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📌PINDex｜Winning in Trading Is Not About Direction — It’s About Execution Most derivatives platforms focus on one question: Can the trade be executed? But long-term results depend on something deeper: •Who executes •How #execution is structured •Whether it can be reproduced consistently over time Direction drives short-term outcomes. Execution...

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PINDex

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25,161 views • 6 months ago •via X (Twitter)

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Most traders make trading too complicated. My mechanical approach is simple: -Wait for Intelligent Balance. -Identify the more probable side. -Trade ONLY in that direction. -Use iFVG as execution. Target fixed R. No setup toward the bias = no trade. Today’s New York session was a perfect example. Intelligent Balance showed 70% for SHORTS. That means I was NOT interested in longs. I did not need to predict every candle. I did not need to catch the top. I did not need to trade both directions. My job was simple: Wait for price to show bearish confirmation toward the more probable side. Once the iFVG appeared in the short direction, that became the mechanical entry. Bias came from Intelligent Balance. Execution came from iFVG. Risk handled the rest. This is why direction matters more than entry. A basic ICT concept becomes much easier when you already know which side you are allowed to trade. Would your trading improve if you stopped asking “long or short?” every 5 minutes and only traded toward the higher probability side?

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2:38

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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25,535 views • 8 months ago