HELIOS
5-minute intraday scalping on liquid large-caps
Sun god — always on, day-cycle, fastest light
What HELIOS does
The fastest of the Five. HELIOS predicts 30 minutes ahead on 148 liquid large-cap equities, specialising in catching short-term microstructure dislocations.
Intraday momentum + microstructure dislocations
How it works
HELIOS is a factorised channel × time attention transformer. It looks at a 200-bar context window of 12 input channels, and predicts the full distribution of the next 6 bars.
Forward pass: input OHLCV + indicator channels → patch-embedded → alternating space/time attention layers → pooled to a single embedding → MLP head predicts 7 quantile levels per horizon step.
Input channels
Every bar is z-score normalised per-window using context-only statistics, so the model sees relative moves rather than absolute prices.
Output
Data & training
Universe
148 symbols
TOP_500_LIQUID
Symbols are the liquid, tradeable instruments that form HELIOS's training population. Each prediction cycle runs forward-tests on the same universe so performance numbers aren't cherry-picked.
Why this training window
We deliberately exclude pre-2010 data. Pre-decimalisation (fractions until April 2001), pre-HFT regime (≤2007), and the 2007-2009 credit crisis reflect a market structure that no longer exists. At 4.4 M parameters, burning capacity on that regime is noise competing for weights with the current market — López de Prado (2018) identifies regime non-stationarity as the primary failure mode of financial ML.
Sensitivity: a pilot trained on 2003-2022 ran validation loss 10-12% worse than the 2010-onwards run.
Live performance
Every prediction HELIOS makes is logged and barrier-evaluated in real time. These numbers reflect closed trades only and update continuously.
Past performance does not guarantee future results. Forward-test outcomes reflect 1:3 R/R barrier simulation; live outcomes may differ due to slippage, spreads, and liquidity.
Honest limitations
Bull-regime test exposure
The 15-week out-of-sample window was a bull-biased period. Model performance in bear regimes (2022-Q4 style) is unverified and likely materially lower.
Transaction costs not modelled
Headline returns exclude round-trip fees (~0.05-0.30% on these instruments). Real-world returns will be lower; headline numbers are upper bounds.
Same-symbol train/test
The model was trained and tested on the same symbol universe (time-split). We validate temporal generalisation, not universe generalisation to unseen tickers.
What we're confident about
Directional skill above random chance (symmetric 1:1 R/R win rate is 49.9%) and reproducible training: we've retrained from scratch 3+ times with identical hyperparameters within ±1% val loss.
Read the full paper
Publication-depth technical report: data cleaning, architecture deep-dive, training dynamics, barrier-simulation methodology, full-window results, sensitivity analyses, and honest-limitations discussion. ~7,000 words.
Compare across the Five
Every model in the Zirdle Five tackles a different trading regime. Head back to the overview to see how they stack up on win-rate, universe size, and out-of-sample returns.