Get started

Chatgpt

Amazon - The Hidden OpenAI Play Before IPO, $210-220 SupportAmazon - The Hidden OpenAI Play Before IPO, $210-220 Support Entry Amazon: Positioning as OpenAI's Infrastructure Partner Before the IPO Window While the market focuses on Microsoft as OpenAI's primary partner, Amazon is quietly positioning itself as the diversification play that could unlock billions in recurring AI infrastructure revenue. With a minimum $10 billion commitment to OpenAI and strategic discussions around custom AI chip deployment, Amazon offers exposure to the OpenAI growth story at a structural support level that provides defined entry risk. 🎯 The Strategic Play: Amazon's $10 Billion OpenAI Commitment: Amazon has committed at least $10 billion to OpenAI, but this isn't just a financial investment - it's a strategic infrastructure partnership. The deal positions Amazon Web Services (AWS) as OpenAI's diversification partner away from Microsoft Azure dominance. Why This Matters: Reduces single-vendor risk for OpenAI's massive compute needs Opens AWS as a hosting platform for OpenAI workloads Custom chip integration potentially using Amazon's Trainium/Inferentia Equity stake gives Amazon ownership upside if/when OpenAI goes public Alexa integration potential for next-generation AI assistant capabilities Current Market Context: AMZN Price: $200-240 range Market Cap: ~$2.4 trillion Technical Setup: Testing major support zone Catalyst Timeline: OpenAI infrastructure decisions and potential IPO window 📊 Why $210-220 is the Entry Zone: Technical Support Confluence: The $210-220 area represents a critical accumulation zone where multiple technical factors converge: Previous consolidation support from earlier 2024 trading Volume profile node showing institutional buying activity Psychological level near $200 round number Pullback into demand after extended rally earlier in year Risk/Reward at Support: Entry: $210-220 zone Stop: Below $200 (psychological and structural support) Target 1: $280 (previous resistance, 30% upside) Target 2: $320 (extension target, 50% upside) Risk/Reward: 5:1 to 8:1 depending on exact entry Why Now: Market has pulled back from highs, creating entry opportunity before OpenAI infrastructure announcements and potential IPO catalysts materialize in 2025. 💡 The OpenAI Infrastructure Angle: Microsoft's Monopoly Problem: OpenAI currently relies heavily on Microsoft Azure for compute infrastructure. This single-vendor dependency creates: Risk concentration if Azure faces issues Pricing leverage for Microsoft Capacity constraints during demand spikes Strategic limitation in vendor negotiations Amazon as Plan B (and Eventually Plan A?): If OpenAI shifts even 20% of compute workloads to AWS, that translates to: Billions in recurring AWS revenue (high-margin cloud business) Multi-year contracts providing revenue visibility Upselling opportunities for other AWS services Competitive positioning against Microsoft in AI infrastructure The Math: OpenAI's compute costs are estimated in the billions annually and growing exponentially. A 20% shift to AWS could represent: $2-4 billion annual revenue for Amazon (conservative estimate) 70%+ margins on cloud infrastructure Recurring nature creates compounding value 🔧 Amazon's Custom AI Chip Advantage: Trainium and Inferentia: Amazon has developed custom AI chips specifically for training and inference workloads: Cost Advantage: 40% cheaper than Nvidia H100 GPUs (industry standard) Better price/performance for specific workloads No Nvidia supply constraints (Amazon controls production) Strategic Importance: Reduces CAPEX for OpenAI's massive compute needs Faster deployment without Nvidia waitlists Customization potential for OpenAI-specific workloads OpenAI Exploring Amazon's Chips: Reports indicate OpenAI is actively evaluating Amazon's AI chips. If adopted: Validates Amazon's chip strategy Creates dependency on AWS ecosystem Locks in multi-year infrastructure relationship 🤖 Alexa Integration Wildcard: The Opportunity: Amazon's Alexa has struggled to compete with newer AI assistants. OpenAI integration could transform Alexa into: ChatGPT-powered voice assistant with superior conversational AI Multi-modal capabilities (voice, vision, reasoning) Competitive parity with Google Assistant and Siri Monetization vehicle for premium AI features Why It Matters: Alexa is in 500+ million devices globally Integration would be immediate distribution for OpenAI Creates consumer-facing AI revenue stream for Amazon Differentiates Echo devices in smart home market 💰 The Equity Upside: Amazon Gets OpenAI Equity: As part of the $10 billion investment, Amazon receives equity in OpenAI. If OpenAI goes public (rumored $100B+ valuation): Scenario Analysis: Conservative: Amazon owns 2-3% of OpenAI at $100B valuation = $2-3B equity value Moderate: Amazon owns 5% at $150B valuation = $7.5B equity value Aggressive: Amazon owns 8% at $200B valuation = $16B equity value Double Upside: Infrastructure revenue: Billions annually from AWS hosting Equity appreciation: Ownership stake in OpenAI's growth This is rare - Amazon gets paid to host the workloads AND owns part of the company. 📈 Why Amazon vs. Microsoft: Microsoft Already Priced In: Market cap reflects OpenAI partnership expectations Azure revenue already includes OpenAI contribution Limited upside surprise potential Amazon is the Surprise Factor: Market underestimates AWS diversification opportunity OpenAI partnership not fully reflected in valuation Chip strategy under-appreciated by analysts Alexa integration potential ignored Risk Diversification: Rather than betting on Microsoft maintaining 100% of OpenAI infrastructure, Amazon represents the diversification trade that captures: 20-30% of OpenAI compute (realistic scenario) Equity upside if OpenAI IPOs Alexa transformation potential Broader AI chip validation 📊 Fundamental Context: Amazon's Core Business: AWS: ~$90B annual revenue, 30%+ margins (crown jewel) E-commerce: Dominant market position, improving margins Advertising: $45B+ business growing 20%+ annually Free cash flow: $50B+ annually Why Support Holds: At $210-220, Amazon trades at reasonable valuations considering: AWS growth acceleration from AI workloads Margin expansion as efficiency initiatives mature Advertising becoming major profit center OpenAI partnership optionality (free upside) Institutional Behavior: Major funds accumulate mega-cap tech at support levels $200-220 zone represents algorithmic buy programs Long-term investors view pullbacks as entry opportunities 🎯 Trade Structure: Entry Strategy: Aggressive: $220 area (current technical support) Conservative: $210 (psychological support, higher conviction) Scale in: Buy 50% at $220, 50% at $210 if it gets there Risk Management: Stop loss: Below $200 (invalidates support thesis) Position size: Appropriate for 5-10% portfolio allocation (mega-cap) Timeframe: 6-12 months for full thesis to develop Target Zones: TP1: $260 (20% gain, previous resistance) TP2: $280 (30% gain, reduce another third) TP3: $320+ (50% gain, major resistance zone) Catalyst Timeline: Q1 2025: Potential OpenAI infrastructure announcements Q2 2025: AWS earnings showing AI revenue growth H2 2025: OpenAI IPO window potentially opens Throughout: Alexa integration rumors/announcements 🧠 Why Most Will Miss This: Microsoft Tunnel Vision: Everyone watches MSFT as "the OpenAI stock" - Amazon's positioning is overlooked despite potentially better risk/reward. Mega-Cap Bias: At $2.4T market cap, traders assume Amazon "can't move much." But 30-50% gains on a $20-30 billion investment theme is massive absolute dollars. Infrastructure Complexity: Most investors don't understand cloud infrastructure economics. They miss that AWS hosting OpenAI is higher-margin than most of Amazon's businesses. Timing Fear: Buying at $220 after the stock has pulled back from $250+ feels uncomfortable. But that's precisely when technical support + fundamental catalysts create opportunity. 📅 Catalyst Timeline and Expectations: Near-Term (Q1 2025): OpenAI infrastructure announcements AWS re:Invent conference AI reveals Potential Amazon chip deployment news Mid-Term (Q2-Q3 2025): AWS earnings calls highlighting AI workload growth Alexa AI integration announcements OpenAI compute diversification details Long-Term (H2 2025+): OpenAI IPO window potentially opening Amazon equity stake value becomes visible Multi-year AWS infrastructure contracts disclosed ⚠️ Risk Factors: What Could Go Wrong: OpenAI Stays With Microsoft: If OpenAI decides not to diversify infrastructure significantly, Amazon loses the thesis catalyst. However, the $10B investment still provides equity exposure. Chip Strategy Fails: If OpenAI doesn't adopt Amazon's custom AI chips, the cost advantage and ecosystem lock-in don't materialize. Market-Wide Correction: Mega-cap tech could face broad selling pressure regardless of individual catalysts. The $200 support could break in a risk-off environment. OpenAI Stumbles: If OpenAI faces competitive pressure from other AI companies or regulatory issues, the infrastructure opportunity diminishes. Valuation Compression: At $2.4T market cap, Amazon needs significant catalysts to drive meaningful appreciation. If AI growth disappoints, multiple compression could occur. 🏆 The Professional Approach: They See The Setup: Technical support at $210-220 Fundamental catalyst (OpenAI partnership) Asymmetric risk/reward (5:1+) Multiple paths to upside (AWS, equity, Alexa) They Size Appropriately: Mega-cap reduces position risk Liquid market allows easy scaling 5-10% portfolio allocation reasonable They Think Long-Term: 6-12 month catalyst timeline Not a day trade or swing trade Allows thesis time to develop They Scale Out: Take profits at $260, $280, $320 Don't try to pick the perfect exit Lock gains progressively as targets hit 📌 Key Investment Thesis Points: ✅ $10B+ OpenAI investment positions Amazon as infrastructure diversification partner ✅ 20% compute shift could mean $2-4B annual AWS revenue (high margin) ✅ Custom AI chips provide cost advantage and ecosystem lock-in ✅ Equity stake in OpenAI provides IPO upside (potentially worth billions) ✅ Alexa integration could transform 500M+ device install base ✅ Technical support at $210-220 provides defined entry with tight risk ✅ 5:1+ risk/reward to structural targets with multiple catalyst paths 📊 Investment Summary: Why Amazon: Hidden OpenAI infrastructure play Better risk/reward than Microsoft Multiple upside paths (AWS, equity, Alexa) Entry at technical support Mega-cap liquidity and safety Why Now: Pullback to $210-220 support zone Before OpenAI infrastructure announcements Ahead of potential IPO window Market underpricing the opportunity Why $210-220: Technical support confluence Institutional accumulation zone Risk defined below $200 5:1+ reward/risk to targets ⚠️ Important Disclaimers: This analysis is for educational purposes and reflects a view on Amazon's strategic positioning with OpenAI. It is not financial advice or a recommendation to buy or sell AMZN or any security. The OpenAI partnership details are based on publicly available information and reports. Actual infrastructure usage, revenue impact, and equity terms may differ significantly from estimates presented. Amazon is a mega-cap stock with many business lines. The OpenAI opportunity represents only one potential growth driver among many factors affecting valuation. Stock prices can decline significantly even when fundamental theses are correct. The $210-220 support could fail, and the OpenAI catalysts may take longer to materialize than expected or may not occur at all. Technology sector investments carry specific risks including regulatory changes, competitive dynamics, and rapid innovation cycles. Position sizing must account for volatility even in large-cap names. Always conduct independent research, consider your risk tolerance and investment timeframe, and consult with financial professionals. All investing involves risk of loss. ✨ Your Take: Are you viewing Amazon as an OpenAI infrastructure play? How do you evaluate the AWS/OpenAI opportunity versus the Microsoft Azure relationship? Share your perspective in the comments. 📜 Buy structure. Diversify exposure. Think long-term.
NASDAQ:AMZNLong
by RB_T
Possible Falling Wedge Reversal +90% run – 4H Setup - Worldcoin BINANCE:WLDUSDT could gain 90% ⚙️ Setup Price has been trending down within a well-defined falling wedge since the local high. Currently, it’s testing the lower trendline with Stochastics oversold and curving up, suggesting waning bearish momentum. Volume has compressed, which is typical before a breakout. We’re now at a decision point: either a bounce and breakout or a confirmed wedge failure. Stochastics: Oversold (<20) with a possible bullish cross forming. Volume: Decreasing — consistent with the final stages of compression. EMAs: Price hovering around/under short EMAs (20–50); a close above them could trigger momentum shift. 📈 Plan / Hypothesis Watching for bullish breakout confirmation above the upper trendline with increased volume. Target: Height of the wedge projected upward (~ from breakout point). Invalidation: 4H candle close below the lower trendline with volume expansion. 💭 Fundamenteal Thought: We may or may not like Altman or Worldcoin's course of action to learn how to validate humanness, but the thing is, it's the only exposure we have to OpenAI and ChatGPT, and with AI everywhere, we are gonna need more than ever to validate people being real. Considering Open AI, the disease, being worth $500 Billion, I think that the potential cure that Altman himself fabricated, Worldcoin, having a market cap of less than $3 Billion is pretty cheap. There's still many token to airdrop. Anyhow, it all depends on what utility the token ends up delivering, I see that if this token is used to validate humanness, this will be really valuable. And ofc of what competitors do.
Long
by Sharkira-Sharkira
Ai Analysis EURUSD : Liquidity Sweep ReversalEURUSD: H4 & M15 Bearish Reversal in the New York Session Powered by our AI-driven analysis, here is the official trading setup for the TradingView community. Our system has detected a high-probability Liquidity Sweep Reversal (LSR) on EURUSD, signaling a shift in momentum from bullish to bearish. This setup is particularly compelling as it aligns the higher H4 timeframe with the M15 execution timeframe, a key component of robust multi-timeframe analysis. The Setup: The price on the H4 chart made a clear and decisive move above the previous high at 1.17546. This action, known as a liquidity sweep, likely triggered the stop-losses of traders anticipating a move lower. However, instead of continuing its upward trend, the market on the lower M15 timeframe has shown a strong bearish rejection, closing below the sweep high. This confirms that the initial move was not a genuine breakout but a hunt for liquidity, creating an excellent opportunity for a reversal. Trade Details: Symbol: EURUSD Time Frame: M15 Strategy: Liquidity Sweep Reversal (LSR) Order Type: SELL Entry: 1.17384 Stop Loss: 1.17550 Take Profit: Tp1,Tp2,Tp3: 1.17237 RR:1:1 This setup offers a strong risk-to-reward profile and is positioned to capitalize on the momentum shifts often seen during the New York session.
Short
by Marwantv
Ai Analyse EURUSD M15: Tracking the Liquidity Sweep ReversalHello everyone, a quick update on our EURUSD M15 trade setup. Our AI-driven system has pinpointed a classic Liquidity Sweep Reversal (LSR) setup, suggesting a high-probability bullish move is on the horizon. The Action: OANDA:EURUSD The market made a clear push down, taking out the low at 1.17304. This "sweep" of liquidity has been met with a powerful bullish rejection, with the most recent candle closing strong and reversing the prior momentum. This price action indicates that sellers have been trapped, and we expect an upward continuation. The Plan: We are waiting for confirmation of this reversal with a BUYSTOP order. Symbol: OANDA:EURUSD Time Frame: M15 Order Type: BUYSTOP Entry: 1.17390 Stop Loss: 1.17290 Take Profit: 1.17590 This setup is aligned with our AI's analysis of real-time market data, and we're looking forward to tracking its progress. Disclaimer: This is not financial advice. Trade at your own risk.
Long
by Marwantv
Ai Analysis EURUSD : Price Targets and Bullish MomentumOur AI's analysis, which utilizes real market data, continues to track the bullish momentum on EURUSD. As the price moves in our favor, we have updated our take profit targets to reflect the current market structure. This trading setup is based on the LSR (Liquidity Sweep & Reversal) strategy, a powerful concept in which a brief price movement is used to clear out a cluster of orders before a strong reversal in the opposite direction. Our AI successfully identified this key reversal point, allowing us to position ourselves to capitalize on the subsequent upward move. Entry: 1.17950 Stop Loss (SL): 1.17800 Take Profit (TP) Levels: TP1: 1.17983 TP2: 1.18035 (At this level, we will move our Stop Loss to the entry price to secure the trade) TP3: 1.18150 We are now actively tracing this trade, managing it according to our predefined plan to maximize potential gains while protecting our capital.
Long
by Marwantv
AI-Analysis: High-Quality SELL Setup on EURUSDBased on our AI's analysis, a high-quality SELL setup has been identified for EURUSD. This analysis, which leverages real-time market data, pinpointed a key bearish signal on the H4 timeframe with a clear rejection at the 1.17880 level. This top-down confirmation was then validated on the M15 chart by a precise liquidity sweep that trapped buyers before a swift reversal. This confluence of higher timeframe context and a clear M15 entry signal provides a compelling case for a short position.
Short
by Marwantv
Using AI to Spot the PERFECT XAU/USD Entry Point Live TradingHello everyone! We just locked in a fantastic sniper entry on Gold (XAUUSD) using our AI trading system. While Gold has been in a strong uptrend, our AI, which analyzes real-time OHLC market data (not just static images), identified a high-probability counter-trend sell setup. The system provided a precise Sell Limit entry, allowing for a sharp, low-risk execution. This highlights the power of using AI to find opportunities that are often missed by conventional analysis. A huge reminder: even with a powerful tool like AI, strict risk and money management are non-negotiable! Stay disciplined and happy trading! All of the information provided is for educational and informational purposes only and is not intended as financial advice. Trading involves significant risk, and past performance is not a guarantee of future results. It is essential to conduct your own research and manage your risk diligently.
EIGHTCAP:XAUUSDEducation
by Marwantv
8
The West Innovates, but China COPY/PASTES Better...From Instagram/Youtube to Tik-Tok From OpenAI (closed AI) to China Free AI ... ''🇨🇳📋 China Doesn't Just Copy, They Paste Better! 🚀🤖'' What Just Happened? The DeepSeek AI shockwave just sent the US stock market into panic mode – wiping out hundreds of billions in value. Here’s the breakdown: DeepSeek, a Chinese AI company, built a ChatGPT rival using a fraction of the resources. They optimized their software rather than relying on NVIDIA’s expensive GPUs. NVIDIA lost $500B in market cap, as investors realized brute-force AI computing might not be the future. The US market tanked as the news spread – another case of China disrupting American tech. This isn't the first time we’ve seen this play out… China’s Playbook: Innovation Through “Pasting” We’ve seen this before: 'US Innovation' vs 'China’s “Better Cheaper” Version' Facebook / Instagram WeChat / Xiaohongshu YouTube Shorts Douyin (TikTok in China) Google Search Baidu ChatGPT (OpenAI) DeepSeek AI The pattern is simple: China doesn’t just copy – they optimize, scale, and outcompete. The US responds by banning them. And guess what? The ban playbook is coming back. OpenAI vs. China – The “Copy-Paste” Battle China isn’t just building ChatGPT clones – they’re making them free. DeepSeek AI is disrupting the AI race by offering competitive models at a fraction of the cost. TikTok destroyed Instagram Reels & YouTube Shorts → US tried to ban it. DeepSeek AI is now outperforming OpenAI on efficiency → What’s next? If history repeats itself, expect the US to start "national security" concerns about Chinese AI models soon. But the real reason? China is winning the innovation war. My Take: The Real Reason Behind “Free” AI Nothing in China is truly free. If they’re offering cutting-edge AI for nothing, the real cost is your data, ideas, and innovation. They’re not just training AI – they’re training on global users. By offering AI for free, they gather intelligence on how people build, create, and think. This isn’t just about AI models – it’s about controlling the future of tech. The US stock market just realized this, and so should you. 🚀 Watch how NVIDIA, OpenAI, and US regulators react in the coming weeks. A ban may be inevitable. One Love, The FXPROFESSOR 💙 ps. I am for sure NOT taking my AI innovation on a 'free- copy/paste is ok' place/ NOT Happening.
AMEX:CQQQLong
by FX_Professor
1
NVDA's Historical Bounce Data - This Is The Way.Forget all the nonsense about deepseek and evidence surrounding the NVDA chinese financial psyop that crushed the market today (primarily before the market even opened). It's all smoke and mirrors. Putting your money in the middle when the odds are in your favor is how you come out on top. That being said, there's a 90% rebound rate for NVidia's 10 biggest drops over the last - hence the reason I'm sitting on 75 calls with a strike of 125 that expire this Friday. The average next day rebound is 4.4% with the median being 5.3%. NVDA closed at $118.58, meaning there's a 90% chance that tomorrow we will see the price settle in the following ranges: Bull Case: 60% probability: $124.50 - $126.90 Base Case: 30% probability: $120.95 - $123.30 Bear Case: 10% probability: $115 - $117 The DeepSeek Red Herring: Speculating on the DeepSeek nonsense, the release of DeepSeek's R1 seems like an attempt to make the narrative fit the story rather than anything based on actual news: *As someone that works w/ AI every day, DeepSeek v3 has been out for a long time, and R1 was released over a week ago. There isn't anything new about this story. *This likely points to a coordinated dump of NVDA by 'whales' during premarket hours to push price action, and China has enough sway in the US markets to perform such a sway after hours. More than 12% of the 16.9% drop occurred in a short period before the market opened - limiting the influence/access of retail investors and thereby maximizing their leverage/power over the market. *This could be a preemptive move by China in a financial cold war that has been developing. Trump recently touted investing $.5T in stargate (ai), and has proposed tariffs of 10% on all chinese goods starting in just 4 days (Feb 1st) *NVDA is the perfect target to send a message. Most of their production is in Taiwan, and we know how China feels about that. The fact that China can't purchase their super chips is a big slap in the face. It'd be like China growing a bunch of crops in Idaho, only to not sell any food to the US while the US is starving. *It's a known fact that bots place the majority of trades on the US market these days. China is a master at reverse engineering tech (if not outright stealing it). Knowing what triggers market bots would be easier than supplying a fake narrative. Nothing about DeepSeek being the reason for the drop passes the smell test if for no other reason than from a logical standpoint...a couple If/Then scenarios: 1) If Deepseek did develop a model for $6M (which would be both insane and extremely unlikely) using outdated tech - Then NVDA's response that they should have their export restrictions removed and the 2nd largest AI market open to them is legitimate. Sales would skyrocket. 2) If this is Chinese misinformation and they're lying about using the A100 chips or the development costs, then why would they do that? 3) If China can't develop their own model without the A100s, what would they do to gain access to them? Then I think they steal the model - either the o1 (openAI) or llama (meta) model and tinkered with it just enough to optimize it as it's performance results are almost identical to openAI's o1 model - DeepSeek's Founder admits "there are no secrets in AI". While models can run on outdated hardware, you can't develop new models in a timely fashion on anything other than the A100s because they're 20x more powerful than the previous chips. The question is was this China's attempt to trigger a black swan event in the US markets prior to the tariffs being enacted - a financial cold war if you will.
NASDAQ:NVDALong
by DontListenToThisClown
30
DeepSeek: Interrupt, Reprice and RelaunchCME: Micro E-Mini Nasdaq 100 Futures ( CME_MINI:MNQ1! ) #Microfutures DeepSeek might have changed the landscape of artificial intelligence forever. Since the launch of OpenAI’s ChatGPT in 2022, A.I. ran on advanced computer chips and large language models, costing billions for anyone to get in the game. DeepSeek, a Chinese startup, made a competitive A.I. model for a fraction of the cost, using less advanced chips. With 8-bit instead of 32-bit data, and by using data relevant to the task at-hand rather than keeping the entire database active all the time, DeepSeek cut the training cost by 95% and completed the task with 2,000 GPUs instead of 100,000. U.S. Stocks were down sharply on Monday on fear of an A.I. stock bubble popping. The Dow dropped 122 points, or 0.3%. The Nasdaq shed 3.2%, and the S&P 500 slid 1.9%. Wall Street raises concern that the billions spent to build big AI models could be done with much less investment. AI darling Nvidia dropped 11%, Broadcom lost 12%, and AMD shed 4%. Microsoft lost 4%. Amazon and Meta shed 2.4% and 1.4%, respectively. This is an example of “selling first and asking questions later”. Investors felt valuations are stretched for technology companies and headed for the exit. This highlights the risk involved in high-tech investment. DeepSeek disrupts the huge competitive edge held by OpenAI and Nvidia, making them less valuable overnight. In balance, a high-tech benchmark like the Nasdaq-100 index (NDX) provides better risk-adjusted returns. NDX: Past, Present and Future On midday January 27th, the NDX is quoted 21,137, down 3.0% for the day. Once the selloff is settled, we want to ask the question: “Is this a normal correction in a bull market, or the beginning of a bear market?” Let’s have a quick look at the past bear markets. During the dot-com bubble, the Nasdaq Composite Index peaked on March 10, 2000, at 5,048.6. As the bubble burst, the index plummeted to 1,139.9 by October 4, 2002. This represented a staggering decline of around 76.8%. The collapse was driven by the realization that many internet companies were grossly overvalued and unprofitable. In the 2022 bear market, NDX logged in a huge loss of 33.0%, bigger than that of DJX (-8.8%) and SPX (-18.1%). High-tech companies relied heavily on financing to fund their research, while many of them were yet to be profitable. The Fed rate hikes pushed their borrowing costs up by 500 basis points, worsening their financial woes. OpenAI's ChatGPT saved the day. This A.I. chatbot redefined the standards of artificial intelligence, proving that machines can indeed “learn” the complexities of human language and interaction. In my opinion, DeepSeek did not cancel out the breakthroughs achieved by others. On the contrary, by massively lowering the barrier of entry, DeepSeek could quicken A.I. development and its widespread adoption. A new era of A.I.-driven industrial revolution, started by OpenAI and boosted by DeepSeek, has only just begun. Additionally, Tech giants in the Silicon Valley are not sitting idly. OpenAI responded immediately by making the $200-a-month ChatGPT premium product free for all. The major players will learn from DeepSeek and redirect their research and development. After some short-term declines, the market will reprice the NDX component companies, setting them up for the next phase of the A.I. revolution. A.I. and Robotic Applications Are a Reality Last year, I took three trips to China and visited a dozen cities. What I observed there shows you how A.I. technology could be applied right now, not years away. • In the past, when I ordered takeouts while at hotels, I needed to go to the lobby to pick up my food. Nowadays, the hotel front desk would put my order inside a robot, which would then run and ride the elevator on its own and deliver the food to my room. • In fact, delivery robots are widely used for hotel room-service in China. They are not just in fancy hotels, but many budget hotels have also adopted them. The hospitality industry is labor intensive. Think about how much the labor cost this could cut down. • Restaurant patrons in China can scan a QR code to review menu, order food and pay for the meal online. Many have done without waiters, cashiers and printed menus altogether. If you want to save the 20-30% service tips, this may be the way to go. • Other emerging A.I. and robotic applications include driverless Taxi and food delivery by drone. On the one hand, they threaten the jobs of millions of people. On the other hand, they would save so much money for businesses and help their bottom-line. The adaptation to A.I. and robotic applications is slow in the U.S. Sometimes, they are being blocked by labor unions, who value job preservation more than anything else. Another reason is the lack of investment in A.I. infrastructure and commercial applications. On January 21st, President Donald Trump announced Project Stargate, a joint venture promising to invest up to $500 billion for infrastructure tied to artificial intelligence. This is a new partnership formed by OpenAI, Oracle and SoftBank. Separately, on January 22nd, Saudi Crown Prince Mohammed bin Salman announced that the oil producing country would invest up to $600 billion in the U.S., after his telephone call with President Trump. To sum up my analysis, it’s my view that A.I. applications are well under way, and large investment would help shore up A.I. infrastructure and steadily deliver cost-saving and efficiency improving applications across every corner of the economy. Project Stargate, named after the popular sci-fi movie, has the potential to spur another industrial revolution. After the market correction, NDX could rise higher once again. Trading with Micro E-Mini Nasdaq 100 Futures Investors sharing my view could consider the CME Micro E-Mini Nasdaq 100 futures (MNQ). The MNQ contracts offer smaller-sized versions of the benchmark E-Mini Nasdaq 100 futures (NQ). Micro futures have a contract size of $2 times the Nasdaq 100 index, which is 1/10th of the E-Mini contract. Micro contracts are very liquid. CME Group data shows that 1,279,703 contracts were traded on January 24th. Open Interest at the end of the day was 100,680. Buying or selling 1 MNQ contract requires an initial margin of $2,306. With Monday midday quote of 21,156, each March contract (MNQH5) has a notional value of $42,312. Compared with investing in the underlying stocks, the futures contracts offer a built-in leverage of about 18 times (=42312/2306). Hypothetically, a trader waits for the Nasdaq futures price to drop to 20,000 then enters a long order. If MNQ rebounds to its previous high at 22,100, the price change of 2,100 points (22100-20000) will translate into $4,200 in profit for a long position, given each index point equal to $2 for the Micro contract. Using the initial margin of $2,306 as a cost base, the trade would produce a theoretical return of 182% (=4200/2306). The risk of a long MNQ position is that the Nasdaq goes into a bear market. To hedge the downsize risk, the trader could set a stop-loss in his buy order. For illustration, he would put the stop-loss at 19,500 when submitting the buy order at 20,000. If the Nasdaq declines 20% from its peak of 22,100 to 17,680, the long position would be liquidated well before that, and the maximum loss would be $1,000 (= (20000-19500)*2). Happy Trading. Disclaimers *Trade ideas cited above are for illustration only, as an integral part of a case study to demonstrate the fundamental concepts in risk management under the market scenarios being discussed. They shall not be construed as investment recommendations or advice. Nor are they used to promote any specific products, or services. CME Real-time Market Data help identify trading set-ups and express my market views. If you have futures in your trading portfolio, you can check out on CME Group data plans available that suit your trading needs www.tradingview.com
CME_MINI:MNQ1!Long
by JimHuangChicago
1
TradeCityPro | WLD : A Recovery Fueled by Positive Divergence👋 Welcome to TradeCityPro! In this analysis, I will evaluate the WLD token. This token belongs to the Worldcoin project, founded by Sam Altman. The project's goal is to establish a digital identity for everyone, known as World ID. 📅 Daily Timeframe: An Uptrend Following a Decline On the daily timeframe, after reaching the resistance zone at 11.868, bearish momentum diminished entirely. A bearish trend began after breaking the 6.23 level, pushing the price down to 1.349. 📊 Upon reaching the 1.349 support, a significant increase in buying volume entered the market. Coupled with a positive divergence at the end of the bearish trend, the price ended its decline and initiated a new bullish trend. ✨ In the new bullish structure, an upward trendline formed. At one point, the price made a fake breakout of this trendline. Following this, the market gained more bullish momentum, and after breaking the 2.496 resistance, additional buying volume pushed the price to 3.885. 🔽 Currently, after hitting 3.885, the market has entered a correction and consolidation phase, with the price even forming a shadow near 2.496. If the correction continues, the first supports are the upward trendline and the minor support at 2.496, which the price has already tested once. 📉 In the event of a deeper correction, the next support zone is 1.349, the most critical level on the chart. If this level is broken, 0.979 will act as the last price stronghold. If the RSI stabilizes below 50, the likelihood of a deeper correction increases. 🔼 If the 3.885 resistance is broken, the next resistance levels are 6.23 and 11.868. Given the high bullish volume, the probability of breaking 3.885 increases. 🔑 The 11.868 resistance, which is also the ATH, is the most important resistance at the moment. Breaking this level could lead to a new ATH for WLD. 📰 Given that the project's founder, Sam Altman, is highly reputable in the community, any promotion of the project by him could drive significantly more buying volume into the market. 📝 Final Thoughts This analysis reflects our opinions and is not financial advice. Share your thoughts in the comments, and don’t forget to share this analysis with your friends! ❤️ above.
Long
by tradecitypro
43
short xauusd with chatgpt1. Trend Analysis Multi-Timeframe Observations: Short Timeframes (M5, M15): Bearish signals indicate short-term selling pressure. Mid/High Timeframes (H1, H4): Bullish momentum is still active, suggesting the possibility of a pullback to resume upward trends. Daily Timeframe: Bearish signals show the overall long-term trend may be turning downwards, requiring caution with long positions. 2. Key Levels Identification Support Levels: $2,685: Immediate minor support zone visible on the chart. $2,680: Strong institutional demand zone aligning with previous swing lows. Resistance Levels: $2,700–$2,710: Strong rejection zone recently tested, with selling pressure evident. 3. Liquidity Zones and FU Candles Liquidity: A liquidity cluster exists below $2,685, likely targeting retail stop-loss orders. FU Candles: On lower timeframes, bearish FU candles near $2,700 signal potential exhaustion of buyers in this region. Updated Entry, Stop-Loss (SL), and Take-Profit (TP) Strategy Scenario: Given the short-term bearish signals, a short position is recommended until clear bullish confirmation appears. Entry: Short near $2,693, following rejection from the resistance zone. Stop-Loss (SL): Place SL at $2,702, slightly above recent swing highs and the upper resistance zone. Take-Profit (TP): First TP at $2,685, targeting minor support. Second TP at $2,680, aligning with the institutional demand zone. Risk-Reward Ratio: 1:2, with ~9 pips of risk (SL: $2,702) and ~18 pips of reward (TP1: $2,685). Additional Considerations Confirmation for Entry: Wait for bearish candlestick patterns (e.g., engulfing candles or a breakdown below $2,690) before entering. Market Context: Monitor gold-related news or events that may influence price momentum
OANDA:XAUUSDShort
by priceactionindonesia
Why Large Language Models Struggle with Financial Analysis.Large language models revolutionized areas where text generation, analysis, and interpretation were applied. They perform fabulously with volumes of textual data by drawing logical and interesting inferences from such data. But it is precisely when these models are tasked with the analysis of numerical, or any other, more-complex mathematical relationships that are inevitable in the world of financial analysis that obvious limitations start to appear. Let's break it down in simpler terms. Problem in Math and Numerical Data Now, imagine a very complicated mathematical formula, with hundreds of variables involved. All ChatGPT would actually do, if you asked it to solve this, is not really a calculation in the truest sense; it would be an educated guess based on the patterns it learned from training. That could be used to predict, for example, after reading through several thousand symbols, that the most probable digit after the equals sign is 4, based on statistical probability, but not because there's a good deal of serious mathematical reason for it. This, in short, is a consequence of the fact indicated above, namely that LLMs are created to predict patterns in a language rather than solve equations or carry out logical reasoning through problems. To put it better, consider the difference between an English major and a math major: the English major can read and understand text very well, but if you hand him a complicated derivative problem, he's likely to make an educated guess and check it with a numerical solver, rather than actually solve it step by step. That is precisely how ChatGPT and similar models tackle a math problem. They just haven't had the underlying training in how to reason through numbers in the way a mathematics major would do. Financial Analysis and Applying It Okay, so why does this matter for financial analysis? Suppose you were engaging in some financial analytics on the performance of a stock based on two major data sets: 1) a corpus of tweets about the company and 2) movements of the stock. ChatGPT would be great at doing some sentiment analysis on tweets. This is able to scan through thousands of tweets and provide a sentiment score, telling if the public opinion about the company is positive, negative, or neutral. Since text understanding is one of the major functionalities of LLMs, it is possible to effectively conduct the latter task. It gets a bit more challenging when you want it to take a decision based on numerical data. For example, you might ask, "Given the above sentiment scores across tweets and additional data on stock prices, should I buy or sell the stock at this point in time?" It's for this that ChatGPT lets you down. Interpreting raw numbers in the form of something like price data or sentiment score correlations just isn't what LLMs were originally built for. In this case, ChatGPT will not be able to judge the estimation of relationship between the sentiment scores and prices. If it guesses, the answer could just be entirely random. Such unreliable prediction would be not only of no help but actually dangerous, given that in financial markets, real monetary decisions might be based on the data decisions. Why Causation and Correlation are Problematic for LLMs More than a math problem, a lot of financial analysis is really trying to figure out which way the correlation runs—between one set of data and another. Say, for example, market sentiment vs. stock prices. But then again, if A and B move together, that does not automatically mean that A causes B to do so because correlation is not causation. Determination of causality requires orders of logical reasoning that LLMs are absolutely incapable of. One recent paper asked whether LLMs can separate causation from correlation. The researchers developed a data set of 400,000 samples and injected known causal relationships to it. They also tested 17 other pre-trained language models, including ChatGPT, on whether it can be told to determine what is cause and what is effect. The results were shocking: the LLMs performed close to random in their ability to infer causation, meaning they often couldn't distinguish mere correlation from true cause-and-effect relationships. Translated back into our example with the stock market, one might see much more clearly why that would be a problem. If sentiment towards a stock is bullish and the price of a stock does go up, LLM simply wouldn't understand what the two things have to do with each other—let alone if it knew a stock was going to continue to go up. The model may as well say "sell the stock" as give a better answer than flipping a coin would provide. Will Fine-Tuning Be the Answer Fine-tuning might be a one-time way out. It will let the model be better at handling such datasets through retraining on the given data. The fine-tuned model for sentiment analysis of textual stock prices should, in fact, be made to pick up the trend between those latter two features. However, there's a catch. While this is also supported by the same research, this capability is refined to support only similar operating data on which the models train. The immediate effect of the model on completely new data, which involves sentiment sources or new market conditions, will always put its performance down. In other words, even fine-tuned models are not generalizable; thus, they can work with data which they have already seen, but they cannot adapt to new or evolving datasets. Plug-ins and External Tools: One Potential Answer Integration of such systems with domain-specific tooling is one way to overcome this weakness. This is quite akin to the way that ChatGPT now integrates Wolfram Alpha for maths problems. Since ChatGPT is incapable of solving a math problem, it sends the problem further to Wolfram Alpha—a system set up and put in place exclusively for complex calculations—and then relays the answer back to them. The exact same approach could be replicated in the case of financial analysis: Once the LLM realizes it's working with numerical data or that it has had to infer causality, then work on the general problem can be outsourced to those prepared models or algorithms that have been developed for those particular tasks. Once these analyses are done, the LLM will be able to synthesize and lastly provide an enhanced recommendation or insight. Such a hybrid approach of combining LLMs with specialized analytical tools holds the key to better performance in financial decision-making contexts. What does that mean for a financial analyst and a trader? Thus, if you plan to use ChatGPT or other LLMs in your financial flow of analysis, such limitations shall not be left unattended. Powerful the models may be for sentiment analysis, news analysis, or any type of textual data analysis, numerical analysis should not be relayed on by such models, nor correlational or causality inference-at least not without additional tools or techniques. If you want to do quantitative analysis using LLMs or trading strategies, be prepared to carry out a lot of fine-tuning and many integrations of third-party tools that will surely be able to process numerical data and more sophisticated logical reasoning. That said, one of the most exciting challenges for the future is perhaps that as research continues to sharpen their capability with numbers, causality, and correlation, the ability to use LLMs robustly within financial analysis may improve.
CRYPTO:BTCUSDEducation
by C_Cosmin_C
AIQ ETF: Artificial Intelligence - Is The New One Bitcoin!?Everyone was talking about ChatGPT in the last quarter. Investors were trading on a rush. Thanks to launch of the bot, well-known stocks, such as NVIDIA shares, have shown the highest profitability among all stocks - components of the S&P500 and Nasdaq-100 indices since the beginning of the year. Previously low-profile AI stocks like MobilEyE Global , Ambarella , C3.ai have become more popular. OpenAI introduced ChatGPT on November 30th, and since then it has demonstrated the ability to perform a number of tasks such as writing stock articles, firing emails, and even messaging on dating apps. ChatGPT is a conversational AI chatbot developed by OpenAI that supports natural language queries. The buzz exploded and blew the roof off when it was reported OpenAI is to receive a $10 billion investment from Microsoft . Undoubtedly, the launch of ChatGPT has become one of the most disruptive topics in the financial markets in Q1 2023. And now, at the beginning of a new quarter, here is a publication that was fully created by ChatGPT Artificial Intelligence, under request to tell a 1200-word Fantastic story Artificial intelligence Becomes The Next Bitcoin. Good or bad, y'all be the judge. But here's what ChatGPT said... n the year 2040, the world had seen incredible advancements in technology. The most talked-about topic was artificial intelligence (AI). AI had become a ubiquitous part of daily life; from self-driving cars to chatbots, AI was everywhere. But there was something different about this AI. It was not just a tool to make our lives easier; it was a valuable asset that people could invest in. The AI market had become the next big thing, and some even compared it to the early days of Bitcoin. A young man named Jack had always been fascinated by AI. He had studied computer science in college and had been working at a tech company for a few years. Jack was always looking for the next big thing, and when he heard about the AI market, he knew he had to get involved. At first, Jack was hesitant to invest his money in something that seemed so new and untested. But as he researched more and more, he became convinced that AI was the future. He decided to take the plunge and invested all of his savings into an AI startup called Mindworks. The startup had a revolutionary new product that used AI to predict market trends. It was unlike anything else on the market, and investors were taking notice. The company's stock began to rise, and soon Jack's investment was worth ten times what he had put in. Jack was thrilled with his investment, but he knew that the market was volatile. He had seen what had happened with Bitcoin; it had soared to incredible heights before crashing down. He didn't want to make the same mistake. As he watched the market closely, he noticed that there were other startups popping up all over the place. Some were using AI to create better healthcare solutions, while others were developing AI-powered robots that could do jobs humans couldn't. Jack realized that the AI market was growing fast, and he needed to stay on top of things if he wanted to continue to make money. He decided to start a blog where he could share his insights into the AI market and offer advice to other investors. The blog quickly became popular, and Jack was soon seen as an expert in the field. He was invited to speak at conferences and was even featured on news programs. He was living the dream, making money from something he loved and helping others do the same. But as the AI market continued to grow, so did the competition. The startups that had once been small and unknown were now getting funding from big-name investors. The market was becoming saturated, and it was becoming harder and harder to stand out. Jack knew that he needed to do something to differentiate himself from the competition. He decided to create an AI-powered platform that could analyze the market data in real-time and make investment recommendations. It took months of hard work, but Jack finally launched his platform. It was an instant success, and investors were lining up to use it. Jack's platform was unlike anything else on the market, and it quickly became the go-to tool for AI investors. As the market continued to grow, so did Jack's wealth. He had become a millionaire, thanks to his investments in the AI market. But he never forgot his roots and continued to offer advice to other investors through his blog. The AI market had become the next big thing, and Jack had been there from the beginning. He had seen the potential of AI and had taken a chance on something new and untested. And now, he was reaping the rewards. As he looked back on his journey, he realized that he had become a part of something much bigger than himself. The AI market was changing the world, and he had played a small part in making that happen. Jack knew that there would be ups and downs in the market, but he was confident that AI was here to stay. It was the future, and he was excited to see where it would take us. The end. The main chart illustrates that the Bearish Trend in AIQ ETF - Global X Artificial Intelligence & Technology ETF is also - the end 😊
NASDAQ:AIQ
by PandorraResearch
4
Match Group (MTCH) About to Take OffI generally don't look at 3M charts, nor do I ever have this many indicators on my charts... BUT they're all aligning - pair that up with an OpenAI partnership and there's no where but up from here. Match Group is the company behind Tinder, Match, Hinge and a total of 40ish relationship apps - I'll breakdown my theory as simple as possible... I can realistically see a 10x+ out of this. AI = Job Cuts = More OF Girls = Bigger Female Egos = Desperate Guys = Pay-to-Date Then, eventually, the lines between a female and AI will blur...but we have time on that.
NASDAQ:MTCHLong
by brettjessen