diff options
Diffstat (limited to 'python')
| -rw-r--r-- | python/analytics/index_data.py | 4 | ||||
| -rw-r--r-- | python/analytics/portfolio.py | 2 | ||||
| -rw-r--r-- | python/exploration/curve_trades.py | 76 | ||||
| -rw-r--r-- | python/notebooks/Curve Trades.ipynb | 32 |
4 files changed, 87 insertions, 27 deletions
diff --git a/python/analytics/index_data.py b/python/analytics/index_data.py index 784b16d4..687481cd 100644 --- a/python/analytics/index_data.py +++ b/python/analytics/index_data.py @@ -119,8 +119,8 @@ def index_returns(df=None, index=None, series=None, tenor=None, from_date=None, df = df.reset_index('date').join(coupon_data).reset_index('tenor') df.tenor = df.tenor.astype(tenor_t) df = df.set_index(['tenor'], append=True) - df['day_frac'] = (df.groupby(level=['index', 'series', 'tenor'])['date']. - diff(). + df['day_frac'] = (df.groupby(level=['index', 'series', 'tenor']).diff(). + date. astype('timedelta64[D]') / 360) df['price_return'] += df.day_frac * df.coupon df = df.drop(['day_frac', 'coupon', 'maturity'], axis=1) diff --git a/python/analytics/portfolio.py b/python/analytics/portfolio.py index fd73e0e5..0f57c674 100644 --- a/python/analytics/portfolio.py +++ b/python/analytics/portfolio.py @@ -12,7 +12,7 @@ def portf_repr(method): obj = args[0] thousands = lambda x: "{:,.2f}".format(x) percent = lambda x: "N/A" if np.isnan(x) else f"{100*x:.2f}%" - header = "Portfolio {}\n\n".format(obj.trade_date) + header = "Portfolio {}\n\n".format(obj.value_date) kwargs = {'formatters': {'Notional': thousands, 'PV': thousands, 'Delta': percent, diff --git a/python/exploration/curve_trades.py b/python/exploration/curve_trades.py index b6d35d3d..3c0a25ad 100644 --- a/python/exploration/curve_trades.py +++ b/python/exploration/curve_trades.py @@ -11,25 +11,29 @@ import numpy as np import matplotlib.pyplot as plt from statsmodels.sandbox.regression.predstd import wls_prediction_std +from scipy.interpolate import interp1d -_engine = dbengine('serenitasdb') +serenitasdb = dbengine('serenitasdb') +dawndb = dbengine('dawndb') def on_the_run(index): - r = _engine.execute("SELECT max(series) FROM index_version WHERE index=%s", + r = serenitasdb.execute("SELECT max(series) FROM index_version WHERE index=%s", (index,)) series, = r.fetchone() return series -def curve_spread_diff(index='IG', rolling=6): +def curve_spread_diff(index='IG', rolling=6, years=3, percentage=False, percentage_base='5yr'): otr = on_the_run(index) ## look at spreads df = get_index_quotes(index, list(range(otr - rolling, otr + 1)), - tenor=['3yr', '5yr', '7yr', '10yr']) + tenor=['3yr', '5yr', '7yr', '10yr'], years=years) spreads = df.groupby(level=['date', 'tenor']).nth(-1)['closespread'].unstack(-1) spreads_diff = spreads.diff(axis=1) del spreads_diff['3yr'] spreads_diff.columns = ['3-5', '5-7', '7-10'] spreads_diff['5-10'] = spreads_diff['5-7'] + spreads_diff['7-10'] + if percentage is True: + spreads_diff = spreads.apply(lambda df: df/df[percentage_base], axis = 1) return spreads_diff def spreads_diff_table(spreads_diff): @@ -133,7 +137,6 @@ def cross_series_curve(index='IG', rolling=6): agg(lambda df: (1 + df).prod() - 1)) plt.plot(monthly_returns_cross_series) - def forward_loss(index='IG'): start_date = (pd.Timestamp.now() - pd.DateOffset(years=3)).date() @@ -238,3 +241,66 @@ def spot_forward(index='IG', series=None, tenors=['3yr', '5yr', '7yr', '10yr']): df = df_0.append(df) df['maturity'] = [b_index.trade_date, maturity_1yr] + b_index.maturities return df.reset_index().set_index('maturity') + +def curve_pos(trade_date, index='IG'): + + ''' + Input trade_date and index + Returns a Portfolio of curve trades ''' + + sql_string = "SELECT * FROM cds where trade_date < %s" + df = pd.read_sql_query(sql_string, dawndb, parse_dates=['trade_date', 'maturity'], + params=[trade_date]) + if index is 'IG': + df = df[df['folder'] == 'SER_IGCURVE'] + elif index is 'HY': + df = df[df['folder'] == 'SER_HYCURVE'] + else: + df = df[df['folder'] == 'SER_ITRXCURVE'] + df.notional = df.apply(lambda x: x.notional * -1 if x.protection == 'Buyer' else x.notional, axis = 1) + df = df.groupby(['security_id', 'maturity']).sum()['notional'] + df = df.iloc[df.nonzero()[0]].reset_index() + + sql_string = "SELECT * FROM index_maturity LEFT JOIN index_version USING (index, series)" + lookup_table = pd.read_sql_query(sql_string, serenitasdb, parse_dates=['maturity']) + + df = df.merge(lookup_table, left_on=['security_id','maturity'], right_on=['redindexcode', 'maturity']) + + indices = [] + sql_string = "SELECT closespread FROM index_quotes where index = %s and series = %s and tenor = %s and date = %s" + for i, row in df[['index', 'tenor', 'series', 'notional']].iterrows(): + temp = Index.from_name(row['index'], row.series, row.tenor) + temp.value_date = trade_date.date() + if row.notional > 0: + temp.direction = 'Seller' + temp.notional = abs(row.notional) + spread_df = pd.read_sql_query(sql_string, serenitasdb, + params=[row['index'], row.series, row.tenor, trade_date.date()]) + temp.spread = spread_df.iloc[0][0] + indices.append(temp) + + return Portfolio(indices) + +def curve_shape(trade_date, index = 'IG', percentile=.95): + + ''' + Returns a function to linearly interpolate between the curve based on maturity (in years)''' + + curve_shape = curve_spread_diff(index, 10, 5, True) + steepness = (curve_shape['10yr']/curve_shape['3yr']) + series = on_the_run(index) + + sql_string = "SELECT closespread FROM index_quotes where index = %s and series = %s and tenor = %s and date = %s" + spread_df = pd.read_sql_query(sql_string, serenitasdb, + params=[index, series, '5yr', trade_date.date()]) + sql_string = "SELECT tenor, maturity FROM index_maturity where index = %s and series = %s" + lookup_table = pd.read_sql_query(sql_string, serenitasdb, parse_dates=['maturity'], params=[index, series]) + + df = curve_shape[steepness == steepness.quantile(percentile, 'nearest')] + df = df * spread_df.iloc[0][0]/df['5yr'][0] + df = df.stack().rename('spread') + df = df.reset_index().merge(lookup_table, on=['tenor']) + df['year_frac'] = (df.maturity - pd.to_datetime(trade_date)).dt.days/365 + return interp1d(np.hstack([0, df.year_frac]), np.hstack([0, df.spread])) + + diff --git a/python/notebooks/Curve Trades.ipynb b/python/notebooks/Curve Trades.ipynb index f0027e40..5637beaf 100644 --- a/python/notebooks/Curve Trades.ipynb +++ b/python/notebooks/Curve Trades.ipynb @@ -9,7 +9,8 @@ "import curve_trades as ct\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", - "from ipywidgets import widgets" + "from ipywidgets import widgets\n", + "import pandas as pd" ] }, { @@ -135,7 +136,7 @@ "metadata": {}, "outputs": [], "source": [ - "model = ct.curve_model()\n", + "model = ct.curve_model('5yr', '10yr')\n", "model_results = ct.curve_model_results(model[0], model[1])" ] }, @@ -145,7 +146,10 @@ "metadata": {}, "outputs": [], "source": [ - "model[1].summary()" + "#Scenario Anslysis on current position\n", + "report_date = (pd.datetime.today() - pd.offsets.BDay(1)).normalize()\n", + "curve_pos = ct.curve_pos(report_date)\n", + "origpv = curve_pos.pv" ] }, { @@ -154,8 +158,9 @@ "metadata": {}, "outputs": [], "source": [ - "#Var on current position\n", - "results = ct.curve_var()" + "flat_curve = ct.curve_shape(report_date, percentile = .05)\n", + "for ind in curve_pos.indices:\n", + " ind.spread = flat_curve((pd.to_datetime(ind.end_date) - trade_date).days/365)" ] }, { @@ -164,19 +169,8 @@ "metadata": {}, "outputs": [], "source": [ - "model_results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results\n", - "#first number: at 5% flatness\n", - "#second number: at predicted curve shape\n", - "#third number: at 5% flatness given duration and spread" + "#PNL in flattening to a 5% case\n", + "curve_pos.pv - origpv" ] }, { @@ -226,7 +220,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.6.5" } }, "nbformat": 4, |