from pyisda.credit_index import CreditIndex
from analytics import BasketIndex
from pyisda.legs import FeeLeg, ContingentLeg
from pyisda.logging import enable_logging

import datetime
import numpy as np
import pandas as pd

from db import dbconn

def all_curves_pv(curves, today_date, jp_yc, start_date, step_in_date, value_date, maturities):
    r = {}
    for d in maturities:
        tenor = {}
        coupon_leg = FeeLeg(start_date, d, True, 1., 1.)
        default_leg = ContingentLeg(start_date, d, True)
        accrued = coupon_leg.accrued(step_in_date)
        tickers = []
        data = []
        for sc in curves:
            coupon_leg_pv = coupon_leg.pv(today_date, step_in_date, value_date, jp_yc, sc, False)
            default_leg_pv = default_leg.pv(today_date, step_in_date, value_date,
                                            jp_yc, sc, 0.4)
            tickers.append(sc.ticker)
            data.append((coupon_leg_pv-accrued, default_leg_pv))
        r[pd.Timestamp(d)] = pd.DataFrame.from_records(data,
                                                       index=tickers,
                                                       columns=['duration', 'protection_pv'])
    return pd.concat(r, axis=1).swaplevel(axis=1).sort_index(axis=1,level=0)


def calibrate_portfolio(index_type, series, tenors=['3yr', '5yr', '7yr', '10yr'],
                        start_date=None):
    index = BasketIndex(index_type, series, tenors)
    r = {}
    if start_date:
        index.index_quotes = index.index_quotes[startdate:]
    for trade_date in index.index_quotes.index.get_level_values(0):
        index.trade_date = trade_date
        index.tweak()
        durations = [index.duration(m) for m in index.maturities]
        thetas = [index.theta(m) for m in index.maturities]
        r[trade_date] = pd.DataFrame({'duration': durations,
                                      'theta': thetas,
                                      'tweak': index.tweaks}, index=tenors)
    return pd.concat(r)

if __name__=="__main__":
    enable_logging()
    index, series = "IG", 28
    df = calibrate_portfolio(index, series, ['3yr', '5yr', '7yr', '10yr'])
    conn = dbconn('serenitasdb')
    with conn.cursor() as c:
        for k, s in df.iterrows():
            c.execute("UPDATE index_quotes SET duration2=%s, theta2=%s "\
                      "WHERE date=%s AND tenor=%s AND index=%s AND series=%s",
                      (s.duration, s.theta, k[0], k[1], index, series))
    conn.commit()
    conn.close()