Tonight's conversation is kind of like writing an "engineering log" together.

At first, it was just a few images, some code, and some of your usual chart-reading habits. You explained how you read the grids, how you judge direction, what you're thinking about when you touch the line in one minute. If these things only stay in your head, they actually warp slowly over time. What we did today was basically pull these ideas out piece by piece and turn them into something that others can understand, and that your future self can understand too.

The process was actually quite interesting.

At first we discovered the problem wasn't really about "whether the system runs." The code could already run, could place orders, could connect to exchanges, could set stop losses and take profits. But strangely, the trading results always felt off somehow. This feeling was subtle—like having a beautifully drawn map, but always feeling like something's wrong with the direction when you're walking on the road.

Later, we gradually figured it out. The real problem actually came down to one sentence:

The grid the AI sees and the grid you see are not the same map.

Once this was clear, the whole direction suddenly became simple. It's not about adding more indicators or making the model smarter—it's about aligning the coordinate system first. Like two people navigating in the same city: if one uses an old map and one uses a new map, no matter how hard you try, you won't end up at the same place.

So what we actually did today was one very important thing: we deconstructed the entire system.

We took what was originally mixed together and broke it into layers.

One layer only handles calculating grids, one only handles judging direction, one only handles checking whether one minute touched the line, and another only handles placing orders. Each layer does only one thing. The benefit is simple—in the future, if anything goes wrong, you only need to see which layer has the problem, instead of doubting the whole system.

Many people building systems rush to pursue complexity. Adding more filters, more conditions, more "smart judgments." But today we went the opposite direction: first make things simple. Grids, direction, triggers—get these three things right first. As for momentum, channels, various market state judgments, put those aside for now. Once the core skeleton is solid, we can add those later.

This is actually a very engineering way of thinking.

Get the bones straight first, then grow the muscles.

In the end, we turned the entire system into a very clear process:

Market data comes in → Calculate grids → Judge direction → Check if one minute touches the line → Safety check → Place order.

No mysterious black boxes, no question of who's smarter than who. Just a small engine with clear rules and clearly defined responsibilities.

I really liked this way of working today. Not constantly changing strategy, but slowly organizing the thoughts in your head into structure. Many times, real progress isn't finding a new method—it's finally making clear what was originally vague.

So today's takeaway actually isn't some lines of code, but something more important:

We finally know what AI528 should look like.

The work ahead is actually very straightforward. Tomorrow, I just need to swap the grid calculation algorithm to the real version, change the direction to the 15-minute HMA60, fill in the safety logic, and the system can start running again. Only then, when we see the results, does it have real meaning.

Sometimes building a system is like building a bridge.

You spend a lot of time at the beginning on the foundation, and it looks like there's no progress, but once the foundation is solid, the bridge actually gets built pretty quickly.

Tonight counts as the day the foundation was completed.

The rest, we'll take slowly tomorrow.