import sys;sys.path.insert(0,'../scripts');from load import m1,SESS
import pandas as pd,numpy as np
N=m1('NIFTY'); rows=[]
for d in SESS:
    g=N[(N.date==d)&(N.hm<='15:14')].reset_index(drop=True)
    lo=g.low.values; hi=g.high.values
    # day low
    i=int(np.nanargmin(lo)); pk=int(np.nanargmax(hi[:i+1]))
    rows.append(dict(date=d,kind='day low',hm=g.hm[i],low=lo[i],peak_hm=g.hm[pk],peak=hi[pk],fall=hi[pk]-lo[i]))
    # zigzag swing lows: fall >=150 from running peak, then reversal >=60
    peak=hi[0];pki=0;cand=None
    for j in range(len(g)):
        if cand is None:
            if hi[j]>peak: peak,pki=hi[j],j
            if peak-lo[j]>=150: cand=j
        else:
            if lo[j]<lo[cand]: cand=j
            if hi[j]-lo[cand]>=60:
                rows.append(dict(date=d,kind='swing low',hm=g.hm[cand],low=lo[cand],peak_hm=g.hm[pki],peak=peak,fall=peak-lo[cand]))
                peak,pki=hi[j],j; cand=None
                # restart peak tracking from here
    # (lows still 'cand' at end of day are the day low or unconfirmed)
L=pd.DataFrame(rows).drop_duplicates(['date','hm']).sort_values(['date','hm'])
L['kind']=L.groupby(['date','hm']).kind.transform('first')
print(L.to_string()); L.to_csv('results/lows_list.csv',index=False)