# 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
