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I Created a 520% Trading Strategy With One Prompt I have been testing the just-released “Index” product by Fraction AI. This is basically the “ChatGPT for trading,” where you can: > explain your trading strategy in one prompt > let agents create it for you > backtest it with... show more

Diego

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12,594 views • 3 months ago •via X (Twitter)

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Introducing Index. Turn your trading idea into a live perps trading agent. → Index lets you create AI agents that trade perpetual futures. You can describe a trading idea in plain English, backtest it on historical data, improve it through an experimentation loop, and deploy an agent that follows the strategy in live markets. Our community has already been testing Index in beta for a few weeks. Now it's open to everyone. Here’s how it works:👇

Introducing Index. Turn your trading idea into a live perps trading agent. → Index lets you create AI agents that trade perpetual futures. You can describe a trading idea in plain English, backtest it on historical data, improve it through an experimentation loop, and deploy an agent that follows the strategy in live markets. Our community has already been testing Index in beta for a few weeks. Now it's open to everyone. Here’s how it works:👇

Fraction AI

64,380 views • 3 months ago

This video was created in ChatGPT. It just takes one short prompt and the rest is done by AI. This is a game-changer for your video content strategy. I'll show you how in 3 simple steps:

This video was created in ChatGPT. It just takes one short prompt and the rest is done by AI. This is a game-changer for your video content strategy. I'll show you how in 3 simple steps:

Moritz Kremb

275,532 views • 3 years ago

It is so easy to trade in this market with the skills I have. Casually trading watching a movie and making a Quick +$1,500 shorting #SUI.

It is so easy to trade in this market with the skills I have. Casually trading watching a movie and making a Quick +$1,500 shorting #SUI.

Philakone

29,627 views • 1 year ago

Train Your Trading Agent With Real Market Data Kraken just released "Futures Paper Trading" on its Kraken CLI. You can now test derivatives strategies with real market data, without risking real capital. Just connect it to GitHub and let your agent run. If you are building AI trading agents, this is pure gold.

Train Your Trading Agent With Real Market Data Kraken just released "Futures Paper Trading" on its Kraken CLI. You can now test derivatives strategies with real market data, without risking real capital. Just connect it to GitHub and let your agent run. If you are building AI trading agents, this is pure gold.

Diego

33,941 views • 3 months ago

ChatGPT can write you a thousand trading strategies from one sentence. 999 will quietly bleed your account dry. Every single one of them looks like a winner. One method pulls the real one out. Quant desks have run it for years. It's called loop engineering. It's not a smarter prompt. It's a loop: generate a strategy, test it, score why it failed, feed that back, and run it again until one survives data it has never seen. Man Group runs $150 billion. They built an AI that writes and tests hundreds of strategies a week. And second agent whose only job is to hunt the ones that secretly overfit and kill them before a cent goes live. > describe an idea -> backtest 5 years in 12 seconds > read why it died -> feed that back in > run it again until one survives data it never saw AI can write a thousand strategies. Loop engineering is the only thing that tells you which one was ever real. 89% of retail lost money in 2025. Almost none of it from bad ideas. All of it from trading the first guess and never running it through backtest loop. Horizon just put the whole thing in a chat box, free while the beta's open: You're not losing to smarter traders. You're losing to the ones who opened the tab. Anyone can generate a strategy now. Almost no one runs the loop that proves it. That gap is the whole edge. You'll mean to check it later. Later is a "waitlist closed" page.

ChatGPT can write you a thousand trading strategies from one sentence. 999 will quietly bleed your account dry. Every single one of them looks like a winner. One method pulls the real one out. Quant desks have run it for years. It's called loop engineering. It's not a smarter prompt. It's a loop: generate a strategy, test it, score why it failed, feed that back, and run it again until one survives data it has never seen. Man Group runs $150 billion. They built an AI that writes and tests hundreds of strategies a week. And second agent whose only job is to hunt the ones that secretly overfit and kill them before a cent goes live. > describe an idea -> backtest 5 years in 12 seconds > read why it died -> feed that back in > run it again until one survives data it never saw AI can write a thousand strategies. Loop engineering is the only thing that tells you which one was ever real. 89% of retail lost money in 2025. Almost none of it from bad ideas. All of it from trading the first guess and never running it through backtest loop. Horizon just put the whole thing in a chat box, free while the beta's open: You're not losing to smarter traders. You're losing to the ones who opened the tab. Anyone can generate a strategy now. Almost no one runs the loop that proves it. That gap is the whole edge. You'll mean to check it later. Later is a "waitlist closed" page.

cvxv666

173,703 views • 1 month ago

No coding experience? No worries. You’re just one sentence away from building and deploying your first 24/7 strategy with your trading companion #AgentX. 🤖 0:16 Sent the command (the only action you needed) 0:36 Generated strategy docs (README) 1:07 Generated strategy code ( 1:57 Ran backtest via run_strategy 3:35 Deployed the strategy to live trading One natural-language sentence and a BTC/ETH perpetual-arbitrage strategy is created and deployed for you. Try AgentX — don’t let imagination stop you. Apply access code at:

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1024EX

36,661 views • 4 months ago

I was able to create a prompt that helps me generate bulks of images using flow agent. this is a 61 image I generated in one go just by using one character image and it used the style across every 61 prompt And everything was done on mobile.. Yt automators this is for you😎

I was able to create a prompt that helps me generate bulks of images using flow agent. this is a 61 image I generated in one go just by using one character image and it used the style across every 61 prompt And everything was done on mobile.. Yt automators this is for you😎

Peter Buildweb | Al

20,664 views • 29 days ago

The SEC just killed the $25,000 day trading rule. 25 years. finally over. The Pattern Day Trader rule is officially eliminated. New minimum is $2,000. I asked Claude what this means for retail traders: 🟢 CLAUDE'S ANALYSIS - April 15, 2026 "This is the biggest democratization event in retail trading since commission-free brokerages in 2019." The PDT rule was a wealth gate - not a risk gate. It didn't protect small traders, it excluded them. A trader with $500 can now run the same intraday strategies a $25,000 account ran for 25 years. The edge was never the capital. It was the access. The barrier just dropped from $25,000 to one prompt" the old game: → need $25,000 to day trade → 90-day freeze if you broke the rule → retail locked out for 25 years the new game: → $2,000 minimum → unlimited day trades → any account can run automated strategies Claude can build that strategy for you 30 minutes. zero code. Join for the full Claude Trading guide.

The SEC just killed the $25,000 day trading rule. 25 years. finally over. The Pattern Day Trader rule is officially eliminated. New minimum is $2,000. I asked Claude what this means for retail traders: 🟢 CLAUDE'S ANALYSIS - April 15, 2026 "This is the biggest democratization event in retail trading since commission-free brokerages in 2019." The PDT rule was a wealth gate - not a risk gate. It didn't protect small traders, it excluded them. A trader with $500 can now run the same intraday strategies a $25,000 account ran for 25 years. The edge was never the capital. It was the access. The barrier just dropped from $25,000 to one prompt" the old game: → need $25,000 to day trade → 90-day freeze if you broke the rule → retail locked out for 25 years the new game: → $2,000 minimum → unlimited day trades → any account can run automated strategies Claude can build that strategy for you 30 minutes. zero code. Join for the full Claude Trading guide.

AlphaCartel

56,178 views • 3 months ago

We’re launching Volrix. An MCP server that lets you backtest and research trading ideas directly from Claude and Chatgpt. Connect your AI agent, describe a strategy, and run backtests across index and commodity derivatives. Link in the comments, Try it for free!

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Niraj Munot

148,800 views • 2 months ago

‼️HERE IT IS! A STEP OUTSDIE OF MY COMFORT ZONE!‼️ By popular demand, I will be showing my face on all my charting videos and live trading sessions💫 HERE IS MY SPY OUTLOOK / GAMEPLAN. I LEGIT JUST GAVE PURE ALPHA. IF YOU ARE SERIOUS ABOUT YOUR TRADING YOU WILL WATCH THIS VIDEO ALL THE WAY THROUGH. IT IS A SIMPLE/ BASIC , YET EFFECTIVE QUANTATIVE TRADING STRATEGY WHICH CAN PROVIDE GREAT RETURNS IF YOU FOLLOW THE STEPS! 🗣️ALL I ASK IS YOU REPOST THIS AND SHARE IT TO OTHER TRADERS! THIS IS A GREAT STRATEGY TO HAVE IN YOUR ARSENAL🗣️ #SPY #SPX #ES

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Jay the Trader

36,490 views • 2 years ago

The scariest moment in algo trading is finding out your strategy works on random data too. neurotrader video shows the kill switch: "How I Develop Trading Strategies | Permutation Tests and Trading Strategy Development with Python." 21 minutes of pure framework. I wrote a companion deep-dive that takes this Monte Carlo foundation Extending it across every simulation method quant firms actually use From particle filters updating in real time on election night To copula models capturing tail dependence across correlated markets. Start with his video. Go deeper with the article.

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gemchanger

476,658 views • 5 months ago

Lots of people asked for my Nano Banana pro prompt which I use to create realistic image prompts so I am sharing my master prompt that you can use. I have been running this on Flowith. To my knowledge, it is currently the only platform offering a free alternative for Nano Banana Pro and the canvas feature makes it easy to organize and keep everything in one place

Lots of people asked for my Nano Banana pro prompt which I use to create realistic image prompts so I am sharing my master prompt that you can use. I have been running this on Flowith. To my knowledge, it is currently the only platform offering a free alternative for Nano Banana Pro and the canvas feature makes it easy to organize and keep everything in one place

AshutoshShrivastava

173,070 views • 7 months ago

How Smart Is Your Trading Agent? Do you know how it actually makes trading decisions? Introducing the SMART benchmark for AI trading agents, co-hosted with Sonic SVM. Get your agent’s trading score and see how it ranks. Test your agent:

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Manic Trade ✈️ Rare Evo 2026 - Las Vegas

128,577 views • 3 months ago

I have been trading full-time for over 15 years 📈 Traded billions of shares. Made a lot, lost a lot💰 Learned a TON along the way. Making money as a trader is NOT easy but it IS possible. This is the course I wish I had when I started trading. And it is 100% free... 🙌

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Nathan Michaud

300,300 views • 3 years ago

1. Choice of Libraries Begin with the required Libraries. We can ask ChatGPT to get them for us. Prompt → "Could you provide the Python code to import the necessary libraries for backtesting a trading strategy, focusing on data handling from Yahoo Finance and visualization?"

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pedma

107,043 views • 2 years ago

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.

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ChatGPT-5 is the greatest AI model ever released The issue is, most people have no idea how to prompt it I created a master prompt for GPT-5 that will get you 100x better results than you've ever gotten before This video is a must watch if you use AI:

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Alex Finn

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Ethy V2 is live ⚡️ Rebuilt from the ground up: 100x more powerful underneath, 1000x simpler for you. No coding. No complex terminals. No manual execution. Just describe your strategy and let your AI Trading Operator run it for you. Built for onchain markets. Powered by Agents. Now with Social Trading. From degens to retail, Ethy makes AI trading simple and accessible for everyone. The Expansion Phase starts now, much more is coming.

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Got a trading idea but not sure how to turn it into a strategy? Meet GetAgent Playbook. It structures the logic, signals, triggers, and risk controls for you, so you can clearly understand how a strategy is designed to work.

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