"""Part B — geometry grid over the seven crown policies (pre-reg 2026-09-09)."""
import itertools, numpy as np, pandas as pd, warnings, importlib.util, sys
from scipy import stats
warnings.filterwarnings("ignore")
spec=importlib.util.spec_from_file_location("cp","crown_policies.py"); cp=importlib.util.module_from_spec(spec)
# import without executing the run at module bottom: exec only the definitions
src=open("crown_policies.py").read().split("\ndf=run()")[0]; exec(src, cp.__dict__)
FP=cp.FP; d=cp.load_nq(); QUAD=cp.QUAD
GRID=list(itertools.product([1.0,1.5,2.0],[60,120,240],[1.5,2.0,3.0]))
def walk(rth, sig_min, side, f5, i5, rr, cap, mult):
    stop_d=max(cp.STOP_FLOOR, mult*cp.atr3(f5,i5)); nxt=rth[rth["min"]>=sig_min]
    if nxt.empty: return None
    entry=nxt.iloc[0]["open"]+(cp.SLIP if side>0 else -cp.SLIP); fill_min=int(nxt.iloc[0]["min"]); stop=entry-side*stop_d; target=entry+side*rr*stop_d
    lows=nxt["low"].values; highs=nxt["high"].values; closes=nxt["close"].values; mins=nxt["min"].values.astype(int)
    for k in range(len(nxt)):
        m=mins[k]; hs = lows[k]<=stop if side>0 else highs[k]>=stop; ht = highs[k]>=target if side>0 else lows[k]<=target
        if hs: return -1.0-cp.SLIP/stop_d
        if ht: return rr
        if m-fill_min>=cap or m>=959: return (side*(closes[k]-entry)-cp.SLIP)/stop_d
    return (side*(closes[-1]-entry)-cp.SLIP)/stop_d
# reuse Part A's signal detection by re-running the session loop with all geometries per signal
rows=[]; prior=None; rth_ranges=[]; prev_day=None
for sday,g in d.groupby("sday"):
    rth=g[(g["min"]>=570)&(g["min"]<960)].reset_index(drop=True); on=g[(g["min"]>=1080)|(g["min"]<570)]
    if len(rth)<300: continue
    rng=rth["high"].max()-rth["low"].min()
    ok = prior is not None and prev_day is not None and (sday-prev_day).days<=4 and len(rth_ranges)>=20 and sday not in QUAD and sday>=pd.Timestamp("2012-01-03").date() and len(on)>=60
    if ok:
        atr20=float(np.mean(rth_ranges[-20:])); pc,vah,val,poc=prior
        o=rth.iloc[0]["open"]; gap=(o-pc)/atr20; onh=on["high"].max(); onl=on["low"].min(); onq=sum(1 for e in cp.ON_EDGES if (onh-onl)/atr20>e); onlast=on.iloc[-1]["close"]
        accept = 1 if onlast>vah else (-1 if onlast<val else 0)
        f5=cp.five_min(rth); c=f5.c.values; orb_h=rth[rth["min"]<585]["high"].max(); orb_l=rth[rth["min"]<585]["low"].min()
        brk=lambda i: (1 if c[i]>onh else (-1 if c[i]<onl else 0))
        def fade(i):
            if i==0: return 0
            if c[i-1]>vah and val<=c[i]<=vah: return -1
            if c[i-1]<val and val<=c[i]<=vah: return 1
            return 0
        def cont(i):
            if accept==1: return 1 if (i>0 and c[i]>vah and any(c[k]<vah for k in range(0,i))) else 0
            if accept==-1: return -1 if (i>0 and c[i]<val and any(c[k]>val for k in range(0,i))) else 0
            return 0
        sigs={}
        sigs["P5_on_break"]=cp.first_signal(f5,brk); sigs["P1_expansion_break"]=sigs["P5_on_break"] if onq>=3 else (None,0)
        sigs["P2_contained_fade"]=cp.first_signal(f5,fade) if onq<=1 else (None,0)
        sigs["P3_gap_fade"]=cp.first_signal(f5,lambda i: 1 if c[i]>pc else 0) if gap<=cp.GAP_LO else (cp.first_signal(f5,lambda i: -1 if c[i]<pc else 0) if gap>=cp.GAP_HI else (None,0))
        sigs["P4_value_cont"]=cp.first_signal(f5,cont) if accept!=0 else (None,0)
        sigs["P6_orb15"]=cp.first_signal(f5,lambda i: (1 if (f5.endmin[i]>585 and c[i]>orb_h) else (-1 if (f5.endmin[i]>585 and c[i]<orb_l) else 0)))
        sigs["P7_regime"]=sigs["P1_expansion_break"] if onq>=3 else (sigs["P2_contained_fade"] if onq<=1 else (None,0))
        for pol,(i5,side) in sigs.items():
            if i5 is None or not side: continue
            for rr,cap,mult in GRID:
                r=walk(rth,int(f5.endmin[i5]),side,f5,i5,rr,cap,mult)
                if r is not None: rows.append((sday,pol,rr,cap,mult,side,r))
    rth_ranges.append(rng); va=cp.value_area(rth); prior=(rth.iloc[-1]["close"],va[1],va[2],va[0]); prev_day=sday
df=pd.DataFrame(rows,columns=["date","pol","rr","cap","mult","side","r"]); df.to_parquet("data/crown_geometry.parquet")
half=pd.Timestamp("2019-01-01").date(); ALPHA=0.05/189
L=[f"# Crown-play geometry grid — results · {pd.Timestamp.now():%Y-%m-%d %H:%M}\n\n{len(df):,} walks = triggers × 27 geometries; α = {ALPHA:.5f}.\n"]
L.append("| policy | target R | cap min | stop ×ATR3 | n | hit% | R/trig | H1 | H2 | +yrs | p vs 0 | verdict |\n|---|---|---|---|---|---|---|---|---|---|---|---|")
out=[]
for (pol,rr,cap,mult),g in df.groupby(["pol","rr","cap","mult"]):
    h1=g[g.date<half].r; h2=g[g.date>=half].r; yrs=g.groupby(pd.to_datetime(g.date).dt.year).r.mean(); pos=int((yrs>0).sum())
    t=stats.ttest_1samp(g.r,0).pvalue if len(g)>2 else 1
    ok= len(g)>=100 and h1.mean()>=0.10 and h2.mean()>=0.10 and pos>=10 and t<ALPHA and g.r.mean()>0
    v="VALIDATED" if ok else ("suggestive" if (len(g)>=100 and h1.mean()>=0.05 and h2.mean()>=0.05) else "refuted")
    out.append((g.r.mean(),pol,rr,cap,mult,len(g),(g.r>=rr-1e-9).mean()*100,h1.mean(),h2.mean(),pos,t,v))
out.sort(reverse=True)
for m,pol,rr,cap,mult,n,hit,h1,h2,pos,t,v in out[:25]: L.append(f"| {pol} | {rr} | {cap} | {mult} | {n} | {hit:.0f} | {m:+.3f} | {h1:+.3f} | {h2:+.3f} | {pos}/15 | {t:.4f} | {v} |")
L.append(f"\n(top 25 of 189 cells by mean R; full grid in data/crown_geometry.parquet)\n\n## Survivors\n"+ (", ".join(f"{o[1]} {o[2]}R/{o[3]}m/{o[4]}×" for o in out if o[11]=="VALIDATED") or "none") + "\n\n## Suggestive\n" + (", ".join(f"{o[1]} {o[2]}R/{o[3]}m/{o[4]}× ({o[0]:+.3f})" for o in out if o[11]=="suggestive") or "none"))
L.append("\n## Geometry marginals (mean R/trigger across all policies)\n"+df.groupby("rr").r.mean().round(3).to_string()+"\n"+df.groupby("cap").r.mean().round(3).to_string()+"\n"+df.groupby("mult").r.mean().round(3).to_string())
res="\n".join(L); open(f"{FP}/studies/2026-09-09-crown-geometry-results.md","w").write(res); print(res)