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from index_data import get_index_quotes, index_returns
import pandas as pd
import math
from scipy.stats.mstats import zscore
import matplotlib.pyplot as plt
index = 'IG'
on_the_run = 28
def curve_spread_diff(index = 'IG', on_the_run = 28):
## look at spreads
df = get_index_quotes(index, list(range(on_the_run-6,on_the_run+1)), tenor=['3yr', '5yr', '7yr', '10yr'])
spreads = df.groupby(level=['date', 'tenor']).nth(-1)['closespread'].unstack(-1)
# remove 'yr'
spreads.columns = [int(col[:-2]) for col in spreads.columns]
spreads = spreads.sort_index(1)
spreads_diff = spreads.diff(axis=1)
spreads_diff = spreads_diff.filter([5, 7, 10])
spreads_diff['5-10'] = spreads_diff[7] + spreads_diff[10]
spreads_diff.columns = ['3-5', '5-7', '7-10', '5-10']
return spreads_diff
def spreads_diff_table(spreads_diff):
df = pd.DataFrame()
df['min'] = spreads_diff.min()
df['max'] = spreads_diff.max()
df['average'] = spreads_diff.mean()
df['current'] = spreads_diff.iloc[-1]
df['zscore'] = pd.Series(zscore(spreads_diff)[-1], index = df.index)
pd.DataFrame(zscore(spreads_diff), index=spreads_diff.index, columns=spreads_diff.columns).plot()
return df
def theta_matrix(index = 'IG', on_the_run = 28):
df = get_index_quotes(index, list(range(on_the_run-6,on_the_run+1)), tenor=['3yr', '5yr', '7yr', '10yr'])
df['theta_per_dur'] = df.theta2/df.duration2
theta_matrix = df.groupby(level=['date', 'tenor','series']).nth(-1)['theta_per_dur']
theta_matrix_1 = theta_matrix.xs(theta_matrix.index.max()[0], level = 0).unstack(0)
return theta_matrix_1[['3yr', '5yr', '7yr', '10yr']]
def on_the_run_theta(index = 'IG', on_the_run = 28):
df = get_index_quotes(index, list(range(on_the_run-6,on_the_run+1)), tenor=['3yr', '5yr', '7yr', '10yr'])
df['theta_per_dur'] = df.theta2/df.duration2
theta_matrix = df.groupby(level=['date', 'tenor']).nth(-1)['theta_per_dur']
theta_matrix.unstack(-1).plot()
def curve_returns(index = 'IG', on_the_run = 28):
## look at returns
df = index_returns(index= index, series=list(range(on_the_run-6,on_the_run+1)), tenor=['3yr', '5yr', '7yr', '10yr'])
## on-the-run returns
returns = df.price_return.unstack(-1).dropna().groupby(level='date').nth(-1)
strategy = ['510', '710', '3510']
strategies_return = pd.DataFrame()
strategies_return[strategy[0]] = 1.78 * returns['5yr'] - returns['10yr']
strategies_return[strategy[1]] = 1.33 * returns['7yr'] - returns['10yr']
strategies_return[strategy[2]] = -2 * returns['3yr']+ 3 * returns['5yr'] - 1 * returns['10yr']
strategies_return_monthly = pd.DataFrame()
for strat in strategy:
strategies_return_monthly[strat] = strategies_return[strat].groupby(pd.TimeGrouper(freq='M')).agg(lambda df:(1+df).prod()-1)
sharpe = {}
monthly_sharpe = {}
for strat in strategy:
sharpe[strat] = strategies_return[strat].mean()/strategies_return[strat].std()*math.sqrt(252)
monthly_sharpe[strat] = strategies_return_monthly[strat].mean()/strategies_return_monthly[strat].std()*math.sqrt(12)
worst_drawdown = {}
for strat in strategy:
worst_drawdown[strat] = strategies_return[strat].nsmallest(10)
return (sharpe, monthly_sharpe, worst_drawdown)
def cross_series_curve(index = 'IG', on_the_run = 28):
df = index_returns(index= index, series=list(range(on_the_run-6,on_the_run+1)), tenor=['3yr', '5yr', '7yr', '10yr'])
## look cross series
returns1 = df.xs(['5yr','IG'], level = ['tenor','index']).price_return.unstack(-1)
price_diff = pd.DataFrame()
for ind in [28,27,26]:
price_diff[ind] = returns1[ind] - 1.55* returns1[ind - 4]
price_diff = price_diff.stack().groupby(level = 'date').nth(-1)
monthly_returns_cross_series = price_diff.groupby(pd.TimeGrouper(freq='M')).agg(lambda df:(1+df).prod()-1)
monthly_returns_cross_series.plot()
def forward_loss():
from db import dbengine, dbconn
serenitasdb = dbengine('serenitasdb')
index = 'IG'
start_date = (pd.Timestamp.now() - pd.DateOffset(years=3)).date()
df = pd.read_sql_query("SELECT date, index, series, tenor, duration, closespread, closespread*duration/100 as indexel " \
"FROM index_quotes WHERE index=%s AND date >= %s " \
"ORDER BY date DESC, series ASC, duration ASC",
serenitasdb, parse_dates=['date'], params=[index, start_date])
df1 = pd.read_sql_query("select index, series, tenor, maturity from index_maturity", serenitasdb, parse_dates=['maturity'])
df = df.merge(df1, on=['index','series','tenor'])
df = df.set_index(['date','index', 'maturity']).dropna()
df = df.groupby(level=['date','index', 'maturity']).nth(-1)
# annual change, to take out some noise
df['fwd_loss_rate'] = df.indexel.diff(2)/df.duration.diff(2)
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