🇨🇭 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.
Many traders focus on the open and close for momentum, while midday is calmer.
1. 9:30 AM – 10:30 AM — Opening Momentum Highest volatility and volume. Great for momentum trades, breakouts, and quick moves as orders flood in.
2. 9:45 AM – 11:00 AM — Directional Trends Early direction often sets; good for trend-following after initial chaos settles.
3. 11:00 AM – 1:00 PM — Sideways/Chop Lower volume, ranging action. Often choppy—best to avoid or trade tight ranges cautiously.
4. 1:00 PM – 3:00 PM — Midday Lull / Steady Action Calmer, more predictable ranges. Suitable for scalps or waiting for setups.
5. 3:00 PM – 4:00 PM — Power Hour / Closing Momentum Volume surges again. Strong moves, reversals, or trend continuations as institutions adjust positions.
Additional common ones:
6. 10:30 AM – 11:30 AM — Post-Open Momentum Fade Volatility drops; trends may extend or reverse.
7. 2:00 PM – 4:00 PM — Afternoon Directional Build Picks up toward close; good for end-of-day trends.
1. 9:30 AM – 10:30 AM — Opening Momentum Highest volatility and volume. Great for momentum trades, breakouts, and quick moves as orders flood in.
2. 9:45 AM – 11:00 AM — Directional Trends Early direction often sets; good for trend-following after initial chaos settles.
3. 11:00 AM – 1:00 PM — Sideways/Chop Lower volume, ranging action. Often choppy—best to avoid or trade tight ranges cautiously.
4. 1:00 PM – 3:00 PM — Midday Lull / Steady Action Calmer, more predictable ranges. Suitable for scalps or waiting for setups.
5. 3:00 PM – 4:00 PM — Power Hour / Closing Momentum Volume surges again. Strong moves, reversals, or trend continuations as institutions adjust positions.
Additional common ones:
6. 10:30 AM – 11:30 AM — Post-Open Momentum Fade Volatility drops; trends may extend or reverse.
7. 2:00 PM – 4:00 PM — Afternoon Directional Build Picks up toward close; good for end-of-day trends.
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Cathie Wood just flagged the sleeper trade inside the AI boom that most people are completely missing.
Everyone has been chasing GPUs. Nvidia, the data center buildout, the chip arms race. That trade has been obvious for two years.
But OpenAI's CFO Sarah Fryer said something quite different: people are going to be really shocked by how agentic AI activates CPUs.
Right now, for every CPU in an AI workload, there are 4 to 5 GPUs. That's the current ratio. Wood thinks that ratio is going to 1 to 1.
Think about what that means. AI inference at scale, agents running autonomously, pipelines executing tasks across systems. The compute mix shifts dramatically away from pure GPU dominance. CPUs become a first-class citizen in the AI stack.
Cathie called it going "back to the future." Intel has taken off. Flex (formerly Flextronics) is booming. Stocks that were giants in the dot-com bubble are resurging because the underlying demand for their products is real again.
The GPU trade made sense at the training stage. You need massive parallel compute to train frontier models.
But agentic AI runs differently. Agents are constantly orchestrating, reasoning, calling APIs, executing workflows. That workload looks a lot more like traditional computing. And traditional computing runs on CPUs.
If Cathie Wood is right about the ratio collapsing to 1:1, the CPU demand signal embedded in the AI buildout is orders of magnitude larger than the market is currently pricing.
Everyone has been chasing GPUs. Nvidia, the data center buildout, the chip arms race. That trade has been obvious for two years.
But OpenAI's CFO Sarah Fryer said something quite different: people are going to be really shocked by how agentic AI activates CPUs.
Right now, for every CPU in an AI workload, there are 4 to 5 GPUs. That's the current ratio. Wood thinks that ratio is going to 1 to 1.
Think about what that means. AI inference at scale, agents running autonomously, pipelines executing tasks across systems. The compute mix shifts dramatically away from pure GPU dominance. CPUs become a first-class citizen in the AI stack.
Cathie called it going "back to the future." Intel has taken off. Flex (formerly Flextronics) is booming. Stocks that were giants in the dot-com bubble are resurging because the underlying demand for their products is real again.
The GPU trade made sense at the training stage. You need massive parallel compute to train frontier models.
But agentic AI runs differently. Agents are constantly orchestrating, reasoning, calling APIs, executing workflows. That workload looks a lot more like traditional computing. And traditional computing runs on CPUs.
If Cathie Wood is right about the ratio collapsing to 1:1, the CPU demand signal embedded in the AI buildout is orders of magnitude larger than the market is currently pricing.
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Eric Schmidt thinks the real limit to AI isn't energy but rather it's cash.
Everyone talks about energy as the bottleneck for AI but Schmidt says that's the wrong constraint.
The real limit is capital.
His math is that roughly $50 billion per gigawatt of AI compute.
To build 10 gigawatts, that's half a trillion dollars and the infrastructure buildout being discussed isn't 10 gigawatts, it's more.
When you add it all up, you're talking about handing an industry a trillion dollars of capital and he asked the obvious question: how many companies or countries on earth can actually do that?
Very, very few.
China could. they have the state apparatus and the willingness to deploy capital at that scale.
America can because of the brilliance of the American capital markets and the ability to raise and deploy private capital at a scale no other financial system in the world can match.
Europe cannot and according to Schmidt, they know it.
This is the part of the AI race that doesn't get enough attention.
The algorithm wars, the model benchmarks, the talent competition, all of that matters but none of it happens without the capital stack behind it.
Everyone talks about energy as the bottleneck for AI but Schmidt says that's the wrong constraint.
The real limit is capital.
His math is that roughly $50 billion per gigawatt of AI compute.
To build 10 gigawatts, that's half a trillion dollars and the infrastructure buildout being discussed isn't 10 gigawatts, it's more.
When you add it all up, you're talking about handing an industry a trillion dollars of capital and he asked the obvious question: how many companies or countries on earth can actually do that?
Very, very few.
China could. they have the state apparatus and the willingness to deploy capital at that scale.
America can because of the brilliance of the American capital markets and the ability to raise and deploy private capital at a scale no other financial system in the world can match.
Europe cannot and according to Schmidt, they know it.
This is the part of the AI race that doesn't get enough attention.
The algorithm wars, the model benchmarks, the talent competition, all of that matters but none of it happens without the capital stack behind it.
However, a separate approval from the CFTC is still required to launch trading.
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Traders trying to predict Trump's next move by going 10x long/short on $OIL