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-rw-r--r--python/notebooks/Option Trades.ipynb28
-rw-r--r--python/notebooks/Realized Vol.ipynb24
-rw-r--r--python/notebooks/Risk Management.ipynb119
-rw-r--r--python/notebooks/Valuation Backtest.ipynb36
4 files changed, 183 insertions, 24 deletions
diff --git a/python/notebooks/Option Trades.ipynb b/python/notebooks/Option Trades.ipynb
index 939d4cce..b72a4d85 100644
--- a/python/notebooks/Option Trades.ipynb
+++ b/python/notebooks/Option Trades.ipynb
@@ -31,21 +31,25 @@
"source": [
"#Ad hoc\n",
"option_delta = Index.from_name('HY', 29, '5yr')\n",
- "option_delta.price = 107.375\n",
- "option1 = BlackSwaption(option_delta, datetime.date(2017, 12, 20), 104, option_type=\"payer\")\n",
- "option2 = BlackSwaption(option_delta, datetime.date(2017, 12, 20), 100, option_type=\"payer\")\n",
- "option1.sigma = .415\n",
- "option2.sigma = .563\n",
- "option1.notional = 25_000_000\n",
- "option2.notional = 25_000_000\n",
- "option1.direction = 'Long'\n",
- "option2.direction = 'Short'\n",
+ "option_delta.price = 107.5\n",
+ "option1 = BlackSwaption(option_delta, datetime.date(2017, 10, 18), 105, option_type=\"payer\")\n",
+ "option2 = BlackSwaption(option_delta, datetime.date(2017, 11, 15), 105, option_type=\"payer\")\n",
+ "option3 = BlackSwaption(option_delta, datetime.date(2017, 12, 20), 105, option_type=\"payer\")\n",
+ "option1.sigma = .459\n",
+ "option2.sigma = .378\n",
+ "option3.sigma = .377\n",
+ "option1.notional = 35_000_000\n",
+ "option2.notional = 15_000_000\n",
+ "option3.notional = 15_000_000\n",
+ "option1.direction = 'Short'\n",
+ "option2.direction = 'Long'\n",
+ "option3.direction = 'Long'\n",
"#option_delta.notional = 1\n",
- "option_delta.notional = option1.notional * option1.delta + option2.notional * option2.delta\n",
+ "option_delta.notional = option1.notional * option1.delta + option2.notional * option2.delta + option3.notional * option3.delta\n",
"option_delta.direction = 'Seller' if option_delta.notional > 0 else 'Buyer'\n",
"option_delta.notional = abs(option_delta.notional)\n",
- "portf = Portfolio([option1, option2, option_delta])\n",
- "spread.plot_trade_scenarios(portf, -.15, .8)"
+ "portf = Portfolio([option1, option2, option3, option_delta])\n",
+ "spread.plot_trade_scenarios(portf, -.15, .3)"
]
},
{
diff --git a/python/notebooks/Realized Vol.ipynb b/python/notebooks/Realized Vol.ipynb
index 4fa97ede..65d70d5f 100644
--- a/python/notebooks/Realized Vol.ipynb
+++ b/python/notebooks/Realized Vol.ipynb
@@ -8,7 +8,7 @@
"source": [
"from option_trades import realized_vol\n",
"from matplotlib import pyplot as plt\n",
- "plt.style.use('ggplot')"
+ "#plt.style.use('ggplot')"
]
},
{
@@ -18,7 +18,7 @@
"outputs": [],
"source": [
"ig28, model_ig = realized_vol(\"IG\", 28, \"5yr\", years=3)\n",
- "hy27, model_hy = realized_vol(\"HY\", 27, \"5yr\", years=3)"
+ "hy28, model_hy = realized_vol(\"HY\", 28, \"5yr\", years=3)"
]
},
{
@@ -36,7 +36,7 @@
"metadata": {},
"outputs": [],
"source": [
- "hy27.plot()"
+ "hy28.plot()"
]
},
{
@@ -53,6 +53,24 @@
"execution_count": null,
"metadata": {},
"outputs": [],
+ "source": [
+ "ig28"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "hy28"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": []
}
],
diff --git a/python/notebooks/Risk Management.ipynb b/python/notebooks/Risk Management.ipynb
new file mode 100644
index 00000000..a650b1c3
--- /dev/null
+++ b/python/notebooks/Risk Management.ipynb
@@ -0,0 +1,119 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import portfolio_var as port\n",
+ "from analytics import Swaption, BlackSwaption, Index, VolatilitySurface, Portfolio\n",
+ "from analytics.scenarios import run_swaption_scenarios, run_index_scenarios, run_portfolio_scenarios\n",
+ "import datetime\n",
+ "import pandas as pd\n",
+ "\n",
+ "#import exploration.swaption_calendar_spread as spread\n",
+ "import exploration.swaption_calendar_spread as spread"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df, spread, dur = port.rel_spread_diff()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#The 95%tile \n",
+ "stress = pd.DataFrame(index = ['widen', 'tighten'], columns=['pts'])\n",
+ "stress.loc['widen'] = df.quantile(.975) \n",
+ "stress.loc['tighten'] = df.quantile(.025)\n",
+ "stress = -stress * spread * dur/100"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#August ME Bond HY Equiv\n",
+ "bond_HY_equiv = .1652\n",
+ "stress['nav_impact'] = bond_HY_equiv * stress"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Swaptions\n",
+ "#Aug 2018: Buy Sept HY payer spread\n",
+ "option_delta = Index.from_tradeid(891)\n",
+ "option1 = BlackSwaption.from_tradeid(10, option_delta)\n",
+ "option2 = BlackSwaption.from_tradeid(11, option_delta)\n",
+ "portf = Portfolio([option1, option2, option_delta])\n",
+ "portf.trade_date = datetime.date(2017, 8, 31)\n",
+ "portf.mark()\n",
+ "orig_pv = portf.pv\n",
+ "orig_ref = portf.ref"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for x, y in stress.pts.iteritems():\n",
+ " portf.ref = orig_ref + y\n",
+ " stress[x] = portf.pv - orig_pv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "stress"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/python/notebooks/Valuation Backtest.ipynb b/python/notebooks/Valuation Backtest.ipynb
index 6326ec98..6a81dcd5 100644
--- a/python/notebooks/Valuation Backtest.ipynb
+++ b/python/notebooks/Valuation Backtest.ipynb
@@ -55,7 +55,7 @@
"outputs": [],
"source": [
"#Regression Intercept\n",
- "results[0]"
+ "round(results[0],1)"
]
},
{
@@ -65,7 +65,7 @@
"outputs": [],
"source": [
"#Sale Difference\n",
- "results[1]"
+ "round(results[1],2)"
]
},
{
@@ -97,10 +97,17 @@
"#difference by source\n",
"nav = ops.get_net_navs()['endbooknav']\n",
"difference = mark.diff_by_source_percentage(df)\n",
- "#difference.to_clipboard()\n",
- "\n",
+ "#difference.to_clipboard()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
"#plot\n",
- "ax = difference.plot(kind = 'bar', legend = True)\n",
+ "ax = difference.plot(kind = 'bar', legend = True, figsize = [10, 3.5])\n",
"\n",
"visible = ax.xaxis.get_ticklabels()[::6]\n",
"for label in ax.xaxis.get_ticklabels():\n",
@@ -110,7 +117,11 @@
"ax.xaxis.set_major_formatter(plt.FixedFormatter(difference.index.to_series().dt.strftime(\"%b %Y\")))\n",
"ax.set_ylabel('NAV Impact vs. Fund Policy (%)')\n",
"vals = ax.get_yticks()\n",
- "ax.set_yticklabels(['{:3.0f}%'.format(x*100) for x in vals])"
+ "ax.set_yticklabels(['{:3.0f}%'.format(x*100) for x in vals])\n",
+ "ax.set_xlabel('')\n",
+ "ax.grid(False, which='major',axis = 'x')\n",
+ "lgd = ax.legend(loc='upper center', bbox_to_anchor=(0.5, -.3), shadow=True, ncol=5)\n",
+ "ax.figure.savefig(\"/home/serenitas/edwin/PythonGraphs/Valuation_2.png\", bbox_extra_artists=(lgd,), bbox_inches='tight')"
]
},
{
@@ -164,7 +175,7 @@
"metadata": {},
"outputs": [],
"source": [
- "mark.annual_performance(results[1])"
+ "round(mark.annual_performance(results[1])*100,2)"
]
},
{
@@ -174,7 +185,7 @@
"outputs": [],
"source": [
"#A positive impact % means the alternative methodology results in a higher NAV than the fund's valuation policy.\n",
- "pd.DataFrame(mark.alt_nav_impact())"
+ "round(pd.DataFrame(mark.alt_nav_impact())*100,2)"
]
},
{
@@ -213,7 +224,14 @@
"execution_count": null,
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "df = ops.trade_performance()\n",
+ "df = df.set_index('trade_date')\n",
+ "df.days_held = df.days_held.dt.days\n",
+ "winner = df[df.percent_gain > 0]\n",
+ "df[df.days_held.notnull()].groupby(pd.TimeGrouper(freq='A')).mean()\n",
+ "df"
+ ]
}
],
"metadata": {
generated by cgit v1.2.3-70-g09d2 (git 2.52.0) at 2025-12-22 05:52:02 +0000