import cvxpy
import datetime
import math
import numpy as np
import pandas as pd

from pandas.tseries.offsets import BDay
from arch import arch_model
from db import dbengine, dbconn
from scipy.interpolate import interp1d
from analytics import Index
from dates import bond_cal
serenitasdb = dbengine('serenitasdb')

def get_daily_pnl(index, series, tenor, coupon=1):
    sql_str = "SELECT date, adjcloseprice AS close, closespread AS spread, duration, theta FROM index_quotes " \
    "WHERE index=%s and series=%s and tenor = %s"
    df = pd.read_sql_query(sql_str, serenitasdb, parse_dates=['date'],
                           index_col=['date'], params=(index, series, tenor))
    df.sort_index(inplace=True)
    df['dt'] = df.index.to_series().diff().astype('timedelta64[D]')
    df['pnl'] = df['close'].ffill().diff() + df.dt/360*coupon
    return df

def daily_spreads(index, series, tenor):
    """computes daily spreads returns

    Parameters
    ----------
    index : string
    series : int
    tenor : string
    """
    sql_str = "SELECT date, closespread AS spread FROM index_quotes " \
              "WHERE index=%s and series=%s and tenor = %s"
    df = pd.read_sql_query(sql_str, serenitasdb, parse_dates=['date'], index_col=['date'],
                           params=(index.upper(), series, tenor))
    df.sort_index(inplace=True)
    return df.spread.pct_change().dropna()

def insert_quotes():
    # backpopulate some version i+1 quotes one day before they start trading so that
    # we get continuous time series in the returns
    dates = pd.DatetimeIndex(['2014-05-21', '2015-02-19', '2015-03-05','2015-06-23'])
    df = pd.read_sql_query("SELECT DISTINCT ON (date) * FROM index_quotes " \
                           "WHERE index='HY' AND tenor='5yr' " \
                           "ORDER BY date, series DESC, version DESC",
                           serenitasdb, parse_dates=['date'], index_col=['date'])
    df = df.loc[dates]
    for tup in df.itertuples():
        result = serenitasdb.execute("SELECT indexfactor, cumulativeloss  FROM index_version " \
                                     "WHERE index = 'HY' AND series=%s AND version in (%s, %s)" \
                                     "ORDER BY version",
                                     (tup.series, tup.version, tup.version+1))
        factor1, cumloss1 = result.fetchone()
        factor2, cumloss2 = result.fetchone()
        recovery = 1-(cumloss2-cumloss1)
        version2_price = (factor1 * tup.closeprice - 100*recovery)/factor2
        print(version2_price)
        serenitasdb.execute("INSERT INTO index_quotes(date, index, series, version, tenor, closeprice)" \
                            "VALUES(%s, %s, %s, %s, %s, %s)",
                            (tup.Index, 'HY', tup.series, tup.version+1, tup.tenor, version2_price))

def index_returns(date=None, years=3, index="IG", tenor="5yr"):
    """computes on the run spread returns"""
    if date is None:
        date = (pd.Timestamp.now() - pd.DateOffset(years=years)).date()
    sql_str = "SELECT date, series, version, closespread AS spread FROM index_quotes " \
              "WHERE index=%s and date>=%s and tenor = %s"
    df = pd.read_sql_query(sql_str, serenitasdb, parse_dates=['date'],
                           index_col=['date', 'series', 'version'],
                           params=(index.upper(), date, tenor))
    df.sort_index(inplace=True)
    return (df.groupby(level=['series', 'version']).
            transform(lambda x: x.pct_change()).
            dropna().
            groupby(level='date').
            last())


def index_price_returns(date=None, years=3, index="IG", tenor="5yr"):
    """computes on the run price returns taking coupons into account"""
    if date is None:
        date = (pd.Timestamp.now() - pd.DateOffset(years=years)).date()
    sql_str = "SELECT date, series, version, closeprice AS price FROM index_quotes " \
              "WHERE index=%s and date>=%s and tenor = %s"
    df = pd.read_sql_query(sql_str, serenitasdb, parse_dates=['date'],
                           index_col=['date', 'series', 'version'],
                           params=(index.upper(), date, tenor))
    df.sort_index(inplace=True)
    ## get rid of holidays
    dates = df.index.levels[0]
    holidays = bond_cal().holidays(start=dates[0], end=dates[-1])
    df = df.loc(axis=0)[dates.difference(holidays),:,:]

    def returns(df, coupon=1):
        df['returns'] = df.price.pct_change() + \
                        coupon * df.index.levels[0].to_series().diff().dt.days/360/df.price
        return df
    return (df.groupby(level=['series', 'version']).
            apply(returns, (1 if index=='IG' else 5,)).
                  groupby(level='date')['returns'].last())

def realized_vol(index, series, tenor, date=None, years=None):
    """computes the realized spread volatility"""
    if date is None:
        if years is None:
            raise ValueError("need to provide at least one of date or years")
        date = (pd.Timestamp.now() - pd.DateOffset(years=years)).date()
    returns = daily_spreads(index, series, tenor)
    # GARCH(1,1) volatility process with constant mean
    am = arch_model(returns)
    res = am.fit(update_freq=0, disp='off')
    return (res.conditional_volatility * math.sqrt(252), res)

def lr_var(res):
    """ computes long run variance of the garch process

    .. math::

    \sigma^2=\frac{\omega}{1-\sum_{i=1}^p \alpha_i + \sum_{i=1}^q \beta_i}

    """
    names = res.model.volatility.parameter_names()
    ## names[0] is omega, rest is alpha[1],..., alpha[p], beta[1],...,beta[q]
    var = res.params[names[0]]/(1 - res.params[names[1:]])
    return math.sqrt(var * 252)

def atm_vol_fun(v, ref_is_price=False, moneyness=0.2):
    f = interp1d(v.strike.values, v.vol.values, fill_value='extrapolate')
    atm_val = v['fwdspread'].iat[0]
    otm_val = atm_val * (1 + moneyness) ## doesn't make sense for HY
    return pd.Series(f(np.array([atm_val, otm_val])), index = ['atm_vol', 'otm_vol'])

def atm_vol(index, series, moneyness=0.2):
    df =  pd.read_sql_query('SELECT quotedate, expiry, strike, vol from swaption_quotes ' \
                            'WHERE index = %s and series = %s',
                            serenitasdb, index_col=['quotedate', 'expiry'],
                            params = (index.upper(), series))
    index_data = pd.read_sql_query(
        'SELECT quotedate, expiry, fwdspread from swaption_ref_quotes ' \
        'WHERE index= %s and series = %s',
        serenitasdb, index_col = ['quotedate', 'expiry'],
        params = (index.upper(), series))

    df = df.join(index_data)
    df = df.groupby(level=['quotedate', 'expiry']).filter(lambda x: len(x)>=2)
    df = df.groupby(level=['quotedate', 'expiry']).apply(atm_vol_fun, index=="HY", moneyness)
    df = df.reset_index(level=-1) #move expiry back to the column
    return df

def atm_vol_date(index, date):
    df =  pd.read_sql_query('SELECT quotedate, series, expiry, strike, vol ' \
                            'FROM swaption_quotes ' \
                            'WHERE index = %s and quotedate >= %s',
                            serenitasdb,
                            index_col=['quotedate', 'expiry', 'series'],
                            params=(index.upper(), date))
    index_data = pd.read_sql_query(
        'SELECT quotedate, expiry, series,  fwdspread FROM swaption_ref_quotes ' \
        'WHERE index= %s and quotedate >= %s',
        serenitasdb, index_col=['quotedate', 'expiry', 'series'],
        params = (index.upper(), date))
    df = df.join(index_data)
    df = df.groupby(df.index).filter(lambda x: len(x)>=2)
    df = df.groupby(level=['quotedate', 'expiry', 'series']).apply(atm_vol_fun)
    df = df.reset_index(level=['expiry', 'series']) #move expiry and series back to the columns
    return df

def rolling_vol(df, col='atm_vol', term=[3]):
    """compute the rolling volatility for various terms"""
    df = df.groupby(df.index).filter(lambda x: len(x)>2)
    def aux(s, col, term):
        k = s.index[0]
        f = interp1d(s.expiry.values.astype('float'), s[col].values, fill_value='extrapolate')
        x = np.array([(k + pd.DateOffset(months=t)).to_datetime64().astype('float') \
                      for t in term])
        return pd.Series(f(x), index=[str(t)+'m' for t in term])

    df = df.groupby(level='quotedate').apply(aux, col, term)
    # MS quotes don't have fwdspread so they end up as NA
    return df.dropna()

def vol_var(percentile=0.99, index='IG'):
    df = atm_vol_date("IG", datetime.date(2014, 6, 11))
    df = rolling_vol(df, term=[1,2,3])
    df = df.sort_index()
    df = df.groupby(df.index.date).last()
    return df.pct_change().quantile(percentile)

def index_rolling_returns(date=None, years=3, index="IG", tenor="5yr"):
    """computes on the run returns"""
    if date is None:
        date = (pd.Timestamp.now() - pd.DateOffset(years=years)).date()
    sql_str = "SELECT date, series, closespread AS spread FROM index_quotes " \
              "WHERE index=%s and date>=%s and tenor = %s"
    df = pd.read_sql_query(sql_str, serenitasdb, parse_dates=['date'], index_col=['date', 'series'],
                           params=(index.upper(), date, tenor))
    df.sort_index(inplace=True)
    df = df.groupby(level='series').pct_change()
    return df.groupby(level='date').last()

def get_index_spread(index, series, date, conn):
    with conn.cursor() as c:
        c.execute("SELECT closespread from index_quotes " \
                  "WHERE index=%s and series=%s and date=%s and tenor='5yr'",
                  (index, series, date))
        try:
            spread, = c.fetchone()
        except TypeError:
            spread = None
    conn.commit()
    return spread

def get_index_ref(index, series, date, expiry, conn):
    with conn.cursor() as c:
        c.execute("SELECT ref, fwdspread from swaption_ref_quotes " \
                  "WHERE index=%s and series=%s and quotedate::date=%s "\
                  "AND expiry=%s ORDER BY quotedate desc",
                  (index, series, date, expiry))
        try:
            ref, fwdspread = c.fetchone()
        except TypeError:
            ref, fwdspread = None, None
    conn.commit()
    return ref, fwdspread

def get_option_pnl(strike, expiry, index, series, start_date, engine):
    for s in [strike, strike+2.5, strike-2.5, strike+5]:
        df = pd.read_sql_query("SELECT quotedate, (pay_bid+pay_offer)/2 AS pay_mid, " \
                               "(rec_bid+rec_offer)/2 AS rec_mid FROM swaption_quotes " \
                               "WHERE strike=%s and expiry=%s and index=%s and series=%s" \
                               "and quotedate>=%s", engine,
                               params=(s, expiry, index, series, start_date),
                               index_col='quotedate',
                               parse_dates=['quotedate'])
        if not df.empty and df.index[0].date() == start_date:
            strike = s
            break
    else:
        raise ValueError("Couldn't find data starting from that date")

    if not pd.api.types.is_datetime64tz_dtype(df.index):
        df.index = df.index.tz_localize('utc')

    df = df.groupby(df.index.normalize()).last()
    if expiry < datetime.date.today():
        spread = get_index_spread(index, series, expiry, engine.raw_connection())
        underlying = Index.from_name(index, series, "5yr", expiry, 1e4)
        underlying.spread = spread
        pv = underlying.pv
        underlying.spread = strike
        if spread > strike:
            pay_mid, rec_mid = pv-underlying.pv, 0
        else:
            pay_mid, rec_mid = 0, underlying.pv - pv
            pv = underlying.pv
        df = df.append(pd.DataFrame([[pay_mid, rec_mid]],
                                    columns=['pay_mid', 'rec_mid'],
                                    index=[pd.Timestamp(expiry, tz='UTC')]))
    return df, strike

def sell_vol_strategy(index="IG", months=3):
    engine = dbengine('serenitasdb')
    conn = engine.raw_connection()
    with conn.cursor() as c1, conn.cursor() as c2:
        c1.execute("SELECT DISTINCT series, expiry FROM " \
                  "swaption_quotes ORDER BY expiry, series desc")
        d = {}
        for series, expiry in c1:
            start_date = BDay().rollback(expiry - pd.DateOffset(months=months)).date()
            if start_date > datetime.date.today():
                break
            c2.execute("SELECT max(quotedate::date) FROM swaption_quotes WHERE " \
                       "index=%s AND series=%s AND expiry=%s AND quotedate<=%s",
                       (index, series, expiry, start_date))
            actual_start_date, = c2.fetchone()
            if actual_start_date is None or (start_date - actual_start_date).days > 5:
                continue
            ref, fwdspread = get_index_ref(index, series, actual_start_date, expiry, conn)
            if fwdspread is None:
                fwdspread = ref + months / 50 #TODO: use actual values
            strike = round(fwdspread/2.5) * 2.5
            pnl, strike = get_option_pnl(strike, expiry, index, series, actual_start_date, engine)
            d[(series, strike, expiry)] = pnl
    conn.commit()
    return d

def aggregate_trades(d):
    r = pd.Series()
    for v in d.values():
        r = r.add(-v.sum(1).diff().dropna(), fill_value=0)
    return r


def compute_allocation(df):
    Sigma = df.cov().values
    gamma = cvxpy.Parameter(sign='positive')
    mu = df.mean().values
    w = cvxpy.Variable(3)
    ret = mu.T*w
    risk = cvxpy.quad_form(w, Sigma)
    prob = cvxpy.Problem(cvxpy.Maximize(ret - gamma * risk),
                         [cvxpy.sum_entries(w) == 1,
                          w >= -2,
                           w <= 2])

    gamma_x = np.linspace(0, 0.02, 500)
    W = np.empty((3, gamma_x.size))
    for i, val in enumerate(gamma_x):
        gamma.value = val
        prob.solve()
        W[:,i] = np.asarray(w.value).squeeze()

    fund_return = mu @ W
    fund_vol= np.array([math.sqrt(W[:,i] @ Sigma @W[:,i]) for i in range(gamma_x.size)])
    return (W, fund_return, fund_vol)


if __name__ == "__main__":
    d1 = sell_vol_strategy(months=1)
    d2 = sell_vol_strategy(months=2)
    d3 = sell_vol_strategy(months=3)
    all_tenors = pd.concat([aggregate_trades(d) for d in [d1, d2, d3]], axis=1)
    all_tenors.columns = ['1m', '2m', '3m']
    all_tenors['optimal'] = ((1.2*all_tenors['1m']).
                             sub(1.2*all_tenors['2m'], fill_value=0).
                             add(all_tenors['3m'], fill_value=0))