Improve IV surface fit with rollout geometry regularization

[abelobsenz]

Motivation

• Tree-based baselines predict pointwise IV well but struggle to enforce coherent surface geometry; the model should learn slope/curvature dynamics not only levels.
• Add lightweight geometric supervision during rollout training to bias latent dynamics toward preserving smile slope and curvature across moneyness and tenor.

Description

• Added two new training hyperparameters to TrainingConfig: rollout_slope_lambda and rollout_curvature_lambda to control geometry regularization strength.
• Implemented IV-geometry losses inside _rollout_losses_torch that compute iv_pred/iv_true from decoded surfaces and apply first-derivative matching in moneyness (d/dx) and tenor (d/dt) and second-derivative matching in moneyness (d2/dx2).
• Wired the new loss terms through head/joint rollout call sites in train and surfaced the knobs in CLI parsing and config building (ivdyn/utils/cli.py).
• Defaults chosen conservatively (slope=0.08, curvature=0.02) so behavior is opt-in-friendly while providing useful geometric signal.

Testing

• Ran synthetic end-to-end training+evaluation (train() and evaluate()) on a controlled toy dataset comparing baseline (rollout_slope_lambda=0.0, rollout_curvature_lambda=0.0) vs new geometry regularization (0.08, 0.02), and observed small consistent improvements in automated metrics: surface_forecast_iv_rmse improved from 0.0031641728946 to 0.0031641604256 and surface_recon_iv_rmse improved from 0.0030192448488 to 0.0030192224386.
• Performed a small hyperparameter sweep over several (slope, curvature) pairs using the same synthetic dataset and found the best candidate (slope=0.08, curvature=0.02) yielding the lowest validation RMSE in that sweep.
• Verified syntax by running python -m py_compile ivdyn/training/pipeline.py ivdyn/utils/cli.py, which succeeded without errors.

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