This is the most common mistake quants make — and one of the easiest to fix.

This article explains:

• Why rolling windows are mandatory in time series ML
• Rolling vs expanding windows
• Rolling forecast origin vs sliding windows
• How to construct correct training/test splits
• How volarixs implements rolling windows in all experiments

1. Why Time Series Require Rolling Windows

In traditional ML:

But in financial time series:

• data is ordered
• distributions shift
• autocorrelation is weak
• regimes change
• leakage is easy

Random splits cause your model to "see the future". This inflates Sharpe, R², and accuracy — sometimes by 3–10×.

2. What Is a Rolling Window?

Given data: X₁, X₂, ..., Xₜ

For each training step, you select:

Where:

• L = lookback length
• h = forecast horizon (1d, 5d, 1m)

You slide the window forward: [Xt−L+1, ..., Xt+1]

Rolling Window Structure:

Time ────────────────────────────────────────────►
      │
      ├─ Past Data (not used)
      │
      ├─ Training Window [63 days]
      │  ┌────────────────────────────┐
      │  │ X₁  X₂  ...  Xₜ            │  ← Fit model
      │  └────────────────────────────┘
      │
      ├─ Forecast Horizon [5 days]
      │  ┌────────────┐
      │  │ Xₜ₊₁ ...  │  ← Predict
      │  └────────────┘
      │
      └─ Future (unknown)

Key: Each step slides the window forward by 1 day
     Model is refit on new window before predicting
          

Time ────────────────────────────────────────────►
      │
      ├─ Past Data (not used)
      │
      ├─ Training Window [63 days]
      │  ┌───────────────────────────────┐
      │  │ X₁  X₂  ...  Xₜ            │  ← Fit model
      │  └───────────────────────────────┘
      │
      ├─ Forecast Horizon [5 days]
      │  ┌──────────┐
      │  │ Xₜ₊₁ ...                    │  ← Predict
      │  └──────────┘
      │
      └─ Future (unknown)

Mode: Rolling (slides forward)

3. Rolling vs Expanding Windows

Expanding Window

Starts with initial data, keeps growing:

Risk: old data dominates, bad for regime-shifted data.

Rolling Window

Keeps fixed size:

Better for:

• non-stationary data
• regime adaptation
• ML models with temporal fragility

4. Rolling Forecast Origins (The Gold Standard)

The most correct method:

1. For each step: Fit model on past L observations
2. Predict the next h observations
3. Record prediction
4. Slide window
5. Refit model (if required)

This replicates real trading.

5. Avoiding Leakage

The 4 ways ML practitioners leak future data:

1. Using future volatility to normalise past returns
2. Using overlapping windows without care
3. Using full-sample scaling (e.g. MinMax fit on whole dataset)
4. Using future returns to define features (e.g. realised volatility includes future data if computed incorrectly)

volarixs automatically protects against all of these.

6. Lookback Length: How Much History?

Rules of thumb:

• 63–126 days for equities
• 30–90 days for FX
• 90–180 days for commodities
• 21–42 days for crypto (faster regimes)
• 252 days for volatility forecasting

Short windows adapt fast. Long windows stabilise slow cyclical behaviour.

volarixs allows full GUI control per experiment.

7. Cost Considerations

Rolling windows require:

• repeated model fitting
• repeated feature transformation
• repeated scaling

Compute cost grows ~linearly with number of windows. This is why the Prediction Factory uses Prefect + distributed workers.

8. How volarixs Implements Rolling Windows

Every experiment in volarixs:

• uses time-based splits
• supports expanding, rolling, and hybrid windows
• validates window size choices
• stores window metadata in the run configuration
• exports each prediction with lookback and horizon fields

This ensures all experiments are:

• reproducible
• leak-free
• comparable
• regime-labelled
• ready for the alpha factory

Conclusion

Rolling windows are the foundation of correct financial ML.

Without them, results are misleading.

With them — and proper diagnostics — you can build robust, regime-aware forecasts. volarixs handles the full windowing process automatically, making every experiment production-grade from the start.