simple_versus_log_returns.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple and Logarithmic Return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's defined and clarify the definitions and usage of simple and logarithmic returns for financial assets like stocks or portfolios.<br>\n",
    "This notebook is based on Panna Miskolczi's \"Note on simple and logarithmic returns\" paper. For our purpose, we use the daily stock prices of the bank Goldman Sachs (ticker: GS), Google (ticker: GOOG) and Apple (ticker: AAPL). and for the risk calculation, we focus on the following risk measures:\n",
    "1. standard deviation\n",
    "2. semivariance\n",
    "3. Value at Risk\n",
    "4. Expected Shortfall"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple return\n",
    "Return is the profit of an investment over a period of time, expressed as a proportion of the original investment. Given $P_0$, the price of an asset at time $0$ and $P_T$, the price of an asset at time $T$. If there is no cash flow in this time $[0,T]$ interval, we therefore speak of a *one-period simple net return* and we introduce the notation $R^S_{[0,T]}[1]$ so that:\n",
    "\n",
    "$R^S_{[0,T]}[1] = \\frac{P_T - P_0}{P_0} = \\frac{P_T}{P_0} - 1$\n",
    "\n",
    "The *one-period gross return* of an asset is given in terms of the simple return:\n",
    "\n",
    "$GrR^S_{[0,T]}[1] = R^S_{[0,T]}[1] + 1$\n",
    "\n",
    "Let's devide the interval $[0,T]$ into $n$ **pair wise disjointed intervals** with $t_0 = 0$ and $t_n = T$:\n",
    "\n",
    "$GrR^S_{[0,T]}[n] = \\prod_{i=1}^{n}{GrR^S_{[t_{i-1},t_i]}[1]} = \\prod_{i=1}^{n}{(1 + R^S_{[t_{i-1},t_i]}[1])}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logarithmic return\n",
    "Abbreviated log return. Let's devide the interval $[0,T]$ into $n$ **equidistant intervals** with $t_0 = 0$ and $t_n = T$. We will assume that on every $[t_{i-1}, t_i]$ interval, the subinterval return $R$ is the same as for the $n-1$ other subinterval.\n",
    "\n",
    "$R = \\frac{R^*_{[0,T]}[1]}{n}$ such that:\n",
    "\n",
    "$GrR^S_{[0,T]}[n] = \\prod_{i=1}^{n}{(1 + R^S_{[t_{i-1},t_i]}[1])} = (1+R)^n = (1 + \\frac{R^*_{[0,T]}[1]}{n})^n$\n",
    "\n",
    "Since $t_0$ and $t_n$ are the 0th and the last time points respectively:\n",
    "\n",
    "$GrR^S_{[0,T]}[n] = \\frac{1}{P_0} * \\prod_{i=1}^{n-1}{\\frac{P_{t_i}}{P_{t_i}}} * P_T = \\frac{P_T}{P_0} = (1 + \\frac{R^*_{[0,T]}[1]}{n})^n$\n",
    "\n",
    "Let's make the length of the $n$ $[t_{i-1}, t_i]$ subintervals tend towards 0 such as $n -> +\\infty$.\n",
    "\n",
    "$\\lim_{n \\to +\\infty}{\\frac{P_T}{P_0}} = \\lim_{n \\to +\\infty}{(1 + \\frac{R^*_{[0,T]}[1]}{n})^n} = e^{R^*_{[0,T]}[1]}$ by the definition of the exponential function.\n",
    "\n",
    "Since we are interested in returns, we apply the logarithm: $ln(\\frac{P_T}{P_0}) = R^*_{[0,T]}[1]$\n",
    "\n",
    "This is called the one-period logarithmic return of an asset. So we define the one-period log return as the logarithm of the one-period simple gross return and we use the notation $R^L_{[0,T]}[1]$ so that:\n",
    "\n",
    "$R^L_{[0,T]}[1] = ln(\\frac{P_T}{P_0}) = ln(1 + R^S_{[0,T]}[1])$\n",
    "\n",
    "$R^L_{[0,T]}[1] = ln(\\frac{P_{t_1}}{P_{t_0}} * \\frac{P_{t_2}}{P_{t_1}} * ... * \\frac{P_{t_n}}{P_{t_{n-1}}}) = \\sum_{i=1}^{n}{ln(\\frac{P_{t_n}}{P_{t_{n-1}}})}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Summary\n",
    "\n",
    "$R^S_{t} = R^S_{[t, t-1]}[1] = \\frac{P_t}{P_{t-1}} - 1$ and $R^S_{t} = e^{R^L_{t}} - 1$\n",
    "\n",
    "$R^L_t = R^L_{[t, t-1]}[1] = ln(\\frac{P_T}{P_{t-1}})$ and $R^L_{t} = ln(1 + R^S_{t})$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying simple and logarithmic returns to portfolios (of assets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating the simple and logarithmic returns of a portfolio differs due to the rules of sums, leading to diverging effects and results that can be sought after. The following notation is used:\n",
    "\n",
    "- $n$ : number of assets in the portfolio\n",
    "- $i$ : reference of an asset in the portfolio\n",
    "- $S_t$ : amoiunt of money invested in the portfolio at time t\n",
    "- $S_{t, i}$ : amount of money invested in an asset i at time t\n",
    "- $P_{t, i}$ : price of asset i at time t\n",
    "- $W_{t, i}$ : relative weights of the asset i in porfolio at time t\n",
    "- $k_i$ : number of asset i in portfolio\n",
    "\n",
    "with: $S_{t, i} = k_i * P_{t,i} = W_{t, i} * S_t$\n",
    "\n",
    "$S_t = \\sum^{n}_{i = 1}{S_{t, i}} = \\sum^{n}_{i = 1}{k_i * P_{t, i}}$\n",
    "\n",
    "$W_{t, i} = \\frac{S_{t, i}}{S_{t}} = \\frac{k_i * P_{t, i}}{\\sum^{n}_{i = 1}{k_i * P_{t, i}}}$ with $\\sum^{n}_{i = 1}{W_{t, i}} = 1$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Simple return of a portfolio\n",
    "Similar to the simple return of an asset, we can define the simple return of a portfolio at time $t$ as the gain (or loss) in value of the profolio relative to the starting value:\n",
    "\n",
    "$R^S_t = \\frac{S_t}{S_{t-1}} - 1$\n",
    "\n",
    "Using the fact that the weights sum up to one and the equation above where is the simple return $R^S_t$ of asset $i$ at time $t$, we can rewrite $S_t$ such as:\n",
    "\n",
    "$S_t = \\sum^{n}_{i = 1}{k_i * P_{t, i}} = \\sum^{n}_{i = 1}{k_i * P_{t-1, i} * (1+R^S_t)} = \\sum^{n}_{i = 1}{S_{t-1, i} * (1+R^S_t)} = \\sum^{n}_{i = 1}{S_{t-1} * W_{t-1,i} * (1+R^S_t)}$\n",
    "\n",
    "This provides the simple return of a portfolio at time $t$:\n",
    "\n",
    "$R^S_t = \\frac{S_t}{S_{t-1}} - 1 = \\sum^{n}_{i=1}{W_{t-1,i}*R^S_{t,i}}$\n",
    "\n",
    "The portfolio simple return is the sum of the weighted simple returns of the constituents of the considered portfolio."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Logarithmic return of a portfolio\n",
    "\n",
    "The logarithmic return of a porfolio, nioted $R^L_t$ at time $t$ can be defined analogously to the logarithmic return of an asset such as:\n",
    "\n",
    "$R^L_t = ln(\\frac{S_t}{S_{t-1}}) = ln(1 + \\sum^{n}_{i=1}{W_{t-1,i}*R^S_{t,i}}) = ln(1 + \\sum^{n}_{i=1}{W_{t-1,i}*E^{R^L_{t,i}})}$ where $R^L_{t,i}$ is the log return of asset $i$ at time $t$.\n",
    "\n",
    "Because of the rules of logarithm, it cannot be written as the sum of the weighted log returns of the constituents of the considered portfolio. \n",
    "\n",
    "$R^L_t = ln(1 + R^S_t) \\approx R^S_t \\approx \\sum^{n}_{i=1}{W_{t-1,i}*R^L_{t,i}}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting Stock Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pandas_type = \"pandas.core.frame.DataFrame\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data retrieved using ``yf.download(ticker)`` is a dictionary containing financial information on a company stock such as Goldman Sachs (or GS). Some of those values, such as bid and ask, may drop to 0 outside of market hours. As such, the price used for the following Jupyter is the one linked to the key 'regularMarketPrice'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def download_stock_data(ticker: str) -> (str, pandas_type):\n",
    "    \"\"\"\n",
    "    Downloads and reshapes stock data based on the yfinance library.\n",
    "    \"\"\"\n",
    "    stock_data = yf.download(ticker)\n",
    "    columns_to_remove = [col for col in stock_data.columns if col != \"Adj Close\"] #lists all column not 'Adj Close'\n",
    "    stock = stock_data.drop(columns = columns_to_remove)\n",
    "    stock = stock[-260:] #260 is the expected number of business days in 1 year (conventional time frame to compute VaR)\n",
    "    return ticker, stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_stock_return(ticker: str, return_type: str, stock: pandas_type) -> (str, pandas_type):\n",
    "    \"\"\"\n",
    "    Calculates daily returns over the chosen timeframe.\n",
    "    Plots stock returns.\n",
    "    \"\"\"\n",
    "    if return_type not in [\"simple\", \"log\"]: return \"ERROR\"\n",
    "    if return_type == \"simple\":\n",
    "        stock = stock[1:].values / stock[:-1] - 1 #computes simple stock returns\n",
    "    else:\n",
    "        stock = np.log(stock[1:].values) - np.log(stock[:-1]) #computes log stock returns\n",
    "    stock.dropna() #drops all NAs\n",
    "    return ticker, stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def standard_deviation(stock: pandas_type) -> float:\n",
    "    \"\"\"\n",
    "    Calculates standard deviation of stock over the chosen timeframe.\n",
    "    \"\"\"\n",
    "    mean = stock.mean()\n",
    "    stock = (stock - mean)**2\n",
    "    return math.sqrt(stock.sum()/(stock.count()-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def semi_variance(stock: pandas_type) -> float:\n",
    "    \"\"\"\n",
    "    Calculates semivariance of stock over the chosen timeframe.\n",
    "    \"\"\"\n",
    "    mean = stock.mean()\n",
    "    stock = stock-mean\n",
    "    stock[stock < 0] = 0\n",
    "    stock = stock ** 2\n",
    "    return float(stock.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def value_at_risk(alpha: float, stock: pandas_type) -> float:\n",
    "    \"\"\"\n",
    "    Calculates value-at-risk over the chosen timeframe.\n",
    "    \"\"\"  \n",
    "    return float(stock.quantile(alpha))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def expected_shortfall(alpha: float, stock: pandas_type) -> float:\n",
    "    \"\"\"\n",
    "    Calculates expected shortfall over the chosen timeframe.\n",
    "    \"\"\"\n",
    "    percentage = value_at_risk(alpha, stock)\n",
    "    stock = stock[stock <= percentage]\n",
    "    return float(stock.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison of simple and log returns with the GS stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 downloaded\n"
     ]
    }
   ],
   "source": [
    "stock_GS = download_stock_data(\"GS\")\n",
    "\n",
    "simple_return_GS = calculate_stock_return(stock_GS[0], \"simple\", stock_GS[1])\n",
    "log_return_GS = calculate_stock_return(stock_GS[0], \"log\", stock_GS[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(12, 6))\n",
    "fig.subplots_adjust(hspace=.2, wspace=.3)\n",
    "axes[0].plot(simple_return_GS[1], linewidth = 2)\n",
    "axes[1].plot(log_return_GS[1], linewidth = 2)\n",
    "axes[2].plot(simple_return_GS[1] - log_return_GS[1], linewidth = 2)\n",
    "axes[2].set_xlabel('Time', fontsize=12)\n",
    "axes[0].set_ylabel('Simple Returns', fontsize=12)\n",
    "axes[1].set_ylabel('Log Returns', fontsize=12)\n",
    "axes[2].set_ylabel('Difference', fontsize=12)\n",
    "for i in range(3):\n",
    "    axes[i].grid(linestyle='-', linewidth='0.5', color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Application on a portfolio of GS, GOOG, and AAPL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 downloaded\n",
      "[*********************100%***********************]  1 of 1 downloaded\n",
      "[*********************100%***********************]  1 of 1 downloaded\n"
     ]
    }
   ],
   "source": [
    "stock_GS = download_stock_data(\"GS\")\n",
    "stock_GOOG = download_stock_data(\"GOOG\")\n",
    "stock_AAPL = download_stock_data(\"AAPL\")\n",
    "\n",
    "simple_return_GS = calculate_stock_return(stock_GS[0], \"simple\", stock_GS[1])\n",
    "log_return_GS = calculate_stock_return(stock_GS[0], \"log\", stock_GS[1]) \n",
    "simple_return_GOOG = calculate_stock_return(stock_GOOG[0], \"simple\", stock_GOOG[1])\n",
    "log_return_GOOG = calculate_stock_return(stock_GOOG[0], \"log\", stock_GOOG[1])\n",
    "simple_return_AAPL = calculate_stock_return(stock_AAPL[0], \"simple\", stock_AAPL[1])\n",
    "log_return_AAPL = calculate_stock_return(stock_AAPL[0], \"log\", stock_AAPL[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculating Returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "simple = [simple_return_GS, simple_return_GOOG, simple_return_AAPL]\n",
    "log = [log_return_GS, log_return_GOOG, log_return_AAPL]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculating Standard Deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0174759861851394, 0.01826404067545584, 0.020043337768029496] [0.017481954970368623, 0.01823054267017098, 0.02017545023896378]\n"
     ]
    }
   ],
   "source": [
    "std_dev_simple = [standard_deviation(stock[1]) for stock in simple]\n",
    "std_dev_log = [standard_deviation(stock[1]) for stock in log]\n",
    "print(std_dev_simple, std_dev_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculating semivariance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0001473140523583097, 0.00016784798186472282, 0.00017525297216067857] [0.00014350518862657556, 0.00016187155691758804, 0.00017196244273322522]\n"
     ]
    }
   ],
   "source": [
    "semivar_simple = [semi_variance(stock[1]) for stock in simple]\n",
    "semivar_log = [semi_variance(stock[1]) for stock in log]\n",
    "print(semivar_simple, semivar_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculating Value at Risk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.04232053663049964, -0.05039427519195436, -0.05475908474260627] [-0.04324227642436307, -0.051708420705281616, -0.05631993770170313]\n"
     ]
    }
   ],
   "source": [
    "var_simple = [value_at_risk(0.01, stock[1]) for stock in simple]\n",
    "var_log = [value_at_risk(0.01, stock[1]) for stock in log]\n",
    "print(var_simple, var_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculating ES/CVaR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.055701573488734425, -0.06287198430470287, -0.07466017547315192] [-0.057417543171843256, -0.06500251101322292, -0.07778368009949184]\n"
     ]
    }
   ],
   "source": [
    "cvar_simple = [expected_shortfall(0.01, stock[1]) for stock in simple]\n",
    "cvar_log = [expected_shortfall(0.01, stock[1]) for stock in log]\n",
    "print(cvar_simple, cvar_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Graphic representation of Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data to plot\n",
    "\n",
    "n_groups = 3\n",
    "simple = []\n",
    "log = []\n",
    "\n",
    "simple.append([(std_dev_simple[0], std_dev_simple[1], std_dev_simple[2]), \"Standard Dev.\"])\n",
    "log.append([(std_dev_log[0], std_dev_log[1], std_dev_log[2])])\n",
    "\n",
    "simple.append([(semivar_simple[0], semivar_simple[1], semivar_simple[2]), \"Semivariance\"])\n",
    "log.append([(semivar_log[0], semivar_log[1], semivar_log[2])])\n",
    "\n",
    "simple.append([tuple(-1*x for x in (var_simple[0], var_simple[1], var_simple[2])), \"VaR\"])\n",
    "log.append([tuple(-1*x for x in (var_log[0], var_log[1], var_log[2]))])\n",
    "\n",
    "simple.append([tuple(-1*x for x in (cvar_simple[0], cvar_simple[1], cvar_simple[2])), \"CVaR/ES\"])\n",
    "log.append([tuple(-1*x for x in (cvar_log[0], cvar_log[1], cvar_log[2]))])\n",
    "\n",
    "for i in range(4):\n",
    "    fig, ax = plt.subplots()\n",
    "    index = np.arange(n_groups)\n",
    "    bar_width = 0.35\n",
    "    opacity = 0.8\n",
    "    \n",
    "    rects1 = plt.bar(index, simple[i][0], bar_width,\n",
    "                     alpha=opacity,\n",
    "                     color='b',\n",
    "                     label='Simple ' + simple[i][1])\n",
    "\n",
    "    rects2 = plt.bar(index + bar_width, log[i][0], bar_width,\n",
    "                     alpha=opacity,\n",
    "                     color='g',\n",
    "                     label='Log ' + simple[i][1])\n",
    "    \n",
    "    plt.xlabel('Company')\n",
    "    plt.ylabel(simple[i][1])\n",
    "    plt.ylim(min(simple[i][0] + log[i][0])*0.98, max(simple[i][0] + log[i][0])*1.02)\n",
    "    plt.title(simple[i][1] + ' per Company')\n",
    "    plt.xticks(index + bar_width/2, ('GS', 'GOOG', 'AAPL'))\n",
    "    plt.legend()\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}