# Option sims (primary trail50-spot, secondary E1) + spot proxy 2022-26 + random same-time baseline (primary exit, REAL).
import sys,time; sys.path.insert(0,'/workspace/research/popular-strategies/scripts'); from plib import *
t0=time.time(); S,PC=load_spot(); opt(); X=pd.read_csv(f'{L}/results/signals_all.csv',parse_dates=['date'])
rows=[]; spx=[]
for r in X.itertuples():
    a=S[r.date]
    spx.append(dict(cand=r.cand,date=r.date,hm=i2hm(r.m),dir=r.dir,R=r.R,spot_pts=spot_proxy_trail(a,r.m,r.dir,r.entry,r.stop,r.R),spot_pts_E1=spot_proxy(a,r.m,r.dir,r.entry,r.stop,r.R,'E1')))
    if r.date<pd.Timestamp('2024-10-21'): continue
    z=sim_trail(a,r.date,r.m,r.dir,r.entry,r.stop,r.R,'spot')
    if z: rows.append(dict(cand=r.cand,date=r.date,hm=i2hm(r.m),dir=r.dir,R=r.R,ex='T50',split=split(r.date),**z))
    z=sim(a,r.date,r.m,r.dir,r.entry,r.stop,r.R,'E1','REAL')
    if z: rows.append(dict(cand=r.cand,date=r.date,hm=i2hm(r.m),dir=r.dir,R=r.R,ex='E1',split=split(r.date),**z))
T=pd.DataFrame(rows); T.to_csv(f'{L}/results/trades_options.csv',index=False)
SP=pd.DataFrame(spx); SP['spot_net']=SP.spot_pts-1.0; SP.to_csv(f'{L}/results/trades_spot.csv',index=False)
print('opt',len(T),T.status.value_counts().to_dict(),round(time.time()-t0),flush=True)
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=[]
for r in X[X.date>=pd.Timestamp('2024-10-21')].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
        z=sim_trail(a,d,r.m,r.dir,en,en-r.dir*r.R,r.R,'spot')
        if z and z['status']=='ok': rr.append(dict(cand=r.cand,src_date=r.date,rep=k,date=d,split=sp,pnl=z['pnl']))
pd.DataFrame(rr).to_csv(f'{L}/results/trades_random.csv',index=False); print('random',len(rr),round(time.time()-t0))