AI model
How LearnAImarkets Works
Published by LearnAImarkets Research Team
By LearnAImarkets Research Team · ·
The decision pipeline
What is LearnAImarkets?
An educational platform that uses AI to analyze European markets every day. It does not invest or trade: it analyzes, predicts a regime (BULL / NEUTRAL / BEAR) for the next 42, 84 and 126 days, and explains why it reaches that conclusion.
What data does it use?
Every day the model ingests prices from 12 European ETFs (Core12) together with macro indicators (rates, yield curve, inflation, employment, credit spreads) from the Federal Reserve. Everything is turned into a daily feature vector that feeds the ensemble.
Yahoo Finance
FRED
76 features
Heterogeneous ensemble
How does the ensemble predict?
Instead of a monolithic model, the final signal comes from combining two very different engines — and each one is itself a small committee. This diversity is what reduces bias and captures uncertainty.
Transformers · neural network
~70%Several sub-Transformers trained with three different fixed seeds, whose readings are averaged. Each reads long sequences of prices+macro and contributes its own reading. Their divergence is not a flaw: it is how the ensemble captures epistemic uncertainty.
XGBoost · decision trees
~30%Complementary tabular model. Captures non-linear interactions and abrupt relationships that the Transformer may smooth out.
What does each signal mean?
BULL REGIME
The model sees a dominant uptrend. Portfolio invested in its monthly top-3; cash only if the VIX Shield acts.
NEUTRAL
Transition zone or sideways market. The portfolio stays as selected at the start of the month, waiting for variables to confirm direction.
BEAR REGIME
The model detects deterioration and its reading turns bearish. It keeps its monthly top-3; cash only rises if VIX is high and rising.
Each state has nuances (what NEUTRAL means, how long a regime lasts, what a label does not imply). They are covered in the dedicated guide.
How does it protect capital?
On top of the ensemble there is a guardrail layer that never moves the signal, but does adjust exposure:
VIX Shield
VIX does not have a single red line. The shield only acts when VIX is high and also rising (above its 10-day average): at VIX ≥25/35/45 the model holds 20%/50%/80% simulated cash and reinvests when VIX turns down. A high but falling VIX does not trigger it: validation showed that reacting to passing scares destroyed returns.
SMA200 Floor
In a confirmed BULL regime (price above the 200-day moving average), the model requires at least 100% invested. It never sits systematically in cash if the trend remains intact.
No regime-based trimming
The regime reading (BULL/NEUTRAL/BEAR) no longer scales exposure: in validation, trimming in BEAR or NEUTRAL reduced return and worsened drawdown. The portfolio is selected once a month (top-3 by signal) and stays invested unless the VIX Shield acts.
Can it be wrong?
Of course. No model is infallible. What you CAN verify is how the system validates itself: the backtest is pure walk-forward (trains on past data, predicts on data it has never seen) and every champion candidate must beat the current one on Sharpe, Alpha AND a panel of out-of-sample thresholds. Past returns do not guarantee future results — but the methodology is transparent, reproducible and auditable.
Does it work? The data speaks.
Walk-forward out-of-sample backtest 2013–2026. Past performance is not a reliable indicator of future performance.
Frequently asked questions
What is Learn AI Markets?
Learn AI Markets is a common search phrase for LearnAImarkets, an educational platform focused on explainable AI market analysis for UCITS ETFs.
Is this investment advice?
No. LearnAImarkets is educational and informational only, not personalized financial advice.
How often is the model updated?
The system publishes daily signals on market days and follows periodic validation and refresh logic.
What assets are covered?
The framework uses a Core12 UCITS ETF universe across equities, fixed income, gold, and factor or sector exposures.
Why does explainability matter?
Because it improves discipline: it makes clear why a signal exists instead of leaving a black-box output to be followed.
Where is a good place to start?
A sensible sequence is the model page (today's signal and why the model reads it that way), then this methodology and the walk-forward validation guide.
Go deeper
LearnAImarkets is an educational platform. Content is informational only and does not constitute personalized financial advice.