KF5 - Dense ANN(5,12,1) Model v5.ipynb

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   "source": [
    "# ANN (5,12,1) Python Code\n",
    "## Sector Volatility Prediction Performance using GARCH Models and Artificial Neural Networks\n",
    "## MSc Thesis\n",
    "## Curtis Nybo, 2020\n",
    "### Version: 5.0"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "My MSc Dissertation paper results for the ANN section are derived using this notebook.\n",
    "\n",
    "I mainly utilize the works of Kenneth R. French for the datasets, Aurélien Géron, and Jason Brownlee. See references section in this notebook for more detail. "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Loading required libraries"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "np.random.seed(1)\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "# Tensorflow (4)\n",
    "from tensorflow.python.keras.models import Sequential, load_model\n",
    "from tensorflow.python.keras.layers.core import Dense\n",
    "from tensorflow.python.keras.layers.recurrent import LSTM\n",
    "from tensorflow.python.keras import optimizers\n",
    "from tensorflow.python.keras.callbacks import EarlyStopping\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error,r2_score,mean_absolute_error\n",
    "from math import sqrt\n",
    "import datetime as dt\n",
    "import time\n",
    "\n",
    "import math"
   ]
  },
  {
   "source": [
    "## Loading the datasets"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sector to be analyzed - This value can be changed between Technology, Consumer Durables, Health, Manufacturing, Other.\n",
    "sector = 'Technology'\n",
    "# Set working Directory\n",
    "os.chdir('C:\\\\Users\\\\CurtisXPS\\\\OneDrive\\\\School\\\\MSc Finance\\\\Dissertation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train and Test set criteria\n",
    "# Read in Ken French dataset [2], add on the path to the directory where data is stored.\n",
    "data = pd.read_csv('Data\\\\Ken F - Five Sector\\\\' + sector + '.csv')\n",
    "data['Date'] = pd.to_datetime(data['Date'])\n",
    "data = data.set_index(['Date'])\n",
    "\n",
    "data['Squared Returns'] = data['Returns']**2\n",
    "\n",
    "dataset = data['Squared Returns']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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137.126502 213.96076 \r\nL 137.398255 214.537075 \r\nL 137.670008 214.308837 \r\nL 137.941761 214.741322 \r\nL 138.213515 211.692172 \r\nL 139.028774 213.940746 \r\nL 139.300528 213.879211 \r\nL 139.844034 214.750272 \r\nL 140.115787 207.172151 \r\nL 140.931047 195.653929 \r\nL 141.2028 209.116407 \r\nL 141.474554 213.858202 \r\nL 141.746307 195.751266 \r\nL 142.01806 209.783843 \r\nL 142.83332 211.731082 \r\nL 143.105073 214.576856 \r\nL 143.376826 210.992663 \r\nL 143.64858 210.81763 \r\nL 143.920333 214.706638 \r\nL 145.007346 214.754375 \r\nL 145.279099 214.445581 \r\nL 145.550852 214.751888 \r\nL 145.822606 210.221301 \r\nL 147.181372 214.164509 \r\nL 147.453125 212.490759 \r\nL 147.724878 213.437527 \r\nL 148.540138 213.537972 \r\nL 148.811891 214.547393 \r\nL 149.083644 214.433025 \r\nL 149.355398 214.469946 \r\nL 149.627151 211.330794 \r\nL 150.442411 214.60408 \r\nL 150.714164 213.140792 \r\nL 150.985917 214.748408 \r\nL 151.529424 214.748408 \r\nL 152.344683 210.409262 \r\nL 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289.580073 212.214287 \r\nL 289.851826 214.690602 \r\nL 290.12358 207.53614 \r\nL 290.395333 213.279401 \r\nL 291.210593 212.813973 \r\nL 291.482346 209.733994 \r\nL 291.754099 203.343311 \r\nL 292.025852 214.751888 \r\nL 292.297606 211.412841 \r\nL 293.112865 212.557391 \r\nL 293.384619 214.338175 \r\nL 293.656372 213.488247 \r\nL 293.928125 214.684759 \r\nL 294.199878 203.568193 \r\nL 295.286891 214.706638 \r\nL 295.558644 214.690602 \r\nL 295.830398 211.453491 \r\nL 296.102151 214.754375 \r\nL 297.189164 212.751319 \r\nL 297.460917 211.84632 \r\nL 297.73267 210.409262 \r\nL 298.004424 214.701542 \r\nL 298.819683 214.469946 \r\nL 299.091437 214.756239 \r\nL 299.36319 212.995967 \r\nL 299.634943 214.644482 \r\nL 299.906696 214.50463 \r\nL 300.721956 214.706638 \r\nL 300.993709 214.644482 \r\nL 301.265463 214.755866 \r\nL 301.537216 214.720437 \r\nL 301.808969 214.18154 \r\nL 302.624229 214.753256 \r\nL 302.895982 212.687671 \r\nL 303.167735 213.224703 \r\nL 303.439489 214.636899 \r\nL 303.711242 212.936297 \r\nL 304.526502 213.749428 \r\nL 304.798255 213.224703 \r\nL 305.070008 211.163718 \r\nL 305.341761 212.906089 \r\nL 305.613515 211.613606 \r\nL 306.428774 214.678668 \r\nL 306.700528 210.683745 \r\nL 306.972281 213.025429 \r\nL 307.244034 214.380317 \r\nL 307.515787 212.249716 \r\nL 308.331047 214.72454 \r\nL 308.6028 212.844927 \r\nL 308.874554 214.293795 \r\nL 309.146307 214.746294 \r\nL 309.41806 213.858202 \r\nL 310.23332 213.920483 \r\nL 310.505073 213.610695 \r\nL 310.776826 214.612658 \r\nL 311.04858 214.32363 \r\nL 311.320333 213.657934 \r\nL 312.135593 213.224703 \r\nL 312.679099 214.481756 \r\nL 312.950852 214.746294 \r\nL 313.222606 213.224703 \r\nL 314.309619 214.755245 \r\nL 314.581372 214.716086 \r\nL 314.853125 214.407168 \r\nL 315.124878 214.32363 \r\nL 315.940138 214.750272 \r\nL 316.483644 214.636899 \r\nL 316.755398 214.755245 \r\nL 317.027151 213.899971 \r\nL 317.842411 214.731998 \r\nL 318.114164 214.735355 \r\nL 318.385917 214.380317 \r\nL 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(dataset[3086:])\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "# Shaping the data and creating Train and Test sets\n",
    "\n",
    "Leveraging the work and code of Jason Brownlee (1)."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "dataset = dataset.values.reshape(-1,1)\n",
    "dataset = scaler.fit_transform(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "2701 385 772\n"
    }
   ],
   "source": [
    "train_size = int(len(dataset) * 0.80)\n",
    "val_size = int(len(dataset) * 0.10)\n",
    "test_size = len(dataset) - train_size\n",
    "train, val, test = dataset[0:(train_size - val_size),:], dataset[(train_size-val_size):train_size,:], dataset[train_size:len(dataset),:]\n",
    "print(len(train), len(val), len(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(dataset, look_back):\n",
    "\tdataX, dataY = [], []\n",
    "\tfor i in range(len(dataset)-look_back-1):\n",
    "\t\ta = dataset[i:(i+look_back), 0]\n",
    "\t\tdataX.append(a)\n",
    "\t\tdataY.append(dataset[i + look_back, 0])\n",
    "\treturn np.array(dataX), np.array(dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "look_back = 5\n",
    "trainX, trainY = create_dataset(train, look_back)\n",
    "valX, valY = create_dataset(val, look_back)\n",
    "testX, testY = create_dataset(test, look_back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\n",
    "testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))\n",
    "valX = np.reshape(valX, (valX.shape[0], 1, valX.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Developing and Training the Dense Network\n",
    "Leveraging Tensorflow (4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Train on 2695 samples, validate on 379 samples\nEpoch 1/60\n2695/2695 [==============================] - 2s 925us/sample - loss: 0.1245 - val_loss: 0.0232\nEpoch 2/60\n2695/2695 [==============================] - 2s 923us/sample - loss: 0.0105 - val_loss: 0.0039\nEpoch 3/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0027 - val_loss: 8.0231e-04\nEpoch 4/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0015 - val_loss: 2.5202e-04\nEpoch 5/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0013 - val_loss: 1.5970e-04\nEpoch 6/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0013 - val_loss: 1.4421e-04\nEpoch 7/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0013 - val_loss: 1.4275e-04\nEpoch 8/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0013 - val_loss: 1.4411e-04\nEpoch 9/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0013 - val_loss: 1.4305e-04\nEpoch 10/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0013 - val_loss: 1.3951e-04\nEpoch 11/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0013 - val_loss: 1.3563e-04\nEpoch 12/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0013 - val_loss: 1.3924e-04\nEpoch 13/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0012 - val_loss: 1.3453e-04\nEpoch 14/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0012 - val_loss: 1.4301e-04\nEpoch 15/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0012 - val_loss: 1.4211e-04\nEpoch 16/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0012 - val_loss: 1.4299e-04\nEpoch 17/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0012 - val_loss: 1.4401e-04\nEpoch 18/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0012 - val_loss: 1.3569e-04\nEpoch 19/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0012 - val_loss: 1.3462e-04\nEpoch 20/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0012 - val_loss: 1.3387e-04\nEpoch 21/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0012 - val_loss: 1.3874e-04\nEpoch 22/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0012 - val_loss: 1.4090e-04\nEpoch 23/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0012 - val_loss: 1.3444e-04\nEpoch 24/60\n2695/2695 [==============================] - 4s 1ms/sample - loss: 0.0011 - val_loss: 1.3849e-04\nEpoch 25/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0011 - val_loss: 1.3629e-04\nEpoch 26/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0011 - val_loss: 1.3148e-04\nEpoch 27/60\n2695/2695 [==============================] - 4s 2ms/sample - loss: 0.0011 - val_loss: 1.2637e-04\nEpoch 28/60\n2695/2695 [==============================] - 4s 1ms/sample - loss: 0.0011 - val_loss: 1.3253e-04\nEpoch 29/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0011 - val_loss: 1.3129e-04\nEpoch 30/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0011 - val_loss: 1.2744e-04\nEpoch 31/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0011 - val_loss: 1.2350e-04\nEpoch 32/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0011 - val_loss: 1.2713e-04\nEpoch 33/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0011 - val_loss: 1.2780e-04\nEpoch 34/60\n2695/2695 [==============================] - 4s 1ms/sample - loss: 0.0011 - val_loss: 1.1942e-04\nEpoch 35/60\n2695/2695 [==============================] - 4s 1ms/sample - loss: 0.0011 - val_loss: 1.2203e-04\nEpoch 36/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0011 - val_loss: 1.1950e-04\nEpoch 37/60\n2695/2695 [==============================] - 4s 1ms/sample - loss: 0.0011 - val_loss: 1.2061e-04\nEpoch 38/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0011 - val_loss: 1.1698e-04\nEpoch 39/60\n2695/2695 [==============================] - 5s 2ms/sample - loss: 0.0011 - val_loss: 1.1798e-04\nEpoch 40/60\n2695/2695 [==============================] - 4s 2ms/sample - loss: 0.0010 - val_loss: 1.1633e-04\nEpoch 41/60\n2695/2695 [==============================] - 4s 1ms/sample - loss: 0.0010 - val_loss: 1.1693e-04\nEpoch 42/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0010 - val_loss: 1.1591e-04\nEpoch 43/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0010 - val_loss: 1.1020e-04\nEpoch 44/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0010 - val_loss: 1.1355e-04\nEpoch 45/60\n2695/2695 [==============================] - 4s 1ms/sample - loss: 0.0010 - val_loss: 1.1191e-04\nEpoch 46/60\n2695/2695 [==============================] - 4s 1ms/sample - loss: 0.0010 - val_loss: 1.1286e-04\nEpoch 47/60\n2695/2695 [==============================] - 5s 2ms/sample - loss: 0.0010 - val_loss: 1.0892e-04\nEpoch 48/60\n2695/2695 [==============================] - 4s 1ms/sample - loss: 0.0010 - val_loss: 1.1087e-04\nEpoch 49/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0010 - val_loss: 1.0747e-04\nEpoch 50/60\n2695/2695 [==============================] - 4s 1ms/sample - loss: 0.0010 - val_loss: 1.0709e-04\nEpoch 51/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0010 - val_loss: 1.0586e-04\nEpoch 52/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0010 - val_loss: 1.0902e-04\nEpoch 53/60\n2695/2695 [==============================] - 4s 1ms/sample - loss: 0.0010 - val_loss: 1.0908e-04\nEpoch 54/60\n2695/2695 [==============================] - 4s 2ms/sample - loss: 0.0010 - val_loss: 1.0779e-04\nEpoch 55/60\n2695/2695 [==============================] - 4s 1ms/sample - loss: 0.0010 - val_loss: 1.0951e-04\nEpoch 56/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0010 - val_loss: 1.1015e-04\nEpoch 57/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0010 - val_loss: 1.0949e-04\nEpoch 58/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0010 - val_loss: 1.1014e-04\nEpoch 59/60\n2695/2695 [==============================] - 4s 1ms/sample - loss: 0.0010 - val_loss: 1.1301e-04\nEpoch 60/60\n2695/2695 [==============================] - 3s 1ms/sample - loss: 0.0010 - val_loss: 1.0701e-04\n"
    }
   ],
   "source": [
    "# Using Keras to fit the Dense network\n",
    "model = Sequential()\n",
    "model.add(Dense(12, input_shape=(1,look_back), activation=\"sigmoid\"))\n",
    "model.add(Dense(1, activation=\"sigmoid\"))\n",
    "\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "\n",
    "history = model.fit(trainX, trainY, epochs=60, batch_size=1, validation_data = (valX, valY), verbose=1)"
   ]
  },
  {
   "source": [
    "# Plotting the Train and Validation Loss Curve\n",
    "\n",
    "Leveraging the works of Aurélien Géron (3)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "def plot_error(train_loss,val_loss):\n",
    "    plt.plot(train_loss,c = 'r')\n",
    "    plt.plot(val_loss,c = 'b')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.title(\"{sector} NN(1,1,1) Train Loss and Validation Loss Curve\".format(sector=sector))\n",
    "    plt.legend(['train','val'],loc = 'upper right')\n",
    "    #plt.savefig('Thesis Document\\\\KF5 Figures\\\\NN (5,1,1)\\\\' + sector + '\\\\' + sector + ' - NN(5,1,1) Train vs. Validation.png')\n",
    "    plt.show()\n",
    "\n",
    "train_error = history.history['loss']\n",
    "val_error = history.history['val_loss']\n",
    "plot_error(train_error,val_error)"
   ]
  },
  {
   "source": [
    "# Results \n",
    "\n",
    "Continuing with the results from the Dense network predictions - Leveraging Jason Brownlee (1) and Aurélien Géron (3)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Train MAE: 0.0001850\nTrain MSE: 0.0000003\nTrain RMSE: 0.0005089\nTest MAE: 0.0002387\nTest MSE: 0.0000007\nTest RMSE: 0.0008082\n"
    }
   ],
   "source": [
    "trainPredict = model.predict(trainX)\n",
    "testPredict = model.predict(testX)\n",
    "\n",
    "trainPredict = trainPredict.reshape((trainPredict.shape[0], trainPredict.shape[2]))\n",
    "testPredict = testPredict.reshape((len(testPredict), 1))\n",
    "\n",
    "trainPredict = scaler.inverse_transform(trainPredict)\n",
    "trainY = scaler.inverse_transform([trainY])\n",
    "testPredict = scaler.inverse_transform(testPredict)\n",
    "testY = scaler.inverse_transform([testY])\n",
    "\n",
    "trainMAE = mean_absolute_error(trainY[0], trainPredict[:,0])\n",
    "print('Train MAE: %.7f' % (trainMAE))\n",
    "trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))\n",
    "trainMSE = trainScore**2\n",
    "print('Train MSE: %.7f' % (trainMSE))\n",
    "print('Train RMSE: %.7f' % (trainScore))\n",
    "\n",
    "testMAE = mean_absolute_error(testY[0], testPredict[:,0])\n",
    "print('Test MAE: %.7f' % (testMAE))\n",
    "testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))\n",
    "testMSE = testScore**2\n",
    "print('Test MSE: %.7f' % (testMSE))\n",
    "print('Test RMSE: %.7f' % (testScore))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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229.6552 \r\nL 149.603378 229.426962 \r\nL 150.000719 229.859447 \r\nL 150.398061 226.810297 \r\nL 150.795403 229.058871 \r\nL 151.192744 228.997336 \r\nL 151.590086 229.55115 \r\nL 151.987427 229.868397 \r\nL 152.384769 222.290276 \r\nL 152.78211 210.772054 \r\nL 153.179452 224.234532 \r\nL 153.576794 228.976327 \r\nL 153.974135 210.869391 \r\nL 154.371477 224.901968 \r\nL 154.768818 226.849207 \r\nL 155.16616 229.694981 \r\nL 155.563501 226.110788 \r\nL 155.960843 225.935755 \r\nL 156.358184 229.824763 \r\nL 156.755526 229.8725 \r\nL 157.152868 229.563706 \r\nL 157.550209 229.870013 \r\nL 157.947551 225.339426 \r\nL 158.344892 227.575195 \r\nL 158.742234 228.024214 \r\nL 159.139575 229.282634 \r\nL 159.536917 227.608884 \r\nL 159.934259 228.555652 \r\nL 160.3316 228.656097 \r\nL 160.728942 229.665518 \r\nL 161.126283 229.55115 \r\nL 161.523625 229.588071 \r\nL 161.920966 226.448919 \r\nL 162.318308 229.722205 \r\nL 162.715649 228.258917 \r\nL 163.112991 229.866533 \r\nL 163.907674 229.866533 \r\nL 164.305016 225.527387 \r\nL 164.702357 229.846518 \r\nL 165.099699 229.665518 \r\nL 165.49704 222.654265 \r\nL 165.894382 223.067605 \r\nL 166.291724 215.756384 \r\nL 166.689065 219.63495 \r\nL 167.086407 229.175228 \r\nL 167.88109 223.183465 \r\nL 168.278431 228.867553 \r\nL 168.675773 227.507072 \r\nL 169.073114 229.563706 \r\nL 169.470456 223.581143 \r\nL 169.867798 229.470596 \r\nL 170.265139 223.692527 \r\nL 170.662481 229.611442 \r\nL 171.059822 228.631359 \r\nL 171.457164 229.704304 \r\nL 171.854505 229.078885 \r\nL 172.251847 225.891375 \r\nL 172.649189 229.868397 \r\nL 173.443872 227.741153 \r\nL 173.841213 229.814321 \r\nL 174.238555 226.281843 \r\nL 174.635896 229.868397 \r\nL 175.033238 227.963052 \r\nL 175.430579 229.599881 \r\nL 175.827921 229.299665 \r\nL 176.225263 228.680586 \r\nL 176.622604 229.713379 \r\nL 177.019946 229.842665 \r\nL 177.417287 225.665747 \r\nL 177.814629 229.038608 \r\nL 178.21197 229.8725 \r\nL 178.609312 228.397526 \r\nL 179.006654 228.084381 \r\nL 179.403995 229.866533 \r\nL 179.801337 229.864419 \r\nL 180.198678 228.911809 \r\nL 180.59602 229.563706 \r\nL 181.390703 229.856588 \r\nL 181.788044 229.23005 \r\nL 182.185386 229.730783 \r\nL 182.582728 229.426962 \r\nL 182.980069 229.870013 \r\nL 183.377411 229.874364 \r\nL 183.774752 229.316448 \r\nL 184.172094 229.118168 \r\nL 184.569435 229.834211 \r\nL 184.966777 226.448919 \r\nL 185.364119 229.247827 \r\nL 185.76146 229.6552 \r\nL 186.158802 229.247827 \r\nL 186.556143 229.156457 \r\nL 186.953485 229.85348 \r\nL 187.350826 229.838562 \r\nL 187.748168 229.4563 \r\nL 188.145509 229.762607 \r\nL 188.542851 228.997336 \r\nL 188.940193 229.819667 \r\nL 189.337534 229.873991 \r\nL 189.734876 229.365303 \r\nL 190.132217 229.747192 \r\nL 190.529559 228.867553 \r\nL 190.9269 229.814321 \r\nL 191.324242 224.701078 \r\nL 191.721583 229.755024 \r\nL 192.118925 227.575195 \r\nL 192.516267 228.477708 \r\nL 192.913608 229.87337 \r\nL 193.31095 228.704828 \r\nL 193.708291 228.822303 \r\nL 194.105633 227.869444 \r\nL 194.502974 228.054422 \r\nL 194.900316 229.118168 \r\nL 195.297658 229.850123 \r\nL 195.694999 229.4563 \r\nL 196.092341 226.323985 \r\nL 196.489682 229.871381 \r\nL 196.887024 229.762607 \r\nL 197.284365 229.38109 \r\nL 197.681707 229.85348 \r\nL 199.271073 229.829612 \r\nL 199.668415 228.315106 \r\nL 200.065756 229.018096 \r\nL 200.463098 226.281843 \r\nL 200.860439 227.224633 \r\nL 201.257781 229.576013 \r\nL 201.655123 229.349266 \r\nL 202.052464 228.024214 \r\nL 202.449806 229.755024 \r\nL 202.847147 229.175228 \r\nL 203.244489 229.730783 \r\nL 203.64183 229.862057 \r\nL 204.039172 229.834211 \r\nL 204.436513 229.299665 \r\nL 204.833855 229.783864 \r\nL 205.231197 229.349266 \r\nL 205.628538 228.72882 \r\nL 206.02588 229.6552 \r\nL 206.423221 229.824763 \r\nL 206.820563 229.8725 \r\nL 207.217904 229.675588 \r\nL 208.012588 229.868397 \r\nL 208.409929 228.287136 \r\nL 209.204612 229.842665 \r\nL 209.601954 229.058871 \r\nL 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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(scaler.inverse_transform(test[look_back:]), label='True Squared Returns')\n",
    "plt.plot(testPredict, label='Forecast')\n",
    "plt.legend(loc='best')\n",
    "plt.ylabel('Volatility')\n",
    "plt.title(\"{sector} NN(1,1,1) Volatility Forecast vs. Squared Returns\".format(sector=sector))\n",
    "#plt.savefig('Thesis Document\\\\KF5 Figures\\\\NN (5,1,1)\\\\' + sector + '\\\\' + sector + ' - NN(5,1,1) Prediction.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(testPredict)\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "# References"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "(1) Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras - Jason Brownlee, PhD - https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/ - 2016\n",
    "\n",
    "(2) Data Library - Kenneth R. French - https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html - 2020\n",
    "\n",
    "(3) Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition - Aurélien Géron - https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/ - 2019\n",
    "\n",
    "(4) TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems - https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf - 2015\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}