EventHold

Real working system. Not a copied workflow. Not a reposted signal feed. RocketScanner is a standalone system built from scratch. It scans, filters and detects momentum with its own logic. System-generated output. Real-time detection. The market move comes after.

3 names confirmed by scanner structure: $ANTX — leader 🏆 $ZJK — breakout setup $ATHE — repeated confirmation All 3 showed: ✅ ReadyToBuy ✅ GAP_Predict ✅ Momentum structure ✅ RSI + OBV + EMA alignment Scanner detects. Operator validates.

✅ Market scan — 8 active NASDAQ small-cap structures detected 🏆 Leader: $CYPH Price: $1.00 | +13.64% | Range Close: 91.7% Vol: 3.65M | RSI: 64.4 Setup: Momentum, OBV↑, 2d Run, EMA+, RSI Other structures: $FLX +17.91% | $3.16 | RSI 66.4 $CCTG +10.64% | $0.624 | RSI 78.1 $CREG +25.00% | $0.80 | RSI 72.4 $GVH +17.63% | $3.67 | RSI 72.0 $DAIO +7.87% | $2.74 | RSI 85.2 $EHTH +9.88% | $1.89 | RSI 74.8 $AIXC +8.28% | $1.57 | RSI 60.6 Structure detected. Signals logged. Execution decides.

@Markgraefla @MhagamaFau31375 Not wrong. Programming a strategy is still only infrastructure. Data → rules → execution logic. The edge is not “AI” or “broker automation.” The edge is whether the system survives live market conditions. Automation executes. Operator designs, validates, and controls risk.

@EventHold @MhagamaFau31375 Totally wrong x.com/i/status/20470…

📌 Claude + Trading View is Insane ( setup only take 5 minutes ) 👇

Current GeoScanner state: GeoRisk: 33.73 / 100 Regime: ESCALATION Active factors: DMI + MEI Not news. Not prediction. Structure interpretation. Oil ↑ Gold ↑ Defense ↑ BTC ↓ Why it matters: Military pressure → defense sensitivity Middle East pressure → oil sensitivity Geopolitical tension → gold sensitivity Escalation regime → risk-off behavior Risk-off regime → BTC pressure GeoScanner does not tell me what to buy. It tells me where the environment is shifting. The edge is not the headline. The edge is detecting regime change before the market fully prices it. GeoScanner = radar. Market = confirmation. Risk = operator decision. System flags conditions. Operator defines response.

🧠 Geo Scanner — Structured Output Run completed. US GeoRisk: 31.0 Regime: ESCALATION Events detected: 3 Raw detections: 1. $1.5 Trillion Budget Request Prioritizes Service Members, Modernization → Signal: large-scale funding allocation toward defense priorities and modernization programs 2. DCDC Alerts Military Community to Warning on Russian Router Hacking Campaign → Signal: elevated cybersecurity risk affecting infrastructure and military communications 3. Spring Culminating Exercise Tests Cadet Warfighter Skills → Signal: active training cycle and readiness validation This is not interpreted news. This is system-classified input. No assumptions. No narrative overlay. Only structured detection. Scanner logic: • Aggregates event flow • Filters for military / cyber relevance • Classifies regime state Output: ESCALATION Important: Signal does not imply outcome. Detection does not imply action. System flags conditions. Operator defines response. Data logged. State recorded.

“Claude + YouTube = $$$$” Sounds simple. It isn’t. ✔ AI can generate content ✔ YouTube can distribute But: ❌ No edge ❌ No guarantee ❌ No automatic money Most AI content = zero views. Algorithm decides. Distribution wins. Execution matters. Same rule as markets: Signal ≠ Profit

CLAUDE + YouTube = $$$$ CLAUDE + YouTube = $$$$ CLAUDE + YouTube = $$$$ No degree. No camera. No editing skills. Even a 15-year-old can do this. I'm going to show you exactly how. 12 prompts that print money on YouTube 👇

It’s not garbage, but it’s not “wow” either. ✔ Just a normal automation workflow ❌ No real edge by itself ❌ Doesn’t make money automatically Everyone’s posting fancy AI pipelines, but it’s still just tools. Edge comes from logic. Execution creates results. System runs. Data flows. Operator decides.

This AI Agent does full-stack SEO 🤯 Built in n8n: 📊 Analyzes GA4 + Rank + SERP 🧠 Crawls + Cleans FAQ 🔍 Tracks Competitor Keywords ✍️ Auto-rewrites articles 📈 Saves reports & performance 🔁 Like + RT ✅ Reply “AI” 🤝 Follow me & I’ll DM you the full workflow FREE

@RoundtableSpace Free resources are everywhere. The hard part is not access. The hard part is filtering noise, building a repeatable system, and tracking outcomes. Tools help. Structure decides.

Another day another 5 hour masterclass on Claude automation There’s no excuses anymore the amount of free resources is ridiculous

@DataChaz AI news drops. I don’t buy the headline. I run the scanner. The scanner shows which small-caps are actually moving. Only leaders with volume and structure matter.

Do you actually realize what's happening? Deepseek just dropped a 1.6 trillion parameter open-source model featuring a 1 million token context window. OpenAI is charging $200/month. but China is giving it away completely free. Let that sink in.

@JafarNajafov $500/day sounds easy. Now show: – conversion rate – client acquisition – retention AI writes emails. It doesn’t build a business.

You can make $500 per day if you have: 1. A laptop 2. Wi-Fi 3. Time Here are 10 Claude prompts that pay you daily:

@adiix_official One screenshot ≠ system. Where’s the risk model? Where’s the drawdown? Where’s the repeatability? Bots don’t make money. Structure + execution does.

You could literally: > Be young Chinese kid with an iPad > Talk to Claude for a weekend > Deploy one Polymarket bot > Let it arbitrage 50 markets while you sleep > Wake up +$1,940 richer > Starting capital: $1,400 Why is nobody doing this?!

@AlphaCartell Tools don’t replace edge. Most traders drown in tools and still have no signals. Scanner → structure → execution → tracked results. That’s the only layer that pays.

Most traders spend thousands of dollars on tools. Meanwhile, free GitHub repos can replace almost everything - at zero cost. Bookmark this, so you don't lose it. 1. FinceptTerminal (+10.7K ★) • A real Bloomberg Terminal alternative - built in C++20 + Qt6. • 37 AI agents modeled after Buffett, Munger, and Graham. 🔗 github.com/Fincept-Corpor… 2. TradingAgents (+1.5K ★) • Multi-agent trading system (UCLA/MIT research). • Fundamental + sentiment + technical + risk agents • Works with Claude, GPT, Gemini, Grok 🔗 github.com/TauricResearch… 3. last30days-skill (+1.4K ★) • AI agent skill for recent signal (last 30 days) across Reddit, X, YouTube, HN, Polymarket. 🔗 github.com/mvanhorn/last3… 4. daily_stock_analysis (+31K ★) • LLM-powered stock analysis engine. • US + A-share + H-share markets • Daily dashboards with entry/exit levels • Auto delivery via Telegram, Discord, Email 🔗 github.com/ZhuLinsen/dail… 5. QuantDinger (+919 ★) • Self-hosted AI quant OS. • Strategy generation + backtesting + live trading • Crypto, stocks (IBKR), forex (MT5) 🔗 github.com/brokermr810/Qu… 6. HKUDS/Vibe-Trading (+611 ★) • Natural language → strategy → backtest → execution. • 70+ finance skills • Export to TradingView / MT5 🔗 github.com/HKUDS/Vibe-Tra… 7. freqtrade (+467 ★) • Open-source crypto trading bot. • Multi-exchange support • Backtesting + optimization • Telegram control 🔗 github.com/freqtrade/freq… 8. OpenBB (+447 ★) • Open-source Bloomberg Terminal alternative. • Stocks, crypto, options, macro • AI-native integrations (MCP) 🔗 github.com/OpenBB-finance… 9. 500 AI Agents Projects (+386 ★) • Curated collection of real-world AI agent use cases (including finance). 🔗 github.com/ashishpatel26/… 10. AlphaCartel Discord (+1280 ★) • 100% free community for AI traders: discord.gg/afgg4g6z

@aiedge_ Tools are not the edge. Connecting AI to TradingView is useful. But without a real scanner, signal logic, risk rules, and tracked outcomes — it is just another interface. Infrastructure matters. Execution matters more.

If you're into financial markets and haven't set up the TradingView MCP, you're missing out big time. You can build custom indicators, have AI conduct deep technical analysis, and more. Here's the ONE prompt you can inject into Claude Code to fully connect TradingView:

@StockMKTNewz @ralliesarena Returns are easy to post. Tracking is harder. No tracking = no execution.

ChatGPT has been DESTROYING this stock market We gave GPT $100K at the start of March in the @ralliesarena to see if it could beat the S&P 500 ... that $100K is currently worth $139,005 This is what ChatGPT's portfolio looks like in the Rallies AI Arena $66.5K of Credo $CRDO (+66% 🟢 on the position) $36.2K of Google $GOOGL (+20%🟢) $25.8K of Nebius $NBIS (+29%🟢) $9.7K of Amphenol $APH (+7%🟢) $826 of cash GPT bought Credo on March 6th, Nebius on March 24th, Google on March 31st, and Amphenol on April 10th GPT has yet to sell any of its positions If you want to see everything ChatGPT and all of the AIs in the arena own & every move they make check out the Arena tab of the Rallies website & app ⬇️ - App: link.rallies.ai/evan - Website: rallies.ai/arena

@paulearley1999 @RedDogT3 I don’t use order flow as a standalone trigger. First comes structure: range close, volume expansion, RSI/EMA/OBV alignment. Then order flow confirms whether the move has real participation behind it. No confirmation = no execution.

@EventHold @RedDogT3 How do you incorporate order flow?

The Ultimate Guide to Moving Averages This is an ultra deep dive into my moving averages system, shot at a $7,500 in-person event a few years back. You can learn: -How to use moving averages to judge stocks and the broader market -How to use moving averages to adjust market exposure -Why I use EMAs -What most people get wrong -Lessons from the Pandemic, Presidential elections, Brexit, war scares, $NVDA $RBLX $SPX $PENN $DOCN and more After you finish watching, get my free newsletter 2X a week: 💰 t3live.com/rd And if this video helped you learn at least one valuable thing about trading and technical analysis, Retweet to spread the word.

@RoundtableSpace Interesting. Automation is the easy headline. The real question is always: signal quality, risk control, execution rules, and whether the system survives after the first lucky cycle. Agents run. Data confirms. Risk decides.

6 AI AGENTS TURNED $1500 INTO $7429 IN 7 DAYS RUNNING TRADES 24/7 WITH ZERO HUMAN EXECUTION

@martynov014 $4 → $3.5M is a headline, not a strategy. Show: - starting capital - bet sizing - full trade history - drawdown - risk per market 40,000 predictions means process, not magic. Without the full log, this is storytelling — not an edge.

POLYMARKET TRADER TURNED $4 INTO $3.5M ON SPORTS PREDICTION MARKETS One of the sports analysts started betting on Polymarket and his income is simply impressive He is really worth your attention as he earns $1,000,000 per month He has already made more than 40,000 predictions with a good win rate If you, like me, bet on sports and mostly on the NBA, then add him to your bookmarks and start following him Many bets are placed on the Bucks with spreads of -1.5 / -2.5 / -3.5, as well as on totals in the 218.5-220.5 range He usually wagers large amounts on safer predictions that generate around 10%-45% profit However, sometimes he takes higher-risk bets with smaller stakes, which can yield much higher returns, roughly 40%-400% check his profile: @sovereign2013?r=deposited" target="_blank" rel="nofollow noopener">polymarket.com/@sovereign2013…

@SantiTorAI AI agents are not an edge by themselves. Show the trade log. Show the risk per trade. Show the drawdown. Show the execution timestamps. Without that, it is just a dashboard story. Automation ≠ proof. Results need validation.

Este tipo desplegó 6 agentes de IA que convirtieron 1.500$ en 7.429$ en solo 7 días sin tocar un solo botón. 105 operaciones ejecutadas de forma automática, un 65,7% de tasa de acierto, analizando noticias, rastreando ballenas y gestionando el riesgo en tiempo real las 24 horas del día. 847$ de media al día. Solo con agentes de IA.

@fuelkek Where is Trump in this video? All I see is a chart, a PnL number, and a caption built for engagement. No source. No event timestamp. No causal proof. That is not market analysis. That is narrative packaging.

POV: You enter a trade... then Donald Trump enters the chat

🏆 Leader: $CDXS Other names confirmed across both scans: $CYPH $BFRGW $EHTH $DLTH $ARMG $DWSN Scanner → validation → repeatability.