diff options
Diffstat (limited to 'python/exploration')
| -rw-r--r-- | python/exploration/curve_trades.py | 57 |
1 files changed, 54 insertions, 3 deletions
diff --git a/python/exploration/curve_trades.py b/python/exploration/curve_trades.py index 310cc9a6..154d2595 100644 --- a/python/exploration/curve_trades.py +++ b/python/exploration/curve_trades.py @@ -1,15 +1,19 @@ -from analytics.index_data import get_index_quotes, index_returns, _serenitas_engine +from analytics.index_data import get_index_quotes, index_returns +from db import dbengine from analytics import Index, Portfolio import pandas as pd import math import statsmodels.formula.api as smf +import analytics.basket_index as bkt import numpy as np +import matplotlib.pyplot as plt + from statsmodels.sandbox.regression.predstd import wls_prediction_std -import matplotlib.pyplot as plt +_engine = dbengine('serenitasdb') def on_the_run(index): - r = _serenitas_engine.execute("SELECT max(series) FROM index_version WHERE index=%s", + r = _engine.execute("SELECT max(series) FROM index_version WHERE index=%s", (index,)) series, = r.fetchone() return series @@ -196,3 +200,50 @@ def spread_fin_crisis(index='IG'): plt.legend(bbox_to_anchor=(.5, -.1), ncol = 2) plt.show() + +def forward_spread(index = 'IG', series = None): + + if series is None: + series = on_the_run(index = index) + tenors = ['3yr', '5yr', '7yr', '10yr'] + b_index = bkt.MarkitBasketIndex(index, series, tenors) + b_index.tweak() + + f_spread = {} + date_range = pd.bdate_range(pd.datetime.today(), max(b_index.maturities), freq='M') + for d in date_range.date: + b_index.trade_date = d + f_spread[d] = b_index.spread() + + return pd.DataFrame(f_spread).transpose() + +def spot_forward(index = 'IG', series = None): + + if series is None: + series = on_the_run(index = index) + tenors = ['3yr', '5yr', '7yr', '10yr'] + b_index = bkt.MarkitBasketIndex(index, series, tenors) + b_index.tweak() + + spread = b_index.spread() + new_maturity = [(d - pd.DateOffset(years=1)).date() for d in b_index.maturities] + new_duration = {} + for i, m in enumerate(new_maturity): + new_duration[b_index.index_desc.tenor[i]] = b_index.duration(m) + dur_df = pd.Series(new_duration) + + one_yr = (b_index.trade_date + pd.DateOffset(years=1)).date() + one_yr_pv = b_index.pv(one_yr, coupon = b_index.index_desc.coupon[0]) + one_yr_dur = b_index.duration((b_index.trade_date + pd.DateOffset(years=1)).date()) + new_spread = ((b_index.pv() - b_index.theta() + b_index.index_desc.coupon[0])/ dur_df + b_index.index_desc.coupon[0])* 1e4 + + df = pd.DataFrame(dict(today_spread=spread, spread_in_one_year=new_spread)) + df.index.name = 'tenor' + df = df.reset_index().merge(b_index.index_desc.tenor.reset_index(), on='tenor') + df.loc[-2] = ['0yr', 0, 0, b_index.trade_date] + df.loc[-1] = ['1yr', 0, (one_yr_pv/one_yr_dur + b_index.index_desc.coupon[0])*1e4, one_yr] + + df['maturity'] = pd.to_datetime(df['maturity']) + df = df.sort_values('maturity') + + return df.set_index('maturity')
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