import pandas as pd,numpy as np,glob,os,json
T='/workspace/research/flow-forensics-2026-10/recov/data/today/'; R='/workspace/research/flow-forensics-2026-10/recov'
def rd(n):
    d=pd.read_csv(T+n+'.csv'); d['hm']=d.ts.str[11:16]; return d.set_index('hm')
N=rd('NIFTY'); F=rd('NIFTYFUT'); BN=rd('BANKNIFTY'); V=rd('INDIAVIX'); BF=rd('BANKNIFTYFUT')
df=pd.DataFrame({'nifty':N.close,'n_low':N.low,'fut':F.close,'fut_oi':F.oi,'fut_vol':F.volume,'bn':BN.close,'bn_low':BN.low,'vix':V.close})
df['basis']=df.fut-df.nifty
for k in (22300,22400,22500): df[f'pe{k}_oi']=rd(f'OPT_{k}PE').oi
for k in (22500,22600,22700): df[f'ce{k}_oi']=rd(f'OPT_{k}CE').oi
df['ce22400']=rd('OPT_22400CE').close; df['ce22450']=rd('OPT_22450CE').close; df['ce22500']=rd('OPT_22500CE').close
df['straddle22400']=rd('OPT_22400CE').close+rd('OPT_22400PE').close
# breadth / heavyweights from 50 stocks
keys=json.load(open('/workspace/research/flow-forensics-2026-10/data/keys.json'))
stk=[s for s in keys if s not in('NIFTY','BANKNIFTY','NIFTYFUT','BANKNIFTYFUT','NIFTYFUT_NOV','INDIAVIX')]
W=pd.read_csv('/workspace/research/flow-forensics-2026-10/data/implied_weights.csv'); print(W.head(2).to_string())
lowt={}; up=pd.DataFrame()
for s in stk:
    x=rd(s); o=x.open.iloc[0]
    up[s]=(x.close>x.open.iloc[0]).astype(int)
    w=x.loc['09:15':'09:45']; lowt[s]=w.low.idxmin()
df['adv_vs_open']=up.sum(axis=1)
lt=pd.Series(lowt); print('stock low-time distribution (09:15-09:45):'); print(lt.value_counts().sort_index().to_string())
top=['HDFCBANK','RELIANCE','ICICIBANK','INFY','BHARTIARTL','LT','ITC','TCS','AXISBANK','KOTAKBANK','SBIN']
print('heavyweight low times:',{s:lowt.get(s) for s in top})
w=df.loc['09:15':'10:15']; pd.set_option('display.width',250); print(w.round(2).to_string())
df.to_csv(f'{R}/results/today_minutes.csv')
print('Nifty low',N.low.min(),N.low.idxmin(),' BN low',BN.low.min(),BN.low.idxmin(),' fut low',F.low.min(),F.low.idxmin(),' VIX high',V.high.max(),V.high.idxmax(),' day high so far',N.high.max(),N.high.idxmax())