cnn_ns.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "4/21 Copy of cnn-ns-5d.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "orfmt-IG0_VV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "0c67d902-4354-4bc2-fc15-ed8e17f216c6"
      },
      "source": [
        "!pip install pyyaml h5py  # Required to save models in HDF5 format"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (3.13)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (2.10.0)\n",
            "Requirement already satisfied: numpy>=1.7 in /usr/local/lib/python3.6/dist-packages (from h5py) (1.18.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from h5py) (1.12.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "wQ30Mz_Y_EoE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "feffacf4-a5dd-454b-8567-d379d165addc"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uerTsMOqWi2S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "22210eea-fa95-412b-db9d-5e729a7c7eee"
      },
      "source": [
        "!pip install kaggle\n",
        "!mkdir /root/.kaggle\n",
        "import json\n",
        "token = {\"username\":\"oliverxie\",\"key\":\"27eb35f64a866446390a5b0d714b6dfe\"}\n",
        "with open('/root/.kaggle/kaggle.json', 'w') as file:\n",
        "    json.dump(token, file)\n",
        "!chmod 600 /root/.kaggle/kaggle.json\n",
        "!kaggle config set -n path -v{/content}\n",
        "!kaggle datasets download \"borismarjanovic/price-volume-data-for-all-us-stocks-etfs\" -p /content\n",
        "!unzip -q -n \\*.zip "
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.6/dist-packages (1.5.6)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.12.0)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.38.0)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.8.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.21.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.6/dist-packages (from kaggle) (2020.4.5.1)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.0.0)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (2.8)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.6/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n",
            "- path is now set to: {/content}\n",
            "price-volume-data-for-all-us-stocks-etfs.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9t-CEKv1W-0F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b2dafae1-bce9-42b1-9730-f54c1c00a11c"
      },
      "source": [
        "!ls ETFs/tecl.us.txt"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ETFs/tecl.us.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bPhDiEpIXgB0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "424b0fca-efe2-4849-e403-1abae4a105ec"
      },
      "source": [
        "%tensorflow_version 2.x\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qpcj5kvFRl2z",
        "colab_type": "text"
      },
      "source": [
        "# Saving Path"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c59g99TKRpIJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pth = 'drive/My Drive/532m_saves'\n",
        "pth += '/cnn-ns-5d-vggm2-sgd_lr0001'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VghwYXoUtDR8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import os\n",
        "# pth = \"training_1/cp.ckpt\"\n",
        "# dir = os.path.dirname(pth)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2hRM8SreRxdx",
        "colab_type": "text"
      },
      "source": [
        "# Generate Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O1YEqxWsafCC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "outputId": "31ba482b-cd38-4605-8003-cfa9d5482534"
      },
      "source": [
        "d = pd.read_csv('ETFs/tecl.us.txt')\n",
        "d.head()\n",
        "d = d.iloc[:,1:-2]\n",
        "\n",
        "print(d.shape)\n",
        "d.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2241, 4)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.8140</td>\n",
              "      <td>2.9930</td>\n",
              "      <td>2.7992</td>\n",
              "      <td>2.9048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.7795</td>\n",
              "      <td>2.8457</td>\n",
              "      <td>2.5638</td>\n",
              "      <td>2.6448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.7870</td>\n",
              "      <td>2.8432</td>\n",
              "      <td>2.6985</td>\n",
              "      <td>2.7330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.7280</td>\n",
              "      <td>2.7280</td>\n",
              "      <td>2.3992</td>\n",
              "      <td>2.4630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.6250</td>\n",
              "      <td>2.6372</td>\n",
              "      <td>2.4532</td>\n",
              "      <td>2.4925</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Open    High     Low   Close\n",
              "0  2.8140  2.9930  2.7992  2.9048\n",
              "1  2.7795  2.8457  2.5638  2.6448\n",
              "2  2.7870  2.8432  2.6985  2.7330\n",
              "3  2.7280  2.7280  2.3992  2.4630\n",
              "4  2.6250  2.6372  2.4532  2.4925"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GQ_CDilkZDO6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def generate_rnn_dataset(array, window_size, prediction_size, offset):\n",
        "    n_samples = len(array) - window_size - prediction_size - offset\n",
        "    X = np.ndarray([n_samples, window_size, array.shape[1]])\n",
        "    y = np.ndarray([n_samples, prediction_size, array.shape[1]])\n",
        "    for i in range(n_samples):\n",
        "        X[i,:] = array[i:i+window_size]\n",
        "        st_pos = i+window_size + 1 + offset\n",
        "        y[i] = array[st_pos: st_pos + prediction_size]\n",
        "\n",
        "    return (X, y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o2yarVJibldm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "790c9e4a-b667-45ab-facb-c08d5a5b4a32"
      },
      "source": [
        "d_np = d.to_numpy()\n",
        "\n",
        "\n",
        "# from sklearn import preprocessing\n",
        "# transformer = preprocessing.StandardScaler()\n",
        "# d = transformer.fit_transform(d)\n",
        "\n",
        "X, y = generate_rnn_dataset(d_np, 100, 5, 0)\n",
        "y = y[:,:,3,np.newaxis]\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.25, random_state=42)\n",
        "\n",
        "print(X.shape)\n",
        "print(y.shape)\n",
        "\n",
        "print(X_train.shape, y_train.shape)\n",
        "print(X_test.shape, y_test.shape)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2136, 100, 4)\n",
            "(2136, 5, 1)\n",
            "(1602, 100, 4) (1602, 5, 1)\n",
            "(534, 100, 4) (534, 5, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OcAK4LWVR1Ot",
        "colab_type": "text"
      },
      "source": [
        "# Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mHzTgxXJeB7x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "fda1ba5d-f232-48cb-c705-dfb649a4ff4d"
      },
      "source": [
        "from functools import partial \n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential, Model\n",
        "from keras.regularizers import l1_l2\n",
        "from tensorflow.keras.layers import (\n",
        "    Conv1D, Flatten, MaxPooling1D, Dense, BatchNormalization, \n",
        "    Reshape, Dropout\n",
        ")\n",
        "\n",
        "Conv1D = partial(\n",
        "    Conv1D, padding='same', \n",
        "    activation='relu',\n",
        "    # kernel_regularizer=l1_l2(1e-3, 1e-3),\n",
        ")\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "def pattern(n_filters, input_shape=None):\n",
        "    if input_shape is None:\n",
        "        model.add(Conv1D(n_filters, kernel_size=3))\n",
        "    else:\n",
        "        model.add(Conv1D(n_filters, kernel_size=3, input_shape=input_shape))\n",
        "    model.add(Conv1D(n_filters, kernel_size=3))\n",
        "    model.add(MaxPooling1D(2))\n",
        "    # model.add(Dropout(0.5))\n",
        "    model.add(BatchNormalization())\n",
        "\n",
        "pattern(64, input_shape=X.shape[1:])\n",
        "pattern(128)\n",
        "pattern(256)\n",
        "pattern(512)\n",
        "# pattern(512)\n",
        "# pattern(512)\n",
        "\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(200))\n",
        "\n",
        "n_output = y.shape[1:]\n",
        "print(n_output)\n",
        "model.add(Dense(np.prod(n_output)))\n",
        "model.add(Reshape(target_shape=n_output))\n",
        "\n",
        "\n",
        "model.build()\n",
        "# model.summary()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "(5, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4lf5_K9SuEmL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.optimizers import SGD\n",
        "model.compile(optimizer=SGD(lr=0.0001, momentum=0.1), loss='mse')\n",
        "# model.compile(optimizer=\"adam\", loss='mse')\n",
        "# model.compile(optimizer='nAdam', loss='mse')\n",
        "\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "mc = ModelCheckpoint(pth+'-best_model.h5',\n",
        "                     monitor='val_loss', mode='min', save_best_only=True,\n",
        "                    #  verbose=True,\n",
        "                     )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MZD2WsvfR4zE",
        "colab_type": "text"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eLFoXPTja6ic",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "39479abf-0b06-4ae9-8882-99a950ebf32f"
      },
      "source": [
        "history = model.fit(\n",
        "    x=X_train, y=y_train,\n",
        "    batch_size=128, epochs=100,\n",
        "    validation_data=(X_test, y_test),\n",
        "    callbacks=[mc]\n",
        ")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "13/13 [==============================] - 1s 106ms/step - loss: 273.3145 - val_loss: 891.7067\n",
            "Epoch 2/100\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 19.4089 - val_loss: 950.6245\n",
            "Epoch 3/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 17.7595 - val_loss: 968.4469\n",
            "Epoch 4/100\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 9.2884 - val_loss: 988.4417\n",
            "Epoch 5/100\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 10.4353 - val_loss: 996.0773\n",
            "Epoch 6/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 14.8925 - val_loss: 997.7964\n",
            "Epoch 7/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 6.0872 - val_loss: 998.2360\n",
            "Epoch 8/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 9.8276 - val_loss: 999.6760\n",
            "Epoch 9/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 8.6435 - val_loss: 979.7445\n",
            "Epoch 10/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 7.4500 - val_loss: 977.9525\n",
            "Epoch 11/100\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 7.9236 - val_loss: 955.2626\n",
            "Epoch 12/100\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 9.6074 - val_loss: 926.6543\n",
            "Epoch 13/100\n",
            "13/13 [==============================] - 0s 26ms/step - loss: 10.7318 - val_loss: 875.5498\n",
            "Epoch 14/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 11.2986 - val_loss: 885.0011\n",
            "Epoch 15/100\n",
            "13/13 [==============================] - 0s 25ms/step - loss: 9.1715 - val_loss: 817.7672\n",
            "Epoch 16/100\n",
            "13/13 [==============================] - 0s 38ms/step - loss: 12.1481 - val_loss: 759.6649\n",
            "Epoch 17/100\n",
            "13/13 [==============================] - 0s 37ms/step - loss: 7.0495 - val_loss: 746.2000\n",
            "Epoch 18/100\n",
            "13/13 [==============================] - 0s 35ms/step - loss: 5.7095 - val_loss: 672.0061\n",
            "Epoch 19/100\n",
            "13/13 [==============================] - 0s 36ms/step - loss: 11.0786 - val_loss: 659.3367\n",
            "Epoch 20/100\n",
            "13/13 [==============================] - 1s 39ms/step - loss: 8.6143 - val_loss: 548.2700\n",
            "Epoch 21/100\n",
            "13/13 [==============================] - 0s 37ms/step - loss: 9.6291 - val_loss: 521.4419\n",
            "Epoch 22/100\n",
            "13/13 [==============================] - 0s 26ms/step - loss: 5.7026 - val_loss: 509.5064\n",
            "Epoch 23/100\n",
            "13/13 [==============================] - 0s 32ms/step - loss: 8.2773 - val_loss: 506.1595\n",
            "Epoch 24/100\n",
            "13/13 [==============================] - 0s 37ms/step - loss: 5.7182 - val_loss: 393.5919\n",
            "Epoch 25/100\n",
            "13/13 [==============================] - 0s 16ms/step - loss: 6.9353 - val_loss: 402.9837\n",
            "Epoch 26/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 7.5870 - val_loss: 440.8731\n",
            "Epoch 27/100\n",
            "13/13 [==============================] - 0s 25ms/step - loss: 10.4618 - val_loss: 288.3558\n",
            "Epoch 28/100\n",
            "13/13 [==============================] - 1s 39ms/step - loss: 5.9471 - val_loss: 236.2938\n",
            "Epoch 29/100\n",
            "13/13 [==============================] - 1s 41ms/step - loss: 5.7113 - val_loss: 231.3740\n",
            "Epoch 30/100\n",
            "13/13 [==============================] - 0s 36ms/step - loss: 16.2821 - val_loss: 207.5395\n",
            "Epoch 31/100\n",
            "13/13 [==============================] - 0s 36ms/step - loss: 5.5357 - val_loss: 149.3813\n",
            "Epoch 32/100\n",
            "13/13 [==============================] - 0s 38ms/step - loss: 10.7283 - val_loss: 118.5736\n",
            "Epoch 33/100\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 4.1364 - val_loss: 139.5050\n",
            "Epoch 34/100\n",
            "13/13 [==============================] - 0s 25ms/step - loss: 9.2485 - val_loss: 82.9951\n",
            "Epoch 35/100\n",
            "13/13 [==============================] - 0s 26ms/step - loss: 9.6382 - val_loss: 56.6270\n",
            "Epoch 36/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 7.4776 - val_loss: 106.3594\n",
            "Epoch 37/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 4.2781 - val_loss: 59.9478\n",
            "Epoch 38/100\n",
            "13/13 [==============================] - 0s 24ms/step - loss: 6.4207 - val_loss: 46.8736\n",
            "Epoch 39/100\n",
            "13/13 [==============================] - 0s 37ms/step - loss: 6.7823 - val_loss: 24.0735\n",
            "Epoch 40/100\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 8.0143 - val_loss: 48.5878\n",
            "Epoch 41/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 4.8688 - val_loss: 59.1706\n",
            "Epoch 42/100\n",
            "13/13 [==============================] - 0s 26ms/step - loss: 5.4283 - val_loss: 18.1896\n",
            "Epoch 43/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 8.3256 - val_loss: 60.2942\n",
            "Epoch 44/100\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 4.8095 - val_loss: 37.1941\n",
            "Epoch 45/100\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 5.8515 - val_loss: 44.5174\n",
            "Epoch 46/100\n",
            "13/13 [==============================] - 0s 24ms/step - loss: 5.2777 - val_loss: 17.9784\n",
            "Epoch 47/100\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 8.5541 - val_loss: 29.9015\n",
            "Epoch 48/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 7.5144 - val_loss: 26.1647\n",
            "Epoch 49/100\n",
            "13/13 [==============================] - 0s 23ms/step - loss: 7.6887 - val_loss: 5.4277\n",
            "Epoch 50/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 7.5053 - val_loss: 28.1917\n",
            "Epoch 51/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 6.7350 - val_loss: 8.4796\n",
            "Epoch 52/100\n",
            "13/13 [==============================] - 0s 24ms/step - loss: 5.8586 - val_loss: 5.1098\n",
            "Epoch 53/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 3.9091 - val_loss: 43.5455\n",
            "Epoch 54/100\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 5.0991 - val_loss: 19.5033\n",
            "Epoch 55/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 3.5875 - val_loss: 11.7651\n",
            "Epoch 56/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 5.3262 - val_loss: 43.1646\n",
            "Epoch 57/100\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 4.8337 - val_loss: 12.9250\n",
            "Epoch 58/100\n",
            "13/13 [==============================] - 0s 24ms/step - loss: 4.0146 - val_loss: 4.2538\n",
            "Epoch 59/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 4.3112 - val_loss: 4.7595\n",
            "Epoch 60/100\n",
            "13/13 [==============================] - 0s 24ms/step - loss: 5.4393 - val_loss: 3.2312\n",
            "Epoch 61/100\n",
            "13/13 [==============================] - 1s 40ms/step - loss: 5.3700 - val_loss: 2.6859\n",
            "Epoch 62/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 4.6594 - val_loss: 4.7691\n",
            "Epoch 63/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 7.9691 - val_loss: 16.9310\n",
            "Epoch 64/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 8.5984 - val_loss: 24.6690\n",
            "Epoch 65/100\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 6.0841 - val_loss: 3.9594\n",
            "Epoch 66/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 4.0038 - val_loss: 26.4372\n",
            "Epoch 67/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 4.6440 - val_loss: 5.8660\n",
            "Epoch 68/100\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 3.3727 - val_loss: 3.9288\n",
            "Epoch 69/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 5.6817 - val_loss: 11.3959\n",
            "Epoch 70/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 2.6643 - val_loss: 10.6103\n",
            "Epoch 71/100\n",
            "13/13 [==============================] - 0s 25ms/step - loss: 3.4144 - val_loss: 2.3449\n",
            "Epoch 72/100\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 5.3937 - val_loss: 19.6067\n",
            "Epoch 73/100\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 4.9009 - val_loss: 9.3140\n",
            "Epoch 74/100\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 8.0873 - val_loss: 85.4794\n",
            "Epoch 75/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 7.2654 - val_loss: 2.9081\n",
            "Epoch 76/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 10.0898 - val_loss: 11.7334\n",
            "Epoch 77/100\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 2.9525 - val_loss: 2.6007\n",
            "Epoch 78/100\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 6.8027 - val_loss: 14.9300\n",
            "Epoch 79/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 6.8223 - val_loss: 2.3485\n",
            "Epoch 80/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 3.4857 - val_loss: 15.5974\n",
            "Epoch 81/100\n",
            "13/13 [==============================] - 0s 24ms/step - loss: 3.6417 - val_loss: 2.0257\n",
            "Epoch 82/100\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 4.4632 - val_loss: 2.2047\n",
            "Epoch 83/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 4.7795 - val_loss: 2.0269\n",
            "Epoch 84/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 4.2236 - val_loss: 8.0460\n",
            "Epoch 85/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 4.4941 - val_loss: 6.2766\n",
            "Epoch 86/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 5.1605 - val_loss: 2.9737\n",
            "Epoch 87/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 3.1430 - val_loss: 8.0006\n",
            "Epoch 88/100\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 3.5152 - val_loss: 4.6403\n",
            "Epoch 89/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 3.2334 - val_loss: 12.3668\n",
            "Epoch 90/100\n",
            "13/13 [==============================] - 0s 16ms/step - loss: 4.5771 - val_loss: 17.0900\n",
            "Epoch 91/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 4.8397 - val_loss: 14.8880\n",
            "Epoch 92/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 3.2601 - val_loss: 5.7807\n",
            "Epoch 93/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 7.0078 - val_loss: 14.3873\n",
            "Epoch 94/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 5.9111 - val_loss: 14.0475\n",
            "Epoch 95/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 5.8123 - val_loss: 13.0292\n",
            "Epoch 96/100\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 2.5897 - val_loss: 7.9727\n",
            "Epoch 97/100\n",
            "13/13 [==============================] - 0s 24ms/step - loss: 4.8520 - val_loss: 1.8587\n",
            "Epoch 98/100\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 3.6946 - val_loss: 19.8128\n",
            "Epoch 99/100\n",
            "13/13 [==============================] - 0s 17ms/step - loss: 3.2984 - val_loss: 6.0390\n",
            "Epoch 100/100\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 6.8139 - val_loss: 6.1780\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7yWgzboW_N6E",
        "colab_type": "text"
      },
      "source": [
        "# Save results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "05MNHzOY0bhk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0a821a57-2b4f-4105-beba-4d807d2d9d4b"
      },
      "source": [
        "print(model)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<tensorflow.python.keras.engine.sequential.Sequential object at 0x7f49715400f0>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kNOsPj4BUbSb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.load_weights(pth +'-best_model.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XcLqQ5i6gCdP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 806
        },
        "outputId": "b9adc718-f229-4ce0-a292-ad4c6bbef37d"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "\n",
        "def pl(y, y_pred):\n",
        "    plt.plot(y, label='Groundtruth')\n",
        "    plt.plot(y_pred, label='Prediction')\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "\n",
        "y_pred = model.predict(X)\n",
        "y = y\n",
        "\n",
        "print(y_pred.shape)\n",
        "\n",
        "def get_i(i):\n",
        "    return y_pred[:,i, 0], y[:,i, 0]\n",
        "\n",
        "\n",
        "plt.figure(figsize=[6,4],\n",
        "           dpi=200\n",
        "           )\n",
        "pl(*get_i(4))\n",
        "\n",
        "\n",
        "\n",
        "# plt.plot(y_train, label='Groundtruth')\n",
        "# plt.plot(model.predict(X_train), label='Training')\n",
        "# plt.plot(model.predict(X_test), label='Testing')\n",
        "title = \"Price Prediction on New Approach of Test-set Separation\"\n",
        "plt.title(title)\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Closing Price (USD)')\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2136, 5, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "piS87NpL_MQQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# np.save(pth+'-predict', model.predict(X))\n",
        "# np.save(pth+'-true', y)\n",
        "\n",
        "np.save(pth+'-train_loss', history.history['loss'])\n",
        "np.save(pth+'-test_loss', history.history['val_loss'])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ES_Z-PTWM5AK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 979
        },
        "outputId": "c634a297-adee-4e81-ef8a-56cd27539050"
      },
      "source": [
        "train_loss = np.load(pth+'-train_loss.npy')\n",
        "test_loss = np.load(pth+'-test_loss.npy')\n",
        "\n",
        "# title = \"True/Predict: CNN | 5days[5] | SGD | random split\"\n",
        "title = 'Losses during Training using\\nNew Approach of Test-set Separation'\n",
        "\n",
        "plt.figure(figsize=[4,5], \n",
        "           dpi=200\n",
        "           )\n",
        "\n",
        "plt.semilogy(train_loss, label='Train loss')\n",
        "plt.semilogy(test_loss, label='Test loss')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.title(title)\n",
        "plt.xlabel('Epoches')\n",
        "plt.show()\n",
        "\n",
        "# plt.plot(train_loss, label='train loss')\n",
        "# plt.plot(test_loss, label='test loss')\n",
        "# plt.legend()\n",
        "# plt.grid(True)\n",
        "# plt.title(title)\n",
        "# plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x1000 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}