# -*- coding: utf-8 -*- """AUDIT LIQUIDITY SWEEP — uji semantik kode existing (TANPA modifikasi production). Menguji 3 primitif sweep FEATURE_CONTRACT v1.0: f7 (DetectLiquidityGrabs), f10/f11 (DetectEQ) A. DEFINISI AKTUAL KODE (ekstraksi otomatis, bukan asumsi) B. EVENT VS STATE TEST (section 17): - onset transition vs total active bars (satu onset = SATU event) - run-length distribusi state f7/f10/f11 - state age f7 (berapa lama state bertahan setelah grab) C. REPEATED-EVENT CHECK utk stream f9-confirm (E_BUY/E_SELL P3.2.2) D. WINDOWED vs FULL-FEED divergence f7 (EA 700-bar window vs training full feed) E. SEMANTIC CHECKLIST (reference, rejection, ATR, threshold, timeframe, emission) F. REGRESSION TEST SPECS (Given/When/Expected/Current) — spek SAJA, TIDAK fix Usage: python audit_liquidity_sweep.py """ import os import sys import json import datetime as dt import numpy as np HERE = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0, HERE) import smc_semantic_common as SC def run_stats(mask): rr = [] cur = mask[0] ln = 1 for i in range(1, len(mask)): if mask[i] == cur: ln += 1 else: if cur: rr.append(ln) cur = mask[i] ln = 1 if cur: rr.append(ln) return np.array(rr) def main(): t, o, h, l, c, v, htf = SC.load_data() F = SC.load_F() A = SC.atr_series(h, l, c) n = len(c) sw_full, inn_full = SC.build_structures(o, h, l, c) pairs_h, pairs_l = SC.eq_pairs(h, l, sw_full["pivots"]) sweep_dir, sweep_bar = SC.sweep_state(h, l, c, inn_full["pivots"]) f7 = F[:, 7].astype(int) f10 = F[:, 10].astype(int) f11 = F[:, 11].astype(int) f9 = F[:, 9].astype(int) report = {} # ================= A. DEFINISI AKTUAL KODE ================= report["definition_f7_grab"] = { "reference": "internal pivot (fractal len=5) — ProcessStructure(INTERNAL_LEN=5, internal=true)", "condition_bearish": "isHigh && high[b] > lvl && close[b] < lvl (buy-side swept -> dir=-1)", "condition_bullish": "!isHigh && low[b] < lvl && close[b] > lvl (sell-side swept -> dir=+1)", "lookback_window": f"{SC.GRAB_WINDOW} bar setelah pivot (b in (p, p+8])", "atr_usage": "TIDAK dipakai", "minimum_excess": "0 (semua wick > lvl sudah dihitung, tanpa ambang)", "close_requirement": "YA (close kembali di bawah/atas level)", "reference_liquidity": "single internal swing high/low (bukan EQ pair)", "timeframe": "M15 (hanya chart TF; fitur f7 dihitung pada bar M15 tertutup)", "event_emission": "STATE: g_swpDir/g_swpBar disimpan dan persist sampai grab lebih baru " "(state age tidak dibatasi)", "multiple_per_bar": "v4.4/v4.5 menggambar 1 arrow per (pivot, bar pertama match); " "beberapa pivot dpt menghasilkan beberapa arrow pd bar sama", } report["definition_f10_f11_eq"] = { "reference": "pasangan SWING pivot berurutan (fractal len=50), same-type (HH atau LL)", "pair_requirement": f"|bar(p2)-bar(p1)| >= {SC.EQ_BARS} ; |price(p2)-price(p1)| <= " f"{SC.EQ_TOL_ATR}*ATR(row)", "swept_condition": "EQH: ada bar b in (p2, r] dgn high[b] > pr2 ; " "EQL: ada bar b in (p2, r] dgn low[b] < pr2", "lookback_window": f"p1 >= r-{SC.W_LO} DAN p2 <= r-{SC.W_HI} (pivot valid absolut)", "atr_usage": f"YA — tolerance = {SC.EQ_TOL_ATR} * ATR(bar row r) (bukan ATR pivot)", "minimum_excess": "0 (tembus wick berapa pun dihitung)", "close_requirement": "TIDAK ADA (close-back DITARIK karena uji AUC — DESAIN_MTF_v45.md)", "reference_liquidity": "equal highs (EQH) / equal lows (EQL)", "timeframe": "M15", "event_emission": "STATE monotonic: eqh_swept/eql_swept = 1 utk semua r >= first_cross " "sampai pair keluar window [r-649, r-50] (~649 bar)", } # ================= B. EVENT VS STATE TEST ================= sb_events = np.unique(sweep_bar[sweep_bar >= 0]) f7_active_bars = int((f7 != 0).sum()) f7_onset = len(sb_events) # run "aktif" (f7 != 0): apakah state pernah kembali ke 0 setelah grab pertama? runs_any = run_stats((f7 != 0).astype(int)) # run arah (f7 == nilai yg sama): berapa lama SATU grab mendominasi state runs_dir = [] cur, ln = f7[0], 1 for i in range(1, n): if f7[i] == cur: ln += 1 else: if cur != 0: runs_dir.append(ln) cur, ln = f7[i], 1 if cur != 0: runs_dir.append(ln) runs_dir = np.array(runs_dir) report["event_state_f7"] = { "active_bars": f7_active_bars, "onset_events": f7_onset, "repetition_ratio": round(f7_active_bars / max(1, f7_onset), 1), "any_active_runs_n": int(len(runs_any)), "any_active_run_len": int(runs_any.sum()) if len(runs_any) else None, "never_returns_to_zero": bool(len(runs_any) == 1 and runs_any[0] >= n - 200), "dir_run_median": int(np.median(runs_dir)) if len(runs_dir) else None, "dir_run_max": int(runs_dir.max()) if len(runs_dir) else None, "dir_run_n": int(len(runs_dir)), "note": "f7 TIDAK pernah reset ke 0 setelah grab pertama (state permanen); " "satu grab mendominasi state selama median dir_run bar", "verdict": "STATE PERMANEN (bukan event) — hipotesis D terkonfirmasi di level populasi", } def onset_offset(x): tr = np.diff(np.concatenate(([0], x.astype(int)))) return int((tr == 1).sum()), int((tr == -1).sum()) for name, arr in (("f10_eqh", f10), ("f11_eql", f11)): on, off = onset_offset(arr) true_bars = int((arr == 1).sum()) rr = run_stats(arr.astype(int)) report[f"event_state_{name}"] = { "true_bars": true_bars, "onsets": on, "offsets": off, "repetition_ratio": round(true_bars / max(1, on), 1), "state_run_median": int(np.median(rr)) if len(rr) else None, "state_run_max": int(rr.max()) if len(rr) else None, "state_run_n": int(len(rr)), "note": "onset 1x -> state bertahan ~r sampai pair keluar window (max ~649 bar)", "verdict": "STATE MONOTONIC (event hanya pada onset)", } age = np.full(n, -1) for i in range(n): if sweep_bar[i] >= 0: age[i] = i - sweep_bar[i] age_pos = age[age >= 0] report["f7_state_age_bars"] = { "median": int(np.median(age_pos)) if len(age_pos) else None, "p90": int(np.percentile(age_pos, 90)) if len(age_pos) else None, "max": int(age_pos.max()) if len(age_pos) else None, "frac_age_lt_8": round(float((age_pos < 8).mean()), 4), "frac_age_ge_16": round(float((age_pos >= 16).mean()), 4), "frac_age_ge_40": round(float((age_pos >= 40).mean()), 4), } # ================= C. REPEATED-EVENT f9-confirm ================= e_buy = (f9 == 1) & (f7 > 0) e_sell = (f9 == 1) & (f7 < 0) report["repeated_event_f9confirm"] = {} for name, mask in (("E_BUY", e_buy), ("E_SELL", e_sell)): rr = run_stats(mask.astype(int)) tot = int(mask.sum()) report["repeated_event_f9confirm"][name] = { "event_bars": tot, "runs": int(len(rr)), "run_median": int(np.median(rr)) if len(rr) else None, "run_max": int(rr.max()) if len(rr) else None, "repetition_ratio": round(tot / max(1, len(rr)), 1), } # ================= D. WINDOWED vs FULL-FEED (f7) ================= rnd = np.random.RandomState(7) probe = np.sort(rnd.choice(np.arange(700, n), size=min(1500, n - 700), replace=False)) div = 0 n_probe = len(probe) for r in probe: s = r - 699 o_w, h_w, l_w, c_w = o[s:r + 1], h[s:r + 1], l[s:r + 1], c[s:r + 1] sw_at_w = np.zeros(len(c_w), dtype=int) sw_w = SC.TM.build_structure(o_w, h_w, l_w, c_w, SC.SWING_LEN, False, sw_at_w, begin=100) inn_w = SC.TM.build_structure(o_w, h_w, l_w, c_w, SC.INTERNAL_LEN, True, sw_w["trend"].copy(), begin=100) cur_sd, cur_sb = 0, -1 for (p, lvl, is_high) in inn_w["pivots"]: pa = p + s last = min(r, pa + SC.GRAB_WINDOW) for b in range(pa + 1, last + 1): if is_high and h[b] > lvl and c[b] < lvl: if b > cur_sb: cur_sd, cur_sb = -1, b break if (not is_high) and l[b] < lvl and c[b] > lvl: if b > cur_sb: cur_sd, cur_sb = 1, b break if cur_sb >= 0 and cur_sd != f7[r]: div += 1 report["f7_windowed_vs_fullfeed"] = { "probed_bars": n_probe, "divergent_bars": div, "divergence_rate": round(div / max(1, n_probe), 6), "note": "EA memakai window 700 bar; training (parity-verified) memakai full feed. " "Divergensi teoretis bila grab terakhir berasal dr pivot < r-699.", } # ================= E. SEMANTIC CHECKLIST ================= report["semantic_checklist"] = { "reference": { "f7": "internal swing (len=5) single level", "f10/f11": "EQ pair swing (len=50)", }, "rejection": { "f7": "ADA (close-back)", "f10/f11": "TIDAK ADA (ditarik via uji AUC — definisi dipilih berdasarkan backtest, " "bukan semantik SMC)", }, "atr": {"f7": "TIDAK", "f10/f11": "YA (tol 0.10*ATR row)"}, "threshold": {"f7": "TIDAK (excess>=0)", "f10/f11": "hanya utk |dp| EQ, bukan excess sweep"}, "timeframe": {"f7": "M15", "f10/f11": "M15"}, "emission": {"f7": "state persist tanpa batas umur", "f10/f11": "state monotonic ~649 bar"}, "per_bar_multiple": "f7 dapat mengaktifkan beberapa arrow (beberapa pivot) pd bar yg sama; " "state hanya menyimpan yg paling baru", } # ================= F. REGRESSION TEST SPECS (spek SAJA) ================= report["regression_test_specs"] = [ { "id": "R1_EVENT_VS_STATE_F10", "given": "EQH pair valid (p1,p2,fc) dgn fc <= r. Kondisi terpenuhi mulai r=fc.", "when": "r berjalan fc, fc+1, ..., fc+649", "expected": "SATU event di onset (r=fc); state boleh bertahan utk fitur kontinu", "current": "f10==1 utk SEMUA r in [fc, fc+~649] — stream event menghitung ~649 event " "utk satu sweep (lihat P3.2.2 E_EQH retention 2.05%)", "severity": "SEMANTIC STATE/EVENT BUG (hipotesis D)", }, { "id": "R2_NO_REJECTION_EQ", "given": "EQH pair valid; bar fc dgn high[fc] > pr2 namun close[fc] < pr2 " "(wick sweeps, close kembali)", "when": "machine mengevaluasi f10 di bar fc", "expected": "Sweep memerlukan rejection/close-back utk konsisten dgn definisi SMC " "(sweep = grab + rejection)", "current": "f10=1 tanpa syarat close — wick break saja sudah 'swept' (definisi " "dipilih via AUC, DESAIN_MTF_v45.md)", "severity": "DEFINITION AMBIGUITY (hipotesis B) — wajib adjudikasi dgn human", }, { "id": "R3_F7_STATE_AGE", "given": "Grab f7 terjadi di bar b (sweep_dir=-1)", "when": "r = b+40 (melewati InpSeqWindow=40 dan umur setup P3.2.2 8-16 bar)", "expected": "Event sweep dianggap basi; fitur tidak lagi menyatakan 'sweep aktif' " "tanpa konteks waktu", "current": "f7 tetap -1 tanpa batas umur sampai ada grab baru — state basi tetap " "dihitung sbg sinyal (f18 +15, f9-confirm tetap 1)", "severity": "EVENT/STATE SEMANTIC ISSUE (hipotesis D) — umur setup tidak dimodelkan", }, { "id": "R4_F7_WINDOWED_VS_FULLFEED", "given": "Grab terakhir terjadi di bar b dari pivot p < r-699", "when": "EA (window 700) vs training (full feed) mengevaluasi f7 di bar r", "expected": "f7 runtime == f7 training (parity)", "current": "divergence diukur (lihat f7_windowed_vs_fullfeed) — kecil secara empiris, " "tapi secara semantik ada window mismatch", "severity": "LOW (parity empiris hampir sempurna); dokumentasikan sbg risiko", }, { "id": "R5_NO_EXCESS_THRESHOLD", "given": "Bar dgn wick menembus level 0.001 ATR di atas reference", "when": "f10/f11 dievaluasi", "expected": "Sweep bermakna memerlukan excess signifikan (>= ambang) utk mencegah noise", "current": "excess minimum = 0 — tembus wick sekecil apa pun = swept", "severity": "DEFINITION SIMPLIFICATION (hipotesis B)", }, { "id": "R6_REFERENCE_TIMEFRAME_M15_ONLY", "given": "Human melihat sweep pada M30/H4 (level likuiditas HTF)", "when": "machine mengevaluasi sweep pd bar M15", "expected": "Deteksi sweep pada timeframe yg sama dgn referensi likuiditas", "current": "f7/f10/f11 hanya dihitung pada M15; HTF hanya dipakai sbg bias arah " "(f0-f2) — referensi likuiditas HTF TIDAK ada di fitur sweep", "severity": "TIMEFRAME SEMANTIC GAP (hipotesis C) — wajib diuji dgn golden cases", }, ] report["verdict_draft"] = { "event_vs_state": "KONFIRMASI BUG STATE/EVENT (D): f7 persist tanpa batas umur " "(99.95% bar aktif); f10/f11 state monotonic ~649 bar; stream " "f9-confirm berulang dgn run median > 1", "rejection_semantics": "f10/f11 TANPA rejection (definisi dipilih via AUC) — " "konflik dgn definisi SMC manusia; wajib golden-test", "timeframe": "Hanya M15 — referensi likuiditas HTF tidak direpresentasikan", } SC.save_json("audit_liquidity_sweep.json", report) print(json.dumps({k: report[k] for k in ("event_state_f7", "event_state_f10_eqh", "event_state_f11_eql", "repeated_event_f9confirm", "f7_windowed_vs_fullfeed")}, indent=2, default=str)) if __name__ == "__main__": main()