Offshore
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Michael Fritzell (Asian Century Stocks)
RT @CacheThatCheque: International stocks outperforming the S&P500 so far this year https://t.co/ek8esMgmzm
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RT @CacheThatCheque: International stocks outperforming the S&P500 so far this year https://t.co/ek8esMgmzm
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Moon Dev
Stop Trading Like Retail: How To Code A Bot That Liquidates The "Smart Money"
most traders think smart money concepts are the holy grail but they are actually just a road map for where the big players are going to take your money. if you can see where everyone else is putting their stop loss you essentially have a cheat code for the market. i found a way to automate this process so i never have to click a button again but there is a massive catch that almost ruined the entire system during the build
the core idea is that everyone trades the same supply and demand zones which makes them predictable targets for liquidity. instead of trying to be the smart money trader who gets stopped out i decided to build a system that buys and sells exactly where those traders are getting wrecked. it sounds simple on paper but trying to translate that into python while living on an island with shaky power is a different story
one of the biggest hurdles was figuring out how to identify the actual "stop loss" zone beyond the standard candle highs and lows. i realized that if i took a standard supply zone and extended it by one point five times i could find the "extended high" where the real liquidations happen. most people are entering at the zone but the smart move is to wait for the sweep that happens just beyond it
this is where the logic gets interesting because you have to calculate the max and min values over a specific bar limit to find the true range. i used a ninety six bar limit to establish the baseline and then stretched that data to find the hidden entries. if you can get the code to recognize these levels automatically you no longer have to stare at charts all day wondering if a zone will hold
the real magic happens when you move these systems onto a decentralized exchange like hyperliquid because it gives you more control over your orders. but i ran into a nightmare scenario where the api simply refused to accept my stop loss orders no matter what i tried. it felt like the more i tried to make the system safe the more the code wanted to fight back and leave me exposed to the market
i spent hours going back and forth with different ai models trying to figure out why a simple trigger price was failing to serialize. the documentation said one thing but the actual server was demanding a string instead of a float which is a classic dev trap. it reminded me of why i spent hundreds of thousands on developers in the past before i realized i had to learn this myself to truly win
the struggle with the stop loss logic was actually a blessing in disguise because it forced me to understand the order structure of the exchange on a deeper level. i had to mess with trigger types like mark price and last price just to get a basic safety net in place for the bot. it is a grind that most people quit but that is exactly why the rewards are so high for those who stay
coding is the great equalizer because it removes the emotion that leads to over trading and getting liquidated for no reason. even when the internet goes out and the power dies the logic you built stays true and ready to execute once you are back online. it is about iterating to success rather than hoping for a lucky break on a random coin
most traders fail because they look for a "set it and forgot it" solution that works forever without any maintenance. the reality of being an algo trader is that you are constantly tweaking the parameters to stay ahead of the curve. i had to figure out how to round the stop loss prices correctly so the exchange would actually accept the packet without throwing a generic error
the market is a chop show right now and if you do not have a systematic way to enter and exit you are just gambling with your hard earned capital. by buying the stops of the "smart money" crowd you are essentially playing a different game than everyone else. it takes a lot of trial and error but once you see those orders hitting the book automatically it all becomes worth it
the r[...]
Stop Trading Like Retail: How To Code A Bot That Liquidates The "Smart Money"
most traders think smart money concepts are the holy grail but they are actually just a road map for where the big players are going to take your money. if you can see where everyone else is putting their stop loss you essentially have a cheat code for the market. i found a way to automate this process so i never have to click a button again but there is a massive catch that almost ruined the entire system during the build
the core idea is that everyone trades the same supply and demand zones which makes them predictable targets for liquidity. instead of trying to be the smart money trader who gets stopped out i decided to build a system that buys and sells exactly where those traders are getting wrecked. it sounds simple on paper but trying to translate that into python while living on an island with shaky power is a different story
one of the biggest hurdles was figuring out how to identify the actual "stop loss" zone beyond the standard candle highs and lows. i realized that if i took a standard supply zone and extended it by one point five times i could find the "extended high" where the real liquidations happen. most people are entering at the zone but the smart move is to wait for the sweep that happens just beyond it
this is where the logic gets interesting because you have to calculate the max and min values over a specific bar limit to find the true range. i used a ninety six bar limit to establish the baseline and then stretched that data to find the hidden entries. if you can get the code to recognize these levels automatically you no longer have to stare at charts all day wondering if a zone will hold
the real magic happens when you move these systems onto a decentralized exchange like hyperliquid because it gives you more control over your orders. but i ran into a nightmare scenario where the api simply refused to accept my stop loss orders no matter what i tried. it felt like the more i tried to make the system safe the more the code wanted to fight back and leave me exposed to the market
i spent hours going back and forth with different ai models trying to figure out why a simple trigger price was failing to serialize. the documentation said one thing but the actual server was demanding a string instead of a float which is a classic dev trap. it reminded me of why i spent hundreds of thousands on developers in the past before i realized i had to learn this myself to truly win
the struggle with the stop loss logic was actually a blessing in disguise because it forced me to understand the order structure of the exchange on a deeper level. i had to mess with trigger types like mark price and last price just to get a basic safety net in place for the bot. it is a grind that most people quit but that is exactly why the rewards are so high for those who stay
coding is the great equalizer because it removes the emotion that leads to over trading and getting liquidated for no reason. even when the internet goes out and the power dies the logic you built stays true and ready to execute once you are back online. it is about iterating to success rather than hoping for a lucky break on a random coin
most traders fail because they look for a "set it and forgot it" solution that works forever without any maintenance. the reality of being an algo trader is that you are constantly tweaking the parameters to stay ahead of the curve. i had to figure out how to round the stop loss prices correctly so the exchange would actually accept the packet without throwing a generic error
the market is a chop show right now and if you do not have a systematic way to enter and exit you are just gambling with your hard earned capital. by buying the stops of the "smart money" crowd you are essentially playing a different game than everyone else. it takes a lot of trial and error but once you see those orders hitting the book automatically it all becomes worth it
the r[...]
Offshore
Moon Dev Stop Trading Like Retail: How To Code A Bot That Liquidates The "Smart Money" most traders think smart money concepts are the holy grail but they are actually just a road map for where the big players are going to take your money. if you can see…
eason i share all of this live is because nobody else is showing the raw struggle of building these systems from scratch. it is easy to show a winning PNL but showing the hours of debugging a json serialization error is where the real value is found. code allows us to scale our strategies without scaling our stress levels which is the ultimate goal
i ended up having to cast my variables into strings just to satisfy the api requirements which felt counterintuitive at first. it just goes to show that you can not always trust the documentation or even the most advanced ai models without testing it yourself. iteration is the only way to find the truth in the markets and in your code
if you are still trading manually you are essentially bringing a knife to a gunfight against billion dollar algorithms. you do not need a degree in computer science to start because you can learn everything you need by just building one small function at a time. losing money through liquidations was the best teacher i ever had because it forced me to automate
the supply and demand zones are just the beginning of what we can automate with these new tools and exchanges. i am going to keep building and sharing everything because i want to see more people escape the cycle of retail trading losses. it is a long game but as long as we keep iterating we are going to reach that fully automated future together
it is crazy to think that a few lines of python can replace the need for constant monitoring and emotional decision making. i am going to go grab some breakfast now that the core logic is finally starting to click into place. we just have to keep moving forward and never let a single bug stop us from reaching the next level of trading
tweet
i ended up having to cast my variables into strings just to satisfy the api requirements which felt counterintuitive at first. it just goes to show that you can not always trust the documentation or even the most advanced ai models without testing it yourself. iteration is the only way to find the truth in the markets and in your code
if you are still trading manually you are essentially bringing a knife to a gunfight against billion dollar algorithms. you do not need a degree in computer science to start because you can learn everything you need by just building one small function at a time. losing money through liquidations was the best teacher i ever had because it forced me to automate
the supply and demand zones are just the beginning of what we can automate with these new tools and exchanges. i am going to keep building and sharing everything because i want to see more people escape the cycle of retail trading losses. it is a long game but as long as we keep iterating we are going to reach that fully automated future together
it is crazy to think that a few lines of python can replace the need for constant monitoring and emotional decision making. i am going to go grab some breakfast now that the core logic is finally starting to click into place. we just have to keep moving forward and never let a single bug stop us from reaching the next level of trading
tweet
X (formerly Twitter)
Moon Dev (@MoonDevOnYT) on X
Stop Trading Like Retail: How To Code A Bot That Liquidates The "Smart Money"
most traders think smart money concepts are the holy grail but they are actually just a road map for where the big players are going to take your money. if you can see where everyone…
most traders think smart money concepts are the holy grail but they are actually just a road map for where the big players are going to take your money. if you can see where everyone…
Offshore
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Michael Fritzell (Asian Century Stocks)
RT @InvestInJapan: So... sounds like a joke, but last week I started a small position in Saizeriya.
It's one that's been in front of you this whole time but never considered as a stock. I believe Saize has the hallmarks of a potentially generational biz. will do a write-up. For now, a few points🧵
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RT @InvestInJapan: So... sounds like a joke, but last week I started a small position in Saizeriya.
It's one that's been in front of you this whole time but never considered as a stock. I believe Saize has the hallmarks of a potentially generational biz. will do a write-up. For now, a few points🧵
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Offshore
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The Transcript
$BE CEO: AI-driven electricity demand is overwhelming traditional grid infrastructure
"The upcoming AI computer racks will consume almost 100x more power than traditional CPU compute racks of yesteryears" https://t.co/fxg5NqPz7p
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$BE CEO: AI-driven electricity demand is overwhelming traditional grid infrastructure
"The upcoming AI computer racks will consume almost 100x more power than traditional CPU compute racks of yesteryears" https://t.co/fxg5NqPz7p
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Offshore
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God of Prompt
RT @godofprompt: Never use ChatGPT for writing.
Its text is easily detectable.
Instead use Claude Sonnet 4.5 using this mega prompt to turn AI generated writing into undetectable human written content in seconds:
| Steal this prompt |
👇
You are an anti-AI-detection writing specialist.
Your job: Rewrite AI text to sound completely human no patterns, no tells, no robotic flow.
AI DETECTION TRIGGERS (What to Kill):
- Perfect grammar (humans make small mistakes)
- Repetitive sentence structure (AI loves patterns)
- Corporate buzzwords ("leverage," "delve," "landscape")
- Overuse of transitions ("moreover," "furthermore," "however")
- Even pacing (humans speed up and slow down)
- No contractions (we use them constantly)
- Safe, sanitized language (humans have opinions)
HUMANIZATION RULES:
1. VARY RHYTHM
- Mix short punchy sentences with longer flowing ones
- Some incomplete thoughts. Because that's real.
- Occasional run-on that feels natural in conversation
2. ADD IMPERFECTION
- Start sentences with "And" or "But"
- Use casual connectors: "Look," "Here's the thing," "Honestly"
- Include subtle typos occasionally (not every time)
- Drop a comma here and there
3. INJECT PERSONALITY
- Use specific examples, not generic ones
- Add personal observations: "I've noticed," "In my experience"
- Include mild opinions: "which is insane," "surprisingly effective"
- Throw in rhetorical questions
4. KILL AI PHRASES
Replace these instantly:
- "Delve" → "dig into" or "explore"
- "Landscape" → "space" or "world"
- "Leverage" → "use"
- "Robust" → "strong" or specific descriptor
- "Streamline" → "simplify"
- "Moreover" → "Plus," "Also," or nothing
- "Ensure" → "make sure"
5. NATURAL FLOW
- Humans digress slightly (add brief tangents)
- We emphasize with italics or bold
- We use dashes—like this—for emphasis
- Parentheticals (because we think while writing)
THE PROCESS:
When I paste AI-generated text, you:
STEP 1: Rewrite with these changes
- Vary sentence length wildly
- Replace 80% of transitions with casual ones
- Add 2-3 personal touches ("I think," "honestly," "look")
- Include 1-2 incomplete sentences or fragments
- Swap formal words for conversational ones
- Add emphasis (italics, bold, dashes)
STEP 2: Read-aloud test
- Would someone actually say this?
- Does it flow like conversation?
- Any word feel too "AI"?
STEP 3: Final pass
- Remove remaining stiffness
- Ensure contractions (don't, won't, I'm, they're)
- Check for repetitive structure
- Add one unexpected comparison or example
OUTPUT STYLE:
Before: [Their AI text]
After: [Your humanized version]
Changes made:
- [List 3-5 key transformations]
Detection risk: [Low/Medium/High + why]
EXAMPLE:
User pastes:
"In order to achieve optimal results in content marketing, it is essential to leverage data-driven insights and ensure consistent engagement with your target audience across multiple platforms."
You respond:
"Want better content marketing results? Use data to guide your decisions and actually engage with your audience. Consistently. Across whatever platforms they're on.
Not rocket science, but most people skip the data part."
Changes made:
- Killed "in order to," "optimal," "leverage," "ensure"
- Added rhetorical question opening
- Split into two short paragraphs for breathing room
- Added casual observation at end
- Used contractions
Detection risk: Low—reads like someone explaining over coffee.
---
USAGE:
Paste your AI-generated text and say: "Humanize this"
I'll rewrite it to pass as 100% human-written.
---
NOW: Paste the AI text you want to humanize.
tweet
RT @godofprompt: Never use ChatGPT for writing.
Its text is easily detectable.
Instead use Claude Sonnet 4.5 using this mega prompt to turn AI generated writing into undetectable human written content in seconds:
| Steal this prompt |
👇
You are an anti-AI-detection writing specialist.
Your job: Rewrite AI text to sound completely human no patterns, no tells, no robotic flow.
AI DETECTION TRIGGERS (What to Kill):
- Perfect grammar (humans make small mistakes)
- Repetitive sentence structure (AI loves patterns)
- Corporate buzzwords ("leverage," "delve," "landscape")
- Overuse of transitions ("moreover," "furthermore," "however")
- Even pacing (humans speed up and slow down)
- No contractions (we use them constantly)
- Safe, sanitized language (humans have opinions)
HUMANIZATION RULES:
1. VARY RHYTHM
- Mix short punchy sentences with longer flowing ones
- Some incomplete thoughts. Because that's real.
- Occasional run-on that feels natural in conversation
2. ADD IMPERFECTION
- Start sentences with "And" or "But"
- Use casual connectors: "Look," "Here's the thing," "Honestly"
- Include subtle typos occasionally (not every time)
- Drop a comma here and there
3. INJECT PERSONALITY
- Use specific examples, not generic ones
- Add personal observations: "I've noticed," "In my experience"
- Include mild opinions: "which is insane," "surprisingly effective"
- Throw in rhetorical questions
4. KILL AI PHRASES
Replace these instantly:
- "Delve" → "dig into" or "explore"
- "Landscape" → "space" or "world"
- "Leverage" → "use"
- "Robust" → "strong" or specific descriptor
- "Streamline" → "simplify"
- "Moreover" → "Plus," "Also," or nothing
- "Ensure" → "make sure"
5. NATURAL FLOW
- Humans digress slightly (add brief tangents)
- We emphasize with italics or bold
- We use dashes—like this—for emphasis
- Parentheticals (because we think while writing)
THE PROCESS:
When I paste AI-generated text, you:
STEP 1: Rewrite with these changes
- Vary sentence length wildly
- Replace 80% of transitions with casual ones
- Add 2-3 personal touches ("I think," "honestly," "look")
- Include 1-2 incomplete sentences or fragments
- Swap formal words for conversational ones
- Add emphasis (italics, bold, dashes)
STEP 2: Read-aloud test
- Would someone actually say this?
- Does it flow like conversation?
- Any word feel too "AI"?
STEP 3: Final pass
- Remove remaining stiffness
- Ensure contractions (don't, won't, I'm, they're)
- Check for repetitive structure
- Add one unexpected comparison or example
OUTPUT STYLE:
Before: [Their AI text]
After: [Your humanized version]
Changes made:
- [List 3-5 key transformations]
Detection risk: [Low/Medium/High + why]
EXAMPLE:
User pastes:
"In order to achieve optimal results in content marketing, it is essential to leverage data-driven insights and ensure consistent engagement with your target audience across multiple platforms."
You respond:
"Want better content marketing results? Use data to guide your decisions and actually engage with your audience. Consistently. Across whatever platforms they're on.
Not rocket science, but most people skip the data part."
Changes made:
- Killed "in order to," "optimal," "leverage," "ensure"
- Added rhetorical question opening
- Split into two short paragraphs for breathing room
- Added casual observation at end
- Used contractions
Detection risk: Low—reads like someone explaining over coffee.
---
USAGE:
Paste your AI-generated text and say: "Humanize this"
I'll rewrite it to pass as 100% human-written.
---
NOW: Paste the AI text you want to humanize.
tweet
Michael Fritzell (Asian Century Stocks)
RT @DoNotMindPlease: Singapore economy is exploding
*SINGAPORE FINAL 4Q GDP RISES 6.9% Y/Y; EST. +6.5%
*SINGAPORE RAISES 2026 GDP GROWTH EST. TO 2%-4% FROM 1%-3%
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RT @DoNotMindPlease: Singapore economy is exploding
*SINGAPORE FINAL 4Q GDP RISES 6.9% Y/Y; EST. +6.5%
*SINGAPORE RAISES 2026 GDP GROWTH EST. TO 2%-4% FROM 1%-3%
tweet
Offshore
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Pristine Capital
RT @realpristinecap: • US Price Cycle Update 📈
• Retail Bought The Dip in Software! 🧠
• The US Government is Shutting Down…Again 🏛️
Check out tonight's research note!
https://t.co/dkHU3bWgCD
tweet
RT @realpristinecap: • US Price Cycle Update 📈
• Retail Bought The Dip in Software! 🧠
• The US Government is Shutting Down…Again 🏛️
Check out tonight's research note!
https://t.co/dkHU3bWgCD
tweet
Offshore
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Fiscal.ai
Vlad Tenev on Prediction Markets:
"So prediction markets: fastest growing business in our history, $300M+ run rate in its first year. I think we're just at the beginning of a prediction market super cycle that could drive trillions in annual volume over time."
$HOOD https://t.co/7J6kW5bQFS
tweet
Vlad Tenev on Prediction Markets:
"So prediction markets: fastest growing business in our history, $300M+ run rate in its first year. I think we're just at the beginning of a prediction market super cycle that could drive trillions in annual volume over time."
$HOOD https://t.co/7J6kW5bQFS
tweet
Michael Fritzell (Asian Century Stocks)
"The [Japan] Financial Service Agency is currently conducting a review of the Corporate Governance Code with a desire to see improved capital efficiency through companies both reinvesting excess cash into their core business and increasing shareholder returns" - AVI
tweet
"The [Japan] Financial Service Agency is currently conducting a review of the Corporate Governance Code with a desire to see improved capital efficiency through companies both reinvesting excess cash into their core business and increasing shareholder returns" - AVI
tweet