from analytics.curve_trades import on_the_run
from analytics.index_data import get_index_quotes, index_returns
from db import dbengine

import pandas as pd
import math
import datetime

dawndb = dbengine('dawndb')
serenitasdb = dbengine('serenitasdb')

def hist_var(portf, index_type='IG', quantile=.05, years=5):
    df = index_returns(index=index_type, years=years,
                       tenor=['3yr', '5yr', '7yr', '10yr'])
    df = (df.reset_index(['index'], drop=True).
          reorder_levels(['date', 'series', 'tenor']))
    returns = df.spread_return.dropna().reset_index('series')
    returns['dist_on_the_run'] = (returns.
                                  groupby('date')['series'].
                                  transform(lambda x: x.max() - x))
    del returns['series']
    returns = returns.set_index('dist_on_the_run', append=True).unstack('tenor')
    returns.columns = returns.columns.droplevel(0)
    portf.reset_pv()

    otr = on_the_run(index_type)
    spreads = pd.DataFrame({'spread': portf.spread,
                            'tenor': [ind.tenor for ind in portf.indices],
                            'dist_on_the_run': [otr - ind.series for ind in portf.indices]})
    spreads = spreads.set_index(['dist_on_the_run', 'tenor'])
    r = []
    for k, g in returns.groupby(level='date', as_index=False):
        shocks = g.reset_index('date', drop=True).stack('tenor')
        shocks.name = 'shocks'
    portf.spread = spreads.spread * (1 + spreads.join(shocks).shocks)
    r.append((k, portf.pnl))
    pnl = pd.DataFrame.from_records(r, columns=['date', 'pnl'], index=['date'])
    return pnl.quantile(quantile) * math.sqrt(12)