import datetime
import numpy as np
import pandas as pd
from analytics.utils import tenor_t
from pandas.tseries.offsets import BDay
from yieldcurve import get_curve
from db import dbengine
from pyisda.legs import FeeLeg, ContingentLeg
from pyisda.curve import SpreadCurve
from pyisda.date import previous_twentieth

serenitas_engine = dbengine('serenitasdb')
tenors = {"IG": ("3yr", "5yr", "7yr", "10yr"),
          "HY": ("3yr", "5yr", "7yr"),
          "EU": ("3yr", "5yr", "7yr", "10yr"),
          "XO": ("3yr", "5yr", "7yr", "10yr")}

sql_str = "INSERT INTO index_risk VALUES(%s, %s, %s)"

def get_legs(index, series, tenors):
    fee_legs = {}
    contingent_legs = {}
    coupons = []
    end_dates = []
    df = pd.read_sql_query("SELECT tenor, maturity, coupon, issue_date "
                           "FROM index_maturity "
                           "WHERE index=%s AND series=%s and tenor IN %s "
                           "ORDER BY maturity",
                           serenitas_engine,
                           params=(index, series, tenors),
                           parse_dates=['maturity', 'issue_date'])
    df.coupon *= 1e-4
    for tenor, maturity, coupon, issue_date in df.itertuples(index=False):
        fee_legs[tenor] = FeeLeg(issue_date, maturity, True, 1., coupon * 1e-4)
        contingent_legs[tenor] = ContingentLeg(issue_date, maturity, True)
        coupons.append(coupon * 1e-4)
    # number of seconds since epoch
    df.maturity = df.maturity.view(int) // int(86400 * 1e9)
    # number of days between 1900-1-1 and epoch
    df.maturity += 134774
    return fee_legs, contingent_legs, df

def credit_curve(value_date, quotes, end_dates, coupons, recoveries, yc):
    step_in_date = value_date + datetime.timedelta(days=1)
    cash_settle_date = pd.Timestamp(value_date) + 3 * BDay()
    start_date = previous_twentieth(value_date)
    sc = SpreadCurve(value_date, yc, start_date,
                     step_in_date, cash_settle_date,
                     end_dates, coupons, quotes,
                     recoveries)
    return sc

conn = serenitas_engine.raw_connection()
for index in ["IG", "HY", "EU", "XO"]:
    if index in ["HY", "XO"]:
        recoveries = np.full(len(tenors[index]), 0.3)
    else:
        recoveries = np.full(len(tenors[index]), 0.4)
    for series in range(18, 31):
        if index in ["EU", "XO"] and series == 30:
            continue
        fee_legs, contingent_legs, df = \
            get_legs(index, series, tenors[index])

        index_quotes = pd.read_sql_query(
            "SELECT id, date, tenor, close_price FROM index_quotes_pre "
            "LEFT JOIN index_risk USING (id) "
            "WHERE index=%s AND series=%s "
            "AND source='MKIT' AND duration is NULL AND tenor IN %s",
            serenitas_engine,
            params=(index, series, tenors[index]),
            parse_dates=['date'],
            index_col='id')
        if index_quotes.empty:
            continue
        index_quotes.tenor = index_quotes.tenor.astype(tenor_t)
        index_quotes = index_quotes.sort_values('tenor')
        index_quotes['close_price'] = 1. - index_quotes['close_price'] / 100
        with conn.cursor() as c:
            for k, v in index_quotes.groupby('date'):
                yc = get_curve(k, "USD" if index in ["IG", "HY"] else "EUR")
                v = v.merge(df, on='tenor')
                sc = credit_curve(k, v.close_price.values, v.maturity.values,
                                  v.coupon.values, recoveries, yc)
                for t in v.tenor.values:
                    risky_annuity = fee_legs[t].(
                        k, step_in_date,
                        cash_settle_date, yc, sc,
                        False)
        conn.commit()
conn.close()