🇨🇭 Global One SI - Prompt
What are we gonna call this? 'Gulf of America' is already taken…
Strait of America of course
🗿1
The White House is reportedly nearing a deal with Anthropic to let the NSA and other US intelligence agencies use its advanced AI models for classified work, according to The New York Times.
The talks come despite the Pentagon previously labeling Anthropic a "supply chain risk" after disputes over contract language tied to surveillance and autonomous weapons use.
The report also says the White House approved a $9 billion request for Nvidia Blackwell chips to support AI use across spy agencies.
The talks come despite the Pentagon previously labeling Anthropic a "supply chain risk" after disputes over contract language tied to surveillance and autonomous weapons use.
The report also says the White House approved a $9 billion request for Nvidia Blackwell chips to support AI use across spy agencies.
🇨🇭 Global One SI - Prompt
$AMD/USD H2 #Indicator #Signal #Trading #GlobalOneAI
Here are some of Wall Street's price targets for $AMD stock
Baird $625
Evercore ISI $579
KeyBanc $530
Bernstein $525
Mizuho $515
Wells Fargo $505
Cantor Fitzgerald $500
Barclays $500
TD Cowen $500
Roth/MKM $500
Aletheia Capital $500
Baird $625
Evercore ISI $579
KeyBanc $530
Bernstein $525
Mizuho $515
Wells Fargo $505
Cantor Fitzgerald $500
Barclays $500
TD Cowen $500
Roth/MKM $500
Aletheia Capital $500
👀1
This is HUGE: OpenAl general purpose model had a breakthrough on famous 80 year old Erdos problem 🤯!
“This is believed to be the first time a general-purpose AI system has autonomously solved a major open problem central to a field of mathematics.”
“The breakthrough involves the ‘unit distance problem,’ originally posed by mathematician Paul Erdős in 1946.”
“For decades, researchers believed square-grid-style constructions were essentially optimal for arranging points with the maximum number of equal distances.”
“But the AI discovered a completely new mathematical construction that produces more unit-distance pairs than previously thought possible.”
“The proof was independently checked by mathematicians and introduces new techniques in discrete geometry and combinatorics.”
“This is important because mathematics is one of the hardest domains for AI. A proof is either correct or wrong. There is no room for vague answers or benchmark tricks.”
“The result suggests future AI systems could help generate new discoveries in physics, algorithms, cryptography, biology, and other frontier sciences.”
“This is believed to be the first time a general-purpose AI system has autonomously solved a major open problem central to a field of mathematics.”
“The breakthrough involves the ‘unit distance problem,’ originally posed by mathematician Paul Erdős in 1946.”
“For decades, researchers believed square-grid-style constructions were essentially optimal for arranging points with the maximum number of equal distances.”
“But the AI discovered a completely new mathematical construction that produces more unit-distance pairs than previously thought possible.”
“The proof was independently checked by mathematicians and introduces new techniques in discrete geometry and combinatorics.”
“This is important because mathematics is one of the hardest domains for AI. A proof is either correct or wrong. There is no room for vague answers or benchmark tricks.”
“The result suggests future AI systems could help generate new discoveries in physics, algorithms, cryptography, biology, and other frontier sciences.”
👀1
Smart money is accumulating the infrastructure powering the next decade.
🚀 Space
⚛️ Quantum
☁️ NeoCloud
⚡ Energy
🧠 AI Infrastructure
These aren’t separate trends.
They fuel each other.
The physical layer of AI is becoming the biggest moat in the market.
Position early or pay a premium later.
🚀 Space
⚛️ Quantum
☁️ NeoCloud
⚡ Energy
🧠 AI Infrastructure
These aren’t separate trends.
They fuel each other.
The physical layer of AI is becoming the biggest moat in the market.
Position early or pay a premium later.
Forwarded from 🛢WTI, Brent Crude Oil, NGAS Trading 📊
🚨Trump says a deal with Iran is "largely negotiated"
Iranian officials are talking about a 14-point framework.
Hormuz could reopen.
But nothing is signed.
Here's what's real and what isn't ⚠️
Let's be precise about what we actually know.
What is confirmed:
→ Trump: deal is "largely negotiated," will include reopening Hormuz and lifting the US naval blockade
→ Iranian officials: a draft 14-point framework exists, providing a basis for further talks
→ Structure: end active hostilities
→ gradual Hormuz reopening
→ 30–60 day negotiation window on nuclear issues and sanctions
Sources: Reuters, CNN, Axios, Al Jazeera, TIME all describe a draft framework, not a signed agreement
What is not confirmed:
→ Hormuz "fully reopened" on day one reporting says gradual, not immediate
→ No transit fees analyst speculation, no agreed text
→ Zero US payments drafts describe partial sanctions relief and frozen asset releases, which contradicts a clean "no payments" framing
→ Full lifting of Iranian oil sanctions phased relief tied to nuclear steps, not unconditional
→ Lebanon as formal ceasefire signatory likely included in de-escalation but not confirmed as a named party
→ Exactly "60 days" for nuclear talks 30–60 day window reported, precise figure unconfirmed ⚡
This is high-probability direction of travel toward Hormuz reopening and partial sanctions relief.
It is not a done deal.
Oil and LNG prices will move on the framework rumour.
They would move hard in the opposite direction if the text falls apart 📊
A signed deal reopens Hormuz, brings back 12.8 mtpa of Qatari LNG capacity over years, and gradually unlocks Iranian barrels.
A collapsed deal keeps the current supply shock and potentially escalates it.
Both outcomes are still on the table.
Nothing is signed yet 🌍
Iranian officials are talking about a 14-point framework.
Hormuz could reopen.
But nothing is signed.
Here's what's real and what isn't ⚠️
Let's be precise about what we actually know.
What is confirmed:
→ Trump: deal is "largely negotiated," will include reopening Hormuz and lifting the US naval blockade
→ Iranian officials: a draft 14-point framework exists, providing a basis for further talks
→ Structure: end active hostilities
→ gradual Hormuz reopening
→ 30–60 day negotiation window on nuclear issues and sanctions
Sources: Reuters, CNN, Axios, Al Jazeera, TIME all describe a draft framework, not a signed agreement
What is not confirmed:
→ Hormuz "fully reopened" on day one reporting says gradual, not immediate
→ No transit fees analyst speculation, no agreed text
→ Zero US payments drafts describe partial sanctions relief and frozen asset releases, which contradicts a clean "no payments" framing
→ Full lifting of Iranian oil sanctions phased relief tied to nuclear steps, not unconditional
→ Lebanon as formal ceasefire signatory likely included in de-escalation but not confirmed as a named party
→ Exactly "60 days" for nuclear talks 30–60 day window reported, precise figure unconfirmed ⚡
This is high-probability direction of travel toward Hormuz reopening and partial sanctions relief.
It is not a done deal.
Oil and LNG prices will move on the framework rumour.
They would move hard in the opposite direction if the text falls apart 📊
A signed deal reopens Hormuz, brings back 12.8 mtpa of Qatari LNG capacity over years, and gradually unlocks Iranian barrels.
A collapsed deal keeps the current supply shock and potentially escalates it.
Both outcomes are still on the table.
Nothing is signed yet 🌍
🇨🇭 Global One SI - Prompt
$GOOGL/USD H2 #Indicator #Signal #Trading #GlobalOneAI
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$GOOGL is now processing 3.2 QUADRILLION tokens per month… up from just 9.7 trillion in May ’24.
Sundar showed the AI flywheel hitting escape velocity.
Search, Gemini, Cloud… every surface is exploding with real usage.
This is parabolic demand at planetary scale.
The market still hasn’t fully priced it in.
$GOOGL remains one of the strongest asymmetric bets in Big Tech.
Sundar showed the AI flywheel hitting escape velocity.
Search, Gemini, Cloud… every surface is exploding with real usage.
This is parabolic demand at planetary scale.
The market still hasn’t fully priced it in.
$GOOGL remains one of the strongest asymmetric bets in Big Tech.
🇨🇭 Global One SI - Prompt
Space gains in one month
WEEKLY PERSPECTIVE
Big week of momentum across the space sector. Here’s where we stand on YTD returns:
$SATL 441%
$IRDM 175%
$SPIR 171%
$BKSY 130%
$PL 117%
$LUNR 114%
$FLY 108%
$VSAT 98%
$RDW 94%
$RKLB 79%
$VOYG 61%
$AMPG 54%
$MDA 40%
$VELO 39%
$ASTI 32%
$ASTS 27%
$MNTS 29%
$SIDU 18%
RECENT IPOs
$HAWK -10%
$YSS -14%
We can see that despite strong momentum in the sector, recent IPOs like HAWK & YSS are underperforming the broader space names.
Buying at peak hype is rarely the best move, patience and selective entry points tend to work better.
Big week of momentum across the space sector. Here’s where we stand on YTD returns:
$SATL 441%
$IRDM 175%
$SPIR 171%
$BKSY 130%
$PL 117%
$LUNR 114%
$FLY 108%
$VSAT 98%
$RDW 94%
$RKLB 79%
$VOYG 61%
$AMPG 54%
$MDA 40%
$VELO 39%
$ASTI 32%
$ASTS 27%
$MNTS 29%
$SIDU 18%
RECENT IPOs
$HAWK -10%
$YSS -14%
We can see that despite strong momentum in the sector, recent IPOs like HAWK & YSS are underperforming the broader space names.
Buying at peak hype is rarely the best move, patience and selective entry points tend to work better.
✍1
$AMPG; The only American company at the intersection of 5G, Open RAN, and $NVDA's AI infrastructure buildout
AMPG just had its radio selected as the central hardware in the world's first open-source Massive MIMO AI-RAN prototype at Northeastern University's Open6G OTIC, powered by NVIDIA $NVDA GPUs. Here's why this is a big deal.
- AMPG is the only American company to design and commercialize an O-RAN CAT B 64T64R Massive MIMO radio. There is no domestic competitor for this certification.
- AI-RAN (AI-native Radio Access Network) is the next trillion-dollar infrastructure wave. Every carrier building out 5G/6G AI networks needs hardware that plugs into AI acceleration stacks like NVIDIA AI Aerial. $AMPG's radio just proved it can.
- ORAN 5G infrastructure is projected at 33%+ CAGR, reaching $42 billion globally by 2030. $AMPG is showing proof that they are well-positioned to massively win in this segment.
$AMPG is poised to generate $50m revenue in 2026 and become profitable too, with ~45% gross margin and an incredible moat. Yet, it is only trading at a market cap of ~$130 million. We are so early.
AMPG just had its radio selected as the central hardware in the world's first open-source Massive MIMO AI-RAN prototype at Northeastern University's Open6G OTIC, powered by NVIDIA $NVDA GPUs. Here's why this is a big deal.
- AMPG is the only American company to design and commercialize an O-RAN CAT B 64T64R Massive MIMO radio. There is no domestic competitor for this certification.
- AI-RAN (AI-native Radio Access Network) is the next trillion-dollar infrastructure wave. Every carrier building out 5G/6G AI networks needs hardware that plugs into AI acceleration stacks like NVIDIA AI Aerial. $AMPG's radio just proved it can.
- ORAN 5G infrastructure is projected at 33%+ CAGR, reaching $42 billion globally by 2030. $AMPG is showing proof that they are well-positioned to massively win in this segment.
$AMPG is poised to generate $50m revenue in 2026 and become profitable too, with ~45% gross margin and an incredible moat. Yet, it is only trading at a market cap of ~$130 million. We are so early.
🇨🇭 Global One SI - Prompt
Photo
How ColdMath Dominates Polymarket Weather Markets (Narrow 1°C Bins + Aggressive Scaling + Multi-Model Bias Correction)
I went through more than 1,200 of his recent trades using Parity to understand the system behind the results
What looked like random activity at first turned out to be an extremely disciplined and sophisticated strategy
1. Ultra-Narrow Bins - The Core of His Strategy
He actively trades very precise values and tight ranges, often just 1 degree. Recent examples include:
"Will the highest temperature in Singapore be 34°C?"
"Will the highest temperature in Kuala Lumpur be 29°C?"
"Will the highest temperature in Houston be between 70-71°F?"
This is not random. Consistently profiting from such narrow outcomes is only possible with very high forecast accuracy.
2. Aggressive Position Scaling (Scaling In)
One of his most distinctive traits is entering the same outcome dozens of times with small amounts ($0.01 to $10–50, sometimes up to $100–200)
Example from today: he made 8–10 separate buys in the same market at 7, 10, 16, 46, 115, and 290 shares.
This is classic technique: minimizing slippage, averaging entry price, and the ability to quickly stop adding to a position if the forecast changes.
3. Extremely High Trade Volume
He trades a lot. Some days he makes dozens, sometimes hundreds of trades. This clearly indicates a fully automated bot
4. Aggressive Betting on 1¢ Yes/No Outcomes
He often builds massive positions in very cheap outcomes (1¢), which shows high confidence in his probability calculation after all adjustments.What we can infer about his strategy:
• He most likely uses a multi-model blend
• He applies bias correction at least at the city + horizon level
• He has a dynamic component that adjusts model weights based on recent performance
• Strong emphasis on real-time data (METAR, etc.)
ColdMath is not just “a bot that pulls ECMWF”
It’s a well-thought-out system that combines multiple forecast sources, bias correction, and extremely disciplined position management through micro-scaling
This is exactly why he delivers outstanding results even with a relatively average win rate (~63-65%)
I went through more than 1,200 of his recent trades using Parity to understand the system behind the results
What looked like random activity at first turned out to be an extremely disciplined and sophisticated strategy
1. Ultra-Narrow Bins - The Core of His Strategy
He actively trades very precise values and tight ranges, often just 1 degree. Recent examples include:
"Will the highest temperature in Singapore be 34°C?"
"Will the highest temperature in Kuala Lumpur be 29°C?"
"Will the highest temperature in Houston be between 70-71°F?"
This is not random. Consistently profiting from such narrow outcomes is only possible with very high forecast accuracy.
2. Aggressive Position Scaling (Scaling In)
One of his most distinctive traits is entering the same outcome dozens of times with small amounts ($0.01 to $10–50, sometimes up to $100–200)
Example from today: he made 8–10 separate buys in the same market at 7, 10, 16, 46, 115, and 290 shares.
This is classic technique: minimizing slippage, averaging entry price, and the ability to quickly stop adding to a position if the forecast changes.
3. Extremely High Trade Volume
He trades a lot. Some days he makes dozens, sometimes hundreds of trades. This clearly indicates a fully automated bot
4. Aggressive Betting on 1¢ Yes/No Outcomes
He often builds massive positions in very cheap outcomes (1¢), which shows high confidence in his probability calculation after all adjustments.What we can infer about his strategy:
• He most likely uses a multi-model blend
• He applies bias correction at least at the city + horizon level
• He has a dynamic component that adjusts model weights based on recent performance
• Strong emphasis on real-time data (METAR, etc.)
ColdMath is not just “a bot that pulls ECMWF”
It’s a well-thought-out system that combines multiple forecast sources, bias correction, and extremely disciplined position management through micro-scaling
This is exactly why he delivers outstanding results even with a relatively average win rate (~63-65%)
✍1
🇨🇭 Global One SI - Prompt
How ColdMath Dominates Polymarket Weather Markets (Narrow 1°C Bins + Aggressive Scaling + Multi-Model Bias Correction) I went through more than 1,200 of his recent trades using Parity to understand the system behind the results What looked like random activity…
You can also find and analyze traders with similar narrow-bin + scaling strategies using Parity Terminal:
https://predictparity.com/
https://predictparity.com/
Parity
Trade prediction markets across platforms. Track top traders, analyze market data, and make informed decisions.
Top 10 Algorithmic Trading Strategies (and how they work) 🧵
1. Pairs Trading
Trades two correlated instruments simultaneously. It goes long on one asset and short on the other to profit from deviations from their historical relationship, expecting the correlation to eventually resume.
2. Scalping
Involves making numerous small trades to capture minimal price differences over a short time. For example, tape reading is used to analyze order flow and timing, enabling scalpers to profit from very brief price fluctuations.
3. Smart Order Routing Trading
Breaks large orders into smaller ones and routes them to different exchanges for the best available prices. An adaptive version adjusts routing dynamically based on real-time market conditions and liquidity.
4. Market-Making Trading
Buys and sells securities to provide liquidity in the market. It often employs statistical arbitrage to identify and exploit short-term price discrepancies between correlated securities.
5. Momentum Strategy
Capitalizes on the continuation of existing price trends. Strategies like using the Relative Strength Index (RSI) or trend-following indicators help traders ride strong market movements in a specific direction.
6. Time-Weighted Average Price (TWAP)
Splits the trade evenly over a set time period regardless of volume, aiming to achieve an average execution price close to the start-to-finish (arrival) price.
7. Volume-Weighted Average Price (VWAP)
Divides orders into smaller trades executed throughout the day at the volume-weighted average price. It takes into account historical volume profiles and market impact to optimize execution, with strategies like Implementation Shortfall VWAP aiming for minimal deviation from the VWAP.
8. Seasonality Trading
Identifies recurring market patterns based on time factors (year, month, week, etc.). For example, the Calendar Spread Strategy exploits seasonal trends by taking positions in futures contracts or options with different expiration dates.
9. Volatility Trading
Focuses on profiting from changes in market volatility by using options, futures, or other derivatives. An example is the Volatility Breakout strategy, which aims to capture significant price movements after periods of low volatility.
10. Machine Learning-Based Strategy
Leverages machine learning algorithms (e.g., Neural Networks, Random Forests, Support Vector Machines) to analyze large datasets and uncover complex patterns, aiding in making data-driven trading decisions.
1. Pairs Trading
Trades two correlated instruments simultaneously. It goes long on one asset and short on the other to profit from deviations from their historical relationship, expecting the correlation to eventually resume.
2. Scalping
Involves making numerous small trades to capture minimal price differences over a short time. For example, tape reading is used to analyze order flow and timing, enabling scalpers to profit from very brief price fluctuations.
3. Smart Order Routing Trading
Breaks large orders into smaller ones and routes them to different exchanges for the best available prices. An adaptive version adjusts routing dynamically based on real-time market conditions and liquidity.
4. Market-Making Trading
Buys and sells securities to provide liquidity in the market. It often employs statistical arbitrage to identify and exploit short-term price discrepancies between correlated securities.
5. Momentum Strategy
Capitalizes on the continuation of existing price trends. Strategies like using the Relative Strength Index (RSI) or trend-following indicators help traders ride strong market movements in a specific direction.
6. Time-Weighted Average Price (TWAP)
Splits the trade evenly over a set time period regardless of volume, aiming to achieve an average execution price close to the start-to-finish (arrival) price.
7. Volume-Weighted Average Price (VWAP)
Divides orders into smaller trades executed throughout the day at the volume-weighted average price. It takes into account historical volume profiles and market impact to optimize execution, with strategies like Implementation Shortfall VWAP aiming for minimal deviation from the VWAP.
8. Seasonality Trading
Identifies recurring market patterns based on time factors (year, month, week, etc.). For example, the Calendar Spread Strategy exploits seasonal trends by taking positions in futures contracts or options with different expiration dates.
9. Volatility Trading
Focuses on profiting from changes in market volatility by using options, futures, or other derivatives. An example is the Volatility Breakout strategy, which aims to capture significant price movements after periods of low volatility.
10. Machine Learning-Based Strategy
Leverages machine learning algorithms (e.g., Neural Networks, Random Forests, Support Vector Machines) to analyze large datasets and uncover complex patterns, aiding in making data-driven trading decisions.