equity-options-pricing-example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "inside-enemy",
   "metadata": {},
   "source": [
    "# Pricing equity options\n",
    "In this notebook I price American equity options using volatility tracking, riskless yield curve construction, and options pricing code from this package. I ensure that all market and economic data required for pricing is obtained from [FRED](https://fred.stlouisfed.org) and [Yahoo-finance](https://finance.yahoo.com) using [pandas-datareader](https://pydata.github.io/pandas-datareader/). Since the lifetime of an option is usually measured using trading\n",
    "days rather than calendar days, I make use of [the pandas-market-calendars library](https://pandas-market-calendars.readthedocs.io/en/latest/index.html) to get correct trading calendars.\n",
    "\n",
    "This notebook can also be used as a tutorial to using the `pricing.curves`, `pricing.options`, `volatility.parameter_estimators`, and `volatility.volatility_trackers` packages in this repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "decreased-lending",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment if running in Google Colaboratory, otherwise the import of the curves module in the cell below will fail\n",
    "#!git clone -l -s /~https://github.com/ilchen/options-pricing.git cloned-repo\n",
    "#%cd cloned-repo\n",
    "\n",
    "# Install the latest version of pandas-datareader, yfinance, and pandas-market-calendars\n",
    "# !pip install pandas-datareader -U\n",
    "# !pip install yfinance -U\n",
    "# !pip install pandas-market-calendars -U"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fancy-spanking",
   "metadata": {},
   "source": [
    "## Constructing a riskless rate curve for USD\n",
    "Since I already covered it in a separate notebook, I will simply import it (along with all imports that this notebook needs). The resultant YieldCurve object is assigned to the `curve` variable.\n",
    "\n",
    "I prepared two alternative rislless rate/yield curves &mdash; one based on US Treasury Yields (you can use it for pricing options maturing up to 30 years in the future) and another based on AMERIBOR rates (you can use it for pricing options maturing up to 2 years in the future). Since AMERIBOR term structure is no longer published by the Federal Reserve Bank of St. Louis, it cannot be used anymore unfortunately..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "personalized-reviewer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Uncomment the below line if you want to price with a yield curve based on US Treasury Yields\n",
    "%run riskless-yield-construction-example.ipynb\n",
    "\n",
    "# Uncomment the below line if you want to price with a yield curve based on AMERIBOR rates\n",
    "#%run riskless-rate-curve-construction-ameribor.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "encouraging-economics",
   "metadata": {},
   "source": [
    "## Estimating and forecasting volatility of the stock, options on which we want to price\n",
    "Now that the riskless curve has been constructed, the next thing to do is obtain an accurate volatility estimate for the stock whose options we want to price. There are various methods for doing it. The most common are the Exponentially Weighted Moving Average (EWMA) and GARCH(1, 1) methods. You can read about them on the Internet or delve into John C. Hull's [Risk Management and Financial Institutions](http://www-2.rotman.utoronto.ca/~hull/riskman/index.html) or [Options, Futures, and Other Derivatives](http://www-2.rotman.utoronto.ca/~hull/ofod/index.html). For the purposes of pricing options GARCH(1, 1) is preferred because it supports volatility forecasting for future maturities by incorporating mean reversion (and volatility of equities lends itself to mean reversion). In this notebook I therefore use GARCH(1, 1).\n",
    "\n",
    "### GARCH(1, 1)\n",
    "In order to use GARCH(1, 1) one first needs to estimate its parameters based on the historical data for the equity whose options you want to price. GARCH(1, 1) has three parameters &mdash; ω, α, and β. I use the maximum likelihood method to estimate them, which is an optimization task. The `volatility.parameter_estimators.GARCHParameterEstimator` class comes to rescure. _Implementation detail_: In that class I use [SciPy](https://docs.scipy.org/doc/scipy/tutorial/optimize.html) to optimize for maximum likelihood. Alternatively I could've used Google's TensorFlow 2.x.\n",
    "\n",
    "Constraint optimization for three variables is not fast. If you are not content with the speed of `volatility.parameter_estimators.GARCHParameterEstimator`, you can use its `volatility.parameter_estimators.GARCHVarianceTargetingParameterEstimator` counterpart. This latter class is \n",
    "faster because it uses the so-called _variance targeting_ method whereby it sets ω based on the sample variance of price changes. Then it optimises for only two variables instead of three as GARCHParameterEstimator does. It's marginally less accurate.\n",
    "\n",
    "*NB* When estimating ω, α, and β it's vital to use _adjusted closing_ prices, i.e. prices that have been adjusted for dividends and stock splits. Failing to do that will result in too high an estimate. Yahoo Finance provides such prices in the `Adj Close` column.\n",
    "\n",
    "#### Estimating GARCH(1, 1) parameters for Apple\n",
    "Since volatility tends to change with time, it's recommended to estimate it based on the most recent 90 or 180 days, or on the same number of days as the duration of the option being priced. In this notebook I use date from the past two years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "continuing-sensitivity",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Objective function: 3787.64268 after 122 iterations\n",
      "Optimal values for GARCH(1, 1) parameters:\n",
      "\tω=0.000004642447, α=0.00987, β=0.96411\n"
     ]
    }
   ],
   "source": [
    "TICKER = 'AAPL'\n",
    "\n",
    "# I'll use price changes over the past two years to estimate GARCH(1, 1) ω, α, and β parameters\n",
    "start = BDay(1).rollback(date.today() - relativedelta(years=+2))\n",
    "ticker = yfin.Ticker(TICKER)\n",
    "\n",
    "data = ticker.history(start=start, actions=False, auto_adjust=False).tz_localize(None)\n",
    "\n",
    "asset_prices = data['Adj Close']\n",
    "\n",
    "vol_estimator = parameter_estimators.GARCHParameterEstimator(asset_prices)\n",
    "print('Optimal values for GARCH(1, 1) parameters:\\n\\tω=%.12f, α=%.5f, β=%.5f'\n",
    "      % (vol_estimator.omega, vol_estimator.alpha, vol_estimator.beta))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "turkish-carter",
   "metadata": {},
   "source": [
    "##### Optional: Estimating with a faster estimator &mdash; GARCHVarianceTargetingParameterEstimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "sunset-project",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.12/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: invalid value encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
      "/usr/local/lib/python3.12/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: invalid value encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
      "/usr/local/lib/python3.12/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: invalid value encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Objective function: 3787.40432 after 158 iterations\n",
      "Optimal values for GARCH(1, 1) parameters:\n",
      "\tω=0.000004914091, α=0.01073, β=0.96296\n"
     ]
    }
   ],
   "source": [
    "var_target_vol_estimator = parameter_estimators.GARCHVarianceTargetingParameterEstimator(asset_prices)\n",
    "print('Optimal values for GARCH(1, 1) parameters:\\n\\tω=%.12f, α=%.5f, β=%.5f'\n",
    "      % (var_target_vol_estimator.omega, var_target_vol_estimator.alpha, var_target_vol_estimator.beta))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "common-projector",
   "metadata": {},
   "source": [
    "As you can see, `GARCHParameterEstimator` is able to achieve a slightly higher value of the objective function than `GARCHVarianceTargetingParameterEstimator`. The higher, the more accurate as we are solving for the maximum value."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "literary-compensation",
   "metadata": {},
   "source": [
    "#### Forecasting volatility for Apple\n",
    "Now that we have obtained optimal values for GARCH(1, 1) parameters ω, α, and β, we can proceed to forecast the volatility of AAPL stock. I created class `volatility.parameter_estimators.GARCHVolatilityTracker` to assist in it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "unlimited-photographer",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volatility of AAPL for term 0.0685 years: 0.20475\n"
     ]
    }
   ],
   "source": [
    "vol_tracker = volatility_trackers.GARCHVolatilityTracker(vol_estimator.omega, vol_estimator.alpha,\n",
    "                                                         vol_estimator.beta, asset_prices)\n",
    "\n",
    "# Let's get volatility forecast for next January options, i.e. maturity on the 3rd Friday of next January\n",
    "from dateutil.relativedelta import FR\n",
    "maturity_date = curve.date + relativedelta(years=1, month=1, day=1, weekday=FR(3))\n",
    "vol = vol_tracker.get_annual_term_volatility_forecast(curve.to_years(maturity_date))\n",
    "print('Volatility of %s for term %.4f years: %.5f' % (TICKER, curve.to_years(maturity_date), vol))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "technological-camel",
   "metadata": {},
   "source": [
    "#### Optional: Visualizing historical and future volatility of Apple stock for 200 business days into the future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "surprised-seattle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Skipping the first n days of volatility forecast\n",
    "n_days_to_skip = 10\n",
    "x = vol_tracker.get_dates()[n_days_to_skip:]\n",
    "fig = plt.figure(figsize=(20, 10))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.plot(x, vol_tracker.get_annual_volatilities()[n_days_to_skip:],\n",
    "        label='Historical Annual Volatility (GARCH(1,1) ω=%.5f, α=%.5f, β=%.5f)' \n",
    "            % (vol_estimator.omega, vol_estimator.alpha, vol_estimator.beta),\n",
    "        linewidth=2, color='k')\n",
    "\n",
    "# Forecasting volatility of Apple stock 200 days into the future\n",
    "future_volatilities = vol_tracker.get_annual_volatility_forecast_for_next_n_days(200)\n",
    "ax.plot(future_volatilities.index, future_volatilities, label='Forecast Annual Volatility', linewidth=1, color='k')\n",
    "ax.hlines(vol_tracker.get_annual_long_term_volatility(), xmin=future_volatilities.index[0], xmax=future_volatilities.index[-1],\n",
    "         label=\"Long-term Annual Volatility\", linestyle='--')\n",
    "ax.legend()\n",
    "ax.grid(True)\n",
    "x_axis = x.append(future_volatilities.index)\n",
    "ax.set_xticks(x_axis[::36])\n",
    "ax.set_xticklabels(x_axis[::36].strftime('%Y-%m-%d'), rotation=45)\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nonprofit-movie",
   "metadata": {},
   "source": [
    "#### Optional: Visualizing the volatility term structure of Apple stock for 5 years into the future\n",
    "A very important notion in pricing options is the _volatility term structure_. It defines the relationship between\n",
    "the maturity of options and their volatilities. In other words it defines what the average volatility for a given maturity is forecast to be. Given the mean reverting nature of volatility, volatility for the longer terms trends towards the mean volatility of a given asset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "complex-contract",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from math import floor, ceil\n",
    "\n",
    "terms = [x / 100 for x in range(5, 505, 5)]\n",
    "term_vols = [vol_tracker.get_annual_term_volatility_forecast(term) * 100 for term in terms]\n",
    "series = pd.Series(term_vols, index=terms)\n",
    "\n",
    "ax = series.plot(figsize=(20,10), grid=True, kind='bar', title='Term structure of ' + TICKER + ' volatility')\n",
    "ax.set_ylim(bottom=floor(series.min()), top=ceil(series.max()))\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter())\n",
    "ax.set_ylabel('Annual Volatility')\n",
    "_ = ax.set_xlabel('Terms in years')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lined-times",
   "metadata": {},
   "source": [
    "## Pricing using Black-Scholes-Merton Pricer\n",
    "The Black-Scholes-Merton model can be used to price European options, including on stock that pays dividends. With enough customizations it can also be used to price American call options (even those paying dividends). This is based on the fact that it is never optimal to exercise an American call option prematurely except on days immediately preceeding ex-dividend days for the stock. The `pricing.options.BlackScholesMertonPricer` class from this repository implements this customization to price American call options.\n",
    "\n",
    "Let's put it to use to price an AAPL call option with a strike of $180. Apple pays dividends. It's essential to take into account dividends that get payed during the lifetime of an option to get an accurate price.\n",
    "\n",
    "Market makers tend to calculate the life time of options using trading days rather than calendar days. The `pricing.options.BlackScholesMertonPricer` class supports both ways of pricing options. It's determined by the `holidays` parameter of its constructor, passing an array of `datetime64[D]` values implies calculating the life of\n",
    "the option using the number trading days to its expiry and dividing by 252. Not passing a value for this parameter\n",
    "will imply calculating the life of the option using the number of calendar days to its expiry and dividing by a value between 365 and 366. It **does** make an impact of about 3\\%-5\\% on the price of the option depending on its maturity, for longer-dated options the impact is smaller. In the below notebook I price based on the trading\n",
    "calendar of the New-York Stock Exchange. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "other-palmer",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAPL AMERICAN call option with strike 180.0 and maturity 2025-01-17, price: 75.80, σ: 0.2047, Δ: 1.000, Γ: 0.000, ν: 0.000\n"
     ]
    }
   ],
   "source": [
    "strike = 180.\n",
    "\n",
    "# yfinance returns the most recent ex-dividend date in the last row\n",
    "#ticker = yfin.Ticker(TICKER)\n",
    "last_divs = ticker.dividends.iloc[-1:]\n",
    "\n",
    "# An approximate rule for Apple's ex-dividend dates -- ex-dividend date is on the first Friday\n",
    "# of the last month of a season if that Friday is the 5th day of the month or later, otherwise\n",
    "# it falls on the second Friday of that month.\n",
    "idx = (pd.date_range(last_divs.index[0].date(), freq='WOM-1FRI', periods=30)[::3])\n",
    "idx = idx.map(lambda dt: dt if dt.day >= 5 else dt+BDay(5))\n",
    "divs = pd.Series([last_divs.iloc[0]] * len(idx), index=idx, name=TICKER + ' Dividends')\n",
    "\n",
    "# Taking the trading calendar into account when pricing\n",
    "import pandas_market_calendars as mcal\n",
    "holidays = mcal.get_calendar('NYSE').holidays().holidays\n",
    "\n",
    "cur_price = asset_prices.iloc[-1]\n",
    "pricer = options.BlackScholesMertonPricer(maturity_date, vol_tracker, strike, curve, cur_price, ticker=TICKER,\n",
    "                                          dividends=divs, opt_type=options.OptionType.AMERICAN, holidays=holidays)\n",
    "print(pricer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "clean-dictionary",
   "metadata": {},
   "source": [
    "Sometimes you might want to price using _an implied volatility_ for a given expiry date. You get the implied volatilty from the option prices of market makers. The `pricing.options.BlackScholesMertonPricer` class supports this. Just enter the implied volatility value instead of passing it a volatility_tracker object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "stunning-airfare",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAPL AMERICAN call option with strike 180.0 and maturity 2025-01-17, price: 75.81, σ: 0.4342, Δ: 0.999, Γ: 0.000, ν: 0.168\n"
     ]
    }
   ],
   "source": [
    "# Pricing with an implied volatility of 43.42% (implied volatility for this contract as of 15th Sep 2023)\n",
    "pricer = options.BlackScholesMertonPricer(maturity_date, .4342, strike, curve, cur_price, ticker=TICKER,\n",
    "                                          dividends=divs, opt_type=options.OptionType.AMERICAN, holidays=holidays)\n",
    "print(pricer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "inside-skirt",
   "metadata": {},
   "source": [
    "As you can see, a different volatility value resulted in a different price.\n",
    "\n",
    "Let's now price an AAPL call option with a longer maturity. This is a little more complex as there are more ex-dividend dates during the option's lifetime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "norman-error",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAPL AMERICAN call option with strike 180.0 and maturity 2025-12-19, price: 82.20, σ: 0.2107, Δ: 0.975, Γ: 0.001, ν: 14.946\n"
     ]
    }
   ],
   "source": [
    "maturity_date = curve.date + relativedelta(years=1, month=12, day=1, weekday=FR(3))\n",
    "pricer = options.BlackScholesMertonPricer(maturity_date, vol_tracker, strike, curve, cur_price, ticker=TICKER,\n",
    "                                          dividends=divs, opt_type=options.OptionType.AMERICAN, holidays=holidays)\n",
    "print(pricer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "expressed-amino",
   "metadata": {},
   "source": [
    "If you wonder where the volatility forecast came from, it came from the GARCH(1, 1) `vol_tracker` object I passed to the BlackScholesMertonPricer. You can easily see this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bridal-grave",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21067968034241882"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vol_tracker.get_annual_term_volatility_forecast(curve.to_years(maturity_date))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "optical-democracy",
   "metadata": {},
   "source": [
    "## Pricing using Binomial Tree Pricer\n",
    "The Binomial Tree model can be used to price any European or American option, including on stock that pays dividends.\n",
    "\n",
    "Let's put it to use to price an AAPL call and put option with a strike of $180 and compare resulting prices for call options. The constructor of the `BinomialTreePricer` class takes exactly the same parameters as its\n",
    "`BlackScholesMertonPricer` counterpart. I will use it using the trading calendar of the NYSE to calculate\n",
    "the lifetime of options priced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "current-export",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAPL AMERICAN call option with strike 180.0 and maturity 2025-12-19, price: 82.18, σ: 0.2107, Δ: 0.976, Γ: 0.001, ν: 12.013\n",
      "Price difference between Black-Scholes-Merton and Binomial Treee pricers: 0.021441\n"
     ]
    }
   ],
   "source": [
    "bin_tree_pricer = options.BinomialTreePricer(maturity_date, vol_tracker, strike, curve, cur_price, ticker=TICKER,\n",
    "                                             dividends=divs, opt_type=options.OptionType.AMERICAN, holidays=holidays)\n",
    "print(bin_tree_pricer)\n",
    "\n",
    "print('Price difference between Black-Scholes-Merton and Binomial Treee pricers: %.6f'\n",
    "      % abs(pricer.get_price() - bin_tree_pricer.get_price()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rubber-torture",
   "metadata": {},
   "source": [
    "Let's now price an AAPL put option with the same maturity. You cannot price put options with a `BlackScholesMertonPricer`, with a `BinomialTreePricer` you can."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fitting-hands",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAPL AMERICAN put option with strike 180.0 and maturity 2025-12-19, price: 0.54, σ: 0.2107, Δ: -0.025, Γ: 0.001, ν: 12.244\n"
     ]
    }
   ],
   "source": [
    "bin_tree_pricer_put = options.BinomialTreePricer(maturity_date, vol_tracker, strike, curve, cur_price, is_call=False,\n",
    "                                                 ticker=TICKER, dividends=divs, opt_type=options.OptionType.AMERICAN,\n",
    "                                                 holidays=holidays)\n",
    "print(bin_tree_pricer_put)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "executed-front",
   "metadata": {},
   "source": [
    "#### Put-Call Parity\n",
    "An important theoretical property of the price of an American option is the so-called put-call parity, which is expressed as:\n",
    "$$S_0 - D - K <= C - P <= S_0 - K\\cdot e^{-r\\cdot T}$$\n",
    "where:\n",
    "* C is the price of an American call option\n",
    "* P is the price of the corresponding American put option\n",
    "* S<sub>0</sub> is the present price of the underlying stock\n",
    "* D is the present value of the dividents whose ex-dividend dates happen before the maturity date of the option\n",
    "* K is the strike price\n",
    "* T is the liefteime of the option expressed in years\n",
    "* r is the riskless rate for maturity T\n",
    "\n",
    "Let's put it to the test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "apparent-appeal",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Put-Call parity: 74.29 <= 81.64 <= 82.63\n"
     ]
    }
   ],
   "source": [
    "from math import exp\n",
    "price_difference = bin_tree_pricer.get_price() - bin_tree_pricer_put.get_price()\n",
    "left_hand_side_expr = bin_tree_pricer.s0 - bin_tree_pricer.get_npv_dividends() - bin_tree_pricer.strike\n",
    "right_hand_side_expr = bin_tree_pricer.s0 - bin_tree_pricer.strike * exp(-bin_tree_pricer.r * bin_tree_pricer.T)\n",
    "\n",
    "assert left_hand_side_expr <= price_difference <= right_hand_side_expr\n",
    "print(f'Put-Call parity: {left_hand_side_expr:.2f} <= {price_difference:.2f} <= {right_hand_side_expr:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "roman-leave",
   "metadata": {},
   "source": [
    "If you wish to price with a given implied volatility, just pass it to the pricer as the second parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "owned-dispatch",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAPL AMERICAN call option with strike 180.0 and maturity 2025-12-19, price: 85.68, σ: 0.3297, Δ: 0.911, Γ: 0.002, ν: 44.252\n",
      "AAPL AMERICAN put option with strike 180.0 and maturity 2025-12-19, price: 4.11, σ: 0.3297, Δ: -0.091, Γ: 0.002, ν: 44.535\n"
     ]
    }
   ],
   "source": [
    "impl_vol = .3297\n",
    "bin_tree_pricer = options.BinomialTreePricer(maturity_date, impl_vol, strike, curve, cur_price, ticker=TICKER,\n",
    "                                             dividends=divs, opt_type=options.OptionType.AMERICAN,\n",
    "                                             holidays=holidays)\n",
    "print(bin_tree_pricer)\n",
    "bin_tree_pricer_put = options.BinomialTreePricer(maturity_date, impl_vol, strike, curve, cur_price, is_call=False,\n",
    "                                                 ticker=TICKER, dividends=divs, opt_type=options.OptionType.AMERICAN,\n",
    "                                                 holidays=holidays)\n",
    "print(bin_tree_pricer_put)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "growing-summer",
   "metadata": {},
   "source": [
    "## Pricing with your own discount curve\n",
    "You might want to prefer to price using your own discount curve. The `YieldCurve` class of the `pricing.curves` package makes it fairly easy to do."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "italian-bermuda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reusing 'relativedelta' offsets defined in Cell 3, which are for 0, 1 month, 3 months, 6 months, 1 year, 2 years,\n",
    "# 3 years, 5 years, 7 years, 10 years, 20 years, 30 years.\n",
    "\n",
    "# Defining a flat curve with a discount rate of 3% and yearly compounding frequency.\n",
    "custom_curve = curves.YieldCurve(today, offsets, [.03]*len(offsets), compounding_freq=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "helpful-butler",
   "metadata": {},
   "source": [
    "Now pricing with out own flat curve and a given implied volatility:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "blind-cotton",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAPL AMERICAN call option with strike 180.0 and maturity 2025-12-19, price: 83.84, σ: 0.3297, Δ: 0.905, Γ: 0.002, ν: 46.552\n",
      "AAPL AMERICAN put option with strike 180.0 and maturity 2025-12-19, price: 4.43, σ: 0.3297, Δ: -0.097, Γ: 0.002, ν: 46.685\n"
     ]
    }
   ],
   "source": [
    "bin_tree_pricer = options.BinomialTreePricer(maturity_date, impl_vol, strike, custom_curve, cur_price,\n",
    "                                             ticker=TICKER, dividends=divs, opt_type=options.OptionType.AMERICAN,\n",
    "                                             holidays=holidays)\n",
    "print(bin_tree_pricer)\n",
    "bin_tree_pricer_put = options.BinomialTreePricer(maturity_date, impl_vol, strike, custom_curve, cur_price,\n",
    "                                                 is_call=False, ticker=TICKER, dividends=divs,\n",
    "                                                 opt_type=options.OptionType.AMERICAN, holidays=holidays)\n",
    "print(bin_tree_pricer_put)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "213d1e24-ddc1-4802-ac07-0ea304c81182",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}