feat(alpha): fitted FillModel coefficients from 500K ES.FUT snapshots

Phase E.0 Task 5c. Ran phase_e_fit_fill_model on the ES.FUT 2024-Q1 MBP-10
+ trade tape (5.2M trades, 3.9M MBP-10 events, 500K snapshots accumulated
at snapshot_interval=50 over a ~24-minute window). Total runtime ~80s.

Empirical fill rates within 60s window:
  - bid_l1: 4.97%   (matches L1 maker-side activity in trending market)
  - ask_l1: 71.34%  (high — most 60s windows see an aggressive buy)

Fitted L1 cloglog coefficients (all 5 features):
  BID L1: β_0=-0.213  β_spread=-2.064  β_imbal=-0.099  β_ofi=-0.006  β_logτ=-0.286
  ASK L1: β_0=+0.016  β_spread=-40.336 β_imbal=+0.041  β_ofi=+0.652  β_logτ=-0.055

Sanity (sign checks all pass):
  - β_spread < 0 both sides   (wider spread → fewer fills) ✓
  - bid β_imbal < 0           (more bid stack → harder to get hit by sell) ✓
  - ask β_ofi > 0             (buying pressure correlates with ask fills) ✓
  - β_logτ < 0 both sides     (quieter markets → slower execution) ✓

L1-only limitation: as documented in the binary header, the parser only
populates levels[0]; L2/L3 in the JSON are L1 with β_0 -= ln(L+1) attenuation.

Default --out-path bumped to config/ml/phase_e_fill_coeffs.json so future
re-runs land in the same committed location.

crates/ml/examples/phase_e_fit_fill_model.rs

@@ -74,7 +74,7 @@ struct Cli {
     #[arg(long, default_value_t = 60.0)]
     window_seconds: f32,
     /// Output JSON path.
-    #[arg(long, default_value = "phase_e_fill_coeffs.json")]
+    #[arg(long, default_value = "config/ml/phase_e_fill_coeffs.json")]
     out_path: PathBuf,
     /// MBP-10 events between snapshot emissions (denser = larger sample).
     #[arg(long, default_value_t = 10)]