Building a Universe-Wide Prediction Grid: Scaling ML From Single Tickers to Thousands of Assets
Most ML workflows start with "Let's test this model on AAPL." But an alpha factory needs predictions for every asset in the universe at multiple horizons from multiple models.
1. What Is a Prediction Grid?
Conceptually, a prediction grid is a large tensor: r̂_{u, m, h, t} where:
- u = asset (ticker, FX pair, crypto, index, etc.)
- m = model (Ridge, XGBoost, LSTM, etc.)
- h = horizon (1d, 5d, 21d, etc.)
- t = forecast date
Each entry stores predicted return (or volatility, or probability) and associated metadata.
2. From Single Ticker to Universe
For U tickers, M models, H horizons, you'd naively run U × M × H independent experiments. This doesn't scale.
We need shared infrastructure: centralized feature stores, hierarchical configuration, and efficient execution models.
3. Data Design for a Prediction Grid
3.1 Centralized Feature Store
Rather than recomputing features per-run, maintain a feature store keyed by (asset, date, feature_set_version). Features computed once per day per asset, versioned transformations.
3.2 Hierarchical Configuration
Separate configuration into universe-level, model template, and execution config. The prediction factory builds runs using Cartesian products where needed, but avoids combinatorial explosion.
4. Execution Model
A practical design:
- Daily schedule: For each business day, load required features and models
- Task graph (Prefect/Celery): tasks by (model, horizon, sector chunk)
- Output: prediction table for day t with (asset_id, model_id, horizon, forecast_date, prediction, metadata_ref)
This is exactly what volarixs' prediction factory is designed to orchestrate.
Prediction Grid Browser
| Ticker | 1d | 5d | 21d |
|---|---|---|---|
| NVDA | 4.74% | -1.80% | -1.52% |
| GOOGL | 2.20% | -4.86% | -4.73% |
| INTC | 0.98% | -3.08% | 4.40% |
| AMD | 0.87% | -2.08% | 1.61% |
| META | 0.39% | -0.86% | -0.21% |
| TSLA | -0.37% | 1.72% | -2.53% |
| AAPL | -0.87% | -4.86% | -1.48% |
| AMZN | -1.71% | 1.77% | -2.65% |
| MSFT | -2.79% | -2.35% | -2.96% |
| NFLX | -3.16% | 3.07% | -2.30% |
This is a simplified, illustrative view of a universe-wide prediction grid — the values are synthetic. It mirrors the shape volarixs is built toward: multi-horizon predictions per asset, across models, with the prediction history and metadata each run already lands feeding cross-sectional analysis.
5. Research Questions Enabled by a Prediction Grid
Once the grid is populated, you can run research that is simply not possible in single-ticker workflows:
- Cross-sectional comparison of model performance by sector, size, liquidity
- Stability of model rankings across time and regimes
- Horizon-consistency: does a model that's good at 1d also work at 21d?
- Factor extraction: PCA/ICA on prediction matrices to identify latent "model factors"
6. How This Maps to volarixs
volarixs splits the work into two surfaces, which is what makes a grid like this tractable:
- Experiments: the wizard you drive by hand — datasets, feature sets, model and target horizon over a chosen time window, on a single asset or a small set
- Factory: production model training and monitoring, with diagnostics and predictions
The pieces a grid is assembled from are already what the platform produces and keeps, run by run:
- multi-horizon predictions per ticker — 1d / 5d / 21d / 63d and beyond — with direction and a confidence component
- stored prediction history, plus the regime context the run was made under
- run results: model, datasets, targets and train/test R²
The full universe-wide grid — every asset, every model, every horizon, sliced on demand — is the shape volarixs is built toward rather than a populated dashboard you query today. But because each run already lands these predictions and their metadata in one place, the move from a single experiment toward that grid is an extension of the same data, not a rebuild.
7. From Prediction Grid to Trade Signals
The grid itself is not yet a trading strategy. You still need filtering, position sizing logic, and portfolio construction rules. But if the prediction grid is well-designed and reproducible, the step from "research" to "production" is much shorter — and far less fragile.