# Run candidate trades (all exits x cost scenarios) + spot proxy; random same-time baseline (REAL cost).
import sys,time; sys.path.insert(0,'/workspace/research/buyer-method-audit/scripts'); from lib import *
t0=time.time(); S,PC=load_spot(); X=pd.read_csv('results/signals_all.csv',parse_dates=['date']); opt()
EX=['E0','E1','E2','E3','E4']; rows=[]; px=[]
for r in X.itertuples():
    a=S[r.date]
    for ex in EX:
        px.append(dict(cand=r.cand,date=r.date,ex=ex,spot_pts=spot_proxy(a,r.m,r.dir,r.entry,r.stop,r.R,ex)))
    if r.date<pd.Timestamp('2024-10-21'): continue
    for ex in EX:
        for cs in ('OLD','REAL','STRESS'):
            z=sim(a,r.date,r.m,r.dir,r.entry,r.stop,r.R,ex,cs)
            if z: rows.append(dict(cand=r.cand,date=r.date,hm=i2hm(r.m),dir=r.dir,R=r.R,ex=ex,scen=cs,split=split(r.date),**z))
T=pd.DataFrame(rows); T.to_csv('results/trades_candidates.csv',index=False); pd.DataFrame(px).to_csv('results/spot_proxy.csv',index=False)
print('cand sims',len(T),T.status.value_counts().to_dict(),round(time.time()-t0),flush=True)
# random baseline
rng=np.random.default_rng(20261026); od=[d for d in sorted(S) if d>=pd.Timestamp('2024-10-21') and nexp(d) is not None]
pool={s:[d for d in od if split(d)==s] for s in ('train','test')}; rr=[]
X2=X[X.date>=pd.Timestamp('2024-10-21')]
for r in X2.itertuples():
    sp=split(r.date)
    for k in range(30):
        d=pool[sp][rng.integers(len(pool[sp]))]; a=S[d]; en=a[3,r.m]
        if np.isnan(en): continue
        st=en-r.dir*r.R
        for ex in EX:
            z=sim(a,d,r.m,r.dir,en,st,r.R,ex,'REAL')
            if z and z['status']=='ok': rr.append(dict(cand=r.cand,src_date=r.date,rep=k,date=d,ex=ex,split=sp,pnl=z['pnl'],gross=z['gross']))
RR=pd.DataFrame(rr); RR.to_csv('results/trades_random.csv',index=False); print('random sims',len(RR),round(time.time()-t0))