0207_kNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# $k$ Nearest Neighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Illustration of Nearest Neighbors\n",
    "\n",
    "Arguably one of the simplest classification algorithm is $k$ nearest neighbors (KNN). Below figure portrays the algorithm's simplicity. We see a training set of five red and five blue dots, representing some label 1 and 0, respectively. The two axes represent two features, e.g. income and credit card balance. If we now add a new test data point $x_0$ (green dot), KNN will label this test data according to its $k$ closest neighbors. In below figure we set $k=3$. The three closest neighbors are: one blue dot and two red dots, resulting in estimated probabilities of 2/3 for the red class and 1/3 for the blue class. Hence KNN will predict that the new data point will belong to the red class. More so, based on the training set the algorithm is able to draw a decision boundary. (Note: In a classification settings with $j$ classes a *decision boundary* is a hyperplane that partitions the underlying vector space into $j$ sets, one for each class.). This is shown with the jagged line that separates background colors cyan (blue label) and light blue (red label). Given any possible pair of feature values, KNN labels the response along the drawn decision boundary. With $k=1$ the boundary line is very jagged. Increasing the number of $k$ will smoothen the decision boundary. This tells us that small values of $k$ will produce large variance but low bias, meaning that each new added training point might change the decision boundary line significantly but the decision boundary separates the training set (almost) correctly. As $k$ increases, variance decreases but bias increases. This is a manifestation of the Bias-Variance Trade-Off as discussed in the script ([Fortmann-Roe (2012)](http://scott.fortmann-roe.com/docs/BiasVariance.html)) and highlights the importance of selecting an adequate value of $k$ - a topic we will pick up in a future chapter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"Graphics/0207_KNN.png\" alt=\"LinearRegClassification\" style=\"width: 1000px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mathematical Description of KNN\n",
    "\n",
    "Having introduced KNN illustratively, let us now define this in mathematical terms. Let our data set be $\\{(x_1, y_1), (x_2, y_2), \\ldots (x_n, y_n)\\}$ with $x_i \\in \\mathbb{R}^p$ and $y_i \\in \\{0, 1\\}\\; \\forall i \\in \\{1, 2, \\ldots, n\\}$. Based on the $k$ neighbors, KNN estimates the conditional probability for class $j$ as the fraction of points in $\\mathcal{N}(k, x_0)$ (the set of the $k$ closest neighbors of $x_0$) whose response values equals $j$ (Russell and Norvig (2009)):\n",
    "\n",
    "$$\\begin{equation}\n",
    "\\Pr(Y = j | X = x_0) = \\frac{1}{k} \\sum_{i \\in \\mathcal{N}(k, x_0)} \\mathbb{I}(y_i = j).\n",
    "\\end{equation}$$\n",
    "\n",
    "Once the probability for each class $j$ is calculated, the  KNN classifier predicts a class label $\\hat{y}_0$ for the new data point $x_0$ by maximizing the conditional probability (Batista and Silva (2009)).\n",
    "\n",
    "$$\\begin{equation}\n",
    "\\hat{y}_0 = \\arg \\max_{j} \\frac{1}{k} \\sum_{i \\in \\mathcal{N}(k,x_0)} \\mathbb{I}(y_i = j)\n",
    "\\end{equation}$$\n",
    "\n",
    "Selecting a new data point $x_0$'s nearest neighbors requires some notion of **distance measure**. Most researchers chose Minkowski's distance, which is often referred to as $L^m$ norm (Guggenbuehler (2015)). The distance between points $x_a$ and $x_b$ in $\\mathbb{R}^p$ is then defined as follows (Russell and Norvig (2009)):\n",
    "\n",
    "$$\\begin{equation}\n",
    "L^m (x_a, x_b) = \\left(\\sum_{i=1}^p |x_{a, i} - x_{b, i}|^m \\right)^{1/m}\n",
    "\\end{equation}$$\n",
    "\n",
    "Using $m=2$, above equation simplifies to the well known Euclidean distance and $m=1$ yields the Manhattten distance. Python's `sklearn` package, short for scikit-learn, offers [several other options](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html) that we will not discuss. \n",
    "\n",
    "Beyond the pure distance measure, it is also possible to weight training data points relative to their distance from a certain point. In above figure distance is weighted uniformly. Alternatively one could weight points by the inverse of their distance (closer neighbors of a query point will have a greater influence than neighbors which are further away) or any other user-defined weighting function. For [further details check `sklearn`'s documentation](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html?highlight=neighbors%20kneighborsclassifier#sklearn.neighbors.KNeighborsClassifier) for details)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Application: Predicting Share Price Movement\n",
    "\n",
    "### Loading Data\n",
    "\n",
    "The application of KNN is shown using simple stock market data. The idea is to predict a stock's movement based on simple features such as:\n",
    "* `Lag1, Lag2`: log returns of the two previous trading days \n",
    "* `SMI`: SMI log return of the previous day\n",
    "\n",
    "The response is a binary variable: if a stock closed above the previous day's closing price it equals 1, and 0 if it fell. We start by loading the necessary packages and stock data from a csv - a procedure we are well acquainted with by now. Thus comments are held short."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "plt.rcParams['font.size'] = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ABBN</th>\n",
       "      <th>ADEN</th>\n",
       "      <th>BAER</th>\n",
       "      <th>CFR</th>\n",
       "      <th>CSGN</th>\n",
       "      <th>GEBN</th>\n",
       "      <th>GIVN</th>\n",
       "      <th>LHN</th>\n",
       "      <th>LONN</th>\n",
       "      <th>NESN</th>\n",
       "      <th>...</th>\n",
       "      <th>ROG</th>\n",
       "      <th>SCMN</th>\n",
       "      <th>SGSN</th>\n",
       "      <th>SLHN</th>\n",
       "      <th>SREN</th>\n",
       "      <th>UBSG</th>\n",
       "      <th>UHR</th>\n",
       "      <th>ZURN</th>\n",
       "      <th>SIK</th>\n",
       "      <th>SMI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-06-26</th>\n",
       "      <td>24.63</td>\n",
       "      <td>73.85</td>\n",
       "      <td>51.35</td>\n",
       "      <td>80.45</td>\n",
       "      <td>13.49</td>\n",
       "      <td>457.0</td>\n",
       "      <td>1947.0</td>\n",
       "      <td>56.25</td>\n",
       "      <td>208.2</td>\n",
       "      <td>85.65</td>\n",
       "      <td>...</td>\n",
       "      <td>251.7</td>\n",
       "      <td>469.3</td>\n",
       "      <td>2375.0</td>\n",
       "      <td>323.9</td>\n",
       "      <td>89.40</td>\n",
       "      <td>15.71</td>\n",
       "      <td>367.5</td>\n",
       "      <td>284.4</td>\n",
       "      <td>6310.0</td>\n",
       "      <td>9121.22</td>\n",
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       "    <tr>\n",
       "      <th>2017-06-27</th>\n",
       "      <td>24.40</td>\n",
       "      <td>73.70</td>\n",
       "      <td>51.65</td>\n",
       "      <td>80.20</td>\n",
       "      <td>13.74</td>\n",
       "      <td>453.8</td>\n",
       "      <td>1931.0</td>\n",
       "      <td>56.40</td>\n",
       "      <td>207.6</td>\n",
       "      <td>84.30</td>\n",
       "      <td>...</td>\n",
       "      <td>252.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>2355.0</td>\n",
       "      <td>322.7</td>\n",
       "      <td>88.80</td>\n",
       "      <td>16.00</td>\n",
       "      <td>362.0</td>\n",
       "      <td>281.7</td>\n",
       "      <td>6280.0</td>\n",
       "      <td>9072.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-28</th>\n",
       "      <td>24.07</td>\n",
       "      <td>73.55</td>\n",
       "      <td>51.65</td>\n",
       "      <td>80.00</td>\n",
       "      <td>13.82</td>\n",
       "      <td>452.5</td>\n",
       "      <td>1927.0</td>\n",
       "      <td>56.35</td>\n",
       "      <td>207.5</td>\n",
       "      <td>85.40</td>\n",
       "      <td>...</td>\n",
       "      <td>251.2</td>\n",
       "      <td>464.9</td>\n",
       "      <td>2342.0</td>\n",
       "      <td>325.3</td>\n",
       "      <td>88.55</td>\n",
       "      <td>16.29</td>\n",
       "      <td>360.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>6195.0</td>\n",
       "      <td>9076.73</td>\n",
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       "    <tr>\n",
       "      <th>2017-06-29</th>\n",
       "      <td>23.63</td>\n",
       "      <td>72.85</td>\n",
       "      <td>51.30</td>\n",
       "      <td>78.60</td>\n",
       "      <td>14.10</td>\n",
       "      <td>446.0</td>\n",
       "      <td>1913.0</td>\n",
       "      <td>55.00</td>\n",
       "      <td>204.7</td>\n",
       "      <td>83.65</td>\n",
       "      <td>...</td>\n",
       "      <td>246.9</td>\n",
       "      <td>463.9</td>\n",
       "      <td>2310.0</td>\n",
       "      <td>323.3</td>\n",
       "      <td>87.80</td>\n",
       "      <td>16.36</td>\n",
       "      <td>353.4</td>\n",
       "      <td>276.3</td>\n",
       "      <td>6145.0</td>\n",
       "      <td>8944.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>23.68</td>\n",
       "      <td>72.90</td>\n",
       "      <td>50.45</td>\n",
       "      <td>79.00</td>\n",
       "      <td>13.86</td>\n",
       "      <td>447.2</td>\n",
       "      <td>1918.0</td>\n",
       "      <td>54.90</td>\n",
       "      <td>207.3</td>\n",
       "      <td>83.45</td>\n",
       "      <td>...</td>\n",
       "      <td>244.2</td>\n",
       "      <td>462.7</td>\n",
       "      <td>2322.0</td>\n",
       "      <td>323.6</td>\n",
       "      <td>87.65</td>\n",
       "      <td>16.24</td>\n",
       "      <td>354.1</td>\n",
       "      <td>279.1</td>\n",
       "      <td>6160.0</td>\n",
       "      <td>8906.89</td>\n",
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       "             ABBN   ADEN   BAER    CFR   CSGN   GEBN    GIVN    LHN   LONN  \\\n",
       "Date                                                                         \n",
       "2017-06-26  24.63  73.85  51.35  80.45  13.49  457.0  1947.0  56.25  208.2   \n",
       "2017-06-27  24.40  73.70  51.65  80.20  13.74  453.8  1931.0  56.40  207.6   \n",
       "2017-06-28  24.07  73.55  51.65  80.00  13.82  452.5  1927.0  56.35  207.5   \n",
       "2017-06-29  23.63  72.85  51.30  78.60  14.10  446.0  1913.0  55.00  204.7   \n",
       "2017-06-30  23.68  72.90  50.45  79.00  13.86  447.2  1918.0  54.90  207.3   \n",
       "\n",
       "             NESN  ...    ROG   SCMN    SGSN   SLHN   SREN   UBSG    UHR  \\\n",
       "Date               ...                                                     \n",
       "2017-06-26  85.65  ...  251.7  469.3  2375.0  323.9  89.40  15.71  367.5   \n",
       "2017-06-27  84.30  ...  252.0  465.0  2355.0  322.7  88.80  16.00  362.0   \n",
       "2017-06-28  85.40  ...  251.2  464.9  2342.0  325.3  88.55  16.29  360.0   \n",
       "2017-06-29  83.65  ...  246.9  463.9  2310.0  323.3  87.80  16.36  353.4   \n",
       "2017-06-30  83.45  ...  244.2  462.7  2322.0  323.6  87.65  16.24  354.1   \n",
       "\n",
       "             ZURN     SIK      SMI  \n",
       "Date                                \n",
       "2017-06-26  284.4  6310.0  9121.22  \n",
       "2017-06-27  281.7  6280.0  9072.92  \n",
       "2017-06-28  278.0  6195.0  9076.73  \n",
       "2017-06-29  276.3  6145.0  8944.04  \n",
       "2017-06-30  279.1  6160.0  8906.89  \n",
       "\n",
       "[5 rows x 21 columns]"
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     "execution_count": 2,
     "metadata": {},
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   "source": [
    "# Import daily shares prices and select window\n",
    "shsPr = pd.read_csv('Data/SMIDataDaily.csv', sep=',',\n",
    "                     parse_dates=['Date'], dayfirst=True,\n",
    "                     index_col=['Date'])\n",
    "shsPr = shsPr['2012-06-01':'2017-06-30']\n",
    "shsPr = shsPr.sort_index()\n",
    "shsPr.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having the data in a proper dataframe, we are now in a position to create the features and response values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate log-returns and label responses: \n",
    "# 'direction' equals 1 if stock closed above \n",
    "# previous day and 0 if it fell.\n",
    "today = np.log(shsPr / shsPr.shift(1))\n",
    "direction = np.where(today >= 0, 1, 0)\n",
    "\n",
    "# Convert 'direction' to dataframe\n",
    "direction = pd.DataFrame(direction, index=today.index, columns=today.columns)\n",
    "\n",
    "# Lag1, 2: t-1 and t-2 returns; excl. smi (in last column)\n",
    "Lag1 = np.log(shsPr.iloc[:, :-1].shift(1) / shsPr.iloc[:, :-1].shift(2))\n",
    "Lag2 = np.log(shsPr.iloc[:, :-1].shift(2) / shsPr.iloc[:, :-1].shift(3))\n",
    "\n",
    "# Previous day return for SMI index\n",
    "smi  = np.log(shsPr.iloc[:, -1].shift(1) / shsPr.iloc[:, -1].shift(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN Algorithm Applied\n",
    "\n",
    "Now comes the difficult part. What we want to achieve is to run the KNN algorithm for every stock and for different hyperparameter $k$ and see how it performs. For this we do the following steps:\n",
    "\n",
    "1. Create a feature matrix `X` containing `Lag1`, `Lag2` and `SMI` data for share i\n",
    "2. Create a response vector `y` with binary direction values\n",
    "3. Split data to training (before 2016-06-30) and test set (after 2016-06-30)\n",
    "4. Run KNN for different values of $k$ (loop)\n",
    "5. Write test score for given $k$ to matrix `scr`\n",
    "6. Once we have run through all $k$'s we proceed with step 1. with share i+1\n",
    "\n",
    "This means we need two loops. The first corresponds to the share (e.g. ABB, Adecco, etc.), the second runs the KNN algorithm for different values of $k$. \n",
    "\n",
    "The reason for this approach is that we are interested in finding any pattern/structure that would provide a successful trading strategy. There is obviously no free lunch. Predicting share price direction is by no means an easy task and we must be well aware that we are in for a difficult job here. If it were simple, neither one of us would be sitting here but run his own fund. But nonetheless, let us see how KNN performs and how homogeneous (or heterogeneous) the results are.\n",
    "\n",
    "As usual our first step is to prepare the ground by loading the necessary package and defining some auxiliary variables. The KNN function we will be using is available through the `sklearn` (short for Scikit-learn) package. We only load the `neighbor` sublibrary which contains the needed KNN function called `KNeigborsClassifier()`. KNN is applied with the default distance metric: Euclidean distance (Minkowski's distance with $m=2$). If we would prefer another distance metric we would have to specify it ([see documentation](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import relevant functions\n",
    "from sklearn import neighbors\n",
    "\n",
    "# k = {1, 3, ..., 200}\n",
    "k = np.arange(1, 200, 2)\n",
    "\n",
    "# Array to store results in. Dimension is [k x m] \n",
    "# with m=20 for the 20 companies (excl. SMI)\n",
    "scr = np.empty(shape=(len(k), len(shsPr.columns)-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(shsPr.columns)-1):\n",
    "    \n",
    "    # 1) Create matrix with feature values of stock i\n",
    "    X = pd.concat([Lag1.iloc[:, i], Lag2.iloc[:, i], smi], axis=1)\n",
    "    X = X[3:]  # Drop first three rows with NaN (due to lag)\n",
    "    \n",
    "    # 2) Remove first three rows of response dataframe\n",
    "    #    to have equal no. of rows for features and response\n",
    "    y = direction.iloc[:, i]\n",
    "    y = y[3:]\n",
    "    \n",
    "    # 3) Split data into training set...\n",
    "    X_train = X[:'2016-06-30']\n",
    "    y_train = y[:'2016-06-30']\n",
    "    # ...and test set.\n",
    "    X_test = X['2016-07-01':]\n",
    "    y_test = y['2016-07-01':]\n",
    "    \n",
    "    # Convert responses to 1xN array (with .ravel() function)\n",
    "    # Note: older versions of Scikit-Learn required this, as of V1.0 not anymore\n",
    "    # y_train = y_train.values.ravel()\n",
    "    # y_test  = y_test.values.ravel()\n",
    "    \n",
    "    for j in range(len(k)):\n",
    "        \n",
    "        # 4) Run KNN\n",
    "        # Instantiate KNN class\n",
    "        knn = neighbors.KNeighborsClassifier(n_neighbors=k[j])\n",
    "        # Fit KNN classifier using training set\n",
    "        knn = knn.fit(X_train, y_train)\n",
    "        \n",
    "        # 5) Extract test score for k[j]\n",
    "        scr[j, i] = knn.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert data to pandas dataframe\n",
    "tickers = shsPr.columns\n",
    "scr = pd.DataFrame(scr, index=k, columns=tickers[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <th>ROG</th>\n",
       "      <th>SCMN</th>\n",
       "      <th>SGSN</th>\n",
       "      <th>SLHN</th>\n",
       "      <th>SREN</th>\n",
       "      <th>UBSG</th>\n",
       "      <th>UHR</th>\n",
       "      <th>ZURN</th>\n",
       "      <th>SIK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.462451</td>\n",
       "      <td>0.533597</td>\n",
       "      <td>0.521739</td>\n",
       "      <td>0.513834</td>\n",
       "      <td>0.509881</td>\n",
       "      <td>0.533597</td>\n",
       "      <td>0.498024</td>\n",
       "      <td>0.494071</td>\n",
       "      <td>0.505929</td>\n",
       "      <td>0.411067</td>\n",
       "      <td>0.549407</td>\n",
       "      <td>0.553360</td>\n",
       "      <td>0.517787</td>\n",
       "      <td>0.521739</td>\n",
       "      <td>0.505929</td>\n",
       "      <td>0.470356</td>\n",
       "      <td>0.490119</td>\n",
       "      <td>0.549407</td>\n",
       "      <td>0.498024</td>\n",
       "      <td>0.529644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.482213</td>\n",
       "      <td>0.470356</td>\n",
       "      <td>0.529644</td>\n",
       "      <td>0.482213</td>\n",
       "      <td>0.577075</td>\n",
       "      <td>0.533597</td>\n",
       "      <td>0.525692</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.525692</td>\n",
       "      <td>0.486166</td>\n",
       "      <td>0.517787</td>\n",
       "      <td>0.573123</td>\n",
       "      <td>0.490119</td>\n",
       "      <td>0.517787</td>\n",
       "      <td>0.541502</td>\n",
       "      <td>0.450593</td>\n",
       "      <td>0.490119</td>\n",
       "      <td>0.557312</td>\n",
       "      <td>0.517787</td>\n",
       "      <td>0.513834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.494071</td>\n",
       "      <td>0.494071</td>\n",
       "      <td>0.537549</td>\n",
       "      <td>0.490119</td>\n",
       "      <td>0.525692</td>\n",
       "      <td>0.573123</td>\n",
       "      <td>0.537549</td>\n",
       "      <td>0.470356</td>\n",
       "      <td>0.501976</td>\n",
       "      <td>0.486166</td>\n",
       "      <td>0.525692</td>\n",
       "      <td>0.513834</td>\n",
       "      <td>0.466403</td>\n",
       "      <td>0.494071</td>\n",
       "      <td>0.521739</td>\n",
       "      <td>0.482213</td>\n",
       "      <td>0.494071</td>\n",
       "      <td>0.553360</td>\n",
       "      <td>0.533597</td>\n",
       "      <td>0.509881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.513834</td>\n",
       "      <td>0.486166</td>\n",
       "      <td>0.509881</td>\n",
       "      <td>0.494071</td>\n",
       "      <td>0.505929</td>\n",
       "      <td>0.553360</td>\n",
       "      <td>0.525692</td>\n",
       "      <td>0.517787</td>\n",
       "      <td>0.474308</td>\n",
       "      <td>0.509881</td>\n",
       "      <td>0.513834</td>\n",
       "      <td>0.482213</td>\n",
       "      <td>0.474308</td>\n",
       "      <td>0.501976</td>\n",
       "      <td>0.525692</td>\n",
       "      <td>0.466403</td>\n",
       "      <td>0.490119</td>\n",
       "      <td>0.561265</td>\n",
       "      <td>0.529644</td>\n",
       "      <td>0.513834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.553360</td>\n",
       "      <td>0.474308</td>\n",
       "      <td>0.509881</td>\n",
       "      <td>0.470356</td>\n",
       "      <td>0.486166</td>\n",
       "      <td>0.533597</td>\n",
       "      <td>0.505929</td>\n",
       "      <td>0.513834</td>\n",
       "      <td>0.458498</td>\n",
       "      <td>0.462451</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.490119</td>\n",
       "      <td>0.462451</td>\n",
       "      <td>0.498024</td>\n",
       "      <td>0.501976</td>\n",
       "      <td>0.470356</td>\n",
       "      <td>0.490119</td>\n",
       "      <td>0.517787</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.470356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ABBN      ADEN      BAER       CFR      CSGN      GEBN      GIVN  \\\n",
       "1  0.462451  0.533597  0.521739  0.513834  0.509881  0.533597  0.498024   \n",
       "3  0.482213  0.470356  0.529644  0.482213  0.577075  0.533597  0.525692   \n",
       "5  0.494071  0.494071  0.537549  0.490119  0.525692  0.573123  0.537549   \n",
       "7  0.513834  0.486166  0.509881  0.494071  0.505929  0.553360  0.525692   \n",
       "9  0.553360  0.474308  0.509881  0.470356  0.486166  0.533597  0.505929   \n",
       "\n",
       "        LHN      LONN      NESN      NOVN       ROG      SCMN      SGSN  \\\n",
       "1  0.494071  0.505929  0.411067  0.549407  0.553360  0.517787  0.521739   \n",
       "3  0.478261  0.525692  0.486166  0.517787  0.573123  0.490119  0.517787   \n",
       "5  0.470356  0.501976  0.486166  0.525692  0.513834  0.466403  0.494071   \n",
       "7  0.517787  0.474308  0.509881  0.513834  0.482213  0.474308  0.501976   \n",
       "9  0.513834  0.458498  0.462451  0.545455  0.490119  0.462451  0.498024   \n",
       "\n",
       "       SLHN      SREN      UBSG       UHR      ZURN       SIK  \n",
       "1  0.505929  0.470356  0.490119  0.549407  0.498024  0.529644  \n",
       "3  0.541502  0.450593  0.490119  0.557312  0.517787  0.513834  \n",
       "5  0.521739  0.482213  0.494071  0.553360  0.533597  0.509881  \n",
       "7  0.525692  0.466403  0.490119  0.561265  0.529644  0.513834  \n",
       "9  0.501976  0.470356  0.490119  0.517787  0.478261  0.470356  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results & Analysis\n",
    "\n",
    "Now let's see the results in an overview. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ABBN</th>\n",
       "      <th>ADEN</th>\n",
       "      <th>BAER</th>\n",
       "      <th>CFR</th>\n",
       "      <th>CSGN</th>\n",
       "      <th>GEBN</th>\n",
       "      <th>GIVN</th>\n",
       "      <th>LHN</th>\n",
       "      <th>LONN</th>\n",
       "      <th>NESN</th>\n",
       "      <th>NOVN</th>\n",
       "      <th>ROG</th>\n",
       "      <th>SCMN</th>\n",
       "      <th>SGSN</th>\n",
       "      <th>SLHN</th>\n",
       "      <th>SREN</th>\n",
       "      <th>UBSG</th>\n",
       "      <th>UHR</th>\n",
       "      <th>ZURN</th>\n",
       "      <th>SIK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.552569</td>\n",
       "      <td>0.506443</td>\n",
       "      <td>0.516561</td>\n",
       "      <td>0.508498</td>\n",
       "      <td>0.517628</td>\n",
       "      <td>0.499328</td>\n",
       "      <td>0.533320</td>\n",
       "      <td>0.490672</td>\n",
       "      <td>0.537510</td>\n",
       "      <td>0.518696</td>\n",
       "      <td>0.545573</td>\n",
       "      <td>0.513360</td>\n",
       "      <td>0.479012</td>\n",
       "      <td>0.517391</td>\n",
       "      <td>0.491383</td>\n",
       "      <td>0.520672</td>\n",
       "      <td>0.491462</td>\n",
       "      <td>0.506680</td>\n",
       "      <td>0.552530</td>\n",
       "      <td>0.529328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.030771</td>\n",
       "      <td>0.010743</td>\n",
       "      <td>0.022617</td>\n",
       "      <td>0.024798</td>\n",
       "      <td>0.016740</td>\n",
       "      <td>0.020342</td>\n",
       "      <td>0.013498</td>\n",
       "      <td>0.019679</td>\n",
       "      <td>0.027272</td>\n",
       "      <td>0.021221</td>\n",
       "      <td>0.019400</td>\n",
       "      <td>0.016129</td>\n",
       "      <td>0.019963</td>\n",
       "      <td>0.021084</td>\n",
       "      <td>0.020671</td>\n",
       "      <td>0.017746</td>\n",
       "      <td>0.014940</td>\n",
       "      <td>0.028037</td>\n",
       "      <td>0.019713</td>\n",
       "      <td>0.027794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.462451</td>\n",
       "      <td>0.470356</td>\n",
       "      <td>0.462451</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.446640</td>\n",
       "      <td>0.458498</td>\n",
       "      <td>0.498024</td>\n",
       "      <td>0.446640</td>\n",
       "      <td>0.458498</td>\n",
       "      <td>0.411067</td>\n",
       "      <td>0.498024</td>\n",
       "      <td>0.474308</td>\n",
       "      <td>0.446640</td>\n",
       "      <td>0.474308</td>\n",
       "      <td>0.450593</td>\n",
       "      <td>0.450593</td>\n",
       "      <td>0.446640</td>\n",
       "      <td>0.458498</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.466403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.529644</td>\n",
       "      <td>0.501976</td>\n",
       "      <td>0.501976</td>\n",
       "      <td>0.490119</td>\n",
       "      <td>0.509881</td>\n",
       "      <td>0.486166</td>\n",
       "      <td>0.525692</td>\n",
       "      <td>0.474308</td>\n",
       "      <td>0.529644</td>\n",
       "      <td>0.509881</td>\n",
       "      <td>0.529644</td>\n",
       "      <td>0.501976</td>\n",
       "      <td>0.462451</td>\n",
       "      <td>0.498024</td>\n",
       "      <td>0.477273</td>\n",
       "      <td>0.516798</td>\n",
       "      <td>0.486166</td>\n",
       "      <td>0.485178</td>\n",
       "      <td>0.541502</td>\n",
       "      <td>0.509881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.553360</td>\n",
       "      <td>0.505929</td>\n",
       "      <td>0.517787</td>\n",
       "      <td>0.509881</td>\n",
       "      <td>0.517787</td>\n",
       "      <td>0.496047</td>\n",
       "      <td>0.533597</td>\n",
       "      <td>0.486166</td>\n",
       "      <td>0.547431</td>\n",
       "      <td>0.525692</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.513834</td>\n",
       "      <td>0.476285</td>\n",
       "      <td>0.517787</td>\n",
       "      <td>0.494071</td>\n",
       "      <td>0.525692</td>\n",
       "      <td>0.494071</td>\n",
       "      <td>0.498024</td>\n",
       "      <td>0.557312</td>\n",
       "      <td>0.525692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.581028</td>\n",
       "      <td>0.513834</td>\n",
       "      <td>0.533597</td>\n",
       "      <td>0.525692</td>\n",
       "      <td>0.525692</td>\n",
       "      <td>0.513834</td>\n",
       "      <td>0.537549</td>\n",
       "      <td>0.505929</td>\n",
       "      <td>0.553360</td>\n",
       "      <td>0.533597</td>\n",
       "      <td>0.558300</td>\n",
       "      <td>0.522727</td>\n",
       "      <td>0.490119</td>\n",
       "      <td>0.534585</td>\n",
       "      <td>0.505929</td>\n",
       "      <td>0.529644</td>\n",
       "      <td>0.498024</td>\n",
       "      <td>0.525692</td>\n",
       "      <td>0.569170</td>\n",
       "      <td>0.545455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.596838</td>\n",
       "      <td>0.533597</td>\n",
       "      <td>0.565217</td>\n",
       "      <td>0.557312</td>\n",
       "      <td>0.577075</td>\n",
       "      <td>0.573123</td>\n",
       "      <td>0.573123</td>\n",
       "      <td>0.537549</td>\n",
       "      <td>0.577075</td>\n",
       "      <td>0.557312</td>\n",
       "      <td>0.588933</td>\n",
       "      <td>0.573123</td>\n",
       "      <td>0.541502</td>\n",
       "      <td>0.565217</td>\n",
       "      <td>0.541502</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.525692</td>\n",
       "      <td>0.573123</td>\n",
       "      <td>0.592885</td>\n",
       "      <td>0.581028</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ABBN        ADEN        BAER         CFR        CSGN        GEBN  \\\n",
       "count  100.000000  100.000000  100.000000  100.000000  100.000000  100.000000   \n",
       "mean     0.552569    0.506443    0.516561    0.508498    0.517628    0.499328   \n",
       "std      0.030771    0.010743    0.022617    0.024798    0.016740    0.020342   \n",
       "min      0.462451    0.470356    0.462451    0.454545    0.446640    0.458498   \n",
       "25%      0.529644    0.501976    0.501976    0.490119    0.509881    0.486166   \n",
       "50%      0.553360    0.505929    0.517787    0.509881    0.517787    0.496047   \n",
       "75%      0.581028    0.513834    0.533597    0.525692    0.525692    0.513834   \n",
       "max      0.596838    0.533597    0.565217    0.557312    0.577075    0.573123   \n",
       "\n",
       "             GIVN         LHN        LONN        NESN        NOVN         ROG  \\\n",
       "count  100.000000  100.000000  100.000000  100.000000  100.000000  100.000000   \n",
       "mean     0.533320    0.490672    0.537510    0.518696    0.545573    0.513360   \n",
       "std      0.013498    0.019679    0.027272    0.021221    0.019400    0.016129   \n",
       "min      0.498024    0.446640    0.458498    0.411067    0.498024    0.474308   \n",
       "25%      0.525692    0.474308    0.529644    0.509881    0.529644    0.501976   \n",
       "50%      0.533597    0.486166    0.547431    0.525692    0.545455    0.513834   \n",
       "75%      0.537549    0.505929    0.553360    0.533597    0.558300    0.522727   \n",
       "max      0.573123    0.537549    0.577075    0.557312    0.588933    0.573123   \n",
       "\n",
       "             SCMN        SGSN        SLHN        SREN        UBSG         UHR  \\\n",
       "count  100.000000  100.000000  100.000000  100.000000  100.000000  100.000000   \n",
       "mean     0.479012    0.517391    0.491383    0.520672    0.491462    0.506680   \n",
       "std      0.019963    0.021084    0.020671    0.017746    0.014940    0.028037   \n",
       "min      0.446640    0.474308    0.450593    0.450593    0.446640    0.458498   \n",
       "25%      0.462451    0.498024    0.477273    0.516798    0.486166    0.485178   \n",
       "50%      0.476285    0.517787    0.494071    0.525692    0.494071    0.498024   \n",
       "75%      0.490119    0.534585    0.505929    0.529644    0.498024    0.525692   \n",
       "max      0.541502    0.565217    0.541502    0.545455    0.525692    0.573123   \n",
       "\n",
       "             ZURN         SIK  \n",
       "count  100.000000  100.000000  \n",
       "mean     0.552530    0.529328  \n",
       "std      0.019713    0.027794  \n",
       "min      0.478261    0.466403  \n",
       "25%      0.541502    0.509881  \n",
       "50%      0.557312    0.525692  \n",
       "75%      0.569170    0.545455  \n",
       "max      0.592885    0.581028  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scr.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ABBN    0.596838\n",
       "ZURN    0.592885\n",
       "NOVN    0.588933\n",
       "SIK     0.581028\n",
       "CSGN    0.577075\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scr.max().nlargest(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following finance theory, returns should be distributed symmetrically. Thus the simplest guess would be to expect a share price to increase on 50% of the days and to decrease on the remaining 50%. Similar to guessing a coin flip, if we would guess an 'up' movement for every day, we obviously would - in the long run - be correct 50% of the times. This would make for a score of 50%. \n",
    "\n",
    "Looking in that light at the above summary, we see some very interesting results. For 10 out of 20 stocks KNN produces test scores of > 50% for even the 0.25th percentile. Let's plot the ones with the highest test-scores (ABBN, ZURN, NOVN, SIK) to see at what value of $k$ the best test-score is achieved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nms = ['ABBN', 'ZURN', 'NOVN', 'SIK']\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "for col in nms:\n",
    "    scr[col].plot(legend=True)\n",
    "plt.axhline(0.50, c='k', ls='--');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Zurich the peak is early (max. score around $k=60$) while the others peak later, i.e. with higher values of $k$. Furthermore, it seems interesting that for $k > 40$ test scores remained (barely) above 50%. If this is indeed a pattern we would have found a trading strategy, wouldn't we? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To further assess our results we look into KNN's prediction of ABB's stock movements. For this we rerun our KNN classifier algorithm for ABBN as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1) Create matrix with feature values of stock i\n",
    "X = pd.concat([Lag1['ABBN'], Lag2['ABBN'], smi], axis=1)\n",
    "X = X[3:]  # Drop first three rows with NaN (due to lag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2) Remove first three rows of response dataframe\n",
    "#    to have equal no. of rows for features and response\n",
    "y = direction['ABBN']\n",
    "y = y[3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3) Split data into training set...\n",
    "X_train = X[:'2016-06-30']\n",
    "y_train = y[:'2016-06-30']\n",
    "# ...and test set.\n",
    "X_test = X['2016-07-01':]\n",
    "y_test = y['2016-07-01':]\n",
    "\n",
    "# Convert responses to 1xN array (with .ravel() function)\n",
    "# Note: older versions of Scikit-Learn required this, as of V1.0 not anymore\n",
    "# y_train = y_train.values.ravel()\n",
    "# y_test  = y_test.values.ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For ABB the maximum score is reached where $k=145$. You can check this with the `scr['ABBN'].idxmax()` command, which provides the index of the maximum value of the selected column. In our case, the index is equivalent to the value of $k$. Thus we run KNN with $k=145$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "145"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scr['ABBN'].idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5968379446640316"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4) Run KNN\n",
    "# Instantiate KNN class for GIVN with k=145\n",
    "knn = neighbors.KNeighborsClassifier(n_neighbors=scr['ABBN'].idxmax())\n",
    "# Fit KNN classifier using training set\n",
    "knn = knn.fit(X_train, y_train)\n",
    "\n",
    "# 5) Extract test score for ABB\n",
    "scr_ABBN = knn.score(X_test, y_test)\n",
    "scr_ABBN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The score of 59.68% is the very same as what we have seen in above summary statistic. Nothing new so far. (Recall that the score is the total of correctly predicted outcomes.)\n",
    "\n",
    "However, the alert reader should by now raise some questions regarding our assumption that 50% of the returns should have been positive. In the long run, this might be true. But our training sample contained only 1'017 records and of these 534 were positive. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5250737463126843"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percentage of 'up' days in training set\n",
    "y_train.sum() / y_train.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Therefore, if we would guess 'up' for every day of our test set and **given the distribution of classes in the test set is exactly as in our training set**, then we would predict the correct movement in 52.51% of the cases. So in that light, the predictive power of our KNN algorithm has to be put in perspective to the 52.51%.\n",
    "\n",
    "In summary, our KNN algorithm has a score of 59.68%. Our best guess (based on the training set) would yield a score of 52.51%. This still displays that overall our KNN algorithm outperforms our best guess. Nonetheless, the margin is smaller than initially thought. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix\n",
    "\n",
    "There are more tools to assess the accuracy of an algorithm. We postpone the discussion of these tools to a later chapter and at this stage restrict ourselves to the discussion of a tool called \"confusion matrix\". \n",
    "\n",
    "A confusion matrix is a convenient way of displaying how our classifier performs. In binary classification (with e.g. response $y \\in \\{0, 1\\}$) there are four prediction categories possible (Ting (2011)):\n",
    "\n",
    "* **True positive**: True response value is 1, predicted value is 1 (\"hit\")\n",
    "* **True negative**: True response value is 0, predicted value is 0 (\"correct rejection\")\n",
    "* **False positive**: True response value is 0, predicted value is 1 (\"False alarm\", Type 1 error)\n",
    "* **False negative**: True response value is 1, predicted value is 0 (\"Miss\", Type 2 error)\n",
    "\n",
    "These information help us to understand how our (KNN) algorithm performed. There are different two ways of arranging confusion matrix. James et al. (2013) follow the convention that column labels indicate the true class label and rows the predicted response class. Others have it transposed such that column labels indicate predicted classes and row labels show true values. We will use the latter approach as it is more common."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"Graphics/0207_ConfusionMatrixExplained.png\" alt=\"ConfusionMatrixExplained\" style=\"width: 800px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run this in Python, we first predict the response value for each data entry in our test matrix `X_test`. Then we arrange the data in a suitable manner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict 'up' (=1) or 'down' (=0) for test set\n",
    "pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted direction  Down   Up\n",
      "True direction                \n",
      "Down                   31   83\n",
      "Up                     19  120\n"
     ]
    }
   ],
   "source": [
    "# Store data in DataFrame\n",
    "cfm = pd.DataFrame({'True direction': y_test,\n",
    "                    'Predicted direction': pred})\n",
    "cfm.replace(to_replace={0:'Down', 1:'Up'}, inplace=True)\n",
    "\n",
    "# Arrange data to confusion matrix\n",
    "print(cfm.groupby(['Predicted direction','True direction']) \\\n",
    "      .size().unstack('Predicted direction'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned before, rows represent the true outcome and columns show what class KNN predicted. In 31 cases, the test set's true response was 'down' (in our case represented by 0) and KNN correctly predicted 'down'. 120 times KNN was correct in predicting an 'up' (=1) movement. 19 returns in the test set were positive but KNN predicted a negative return. And in 83 out of 253 cases KNN predicted an 'up' movement whereas in reality the stock price decreased. The KNN score of 59.68% for ABB is the sum of true positive and negative (31 + 120) in relation to the total number of predictions (253 = 31 + 19 + 83 + 120). The error rate is 1 - score or (19 + 83)/253.\n",
    "\n",
    "Class-specific performance is also helpful to better understand results. The related terms are **sensitivity** and **specifity**. In the above case, sensitivity is the percentage of true 'up' movements that are identified. A good 86.3% (= 120 / (19 + 120)). The specifity is the percentage of 'down' movements that are correctly identified, here a poor 27.2% (= 31 / (31 + 83)). More on this in the next chapter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because confusion matrices are important to analyze results, `Scikit-learn` has its own [command to generate it](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html). It is part of the `metrics` sublibrary. The difficulty is that in contrast to above (manually generated) table, the function's output provides no labels. Therefore one must be sure to know which value are where. Here's the code to generate the confusion matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 31,  83],\n",
       "       [ 19, 120]], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Confusion matrix\n",
    "cm = confusion_matrix(y_test, pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general it is tremendously helpful to visualize results. At the time of writing in February 2022, Anaconda shipped with `Sklearn` version 0.24.2. This `Sklearn` version uses a confusion matrix-plotting function that is deprecated in versioun 1.0. Therefore, be aware that below plotting function only works for versions < 1.0. Details see [here](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.ConfusionMatrixDisplay.html#sklearn.metrics.ConfusionMatrixDisplay). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "ConfusionMatrixDisplay(cm, display_labels = ['Down', 'Up']).plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion matrix generated from estimator-object itself\n",
    "ConfusionMatrixDisplay.from_estimator(estimator = knn, \n",
    "                                      X = X_test, y = y_test,\n",
    "                                      display_labels = ['Down', 'Up']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To plot the normalized figures, add normalize = 'true', 'pred', or 'all'\n",
    "# To show possible options, this time with different color scheme\n",
    "ConfusionMatrixDisplay.from_estimator(estimator = knn, \n",
    "                                      X = X_test, y = y_test,\n",
    "                                      cmap = plt.cm.Blues,\n",
    "                                      normalize = 'all',\n",
    "                                      display_labels = ['Down', 'Up']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Further Ressources\n",
    "\n",
    "\n",
    "In writing this notebook, many ressources were consulted. For internet ressources the links are provided within the textflow above and will therefore not be listed again. Beyond these links, the following ressources were consulted and are recommended as further reading on the discussed topics:\n",
    "\n",
    "* Batista, Gustavo, and Diego Furtado Silva, 2009, How k-nearest neighbor parameters affect its performance, in *Argentine Symposium on Artificial Intelligence*, 1–12, sn.\n",
    "* Fortmann-Roe, Scott, 2012, Understanding the Bias-Variance Tradeoff from website, http://scott.fortmann-roe.com/docs/BiasVariance.html, 08/15/17.\n",
    "* Guggenbuehler, Jan P., 2015, Predicting net new money using machine learning algorithms and newspaper articles, Technical report, University of Zurich, Zurich.\n",
    "* James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani, 2013, *An Introduction to Statistical Learning: With Applications in R* (Springer Science & Business Media, New York, NY).\n",
    "* Müller, Andreas C., and Sarah Guido, 2017, *Introduction to Machine Learning with Python* (O’Reilly Media, Sebastopol, CA).\n",
    "* Russell, Stuart, and Peter Norvig, 2009, *Artificial Intelligence: A Modern Approach* (Prentice Hall Press, Upper Saddle River, NJ).\n",
    "* Ting, Kai Ming, 2011, Confusion matrix, in Claude Sammut, and Geoffrey I. Webb, eds., *Encyclopedia of Machine Learning* (Springer Science & Business Media, New York, NY)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "System:\n",
      "    python: 3.9.7 (default, Sep 16 2021, 16:59:28) [MSC v.1916 64 bit (AMD64)]\n",
      "executable: C:\\Users\\Ben Zimmermann\\anaconda3\\python.exe\n",
      "   machine: Windows-10-10.0.19045-SP0\n",
      "\n",
      "Python dependencies:\n",
      "      sklearn: 1.1.1\n",
      "          pip: 22.1.2\n",
      "   setuptools: 58.0.4\n",
      "        numpy: 1.20.3\n",
      "        scipy: 1.7.1\n",
      "       Cython: 0.29.24\n",
      "       pandas: 1.3.4\n",
      "   matplotlib: 3.4.3\n",
      "       joblib: 1.1.0\n",
      "threadpoolctl: 2.2.0\n",
      "\n",
      "Built with OpenMP: True\n",
      "\n",
      "threadpoolctl info:\n",
      "       filepath: C:\\Users\\Ben Zimmermann\\anaconda3\\Library\\bin\\mkl_rt.1.dll\n",
      "         prefix: mkl_rt\n",
      "       user_api: blas\n",
      "   internal_api: mkl\n",
      "        version: 2021.4-Product\n",
      "    num_threads: 2\n",
      "threading_layer: intel\n",
      "\n",
      "       filepath: C:\\Users\\Ben Zimmermann\\anaconda3\\Lib\\site-packages\\sklearn\\.libs\\vcomp140.dll\n",
      "         prefix: vcomp\n",
      "       user_api: openmp\n",
      "   internal_api: openmp\n",
      "        version: None\n",
      "    num_threads: 4\n",
      "\n",
      "       filepath: C:\\Users\\Ben Zimmermann\\anaconda3\\Library\\bin\\libiomp5md.dll\n",
      "         prefix: libiomp\n",
      "       user_api: openmp\n",
      "   internal_api: openmp\n",
      "        version: None\n",
      "    num_threads: 4\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "sklearn.show_versions()"
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}