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)


def rel_spread_diff(report_date=datetime.date.today(), index='HY', rolling=10):
    otr = on_the_run(index)
    ## look at spreads
    df = get_index_quotes(index, list(range(otr - rolling, otr + 1)),
                          tenor=['3yr', '5yr', '7yr', '10yr'], years=5)

    duration = df.duration.xs((report_date, '5yr', otr), level=['date', 'tenor', 'series'])[-1]

    df = df.xs('5yr', level='tenor')['closespread'].groupby(['date']).last()
    df = df.loc['2013-01-15':report_date]
    curr_spread = df.iloc[-1]
    df = df.pct_change(freq='22B').dropna()

    return df.groupby('date').last(), curr_spread, duration


def get_pos(report_date, strategy=None):
    if strategy is None:
        return pd.read_sql_query("SELECT * from list_cds_marks(%s)",
                                 dawndb, params=(report_date,))
    else:
        return pd.read_sql_query("SELECT * from list_cds_marks(%s, %s)",
                                 dawndb, params=(report_date, strategy))

def cleared_cds_margins(report_date=datetime.date.today()):

    df = get_pos(report_date)

    #Cap Allocation for Deltas
    percentile = .95 #monthly 90%tile case...
    shocks, widen, tighten, onTR_dur, onTR_spread = {}, {}, {}, {}, {}
    for ind in ['IG', 'HY', 'EU']:
        shocks[ind], onTR_spread[ind], onTR_dur[ind] = rel_spread_diff(report_date, index=ind)
        widen[ind] = shocks[ind].quantile(percentile)
        tighten[ind] = shocks[ind].quantile(1-percentile)

    df['onTR_notional'] = df.apply(lambda df:
                                   df.notional * df.factor * df.duration / onTR_dur[df.p_index], axis=1)
    df['widen'] = df.apply(lambda df:
                           df.onTR_notional * onTR_spread[df.p_index] * onTR_dur[df.p_index] * widen[df.p_index]/10000, axis=1)
    df['tighten'] = df.apply(lambda df:
                             df.onTR_notional * onTR_spread[df.p_index] * onTR_dur[df.p_index] * tighten[df.p_index]/10000, axis=1)
    delta_alloc = df.groupby('strategy').sum()
    delta_alloc['total'] = delta_alloc.apply(lambda df: max(abs(df.widen), abs(df.tighten)), axis=1)
    return delta_alloc