import sys
sys.path.append("..")
from graphics import plot_time_color_map
import analytics.tranche_functions as tch
import analytics.tranche_basket as bkt
import analytics.basket_index as idx_bkt
import numpy as np
import pandas as pd

from analytics import Swaption, BlackSwaption, CreditIndex, BlackSwaptionVolSurface, Portfolio, ProbSurface
from analytics.scenarios import run_swaption_scenarios, run_index_scenarios, run_portfolio_scenarios, run_tranche_scenarios
from scipy.interpolate import interp1d

from datetime import date
from db import dbengine
engine  = dbengine('serenitasdb')

def rv_calc1():
    #let's do IG27 from IG29, need to get the quotes from risk_numbers_new not just random ones
    #Get IG29-1 year shortened rho with TLP, compare to IG27 5y rho
    index = 'IG'
    series = 29
    series2 = series -2
    tenor = '5yr'
    shortened = 4
    method = 'TLP'

    #Read existing results, find which ones need to run
    try:
        results = pd.read_csv(f"/home/serenitas/edwin/Python/rv_{index}{series}.csv",
                              parse_dates=['date'], index_col=['date'])
    except IOError:
        results = pd.DataFrame()
    sql_string = "select distinct date from risk_numbers_new where index = %s and series = %s order by date desc"
    df = pd.read_sql_query(sql_string, engine, params=(index, series), parse_dates=['date'])
    df1 = pd.read_sql_query(sql_string, engine, params=(index, series2), parse_dates=['date'])
    df = df.merge(df1, on=['date'])
    df = df[~df.date.isin(results.index)]

    rho_tlp, pv_tlp, rho_prev_index, pv_prev_index = [], [], [], []

    tranche = bkt.TrancheBasket('IG', series, '5yr')
    tranche2 = bkt.TrancheBasket('IG', series2, '5yr')

    for trade_date in df.date:
        tranche.trade_date = trade_date
        tranche2.trade_date = trade_date
        tranche.build_skew()
        tranche.rho = tranche.map_skew(tranche, method, 4)
        pv = tranche.tranche_pvs().bond_price
        rho_tlp.append(tranche.rho[1:-1])
        pv_tlp.append(pv)

        tranche2.build_skew()
        rho_prev_index.append(tranche2.rho[1:-1])

        tranche.rho = tranche2.rho
        pv = tranche.tranche_pvs(shortened=4).bond_price
        pv_prev_index.append(pv)

    temp1 = pd.DataFrame(rho_tlp, index=df.date, columns=['3_rho_tlp', '7_rho_tlp', '15_rho_tlp'])
    temp2 = pd.DataFrame(pv_tlp, index=df.date, columns=['03_pv_tlp', '37_pv_tlp', '715_pv_tlp', '15100_pv_tlp'])
    temp3 = pd.DataFrame(rho_prev_index, index=df.date, columns=['3_rho_ig27', '7_rho_ig27', '15_rho_ig27'])
    temp4 = pd.DataFrame(pv_prev_index, index=df.date, columns=['03_pv_ig27', '37_pv_ig27', '715_pv_ig27', '15100_pv_ig27'])

    results = results.append(pd.concat([temp1, temp2, temp3, temp4], axis=1))

    result.to_csv("/home/serenitas/edwin/Python/rv_" + index + series + ".csv")

def dispersion():

    from quantlib.time.api import Schedule, Rule, Date, Period, WeekendsOnly
    from quantlib.settings import Settings

    curves = {}
    maturities = {}
    settings = Settings()
    for series in [24, 25, 26, 27, 28, 29]:
        index_temp = idx_bkt.MarkitBasketIndex('IG', series, ["5yr",], value_date=trade_date)
        maturities[series] = index_temp.maturities[0]
        cds_schedule = Schedule.from_rule(settings.evaluation_date, Date.from_datetime(maturities[series]),
                                      Period('3M'), WeekendsOnly(), date_generation_rule=Rule.CDS2015)
        sm, tickers = index_temp.survival_matrix(cds_schedule.to_npdates().view('int') + 134774)
        curves[series] = pd.DataFrame(1 - sm, index=tickers, columns=cds_schedule)
        #temp = (pd.to_datetime(maturities[series]) - datetime.datetime(1970,1,1)).days + 134774
        #curves[series] = pd.concat([c.to_series() for _,_, c in index_temp.items()], axis=1)
    curve_df = pd.concat(curves).stack()
    curve_df.index.rename(['series', 'maturity', 'name'], inplace=True)
    disp = {}
    for series in [24, 25, 26, 27, 28, 29]:
        temp = curve_df.xs([series, maturities[series].strftime('%Y-%m-%d')])
        temp = temp[pd.qcut(temp, 10, labels=False) == 9]
        disp[series] = temp.std()/temp.mean()
    dispersion = pd.concat(disp)
    curve_df.groupby(['series', 'maturity']).mean()
    curve_df.groupby(['series', 'maturity']).std()