Offshore
Video
Dimitry Nakhla | Babylon Capital®
RT @DimitryNakhla: When you kept buying this $AMZN dip all the way down to $200… and now you’re out of dry powder below $200 https://t.co/6gAqHaIeoU
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RT @DimitryNakhla: When you kept buying this $AMZN dip all the way down to $200… and now you’re out of dry powder below $200 https://t.co/6gAqHaIeoU
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Illiquid
Struggled to build a SE Asia data centre and semicon fab basket after I sold Wasion but found two microcaps yesterday!
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Struggled to build a SE Asia data centre and semicon fab basket after I sold Wasion but found two microcaps yesterday!
Chinese AI CAPEX (include SEA buildout as well) will surprise us tweet
X (formerly Twitter)
Zephyr (@zephyr_z9) on X
Chinese AI CAPEX (include SEA buildout as well) will surprise us
Offshore
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The Transcript
Spotify is leveraging AI “Honk” system to accelerate engineering velocity and feature deployment:
$SPOT https://t.co/qa4l9QIsMT
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Spotify is leveraging AI “Honk” system to accelerate engineering velocity and feature deployment:
$SPOT https://t.co/qa4l9QIsMT
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Offshore
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Brady Long
RT @thisguyknowsai: R.I.P basic RAG ☠️
Graph-enhanced retrieval is the new king.
OpenAI, Anthropic, and Microsoft engineers don't build RAG systems like everyone else.
They build knowledge graphs first.
Here are 7 ways to use graph RAG instead of vector search: https://t.co/HdEjy6RslX
tweet
RT @thisguyknowsai: R.I.P basic RAG ☠️
Graph-enhanced retrieval is the new king.
OpenAI, Anthropic, and Microsoft engineers don't build RAG systems like everyone else.
They build knowledge graphs first.
Here are 7 ways to use graph RAG instead of vector search: https://t.co/HdEjy6RslX
tweet
Offshore
Video
Moon Dev
Zero Fluff: A Developer’s Stress-Test of the 192,726% ROI Strategy
if you find a strategy that claims a one hundred and ninety two thousand percent return your first instinct should be to run in the opposite direction because the market is designed to hunt down anyone who believes in magic bullets. the reality is that ninety nine percent of back tests are just lies dressed up in pretty data but there is a specific way to peel back the layers and find the truth before you lose a single dollar of your actual capital
most traders spend their entire lives chasing indicators that look like they print money on a static chart only to watch their account balance vanish the second they turn on a live bot. i spent years losing money with liquidations and over trading because i thought i could outsmart the tape with my own eyes instead of using code as the ultimate filter
i believe that code is the great equalizer because it does not care about your ego or how much you want a trade to work. it only cares about the logic you give it and through my own failures i realized that automating my trading was the only way to escape the cycle of being a liquidity provider for the big institutions
before i learned to code i spent hundreds of thousands on developers for apps because i thought engineering was some kind of dark art i would never be able to master. then i decided to learn live on youtube and now i have fully automated systems doing the heavy lifting for me while most people are still staring at candles at three in the morning
the secret that the industry hides from you is that the best strategies are often the ones that look the most boring on paper. a simple moving average reversal might sound like beginner stuff but when you stress test it against hundreds of weeks of data and double the commission to account for slippage you start to see where the real edge lives
many people ask if i account for fees and slippage when i show these massive returns and the answer is that i always use a multiplier to make sure the results are grounded in reality. if a strategy can still survive when you are paying double the standard commission then you might have actually found something worth incubating with tiny size
most back tests fail because of something called over optimization where you search for parameters so long that you eventually find a profitable line that only works on past data. the real test is seeing how an unoptimized version performs because if the core logic is weak then no amount of math will save it in a live environment
one of the most important steps in this journey is understanding in sample and out of sample testing to ensure your strategy is robust and not just a fluke of a specific period. i test every idea against multiple data sets including hundreds of weeks of bitcoin data because if it cannot survive a two year window it has no business being in my portfolio
i am skeptical of anything that shows a six figure percentage return even when it is my own code which is why i share everything openly for you to fork and test for yourself. you can find the entire source code in my github because i want you to be able to look under the hood and decide if the math makes sense to you before you ever take a risk
the path to success in this game is not about finding a plug and play bot that makes a million dollars overnight but about building a process of research and iteration. i show the good the bad and the ugly parts of this process every single day because i want to bridge the gap between retail traders and the systematic machines that currently dominate the market
even if a back test looks like the holy grail i will always start with tiny size in the live market to see if the execution matches the theory. you should never trade an amount that keeps you up at night because the moment emotion enters the equation you have already lost the battle against the machines
learning to code is the only way to truly own your time and [...]
Zero Fluff: A Developer’s Stress-Test of the 192,726% ROI Strategy
if you find a strategy that claims a one hundred and ninety two thousand percent return your first instinct should be to run in the opposite direction because the market is designed to hunt down anyone who believes in magic bullets. the reality is that ninety nine percent of back tests are just lies dressed up in pretty data but there is a specific way to peel back the layers and find the truth before you lose a single dollar of your actual capital
most traders spend their entire lives chasing indicators that look like they print money on a static chart only to watch their account balance vanish the second they turn on a live bot. i spent years losing money with liquidations and over trading because i thought i could outsmart the tape with my own eyes instead of using code as the ultimate filter
i believe that code is the great equalizer because it does not care about your ego or how much you want a trade to work. it only cares about the logic you give it and through my own failures i realized that automating my trading was the only way to escape the cycle of being a liquidity provider for the big institutions
before i learned to code i spent hundreds of thousands on developers for apps because i thought engineering was some kind of dark art i would never be able to master. then i decided to learn live on youtube and now i have fully automated systems doing the heavy lifting for me while most people are still staring at candles at three in the morning
the secret that the industry hides from you is that the best strategies are often the ones that look the most boring on paper. a simple moving average reversal might sound like beginner stuff but when you stress test it against hundreds of weeks of data and double the commission to account for slippage you start to see where the real edge lives
many people ask if i account for fees and slippage when i show these massive returns and the answer is that i always use a multiplier to make sure the results are grounded in reality. if a strategy can still survive when you are paying double the standard commission then you might have actually found something worth incubating with tiny size
most back tests fail because of something called over optimization where you search for parameters so long that you eventually find a profitable line that only works on past data. the real test is seeing how an unoptimized version performs because if the core logic is weak then no amount of math will save it in a live environment
one of the most important steps in this journey is understanding in sample and out of sample testing to ensure your strategy is robust and not just a fluke of a specific period. i test every idea against multiple data sets including hundreds of weeks of bitcoin data because if it cannot survive a two year window it has no business being in my portfolio
i am skeptical of anything that shows a six figure percentage return even when it is my own code which is why i share everything openly for you to fork and test for yourself. you can find the entire source code in my github because i want you to be able to look under the hood and decide if the math makes sense to you before you ever take a risk
the path to success in this game is not about finding a plug and play bot that makes a million dollars overnight but about building a process of research and iteration. i show the good the bad and the ugly parts of this process every single day because i want to bridge the gap between retail traders and the systematic machines that currently dominate the market
even if a back test looks like the holy grail i will always start with tiny size in the live market to see if the execution matches the theory. you should never trade an amount that keeps you up at night because the moment emotion enters the equation you have already lost the battle against the machines
learning to code is the only way to truly own your time and [...]
Offshore
Moon Dev Zero Fluff: A Developer’s Stress-Test of the 192,726% ROI Strategy if you find a strategy that claims a one hundred and ninety two thousand percent return your first instinct should be to run in the opposite direction because the market is designed…
your financial future because it allows you to test thousands of ideas in a fraction of the time it would take to trade them manually. you do not need to be a math genius to get started because libraries like ta lib do most of the heavy lifting by providing access to almost any indicator you can imagine
i use order flow and liquidation data to add a layer of depth to my strategies that standard indicators simply cannot provide. when you combine the power of python with live data from platforms like hyperliquid you are no longer guessing what might happen but reacting to what is actually happening in the order book
there is a specific road map that takes you from a beginner to an expert and it involves mastering the art of the back test before you ever worry about live execution. most people skip the hard work of research and then wonder why their bots fail the first time the market gets volatile
it is a common limiting belief that you need to be a software engineer to build these systems but the truth is that ai agents like claude can help you write the logic if you give them a solid template to work with. i have built my own templates that allow me to throw any idea at an ai and get a working back test in minutes which has completely changed the speed at which i can iterate
if you are not committed to the top one percent of this industry you will probably just keep scrolling and looking for the next easy signal. but if you are ready to stop being a victim of your own emotions then the tools are right in front of you and they do not cost a fortune to access
the simplest strategies like mean reversion often outperform the most complex neural networks because they are easier to debug and less likely to break when the market structure shifts. i have found that the more moving parts a bot has the more ways it can fail when you are not looking
everything i build is shared in the spirit of collaboration because this industry is much more fun when we are all growing together. i want to see what you find when you take my code and apply your own ideas to it because that is how we all find the next big breakthrough
at the end of the day the goal is not just to have a high roi but to have a system that gives you the freedom to step away from the computer. you can have the best back test in the world but if you are still stressed every time a trade opens then you have not truly achieved automation
the great equalizer is waiting for you to take the first step and learn how to translate your ideas into logic that never sleeps. stop staring at the candles and start building the systems that will trade them for you
tweet
i use order flow and liquidation data to add a layer of depth to my strategies that standard indicators simply cannot provide. when you combine the power of python with live data from platforms like hyperliquid you are no longer guessing what might happen but reacting to what is actually happening in the order book
there is a specific road map that takes you from a beginner to an expert and it involves mastering the art of the back test before you ever worry about live execution. most people skip the hard work of research and then wonder why their bots fail the first time the market gets volatile
it is a common limiting belief that you need to be a software engineer to build these systems but the truth is that ai agents like claude can help you write the logic if you give them a solid template to work with. i have built my own templates that allow me to throw any idea at an ai and get a working back test in minutes which has completely changed the speed at which i can iterate
if you are not committed to the top one percent of this industry you will probably just keep scrolling and looking for the next easy signal. but if you are ready to stop being a victim of your own emotions then the tools are right in front of you and they do not cost a fortune to access
the simplest strategies like mean reversion often outperform the most complex neural networks because they are easier to debug and less likely to break when the market structure shifts. i have found that the more moving parts a bot has the more ways it can fail when you are not looking
everything i build is shared in the spirit of collaboration because this industry is much more fun when we are all growing together. i want to see what you find when you take my code and apply your own ideas to it because that is how we all find the next big breakthrough
at the end of the day the goal is not just to have a high roi but to have a system that gives you the freedom to step away from the computer. you can have the best back test in the world but if you are still stressed every time a trade opens then you have not truly achieved automation
the great equalizer is waiting for you to take the first step and learn how to translate your ideas into logic that never sleeps. stop staring at the candles and start building the systems that will trade them for you
tweet
Offshore
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Michael Fritzell (Asian Century Stocks)
RT @anonymous3nibrv: Yakult announces 2.56% s/o buyback with 8.08% of issued share cancellation. Another cash hoarding company that realises: we don't need this much cash. 2 yrs ago i felt many corp gov changes were 'reactive' - recently i feel they are more 'proactive'. https://t.co/8hihJTh5wt
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RT @anonymous3nibrv: Yakult announces 2.56% s/o buyback with 8.08% of issued share cancellation. Another cash hoarding company that realises: we don't need this much cash. 2 yrs ago i felt many corp gov changes were 'reactive' - recently i feel they are more 'proactive'. https://t.co/8hihJTh5wt
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Michael Fritzell (Asian Century Stocks)
"Buy SaaS. Not overpriced. Mostly moats."
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"Buy SaaS. Not overpriced. Mostly moats."
https://t.co/KRlTbtnA8A tweet
X (formerly Twitter)
Finbarr Taylor (@finbarr) on X
In Defense of SaaS
Offshore
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Michael Fritzell (Asian Century Stocks)
The bull case for Midea
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The bull case for Midea
South Korea uses more industrial robots per worker than any other country—
This chart shows one way to compare automated manufacturing across countries — it plots the number of robots per 1,000 manufacturing employees.
The chart shows very large differences between countries. South Korea stands out, with more than one robot for every ten manufacturing workers.
Singapore comes second, and China ranks third, close to Germany. The United States sits in the middle, close to the European average, below Switzerland, Denmark and Slovenia.
This perspective shows industrial robot adoption in relative terms. In another Data Insight, I looked at robot adoption in absolute terms. From that perspective, China stands out by a large margin: it’s a large economy with a huge manufacturing sector, and it has by far the largest stock of industrial robots.
Much of this expansion has happened recently: China’s annual installations increased 12-fold over a decade, helping it catch up to South Korea in terms of robots per worker.
(This Data Insight was written by @EOrtizOspina.) tweet