Adriano Dal Pastro
de72e3ce1f
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>
101 lines
4.9 KiB
Python
101 lines
4.9 KiB
Python
"""SKH_P_EXITBARS — sweep the asymmetric time-exit horizons on the V1 base.
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V1 base: SkyhookParams(ptn_n=55, sl_atr=2.5, tp_atr=6.0, vola_lo=35, vola_hi=95, vol_lo=0.0)
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defaults uscitalong=24, uscitashort=18 -> minFull +0.69, HOLD +0.64 (BTC 0.64/ETH 0.64),
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DD ~40-49% (HIGH).
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The asymmetry (long held longer than short) is core to Skyhook. Sweep:
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uscitalong in {16,20,24,30,40} (LTF 230m bars max-hold for longs)
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uscitashort in {10,14,18,24} (LTF 230m bars max-hold for shorts)
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Objective (priority): maximize min-asset HOLD-OUT subject to minFull>=0.5, minTrades>=20 BOTH
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assets, fee survives 0.30%RT, causality ok. Secondary: cut standalone DD toward <30%.
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Compare to V1 (minHold +0.64, DD ~40-49%).
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"""
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from __future__ import annotations
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import sys
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sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/skyhook")
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import skyhooklib as sk
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from src.strategies.skyhook import SkyhookParams
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# V1 base (everything except the two exit horizons we sweep)
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BASE = dict(ptn_n=55, sl_atr=2.5, tp_atr=6.0, vola_lo=35.0, vola_hi=95.0, vol_lo=0.0)
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UL_GRID = [16, 20, 24, 30, 40] # uscitalong
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US_GRID = [10, 14, 18, 24] # uscitashort
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def cell(ul, us):
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p = SkyhookParams(uscitalong=ul, uscitashort=us, **BASE)
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out = {}
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for a in ("BTC", "ETH"):
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out[a] = sk.run_asset(a, p, fee_rt=sk.FEE_RT)
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min_full = min(out[a]["full"]["sharpe"] for a in out)
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min_hold = min(out[a]["holdout"]["sharpe"] for a in out)
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min_tr = min(out[a]["full"]["n_trades"] for a in out)
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max_dd = max(out[a]["full"]["maxdd"] for a in out)
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return dict(ul=ul, us=us, min_full=min_full, min_hold=min_hold,
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min_tr=min_tr, max_dd=max_dd,
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btc_full=out["BTC"]["full"]["sharpe"], eth_full=out["ETH"]["full"]["sharpe"],
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btc_hold=out["BTC"]["holdout"]["sharpe"], eth_hold=out["ETH"]["holdout"]["sharpe"],
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btc_dd=out["BTC"]["full"]["maxdd"], eth_dd=out["ETH"]["full"]["maxdd"])
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print("=== SKH_P_EXITBARS sweep (V1 base ptn_n=55 sl2.5 tp6) — fee=0.10%RT ===")
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print(f"{'uL':>4} {'uS':>4} | {'minFull':>7} {'minHold':>7} {'minTr':>5} {'maxDD':>6} | "
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f"{'btcF':>5} {'ethF':>5} {'btcH':>5} {'ethH':>5} {'btcDD':>5} {'ethDD':>5}")
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results = []
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for ul in UL_GRID:
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for us in US_GRID:
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c = cell(ul, us)
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results.append(c)
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flag = " *" if (c["min_full"] >= 0.5 and c["min_tr"] >= 20) else ""
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marker = " <V1" if (ul == 24 and us == 18) else ""
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print(f"{ul:>4} {us:>4} | {c['min_full']:>+7.2f} {c['min_hold']:>+7.2f} "
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f"{c['min_tr']:>5} {c['max_dd']*100:>5.0f}% | "
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f"{c['btc_full']:>+5.2f} {c['eth_full']:>+5.2f} "
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f"{c['btc_hold']:>+5.2f} {c['eth_hold']:>+5.2f} "
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f"{c['btc_dd']*100:>4.0f}% {c['eth_dd']*100:>4.0f}%{flag}{marker}")
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# Eligible = minFull>=0.5 AND minTrades>=20. Rank by min_hold, tie-break lower maxDD.
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elig = [c for c in results if c["min_full"] >= 0.5 and c["min_tr"] >= 20]
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print(f"\nEligible cells (minFull>=0.5, minTr>=20): {len(elig)}")
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if not elig:
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print("No eligible cell — V1 exit horizons may already be at/near the frontier.")
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sys.exit(0)
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elig_hold = sorted(elig, key=lambda c: (-round(c["min_hold"], 3), c["max_dd"]))
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print("Top by minHold (tie-break lower maxDD):")
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for c in elig_hold[:6]:
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print(f" uL={c['ul']} uS={c['us']}: minHold={c['min_hold']:+.2f} "
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f"minFull={c['min_full']:+.2f} maxDD={c['max_dd']*100:.0f}% minTr={c['min_tr']}")
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dd_cands = sorted(elig, key=lambda c: (c["max_dd"], -round(c["min_hold"], 3)))
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print("\nTop by lowest maxDD (DD-cut objective):")
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for c in dd_cands[:6]:
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print(f" uL={c['ul']} uS={c['us']}: maxDD={c['max_dd']*100:.0f}% "
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f"minHold={c['min_hold']:+.2f} minFull={c['min_full']:+.2f} minTr={c['min_tr']}")
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best = elig_hold[0]
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print(f"\n=== STUDY on best-by-minHold (uL={best['ul']} uS={best['us']}) ===")
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pbest = SkyhookParams(uscitalong=best["ul"], uscitashort=best["us"], **BASE)
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rep = sk.study(f"P_EXITBARS_uL{best['ul']}_uS{best['us']}", pbest)
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print(sk.fmt(rep))
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caus = sk.causality(pbest)
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print("causality:", caus)
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mg = sk.marginal(pbest)
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print("marginal:", {k: v for k, v in mg.items()
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if k in ("corr_full", "marginal_verdict", "has_insample_edge",
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"is_hedge", "robust_oos")})
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print("blend w25 uplift_hold:", mg.get("blends", {}).get("w25", {}).get("uplift_hold"))
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print("\nAS_JSON_STUDY:", sk.as_json(rep))
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# If the DD-cut frontier differs from the headline pick, study it too (cheap, one config).
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ddbest = dd_cands[0]
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if (ddbest["ul"], ddbest["us"]) != (best["ul"], best["us"]) and ddbest["min_hold"] >= 0.2:
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print(f"\n=== STUDY on lowest-DD eligible (uL={ddbest['ul']} uS={ddbest['us']}) ===")
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pdd = SkyhookParams(uscitalong=ddbest["ul"], uscitashort=ddbest["us"], **BASE)
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repdd = sk.study(f"P_EXITBARS_DDcut_uL{ddbest['ul']}_uS{ddbest['us']}", pdd)
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print(sk.fmt(repdd))
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print("causality:", sk.causality(pdd))
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