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HUDA Market Sec 56, #Gurugram Open Sewer, Water Logging, and Waste Dump - Everything in 1 single screen. Dare for MCG commissioner to cross this market on his feet. Nayab Saini CMO Haryana Central Pollution Control Board MCG DC Gurugram Sanjiv Kapoor @gsam2411 खुरपेंच ravish kumar Manohar Lal Narendra...

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Vivek Lamba 🇮🇳

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12,015 次观看 • 1 年前 •via X (Twitter)

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Dwarka express Road. How is this acceptable in any civilised country? Why government is not holding one responsible? DC Gurugram MCG CMO Haryana GMDA PMO India Nayab Saini Central Pollution Control Board Nitin Gadkari Office Of Nitin Gadkari Narendra Modi
0:37

Dwarka express Road. How is this acceptable in any civilised country? Why government is not holding one responsible? DC Gurugram MCG CMO Haryana GMDA PMO India Nayab Saini Central Pollution Control Board Nitin Gadkari Office Of Nitin Gadkari Narendra Modi

मेरा गुड़गांव GurGaon 🇮🇳

10,581 次观看 • 1 年前

🔺 SAME SPOT ‼️ Wazirabad Again, Trolley moved ahead towards road. Cows are joined with Buffalo’s MCG DC Gurugram #YeThikKarkeDikhao खुरपेंच Jasmine @gsam2411 DIPRO Gurugram ashishkashyap Dr Aniruddha Malpani, MD DEEPAK AHUJA Sanjiv Kapoor SUHEL SETH @NamitaBalyan Central Pollution Control Board CMO Haryana Narendra Modi Dr. Leena Dhankhar Gurugram News गुरुग्राम न्यूज़ Abhinav Pandey ravish kumar Ravindra Singh Sheoran The Exploited TaxPayer Saurabh shukla Ajit Anjum Manohar Lal Gurgaon LIVE! #YeThikKarkeDikhao खुरपेंच khurpench wala
0:21

🔺 SAME SPOT ‼️ Wazirabad Again, Trolley moved ahead towards road. Cows are joined with Buffalo’s MCG DC Gurugram #YeThikKarkeDikhao खुरपेंच Jasmine @gsam2411 DIPRO Gurugram ashishkashyap Dr Aniruddha Malpani, MD DEEPAK AHUJA Sanjiv Kapoor SUHEL SETH @NamitaBalyan Central Pollution Control Board CMO Haryana Narendra Modi Dr. Leena Dhankhar Gurugram News गुरुग्राम न्यूज़ Abhinav Pandey ravish kumar Ravindra Singh Sheoran The Exploited TaxPayer Saurabh shukla Ajit Anjum Manohar Lal Gurgaon LIVE! #YeThikKarkeDikhao खुरपेंच khurpench wala

Vivek Lamba 🇮🇳

28,350 次观看 • 1 年前

Shocking inefficiency by GMDA 🆘️ The Bajghera Underpass on #DwarkaExpressway flooded because water from Old Bajghera Rail Over Bridge & Palam Vihar was routed into #NHAI's Dwarka Expressway Underpass instead of the main BIG drain along the surface road. Is it done purposefully? DC Gurugram DIPRO Gurugram MCG GMDA are NOT PERMORMING as per expectations of the Gurgaon residents. The volume of water drained in underpass was such that all 7 high capacity pumps failed to dewater the underpass leaving residents & commuters to suffer. Is #GMDA intentionally sabotaging developments happening around Dwarka Expressway #Gurgaon? This is unacceptable! #Gurugram #GurgaonRains #GurugramRains Kindly take STRICT ACTION against the responsible persons behind this wrong doings. To: Sh. Nitin Gadkari Office Of Nitin Gadkari Nayab Saini PMO India Narendra Modi VAIBHAV DANGE वैभव डांगे Build India with Vaibhav Dange NHAI MORTHINDIA Gurugram Police Gurugram Traffic Police CMO Haryana DXP-GDA Manohar Lal Larsen & Toubro
0:07

Shocking inefficiency by GMDA 🆘️ The Bajghera Underpass on #DwarkaExpressway flooded because water from Old Bajghera Rail Over Bridge & Palam Vihar was routed into #NHAI's Dwarka Expressway Underpass instead of the main BIG drain along the surface road. Is it done purposefully? DC Gurugram DIPRO Gurugram MCG GMDA are NOT PERMORMING as per expectations of the Gurgaon residents. The volume of water drained in underpass was such that all 7 high capacity pumps failed to dewater the underpass leaving residents & commuters to suffer. Is #GMDA intentionally sabotaging developments happening around Dwarka Expressway #Gurgaon? This is unacceptable! #Gurugram #GurgaonRains #GurugramRains Kindly take STRICT ACTION against the responsible persons behind this wrong doings. To: Sh. Nitin Gadkari Office Of Nitin Gadkari Nayab Saini PMO India Narendra Modi VAIBHAV DANGE वैभव डांगे Build India with Vaibhav Dange NHAI MORTHINDIA Gurugram Police Gurugram Traffic Police CMO Haryana DXP-GDA Manohar Lal Larsen & Toubro

प्रखर Dwarka Expressway |🇮🇳

19,922 次观看 • 2 年前

🚨Famous Waziabad Chowk Again Govt has opened 1 more liquor shop just next to first one. MCG removed the trolly and just kept it little left and a new garbage place is being developed. Govt has no process to collect garbage from home, expect citizens to throw garbage on main roads (centre of city) so that it becomes easy for them to collect from 1 single point. This is actually a strategy by MCG GMDA #YeThikKarkeDikhao Dr. Leena Dhankhar SUHEL SETH खुरपेंच Amaltas Maj Gen (Dr) YashMor Dr Aniruddha Malpani, MD Anuradha Tiwari @NamitaBalyan DC Gurugram Nayab Saini Sanjiv Kapoor Gurgaon LIVE! मेरा गुड़गांव GurGaon 🇮🇳 Central Pollution Control Board
0:42

🚨Famous Waziabad Chowk Again Govt has opened 1 more liquor shop just next to first one. MCG removed the trolly and just kept it little left and a new garbage place is being developed. Govt has no process to collect garbage from home, expect citizens to throw garbage on main roads (centre of city) so that it becomes easy for them to collect from 1 single point. This is actually a strategy by MCG GMDA #YeThikKarkeDikhao Dr. Leena Dhankhar SUHEL SETH खुरपेंच Amaltas Maj Gen (Dr) YashMor Dr Aniruddha Malpani, MD Anuradha Tiwari @NamitaBalyan DC Gurugram Nayab Saini Sanjiv Kapoor Gurgaon LIVE! मेरा गुड़गांव GurGaon 🇮🇳 Central Pollution Control Board

Vivek Lamba 🇮🇳

35,363 次观看 • 1 年前

URGENT MCG Bharti Manish Harsana Central Pollution Control Board #TigraChowk, Sector 57, facing Lotus Valley School & Nirvana The Close Apartments, Sector 50, #Ward15. This is sewage water, not rainwater. It has remained unresolved for more than 48 hours, turning a major thoroughfare into a large, foul-smelling sewage lake. The overpowering ammonia/urine stench and acrid fumes are unbearable, forcing pedestrians and cyclists to navigate through it. More than a lakh people live in this area, many of them community workers and domestic staff who do not have organised RWA bodies to represent them. Many live in Tigra and say their concerns are not heard by landlords. Pradeep Dahiya ji Yash Jaluka ji Our ward 15 has such a MCG sanitation department, JE, SDO and ward councillor safai karamchsris MCG contractors and engineers…How can a sewage lake be allowed to remain in the middle of one of the busiest thoroughfares for more than 48 hours? Please send the concerned team for an urgent site inspection, identify the source and clear the sewage immediately. Kindly acknowledge and confirm action taken. Thank you Directorate of Urban Local Bodies, Haryana Mukesh Sharma DC Gurugram
1:01

URGENT MCG Bharti Manish Harsana Central Pollution Control Board #TigraChowk, Sector 57, facing Lotus Valley School & Nirvana The Close Apartments, Sector 50, #Ward15. This is sewage water, not rainwater. It has remained unresolved for more than 48 hours, turning a major thoroughfare into a large, foul-smelling sewage lake. The overpowering ammonia/urine stench and acrid fumes are unbearable, forcing pedestrians and cyclists to navigate through it. More than a lakh people live in this area, many of them community workers and domestic staff who do not have organised RWA bodies to represent them. Many live in Tigra and say their concerns are not heard by landlords. Pradeep Dahiya ji Yash Jaluka ji Our ward 15 has such a MCG sanitation department, JE, SDO and ward councillor safai karamchsris MCG contractors and engineers…How can a sewage lake be allowed to remain in the middle of one of the busiest thoroughfares for more than 48 hours? Please send the concerned team for an urgent site inspection, identify the source and clear the sewage immediately. Kindly acknowledge and confirm action taken. Thank you Directorate of Urban Local Bodies, Haryana Mukesh Sharma DC Gurugram

Ruchika Sethi

13,096 次观看 • 29 天前

It must take enormous motivation to get ready every morning, commute from Ballabgarh towards Gurugram, arrive at a bus stand like this -and still take out your phone, record it, share it and hope that this time someone will care enough to act. Our dear member Sumit Tayal has been doing exactly that for 3 years. 3 years of documenting and reporting waste dumping and burning. 3 years of raising concerns. 3 years of hoping the civic authorities -including CAQM/CPCB -will hold the local authorities accountable and fix ground situation And yet, here we are. Open dumping. Open burning. Food waste, cups, plates, fruit waste, straws and plastic around vendor stalls. Stray cattle. Open defecation. Road dust. Filth. As Sumit says in the video: “सामने ही पुलिस चौकी और नगर निगम फरीदाबाद का कार्यालय भी है, तब ये हाल है।” The police post is right there. The Municipal Corporation Faridabad office is right there. Then how does this become #normal? PMO India -how does the government expect people to shift to public transport to reduce congestion and emissions when this is what awaits them? We aren’t asking for an airport lounge. We are asking for a decent bus stand in 2026: clean surroundings, functional toilets, drinking water, proper waste collection, source reduction and segregation, a system for vendors to manage their waste, no dumping or burning and a paved, usable approach road. And yes, we have all seen how quickly public spaces can be cleaned and made presentable when a VIP event is expected. So clearly, it can be done. But perhaps ordinary commuters know the truth: #We_are_not_VIPs_But_We_pay_taxes, use public transport and ask for basic civic infrastructure -while being lectured about our bhagidari. mool chand sharma ji a bus stand in 2026 should not look like 1426. Vipul Goel ji The SWM Rules, 2026 are in force. Three years of citizen documentation is enough. Please inspect. Please enforce. Please fix. And please tell citizens what action has been taken. Sumit has done his part. Now, who will do theirs? CMO Haryana Haryana Roadways Chief Secretary Haryana Directorate of Urban Local Bodies, Haryana Haryana State Pollution Control Board HSPCB, Ballabgarh region Central Pollution Control Board Commission for Air Quality Management #Ballabgarh #Faridabad #HaryanaRoadways #PublicTransport #WasteManagement #SWMRules2026 #OpenDumping #WasteBurning #CleanAirBharat #CivicAccountability #SmartCity Press Trust of India MoEF&CC Ministry of Health
1:48

It must take enormous motivation to get ready every morning, commute from Ballabgarh towards Gurugram, arrive at a bus stand like this -and still take out your phone, record it, share it and hope that this time someone will care enough to act. Our dear member Sumit Tayal has been doing exactly that for 3 years. 3 years of documenting and reporting waste dumping and burning. 3 years of raising concerns. 3 years of hoping the civic authorities -including CAQM/CPCB -will hold the local authorities accountable and fix ground situation And yet, here we are. Open dumping. Open burning. Food waste, cups, plates, fruit waste, straws and plastic around vendor stalls. Stray cattle. Open defecation. Road dust. Filth. As Sumit says in the video: “सामने ही पुलिस चौकी और नगर निगम फरीदाबाद का कार्यालय भी है, तब ये हाल है।” The police post is right there. The Municipal Corporation Faridabad office is right there. Then how does this become #normal? PMO India -how does the government expect people to shift to public transport to reduce congestion and emissions when this is what awaits them? We aren’t asking for an airport lounge. We are asking for a decent bus stand in 2026: clean surroundings, functional toilets, drinking water, proper waste collection, source reduction and segregation, a system for vendors to manage their waste, no dumping or burning and a paved, usable approach road. And yes, we have all seen how quickly public spaces can be cleaned and made presentable when a VIP event is expected. So clearly, it can be done. But perhaps ordinary commuters know the truth: #We_are_not_VIPs_But_We_pay_taxes, use public transport and ask for basic civic infrastructure -while being lectured about our bhagidari. mool chand sharma ji a bus stand in 2026 should not look like 1426. Vipul Goel ji The SWM Rules, 2026 are in force. Three years of citizen documentation is enough. Please inspect. Please enforce. Please fix. And please tell citizens what action has been taken. Sumit has done his part. Now, who will do theirs? CMO Haryana Haryana Roadways Chief Secretary Haryana Directorate of Urban Local Bodies, Haryana Haryana State Pollution Control Board HSPCB, Ballabgarh region Central Pollution Control Board Commission for Air Quality Management #Ballabgarh #Faridabad #HaryanaRoadways #PublicTransport #WasteManagement #SWMRules2026 #OpenDumping #WasteBurning #CleanAirBharat #CivicAccountability #SmartCity Press Trust of India MoEF&CC Ministry of Health

CitizensForCleanAirBharat

30,372 次观看 • 1 个月前

A few months back, I was called by the embassy to arrange a meeting about Zimbabwean goods. I fobbed them off. However, they then roped in another businessman to go see them. He called me & asked for advice. I told him they a bunch of idiots & can go hang. He was already on his way, so he ended up going to have a listen to what they said. Just to jog your memory, remember Hopewell visited me a few months back? We actually spoke about it briefly. The embassy & its incompetent idiots that work there have this GRAND idea that they are going to bring in Zimbabwean goods & sell them. With the help of ZimTrade, apparently. They actually believe they can get someone here in the UK to be the sole distributor for Mazoe. Anyway, cut a long story short, the embassy is really trying hard to rope in investors to bring in dry goods from Zimbabwe. They want to get their fingers in the pie. Well, all I can say is those fingers will get burnt because the pie is hot. Dry goods are shipped into this country 🇬🇧 by Zimbabweans. Black Zimbabweans & myself! We hold a monopoly on the market & no matter how much the embassy or some guy wants to try convince you to invest you will fail. We have been doing this for years & know exactly how it works. Those products you find in your favourite shops come from either of us & we work together. Ask the last 6 people that have tried to enter the market over the past 5 years what happend to them. 1 container 2 container NONE! We will soak up your first container so you think you done well the moment your second container is on the water we will dump your first container on the market. It will be flooded that you won't even sell a single case of that second container. We know how it works & nobody can get in our way. Coke cola owns Schweppes & they would never drop the likes of us who supply the majority of the UK for someone who doesn't have a market at all. You can have 1 million followers & will fail. We will make sure of that. We control the market. So before you even think of sinking £30k into shipping a 40ft container to the UK remember there is us who you will have to deal with & you don't want to price war with us. Consider this a friendly warning Mr Chinono! This is our turf & you will lose a lot of money. I have NDA's signed by businessmen who have asked for my advice when it comes to importation of dry goods if any of them have tried to rope you in I will sue them to Kingdom come. This is a very cutthroat industry & we will crush you & any other person who gets in our way, like how we have been crushing people for years & nothing will change. Anyone who want to bring dry goods into this country comes through me. We will see if we can fit you into the circle. That's the only way you will succeed otherwise its war. Again, consider this a friendly warning!
3:43

A few months back, I was called by the embassy to arrange a meeting about Zimbabwean goods. I fobbed them off. However, they then roped in another businessman to go see them. He called me & asked for advice. I told him they a bunch of idiots & can go hang. He was already on his way, so he ended up going to have a listen to what they said. Just to jog your memory, remember Hopewell visited me a few months back? We actually spoke about it briefly. The embassy & its incompetent idiots that work there have this GRAND idea that they are going to bring in Zimbabwean goods & sell them. With the help of ZimTrade, apparently. They actually believe they can get someone here in the UK to be the sole distributor for Mazoe. Anyway, cut a long story short, the embassy is really trying hard to rope in investors to bring in dry goods from Zimbabwe. They want to get their fingers in the pie. Well, all I can say is those fingers will get burnt because the pie is hot. Dry goods are shipped into this country 🇬🇧 by Zimbabweans. Black Zimbabweans & myself! We hold a monopoly on the market & no matter how much the embassy or some guy wants to try convince you to invest you will fail. We have been doing this for years & know exactly how it works. Those products you find in your favourite shops come from either of us & we work together. Ask the last 6 people that have tried to enter the market over the past 5 years what happend to them. 1 container 2 container NONE! We will soak up your first container so you think you done well the moment your second container is on the water we will dump your first container on the market. It will be flooded that you won't even sell a single case of that second container. We know how it works & nobody can get in our way. Coke cola owns Schweppes & they would never drop the likes of us who supply the majority of the UK for someone who doesn't have a market at all. You can have 1 million followers & will fail. We will make sure of that. We control the market. So before you even think of sinking £30k into shipping a 40ft container to the UK remember there is us who you will have to deal with & you don't want to price war with us. Consider this a friendly warning Mr Chinono! This is our turf & you will lose a lot of money. I have NDA's signed by businessmen who have asked for my advice when it comes to importation of dry goods if any of them have tried to rope you in I will sue them to Kingdom come. This is a very cutthroat industry & we will crush you & any other person who gets in our way, like how we have been crushing people for years & nothing will change. Anyone who want to bring dry goods into this country comes through me. We will see if we can fit you into the circle. That's the only way you will succeed otherwise its war. Again, consider this a friendly warning!

Adamski Jahman

188,001 次观看 • 1 年前

The Tradingview Source Code Hack: How Claude Code Actually Makes You Profitable building a trading bot in 2026 is less about being a genius coder and more about having the guts to stop letting your emotions drive your portfolio into a ditch. i spent years thinking that code was some secret language for the elite while i was busy getting liquidated on trades that should have been easy wins. the truth is that the simplest piece of automation can be the difference between a blown account and a system that actually grows while you sleep most traders are stuck in a loop of staring at charts until their eyes bleed and then making a panic decision at the worst possible moment. i used to be that guy who spent hundreds of thousands of dollars on developers because i thought i was too far behind to learn it myself. the moment i realized that code is the great equalizer was the moment i stopped being a victim of market makers who can see every single move i make the hardest part of trading is the transition from manual clicking to full automation and that is where most people quit because they think it is an all or nothing game. you do not need a bot that does everything on day one but you do need a way to stop yourself from over trading and hitting the market button like a slot machine. imagine if you could just tell a script to enter a position for you slowly over time so you never have to worry about catching a falling knife ever again the secret weapon in this 2026 landscape is something i call the easy bot which is basically a remote control for your trading discipline. most people use too much leverage and never take profits because their brain tells them it will go higher right before the dump hits. by using a chunking entry system you can tell your bot to buy a position piece by piece which averages your entry and keeps your heart rate low most traders do not realize that exchanges and market makers can see your stop loss sitting right there on the order book like a giant target. if you are tired of getting stopped out only to see the price immediately reverse in your direction then you need to understand the power of a ghost stop loss. a ghost stop loss lives in your code and not on the exchange so the market makers have no idea where your pain point is until the bot actually sends the close order this invisible protection is a game changer for anyone trading on high volatility chains like solana or hyperliquid where the wick hunting is relentless. i learned this the hard way after watching my manual trades get hunted over and over while my automated systems stayed perfectly safe in the shadows. once you have a bot monitoring your positions for you the stress of a potential liquidation basically vanishes because the machine does not hesitate when it is time to exit the real fun begins when you start thinking like a market maker instead of a gambler who is just hoping for a moon shot. instead of guessing where the top is you can set up a simple logic loop that says buy under this price and sell over that price all day long. this allows you to capture the sideways chop that normally drains a manual trader through fees and bad entries while you are out at the beach or focusing on your business breakout trading is another area where manual traders lose their edge because they are either too slow to react or they enter way too late on a fake out. a simple script can sit there and monitor price action 24/7 with more patience than any human being could ever dream of having. when the breakout actually happens the bot triggers the entry in milliseconds while you are still trying to unlock your phone and open the exchange app i started learning to code live on youtube because i wanted to prove that anyone can do this if they are willing to iterate to success. it is not about getting it perfect on the first try but about building the foundation with functions that get the position and check the token price. every single bot i run today is just a combination of these basic building blocks that i have refined over the last five years of building in public if you are still trading by hand you are essentially bringing a knife to a gunfight in a market that is increasingly dominated by ai and high frequency systems. my goal with the road map and the open source code is to give you the same tools the big players have without you having to spend a fortune on devs. code allows you to backtest your ideas against historical data so you can see if your strategy actually works before you risk a single dollar of your hard earned capital the journey from a hand trader to a pseudo automated trader is the most important step you will ever take for your financial freedom. as you start to automate your entries and exits you will notice that your life gets better because the machine handles the boredom and the stress. eventually you will find that you are no longer chasing the market but instead you are letting your systems do the work while you live your life the way you want to i believe that the era of the manual retail trader is coming to an end but the era of the retail coder is just getting started. through the pain of my own liquidations i found the path to automation and i am never going back to the old way of doing things. keep building and keep iterating because the great equalizer is right there in the terminal waiting for you to take control of your future
30:38

The Tradingview Source Code Hack: How Claude Code Actually Makes You Profitable building a trading bot in 2026 is less about being a genius coder and more about having the guts to stop letting your emotions drive your portfolio into a ditch. i spent years thinking that code was some secret language for the elite while i was busy getting liquidated on trades that should have been easy wins. the truth is that the simplest piece of automation can be the difference between a blown account and a system that actually grows while you sleep most traders are stuck in a loop of staring at charts until their eyes bleed and then making a panic decision at the worst possible moment. i used to be that guy who spent hundreds of thousands of dollars on developers because i thought i was too far behind to learn it myself. the moment i realized that code is the great equalizer was the moment i stopped being a victim of market makers who can see every single move i make the hardest part of trading is the transition from manual clicking to full automation and that is where most people quit because they think it is an all or nothing game. you do not need a bot that does everything on day one but you do need a way to stop yourself from over trading and hitting the market button like a slot machine. imagine if you could just tell a script to enter a position for you slowly over time so you never have to worry about catching a falling knife ever again the secret weapon in this 2026 landscape is something i call the easy bot which is basically a remote control for your trading discipline. most people use too much leverage and never take profits because their brain tells them it will go higher right before the dump hits. by using a chunking entry system you can tell your bot to buy a position piece by piece which averages your entry and keeps your heart rate low most traders do not realize that exchanges and market makers can see your stop loss sitting right there on the order book like a giant target. if you are tired of getting stopped out only to see the price immediately reverse in your direction then you need to understand the power of a ghost stop loss. a ghost stop loss lives in your code and not on the exchange so the market makers have no idea where your pain point is until the bot actually sends the close order this invisible protection is a game changer for anyone trading on high volatility chains like solana or hyperliquid where the wick hunting is relentless. i learned this the hard way after watching my manual trades get hunted over and over while my automated systems stayed perfectly safe in the shadows. once you have a bot monitoring your positions for you the stress of a potential liquidation basically vanishes because the machine does not hesitate when it is time to exit the real fun begins when you start thinking like a market maker instead of a gambler who is just hoping for a moon shot. instead of guessing where the top is you can set up a simple logic loop that says buy under this price and sell over that price all day long. this allows you to capture the sideways chop that normally drains a manual trader through fees and bad entries while you are out at the beach or focusing on your business breakout trading is another area where manual traders lose their edge because they are either too slow to react or they enter way too late on a fake out. a simple script can sit there and monitor price action 24/7 with more patience than any human being could ever dream of having. when the breakout actually happens the bot triggers the entry in milliseconds while you are still trying to unlock your phone and open the exchange app i started learning to code live on youtube because i wanted to prove that anyone can do this if they are willing to iterate to success. it is not about getting it perfect on the first try but about building the foundation with functions that get the position and check the token price. every single bot i run today is just a combination of these basic building blocks that i have refined over the last five years of building in public if you are still trading by hand you are essentially bringing a knife to a gunfight in a market that is increasingly dominated by ai and high frequency systems. my goal with the road map and the open source code is to give you the same tools the big players have without you having to spend a fortune on devs. code allows you to backtest your ideas against historical data so you can see if your strategy actually works before you risk a single dollar of your hard earned capital the journey from a hand trader to a pseudo automated trader is the most important step you will ever take for your financial freedom. as you start to automate your entries and exits you will notice that your life gets better because the machine handles the boredom and the stress. eventually you will find that you are no longer chasing the market but instead you are letting your systems do the work while you live your life the way you want to i believe that the era of the manual retail trader is coming to an end but the era of the retail coder is just getting started. through the pain of my own liquidations i found the path to automation and i am never going back to the old way of doing things. keep building and keep iterating because the great equalizer is right there in the terminal waiting for you to take control of your future

Moon Dev

38,657 次观看 • 7 个月前

Chamath Palihapitiya just said what Silicon Valley is terrified to say out loud. On Joe Rogan. To millions of people. Without flinching. Chamath: “The only person that we can trust is Elon.” Not whispered at a dinner party. Not buried in a podcast nobody listens to. Said on the record. Full weight behind it. And then he told you why. Chamath: “I feel like he’s the least corruptible. He’s the most independent thinking. And I think he’s the one that has an actual empathy for people.” One of the sharpest capital allocators in Silicon Valley history looked at every founder building AI. Every single one. And chose the one the media spends the most energy telling you to hate. That alone should stop you cold. Chamath: “Then there are folks where there’s just an insane profit motive.” He’s talking about OpenAI. He’s talking about Google. He’s talking about companies that swallowed billions from Wall Street and now answer to shareholders before they answer to humanity. Chamath: “They’re less in control of the businesses that they run.” The people building the most powerful technology in human history do not control their own companies. Their boards do. Their investors do. Their liquidation preferences do. And these are the ones we’re trusting with superintelligence. Chamath: “He’s like, I need to get to Mars.” This is the fracture line nobody wants to touch. Every other AI founder is optimizing for the next earnings call. The next funding round. The next quarterly number that keeps the machine fed. Elon is optimizing for the next planet. One group builds to satisfy investors. The other builds to survive as a species. Those aren’t different strategies. Those are different operating systems running on different hardware. And it changes everything about how you build. When your time horizon is 90 days, you cut corners. You monetize behavior. You trade safety for speed because the board needs a number by Friday. When your time horizon is interplanetary, you can’t afford a single shortcut. Because shortcuts don’t survive launch. Chamath: “Where is this going to end up?” The only question that matters. And nobody in power wants you asking it. Because the answer comes down to who gets there first. If it’s a company owned by Wall Street, superintelligence becomes the most sophisticated extraction engine ever built. Every decision optimized. Every behavior predicted. Every market captured. Not for you. For the balance sheet. If it’s someone who can’t be bought, pressured, or voted out by a board of directors, there’s at least a chance it bends toward something bigger than quarterly revenue. History never remembers who built the most powerful technology. It remembers who controlled it. And what they used it for. The only founder in AI who cannot be fired by a board, leveraged by an investor, or replaced by a shareholder vote is the one they spend the most energy telling you not to trust. Ask yourself why.
1:16

Chamath Palihapitiya just said what Silicon Valley is terrified to say out loud. On Joe Rogan. To millions of people. Without flinching. Chamath: “The only person that we can trust is Elon.” Not whispered at a dinner party. Not buried in a podcast nobody listens to. Said on the record. Full weight behind it. And then he told you why. Chamath: “I feel like he’s the least corruptible. He’s the most independent thinking. And I think he’s the one that has an actual empathy for people.” One of the sharpest capital allocators in Silicon Valley history looked at every founder building AI. Every single one. And chose the one the media spends the most energy telling you to hate. That alone should stop you cold. Chamath: “Then there are folks where there’s just an insane profit motive.” He’s talking about OpenAI. He’s talking about Google. He’s talking about companies that swallowed billions from Wall Street and now answer to shareholders before they answer to humanity. Chamath: “They’re less in control of the businesses that they run.” The people building the most powerful technology in human history do not control their own companies. Their boards do. Their investors do. Their liquidation preferences do. And these are the ones we’re trusting with superintelligence. Chamath: “He’s like, I need to get to Mars.” This is the fracture line nobody wants to touch. Every other AI founder is optimizing for the next earnings call. The next funding round. The next quarterly number that keeps the machine fed. Elon is optimizing for the next planet. One group builds to satisfy investors. The other builds to survive as a species. Those aren’t different strategies. Those are different operating systems running on different hardware. And it changes everything about how you build. When your time horizon is 90 days, you cut corners. You monetize behavior. You trade safety for speed because the board needs a number by Friday. When your time horizon is interplanetary, you can’t afford a single shortcut. Because shortcuts don’t survive launch. Chamath: “Where is this going to end up?” The only question that matters. And nobody in power wants you asking it. Because the answer comes down to who gets there first. If it’s a company owned by Wall Street, superintelligence becomes the most sophisticated extraction engine ever built. Every decision optimized. Every behavior predicted. Every market captured. Not for you. For the balance sheet. If it’s someone who can’t be bought, pressured, or voted out by a board of directors, there’s at least a chance it bends toward something bigger than quarterly revenue. History never remembers who built the most powerful technology. It remembers who controlled it. And what they used it for. The only founder in AI who cannot be fired by a board, leveraged by an investor, or replaced by a shareholder vote is the one they spend the most energy telling you not to trust. Ask yourself why.

Dustin

143,786 次观看 • 4 个月前

Here is another recording of Manisha Swami on the day she was supposed to reprimand Adarsh Kumar Mishra because he was harassing female employees. But instead of doing that, she misused her position, misused the idea that she is a woman and went on a meaningless tirade about Gender sensitization. While listening to this, do remember this is a person who has cleared UPSC and pay attention to everything she says. I would like to know your opinion on how this is a gender sensitization workshop. She says "I can say hello to anybody but nobody can say hello to me". I don't know who she was referring to about being blocked but it was definitely not me. Because last reply from her to me is "Ok". There is a reason foreign policy is in shambles. It's because people like S Jaishankar, Manisha Swami, Suresh Reddy, B Vanlalvawna, Sanjiv Ranjan(IORA, Mauritius), Puneet Agrawal, Monika Aggarwal, Harish Baxla, Suraj Mohan, Gayatri Gosain, Adarsh Kumar Mishra, Yashashri Shukla(CVC) and Puneet Meena (DS[CPIO]&CVC) have been looting the country openly. I have not put even a single name above randomly. If anyone has seen my original complaint, you would have noticed that I had also written highly about good officers in the Ministry. But unfortunately there are very few of these. Otherwise all this would not have happened with me so openly without any restraints. After this case, it will be definitely clear whether India is an Independent nation or a country ruled by corrupt civil servants. I am attaching a copy of the FIR which I have tried to file but which was not accepted by police. In case audio does not play on twitter, here is the link I hope you are listening Narendra Modi PMO India Rahul Gandhi Subramanian Swamy Pawan Khera 🇮🇳 ಪವನ್ ಖೇರಾ Shashi Tharoor Priyanka Chaturvedi🇮🇳 Somnath Bharti सोमनाथ भारती Revant Himatsingka “Food Pharmer” खुरपेंच Anshul Saxena Abhi and Niyu Abhijit Iyer-Mitra Indira Jaising englishaugust Vir Das Deepika Narayan Bhardwaj Mahua Moitra Sagarika Ghose Sucheta Dalal Ramachandra Guha Sonam Wangchuk Sanket Upadhyay Saurav Das Sidhant Sibal ravish ndtv Dr Aniruddha Malpani, MD Sandeep Manudhane Akash Gupta Mohak Mangal AajTak NDTV The Hindu IndiaToday Satish Acharya Yogendra Yadav Prakash Raj Zee News Al Jazeera World BBC News Hindi The Wire The Caravan newslaundry Govind Pratap Singh | GPS Manisha Pande I have started something purely out of sheer luck and chance which I hope is going to bring a good change in my country but I hope that world you are listening and that this man Dr. S. Jaishankar is a wolf in sheep's clothing and I hope you are going to definitely ban him United Nations United NationsHumanRights
10:42

Here is another recording of Manisha Swami on the day she was supposed to reprimand Adarsh Kumar Mishra because he was harassing female employees. But instead of doing that, she misused her position, misused the idea that she is a woman and went on a meaningless tirade about Gender sensitization. While listening to this, do remember this is a person who has cleared UPSC and pay attention to everything she says. I would like to know your opinion on how this is a gender sensitization workshop. She says "I can say hello to anybody but nobody can say hello to me". I don't know who she was referring to about being blocked but it was definitely not me. Because last reply from her to me is "Ok". There is a reason foreign policy is in shambles. It's because people like S Jaishankar, Manisha Swami, Suresh Reddy, B Vanlalvawna, Sanjiv Ranjan(IORA, Mauritius), Puneet Agrawal, Monika Aggarwal, Harish Baxla, Suraj Mohan, Gayatri Gosain, Adarsh Kumar Mishra, Yashashri Shukla(CVC) and Puneet Meena (DS[CPIO]&CVC) have been looting the country openly. I have not put even a single name above randomly. If anyone has seen my original complaint, you would have noticed that I had also written highly about good officers in the Ministry. But unfortunately there are very few of these. Otherwise all this would not have happened with me so openly without any restraints. After this case, it will be definitely clear whether India is an Independent nation or a country ruled by corrupt civil servants. I am attaching a copy of the FIR which I have tried to file but which was not accepted by police. In case audio does not play on twitter, here is the link I hope you are listening Narendra Modi PMO India Rahul Gandhi Subramanian Swamy Pawan Khera 🇮🇳 ಪವನ್ ಖೇರಾ Shashi Tharoor Priyanka Chaturvedi🇮🇳 Somnath Bharti सोमनाथ भारती Revant Himatsingka “Food Pharmer” खुरपेंच Anshul Saxena Abhi and Niyu Abhijit Iyer-Mitra Indira Jaising englishaugust Vir Das Deepika Narayan Bhardwaj Mahua Moitra Sagarika Ghose Sucheta Dalal Ramachandra Guha Sonam Wangchuk Sanket Upadhyay Saurav Das Sidhant Sibal ravish ndtv Dr Aniruddha Malpani, MD Sandeep Manudhane Akash Gupta Mohak Mangal AajTak NDTV The Hindu IndiaToday Satish Acharya Yogendra Yadav Prakash Raj Zee News Al Jazeera World BBC News Hindi The Wire The Caravan newslaundry Govind Pratap Singh | GPS Manisha Pande I have started something purely out of sheer luck and chance which I hope is going to bring a good change in my country but I hope that world you are listening and that this man Dr. S. Jaishankar is a wolf in sheep's clothing and I hope you are going to definitely ban him United Nations United NationsHumanRights

Rohan

17,494 次观看 • 1 年前

The Great Equalizer: How I Iterated Through 90+ Strategies to Automate My Financial Freedom ninety strategies sounds like a death wish but it is actually the only way to find your edge in a market designed to liquidate you. most traders are out here gambling with their rent money while the big players are using automated systems to harvest their liquidations. i know this because i spent hundreds of thousands of dollars on developers for apps thinking i could never code myself. i was getting wrecked by over trading and watching my accounts hit zero while i slept. code became the great equalizer for me because it removed the emotion that was killing my bankroll. i decided to learn to code live so i could iterate to success and now i have fully automated systems trading for me instead of getting liquidated by every wick. i just saw someone lose ten million dollars in a single month because they were trading by hand and got addicted to the screen. you have to understand that if you are not automating you are the exit liquidity for someone who is. the reality of advanced futures trading is not about finding one holy grail bot that prints money forever. it is about research and back testing until you find a strategy that has a statistical advantage. one of the most slept on concepts is variable risk scaling where you actually change your position size based on how volatile the market is. instead of just betting the same amount every time you increase your size when volatility is low and scale back when the market starts moving like crazy. this keeps you in the game during the draw downs that usually wipe people out. most people do the opposite and revenge trade with bigger size when they are losing which is the fastest way to the cemetery. i used to think i needed to be the smartest guy in the room to make this work but i realized i just needed to be the most disciplined with my risk parameters. there is a secret hidden in funding rates and basis trading that most retail traders never even look at. while everyone else is trying to guess if bitcoin is going to the moon or the floor you can actually make consistent money through funding rate arbitrage. you basically buy the asset in the spot market and simultaneously sell it in the futures market when the funding rate is high. you just sit there and collect the interest payments from the gamblers who are over leveraged on the other side. it is basically free money if you can manage the fees and keep your execution precise. i used to ignore these low yield plays because i wanted the big home runs but those home runs usually came with massive strikeouts. now i look for these carry trades as a way to keep the equity curve moving up and to the right while others are sweating over every price change. most traders fail because they use lagging indicators and expect them to predict the future with one hundred percent accuracy. the truth is that even the best trend following strategies like the golden cross or moving average crossovers only have about sixty five percent accuracy. you have to combine these with filters like the average directional index or relative strength index to make sure you are not just buying a fake breakout. a lot of people get chopped up in sideways markets because they do not have a trend strength filter to tell them to stay out of the trade. i learned to use multiple time frames to confirm my breakouts because if the one hour and the four hour charts are not saying the same thing then the trade is probably a trap. you have to be a searcher looking for those golden nuggets of alpha buried in mountains of data. i used to think that machine learning and genetic algorithms were just buzzwords that did not actually work for trading. then i realized that the 1990s tech trap is real and if you are still using basic indicators without any optimization you are decades behind. genetic algorithms are wild because they simulate natural selection to find the best parameters for your strategy through trial and error. you can actually build an environment where your bot learns from its own mistakes and optimizes its decision making process over time. i spent so much time thinking i was not smart enough to do this but once i started iterating live i found that the machines are much better at following rules than i ever was. code is the only way to compete with the high frequency firms that are looking for any tiny mispricing in the order book. slippage and bad execution will eat your profits faster than a bad trade ever could if you are not careful. most people just hit the market buy button and pay the spread and the fees without a second thought. you should be using smart order routing and limit orders to capture the bid ask spread instead of paying it to the market makers. i started using time weighted average price execution to spread my larger orders out over time so i did not move the market against myself. it is these tiny details in execution that separate the professional quants from the people who are just playing around. i had to learn this the hard way after losing a fortune on bad entries and exits that could have been avoided with a few lines of code. the ultimate goal of all of this is to build a compounding machine that grows your capital while you are living your life. you have to automate the reinvestment of your profits so that your position sizes grow as your account grows without you having to manually adjust anything. i like to use automated compounding algorithms that take a portion of my wins and put them back into the systems that are performing the best. this creates a snowball effect where your returns start to accelerate as the base capital increases. it took me years to realize that i did not need to be at the desk for eighteen hours a day to make life changing money. i just needed to build a system that was smarter and more disciplined than my own human brain. cross asset skew and volatility surface arbitrage are where the real quants play when the market gets efficient. you can look for mispricings between highly correlated assets like bitcoin and ethereum and trade the spread between them. when one asset gets overvalued relative to the other you short the leader and long the laggard until they revert back to the mean. this is a much safer way to trade because you are not betting on the direction of the market but rather the relationship between two assets. i spent a lot of money trying to guess the next big move before i realized that trading the relationship between assets was much more consistent. iteration is the only way to find these winks in the market that the average trader is completely blind to. it is a cold world in finance and most people are out here trying to step on your neck to get ahead. i believe that sharing this knowledge is important because code is the only thing that can give a regular person a fighting chance against the institutions. i started from zero and learned everything through failing and losing money until i finally figured out how to automate. now i spend my time building and testing instead of worrying about the next liquidation candle. you have to decide today if you want to keep being the exit liquidity or if you want to start building your own systems. the tools are all there and the data is accessible if you are willing to put in the work and stop negotiating with yourself. successful trading is not about being lucky it is about being prepared and having a system that can handle any market regime. whether the market is in a bull run or a total crash your bots should know exactly what to do based on the rules you have coded into them. i use risk weighted allocation to make sure that my capital is always moving toward the strategies with the highest sharp ratio and the lowest volatility. this keeps the portfolio stable even when the crypto market is going through its typical insane swings. i finally found peace in this game because i know that my automated systems are following the math while everyone else is following their feelings. code is the great equalizer and it is time for you to start using it to protect your future and build your empire there are over ninety strategies you can test and most of them will not work for your specific style but you only need one or two to change your life. i have built a fat list of ideas from research and i spend every day back testing and refining them to stay ahead of the curve. do not let the fear of coding stop you from taking control of your financial destiny because i am living proof that anyone can learn. i would rather spend my time iterating to success than getting liquidated by some random news event that i could not predict. the journey from losing hundreds of thousands to fully automated success was long but it was the best investment i ever made. keep your heart open and lead with love in this game and i promise the universe will start passing you those golden nuggets of alpha you have been searching for
2:40:29

The Great Equalizer: How I Iterated Through 90+ Strategies to Automate My Financial Freedom ninety strategies sounds like a death wish but it is actually the only way to find your edge in a market designed to liquidate you. most traders are out here gambling with their rent money while the big players are using automated systems to harvest their liquidations. i know this because i spent hundreds of thousands of dollars on developers for apps thinking i could never code myself. i was getting wrecked by over trading and watching my accounts hit zero while i slept. code became the great equalizer for me because it removed the emotion that was killing my bankroll. i decided to learn to code live so i could iterate to success and now i have fully automated systems trading for me instead of getting liquidated by every wick. i just saw someone lose ten million dollars in a single month because they were trading by hand and got addicted to the screen. you have to understand that if you are not automating you are the exit liquidity for someone who is. the reality of advanced futures trading is not about finding one holy grail bot that prints money forever. it is about research and back testing until you find a strategy that has a statistical advantage. one of the most slept on concepts is variable risk scaling where you actually change your position size based on how volatile the market is. instead of just betting the same amount every time you increase your size when volatility is low and scale back when the market starts moving like crazy. this keeps you in the game during the draw downs that usually wipe people out. most people do the opposite and revenge trade with bigger size when they are losing which is the fastest way to the cemetery. i used to think i needed to be the smartest guy in the room to make this work but i realized i just needed to be the most disciplined with my risk parameters. there is a secret hidden in funding rates and basis trading that most retail traders never even look at. while everyone else is trying to guess if bitcoin is going to the moon or the floor you can actually make consistent money through funding rate arbitrage. you basically buy the asset in the spot market and simultaneously sell it in the futures market when the funding rate is high. you just sit there and collect the interest payments from the gamblers who are over leveraged on the other side. it is basically free money if you can manage the fees and keep your execution precise. i used to ignore these low yield plays because i wanted the big home runs but those home runs usually came with massive strikeouts. now i look for these carry trades as a way to keep the equity curve moving up and to the right while others are sweating over every price change. most traders fail because they use lagging indicators and expect them to predict the future with one hundred percent accuracy. the truth is that even the best trend following strategies like the golden cross or moving average crossovers only have about sixty five percent accuracy. you have to combine these with filters like the average directional index or relative strength index to make sure you are not just buying a fake breakout. a lot of people get chopped up in sideways markets because they do not have a trend strength filter to tell them to stay out of the trade. i learned to use multiple time frames to confirm my breakouts because if the one hour and the four hour charts are not saying the same thing then the trade is probably a trap. you have to be a searcher looking for those golden nuggets of alpha buried in mountains of data. i used to think that machine learning and genetic algorithms were just buzzwords that did not actually work for trading. then i realized that the 1990s tech trap is real and if you are still using basic indicators without any optimization you are decades behind. genetic algorithms are wild because they simulate natural selection to find the best parameters for your strategy through trial and error. you can actually build an environment where your bot learns from its own mistakes and optimizes its decision making process over time. i spent so much time thinking i was not smart enough to do this but once i started iterating live i found that the machines are much better at following rules than i ever was. code is the only way to compete with the high frequency firms that are looking for any tiny mispricing in the order book. slippage and bad execution will eat your profits faster than a bad trade ever could if you are not careful. most people just hit the market buy button and pay the spread and the fees without a second thought. you should be using smart order routing and limit orders to capture the bid ask spread instead of paying it to the market makers. i started using time weighted average price execution to spread my larger orders out over time so i did not move the market against myself. it is these tiny details in execution that separate the professional quants from the people who are just playing around. i had to learn this the hard way after losing a fortune on bad entries and exits that could have been avoided with a few lines of code. the ultimate goal of all of this is to build a compounding machine that grows your capital while you are living your life. you have to automate the reinvestment of your profits so that your position sizes grow as your account grows without you having to manually adjust anything. i like to use automated compounding algorithms that take a portion of my wins and put them back into the systems that are performing the best. this creates a snowball effect where your returns start to accelerate as the base capital increases. it took me years to realize that i did not need to be at the desk for eighteen hours a day to make life changing money. i just needed to build a system that was smarter and more disciplined than my own human brain. cross asset skew and volatility surface arbitrage are where the real quants play when the market gets efficient. you can look for mispricings between highly correlated assets like bitcoin and ethereum and trade the spread between them. when one asset gets overvalued relative to the other you short the leader and long the laggard until they revert back to the mean. this is a much safer way to trade because you are not betting on the direction of the market but rather the relationship between two assets. i spent a lot of money trying to guess the next big move before i realized that trading the relationship between assets was much more consistent. iteration is the only way to find these winks in the market that the average trader is completely blind to. it is a cold world in finance and most people are out here trying to step on your neck to get ahead. i believe that sharing this knowledge is important because code is the only thing that can give a regular person a fighting chance against the institutions. i started from zero and learned everything through failing and losing money until i finally figured out how to automate. now i spend my time building and testing instead of worrying about the next liquidation candle. you have to decide today if you want to keep being the exit liquidity or if you want to start building your own systems. the tools are all there and the data is accessible if you are willing to put in the work and stop negotiating with yourself. successful trading is not about being lucky it is about being prepared and having a system that can handle any market regime. whether the market is in a bull run or a total crash your bots should know exactly what to do based on the rules you have coded into them. i use risk weighted allocation to make sure that my capital is always moving toward the strategies with the highest sharp ratio and the lowest volatility. this keeps the portfolio stable even when the crypto market is going through its typical insane swings. i finally found peace in this game because i know that my automated systems are following the math while everyone else is following their feelings. code is the great equalizer and it is time for you to start using it to protect your future and build your empire there are over ninety strategies you can test and most of them will not work for your specific style but you only need one or two to change your life. i have built a fat list of ideas from research and i spend every day back testing and refining them to stay ahead of the curve. do not let the fear of coding stop you from taking control of your financial destiny because i am living proof that anyone can learn. i would rather spend my time iterating to success than getting liquidated by some random news event that i could not predict. the journey from losing hundreds of thousands to fully automated success was long but it was the best investment i ever made. keep your heart open and lead with love in this game and i promise the universe will start passing you those golden nuggets of alpha you have been searching for

Moon Dev

11,196 次观看 • 7 个月前

Stop Gambling, Start Engineering: The Ultimate Guide To CCXT Algorithmic Trading most traders are essentially walking into a high stakes casino with a blindfold on while the house has a high speed laser aimed directly at their bankroll. if you have ever felt the soul crushing weight of a liquidation notification at three in the morning then you know the market is a 24/7 beast that eats human emotion for breakfast there is a hidden bridge that connects your laptop to almost every major crypto exchange in existence and once you cross it the game changes forever. my name is moon dev 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 so i learned to code as in the past i spent hundreds of thousands on devs for app, thinking i would not be able to code myself w/ bots you must iterate to success so i decided to learn live on youtube, and now we are here, fully automated systems trading for me instead of getting liquidated. the secret weapon behind this transition is a library called ccxt which acts as a universal translator for exchanges like binance, bybit, and kucoin most people think they need to spend years studying computer science just to place a single trade via code but that is a lie designed to keep you on the sidelines. the reality is that once you understand how to initialize a connection you can control your entire portfolio with just a few lines of logic. it starts with importing the library and setting up your credentials in a way that doesn't leave your keys exposed to the world the first mistake that bankrupts most manual traders is the inability to act fast enough when the trend shifts. when you build a bot the first thing you need to master is the market order because it allows you to enter or exit a position instantly regardless of the price. it is the ultimate panic button for when a strategy goes south or a massive opportunity presents itself while market orders are great for speed they are the fastest way to get eaten alive by fees if you are not careful. this is where the limit order comes into play allowing you to dictate exactly what price you are willing to pay for an asset. by using a create limit order function you can place your bids and asks in the order book and wait for the market to come to you most traders forget that once an order is placed it stays active until it is either filled or manually removed. i have seen countless accounts go to zero because a bot kept piling on buy orders without ever checking to see if the previous ones were canceled. the cancel all orders function is the invisible shield that prevents your algorithm from accidentally over leveraging your account the real magic happens when you realize you can cancel more than just basic limit orders. there are untriggered conditional orders like stop losses and take profits that often hide in the background of an exchange waiting to ruin your day. by passing specific parameters into your cancel function you can wipe the slate clean and ensure your bot is starting from a neutral state every single time if you want to know what the whales are doing before it shows up on a candle chart then you need to be looking at the raw order book. fetching the order book gives you a direct view of every single bid and ask currently sitting on the exchange. this is the most honest data you can get because it represents real money waiting to be filled at specific price levels you can actually parse this data to find the exact top of the bid and the bottom of the ask to ensure your bot always gets the best possible entry. most retail traders are looking at delayed charts while your bot is reading the tape in real time and calculating the spread. this allows you to place orders that are optimized for the current liquidity rather than just guessing where the price might go one of the biggest hurdles in automation is managing the sheer volume of data that an exchange throws at you. when you fetch open high low close volume data you are getting the historical heartbeat of an asset across any timeframe you choose. this data is the foundation of every technical indicator from simple moving averages to complex machine learning models the problem is that raw data is often a mess of lists and dictionaries that are impossible for a human or a simple script to read efficiently. this is why we use pandas to convert that garbage into a structured data frame that looks exactly like a clean spreadsheet. once your data is in a data frame you can calculate rsi or macd with a single line of code and visualize the entire market structure the path to becoming a successful automated trader is not a sprint but a series of iterations toward a system that works. i chose to learn this live in front of the world because i wanted to prove that anyone can escape the cycle of over trading. you don't need a million dollars to start but you do need a system that removes the human element from the equation if you are still clicking buttons on a website then you are competing against machines that can process thousands of data points per second. it is time to stop playing a rigged game and start building your own edge in the market. the code is there for anyone to grab and the only thing standing between you and a fully automated portfolio is the willingness to sit down and write the first line every algorithm you build is a brick in a wall that protects your capital from the emotional swings of the crypto market. i spend my days refining these systems and sharing the process because i know how lonely it feels to lose everything to a flash crash. we are building a community where code is the tool and financial freedom is the goal the final step is realizing that your balance is just a number that your bot needs to manage with cold logic. by fetching your balance frequently your bot can calculate position sizes based on your total equity ensuring that no single trade can ever wipe you out. this is the difference between gambling and systematic trading and it is accessible to anyone with an internet connection i hope you take these tools and start building something that allows you to sleep peacefully while the markets do their thing. the industry is secretive for a reason but we are breaking those walls down one line of code at a time. the journey is long but the reward of never having to worry about a liquidation again is worth every second of the struggle
1:09:25

Stop Gambling, Start Engineering: The Ultimate Guide To CCXT Algorithmic Trading most traders are essentially walking into a high stakes casino with a blindfold on while the house has a high speed laser aimed directly at their bankroll. if you have ever felt the soul crushing weight of a liquidation notification at three in the morning then you know the market is a 24/7 beast that eats human emotion for breakfast there is a hidden bridge that connects your laptop to almost every major crypto exchange in existence and once you cross it the game changes forever. my name is moon dev 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 so i learned to code as in the past i spent hundreds of thousands on devs for app, thinking i would not be able to code myself w/ bots you must iterate to success so i decided to learn live on youtube, and now we are here, fully automated systems trading for me instead of getting liquidated. the secret weapon behind this transition is a library called ccxt which acts as a universal translator for exchanges like binance, bybit, and kucoin most people think they need to spend years studying computer science just to place a single trade via code but that is a lie designed to keep you on the sidelines. the reality is that once you understand how to initialize a connection you can control your entire portfolio with just a few lines of logic. it starts with importing the library and setting up your credentials in a way that doesn't leave your keys exposed to the world the first mistake that bankrupts most manual traders is the inability to act fast enough when the trend shifts. when you build a bot the first thing you need to master is the market order because it allows you to enter or exit a position instantly regardless of the price. it is the ultimate panic button for when a strategy goes south or a massive opportunity presents itself while market orders are great for speed they are the fastest way to get eaten alive by fees if you are not careful. this is where the limit order comes into play allowing you to dictate exactly what price you are willing to pay for an asset. by using a create limit order function you can place your bids and asks in the order book and wait for the market to come to you most traders forget that once an order is placed it stays active until it is either filled or manually removed. i have seen countless accounts go to zero because a bot kept piling on buy orders without ever checking to see if the previous ones were canceled. the cancel all orders function is the invisible shield that prevents your algorithm from accidentally over leveraging your account the real magic happens when you realize you can cancel more than just basic limit orders. there are untriggered conditional orders like stop losses and take profits that often hide in the background of an exchange waiting to ruin your day. by passing specific parameters into your cancel function you can wipe the slate clean and ensure your bot is starting from a neutral state every single time if you want to know what the whales are doing before it shows up on a candle chart then you need to be looking at the raw order book. fetching the order book gives you a direct view of every single bid and ask currently sitting on the exchange. this is the most honest data you can get because it represents real money waiting to be filled at specific price levels you can actually parse this data to find the exact top of the bid and the bottom of the ask to ensure your bot always gets the best possible entry. most retail traders are looking at delayed charts while your bot is reading the tape in real time and calculating the spread. this allows you to place orders that are optimized for the current liquidity rather than just guessing where the price might go one of the biggest hurdles in automation is managing the sheer volume of data that an exchange throws at you. when you fetch open high low close volume data you are getting the historical heartbeat of an asset across any timeframe you choose. this data is the foundation of every technical indicator from simple moving averages to complex machine learning models the problem is that raw data is often a mess of lists and dictionaries that are impossible for a human or a simple script to read efficiently. this is why we use pandas to convert that garbage into a structured data frame that looks exactly like a clean spreadsheet. once your data is in a data frame you can calculate rsi or macd with a single line of code and visualize the entire market structure the path to becoming a successful automated trader is not a sprint but a series of iterations toward a system that works. i chose to learn this live in front of the world because i wanted to prove that anyone can escape the cycle of over trading. you don't need a million dollars to start but you do need a system that removes the human element from the equation if you are still clicking buttons on a website then you are competing against machines that can process thousands of data points per second. it is time to stop playing a rigged game and start building your own edge in the market. the code is there for anyone to grab and the only thing standing between you and a fully automated portfolio is the willingness to sit down and write the first line every algorithm you build is a brick in a wall that protects your capital from the emotional swings of the crypto market. i spend my days refining these systems and sharing the process because i know how lonely it feels to lose everything to a flash crash. we are building a community where code is the tool and financial freedom is the goal the final step is realizing that your balance is just a number that your bot needs to manage with cold logic. by fetching your balance frequently your bot can calculate position sizes based on your total equity ensuring that no single trade can ever wipe you out. this is the difference between gambling and systematic trading and it is accessible to anyone with an internet connection i hope you take these tools and start building something that allows you to sleep peacefully while the markets do their thing. the industry is secretive for a reason but we are breaking those walls down one line of code at a time. the journey is long but the reward of never having to worry about a liquidation again is worth every second of the struggle

Moon Dev

14,105 次观看 • 7 个月前

I Built a 37.0 Profit Factor Bot by Cracking Every TradingView Source Code tradingview is a gold mine hiding in plain sight and i just found the master key to unlock every single secret hidden within its community scripts. most traders spend their entire lives staring at candles and hoping for a miracle while the actual alpha is buried in the open source code that nobody bothers to look at. i used to be that guy who sat there getting liquidated at three in the morning because i thought i could outplay the market with my gut feeling and some drawings on a screen. it turns out that the game is completely rigged against you if you are trading manually but there is a specific way to flip the script. i am going to show you how to stop guessing and start knowing exactly what works across every possible market condition before you ever risk a single dollar. i spent years losing money and thousands on developers because i thought i was not smart enough to code the systems myself but i was wrong. the first step to cracking the market is realizing that every indicator on the super charts has a source code section that is completely open to the public. you can literally scroll through the community scripts and pull the exact logic for thousands of different strategies that people claim are the holy grail of trading. but the secret is not just having the code because most of these indicators are actually garbage that will blow your account up in a week. this is where the real loop opens because you need a way to test these ideas across twenty five different data sets in seconds rather than months. i use a custom setup with ai agents specifically a sub agent i call the backtest architect to handle the heavy lifting of turning pine script into python code. the goal is to create a factory where you can feed in a raw indicator and get back a full report on its expectancy and profit factor without lifting a finger. most people find one strategy and marry it for life but a real data dog knows that you have to iterate to success or you will get left behind. i am running eighty one different backtests right now because i know that ninety percent of what i find will be trash but that remaining ten percent is where the wealth is made. the backtest architect knows exactly how to structure the folders and data paths so that we are testing everything from the base indicator to complex versions with filters. you might think that popular tools like fibonacci or order blocks are the way to go because everyone on social media talks about them like they are law. but when i actually ran the numbers through the machine the results were embarrassing and most of those strategies just resulted in negative expectancy. it is a dangerous trap to follow the crowd into a trade just because some guru said a certain level was important when the data shows it is a coin flip at best. the dynamic swing indicator was one of the few that actually held its weight during the recent massive testing sessions we ran. it was pulling in profit factors of over thirty seven with annualized returns that look too good to be true until you see the trade list. we combined it with filters like the adx and the money flow index to see if we could refine the signals and the results were absolutely staggering. when you have a system that can run through forty data sets while you are drinking tea you realize that manual trading is a form of self harm. i realized this after spending hundreds of thousands on apps and devs only to find out that i could just learn to build these bots myself live on the internet. the speed of iteration is the only thing that matters in this game because the faster you can fail the faster you can find the one strategy that actually prints. one of the biggest hurdles i faced was thinking that i needed to be a math genius or a senior engineer to automate my trading systems. the truth is that code is the great equalizer because it allows a regular person to compete with massive hedge funds by using the same logic and speed. i decided to learn everything in public because i wanted people to see the process of losing money with liquidations and then finally finding a path to automation. the reality of the market is that it moves in cycles and what worked yesterday will almost certainly fail tomorrow unless you are constantly testing. that is why i built the agents to automatically look through the results folder and rank the top performers based on a composite score. it takes all the emotion out of the process because i am no longer looking for a reason to enter a trade i am just looking at a csv file that tells me the truth. if you are still drawing lines on a chart and hoping for the best you are basically playing a game of chance against a high speed casino. the transition from a manual trader to a systems builder is the single most important pivot you will ever make in your life. it is not about being right or wrong it is about having a positive expectancy that has been proven across thousands of trades and multiple years of history. i had to fix a few errors in the short selling logic where the agents were getting confused between maximum and minimum values for take profit levels. these tiny bugs are the difference between a winning system and a blown account so you have to be willing to dive into the code and refine the machine. but once the system is tuned and the sub agents are running it becomes a beautiful workflow that functions entirely without your input. we are currently moving through the editors picks and the trending indicators one by one because i want to have a database of every single strategy on the platform. being a data dog means you never stop searching for that edge and you never settle for a strategy that just looks okay on a single chart. you have to demand excellence from your code because the market will not give you a single inch of mercy if you are lazy with your research. the ultimate goal is to have fully automated systems trading for you so you can focus on scaling rather than staring at a screen for ten hours a day. i am already up to over eighty backtests in this single session and i plan on hitting hundreds more by the end of the week. once you realize that you can crack the code of any indicator you see on the internet you will never look at a chart the same way again. this is the power of using agents to bridge the gap between a raw idea and a finished trading bot that actually works in the real world. i am done with getting liquidated and i am done with the stress of over trading because the code handles everything with cold precision. the path to success is paved with data and if you are not willing to automate your process you are just waiting for your next liquidation to happen
49:49

I Built a 37.0 Profit Factor Bot by Cracking Every TradingView Source Code tradingview is a gold mine hiding in plain sight and i just found the master key to unlock every single secret hidden within its community scripts. most traders spend their entire lives staring at candles and hoping for a miracle while the actual alpha is buried in the open source code that nobody bothers to look at. i used to be that guy who sat there getting liquidated at three in the morning because i thought i could outplay the market with my gut feeling and some drawings on a screen. it turns out that the game is completely rigged against you if you are trading manually but there is a specific way to flip the script. i am going to show you how to stop guessing and start knowing exactly what works across every possible market condition before you ever risk a single dollar. i spent years losing money and thousands on developers because i thought i was not smart enough to code the systems myself but i was wrong. the first step to cracking the market is realizing that every indicator on the super charts has a source code section that is completely open to the public. you can literally scroll through the community scripts and pull the exact logic for thousands of different strategies that people claim are the holy grail of trading. but the secret is not just having the code because most of these indicators are actually garbage that will blow your account up in a week. this is where the real loop opens because you need a way to test these ideas across twenty five different data sets in seconds rather than months. i use a custom setup with ai agents specifically a sub agent i call the backtest architect to handle the heavy lifting of turning pine script into python code. the goal is to create a factory where you can feed in a raw indicator and get back a full report on its expectancy and profit factor without lifting a finger. most people find one strategy and marry it for life but a real data dog knows that you have to iterate to success or you will get left behind. i am running eighty one different backtests right now because i know that ninety percent of what i find will be trash but that remaining ten percent is where the wealth is made. the backtest architect knows exactly how to structure the folders and data paths so that we are testing everything from the base indicator to complex versions with filters. you might think that popular tools like fibonacci or order blocks are the way to go because everyone on social media talks about them like they are law. but when i actually ran the numbers through the machine the results were embarrassing and most of those strategies just resulted in negative expectancy. it is a dangerous trap to follow the crowd into a trade just because some guru said a certain level was important when the data shows it is a coin flip at best. the dynamic swing indicator was one of the few that actually held its weight during the recent massive testing sessions we ran. it was pulling in profit factors of over thirty seven with annualized returns that look too good to be true until you see the trade list. we combined it with filters like the adx and the money flow index to see if we could refine the signals and the results were absolutely staggering. when you have a system that can run through forty data sets while you are drinking tea you realize that manual trading is a form of self harm. i realized this after spending hundreds of thousands on apps and devs only to find out that i could just learn to build these bots myself live on the internet. the speed of iteration is the only thing that matters in this game because the faster you can fail the faster you can find the one strategy that actually prints. one of the biggest hurdles i faced was thinking that i needed to be a math genius or a senior engineer to automate my trading systems. the truth is that code is the great equalizer because it allows a regular person to compete with massive hedge funds by using the same logic and speed. i decided to learn everything in public because i wanted people to see the process of losing money with liquidations and then finally finding a path to automation. the reality of the market is that it moves in cycles and what worked yesterday will almost certainly fail tomorrow unless you are constantly testing. that is why i built the agents to automatically look through the results folder and rank the top performers based on a composite score. it takes all the emotion out of the process because i am no longer looking for a reason to enter a trade i am just looking at a csv file that tells me the truth. if you are still drawing lines on a chart and hoping for the best you are basically playing a game of chance against a high speed casino. the transition from a manual trader to a systems builder is the single most important pivot you will ever make in your life. it is not about being right or wrong it is about having a positive expectancy that has been proven across thousands of trades and multiple years of history. i had to fix a few errors in the short selling logic where the agents were getting confused between maximum and minimum values for take profit levels. these tiny bugs are the difference between a winning system and a blown account so you have to be willing to dive into the code and refine the machine. but once the system is tuned and the sub agents are running it becomes a beautiful workflow that functions entirely without your input. we are currently moving through the editors picks and the trending indicators one by one because i want to have a database of every single strategy on the platform. being a data dog means you never stop searching for that edge and you never settle for a strategy that just looks okay on a single chart. you have to demand excellence from your code because the market will not give you a single inch of mercy if you are lazy with your research. the ultimate goal is to have fully automated systems trading for you so you can focus on scaling rather than staring at a screen for ten hours a day. i am already up to over eighty backtests in this single session and i plan on hitting hundreds more by the end of the week. once you realize that you can crack the code of any indicator you see on the internet you will never look at a chart the same way again. this is the power of using agents to bridge the gap between a raw idea and a finished trading bot that actually works in the real world. i am done with getting liquidated and i am done with the stress of over trading because the code handles everything with cold precision. the path to success is paved with data and if you are not willing to automate your process you are just waiting for your next liquidation to happen

Moon Dev

26,242 次观看 • 6 个月前

Do you want another ripple:native thesis on how Ripple is positioning XRP to modernize the whole financial system? Look at private credit. This is one of those markets most people never think about because it does not move like stocks, crypto, or even government bonds. A private-credit loan can be worth hundreds of millions of dollars. The borrower pays interest. The lender earns a return. The asset itself can be valuable. But there is one huge problem. It can be extremely hard to move. That is exactly what caught my attention in the Sandy Kaul and Anant Kumar discussion. Anant Kumar, from Benefit Street Partners, described the issue in a very simple way. Private credit has limited ownership. And it has almost no real secondary-market liquidity. A lender can originate a huge loan, but once that loan is sitting inside a fund, selling pieces of it is not as simple as selling a stock. That capital can stay trapped. Now imagine the same loan becoming digital. Not changing the economics of the loan. Not changing who the borrower is. Not changing who remains lender of record. Just changing how ownership can be represented. Instead of one giant $100M position sitting inside one structure, that loan could be represented as millions of smaller digital interests. Suddenly something that was hard to divide becomes divisible. Something that barely traded could potentially develop a secondary market. Something trapped inside one fund could become easier to distribute among approved investors. That is the part people should focus on. Because this is not some random idea coming from crypto Twitter. Sandy Kaul is Head of Digital Assets and Innovation at Franklin Templeton. Franklin Templeton manages roughly $1.78T. Anant Kumar is from Benefit Street Partners. And Franklin Templeton itself just closed a $1.5B Collateralized Fund Obligation tied to private equity secondaries and U.S. middle-market direct lending through Benefit Street Partners. So when they are talking about the problem of private-credit liquidity, they are talking about a market they actually operate inside. And this is where my ripple:native thesis gets much bigger. Because XRP Ledger is being built around the exact same problem. Not just payments. Not just moving stablecoins. Credit. Liquidity. Tokenized ownership. Secondary markets. Institutional lending. Collateral. That is what starts connecting everything. Private credit is already one of the largest categories inside tokenized real-world assets. Franklin Templeton’s own research says tokenized RWAs grew from around $5B in 2023 to more than $25B by early 2026. Private credit, Treasuries and real estate make up a major part of that growth. That tells me something important. Wall Street is not only tokenizing cash. It is beginning to tokenize assets that traditionally sit in some of the least liquid corners of finance. And private credit may be one of the biggest opportunities because liquidity is exactly where the pain is. Now look at XRPL. In 2025, VERT launched structured-credit infrastructure using XRP Ledger and its EVM sidechain. Its first live transaction was a BRL 700M Agribusiness Receivables Certificate. Roughly $130M. That is real structured credit. Recorded through infrastructure using XRPL. So when I hear Sandy Kaul and Anant Kumar talking about tokenizing private loans, I do not have to imagine whether XRPL could ever touch this market. It already has. That is only the beginning of the setup. The bigger piece is what Ripple is building directly into the network. The XRPL Lending Protocol. This is where everything starts making sense. Ripple has been very clear about the next stage of tokenization. Putting an asset onchain is not enough. A Treasury token sitting in a wallet is still just an asset sitting in a wallet. A private-credit token sitting in a wallet is still just a loan represented digitally. The real transformation happens when those assets can enter functioning capital markets. Borrowing. Lending. Liquidity. Collateral. Credit. That is exactly where the XRPL Lending Protocol is headed. Ripple explicitly names private credit among the assets that can move into this infrastructure, alongside Treasuries, money-market funds, stablecoins and commodities. That is a huge detail. Because private credit is not some side use case Ripple accidentally fits. It is literally one of the categories they are building around. Now add XLS-65. The Single Asset Vault design. This allows assets from multiple depositors to be pooled into one onchain vault. And that vault can hold XRP. Trust-line tokens. Or Multi-Purpose Tokens. Think about what that means in plain English. Today, one large institution may have to fund a giant private loan. Tomorrow, capital can potentially be pooled digitally. Thousands of approved investors contribute. The capital sits inside a common structure. A loan gets funded. The returns flow back through that structure. That is extremely close to what Anant Kumar is talking about when he says one loan could be split into smaller pieces. Now add XLS-66. The Lending Protocol. Fixed-term, uncollateralized lending. Credit underwriting stays offchain. The actual loan can be created and managed onchain. That detail matters more than people realize. Private credit is not anonymous DeFi. The borrower is evaluated. Creditworthiness matters. Interest matters. Terms matter. Default matters. Underwriting matters. XRPL is not trying to throw away that traditional credit process. It is trying to put the financial infrastructure around it onchain. That is why this feels much more institutional than a normal crypto lending protocol. And then you get to the liquidity problem. This is where Anant Kumar’s point becomes the whole thesis. Private-credit loans barely trade. If investors want redemptions, funds can have a problem. The assets may be good. The borrowers may be paying. But there may not be a deep market to sell into. That is trapped capital. Tokenization attacks that directly. Imagine one $100M private loan. Instead of treating it as one huge block, it becomes millions of smaller digital interests. Approved institutions can own pieces. Funds can rebalance. Banks can distribute exposure. Ownership can move without the whole loan changing hands as one giant object. Now put those interests on XRPL. They can be issued digitally. Held digitally. Transferred digitally. Settled digitally. Traded inside controlled markets. Used inside lending infrastructure. That is a completely different market structure. And XRPL is also building the control layer institutions need. Permissioned Domains. Permissioned DEXes. Credentials. Deep Freeze. Confidential Transfers. This is important because a bank is not going to take a $500M private-credit position and make it freely available to every random wallet in the world. Institutions need to control who can hold these assets. Who can trade them. Which jurisdiction they come from. Whether they satisfy eligibility rules. XRPL is being built for exactly that. You can have public blockchain infrastructure while still creating controlled markets where only approved participants transact. That solves one of the biggest objections banks have to permissionless finance. They do not need to choose between old closed systems and completely open anonymous markets. They can have digital assets with institutional rules built around them. That is where Permissioned DEXes become powerful. Imagine a tokenized private loan. Only approved investors can trade it. The loan still exists. The lender still exists. The borrower still exists. But now there is a secondary market. A fund needs liquidity? It can sell part of the position. Another institution wants exposure? It can buy a smaller piece. The market no longer depends on one giant bilateral transfer. That is how tokenization can start unlocking liquidity. And the more I look at this, the more I think ripple:native is being positioned for a much bigger role than people realize. Because every new tokenized asset creates another liquidity problem. Private credit token A. Private credit token B. Treasuries. Money-market funds. Stablecoins. Commercial paper. Tokenized deposits. Fund interests. Every asset needs somewhere to trade. Every institution needs somewhere to move value. Every market needs liquidity. You cannot have deep direct markets between every possible pair. That is where a common bridge asset becomes valuable. Private-credit token → ripple:native → RLUSD. RLUSD → ripple:native → another private-credit token. A European institution holds EUR liquidity and wants a U.S. private-credit position. EUR liquidity → ripple:native → RLUSD → tokenized credit. A fund wants to exit one credit position and move into another. Credit token A → ripple:native → RLUSD → credit token B. The more markets appear, the more possible routes exist. And the value of a common liquid bridge increases with the number of things it can connect. That is the part I think people still underestimate. ripple:native does not need every private-credit transaction to use XRP. It needs XRP to become useful wherever direct liquidity is weak. If XRPL becomes home to hundreds or thousands of tokenized credit instruments, there will always be fragmented liquidity somewhere. That is where deep XRP markets become valuable. Now add another piece that gets almost no attention. XRP itself can sit inside XLS-65 vault infrastructure. So XRP does not only have a potential role as bridge liquidity. It can also become pooled capital. That creates a completely different path. XRP goes into a vault. Vault capital gets pooled. The lending infrastructure uses that capital. Borrowers receive credit. Interest flows back through the structure. Now XRP is not just moving between markets. It is potentially sitting inside the capital base of the credit market itself. That is where the phrase “XRP utility is growing across payments, liquidity and credit markets” starts to make much more sense. Those are three completely different engines. Payments move value. Liquidity connects assets. Credit makes capital productive. Ripple is building around all three. Then you have ZILO and Licuido. Ripple invested in both to expand regulated transfer agency, tokenized issuance and collateral mobility on XRPL. That matters because a private-credit market is not just about issuing a token. Someone has to manage ownership records. Transfers. Servicing. Restrictions. Collateral. Secondary transactions. Settlement. If Ripple keeps adding these pieces, XRPL starts looking less like a blockchain with tokens on it and more like an operating system for financial assets. That is why Sandy Kaul’s broader thinking matters too. She has argued that blockchain is moving toward becoming a universal liquidity layer. Stablecoins. Tokenized cash. Lending. Collateral. Those are exactly the pieces appearing around XRPL. And I think private credit could be where this becomes impossible to ignore. Because the pain is so obvious. Imagine owning a valuable asset you cannot easily sell. That is private credit today. Imagine a fund holding billions in loans that barely trade. The assets are generating income. But if investors suddenly want cash, the fund cannot just tap a button and sell a fraction instantly. That is a huge weakness. Tokenization changes the unit of ownership. XRPL changes the infrastructure around that ownership. Permissioned markets change who can trade it. Lending turns those assets into productive capital. ripple:native can connect the liquidity between everything. That is the full setup. And now take it to the bullish extreme. Imagine private-credit managers start tokenizing at scale. A $500M fund does not hold 50 giant, isolated loan positions anymore. Each one becomes digitally represented. A $100M loan becomes 100M digital units worth $1 each. Approved investors can own smaller pieces. Funds can rebalance positions instead of selling whole loans. Banks can distribute exposure. Family offices can participate. Institutions can move capital without waiting for one buyer willing to absorb the entire block. Now imagine those assets living on XRPL. A fund wants to raise liquidity. It sells tokenized interests through a Permissioned DEX. Another approved institution takes the other side. Settlement happens digitally. RLUSD provides the dollar liquidity. XRP can bridge where direct liquidity is thin. The fund gets cash. The buyer gets credit exposure. The loan keeps performing. Nothing has to be dismantled. That is a much more efficient market. Then lending infrastructure goes live. An institution holds $200M of tokenized private credit. It does not want to sell. It wants liquidity. Instead of exiting the position, it uses that asset inside XRPL credit infrastructure. Capital gets unlocked. The institution receives liquidity. Moves into RLUSD. Then routes part of that capital through XRP into EUR. Now look at what XRP is sitting between. Private credit. Stablecoin liquidity. FX. Lending. Collateral. Global settlement. That is not a small use case. Now scale it. $100B of private credit on XRPL. Then $500B. Then $1T. Thousands of tokenized loans. Thousands of institutions. Loans constantly being issued. Traded. Financed. Pledged. Refinanced. Settled. Each new asset adds another market. Each new market needs liquidity. Each new participant creates another flow. And a common liquid bridge becomes more valuable as the network gets more complex. That is where ripple:native can become institutional credit-market liquidity. Not just a payment token. Not just a crypto trade. Liquidity sitting underneath a digital credit economy. And if that starts happening at hundreds of billions or trillions in scale, the XRP price conversation changes too. Market makers need inventory. Liquidity providers need inventory. Vaults can hold XRP. More XRP gets deployed inside financial infrastructure. The amount of financial value XRP markets have to support gets larger. If XRP is worth $1, $1B of XRP liquidity requires 1B XRP. At $10, it takes 100M. At $100, 10M. The higher the value of XRP, the more dollar liquidity each unit can represent. So if XRPL ever becomes a serious home for institutional private credit, the market may eventually have to price XRP around a completely different economic role. That is the thesis I keep coming back to. Sandy Kaul is talking about tokenizing private credit. Anant Kumar is talking about solving access and liquidity. Benefit Street Partners is operating directly in that market. Franklin Templeton is already deep in private markets. VERT has already put real structured-credit activity onto XRPL infrastructure. Ripple is building the Lending Protocol. XLS-65 can pool capital. XLS-66 can create fixed-term credit. Permissioned DEXes can create controlled secondary markets. Credentials can control eligibility. ZILO and Licuido expand issuance and collateral mobility. And ripple:native sits inside the liquidity and credit architecture. These are not separate stories to me anymore. They are all pieces of the same direction. Credit becomes digital. Digital credit becomes easier to divide. Divided credit becomes easier to trade. Tradable credit needs liquidity. Liquidity needs infrastructure. XRPL is being built for that infrastructure. And ripple:native can become part of the capital moving underneath it. That is why I think this private-credit conversation is one of the most underrated ripple:native theses right now. The endgame is not simply banks sending XRP across borders. The endgame could be XRP sitting inside a financial system where trillions of dollars of loans, Treasuries, stablecoins, funds and collateral move through the same liquidity network. That is a much bigger market than payments alone. And if Ripple gets this right, private credit may end up being one of the places where the world finally understands what they have been building. Remember this thesis when private credit starts moving onchain. If you understand where private credit is heading, you understand why I’m watching ripple:native.
0:24

Do you want another ripple:native thesis on how Ripple is positioning XRP to modernize the whole financial system? Look at private credit. This is one of those markets most people never think about because it does not move like stocks, crypto, or even government bonds. A private-credit loan can be worth hundreds of millions of dollars. The borrower pays interest. The lender earns a return. The asset itself can be valuable. But there is one huge problem. It can be extremely hard to move. That is exactly what caught my attention in the Sandy Kaul and Anant Kumar discussion. Anant Kumar, from Benefit Street Partners, described the issue in a very simple way. Private credit has limited ownership. And it has almost no real secondary-market liquidity. A lender can originate a huge loan, but once that loan is sitting inside a fund, selling pieces of it is not as simple as selling a stock. That capital can stay trapped. Now imagine the same loan becoming digital. Not changing the economics of the loan. Not changing who the borrower is. Not changing who remains lender of record. Just changing how ownership can be represented. Instead of one giant $100M position sitting inside one structure, that loan could be represented as millions of smaller digital interests. Suddenly something that was hard to divide becomes divisible. Something that barely traded could potentially develop a secondary market. Something trapped inside one fund could become easier to distribute among approved investors. That is the part people should focus on. Because this is not some random idea coming from crypto Twitter. Sandy Kaul is Head of Digital Assets and Innovation at Franklin Templeton. Franklin Templeton manages roughly $1.78T. Anant Kumar is from Benefit Street Partners. And Franklin Templeton itself just closed a $1.5B Collateralized Fund Obligation tied to private equity secondaries and U.S. middle-market direct lending through Benefit Street Partners. So when they are talking about the problem of private-credit liquidity, they are talking about a market they actually operate inside. And this is where my ripple:native thesis gets much bigger. Because XRP Ledger is being built around the exact same problem. Not just payments. Not just moving stablecoins. Credit. Liquidity. Tokenized ownership. Secondary markets. Institutional lending. Collateral. That is what starts connecting everything. Private credit is already one of the largest categories inside tokenized real-world assets. Franklin Templeton’s own research says tokenized RWAs grew from around $5B in 2023 to more than $25B by early 2026. Private credit, Treasuries and real estate make up a major part of that growth. That tells me something important. Wall Street is not only tokenizing cash. It is beginning to tokenize assets that traditionally sit in some of the least liquid corners of finance. And private credit may be one of the biggest opportunities because liquidity is exactly where the pain is. Now look at XRPL. In 2025, VERT launched structured-credit infrastructure using XRP Ledger and its EVM sidechain. Its first live transaction was a BRL 700M Agribusiness Receivables Certificate. Roughly $130M. That is real structured credit. Recorded through infrastructure using XRPL. So when I hear Sandy Kaul and Anant Kumar talking about tokenizing private loans, I do not have to imagine whether XRPL could ever touch this market. It already has. That is only the beginning of the setup. The bigger piece is what Ripple is building directly into the network. The XRPL Lending Protocol. This is where everything starts making sense. Ripple has been very clear about the next stage of tokenization. Putting an asset onchain is not enough. A Treasury token sitting in a wallet is still just an asset sitting in a wallet. A private-credit token sitting in a wallet is still just a loan represented digitally. The real transformation happens when those assets can enter functioning capital markets. Borrowing. Lending. Liquidity. Collateral. Credit. That is exactly where the XRPL Lending Protocol is headed. Ripple explicitly names private credit among the assets that can move into this infrastructure, alongside Treasuries, money-market funds, stablecoins and commodities. That is a huge detail. Because private credit is not some side use case Ripple accidentally fits. It is literally one of the categories they are building around. Now add XLS-65. The Single Asset Vault design. This allows assets from multiple depositors to be pooled into one onchain vault. And that vault can hold XRP. Trust-line tokens. Or Multi-Purpose Tokens. Think about what that means in plain English. Today, one large institution may have to fund a giant private loan. Tomorrow, capital can potentially be pooled digitally. Thousands of approved investors contribute. The capital sits inside a common structure. A loan gets funded. The returns flow back through that structure. That is extremely close to what Anant Kumar is talking about when he says one loan could be split into smaller pieces. Now add XLS-66. The Lending Protocol. Fixed-term, uncollateralized lending. Credit underwriting stays offchain. The actual loan can be created and managed onchain. That detail matters more than people realize. Private credit is not anonymous DeFi. The borrower is evaluated. Creditworthiness matters. Interest matters. Terms matter. Default matters. Underwriting matters. XRPL is not trying to throw away that traditional credit process. It is trying to put the financial infrastructure around it onchain. That is why this feels much more institutional than a normal crypto lending protocol. And then you get to the liquidity problem. This is where Anant Kumar’s point becomes the whole thesis. Private-credit loans barely trade. If investors want redemptions, funds can have a problem. The assets may be good. The borrowers may be paying. But there may not be a deep market to sell into. That is trapped capital. Tokenization attacks that directly. Imagine one $100M private loan. Instead of treating it as one huge block, it becomes millions of smaller digital interests. Approved institutions can own pieces. Funds can rebalance. Banks can distribute exposure. Ownership can move without the whole loan changing hands as one giant object. Now put those interests on XRPL. They can be issued digitally. Held digitally. Transferred digitally. Settled digitally. Traded inside controlled markets. Used inside lending infrastructure. That is a completely different market structure. And XRPL is also building the control layer institutions need. Permissioned Domains. Permissioned DEXes. Credentials. Deep Freeze. Confidential Transfers. This is important because a bank is not going to take a $500M private-credit position and make it freely available to every random wallet in the world. Institutions need to control who can hold these assets. Who can trade them. Which jurisdiction they come from. Whether they satisfy eligibility rules. XRPL is being built for exactly that. You can have public blockchain infrastructure while still creating controlled markets where only approved participants transact. That solves one of the biggest objections banks have to permissionless finance. They do not need to choose between old closed systems and completely open anonymous markets. They can have digital assets with institutional rules built around them. That is where Permissioned DEXes become powerful. Imagine a tokenized private loan. Only approved investors can trade it. The loan still exists. The lender still exists. The borrower still exists. But now there is a secondary market. A fund needs liquidity? It can sell part of the position. Another institution wants exposure? It can buy a smaller piece. The market no longer depends on one giant bilateral transfer. That is how tokenization can start unlocking liquidity. And the more I look at this, the more I think ripple:native is being positioned for a much bigger role than people realize. Because every new tokenized asset creates another liquidity problem. Private credit token A. Private credit token B. Treasuries. Money-market funds. Stablecoins. Commercial paper. Tokenized deposits. Fund interests. Every asset needs somewhere to trade. Every institution needs somewhere to move value. Every market needs liquidity. You cannot have deep direct markets between every possible pair. That is where a common bridge asset becomes valuable. Private-credit token → ripple:native → RLUSD. RLUSD → ripple:native → another private-credit token. A European institution holds EUR liquidity and wants a U.S. private-credit position. EUR liquidity → ripple:native → RLUSD → tokenized credit. A fund wants to exit one credit position and move into another. Credit token A → ripple:native → RLUSD → credit token B. The more markets appear, the more possible routes exist. And the value of a common liquid bridge increases with the number of things it can connect. That is the part I think people still underestimate. ripple:native does not need every private-credit transaction to use XRP. It needs XRP to become useful wherever direct liquidity is weak. If XRPL becomes home to hundreds or thousands of tokenized credit instruments, there will always be fragmented liquidity somewhere. That is where deep XRP markets become valuable. Now add another piece that gets almost no attention. XRP itself can sit inside XLS-65 vault infrastructure. So XRP does not only have a potential role as bridge liquidity. It can also become pooled capital. That creates a completely different path. XRP goes into a vault. Vault capital gets pooled. The lending infrastructure uses that capital. Borrowers receive credit. Interest flows back through the structure. Now XRP is not just moving between markets. It is potentially sitting inside the capital base of the credit market itself. That is where the phrase “XRP utility is growing across payments, liquidity and credit markets” starts to make much more sense. Those are three completely different engines. Payments move value. Liquidity connects assets. Credit makes capital productive. Ripple is building around all three. Then you have ZILO and Licuido. Ripple invested in both to expand regulated transfer agency, tokenized issuance and collateral mobility on XRPL. That matters because a private-credit market is not just about issuing a token. Someone has to manage ownership records. Transfers. Servicing. Restrictions. Collateral. Secondary transactions. Settlement. If Ripple keeps adding these pieces, XRPL starts looking less like a blockchain with tokens on it and more like an operating system for financial assets. That is why Sandy Kaul’s broader thinking matters too. She has argued that blockchain is moving toward becoming a universal liquidity layer. Stablecoins. Tokenized cash. Lending. Collateral. Those are exactly the pieces appearing around XRPL. And I think private credit could be where this becomes impossible to ignore. Because the pain is so obvious. Imagine owning a valuable asset you cannot easily sell. That is private credit today. Imagine a fund holding billions in loans that barely trade. The assets are generating income. But if investors suddenly want cash, the fund cannot just tap a button and sell a fraction instantly. That is a huge weakness. Tokenization changes the unit of ownership. XRPL changes the infrastructure around that ownership. Permissioned markets change who can trade it. Lending turns those assets into productive capital. ripple:native can connect the liquidity between everything. That is the full setup. And now take it to the bullish extreme. Imagine private-credit managers start tokenizing at scale. A $500M fund does not hold 50 giant, isolated loan positions anymore. Each one becomes digitally represented. A $100M loan becomes 100M digital units worth $1 each. Approved investors can own smaller pieces. Funds can rebalance positions instead of selling whole loans. Banks can distribute exposure. Family offices can participate. Institutions can move capital without waiting for one buyer willing to absorb the entire block. Now imagine those assets living on XRPL. A fund wants to raise liquidity. It sells tokenized interests through a Permissioned DEX. Another approved institution takes the other side. Settlement happens digitally. RLUSD provides the dollar liquidity. XRP can bridge where direct liquidity is thin. The fund gets cash. The buyer gets credit exposure. The loan keeps performing. Nothing has to be dismantled. That is a much more efficient market. Then lending infrastructure goes live. An institution holds $200M of tokenized private credit. It does not want to sell. It wants liquidity. Instead of exiting the position, it uses that asset inside XRPL credit infrastructure. Capital gets unlocked. The institution receives liquidity. Moves into RLUSD. Then routes part of that capital through XRP into EUR. Now look at what XRP is sitting between. Private credit. Stablecoin liquidity. FX. Lending. Collateral. Global settlement. That is not a small use case. Now scale it. $100B of private credit on XRPL. Then $500B. Then $1T. Thousands of tokenized loans. Thousands of institutions. Loans constantly being issued. Traded. Financed. Pledged. Refinanced. Settled. Each new asset adds another market. Each new market needs liquidity. Each new participant creates another flow. And a common liquid bridge becomes more valuable as the network gets more complex. That is where ripple:native can become institutional credit-market liquidity. Not just a payment token. Not just a crypto trade. Liquidity sitting underneath a digital credit economy. And if that starts happening at hundreds of billions or trillions in scale, the XRP price conversation changes too. Market makers need inventory. Liquidity providers need inventory. Vaults can hold XRP. More XRP gets deployed inside financial infrastructure. The amount of financial value XRP markets have to support gets larger. If XRP is worth $1, $1B of XRP liquidity requires 1B XRP. At $10, it takes 100M. At $100, 10M. The higher the value of XRP, the more dollar liquidity each unit can represent. So if XRPL ever becomes a serious home for institutional private credit, the market may eventually have to price XRP around a completely different economic role. That is the thesis I keep coming back to. Sandy Kaul is talking about tokenizing private credit. Anant Kumar is talking about solving access and liquidity. Benefit Street Partners is operating directly in that market. Franklin Templeton is already deep in private markets. VERT has already put real structured-credit activity onto XRPL infrastructure. Ripple is building the Lending Protocol. XLS-65 can pool capital. XLS-66 can create fixed-term credit. Permissioned DEXes can create controlled secondary markets. Credentials can control eligibility. ZILO and Licuido expand issuance and collateral mobility. And ripple:native sits inside the liquidity and credit architecture. These are not separate stories to me anymore. They are all pieces of the same direction. Credit becomes digital. Digital credit becomes easier to divide. Divided credit becomes easier to trade. Tradable credit needs liquidity. Liquidity needs infrastructure. XRPL is being built for that infrastructure. And ripple:native can become part of the capital moving underneath it. That is why I think this private-credit conversation is one of the most underrated ripple:native theses right now. The endgame is not simply banks sending XRP across borders. The endgame could be XRP sitting inside a financial system where trillions of dollars of loans, Treasuries, stablecoins, funds and collateral move through the same liquidity network. That is a much bigger market than payments alone. And if Ripple gets this right, private credit may end up being one of the places where the world finally understands what they have been building. Remember this thesis when private credit starts moving onchain. If you understand where private credit is heading, you understand why I’m watching ripple:native.

X Finance Bull

16,025 次观看 • 24 天前

In 2016, Marvell's largest design win was a Wi-Fi chip in the Barbie Dream House (Save this). That is a documented fact about one of the most remarkable corporate transformations in semiconductor history. Ten years and $36 billion in acquisitions later, Marvell is now the company that Jensen Huang invites onto the COMPUTEX stage, the same stage where he announced a $2 billion strategic investment into the company. Over 75% of Marvell's revenue today comes from data centers. To understand what Marvell actually is now, you need to understand what Matt Murphy did when he walked in as CEO in 2016. The company had stagnant growth, governance scandals, and a business model built around chips for hard drives, printers, and consumer electronics, exactly the wrong place to be as the cloud era was beginning. Murphy made a ruthless decision to kill every low margin consumer business and go all in on data infrastructure. Then he went shopping. 2018 - Acquired Cavium for $6 billion, bringing ARM-based network processors and the foundation for cloud infrastructure compute. 2019 - Acquired Avera Semiconductor, formerly IBM's custom silicon team, which gave Marvell the ability to design bespoke ASICs for hyperscalers. This is what opened the door to Amazon, Microsoft, and Google design wins. 2021 - Acquired Inphi for $8.2 billion, securing leadership in high-speed optical interconnect, the technology that moves data between and within data centers at the speed of light. 2021 - Acquired Innovium, adding cloud-optimized Ethernet switching to the portfolio. 2025/2026 - Acquired Celestial AI for $3.25 billion, bringing photonic fabric technology that places optical connections directly inside the chip package itself. Each acquisition followed the same formula, buy the technology that will be absolutely essential in the next generation of computing before anyone else is paying attention. Now here's the vision Murphy laid out at COMPUTEX 2026, and why it's the most important thing he's ever said publicly. He made one central argument, AI scaling is no longer limited by compute or memory but rather limited by connectivity. Training a frontier model requires tens of thousands and eventually millions of processors working as a single engine and making that happen is a connectivity problem above all else. Today, data centers are constrained by copper. Copper traces connecting chips inside a server can only move data so far, so fast, before bandwidth collapses and latency rises, that's why today's AI servers have to bundle everything, CPUs, GPUs, memory onto the same physical board sitting centimeters apart. When you replace copper with optics, distance disappears entirely. An optically connected server rack can communicate with another rack in a different building at the same bandwidth and latency as if they were the same machine. Memory can sit in one physical location, compute in another, networking in a third and a software orchestration layer composes the exact ratio the workload needs, on the fly, in real time. Murphy called this a data center without distance, a globally optically interconnected infrastructure where the rigid physical boundaries of today's servers begin to disappear entirely, and data centers function as one unified system. That is not a 10 year vision because Marvell's CPO (co-packaged optics) products are sampling in 2027 with volume shipments beginning 2028. Nvidia's Vera Rubin platform has already adopted Spectrum-X Ethernet Photonics, the first CPO switch in commercial production. The reason this makes Marvell's TAM almost impossible to cap is the following. Right now, Marvell's addressable market is the optical interconnect market, a segment projected to be worth $200 billion per year by end of decade. But if the data center without distance architecture actually materializes and the evidence suggests it will, then Marvell's TAM is not just the optical interconnect market but rather every connection in every data center on earth. Bullish on Marvel! Come join Milk Road Pro for just a $1, If you want the full Marvell breakdown on where it sits in our AI infrastructure portfolio, and our entire AI thesis. Link below!
3:05

In 2016, Marvell's largest design win was a Wi-Fi chip in the Barbie Dream House (Save this). That is a documented fact about one of the most remarkable corporate transformations in semiconductor history. Ten years and $36 billion in acquisitions later, Marvell is now the company that Jensen Huang invites onto the COMPUTEX stage, the same stage where he announced a $2 billion strategic investment into the company. Over 75% of Marvell's revenue today comes from data centers. To understand what Marvell actually is now, you need to understand what Matt Murphy did when he walked in as CEO in 2016. The company had stagnant growth, governance scandals, and a business model built around chips for hard drives, printers, and consumer electronics, exactly the wrong place to be as the cloud era was beginning. Murphy made a ruthless decision to kill every low margin consumer business and go all in on data infrastructure. Then he went shopping. 2018 - Acquired Cavium for $6 billion, bringing ARM-based network processors and the foundation for cloud infrastructure compute. 2019 - Acquired Avera Semiconductor, formerly IBM's custom silicon team, which gave Marvell the ability to design bespoke ASICs for hyperscalers. This is what opened the door to Amazon, Microsoft, and Google design wins. 2021 - Acquired Inphi for $8.2 billion, securing leadership in high-speed optical interconnect, the technology that moves data between and within data centers at the speed of light. 2021 - Acquired Innovium, adding cloud-optimized Ethernet switching to the portfolio. 2025/2026 - Acquired Celestial AI for $3.25 billion, bringing photonic fabric technology that places optical connections directly inside the chip package itself. Each acquisition followed the same formula, buy the technology that will be absolutely essential in the next generation of computing before anyone else is paying attention. Now here's the vision Murphy laid out at COMPUTEX 2026, and why it's the most important thing he's ever said publicly. He made one central argument, AI scaling is no longer limited by compute or memory but rather limited by connectivity. Training a frontier model requires tens of thousands and eventually millions of processors working as a single engine and making that happen is a connectivity problem above all else. Today, data centers are constrained by copper. Copper traces connecting chips inside a server can only move data so far, so fast, before bandwidth collapses and latency rises, that's why today's AI servers have to bundle everything, CPUs, GPUs, memory onto the same physical board sitting centimeters apart. When you replace copper with optics, distance disappears entirely. An optically connected server rack can communicate with another rack in a different building at the same bandwidth and latency as if they were the same machine. Memory can sit in one physical location, compute in another, networking in a third and a software orchestration layer composes the exact ratio the workload needs, on the fly, in real time. Murphy called this a data center without distance, a globally optically interconnected infrastructure where the rigid physical boundaries of today's servers begin to disappear entirely, and data centers function as one unified system. That is not a 10 year vision because Marvell's CPO (co-packaged optics) products are sampling in 2027 with volume shipments beginning 2028. Nvidia's Vera Rubin platform has already adopted Spectrum-X Ethernet Photonics, the first CPO switch in commercial production. The reason this makes Marvell's TAM almost impossible to cap is the following. Right now, Marvell's addressable market is the optical interconnect market, a segment projected to be worth $200 billion per year by end of decade. But if the data center without distance architecture actually materializes and the evidence suggests it will, then Marvell's TAM is not just the optical interconnect market but rather every connection in every data center on earth. Bullish on Marvel! Come join Milk Road Pro for just a $1, If you want the full Marvell breakdown on where it sits in our AI infrastructure portfolio, and our entire AI thesis. Link below!

Milk Road AI

21,550 次观看 • 3 个月前

Made $530,000 with Ai Bot that started with $313. Didn't know how to code. Now this bots run 24/7 printing money while sleeping. I've made the exact step-by-step guide to build this Claude Code Polymarket trading bot. Prompts. Code. Risk settings. Paper trading checklist. Everything from zero to running bot. It's free. For 24 hours. After that I'm charging $499 for it. To grab it right now: 1. Comment "Claude Bot" 2. Like and Retweet this post 3. Follow me Himanshu Kumar ( I can't send DMs to non-followers ) I'm DMing everyone who Complete the 3 steps. I spent hundreds of thousands hiring developers because he was too scared to learn. Then learned Claude Code. Built algorithmic trading systems. $313 → $530,000. You have the same tools available right now. And you're using them to ask ChatGPT for Instagram captions. This attached video is a goldmine. Full live walkthrough. Claude Code building actual Polymarket trading bots. From zero. Every line of code. Every decision explained. Now let me break down why everything you're doing in trading is wrong and exactly how to fix it. Save this post. You'll hate yourself if you lose it. ↓ Let's start with why you keep losing money. You already know the answer. You just won't admit it. You overtrade. Every. Single. Day. You see a candle move. You feel something. You enter. No plan. No edge. No reason. Just feelings. Then it goes against you. You feel something else. Panic. Anger. Denial. You move your stop loss. Or you didn't set one at all. "It'll come back." It doesn't come back. So you take another trade. A revenge trade. Bigger size this time. Because you need to "make it back." That one fails too. Now you're emotional. Now you're tilted. Now you're using leverage you have no business touching. 40x. 50x. 100x. On a trade you entered because a candle looked "bullish" and some guy on Twitter said "send it." You get liquidated. Close the laptop. Punch something. Tell yourself you'll be "more disciplined" tomorrow. Tomorrow comes. Same cycle. Same result. Same liquidation. You've been doing this for months. Maybe years. And you still think the problem is your strategy. The problem isn't your strategy. The problem is you. Save this post right now. What I'm about to show you is the only way to remove yourself from the equation. Follow Himanshu Kumar so you don't miss any of this. ↓ Here's what's actually killing your account. It's not the market. The market doesn't care about you. It's not your indicators. RSI works fine. MACD works fine. They all "work." It's not your timeframe. It's not your broker. It's not the "manipulation." It's four things: 1. Emotions. You hold losers because hope feels better than loss. You cut winners because fear feels stronger than greed. You size up when angry. You skip trades when scared. Your emotional state determines your position size. That's insane. And you know it's insane. But you keep doing it. 2. Overtrading. You take 15 trades a day. Maybe 5 of them had actual setups. The other 10 were boredom. Boredom trades are the most expensive hobby in human history. 3. Leverage. You use 20x-50x on trades where you're not even sure about the direction. That's not trading. That's a casino with a nicer interface. 4. Fees. You're smashing market orders. Paying spread. Paying commission. On 15 trades a day. Your broker makes more money from your account than you do. Think about that. Your broker is profitable on your account. You're not. You're the product. Not the trader. These four things are why 90% of traders lose. Not bad luck. Not the market. You. Save this post and follow Himanshu Kumar because the solution is coming next. ↓ The solution is painfully obvious. Remove yourself from the equation. Not partially. Not "I'll be more disciplined." Not "I'll journal my trades." Not "I'll meditate before trading." Completely remove yourself. Build a bot. Let the bot trade. You go live your life. The bot doesn't feel emotions. The bot doesn't overtrade. The bot doesn't use reckless leverage. The bot doesn't smash market orders and bleed fees. The bot follows the rules. Every single time. Without exception. Without "just this once." Without "I have a feeling about this one." Rules in. Execution out. No human in the middle to mess everything up. That's algorithmic trading. And before your ego jumps in with "but I'm different, I have discipline" — No you don't. Your account balance proves you don't. If you had discipline, your account would be green. It's not. So you don't. Accept it. Automate it. Move on. This is the hardest truth in trading. Your discipline will always fail. A bot's won't. Save this post. Follow Himanshu Kumar for the exact bot setup that removes your emotions permanently. ↓ "But I don't know how to code." Neither did he. The guy in this video didn't know how to code for most of his life. Got held back in 7th grade. People counted him out early. Spent years building apps and SaaS businesses without writing a single line of code. Hired developers on Upwork instead. Spent hundreds of thousands of dollars paying other people to build what he could have built himself. Because he was scared to learn. That fear cost him years. And hundreds of thousands of dollars. Sound familiar? You're doing the same thing right now. Not with developers. But with your time. You're spending thousands of hours trading manually because you're scared to learn the thing that would make trading automatic. The fear of learning to code is costing you more than any bad trade ever did. Because every month you trade manually is a month of emotional decisions, overleveraged entries, and unnecessary losses that a bot would never make. And here's the thing that should really frustrate you: AI does the hard parts now. You don't need a computer science degree. You don't need to work at a hedge fund. You don't need to be "good at math." Claude Code writes the code for you. You just need to think clearly about trading ideas. That's it. If you can describe a strategy in English, Claude can build it in Python. "I don't know how to code" stopped being a valid excuse in 2024. It's 2026. You're 2 years late on that excuse. Find a new one. Or stop making excuses entirely. Save this post. Follow Himanshu Kumar because I'm showing you how people with zero coding experience are building profitable bots. ↓ The process that actually makes money. Three letters. R. B. I. Research. Backtest. Implement. That's it. That's the entire process. Every single day. Research: Find an idea. A pattern. A market inefficiency. Don't trade it yet. Don't even think about trading it yet. Just research it. Backtest: Test the idea against historical data. Does it work? Not "does it look good on one chart." Does it work across thousands of trades? Across different market conditions? Across in-sample AND out-of-sample data? If no, kill it. Find another idea. If yes, move to step 3. Implement: Build the bot. Deploy it. Paper trade first. Then live with small size. Scale only on evidence. Research. Backtest. Implement. Every day. No exceptions. You know what your current process is? Feel. Enter. Pray. F. E. P. Feel bullish. Enter a trade. Pray it works. That's not a process. That's gambling with a TradingView subscription. RBI is the only process that works. Save this post. Tattoo it on your forearm. Follow Himanshu Kumar for daily RBI breakdowns. ↓ What Claude Code actually does that your manual process can't. You can maybe test 3-5 strategy ideas per week. Manually adjusting parameters. Manually checking results. Manually writing code (badly). Claude Code tests 50-100 ideas per week. With parallel agents running simultaneously. Multiple strategies being built, tested, and validated at the same time. While you sleep. The guy in this video spends 4-8 hours a day building systems with Claude Code. Not trading. Building. Research. Backtest. Implement. Then iterate. Improve. Optimize. Every day the systems get better. Every day the edge compounds. Every day the bots get smarter. While you? You spend 4-8 hours a day staring at charts making the same mistakes you made last month. Same indicators. Same patterns. Same entries. Same losses. He's iterating forward. You're running in circles. Same 8 hours per day. Completely different outcomes. Because he's building systems. And you're feeding a casino. Stop feeding the casino. Start building the machine. Save this post and follow Himanshu Kumar for the Claude Code workflow that iterates strategies while you sleep. ↓ Jim Simons. That's the benchmark. You probably don't know who Jim Simons is. And that tells me everything about how seriously you take trading. Jim Simons. Mathematician. Founded Renaissance Technologies. Built a net worth of $31 billion. 100% from algorithmic trading. Not one single manual trade. Not one "gut feeling" entry. Not one RSI divergence. Not one "smart money concept." Algorithms. Bots. Systems. Data. $31 billion. His fund averaged 66% annual returns for over 30 years. While you're excited about making $200 on a trade that you'll give back tomorrow. The best trader in human history never placed a manual trade in his life. And you think your edge is staring at a 5-minute chart with bloodshot eyes at 2 AM? Your edge is building the system. Not being inside it. Jim Simons is the benchmark. Everything else is noise. Save this post. Follow Himanshu Kumar because I'm building toward the same goal and showing every step publicly. ↓ What you need to understand about patience. This is not get-rich-overnight. The guy in this video says it directly: "This channel is not for people looking to get rich overnight. It's not plug and play. There are no shortcuts. If you're impatient, this probably isn't for you." And that's exactly why most people will fail at this. Because you want results now. Today. This trade. You don't want to spend a week building a bot. You don't want to paper trade for 2 weeks. You don't want to test 50 ideas to find 1 that works. You want to copy someone's bot, run it live with your rent money, and be rich by Friday. That's why you'll be broke by Friday. The guy making $2.3M spent months iterating. Testing. Failing. Rebuilding. Testing again. He was patient when you would have quit. He was calm when you would have panicked. He was consistent when you would have given up. Patience isn't just a virtue in trading. It's the only virtue. Without it, everything else fails. Impatience is the most expensive personality trait in trading. Save this post. Follow Himanshu Kumar and learn to build systems with the patience that actually pays. ↓ The live streams where the real learning happens. The YouTube video is the trailer. The live streams are the movie. Real-time bot building. Real-time questions answered. Real code shown. Real mistakes made and fixed. Not polished highlight reels where everything works perfectly. Actual development. Where things break. Where strategies fail. Where code doesn't compile. Where the fix takes 2 hours. Because that's what real development looks like. And seeing the messy parts is more valuable than any polished tutorial. Because when your bot breaks at 3 AM, you need to know how to fix it. Not just how to celebrate when it works. The streams mix beginner and advanced. Start with how to automate trading. How to use AI for code generation. Then dive into the daily work. Claude Code. Parallel agents. Constant iteration. Live debugging. 4-8 hours of real algorithmic trading development. Live. Uncut. No filter. Most "trading education" shows you the wins. This shows you the work. Save this post. Follow Himanshu Kumar for the stream schedules and breakdowns. ↓ The belief that changes everything. Code is the greatest equalizer. Not money. Not connections. Not a degree. Not where you grew up. Not what school you went to. Code. Once you can build systems, you can build anything. For the rest of your life. A trading bot today. A SaaS product tomorrow. An automation business next month. A completely different life next year. The skill isn't "algorithmic trading." The skill is building systems. And that skill transfers to everything. The guy who can build a trading bot can also build a lead gen tool. Can also build a content pipeline. Can also build a SaaS product. Can also build literally anything that runs on logic and code. One skill. Infinite applications. And AI makes learning it 100x easier than it was 5 years ago. You don't need to be smart. You don't need talent. You need Claude Code and the willingness to sit down and build something instead of consuming content about building something. Building is the skill. Everything else is entertainment disguised as education. Save this post. Follow Himanshu Kumar because I'm showing you how to build, not just how to watch. ↓ If any of this applies to you, pay attention. If you've lost money from overtrading. If you've been liquidated. If you know trading is the vehicle but manual execution keeps crashing you. If you've tried "being more disciplined" and it never lasted more than a week. If you keep saying "next month I'll start automating." If you've spent more money on courses than you've made from trading. There is a better way. It's not a magic indicator. It's not a signal group. It's not a $997 mentorship from a guy who makes money teaching, not trading. It's building your own system. A system that trades without emotion. A system that follows rules without exception. A system that runs while you sleep. A system that compounds while you live your life. That's the answer. It's always been the answer. You've just been too scared to accept that the solution requires building something instead of buying something. ↓ What the next 30 days look like if you actually commit. Week 1: Watch the video. Learn Claude Code basics. Build your first simple strategy. Run your first backtest. Week 2: Iterate. Let Claude improve the strategy. Run Monte Carlo validation. Paper trade. Week 3: Go live with $50-100. Tiny positions. Watch every trade. Compare to paper results. Week 4: Scale based on evidence. Not based on excitement. Not based on one good day. Based on data. 30 days from now you either have a running bot that trades without your emotions destroying every position. Or you're exactly where you are right now. Reading another post. Making another promise. Breaking it by Tuesday. Same 30 days either way. Different actions. Different results. Different life. ↓ Full video tutorial attached. Live bot building with Claude Code. From zero to running Polymarket trading bot. Every line of code. Every decision explained. The video is free. Claude Code is available now. The market is open 24/7. The only thing standing between you and a profitable trading bot is the same thing that's been standing there for months. You. Get out of your own way. Follow Himanshu Kumar for daily AI trading bot breakdowns, live build sessions, and the full RBI process. Save this post. Watch the video. Build the bot. Or keep trading manually and keep losing. The choice has never been easier. And you've never been more stubborn about making the wrong one.
18:53

Made $530,000 with Ai Bot that started with $313. Didn't know how to code. Now this bots run 24/7 printing money while sleeping. I've made the exact step-by-step guide to build this Claude Code Polymarket trading bot. Prompts. Code. Risk settings. Paper trading checklist. Everything from zero to running bot. It's free. For 24 hours. After that I'm charging $499 for it. To grab it right now: 1. Comment "Claude Bot" 2. Like and Retweet this post 3. Follow me Himanshu Kumar ( I can't send DMs to non-followers ) I'm DMing everyone who Complete the 3 steps. I spent hundreds of thousands hiring developers because he was too scared to learn. Then learned Claude Code. Built algorithmic trading systems. $313 → $530,000. You have the same tools available right now. And you're using them to ask ChatGPT for Instagram captions. This attached video is a goldmine. Full live walkthrough. Claude Code building actual Polymarket trading bots. From zero. Every line of code. Every decision explained. Now let me break down why everything you're doing in trading is wrong and exactly how to fix it. Save this post. You'll hate yourself if you lose it. ↓ Let's start with why you keep losing money. You already know the answer. You just won't admit it. You overtrade. Every. Single. Day. You see a candle move. You feel something. You enter. No plan. No edge. No reason. Just feelings. Then it goes against you. You feel something else. Panic. Anger. Denial. You move your stop loss. Or you didn't set one at all. "It'll come back." It doesn't come back. So you take another trade. A revenge trade. Bigger size this time. Because you need to "make it back." That one fails too. Now you're emotional. Now you're tilted. Now you're using leverage you have no business touching. 40x. 50x. 100x. On a trade you entered because a candle looked "bullish" and some guy on Twitter said "send it." You get liquidated. Close the laptop. Punch something. Tell yourself you'll be "more disciplined" tomorrow. Tomorrow comes. Same cycle. Same result. Same liquidation. You've been doing this for months. Maybe years. And you still think the problem is your strategy. The problem isn't your strategy. The problem is you. Save this post right now. What I'm about to show you is the only way to remove yourself from the equation. Follow Himanshu Kumar so you don't miss any of this. ↓ Here's what's actually killing your account. It's not the market. The market doesn't care about you. It's not your indicators. RSI works fine. MACD works fine. They all "work." It's not your timeframe. It's not your broker. It's not the "manipulation." It's four things: 1. Emotions. You hold losers because hope feels better than loss. You cut winners because fear feels stronger than greed. You size up when angry. You skip trades when scared. Your emotional state determines your position size. That's insane. And you know it's insane. But you keep doing it. 2. Overtrading. You take 15 trades a day. Maybe 5 of them had actual setups. The other 10 were boredom. Boredom trades are the most expensive hobby in human history. 3. Leverage. You use 20x-50x on trades where you're not even sure about the direction. That's not trading. That's a casino with a nicer interface. 4. Fees. You're smashing market orders. Paying spread. Paying commission. On 15 trades a day. Your broker makes more money from your account than you do. Think about that. Your broker is profitable on your account. You're not. You're the product. Not the trader. These four things are why 90% of traders lose. Not bad luck. Not the market. You. Save this post and follow Himanshu Kumar because the solution is coming next. ↓ The solution is painfully obvious. Remove yourself from the equation. Not partially. Not "I'll be more disciplined." Not "I'll journal my trades." Not "I'll meditate before trading." Completely remove yourself. Build a bot. Let the bot trade. You go live your life. The bot doesn't feel emotions. The bot doesn't overtrade. The bot doesn't use reckless leverage. The bot doesn't smash market orders and bleed fees. The bot follows the rules. Every single time. Without exception. Without "just this once." Without "I have a feeling about this one." Rules in. Execution out. No human in the middle to mess everything up. That's algorithmic trading. And before your ego jumps in with "but I'm different, I have discipline" — No you don't. Your account balance proves you don't. If you had discipline, your account would be green. It's not. So you don't. Accept it. Automate it. Move on. This is the hardest truth in trading. Your discipline will always fail. A bot's won't. Save this post. Follow Himanshu Kumar for the exact bot setup that removes your emotions permanently. ↓ "But I don't know how to code." Neither did he. The guy in this video didn't know how to code for most of his life. Got held back in 7th grade. People counted him out early. Spent years building apps and SaaS businesses without writing a single line of code. Hired developers on Upwork instead. Spent hundreds of thousands of dollars paying other people to build what he could have built himself. Because he was scared to learn. That fear cost him years. And hundreds of thousands of dollars. Sound familiar? You're doing the same thing right now. Not with developers. But with your time. You're spending thousands of hours trading manually because you're scared to learn the thing that would make trading automatic. The fear of learning to code is costing you more than any bad trade ever did. Because every month you trade manually is a month of emotional decisions, overleveraged entries, and unnecessary losses that a bot would never make. And here's the thing that should really frustrate you: AI does the hard parts now. You don't need a computer science degree. You don't need to work at a hedge fund. You don't need to be "good at math." Claude Code writes the code for you. You just need to think clearly about trading ideas. That's it. If you can describe a strategy in English, Claude can build it in Python. "I don't know how to code" stopped being a valid excuse in 2024. It's 2026. You're 2 years late on that excuse. Find a new one. Or stop making excuses entirely. Save this post. Follow Himanshu Kumar because I'm showing you how people with zero coding experience are building profitable bots. ↓ The process that actually makes money. Three letters. R. B. I. Research. Backtest. Implement. That's it. That's the entire process. Every single day. Research: Find an idea. A pattern. A market inefficiency. Don't trade it yet. Don't even think about trading it yet. Just research it. Backtest: Test the idea against historical data. Does it work? Not "does it look good on one chart." Does it work across thousands of trades? Across different market conditions? Across in-sample AND out-of-sample data? If no, kill it. Find another idea. If yes, move to step 3. Implement: Build the bot. Deploy it. Paper trade first. Then live with small size. Scale only on evidence. Research. Backtest. Implement. Every day. No exceptions. You know what your current process is? Feel. Enter. Pray. F. E. P. Feel bullish. Enter a trade. Pray it works. That's not a process. That's gambling with a TradingView subscription. RBI is the only process that works. Save this post. Tattoo it on your forearm. Follow Himanshu Kumar for daily RBI breakdowns. ↓ What Claude Code actually does that your manual process can't. You can maybe test 3-5 strategy ideas per week. Manually adjusting parameters. Manually checking results. Manually writing code (badly). Claude Code tests 50-100 ideas per week. With parallel agents running simultaneously. Multiple strategies being built, tested, and validated at the same time. While you sleep. The guy in this video spends 4-8 hours a day building systems with Claude Code. Not trading. Building. Research. Backtest. Implement. Then iterate. Improve. Optimize. Every day the systems get better. Every day the edge compounds. Every day the bots get smarter. While you? You spend 4-8 hours a day staring at charts making the same mistakes you made last month. Same indicators. Same patterns. Same entries. Same losses. He's iterating forward. You're running in circles. Same 8 hours per day. Completely different outcomes. Because he's building systems. And you're feeding a casino. Stop feeding the casino. Start building the machine. Save this post and follow Himanshu Kumar for the Claude Code workflow that iterates strategies while you sleep. ↓ Jim Simons. That's the benchmark. You probably don't know who Jim Simons is. And that tells me everything about how seriously you take trading. Jim Simons. Mathematician. Founded Renaissance Technologies. Built a net worth of $31 billion. 100% from algorithmic trading. Not one single manual trade. Not one "gut feeling" entry. Not one RSI divergence. Not one "smart money concept." Algorithms. Bots. Systems. Data. $31 billion. His fund averaged 66% annual returns for over 30 years. While you're excited about making $200 on a trade that you'll give back tomorrow. The best trader in human history never placed a manual trade in his life. And you think your edge is staring at a 5-minute chart with bloodshot eyes at 2 AM? Your edge is building the system. Not being inside it. Jim Simons is the benchmark. Everything else is noise. Save this post. Follow Himanshu Kumar because I'm building toward the same goal and showing every step publicly. ↓ What you need to understand about patience. This is not get-rich-overnight. The guy in this video says it directly: "This channel is not for people looking to get rich overnight. It's not plug and play. There are no shortcuts. If you're impatient, this probably isn't for you." And that's exactly why most people will fail at this. Because you want results now. Today. This trade. You don't want to spend a week building a bot. You don't want to paper trade for 2 weeks. You don't want to test 50 ideas to find 1 that works. You want to copy someone's bot, run it live with your rent money, and be rich by Friday. That's why you'll be broke by Friday. The guy making $2.3M spent months iterating. Testing. Failing. Rebuilding. Testing again. He was patient when you would have quit. He was calm when you would have panicked. He was consistent when you would have given up. Patience isn't just a virtue in trading. It's the only virtue. Without it, everything else fails. Impatience is the most expensive personality trait in trading. Save this post. Follow Himanshu Kumar and learn to build systems with the patience that actually pays. ↓ The live streams where the real learning happens. The YouTube video is the trailer. The live streams are the movie. Real-time bot building. Real-time questions answered. Real code shown. Real mistakes made and fixed. Not polished highlight reels where everything works perfectly. Actual development. Where things break. Where strategies fail. Where code doesn't compile. Where the fix takes 2 hours. Because that's what real development looks like. And seeing the messy parts is more valuable than any polished tutorial. Because when your bot breaks at 3 AM, you need to know how to fix it. Not just how to celebrate when it works. The streams mix beginner and advanced. Start with how to automate trading. How to use AI for code generation. Then dive into the daily work. Claude Code. Parallel agents. Constant iteration. Live debugging. 4-8 hours of real algorithmic trading development. Live. Uncut. No filter. Most "trading education" shows you the wins. This shows you the work. Save this post. Follow Himanshu Kumar for the stream schedules and breakdowns. ↓ The belief that changes everything. Code is the greatest equalizer. Not money. Not connections. Not a degree. Not where you grew up. Not what school you went to. Code. Once you can build systems, you can build anything. For the rest of your life. A trading bot today. A SaaS product tomorrow. An automation business next month. A completely different life next year. The skill isn't "algorithmic trading." The skill is building systems. And that skill transfers to everything. The guy who can build a trading bot can also build a lead gen tool. Can also build a content pipeline. Can also build a SaaS product. Can also build literally anything that runs on logic and code. One skill. Infinite applications. And AI makes learning it 100x easier than it was 5 years ago. You don't need to be smart. You don't need talent. You need Claude Code and the willingness to sit down and build something instead of consuming content about building something. Building is the skill. Everything else is entertainment disguised as education. Save this post. Follow Himanshu Kumar because I'm showing you how to build, not just how to watch. ↓ If any of this applies to you, pay attention. If you've lost money from overtrading. If you've been liquidated. If you know trading is the vehicle but manual execution keeps crashing you. If you've tried "being more disciplined" and it never lasted more than a week. If you keep saying "next month I'll start automating." If you've spent more money on courses than you've made from trading. There is a better way. It's not a magic indicator. It's not a signal group. It's not a $997 mentorship from a guy who makes money teaching, not trading. It's building your own system. A system that trades without emotion. A system that follows rules without exception. A system that runs while you sleep. A system that compounds while you live your life. That's the answer. It's always been the answer. You've just been too scared to accept that the solution requires building something instead of buying something. ↓ What the next 30 days look like if you actually commit. Week 1: Watch the video. Learn Claude Code basics. Build your first simple strategy. Run your first backtest. Week 2: Iterate. Let Claude improve the strategy. Run Monte Carlo validation. Paper trade. Week 3: Go live with $50-100. Tiny positions. Watch every trade. Compare to paper results. Week 4: Scale based on evidence. Not based on excitement. Not based on one good day. Based on data. 30 days from now you either have a running bot that trades without your emotions destroying every position. Or you're exactly where you are right now. Reading another post. Making another promise. Breaking it by Tuesday. Same 30 days either way. Different actions. Different results. Different life. ↓ Full video tutorial attached. Live bot building with Claude Code. From zero to running Polymarket trading bot. Every line of code. Every decision explained. The video is free. Claude Code is available now. The market is open 24/7. The only thing standing between you and a profitable trading bot is the same thing that's been standing there for months. You. Get out of your own way. Follow Himanshu Kumar for daily AI trading bot breakdowns, live build sessions, and the full RBI process. Save this post. Watch the video. Build the bot. Or keep trading manually and keep losing. The choice has never been easier. And you've never been more stubborn about making the wrong one.

Himanshu Kumar

38,153 次观看 • 5 个月前

I Spent $100k On Developers Before Learning This: Build Your AI Bot Today the blueprint to building your first ai trading bot without a degree or a single clue where to start is hidden in plain sight. most people think you need a stanford degree or some crazy math background to build these systems but i spent ten years in tech scared to code for that exact reason. i thought it was only for the geniuses and the nerds while i was just a guy who played video games and wanted his time back the reality is that code is the great equalizer because it doesn't care who you are or where you came from. i lost hundreds of thousands of dollars hiring developers who did shoddy work and i lost even more through liquidations and over trading because i was too emotional to follow my own rules. i knew i had to automate everything if i wanted to survive this game so i decided to learn live on youtube and iterate my way to success everyone is looking for the holy grail indicator that prints money while they sleep but they are looking in the wrong place. the real secret isn't a magical line on a chart but a process i call the rbi system which stands for research backtest and implement. most traders fail because they try to build a bot before they even know if their strategy worked in the past which is basically just gambling with extra steps you have to start with deep research into a strategy like supply and demand zones where you buy where the banks buy and sell where they sell. once you have a solid idea you must backtest it against years of data to see if it actually has an edge. if it doesn't work in the past it definitely won't work in the future but if it shows promise then you move to the implementation phase with small size there is a hidden cost to automation that can wipe out your profits before you even place a trade if you aren't careful. i found myself overusing api credits and running up a massive bill just to fetch wallet balances and token lists. if your bot is calling the exchange every five seconds just to see how much money you have you are essentially burning cash for no reason you can use ai tools like cursor to help you write the python code even if you are a total beginner. i still use ai to explain complex functions and identify where my code is being inefficient or chewing through credits. i had to refactor my entire dashboard and timer logic to only check balances every thirty minutes instead of every few seconds to save those precious credits the man who made thirty one billion dollars in the markets had one rule he never broke throughout his entire career. jim simons was the greatest algorithmic trader to ever live and he proved that systems will always beat human intuition over a long enough timeline. his secret wasn't some complex formula that no one else could understand but a commitment to a specific way of thinking simons always said you just have to make your systems better and better because that is what everyone else is trying to do. the game never really ends because the markets are always evolving and your edge will eventually decay if you don't iterate. this is why i build in public and show every step of the process because the iteration is where the actual money is made the reason you get liquidated isn't the market or the whales or some conspiracy against your small account. the real reason is the conversation you have with yourself at two in the morning when you are down on a trade and decide to move your stop loss. humans are built for survival not for trading and our emotions like fomo and fear will always sabotage our results when you automate your trading you are essentially signing a non negotiable contract with yourself that the bot will execute without question. if the plan says to sell fifty percent in an uptrend and ninety five percent in a downtrend the bot does it every single time. it doesn't feel the panic when a red candle drops or the greed when a green one spikes it just follows the code i used to spend all day staring at screens chasing bars up and down thinking that more screen time equaled more profit. i got into trading to get my time back but i ended up becoming a slave to the charts until i finally learned to code. now i have fully automated systems trading for me instead of getting liquidated because i removed the weakest link in the system which was me you don't need to spend ten years learning how to code before you can start building your own trading bots. if you spend three to six months getting the gist of python and using ai to bridge the gap you can start building immediately. start with a simple supply and demand bot that looks for major coin trends and only enters when the odds are heavily in your favor by checking the trend of bitcoin ethereum and solana simultaneously you can ensure you aren't fighting the overall market direction. i look for at least two out of those three to be trending before my bot is even allowed to look for an entry. this simple filter alone can save you from thousands of dollars in paper cuts during choppy sideways markets if you can't fly then run and if you can't run then walk but by all means you must keep moving toward automation. the process of taking an idea out of your brain and putting it into a system is the most secretive and valuable skill in the world. don't follow the pack and try to solve the same problems as everyone else but find your own edge and code it into existence the deal you make with yourself at the start of your journey is what determines if you will actually make it or not. i made a contract with myself to learn live and show everything because i believe that transparency is the only way to truly learn this craft. stick to your plan and iterate every single day because the systems you build today are the equalizers that will change your life tomorrow
50:50

I Spent $100k On Developers Before Learning This: Build Your AI Bot Today the blueprint to building your first ai trading bot without a degree or a single clue where to start is hidden in plain sight. most people think you need a stanford degree or some crazy math background to build these systems but i spent ten years in tech scared to code for that exact reason. i thought it was only for the geniuses and the nerds while i was just a guy who played video games and wanted his time back the reality is that code is the great equalizer because it doesn't care who you are or where you came from. i lost hundreds of thousands of dollars hiring developers who did shoddy work and i lost even more through liquidations and over trading because i was too emotional to follow my own rules. i knew i had to automate everything if i wanted to survive this game so i decided to learn live on youtube and iterate my way to success everyone is looking for the holy grail indicator that prints money while they sleep but they are looking in the wrong place. the real secret isn't a magical line on a chart but a process i call the rbi system which stands for research backtest and implement. most traders fail because they try to build a bot before they even know if their strategy worked in the past which is basically just gambling with extra steps you have to start with deep research into a strategy like supply and demand zones where you buy where the banks buy and sell where they sell. once you have a solid idea you must backtest it against years of data to see if it actually has an edge. if it doesn't work in the past it definitely won't work in the future but if it shows promise then you move to the implementation phase with small size there is a hidden cost to automation that can wipe out your profits before you even place a trade if you aren't careful. i found myself overusing api credits and running up a massive bill just to fetch wallet balances and token lists. if your bot is calling the exchange every five seconds just to see how much money you have you are essentially burning cash for no reason you can use ai tools like cursor to help you write the python code even if you are a total beginner. i still use ai to explain complex functions and identify where my code is being inefficient or chewing through credits. i had to refactor my entire dashboard and timer logic to only check balances every thirty minutes instead of every few seconds to save those precious credits the man who made thirty one billion dollars in the markets had one rule he never broke throughout his entire career. jim simons was the greatest algorithmic trader to ever live and he proved that systems will always beat human intuition over a long enough timeline. his secret wasn't some complex formula that no one else could understand but a commitment to a specific way of thinking simons always said you just have to make your systems better and better because that is what everyone else is trying to do. the game never really ends because the markets are always evolving and your edge will eventually decay if you don't iterate. this is why i build in public and show every step of the process because the iteration is where the actual money is made the reason you get liquidated isn't the market or the whales or some conspiracy against your small account. the real reason is the conversation you have with yourself at two in the morning when you are down on a trade and decide to move your stop loss. humans are built for survival not for trading and our emotions like fomo and fear will always sabotage our results when you automate your trading you are essentially signing a non negotiable contract with yourself that the bot will execute without question. if the plan says to sell fifty percent in an uptrend and ninety five percent in a downtrend the bot does it every single time. it doesn't feel the panic when a red candle drops or the greed when a green one spikes it just follows the code i used to spend all day staring at screens chasing bars up and down thinking that more screen time equaled more profit. i got into trading to get my time back but i ended up becoming a slave to the charts until i finally learned to code. now i have fully automated systems trading for me instead of getting liquidated because i removed the weakest link in the system which was me you don't need to spend ten years learning how to code before you can start building your own trading bots. if you spend three to six months getting the gist of python and using ai to bridge the gap you can start building immediately. start with a simple supply and demand bot that looks for major coin trends and only enters when the odds are heavily in your favor by checking the trend of bitcoin ethereum and solana simultaneously you can ensure you aren't fighting the overall market direction. i look for at least two out of those three to be trending before my bot is even allowed to look for an entry. this simple filter alone can save you from thousands of dollars in paper cuts during choppy sideways markets if you can't fly then run and if you can't run then walk but by all means you must keep moving toward automation. the process of taking an idea out of your brain and putting it into a system is the most secretive and valuable skill in the world. don't follow the pack and try to solve the same problems as everyone else but find your own edge and code it into existence the deal you make with yourself at the start of your journey is what determines if you will actually make it or not. i made a contract with myself to learn live and show everything because i believe that transparency is the only way to truly learn this craft. stick to your plan and iterate every single day because the systems you build today are the equalizers that will change your life tomorrow

Moon Dev

11,726 次观看 • 7 个月前

POST OFFICE KNEW ITS TILL BUTTON WAS ROBBING PEOPLE SINCE 2019 A subpostmaster called Denis O'Donnell messaged me on LinkedIn. He follows this page and asked me to help amplify a story he has spent six years trying to get anyone to take seriously. After checking it out I understand why he is still fighting. In 2019 O'Donnell ran a Post Office branch in Prestatyn, Wales. He noticed something wrong with the till. A screen icon on the Horizon system does two opposite jobs, take money and pay money. When several transactions stack up together, Horizon can hand cash to a customer and then log it as a payment the other way, creating an instant shortfall the subpostmaster gets blamed for. O'Donnell wrote to the top. He sent letters to then CEO Nick Read and communications boss Mark Davies. Post Office's answer was that it was a one off mistake, fixed within days. Convenient timing too, since this was 6 months after Post Office had just been humiliated in the High Court by subpostmasters who proved Horizon was faulty all along. O'Donnell kept pushing for years. He eventually got the story to ComputerWeekly and its reporter Karl Flinders Karl Flinders, and to Ron Warmington, the forensic investigator at Second Sight who was one of the original people who blew open the whole Horizon scandal back in 2012. Warmington called this new defect sufficiently serious to write directly to the public inquiry. He also called Post Office's first response to questions about it arrogant and dismissive, which by now is basically the company's official slogan. Post Office finally agreed to investigate and warn branches in December 2025. Six years after O'Donnell first told them. Their own spokesperson said it is impossible to know the true impact but they believe it is limited, based on checking 1 year of error logs out of more than 24 years Horizon has been running. O'Donnell calls that maths a joke, and it is hard to disagree when millions of transactions across two and a half decades got reduced to a single year sample. O'Donnell is trying to warn other subpostmasters and their families that the Post Office Process Review scheme closes to new applications on 30 September 2026. This scheme covers losses caused by Post Office products, policies or processes, separate from the Horizon Shortfall Scheme which already closed in January and the GLO scheme closing this month. If people affected by this specific defect do not apply before the deadline, they may lose the chance entirely. Post Office spent over two decades telling subpostmasters the system never lies. Then it spent six more years telling one particular whistleblower his bug did not matter. Forgive people for not trusting the next apology on the first read. If you are a current or former subpostmaster affected by this or unsure about the Process Review deadline, get proper advice before it closes. SOURCES ComputerWeekly Karl Flinders Post Office Post Office Horizon IT Inquiry BBC News (UK)
1:51

POST OFFICE KNEW ITS TILL BUTTON WAS ROBBING PEOPLE SINCE 2019 A subpostmaster called Denis O'Donnell messaged me on LinkedIn. He follows this page and asked me to help amplify a story he has spent six years trying to get anyone to take seriously. After checking it out I understand why he is still fighting. In 2019 O'Donnell ran a Post Office branch in Prestatyn, Wales. He noticed something wrong with the till. A screen icon on the Horizon system does two opposite jobs, take money and pay money. When several transactions stack up together, Horizon can hand cash to a customer and then log it as a payment the other way, creating an instant shortfall the subpostmaster gets blamed for. O'Donnell wrote to the top. He sent letters to then CEO Nick Read and communications boss Mark Davies. Post Office's answer was that it was a one off mistake, fixed within days. Convenient timing too, since this was 6 months after Post Office had just been humiliated in the High Court by subpostmasters who proved Horizon was faulty all along. O'Donnell kept pushing for years. He eventually got the story to ComputerWeekly and its reporter Karl Flinders Karl Flinders, and to Ron Warmington, the forensic investigator at Second Sight who was one of the original people who blew open the whole Horizon scandal back in 2012. Warmington called this new defect sufficiently serious to write directly to the public inquiry. He also called Post Office's first response to questions about it arrogant and dismissive, which by now is basically the company's official slogan. Post Office finally agreed to investigate and warn branches in December 2025. Six years after O'Donnell first told them. Their own spokesperson said it is impossible to know the true impact but they believe it is limited, based on checking 1 year of error logs out of more than 24 years Horizon has been running. O'Donnell calls that maths a joke, and it is hard to disagree when millions of transactions across two and a half decades got reduced to a single year sample. O'Donnell is trying to warn other subpostmasters and their families that the Post Office Process Review scheme closes to new applications on 30 September 2026. This scheme covers losses caused by Post Office products, policies or processes, separate from the Horizon Shortfall Scheme which already closed in January and the GLO scheme closing this month. If people affected by this specific defect do not apply before the deadline, they may lose the chance entirely. Post Office spent over two decades telling subpostmasters the system never lies. Then it spent six more years telling one particular whistleblower his bug did not matter. Forgive people for not trusting the next apology on the first read. If you are a current or former subpostmaster affected by this or unsure about the Process Review deadline, get proper advice before it closes. SOURCES ComputerWeekly Karl Flinders Post Office Post Office Horizon IT Inquiry BBC News (UK)

Artur Nadolny

47,598 次观看 • 2 个月前

The 40,000% ROI "Bug": How Claude Code Cracked the TradingView Holy Grail most people think the elite traders at the top of the mountain have some secret indicator or a hidden math formula that gives them a forty thousand percent return. they assume the game is rigged against the small player and that you need a multi million dollar budget just to get a seat at the table. the truth is that the holy grail of trading is actually hidden in plain sight inside a community tab that most people scroll past every single day i spent years losing money to liquidations and over trading because i thought i had to manually predict where the price was going next. i even spent hundreds of thousands of dollars on developers to build apps for me because i was convinced that i would never be able to code the systems myself. it turns out that once you stop trying to be a genius and start using the tools that are already available you can crack the code to unlimited trading strategies the secret is not in a single indicator but in the process of research back test and implement. if you go to the community section of trading view you will find an endless stream of source code for indicators that people have built over decades. most traders just slap these on a chart and hope for the best but if you are a data dog like me you know that a chart is just a pretty picture that lies to you i believe that code is the great equalizer because it allows us to take these public ideas and turn them into fully automated systems that trade for us while we sleep. i decided to learn to code live on youtube to show everyone that you can iterate your way to success without being a math wizard or a stanford graduate. now i have fully automated systems that manage my capital instead of getting liquidated by emotional decisions in the middle of the night the biggest trap in the trading world is something called repainting and it is the reason why so many strategy back tests look like they are printing money when they are actually just a scam. repainting happens when an indicator looks at future data to tell you what happened in the past which makes every buy and sell signal look like a perfect entry at the top and bottom. if you trust a back test on a basic chart without understanding the logic underneath you are just building a house on a foundation of sand this is why i transitioned all of my serious work into python because python does not lie to you. in python you can control the data flow tick by tick and bar by bar to ensure that no future data is leaking into your strategy. i built a back test architect which is a specialized sub agent that knows exactly how to take a simple idea and test it against twenty five different data sources all at once when you run a strategy across btc eth apple google and tesla you start to see the real truth about whether a strategy has an edge or if it was just a lucky fluke on one chart. i saw one strategy this week that showed a one million percent return which sounds like a total lie but the data does not have an ego. even if a number looks insane you have to investigate it and incubate it with tiny size to see if it holds up in the live market you must treat your trading like a business where you are the manager and the code is your team of tireless employees. i have sub agents running for me right now that act as masters of specific tasks like converting pine script into python or optimizing exit logic. if you are not using these specialized ai assistants in your workflow you are essentially trying to build a skyscraper with a hand saw while everyone else is using heavy machinery most people get stuck in the beginner phase because they think they need to write every single line of code from scratch. the reality is that the best developers are just really good at importing the hard work of others and connecting it like lego blocks. i use a library called ccxt that allows my bots to communicate with every major exchange in the world with just a few lines of script which saves me months of development time the reason i show everything live is because the industry is filled with gatekeepers who want to keep the secrets of automation to themselves. they want you to stay as a manual trader who pays high fees and provides liquidity for their algorithms. once you learn to automate you are no longer a victim of the market but a participant in the architecture of the financial system if you are sitting there right now feeling defeated because you just got smoked on a trade or you missed a massive pump you have to realize that those emotions are your greatest enemy. a computer does not feel fomo and it does not get tilted after a loss; it just waits for the next signal that fits the parameters you defined. my mission is to help you get to a place where you can walk away from the screen and let the machines do the heavy lifting learning to code is actually much easier than learning a second language because the syntax is logical and the feedback is immediate. i spent ten years in tech scared to touch a keyboard for anything other than emails because i thought i was not smart enough for engineering. once i realized that code is just logic i was able to build my first profitable bot within a few months and i have never looked back the transition from a manual trader to an algorithmic expert is about building a robust framework for testing your ideas as fast as possible. you want to be able to find an indicator on trading view convert it to python and run it against years of historical data in less than five minutes. if you can do that you have a higher chance of success than ninety nine percent of the people who are just drawing lines on a screen one of the most powerful strategies i found recently combines the squeeze momentum indicator with smart money concepts. when you test these individually they might show a decent return but when you combine them and add a filter like the adx you can find setups that have a massive expectancy. the key is to look for strategies that show positive returns across multiple different asset classes and time frames simultaneously even if a strategy looks like it is printing a forty thousand percent return you must always remain skeptical and look for the catch. i always incubate my new ideas with tiny capital for at least a few weeks to see how they handle real world slippage and fees. a back test is a map of the past but the live market is a wilderness that changes every single day this is why i believe in the rbi method which stands for research back test and implement. you spend your mornings looking for new ideas your afternoons stress testing them with ai and your evenings deploying the winners to the market. it is a systematic approach to wealth that removes the need for luck or guessing what a celebrity is going to tweet next the most successful traders in history like jim simons did not sit around looking at rsi levels on a fifteen minute chart. they built systems that identified mathematical edges and then scaled those systems until they were managing billions of dollars. you do not need thirty one billion dollars to change your life but you do need the discipline to stop trading like a human and start thinking like a system i give away so much for free on youtube because i want to build a community of data dogs who are all chasing the same goal of financial freedom through automation. when we work together and share our findings we can collectively identify edges that nobody else is looking at. the world is moving towards an ai dominated economy and if you are not learning to control the machines you are going to be controlled by them the road to automation is not a straight line and you will run into bugs that make you want to throw your computer out the window. but every time you fix an error and every time you optimize a script you are getting one step closer to a life where you own your time. code really is the great equalizer and it is waiting for you to pick it up and start building your own future if you can fly then run and if you can run then walk but whatever you do you must keep moving forward in this journey. trading can be heartless but the logic of code is always fair and consistent. stop being the liquidity for someone else's bot and start building the walls that will protect your capital forever
2:02:43

The 40,000% ROI "Bug": How Claude Code Cracked the TradingView Holy Grail most people think the elite traders at the top of the mountain have some secret indicator or a hidden math formula that gives them a forty thousand percent return. they assume the game is rigged against the small player and that you need a multi million dollar budget just to get a seat at the table. the truth is that the holy grail of trading is actually hidden in plain sight inside a community tab that most people scroll past every single day i spent years losing money to liquidations and over trading because i thought i had to manually predict where the price was going next. i even spent hundreds of thousands of dollars on developers to build apps for me because i was convinced that i would never be able to code the systems myself. it turns out that once you stop trying to be a genius and start using the tools that are already available you can crack the code to unlimited trading strategies the secret is not in a single indicator but in the process of research back test and implement. if you go to the community section of trading view you will find an endless stream of source code for indicators that people have built over decades. most traders just slap these on a chart and hope for the best but if you are a data dog like me you know that a chart is just a pretty picture that lies to you i believe that code is the great equalizer because it allows us to take these public ideas and turn them into fully automated systems that trade for us while we sleep. i decided to learn to code live on youtube to show everyone that you can iterate your way to success without being a math wizard or a stanford graduate. now i have fully automated systems that manage my capital instead of getting liquidated by emotional decisions in the middle of the night the biggest trap in the trading world is something called repainting and it is the reason why so many strategy back tests look like they are printing money when they are actually just a scam. repainting happens when an indicator looks at future data to tell you what happened in the past which makes every buy and sell signal look like a perfect entry at the top and bottom. if you trust a back test on a basic chart without understanding the logic underneath you are just building a house on a foundation of sand this is why i transitioned all of my serious work into python because python does not lie to you. in python you can control the data flow tick by tick and bar by bar to ensure that no future data is leaking into your strategy. i built a back test architect which is a specialized sub agent that knows exactly how to take a simple idea and test it against twenty five different data sources all at once when you run a strategy across btc eth apple google and tesla you start to see the real truth about whether a strategy has an edge or if it was just a lucky fluke on one chart. i saw one strategy this week that showed a one million percent return which sounds like a total lie but the data does not have an ego. even if a number looks insane you have to investigate it and incubate it with tiny size to see if it holds up in the live market you must treat your trading like a business where you are the manager and the code is your team of tireless employees. i have sub agents running for me right now that act as masters of specific tasks like converting pine script into python or optimizing exit logic. if you are not using these specialized ai assistants in your workflow you are essentially trying to build a skyscraper with a hand saw while everyone else is using heavy machinery most people get stuck in the beginner phase because they think they need to write every single line of code from scratch. the reality is that the best developers are just really good at importing the hard work of others and connecting it like lego blocks. i use a library called ccxt that allows my bots to communicate with every major exchange in the world with just a few lines of script which saves me months of development time the reason i show everything live is because the industry is filled with gatekeepers who want to keep the secrets of automation to themselves. they want you to stay as a manual trader who pays high fees and provides liquidity for their algorithms. once you learn to automate you are no longer a victim of the market but a participant in the architecture of the financial system if you are sitting there right now feeling defeated because you just got smoked on a trade or you missed a massive pump you have to realize that those emotions are your greatest enemy. a computer does not feel fomo and it does not get tilted after a loss; it just waits for the next signal that fits the parameters you defined. my mission is to help you get to a place where you can walk away from the screen and let the machines do the heavy lifting learning to code is actually much easier than learning a second language because the syntax is logical and the feedback is immediate. i spent ten years in tech scared to touch a keyboard for anything other than emails because i thought i was not smart enough for engineering. once i realized that code is just logic i was able to build my first profitable bot within a few months and i have never looked back the transition from a manual trader to an algorithmic expert is about building a robust framework for testing your ideas as fast as possible. you want to be able to find an indicator on trading view convert it to python and run it against years of historical data in less than five minutes. if you can do that you have a higher chance of success than ninety nine percent of the people who are just drawing lines on a screen one of the most powerful strategies i found recently combines the squeeze momentum indicator with smart money concepts. when you test these individually they might show a decent return but when you combine them and add a filter like the adx you can find setups that have a massive expectancy. the key is to look for strategies that show positive returns across multiple different asset classes and time frames simultaneously even if a strategy looks like it is printing a forty thousand percent return you must always remain skeptical and look for the catch. i always incubate my new ideas with tiny capital for at least a few weeks to see how they handle real world slippage and fees. a back test is a map of the past but the live market is a wilderness that changes every single day this is why i believe in the rbi method which stands for research back test and implement. you spend your mornings looking for new ideas your afternoons stress testing them with ai and your evenings deploying the winners to the market. it is a systematic approach to wealth that removes the need for luck or guessing what a celebrity is going to tweet next the most successful traders in history like jim simons did not sit around looking at rsi levels on a fifteen minute chart. they built systems that identified mathematical edges and then scaled those systems until they were managing billions of dollars. you do not need thirty one billion dollars to change your life but you do need the discipline to stop trading like a human and start thinking like a system i give away so much for free on youtube because i want to build a community of data dogs who are all chasing the same goal of financial freedom through automation. when we work together and share our findings we can collectively identify edges that nobody else is looking at. the world is moving towards an ai dominated economy and if you are not learning to control the machines you are going to be controlled by them the road to automation is not a straight line and you will run into bugs that make you want to throw your computer out the window. but every time you fix an error and every time you optimize a script you are getting one step closer to a life where you own your time. code really is the great equalizer and it is waiting for you to pick it up and start building your own future if you can fly then run and if you can run then walk but whatever you do you must keep moving forward in this journey. trading can be heartless but the logic of code is always fair and consistent. stop being the liquidity for someone else's bot and start building the walls that will protect your capital forever

Moon Dev

245,471 次观看 • 7 个月前