CETU
Cetus Capital Acquisition Corp. Class A Common Stock
stock NASDAQ

I've been building software in fintech for about a decade (sold a trading infra company to Coinbase a few years back) and the current wave of "AI trading agents" is making me lose my mind.
Every week there's a new bot promising autonomous trading powered by "proprietary AI." Of course most of them are just if/then rules hitting an exchange API with a ChatGPT skin on top. The industry even has a name for it now - "agent washing" - and it's gotten bad enough that the SEC started filing enforcement actions for people hocking fake agents. They charged two investment advisers in March 2024 for straight up lying about their AI capabilities, and by 2025 they'd set up an entire unit ("CETU") with AI washing as a priority. A founder raised $42M claiming his app used AI when it was literally contractors doing the work manually.
The ArtificialIntelligence sub landed on a pretty good litmus test that I think everyone here should apply before touching any of these tools:
1. Does it take initiative, or does it wait for every instruction? If you have to tell it exactly what to do every time, it's a script with a chat UI.
2. Does it handle unexpected situations, or does it crash and need re-prompting? A real agent adapts when the plan breaks. A wrapper just fails.
3. Does it use external tools (APIs, data sources, code execution) or does it only generate text? Most "AI trading bots" are just generating text responses about markets. That's a chatbot, not an agent.
4. Does it remember context across a multi-step task without you repeating yourself? If every interaction is stateless, you don't have an agent. You have autocomplete.
The reliability math alone should scare people. Carnegie Mellon built a benchmark (TheAgentCompany) that tested agents on realistic multi-step tasks. The best model they tested completed 24% of tasks autonomously. That's the best one!! And if you assume even 95% reliability per step, a 20-step workflow has about **a 36% chance of finishing without error**. Can't imagine trusting that with my portfolio.
Meanwhile at Consensus Hong Kong this year, Bitget's CEO said the quiet part out loud: current AI trading bots are trained on limited historical data and fall apart when markets do something genuinely unfamiliar - like the 10/10 liquidation cascade. In these cases, the ones where your whole P/L is decided, human intervention is still fully required.
**Here's what I think almost everyone in this space gets wrong:**
They're trying to build agents that make trading decisions. IMO, that's the wrong problem to automate. The decision is the hard part. It's the part that requires judgment, risk tolerance, conviction, and context that no model actually has about your specific situation. Delegating that to an agent is how you get blown up.
The right problem to automate is everything around the decision. The research, the monitoring, the risk math... the pattern recognition across your own trade history. The stuff that a serious trader does (or should be doing) but that takes hours and is brutally tedious to maintain manually.
**Think about what a firm like Bridgewater does.** They don't have a magic algorithm. They have investment theses that research analysts refine over time and pressure-test against current conditions. A risk desk optimizing risk-return profiles. Analysts providing inputs to all the above based on their reading of current events. It's not a crystal ball - it's disciplined analysis compounding over time.
That's the gap agents should be filling. Not replacing your judgment - augmenting your operation so you can make better calls with less manual overhead. Decision support, not decision making. It's not about a super-smart agent, it's about how smart you could be if you had the resources of a group at Citadel.
**So I've been experimenting with this kind of thing over the past few months.** I have a multi-agent setup where each agent has a narrow, well-defined job - monitoring on-chain activity, running quantitative checks against my positions, watching for news and sentiment shifts in my specific holdings, tracking risk metrics I've defined myself. They share a common data layer so they're all working from the same picture of my portfolio. None of them execute trades. They surface information, flag anomalies, and make sure I'm not missing something obvious at 2am when I'm not looking at charts.
It's early and I genuinely don't know yet whether this will meaningfully improve my returns or just be a simple way to feel informed. But so far it's been working really well - I regularly find myself acting on information I probably would never have noticed before.
And the architecture feels right in a way that "autonomous trading bot" never did to me. The agents pass the 4-question test above - they use real tools, maintain context, adapt when data changes - and more importantly, I'm still the one making the call, just with a much better picture of what's actually happening.
Curious whether anyone else has landed in a similar place - using agents for the support layer rather than trying to close the loop on execution. Or if the consensus here is still that the only valid use of automation in trading is fully systematic strategies with tight parameters, that it's really not algo trading if the algorithm doesn't do everything. I could see arguments either way.

I've been building software in fintech for about a decade (sold a trading infra company to Coinbase a few years back) and the current wave of "AI trading agents" is making me lose my mind.
Every week there's a new bot promising autonomous trading powered by "proprietary AI." Of course most of them are just if/then rules hitting an exchange API with a ChatGPT skin on top. The industry even has a name for it now - "agent washing" - and it's gotten bad enough that the SEC started filing enforcement actions for people hocking fake agents. They charged two investment advisers in March 2024 for straight up lying about their AI capabilities, and by 2025 they'd set up an entire unit ("CETU") with AI washing as a priority. A founder raised $42M claiming his app used AI when it was literally contractors doing the work manually.
The ArtificialIntelligence sub landed on a pretty good litmus test that I think everyone here should apply before touching any of these tools:
1. Does it take initiative, or does it wait for every instruction? If you have to tell it exactly what to do every time, it's a script with a chat UI.
2. Does it handle unexpected situations, or does it crash and need re-prompting? A real agent adapts when the plan breaks. A wrapper just fails.
3. Does it use external tools (APIs, data sources, code execution) or does it only generate text? Most "AI trading bots" are just generating text responses about markets. That's a chatbot, not an agent.
4. Does it remember context across a multi-step task without you repeating yourself? If every interaction is stateless, you don't have an agent. You have autocomplete.
The reliability math alone should scare people. Carnegie Mellon built a benchmark (TheAgentCompany) that tested agents on realistic multi-step tasks. The best model they tested completed 24% of tasks autonomously. That's the best one!! And if you assume even 95% reliability per step, a 20-step workflow has about **a 36% chance of finishing without error**. Can't imagine trusting that with my portfolio.
Meanwhile at Consensus Hong Kong this year, Bitget's CEO said the quiet part out loud: current AI trading bots are trained on limited historical data and fall apart when markets do something genuinely unfamiliar - like the 10/10 liquidation cascade. In these cases, the ones where your whole P/L is decided, human intervention is still fully required.
**Here's what I think almost everyone in this space gets wrong:**
They're trying to build agents that make trading decisions. IMO, that's the wrong problem to automate. The decision is the hard part. It's the part that requires judgment, risk tolerance, conviction, and context that no model actually has about your specific situation. Delegating that to an agent is how you get blown up.
The right problem to automate is everything around the decision. The research, the monitoring, the risk math... the pattern recognition across your own trade history. The stuff that a serious trader does (or should be doing) but that takes hours and is brutally tedious to maintain manually.
**Think about what a firm like Bridgewater does.** They don't have a magic algorithm. They have investment theses that research analysts refine over time and pressure-test against current conditions. A risk desk optimizing risk-return profiles. Analysts providing inputs to all the above based on their reading of current events. It's not a crystal ball - it's disciplined analysis compounding over time.
That's the gap agents should be filling. Not replacing your judgment - augmenting your operation so you can make better calls with less manual overhead. Decision support, not decision making. It's not about a super-smart agent, it's about how smart you could be if you had the resources of a group at Citadel.
**So I've been experimenting with this kind of thing over the past few months.** I have a multi-agent setup where each agent has a narrow, well-defined job - monitoring on-chain activity, running quantitative checks against my positions, watching for news and sentiment shifts in my specific holdings, tracking risk metrics I've defined myself. They share a common data layer so they're all working from the same picture of my portfolio. None of them execute trades. They surface information, flag anomalies, and make sure I'm not missing something obvious at 2am when I'm not looking at charts.
It's early and I genuinely don't know yet whether this will meaningfully improve my returns or just be a simple way to feel informed. But so far it's been working really well - I regularly find myself acting on information I probably would never have noticed before.
And the architecture feels right in a way that "autonomous trading bot" never did to me. The agents pass the 4-question test above - they use real tools, maintain context, adapt when data changes - and more importantly, I'm still the one making the call, just with a much better picture of what's actually happening.
Curious whether anyone else has landed in a similar place - using agents for the support layer rather than trying to close the loop on execution. Or if the consensus here is still that the only valid use of automation in trading is fully systematic strategies with tight parameters, that it's really not algo trading if the algorithm doesn't do everything. I could see arguments either way.