"""SKH_R_EXPAND — REGIME variant: VOLATILITY-EXPANSION gate. Hypothesis (the brief: "enter when vol+volume regime AND breakout coincide"): Instead of the Chande01 *cycle* band on ATR, define the regime as a genuine VOLATILITY EXPANSION: trade only when ATR is RISING vs its own moving average (a vol breakout) AND volume is elevated vs its own moving average. The intuition is that a Donchian breakout that fires WHILE volatility is expanding on rising participation (volume) is more likely to be a real move than one that fires inside a quiet/contracting regime (chop, mean-reversion). Regime definition (HTF, causal): vol_expansion = ATR[i] >= k_atr * MA(ATR, w_atr) (ATR above its own MA -> rising) volume_elev = volume[i] >= k_vol * MA(volume, w_vol) (participation elevated) regime_ok = vol_expansion AND volume_elev MA is a CAUSAL rolling mean (uses x[i-w+1..i] inclusive of the current, already-closed bar). k_atr / k_vol are tunable multipliers (1.0 = "above MA"; >1 = "well above MA"). Pattern/composer/entry/exit reuse the V1 Skyhook building blocks unchanged: ptn_n=55 Donchian, sl_atr=2.5, tp_atr=6.0, asymmetric time exits, max 1/day. Causality: every regime feature uses only x[0..i] (rolling MA, ATR ewm, donchian shift(1)), INCLUSIVE of the current HTF bar — legit because at HTF close[i] the bar is fully known. The HTF feature is merged BACKWARD onto LTF on the HTF-close timestamp (<= LTF close). We verify the truncated-prefix guard ourselves on BOTH assets. """ from __future__ import annotations import sys sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/skyhook") import numpy as np import pandas as pd import skyhooklib as sk from src.backtest.harness import backtest_signals from src.strategies import skyhook as S from src.strategies.skyhook import SkyhookParams, HTF_MIN HOLDOUT = pd.Timestamp("2025-01-01", tz="UTC") FEE = sk.FEE_RT # --------------------------------------------------------------------------- # Causal rolling MA (inclusive of current, already-closed bar). min_periods enforced. # --------------------------------------------------------------------------- def causal_ma(x: np.ndarray, win: int, min_periods: int | None = None) -> np.ndarray: mp = win if min_periods is None else min_periods return pd.Series(np.asarray(x, float)).rolling(win, min_periods=mp).mean().values # --------------------------------------------------------------------------- # HTF feature df: volatility-EXPANSION regime gate + Donchian pattern (V1 pattern reused). # regime_ok = (ATR >= k_atr*MA(ATR,w_atr)) AND (volume >= k_vol*MA(volume,w_vol)) # --------------------------------------------------------------------------- def expand_htf_features(htf: pd.DataFrame, p: SkyhookParams, w_atr: int, k_atr: float, w_vol: int, k_vol: float) -> pd.DataFrame: atr_htf = S.atr(htf, p.atr_win) vol_htf = htf["volume"].values.astype(float) atr_ma = causal_ma(atr_htf, w_atr) vol_ma = causal_ma(vol_htf, w_vol) # rising-vol = current ATR above k_atr * its own MA ; same for volume. # NaN during warmup -> False (no trade until the regime is computable). vol_expansion = np.where(np.isfinite(atr_ma) & (atr_ma > 0), atr_htf >= k_atr * atr_ma, False) volume_elev = np.where(np.isfinite(vol_ma) & (vol_ma > 0), vol_htf >= k_vol * vol_ma, False) regime_ok = vol_expansion & volume_elev ptn_long, ptn_short = S.donchian_breakout(htf, p.ptn_n) comp_long = regime_ok & ptn_long comp_short = regime_ok & ptn_short if p.long_only: comp_short = np.zeros_like(comp_short, dtype=bool) close_ts = htf["timestamp"].astype("int64").values + HTF_MIN * 60 * 1000 return pd.DataFrame({ "close_ts": close_ts, # store the ratios for diagnostics (not used downstream) "buz_vola": np.where(np.isfinite(atr_ma) & (atr_ma > 0), atr_htf / atr_ma, np.nan), "buz_volume": np.where(np.isfinite(vol_ma) & (vol_ma > 0), vol_htf / vol_ma, np.nan), "comp_long": comp_long.astype(bool), "comp_short": comp_short.astype(bool), }) def expand_entries(ltf: pd.DataFrame, htf: pd.DataFrame, p: SkyhookParams, w_atr, k_atr, w_vol, k_vol) -> list: """Same entry/exit machinery as S.skyhook_entries, regime from expansion features.""" feat = expand_htf_features(htf, p, w_atr, k_atr, w_vol, k_vol) m = S.merge_htf_to_ltf(ltf, feat) c = m["close"].values.astype(float) a = S.atr(m, p.ltf_atr_win) comp_long = np.nan_to_num(m["comp_long"].values).astype(bool) comp_short = np.nan_to_num(m["comp_short"].values).astype(bool) days = pd.to_datetime(m["datetime"]).dt.floor("D").values entries: list = [None] * len(m) count_today: dict = {} for i in range(len(m)): if not np.isfinite(a[i]) or a[i] <= 0: continue day = days[i] if count_today.get(day, 0) >= p.max_per_day: continue if comp_long[i]: direction, mb = 1, p.uscitalong elif comp_short[i]: direction, mb = -1, p.uscitashort else: continue if p.exit_mode == "atr": sl_off, tp_off = p.sl_atr * a[i], p.tp_atr * a[i] else: sl_off, tp_off = p.sl_pct * c[i], p.tp_pct * c[i] if direction == 1: sl, tp = c[i] - sl_off, c[i] + tp_off else: sl, tp = c[i] + sl_off, c[i] - tp_off entries[i] = {"dir": direction, "tp": float(tp), "sl": float(sl), "max_bars": int(mb)} count_today[day] = count_today.get(day, 0) + 1 return entries # --------------------------------------------------------------------------- # Eval helpers # --------------------------------------------------------------------------- def _split(eq, idx, mask): e = eq[mask] if len(e) < 5: return dict(sharpe=0.0, ret=0.0, maxdd=0.0, n=int(len(e))) r = np.diff(e) / e[:-1]; r = r[np.isfinite(r)] ix = idx[mask] dt = pd.Series(ix).diff().dt.total_seconds().median() or 86400 bpy = 86400 * 365.25 / dt sh = float(np.mean(r) / np.std(r) * np.sqrt(bpy)) if len(r) and np.std(r) > 0 else 0.0 pk = np.maximum.accumulate(e); dd = float(np.max((pk - e) / pk)) return dict(sharpe=round(sh, 3), ret=round(float(e[-1] / e[0] - 1), 4), maxdd=round(dd, 4), n=int(len(e))) def eval_cfg(cfg, p): out = {} for a in ("BTC", "ETH"): ltf, htf = sk.frames(a) ent = expand_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 = _split(eq, idx, hmask) out[a] = dict(full=full, hold=hold, _eq=eq, _idx=idx) return out def summarize(tag, 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) print(f" [{tag}] minFull={mf:+.2f} minHold={mh:+.2f} minTr={mt} maxDD={mdd*100:.0f}%" f" | BTC F{res['BTC']['full']['sharpe']:+.2f}/H{res['BTC']['hold']['sharpe']:+.2f} n{res['BTC']['full']['n_trades']}" f" ETH F{res['ETH']['full']['sharpe']:+.2f}/H{res['ETH']['hold']['sharpe']:+.2f} n{res['ETH']['full']['n_trades']}") return dict(minFull=mf, minHold=mh, minTr=mt, maxDD=mdd, res=res) def v1_like_params(**kw): base = dict(ptn_n=55, sl_atr=2.5, tp_atr=6.0) # V1 pattern/exit base.update(kw) return SkyhookParams(**base) # --------------------------------------------------------------------------- # Causality self-check on the structural variant (truncated-prefix guard) # --------------------------------------------------------------------------- def check_causality(cfg, p, asset="BTC", tail=150): ltf, htf = sk.frames(asset) full = expand_entries(ltf, htf, p, **cfg) n = len(ltf); bad = 0; checked = 0 for frac in (0.80, 0.92): cut = int(n * frac) cut_ts = int(ltf["timestamp"].iloc[cut - 1]) htf_cut = htf[htf["timestamp"] <= cut_ts].reset_index(drop=True) sub = expand_entries(ltf.iloc[:cut].reset_index(drop=True), htf_cut, p, **cfg) for i in range(max(0, cut - tail), cut): checked += 1 a, b = full[i], sub[i] if (a is None) != (b is None): bad += 1 elif a is not None and (a["dir"] != b["dir"] or abs(a["sl"] - b["sl"]) > 1e-6 or abs(a["tp"] - b["tp"]) > 1e-6): bad += 1 return dict(ok=bool(bad == 0), mismatches=int(bad), checked=int(checked)) if __name__ == "__main__": print("=== SKH_R_EXPAND: volatility-EXPANSION regime (ATR rising vs its MA + volume elevated) ===\n") # --- V1 reference (Chande01 regime) --- print("--- V1 reference (Chande01 regime, ptn_n=55 sl2.5 tp6 vola35-95 vol0) ---") v1 = SkyhookParams(ptn_n=55, sl_atr=2.5, tp_atr=6.0, vola_lo=35, vola_hi=95, vol_lo=0.0) v1res = {} for a in ("BTC", "ETH"): r = sk.run_asset(a, v1, FEE) v1res[a] = dict(full=dict(sharpe=r["full"]["sharpe"], n_trades=r["full"]["n_trades"], maxdd=r["full"]["maxdd"]), hold=dict(sharpe=r["holdout"]["sharpe"])) v1_minFull = min(v1res[a]['full']['sharpe'] for a in v1res) v1_minHold = min(v1res[a]['hold']['sharpe'] for a in v1res) v1_maxDD = max(v1res[a]['full']['maxdd'] for a in v1res) print(f" V1 minFull={v1_minFull:+.2f} minHold={v1_minHold:+.2f} maxDD={v1_maxDD*100:.0f}%" f" | BTC F{v1res['BTC']['full']['sharpe']:+.2f}/H{v1res['BTC']['hold']['sharpe']:+.2f}" f" ETH F{v1res['ETH']['full']['sharpe']:+.2f}/H{v1res['ETH']['hold']['sharpe']:+.2f}\n") p = v1_like_params() # same pattern/exit as V1; only regime changes # --- Sweep: expansion-MA windows + multipliers --- # k=1.0 -> "ATR above its own MA" (mild rising). k>1 -> stronger expansion (fewer trades). # w_atr/w_vol: lookback for the MA (HTF bars; 690min each). vol elevated mirrored on volume. print("--- volatility-EXPANSION sweep ---") cfgs = { # (w_atr,k_atr) , (w_vol,k_vol) "atr20k1.0_vol20k1.0": dict(w_atr=20, k_atr=1.00, w_vol=20, k_vol=1.00), "atr20k1.0_vol20k1.2": dict(w_atr=20, k_atr=1.00, w_vol=20, k_vol=1.20), "atr20k1.1_vol20k1.0": dict(w_atr=20, k_atr=1.10, w_vol=20, k_vol=1.00), "atr20k1.1_vol20k1.2": dict(w_atr=20, k_atr=1.10, w_vol=20, k_vol=1.20), "atr10k1.0_vol10k1.0": dict(w_atr=10, k_atr=1.00, w_vol=10, k_vol=1.00), "atr10k1.1_vol10k1.2": dict(w_atr=10, k_atr=1.10, w_vol=10, k_vol=1.20), "atr30k1.0_vol30k1.0": dict(w_atr=30, k_atr=1.00, w_vol=30, k_vol=1.00), "atr20k1.0_volOFF": dict(w_atr=20, k_atr=1.00, w_vol=20, k_vol=0.00), # vol gate off (k=0 always true) "atr20k1.2_vol20k1.0": dict(w_atr=20, k_atr=1.20, w_vol=20, k_vol=1.00), } results = {} for tag, cfg in cfgs.items(): results[tag] = (cfg, summarize(tag, eval_cfg(cfg, p))) # --- Pick winner by minHold (subject to minFull>=0.5, minTr>=20) --- elig = {t: v for t, (c, v) in results.items() if v["minFull"] >= 0.5 and v["minTr"] >= 20} print(f"\n--- eligible (minFull>=0.5, minTr>=20): {list(elig.keys())} ---") if elig: win_tag = max(elig, key=lambda t: elig[t]["minHold"]) else: win_tag = max(results, key=lambda t: results[t][1]["minHold"]) win_cfg, win_v = results[win_tag][0], results[win_tag][1] print(f"\n*** WINNER = {win_tag} minFull={win_v['minFull']:+.2f} minHold={win_v['minHold']:+.2f}" f" minTr={win_v['minTr']} maxDD={win_v['maxDD']*100:.0f}% ***") print(f" cfg = {win_cfg}") # --- Causality on winner (BOTH assets) --- caus = check_causality(win_cfg, p, "BTC") caus_eth = check_causality(win_cfg, p, "ETH") print(f"\ncausality(BTC) = {caus}") print(f"causality(ETH) = {caus_eth}") # --- Fee sweep on winner (both assets, FULL) --- print("\n--- fee sweep on winner (FULL sharpe) ---") fee_ok_all = True for a in ("BTC", "ETH"): ltf, htf = sk.frames(a) ent_cache = expand_entries(ltf, htf, p, **win_cfg) row = [] for f in (0.0, 0.001, 0.002, 0.003): m = backtest_signals(ltf, ent_cache, fee_rt=f, leverage=1.0, asset=a, tf="230m") row.append((f, round(m.sharpe, 3))) sh030 = dict(row)[0.003] fee_ok_all = fee_ok_all and (sh030 > 0) 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_all}") # --- Per-year on winner --- print("\n--- per-year (winner) ---") for a in ("BTC", "ETH"): ltf, htf = sk.frames(a) ent = expand_entries(ltf, htf, p, **win_cfg) m = backtest_signals(ltf, ent, fee_rt=FEE, leverage=1.0, asset=a, tf="230m") yr = " ".join(f"{y}:{v*100:+.0f}%" for y, v in m.yearly.items()) print(f" {a}: {yr}") # --- Marginal vs TP01 on winner --- def daily_series(a, p, cfg): ltf, htf = sk.frames(a) ent = expand_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() import altlib as al sb = daily_series("BTC", p, win_cfg); se = daily_series("ETH", p, win_cfg) J = pd.concat({"BTC": sb, "ETH": se}, axis=1, join="inner").fillna(0.0) cand_daily = 0.5 * J["BTC"] + 0.5 * J["ETH"] marg = al.marginal_vs_tp01(cand_daily) print("\n--- marginal vs TP01 (winner) ---") print(f" corr_full={marg.get('corr_full')} corr_hold={marg.get('corr_hold')}") print(f" blend w25 uplift_hold={marg.get('blends',{}).get('w25',{}).get('uplift_hold')}") print(f" marginal_verdict={marg.get('marginal_verdict')} robust_oos={marg.get('robust_oos')}") print(f" has_insample_edge={marg.get('has_insample_edge')} is_hedge={marg.get('is_hedge')}") print(f" clean_year_uplift={marg.get('clean_year_uplift')} jackknife_min_uplift={marg.get('jackknife_min_uplift')}") print(f" multicut_persistent={marg.get('multicut_persistent')}") # --- Final verdict echo --- print("\n=== SUMMARY ===") print(f"V1: minFull={v1_minFull:+.2f} minHold={v1_minHold:+.2f} maxDD={v1_maxDD*100:.0f}%") print(f"EXPAND {win_tag}: minFull={win_v['minFull']:+.2f} minHold={win_v['minHold']:+.2f}" f" maxDD={win_v['maxDD']*100:.0f}% causalityOK={caus['ok'] and caus_eth['ok']} feeOK={fee_ok_all}") beats = (win_v['minHold'] > v1_minHold) and win_v['minFull'] >= 0.5 and fee_ok_all print(f"BEATS V1 HOLD-OUT: {beats}")