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Spent several weeks refining an AI-driven "Alpha Scanner," developed based on deep research models (such as Gemini's deep research capabilities).
What is the core logic?
It's not simply reviewing assets that have performed strongly in the past, but rather having AI think like a "trading strategy supervisor." What's different about this approach? Traditional methods only look at historical data; the new method forces AI to extract Alpha from a strategic level.
In other words—it's not about "what has made money before," but rather "what kind of thinking framework can continuously discover opportunities." These are two different things.
The tool is still being refined, but the logical framework is already usable. For those involved in strategy research and market analysis, this approach is worth referencing.
Having strong in-depth research ability is good, but the question is whether AI can truly come up with a sustainable and effective framework, or if it's just more fancy backtesting.
If the framework is usable, just use it first; after all, it's more reliable than pure historical data.
The biggest risk with this kind of thing is discovering it becomes invalid after half a year.
By the way, have you actually run the data? I'd like to see how the results turn out.
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Using historical data to make money doesn't mean the framework can make money. I agree with this logic, but in reality, frameworks often underperform too.
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Wait, are we saying AI can replace manual monitoring of the market? Then what chance do we retail investors have left haha.
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Deep research models are indeed different, but in the end, it still depends on actual market performance.
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It's worth pondering, but hopefully it's not another "looks impressive but actually loses money" situation.
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This approach is essentially just asking questions from a different perspective, feels like giving AI a product manager role.
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The tools are still being refined, let's wait and see. Anyway, I've heard too many times about Alpha scanners.
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Forcing AI to think from a strategic level... can it really break out of the data trap? I'm a bit skeptical.
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A few weeks of work, the cost and effort are quite substantial.
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The key is whether this framework can be reused and applied to other markets.
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Another万能药 for AI, I bet five bucks it will be shelved in half a year
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A framework alone is worth little, the key is whether the model can adapt to the market's way of doing things
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Gemini in-depth research? Or just that hallucination stuff again
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Interesting, but Alpha has always been fast food—effective today, invalid tomorrow
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Wait, can this thing really be distilled from a strategic level? Or is it just a beautified version of parameter tuning
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I just want to know what the backtest return rate is, don’t just talk about the ideas, brother
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The framework idea is good, but what about real backtest data? That’s the real key, right?
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It sounds good to let AI be the strategy manager, but the question is, can it really avoid pitfalls?
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Thinking frameworks are indeed more reliable than historical data, but with the market changing so quickly, can this tool keep up?
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Interesting, but I still trust my intuition and on-chain data more.
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Wait, isn’t this still training on historical data? How is it different?
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Polishing for a few weeks and then wanting to revolutionize the trading world—you're really bold. I’ll wait and see.
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Learning the logical framework is worthwhile, but isn’t the word Alpha being overused now?
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Constantly finding opportunities sounds too good to be true. Are there any practical cases?
To put it nicely, it's still just training AI with past data; the essence hasn't changed.
Fundamental game theory isn't that simple; whale trading strategies can't be learned by AI.
The framework logic sounds good, but where is the real Alpha in the chip distribution? Can your scanner see it?
Deep research is indeed interesting, but the key is whether it can capture the moment of market structure shifts.
Another highly touted tool, but ultimately, performance in real-world trading is what matters.
AI's way of thinking is worlds apart from a trader’s intuition; this part might be overthought.
Instead of optimizing AI logic, it's better to focus on capital flow; that's the real source of signals.
The concept is indeed fresh, but retail investors are still likely to end up as bagholders.
Having AI as the strategy director is an interesting perspective, but whether it can truly make money depends on real trading data.
Gemini's in-depth research capability is indeed impressive, but I don't know how to integrate it into the trading system.
The framework is usable, but the tools are still being refined. It feels a bit early; let's wait and see the implementation results.
The difference between traditional methods and new approaches is well said, but the key is whether the new logic can truly sustain outperformance.
If this thing can really find persistent Alpha, it will rewrite the game rules and is worth paying attention to.
Logically, there's nothing wrong, but the execution might be more difficult than expected.