import datetime
import pandas as pd
from analytics.index import CreditIndex
from db import serenitas_pool
from statistics import median


def get_refids(index, series, expiry, value_date=datetime.date.today(),
               sources=["GS", "MS", "CITI"]):
    sql_str = ("SELECT ref_id, ref, quotedate FROM swaption_ref_quotes "
               "WHERE quotedate::date=%s "
               "  AND quote_source=%s "
               "  AND index=%s AND series=%s"
               "  AND expiry=%s "
               "ORDER BY quotedate DESC LIMIT 1")
    d = {}
    conn = serenitas_pool.getconn(__name__)
    with conn.cursor() as c:
        for s in sources:
            c.execute(sql_str, (value_date, s, index, series, expiry))
            d[s] = c.fetchone()
    return d

def adjust_stacks(index_type, series, expiry,
                  value_date=datetime.date.today(),
                  sources=["GS", "MS", "CITI"], common_ref=None):
    conn = serenitas_pool.getconn(__name__)
    d = get_refids(index_type, series, expiry, value_date, sources)
    if common_ref is None:
        common_ref = median(v[1] for v in d.values())
    index = CreditIndex(index_type, series, "5yr", value_date=value_date, notional=10000.)
    index.ref = common_ref
    old_pv = index.pv
    quotes = {}
    for s, (ref_id, ref, _) in d.items():
        index.ref = ref
        dindex_pv = index.pv - old_pv
        df = pd.read_sql_query("SELECT strike, pay_bid, pay_offer, delta_pay, "
                               "rec_bid, rec_offer, delta_rec FROM swaption_quotes "
                               "WHERE ref_id=%s ORDER BY strike",
                               conn,
                               params=(ref_id,),
                               index_col=['strike'])
        if s == "GS":
            df['delta_rec'] = 1 - df['delta_pay']
            if index_type == "HY":
                df[['pay_bid', 'pay_offer', 'rec_bid', 'rec_offer']] *=100
        if s == "CITI":
            df['delta_rec'] *= -1
        if dindex_pv != 0.:
            df[['pay_bid', 'pay_offer']] = df[['pay_bid', 'pay_offer']].sub(
                df.delta_pay * dindex_pv, axis=0)
            df[['rec_bid', 'rec_offer']] = df[['rec_bid', 'rec_offer']].add(
                df.delta_rec * dindex_pv, axis=0)
        quotes[s] = df
    quotes = pd.concat(quotes, names=['source'])
    quotes = quotes.swaplevel('source', 'strike').sort_index()
    inside_quotes = pd.concat([
        quotes[['pay_bid', 'rec_bid']].groupby(level='strike').max(),
        quotes[['pay_offer', 'rec_offer']].groupby(level='strike').min()],
                              axis=1
    ).sort_index(axis=1)
    quotes = quotes.unstack('source')
    d = {}
    for k in ['pay_bid', 'rec_bid']:
        #quotes[k].style.apply(highlight_max, axis=1)
        df = pd.concat([quotes[k], inside_quotes[k]], axis=1)
        d[k] = df.rename(columns={k: 'Best'})
    for k in ['pay_offer', 'rec_offer']:
        #quotes[k].style.apply(highlight_min, axis=1)
        df = pd.concat([inside_quotes[k], quotes[k]], axis=1)
        d[k] = df.rename(columns={k: 'Best'})
    serenitas_pool.putconn(conn, __name__)
    return common_ref, pd.concat(d, axis=1)