ask it to create sixty different variations of a strategy. we just created five different parabolic sar versions today and found that the long only setup was the only one worth keeping. it returned sixteen thousand percent on the soul data set because it stayed out of the short side traps shorting crypto is extremely dangerous and usually not worth the stress for most people. i have found that focusing on long only strategies with a tight trail stop is the most consistent way to grow an account. the sub agent architect allowed me to verify this across twenty five data sources in less than ten minutes. this speed of iteration is the only way to stay ahead of the curve in an industry that changes every few seconds the dca bot i mentioned earlier is still grinding away and buying the dips as we speak. i have built it to be a long term play where i am slowly accumulating a position in housecoin based on smas. if the price stays under the moving average the bot keeps buying and if it goes above then it sits on its hands. it is a simple logic but it removes the human desire to "buy the moon" when the price is already overextended i found that the camarilla pivot indicator was mostly trash today when we ran the numbers. even though it looks fancy on a chart the backtest showed negative expectancy across almost every asset we tried. this is why backtesting is so important because it kills the "indicator porn" that influencers use to sell you courses. i would much rather know that a strategy is a loser now than find out after i put real money on the line the true secret to using claude code is to treat it like a partner and not just a tool. i ask it to find anomalies and then i ask it to prove me wrong by testing it against the worst market conditions in history. if a strategy can survive the 2022 crypto crash and the 2020 stock market dip then i might consider it for a live run. we are stepping on the gas every single day because there are always new anomalies popping up if you are fast enough to find them i have uploaded over twenty five new backtests to the github today for everyone to use. code is the equalizer because it does not care about your background or how much money you started with. if you can write the logic and prove the edge then the market has to pay you. i am going to keep building in public and showing the wins and the losses because that is the only way to stay real in this space the final piece of the puzzle is the mindset of iteration over perfection. i would rather run a hundred messy backtests today than spend a month trying to write one perfect script. the ai allows me to fail fast so that i can find the winners that actually move the needle. my housecoin dca bot is a testament to that philosophy of just building and letting the systems do the heavy lifting for me if you are still trading by hand you are playing a game that is rigged against you by the biggest firms in the world. they have the best servers and the best data and the best phds but they do not have your specific creativity. when you combine your ideas with the power of claude code you are creating a custom weapon that they have never seen before. i will see you in the code and we will keep chasing the goat until we find that ultimate edge

Moon Dev

18,968 次观看 • 7 个月前

Just finished a huge UPGRADE to my Polymarket Arbitrage Trading Bot 📈 $41,514 +EV - [ Right Now] ✳️ 1,032% Spread - [ Right Now] #⃣ 3X More Arbitrage Opportunities I had to break a lot of rules to get this to work, If Polymarket finds out I might be in trouble... But it was worth it! I was able to bypass the restrictions that are holding back other Arbitrage Trading Bots Here’s how it all works... MARKET MATCHING The bot is looking for arbitrages across 5 different Prediction Markets To do that we are indexing millions of individual markets To try to find the few thousand functionally identical pairs between platforms That's billions of potential matches To find the few thousand market pairs that are functionally equivalent between platforms we are using a four part matching system: 1. Keyword extraction, ranking & matching 2. Trigram, Jaccard & Vector hybrid matching algorithm of the market titles, close conditions, alternative titles & outcomes 3. LLM Prompt matching checking for functional equivalence of market rules -> Incredibly inconsistent, hence the need for part 4, building a system like this at scale will open your eyes to the shortcomings of AI 4. Human verification + 99.8% Accuracy + 6,320 Markets Matched Once we have our markets, we move onto.. ARBITRAGE DETECTION - [ UPGRADED ] To detect if there is an arbitrage we need two things Market Odds & Orderbooks Market Odds: This will show us the ‘Spread’, if the sum of Market A YES & Market B NO is less than $1, or vice versa, we have a potential arbitrage Orderbooks: This will show us the "EV", much we can arbitrage profit we can extract, according to the available liquidity and slippage of both orderbooks This is where we had been severely limited in the past, due to inadequacies of the WebSocket feeds and API rate limits Polymarket: Orderbook initial dumps & entire orderbook price levels missing when connecting hundreds of markets to the WS feed, undisclosed multi-WS rate limits Opinion: API rate limits, WS delta updates missing, WS delta updates sent in wrong order, asks sent below best bid, airdrop farming bots posting and filling their own orders breaking WS feed. Kalshi: Rate limits and minor book inaccuracies at scale PredictFun & Probable: Surprisingly accurate as of current, monitoring how they handle increasing volumes To get past these limits and scale the Arbitrage Finder we built some advanced new systems 1. Multiple Instances Instead of scaling vertically we moved to scaling horizontally, a central controller handles the deployment & management of multiple proxied worker instances that each keep a local record of a subset of the market orderbooks and detect arbitrage opportunities as soon as dif updates are received These worker instances feed the orderbook data back to the main controller which aggregates all information in one place and formats along them with relevant metadata to be fetched by our trading interfaces and applications 2. Handling “Junk Data” One of the most challenging parts of scaling this application is dealing with the inaccuracies of the data provided by the APIs that we refer to as ‘Junk Data’ Some are easy to deal with: - Book updates returned in the wrong order required an additional ‘lastTimestamp’ value at each book level which was referenced before any future updates are applied, if diff update timestamp was prior to lastTimestamp the dif update is ignored. - Missing book dumps / levels reduced almost entirely by reducing the number of CLOB tokens per WS connection - Dif ask/bid flips appearing at impossible levels are not applied Some were a lot more challenging: - Missing book updates were only detectable with revalidation & comparison, we don’t know what we don't know until we know we don't know it. More complex revalidation triggers and short recycling periods minimize the issue With these updates we can scale the number of local orderbooks we are handling at one time: Before: ~4,000 orderbooks After: ~10,000 orderbooks This, along with the improvements in orderbook accuracy, has increased arb density by 3X Meaning we’re finding 3X the amount of opportunities as before 3. Rate limit bypass To bypass the API limits that limit the quantity of markets we can subscribe to at once we had to ██████ ███ █ ██████ █████ █████████ TRADING SYSTEMS - [ NEW ] The data is only as good as you can display it, ultimately the format in which the data is served will determine how efficiently it can be acted upon We’ve created a system of interconnected tools that enable us to trade these opportunities, each with a different specific use case 1. AlertPilot Trading Terminal A dashboard displaying all the hundreds of arbitrage opportunities the bot has found across 5 different prediction markets in real time + Arbitrage Calculator, showing you exactly how much to bid to take advantage of the arbitrage according to your bankroll, fees & slippage + Double Price Chart, which helps traders to estimate how long their arbitrage take to close + Strategy Guide, explaining how to execute arbitrage trades most effectively to maximize profits + Position Manager, connect your wallets to see your open arbitrage positions, EV, profits & exits 2. AlertPilot Telegram Bot A system for getting alerts on all new arbitrage opportunities immediately, EV, Spreads & market links + Custom Settings, only see the arbitrages you’d want to take with user specific settings + Position Sell Alerts, connected to the AlertPilot Position Manager, get alerts to your phone when its time to sell your arbitrage positions + All 5 Markets, alerts on all 5 supported prediction markets: Polymarket, Kalshi, Opinion, Probable & PredictFun 3. AlertPilot Discord Bot Private chat rooms and arbitrage alerts on the AlertPilot Discord Group + Custom Alerts, the best arbitrage alerts are sent to the discord channel + Support, traders answering your questions on Arbitrage Trading 4. Arbitrage Trading Terminal [ SOON ] A trading terminal built specifically for Arbitrage + Atomic Execution, enter positions on two platforms at the same time + Visualize the Arbitrage, trade with both charts in one place, see the gap close as you take your positions + Manage positions across multiple prediction markets in one place

SecureZero 

33,200 次观看 • 7 个月前

What a ride! Made by using GPT Image 2 + Seedance 2.0 on Fish Creative Prompt reference_handling: "Image generation strictly for driver facial and wardrobe styling reference only — calm, composed features silver wristwatch on left wrist Image strictly for sports car styling and cabin reference only — low, wide Italian wedge-shaped body + bright yellow paint + strongly geometric body lines + hexagonal front intake + Y-shaped LED headlights + gloss black multi-spoke wheels + black leather cabin with orange stitching + left-hand-drive cabin (driver seat on left) + across this sequence the driver-side window (left side of car) is rolled down only for the cockpit reveal shot, all other windows remain as-is throughout. Image strictly for spire architectural geometry, Dubai downtown skyline, and warm hazy midday atmosphere reference only — tapered glass-and-steel spire that widens progressively toward the base + dense glass high-rise skyline below + wide multi-lane boulevard. Do not reproduce any specific camera angle, composition, or caption elements from the reference images" style: "REAL AERIAL + AUTOMOTIVE CINEMATOGRAPHY PLATE — not CGI rendering, not game-engine rendering, not an animated/illustrated look." visual_feel: "Strong overhead midday light + warm hazy atmosphere softening the horizon. Color strictly natural and true-to-life — not oversaturated, not faded, not washed out. Continuous soft haze and atmospheric layering from spire tip down to street level. Every camera move, whether aerial or ground-tracking, strictly gimbal-level smooth — absolutely no handheld feel, no shake, no roll or tilt, even during the FPV-paced dive segment or the accelerating side-pass segments. 16:9 frame + no stylized film-grain treatment, aiming for genuine cinematography texture across every shot" duration: "30 seconds (8-shot sequence)" aspect_ratio: "16:9" character_modeling: driver_suited_woman: base: " appearance and wardrobe strictly per Image generation reference. Present in the car throughout the sequence, but the face is strictly clearly visible only during the 0:20–0:21 cockpit reveal shot — in every other shot the face is strictly not shown or not resolvable, whether by camera position, angle, or framing" wardrobe: silver wristwatch on left wrist . complete, with no wrinkling, misalignment, or missing pieces throughout" presence: "In shots where the driver is not the subject (0:01–0:19, 0:22–0:30), the driver strictly remains seated in the left-side driving position, present but strictly not resolved facially due to camera side, distance, or angle. During the 0:20–0:21 cockpit reveal shot only, the face and posture are strictly fully clear — visibility achieved via a right-side cockpit camera position looking across the cabin, with natural light and open sightline entering through the already-lowered driver-side window (left side of car) forming an angled depth-of-view channel — strictly NOT via looking directly through a window immediately adjacent to the camera" sports_car_yellow: identity: "Low, wide Italian wedge-shaped supercar + bright yellow paint + strongly geometric body + hexagonal front intake + Y-shaped LED headlights + gloss black multi-spoke wheels + black leather cabin with orange stitching + left-hand-drive cabin, driver seat on left — appearance strictly per image2 reference. Strictly only this one car appears across all 8 shots + doors strictly closed throughout + strictly only the driver-side window (left side of car) is rolled down, and only for the 0:20–0:21 cockpit shot + all other windows strictly remain closed/unchanged throughout + left-hand-drive position strictly remains on the left side of the car in every shot — not mirrored, not flipped, regardless of which side the camera is on" physics: "In every shot showing the car in motion, tires strictly show real, visible load deformation on turns + suspension strictly compresses and rebounds continuously with road surface undulation + body strictly shows slight roll and pitch matching cornering or acceleration — strictly not a rigid-glide, zero-deformation model feel. The car strictly stays lane-centered along the boulevard's true path — strictly no crossing lines, no drifting, no hugging the curb" environment_spire_and_skyline: setting_lock: "Tapered glass-and-steel spire structure — body progressively widens toward the base + spire tip is the sequence's starting point + below is Dubai downtown's dense glass high-rise skyline and wide multi-lane boulevard + warm hazy midday light, architectural geometry, skyline, and atmosphere strictly per image3 reference. Shot 1 (0:01–0:12) strictly covers the spire exterior and the high-altitude-to-street transition. Shots 2–8 (0:12–0:30) strictly take place entirely at street level, on or beside the boulevard, with the skyline visible as background context only" cinematic_storyboard: shot_1_spire_descent_0_01_0_12: camera: "Continuous aerial dive — the same FPV-paced descent arc as the master establishing move: 0:01–0:02 camera approaches and briefly hovers directly above the spire tip, gimbal strictly steady, no roll or tilt. 0:02–0:12 camera descends along one continuous curved arc, vertical speed component smoothly decaying while horizontal speed component smoothly increasing, no perceptible docking point or speed jump. As the arc resolves near street level, the camera settles into a position that spotlights the yellow car on the right side of frame — car held in the right third of the composition as the shot closes. Lens strictly 35–50mm cine prime throughout, no zoom, no digital zoom, straight architectural lines keep true perspective." action: "Spire tip and city grid fill the frame at the open, then dissolve into recognizable streets and blocks as the dive continues. The yellow car appears as a small point mid-descent and grows continuously larger, coming to rest spotlighted on the right side of frame by 0:12, driving forward along the boulevard, wheels rotating forward, no reverse." lighting: "Strong overhead midday light at the spire tip with long shadows; as the descent continues, glass-facade and ground reflections shift continuously and smoothly with the changing angle — no abrupt lens-flare flicker." vfx: "Ground detail and color progressively sharpen through the descent, no sudden clarity jump. Ground shadows strictly limited to the car's own cast shadow — no operator or camera-rig shadow anywhere in frame." sfx: "High-altitude wind roar at the open, fading continuously into rising engine sound and city ambience as the car comes into view. No music, no voiceover, no captions." shot_2_side_pass_0_12_0_15: camera: "Hard cut to a static lateral profile position — camera holds a fixed side-view framing of the car, 35–50mm cine prime, gimbal-locked, no handheld sway. As the car accelerates, the camera lets it pull ahead and overtake past the camera's position, exiting frame screen-right." action: "Car holds briefly in profile, then accelerates hard — visible squat of the rear suspension under acceleration, tires gripping without slip, body pitching slightly rearward under load — before overtaking and leaving frame past the camera." lighting: "Even natural daylight, sun still overhead-midday, car's yellow paint reading true and saturated against the boulevard背景, no flat frontal wash." vfx: "Real suspension compression and rebound as the car surges forward. No motion blur artifacts beyond natural shutter response; no CG float." sfx: "Engine note rises sharply with the acceleration, a clean Doppler pass as the car overtakes the camera position; no music." shot_3_center_mirror_0_15_0_17: camera: "Hard cut to an interior point-of-view through the car's center rear-view mirror — camera framed as if looking through the mirror glass from just behind/above the driver's eyeline, mirror surface visibly framing the receding view." action: "Through the mirror, the boulevard and the Dubai skyline recede behind the car as it continues forward at speed; slight natural mirror-glass vignette at the frame edge." lighting: "Cabin interior in soft ambient light, mirror glass reflecting the bright exterior daylight and skyline without glare washing out the reflected image." vfx: "Mirror reflection stays optically clean and stable — no double image, no warping; road and skyline motion in the reflection reads as physically continuous with forward travel." sfx: "Muffled cabin-interior tone to engine and wind noise (heard as if from inside the car); no music, no dialogue." shot_4_front_view_0_18_0_19: camera: "Hard cut to a nose-on front view of the car — camera positioned directly ahead on the boulevard, framing the grille, headlights, and hood centered in frame, lens 35–50mm cine prime, static or minimal push, gimbal-steady." action: "Car approaches head-on at a steady, controlled speed, Y-shaped LED headlights and hexagonal intake clearly readable, wheels visibly rotating forward." lighting: "Overhead midday sun catches the hood and windshield with clean natural highlights, no artificial front-fill look." vfx: "Subtle heat-haze shimmer off the hot asphalt ahead of the car for realism; no CG gloss on the paint." sfx: "Engine sound growing louder as the car closes distance toward camera; no music." shot_5_cockpit_reveal_0_20_0_21: camera: "Hard cut to a right-side cockpit angle — camera positioned to the right-front of the car, sightline crossing through the windshield and, aided by the already-lowered driver-side (left) window, resolving the driver clearly inside the cabin. This is the sequence's only driver-reveal shot." action: "Driver's face and posture are fully visible — one hand resting lightly on the wheel, eyes on the road ahead, expression calm and composed, natural unstiff posture. Car maintains the same forward direction and steady speed with no lens or vehicle behavior change during the shot." lighting: "Even natural daylight, light falling cleanly across the yellow paint, windshield, and driver's face; windshield reflection kept light enough not to obscure visibility." vfx: "Windshield glass stays transparent and reflection-light, no glare occlusion of the driver." sfx: "Steady engine hum plus faint city ambience; strictly no dramatic sound swell or music entering at the reveal moment." shot_6_straight_road_rear_3_4_0_22_0_24: camera: "Hard cut to a rear-bumper 3/4 angle — camera positioned low and behind, off to one side, framing the car driving away down a straight stretch of boulevard, lens 35–50mm cine prime, gimbal-smooth tracking that holds pace with the car." action: "Car drives straight down the boulevard at a steady cruising speed, lane-centered, taillights and rear three-quarter bodywork clearly visible, wheels rotating forward, no drift or lane departure." lighting: "Overhead midday sun, road surface and rear bodywork evenly lit, skyline visible in soft haze in the background." vfx: "Light heat-shimmer off the straight road surface; ground shadow strictly limited to the car's own cast shadow." sfx: "Steady, sustained engine tone at cruising speed plus ambient city sound; no music." shot_7_side_pass_0_25_0_28: camera: "Hard cut back to a static lateral profile position, mirroring shot 2's setup — camera holds the side view of the car, gimbal-locked, no handheld sway." action: "Car holds briefly in profile again, then accelerates a second time and overtakes past the camera, exiting frame — same physical behavior as the first side-pass (visible suspension squat, tire grip, body pitch)." lighting: "Consistent overhead midday daylight, same natural exposure as shot 2 for continuity." vfx: "Real suspension compression and rebound under acceleration; clean natural motion, no CG float." sfx: "Engine note rising sharply into the pass, clean Doppler effect as the car overtakes camera; no music." shot_8_static_3_4_close_0_29_0_30: camera: "Hard cut to a static, locked-off 3/4 angle — camera fixed in position, no movement, gimbal-perfect stillness, framing a 3/4 view of the boulevard as the car enters and exits frame to close the sequence." action: "Car drives through the static frame at a steady speed and exits, completing the sequence; wheels rotating forward, no reverse, no lingering hold after exit." lighting: "Same consistent overhead midday daylight and natural color grade as the rest of the sequence, no shift in exposure for the closing shot." vfx: "Ground shadow strictly limited to the car's own cast shadow; no operator or rig shadow in frame." sfx: "Engine sound passing through and fading as the car exits frame; no music, no voiceover, no captions at any point in the closing shot." production_notes: multi_shot_cut_lock: "This sequence is strictly 8 distinct shots joined by hard cuts at the following points: 0:12, 0:15, 0:18, 0:20, 0:22, 0:25, 0:29 — strictly no smooth transitions, no cross-dissolves, no whip-pans between shots, no morphing between camera setups. Each shot is a clean cut to a new fixed or moving camera setup as specified; only shot 1 (0:01–0:12) is itself one continuous unbroken aerial move. Across every cut, the following must remain continuous: the car's identity and paint color, the boulevard geography and skyline, the direction of travel, the lighting direction and quality, and the absence of music/dialogue/captions." gimbal_stabilization_lock: "Every shot, aerial or ground-based, strictly holds gimbal-level smoothness — absolutely no handheld shake, no roll, no tilt, no high-frequency jitter, in any of the 8 shots, including the accelerating side-pass shots." optics_lock: "Every ground/tracking shot strictly uses a 35–50mm cinema prime feel (Sony Cine prime lens character), unchanged within each shot — strictly no zoom in, no zoom out, no digital zoom of any kind in any shot. Straight lines of buildings and roads strictly retain true perspective — strictly no wide-angle distortion, no fisheye curvature." forward_motion_lock: "In every shot, the car strictly drives forward — nose pointed in the direction of travel except where explicitly framed nose-on toward camera (shot 4), and never reversing. Wheels strictly rotate continuously in the direction of travel, reverse rotation strictly forbidden in any shot." vehicle_structural_identity_lock: "Strictly only this one car appears across all 8 shots — a second car of the same or different model is strictly forbidden. Doors strictly remain closed in every shot. Left-hand-drive layout strictly remains unchanged across all 8 shots — driver's seat strictly on the left side of the car in every shot, never mirrored or flipped regardless of camera side. The driver-side (left) window is strictly rolled down only during shot 5 (cockpit reveal, 0:20–0:21); in every other shot all windows strictly remain closed/unchanged." driver_appearance_window_lock: "The driver's face is strictly clearly visible only during shot 5 (0:20–0:21) — in shots 1–4 and 6–8 the face is strictly not resolved, whether due to distance, angle, motion, or framing. During shot 5, visibility is strictly achieved via the right-side cockpit camera angle through the windshield, aided by the already-lowered driver-side window — strictly NOT via a window directly facing the camera." no_operator_shadow_lock: "In every shot, the ground strictly shows only the car's own cast shadow — strictly no human-shaped shadow, camera-operator silhouette, photographer's figure, or rig shadow cast anywhere in any of the 8 shots. The aerial shot (shot 1) is strictly pure drone photography with no physical rig or ground-crew trace; the ground shots (2–8) are strictly framed with no visible operator, crew, or equipment in frame." audio_lock: "Sound throughout the sequence is strictly authentic diegetic sound only — wind, engine, and city ambience, shifting naturally shot to shot — strictly no background music or score of any kind, including any hidden musical layer, in any of the 8 shots + strictly no voiceover or dialogue of any kind + strictly no captions or on-screen text of any kind at any point." critical_constraint: "8 hard-cut shots across 30 seconds, cut points strictly at 0:12, 0:15, 0:18, 0:20, 0:22, 0:25, 0:29 — strictly no dissolves or blended transitions. Shot 1 is one continuous uncut aerial descent from the spire tip to street level, ending with the car spotlighted screen-right. Shots 2 and 7 are matching static side-profile setups where the car accelerates and passes the camera. Shot 5 is the sequence's only driver-face reveal, via the right-side cockpit angle through the windshield with the driver-side window down — strictly not visible in any other shot. The car is strictly the same single yellow LHD supercar throughout, doors closed except for the driver-side window during shot 5, always driving forward, wheels never reversing. All camera work is strictly gimbal-smooth with a 35–50mm cine-prime feel, no zoom, no distortion, no handheld shake in any shot. Ground shadows throughout strictly show only the car's own shadow, no operator or rig trace. Audio is strictly diegetic only — no music, no voiceover, no captions, throughout the entire 30 seconds." avoid: "dissolves, cross-fades, morphs, or whip-pans between shots — cuts must be hard cuts only, any shot other than shot 5 showing the driver's face clearly, the driver-side window rolled down in any shot other than shot 5, the passenger-side or any other window rolled down at any point, a second car appearing in any shot, car doors opening in any shot, the car reversing or wheels rotating backward in any shot, mirrored or flipped left-hand-drive layout, driver's seat appearing on the right side of the car, human-shaped shadow, camera-operator silhouette, or rig shadow in any of the 8 shots, camera shake, handheld feel, roll, tilt, or high-frequency jitter in any shot, zoom in, zoom out, digital zoom, wide-angle distortion, fisheye distortion, or curved building/road lines in any shot, smooth continuous single-take treatment of the whole 30 seconds (the sequence is strictly multi-shot with hard cuts, not one unbroken take beyond shot 1), background music, score, melody, hidden musical layer, voiceover, dialogue, narration, captions, or on-screen text at any point, CGI-rendered look, game-engine feel, plasticky car-paint gloss, static hovering with no sense of gravity" animation_style: "Shot 1 plays out as one continuous real-time aerial move; shots 2–8 are each a distinct, clean hard-cut setup, every shot internally in real time with no speed ramping — the sense of pace across the sequence comes from the editing rhythm of the cuts, not from slow motion or time manipulation within any single shot"

Aaliya

11,907 次观看 • 1 个月前

$AMD is easily a $1,200 stock IMO| CPUs TAM 🧵 Not Financial Advice! DYOR! In this thread, I want to discuss the actual TAM for CPUs data center for just 2026, where many are giving different ranges, where I don't agree with. I will explain in detail why I disagree with these research firms and financial analysts using Math. And this thread should not be treated as Financial Advice. I'm just explaining my research and thought process so we can have a discussion. In 2024/2025, I gave out $620 PT for FY2026 was too conservative for AMD potential. At the time, It was early and many were just laughing, that PT was unrealistic and the AI world is run on GPUs only. Today, most of these folks are laughing with me. That is ok, I dont offer financial advice, and I do not need everyone to agree with me. I respect other opinions. If you enjoy this kind of thread, slap the like/repost/bookmark. If you want to support my work further and gain more in-depth analysis, consider subscribe! In early 2026, hyperscalers, enterprises, and OEMs are scrambling as Intel and AMD server CPUs are largely sold out for the year, with prices jumping 10–20% and lead times stretching from weeks to months (or longer for certain SKUs). What was once a GPU dominated story has flipped: the shift to explosive Agentic AI with its multi-step reasoning loops, tool calling, multi-agent orchestration, real-time data movement, and reinforcement learning, is dramatically tightening CPU:GPU ratios from the old training-era 1:4–8 all the way to 1:1 to 5:1 or even CPU-heavy configurations. CEOs across NVIDIA, AMD, Intel, Google, Meta, Microsoft, and public companies have been sounding the alarm on CNBC, Bloomberg, and earnings calls. CPUs are “cool again,” and in many agentic deployments they are becoming the new bottleneck alongside (or even ahead of) GPUs and custom ASICs. In 2025, roughly 12-15m AI GPUs + AI ASICs GPUs shipped, and is expect to be 15-20m units by 2026, where it suggesting Training demand is not going away. The actual TAM is structural, multiplicative demand that has already forced AMD to double its long-term server CPU TAM forecast to >$120 billion by 2030 (>35% CAGR), with Dr. Lisa Su noting Q2 2026 server CPU sales expected to surge 70%+ year-over-year and demand “far exceeding expectations.” At the same time, AMD’s secured 30–40% share of TSMC’s initial 2nm capacity (behind only Apple’s >50%) positions it to ramp Zen 6-based EPYC Venice exactly when this agentic wave hits hardest but even that aggressive five-fab 2nm expansion (with plans scaling toward 11 total advanced facilities) cannot instantly close the gap in the near-term. Supply constraints on wafers, advanced packaging, and power are compounding the squeeze, just as hyperscalers forward-buy and lock in long-term deals. 1. The actual potential TAM Various sources and institutions are giving $50-$160-$200B CPUs TAM toward 2030, and i disagree, where supply is severely behind vs Demand by at least 2-3 years or even longer by some estimates. The actual TAM will probably be 15-20m for FY2026. The typical average selling price from low to high end is $5,000 to $15,000, but due to rising memory, and different inflationary pressures on Semi, it would be more logical to think between $7,000-17,000. A. CPU:GPU Ratio at 1:1 A basic calucation at mid range =12,000 x 15-20m CPUs= $180-$240B TAM B. CPU:GPU Ratio at 5:1 = $12,000 x 75m-100m CPUs= $900B-$1.2T TAM Of course TSMC cannot even supply 20% of this massive inflection TAM in 2026. But do we think of Demand for TAM or Supply for TAM? Hence we are seeing massive 2nm Ramp from TSMC for $AMD. IMO, conservatively, I would take down 15-20% on 1:1 or $135-$192B TAM for just 2026. Im not even talking about 2030. We are just months into this, it is impossible to estimate Cagr atm, but this is 1-5 agents running tasks, I wrote a thread on 24/7 autonomous agents thread, where companies could use 50-250 agents to run tasks for them 24/7. It would require a different structural CPU:GPU to bring down the cost of token as well as handling the Orchestration bottleneck. GPUs would be useless and sit idle waiting for CPU due to highly CPU-intensive nature. The cost per Million tokens must come down more rapidly for this 50-250 autonomous agents to work, otherwise the token cost would be too enormous. Helios Rack is estimated to bring inference cost down to $0.0003-$0.0005/M tokens with 18 EPYC Venices along with 72 MI455x and other chips+ Components. A heavier or CPUs dense rack would bring down inference cost further. EPYC Verano(2027 gen 7 AI-optimized) is expected to drive inference costs meaningfully lower than the Venice baseline likely to the $0.00002–$0.00025 per million tokens range (or even sub-$0.00015 in highly optimized agentic/batch workloads). Verano have higher core counts than Venice, LPDDR5X SOCAMM2 memory support, more AI optimized and Next-Gen rack density & efficiency. 2. $AMD secured at least 30-40% of TSMC 2nm capacity and Memory from Samsung through 2028-2030. 2 2nm fabs are entering ramping phase toward 60-65k wafers per months and 5 dedicated 2nm fabs entering mass production/ramp in 2026. Will link sub threads below if you are interest for full detail. Apple is reported to secure 50%+ 2nm capacity for Iphone 18 and Mac chips and AMD secured at least 30-40% capacity while $NVDA $AVGO $ARM $AMZN $GOOGL and others are on 3nm. This broader aggressive ramp from TSMC to target up to 11 fabs is to address $AMD massive growth ahead. Where $ARM is facing massive CPUs supply constraints as they have to compete with other Mega Cap players on 3nm allocation. And $INTC is also facing supply constraints for data center CPUs and PC per management with lead times extrended to longer than 12 weeks. Dr. Su is aiming for higher than 50%+ Market share, and I believe it is achievable in 2026 or 2027 as AMD has the strongest CPUs offerings. Dr. Su did not want to take advantage of the shortage and she said during the Q1 earning call, AMD is prioritizing Units shipped while guiding margin to be inching 60%. If Jensen were in charge, I'm sure margin would be 70-75% in this kind of severe CPUs shortage condition. But that is not how Dr. Su operates for more than a decade. She wants most market share. So we will see it in revenue growth, but as TSMC ramps faster and faster, AMD Operating and FCF margin will massively improve vs prior decade. A significantly higher margin profile than before. 3. How I came up with $1,200 withint 12-18 months? At $1,200/ share, that would be around $2 Trillion MC. I expect FY2027 revenue to be $124-$144B where data center revenue dominates overall revenue. AI GPUs: I will stick to the lowest end so show u that I'm conservative at $18B for each GW vs $NVDA Rubin is $30B+ (most likely Helios Rack in the $20B+ due to memory price rising). We know deals with OpenAI and Meta are around 12GW and additional multi-customers at multi-GW scale were hinted and will be revealed as we get to July 22-23 2026 Advancing AI event. For now I will conservatively add a bit more to this model. (3-6GW Helios Rack Range) EPYC Venice is reported to be in $15,000-$20,000. However large customers will likely to enjoy $10-$12k discount. I expect AMD to be able to ramp 7m EPYC Venice for entire 2026 and 3-4m of EPYC Verano(higher price than Venice). If we take an average selling price of $10,000 to be on the conservative side. Take down another 30% to be even more conservative on projection. I like to be conservative. That would be ~ 7m EPYC CPUs(Venice + Verano) for FY2027 or 583,000 units per month or 15,000 additional 2nm wafers per month which is completely reasonable for current TSMC Ramp, and I may be too conservative here. EPYC Verano and MI500 series will also be on 2nm. AI GPUs: 3GW x $18B= $54B EPYC CPUs: $10k x 7m CPUs= $70B = Data center revenue alone is $124B Other segments= probably in the $20-$25B FY 2027. FY2027 revenue = $124-$149B At 7m EPYC CPUs for entire 2027, that would be more than 50% market share when we comp it to availability from supply side, not from total Demand. It is possible that TSMC could significantly ramp even more capacity in 2027, so we will see. Metric Q1 2026 FY2027 Gross Margin 55-56% 60-62% Operating Margin 25-26% 32-35% Net Income Margin ~22% 26-30% FCF Margin 25% 28-30% At $124-$149B Revenue FY 2027 Net Income would be $32-$44B EPS would be $20-$27 (GAAP) Non-GAAP would be $25-$31 At $1,200 a share or $2T valuation that would be: 13.4-16x Price to Sales (P/S) 38-48 P/E At this kind of growth of AI SuperCycle, I think it is very reasonable valuation. If we use today at $406/share or $661B MC: 2027 P/S = 4.4x-5.3x 2027 P/E = 13x-16x Is AMD today expensive or cheap to you? Above is already a very conservative where I trimmed 20-30% of doable units. Meaning, there could be upside if TSMC is able to ramp meaningfully like they are planning. Conclusion: A $1,200 per share valuation IMO for AMD in FY2027 is not expensive at all; it is, in fact, conservative when viewed against the structural explosion in agentic AI demand we have mapped out. With server CPU TAM potentially scaling into the $100–$200B+ range in just CPU:GPU 1:1 Ratio for just 2026. AMD positioned to capture 50%+ share thanks to its 2nm TSMC allocation advantage and full-stack leadership, the company could realistically deliver $124–149B in total revenue and $25–$31+ non-GAAP EPS. At those levels, $1,200 implies a 2027 P/E = 13x-16x. Entirely reasonable for a company that will have become the clear Inference Queen (and in many workloads the preferred) AI infrastructure provider, with operating margins expanding above 30% and tens of billions in high-margin rack-scale AI revenue. Dr. Lisa Su was right presciently so about the Agentic AI inflection all the way back to her early 2022–2023 commentary on the coming shift from pure training to inference and orchestration-heavy workloads. While the broader market only fully woke up to this in 2026 when she doubled AMD’s long-term server CPU TAM forecast to >$120B by 2030 (with >35% CAGR), Dr. Su and her team have consistently positioned the company at the center of the CPU renaissance. The explosive demand we are seeing today, sold-out lines, rising ASPs, and hyperscalers forward-buying entire gigawatts of Helios-class systems is exactly the outcome she forecasted years ago. Not Financial Advice! DYOR!

Mike

417,587 次观看 • 5 个月前

Made $530,000 with Ai Bot that started with $313. Didn't know how to code. Now this bots run 24/7 printing money while sleeping. I've made the exact step-by-step guide to build this Claude Code Polymarket trading bot. Prompts. Code. Risk settings. Paper trading checklist. Everything from zero to running bot. It's free. For 24 hours. After that I'm charging $499 for it. To grab it right now: 1. Comment "Claude Bot" 2. Like and Retweet this post 3. Follow me Himanshu Kumar ( I can't send DMs to non-followers ) I'm DMing everyone who Complete the 3 steps. I spent hundreds of thousands hiring developers because he was too scared to learn. Then learned Claude Code. Built algorithmic trading systems. $313 → $530,000. You have the same tools available right now. And you're using them to ask ChatGPT for Instagram captions. This attached video is a goldmine. Full live walkthrough. Claude Code building actual Polymarket trading bots. From zero. Every line of code. Every decision explained. Now let me break down why everything you're doing in trading is wrong and exactly how to fix it. Save this post. You'll hate yourself if you lose it. ↓ Let's start with why you keep losing money. You already know the answer. You just won't admit it. You overtrade. Every. Single. Day. You see a candle move. You feel something. You enter. No plan. No edge. No reason. Just feelings. Then it goes against you. You feel something else. Panic. Anger. Denial. You move your stop loss. Or you didn't set one at all. "It'll come back." It doesn't come back. So you take another trade. A revenge trade. Bigger size this time. Because you need to "make it back." That one fails too. Now you're emotional. Now you're tilted. Now you're using leverage you have no business touching. 40x. 50x. 100x. On a trade you entered because a candle looked "bullish" and some guy on Twitter said "send it." You get liquidated. Close the laptop. Punch something. Tell yourself you'll be "more disciplined" tomorrow. Tomorrow comes. Same cycle. Same result. Same liquidation. You've been doing this for months. Maybe years. And you still think the problem is your strategy. The problem isn't your strategy. The problem is you. Save this post right now. What I'm about to show you is the only way to remove yourself from the equation. Follow Himanshu Kumar so you don't miss any of this. ↓ Here's what's actually killing your account. It's not the market. The market doesn't care about you. It's not your indicators. RSI works fine. MACD works fine. They all "work." It's not your timeframe. It's not your broker. It's not the "manipulation." It's four things: 1. Emotions. You hold losers because hope feels better than loss. You cut winners because fear feels stronger than greed. You size up when angry. You skip trades when scared. Your emotional state determines your position size. That's insane. And you know it's insane. But you keep doing it. 2. Overtrading. You take 15 trades a day. Maybe 5 of them had actual setups. The other 10 were boredom. Boredom trades are the most expensive hobby in human history. 3. Leverage. You use 20x-50x on trades where you're not even sure about the direction. That's not trading. That's a casino with a nicer interface. 4. Fees. You're smashing market orders. Paying spread. Paying commission. On 15 trades a day. Your broker makes more money from your account than you do. Think about that. Your broker is profitable on your account. You're not. You're the product. Not the trader. These four things are why 90% of traders lose. Not bad luck. Not the market. You. Save this post and follow Himanshu Kumar because the solution is coming next. ↓ The solution is painfully obvious. Remove yourself from the equation. Not partially. Not "I'll be more disciplined." Not "I'll journal my trades." Not "I'll meditate before trading." Completely remove yourself. Build a bot. Let the bot trade. You go live your life. The bot doesn't feel emotions. The bot doesn't overtrade. The bot doesn't use reckless leverage. The bot doesn't smash market orders and bleed fees. The bot follows the rules. Every single time. Without exception. Without "just this once." Without "I have a feeling about this one." Rules in. Execution out. No human in the middle to mess everything up. That's algorithmic trading. And before your ego jumps in with "but I'm different, I have discipline" — No you don't. Your account balance proves you don't. If you had discipline, your account would be green. It's not. So you don't. Accept it. Automate it. Move on. This is the hardest truth in trading. Your discipline will always fail. A bot's won't. Save this post. Follow Himanshu Kumar for the exact bot setup that removes your emotions permanently. ↓ "But I don't know how to code." Neither did he. The guy in this video didn't know how to code for most of his life. Got held back in 7th grade. People counted him out early. Spent years building apps and SaaS businesses without writing a single line of code. Hired developers on Upwork instead. Spent hundreds of thousands of dollars paying other people to build what he could have built himself. Because he was scared to learn. That fear cost him years. And hundreds of thousands of dollars. Sound familiar? You're doing the same thing right now. Not with developers. But with your time. You're spending thousands of hours trading manually because you're scared to learn the thing that would make trading automatic. The fear of learning to code is costing you more than any bad trade ever did. Because every month you trade manually is a month of emotional decisions, overleveraged entries, and unnecessary losses that a bot would never make. And here's the thing that should really frustrate you: AI does the hard parts now. You don't need a computer science degree. You don't need to work at a hedge fund. You don't need to be "good at math." Claude Code writes the code for you. You just need to think clearly about trading ideas. That's it. If you can describe a strategy in English, Claude can build it in Python. "I don't know how to code" stopped being a valid excuse in 2024. It's 2026. You're 2 years late on that excuse. Find a new one. Or stop making excuses entirely. Save this post. Follow Himanshu Kumar because I'm showing you how people with zero coding experience are building profitable bots. ↓ The process that actually makes money. Three letters. R. B. I. Research. Backtest. Implement. That's it. That's the entire process. Every single day. Research: Find an idea. A pattern. A market inefficiency. Don't trade it yet. Don't even think about trading it yet. Just research it. Backtest: Test the idea against historical data. Does it work? Not "does it look good on one chart." Does it work across thousands of trades? Across different market conditions? Across in-sample AND out-of-sample data? If no, kill it. Find another idea. If yes, move to step 3. Implement: Build the bot. Deploy it. Paper trade first. Then live with small size. Scale only on evidence. Research. Backtest. Implement. Every day. No exceptions. You know what your current process is? Feel. Enter. Pray. F. E. P. Feel bullish. Enter a trade. Pray it works. That's not a process. That's gambling with a TradingView subscription. RBI is the only process that works. Save this post. Tattoo it on your forearm. Follow Himanshu Kumar for daily RBI breakdowns. ↓ What Claude Code actually does that your manual process can't. You can maybe test 3-5 strategy ideas per week. Manually adjusting parameters. Manually checking results. Manually writing code (badly). Claude Code tests 50-100 ideas per week. With parallel agents running simultaneously. Multiple strategies being built, tested, and validated at the same time. While you sleep. The guy in this video spends 4-8 hours a day building systems with Claude Code. Not trading. Building. Research. Backtest. Implement. Then iterate. Improve. Optimize. Every day the systems get better. Every day the edge compounds. Every day the bots get smarter. While you? You spend 4-8 hours a day staring at charts making the same mistakes you made last month. Same indicators. Same patterns. Same entries. Same losses. He's iterating forward. You're running in circles. Same 8 hours per day. Completely different outcomes. Because he's building systems. And you're feeding a casino. Stop feeding the casino. Start building the machine. Save this post and follow Himanshu Kumar for the Claude Code workflow that iterates strategies while you sleep. ↓ Jim Simons. That's the benchmark. You probably don't know who Jim Simons is. And that tells me everything about how seriously you take trading. Jim Simons. Mathematician. Founded Renaissance Technologies. Built a net worth of $31 billion. 100% from algorithmic trading. Not one single manual trade. Not one "gut feeling" entry. Not one RSI divergence. Not one "smart money concept." Algorithms. Bots. Systems. Data. $31 billion. His fund averaged 66% annual returns for over 30 years. While you're excited about making $200 on a trade that you'll give back tomorrow. The best trader in human history never placed a manual trade in his life. And you think your edge is staring at a 5-minute chart with bloodshot eyes at 2 AM? Your edge is building the system. Not being inside it. Jim Simons is the benchmark. Everything else is noise. Save this post. Follow Himanshu Kumar because I'm building toward the same goal and showing every step publicly. ↓ What you need to understand about patience. This is not get-rich-overnight. The guy in this video says it directly: "This channel is not for people looking to get rich overnight. It's not plug and play. There are no shortcuts. If you're impatient, this probably isn't for you." And that's exactly why most people will fail at this. Because you want results now. Today. This trade. You don't want to spend a week building a bot. You don't want to paper trade for 2 weeks. You don't want to test 50 ideas to find 1 that works. You want to copy someone's bot, run it live with your rent money, and be rich by Friday. That's why you'll be broke by Friday. The guy making $2.3M spent months iterating. Testing. Failing. Rebuilding. Testing again. He was patient when you would have quit. He was calm when you would have panicked. He was consistent when you would have given up. Patience isn't just a virtue in trading. It's the only virtue. Without it, everything else fails. Impatience is the most expensive personality trait in trading. Save this post. Follow Himanshu Kumar and learn to build systems with the patience that actually pays. ↓ The live streams where the real learning happens. The YouTube video is the trailer. The live streams are the movie. Real-time bot building. Real-time questions answered. Real code shown. Real mistakes made and fixed. Not polished highlight reels where everything works perfectly. Actual development. Where things break. Where strategies fail. Where code doesn't compile. Where the fix takes 2 hours. Because that's what real development looks like. And seeing the messy parts is more valuable than any polished tutorial. Because when your bot breaks at 3 AM, you need to know how to fix it. Not just how to celebrate when it works. The streams mix beginner and advanced. Start with how to automate trading. How to use AI for code generation. Then dive into the daily work. Claude Code. Parallel agents. Constant iteration. Live debugging. 4-8 hours of real algorithmic trading development. Live. Uncut. No filter. Most "trading education" shows you the wins. This shows you the work. Save this post. Follow Himanshu Kumar for the stream schedules and breakdowns. ↓ The belief that changes everything. Code is the greatest equalizer. Not money. Not connections. Not a degree. Not where you grew up. Not what school you went to. Code. Once you can build systems, you can build anything. For the rest of your life. A trading bot today. A SaaS product tomorrow. An automation business next month. A completely different life next year. The skill isn't "algorithmic trading." The skill is building systems. And that skill transfers to everything. The guy who can build a trading bot can also build a lead gen tool. Can also build a content pipeline. Can also build a SaaS product. Can also build literally anything that runs on logic and code. One skill. Infinite applications. And AI makes learning it 100x easier than it was 5 years ago. You don't need to be smart. You don't need talent. You need Claude Code and the willingness to sit down and build something instead of consuming content about building something. Building is the skill. Everything else is entertainment disguised as education. Save this post. Follow Himanshu Kumar because I'm showing you how to build, not just how to watch. ↓ If any of this applies to you, pay attention. If you've lost money from overtrading. If you've been liquidated. If you know trading is the vehicle but manual execution keeps crashing you. If you've tried "being more disciplined" and it never lasted more than a week. If you keep saying "next month I'll start automating." If you've spent more money on courses than you've made from trading. There is a better way. It's not a magic indicator. It's not a signal group. It's not a $997 mentorship from a guy who makes money teaching, not trading. It's building your own system. A system that trades without emotion. A system that follows rules without exception. A system that runs while you sleep. A system that compounds while you live your life. That's the answer. It's always been the answer. You've just been too scared to accept that the solution requires building something instead of buying something. ↓ What the next 30 days look like if you actually commit. Week 1: Watch the video. Learn Claude Code basics. Build your first simple strategy. Run your first backtest. Week 2: Iterate. Let Claude improve the strategy. Run Monte Carlo validation. Paper trade. Week 3: Go live with $50-100. Tiny positions. Watch every trade. Compare to paper results. Week 4: Scale based on evidence. Not based on excitement. Not based on one good day. Based on data. 30 days from now you either have a running bot that trades without your emotions destroying every position. Or you're exactly where you are right now. Reading another post. Making another promise. Breaking it by Tuesday. Same 30 days either way. Different actions. Different results. Different life. ↓ Full video tutorial attached. Live bot building with Claude Code. From zero to running Polymarket trading bot. Every line of code. Every decision explained. The video is free. Claude Code is available now. The market is open 24/7. The only thing standing between you and a profitable trading bot is the same thing that's been standing there for months. You. Get out of your own way. Follow Himanshu Kumar for daily AI trading bot breakdowns, live build sessions, and the full RBI process. Save this post. Watch the video. Build the bot. Or keep trading manually and keep losing. The choice has never been easier. And you've never been more stubborn about making the wrong one.

Himanshu Kumar

38,373 次观看 • 6 个月前

$AMD| The FOMO to buy AMD Chips is NOW 🧵 Not Financial Advice! DYOR! Research Purpose Only! The Inference Queen is the biggest winner in Agentic AI where all other CPUs are struggling to compete with a 2yr old EPYC Turin and EPYC Venice is in mass production phase. AMD stresses deployability today on standard x86 platforms (no proprietary architectures required), full software compatibility, and open standards. This positions Venice + Helios as a practical, high-density alternative to competing solutions while underscoring that agentic AI shifts the balance toward CPU-rich racks alongside GPUs, and most importantly, lowering the cost of token to accelerate adoption and innovation. Context: The Wall Street Journal yesterday came out with an article that OpenAI is condiering drasstically lowering the token prices to win more customers from Anthropic. The narrative "they" are trying to exacerbate the current AI selloff won't last long. This is a fundamental misunderstanding of what is going on, or what I already discussed for months and years. Followers and Subscribers already knew this for years, that this day would come, where token cost will bcome the central discussion among enterprises as there is no such thing as unlimited budget or Tokenmaxxing when they use $NVDA chips or In-house Hyperscalers chips. I will link various threads if you are interested in understanding the full picture from supply chain to recent TSMC Rapid 2nm expansion up to 12 Fabs total by 2027/2028. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. The OpenAI-AMD 1GW Helios deployment (starting H2 2026) represents a pivotal vertical integration move that directly supercharges the inference economics. This isn't incremental; it's a structural shift toward ownership of massive, optimized rack-scale capacity, enabling the lowest token costs and triggering the enterprise adoption flywheel. We need to be honest, $AMD is the only company that made a big bet on Inference since the day Chatgpt became sensational where $NVDA and others were betting big on Training. At the end of the day, Token bill from Anthropic has to obey economics. Meaning the bills rise, companies have to get more out of it to justify the cost. It cannot be an unlimited inference budget, and it has to show up on efficiency, profitability and operating leverage. 1. Tokenomics After you understand this, you will understand why Citi cited Anthropic is likely to sign a deal with $AMD along with Hyperscalers, AI Labs, Sovereign AI like Softbank 5GW in France and many other countries. However, OpenAI and $META are now wanting faster deployment, and they are AMD shareholders now, they have prioritized allocation. Anthropic and Hyperscalers just cannot compete when Helios Rack lower token cost to$0.0003–$0.0005 per million tokens at GW scale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens Now, OpenAI, META and Hyperscalers can lower Inference cost even further with $AMD EPYC Venice "dense rack" or Agentic AI Rack. AMD published a detailed technical blog emphasizing that the future of agentic AI autonomous, multi-step AI systems requiring heavy orchestration, databases, caching, APIs, and control planes demands massive CPU-dense rack-scale infrastructure, not just GPUs. The catalyst prominently positions their upcoming 6th Gen EPYC "Venice" processors as the key enabler for next-generation dense racks, delivering leadership throughput under real-world power, cooling, and density constraints. ~EPYC Venice (Zen 6 architecture, up to 256 cores / 512 threads per socket) is projected to deliver exceptional rack-level performance. In AMD’s modeled 100 kW rack comparisons, Venice-powered systems are expected to achieve ~3.30x the throughput of NVIDIA’s Vera (88-core Olympus) baseline across a broad mix of agentic-supporting workloads. ~This builds on current-generation 5th Gen EPYC "Turin" (up to 192 cores), which already delivers ~2.37x rack throughput vs. Vera and ~1.6x vs. Intel’s Xeon 6980P (128 cores). ~ Liquid-cooled Turin deployments already support >27,000 CPU cores per rack today. Venice is architected to push this beyond 36,000 cores in the same rack class, dramatically increasing concurrent agent capacity and overall infrastructure efficiency. 2. Ownership vs renting compute from Hyperscalers matter to OpenAI and only owning $AMD chips can meaningfully lower token cost for enterprises. ~Eliminates cloud overhead: No provider margins, utilization buffers, or egress fees. Direct control over power contracts, cooling, scheduling, and orchestration at dedicated facilities. ~Helios optimizations at GW scale: Rack-level density (1.4+ exaFLOPS FP8 per rack), high HBM4 bandwidth, EPYC orchestration for agentic workloads, and superior TCO/TDP. AMD's long-standing focus on tokens per dollar/watt shines here 20-40%+ efficiency edges in inference-heavy scenarios. ~At 1GW+ optimized deployment, inference hits $0.0003–$0.0005 per million tokens (community/analyst models tied to Helios metrics). This is dramatically lower than typical rented/cloud equivalents, especially for high-volume output tokens in agentic flows. High token bills today, enterprises running heavy agentic/coding/analysis workloads can face $50-100M+/month at current API rates (flagship models $5-30+/M output, scaled to massive volumes). Post-Helios compression, same volume will drop to $10-15M/month (or better) via lower underlying costs passed through as pricing flexibility, volume tiers, caching, or batch discounts. ROI thresholds collapse. More companies greenlight pilots → production → massive scaling. Agentic AI (autonomous workflows) multiplies token demand exponentially, but affordability removes the friction. OpenAI gains flexibility, Unlike more cloud-dependent rivals (Anthropic), they can lower effective pricing, offer aggressive enterprise bundles, or absorb volume without margin destruction directly tackling "high token bill" complaints while maintaining profitability as usage explodes. 3. Agentic AI Models shifted CPU:GPU Ratio to 1:1 toward 3-5:1 with Explosively Token-Hungry Workloads Agentic AI (autonomous, multi-step agents with planning, tool use, iteration, and self-correction) is fundamentally more compute and token intensive than conversational or single-turn generative AI. Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. ~Agents often generate 10–100x+ more tokens per task due to iterative reasoning chains, multiple tool calls, verification loops, and long-context orchestration. ~Goldman Sachs forecasts token consumption multiplying 24x by 2030 (to 120 quadrillion tokens/month) largely driven by agentic adoption in consumer and enterprise. ~Enterprise data shows agent-pattern workloads growing at 680% annualized rates, projected to surpass conversational AI in token volume by Q3 2026. ~Daily enterprise agent token consumption is already in the billions, with complex workflows (coding, workflows, analysis) amplifying this dramatically. 4. Competitive Edge: Winning Customers from Anthropic Anthropic’s Claude models (especially Opus/Sonnet) excel in complex reasoning and agentic coding, commanding premium positioning. However, their higher underlying costs (heavier reliance on third-party cloud with margins) limit pricing flexibility compared to OpenAI’s owned Helios capacity. Anthropic is on track to generate $10.9 billion in Q2 revenue. The company expects to achieve its first-ever quarterly adjusted operating profit of $559 million. However, sustaining full-year profitability remains challenging due to immense computing and model training costs The truth is, Anthropic has no choice but to buy as much $AMD chips as possible if they want to compete with OpenAI or get investors attention. This 5% adjusted operating profit to revenue ratio is just pathetic. Current pricing dynamics (2026): OpenAI already undercuts on many tiers ( flagship output tokens significantly cheaper than equivalent Claude Opus). Nano/mini models offer 5–10x advantages for volume work. Anthropic holds edges in long-context flat pricing and certain reasoning quality. OpenAI after Helios Rack Ownership, At $0.0003–$0.0005/M effective costs, OpenAI gains massive headroom to: ~Aggressively discount high-volume agentic tiers or bundles. ~Offer “unlimited” enterprise plans or usage-based models that Anthropic struggles to match without margin erosion. ~Target cost-sensitive, high-throughput agent deployments (dev tools, automation platforms) where token bills explode. Enterprises facing $ millions in monthly agentic bills will migrate to the provider delivering better economics at scale. OpenAI’s combination of strong models (o-series reasoning) + lowest TCO positions it to erode Anthropic’s enterprise share, especially as agentic becomes the dominant token consumer. Cheaper tokens expand the total addressable market dramatically. This feeds the data/model improvement loop, justifying further capex. AMD benefits from proven scale pulling in more customers (Meta, Oracle, Microsfot, Amazon, Softbank, TensorWave, LumaAI ... already aligned on Helios). Conclusion: Dr. Lisa Su has been laser focused on inference economics since at least 2022–2023, repeatedly emphasizing that the real battleground for AI scalability would be TCO, power efficiency (TDP), and ultimately tokens per dollar and per watt not just raw training FLOPS. While many viewed inference as a secondary, commoditized workload, Dr. Su architected AMD’s roadmap around rack-scale systems optimized for high-volume, sustained inference that would dominate as models matured and usage exploded. Helios represents the culmination of that multi-year bet: a fully integrated, open platform designed precisely for the economics of massive token throughput. This deep, strategic partnership with OpenAI starting with the 1GW Helios deployment in H2 2026 and scaling to 6GW, is the embodiment of that shared vision. Both companies foresaw a future where agentic AI models evolve to become extraordinarily token-hungry: autonomous agents executing complex, iterative workflows with planning, tool use, verification loops, and long-context reasoning. These workloads can consume 100x+ more tokens per task than traditional chat or single-turn generation, driving exponential demand as capabilities improve and enterprises deploy them at scale. By owning and optimizing this massive Helios capacity at GW scale, OpenAI achieves inference costs as low as $0.0003–$0.0005 per million tokens. This structural cost advantage allows OpenAI to absorb the coming token explosion profitably, dramatically lower effective pricing for enterprises, and win high-volume agentic workloads from higher-cost competitors like Anthropic. What was once a prohibitive monthly token bill becomes an affordable accelerator for productivity and innovation. The OpenAI-AMD alliance validates Dr. Su’s prescient strategy and turns the Agentic flywheel into reality: Collapsing inference costs → explosive token consumption → richer data and better models → accelerate greater demand. This partnership doesn’t just address today’s economics, it positions both leaders at the center of the infrastructure buildout that will power AI’s next decade. By delivering the lowest inference economics at scale, OpenAI not only solves enterprise bill pain but gains a decisive weapon to win share from higher-cost rivals like Anthropic. And that is why OpenAI and $META will deploy EPYC Dense Rack Not Financial Advice! DYOR! Research Purpose Only!

Mike

84,951 次观看 • 3 个月前

In 1998, Warren Buffett and Charlie Munger spent 4 hours explaining why the smartest people in finance keep going broke. It might be the most valuable finance lecture ever recorded: 1. The smartest people in finance went completely broke. Long-term Capital Management had 16 people with possibly the highest average IQ of any firm in the country, 350 to 400 combined years of experience, and most of their own net worth in the fund. They still went bankrupt. Buffett said if he ever wrote a book it would be called why smart people do dumb things. 2. Life and markets have no relation to sigmas. Buffett keeps a 1901 newspaper on his office wall. Northern Pacific went from $170 to $1,000 a share in a single day when two buyers accidentally cornered the stock. A brewer who had shorted it, facing a margin call, dove into a vat of hot beer. That man probably understood sigmas and knew such a move was impossible. Buffett has never wanted to end up in the vat. 3. Beta and sigmas tell you nothing about the risk of going broke. the LTCM team relied on mathematics and believed a six- or seven-sigma event could not touch them. they were wrong. history does not tell you the probabilities of future financial events. the real risk is a permanent blind spot in something crucial, often caused by knowing a great deal about something else. 4. To a man with a hammer, every problem looks like a nail. Munger's explanation for why brilliant people do dumb things. They learn a set of mathematical techniques and then twist every problem to fit the solution they already know. Combine that with a poor grasp of history, and you get people with advanced degrees blowing themselves up. 5. To make money they did not need, they risked money they did need. That is just plain foolish, Buffett says, no matter your IQ. Hand him a gun with a million chambers and one bullet, offer any sum to put it to his temple and pull once, and he will not do it. there is nothing on the upside that justifies the downside. people do this financially all the time without thinking. 6. The major banks all had risk models and had no idea what they owned. they met weekly at risk committees, printed all the statistics in neat columns, and did not have the faintest idea what risk they were carrying. The rare and essential quality is someone who can contemplate perils that have not popped up yet, the ones no past model contains. 7. A chief risk officer often just makes you feel good while you do dumb things. munger compares him to the Delphic oracle who convinced the Persian king to attack. he has a PhD and does advanced math, but he tortures reality to defend a model that does not hold under extreme conditions. all that computation makes you feel like you clobbered the risk when you have only clobbered your own head. 8. The whole quant risk system just changed the shape of the curve and kept going. Munger notes the business schools "improved" by throwing away the Gaussian curve and drawing a different one. They talk about fat tails now, but they still have no idea how fat to make them. he and Buffett always knew the tails were there, and used to roll their eyes at the risk-control people at Salomon. 9. Never risk what you have and need for what you do not have and do not need. Buffett will not explain to his family, who hold most of their net worth in Berkshire, that they went broke on a 100-to-1 gamble. Their returns get penalized 99 years out of 100 by being too conservative, and in the hundredth year they survive when others do not. 10. Build the business so that if the world stops working tomorrow, you have no problem. Berkshire double-layers its protection. First, they behave so no rational person questions their credit, then they hold so much liquidity that if the world suddenly hated their credit, they would not notice for months. It gives up higher returns 99% of the time and survives the one time others do not. 11. The real danger is a risk that has never happened before. Buffett wants someone who can imagine perils that have not yet appeared, the ones no model contains. The major institutions all had models, and that inability to envision the unprecedented is exactly what proved fatal. He and Munger spend a lot of time thinking about things that could hit them out of the blue that others leave out entirely. 12. Investing is simple, but not easy. The framework is not complicated. you did not need a high IQ to buy junk bonds in 2002 or stocks at low multiples in 1974. you just needed the courage of your convictions and the willingness to act when everyone else was paralyzed. Following logic rather than emotion is obvious, and yet some people find it almost impossible. 13. You cannot get rich with a weathervane. Buffett and Munger pay no attention to predictions about the economy or the market. People love predictions, entire industries are built on them, but it is like the king hiring a forecaster to read sheep guts. They have never made or avoided a single business purchase because of a macro view. 14. Name one super-wealthy economist. Munger's challenge. All these economists with 160 IQs spend their lives studying markets, and you cannot find one who got rich buying securities. Even Keynes tried to predict the credit cycle, broke a couple of times, and only did well once he switched to buying good businesses cheap and concentrating. 15. Focus only on what is important and knowable. Some things are important but unknowable, like whether someone drops a nuclear weapon tomorrow. Some things are knowable but unimportant. You narrow your attention to the small set of things that are both important and knowable, and you ignore everything else. 16. The market is there to serve you, not to instruct you. This is Graham's chapter eight, and Buffett calls it enormously important. When people talk about momentum or charts, they are saying the market instructs you. It does not. It just quotes prices. When it does something silly, you get a chance to act. Otherwise you go play bridge and check again tomorrow. 17. You can make a decision in five minutes or not at all. Buffett and Munger act fast because they rule out enormous territory in advance. Munger blots out startups entirely, and half a dozen other filters, so what remains is small enough to judge instantly. If they cannot decide in five minutes, they will not learn enough in five months to make up for going in deficient. 18. You can make a lot of money on a Sunday. Buffett said the calls you get on a Sunday, when things are truly screwed up, are the ones you make money on. All you have to do is be the collie and not the caller. You never get in a position where the other party can call your tune, so you can always play out your hand. 19. You are not right because others agree with you. Ben Graham said you are neither right nor wrong because the crowd disagrees. You are right because your facts and reasoning are right. Being contrarian has no special virtue over being a trend follower. All that matters is whether the facts are correct and the logic is sound. 20. Know where the edge of your circle of competence is. Buffett says the size of your circle does not matter. Knowing its perimeter does. You do not have to understand 90% of businesses. You just have to know something real about the few you actually put money into, and honestly recognize the ones you do not understand and walk away. 21. Intrinsic value is just the cash a business will produce, discounted back. Buffett thinks of every business as a bond with coupons that are not printed on it. Your job as an investor is to estimate those future coupons. If you cannot estimate them, like in a high-tech company, you pass. Investing is putting out money to get more back from what the asset produces, not from selling it to someone else. 22. The best businesses earn a royalty and need little capital. Coca-Cola sells a formula and takes a cut of every drink. Magazines like People operate on negative capital because subscribers pay in advance. The great businesses are the ones that can grow very large while needing almost no capital, which is why consumer businesses with pricing power are so valuable. 23. You only have to find one good idea, not twenty. Munger said you cannot find twenty deeply mispriced things, and Buffett agreed you do not need to. You do not have to have tons of good ideas in this business. You just need one good idea that is worth a ton, occasionally. For small sums, Buffett said he would have been 100% in Korea a few years earlier, where great companies traded at three times earnings. 24. The trick is measuring everything against your best opportunity. Munger calls this opportunity cost, the doctrine from the first page of the economics textbook that modern portfolio theory somehow ignored. Once you have found the best thing you understand, you measure every other option against it. The higher your default option, the more you can reject. 25. Modern portfolio theory is, in Munger's words, asinine. Most people will not find thousands of equally good things. They will find a few where one or two are far better than anything else they know. The right way to invest is to concentrate on your best opportunity cost, not to diversify into mediocrity because a model told you to. 26. Big opportunities must be seized, and seized big. Buffett says imagine you got a punch card with only twenty punches for your whole life, one per financial decision. You would think hard about each one, make fewer and better bets, and probably never use all twenty. The discipline of scarcity would make you rich. Dabbling in a bull market because it is easy is how people lose. 27. America has always been full of reasons to sell, and wrong every time. Coca-Cola went public in 1919 at $40, dropped to $19 within a year, and then faced the great depression, World War, and atomic bombs. One share reinvested is worth millions now. The country's opportunities have always won out over its problems. It is investors, not the economy, who tend to be their own worst enemy.

Jaynit

104,045 次观看 • 3 个月前

"The label just says No Brand" GRWM w Desi Generated on Atlas Cloud Wan 3.0 Prime Esr upscale stitched together on ElevenCreative studio prompt Desi: South Asian woman, mid-20s, warm brown skin with natural, realistic texture — visible pores, subtle natural sheen, no airbrushed smoothness. Expressive dark brown eyes, defined brows, full lips. Long dark wavy hair, undone/messy at the start. No jewelry until the earrings beat. Gold jhumka earrings and thin gold necklace are her fixed accessories once applied. Environment — Desi's Bedroom: Modest, lived-in bedroom, soft neutral-toned walls, single window with sheer curtains, warm diffused morning light with real shadow falloff. Wooden/laminate flooring, small woven area rug. Flows from bed area to vanity/mirror corner to a closet/wardrobe area to a doorway. Bed: simple platform frame, unmade, muted duvet, mismatched pillows, folded throw, nightstand with a plain ceramic mug, analog alarm clock, small lamp. Vanity: wooden dresser, plain oval mirror, small ceramic dish holding her rings/studs/jhumka earrings, a plain glass jar labeled "NO BRAND" in small black sans-serif text, a plain wooden hairbrush — all visible on the surface from the start. Wardrobe area: simple open wardrobe/hangers with a small handful of plain unbranded clothing already visible. Doorway: simple wooden door, softly lit hallway beyond. Visual style (all blocks): Cinematic realism, not commercial polish. Soft natural film-like grain, warm slightly imperfect color grading true to real morning light. Skin genuinely textured, never smoothed or beauty-filtered. Smooth floating steadicam/gimbal camera movement throughout, cinematic framing sensibility, not raw flat phone footage. Audio style (all blocks): Warm, natural, close-mic room-tone quality, not sterile or studio-clean. Real object-contact sounds, natural room reverb on her voice. No music, no artificial sound effects. Standing negatives (repeat in every block): No AI morphing of any kind — no shifting, warping, or unstable facial features, body proportions, or object shapes. No items suddenly appearing or disappearing in frame — every object must already be visible in its place before she interacts with it. No over-polished or beauty-filtered skin — natural texture and imperfection required throughout. No overly clean, sterile, or commercial visual grade. No duplicate Desi in any frame. No identity drift from block to block. Every product/container shows either no text/logo or exactly the plain "NO BRAND" mark — simple black sans-serif on plain packaging. No recognizable real-world brand names, logos, or trademarked packaging/clothing design anywhere in frame. No extra fingers, fused hands, or distorted anatomy. No text overlays or captions. No music — natural ambient audio only. Mirror reflections physically accurate and consistent; camera never visible in any reflection. No sudden speed changes, no time-lapse compression, no rushed dialogue delivery. BLOCK 1 — [0:00–0:15] The Alarm, Waking, Yawn, First Line Continuity note: Opening block — no prior frame to match. Action: Dark, close frame on Desi's sleeping face, soft warm morning light beginning to catch her skin's natural texture. A phone alarm begins ringing on the nightstand. Hold on her unmoving for a real beat as it rings. Her hand fumbles slowly toward the sound, silences it. She lies still a moment longer, then opens her eyes slowly, squinting at the light. She yawns — full, unhurried, genuine — stretching slightly where she lies. A long pause settles after. Dialogue: (after the yawn, slow, groggy, real pauses) "Okay... (pause) ...so it is way too early for this..." (long pause, slow exhale) "...but let's get ready anyway." Camera: Static-to-slow-float — begins locked close on her face, only beginning the faintest float as she finishes speaking, ending the block just as she starts to shift toward sitting up. Block-specific negatives: The alarm sound must play first, before any dialogue or visible movement. Do not have her wake up abruptly or sit up quickly — the entire block stays in slow, groggy stillness. Do not begin the standing/sitting motion before the final line completes. BLOCK 2 — [0:15–0:30] Five More Minutes + Coffee Stretch Continuity note: Opens exactly where Block 1 ended — Desi still lying down, having just finished her line, beginning to shift toward sitting up. Match her position, the room's lighting, and her expression precisely to Block 1's final frame. Action: She sits up fully, stretches with a real, full arms-overhead extension, laughs softly at herself. She reaches for the ceramic mug already present on the nightstand, takes a slow sip, holds it a moment, exhales contentedly. Only after finishing the sip does she swing her legs out and stand, unhurried. Dialogue: "Five more minutes..." (pause, small laugh) "...turned into not five minutes." (beat) "Classic." (pause, after the sip) "Okay." (beat) "...That helps." Camera: Smooth floating steadicam, holding a steady elevated angle through the stretch, following her rise to standing by the block's end. Block-specific negatives: Do not skip or shorten the full stretch. The sip of coffee must complete before she stands — no simultaneous drinking-and-standing. Do not have her rush to her feet; the stand is the final motion of the block, unhurried. BLOCK 3 — [0:30–0:45] Moisturizer Continuity note: Opens exactly where Block 2 ended — Desi just risen to standing beside the bed. This block covers her walk to the vanity and the full moisturizer application. Match her position and the room's light to Block 2's final frame. Action: Camera drifts with her as she walks unhurried from the bed to the vanity, arriving at the mirror. The plain glass jar labeled "NO BRAND" is already visible on the vanity surface as she arrives. She unscrews the lid slowly, scoops a small amount, and applies it in full, unhurried circular motions across her face — the entire application shown start to finish within this block. Dialogue: (spoken only once the application is fully finished, near the block's end) "This is like..." (pause) "...the one step I actually never skip." (beat) "Everything else is negotiable." Camera: Floating lateral tracking during the walk, settling into a steady medium shot at the mirror for the application itself. Block-specific negatives: The jar must already be visible on the vanity before she reaches for it — no appearing mid-motion. The application must be shown completing in full before any dialogue begins. Do not rush the walk to the mirror to make room for the application — both get real, unhurried time within the 15 seconds. BLOCK 4 — [0:45–1:00] Hairbrush + Earrings Continuity note: Opens exactly where Block 3 ended — Desi standing at the mirror, moisturizer just applied. Match her position and expression to Block 3's final frame. Action: She picks up the plain wooden hairbrush, already resting on the vanity, and brushes out her hair with several full, unhurried strokes, working through it completely. She sets the brush down, then picks up her gold jhumka earrings from the ceramic dish and puts them on one at a time — each fully finished and settled before she begins the second. Dialogue: (mid-brushing) "Yeah, I don't even know what's in that jar anymore." (soft laugh) "The label just says 'No Brand.'" (beat, as she puts in the second earring) "...Works though." Camera: Steady medium-close shot at the mirror, holding through both the brushing and the earrings without cutting away. Block-specific negatives: The hairbrush and earrings must both already be visible on the vanity from the block's start. The first earring must be fully in place before she begins the second — no simultaneous application. Do not shorten the number of brush strokes to save time; let the motion read as genuinely unhurried. BLOCK 5 — [1:00–1:15] Wardrobe Change (Revised: Explicit Detail + Locked Outfit) Continuity note: Opens exactly where Block 4 ended — Desi at the mirror, both earrings now in place. This block covers her moving to the wardrobe area and changing. New Outfit — Locked Reference (carries into Block 6 and any future scenes): A simple, plain white or cream-colored short-sleeve cotton top with a round or slightly scooped neckline, no graphics, no visible branding — soft, everyday fabric with natural draping and slight creasing. Paired with plain straight-leg or slightly relaxed-fit trousers in a muted neutral tone (soft khaki, warm taupe, or dark grey), no visible logos, no stitched branding on pockets or waistband. Simple flat sandals or bare feet, consistent with the casual, staying-home-in-the-morning energy of the piece. This exact combination — top, trousers, footwear — must remain identical through Block 6 and any subsequent generation; treat it as a locked reference image once established. Action, explicitly detailed: Camera follows her as she walks from the vanity toward the wardrobe area, where the clothing has been visible on the rod/hangers since the environment was established — including the specific white/cream top and neutral trousers she will select, already hanging there from the start. She reaches the wardrobe, looks over the few items for a moment, unhurried, and lifts the plain white top and the neutral trousers off their hangers, laying them briefly over her arm or the chair. She turns slightly away from camera or angles herself naturally at the wardrobe (matter-of-fact, not performative), and removes her sleep t-shirt first — pulling it up and off in one unhurried motion, setting it aside on the chair or a nearby surface. She then pulls the plain white top on over her head, arms finding the sleeves naturally, settling the fabric down over her torso with a small adjusting tug at the hem. She then steps out of her sleep bottoms or loungewear pants — sitting briefly on the edge of the chair or bed if needed for balance, one leg at a time — and pulls on the neutral trousers, standing to fasten or settle them at the waist, a small natural motion to smooth them down her hips. Once fully dressed, she does a small self-check — glancing down at the outfit, smoothing the top's hem once more, rolling one sleeve cuff slightly — before turning back toward the mirror. Dialogue: "Okay..." (pause, mid-change) "...let's see." (beat, once dressed, considering the outfit) "...Yeah. That works." Camera: Floating steadicam following her to the wardrobe, holding a respectful medium distance throughout the change — matter-of-fact framing, never a close or lingering angle, treating the change the way an ordinary morning routine would actually look on camera, not stylized or drawn out for effect. Block-specific negatives: The white top and neutral trousers must already be visible hanging in the wardrobe from the start of the block — no appearing only once she reaches for them. The change must be shown as a clear, complete sequence — sleep shirt removed, new top pulled on, sleep bottoms removed, new trousers pulled on — with no skipped steps or instant swaps between states. The change must read as ordinary and unhurried but not sexualized or dwelt upon — medium-distance framing throughout, natural body positioning, nothing posed. No outfit teleporting at any point — every layer change must be visibly performed by her, not implied through a cut or dissolve (there are no cuts in this piece regardless). This exact outfit (plain white/cream short-sleeve top, neutral trousers, sandals or bare feet) is now the locked wardrobe reference for the remainder of the piece — it must not vary in color, cut, or detail in Block 6 or any subsequent scene. BLOCK 6 — 1:15–1:30 FINAL LOOK → WALK OUT CONTINUITY OPEN Block 6 begins immediately from the final frame of Block 5. There is no cut. Desi stands in front of the mirror wearing the exact outfit established in Block 5. Her: white/cream top; neutral trousers; footwear; gold jhumka earrings; gold necklace; hairstyle; makeup; facial features remain exactly consistent. --- 1:15–1:20 FINAL MIRROR CHECK Desi faces the oval mirror. The camera floats gently into position behind/alongside her. She looks at her reflection. A genuine pause. She looks down briefly at the outfit. Smooths the top once. Looks back at herself. A tiny satisfied nod. No exaggerated posing. No influencer-style presentation. She simply looks like someone deciding that she is ready enough to leave. --- 1:20–1:26 WALK TOWARD DOOR Desi turns away from the mirror. The camera smoothly follows. She walks through the bedroom toward the doorway. Her pace remains natural and unhurried. The camera never overtakes her. No whip-pan. No sudden acceleration. No speed ramp. The hallway beyond the doorway is already visible. Morning light spills naturally from the hallway. Dialogue Desi: "Okay." pause "Human." beat "Presentable." pause "Good enough." A small genuine laugh. --- 1:26–1:30 EXIT Desi reaches the doorway. She opens the wooden door. Warm hallway light falls across her. She steps through. The camera continues following her naturally. Just before she disappears completely, she turns her head slightly toward the camera. A genuine late-arriving smile. Dialogue "Alright..." pause "...let's go be a person today." beat "Bye." She exits. The camera remains inside the bedroom. It settles naturally on the empty doorway. Hold for one quiet beat. END. --- MASTER NEGATIVES & CONSTRAINTS BLOCKS 5–6 CONTINUITY One continuous physical camera take. Zero hard cuts. Zero hidden cuts. Zero jump cuts. Zero dissolves. Zero fades between states. Zero time skips. Zero time-lapse. Zero teleportation. Zero duplicated footage. Zero sudden camera repositioning. WHIP-PAN ONE WHIP-PAN ONLY. It occurs exclusively during the wardrobe change in Block 5. The whip-pan must: physically originate from the camera's current position; move through the same bedroom; temporarily obscure Desi; return naturally to the wardrobe area. It must never be used as a disguised cut. No whip-pan during Block 6. --- NO MORPHING / NO MESHING Absolutely no: face morphing; facial meshing; body morphing; wardrobe morphing; texture morphing; object morphing; environmental morphing; frame interpolation artifacts; identity blending; unstable facial geometry; unstable hands; unstable hair; unstable jewelry. Desi must remain the same woman throughout the entire sequence. --- IDENTITY LOCK Do not alter: eyes; eyebrows; nose; lips; jaw; cheekbones; skin tone; face shape; body proportions; age; hair texture; hairline. No identity drift. No alternate version of Desi. No duplicate Desi. No phantom Desi. --- WARDROBE CHANGE The wardrobe change must be physically believable. No: instant outfit swap; outfit teleportation; clothing appearing on her body; clothing disappearing from her body; clothing growing onto her; magical transition; glowing transformation; particle effect; dissolve; morph. The single whip-pan simply gives privacy while maintaining the continuous physical flow of the camera. --- OBJECT CONTINUITY No object may suddenly appear or disappear. The following must remain spatially consistent: wardrobe; clothing; chair; vanity; mirror; jewelry; dresser; plants; rug; door; hallway. The white/cream top and neutral trousers must be visibly hanging in the wardrobe before Desi selects them. --- OUTFIT LOCK Once Desi finishes changing: WHITE/CREAM SHORT-SLEEVE TOP + EXACT NEUTRAL TROUSERS + SAME FOOTWEAR must remain unchanged through the end. No recoloring. No redesign. No different sleeve length. No different trouser cut. No different footwear. No spontaneous accessories. --- MIRROR CONSISTENCY Mirror reflection must be physically accurate at all times. One Desi. Reflection matches her movements exactly. No reflection drift. No alternate face. No duplicated reflection. No delayed reflection. Camera never appears in the mirror. --- ANATOMY No: extra fingers; missing fingers; fused fingers; duplicated hands; malformed hands; warped arms; elongated limbs; distorted torso; unnatural joints; anatomical meshing. --- VISUAL STYLE Cinematic realism. Natural morning light. Natural skin pores and texture. Subtle film-like grain. Slightly imperfect real-world color. No: beauty filter; plastic skin; excessive skin smoothing; glossy commercial lighting; HDR; hyper-clean advertising aesthetic; artificial glow; excessive sharpening. --- AUDIO Continuous natural audio only: footsteps; fabric movement; clothing rustle; jewelry movement; room ambience; natural breathing; door movement; hallway ambience; Desi's natural voice. No music. No cinematic impact sounds. No artificial transition sounds. No sound effect accompanying the whip-pan. --- VOICE Warm low-mid female voice. Natural morning rasp. Close and intimate. Slow. Unhurried. Conversational. Real pauses between phrases. Never sounds like a commercial, narrator, actress, or influencer reading a script. Do not compress dialogue to fit the timestamps. Natural delivery takes priority. --- FINAL GENERATION LOCK ONE DESI. ONE BEDROOM. ONE CAMERA. ONE CONTINUOUS PHYSICAL FLOW. ONE WHIP-PAN ONLY — BLOCK 5 WARDROBE CHANGE. NO CUTS. NO MORPHING. NO MESHING. NO TELEPORTATION. NO IDENTITY DRIFT. NO OBJECT POP-IN. NO WARDROBE TELEPORTATION. NO DUPLICATION. NO TIME-LAPSE. NO RUSHED MOTION. Block 6 must begin exactly where Block 5 ends, with the exact same Desi, exact same face, exact same jewelry, exact same hairstyle, exact same outfit, exact same environment, and exact same spatial continuity. #AtlasCloud

Kayno

21,304 次观看 • 1 个月前

Three days ago I asked myself a dumb question. It was so stupid I was actually ashamed to Google it. Can AI earn money while I sleep? Not saving time. Not automating routine. I mean putting real money into my account while I am not looking at the screen. Everyone says ClawdBot will change how we work. Automation. Task management. Smart replies. But I was sitting in my kitchen thinking about something else entirely. You know that feeling when you look at a tool and realize everyone is using only 1% of its potential? It is like being given a race car and only using it to drive to the store for bread. I decided to test it. I started a notebook. I record everything. > Day One I started with something simple. I gave Clawdbot a task. Find wallets on Polymarket where the numbers do not add up. Where the profit is too high for the win rate. Where the result smells like a system rather than luck. It thought for 14 minutes. I had time to pour a coffee and forget about it. Then the screen flashed. 4 addresses. I scrolled through the first three in a minute. Big bets on politics. They guessed the election. Classic. On the fourth one I stopped. Not because it was the most profitable but because I did not understand what I was looking at. The wallet was not trading politics or sports or anything people write reviews about. It was trading the weather. I read it three times. Weather. Will it be 9 degrees in London tomorrow? Will it rain in Tokyo? These are markets I would not even click on by accident. Then I looked at the numbers. > It started with $27. It is now at $63,853. $27 is two trips to McDonald's. It is nothing. $63,853 is a new car or a down payment on an apartment. It is two years of someone's salary. Between those two numbers was only one thing. Thousands of bets on rain. I closed the tab. Opened it again. Checked if it was a glitch. Real dollars. On markets that look like a bad joke. > Day Two I could not get that wallet out of my head. I went to look at its transaction history. I expected to find one big win that explained everything. A lucky hurricane forecast. Instead I saw thousands of small bets. Boring. "Will the temperature in New York be above 15 degrees?" Then I noticed the detail that finally broke my brain. Its win rate: 33%. It loses more often than it wins. 2 out of 3 bets go to zero. Any normal person with that result would be posting about how the market is unfair. Yet this wallet is sitting on $63,000 in profit. How? I started deconstructing the trades. After an hour I got it. When it loses, it loses 10 or 20 cents. When it wins, it takes $1.00. Loses 9 times in a row? Lost $1.80. Wins 1 time? Got $10.00. > This is not trading. It is math that works as long as you do not interfere with your emotions. Here is how it works. Weather is one of the most predictable things on the planet. Governments invest billions in satellites. Data is updated every 2 or 3 hours. Precision to a tenth of a degree. This data is public. But Polymarket is not a weather station. It updates its markets with a delay of 6 or 8 hours. Imagine the situation. 6 AM. The weather service updated the forecast. The probability that London reaches 9 degrees tomorrow rose to 80%. Algorithms everywhere already recalculated the data. But on Polymarket the YES button is still sitting there for 10 cents. Because the market has not woken up yet. This bot sees the difference. It buys YES for 10 cents when the real probability is already 80%. It is not guessing. It is buying what is essentially already known. It just waits a day and collects the dollar. 10 cents turn into a dollar. On information available to anyone who can read weather APIs. That evening I called a friend. He has been trading for 3 years. He sits in analytical chats. Draws support levels. I asked him: "How was the last month?" "I broke even. The market is tough right now. Too much noise." I looked at the screen. A bot betting on rain with a 33% win rate. Profit: $63,853. My friend with 3 years of experience and hundreds of hours of analysis. Profit: $0. Who is doing it wrong? I am not asking you to take my word for it. The blockchain does not lie: > Day Three I decided to dig deeper. I looked at the wallet description. I expected something complex. A hedge fund. A team of developers. Secret data sources. I found one line: Claude plus public weather APIs. Ordinary Claude. The one on your phone. Connected to free weather services. No secret stations. No insiders. No millions for infrastructure. Just an AI doing what any of us could do. But we are too lazy. Or bored. Or we think it is too simple to work. If someone already built this with basic Claude and free APIs... What happens when Clawdbot gets direct access to trading? > Day Four I watched the wallet in real time. First bet: loss. Second bet: loss. Third bet: loss. I thought: this is it. The statistics are collapsing. Fourth bet: loss. Fifth bet: loss. Down $12 in an hour. I was ready to write a post about how I overestimated this. Sixth bet: Temperature in Chicago. Win. +$87. Seventh bet: Win. +$94. By evening: 9 losses. 5 wins. Daily total: +$385. No emotions. No posts about injustice. No strategy changes after a loss. Just the next bet. I wrote to my friend. The one who has been trading for 3 years. "How was your day?" "Down $200. Market makers caught my stop loss again." I looked at the screen. A bot with no posts and no loud claims. +$385 for the day on rain bets. My friend with 3 years of experience and dozens of books. Minus $200 and a post about how the system is against him. > Day Five I woke up with a thought that kept me up all night. It finally hit me. It is not about the weather. It is not about APIs. It is not that the bot is "smarter". > It is about what the bot does NOT have: an ego that hates being wrong. No urge to revenge-trade. No boredom from repetition. My friend trades against the market. He tries to be smarter than the crowd. This bot trades against human nature. And nature loses every day. Clawdbot found me this wallet in 14 minutes. The weather bot turned $27 into $63,000 on markets everyone else thinks are trash. Both use the same principle. Do something simple. Remove emotions. Repeat. I do not know when Clawdbot will start trading on its own. Maybe in a month. Maybe in a year. But I know one thing. While we discuss if it is possible... Someone already set up their bot and went to live their life. Right now as you read this. Somewhere a weather service updated a forecast. Polymarket is sleeping. The bot is already entering a position. And my friend is writing a post about how market makers do not let honest people earn. Guess who wakes up tomorrow with money in their account?

Blaze

29,808 次观看 • 8 个月前