9장_주가딥러닝_colab.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "9장_주가딥러닝.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1PNxQo_-8QbtrcjcbO4SV4xvk3aMzIGHK",
      "authorship_tag": "ABX9TyOHNBBtEgnU027Ig7jhhUJd",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dudungE/AI_Finance/blob/master/9%EC%9E%A5_%EC%A3%BC%EA%B0%80%EB%94%A5%EB%9F%AC%EB%8B%9D_colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YgLQdHaIiY_B"
      },
      "source": [
        "# 활성화함수\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "1k6blP0hi1b2",
        "outputId": "a18d095f-eded-458e-8171-573aa4cc2b6f"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def stepfunc(x):\n",
        "    return np.where(x <= 0, 0, 1)\n",
        "\n",
        "x = np.arange(-10, 10, 0.1)\n",
        "y = stepfunc(x)\n",
        "\n",
        "plt.plot(x, y)\n",
        "plt.title('step function')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "qpmBM7hti5oK",
        "outputId": "78eb73df-93db-4a8f-f014-bb4442d60fd0"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def sigmoid(x):\n",
        "    return 1 / (1 + np.exp(-x)) \n",
        "\n",
        "x = np.arange(-10, 10, 0.1)\n",
        "y = sigmoid(x) \n",
        "\n",
        "plt.plot(x, y)\n",
        "plt.title('sigmoid function')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "ysJzT9Nui5lu",
        "outputId": "d87fd869-169c-4d13-93ce-f8f39de39a11"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def tanh(x):\n",
        "    return (np.exp(x) - np.exp(-x)) / (np.exp(x) + np.exp(-x))  \n",
        "\n",
        "x = np.arange(-10, 10, 0.1) \n",
        "y = tanh(x) \n",
        "\n",
        "plt.plot(x, y)\n",
        "plt.title('tanh function')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "2yYfwPuDi5i6",
        "outputId": "45578697-6a08-4cb4-fe66-d6aa2771f5fe"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def relu(x):\n",
        "    return np.maximum(0, x)\n",
        "\n",
        "x = np.arange(-10, 10, 0.1)\n",
        "y = relu(x)\n",
        "\n",
        "plt.plot(x, y)\n",
        "plt.title('ReLU function')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oTbCX6rWi5ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "b4b88f61-49de-4616-e659-893cfb2c7e20"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def softmax(x):\n",
        "    return np.exp(x) / np.sum(np.exp(x))\n",
        "\n",
        "x = np.arange(-5, 5, 0.1)\n",
        "y = softmax(x)\n",
        "\n",
        "plt.plot(x, y)\n",
        "plt.title('softmax function')\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "\n",
        "print('\\n',softmax([1,1,2]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " [0.21194156 0.21194156 0.57611688]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XN92EMJ6uCut"
      },
      "source": [
        "# 다층 퍼셉트론 구현\n",
        "## 순전파"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o3wp7B7Fi7xt",
        "outputId": "281c9e06-ecde-40b2-8372-46ac8ea81ae4"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "# 입력층 -> 은닉층(1층)\n",
        "X = np.array([10, 20])  \n",
        "W1 = np.array([[0.1, 0.3, 0.5], [0.2, 0.4, 0.6]])  # weight 2x3개\n",
        "B1 = np.array([1, 2, 3])                           # bias 3개\n",
        "\n",
        "def sigmoid(x):                                    # 활성화 함수\n",
        "    return 1 / (1 + np.exp(-x))\n",
        "\n",
        "A1 = np.dot(X, W1) + B1\n",
        "Z1 = sigmoid(A1)\n",
        "\n",
        "print('A1 :', A1)  # 입력신호\n",
        "print('Z1 :', Z1)  # 출력신호"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A1 : [ 6. 13. 20.]\n",
            "Z1 : [0.99752738 0.99999774 1.        ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xocjoBTci7vK",
        "outputId": "054a5288-45b8-4b07-89bd-f167bdcf40b2"
      },
      "source": [
        "# 은닉층(1층) -> 출력층(2층)\n",
        "W2 = np.array([[0.1, 0.4], [0.2, 0.5], [0.3, 0.6]])  # weight 3x2개\n",
        "B2 = np.array([0.1, 0.2])                            # bias 2개\n",
        "\n",
        "A2 = np.dot(Z1, W2) + B2    # Z1 : [0.99752738 0.99999774 1.]\n",
        "Y = sigmoid(A2) \n",
        "\n",
        "print('A2 :', A2)\n",
        "print('Y  :', Y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A2 : [0.69975229 1.69900982]\n",
            "Y  : [0.66813285 0.84540537]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NZAKSHfpig52"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nWKzv2_6ihvk"
      },
      "source": [
        "# 선형 회귀 모델 학습 과정 \n",
        "## 오차역전파"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "A-Wczl_vi7s_",
        "outputId": "a83258b2-5d3d-4e66-a090-a642280ff6b1"
      },
      "source": [
        "import matplotlib.pylab as plt\n",
        "import tensorflow as tf\n",
        "\n",
        "# y = ax + b에서 a, b 찾기\n",
        "\n",
        "# 실제 data ( y = 1x+1)\n",
        "x_data = [1, 2, 3, 4, 5]\n",
        "y_data = [2, 3, 4, 5, 6]\n",
        "\n",
        "# weight와 bias 초기설정\n",
        "w = tf.Variable(2.0)\n",
        "b = tf.Variable(0.7)\n",
        "learn_rate = 0.01\n",
        "\n",
        "print(f'step|    w|    b| cost')\n",
        "print(f'----|-----|-----|-----')\n",
        "\n",
        "for i in range(1, 1101): \n",
        "    with tf.GradientTape() as tape:              # Gradient Tape(이전 연산 과정 tape에 차곡차곡 기록)\n",
        "        hypothesis = w * x_data + b              # 가설 설정\n",
        "        cost = tf.reduce_mean((hypothesis - y_data)**2) # MSE 오차계산, =tf.losses.mean_squared_error(y, y_hat)\n",
        "\n",
        "    dw, db = tape.gradient(cost, [w, b])  # cost를 w와 b에 대해 미분 \n",
        "    w.assign_sub(learn_rate * dw) # w = w - lr*dw\n",
        "    b.assign_sub(learn_rate * db) # b = b - lr*db\n",
        "    \n",
        "    if i in [1, 3, 5, 10, 1000, 1100]:\n",
        "        print(f\"{i:4d}| {w.numpy():.2f}| {b.numpy():.2f}| {cost:.2f}\")\n",
        "        plt.figure(figsize=(7, 7))\n",
        "        plt.title(f'[Step {i:d}]  h(x) = { w.numpy():.2f}x + {b.numpy():.2f}')\n",
        "        plt.plot(x_data, y_data, 'o') \n",
        "        plt.plot(x_data, w * x_data + b, 'r', label='hypothesis') \n",
        "        plt.xlabel('x_data')\n",
        "        plt.ylabel('y_data')\n",
        "        plt.xlim(0, 6)\n",
        "        plt.ylim(1, 7)\n",
        "        plt.legend(loc='best')\n",
        "        plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "step|    w|    b| cost\n",
            "----|-----|-----|-----\n",
            "   1| 1.80| 0.65| 9.29\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "   3| 1.53| 0.57| 3.19\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "   5| 1.37| 0.53| 1.12\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "  10| 1.20| 0.50| 0.12\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "1000| 1.01| 0.98| 0.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "1100| 1.00| 0.99| 0.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k16ftD_bqMa0"
      },
      "source": [
        "# 주가딥러닝"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cC2l4TwAnQfM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c417975-6276-436b-bf33-d9ed7af02004"
      },
      "source": [
        "!pip install pykrx"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pykrx\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1f/51/7e44542028474efe2ee28831b24080362f88e601045e5327c2f01c0ee7c7/pykrx-1.0.14-py3-none-any.whl (81kB)\n",
            "\u001b[K     |████████████████████████████████| 81kB 3.6MB/s \n",
            "\u001b[?25hCollecting datetime\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/73/22/a5297f3a1f92468cc737f8ce7ba6e5f245fcfafeae810ba37bd1039ea01c/DateTime-4.3-py2.py3-none-any.whl (60kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 5.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from pykrx) (2.23.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from pykrx) (1.19.5)\n",
            "Collecting deprecated\n",
            "  Downloading https://files.pythonhosted.org/packages/fb/73/994edfcba74443146c84b91921fcc269374354118d4f452fb0c54c1cbb12/Deprecated-1.2.12-py2.py3-none-any.whl\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from pykrx) (1.1.5)\n",
            "Requirement already satisfied: xlrd in /usr/local/lib/python3.7/dist-packages (from pykrx) (1.1.0)\n",
            "Collecting zope.interface\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/bb/a7/94e1a92c71436f934cdd2102826fa041c83dcb7d21dd0f1fb1a57f6e0620/zope.interface-5.4.0-cp37-cp37m-manylinux2010_x86_64.whl (251kB)\n",
            "\u001b[K     |████████████████████████████████| 256kB 34.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from datetime->pykrx) (2018.9)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->pykrx) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->pykrx) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->pykrx) (2020.12.5)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->pykrx) (1.24.3)\n",
            "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.7/dist-packages (from deprecated->pykrx) (1.12.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->pykrx) (2.8.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from zope.interface->datetime->pykrx) (56.1.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->pykrx) (1.15.0)\n",
            "Installing collected packages: zope.interface, datetime, deprecated, pykrx\n",
            "Successfully installed datetime-4.3 deprecated-1.2.12 pykrx-1.0.14 zope.interface-5.4.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvNQUA4dnQhZ"
      },
      "source": [
        "from pykrx import stock\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df = stock.get_market_ohlcv_by_date(\"20160104\", \"2021054\", \"005930\")\n",
        "dfx=df[['시가','고가','저가','거래량','종가']]\n",
        "dfx.tail(3)\n",
        "data=dfx"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "id": "pV-Nl1bKnbAz",
        "outputId": "2cb00001-aa00-4de3-c99c-506923ba4b36"
      },
      "source": [
        "#데이터정규화\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "numer=data-np.min(data,0)  # 옵션값 0은 열단위에서 구하는 최소값, 1은 행단위에서 구하는 최소값 \n",
        "deno=np.max(data,0)-np.min(data,0)\n",
        "\n",
        "dfx1=numer/(deno+1e-7)          # 오류 cover위해 분모에 작은 수 추가\n",
        "\n",
        "dfx1"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "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",
              "        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>시가</th>\n",
              "      <th>고가</th>\n",
              "      <th>저가</th>\n",
              "      <th>거래량</th>\n",
              "      <th>종가</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>날짜</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2016-01-04</th>\n",
              "      <td>0.279070</td>\n",
              "      <td>0.260331</td>\n",
              "      <td>0.269274</td>\n",
              "      <td>0.003399</td>\n",
              "      <td>0.023072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-05</th>\n",
              "      <td>0.266224</td>\n",
              "      <td>0.251653</td>\n",
              "      <td>0.265028</td>\n",
              "      <td>0.002392</td>\n",
              "      <td>0.023949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-06</th>\n",
              "      <td>0.267553</td>\n",
              "      <td>0.249587</td>\n",
              "      <td>0.261006</td>\n",
              "      <td>0.004061</td>\n",
              "      <td>0.014311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-07</th>\n",
              "      <td>0.258250</td>\n",
              "      <td>0.244421</td>\n",
              "      <td>0.257207</td>\n",
              "      <td>0.003127</td>\n",
              "      <td>0.010806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-08</th>\n",
              "      <td>0.257586</td>\n",
              "      <td>0.245041</td>\n",
              "      <td>0.259888</td>\n",
              "      <td>0.002854</td>\n",
              "      <td>0.013143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-04-28</th>\n",
              "      <td>0.921373</td>\n",
              "      <td>0.859504</td>\n",
              "      <td>0.917318</td>\n",
              "      <td>0.172710</td>\n",
              "      <td>0.870035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-04-29</th>\n",
              "      <td>0.912514</td>\n",
              "      <td>0.852273</td>\n",
              "      <td>0.910615</td>\n",
              "      <td>0.221480</td>\n",
              "      <td>0.864194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-04-30</th>\n",
              "      <td>0.906977</td>\n",
              "      <td>0.848140</td>\n",
              "      <td>0.910615</td>\n",
              "      <td>0.206777</td>\n",
              "      <td>0.861273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-05-03</th>\n",
              "      <td>0.897010</td>\n",
              "      <td>0.851240</td>\n",
              "      <td>0.905028</td>\n",
              "      <td>0.173967</td>\n",
              "      <td>0.864194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-05-04</th>\n",
              "      <td>0.906977</td>\n",
              "      <td>0.853306</td>\n",
              "      <td>0.913966</td>\n",
              "      <td>0.138778</td>\n",
              "      <td>0.877336</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1311 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  시가        고가        저가       거래량        종가\n",
              "날짜                                                          \n",
              "2016-01-04  0.279070  0.260331  0.269274  0.003399  0.023072\n",
              "2016-01-05  0.266224  0.251653  0.265028  0.002392  0.023949\n",
              "2016-01-06  0.267553  0.249587  0.261006  0.004061  0.014311\n",
              "2016-01-07  0.258250  0.244421  0.257207  0.003127  0.010806\n",
              "2016-01-08  0.257586  0.245041  0.259888  0.002854  0.013143\n",
              "...              ...       ...       ...       ...       ...\n",
              "2021-04-28  0.921373  0.859504  0.917318  0.172710  0.870035\n",
              "2021-04-29  0.912514  0.852273  0.910615  0.221480  0.864194\n",
              "2021-04-30  0.906977  0.848140  0.910615  0.206777  0.861273\n",
              "2021-05-03  0.897010  0.851240  0.905028  0.173967  0.864194\n",
              "2021-05-04  0.906977  0.853306  0.913966  0.138778  0.877336\n",
              "\n",
              "[1311 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g_IcBN41na7-"
      },
      "source": [
        "dfy1=dfx1['종가']           # dfy1에 종가데이터만 입력\n",
        "                          \n",
        "x = dfx1.values.tolist()       # 텐서플로우작업은 모두 values로 변경해야함 (numpy array로 변경)\n",
        "y = dfy1.values.tolist()"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L6QhCUkpm7Nu",
        "outputId": "73a54816-25de-42fd-dd9d-b88dbadbeb15"
      },
      "source": [
        "dfx1.values[:,[1,2]][:10]"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.26033058, 0.26927374],\n",
              "       [0.25165289, 0.26502793],\n",
              "       [0.24958678, 0.26100559],\n",
              "       [0.24442149, 0.2572067 ],\n",
              "       [0.24504132, 0.25988827],\n",
              "       [0.24090909, 0.25608939],\n",
              "       [0.24090909, 0.25564246],\n",
              "       [0.23946281, 0.25653631],\n",
              "       [0.23595041, 0.25273743],\n",
              "       [0.23801653, 0.25117318]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u2WxTnr2n7hG",
        "outputId": "c07d30ed-df5e-4d43-ada3-c78dc1187a86"
      },
      "source": [
        "print(x[0:]) \n",
        "print(y[0])   # 종가자료만"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.27906976744155143, 0.26033057851212776, 0.2692737430164589, 0.00339887048922467, 0.023072429906508363], [0.2662236987815435, 0.2516528925617235, 0.26502793296059773, 0.002391885108811546, 0.02394859813080615], [0.2675526024360271, 0.2495867768592463, 0.26100558659188716, 0.004061206134326776, 0.014310747663530504], [0.258250276854642, 0.2444214876030533, 0.25720670391032713, 0.0031270064726580072, 0.01080607476633936], [0.25758582502740024, 0.24504132231379644, 0.2598882681561342, 0.00285432302155809, 0.01314252336446679], [0.2560354374305027, 0.24090909090884202, 0.2560893854745742, 0.0026717662956765374, 0.007593457943914145], [0.2542635658911913, 0.24090909090884202, 0.25564245810027303, 0.0022842623489642325, 0.005841121495318573], [0.25537098560326094, 0.239462809917108, 0.2565363128488754, 0.0015870010752420604, 0.006425233644850431], [0.2504983388701545, 0.23595041322289675, 0.25273743016731537, 0.0023145925001342906, 0.0035046728971911438], [0.25249169435187985, 0.23801652892537395, 0.25117318435726127, 0.002319486960454541, 0.0017523364485955719], [0.24097452934635552, 0.23409090909066727, 0.24312849161984007, 0.0035458704004267573, 0.0], [0.2498338870429127, 0.24194214876008063, 0.2520670391058636, 0.0022948817775776263, 0.01314252336446679], [0.25692137320015845, 0.2396694214873557, 0.25296089385446596, 0.0018498402385032843, 0.0035046728971911438], [0.25094130675498233, 0.23863636363611712, 0.25139664804441186, 0.002107851382082089, 0.0014602803738296432], [0.2535991140639495, 0.2413223140493375, 0.2558659217874236, 0.0016336424029997397, 0.012266355140169003], [0.2595791805091256, 0.24297520661131924, 0.2583240223460801, 0.001770786952923495, 0.010514018691573432], [0.2558139534880888, 0.23904958677661256, 0.25385474860306834, 0.0016875811274792401, 0.0032126168224252153], [0.24939091915808484, 0.2427685950410715, 0.25162011173156246, 0.0030588937454411307, 0.014310747663530504], [0.2578073089698142, 0.2413223140493375, 0.25452513966452006, 0.0034982324631016065, 0.0055490654205526445], [0.25249169435187985, 0.2376033057848785, 0.24938547486005655, 0.0048389048735835586, 0.0070093457943822875], [0.255149501660847, 0.24028925619809888, 0.25720670391032713, 0.002878861763797173, 0.01080607476633936], [0.2571428571425724, 0.24090909090884202, 0.2563128491617248, 0.0018420445369977272, 0.00876168224297786], [0.25470653377601915, 0.23801652892537395, 0.25407821229021893, 0.0019323927310088633, 0.005841121495318573], [0.25470653377601915, 0.23987603305760344, 0.2565363128488754, 0.0027457590193414975, 0.00876168224297786], [0.2560354374305027, 0.2415289256195852, 0.2583240223460801, 0.0020828475553781862, 0.011098130841105289], [0.2476190476187734, 0.23491735537165814, 0.24983240223435774, 0.003380300330950779, 0.0011682242990637146], [0.25027685492774054, 0.23780991735512622, 0.2507262569829601, 0.0028373695854714315, 0.0011682242990637146], [0.2555924695456749, 0.2396694214873557, 0.25564245810027303, 0.0020280561760465153, 0.008177570093446003], [0.25647840531533056, 0.2435950413220624, 0.2585474860332307, 0.0019976595842386264, 0.012266355140169003], [0.2611295681060231, 0.24814049586751225, 0.26122905027903776, 0.00272475270434712, 0.01723130841118979], [0.2664451827239574, 0.24855371900800768, 0.26324022346339304, 0.0024636299242298755, 0.017815420560721648], [0.2629014396453345, 0.24690082644602593, 0.26234636871479067, 0.0019583599469613225, 0.018691588785019433], [0.26356589147257636, 0.24628099173528276, 0.260558659217586, 0.0019367335193471848, 0.014310747663530504], [0.2611295681060231, 0.2456611570245396, 0.2621229050276401, 0.0016352591251869717, 0.016063084112126078], [0.26090808416360917, 0.2435950413220624, 0.259441340781833, 0.0015556410941856152, 0.013434579439232718], [0.2595791805091256, 0.24524793388404417, 0.2618994413404895, 0.001479799106101013, 0.015478971962594219], [0.26135105204843706, 0.24524793388404417, 0.2618994413404895, 0.0019638080792635016, 0.013434579439232718], [0.2611295681060231, 0.2466942148757782, 0.26279329608909185, 0.0031966805548639234, 0.01518691588782829], [0.2657807308967156, 0.24938016528899856, 0.2672625698321036, 0.0026174510742493252, 0.020735981308380936], [0.2686600221480967, 0.25206611570221893, 0.2686033519550071, 0.0024249836198912476, 0.027453271027997293], [0.2702104097449942, 0.2537190082642007, 0.2686033519550071, 0.002231187352776541, 0.02599299065416765], [0.2702104097449942, 0.25433884297494386, 0.2715083798879648, 0.0014547842059574711, 0.02832943925229508], [0.270874861572236, 0.2528925619832098, 0.26502793296059773, 0.002394099796739261, 0.01927570093455129], [0.26312292358774847, 0.2477272727270168, 0.26301675977624245, 0.002030647360921942, 0.019859813084083147], [0.2675526024360271, 0.25537190082618244, 0.2683798882678566, 0.003139763075121646, 0.028913551401826935], [0.2713178294570639, 0.2588842975203937, 0.2717318435751154, 0.00295909990741829, 0.035922897196209225], [0.28062015503844895, 0.2630165289253481, 0.2791061452510848, 0.002404641711275185, 0.037675233644804795], [0.27796234772948175, 0.26115702479311864, 0.278435754189633, 0.001913578957062924, 0.03709112149527294], [0.2781838316718957, 0.26095041322287094, 0.27999999999968717, 0.001626201051562617, 0.037967289719570724], [0.28017718715362105, 0.26776859504104567, 0.2808938547482895, 0.0027336889701354504, 0.04001168224293222], [0.28305647840500214, 0.26404958677658674, 0.28223463687119305, 0.0024950009787259595, 0.042932242990591514], [0.2821705426353464, 0.26425619834683445, 0.2811173184354401, 0.0020061750593206905, 0.04117990654199594], [0.28062015503844895, 0.26425619834683445, 0.28201117318404245, 0.0022464908463570524, 0.0417640186915278], [0.2810631229232768, 0.26425619834683445, 0.28201117318404245, 0.0021860963065682623, 0.044684579439187085], [0.2832779623474161, 0.2665289256195594, 0.28290502793264477, 0.002422813225722087, 0.04556074766348487], [0.2841638981170718, 0.2665289256195594, 0.28558659217845184, 0.0015884406223950754, 0.04731308411208044], [0.28527131782914145, 0.2685950413220366, 0.2878212290499577, 0.0013423002061088232, 0.04906542056067601], [0.286600221483625, 0.2685950413220366, 0.287150837988506, 0.0019096589594308681, 0.0478971962616123], [0.2901439645622479, 0.27292355371872634, 0.29094972067006597, 0.0029633078144809485, 0.053154205607399016], [0.28925802879259216, 0.2714772727269923, 0.2900558659214636, 0.004236864107313496, 0.05432242990646273], [0.2877076411956947, 0.27045454545426606, 0.28402234636839774, 0.0030026295986375295, 0.044684579439187085], [0.2832779623474161, 0.2696280991732752, 0.28581005586560243, 0.0020823160302755345, 0.05227803738310123], [0.2877076411956947, 0.26838842975178884, 0.28156424580974126, 0.002616864181948481, 0.03913551401863444], [0.2810631229232768, 0.2667355371898071, 0.28335195530694596, 0.0020401040783732853, 0.046436915887782655], [0.2879291251381086, 0.2685950413220366, 0.2811173184354401, 0.0028657508112651, 0.0417640186915278], [0.2810631229232768, 0.2621900826443572, 0.2770949720667295, 0.0027890450948886885, 0.03504672897191144], [0.2759689922477564, 0.2626033057848527, 0.278435754189633, 0.001336486650298571, 0.04088785046723001], [0.28128460686569073, 0.26466942148732986, 0.28290502793264477, 0.001484438877309576, 0.04351635514012337], [0.2879291251381086, 0.269008264462532, 0.2880446927371083, 0.003713223293684546, 0.05081775700927159], [0.28992248061983394, 0.27066115702451377, 0.2882681564242589, 0.0015126207811897502, 0.05081775700927159], [0.28682170542603896, 0.2696280991732752, 0.2887150837985601, 0.0014226490841263257, 0.05052570093450566], [0.286600221483625, 0.26776859504104567, 0.2867039106142048, 0.0016030575627179948, 0.04731308411208044], [0.2839424141746579, 0.2685950413220366, 0.2864804469270542, 0.001904321561525075, 0.05052570093450566], [0.2879291251381086, 0.2688016528922843, 0.2878212290499577, 0.0016066342837212546, 0.04906542056067601], [0.286600221483625, 0.26735537190055025, 0.28536312849130124, 0.0012715630736976039, 0.04497663551395301], [0.28460686600189966, 0.2654958677683208, 0.28402234636839774, 0.0010182802888444706, 0.04526869158871894], [0.28460686600189966, 0.2696280991732752, 0.2867039106142048, 0.0018481681491178594, 0.04964953271020787], [0.286600221483625, 0.2685950413220366, 0.2873743016756566, 0.00183759301426302, 0.05081775700927159], [0.2879291251381086, 0.2685950413220366, 0.28178770949689186, 0.0032452818814376304, 0.04059579439246408], [0.27906976744155143, 0.2617768595038618, 0.2775418994410307, 0.003599731610828788, 0.03475467289714551], [0.27619047619017034, 0.2607438016526232, 0.2782122905024824, 0.0015563940880810383, 0.03621495327097515], [0.28062015503844895, 0.262396694214605, 0.2806703910611389, 0.0017503343099110466, 0.039427570093400366], [0.2817275747505186, 0.2665289256195594, 0.28201117318404245, 0.0032562002929212657, 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            "0.023072429906508363\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ihsvCMjun7li",
        "outputId": "8ecee534-d283-430e-cde4-3bc3245ee9fe"
      },
      "source": [
        "data_x=[]; data_y=[]\n",
        "win_size=10\n",
        "len(y) # 종가 개수"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1311"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dOg1ENo5n7qB"
      },
      "source": [
        "for i in range(len(y)-win_size):            # 1311-10=1301 (0 부터 1300까지)\n",
        "  _x=x[i:i+win_size]  # 다음 날 종가 포함 X(window 10)\n",
        "  _y=y[i+win_size]    # 다음 날 종가 포함 O(window 10 다음 날 종가) \n",
        "  data_x.append(_x) ; data_y.append(_y)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o2fUO5-vvHwC",
        "outputId": "9347d307-d575-4c10-9915-b8b87efd7685"
      },
      "source": [
        "x[0:10]"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[0.27906976744155143,\n",
              "  0.26033057851212776,\n",
              "  0.2692737430164589,\n",
              "  0.00339887048922467,\n",
              "  0.023072429906508363],\n",
              " [0.2662236987815435,\n",
              "  0.2516528925617235,\n",
              "  0.26502793296059773,\n",
              "  0.002391885108811546,\n",
              "  0.02394859813080615],\n",
              " [0.2675526024360271,\n",
              "  0.2495867768592463,\n",
              "  0.26100558659188716,\n",
              "  0.004061206134326776,\n",
              "  0.014310747663530504],\n",
              " [0.258250276854642,\n",
              "  0.2444214876030533,\n",
              "  0.25720670391032713,\n",
              "  0.0031270064726580072,\n",
              "  0.01080607476633936],\n",
              " [0.25758582502740024,\n",
              "  0.24504132231379644,\n",
              "  0.2598882681561342,\n",
              "  0.00285432302155809,\n",
              "  0.01314252336446679],\n",
              " [0.2560354374305027,\n",
              "  0.24090909090884202,\n",
              "  0.2560893854745742,\n",
              "  0.0026717662956765374,\n",
              "  0.007593457943914145],\n",
              " [0.2542635658911913,\n",
              "  0.24090909090884202,\n",
              "  0.25564245810027303,\n",
              "  0.0022842623489642325,\n",
              "  0.005841121495318573],\n",
              " [0.25537098560326094,\n",
              "  0.239462809917108,\n",
              "  0.2565363128488754,\n",
              "  0.0015870010752420604,\n",
              "  0.006425233644850431],\n",
              " [0.2504983388701545,\n",
              "  0.23595041322289675,\n",
              "  0.25273743016731537,\n",
              "  0.0023145925001342906,\n",
              "  0.0035046728971911438],\n",
              " [0.25249169435187985,\n",
              "  0.23801652892537395,\n",
              "  0.25117318435726127,\n",
              "  0.002319486960454541,\n",
              "  0.0017523364485955719]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iw39IaIcoEhS"
      },
      "source": [
        "#훈련용 데이터 테스트데이터\n",
        "train_size=int(len(data_y)*0.7)\n",
        "trainX=np.array(data_x[0:train_size])\n",
        "trainY=np.array(data_y[0:train_size])\n",
        "\n",
        "test_size=int(len(data_y)-train_size)\n",
        "testX=np.array(data_x[test_size:len(data_x)])\n",
        "testY=np.array(data_y[test_size:len(data_y)])"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "inUYOuwHoEfA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "002f4871-2f5b-41fe-c342-4f07f25254d9"
      },
      "source": [
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense, LSTM, Dropout\n",
        "\n",
        "# 모델 생성\n",
        "model = Sequential()\n",
        "model.add(LSTM(units=10, activation='relu', return_sequences=True, input_shape=(win_size, 5)))   \n",
        "# input_shape값의 10, 5는 10일간의 자료와 시가,고가,저가,거래량,종가의 5개자료\n",
        "model.add(Dropout(0.1))\n",
        "model.add(LSTM(units=10, activation='relu'))\n",
        "model.add(Dropout(0.1))\n",
        "model.add(Dense(units=1))\n",
        "model.summary()\n",
        "\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "model.fit(trainX, trainY, epochs=50, batch_size=30)\n",
        "pred_y = model.predict(testX)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm (LSTM)                  (None, 10, 10)            640       \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 10, 10)            0         \n",
            "_________________________________________________________________\n",
            "lstm_1 (LSTM)                (None, 10)                840       \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 10)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 1,491\n",
            "Trainable params: 1,491\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/50\n",
            "31/31 [==============================] - 3s 8ms/step - loss: 0.0607\n",
            "Epoch 2/50\n",
            "31/31 [==============================] - 0s 9ms/step - loss: 0.0157\n",
            "Epoch 3/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0126\n",
            "Epoch 4/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0110\n",
            "Epoch 5/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0070\n",
            "Epoch 6/50\n",
            "31/31 [==============================] - 0s 9ms/step - loss: 0.0048\n",
            "Epoch 7/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0048\n",
            "Epoch 8/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0038\n",
            "Epoch 9/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0030\n",
            "Epoch 10/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0030\n",
            "Epoch 11/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0032\n",
            "Epoch 12/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0030\n",
            "Epoch 13/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0027\n",
            "Epoch 14/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0027\n",
            "Epoch 15/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0026\n",
            "Epoch 16/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0028\n",
            "Epoch 17/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0031\n",
            "Epoch 18/50\n",
            "31/31 [==============================] - 0s 9ms/step - loss: 0.0029\n",
            "Epoch 19/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0027\n",
            "Epoch 20/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0025\n",
            "Epoch 21/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0025\n",
            "Epoch 22/50\n",
            "31/31 [==============================] - 0s 9ms/step - loss: 0.0025\n",
            "Epoch 23/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0023\n",
            "Epoch 24/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0021\n",
            "Epoch 25/50\n",
            "31/31 [==============================] - 0s 9ms/step - loss: 0.0021\n",
            "Epoch 26/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0019\n",
            "Epoch 27/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0018\n",
            "Epoch 28/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0016\n",
            "Epoch 29/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0018\n",
            "Epoch 30/50\n",
            "31/31 [==============================] - 0s 9ms/step - loss: 0.0017\n",
            "Epoch 31/50\n",
            "31/31 [==============================] - 0s 9ms/step - loss: 0.0020\n",
            "Epoch 32/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0016\n",
            "Epoch 33/50\n",
            "31/31 [==============================] - 0s 9ms/step - loss: 0.0016\n",
            "Epoch 34/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0020\n",
            "Epoch 35/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0017\n",
            "Epoch 36/50\n",
            "31/31 [==============================] - 0s 9ms/step - loss: 0.0017\n",
            "Epoch 37/50\n",
            "31/31 [==============================] - 0s 9ms/step - loss: 0.0014\n",
            "Epoch 38/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0015\n",
            "Epoch 39/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0016\n",
            "Epoch 40/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0016\n",
            "Epoch 41/50\n",
            "31/31 [==============================] - 0s 9ms/step - loss: 0.0013\n",
            "Epoch 42/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0013\n",
            "Epoch 43/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0013\n",
            "Epoch 44/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0014\n",
            "Epoch 45/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0013\n",
            "Epoch 46/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0013\n",
            "Epoch 47/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0014\n",
            "Epoch 48/50\n",
            "31/31 [==============================] - 0s 9ms/step - loss: 0.0012\n",
            "Epoch 49/50\n",
            "31/31 [==============================] - 0s 9ms/step - loss: 0.0012\n",
            "Epoch 50/50\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 0.0013\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "j6CC2e8LoEZW",
        "outputId": "415af0f4-a5f4-4e54-e2e0-b9a0ec2abc3b"
      },
      "source": [
        "# Visualising the results\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure()\n",
        "plt.plot(testY, color='red', label='real SEC stock price')\n",
        "plt.plot(pred_y, color='blue', label='predicted SEC stock price')\n",
        "plt.title('SEC stock price prediction')\n",
        "plt.xlabel('time')\n",
        "plt.ylabel('stock price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "findfont: Font family ['Malgun Gothic'] not found. Falling back to DejaVu Sans.\n",
            "findfont: Font family ['Malgun Gothic'] not found. Falling back to DejaVu Sans.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c7yhHxpZn7sW",
        "outputId": "b986f8e1-4de4-48e4-cf25-bf1789295d44"
      },
      "source": [
        "print(\"Tomorrow's SEC price(y) :\", df['종가'].iloc[-1] * pred_y[-1] / dfx1.종가.iloc[-1], 'KRW')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tomorrow's SEC price(y) : [84772.65653089] KRW\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oNIrYvWroRuD",
        "outputId": "f1a44f9d-c216-41da-e2d0-a210f4b7beda"
      },
      "source": [
        "# raw_df.close[-1] : dfy.close[-1] = y : pred_y[-1]\n",
        "print(\"Tomorrow's SEC price :\", df['종가'].iloc[-1] * pred_y[-1] / dfy1[-1:], 'KRW')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tomorrow's SEC price : 날짜\n",
            "2021-05-04    84772.656531\n",
            "Name: 종가, dtype: float64 KRW\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cslAQR6zoRvt",
        "outputId": "4047f48c-ce03-4c69-9d7e-df52ed3929f4"
      },
      "source": [
        "df['종가'].iloc[-1]"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "82600"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dyikQVKMoRyv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f7fcbeb4-c46d-483d-a335-b1c4559a53c1"
      },
      "source": [
        "dfy1[-1:]"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "날짜\n",
              "2021-05-04    0.877336\n",
              "Name: 종가, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LHFDSRAdSfLJ"
      },
      "source": [
        ""
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TCEsu6JoPScm"
      },
      "source": [
        "# 교재코드\n",
        "### 데이터 불러오기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L80WAlt9i5cy",
        "outputId": "ca0b1fea-9781-49dd-a8bf-4ac0f0ba78a8"
      },
      "source": [
        "raw_df = stock.get_market_ohlcv_by_date(\"20190504\", \"20210514\", \"005930\")\n",
        "dfx=raw_df[['시가','고가','저가','거래량','종가']]\n",
        "dfx.tail(3)\n",
        "data=dfx\n",
        "\n",
        "\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense, LSTM, Dropout\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "# from Investar import Analyzer\n",
        "\n",
        "\n",
        "# raw_df = mk.get_daily_price('삼성전자', '2018-05-04', '2020-01-22')\n",
        "\n",
        "window_size = 10 \n",
        "data_size = 5\n",
        "\n",
        "def MinMaxScaler(data):\n",
        "    \"\"\"최솟값과 최댓값을 이용하여 0 ~ 1 값으로 변환\"\"\"\n",
        "    numerator = data - np.min(data, 0)\n",
        "    denominator = np.max(data, 0) - np.min(data, 0)\n",
        "    # 0으로 나누기 에러가 발생하지 않도록 매우 작은 값(1e-7)을 더해서 나눔\n",
        "    return numerator / (denominator + 1e-7)\n",
        "\n",
        "# dfx = raw_df[['open','high','low','volume', 'close']]\n",
        "dfx = MinMaxScaler(dfx)\n",
        "dfy = dfx[['종가']]\n",
        "\n",
        "x = dfx.values.tolist()\n",
        "y = dfy.values.tolist()\n",
        "\n",
        "data_x = []\n",
        "data_y = []\n",
        "\n",
        "for i in range(len(y) - window_size):\n",
        "    _x = x[i : i + window_size] # 다음 날 종가(i+windows_size)는 포함되지 않음\n",
        "    _y = y[i + window_size]     # 다음 날 종가\n",
        "    data_x.append(_x)\n",
        "    data_y.append(_y)\n",
        "print(_x, \"->\", _y)\n",
        "\n",
        "train_size = int(len(data_y) * 0.7)\n",
        "train_x = np.array(data_x[0 : train_size])\n",
        "train_y = np.array(data_y[0 : train_size])\n",
        "\n",
        "test_size = len(data_y) - train_size\n",
        "test_x = np.array(data_x[train_size : len(data_x)])\n",
        "test_y = np.array(data_y[train_size : len(data_y)])"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.8387755102023698, 0.7388127853867784, 0.83556012332819, 0.18154256421499085, 0.8132530120465598], [0.8285714285697376, 0.7315068493137324, 0.83556012332819, 0.16608527117795088, 0.8092369477895397], [0.8102040816309996, 0.736986301368517, 0.8252826310363304, 0.13159314998806848, 0.8132530120465598], [0.8285714285697376, 0.7406392694050399, 0.8417266187033058, 0.09459898123445107, 0.83132530120315], [0.8244897959166847, 0.7351598173502554, 0.8396711202449338, 0.1471598614038524, 0.8253012048176198], [0.8265306122432111, 0.7315068493137324, 0.83556012332819, 0.1134853454460102, 0.8172690763035798], [0.8367346938758434, 0.7570776255693935, 0.8417266187033058, 0.1743720340826548, 0.8433734939742101], [0.8408163265288963, 0.7406392694050399, 0.8273381294947023, 0.2862660109216279, 0.8032128514040096], [0.8061224489779467, 0.7150684931493788, 0.8006166495358672, 0.3656096207893565, 0.7791164658618893], [0.7673469387739442, 0.6858447488571948, 0.7718396711186601, 0.31529309212752715, 0.7489959839342389]] -> [0.7811244979903993]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tS0bjPLgPVYw"
      },
      "source": [
        "### 모델돌리기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OQqY54zbi7qa",
        "outputId": "bafc7d8a-57a3-47eb-9407-16231d2766cc"
      },
      "source": [
        "# 모델 생성\n",
        "model = Sequential()\n",
        "model.add(LSTM(units=10, activation='relu', return_sequences=True, input_shape=(window_size, data_size)))\n",
        "model.add(Dropout(0.1))\n",
        "model.add(LSTM(units=10, activation='relu'))\n",
        "model.add(Dropout(0.1))\n",
        "model.add(Dense(units=1))\n",
        "model.summary()\n",
        "\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "model.fit(train_x, train_y, epochs=60, batch_size=30)\n",
        "pred_y = model.predict(test_x)\n",
        "\n",
        "# Visualising the results\n",
        "plt.figure()\n",
        "plt.plot(test_y, color='red', label='real SEC stock price')\n",
        "plt.plot(pred_y, color='blue', label='predicted SEC stock price')\n",
        "plt.title('SEC stock price prediction')\n",
        "plt.xlabel('time')\n",
        "plt.ylabel('stock price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_8 (LSTM)                (None, 10, 10)            640       \n",
            "_________________________________________________________________\n",
            "dropout_8 (Dropout)          (None, 10, 10)            0         \n",
            "_________________________________________________________________\n",
            "lstm_9 (LSTM)                (None, 10)                840       \n",
            "_________________________________________________________________\n",
            "dropout_9 (Dropout)          (None, 10)                0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 1,491\n",
            "Trainable params: 1,491\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/60\n",
            "12/12 [==============================] - 2s 8ms/step - loss: 0.0396\n",
            "Epoch 2/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0226\n",
            "Epoch 3/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0117\n",
            "Epoch 4/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0085\n",
            "Epoch 5/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0066\n",
            "Epoch 6/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0088\n",
            "Epoch 7/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0087\n",
            "Epoch 8/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0056\n",
            "Epoch 9/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0063\n",
            "Epoch 10/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0082\n",
            "Epoch 11/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0057\n",
            "Epoch 12/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0045\n",
            "Epoch 13/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0052\n",
            "Epoch 14/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0051\n",
            "Epoch 15/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0039\n",
            "Epoch 16/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0044\n",
            "Epoch 17/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0052\n",
            "Epoch 18/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0044\n",
            "Epoch 19/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0052\n",
            "Epoch 20/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0039\n",
            "Epoch 21/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0036\n",
            "Epoch 22/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0041\n",
            "Epoch 23/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0040\n",
            "Epoch 24/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0031\n",
            "Epoch 25/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0035\n",
            "Epoch 26/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0032\n",
            "Epoch 27/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0027\n",
            "Epoch 28/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0035\n",
            "Epoch 29/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0040\n",
            "Epoch 30/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0026\n",
            "Epoch 31/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0026\n",
            "Epoch 32/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0030\n",
            "Epoch 33/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0027\n",
            "Epoch 34/60\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.0029\n",
            "Epoch 35/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0037\n",
            "Epoch 36/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0024\n",
            "Epoch 37/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0038\n",
            "Epoch 38/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0030\n",
            "Epoch 39/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0027\n",
            "Epoch 40/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0029\n",
            "Epoch 41/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0033\n",
            "Epoch 42/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0030\n",
            "Epoch 43/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0030\n",
            "Epoch 44/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0034\n",
            "Epoch 45/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0037\n",
            "Epoch 46/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0029\n",
            "Epoch 47/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0029\n",
            "Epoch 48/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0026\n",
            "Epoch 49/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0032\n",
            "Epoch 50/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0035\n",
            "Epoch 51/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0029\n",
            "Epoch 52/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0027\n",
            "Epoch 53/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0035\n",
            "Epoch 54/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0027\n",
            "Epoch 55/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0027\n",
            "Epoch 56/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0031\n",
            "Epoch 57/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0028\n",
            "Epoch 58/60\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.0026\n",
            "Epoch 59/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0024\n",
            "Epoch 60/60\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mKWeWZkji7nq",
        "outputId": "db371330-cef6-4482-a734-eff082ebe6cd"
      },
      "source": [
        "# raw_df.close[-1] : dfy.close[-1] = x : pred_y[-1]\n",
        "print(\"Tomorrow's SEC price :\", raw_df['종가'].iloc[-1] * pred_y[-1] / dfy[-1:], 'KRW')"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tomorrow's SEC price :                       종가\n",
            "날짜                      \n",
            "2021-05-14  90121.043354 KRW\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "id": "hXbHHdldi5aY",
        "outputId": "fd17fa67-1f5d-4e9a-8d38-491cf1c4f4db"
      },
      "source": [
        "raw_df['종가'].iloc[-1] * pred_y[-1] / dfy[-1:]"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "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",
              "        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>종가</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>날짜</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-22</th>\n",
              "      <td>66355.704729</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      종가\n",
              "날짜                      \n",
              "2020-01-22  66355.704729"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0JU9-b76oySG"
      },
      "source": [
        "# 다른기간"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0krPd5W2oaBo",
        "outputId": "7ab98c1b-ffee-4388-90e4-c3f920c40a5f"
      },
      "source": [
        "raw_df = stock.get_market_ohlcv_by_date(\"20180504\", \"20210507\", \"005930\")\n",
        "dfx=raw_df[['시가','고가','저가','거래량','종가']]\n",
        "dfx.tail(3)\n",
        "data=dfx\n",
        "\n",
        "\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense, LSTM, Dropout\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "# from Investar import Analyzer\n",
        "\n",
        "\n",
        "# raw_df = mk.get_daily_price('삼성전자', '2018-05-04', '2021-05-10')\n",
        "\n",
        "window_size = 10 \n",
        "data_size = 5\n",
        "\n",
        "def MinMaxScaler(data):\n",
        "    \"\"\"최솟값과 최댓값을 이용하여 0 ~ 1 값으로 변환\"\"\"\n",
        "    numerator = data - np.min(data, 0)\n",
        "    denominator = np.max(data, 0) - np.min(data, 0)\n",
        "    # 0으로 나누기 에러가 발생하지 않도록 매우 작은 값(1e-7)을 더해서 나눔\n",
        "    return numerator / (denominator + 1e-7)\n",
        "\n",
        "# dfx = raw_df[['open','high','low','volume', 'close']]\n",
        "dfx = MinMaxScaler(dfx)\n",
        "dfy = dfx[['종가']]\n",
        "\n",
        "x = dfx.values.tolist()\n",
        "y = dfy.values.tolist()\n",
        "\n",
        "data_x = []\n",
        "data_y = []\n",
        "\n",
        "for i in range(len(y) - window_size):\n",
        "    _x = x[i : i + window_size] # 다음 날 종가(i+windows_size)는 포함되지 않음\n",
        "    _y = y[i + window_size]     # 다음 날 종가\n",
        "    data_x.append(_x)\n",
        "    data_y.append(_y)\n",
        "print(_x, \"->\", _y)\n",
        "\n",
        "train_size = int(len(data_y) * 0.7)\n",
        "train_x = np.array(data_x[0 : train_size])\n",
        "train_y = np.array(data_y[0 : train_size])\n",
        "\n",
        "test_size = len(data_y) - train_size\n",
        "test_x = np.array(data_x[train_size : len(data_x)])\n",
        "test_y = np.array(data_y[train_size : len(data_y)])\n",
        "\n",
        "# 모델 생성\n",
        "model = Sequential()\n",
        "model.add(LSTM(units=10, activation='relu', return_sequences=True, input_shape=(window_size, data_size)))\n",
        "model.add(Dropout(0.1))\n",
        "model.add(LSTM(units=10, activation='relu'))\n",
        "model.add(Dropout(0.1))\n",
        "model.add(Dense(units=1))\n",
        "model.summary()\n",
        "\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "model.fit(train_x, train_y, epochs=60, batch_size=30)\n",
        "pred_y = model.predict(test_x)\n",
        "\n",
        "# Visualising the results\n",
        "plt.figure()\n",
        "plt.plot(test_y, color='red', label='real SEC stock price')\n",
        "plt.plot(pred_y, color='blue', label='predicted SEC stock price')\n",
        "plt.title('SEC stock price prediction')\n",
        "plt.xlabel('time')\n",
        "plt.ylabel('stock price')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# raw_df.close[-1] : dfy.close[-1] = x : pred_y[-1]\n",
        "print(\"Tomorrow's SEC price :\", raw_df['종가'].iloc[-1] * pred_y[-1] / dfy[-1:], 'KRW')"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.8599810785224976, 0.7668918918905965, 0.8651471984788885, 0.11092263406999359, 0.8394024276361542], [0.8410596026474152, 0.7652027027014101, 0.8499525166175689, 0.15597909448893402, 0.8468720821646183], [0.8599810785224976, 0.7753378378365281, 0.8689458689442185, 0.1290273885412751, 0.8599439775894305], [0.8656575212850224, 0.7719594594581555, 0.8670465337115536, 0.09936015335848986, 0.8487394957967344], [0.8656575212850224, 0.7702702702689691, 0.8594491927808937, 0.1302709443063361, 0.8338001867398062], [0.8505203405849564, 0.7584459459446647, 0.8480531813849039, 0.18154256421499085, 0.826330532211342], [0.8410596026474152, 0.7516891891879194, 0.8480531813849039, 0.16608527117795088, 0.82259570494711], [0.824030274359841, 0.7567567567554785, 0.8385565052215792, 0.13159314998806848, 0.826330532211342], [0.8410596026474152, 0.7601351351338511, 0.8537511870828989, 0.09459898123445107, 0.8431372549003863], [0.8372753074723988, 0.7550675675662921, 0.8518518518502339, 0.1471598614038524, 0.8375350140040382]] -> [0.8300653594755741]\n",
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_4 (LSTM)                (None, 10, 10)            640       \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 10, 10)            0         \n",
            "_________________________________________________________________\n",
            "lstm_5 (LSTM)                (None, 10)                840       \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 10)                0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 1,491\n",
            "Trainable params: 1,491\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/60\n",
            "17/17 [==============================] - 2s 6ms/step - loss: 0.0305\n",
            "Epoch 2/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0079\n",
            "Epoch 3/60\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 0.0046\n",
            "Epoch 4/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0037\n",
            "Epoch 5/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0035\n",
            "Epoch 6/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0022\n",
            "Epoch 7/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0021\n",
            "Epoch 8/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0026\n",
            "Epoch 9/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0027\n",
            "Epoch 10/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0023\n",
            "Epoch 11/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0019\n",
            "Epoch 12/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0019\n",
            "Epoch 13/60\n",
            "17/17 [==============================] - 0s 5ms/step - loss: 0.0017\n",
            "Epoch 14/60\n",
            "17/17 [==============================] - 0s 5ms/step - loss: 0.0022\n",
            "Epoch 15/60\n",
            "17/17 [==============================] - 0s 5ms/step - loss: 0.0017\n",
            "Epoch 16/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0017\n",
            "Epoch 17/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0020\n",
            "Epoch 18/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0018\n",
            "Epoch 19/60\n",
            "17/17 [==============================] - 0s 5ms/step - loss: 0.0018\n",
            "Epoch 20/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0017\n",
            "Epoch 21/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0014\n",
            "Epoch 22/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0018\n",
            "Epoch 23/60\n",
            "17/17 [==============================] - 0s 5ms/step - loss: 0.0018\n",
            "Epoch 24/60\n",
            "17/17 [==============================] - 0s 5ms/step - loss: 0.0019\n",
            "Epoch 25/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0015\n",
            "Epoch 26/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0013\n",
            "Epoch 27/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0017\n",
            "Epoch 28/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0017\n",
            "Epoch 29/60\n",
            "17/17 [==============================] - 0s 5ms/step - loss: 0.0014\n",
            "Epoch 30/60\n",
            "17/17 [==============================] - 0s 5ms/step - loss: 0.0016\n",
            "Epoch 31/60\n",
            "17/17 [==============================] - 0s 5ms/step - loss: 0.0017\n",
            "Epoch 32/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0014\n",
            "Epoch 33/60\n",
            "17/17 [==============================] - 0s 5ms/step - loss: 0.0014\n",
            "Epoch 34/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0014\n",
            "Epoch 35/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0017\n",
            "Epoch 36/60\n",
            "17/17 [==============================] - 0s 5ms/step - loss: 0.0014\n",
            "Epoch 37/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0012\n",
            "Epoch 38/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0015\n",
            "Epoch 39/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0014\n",
            "Epoch 40/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0011\n",
            "Epoch 41/60\n",
            "17/17 [==============================] - 0s 5ms/step - loss: 0.0013\n",
            "Epoch 42/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0012\n",
            "Epoch 43/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0011\n",
            "Epoch 44/60\n",
            "17/17 [==============================] - 0s 5ms/step - loss: 0.0015\n",
            "Epoch 45/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0011\n",
            "Epoch 46/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0011\n",
            "Epoch 47/60\n",
            "17/17 [==============================] - 0s 5ms/step - loss: 0.0012\n",
            "Epoch 48/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0013\n",
            "Epoch 49/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0014\n",
            "Epoch 50/60\n",
            "17/17 [==============================] - 0s 5ms/step - loss: 0.0011\n",
            "Epoch 51/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0011\n",
            "Epoch 52/60\n",
            "17/17 [==============================] - 0s 5ms/step - loss: 0.0010\n",
            "Epoch 53/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0011\n",
            "Epoch 54/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0012\n",
            "Epoch 55/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0012\n",
            "Epoch 56/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 9.8944e-04\n",
            "Epoch 57/60\n",
            "17/17 [==============================] - 0s 5ms/step - loss: 0.0011\n",
            "Epoch 58/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 9.2573e-04\n",
            "Epoch 59/60\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0011\n",
            "Epoch 60/60\n",
            "17/17 [==============================] - 0s 5ms/step - loss: 0.0010\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Tomorrow's SEC price :                       종가\n",
            "날짜                      \n",
            "2021-05-07  80680.997576 KRW\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vATKgqwRoZ_P",
        "outputId": "f5c16bee-a821-45a8-b538-e9c334ae20b0"
      },
      "source": [
        "# raw_df.close[-1] : dfy.close[-1] = x : pred_y[-1]\n",
        "print(\"Tomorrow's SEC price :\", raw_df['종가'].iloc[-1] * pred_y[-1] / dfy[-1:], 'KRW')"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tomorrow's SEC price :                       종가\n",
            "날짜                      \n",
            "2021-05-07  80680.997576 KRW\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VUQaFBT6w1Oj"
      },
      "source": [
        ""
      ],
      "execution_count": 24,
      "outputs": []
    }
  ]
}