spec: profitability roadmap — 11 findings from deep research

4 critical root causes fixed this session (stop-loss, dense shaping,
action aliasing, HFT objective weight). 7 remaining improvements
documented with priority order: three-phase hyperopt, behavioral
cloning warm start, curriculum learning, ensemble, multi-timeframe,
attention, Decision Transformer.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

docs/superpowers/specs/2026-03-23-profitability-roadmap.md

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+# Profitability Roadmap — From 0.12% Win Rate to Production Trading
+
+## Implemented (this session)
+
+1. **Stop-loss 0.3%→1%, TP 0.5%→2%** — noise was killing 99.88% of trades ✅
+2. **Dense shaping 0.1x→0.01x** — trade completion now dominates ✅
+3. **Action aliasing fix** — 5-bar hold override corrupted Q-values ✅
+4. **HFT activity weight 25%→5%** — hyperopt now rewards selectivity ✅
+
+## Next priorities
+
+### P5: Three-phase hyperopt (5D→8D→5D)
+- Phase 1 (5D): lr, gamma, batch_size, w_dsr, w_pnl
+- Phase 2 (8D): kelly, dd_threshold, loss_aversion, sl/tp levels, time_decay, entropy, cql_alpha
+- Phase 3 (5D): hidden_dim, num_atoms, branch_hidden, n_steps, tau
+- PSO 100 iterations × 30 particles per phase = 3000 evaluations
+
+### P6: Behavioral cloning warm start
+- Generate expert demonstrations from MA crossover (20/50 EMA, ADX>25 filter)
+- Pre-fill replay buffer: 50% expert, 50% online for first 10 epochs
+- CQL pre-training with cql_alpha=1.0 for 10 epochs, then 0.1 online
+
+### P7: Curriculum learning
+- Phase 1: only windows where ADX>30 (strong trends, easy)
+- Phase 2: all windows, trending weighted 2x
+- Phase 3: equal weight (full generalization)
+
+### P8: Ensemble of 3-5 agents
+- Different random seeds, average Q-values
+- KL-divergence diversity loss (weight 0.01)
+- Infrastructure exists in `ensemble_network.rs`
+
+### P9: Multi-timeframe features
+- 5-min and 15-min: close_return, volume_ratio, spread_ratio, RSI
+- +8 features → state_dim stays 64 (reuse padding)
+
+### P10: Temporal attention
+- Self-attention over last N state embeddings
+- `use_attention: bool` already in DQN config
+
+### P11: Decision Transformer pre-training
+- Return-conditioned sequence model on historical data
+- LoRA fine-tuning for online adaptation