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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 42, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import QuantLib as ql\n", | |
| "from datetime import date\n", | |
| "import pandas\n", | |
| "import plotly.express as px\n", | |
| "import csv" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# one option example\n", | |
| "# http://gouthamanbalaraman.com/blog/american-option-pricing-quantlib-python.html\n", | |
| "\n", | |
| "# date_mat = ql.Date(31, 12, 2019)\n", | |
| "# price_spot = 105\n", | |
| "# price_strike = 100\n", | |
| "# vol = 0.2\n", | |
| "# dividend_rate = 0.02\n", | |
| "# option_type = ql.Option.Call\n", | |
| "\n", | |
| "# risk_free_rate = 0.0153\n", | |
| "# day_count = ql.Actual365Fixed()\n", | |
| "# calendar = ql.UnitedStates()\n", | |
| "\n", | |
| "# date_calc = ql.Date(17, 12, 2019)\n", | |
| "# ql.Settings.instance().evaluationDate = date_calc\n", | |
| "\n", | |
| "# payoff = ql.PlainVanillaPayoff(option_type, price_strike)\n", | |
| "# settlement = date_calc\n", | |
| "# am_exercise = ql.AmericanExercise(settlement, date_mat)\n", | |
| "# american_option = ql.VanillaOption(payoff, am_exercise)\n", | |
| "\n", | |
| "# spot_handle = ql.QuoteHandle(ql.SimpleQuote(price_spot))\n", | |
| "# flat_ts = ql.YieldTermStructureHandle(ql.FlatForward(date_calc, risk_free_rate, day_count))\n", | |
| "# dividend_yield = ql.YieldTermStructureHandle(ql.FlatForward(date_calc, dividend_rate, day_count))\n", | |
| "# flat_vol_ts = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(date_calc, calendar, vol, day_count))\n", | |
| "\n", | |
| "# bsm_process = ql.BlackScholesMertonProcess(spot_handle, dividend_yield, flat_ts, flat_vol_ts)\n", | |
| "\n", | |
| "# steps = 200\n", | |
| "\n", | |
| "# binomial_engine = ql.BinomialVanillaEngine(bsm_process, 'crr', steps)\n", | |
| "# american_option.setPricingEngine(binomial_engine)\n", | |
| "# print (american_option.NPV())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 30, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def npv_single(date_mat, \n", | |
| " price_spot, \n", | |
| " price_strike, \n", | |
| " vol, \n", | |
| " option_type, \n", | |
| " risk_free_rate, \n", | |
| " date_calc,\n", | |
| " dividend_rate=0,\n", | |
| " steps=200):\n", | |
| " if date_mat < date_calc:\n", | |
| " return 0\n", | |
| " \n", | |
| " day_count = ql.Actual365Fixed()\n", | |
| " calendar = ql.UnitedStates()\n", | |
| "\n", | |
| " ql.Settings.instance().evaluationDate = date_calc\n", | |
| "\n", | |
| " payoff = ql.PlainVanillaPayoff(option_type, price_strike)\n", | |
| " settlement = date_calc\n", | |
| " am_exercise = ql.AmericanExercise(settlement, date_mat)\n", | |
| " american_option = ql.VanillaOption(payoff, am_exercise)\n", | |
| "\n", | |
| " spot_handle = ql.QuoteHandle(ql.SimpleQuote(price_spot))\n", | |
| " flat_ts = ql.YieldTermStructureHandle(ql.FlatForward(date_calc, risk_free_rate, day_count))\n", | |
| " dividend_yield = ql.YieldTermStructureHandle(ql.FlatForward(date_calc, dividend_rate, day_count))\n", | |
| " flat_vol_ts = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(date_calc, calendar, vol, day_count))\n", | |
| "\n", | |
| " bsm_process = ql.BlackScholesMertonProcess(spot_handle, dividend_yield, flat_ts, flat_vol_ts)\n", | |
| "\n", | |
| " binomial_engine = ql.BinomialVanillaEngine(bsm_process, 'crr', steps)\n", | |
| " american_option.setPricingEngine(binomial_engine)\n", | |
| " \n", | |
| " return american_option.NPV()\n", | |
| "\n", | |
| "def npv_portfolio(options, date_calc=None):\n", | |
| " \n", | |
| " def call_npv(option, date_calc):\n", | |
| " \n", | |
| " date_mat = ql.DateParser.parse(option['date_mat'], '%m/%d/%Y')\n", | |
| " price_spot = option['price_spot']\n", | |
| " price_strike = option['price_strike']\n", | |
| " vol = option['vol']\n", | |
| " option_type = ql.Option.Call if option['option_type'] == 'call' else ql.Option.Put\n", | |
| " risk_free_rate = option['risk_free_rate']\n", | |
| " date_calc = ql.DateParser.parse(date_calc, '%m/%d/%Y') if date_calc is not None else ql.Date(date.today().day, date.today().month, date.today().year)\n", | |
| " \n", | |
| "# print('Calculating NPV on %s for %s' % (date_calc, option))\n", | |
| " \n", | |
| " return npv_single(date_mat, price_spot, price_strike, vol, option_type, risk_free_rate, date_calc)\n", | |
| " \n", | |
| " npvs = [call_npv(option, date_calc) for option in options]\n", | |
| " return npvs" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 31, | |
| "metadata": {}, | |
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| { | |
| "data": { | |
| "text/plain": [ | |
| "5.256971896281935" | |
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| }, | |
| "execution_count": 31, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "npv_single(ql.Date(31, 12, 2019), 105, 100, 0.2, ql.Option.Call, 0.0153, ql.Date(17, 12, 2019))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 54, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[{'date_mat': '12/31/2019', 'price_spot': 150, 'price_strike': 145, 'vol': 0.1, 'option_type': 'call', 'risk_free_rate': 0.0153}, {'date_mat': '12/31/2019', 'price_spot': 150, 'price_strike': 145, 'vol': 0.1, 'option_type': 'put', 'risk_free_rate': 0.0153}, {'date_mat': '12/14/2019', 'price_spot': 150, 'price_strike': 145, 'vol': 0.1, 'option_type': 'call', 'risk_free_rate': 0.0153}]\n", | |
| "[{'date_mat': '12/31/2019', 'price_spot': 150.0, 'price_strike': 145.0, 'vol': 0.1, 'option_type': 'call', 'risk_free_rate': 0.0153}, {'date_mat': '12/31/2019', 'price_spot': 150.0, 'price_strike': 145.0, 'vol': 0.1, 'option_type': 'put', 'risk_free_rate': 0.0153}, {'date_mat': '12/14/2019', 'price_spot': 150.0, 'price_strike': 145.0, 'vol': 0.1, 'option_type': 'call', 'risk_free_rate': 0.0153}]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# you can input it as a series of dicts like this\n", | |
| "options = [\n", | |
| " {'date_mat': '12/31/2019', 'price_spot': 150, 'price_strike': 145, 'vol': 0.1, 'option_type': 'call', 'risk_free_rate': 0.0153},\n", | |
| " {'date_mat': '12/31/2019', 'price_spot': 150, 'price_strike': 145, 'vol': 0.1, 'option_type': 'put', 'risk_free_rate': 0.0153},\n", | |
| " {'date_mat': '12/14/2019', 'price_spot': 150, 'price_strike': 145, 'vol': 0.1, 'option_type': 'call', 'risk_free_rate': 0.0153}\n", | |
| "]\n", | |
| "\n", | |
| "print(options)\n", | |
| "\n", | |
| "# or read it as a CSV:\n", | |
| "with open('input.csv') as f:\n", | |
| " def try_parse(i):\n", | |
| " try:\n", | |
| " return float(i)\n", | |
| " except ValueError:\n", | |
| " return i\n", | |
| " \n", | |
| " options = [{k: try_parse(v) for k, v in row.items()}\n", | |
| " for row in csv.DictReader(f, skipinitialspace=True)]\n", | |
| "\n", | |
| "print(options)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 55, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "NPV for the portfolio on a single day\n", | |
| "[5.160771469992204, 0.06373640388023276, 0]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# single day\n", | |
| "npv_single_day = npv_portfolio(options, '12/15/2019')\n", | |
| "print('NPV for the portfolio on a single day')\n", | |
| "print(npv_single_day)" | |
| ] | |
| }, | |
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| "execution_count": 56, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "NPV for the portfolio through many days\n", | |
| "['12/01/2019', '12/02/2019', '12/03/2019', '12/04/2019', '12/05/2019', '12/06/2019', '12/07/2019', '12/08/2019', '12/09/2019', '12/10/2019', '12/11/2019', '12/12/2019', '12/13/2019', '12/14/2019', '12/15/2019', '12/16/2019', '12/17/2019']\n", | |
| "[10.749238942389724, 10.704640863591793, 10.660393784725297, 10.616530476295633, 10.572818807231293, 10.533564863217965, 10.495375084179667, 10.457573169750974, 10.420062550922573, 10.381966629360743, 10.349532054451956, 10.311536502606305, 10.282241319495178, 5.249110061827135, 5.224507873872437, 5.1992479254020125, 5.175721932161299]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# date range\n", | |
| "dates = list(pandas.date_range('12/1/2019', '12/17/2019').strftime('%m/%d/%Y'))\n", | |
| "npvs = [sum(npv_portfolio(options, date)) for date in dates]\n", | |
| "print('NPV for the portfolio through many days')\n", | |
| "print(dates)\n", | |
| "print(npvs)\n", | |
| "# NPV jumps on 12/15 because one of the call options expires at 12/14" | |
| ] | |
| }, | |
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\"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"date\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"npv\"}}},\n", | |
| " {\"responsive\": true}\n", | |
| " ).then(function(){\n", | |
| " \n", | |
| "var gd = document.getElementById('ab3e6c33-5f5b-4876-8978-c618b5e9ba9e');\n", | |
| "var x = new MutationObserver(function (mutations, observer) {{\n", | |
| " var display = window.getComputedStyle(gd).display;\n", | |
| " if (!display || display === 'none') {{\n", | |
| " console.log([gd, 'removed!']);\n", | |
| " Plotly.purge(gd);\n", | |
| " observer.disconnect();\n", | |
| " }}\n", | |
| "}});\n", | |
| "\n", | |
| "// Listen for the removal of the full notebook cells\n", | |
| "var notebookContainer = gd.closest('#notebook-container');\n", | |
| "if (notebookContainer) {{\n", | |
| " x.observe(notebookContainer, {childList: true});\n", | |
| "}}\n", | |
| "\n", | |
| "// Listen for the clearing of the current output cell\n", | |
| "var outputEl = gd.closest('.output');\n", | |
| "if (outputEl) {{\n", | |
| " x.observe(outputEl, {childList: true});\n", | |
| "}}\n", | |
| "\n", | |
| " })\n", | |
| " };\n", | |
| " });\n", | |
| " </script>\n", | |
| " </div>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# plotting\n", | |
| "df_plot = pandas.DataFrame({'date': dates, 'npv': npvs})\n", | |
| "fig = px.line(df_plot, x='date', y='npv')\n", | |
| "fig.show()" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "language": "python", | |
| "name": "python3" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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