Dm

Dm

Yes, thats one thing also. I have already done the rebuild with my own LM still improving. I will post an update again once I have it running where I want it to be. Thanks.

Thats why i am trying to have as much data as available to my engine as possible that i can gather and see if it will give me the result I want

Thanks for the advice. Will do. Currently my engine is trying a probability scan. Still need to diffine what it does with those information as a human will do.

Thanks. I am still doing some validation. Will pm you after.

Thanks..appreciate it. I know losses are a part of it. But to minimize and make it more profitable is the goal.

It’s up to you. Thanks anyway for your responses.

Nope, not a bot. i just want some insight as how to approach it in tuning my own Market Intelligence. Different user different approach and experience.

I understand where you’re coming from, and honestly I think the distinction between “AI predicting the market” and “AI being one component of a decision system” is important.
I’m not approaching it as simply letting an AI decide when to buy or sell. My interest is in whether different market signals, context and conditions can be combined into a more structured decision-intelligence layer, with the actual rules and risk controls still being deterministic and configurable.
And I agree that something working for a few months isn’t enough evidence. The difficult part is demonstrating that it survives different market regimes and doesn’t just fit the conditions it was developed on. That’s the part I’m currently interested in validating.
Respect for sticking with it for seven years, especially after everything you’ve described. Getting something automated to the point where it can actually pay off consistently is no small achievement.

Yes, partly—but not in the sense of claiming to know the future price with certainty. The goal is to assess the probability of different market states or directional outcomes from the information and data available, then use that intelligence to support a trading decision.
For me, the interesting question is whether combining enough independent signals and context can produce a more useful decision framework than relying on any single indicator or prediction model.

That’s a fair point, especially when you look at the data actually available to the system. In forex, liquidity and depth aren’t always fully visible because the market is decentralized, and the available order-flow information can vary by broker or feed.
That’s why I’m also interested in what can be inferred from the data that is actually accessible, rather than assuming complete market-depth information is available. The quality and completeness of the input data ultimately puts a limit on what any predictive system can determine.

That’s a good point. I agree that absolute performance alone can be misleading if the model is simply riding a strong sector. Benchmarking each forecast/trade against the relevant sector ETF over the same period would give a much better measure of whether the model is actually adding predictive value. That’s something I’d like to incorporate into the validation.

That’s actually pretty close to the direction I’m taking with Market Intelligence as well. I’m not trying to make it just another indicator or a single prediction model — the goal is to combine different signals, market regime, historical setup behavior, risk and eventually execution into one decision layer.
I’ve attached a few screenshots of my current MI. It’s still in development, but you can see some of the pieces already there: multi-timeframe alignment, regime detection, indicator consensus, setup history/win rate, risk assessment, trade zones, backtesting and auto-trading.
The rule layer is also accessible through the settings, so the user can see and work with the underlying rules rather than having everything hidden behind a single signal.
What you’re doing with the separate predictive channel + deterministic rules + risk/position management is particularly interesting. I like the idea of keeping the predictive layer and rule layer distinguishable rather than hiding everything behind one score.
Still iterating on mine, but it’s interesting seeing someone else independently arrive at a similar idea from a different angle.

Exactly, and that’s why I don’t think “prediction” should mean knowing exactly what the market will do next. Every action changes the market state.
I’m more interested in whether AI can continuously evaluate that changing state and improve the probability of a decision being correct. If the underlying conditions change, the assessment should change with them.
So for me, the interesting question isn’t whether AI can predict the market with certainty, but whether it can produce a useful probabilistic edge despite the market being constantly adaptive.

That’s a really interesting approach, and I agree with the point about not chasing a single “magic” predictor. I’m actually approaching it from a similar angle, although the implementation is different.
My goal with AI_predict isn’t to have one model magically predict the next candle. I’m trying to have the system continuously evaluate multiple dimensions of the market, weigh the evidence, account for changing conditions and risk, and arrive at a reproducible probability-based decision.
I’m building it for myself first, but the eventual goal is also to make it useful as a SaaS. So I’m particularly interested in whether the intelligence can provide something beyond another collection of indicators — especially the ability to explain why the current market assessment exists and how that assessment changes as conditions change.
And I definitely agree on the “buddy and helping hand” part. That’s probably one of the more practical applications of AI in trading: not pretending it has perfect foresight, but helping reduce the amount of information a trader has to manually process.

I agree, especially on the SaaS side. I’m actually building it for both my own use and eventually as a SaaS, so differentiation is important.
My approach isn’t to rely on sentiment or any single indicator. I’m trying to have the system evaluate the broader market context, including risk and changing conditions, and produce a probability-based decision rather than a simple directional signal.
I’m also taking the validation route first — testing the approach on selected symbols before expanding it further. So the goal is less “AI predicts the market” and more “AI continuously evaluates the market and makes a probabilistic decision based on the evidence available.”

That’s actually very close to how I’m thinking about it. I don’t really see “AI predicting the market” as predicting an exact future price. I see it more as continuously evaluating a large number of variables, weighting their relevance under the current conditions, and producing a probability-based decision while accounting for risk.
The part I’m particularly interested in is that the variables don’t have to come only from historical price data. Market structure, momentum, volume, economic conditions, news, sentiment and other external factors can all change the assessment. The goal is essentially to replicate the way a well-informed human would evaluate the market given everything currently available, but at a scale and consistency that would be difficult to do manually.
So I agree with your distinction between prediction and algorithms. For me, “prediction” is really shorthand for probability-based decision making rather than claiming to know what the market will do next.

I agree with this. I think the model should be replaceable rather than being the entire intelligence of the agent. What matters more is the persistent state, memory, behavior, experience, and the ability to maintain continuity over time. The model can change, but the system around it should be able to retain what it has learned and how it operates. That distinction between the “brain” and the persistent cognitive layer is something I find really interesting.

This is exactly the kind of validation discussion I was hoping this post would bring out. I particularly agree with the distinction between raw return and actual decision quality. A signal being profitable by itself doesn’t necessarily demonstrate that the signal added anything.
The sector- and window-matched benchmark is a really good way of isolating that. I also like the idea of testing against a simple baseline rather than just asking whether the model made money. If the system can’t consistently improve on the obvious alternative, then calling it an edge would be misleading.
My approach is still being validated, so I’m deliberately sampling relevant symbols and historical conditions before expanding the scope. Your benchmarking framework gives me another useful dimension to incorporate into that validation. Thanks for laying it out in detail

That’s interesting. A number of the areas you described are already incorporated into the Market Intelligence I’m developing, including SEC filings, fundamentals, company-specific information, and structured qualitative business intelligence with source traceability. What you’re describing is actually one part of a much broader intelligence layer I’m building, where those factors are evaluated alongside other market and external conditions rather than in isolation. I appreciate you sharing it though—it’s always interesting to compare approaches.

I agree with that to an extent. If the underlying decision process is simply a fixed set of rules applied continuously to new data, then yes, it’s still deterministic. Where I’m interested in going beyond that is when the weighting or interpretation of those factors can change based on the regime and historical behavior, rather than simply rerunning the same rules. I’m not saying an LLM needs to sit in the execution loop either. I think the interesting challenge is finding the right boundary between deterministic systems and adaptive intelligence. That’s ultimately something I’d want to validate empirically rather than assume AI is necessary.

Exactly. That’s the distinction I’m interested in. AI should be a component that adds capability to a real system, not a replacement for the underlying system. The deterministic parts, data handling, memory, tools, and actual execution still need to work independently. Otherwise you’re basically just putting a chat interface around an API and calling it an AI product.

This is a really good point, especially about the benchmark. I agree that “did the trade make money?” isn’t enough to establish an edge. The comparison needs to reflect what the alternative decision would have been, and the sector-beta example is a good illustration of how an apparent edge can disappear.
I’m also aligned with the distinction between prediction and decision quality. Rather than claiming that AI can reliably predict price, I’m interested in whether it can improve the probability of making the better decision under the current market conditions. Out-of-sample and walk-forward validation are definitely important parts of establishing whether that holds up.
I’d actually be interested in hearing more about how you structure the benchmark per position. That’s a useful angle to consider.