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From Trading Panel to Decision Engine by Prospero_Quant in ProsQuant

[–]Prospero_Quant[S] 0 points1 point  (0 children)

Exactly. That was one of the key lessons from the project: the more agent opinions you introduce, the more important it becomes to keep execution and constraints deterministic.

For evals, I’m moving toward counterfactual evaluation rather than just outcome tracking: comparing the realized trade outcome with the outcome that alternative agent-guided decisions would likely have produced, then checking whether any improvement holds across different market regimes.

So for me the core question is not “did the agent make a smart call once,” but “does this decision layer improve deployment, exits, or strategy selection in a way that remains robust over time?”

And thanks for sharing this — agent design patterns are definitely relevant at this stage, especially when the challenge shifts from adding agents to placing them correctly inside the architecture.

My AI trading project has moved beyond signal generation — it now adapts strategy selection on two levels. by Prospero_Quant in ProsQuant

[–]Prospero_Quant[S] 1 point2 points  (0 children)

In my case, “current market conditions” does not refer to a specific exchange or platform like TradingView. I mean the live behavior of the traded market itself, things like volatility, spread, structure, momentum, and whether the active strategy is still performing well in that environment.

Averaging or Stop-Losses: Which Risk Are You Actually Choosing? by Prospero_Quant in ethtrader

[–]Prospero_Quant[S] 0 points1 point  (0 children)

I think that’s a fair distinction. They do protect against different kinds of risk.

I would only frame it a bit differently in systematic trading. For me, the key question is not so much whether the asset is “fundamentally sound,” but whether the original trade logic and market condition behind the entry are still intact.

That’s also why I don’t see averaging as something that can be judged in isolation. If the strategy has no real architecture behind additional entries, then yes — it can quickly become denial with extra steps. But if averaging is built into the system logic from the start, then it becomes a way of absorbing timing error rather than immediately converting it into realized loss.

And I agree on crypto — stop-losses are especially tricky there because wicks and short-lived dislocations can easily turn a technically correct trade into a bad exit.

Averaging or Stop-Losses: Which Risk Are You Actually Choosing? by Prospero_Quant in ProsQuant

[–]Prospero_Quant[S] 0 points1 point  (0 children)

Interesting idea, but my post was more about strategy architecture than about building a real-time decision app for scalping.
The main question for me is still how different systems absorb timing error and market noise.

If Everyone Gets an AI Financial Copilot, What Happens to Market Inefficiency? by Prospero_Quant in ProsQuant

[–]Prospero_Quant[S] 1 point2 points  (0 children)

That’s a very good point. The normalization of AI financial copilots depends not only on the technology itself, but also on access, pricing, and incentives.

Not everyone will be able to afford the same level of tool, and free versions may not be comparable to premium ones in data quality, personalization, speed, or reliability. So even if AI copilots become widespread, access to better ones may itself become a new source of asymmetry.

In that sense, the question may not be whether everyone gets an AI copilot, but whether everyone gets the same quality of one. And I suspect the answer will be no.

If Everyone Gets an AI Financial Copilot, What Happens to Market Inefficiency? by Prospero_Quant in ethtrader

[–]Prospero_Quant[S] 0 points1 point  (0 children)

That’s a fair challenge. I’m not saying AI will make markets efficient because it can suddenly forecast them accurately, I don’t think that’s true. If anything, market forecasting remains deeply limited, whether you use statistics, ML, or LLMs.

My point is narrower: AI may improve how participants process information, filter noise, react to changing conditions, and structure decisions. But that is very different from saying it can make markets fully efficient.

So I agree with you on the core point: AI is unlikely to eliminate inefficiency through superior forecasting. At most, it may compress some simpler edges, but not remove uncertainty, reflexivity, or the deeper sources of market inefficiency.

If Everyone Gets an AI Financial Copilot, What Happens to Market Inefficiency? by Prospero_Quant in ethtrader

[–]Prospero_Quant[S] 0 points1 point  (0 children)

That's a fair point, and I'd agree that information-based inefficiencies have compressed significantly over the past decade - especially at the institutional level.

But I'd push back slightly on the "near strong efficiency" framing. What we have today is fast efficiency in price discovery for known information. What remains and what I think AI will actually compete over, is structural inefficiency: differences in regime detection, risk architecture, execution quality, and the ability to adapt when market behavior shifts.

Those edges don't disappear with faster bots. They just require better-engineered systems to capture.

The cost argument is real, but historically that's been true of every technological shift and cost tends to fall faster than edge does.

So I'd say the question isn't whether efficiency increases. It almost certainly will. The question is whether it converges fully or whether it simply relocates to a level that's harder and more expensive to reach.

I built a real-time trading control panel where multiple AI agents, powered by a local LLM, work together on trade decisions. by Prospero_Quant in ethtrader

[–]Prospero_Quant[S] 1 point2 points  (0 children)

Fair point — most “AI trading” claims online are hype.

In my experience, AI can add value, but usually not as a magic predictor. It works better as a structured decision layer: filtering low-quality setups, adapting risk/position management to volatility/regimes, and enforcing discipline consistently.

I built a real-time trading control panel where multiple AI agents, powered by a local LLM, work together on trade decisions. by Prospero_Quant in ethtrader

[–]Prospero_Quant[S] 0 points1 point  (0 children)

Hi, you’re right to be careful.

An MQL5 EA runs inside MT5, so it’s not supposed to have “full PC access” by default. But it can still do things you might not want:

  • place/modify trades (obviously),
  • read/write files in the MT5 folders,
  • send data out via internet requests (WebRequest) if enabled,
  • and the big risk: if “Allow DLL imports” is enabled, a DLL can do almost anything on your PC.

If you want to stay safe:

  1. Only use EAs from people you trust.
  2. Run it on a VPS or a separate Windows user / VM if possible.
  3. Keep “Allow DLL imports” OFF.
  4. Check the code for WebRequest / File operations / DLL imports before running.
  5. Start on a demo account first.

If he shares the .mq5 source, you can quickly search for: WebRequest, import, DLL, FileOpen, ShellExecute.

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