My most unforgettable failure happened at a moment when I thought I had the best chance. The data on the market chart was as clear as could be, and I was even a bit complacent, thinking I had done enough homework. But I ended up making a terrible trade. Only in retrospect did I realize: the data itself was not the problem; the issue was that it arrived too late, and my understanding of the underlying logic was biased. Plus, I blindly applied it without considering the actual market environment. That lesson completely changed my perspective — accurate data is just the foundation; it’s far from the end point of trading.

I’ve noticed many traders fall into the same trap: thinking that correct data equals correct decisions. But the market’s logic is actually quite twisted. It doesn’t reward those who get accurate data; it rewards those who understand the data at the right moment and dare to act decisively. The two seem similar, but in reality, they are worlds apart.

This is exactly the problem many projects aim to solve. Instead of piling up a bunch of data indicators, it’s better to turn them into actionable intelligence that’s truly usable, verifiable by oneself, and resilient in large-scale trading scenarios. Speed must not be compromised, and costs should not be added unnecessarily. It sounds like a technical detail, but that’s why so many smart traders holding high-quality data still end up losing money inexplicably.

The core contradiction is simple: accuracy and practicality are two completely different dimensions. Accuracy only asks whether the numbers are right; practicality asks whether these numbers can be useful in your trading decisions.

Experienced traders have all gone through these painful lessons: data providers give indicators that are correct, but the market has already moved on, and by the time you react, you’re just taking over the position; the data itself is accurate, but no one tells you how to use it; signals come too late, the market has already shifted, and the data becomes just a post-hoc analysis. These seemingly different pitfalls all point to the same fundamental issue: there’s always something between the data and decision-making, and that “something” often determines whether you make a profit or a loss.