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If you could start over in crypto again, what would you do differently? Give me REAL SOLID answers below 💪

Correlation or Causality? Many traders struggle to separate correlation from causality. The human mind craves simple explanations and tends to think in absolutes, which makes it easy to confuse the two. Even in scientific studies, this mix-up is a common problem. Let’s break down the difference and explore how understanding it can improve our decision-making in the markets&life. Correlation: Correlation is a relationship between two things that happen at the same time, but one does not necessarily cause the other. For example: You start resistance training regularly, and over time your muscles grow and become stronger. At the same time, you also begin listening to more podcasts and learning a lot about the world. These two things are correlated, because you usually listen to podcasts on your way to the gym or while training. But the podcasts are not what make your muscles grow, and your stronger muscles are not what give you new knowledge. They simply occur together, without being the root cause of each other. Just because two things happen together doesn’t mean one causes the other. Causality: Causality means there is a direct cause-and-effect relationship between two things. In our example: Lifting weights and working against resistance directly causes your muscles to grow. And listening to your favourite podcasts most likely causes you to gain new knowledge(depending what you listen to). Here, one action is clearly the reason for the result. If one thing directly makes another happen, that’s causality. How to apply this to the Market: When analysing your performance, connecting macroeconomic dots, interpreting whale hints, or simply trying to better understand the underlying mechanics of the market, it’s crucial to make a clear distinction between what is causal and what is merely correlated. Just because something happened before doesn’t mean it caused the outcome. Often, two events occur together because of a third factor that isn’t immediately visible. Without recognising this, you risk building your decisions on false assumptions. Example in the of a correlation in the market: Imagine Bitcoin rallies sharply on the same days that the S&P 500 also rises. At first glance, it’s easy to assume the stock market rally causes Bitcoin to move higher. In reality, both may simply be reacting to a third factor you haven’t recognised yet, for example, central bank policy, changes in global liquidity, or shifts in whales interests. In this case, the moves are correlated (they happen together), but the causality lies in a much broader set of factors, not in stocks “causing” Bitcoin to rise. If you mistake such patterns for causality, you risk falling into traps in the highly predatory environment of the market. Example of a causality in the market: Looking at the underlying mechanics of the market, there are definitely causalities, but they’re often hidden beneath layers of noise, deception, and the endless shades of grey that disguise simple truths. The architects of the game understand this well: If people could easily see the true causalities, they would start winning more consistently. And that is, of course, not in the interest of those who profit from others mistakes. A clear example of a causality is liquidity and price movement. When a large market order consumes available liquidity on the order book, price must move to the next level where liquidity is offered. This isn’t correlation: It’s a direct mechanical cause-and-effect: demand (or supply) eats through the book, and the price shifts as a result. Find such causalities and apply them to your edge to increase your probabilities, but never fall for the illusion of certainty. Not just useful as trader: Not just in trading, but in any skill that requires an analytical mindset, knowing the difference between correlation and causality is crucial. Even in scientific research, this distinction is one of the greatest challenges to overcome. Take supplement trials or diet studies, for example. At first glance, the results may look convincing. But in reality, countless hidden factors influence the outcome and can easily distort the findings. It’s not just genetics, but lifestyle differences, habits, and environmental influences all play their role. In an ideal study, every participant would have identical genetics and follow the exact same routine every second of the study. Of course, that’s impossible. Which means the results of such research are never perfect and rarely apply equally to everyone. Add to that the fact that many scientists, whether for financial motives or personal bias, often look to prove a certain result. This colours the study, making them focus on the first correlation that fits their interest rather than searching for the real cause. If you truly want principles that can be applied across situations, you need to uncover the real causalities, not be distracted by simple correlations. Find the root cause, the rules that hold true regardless of perception and you dramatically increase your probabilities of success. Still, even in true causalities, there will always remain a margin of unknowns. Perfection is not required for something to be understood and applied. Causalities are the real rules of the universe, not the coloured perceptions of the biased observer.

Tokenization of Real World Asset $RWA

5/5 - Dubai’s #Bitcoin rent experiment is more than a headline, it’s a real-world test of crypto as money, not just an investment. Would you pay your rent in $BTC if you had the chance? 🧐

#BTC Bitcoin is putting in a real effort to confirm the end to Price Discovery Correction 2 (red) A key confirmation would be to reclaim ~$114k as support to effectively resynchronise with the previous Re-Accumulation Range That would bring Bitcoin one step closer to beginning Price Discovery Uptrend 3 $BTC #Crypto #Bitcoin