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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 datetime
import statsmodels.formula.api as smf
from statsmodels.sandbox.regression.predstd import wls_prediction_std
import numpy as np
import matplotlib.pyplot as plt
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_by_series(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 ratio_within_series(index = 'IG', on_the_run = 28, param = 'duration', groupby_level = ['date', 'series']):
df = get_index_quotes(index, list(range(on_the_run-6,on_the_run+1)), tenor=['3yr', '5yr', '7yr', '10yr'])
r = {}
for i,g in df.groupby(level=groupby_level):
five_yr = g.xs('5yr', level = 'tenor')[param]
r[i] = g[param]/five_yr[-1]
df1 = pd.concat(r)
dftemp= pd.DataFrame(df1.groupby(level=['date', 'tenor','series']).nth(-1).rename(param+'_ratio_to_5yr'))
df2 = df.groupby(level=['date', 'tenor','series']).nth(-1).merge(dftemp, left_index=True, right_index=True)
return df2.unstack(-2)
def curve_3_5_10(df):
#buy 3y, sell 5y, buy 10y
df['3_5_10'] = - df.theta2['3yr'] / df.duration_ratio_to_5yr['3yr'] \
+ 2 * df.theta2['5yr'] \
- df.theta2['10yr'] / df.duration_ratio_to_5yr['10yr']
df['3_5_10'].dropna().unstack(-1).plot()
def curve_5_10(df):
#buy sell 5y, buy 10y
df['5_10'] = df.theta2['5yr'] - df.theta2['10yr'] / df.duration_ratio_to_5yr['10yr']
df['5_10'].dropna().unstack(-1).plot()
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)
results = pd.DataFrame()
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).mean()
results = results.append(sharpe, ignore_index=True)
results = results.append(monthly_sharpe, ignore_index=True)
results = results.append(worst_drawdown, ignore_index=True)
results['results'] = ['Sharpe','Monthly Sharpe','Mean Worst 10 Days Drawdown']
return results.set_index('results')
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 - 3y to 5y
returns1 = df.xs(['5yr', index], level = ['tenor','index']).price_return.unstack(-1)
price_diff = pd.DataFrame()
for ind in list(range(on_the_run-2, on_the_run+1)):
price_diff[ind] = returns1[ind] - 1.6* 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)
plt.plot(monthly_returns_cross_series)
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)
def read_IG_curve_pos():
from analytics import Index, Portfolio
trade_1 = Index.from_tradeid(886)
trade_2 = Index.from_tradeid(885)
trade_3 = Index.from_tradeid(884)
portf = Portfolio([trade_1, trade_2, trade_3])
def curve_model():
#OLS model
df = ratio_within_series(param = 'closespread', groupby_level = ['date', 'series'])
df = df.groupby(level='date').last()
df = pd.concat([df.duration['5yr'],df.closespread['5yr'], df.closespread_ratio_to_5yr['10yr']], axis = 1, keys=['duration', 'closespread', 'ratio'])
results = smf.ols('np.log(ratio) ~ np.log(duration) + np.log(closespread)', data=df).fit()
df['predicted'] = np.exp(results.predict())
results.summary()
prstd_ols, iv_l, iv_u = wls_prediction_std(results)
df['up_2_stdev'] = np.exp(iv_u)
df['down_2_stdev'] = np.exp(iv_l)
#dr/dspread = exp(k) + spread_coeff * duration ^ dur_coeff * spread ^ (spread_coeff-1)
df['dr_dspread'] = np.exp(results.params[0]) * results.params[2] * df.duration ** results.params[1] * df.closespread ** (results.params[2] -1)
df['beta'] = df.dr_dspread * df.closespread
return df
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