Discussing the article: "Foundation Models for Trading (Part III): Running a Generative AI Forecaster Inside MetaTrader 5"
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Check out the new article: Foundation Models for Trading (Part III): Running a Generative AI Forecaster Inside MetaTrader 5.
We demonstrate live forecast charting with Kronos in MQL5 and an evaluation pipeline designed to be look‑ahead‑safe. Deterministic dumps are scored per horizon against a random‑walk baseline (15,113 predictions), then fed to a simple Strategy Tester rule on EURUSD H1. The findings show no edge, and illustrate how a high threshold can yield a persuasive but low‑information backtest with very few trades.
Across Part 1 and Part 2, we built a complete native MQL5 port of Kronos and proved, stage by stage, that it reproduces the PyTorch model: exact encoder tokens, decoder and predictor logits within floating-point noise, and autoregressive generation verified through the context-sliding boundary. That answers only the easy question. Fidelity to PyTorch is not the same as forecasting skill. A faithful port of a model that cannot predict the market is still a model that cannot predict the market.
The distinction matters more than it might seem. Verification is a closed question with a right answer: either our matrices agree with the reference or they do not, and a tolerance of 1e-06 settles the argument. Forecasting skill is an open question with no reference to check against, only the market, which is happy to hand you a convincing-looking result that means nothing at all. The tools that settle the first question are useless for the second. The habits that made the port trustworthy, namely tight tolerances and exact comparisons, can also leave you overconfident when you switch to measuring value.
This part asks the hard question honestly. First, we put the model to work in the most satisfying way: an Expert Advisor that draws Kronos's forecast candles directly on the chart. This lets you watch it predict in real time. Then we stop admiring the picture and measure it. We dump forecasts across a stretch of history under strict look-ahead discipline, score them against a naive baseline, and run a deliberately simple strategy through the Strategy Tester. The result is a negative one, and reported plainly, because a negative result that is honestly arrived at is worth more than a positive one that is not.
Author: Muhammad Minhas Qamar