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Chips & SaaS
RT @fiscal_ai: Google is the most profitable company in the world.
Can any company surpass them in the next 10 years?
$GOOGL $AAPL $MSFT $AMZN $NVDA https://t.co/pCqL4TwXWw
tweet
RT @fiscal_ai: Google is the most profitable company in the world.
Can any company surpass them in the next 10 years?
$GOOGL $AAPL $MSFT $AMZN $NVDA https://t.co/pCqL4TwXWw
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Offshore
Video
Moon Dev
Chasing Simons: How I Used DeepSeek R1 to Build an Autonomous 24/7 Quant Fleet
most traders are getting wrecked by high frequency firms while a new open source model just handed us the keys to the kingdom. if you think you need a computer science degree to build a profitable system you are already losing a war you haven't even joined yet. there is a specific reason why deepseek r1 is scaring the legacy ai giants and it has everything to do with how it thinks through complex trading logic
i spent hundreds of thousands of dollars on developers in the past because i was convinced i couldn't handle the syntax myself. that mistake cost me more than just money it cost me the ability to iterate at the speed of the market. when you realize that code is the great equalizer you start to see that liquidations are just a symptom of emotional trading
the entry of deepseek r1 into the space changes the math for every solo trader. it uses a reasoning process that doesn't just guess the next word but actually builds a mental model of the code before it starts typing. i started using it to build out a funding rate bot because i wanted a system that could earn while i slept without the stress of directional guessing
most people look at funding rates as a boring small percentage play but they miss the compounding power of a bot that never gets tired. r1 was able to handle the complex api calls for hyperliquid with a precision that usually requires hours of debugging. the trick is in how you prompt the model to think before it executes the python script
if you just ask for a bot it will give you a template but if you ask it to reason through the risk management first you get a weapon. i found that giving the ai a clear role as a quantitative engineer makes it prioritize the edge cases that usually blow up accounts. this is how we move from gambling on candle sticks to running a serious business
the infrastructure is the part that usually trips people up especially when they try to connect to an exchange for the first time. hyperliquid has become my go to because the transparency is unmatched and the api is built for speed. r1 helped me bridge the gap between a raw idea and a script that actually places orders in the order book
one of the biggest loops to close is the fear of losing your entire balance due to a coding error. i mitigate this by having the ai write its own test cases before we ever go live with a single dollar. seeing the bot pass a simulation built by a reasoning model gives you the confidence to finally turn off the manual terminal
the real magic happened when i asked the model to optimize my social arbitrage strategy. it recognized patterns in how news moves small cap tokens that i had overlooked for years. it turns out that speed is only half the battle the other half is knowing exactly which data points to ignore
iterating to success means you are going to see errors and that is where most people quit and go back to over trading. with r1 you just feed the error back into the reasoning loop and it explains exactly why the logic failed. it feels like having a senior developer sitting next to you who never gets frustrated and only costs pennies to run
i remember the days when i would stay up until four am staring at forty x leverage positions and praying for a bounce. code changed that by removing the man from the middle of the trade and replacing him with a cold logic. now i spend my time building more bots instead of hoping the market does what i want it to do
deepseek is especially good at handling the python libraries like ccxt or the specific sdks for decentralized exchanges. it understood the difference between a market order and a limit order in a way that prevented unnecessary slippage. every basis point you save in execution adds up to a massive difference at the end of the year
the scariest part for the big players is that this technology is now accessible to anyone with a laptop and an internet connection. the moat t[...]
Chasing Simons: How I Used DeepSeek R1 to Build an Autonomous 24/7 Quant Fleet
most traders are getting wrecked by high frequency firms while a new open source model just handed us the keys to the kingdom. if you think you need a computer science degree to build a profitable system you are already losing a war you haven't even joined yet. there is a specific reason why deepseek r1 is scaring the legacy ai giants and it has everything to do with how it thinks through complex trading logic
i spent hundreds of thousands of dollars on developers in the past because i was convinced i couldn't handle the syntax myself. that mistake cost me more than just money it cost me the ability to iterate at the speed of the market. when you realize that code is the great equalizer you start to see that liquidations are just a symptom of emotional trading
the entry of deepseek r1 into the space changes the math for every solo trader. it uses a reasoning process that doesn't just guess the next word but actually builds a mental model of the code before it starts typing. i started using it to build out a funding rate bot because i wanted a system that could earn while i slept without the stress of directional guessing
most people look at funding rates as a boring small percentage play but they miss the compounding power of a bot that never gets tired. r1 was able to handle the complex api calls for hyperliquid with a precision that usually requires hours of debugging. the trick is in how you prompt the model to think before it executes the python script
if you just ask for a bot it will give you a template but if you ask it to reason through the risk management first you get a weapon. i found that giving the ai a clear role as a quantitative engineer makes it prioritize the edge cases that usually blow up accounts. this is how we move from gambling on candle sticks to running a serious business
the infrastructure is the part that usually trips people up especially when they try to connect to an exchange for the first time. hyperliquid has become my go to because the transparency is unmatched and the api is built for speed. r1 helped me bridge the gap between a raw idea and a script that actually places orders in the order book
one of the biggest loops to close is the fear of losing your entire balance due to a coding error. i mitigate this by having the ai write its own test cases before we ever go live with a single dollar. seeing the bot pass a simulation built by a reasoning model gives you the confidence to finally turn off the manual terminal
the real magic happened when i asked the model to optimize my social arbitrage strategy. it recognized patterns in how news moves small cap tokens that i had overlooked for years. it turns out that speed is only half the battle the other half is knowing exactly which data points to ignore
iterating to success means you are going to see errors and that is where most people quit and go back to over trading. with r1 you just feed the error back into the reasoning loop and it explains exactly why the logic failed. it feels like having a senior developer sitting next to you who never gets frustrated and only costs pennies to run
i remember the days when i would stay up until four am staring at forty x leverage positions and praying for a bounce. code changed that by removing the man from the middle of the trade and replacing him with a cold logic. now i spend my time building more bots instead of hoping the market does what i want it to do
deepseek is especially good at handling the python libraries like ccxt or the specific sdks for decentralized exchanges. it understood the difference between a market order and a limit order in a way that prevented unnecessary slippage. every basis point you save in execution adds up to a massive difference at the end of the year
the scariest part for the big players is that this technology is now accessible to anyone with a laptop and an internet connection. the moat t[...]
Offshore
Moon Dev Chasing Simons: How I Used DeepSeek R1 to Build an Autonomous 24/7 Quant Fleet most traders are getting wrecked by high frequency firms while a new open source model just handed us the keys to the kingdom. if you think you need a computer science…
hat the hedge funds built around their proprietary systems is evaporating. we are now in an era where the best ideas win not just the ones with the most funding
a lot of people ask me if they should start with gpt four or claude but i tell them to look at the reasoning tokens of r1. there is a depth to the output that allows it to catch logical fallacies in your trading plan before they cost you real money. it is about building a system that is robust enough to survive a black swan event
my journey from getting liquidated to running a fully automated system was paved with a lot of mistakes and expensive lessons. i share all of this because i want you to realize that you are closer to your goals than you think. the bot we built today is just the beginning of a larger shift in how we approach wealth
when the market is volatile most traders panic and make the worst decisions of their careers. my bots just see another data point and execute the risk controls that were programmed into them months ago. that level of peace of mind is the real reason why i advocate for automation every single day
we are moving into a phase where your ability to communicate with ai will dictate your financial trajectory. it is no longer about how well you can click a buy button but how well you can architect a system. r1 is just the first of many tools that will make this process even faster
if you keep waiting for the perfect time to start you are just giving the institutions more time to take your money. the barrier to entry is gone and the tools are literally sitting on your screen right now. it is time to stop being the liquidity for someone else and start being the one who owns the systems
i will keep iterating and building in the open because that is how we all get better. the bots are trading right now while i talk to you and that is the power of code. i hope you take this and start building your own version of an automated future
tweet
a lot of people ask me if they should start with gpt four or claude but i tell them to look at the reasoning tokens of r1. there is a depth to the output that allows it to catch logical fallacies in your trading plan before they cost you real money. it is about building a system that is robust enough to survive a black swan event
my journey from getting liquidated to running a fully automated system was paved with a lot of mistakes and expensive lessons. i share all of this because i want you to realize that you are closer to your goals than you think. the bot we built today is just the beginning of a larger shift in how we approach wealth
when the market is volatile most traders panic and make the worst decisions of their careers. my bots just see another data point and execute the risk controls that were programmed into them months ago. that level of peace of mind is the real reason why i advocate for automation every single day
we are moving into a phase where your ability to communicate with ai will dictate your financial trajectory. it is no longer about how well you can click a buy button but how well you can architect a system. r1 is just the first of many tools that will make this process even faster
if you keep waiting for the perfect time to start you are just giving the institutions more time to take your money. the barrier to entry is gone and the tools are literally sitting on your screen right now. it is time to stop being the liquidity for someone else and start being the one who owns the systems
i will keep iterating and building in the open because that is how we all get better. the bots are trading right now while i talk to you and that is the power of code. i hope you take this and start building your own version of an automated future
tweet
Offshore
Video
Moon Dev
I wasn’t going to post this and say goodbye bc of openclaw
But I’ve decided to give you the heads up
On Thursday I will be gone, heads down, I found something huge. https://t.co/FjgsOaYPFf
tweet
I wasn’t going to post this and say goodbye bc of openclaw
But I’ve decided to give you the heads up
On Thursday I will be gone, heads down, I found something huge. https://t.co/FjgsOaYPFf
tweet
Offshore
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Moon Dev
automated trading closing
ive figured out automated trading for myself and hundreds of people like you and I
i teach you step by step how to automate your trading
and give you all the quant tools you need to crush it
but im closing that opportunity, if you want all access to everything i know about automated trading
this is your last chance: https://t.co/EHUr5aAxhF
dont miss it
moon dev
tweet
automated trading closing
ive figured out automated trading for myself and hundreds of people like you and I
i teach you step by step how to automate your trading
and give you all the quant tools you need to crush it
but im closing that opportunity, if you want all access to everything i know about automated trading
this is your last chance: https://t.co/EHUr5aAxhF
dont miss it
moon dev
tweet
AkhenOsiris
It's consensus now, we got that going for us!
tweet
It's consensus now, we got that going for us!
JUNE 2028.
The S&P is down 38% from its highs. Unemployment just printed 10.2%. Private credit is unraveling. Prime mortgages are cracking. AI didn’t disappoint. It exceeded every expectation.
What happened?
https://t.co/JzzwCrbJgS tweet
AkhenOsiris
Are all Citrini readers reading about impending bear market mulling over their discretionary substack subscriptions? 😂
tweet
Are all Citrini readers reading about impending bear market mulling over their discretionary substack subscriptions? 😂
JUNE 2028.
The S&P is down 38% from its highs. Unemployment just printed 10.2%. Private credit is unraveling. Prime mortgages are cracking. AI didn’t disappoint. It exceeded every expectation.
What happened?
https://t.co/JzzwCrbJgS tweet
Offshore
Photo
AkhenOsiris
$CRWD CEO Kurtz responds to Claude Cybersecurity
tweet
$CRWD CEO Kurtz responds to Claude Cybersecurity
Can AI replace security platforms? I asked Claude to build a CrowdStrike replacement.
Claude: "I have to be straightforward: building a replacement for CrowdStrike isn't something I can do here. CrowdStrike is a massive platform built by thousands of engineers over a decade-plus. Its core capabilities include real-time kernel-level endpoint monitoring across millions of devices, a proprietary threat intelligence graph built from trillions of security events...That's not something you can replicate with a script — it's an infrastructure product."
If you want to create AI, you need GPUs. If you want to deploy AI, you need security. That's not a hallucination – it's a fact.
Watch the demo → https://t.co/77OnRmpjUx tweet
Moon Dev
Renting Wall Street Power For $1/Hour: Use Cloud GPUs And Python To Automate Your Trading Edge
no more paid courses needed because the actual secrets to building an edge are hidden in sight within the code you write yourself instead of some guru’s outdated pdf. most people think using ai in trading is just asking a chatbot where bitcoin is going next but they are looking at the wrong data entirely.
my name is moon dev and i believe that code is the great equalizer because through losing money with liquidations and over trading i knew i had to automate my trading. in the past i spent hundreds of thousands on devs for apps thinking i would not be able to code myself but with bots you must iterate to success.
i decided to learn live on youtube and now we are here with fully automated systems trading for me instead of getting liquidated. if you have ever felt that punch in the gut when a position gets wiped out you know why i am obsessed with removing the human heart from the execution.
to actually win in this game you have to follow a process called the rbi system which stands for research backtest and implement. most beginners fail because they try to build a bot immediately without knowing if their strategy even has a statistical edge in the historical data.
the r is for research where you look at academic papers on google scholar or listen to veteran quants on podcasts to find ideas that have worked for decades. once you have a basket of ideas you move to the b which is backtesting to see how that idea would have performed over the last few years.
if the backtest shows you a profit only then do you move to the i for implementation with tiny size to see if it works in the live market. you are probably wondering why everyone else is looking at the same open high low close volume data while getting chopped up every single day.
the real alpha is hidden in unique data specifically liquidation data because when traders get forced out of their positions it creates a massive vacuum that price often fills. everyone else is looking at what already happened while we are looking at the forced selling and buying that is about to drive the next move.
this is where the machine learning comes in because we use something called an lstm or long short term memory neural network to find patterns in these sequences. lstms are specialized brains for numerical time series data which is why they are much better at predicting price moves than something like chatgpt.
chatbots are great for writing code or chatting but they suck at predicting numbers because they were not built for time series data. a transformer model like the one inside cursor or claude looks at text context while our lstm looks at the relationship between liquidation spikes and five minute price targets.
you might be tempted to run these models on your local computer but you will quickly realize that training on 17 million records can take 25 hours or more. i realized that as a trader i am making a trade of either dollars or time when i decide where to train my models.
you can actually rent high end gpus in the cloud like an rtx 4090 or an a100 for less than a dollar an hour and get 20 hours of your life back. training a model 10 times faster than your home computer means you can iterate and find winning strategies while the rest of the world is still waiting for their progress bar.
one of the biggest roadblocks you will hit is when your data has infinite values because some altcoin prices are so close to zero they break the math. we found nearly 8000 infinite targets in our data set which would have caused an infinite loss during training and crashed the entire model.
instead of just deleting that data we implemented a capping strategy to keep the math sane while preserving the raw alpha of those small cap liquidations. we do not want to discriminate against small coins because a 100k liquidation on a tiny asset can still be a massive signal for the broader market.
[...]
Renting Wall Street Power For $1/Hour: Use Cloud GPUs And Python To Automate Your Trading Edge
no more paid courses needed because the actual secrets to building an edge are hidden in sight within the code you write yourself instead of some guru’s outdated pdf. most people think using ai in trading is just asking a chatbot where bitcoin is going next but they are looking at the wrong data entirely.
my name is moon dev and i believe that code is the great equalizer because through losing money with liquidations and over trading i knew i had to automate my trading. in the past i spent hundreds of thousands on devs for apps thinking i would not be able to code myself but with bots you must iterate to success.
i decided to learn live on youtube and now we are here with fully automated systems trading for me instead of getting liquidated. if you have ever felt that punch in the gut when a position gets wiped out you know why i am obsessed with removing the human heart from the execution.
to actually win in this game you have to follow a process called the rbi system which stands for research backtest and implement. most beginners fail because they try to build a bot immediately without knowing if their strategy even has a statistical edge in the historical data.
the r is for research where you look at academic papers on google scholar or listen to veteran quants on podcasts to find ideas that have worked for decades. once you have a basket of ideas you move to the b which is backtesting to see how that idea would have performed over the last few years.
if the backtest shows you a profit only then do you move to the i for implementation with tiny size to see if it works in the live market. you are probably wondering why everyone else is looking at the same open high low close volume data while getting chopped up every single day.
the real alpha is hidden in unique data specifically liquidation data because when traders get forced out of their positions it creates a massive vacuum that price often fills. everyone else is looking at what already happened while we are looking at the forced selling and buying that is about to drive the next move.
this is where the machine learning comes in because we use something called an lstm or long short term memory neural network to find patterns in these sequences. lstms are specialized brains for numerical time series data which is why they are much better at predicting price moves than something like chatgpt.
chatbots are great for writing code or chatting but they suck at predicting numbers because they were not built for time series data. a transformer model like the one inside cursor or claude looks at text context while our lstm looks at the relationship between liquidation spikes and five minute price targets.
you might be tempted to run these models on your local computer but you will quickly realize that training on 17 million records can take 25 hours or more. i realized that as a trader i am making a trade of either dollars or time when i decide where to train my models.
you can actually rent high end gpus in the cloud like an rtx 4090 or an a100 for less than a dollar an hour and get 20 hours of your life back. training a model 10 times faster than your home computer means you can iterate and find winning strategies while the rest of the world is still waiting for their progress bar.
one of the biggest roadblocks you will hit is when your data has infinite values because some altcoin prices are so close to zero they break the math. we found nearly 8000 infinite targets in our data set which would have caused an infinite loss during training and crashed the entire model.
instead of just deleting that data we implemented a capping strategy to keep the math sane while preserving the raw alpha of those small cap liquidations. we do not want to discriminate against small coins because a 100k liquidation on a tiny asset can still be a massive signal for the broader market.
[...]
Offshore
Moon Dev Renting Wall Street Power For $1/Hour: Use Cloud GPUs And Python To Automate Your Trading Edge no more paid courses needed because the actual secrets to building an edge are hidden in sight within the code you write yourself instead of some guru’s…
a massive shift happens when you realize you should look at all crypto liquidations but only predict the price of bitcoin specifically. by filtering 17 million records to just btc targets while keeping global market sentiment we create a much more powerful signal for the king of crypto.
it is easy to feel defeated when the market is rigged against you but code allows you to remove the one thing that always fails which is your emotions. jim simons built a net worth of over 31 billion dollars because he realized there are patterns a robot can trade that a human can never see.
the goal isn’t to find a magic bot that makes you a million dollars overnight because that is just a scam designed to drain your wallet. the goal is to build a swarm of small profitable systems that give you your time and your freedom back so you can be free as a bird.
if you are tired of the red candle emails and the emotional rollercoaster the only way out is through automation and relentless testing of your ideas. just keep making your systems better and better every single day because that is exactly what the top 0.1 percent are doing right now.
tweet
it is easy to feel defeated when the market is rigged against you but code allows you to remove the one thing that always fails which is your emotions. jim simons built a net worth of over 31 billion dollars because he realized there are patterns a robot can trade that a human can never see.
the goal isn’t to find a magic bot that makes you a million dollars overnight because that is just a scam designed to drain your wallet. the goal is to build a swarm of small profitable systems that give you your time and your freedom back so you can be free as a bird.
if you are tired of the red candle emails and the emotional rollercoaster the only way out is through automation and relentless testing of your ideas. just keep making your systems better and better every single day because that is exactly what the top 0.1 percent are doing right now.
tweet
X (formerly Twitter)
Moon Dev (@MoonDevOnYT) on X
Renting Wall Street Power For $1/Hour: Use Cloud GPUs And Python To Automate Your Trading Edge
no more paid courses needed because the actual secrets to building an edge are hidden in sight within the code you write yourself instead of some guru’s outdated…
no more paid courses needed because the actual secrets to building an edge are hidden in sight within the code you write yourself instead of some guru’s outdated…