examples/Parallel Computing/Monte Carlo Options.ipynb

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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Parallel Monto-Carlo options pricing"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook shows how to use `IPython.parallel` to do Monte-Carlo options pricing in parallel. We will compute the price of a large number of options for different strike prices and volatilities."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Problem setup"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "import sys\n",
      "import time\n",
      "from IPython.parallel import Client\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here are the basic parameters for our computation."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "price = 100.0  # Initial price\n",
      "rate = 0.05  # Interest rate\n",
      "days = 260  # Days to expiration\n",
      "paths = 10000  # Number of MC paths\n",
      "n_strikes = 6  # Number of strike values\n",
      "min_strike = 90.0  # Min strike price\n",
      "max_strike = 110.0  # Max strike price\n",
      "n_sigmas = 5  # Number of volatility values\n",
      "min_sigma = 0.1  # Min volatility\n",
      "max_sigma = 0.4  # Max volatility"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "strike_vals = np.linspace(min_strike, max_strike, n_strikes)\n",
      "sigma_vals = np.linspace(min_sigma, max_sigma, n_sigmas)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Strike prices: \", strike_vals\n",
      "print \"Volatilities: \", sigma_vals"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Strike prices:  [  90.   94.   98.  102.  106.  110.]\n",
        "Volatilities:  [ 0.1    0.175  0.25   0.325  0.4  ]\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Monte-Carlo option pricing function"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following function computes the price of a single option.  It returns the call and put prices for both European and Asian style options."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def price_option(S=100.0, K=100.0, sigma=0.25, r=0.05, days=260, paths=10000):\n",
      "    \"\"\"\n",
      "    Price European and Asian options using a Monte Carlo method.\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    S : float\n",
      "        The initial price of the stock.\n",
      "    K : float\n",
      "        The strike price of the option.\n",
      "    sigma : float\n",
      "        The volatility of the stock.\n",
      "    r : float\n",
      "        The risk free interest rate.\n",
      "    days : int\n",
      "        The number of days until the option expires.\n",
      "    paths : int\n",
      "        The number of Monte Carlo paths used to price the option.\n",
      "\n",
      "    Returns\n",
      "    -------\n",
      "    A tuple of (E. call, E. put, A. call, A. put) option prices.\n",
      "    \"\"\"\n",
      "    import numpy as np\n",
      "    from math import exp,sqrt\n",
      "    \n",
      "    h = 1.0/days\n",
      "    const1 = exp((r-0.5*sigma**2)*h)\n",
      "    const2 = sigma*sqrt(h)\n",
      "    stock_price = S*np.ones(paths, dtype='float64')\n",
      "    stock_price_sum = np.zeros(paths, dtype='float64')\n",
      "    for j in range(days):\n",
      "        growth_factor = const1*np.exp(const2*np.random.standard_normal(paths))\n",
      "        stock_price = stock_price*growth_factor\n",
      "        stock_price_sum = stock_price_sum + stock_price\n",
      "    stock_price_avg = stock_price_sum/days\n",
      "    zeros = np.zeros(paths, dtype='float64')\n",
      "    r_factor = exp(-r*h*days)\n",
      "    euro_put = r_factor*np.mean(np.maximum(zeros, K-stock_price))\n",
      "    asian_put = r_factor*np.mean(np.maximum(zeros, K-stock_price_avg))\n",
      "    euro_call = r_factor*np.mean(np.maximum(zeros, stock_price-K))\n",
      "    asian_call = r_factor*np.mean(np.maximum(zeros, stock_price_avg-K))\n",
      "    return (euro_call, euro_put, asian_call, asian_put)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can time a single call of this function using the `%timeit` magic:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit -n1 -r1 print price_option(S=100.0, K=100.0, sigma=0.25, r=0.05, days=260, paths=10000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(12.478072469211625, 7.5692079226372924, 6.9498346596114704, 4.5592719279729934)\n",
        "1 loops, best of 1: 111 ms per loop\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Parallel computation across strike prices and volatilities"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The Client is used to setup the calculation and works with all engines."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "rc = Client()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A `LoadBalancedView` is an interface to the engines that provides dynamic load\n",
      "balancing at the expense of not knowing which engine will execute the code."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "view = rc.load_balanced_view()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Submit tasks for each (strike, sigma) pair. Again, we use the `%%timeit` magic to time the entire computation."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "async_results = []"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "%%timeit -n1 -r1\n",
      "\n",
      "for strike in strike_vals:\n",
      "    for sigma in sigma_vals:\n",
      "        # This line submits the tasks for parallel computation.\n",
      "        ar = view.apply_async(price_option, price, strike, sigma, rate, days, paths)\n",
      "        async_results.append(ar)\n",
      "\n",
      "rc.wait(async_results)  # Wait until all tasks are done."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 loops, best of 1: 810 ms per loop\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(async_results)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "30"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Process and visualize results"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Retrieve the results using the `get` method:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "results = [ar.get() for ar in async_results]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Assemble the result into a structured NumPy array."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "prices = np.empty(n_strikes*n_sigmas,\n",
      "    dtype=[('ecall',float),('eput',float),('acall',float),('aput',float)]\n",
      ")\n",
      "\n",
      "for i, price in enumerate(results):\n",
      "    prices[i] = tuple(price)\n",
      "\n",
      "prices.shape = (n_strikes, n_sigmas)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the value of the European call in (volatility, strike) space."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure()\n",
      "plt.contourf(sigma_vals, strike_vals, prices['ecall'])\n",
      "plt.axis('tight')\n",
      "plt.colorbar()\n",
      "plt.title('European Call')\n",
      "plt.xlabel(\"Volatility\")\n",
      "plt.ylabel(\"Strike Price\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "<matplotlib.text.Text at 0x1100a3290>"
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5lFKekORFSb6e5H611i+u36fWelzG1p9Z9/ljkxyb5NG11tfNci0WBN7em0fbp5ZS9l/7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\nElEG2IkoA9MkzgCDI8gAq0iYAXYizMB0iTPAIAgywKoSZYC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       "text": [
        "<matplotlib.figure.Figure at 0x10ffc7c10>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the value of the Asian call in (volatility, strike) space."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure()\n",
      "plt.contourf(sigma_vals, strike_vals, prices['acall'])\n",
      "plt.axis('tight')\n",
      "plt.colorbar()\n",
      "plt.title(\"Asian Call\")\n",
      "plt.xlabel(\"Volatility\")\n",
      "plt.ylabel(\"Strike Price\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "<matplotlib.text.Text at 0x11009b3d0>"
       ]
      },
      {
       "metadata": {
        "png": {
         "height": 407,
         "width": 562
        }
       },
       "output_type": "display_data",
       "png": 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HpFmb5k9qre+cxXdjHzEGmJYYA6tNjAHGIcYwzDgNIc3kjTsn+UKaCR4nlFKy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\nPY495r2BPjrggAPulOTIJD+f5MZJvpjkj5I8s9b6o5Xrn3r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       "text": [
        "<matplotlib.figure.Figure at 0x10ffc7210>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the value of the European put in (volatility, strike) space."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure()\n",
      "plt.contourf(sigma_vals, strike_vals, prices['eput'])\n",
      "plt.axis('tight')\n",
      "plt.colorbar()\n",
      "plt.title(\"European Put\")\n",
      "plt.xlabel(\"Volatility\")\n",
      "plt.ylabel(\"Strike Price\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "<matplotlib.text.Text at 0x1109ef450>"
       ]
      },
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l7FHOjD2SloKhJzPGHuXGdXukOcYelcrYo5w53SOpboaeDHkpl3Jk7JESY49K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\n4dkPdTKjCz1JfiNN5Hl/khPXpndKKVdI8uwkj0ryJ0lO3uS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       "text": [
        "<matplotlib.figure.Figure at 0x1109e0310>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the value of the Asian put in (volatility, strike) space."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure()\n",
      "plt.contourf(sigma_vals, strike_vals, prices['aput'])\n",
      "plt.axis('tight')\n",
      "plt.colorbar()\n",
      "plt.title(\"Asian Put\")\n",
      "plt.xlabel(\"Volatility\")\n",
      "plt.ylabel(\"Strike Price\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "<matplotlib.text.Text at 0x1109e3b10>"
       ]
      },
      {
       "metadata": {
        "png": {
         "height": 407,
         "width": 562
        }
       },
       "output_type": "display_data",
       "png": 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Mm2ArQhlWZ3jejIAGGEc4A8xDOAPMQzjDOuvjoF8WxFBgYC8GAgPzMBAYmIeh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\nhTTLoABYcqZmgCGypAlg+QxyUmaMY9rb48Y+q1V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       "text": [
        "<matplotlib.figure.Figure at 0x110dd2c50>"
       ]
      }
     ],
     "prompt_number": 24
    }
   ],
   "metadata": {}
  }
 ]
}