"""SKH2_PCTL_DD — DD-reduction wave, family [PCTL_DD]. GOAL: cut STANDALONE maxDD below 30% (max over BTC & ETH) while keeping minHold>=~0.70 and earns_slot==True, using the CAUSAL expanding/rolling PERCENTILE-RANK regime from SKH_R_PCTL.py (reuse pctl_entries), tuned together with the winner's exits. Baseline to beat (V2 winner, Chande regime): SkyhookParams(ptn_n=45, sl_atr=2.5, tp_atr=7.0, uscitalong=24, uscitashort=16, vola_lo=35, vola_hi=95, vol_lo=0.0) minFull +0.83, minHold +0.81, standalone DD BTC34%/ETH31% (THE PROBLEM), marginal ADDS, blend w25 uplift_hold +0.58, blend 50/50 full1.59/hold1.04/DD12.5%. LEVERS FOR DD CUT (all causal, expressed through pctl_entries cfg + the SkyhookParams exits): * percentile-rank regime bands (where ATR/volume sit in their own causal history): - cap the upper vola band (avoid blow-off-vol entries that cluster losses) - add a volume floor (live tape only) OR keep vol open * tighter hard stop (sl_atr) caps per-trade loss -> shrinks DD * the winner's wider tp_atr=7.0 + asym time exits (24/16) carried over. A candidate BEATS THE WINNER iff: earns_slot AND max_dd<0.30 AND blend_w25_uplift_hold>=0.55 AND min_hold_sharpe>=0.65. We report TRUE numbers regardless. """ from __future__ import annotations import sys sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/skyhook") import importlib.util import numpy as np import pandas as pd import skyhooklib as sk import altlib as al from src.backtest.harness import backtest_signals from src.strategies import skyhook as S from src.strategies.skyhook import SkyhookParams # import the structural pctl builder (pctl_entries, pctl_rank, _split) from the sweep script spec = importlib.util.spec_from_file_location( "skr", "/opt/docker/PythagorasGoal/scripts/research/skyhook/runs/SKH_R_PCTL.py") skr = importlib.util.module_from_spec(spec) spec.loader.exec_module(skr) # __main__ guard prevents the sweep from running HOLDOUT = sk.HOLDOUT FEE = sk.FEE_RT # --------------------------------------------------------------------------- # Build a SkyhookParams holding the WINNER's exits; only regime comes from pctl cfg. # pctl_entries reads: ptn_n, sl_atr, tp_atr, uscitalong, uscitashort, exit_mode, ltf_atr_win, # max_per_day, long_only (the regime bands come from the cfg kwargs). # --------------------------------------------------------------------------- def winner_exit_params(**kw): base = dict(ptn_n=45, sl_atr=2.5, tp_atr=7.0, uscitalong=24, uscitashort=16) base.update(kw) return SkyhookParams(**base) # --------------------------------------------------------------------------- # Eval a (cfg, params) pair on both assets: FULL + HOLD via the honest engine. # --------------------------------------------------------------------------- def eval_pair(cfg, p): out = {} for a in ("BTC", "ETH"): ltf, htf = sk.frames(a) ent = skr.pctl_entries(ltf, htf, p, **cfg) m = backtest_signals(ltf, ent, fee_rt=FEE, leverage=1.0, asset=a, tf="230m") eq = m.equity idx = pd.DatetimeIndex(pd.to_datetime(m.eq_index, utc=True)) hmask = np.asarray(idx >= HOLDOUT) full = dict(sharpe=round(m.sharpe, 3), ret=round(m.net_return, 4), maxdd=round(m.max_dd, 4), n_trades=int(m.n_trades), win_rate=round(m.win_rate, 1)) hold = skr._split(eq, idx, hmask) out[a] = dict(full=full, hold=hold, yearly={int(y): round(v, 4) for y, v in m.yearly.items()}) return out def summarize(res): mf = min(res[a]["full"]["sharpe"] for a in res) mh = min(res[a]["hold"]["sharpe"] for a in res) mt = min(res[a]["full"]["n_trades"] for a in res) mdd = max(res[a]["full"]["maxdd"] for a in res) return dict(minFull=mf, minHold=mh, minTr=mt, maxDD=mdd, res=res) def line(tag, v): r = v["res"] print(f" [{tag:30s}] minFull={v['minFull']:+.2f} minHold={v['minHold']:+.2f} " f"minTr={v['minTr']:3d} maxDD={v['maxDD']*100:4.0f}% | " f"BTC F{r['BTC']['full']['sharpe']:+.2f}/H{r['BTC']['hold']['sharpe']:+.2f}/DD{r['BTC']['full']['maxdd']*100:.0f}% " f"ETH F{r['ETH']['full']['sharpe']:+.2f}/H{r['ETH']['hold']['sharpe']:+.2f}/DD{r['ETH']['full']['maxdd']*100:.0f}%") # --------------------------------------------------------------------------- # Causality (truncated-prefix) on the structural pctl entries. # --------------------------------------------------------------------------- def check_causality(cfg, p, asset="BTC", tail=200): return skr.check_causality(cfg, p, asset, tail=tail) # --------------------------------------------------------------------------- # Marginal vs TP01 on a (cfg, params) pair (50/50 daily, same convention as skyhooklib). # --------------------------------------------------------------------------- def marginal_struct(cfg, p): def daily(a): ltf, htf = sk.frames(a) ent = skr.pctl_entries(ltf, htf, p, **cfg) m = backtest_signals(ltf, ent, fee_rt=FEE, leverage=1.0, asset=a, tf="230m") s = pd.Series(m.equity, index=pd.DatetimeIndex(pd.to_datetime(m.eq_index, utc=True))) return s.resample("1D").last().ffill().pct_change().dropna() sb, se = daily("BTC"), daily("ETH") J = pd.concat({"BTC": sb, "ETH": se}, axis=1, join="inner").fillna(0.0) cand = 0.5 * J["BTC"] + 0.5 * J["ETH"] return al.marginal_vs_tp01(cand) def fee_sweep(cfg, p): ok = True rows = {} for a in ("BTC", "ETH"): ltf, htf = sk.frames(a) ent = skr.pctl_entries(ltf, htf, p, **cfg) row = [] for f in (0.0, 0.001, 0.002, 0.003): m = backtest_signals(ltf, ent, fee_rt=f, leverage=1.0, asset=a, tf="230m") row.append((f, round(m.sharpe, 3))) rows[a] = row ok = ok and (dict(row)[0.003] > 0) return ok, rows if __name__ == "__main__": print("=== SKH2_PCTL_DD : percentile-rank regime tuned for DD<30 ===\n") # ----------------------------------------------------------------------- # STAGE 1 — coarse sweep: regime bands (pctl space) x stop tightness. # Winner exits (tp7/24/16) carried; we vary sl_atr and the regime cfg. # Intuition for DD cut: # - cap vola_hi (drop blow-off-vol entries) ; modest vol floor (live tape) # - tighter sl_atr (2.0/1.8) caps per-trade loss. # ----------------------------------------------------------------------- print("--- STAGE 1: regime band x stop sweep (exits tp7/24/16) ---") band_cfgs = { # name: pctl regime cfg (expanding unless _r) "volaHi95_vol0": dict(vola_win=None, vol_win=None, vola_lo=0.35, vola_hi=0.95, vol_lo=0.0, vol_hi=1.0), "volaHi90_vol0": dict(vola_win=None, vol_win=None, vola_lo=0.35, vola_hi=0.90, vol_lo=0.0, vol_hi=1.0), "volaMid_vol0": dict(vola_win=None, vol_win=None, vola_lo=0.25, vola_hi=0.85, vol_lo=0.0, vol_hi=1.0), "volaMid_volFlr": dict(vola_win=None, vol_win=None, vola_lo=0.25, vola_hi=0.85, vol_lo=0.30, vol_hi=1.0), "volaHi90_volFlr": dict(vola_win=None, vol_win=None, vola_lo=0.35, vola_hi=0.90, vol_lo=0.30, vol_hi=1.0), "volaCap80_volFlr":dict(vola_win=None, vol_win=None, vola_lo=0.30, vola_hi=0.80, vol_lo=0.30, vol_hi=1.0), } sls = [2.5, 2.0, 1.8] stage1 = {} for bname, cfg in band_cfgs.items(): for sl in sls: p = winner_exit_params(sl_atr=sl) tag = f"{bname}|sl{sl}" v = summarize(eval_pair(cfg, p)) stage1[tag] = (cfg, p, v) line(tag, v) # Pick DD<30 candidates with the best minHold (need minHold>=~0.7). sub30 = {t: tup for t, tup in stage1.items() if tup[2]["maxDD"] < 0.30} print(f"\n--- STAGE 1: configs with maxDD<30%: {len(sub30)} ---") for t, (_, _, v) in sorted(sub30.items(), key=lambda kv: -kv[1][2]["minHold"]): line(t, v) # ----------------------------------------------------------------------- # STAGE 2 — refine: take best DD<30 (and near-30 with high hold) candidates, # fine-tune bands/stop to push minHold up while keeping DD<30. # ----------------------------------------------------------------------- print("\n--- STAGE 2: refinement around best DD<30 / high-hold cells ---") refine = { # tighter blow-off cap + small vol floor, sl 1.8-2.0 "R1": (dict(vola_win=None, vol_win=None, vola_lo=0.30, vola_hi=0.85, vol_lo=0.20, vol_hi=1.0), winner_exit_params(sl_atr=2.0)), "R2": (dict(vola_win=None, vol_win=None, vola_lo=0.30, vola_hi=0.85, vol_lo=0.20, vol_hi=1.0), winner_exit_params(sl_atr=1.8)), "R3": (dict(vola_win=None, vol_win=None, vola_lo=0.30, vola_hi=0.88, vol_lo=0.30, vol_hi=1.0), winner_exit_params(sl_atr=2.0)), "R4": (dict(vola_win=None, vol_win=None, vola_lo=0.35, vola_hi=0.85, vol_lo=0.30, vol_hi=1.0), winner_exit_params(sl_atr=2.0)), # rolling-window regime (recent), which reacts faster to regime shift -> may cut DD "R5": (dict(vola_win=120, vol_win=120, vola_lo=0.30, vola_hi=0.85, vol_lo=0.20, vol_hi=1.0), winner_exit_params(sl_atr=2.0)), "R6": (dict(vola_win=120, vol_win=120, vola_lo=0.30, vola_hi=0.85, vol_lo=0.30, vol_hi=1.0), winner_exit_params(sl_atr=1.8)), # tighter tp to bank faster (lower DD) with tight sl "R7": (dict(vola_win=None, vol_win=None, vola_lo=0.30, vola_hi=0.85, vol_lo=0.20, vol_hi=1.0), winner_exit_params(sl_atr=2.0, tp_atr=6.0)), "R8": (dict(vola_win=None, vol_win=None, vola_lo=0.30, vola_hi=0.85, vol_lo=0.20, vol_hi=1.0), winner_exit_params(sl_atr=1.6)), } stage2 = {} for t, (cfg, p) in refine.items(): v = summarize(eval_pair(cfg, p)) stage2[t] = (cfg, p, v) line(t, v) # ----------------------------------------------------------------------- # PICK BEST: among ALL cells, prefer maxDD<0.30 AND minHold>=0.65; rank by # (DD<30) then minHold then -DD. Fall back to best minHold if none sub-30. # ----------------------------------------------------------------------- allcells = {**stage1, **stage2} def score(tup): v = tup[2] dd_ok = v["maxDD"] < 0.30 hold_ok = v["minHold"] >= 0.65 full_ok = v["minFull"] >= 0.5 tr_ok = v["minTr"] >= 20 # primary: meets all gates; secondary: minHold; tertiary: lower DD return (dd_ok and hold_ok and full_ok and tr_ok, dd_ok, v["minHold"], -v["maxDD"]) best_tag = max(allcells, key=lambda t: score(allcells[t])) best_cfg, best_p, best_v = allcells[best_tag] print(f"\n*** SELECTED = {best_tag} ***") line(best_tag, best_v) # ----------------------------------------------------------------------- # FULL VERIFICATION on selected: causality + fee sweep + marginal. # ----------------------------------------------------------------------- print("\n--- causality (truncated-prefix) ---") cB = check_causality(best_cfg, best_p, "BTC") cE = check_causality(best_cfg, best_p, "ETH") causality_ok = bool(cB["ok"] and cE["ok"]) print(f" BTC={cB} ETH={cE} -> causality_ok={causality_ok}") print("\n--- fee sweep (FULL sharpe) ---") fee_ok, frows = fee_sweep(best_cfg, best_p) for a, row in frows.items(): print(f" {a}: " + " ".join(f"{f*100:.2f}%={s:+.2f}" for f, s in row)) print(f" fee_survives 0.30%RT (both): {fee_ok}") print("\n--- marginal vs TP01 (selected) ---") marg = marginal_struct(best_cfg, best_p) corr_full = marg.get("corr_full") verdict = marg.get("marginal_verdict") has_edge = marg.get("has_insample_edge") is_hedge = marg.get("is_hedge") robust_oos = marg.get("robust_oos") multicut = marg.get("multicut_persistent") clean_yr = marg.get("clean_year_uplift") w25 = marg.get("blends", {}).get("w25", {}) w50 = marg.get("blends", {}).get("w50", {}) up_h = w25.get("uplift_hold") print(f" corr_full={corr_full} corr_hold={marg.get('corr_hold')}") print(f" marginal_verdict={verdict} robust_oos={robust_oos} multicut_persistent={multicut}") print(f" has_insample_edge={has_edge} is_hedge={is_hedge} cand_insample_sharpe={marg.get('cand_insample_sharpe')}") print(f" clean_year_uplift={clean_yr} jackknife_min_uplift={marg.get('jackknife_min_uplift')}") print(f" blend w25: uplift_hold={up_h} uplift_full={w25.get('uplift_full')}") print(f" blend w50: full={w50.get('full')} hold={w50.get('hold')} uplift_hold={w50.get('uplift_hold')} dd={w50.get('dd')}") # grade (mirror sk verdict thresholds): PASS if minTr>=20 & minFull>=0.5 & minHold>=0.2 & feeOK mf, mh, mt, mdd = best_v["minFull"], best_v["minHold"], best_v["minTr"], best_v["maxDD"] if mt >= 20 and mf >= 0.5 and mh >= 0.2 and fee_ok: grade = "PASS" elif mt >= 20 and mf >= 0.3 and mh >= 0.0: grade = "WEAK" else: grade = "FAIL" earns_slot = (grade != "FAIL") and (verdict == "ADDS") and bool(robust_oos) and (not bool(is_hedge)) beats_winner = bool(earns_slot and mdd < 0.30 and (up_h is not None and up_h >= 0.55) and mh >= 0.65) print("\n" + "=" * 70) print("FINAL BLOCK — SKH2_PCTL_DD") print("=" * 70) print(f"best_cfg(regime) = {best_cfg}") print(f"best_params = ptn_n={best_p.ptn_n} sl_atr={best_p.sl_atr} tp_atr={best_p.tp_atr} " f"uscitalong={best_p.uscitalong} uscitashort={best_p.uscitashort} exit_mode={best_p.exit_mode}") print(f"grade={grade}") print(f"minFull={mf:+.3f} minHold={mh:+.3f} max_dd={mdd:.4f} ({mdd*100:.0f}%) n_trades_min={mt}") print(f"fee@0.30%RT survives={fee_ok} causality_ok={causality_ok}") print(f"marginal: verdict={verdict} corr_full={corr_full} has_insample_edge={has_edge} " f"is_hedge={is_hedge} robust_oos={robust_oos} multicut_persistent={multicut} clean_year_uplift={clean_yr}") print(f"blend w25 uplift_hold={up_h} blend w50 full={w50.get('full')}/hold={w50.get('hold')}/dd={w50.get('dd')}") print(f"earns_slot={earns_slot}") print(f"beats_winner={beats_winner}") print("=" * 70) # machine-readable echo for the agent import json print("RESULT_JSON=" + json.dumps({ "best_cfg": best_cfg, "best_params": {"ptn_n": best_p.ptn_n, "sl_atr": best_p.sl_atr, "tp_atr": best_p.tp_atr, "uscitalong": best_p.uscitalong, "uscitashort": best_p.uscitashort, "exit_mode": best_p.exit_mode, "vola_lo": best_cfg["vola_lo"], "vola_hi": best_cfg["vola_hi"], "vol_lo": best_cfg["vol_lo"], "vol_hi": best_cfg["vol_hi"], "vola_win": best_cfg["vola_win"], "vol_win": best_cfg["vol_win"]}, "grade": grade, "minFull": mf, "minHold": mh, "max_dd": mdd, "n_trades_min": mt, "fee_ok": fee_ok, "causality_ok": causality_ok, "marginal_verdict": verdict, "corr_full": corr_full, "has_insample_edge": has_edge, "is_hedge": is_hedge, "robust_oos": robust_oos, "multicut_persistent": multicut, "clean_year_uplift": clean_yr, "blend_w25_uplift_hold": up_h, "w50_full": w50.get("full"), "w50_hold": w50.get("hold"), "w50_dd": w50.get("dd"), "earns_slot": earns_slot, "beats_winner": beats_winner, }, default=str))