feat(skyhook): SKH01-V2-DD — asymmetric %-exits cut standalone DD <30% (2-wave agent research)

Second agent wave (skyhook-improve-v2, 14 DD-reduction families, each adversarially
verified by 2 skeptics) beats the prior winner on the only unmet goal (DD<30%).

Winner = ASYM_LS -> promoted to engine as SKH01_V2_DD:
  same signal (ptn_n=45, vola[35,95], vol_lo=0, exit-bars 24/16) but exits switched
  from ATR to FIXED-PCT ASYMMETRIC — long sl4%/tp10%, short sl2%(tighter)/tp8%.
  The tight short %-SL caps the per-trade loss that forms the maxDD in vol spikes.

Verified (sk.study, independent re-run): standalone maxDD BTC 21.4% / ETH 27.4% (<30%),
minFull +0.99, minHold +1.26, causality 0/400 both assets, fee-surviving to 0.40%RT,
marginal vs TP01 ADDS (corr 0.09, in-sample edge, robust_oos, multicut, clean-year +0.57),
blend 0.75*TP01+0.25*SKH uplift_hold +0.87; blend 50/50 full 1.84/hold 1.59/DD 10.7%.
Plateau (not knife-edge); both skeptics holds_up=high, killer=null.

Engine: per-direction short exit overrides (exit_mode_short/sl_*_short/tp_*_short),
backward-compatible (None -> symmetric, V1/intermediate-winner unchanged). +3 tests (8/8 pass).

Lessons: DD is cut by changing the exit MECHANISM (%-SL, L/S asymmetry, ensembles), NOT by
entry-only kill-switch / vol-target / cadence. PATTERN_CONF killed as overfit (knife-edge).
PCTL_DD unverified (rate-limit) and ENS_PARAM/TPSL_DD recency/hedge-loaded -> forward-monitor.
NOT yet wired to live sleeves: re-verify blend@0.25 + causality on execution code before deploy.

Includes both waves' research scripts (runs/SKH_* wave 1, runs/SKH2_* wave 2).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

scripts/research/skyhook/runs/SKH_P_LOCAL_v1ref.py

@@ -0,0 +1,18 @@
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/skyhook")
+import skyhooklib as sk
+from src.strategies.skyhook import SkyhookParams
+
+V1 = SkyhookParams(ptn_n=55, sl_atr=2.5, tp_atr=6.0, vola_lo=35.0, vola_hi=95.0, vol_lo=0.0)
+print("=== V1 reference ===")
+rep = sk.study("SKH01-V1", V1)
+print(sk.fmt(rep))
+print("causality:", sk.causality(V1))
+mg = sk.marginal(V1)
+keys = ("corr_full","corr_hold","marginal_verdict","has_insample_edge","is_hedge",
+        "robust_oos","multicut_persistent","clean_year_uplift","jackknife_min_uplift",
+        "cand_insample_sharpe","cand_full_sharpe")
+print("marginal:", {k: mg.get(k) for k in keys})
+print("blend w25 uplift_hold:", mg.get("blends",{}).get("w25",{}).get("uplift_hold"))
+print("blend w25 uplift_full:", mg.get("blends",{}).get("w25",{}).get("uplift_full"))
+print("multicut:", mg.get("multicut_uplift"))