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"nbformat": 2,
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"metadata": {
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"metadata": {
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"name": "mcpricer"
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"name": "mcpricer"
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},
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},
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"nbformat": 2,
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"worksheets": [
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"worksheets": [
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"cells": [
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"cells": [
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{
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{
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"source": "# Parallel Monto-Carlo options pricing",
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"cell_type": "markdown",
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"cell_type": "markdown"
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"source": [
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"# Parallel Monto-Carlo options pricing"
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]
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},
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},
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{
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{
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"source": "## Problem setup",
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"cell_type": "markdown",
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"cell_type": "markdown"
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"source": [
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"## Problem setup"
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]
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"collapsed": true,
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"input": [
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"import sys",
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"import time",
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"from IPython.parallel import Client",
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"import numpy as np",
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"from mckernel import price_options",
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"from matplotlib import pyplot as plt"
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],
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"language": "python",
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"language": "python",
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"outputs": [],
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"outputs": [],
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"collapsed": true,
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"prompt_number": 1
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"prompt_number": 1,
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"input": "import sys\nimport time\nfrom IPython.parallel import Client\nimport numpy as np\nfrom mckernel import price_options\nfrom matplotlib import pyplot as plt"
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"collapsed": true,
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"input": [
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"cluster_profile = \"default\"",
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"price = 100.0 # Initial price",
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"rate = 0.05 # Interest rate",
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"days = 260 # Days to expiration",
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"paths = 10000 # Number of MC paths",
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"n_strikes = 6 # Number of strike values",
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"min_strike = 90.0 # Min strike price",
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"max_strike = 110.0 # Max strike price",
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"n_sigmas = 5 # Number of volatility values",
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"min_sigma = 0.1 # Min volatility",
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"max_sigma = 0.4 # Max volatility"
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],
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"language": "python",
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"language": "python",
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"outputs": [],
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"outputs": [],
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"collapsed": true,
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"prompt_number": 2
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"prompt_number": 2,
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"input": "cluster_profile = \"default\"\nprice = 100.0 # Initial price\nrate = 0.05 # Interest rate\ndays = 260 # Days to expiration\npaths = 10000 # Number of MC paths\nn_strikes = 5 # Number of strike values\nmin_strike = 90.0 # Min strike price\nmax_strike = 110.0 # Max strike price\nn_sigmas = 5 # Number of volatility values\nmin_sigma = 0.1 # Min volatility\nmax_sigma = 0.4 # Max volatility"
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"collapsed": true,
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"input": [
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"strike_vals = np.linspace(min_strike, max_strike, n_strikes)",
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"sigma_vals = np.linspace(min_sigma, max_sigma, n_sigmas)"
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],
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"language": "python",
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"language": "python",
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"outputs": [],
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"outputs": [],
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"collapsed": true,
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"prompt_number": 3
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"prompt_number": 3,
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"input": "strike_vals = np.linspace(min_strike, max_strike, n_strikes)\nsigma_vals = np.linspace(min_sigma, max_sigma, n_sigmas)"
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},
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},
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{
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{
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"source": "## Parallel computation across strike prices and volatilities",
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"cell_type": "markdown",
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"cell_type": "markdown"
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"source": [
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"## Parallel computation across strike prices and volatilities"
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]
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},
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{
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{
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"source": "The Client is used to setup the calculation and works with all engines.",
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"cell_type": "markdown",
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"cell_type": "markdown"
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"source": [
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"The Client is used to setup the calculation and works with all engines."
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]
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"cell_type": "code",
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"cell_type": "code",
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"collapsed": true,
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"input": [
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"c = Client(profile=cluster_profile)"
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],
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"language": "python",
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"language": "python",
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"outputs": [],
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"outputs": [],
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"collapsed": true,
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"prompt_number": 4
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"prompt_number": 4,
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"input": "c = Client(profile=cluster_profile)"
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},
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},
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{
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{
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"source": "A LoadBalancedView is an interface to the engines that provides dynamic load \nbalancing at the expense of not knowing which engine will execute the code.",
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"cell_type": "markdown",
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"cell_type": "markdown"
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"source": [
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"A LoadBalancedView is an interface to the engines that provides dynamic load",
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"balancing at the expense of not knowing which engine will execute the code."
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]
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},
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"cell_type": "code",
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"cell_type": "code",
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"collapsed": true,
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"input": [
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"view = c.load_balanced_view()"
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],
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"language": "python",
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"language": "python",
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"outputs": [],
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"collapsed": true,
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"prompt_number": 5
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"prompt_number": 5,
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"input": "view = c.load_balanced_view()"
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},
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"cell_type": "code",
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"print \"Strike prices: \", strike_vals",
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"print \"Volatilities: \", sigma_vals"
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],
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"language": "python",
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"language": "python",
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"outputs": [
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"outputs": [
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{
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{
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"output_type": "stream",
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"output_type": "stream",
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"text": "Strike prices: [ 90. 95. 100. 105. 110.]\nVolatilities: [ 0.1 0.175 0.25 0.325 0.4 ]"
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"stream": "stdout",
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"text": [
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"Strike prices: [ 90. 94. 98. 102. 106. 110.]",
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"Volatilities: [ 0.1 0.175 0.25 0.325 0.4 ]"
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]
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}
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}
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],
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],
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"collapsed": false,
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"prompt_number": 6
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"prompt_number": 6,
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"input": "print \"Strike prices: \", strike_vals\nprint \"Volatilities: \", sigma_vals"
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},
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},
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{
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{
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"source": "Submit tasks for each (strike, sigma) pair.",
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"cell_type": "markdown",
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"cell_type": "markdown"
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"source": [
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"Submit tasks for each (strike, sigma) pair."
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]
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},
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"cell_type": "code",
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"cell_type": "code",
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"collapsed": true,
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"input": [
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"t1 = time.time()",
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"async_results = []",
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"for strike in strike_vals:",
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" for sigma in sigma_vals:",
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" ar = view.apply_async(price_options, price, strike, sigma, rate, days, paths)",
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" async_results.append(ar)"
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],
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"language": "python",
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"language": "python",
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"outputs": [],
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"outputs": [],
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"collapsed": true,
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"prompt_number": 7
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"prompt_number": 7,
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"input": "t1 = time.time()\nasync_results = []\nfor strike in strike_vals:\n for sigma in sigma_vals:\n ar = view.apply_async(price_options, price, strike, sigma, rate, days, paths)\n async_results.append(ar)"
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},
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"cell_type": "code",
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"print \"Submitted tasks: \", len(async_results)"
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],
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"language": "python",
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"outputs": [
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"outputs": [
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"output_type": "stream",
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"output_type": "stream",
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"text": "Submitted tasks: 25"
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"stream": "stdout",
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"text": [
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"Submitted tasks: 30"
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]
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}
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}
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],
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],
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"collapsed": false,
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"prompt_number": 8
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"prompt_number": 8,
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"input": "print \"Submitted tasks: \", len(async_results)"
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},
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},
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{
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{
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"source": "Block until all tasks are completed.",
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"cell_type": "markdown",
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"cell_type": "markdown"
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"source": [
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"Block until all tasks are completed."
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]
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"cell_type": "code",
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"c.wait(async_results)",
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"t2 = time.time()",
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"t = t2-t1",
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"",
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"print \"Parallel calculation completed, time = %s s\" % t"
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],
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"language": "python",
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"language": "python",
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"outputs": [
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"outputs": [
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{
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{
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"output_type": "stream",
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"output_type": "stream",
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"text": "Parallel calculation completed, time = 2.57819604874 s"
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"stream": "stdout",
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"text": [
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"Parallel calculation completed, time = 4.46057891846 s"
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]
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}
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}
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],
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],
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"collapsed": false,
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"prompt_number": 9
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"prompt_number": 9,
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"input": "c.wait(async_results)\nt2 = time.time()\nt = t2-t1\n\nprint \"Parallel calculation completed, time = %s s\" % t"
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},
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},
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{
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{
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"source": "## Process and visualize results",
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"cell_type": "markdown",
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"cell_type": "markdown"
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"source": [
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"## Process and visualize results"
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]
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},
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},
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{
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{
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"source": "Get the results using the `get` method:",
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"cell_type": "markdown",
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"cell_type": "markdown"
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"source": [
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"Get the results using the `get` method:"
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]
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},
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},
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"cell_type": "code",
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"cell_type": "code",
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"collapsed": true,
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"input": [
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"results = [ar.get() for ar in async_results]"
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],
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"language": "python",
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"language": "python",
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"outputs": [],
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"input": "results = [ar.get() for ar in async_results]\n"
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"source": "Assemble the result into a structured NumPy array.",
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"input": [
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"prices = np.empty(n_strikes*n_sigmas,",
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" dtype=[('ecall',float),('eput',float),('acall',float),('aput',float)]",
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"prices.shape = (n_strikes, n_sigmas)"
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"collapsed": true,
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"prompt_number": 11
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"prompt_number": 11,
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"input": "prices = np.empty(n_strikes*n_sigmas,\n dtype=[('ecall',float),('eput',float),('acall',float),('aput',float)]\n)\n\nfor i, price in enumerate(results):\n prices[i] = tuple(price)\n \nprices.shape = (n_strikes, n_sigmas)\nstrike_mesh, sigma_mesh = np.meshgrid(strike_vals, sigma_vals)"
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"source": "Plot the value of the European call in (volatility, strike) space.",
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"Plot the value of the European call in (volatility, strike) space."
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"plt.contourf(sigma_vals, strike_vals, prices['ecall'])",
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"plt.colorbar()",
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"plt.title('European Call')",
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|
"plt.xlabel(\"Volatility\")",
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"plt.ylabel(\"Strike Price\")"
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"prompt_number": 12,
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"text": "<matplotlib.text.Text at 0x2c54750>"
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"text": [
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"<matplotlib.text.Text at 0x106b618d0>"
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"output_type": "display_data",
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"output_type": "display_data",
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lF/exgIFcET0xzCKg1XZm6uuyWcPR0ZG6ujqcna93yK6rq0OttvIO8mMzod9G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\n5MgRsrKy2u6XZZl58+Z1v3I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8/fTTmDp1KmJiYlBTU4PXX38dI0aMwB133IG9e/fKPYLyJLUH0CZPDTFXxaa563niztq6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\nxkREGvD/vLHqAgUOcYsAAAAASUVORK5CYII=\n"
|
|
168
|
}
|
|
268
|
}
|
|
169
|
],
|
|
269
|
],
|
|
170
|
"collapsed": false,
|
|
270
|
"prompt_number": 12
|
|
171
|
"prompt_number": 12,
|
|
|
|
|
172
|
"input": "plt.contourf(sigma_mesh, strike_mesh, prices['ecall'])\nplt.axis('tight')\nplt.colorbar()\nplt.title('European Call')\nplt.xlabel(\"Volatility\")\nplt.ylabel(\"Strike Price\")"
|
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|
|
|
173
|
},
|
|
271
|
},
|
|
174
|
{
|
|
272
|
{
|
|
175
|
"source": "Plot the value of the Asian call in (volatility, strike) space.",
|
|
273
|
"cell_type": "markdown",
|
|
176
|
"cell_type": "markdown"
|
|
274
|
"source": [
|
|
|
|
|
275
|
"Plot the value of the Asian call in (volatility, strike) space."
|
|
|
|
|
276
|
]
|
|
177
|
},
|
|
277
|
},
|
|
178
|
{
|
|
278
|
{
|
|
179
|
"cell_type": "code",
|
|
279
|
"cell_type": "code",
|
|
|
|
|
280
|
"collapsed": false,
|
|
|
|
|
281
|
"input": [
|
|
|
|
|
282
|
"plt.figure()",
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|
|
|
|
283
|
"plt.contourf(sigma_vals, strike_vals, prices['acall'])",
|
|
|
|
|
284
|
"plt.axis('tight')",
|
|
|
|
|
285
|
"plt.colorbar()",
|
|
|
|
|
286
|
"plt.title(\"Asian Call\")",
|
|
|
|
|
287
|
"plt.xlabel(\"Volatility\")",
|
|
|
|
|
288
|
"plt.ylabel(\"Strike Price\")"
|
|
|
|
|
289
|
],
|
|
180
|
"language": "python",
|
|
290
|
"language": "python",
|
|
181
|
"outputs": [
|
|
291
|
"outputs": [
|
|
182
|
{
|
|
292
|
{
|
|
183
|
"output_type": "pyout",
|
|
293
|
"output_type": "pyout",
|
|
184
|
"prompt_number": 13,
|
|
294
|
"prompt_number": 13,
|
|
185
|
"text": "<matplotlib.text.Text at 0x2e14610>"
|
|
295
|
"text": [
|
|
|
|
|
296
|
"<matplotlib.text.Text at 0x106bd90d0>"
|
|
|
|
|
297
|
]
|
|
186
|
},
|
|
298
|
},
|
|
187
|
{
|
|
299
|
{
|
|
188
|
"output_type": "display_data",
|
|
300
|
"output_type": "display_data",
|
|
189
|
"png": 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ebtVCtksyoi8YjotW0SWqRzQbY4xAu8VjtYoHQ1lBiGgZDkdm/1nGU4Ghu6Ho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\nHnpIf+ncBiNEaGfb+rR5EKD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ewHwYYfEYYTFchvjq1av44x//iNOnT2Ps2LH45S9/qe+nLlNjiuwBzIcRFosBFsvl22DO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\nEUnGEBMRSfb/3Vuei4TCOWYAAAAASUVORK5CYII=\n"
|
|
190
|
}
|
|
302
|
}
|
|
191
|
],
|
|
303
|
],
|
|
192
|
"collapsed": false,
|
|
304
|
"prompt_number": 13
|
|
193
|
"prompt_number": 13,
|
|
|
|
|
194
|
"input": "plt.contourf(sigma_mesh, strike_mesh, prices['acall'])\nplt.axis('tight')\nplt.colorbar()\nplt.title(\"Asian Call\")\nplt.xlabel(\"Volatility\")\nplt.ylabel(\"Strike Price\")"
|
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|
|
195
|
},
|
|
305
|
},
|
|
196
|
{
|
|
306
|
{
|
|
197
|
"source": "Plot the value of the European put in (volatility, strike) space.",
|
|
307
|
"cell_type": "markdown",
|
|
198
|
"cell_type": "markdown"
|
|
308
|
"source": [
|
|
|
|
|
309
|
"Plot the value of the European put in (volatility, strike) space."
|
|
|
|
|
310
|
]
|
|
199
|
},
|
|
311
|
},
|
|
200
|
{
|
|
312
|
{
|
|
201
|
"cell_type": "code",
|
|
313
|
"cell_type": "code",
|
|
|
|
|
314
|
"collapsed": false,
|
|
|
|
|
315
|
"input": [
|
|
|
|
|
316
|
"plt.figure()",
|
|
|
|
|
317
|
"plt.contourf(sigma_vals, strike_vals, prices['eput'])",
|
|
|
|
|
318
|
"plt.axis('tight')",
|
|
|
|
|
319
|
"plt.colorbar()",
|
|
|
|
|
320
|
"plt.title(\"European Put\")",
|
|
|
|
|
321
|
"plt.xlabel(\"Volatility\")",
|
|
|
|
|
322
|
"plt.ylabel(\"Strike Price\")"
|
|
|
|
|
323
|
],
|
|
202
|
"language": "python",
|
|
324
|
"language": "python",
|
|
203
|
"outputs": [
|
|
325
|
"outputs": [
|
|
204
|
{
|
|
326
|
{
|
|
205
|
"output_type": "pyout",
|
|
327
|
"output_type": "pyout",
|
|
206
|
"prompt_number": 14,
|
|
328
|
"prompt_number": 14,
|
|
207
|
"text": "<matplotlib.text.Text at 0x32ee190>"
|
|
329
|
"text": [
|
|
|
|
|
330
|
"<matplotlib.text.Text at 0x106d34150>"
|
|
|
|
|
331
|
]
|
|
208
|
},
|
|
332
|
},
|
|
209
|
{
|
|
333
|
{
|
|
210
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"output_type": "display_data",
|
|
334
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"output_type": "display_data",
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211
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"png": 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+JELc0IgQv5e9eRNLR2dwzfYSGjEdsVMjaWA2P1BPrtylGAzPGnuCc48jNRXJ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\nH1+m+kgqUZ4V2FmJMDdUZQWQ9DuYcgk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lypUrhIWFUVBQQFRUFA6Hg7S0NABSUlIoKiri/PnztGjRouYJOijaFKZgidD+JP1+079b\n31qetMS79W2PGWD+d+qbhdfv1pfcquJWacEx98fWu4ba1vvl5lqvu/tT/Pd//7fr68GDB9eYWUdG\nRpKfn0/nzp357rvvaNeunVelabobxG63k5WVhaqqZGRk4HA42LVrFxMnTgTg2LFjREZG1h7UWlMC\nv6QepMMWhmLwkQjA2LFj2bdvH6qqsnfvXuLi4qo97nA4yMjIACA9PZ3evXt7dV6/h7XNZnMN2les\nWMGrr75KXFwcSUlJOBwO7HY73377LZ07d2bSpEm8++67/i7Be4p+SweSBLY5WGIcAoYM7Kq5N2zY\nMMaNG0dcXBxRUVE8/PDDHDp0yDXynTRpEqdPnyYxMZHDhw/z/PPPe7eG4T98wB+UwCyjN7OPRMAa\nH2Bg+nFIpXp8gIHfPnzgkJfHdpcPH9CHoncBgSEdtjlIh21NEtaVFL0LCAwJbHOQwLYeCeuqFL0L\nCAwrBLYVSGBbi4T19RQsEdpmD2wrdNcggW0lEtbuKHoXoD0JbHOwTGBbnIR1XRS9C9CeBLYwDIt3\n1xLWnih6F6A9CWzjs0x3beHAlrD2hqJ3AdqTwDY+CWxzk7D2lqJ3AdqTwDY+CWzzkrCuD0XvArQn\ngW18EtjmJGFdX4reBWhPAtv4JLDNR8LaF4reBWhPAtv4JLDNRcLaV4reBWhPAtv4LBPYFiBh3RCK\n3gVoTwLb+CSwzUHCuqEUvQvQngS2EPqTsPYHRe8CtCeBbWzSXRufhLW/KHoXoD2zB7bZSWAbm4S1\nPyl6F6A9Mwe22btrkMA2Mglrf1P0LkB7EtjGJoFtTB7D+sqVK6xYsYJnn32WrKwszp8/H4i6jE3R\nuwDtSWAbmwS28XgM62eeeYajR4+Snp7OgQMHmDZtWiDqMj5F7wK0J4FtbBLY/nHixAkSEhLo378/\nAHv37uWee+4hISGBCRMmUF5eXu34ZcuWER8fj9PpxOl0smnTJq/W8RjWmZmZ/Pu//zvNmjVjypQp\nnD59uv4/jVUpehegPQlsY5PAbrgZM2YwYMAAbDYbAC+88AIrVqwgKyuLU6dO8T//8z/Vjr9w4QJT\np04lNTWV1NRUxo0b59U6HsO6UaNG1b4vLi729mcQIIFtcFYIbNEw27dvJyUlBVVVgYoG1263U15e\nzv/93//V+pxt27aRnJzM8OHDyc/P92odj2HdqlUrPvjgA8rLy9myZQu33HJLPX4MAUhgG5zZA1u6\na8/S9oOy9tqtqpCQEFdQV/WnP/2Jjh070q9fv2r3h4eHExUVxe7du+nevTszZ870qgabWtsqVXzz\nzTc89thjHDhwgG7durFx40buuOMOr07eEDabDZx1lmY8it4FaG9w0gd6l6CZp1indwmaGnZ4p94l\n+MzWnVoDs17nsNnYpnr3D9dw285q66WlpfEv//Iv7Nq1C4D169ezevVqdu/ezY033uj2PAcPHuTR\nRx/l8OHDHtf02Fl37NiRzz77jKKiIrKzswMS1Kal6F2A9qTDNi7psP3jrbfeYt26dXz66ae1BvWS\nJUtYvXo1ABkZGcTFxXl1Xo9h/cUXXzBnzhwApk6dyoEDB+pTt7ieoncB2pPANi4JbN/YbDbXC4wT\nJkygrKyMkSNH4nQ6+fjjjzl8+DBjxowBYPDgwaxbt46YmBj27NnD4sWLvVvD0xjkd7/7HZMnT2bI\nkCHs2rWLRYsW8dlnnzXwR/OiMDOOQapS9C5AezISMS6jjUT0HoMEgsfOuri4mCFDhgDQv39/Ll++\nrHlRlqDoXYD2pMM2Lumwg4/HsC4qKnIF9OXLl7ly5YrmRVmGoncB2pPANi4J7ODiMayHDh1Kv379\nWLx4MQMHDqyxDUU0kKJ3AdqTwDYuCezg4XFmXV5ezjvvvENubi49evRg/PjxhIRo//5Ppp9ZX0/R\nuwDtyQzbuIJ9hm2FmbXHsNaL5cIaJLANTgJbP1YIa7ct8ogRIwC45ZZbuPXWW123li1bBqw4YT4y\nEhHCN2476++//57WrVvX+sZN7du317gsi3bWIN21CZi5ww7W7trSnXXr1q0B2Lp1K+3bt692ExpS\n9C5Ae2bursHcHba84Kgfj68UfvjhhzXej1VoTNG7AO1JYBuXBLY+PIa10+lk9OjRfPDBB3z00Ud8\n/PHHgairQmpO4NYKNoreBWhPAtu4JLADz+NukOTkZNfvvFdKTU3VtCj4ZWZNNjh7ab5WUFP0LkB7\nMsM2rmCZYVthZh3cW/fIrvjGyoGt6F1AYEhgG1cwBLYVwtrtGOSbb77hySef5MUXX6SoqCiQNdUk\n4xBhcDISEQ3lNqwnTpxImzZtyM/PZ9asWYGsqXYS2KZm9vk1SGCLhnEb1oWFhcybN49Vq1aRnZ0d\nyJpEbRS9C9CeBLaxSWBry21Y33zzzQA0bdqUZs2aBaygOlm5u7YICWxjk8DWjtuwrroD5PrdILqy\ncmArehcQGBLYxiaBrQ23u0GioqIYPXo0qqry7rvvur622WwsWbJE+8Kq7gapjewQMTWz7w6pJLtE\n/MPSu0HGjx9P06ZNiYiIcH1deQsK0mGbmhW6a5AOW3jPGPus3bFydw2WCG3psI0vEB22pTtrX5w4\ncYKEhAT69+8PQF5eHrGxsSQlJTF37lzXcbNnz6ZHjx7079+f48eP+76glbtri5AOWwQ7b3OvUklJ\nCYMGDSIhIYFRo0ZRXFzs1Tp+DesZM2YwYMAA1wuSzz77LMuWLSM9PZ3s7Gx27NhBbm4uOTk5HDx4\nkHnz5vH44483bFErB7aidwGBIYFtbGYfh3iTe1WtXLmSxMREsrKyiI6OZtGiRV6t49ew3r59Oykp\nKa7/HuTk5OBwOACw2+2kpqayb98+kpKSAIiPjyc3N5eSkpKGLSyBLUxCAtt4vMm9qvbt20ffvn3d\nPu5OqLcFlZWVERpa9+EhISHV5jhNmjShUaNGALRt25Yvv/ySixcvut4rOzw8nMjISIqLiwkPD6/l\njG9U+Tr2l5uoQcH0of1J+v2WmV+v5UlTzrC3xwzw2/w6bX/FLVCOpp3lWNq5Wh/zJveqqpqBbdq0\n8frtPDyG9bFjx3jkkUe4cOEC//zP/0xMTIzrXwVPVFXlypUrhIWFUVBQQLt27bjxxhspKCgAoLS0\nlEuXLhEZGenmDE94tQ5Q0V1b/QVHk5PANj5/BXZyz4pbpQVrG3xKoI7/2ST/cnMtONLtOa7Pvaio\nqGqPR0ZGkp+fT+fOnfnuu+9o166dV7V5HINMnTqV5cuX065dO+655x4WLFjg1YmhosXPyspCVVUy\nMjLo3bs3DoeDzMxMADIzM7Hb7V6fzyMZh5ieVebXICMRo7o+9ypHIpUcDgcZGRkApKen07t3b6/O\n6zGsf/75Z/r06YPNZsNut3P16tU6j7fZbK5B+4oVK3j11VeJi4sjKSkJh8NBdHQ0Q4YMoW/fvsyZ\nM4fXX3/dq0K9JoFtehLYxme2wPaUe4cOHWLMmDEATJo0idOnT5OYmMjhw4d5/vnnvVvD0z7rrl27\ncuTIEfr168euXbuIjo5u2HY7L3m1z7ouVh6JKHoXEBhWGYmAefdh+2uG7a991oPVrV4d+4ltZPDt\nsx4xYgQPPPAABQUFDB8+nKFDhwaiLtEQit4FBIZ02MZntg5bSx7D+uWXX2bYsGH069ePe++9l3/9\n138NRF0NZ+VxiIVIYBufBLZ3PI5BSktLady4sev77Oxs/74o6K6who5BKsk4xBJkJGJ8DRmJyBgE\neO6551xf/+1vf2PUqFGaFuR3Vu6wFb0LCBzpsI1POuy6eQzrvLw8/uM//oMff/yRoUOHsmzZskDU\nJfxF0bsAoQUJbOvxGNabN29GURQGDRrElClTGDnS/WbwoGXl7tpCrNRdgwS21bgN6+PHj3P8+HF+\n+OEHnnvuOX7961+TkpISkG17mrByYCt6FxA4EtjmIIFdk9sXGNu3b+/247xOnTqlaVHgxxcYrycv\nOFqClV5wBHnR0QovMBr7wwd8JYFtCRLYxidhfY3bMcjf//534No4pOpNCCOQkYjxyTjkGred9Zgx\nY9iyZUut4xBDj0EqSXdtGdJhG5+nDtsKnbU1xyCVJLAtQwLb+OoKbCuEtcete4888kgg6tCH7BCx\nDBmJGJ/VRyIew/rMmTOcP38+ELWIQFP0LiCwJLCNz8qB7TGsb7/9duLi4pg2bRozZsxg5syZgagr\ncKzcXVuQBLbxWTWwPYZ1y5Ytefjhh4mMjKRp06Y0bdo0EHUFlpUDW9G7gMCTwDY+Kwa2x89gHDZs\nGLGx1z6oNifHpMFm5c9wVLBkaFuJGT/T0Z8fwGsEHjvr6z9y5tlnn9WsGN1Jh20ZVuuuQTpso3Mb\n1gUFBcTHx7N//37i4+OJj4+na9euAd+uIgJI0buAwJLANgerBHad+6wzMzOZMmUKy5YtQ1VVQkJC\niI6OpkWLFtoXFoh91u5YdRwClgtssN4e7EpmGosMt+00/T5rj78U8/rrrzN58uRA1eOia1iDBLbF\nSGAbm55h/dJLL5GRkQFUfLJWSEgIe/fudR3bqVMn2rVrB0BoaCi7du3yrb66wvry5cs0adIEVVXZ\ns2cPN9xwA4mJiT4tVO/C9A5rkMC2GAls4wqWznrNmjXs37+fN99803Vfhw4d/PIWHW5n1tu3b6dj\nx44ArFu3jilTpvDEE0+wefPmBi8qDEDRu4DAs+IMG8w5x9ZDeXk5K1as4Omnn652f3FxMePHj8du\nt7NmzRqfz+92694rr7zCzp0V22LefvttPvjgA2699VaGDBnC73//e58XNBQrb+cDS27p+yT9fkt2\n2Gbc2udPP6Yd42zal3Ues3PnTm688Ubi4uKq3R8WFsbs2bOJiIigR48e/Pa3v+XOO++sdw1uxyDR\n0dEcOXKE8vJyWrZsyQ8//IDNZiM+Pp7c3Nx6L1TvwoJhDFLJyoENlgtskJGI0fhrDMJnXp6jr63G\neoMHD2b06NH84Q9/cPu0ESNG8OijjzJixIh61+d2DBIREQHAP/7xD9q2bet6m9TLly/XexHDs/L+\na7BkWMtIRNRHXl4eubm5PPTQQ9XuLywspF+/fly4cIHi4mKOHj1KdHS0T2u4DetGjRrx1Vdf8eGH\nH+JwOAD43//9Xxo3buzTQoYngW05EtjCWytXruSxxx4jPDwcgOnTp7Nz505atmxJjx49iImJISkp\nifnz57teC6wvt2OQPXv2cN9997l2grRq1YqePXuydu3agHzCeVCNQSrJOMSSZCQS/IJhDKK1Orfu\nlZSUABAeHs7FixfJz8/nrrvuCkxhwRjWIIGt6F2APqwa2GCM0LZCWNf53iDh4eGutr5Zs2YBC+qg\nJuMQS7LqSARkLBIsPL6Rk6iFBLYlSWALPUlYC98oehegDwlsoRcJa19ZvbsGCWwLksDWj4R1Q0hg\nS2BbkAS2PiSsG0oCWwLbgiSwA0/C2h8ksC1LAlsEioS18A9F7wL0I4EtAkHC2l+ku5bAtqi1PCmh\nHQAS1v4kgS2BbWES2NqSsPY3CWwJbAuTwNaOhLXQhqJ3AfqRwJbA1oKEtRaku66g6F2AfiSwJbD9\nTcJaKxLYFRS9C9CPBLYEtj9JWGtJAruConcB+pHAlsD2FwlrrUlgW54EtgS2P0hYi8BQ9C5AXxLY\nEtgNJWEdCNJdV1D0LkBfEtjyyzMNIWEdKBLYFRS9C9CX1QMbpMv2leZhfenSJUaOHEn37t1JSkri\n888/Z/To0fTp0wen04nT6eTYsWNalxEcJLArKHoXoC8JbHMFdnZ2Nt26dXPl2YwZM6o9vnXrVtfj\n7733ns/rhDa0UE82btxIaGgohw4d4m9/+xv33Xcft956K5s3byYqKkrr5UWwUrB0aH+Sfr+lP4QX\nKgLbCB/G68nly5eJj4/n7bffrvFYaWkpM2fO5IsvvgDg7rvvZuDAgTRv3rze62jeWTdp0oSzZ89S\nWlpKfn4+x48f5/LlyyxatIjExESmTp1KaWmp1mUED+mur1H0LkBf0mGbo8NWVZWvv/6a4cOH43A4\n2Lt3r+ux48eP065dO5o3b07z5s3p0KEDWVlZPq2jeWc9btw4Dhw4QO/evUlISCAyMpLw8HBSUlJY\nvnw5I0YVda8kAAAPeElEQVSMYMOGDUyYMKGWZ79R5evYX24mkJoDzl56VxEcFCwd2tJh+9ZhH007\ny7G0c/4vRnFz/7k0OJ9W60NhYWGEhYWxceNGcnNzGTNmDP/4xz8AuHjxIq1bt3Yd27ZtW4qLi30q\nzaaqqurTM31QXl5OeHg4Z8+eJSIiAoBly5Zx6tQpli9fXr0wmw3IDlRp+pDArqDoXYD+rB7YQING\nIsNtO2lolNlsNnB6eY5UW63rqarKTTfdxOnTp2nRogUnTpzgySefJD09HYCkpCSWLl3KPffcU+/6\nNB+DbNmyhZEjRwKwe/duevTowZNPPsm+fftQVZW9e/cSFxendRnBSUYiFRS9C9CfjESMOxLZuXMn\nEydOBODYsWNERkbSokULADp16kRhYSEXLlzg7Nmz5Ofnc/fdd/u0juZhfe+991JcXMxvfvMb5syZ\nw4YNG7j33nsZN24ccXFxREVF8fDDD2tdhgh2it4F6E8C25h7sRMSEvj222/p3LkzkyZN4t1332X6\n9Ons2LGDsLAwVq1axX333YfT6eRPf/oTzZo182mdgI5B6sMSY5BKMg65RtG7AP3JSKRCfcYiwTIG\n0ZL8UkwwkHHINYreBehPOuwKRuuwtSZhHSwksK9R9C5AfxLYFSSwr5GwFsFJ0bsA/UlgV5DAriBh\nHUyku65O0bsA/UlgV5DAlrAOPhLY4joS2BWsHtgS1sFIAvsaRe8CgoMEdgUrB7aEdbCSwL5G0buA\n4CCBXcGqgS1hLYxB0buA4CCBXcGKgS1hHcyku65O0buA4CCBXcFqgS1hHewksKtT9C5ABBMrBbaE\ntRAGJN31NVYJbAlrI5DuujpF7wKCgwS2tUhYG4UEdnWK3gUEBwls65CwFsal6F1AcJDAtgYJayOR\n7romRe8CgoMEtvlJWAvjU/QuIDhIYJubhLXRSHct6iCBbV4S1kYkgV2ToncBwUMC25wkrIV5KHoX\nEDwksM1HwtqopLuunaJ3AcJqysvLGT9+PL169cJut5OZmVnt8U6dOuF0OnE6nfTv39/ndSSsjUwC\nu3aK3gUEB+muA+PTTz+lsLCQnJwcli5dyqxZs6o9fvXqVVJTU0lNTWXXrl0+ryNhLYSJSWBrb8iQ\nIXz44YcAnD59usbjxcXFjB8/Hrvdzpo1a3xex6YG+vPUvWSz2YBsvcswBmcvvSsIToreBQSPwUkf\n6F2Cpj6xjaShUWaz2cDp5TlSbTXWO3PmDElJSbz55pv07t3bdX+bNm1IS0sjIiKCHj16kJ6ezp13\n3lnv+kLr/QwRfFJzJLBroyCB/YtP0u83fWD7hdvR4he/3Gp37tw5hg4dyosvvlgtqAHy8/NdX9vt\ndo4fP+5TWMsYRJiboncBwUNGIg0RCzxR5XbN2bNnGThwIFOmTOHhhx+u9lhhYSH9+vXjwoULFBcX\nc/ToUaKjo32qQMLaLOTFRvcUvQsIHhLY/rdq1SpOnTrF+vXrcTqdPPjgg0yfPp0dO3bQsmVLevTo\nQUxMDElJScyfP5+OHTv6tI7MrM1ERiF1U/QuIHiYbSTit5m115ljb/B69SWdtZlIdy28JB228UhY\nm40EtnuK3gUEFwlsY5GwFtai6F2AEL6RsDYj6a7rpuhdQPCQ7to4JKzNSgK7boreBQQPCWxjkLAW\nQkhgG4CEtZlJd103Re8CgosEdnCTsDY7Cey6KXoXEFwksIOXhLUQit4FBBcJ7OAkYW0F0l17puhd\nQHCRwA4+EtZCVFL0LiC4SGAHFwlrq5DuWvhAAjt4SFhbiQS2Z4reBQhROwlrIa6n6F1AcJHuOjhI\nWFuNdNfeUfQuILhIYOtPwtqKJLC9o+hdQHCRwNaXhLUQwmsS2PqRsLYq6a69o+hdQPCRwNaHhLWV\nSWB7R9G7gOAjgR14EtZCeEPRu4DgI4EdWJqH9aVLlxg5ciTdu3cnKSmJzz//nLy8PGJjY0lKSmLu\n3LlalxBAX+hdQD19Ybzu+lyafmsrPj7vYJofiwiAetQrgV1h9uzZ9OjRg/79+3P8+PFqj23dupVu\n3brhdDp57733fF5D87DeuHEjoaGhHDp0iLfeeovHH3+c5557jmXLlpGenk52djY7duzQuowAMWBY\nG835NH3XV3x4zqE0PxehsXrWa/XAzs3NJScnh4MHDzJv3jwef/xx12OlpaXMnDmTzMxM/uu//otn\nnnmGCxcu+LSO5mHdpEkTzp49S2lpKfn5+Xz55ZdkZWXhcDgAsNvtpKamal2GqIvRumshgkhmZiZ9\n+/YFID4+ntzcXEpKSgA4fvw47dq1o3nz5jRv3pwOHTqQlZXl0zqah/W4ceO466676N27Nx988AG3\n3HIL58+fp1GjRgC0bduWoqIircsQnkhge0/Ru4DgY+XuuqioiFatWgEQHh5OZGQkxcXFAFy8eJHW\nrVu7jm3btq3rsfoKbXipHhYIDWXlypUAlJeXs3r1alq0aMGVK1cICwujoKCAqKgoN8+2a12eBt7Q\nu4B6qlKvUf6Dc3qB3hXU/1qtD4Ka68OHej/RoIzA8y5zIiIiXF9HRkZSUFAAVIw9Ll26RGRkJAC3\n3HKL6zGA7777jnbt2vlUmeZhvWXLFt5//322bt3K7t27iY2NpW3btmRlZdGnTx8yMjJYtGhRjeep\nqqp1aUII4eJr5jgcDqZPnw5UjETs9muB36lTJwoLC7lw4QJXr14lPz+fu+++26d1bKrGqVhUVMQD\nDzxAfn4+TZo0YdOmTURERDBx4kS+//577r33XhYsMFjXIYQQVfzxj39k+/btlJaWsmHDBtasWcOg\nQYMYOHAgu3fv5pVXXuHHH39k4cKFDB8+3Kc1NA9rIYQQDafLL8XUtSfx3LlzjB8/nrCwMNd9JSUl\nDBo0iISEBEaNGuXzgL4h6lvzsmXLiI+Px+l04nQ62bRpU6BLrrPm1atXExsbS3x8PGvXrgX0v871\nrTfYr/H8+fPp1asXPXv2ZOHChYD+19iXmvW+znXVC1BWVkZsbCwTJkwAguMaa0INsM8//1x1Op2q\nqqpqenq6arfbqz0+Y8YMddWqVWpoaKjrviVLlqgLFixQVVVVFy5cqL7wwguBK1j1rWZFUdQNGzYE\ntM6q6qr53Llz6m233aaWlJSoly5dUm+66Sb1559/1vU6+1JvMF/jH3/8Ue3bt69aWlqqlpWVqW3b\ntlW/+uqroP6z7K5mPa+zp797qqqqixcvVu12uzphwgRVVfXPC60EvLOua08iwOLFi5k8eXK15+zb\nt8/1HD32ZftSM8C2bdtITk5m+PDh5OfnB6xeqLvmFi1aUFBQQJMmTSgsLKSkpISysjJdr7Mv9aqq\nGrTX+OabbyYtLQ2AgwcPEhISwq9+9aug/rPsrmY9r7Onv3snT55k+/btTJgwwfUCod7XWCsBD+u6\n9iQC2Gy2Gs+pulexTZs2Ad+X7UvN4eHhREVFsXv3brp3787MmTMDVi94rhng6tWrTJs2jZdeeomI\niAhdr7Mv9f7TP/1T0F/jJUuWMHjwYJ566imaNWsW9H+Wa6tZz+tcV72qqjJp0iSWLFni+r0N0D8v\ntBLwsK5rT2Jdz6n817wh+xR95UvNs2bN4s9//jOhoaGMGDGCY8eOBaJUF081l5eXM3HiRFq1asXs\n2bNdz9HrOvtSb7BfY4C5c+fy7bffsmnTJnJycgzxZ/n6mvW8znXV++6773LbbbeRmJhYbdud3tdY\nKwEPa4fDQWZmJlBzT2Jdz8nIyAAgPT2d3r17a1pjbevXt+YlS5awevVqADIyMoiLi9O0xuvVVXN5\neTkTJkygUaNGrhfrKp+j13X2pd5gvsbZ2dn07t2b8vJyGjduTJMmTbhy5UpQ/1murebS0lJee+01\n3a5zXfWeOXOGr776CqfTyeLFi/noo49YunSp7tdYM3oMypcuXaomJSWpdrtdzcvLU5955hn1008/\nrXZMWFiY6+vS0lL1scceUxMSEtThw4erJSUlgS653jUfOXJEjY6OVqOjo9Xhw4erhYWFgS7Zbc2f\nffaZGhoaqiYnJ7tuBQUFul/n+tSbn58f1Nf46tWr6tSpU9UuXbqoiYmJ6vz581VVDe4/y+5q1vs6\ne/N3b/369a4XGIPhGmtB9lkLIYQByIcPCCGEAUhYCyGEAUhYCyGEAUhYCyGEAUhYC78ZN26c6/0k\nqtq2bRsJCQl1PldRFGbMmOHVOlXfH3jHjh0oigLA+vXrGTVqVI37AX7++WfOnz/v1fmFCEYS1sJv\nZs+ezeuvv87PP/9c7f4lS5Z4/GDk2n4LtDaZmZnV3mJy4MCBrlCueo6q9wMMGzbMtV9XCCOSsBZ+\n07VrV+x2O+vXr3fdl5mZyU8//cTQoUP5z//8TxITE0lKSqJXr16MGzeOwsLCGuc5efIkQ4cO5be/\n/S233347Tz/9NFevXmXVqlVMmTKFvLw8UlJS+OKLL6p101V3oVbeX1xcTHJyMvv372f27NlMmzaN\n+fPnM3bs2Gprjh8/3vWJRkIEJZ33eQuTycrKUm+//Xb16tWrqqqq6rBhw9TNmzereXl5asuWLdWT\nJ0+qqqqq5eXl6qxZs9QHHnhAVVVVnT9/vvr888+rqqqqH3/8sbpt2zZVVVX18uXLavv27dWMjAxV\nVVU1LS1N7dmzp2u99evXu87x9ttv1/q1qqpqcnKy+tFHH6mqqqoXL15Ub7vtNvXIkSOqqqpqXl6e\n2rFjR7W0tFSbiyKEH0hnLfzKbrfTrl07tm7dyokTJ/jyyy8ZPXo0hw8fJjY2lo4dOwIVI4uxY8ey\nf//+Guf48ccf+eMf/0ifPn0YOHAg58+f58KFC0DNj166/ntvREREMG/ePF566SUAFixYwNy5c6u9\nH7kQwUbzz2AU1jN79mzmzJlDTEwMzz//PCEhIcTExHDgwAG++eYbOnbsSHl5OX/5y1+Ij4+v8fwp\nU6aQlpZG9+7dKS4upkuXLq7HQkJCuHTpUr1rCgkJqfbuck888QTLly/nrbfe4sCBA7zzzju+/bBC\nBIiEtfC7AQMGMGfOHHbs2OF6A6A777yTtWvXMnbsWBo3bszly5e54447XI/bbDbXC4SKovDQQw9x\n22230aZNGzp37kxZWRkA0dHRNG/enMTERP785z9Xe567rwFGjhyJoiikpaXx+uuvExoayquvvsp9\n993H5s2bq73FphDBSN4bRFhWeno6kydP5siRI17vRhFCLzKzFpakqiovvPACL7/8sgS1MATprIUQ\nwgCksxZCCAOQsBZCCAOQsBZCCAOQsBZCCAOQsBZCCAOQsBZCCAP4fxQq2ihDo4F8AAAAAElFTkSu\nQmCC\n"
|
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212
|
}
|
|
336
|
}
|
|
213
|
],
|
|
337
|
],
|
|
214
|
"collapsed": false,
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338
|
"prompt_number": 14
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215
|
"prompt_number": 14,
|
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|
216
|
"input": "plt.contourf(sigma_mesh, strike_mesh, prices['eput'])\nplt.axis('tight')\nplt.colorbar()\nplt.title(\"European Put\")\nplt.xlabel(\"Volatility\")\nplt.ylabel(\"Strike Price\")"
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217
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},
|
|
339
|
},
|
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218
|
{
|
|
340
|
{
|
|
219
|
"source": "Plot the value of the Asian put in (volatility, strike) space.",
|
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341
|
"cell_type": "markdown",
|
|
220
|
"cell_type": "markdown"
|
|
342
|
"source": [
|
|
|
|
|
343
|
"Plot the value of the Asian put in (volatility, strike) space."
|
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344
|
]
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|
221
|
},
|
|
345
|
},
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222
|
{
|
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346
|
{
|
|
223
|
"cell_type": "code",
|
|
347
|
"cell_type": "code",
|
|
|
|
|
348
|
"collapsed": false,
|
|
|
|
|
349
|
"input": [
|
|
|
|
|
350
|
"plt.figure()",
|
|
|
|
|
351
|
"plt.contourf(sigma_vals, strike_vals, prices['aput'])",
|
|
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|
|
352
|
"plt.axis('tight')",
|
|
|
|
|
353
|
"plt.colorbar()",
|
|
|
|
|
354
|
"plt.title(\"Asian Put\")",
|
|
|
|
|
355
|
"plt.xlabel(\"Volatility\")",
|
|
|
|
|
356
|
"plt.ylabel(\"Strike Price\")"
|
|
|
|
|
357
|
],
|
|
224
|
"language": "python",
|
|
358
|
"language": "python",
|
|
225
|
"outputs": [
|
|
359
|
"outputs": [
|
|
226
|
{
|
|
360
|
{
|
|
227
|
"output_type": "pyout",
|
|
|
|
|
228
|
"prompt_number": 15,
|
|
|
|
|
229
|
"text": "<matplotlib.text.Text at 0x3487c50>"
|
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|
|
|
230
|
},
|
|
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|
|
231
|
{
|
|
|
|
|
232
|
"output_type": "display_data",
|
|
361
|
"output_type": "display_data",
|
|
233
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"png": 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AHMWw/9Yh3UGGLv5JfQYd1d+zjtF6lOIFMRUuyGadpGWUwmtNb5Bz5v+s38lh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\nozSwVrOGy3XT+gD0bXzueb+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mr9DIizMnWZVlehxfeRLxwcpdG5qKBBeWq+EvgwkV67V9\n4UxdWMe4FyE4S6Qz5jkoKAiRkbeG+bVt2xbh4eE4ePCg2efhxZmTNGM9Rfr7BUj0rgRR6CayZZcE\ndNoj/eF110Q3CIZ+rGM06lfiC8C6zS4qiC1+CGj+e/LMmTM4ceIEBgwwf7alXMbzcA4uZfUF9J8Q\niHy/ANTUSf8u01w5O50xLMKI9GA96yjN2lQTjUd9siESaTU3VROCG+QB+NHtVjunMae08Sd0Gbc+\nWqDRaPDoo4/igw8+gIeHh9nvzzd45WSlR4w3tP1CUFKpvALt7ELR670aFKqkPWPyLZ+foVEdYR3j\nHj2piPHi+9bb4NXkmhNxz/vV19dj7NixGDNmDF588UWLMvBmDU5WzuRUoW5nIXoHSuvOzRrq9QTV\nq9WQ5iZRv/uhRpodg4VEgAHRrGOAUoqZM2fi/vvvt7gwA7w4czJ0/XIdzqzOR38Frg1ddFKFPtnS\nbn/ONfjDV5TO6Ig7nSTsx2Onp6dj06ZN+O233xAVFYWoqCjs2bPH7PPwZg1OtrzbucBjSi+UKq6J\ngyLhbR2yfKS73shI1xJEeXzBOsY9XCjFq8JC5s0a1sDvnDnZqirXI+BsiQJHcRAcXqJGRwlP7/6l\nrj18xc6sY9xDT6R7zczFizMna3l7ryOxrZZ1DKurvkHg9rVa0sOpjtSyb0JQMl6cOdlLW3UOoQrc\nPPZkuhNiC6Q7vfub2q7wpsrfy48VXpw52TPWU+h+LYLaRWnNG8D+D10QKdnp3QQX6qS5nKgS8OLM\nKcKF49XoR26yjmF9lOD8B2q0kej07g3aXvCg3qxjKBIvzpxipG24hAEKHF5XXiygw0/S3NxKDxVu\n1kt3I1g548WZU5TTX55FgJfymjcO7nJCfLEEFx0CsL76frhSaY/NliNenDlFuXFFjw6Xr7COYROp\nS13RyyC9H9kbcIVo6M86huJI73+a41rp8O5yJAYqb3idQU9QuVoN85fQsb0vaiKholIe+Cc/vDhz\nipTxyTnc1055w+su5qsQniG9JoQLogfcjBGsYygKL86cItXXijCmXYCrk/Lan9O+dEHcTeltavq9\nth8g4VmNcsOLM6dY53I1iHWpZB3DJg6+64bOorR+fPMMfvChYaxjKIa0/nc5zspSPruIfu2lu4CQ\npbRVBM6b1ZDa/fMvWmkuJypHvDhzilf0zVm08VRe80Z+pgoD8qU1vTtJHwRfsQvrGIrAizOneNcv\n16Hr1ausY9jE/o9dEKWT1vTug3xBJKvgxZlzCAd3lCEhSMc6hvVRgjMfqNFOQtO7v68NgQ8NZB1D\n9nhx5hxG9pqz6NZWecPrrpcI6JErpeYNgrN8QaRW48WZcxh1NSJUWZfgrFJe+/OBTc4IltAwto3a\n++BBfVjHkDVenDmHcjq7EgM9qljHsLp6PUGXo9K5e66HCtf1vO25NXhx5hxOyuoLiAxW3vC6jC+c\nESChtud1NeFwo9JcTU8OeHHmHFLJ9+fg666s5g19LUGP49JZuU4DF+gNfNyzpXhx5hxS2YVa9LxZ\nzjqG1WVsdEFbCd09b6yJhBOV2lQZeeDFmXNY2d9fweDgWtYxrKpOS9DrlHTunotFNZyNkaxjyJJd\ni/PmzZsxZMgQhIeH49NPPwUAaDQaTJgwAZ07d8bEiRNRXV1tz0g2lMM6gAXklrn1eQ+tPYsQfzsO\nr7uaZPO3yPzcBf5WvHsW01Jbdfy32n4gEhpJYg8pKSkICwvDfffdh5UrV1p0DrsV58rKSrz++uvY\ntm0bsrKysGbNGlRWVmLVqlXo3LkzTp8+jY4dO+KTTz6xVyQbO8g6gAXklrn1eXUaI9SHL8PJXsPr\nypJs/hY6DUFYofXunml6WquOP2nwgZcYbqU08jBv3jysXr0a+/btw0cffYRr166ZfQ67FecDBw4g\nOjoafn5+8PT0xLBhw5CRkYHs7GzMnDkTrq6umDFjBrKysuwVieMAAKcO3ES8l4Z1DKvK/twFvqxD\n3GG3Ay2IVFl5ayXExMREhISEYOTIkRbVNbttXZCYmIi//e1vOH/+PNzc3LB79264uroiJycHoaGh\nAIDQ0FBkZ2c3enx0tLyG5JSWOiM4mGe2JWvmrc65ijETPXBFa9t1KkovA8FdbfoW/48gvNQVhR1b\nv+HtZYGgo6p193FaMQghYhT0gnzWOPHxaflrrq+n0P5h0507axoA9O7dG5mZmRg7dqxZ72+34uzh\n4YFly5bh+eefR2VlJfr06QNXV1dQatqfk7m5PW2c0PpKSy1ra2JJbpmtmtdOrTqlqa/b5X1y37He\nuYoXLWz1OeT2N3FlZR+TXufp6WmT97frpl/jxo3DuHHjAACPPfYYRo0ahdzcXOTn5yMqKgr5+fmI\nibn3zx9TCzjHcZw1tKbmxMTE4J///GfD5ydOnMCoUaPMPo9dR2uUlZUBAPbt24fjx48jOjoasbGx\nWGJI63cAAAdhSURBVLduHXQ6HdatW4e4OL5gCsdx8uXjc2tNkZSUFBQVFWHv3r2IjTV/Kjuhdrwt\nTUxMRFlZGby8vPDRRx9hwIAB0Gg0eOKJJ3D48GFER0dj06ZNNvszgeM4zh6Sk5Mxa9Ys1NfX44UX\nXsALL7xg/kkoQ8nJyTQ0NJT26NGDrlix4p7n8/PzaVxcHHV1daVLliwx61hbaU3mkJAQ2qdPHxoZ\nGUljYmLsFbnFzJs2baJ9+/alffv2pdOnT6cFBQUmHyu1vFK9xtu2baN9+/alERERdMyYMTQ7O9vk\nY6WYmcV1NvU6ZWdnU5VKRbds2WL2sVLCtDhHRkbS5ORkWlRURHv16kXLy8vver6srIzm5OTQ+fPn\n31PoWjpWipm7dOlCr1+/bpecd2op84EDB+jNmzcppZRu2LCBPvHEEyYfK7W8Ur3G1dXVDf9OSkqi\nCQkJJh8rxcwsrrMp18lgMNBhw4bRsWPH3lWcWV3j1mA2fduUsYDt2rVD//794ezsbPaxUst8G7Vz\n56YpmQcOHNjQTjZ27FgkJyebfKyU8t4mxWvs4eFx1+vd3NxMPlZqmW+z53U29TqtXLkSU6dORbt2\n7cw+VmqYFeemxgLa+tjWaO37EkIwfPhwTJw4ETt27LBFxHuYm3nNmjUNI2pYXOfW5AWkfY23bt2K\nLl26YMaMGVi7dq1Zx0oh85o1axoet/d1NiVvcXExtm/fjtmzZzdkNPVYKbLrUDpHl56ejuDgYOTn\n52PcuHEYMGAAgoKCWMdqsG/fPmzatAkHDhxgHcUkjeWV8jWeNGkSJk2ahG+++QYTJ07E4cOHWUdq\n0Z2ZJ02a1JBZitf5xRdfxKJFi0AIAb3VZMs0T2sxu3OOiYnBqVOnGj4/ceKEycPoWnNsa7T2fYOD\ngwEAYWFhGD9+PH788UerZ/wjUzMfPXoUs2bNwo4dO+Dr62vWsVLJC0j7Gt/26KOPoqSkBDqdDv37\n95fF9/KdmQH7X2dT8h46dAiPPfYYunbtiu+//x5z5szBjh07mNWLVmPZ4H27kf78+fPNNtK/9tpr\nTXYItnSstVmauaamhlZVVVFKb3Ua9u7dm168eFESmS9cuEB79OhBMzMzzT5WSnmlfI3PnDlDRVGk\nlFK6a9cuOnr0aJOPlVpmVtfZnOv09NNP0++//96iY6WCaXFOSkqioaGhtHv37nT58uWUUko/+eQT\n+sknn1BKKS0tLaUdO3ak3t7e1NfXl3bq1IlqNJomj5Vy5rNnz9KIiAgaERFBhw8fTj/77DPJZJ45\ncyb19/enkZGR9wyNYnGdLc0r5Wu8ePFiGh4eTiMjI+kzzzxDjx071uyxUs7M6jq3lPdOfyzOrK5x\na9h1EgrHcRxnGr4TCsdxnATx4sxxHCdBvDhzHMdJEC/OHMdxEsSLM2dzw4cPxy+//HLXY8uWLcOc\nOXMafX2XLl1QUVHR7DnfeefuleTj4+MBAEVFRejT59Yi6QcPHsS8efMA3FolLCMjw6L8HMcCL86c\nzU2fPh1ff/31XY998803ePzxxxt9/e1pt81ZuPDunTnS09PveU3//v2xfPlyAMD+/ftlM/OR4wBe\nnDk7mDJlCnbt2gWDwQDg1t1tSUkJ9Ho9xowZg/j4eHz66aeNHjtp0iT069cPw4cPx9atWwEA//73\nv6HT6RAVFYUnn3wSQONbBSUlJWHcuHG4cOECVq9ejQ8++ADR0dFIS0tDt27dGvJUVVWhW7duMBqN\ntvjyOc4ifG0Nzub8/f0xYMAA7N69G+PHj8fXX3+NqVOn4i9/+Qv27NmDNm3aYNSoUYiPj0dYWNhd\nx65btw5+fn6oqqrC0KFDMWnSJCxatAgfffTRXWtTNHe3HRISglmzZsHLywv/+Mc/AABDhw7Frl27\nMGHCBHz99deYMmUKVCrbbu7Kcebgd86cXdzZtPHNN99gypQpCAsLQ48ePeDn54epU6c2urrZ119/\njQceeADx8fE4d+4cjh07ZtH70z8shPPss89i/fr1AIANGzbgmWeesei8HGcrvDhzdjF+/Hj8+uuv\nOHz4MLRa7T13upTSex47d+4cVq1ahe+++w7Hjh1D165dcePGDYve/4/nHjRoEIqKipCUlASj0Yje\nvXtbdF6OsxVenDm78PT0xLBhw/DMM8/g8ccfR1xcHE6dOoWzZ8/ixo0b2Lp1K8aPH3/XMSUlJWjX\nrh38/f2Rnp6OvLy8hufatWsHrVZr8vuHhISgvLz8rseeeuop/OlPf8KMGTNa98VxnA3w4szZzfTp\n03Hs2DFMnz4dhBCsXr0ac+fOxdixYzFz5syGBdFv3+UOHjwYISEhCAsLw7JlyzBixIiGc82dOxcJ\nCQkNHYJ33hk39u+RI0fi4MGDiIqKahjZ8fjjj+PGjRuYPn26bb9wjrMAX/iIc1ibN2/G/v37G3Yl\n4Tgp4aM1OIc0d+5cpKenY+fOnayjcFyj+J0zx3GcBPE2Z47jOAnixZnjOE6CeHHmOI6TIF6cOY7j\nJIgXZ47jOAnixZnjOE6C/g98ZDDk0wdCzQAAAABJRU5ErkJggg==\n"
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TTyt612WHF+ZlIbZgkOVhkJXxlCB7fIinTZsGs9mMkydPYvz48Th69Ci2bt0qbAE2F+al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\nAeyS/5wqC8i5AAAAAElFTkSuQmCC\n"
|
|
234
|
}
|
|
363
|
}
|
|
235
|
],
|
|
364
|
],
|
|
236
|
"collapsed": false,
|
|
365
|
"prompt_number": 15
|
|
237
|
"prompt_number": 15,
|
|
366
|
},
|
|
238
|
"input": "plt.contourf(sigma_mesh, strike_mesh, prices['aput'])\nplt.axis('tight')\nplt.colorbar()\nplt.title(\"Asian Put\")\nplt.xlabel(\"Volatility\")\nplt.ylabel(\"Strike Price\")\n"
|
|
367
|
{
|
|
|
|
|
368
|
"cell_type": "code",
|
|
|
|
|
369
|
"collapsed": true,
|
|
|
|
|
370
|
"input": [
|
|
|
|
|
371
|
"plt.show()"
|
|
|
|
|
372
|
],
|
|
|
|
|
373
|
"language": "python",
|
|
|
|
|
374
|
"outputs": [],
|
|
|
|
|
375
|
"prompt_number": 16
|
|
239
|
}
|
|
376
|
}
|
|
240
|
]
|
|
377
|
]
|
|
241
|
}
|
|
378
|
}
|