malkhut(docs): fill quality documentation — README, integration, OB study

README: Fill Quality section (core optimization target, metrics, reward
function, PerformanceMatrix, CMA-ES integration)

HftBacktestCWM integration doc: FillQuality dataclass, fill_value_score
computation, reward function weighting, PerformanceMatrix tracking

OB microstructure study: Section 13 — Fill Quality Optimization,
per-asset expectations, optimization strategy, connection to OB dynamics

Fill quality is MALKHUT's core aim: the system learns to get better fills
(faster, better-priced, less adverse selection) across regimes and venues.

MALKHUT/docs/HFTBACKTEST_CWM_INTEGRATION.md

@@ -357,3 +357,40 @@ Swapping the implementation behind that protocol is a textbook Strategy pattern.
 The planner doesn't know or care whether the book is synthesized or hftbacktest.
 The reward function is pure math on (prev_state, action, next_state) — identical
 regardless of how next_state was computed.
+
+## Fill Quality Tracking (CORE Optimization Target)
+
+Every CWM transition now computes `FillQuality` metrics on the resulting state:
+
+```python
+@dataclass(frozen=True, slots=True)
+class FillQuality:
+    filled: bool                    # Did this action produce a fill?
+    fill_qty: float                 # How much was filled?
+    fill_price: float               # At what price?
+    slippage_bps: float             # Aggressive: distance from mid
+    price_improvement_bps: float    # Passive: improvement over touch
+    levels_consumed: int            # Queue depth consumed
+    is_maker_fill: bool             # Passive (LIMIT) vs aggressive (CROSS)
+    rolling_fill_rate: float        # EMA of recent fill success
+    post_fill_adverse_bps: float    # Price movement after fill
+    fill_value_score: float         # Composite: quality - adverse
+```
+
+The `fill_value_score` is the PRIMARY optimization target:
+- For maker fills: `price_improvement_bps - abs(post_fill_adverse) * 0.5`
+- For taker fills: `(spread_bps - slippage_bps) - abs(post_fill_adverse) * 0.5`
+
+Both `MinimalCryptoLOBCWM` and `HftBacktestCWM` compute these identically.
+
+The reward function weights fill quality via `w_fill_probability` (default 0.5):
+```
+reward = w_fill_probability * fill_value_score  ← PRIMARY
+       + w_expected_pnl * pnl                   ← secondary
+       - w_adverse_selection * toxicity
+       ...
+```
+
+PerformanceMatrix stores `avg_fill_rate`, `avg_slippage_bps`, `avg_price_improvement_bps`,
+`avg_fill_value_score` per (regime, strategy, venue) — enabling:
+"Which strategy achieves the best fill quality in regime X on venue Y?"