Why does guessing macro data correctly not always lead to profitable trades? Back in the day, whenever there was non-farm payroll data, CPI, or an interest rate meeting, I always wanted to bet on the outcome in advance: bad data means going long, strong data means going short. Later, after losing a lot, I realized that the market doesn’t trade on whether the data is good or bad—it trades on the gap between the data and expectations. Cooling employment might seem favorable for easing policies, but if the market has already priced it in, the announcement could lead to profit-taking instead. Strong data should theoretically suppress risk assets, but as long as it’s not as strong as expected, $BTC could still rally. What’s even trickier is that liquidity thins out the moment data is released. Prices might first wipe out long positions, then short positions, and even if the direction eventually aligns with your prediction, your position might already have been liquidated. I used to open high-leverage positions before data releases, thinking my logic was solid. But as soon as the numbers came out, one sharp move would hit my stop-loss, and only then would the market move in the direction I had anticipated. It was at that moment I understood: having the right view doesn’t mean your trade structure is sound. Before major data releases, what really needs to be assessed isn’t just the outcome, but also how much the market has already priced in, whether positions are overcrowded, and whether you can handle sudden volatility. When there’s no clear edge, waiting for the market to complete its initial reaction is often more important than trying to capture those few seconds. Remember: data determines the narrative, while the gap between expectations determines price direction. Guessing the numbers is just knowledge—surviving the volatility is trading.