diff options
Diffstat (limited to 'python/notebooks/Option Trades.ipynb')
| -rw-r--r-- | python/notebooks/Option Trades.ipynb | 312 |
1 files changed, 0 insertions, 312 deletions
diff --git a/python/notebooks/Option Trades.ipynb b/python/notebooks/Option Trades.ipynb deleted file mode 100644 index 32cd5b04..00000000 --- a/python/notebooks/Option Trades.ipynb +++ /dev/null @@ -1,312 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import datetime\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "from graphics import plot_color_map\n", - "from analytics import Swaption, BlackSwaption, BlackSwaptionVolSurface, CreditIndex, Portfolio\n", - "from analytics.scenarios import run_swaption_scenarios, run_index_scenarios, run_portfolio_scenarios, run_portfolio_scenarios_module\n", - "from scipy.interpolate import SmoothBivariateSpline\n", - "from db import dbengine\n", - "\n", - "from db import dbconn\n", - "from analytics import init_ontr\n", - "conn = dbconn('dawndb')\n", - "init_ontr()\n", - "pd.options.display.float_format = \"{:,.2f}\".format" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Delta Chart: Red = Long Risk, Blue = Short Risk" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Trade Analysis - see if the trade is net positive gamma\n", - "index = 'IG'\n", - "series = 32\n", - "option_delta = CreditIndex(index, series, '5yr') \n", - "option_delta.spread = 64\n", - "option1 = BlackSwaption(option_delta, datetime.date(2019, 7, 17), 60, option_type=\"payer\") \n", - "option2 = BlackSwaption(option_delta, datetime.date(2019, 7, 17), 75, option_type=\"payer\") \n", - "option1.sigma = .394 \n", - "option2.sigma = .52\n", - "option1.notional = 200_000_000 \n", - "option2.notional = 200_000_000 \n", - "option1.direction = 'Long' \n", - "option2.direction = 'Short' \n", - "option_delta.notional = option1.delta * option1.notional + option2.delta * option2.notional\n", - "portf = Portfolio([option1, option2, option_delta], trade_ids=['opt1', 'opt2', 'delta'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "portf" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_trade_scenarios(portf, shock_min=-.15, shock_max=.2, vol_time_roll=True):\n", - " portf.reset_pv()\n", - " end_date = min(portf.swaptions, key=lambda x: x.exercise_date).exercise_date\n", - " date_range = pd.bdate_range(portf.value_date,\n", - " end_date - pd.tseries.offsets.BDay(), freq='3B')\n", - " vol_shock = np.arange(-.15, .31, 0.01)\n", - " spread_shock = np.arange(shock_min, shock_max, 0.01)\n", - " index = portf.indices[0].index_type\n", - " vol_surface = {}\n", - " for trade in portf.swaptions:\n", - " vs = BlackSwaptionVolSurface(trade.index.index_type, trade.index.series, \n", - " value_date=portf.value_date, interp_method = \"bivariate_linear\")\n", - " vol_surface[(trade.index.index_type, trade.index.series)] = vs[vs.list(option_type='payer')[-1]]\n", - " \n", - " df = run_portfolio_scenarios(portf, date_range, params=[\"pnl\",\"delta\"],\n", - " spread_shock = spread_shock,\n", - " vol_shock = vol_shock,\n", - " vol_surface = vol_surface)\n", - " df = df.reset_index()\n", - " df.vol_shock = df.vol_shock.round(2)\n", - "\n", - " if index == 'HY':\n", - " df['price'] = 100 + (500 - portf.indices[0].spread * (1 + df.spread_shock)) \\\n", - " * abs(portf.indices[0].DV01) / portf.indices[0].notional * 100\n", - " df = df.set_index(['date', 'price', 'vol_shock'])\n", - " sort_order = [True, False]\n", - " else:\n", - " df['spread'] = portf.indices[0].spread * (1 + df.spread_shock)\n", - " df = df.set_index(['date', 'spread', 'vol_shock'])\n", - " sort_order = [True, True]\n", - " \n", - " pnl = df.xs('pnl', axis=1, level=1).sum(axis=1)\n", - " for trade_id, t in portf.items():\n", - " if isinstance(t, BlackSwaption):\n", - " df[(trade_id, 'delta')] *= -t.notional \n", - " delta = df.xs('delta', axis=1, level=1).sum(axis=1).xs(0, level='vol_shock')\n", - " delta += sum([x.notional * -1 if x.direction == 'Buyer' else 1 for x in portf.indices])\n", - "\n", - " pnl.name = 'pnl'\n", - " delta.name = 'delta'\n", - "\n", - " plot_color_map(pnl.xs(0, level='vol_shock'), sort_order)\n", - " plot_color_map(delta, sort_order)\n", - " plot_color_map(pnl.loc[date_range[-1]], sort_order)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def calc_simple_scenario(portf, shock_min=-.15, shock_max=.2):\n", - " portf.reset_pv()\n", - " end_date = min(portf.swaptions, key=lambda x: x.exercise_date).exercise_date\n", - " date_range = pd.bdate_range(portf.value_date,\n", - " end_date - pd.tseries.offsets.BDay(), freq='3B')\n", - " vol_shock = [0]\n", - " spread_shock = np.arange(shock_min, shock_max, 0.01)\n", - " index = portf.indices[0].index_type\n", - " vol_surface = {}\n", - " for trade in portf.swaptions:\n", - " vs = BlackSwaptionVolSurface(trade.index.index_type, trade.index.series, \n", - " value_date=portf.value_date, interp_method = \"bivariate_linear\")\n", - " vol_surface[(trade.index.index_type, trade.index.series)] = vs[vs.list(option_type='payer')[-1]]\n", - "\n", - " df = run_portfolio_scenarios(portf, date_range, params=[\"pnl\"],\n", - " spread_shock = spread_shock,\n", - " vol_shock = vol_shock,\n", - " vol_surface = vol_surface)\n", - "\n", - " return df.xs('pnl', axis=1, level=1).sum(axis=1) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_trade_scenarios(portf)\n", - "plot_trade_scenarios(portf, -.15, .8, vol_time_roll=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Dec Jan 2017 Trade\n", - "option_delta = CreditIndex.from_tradeid(864)\n", - "option1 = BlackSwaption.from_tradeid(3, option_delta)\n", - "option2 = BlackSwaption.from_tradeid(4, option_delta)\n", - "portf = Portfolio([option1, option2, option_delta], trade_ids=['opt1', 'opt2', 'delta'])\n", - "#plot_trade_scenarios(portf)\n", - "\n", - "#Feb 2017: Sell May Buy April Calendar Trade\n", - "option_delta = CreditIndex.from_tradeid(870)\n", - "option1 = BlackSwaption.from_tradeid(5, option_delta)\n", - "option2 = BlackSwaption.from_tradeid(6, option_delta)\n", - "portf = Portfolio([option1, option2, option_delta], trade_ids=['opt1', 'opt2', 'delta'])\n", - "#plot_trade_scenarios(portf)\n", - "\n", - "#April 2017: Sell May Buy June Calendar Trade\n", - "option_delta = CreditIndex.from_tradeid(874)\n", - "option1 = BlackSwaption.from_tradeid(7, option_delta)\n", - "option2 = BlackSwaption.from_tradeid(8, option_delta)\n", - "portf = Portfolio([option1, option2, option_delta], trade_ids=['opt1', 'opt2', 'delta'])\n", - "#plot_trade_scenarios(portf)\n", - "\n", - "#June July 2017 Calendar Trade\n", - "option_delta_pf = CreditIndex.from_tradeid(874)\n", - "option_delta2_pf = CreditIndex.from_tradeid(879)\n", - "\n", - "option1_pf = BlackSwaption.from_tradeid(7, option_delta_pf)\n", - "option2_pf = BlackSwaption.from_tradeid(9, option_delta_pf)\n", - "option_delta_pf.notional = 50_335_169\n", - "\n", - "portf = Portfolio([option1_pf, option2_pf, option_delta_pf], trade_ids=['opt1', 'opt2', 'delta'])\n", - "portf.value_date = datetime.date(2017, 5, 17)\n", - "portf.mark()\n", - "#plot_trade_scenarios(portf)\n", - "\n", - "#July 2017: Buy Sept HY payer spread\n", - "option_delta = CreditIndex.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], trade_ids=['opt1', 'opt2', 'delta'])\n", - "#plot_trade_scenarios(portf)\n", - "\n", - "#March 2019: May Bull Risky\n", - "option_delta = CreditIndex.from_tradeid(1063)\n", - "option1 = BlackSwaption.from_tradeid(41, option_delta)\n", - "option2 = BlackSwaption.from_tradeid(40, option_delta)\n", - "portf = Portfolio([option1, option2, option_delta], trade_ids=['opt1', 'opt2', 'delta'])\n", - "results = calc_simple_scenario(portf, shock_min=-.3, shock_max=.3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Look at steepness of volatility - 30 days, .85 vs .15 payer deltas on HY\n", - "days = 30\n", - "delta1 = .85\n", - "delta2 = .15\n", - "index = 'HY'\n", - "\n", - "sql_str = \"select b.quotedate, b.ref, b.ref_id, b.expiry, a.delta_pay, a.vol from \" \\\n", - " \"swaption_quotes a join swaption_ref_quotes b on a.ref_id = b.ref_id and index = %s\"\n", - "df = pd.read_sql_query(sql_str, dbengine('serenitasdb'), \n", - " index_col=['quotedate'], parse_dates={'quotedate': {'utc': True}}, params=[index])\n", - "df['days_expiry'] = (df.expiry - df.index.date).dt.days\n", - "r_1 = []\n", - "for i, g in df.groupby(pd.Grouper(freq='D', level='quotedate')):\n", - " r = []\n", - " for i_1, g_1 in g.groupby(['days_expiry', 'delta_pay']):\n", - " r.append([i_1[0], i_1[1], g_1['vol'].mean()])\n", - " if len(r) > 0:\n", - " temp = np.dstack(r)\n", - " f = SmoothBivariateSpline(temp[0][0], temp[0][1], temp[0][2])\n", - " r = (f(days, delta1) - f(days, delta2))[0][0]\n", - " r_1.append([i, r])\n", - " else:\n", - " pass\n", - "df_1 = pd.DataFrame(r_1, columns=['date', 'steepness'])\n", - "df_1.set_index('date').plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Current Positions\n", - "today = datetime.date.today()\n", - "swaption_sql_string = (\"select id, folder, expiration_date from swaptions where date(expiration_date) \"\n", - " \"> %s and swap_type = 'CD_INDEX_OPTION' \"\n", - " \"AND trade_date <= %s AND termination_date iS NULL\")\n", - "index_sql_string = (\"SELECT id, folder, sum(notional * case when protection='Buyer' then -1 else 1 end) \"\n", - " \"OVER (partition by security_id, attach) AS ntl_agg \"\n", - " \"FROM cds WHERE swap_type='CD_INDEX' AND termination_cp IS null \"\n", - " \"AND trade_date <= %s AND maturity > %s\")\n", - "conn = dawn_engine.raw_connection()\n", - "with conn.cursor() as c:\n", - " c.execute(swaption_sql_string, (today, today))\n", - " swaption_trades = [[dealid, f\"{folder}_{dealid}\", expiration_date] for dealid, folder, expiration_date in c]\n", - " c.execute(index_sql_string, (today, today))\n", - " index_trades = [[dealid, f\"{folder}_{dealid}\"] for dealid, folder, ntl in c if ntl != 0]\n", - "conn.close()\n", - "\n", - "portf = Portfolio([BlackSwaption.from_tradeid(dealid) for dealid, _, _ in swaption_trades],\n", - " [trade_id for _, trade_id, _ in swaption_trades])\n", - "index_trades = list(filter(lambda x: \"CURVE\" not in x[1], index_trades))\n", - "index_trades = list(filter(lambda x: \"SER_IGINDX\" not in x[1], index_trades))\n", - "index_trades = list(filter(lambda x: \"HEDGE_MBS\" not in x[1], index_trades))\n", - "index_trades = list(filter(lambda x: \"IGINX\" not in x[1], index_trades))\n", - "\n", - "for trade_id, name in index_trades:\n", - " portf.add_trade(CreditIndex.from_tradeid(trade_id), name)\n", - " \n", - "portf.value_date = today\n", - " \n", - "results = calc_simple_scenario(portf, shock_min=-.3, shock_max=.3)" - ] - }, - { - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} |