X-quantization

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Even if the strategic logic (trend following, statistical arbitrage) is the same, differences in AI application methods can completely dismantle performance correlation. Traditional quantitative trading is a "competition on the same track," whereas in the AI era, quantitative trading is a "contest across different dimensions." Under the gap of these dimensions, the results naturally vary greatly—one making money, the other losing money—a divergence situation.
Essentially, its AI application has formed a closed-loop ecosystem of "data - algorithms - computing power":
Data side: Integrates ove

This time I really bought it.
This time it was really copied.

It's not that we don't buy the dip, but rather we buy slowly, gradually, and in batches, with a rhythm to it. Let those who are still alive buy first, and let those with lighter positions buy first, achieving a lead-follow effect, while also considering individual family circumstances for specific buying.
It is not blindly buying the dip, but rather strategically buying the dip with precision, depth, and a tentative approach. Let those who understand cutting losses lead the buying, allow those who remain calm under pressure to buy, and apply strong will throughout the buying process, w

Cloudfare down
crypto down
gg

AI Cryptocurrency Trading is currently nowhere near the threshold of trading systems. Six models running simultaneously ended up with two companies not losing, which is essentially equivalent to rolling dice for a good outcome, rather than having a systematic advantage. Especially strategies like deepseek that are almost never short; as long as the market fluctuates slightly upwards during the cycle, it naturally appears decent, but once there is a downward trend, it exposes the fatal flaw of lacking a stop loss mechanism, making it no different from human traders. However, this is very suitab