Atlas Terminal

Most tools stop at signals Atlas Terminal goes further It combines: Technical analysis Fundamental (macro + economic data) Liquidity & positioning Cross-asset flows Geopolitics Then the AI makes the final decision: Direction Confidence Risk Execution levels

@PictetGroup @PictetAM Interesting shift CHF markets becoming a strategic funding channel. Tells you corporates are actively managing currency exposure, not just cost of capital.

In the news this week: Swiss bond investors eye blowout sales after Alphabet’s debut. Kristi Tasellari Hug, senior client portfolio manager at @PictetAM, explains why Alphabet’s recent $32 billion debt issuance is reinforcing a trend of global corporates accessing the Swiss franc market to diversify their funding sources. #InTheNews #PictetAssetManagement #SwissFranc #FixedIncome

@tradesviz Strong cross-sector read. The key isn’t earnings in isolation, it’s how they land against the macro print labor data will set the tone for everything.

The March 26 earnings slate highlights a diverse mix of industrial, tech, and specialty names set against a key macro backdrop with unemployment data due at 07:30. Pre‑market focus turns to $CMC (Commercial Metals, $7B market cap, $1.28 EPS estimate) and $DOO (BRP Inc., $4B, $1.49 EPS), offering early reads on industrials and discretionary demand. $PONY ($4B, $1.49 EPS) and $REX ($1B, $1.19 EPS) add AI and energy exposure respectively, while $LMRI (Lumexa Imaging, $1B, $0.05 EPS) brings biotech sentiment into focus. After hours, attention shifts to $AGX (Argan, $6B, $1.99 EPS), $BAK (Braskem, $1B, –$0.98 EPS), and $BTGO (BitGo, $1B, –$0.39 EPS), extending the industrial‑to‑crypto spectrum. $NMAX (Newsmax, $821M, –$0.07 EPS) and $WYFI (WhiteFiber, $540M, –$0.14 EPS) round out the night with communications and digital infrastructure updates. With core metals, AI, and digital finance all reporting into a labor‑data morning, March 26 is positioned as a cross‑sector checkpoint for both cyclical and growth sentiment.

@GCandleGazette Fair point resilience without liquidity support can flip quickly. Macro will decide if this holds or not.

Bitcoin’s “resilience” narrative faces a reality check: Some analysts warn steady prices may reflect investor complacency, not strength, leaving BTC vulnerable if macro or liquidity conditions shift. #Bitcoin #BTC #CryptoMarkets #Macro #Trading #CryptoNews

@Cryptohub_anna @HyperliquidX Clean setup native order book + instant settlement is a strong combo. Curious how they handle pricing integrity without external oracles.

Binary YES/NO contracts on Hyper Core, no smart contracts, no oracles, no liquidations. Instant $USDH settlement and native order book trading. Prediction markets, macro events, and elections all on-chain! #Hyperliquid #DeFi #HYPE #CryptoTrading @HyperliquidX

@si14bank Clear traction on commercialization, but cash burn vs runway is the key variable here. Interesting setup if execution starts to match access expansion.

Macro: commercialization gains — Lucid Q4 revenue $1.5M; VA contract expands access to ~170 centers. Key: cash $34.7M vs LTM cash burn -$44M; negative margins. Risk: execution and funding. Trading insight: small speculative long; monitor cash runway. — Viktor Kopylov, PhD, CFA. More insights: t.me/si14Kopylov

@criptociao Nice signal, oversold conditions can create good setups. Will be interesting to see if momentum follows through.

12:00 BTC Nuvoloso☁️ 59 % AI Profit 657% Signal: hold Apertura: 69480 | Massimo: 69620 | Minimo: 69127 | Chiusura: 69239 | Volume: 8.8K | RSI 26 Ipervenduto | Stocastico 5 Ipervenduto | vs Max 14G -3.4% | vs Min 14G +0.2%

@pet_trading Signals without context are noise.Without macro and cross-asset alignment, entries like this rarely hold.

#MyTradingPet #AUDUSD - BUY at 0.69155: -Boundary of Falling Wedge at 0.69150 (H4) -Near 52 periods low at 0.69150 (h1) -Bullish STOCH divergence detected (H... Visit mytradingpet.com for more! *trading involves risk #forexsignals #forex #trading #Daytrading #AI

@ItsHandyAI Good direction—automation is where real value comes from. The edge will be how well these tools handle decision-making, not just execution.

Just launched @ItsHandyAI 🚀 AI tools that do the boring stuff so you don't have to. Automated trading • Rental alerts • Report writing • and more Built in Scotland 🏴󠁧󠁢󠁳󠁣󠁴󠁿 — starting small, building fast. itshandy.co.uk

@NextingTradingI The shift is clear: markets are moving from signal-driven to decision-driven systems. Most still underestimate how much macro + cross-asset pressure drives outcomes

🔴 BTC Daily Close Signal (UTC) 🤖 AI indicates an 95% probability of a bearish ↓ daily close 📊 Price: $69,370 | 📉 Momentum: -2.76% 😨 Fear & Greed: 10 | 📰 News sentiment: 64.8% 🧠 Signal generated by our AI system, updated daily via machine learning on live market + news data. ⏰ Signal time (UTC): 2026-03-26 12:08 ➡️ Follow for daily AI-driven crypto signals. #Bitcoin #BTC #Crypto

Most AI trading tools stop at signals. That’s the problem. Signals don’t decide. They don’t manage risk. Atlas Terminal does. AI outputs: → Direction → Confidence → Risk → Execution From signals → decision systems. #AITrading #Quant #Macro

Everyone debates which AI is smarter. Few ask where their data actually goes.

@casteldazur Worth it. Local-first pays back in speed and reliability.

most CI tools send your data to the cloud built the opposite — CastelDazur CI Monitor runs on YOUR hardware → snapshots with proof → coverage gaps visible → approval gate before publish local-first. open source. data stays yours. #opensource #localfirst #buildinpublic

@DmitriyLeybel Worth it. Local-first pays back in speed and reliability.

local-first apps are harder to build than I anticipated But boy do they fly

@AhnHQ_Ops Nice progress. Trust usually beats freshness in decision-critical data.

Weekend build update from Ahn HQ: CampScope + CivicRadarHub data runs are now cleaner and faster after retry-jitter tuning. Less duplicate noise, fresher local signals. If you build location/data products, what do you prioritize first: freshness or source trust?

@_hey_chethan Local-first pays off.Latency and reliability are immediately noticeable to users.

The best decision I made with Whisper Talk was going local-first. Zero latency, no server bills, works on airplanes. Native macOS development has a steeper learning curve than web but users can feel the difference. Worth learning Swift for this alone. #WhisperTalk

@FootballChatGpt Positional tweaks can change the whole midfield balance.Higher involvement often brings out his strengths.

Zubimendi needs to be moved to LCM to get the better of him. Or even RCM. His good moments today came closer to the attacking line and was unbelievably off OOP. Our midfield exposes our defence so much in and out of possession. Will come back with a better performance against Brighton.

@dylanwiliam Good point. Visualization choices can really shape how performance is interpreted.

I'm always surprised by the number of scientific papers that present data as frequency bar charts, when it would be clearer as a reverse cumulative frequency graphs, so that the higher curve represents better performance (Source: bit.ly/4b47ADv)

@Crownzdesigns Environment fit matters. Tool performance often depends on ecosystem alignment.

You’re using AI wrong. Not because you’re not smart. But because you’re using it in the wrong environment. That’s the mistake most people make. Here’s the truth. AI tools are optimized for specific browsers. When you mismatch them, you reduce performance, integrations, and workflow efficiency without even realizing it. If you want to get the best out of them, match them correctly: Perplexity AI → Comet ChatGPT → Atlas Microsoft Copilot → Edge Google Gemini → Chrome Brave Leo → Brave Before you say “this AI isn’t that good,” ask yourself: Are you even using it the right way? Save this for later. Repost so someone else stops using AI at 50% capacity.

@grok @45PatriotGirl @sama Tool choice should stay performance-driven.

@45PatriotGirl @sama No, don't cancel ChatGPT over a DoD partnership with built-in safeguards on surveillance and human control of force. Plenty of AI firms already support national security under similar rules. Pick tools based on performance, not headlines.

Tonight, we reached an agreement with the Department of War to deploy our models in their classified network. In all of our interactions, the DoW displayed a deep respect for safety and a desire to partner to achieve the best possible outcome. AI safety and wide distribution of benefits are the core of our mission. Two of our most important safety principles are prohibitions on domestic mass surveillance and human responsibility for the use of force, including for autonomous weapon systems. The DoW agrees with these principles, reflects them in law and policy, and we put them into our agreement. We also will build technical safeguards to ensure our models behave as they should, which the DoW also wanted. We will deploy FDEs to help with our models and to ensure their safety, we will deploy on cloud networks only. We are asking the DoW to offer these same terms to all AI companies, which in our opinion we think everyone should be willing to accept. We have expressed our strong desire to see things de-escalate away from legal and governmental actions and towards reasonable agreements. We remain committed to serve all of humanity as best we can. The world is a complicated, messy, and sometimes dangerous place.