🇨🇭 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.
This media is not supported in your browser
VIEW IN TELEGRAM
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.
This media is not supported in your browser
VIEW IN TELEGRAM
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.
Please open Telegram to view this post
VIEW IN TELEGRAM
This media is not supported in your browser
VIEW IN TELEGRAM
Traders trying to predict Trump's next move by going 10x long/short on $OIL
🇨🇭 Global One SI - Prompt
Every single week it’s the same story: “DOT COM BUBBLE” “MARKET CRASH COMING” “MICHAEL BURRY SHORTING EVERYTHING” “END OF THE BULL MARKET” Meanwhile… $SPY keeps printing NEW ALL TIME HIGHS. 📈 Look at the Monthly Chart: ✅ Multi-year uptrend still…
$SPY is the perfect example of why fighting the primary trend is one of the most expensive mistakes in the market.
For 2 years:
→ “crash coming”
→ “recession imminent”
→ “AI bubble”
→ “bear market rally”
Meanwhile the market kept doing one thing:
printing HIGHER highs. 📈
And now look at this monthly structure…
The US500/SPY chart is starting to look absolutely insane:
- multi-year uptrend intact
- massive V-shaped recovery from the 2025 panic
- liquidity keeps rotating back into equities
- AI/tech leadership still dominant
- every macro scare keeps getting absorbed
What most people still don’t understand:
This bull market is no longer driven ONLY by speculation.
It’s being driven by:
-> AI infrastructure spending
-> data center expansion
->robotics
->automation
-> cloud growth
-> institutional capital concentration
-> retirement/passive inflows
The amount of money chasing quality assets remains enormous.
And technically?
The chart honestly looks like a long-term institutional accumulation staircase.
Every “crash” headline became fuel for another leg higher.
At this point:
$800 SPY no longer sounds crazy at all over time.
The market keeps rewarding:
→ patience
→ compounding
→ buying fear
→ staying invested
Bulls still fully in control until proven otherwise. 👀🔥
#SPY #SPX #Investing #Stocks #BullMarket
For 2 years:
→ “crash coming”
→ “recession imminent”
→ “AI bubble”
→ “bear market rally”
Meanwhile the market kept doing one thing:
printing HIGHER highs. 📈
And now look at this monthly structure…
The US500/SPY chart is starting to look absolutely insane:
- multi-year uptrend intact
- massive V-shaped recovery from the 2025 panic
- liquidity keeps rotating back into equities
- AI/tech leadership still dominant
- every macro scare keeps getting absorbed
What most people still don’t understand:
This bull market is no longer driven ONLY by speculation.
It’s being driven by:
-> AI infrastructure spending
-> data center expansion
->robotics
->automation
-> cloud growth
-> institutional capital concentration
-> retirement/passive inflows
The amount of money chasing quality assets remains enormous.
And technically?
The chart honestly looks like a long-term institutional accumulation staircase.
Every “crash” headline became fuel for another leg higher.
At this point:
$800 SPY no longer sounds crazy at all over time.
The market keeps rewarding:
→ patience
→ compounding
→ buying fear
→ staying invested
Bulls still fully in control until proven otherwise. 👀🔥
#SPY #SPX #Investing #Stocks #BullMarket
💯1
What the US buys from China?
→ Smartphones: $51.5B 45% of all US imports
→ Computers: $36.7B 26%
→ Batteries: $18.1B 56%
→ Toys: $14.4B 76%
→ Video game consoles: $7.2B 78%
What China buys from the US:
→ Petroleum gases: $14.0B 16%
→ Soybeans: $12.0B 23%
→ Semiconductors: $11.8B 3%
→ Jet engines & gas turbines: $6.6B 54%
→ Aircraft & spacecraft: $5.3B 58%
The energy number is the one that matters most right now.
China buys $14B of US petroleum gases annually 16% of its total gas imports
The asymmetry is stark.
China sells the US consumer electronics it can't quickly source elsewhere.
The US sells China the energy, food, and aerospace technology it needs to keep its economy running.
→ Smartphones: $51.5B 45% of all US imports
→ Computers: $36.7B 26%
→ Batteries: $18.1B 56%
→ Toys: $14.4B 76%
→ Video game consoles: $7.2B 78%
What China buys from the US:
→ Petroleum gases: $14.0B 16%
→ Soybeans: $12.0B 23%
→ Semiconductors: $11.8B 3%
→ Jet engines & gas turbines: $6.6B 54%
→ Aircraft & spacecraft: $5.3B 58%
The energy number is the one that matters most right now.
China buys $14B of US petroleum gases annually 16% of its total gas imports
The asymmetry is stark.
China sells the US consumer electronics it can't quickly source elsewhere.
The US sells China the energy, food, and aerospace technology it needs to keep its economy running.
🇨🇭 Global One SI - Prompt
$MU/USD H2 #Indicator #Signal #Trading #GlobalOneAI Globalone.ch #Prompt #AI
$MU is becoming the PERFECT example of what happens when a true AI infrastructure leader enters a full memory supercycle.
People still think AI is only:
→ GPUs
→ chatbots
→ software hype
Meanwhile the REAL arms race is happening inside:
→ HBM memory
→ DRAM
→ NAND
→ bandwidth
→ data throughput
→ inference infrastructure
And Micron sits directly at the center of it.
Now look at this MONTHLY chart:
absolute vertical expansion
monstrous OBV breakout
institutional accumulation accelerating
momentum in full price discovery mode
buyers chasing every dip aggressively
This is not random speculation anymore.
This is Wall Street repricing the entire memory industry around:
AI demand + hyperscaler capex + supply shortages.
And honestly?
The scariest part is that the market may STILL be underestimating how massive AI memory demand could become over the next few years.
AI models are exploding in size.
Inference workloads are exploding.
Data centers are exploding.
And NONE of that works without memory.
That’s why:
$MU
$SNDK
$WDC
$STX
have suddenly become some of the strongest institutional momentum names in the market.
The chart now looks like a full momentum monster:
✅ higher highs
✅ higher lows
✅ trend acceleration
✅ huge relative strength
✅ TTM momentum expansion
✅ no real signs of exhaustion yet
This doesn’t even feel like a normal semiconductor cycle anymore.
It feels like the market is pricing a complete AI infrastructure rebuild.
Next $950??
#MU #Micron #AI #Semiconductors #Stocks #Investing
People still think AI is only:
→ GPUs
→ chatbots
→ software hype
Meanwhile the REAL arms race is happening inside:
→ HBM memory
→ DRAM
→ NAND
→ bandwidth
→ data throughput
→ inference infrastructure
And Micron sits directly at the center of it.
Now look at this MONTHLY chart:
absolute vertical expansion
monstrous OBV breakout
institutional accumulation accelerating
momentum in full price discovery mode
buyers chasing every dip aggressively
This is not random speculation anymore.
This is Wall Street repricing the entire memory industry around:
AI demand + hyperscaler capex + supply shortages.
And honestly?
The scariest part is that the market may STILL be underestimating how massive AI memory demand could become over the next few years.
AI models are exploding in size.
Inference workloads are exploding.
Data centers are exploding.
And NONE of that works without memory.
That’s why:
$MU
$SNDK
$WDC
$STX
have suddenly become some of the strongest institutional momentum names in the market.
The chart now looks like a full momentum monster:
✅ higher highs
✅ higher lows
✅ trend acceleration
✅ huge relative strength
✅ TTM momentum expansion
✅ no real signs of exhaustion yet
This doesn’t even feel like a normal semiconductor cycle anymore.
It feels like the market is pricing a complete AI infrastructure rebuild.
Next $950??
#MU #Micron #AI #Semiconductors #Stocks #Investing
💯1
🇨🇭 Global One SI - Prompt
$IBM as expected
$IBM 👀
One of the most overlooked AI / Quantum infrastructure charts in the entire market right now.
Everyone keeps chasing the small-cap quantum names…
Meanwhile:
$IBM quietly keeps building REAL enterprise AI + quantum infrastructure at global scale.
And now the chart is starting to wake up aggressively:
✅ major downtrend broken
✅ powerful reclaim above key resistance
✅ OBV exploding higher
✅ institutional accumulation visible
✅ momentum expansion triggering
✅ massive volume confirmation
This is what makes the setup interesting:
$IBM is not a speculative “story stock.”
It already has:
⚡ enterprise relationships
⚡ AI monetization
⚡ cloud infrastructure
⚡ quantum leadership
⚡ government/corporate integration
⚡ global credibility
And the market is finally starting to reprice the company around:
AI + Quantum + Enterprise Infrastructure.
Technically this breakout looks VERY important.
The reclaim above the $245-250 zone changed the entire structure of the chart.
Now the next major focus becomes:
→ continuation momentum
→ trend acceleration
→ institutional rotation into “safer” AI names
And honestly?
If quantum keeps becoming a bigger market narrative…
$IBM could become one of the “serious money” quantum plays while retail keeps gambling on tiny caps.
The chart suddenly looks alive again. 🔥
#IBM #QuantumComputing #AI #Stocks #Investing
One of the most overlooked AI / Quantum infrastructure charts in the entire market right now.
Everyone keeps chasing the small-cap quantum names…
Meanwhile:
$IBM quietly keeps building REAL enterprise AI + quantum infrastructure at global scale.
And now the chart is starting to wake up aggressively:
✅ major downtrend broken
✅ powerful reclaim above key resistance
✅ OBV exploding higher
✅ institutional accumulation visible
✅ momentum expansion triggering
✅ massive volume confirmation
This is what makes the setup interesting:
$IBM is not a speculative “story stock.”
It already has:
⚡ enterprise relationships
⚡ AI monetization
⚡ cloud infrastructure
⚡ quantum leadership
⚡ government/corporate integration
⚡ global credibility
And the market is finally starting to reprice the company around:
AI + Quantum + Enterprise Infrastructure.
Technically this breakout looks VERY important.
The reclaim above the $245-250 zone changed the entire structure of the chart.
Now the next major focus becomes:
→ continuation momentum
→ trend acceleration
→ institutional rotation into “safer” AI names
And honestly?
If quantum keeps becoming a bigger market narrative…
$IBM could become one of the “serious money” quantum plays while retail keeps gambling on tiny caps.
The chart suddenly looks alive again. 🔥
#IBM #QuantumComputing #AI #Stocks #Investing
🇨🇭 Global One SI - Prompt
$VELO/USD H2 #Indicator #Signal #Trading #GlobalOneAI
$VELO continues acting like a monster momentum chart.
This isn’t a random spike anymore.
The stock has been building a series of:
→ higher highs
→ higher lows
→ repeated momentum expansions
→ strong dip buying
Now it’s pushing back toward the major $24 resistance area again.
That’s the key level on the chart.
If bulls clear that zone cleanly…
this could enter another aggressive expansion phase very quickly.
Volume keeps supporting the move and momentum indicators remain strong despite the huge run already achieved.
This is the type of chart momentum traders love:
trend intact
volatility expansion
strong relative strength
leadership behavior
Definitely one of the strongest speculative tech momentum names right now.
#VELO #AI #Tech #Stocks #Trading
This isn’t a random spike anymore.
The stock has been building a series of:
→ higher highs
→ higher lows
→ repeated momentum expansions
→ strong dip buying
Now it’s pushing back toward the major $24 resistance area again.
That’s the key level on the chart.
If bulls clear that zone cleanly…
this could enter another aggressive expansion phase very quickly.
Volume keeps supporting the move and momentum indicators remain strong despite the huge run already achieved.
This is the type of chart momentum traders love:
trend intact
volatility expansion
strong relative strength
leadership behavior
Definitely one of the strongest speculative tech momentum names right now.
#VELO #AI #Tech #Stocks #Trading
💯1