# DQN Generalization Gap — Action Plan

## Problem
In-sample Sharpe +0.57, OOS Sharpe -1.30 on H100. Model performs worse than random OOS.
100K+ IS trades vs 211 OOS trades — model learned IS-specific trading patterns.

## Root Causes (from codebase analysis)
1. **Deterministic episodes** — same start points every epoch, enables memorization
2. **CQL alpha=0.1** — effectively disabled (1.5% of gradient budget)
3. **Fixed simulation params** — spread=1bp, tx_cost=1.0, fill_prob=0.85 never vary
4. **Single-fold validation** — 80/20 time split, no purging/embargo
5. **No data augmentation** — pixel-perfect price sequences

## Execution Order

### Phase 1: Quick Wins (1-2 days)

**1a. Domain Randomization (Rust only, per-epoch)**
- `training_loop.rs` lines 900-990: randomize before kernel launch
- spread: U[0.5bp, 3.0bp], tx_cost: U[0.5, 2.5], fill probs: randomized
- Episode starts: add ±25% stride jitter (line 935)
- Variable episode length: U[250, 750] timesteps

**1b. CQL Alpha + Gradient Budget**
- `config.rs` line 1509: `cql_alpha: 0.1 → 1.0`
- `fused_training.rs` line 51: `CQL_GRAD_BUDGET: 0.15 → 0.25`
- C51 budget: 0.70 → 0.60
- Add `cql_alpha` warmup synchronized with c51_warmup

### Phase 2: Walk-Forward (3-5 days)

**2a. Enable GPU walk-forward by default**
- `config.rs`: `enable_gpu_walk_forward: true`
- Configure 6-8 folds with purge gap = n_steps bars
- Embargo gap = 100 bars (DSR EMA window)
- Model selection: median OOS Sharpe across folds

### Phase 3: Multi-Instrument (2-3 weeks)

**3a. ES + NQ dual training**
- Shared trunk, instrument-specific advantage heads
- Normalized rewards (vol-adjusted, instrument-agnostic)
- Data loading for multiple .dbn.zst files

## Expected Impact
- Phase 1: 50-70% generalization gap reduction
- Phase 2: 30-50% PBO reduction
- Phase 3: 8-12% OOS improvement