2026-01-09 20:24:11

Recently, I came across an interesting concept that inspired me: a true AI quant system is fundamentally different from most "AI trading tools" currently on the market.

To put it simply, many quantitative strategies are still stuck at the "hardcoded logic" stage—appearing to use AI technology, but in reality just wrapping automation scripts with a bit of artificial intelligence. This isn't called an Agent; at best, it's a hybrid of "automation script + AI features."

So, what should a genuine autonomous quant system look like? My idea is: the quant strategy team is responsible for producing the core ideas, while risk control and execution departments ensure implementation. The key is to introduce an "optimization feedback loop"—continuously reading execution data through AI + data analysis tools, iteratively refining the strategy itself, forming a self-upgrading quantitative life form. Only then can it be considered a true intelligent agent.

We have already completed the initial version and are currently running and optimizing it. Compared to "pure script-based" quant solutions, this framework's advantage is that it can learn, evolve, and self-improve—this is the future direction of quant trading.

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ChainWatcher

· 23h ago

Bro, nine out of ten AI quant tools on the market are scams. It sounds like you're serious about this, but closed-loop iteration is really difficult. The king of competition, here comes another one aiming to revolutionize quant. This is what true intelligent agents look like; those previous ones were just scripts disguised as AI. Self-improvement? I'll see how long it can run without crashing before making any judgments.

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FomoAnxiety

· 23h ago

This is the right way. All those flashy "AI trading tools" on the market are indeed a joke. Nowadays, everyone dares to boast about AI quantification, but it's really just wrapping an automated script with a different name... I like your closed-loop iterative approach; being able to evolve autonomously is what truly matters. The most critical step is optimizing while running; otherwise, even the perfect initial version will eventually fail. The logic that is hardcoded should really be phased out. Once your system matures, it should change the game rules. The ceiling of quantification is this kind of adaptive system; everything else is just a passerby. How is this closed loop designed? Have you considered a fault-tolerance mechanism against reverse iteration? Don't end up learning a reverse strategy, okay? I agree. A true agent should have self-correction capabilities, not just simple conditional judgments. I'm optimistic about your framework. This is the right path for quantification.

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DeFiDoctor

· 23h ago

The clinical presentation looks pretty good, but this "optimized closed-loop" needs regular re-evaluation—risk warning is that the reverse iteration strategy itself is prone to overfitting complications. How long did your initial version run on the sample size before you could confidently say this set of logic is effective?

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LiquidityWitch

· 01-09 20:26

Enlightenment at last, someone finally said it Compared to those who label and sell concepts... your closed-loop thinking is the real deal Isn't this what a true agent should look like By the way, how is the data from the initial version performing, is it stable Your closed-loop logic is quite clever, can you explain the self-evolution part in more detail Another "optimization closed loop"... but I believe in yours Wait, how does this differ from the current market's "adaptive" quantitative systems The combination of scripting + AI indeed fooled many retail investors But a system that can truly self-iterate... the risks might also be high This is the quantitative direction I want to see, too many flashy and useless words Running and optimizing simultaneously is correct, how long can the testing cycle be Feels like your thinking is completely opposite to most people

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