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, ForwardIndex
from analytics.index_data import index_returns

serenitasdb = dbengine('serenitasdb')

def realized_vol(index, series, tenor='5yr', date=None, years=None, return_type='spread'):
    """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 = index_returns(index=index, series=series, tenor=tenor, years=None).dropna()
    # GARCH(1,1) volatility process with constant mean, scale to help with fitting
    scale = 10
    am = arch_model(scale * returns[return_type+'_return'])
    res = am.fit(update_freq=0, disp='off')
    return (res.conditional_volatility * math.sqrt(252)/scale, res)

def lr_var(res):
    r""" 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_calc(df, index_type, moneyness):
    r = np.empty((len(df.index.unique()), 3))
    i = 0
    index_keys = []
    for s, g1 in df.groupby(level='series'):
        index = Index.from_name(index_type, s, '5yr')
        for date, g2 in g1.groupby(pd.Grouper(level='quotedate', freq='D')):
            if not g2.empty:
                index.trade_date = date.date()
            for (ref, expiry), g3 in g2.reset_index('expiry').groupby(['ref', 'expiry']):
                index.ref = ref
                atm_val = forward_spread = ForwardIndex(index, expiry, False).forward_spread
                otm_val = atm_val * (1 + moneyness)
                if index._quote_is_price:
                    index.spread = atm_val
                    atm_val = index.price
                    index.spread = otm_val
                    otm_val = index.price
                for quotedate, v in g3.groupby(level='quotedate'):
                    f = interp1d(v.strike.values, v.vol.values, fill_value='extrapolate')
                    r[i, 0] = forward_spread
                    r[i, 1:] = f([atm_val, otm_val])
                    i += 1
                    index_keys.append((quotedate, expiry, s))
    df = pd.DataFrame(data=r,
                      index=pd.MultiIndex.from_tuples(index_keys,
                                                      names=['quotedate', 'expiry', 'series']),
                      columns=['forward_spread', 'atm_vol', 'otm_vol'])
    df['T'] = df.index.get_level_values('expiry').values.astype('datetime64[D]') - \
              df.index.get_level_values('quotedate').values.astype('datetime64[D]')
    df['T'] = df['T'].dt.days / 365
    return df

def atm_vol(index, date, series=None, moneyness=0.2):
    extra_filter = ''
    params = (index.upper(), date)
    if series:
        extra_filter = ' AND series=%s'
        params = params + (series,)
    sql_str = "SELECT * from swaption_ref_quotes JOIN swaption_quotes " \
              "USING (quotedate, index, series, expiry) WHERE index=%s " \
              f"and quotedate>=%s {extra_filter} ORDER BY quotedate ASC"

    df = pd.read_sql_query(sql_str, serenitasdb, params=params)
    df.quotedate = pd.to_datetime(df.quotedate, utc=True).dt.tz_convert('America/New_York')
    df = df.set_index(['quotedate', 'index', 'series', 'expiry'])
    df = df.groupby(level=['quotedate', 'index', 'series', 'expiry']).filter(lambda x: len(x)>2)
    return atm_vol_calc(df, index, moneyness)

def rolling_vol(df, col='atm_vol', term=[3]):
    """compute the rolling volatility for various terms"""
    df = df.reset_index(level=['expiry', 'series'])
    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=0.975, index='IG', start_date=datetime.date(2014, 6, 11)):
    """compute lo and hi percentiles of atm volatility daily change

    Probably overestimates:
    - we don't check that the quotes come from the same dealer
    - we should group it by series
    """
    df = atm_vol(index, start_date)
    df = rolling_vol(df, term=[1,2,3])
    df = df.sort_index()
    df = df.groupby(df.index.date).nth(-1)
    return df.diff().quantile([1-percentile, percentile])

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()).nth(-1)
    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))
    import datetime
    import statsmodels.formula.api as smf
    ## HY
    df = atm_vol("HY", datetime.date(2017, 3, 20))
    df['forward_spread'] *= 1e-4
    df['log_forward_spread'] = np.log(df['forward_spread'])
    df['log_atm_vol'] = np.log(df['atm_vol'])
    df_hy28 = df.xs(28, level='series')
    results = smf.ols('log_atm_vol ~ log_forward_spread + T', data=df_hy28).fit()
    beta_hy28 = 1 + results.params.log_forward_spread
    print(results.summary())

    ## IG
    df = atm_vol("IG", datetime.date(2017, 3, 20))
    df['forward_spread'] *= 1e-4
    df['log_forward_spread'] = np.log(df['forward_spread'])
    df['log_atm_vol'] = np.log(df['atm_vol'])
    df_ig28 = df.xs(28, level='series')
    results = smf.ols('log_atm_vol ~ log_forward_spread + T', data=df_ig28).fit()
    beta_ig28 = 1 + results.params.log_forward_spread
    print(results.summary())