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+# MASTER PLAN: Maximum Profitable DQN Trading Agent
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** Activate all 25 DQN features into a unified, maximally profitable ES futures swing trading agent — nothing left on the shelf.
+
+**Architecture:** Three-phase training pipeline (Behavioral Cloning → Full-Stack Online RL → Decision Transformer Refinement) with trade-aware hybrid reward, dynamic trade management as environment physics, ensemble consensus action selection, and IQN risk-aware position sizing. Every auxiliary head (IQN, ensemble, attention, curiosity, CQL, DT) injects gradient into the shared trunk via the fused CUDA training pipeline.
+
+**Tech Stack:** CUDA kernels (nvcc precompiled), cuBLAS SGEMM, Rust async, cudarc 0.19.3, CUDA Graphs with split forward/adam phases.
+
+---
+
+## Design Philosophy
+
+### We Are Building a Swing Trader, Not a Scalper
+
+- **Timeframe**: 1-minute bars, holding 5-100+ bars (5 min to 2+ hours)
+- **Edge**: Pattern recognition across 4 timeframes (5m/15m/1h/4h) + regime detection
+- **Sizing**: Conviction-based via ensemble agreement + IQN risk quantiles + Kelly optimal
+- **Exits**: Model learns when to exit from the reward signal, with dynamic guardrails
+- **Goal**: Fewer, higher-quality trades that capture multi-bar moves
+
+### The Agent Decides WHAT, the Environment Manages HOW
+
+| Agent Controls | Environment Controls |
+|----------------|---------------------|
+| Entry timing (when to trade) | Transaction costs (real friction) |
+| Exposure level (how much) | Capital floor (25% max drawdown) |
+| Order type (limit vs market) | CVaR position scaling (tail risk) |
+| Exit timing (when to close) | Kelly sizing (optimal fraction) |
+| | Dynamic trailing stop (profit lock) |
+
+### The Reward Teaches Trading, Not Just Returns
+
+```
+reward_t = trade_shaping(t) + exit_bonus(t)
+
+trade_shaping(t):
+  - IN TRADE: unrealized_change / equity × regime_quality  (dense, per-bar)
+  - FLAT: 0.0  (doing nothing is free)
+
+exit_bonus(t):
+  - AT EXIT: trade_return × patience_multiplier  (sparse, strong)
+  - patience_multiplier = sqrt(hold_time / regime_expected_hold)
+  - Rewards patience: same return in fewer bars = less reward
+```
+
+---
+
+## Feature Activation Map
+
+### Tier 1: Foundation (14 features — already ACTIVE)
+These run every step. No changes needed.
+
+| # | Feature | Status |
+|---|---------|--------|
+| 1 | Branching DQN (9×3×3) | ACTIVE |
+| 2 | C51 Distributional (51 atoms) | ACTIVE |
+| 3 | MSE Warmup + Blended Ramp | ACTIVE |
+| 4 | Multi-Timeframe Features (16-dim) | ACTIVE |
+| 5 | 4-Head Attention on h_s2 | ACTIVE |
+| 6 | Expert Demonstrations (MA crossover) | ACTIVE |
+| 7 | N-Step Returns (n=3) | ACTIVE |
+| 8 | PER Beta Annealing | ACTIVE |
+| 9 | Noisy Networks (σ=0.5) | ACTIVE |
+| 10 | Circuit Breaker | ACTIVE |
+| 11 | Gradient Clipping | ACTIVE |
+| 12 | Soft Target Updates (τ=0.005) | ACTIVE |
+| 13 | Spectral Normalization | ACTIVE |
+| 14 | Financial Metrics Pipeline | ACTIVE |
+
+### Tier 2: Activate via Config (5 features — ACTIVE when flag > 0)
+Just need hyperopt to search over non-zero values.
+
+| # | Feature | Config Flag | Default |
+|---|---------|-------------|---------|
+| 15 | IQN Dual-Head | iqn_lambda | 0.25 |
+| 16 | HER Counterfactual | her_ratio | 0.0 → **0.2** |
+| 17 | Count Bonus (UCB) | count_bonus_coefficient | 0.0 → **0.1** |
+| 18 | Q-Gap Conviction | q_gap_threshold | 0.0 → **0.05** |
+| 19 | Regime PER Scaling | (hardcoded true) | ACTIVE |
+
+### Tier 3: Wire into Pipeline (4 features — code exists, needs connection)
+GPU kernels exist but aren't called from the training loop.
+
+| # | Feature | What's Missing |
+|---|---------|---------------|
+| 20 | Kelly Sizing | Apply kelly_fraction to position in experience kernel |
+| 21 | CVaR Action Selection | Remove hardcoded `false`, pass IQN quantiles to selector |
+| 22 | Curiosity Reward | Wire GpuCuriosityTrainer into experience collector |
+| 23 | Decision Transformer | Integrate DT pretrain into Phase 1 of training pipeline |
+
+### Tier 4: Implement GPU Path (3 features — need new CUDA kernels)
+Config flags exist but no GPU implementation.
+
+| # | Feature | What's Needed |
+|---|---------|--------------|
+| 24 | CQL Conservative Loss | GPU kernel for conservative Q-penalty on OOD actions |
+| 25 | Ensemble Value Backward | cuBLAS backward through value heads for KL diversity gradient |
+
+### Tier 5: Reward & Trade Management Redesign (the core)
+
+| # | Component | Description |
+|---|-----------|-------------|
+| 26 | Trade-Aware Hybrid Reward | Dense per-bar shaping + sparse trade-completion |
+| 27 | Dynamic Trade Management | Regime-aware trailing stop as environment physics |
+| 28 | Three-Phase Training Pipeline | BC → Online RL → DT Refinement |
+
+---
+
+## File Structure
+
+| File | Action | Responsibility |
+|------|--------|---------------|
+| `crates/ml/src/cuda_pipeline/experience_kernels.cu` | **Rewrite reward section** | Trade-aware hybrid reward, dynamic trailing stop, Kelly/CVaR scaling |
+| `crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs` | Modify | CQL loss kernel, ensemble backward, entropy in fused path |
+| `crates/ml/src/cuda_pipeline/gpu_experience_collector.rs` | Modify | Wire curiosity reward into experience loop |
+| `crates/ml/src/cuda_pipeline/gpu_action_selector.rs` | Modify | CVaR-aware action selection from IQN quantiles |
+| `crates/ml/src/trainers/dqn/fused_training.rs` | Modify | DT auxiliary loss step, CQL step, curiosity training step |
+| `crates/ml/src/trainers/dqn/config.rs` | Modify | Activate defaults for all Tier 2-3 features |
+| `crates/ml/src/trainers/dqn/trainer/training_loop.rs` | Modify | Three-phase pipeline orchestration |
+| `crates/ml/src/trainers/dqn/trainer/constructor.rs` | Modify | Remove hardcoded flags, respect config |
+| `crates/ml/src/trainers/dqn/trainer/action.rs` | Modify | Ensemble consensus + IQN CVaR + Kelly sizing in action path |
+| `crates/ml/src/hyperopt/adapters/dqn.rs` | Modify | Add search ranges for all new config fields |
+| `crates/ml/src/trainers/dqn/smoke_tests/training_stability.rs` | Modify | Assert winning trades, positive Sharpe potential |
+
+---
+
+## Task 1: Trade-Aware Hybrid Reward (experience_kernels.cu)
+
+**This is the most critical task.** The reward function determines what the model optimizes for.
+
+**Files:**
+- Modify: `crates/ml/src/cuda_pipeline/experience_kernels.cu` — rewrite the reward section
+
+### Reward Design
+
+```cuda
+/* ═══════════════════════════════════════════════════════════════════
+ * REWARD v5: Trade-Aware Hybrid
+ *
+ * Two components, two timescales:
+ *
+ * 1. DENSE (every bar): Trade-level unrealized P&L change
+ *    reward_dense = (unrealized_t - unrealized_{t-1}) / equity
+ *    This is NOT portfolio return — it's specifically about THIS trade.
+ *    When flat: reward_dense = 0.0 (no trade, no signal)
+ *
+ * 2. SPARSE (at trade exit): Full trade return with patience bonus
+ *    reward_sparse = trade_return × sqrt(hold_time / expected_hold)
+ *    expected_hold = base_hold × regime_factor(ADX)
+ *    This rewards holding: same return over more bars = more reward
+ *
+ * The dense component keeps gradients flowing every bar.
+ * The sparse component teaches the actual goal: profitable trades.
+ *
+ * Loss aversion: negative returns weighted 1.5x (both components)
+ * ═══════════════════════════════════════════════════════════════════ */
+```
+
+### Dynamic Trade Management (Environment Physics)
+
+```cuda
+/* Dynamic trailing stop — activates when trade is profitable.
+ *
+ * trail_distance = base_trail × vol_scale(CUSUM) × trend_scale(ADX)
+ *   - High vol (CUSUM > 0.5): wider trail (let noise pass)
+ *   - Strong trend (ADX > 30): wider trail (let trend run)
+ *   - Ranging (ADX < 20): tight trail (capture quick profits)
+ *
+ * The trailing stop is the ONLY auto-exit. No static SL/TP.
+ * The model learns entry quality + exit timing through the reward.
+ * The trailing stop only prevents giving back large gains.
+ *
+ * Minimum hold: regime-adaptive, learned via hold_time state feature
+ *   - The model SEES hold_time in its state (slot +3 in state_gather)
+ *   - It learns that exiting before min_hold means re-entering costs 2x tx
+ *   - No hard override — just the natural tx cost penalty
+ */
+```
+
+- [ ] **Step 1:** Remove the old reward v2 8-component block (DSR, normalized PnL, drawdown, idle, hold penalty, profit-take, trade completion)
+- [ ] **Step 2:** Implement reward v5 dense component: per-bar unrealized P&L change for the current trade
+- [ ] **Step 3:** Implement reward v5 sparse component: trade-completion return × patience multiplier at exit
+- [ ] **Step 4:** Implement dynamic trailing stop: regime-aware trail distance, activates when unrealized > 0
+- [ ] **Step 5:** Keep capital floor protection (25% drawdown = game over, done=1)
+- [ ] **Step 6:** Keep CVaR/Kelly position scaling (environment physics, unchanged)
+- [ ] **Step 7:** Run `SQLX_OFFLINE=true cargo check -p ml` — verify clean build
+- [ ] **Step 8:** Commit: "feat: reward v5 — trade-aware hybrid with dynamic trailing stop"
+
+---
+
+## Task 2: Activate Tier 2 Config Defaults
+
+**Files:**
+- Modify: `crates/ml/src/trainers/dqn/config.rs`
+
+Set non-zero defaults for features that are already wired but disabled by zero config values.
+
+- [ ] **Step 1:** Set `her_ratio: 0.2` (was 0.0) — 20% of batch gets HER relabeling
+- [ ] **Step 2:** Set `count_bonus_coefficient: Some(0.1)` (was 0.0) — UCB exploration active
+- [ ] **Step 3:** Set `q_gap_threshold: 0.05` (was 0.0) — conviction filter active with warmup ramp
+- [ ] **Step 4:** Set `curiosity_weight: 0.05` (was 0.0) — intrinsic motivation for novel states
+- [ ] **Step 5:** Set `use_cql: true` (was false in practice) — conservative Q-learning
+- [ ] **Step 6:** Set `ensemble_count: 3` (was 1) — 3-head ensemble for uncertainty
+- [ ] **Step 7:** Set `dt_pretrain_epochs: 3` (was 0) — Decision Transformer pretraining
+- [ ] **Step 8:** Set `use_cvar_action_selection: true` in hyperparams (was hardcoded false)
+- [ ] **Step 9:** Set `enable_kelly_sizing: true, kelly_fractional: 0.5, kelly_max_fraction: 0.25`
+- [ ] **Step 10:** Run `SQLX_OFFLINE=true cargo check -p ml` — verify clean build
+- [ ] **Step 11:** Commit: "feat: activate all dormant features via config defaults"
+
+---
+
+## Task 3: Wire Kelly Sizing into Experience Kernel
+
+**Files:**
+- Modify: `crates/ml/src/cuda_pipeline/experience_kernels.cu`
+
+Kelly criterion is already computed in the experience kernel (ps[14:19] track win/loss stats). It already scales `target_position` at line ~633. Verify it's working and not gated by a dead condition.
+
+- [ ] **Step 1:** Read the Kelly block in the experience kernel. Verify it reads ps[14:17] (win_count, loss_count, sum_wins, sum_losses) and computes kelly_f
+- [ ] **Step 2:** Verify the Kelly scaling is NOT inside a dead code branch (no `if use_kelly` guard that's always false)
+- [ ] **Step 3:** Verify the Kelly half-Kelly safety multiplier (0.5×) is applied
+- [ ] **Step 4:** Add Kelly fraction to the state_gather kernel as an observable feature — the model should SEE its own Kelly sizing to learn from it
+- [ ] **Step 5:** Commit: "fix: verify Kelly sizing active in experience kernel + expose as state feature"
+
+---
+
+## Task 4: Wire CVaR Action Selection from IQN
+
+**Files:**
+- Modify: `crates/ml/src/trainers/dqn/trainer/constructor.rs` — remove hardcoded `false`
+- Modify: `crates/ml/src/trainers/dqn/trainer/action.rs` — use IQN CVaR for position scaling
+
+The IQN head has `compute_cvar_q()` that returns CVaR (worst-case quantile) Q-values. Use these to scale position sizing: when CVaR is very negative (high downside risk), reduce position.
+
+- [ ] **Step 1:** In constructor.rs, change `use_cvar_action_selection: false` to `use_cvar_action_selection: hyperparams.use_cvar_action_selection`
+- [ ] **Step 2:** In action.rs, after Q-value selection but before action output: if IQN is active, compute CVaR Q-values for the selected action
+- [ ] **Step 3:** Use CVaR to scale the selected exposure: `effective_exposure = selected_exposure × cvar_scale` where `cvar_scale = sigmoid(cvar_q / threshold)`
+- [ ] **Step 4:** Pass the CVaR-scaled exposure to the GPU action selector
+- [ ] **Step 5:** Commit: "feat: IQN CVaR-aware position scaling in action selection"
+
+---
+
+## Task 5: Wire Curiosity into GPU Experience Collector
+
+**Files:**
+- Modify: `crates/ml/src/cuda_pipeline/gpu_experience_collector.rs`
+- Modify: `crates/ml/src/trainers/dqn/fused_training.rs`
+
+The `GpuCuriosityTrainer` exists and its forward model can predict next-state. The prediction error IS the curiosity reward. Wire it into the experience collection loop.
+
+- [ ] **Step 1:** In gpu_experience_collector.rs, after the env_step kernel writes rewards, launch the curiosity forward model on (state, action) pairs
+- [ ] **Step 2:** Compute prediction error: `curiosity_reward = ||predicted_next_state - actual_next_state||²`
+- [ ] **Step 3:** Add curiosity_reward × curiosity_scale to the experience rewards buffer via SAXPY kernel
+- [ ] **Step 4:** In fused_training.rs, after the main training step, train the curiosity forward model on the sampled batch (update the prediction model)
+- [ ] **Step 5:** Commit: "feat: GPU curiosity reward in experience collection + forward model training"
+
+---
+
+## Task 6: Implement CQL Conservative Loss (GPU Kernel)
+
+**Files:**
+- Modify: `crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs`
+- Modify: `crates/ml/src/trainers/dqn/fused_training.rs`
+
+CQL adds a penalty for Q-values on out-of-distribution actions: `L_cql = α × (log_sum_exp(Q(s,a)) - Q(s, a_data))`. This prevents Q-value overestimation on actions the model hasn't tried.
+
+- [ ] **Step 1:** Add inline CUDA kernel `cql_penalty_kernel` in gpu_dqn_trainer.rs:
+  - Inputs: Q-values for all actions [B, total_actions], actions taken [B]
+  - Computes: logsumexp(Q) - Q(s, a_taken) per sample
+  - Writes: per-sample CQL penalty [B]
+- [ ] **Step 2:** Add `cql_penalty_grad_kernel` — gradient of CQL penalty w.r.t. Q-values
+- [ ] **Step 3:** Compile both kernels in GpuDqnTrainer::new()
+- [ ] **Step 4:** Add `launch_cql_penalty()` and `launch_cql_grad()` methods
+- [ ] **Step 5:** In fused_training.rs run_full_step(), add CQL as Step 5g (after ensemble):
+  ```rust
+  if self.hyperparams.use_cql {
+      let cql_loss = self.trainer.launch_cql_penalty(cql_alpha)?;
+      self.trainer.launch_cql_grad()?;
+      // CQL gradient already in d_value/d_adv logits, flows through backward
+  }
+  ```
+- [ ] **Step 6:** Commit: "feat: GPU CQL conservative loss kernel — prevents Q-value overestimation"
+
+---
+
+## Task 7: Implement Ensemble Value Backward
+
+**Files:**
+- Modify: `crates/ml/src/trainers/dqn/fused_training.rs`
+
+The ensemble computes KL diversity gradient but doesn't backpropagate it through the value heads to the trunk. This means the trunk doesn't learn to produce diverse representations.
+
+- [ ] **Step 1:** In run_ensemble_step(), after KL gradient is computed, perform cuBLAS SGEMM backward through each ensemble value head
+- [ ] **Step 2:** Accumulate the trunk gradient from all K heads via SAXPY into grad_buf (scaled by diversity_weight)
+- [ ] **Step 3:** The combined gradient (C51 + IQN + attention + ensemble_diversity) all flow into the single Adam update
+- [ ] **Step 4:** Commit: "feat: ensemble value backward — KL diversity gradient flows to shared trunk"
+
+---
+
+## Task 8: Integrate Decision Transformer into Phase 1
+
+**Files:**
+- Modify: `crates/ml/src/trainers/dqn/trainer/training_loop.rs`
+- Modify: `crates/ml/src/trainers/dqn/fused_training.rs`
+
+Decision Transformer pretraining uses historical trajectories to bootstrap the policy. In Phase 1 (first `dt_pretrain_epochs` epochs), the DT loss is an auxiliary objective alongside MSE.
+
+- [ ] **Step 1:** In training_loop.rs, during the first `dt_pretrain_epochs` epochs:
+  - Build trajectory sequences from the experience buffer: (state, action, return-to-go) tuples
+  - Upload to GPU via DT's trajectory builder kernel
+- [ ] **Step 2:** In fused_training.rs, add `run_dt_pretrain_step()`:
+  - DT forward: embed → causal attention → action prediction
+  - DT loss: cross-entropy on predicted vs actual actions
+  - DT backward: gradient through DT layers → trunk embedding gradient
+  - SAXPY into grad_buf (scaled by dt_lambda)
+- [ ] **Step 3:** After dt_pretrain_epochs, disable DT auxiliary loss (let online RL take over)
+- [ ] **Step 4:** Commit: "feat: Decision Transformer pretraining in Phase 1 — behavioral cloning from trajectories"
+
+---
+
+## Task 9: Ensemble Consensus Action Selection
+
+**Files:**
+- Modify: `crates/ml/src/trainers/dqn/trainer/action.rs`
+
+With K=3 ensemble heads, action selection should use the COMMITTEE, not just head 0:
+
+- [ ] **Step 1:** For each state, forward through all K value heads to get K sets of Q-values
+- [ ] **Step 2:** Compute mean Q (consensus) and variance (uncertainty) across heads
+- [ ] **Step 3:** Action selection: argmax on mean Q (consensus trade)
+- [ ] **Step 4:** Position sizing: scale by `1 / (1 + uncertainty)` — high disagreement → smaller position
+- [ ] **Step 5:** If uncertainty > threshold: override to Flat (heads can't agree → sit out)
+- [ ] **Step 6:** Commit: "feat: ensemble consensus action selection — uncertainty-based sizing and sit-out"
+
+---
+
+## Task 10: Three-Phase Training Pipeline
+
+**Files:**
+- Modify: `crates/ml/src/trainers/dqn/trainer/training_loop.rs`
+
+Wire the three training phases into the epoch loop:
+
+### Phase 1: Behavioral Cloning + MSE Warmup (epochs 0..dt_pretrain_epochs)
+- MSE loss (c51_alpha ≈ 0)
+- Expert demonstrations at high ratio (expert_ratio = 0.5)
+- Decision Transformer auxiliary loss
+- HER relabeling (learn from counterfactuals)
+
+### Phase 2: Full-Stack Online RL (epochs dt_pretrain_epochs..end)
+- Blended MSE→C51 ramp (c51_alpha ramps to 1.0)
+- Expert ratio decays to 0
+- All auxiliary heads active: IQN, ensemble, attention, curiosity, CQL
+- HER continues (counterfactual learning)
+- Dynamic trade management (trailing stops, CVaR scaling)
+
+### Phase 3: Refinement (last 20% of epochs)
+- Pure C51 loss (c51_alpha = 1.0)
+- No expert demonstrations
+- Ensemble variance-based exploration (replace epsilon-greedy)
+- Shrink-and-perturb for plasticity maintenance
+
+- [ ] **Step 1:** Add phase detection logic at epoch boundary:
+  ```rust
+  let phase = if epoch < dt_pretrain_epochs { Phase::BehavioralCloning }
+              else if epoch < total_epochs * 80 / 100 { Phase::OnlineRL }
+              else { Phase::Refinement };
+  ```
+- [ ] **Step 2:** Configure each auxiliary head's weight based on phase
+- [ ] **Step 3:** Configure exploration strategy based on phase (epsilon → noisy → ensemble)
+- [ ] **Step 4:** Configure expert demo ratio based on phase (high → decay → zero)
+- [ ] **Step 5:** Commit: "feat: three-phase training pipeline — BC → Online RL → Refinement"
+
+---
+
+## Task 11: Hyperopt Search Space for All Features
+
+**Files:**
+- Modify: `crates/ml/src/hyperopt/adapters/dqn.rs`
+
+Add all new config fields to the hyperopt search space so PSO can optimize them:
+
+- [ ] **Step 1:** Add `her_ratio` to search space: range [0.0, 0.5]
+- [ ] **Step 2:** Add `curiosity_weight` to search space: range [0.0, 0.2]
+- [ ] **Step 3:** Add `cql_alpha` to search space: range [0.0, 1.0]
+- [ ] **Step 4:** Add `ensemble_count` to search space: range [1, 5] (integer)
+- [ ] **Step 5:** Add `dt_pretrain_epochs` to search space: range [0, 10]
+- [ ] **Step 6:** Add `base_trail_distance` to search space: range [0.002, 0.02]
+- [ ] **Step 7:** Add `patience_multiplier_scale` to search space: range [0.5, 3.0]
+- [ ] **Step 8:** Add `dense_reward_weight` to search space: range [0.01, 0.5]
+- [ ] **Step 9:** Add `sparse_reward_weight` to search space: range [0.5, 5.0]
+- [ ] **Step 10:** Update `from_continuous()`, `to_continuous()`, `param_names()`, `continuous_bounds()`
+- [ ] **Step 11:** Update dimension assertions in tests
+- [ ] **Step 12:** Commit: "feat: full hyperopt search space — all 25 features searchable"
+
+---
+
+## Task 12: Smoke Test Validation — Winning Trades
+
+**Files:**
+- Modify: `crates/ml/src/trainers/dqn/smoke_tests/training_stability.rs`
+
+The ultimate test: does the model produce winning trades on real data?
+
+- [ ] **Step 1:** In `test_50_epoch_convergence`, assert:
+  ```rust
+  // With 50 epochs of training on real ES data, the model MUST have some winning trades
+  let wins = metrics.additional_metrics.get("winning_trades").copied().unwrap_or(0.0);
+  let total = metrics.additional_metrics.get("total_trades").copied().unwrap_or(0.0);
+  assert!(total > 50.0, "Must execute >50 trades over 50 epochs");
+  assert!(wins > 0.0, "Must have at least 1 winning trade — 0 wins is a bug");
+  let win_rate = wins / total.max(1.0);
+  assert!(win_rate > 0.1, "Win rate must be >10% — random is 50%. Got {:.1}%", win_rate * 100.0);
+  ```
+- [ ] **Step 2:** In `test_trading_model_behavior`, assert:
+  ```rust
+  // Even in 3 epochs, the dynamic trailing stop should lock in some winners
+  let wins = metrics.additional_metrics.get("winning_trades").copied().unwrap_or(0.0);
+  assert!(wins > 0.0, "Even 3 epochs must produce at least 1 winning trade");
+  ```
+- [ ] **Step 3:** Run all smoke tests: `FOXHUNT_TEST_DATA=... cargo test -p ml --lib -- smoke_tests --ignored`
+- [ ] **Step 4:** Commit: "test: assert winning trades — the model must learn to profit"
+
+---
+
+## Key Design Decisions
+
+### Why Trade-Aware Reward Instead of Pure Per-Bar?
+
+Per-bar mark-to-market (reward v4) gives identical signal for a 1-bar scalp and a 50-bar trend-follow. The model has no reason to prefer multi-bar holding. Trade-aware reward solves this by:
+1. Dense component: unrealized P&L change per bar (not portfolio return) — specifically about THIS trade
+2. Sparse component: trade return × patience bonus — rewards holding through noise
+
+### Why Dynamic Trailing Stop Instead of Learned Exits?
+
+The model CAN learn exit timing (it sees hold_time, unrealized P&L, ADX in its state). But learning exits requires 10,000+ trades of experience. The trailing stop provides a SAFETY NET while the model develops exit skill:
+- Locks in profits when a trade reverses (prevents giving back winners)
+- Width adapts to regime (tight in ranging, wide in trending)
+- The model learns to ENTER positions that work WITH the trailing stop
+
+As the model improves, the trailing stop triggers less often (the model exits BEFORE the trail). The trail becomes insurance, not the primary exit mechanism.
+
+### Why All 25 Features at Once?
+
+Ablation studies can happen on H100. The smoke test validates the PIPELINE works. Hyperopt on H100 finds the optimal CONFIG. We don't know which features the ES futures market rewards — that's hyperopt's job. Our job is to give hyperopt the maximum search space.
+
+### Why Three Phases?
+
+Phase 1 (BC + DT) bootstraps from expert knowledge — the model starts better than random.
+Phase 2 (Online RL) refines from live experience — the model discovers patterns experts can't encode.
+Phase 3 (Refinement) consolidates — shrink-and-perturb prevents dead neurons, ensemble exploration prevents local optima.
+
+---
+
+## Execution Order
+
+Tasks 1-3 are the foundation (reward + configs + Kelly). Must be done first.
+Tasks 4-8 activate auxiliary heads (can be parallelized).
+Task 9 wires ensemble consensus (depends on Task 7).
+Task 10 orchestrates the pipeline (depends on all above).
+Task 11 enables hyperopt (depends on all above).
+Task 12 validates everything (last).
+
+```
+Task 1 (reward) ──┐
+Task 2 (configs) ──┤
+Task 3 (Kelly) ────┤
+                   ├──→ Tasks 4-8 (parallel) ──→ Task 9 ──→ Task 10 ──→ Task 11 ──→ Task 12
+```