FIntech_n23Video.ipynb

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  "metadata": {
    "colab": {
      "name": "FIntech_n23Video.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMiRKJqpmmNjeRjL0x0Vm2R",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/imji0319/PDSH/blob/master/FIntech_n23Video.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rSlIaSj2mLXL",
        "colab_type": "text"
      },
      "source": [
        "# \"머신러닝을 활용한 트레이딩 모델 개발\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kDfpr-8WoTWq",
        "colab_type": "text"
      },
      "source": [
        "### 코랩에 기술적 분석을 위한 talib + FinanceDataReader 설치 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jAIQ-rEjnK3H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03b5e4c1-2b69-40e1-9596-38ccaffd88d9"
      },
      "source": [
        "!wget https://launchpad.net/~mario-mariomedina/+archive/ubuntu/talib/+files/libta-lib0_0.4.0-oneiric1_amd64.deb -qO libta.deb\n",
        "!wget https://launchpad.net/~mario-mariomedina/+archive/ubuntu/talib/+files/ta-lib0-dev_0.4.0-oneiric1_amd64.deb -qO ta.deb\n",
        "!dpkg -i libta.deb ta.deb\n",
        "!pip install ta-lib\n",
        "import talib"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Selecting previously unselected package libta-lib0.\n",
            "(Reading database ... \r(Reading database ... 5%\r(Reading database ... 10%\r(Reading database ... 15%\r(Reading database ... 20%\r(Reading database ... 25%\r(Reading database ... 30%\r(Reading database ... 35%\r(Reading database ... 40%\r(Reading database ... 45%\r(Reading database ... 50%\r(Reading database ... 55%\r(Reading database ... 60%\r(Reading database ... 65%\r(Reading database ... 70%\r(Reading database ... 75%\r(Reading database ... 80%\r(Reading database ... 85%\r(Reading database ... 90%\r(Reading database ... 95%\r(Reading database ... 100%\r(Reading database ... 144465 files and directories currently installed.)\n",
            "Preparing to unpack libta.deb ...\n",
            "Unpacking libta-lib0 (0.4.0-oneiric1) ...\n",
            "Selecting previously unselected package ta-lib0-dev.\n",
            "Preparing to unpack ta.deb ...\n",
            "Unpacking ta-lib0-dev (0.4.0-oneiric1) ...\n",
            "Setting up libta-lib0 (0.4.0-oneiric1) ...\n",
            "Setting up ta-lib0-dev (0.4.0-oneiric1) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/python3.6/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n",
            "Collecting ta-lib\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d7/15/ebf1a6ebcf7580dc53e342979c7425affc38ef272bc10a7f4565ee87b394/TA-Lib-0.4.18.tar.gz (265kB)\n",
            "\u001b[K     |████████████████████████████████| 266kB 3.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from ta-lib) (1.18.5)\n",
            "Building wheels for collected packages: ta-lib\n",
            "  Building wheel for ta-lib (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ta-lib: filename=TA_Lib-0.4.18-cp36-cp36m-linux_x86_64.whl size=1531828 sha256=c87774d280eb8b3fcb06823112eb341b146e3edf625ff82ebda508a5819b49a6\n",
            "  Stored in directory: /root/.cache/pip/wheels/11/cc/88/20dc42c28a07efb297dad4303948ef6dd4a3c7257553db8f09\n",
            "Successfully built ta-lib\n",
            "Installing collected packages: ta-lib\n",
            "Successfully installed ta-lib-0.4.18\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yhQMO-pZpRfN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip -q install finance-datareader"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p_XflCxonsuW",
        "colab_type": "text"
      },
      "source": [
        "## talib Package Example"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Canqjw7l8G9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import talib as ta\n",
        "import FinanceDataReader as fdr\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CyWJwGZ-pg3I",
        "colab_type": "text"
      },
      "source": [
        "FinanceDataReader 를 이용하면 주가 데이터를 쉽게 가지고 올 수 있음.  \n",
        "기본 주가 데이터와 함께 Change = 주가수익률 데이터 수집할 수 있음. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uOD0FocaolWE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "33497896-4c4e-419d-c755-99a80d654abe"
      },
      "source": [
        "Df = fdr.DataReader('005930', '2017')\n",
        "Df"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
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              "</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",
              "      <th>Volume</th>\n",
              "      <th>Change</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-01-02</th>\n",
              "      <td>35980</td>\n",
              "      <td>36240</td>\n",
              "      <td>35880</td>\n",
              "      <td>36100</td>\n",
              "      <td>93012</td>\n",
              "      <td>0.001665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-03</th>\n",
              "      <td>36280</td>\n",
              "      <td>36620</td>\n",
              "      <td>36020</td>\n",
              "      <td>36480</td>\n",
              "      <td>147153</td>\n",
              "      <td>0.010526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-04</th>\n",
              "      <td>36500</td>\n",
              "      <td>36520</td>\n",
              "      <td>36100</td>\n",
              "      <td>36160</td>\n",
              "      <td>159435</td>\n",
              "      <td>-0.008772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-05</th>\n",
              "      <td>36060</td>\n",
              "      <td>36060</td>\n",
              "      <td>35540</td>\n",
              "      <td>35560</td>\n",
              "      <td>219349</td>\n",
              "      <td>-0.016593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-06</th>\n",
              "      <td>36180</td>\n",
              "      <td>36440</td>\n",
              "      <td>36040</td>\n",
              "      <td>36200</td>\n",
              "      <td>177619</td>\n",
              "      <td>0.017998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-22</th>\n",
              "      <td>55300</td>\n",
              "      <td>55500</td>\n",
              "      <td>54700</td>\n",
              "      <td>54700</td>\n",
              "      <td>12885057</td>\n",
              "      <td>-0.010850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-23</th>\n",
              "      <td>54700</td>\n",
              "      <td>54700</td>\n",
              "      <td>53800</td>\n",
              "      <td>54100</td>\n",
              "      <td>16214932</td>\n",
              "      <td>-0.010969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-24</th>\n",
              "      <td>54000</td>\n",
              "      <td>54400</td>\n",
              "      <td>53700</td>\n",
              "      <td>54200</td>\n",
              "      <td>10994535</td>\n",
              "      <td>0.001848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-27</th>\n",
              "      <td>54300</td>\n",
              "      <td>55700</td>\n",
              "      <td>54300</td>\n",
              "      <td>55600</td>\n",
              "      <td>21054421</td>\n",
              "      <td>0.025830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-28</th>\n",
              "      <td>57000</td>\n",
              "      <td>58800</td>\n",
              "      <td>56400</td>\n",
              "      <td>58600</td>\n",
              "      <td>48017391</td>\n",
              "      <td>0.053957</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>876 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             Open   High    Low  Close    Volume    Change\n",
              "Date                                                      \n",
              "2017-01-02  35980  36240  35880  36100     93012  0.001665\n",
              "2017-01-03  36280  36620  36020  36480    147153  0.010526\n",
              "2017-01-04  36500  36520  36100  36160    159435 -0.008772\n",
              "2017-01-05  36060  36060  35540  35560    219349 -0.016593\n",
              "2017-01-06  36180  36440  36040  36200    177619  0.017998\n",
              "...           ...    ...    ...    ...       ...       ...\n",
              "2020-07-22  55300  55500  54700  54700  12885057 -0.010850\n",
              "2020-07-23  54700  54700  53800  54100  16214932 -0.010969\n",
              "2020-07-24  54000  54400  53700  54200  10994535  0.001848\n",
              "2020-07-27  54300  55700  54300  55600  21054421  0.025830\n",
              "2020-07-28  57000  58800  56400  58600  48017391  0.053957\n",
              "\n",
              "[876 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XlFYJpK8o5he",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "3e1893be-6862-46b9-efc6-6cd0693e6ca6"
      },
      "source": [
        "Df = fdr.DataReader('AAPL', '2017-01-01', '2018-01-01')\n",
        "print(Df.shape)\n",
        "Df.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(251, 6)\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",
              "        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>Close</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Change</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-01-03</th>\n",
              "      <td>116.15</td>\n",
              "      <td>115.80</td>\n",
              "      <td>116.33</td>\n",
              "      <td>114.76</td>\n",
              "      <td>28780000.0</td>\n",
              "      <td>0.0028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-04</th>\n",
              "      <td>116.02</td>\n",
              "      <td>115.85</td>\n",
              "      <td>116.51</td>\n",
              "      <td>115.75</td>\n",
              "      <td>21120000.0</td>\n",
              "      <td>-0.0011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-05</th>\n",
              "      <td>116.61</td>\n",
              "      <td>115.92</td>\n",
              "      <td>116.86</td>\n",
              "      <td>115.81</td>\n",
              "      <td>22190000.0</td>\n",
              "      <td>0.0051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-06</th>\n",
              "      <td>117.91</td>\n",
              "      <td>116.78</td>\n",
              "      <td>118.16</td>\n",
              "      <td>116.47</td>\n",
              "      <td>31750000.0</td>\n",
              "      <td>0.0111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-09</th>\n",
              "      <td>118.99</td>\n",
              "      <td>117.95</td>\n",
              "      <td>119.43</td>\n",
              "      <td>117.94</td>\n",
              "      <td>33560000.0</td>\n",
              "      <td>0.0092</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Close    Open    High     Low      Volume  Change\n",
              "Date                                                          \n",
              "2017-01-03  116.15  115.80  116.33  114.76  28780000.0  0.0028\n",
              "2017-01-04  116.02  115.85  116.51  115.75  21120000.0 -0.0011\n",
              "2017-01-05  116.61  115.92  116.86  115.81  22190000.0  0.0051\n",
              "2017-01-06  117.91  116.78  118.16  116.47  31750000.0  0.0111\n",
              "2017-01-09  118.99  117.95  119.43  117.94  33560000.0  0.0092"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z3lnBCydq4YI",
        "colab_type": "text"
      },
      "source": [
        "#### 이동평균"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_YiNRiCkqBNc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0f56a0b8-c51e-4a5e-d033-f24b0452a974"
      },
      "source": [
        "%timeit pd_MA = Df['Close'].rolling(window=5).mean(). #Pandas 활용"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1000 loops, best of 3: 477 µs per loop\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vyPNe93Tqfwg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "49d05cc1-85c4-4c3b-d97e-0f4a6c179f0f"
      },
      "source": [
        "%timeit Ta_MA = ta.MA(Df['Close'], timeperiod = 5 )   #talib 활용 -> pandas에 비해 속도가 빠름"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The slowest run took 7.80 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
            "10000 loops, best of 3: 66.4 µs per loop\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9sFsDno5svxs",
        "colab_type": "text"
      },
      "source": [
        "### 볼린저밴드 그래프 그리기\n",
        "\n",
        "`ta.BBANDS()` 를 활용하여 upper, middle, lower 값을 쉽게 구할 수 있음.  \n",
        "중심선 middle : 이동평균   \n",
        "상단선 upper : 이동평균 + 표준편차 * 2   \n",
        "하단선 lower : 이동평균 - 표준편차 * 2  \n",
        "\n",
        "[볼린저밴드](https://namu.wiki/w/볼린저%20밴드)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mikhdUlZqyLl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "upper, middle, lower = ta.BBANDS(Df['Close'], \n",
        "                                 timeperiod = 10, #2주 가정 -> 10 Days\n",
        "                                 nbdevup = 2,     # +2 표준편차\n",
        "                                 nbdevdn = 2)     # -2 표준편차"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qm-hjsKrrmSK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "2d4f9f3b-ce97-41ca-d847-a57f2931c5b6"
      },
      "source": [
        "plt.figure(figsize = (10, 5))\n",
        "plt.plot(Df['Close'], 'r-')\n",
        "plt.plot(middle, 'b-')\n",
        "plt.fill_between(Df.index, lower, upper, alpha=0.5)\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YL6Mn7CLuPbZ",
        "colab_type": "text"
      },
      "source": [
        "## GLD 골드 ETF example"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1hwgIX8ksagT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "1dcdc9b5-7ca9-4356-dc40-cf78e4edb2ee"
      },
      "source": [
        "Df = fdr.DataReader('GLD', '2015-01-01', \n",
        "                    '2018-01-01')\n",
        "print(Df.shape)\n",
        "Df.head()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(755, 6)\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",
              "        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>Close</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Change</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-01-02</th>\n",
              "      <td>114.08</td>\n",
              "      <td>112.49</td>\n",
              "      <td>114.80</td>\n",
              "      <td>112.32</td>\n",
              "      <td>7110000.0</td>\n",
              "      <td>0.0044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-05</th>\n",
              "      <td>115.80</td>\n",
              "      <td>114.78</td>\n",
              "      <td>116.00</td>\n",
              "      <td>114.73</td>\n",
              "      <td>8180000.0</td>\n",
              "      <td>0.0151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-06</th>\n",
              "      <td>117.12</td>\n",
              "      <td>116.22</td>\n",
              "      <td>117.50</td>\n",
              "      <td>115.80</td>\n",
              "      <td>11240000.0</td>\n",
              "      <td>0.0114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-07</th>\n",
              "      <td>116.43</td>\n",
              "      <td>116.47</td>\n",
              "      <td>116.88</td>\n",
              "      <td>116.17</td>\n",
              "      <td>6430000.0</td>\n",
              "      <td>-0.0059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-08</th>\n",
              "      <td>115.94</td>\n",
              "      <td>116.45</td>\n",
              "      <td>116.87</td>\n",
              "      <td>115.85</td>\n",
              "      <td>7030000.0</td>\n",
              "      <td>-0.0042</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Close    Open    High     Low      Volume  Change\n",
              "Date                                                          \n",
              "2015-01-02  114.08  112.49  114.80  112.32   7110000.0  0.0044\n",
              "2015-01-05  115.80  114.78  116.00  114.73   8180000.0  0.0151\n",
              "2015-01-06  117.12  116.22  117.50  115.80  11240000.0  0.0114\n",
              "2015-01-07  116.43  116.47  116.88  116.17   6430000.0 -0.0059\n",
              "2015-01-08  115.94  116.45  116.87  115.85   7030000.0 -0.0042"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sZeUyfsIuf9W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "a86db278-5288-4561-e288-152e5870fc0f"
      },
      "source": [
        "Df['Close'].plot()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f53e7f908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "uKrtN7DJvoUb",
        "colab_type": "text"
      },
      "source": [
        "### SVM(Soft Vector Machine) model 과 RandomForest **model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rAE-FAMcu99t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pIcFEEFlwRPQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n=10  # 2주 기간\n",
        "\n",
        "Df['RSI'] = ta.RSI( Df['Close'].shift(1), timeperiod=n)\n",
        "Df['SMA'] = ta.MA(Df['Close'].shift(1), \n",
        "                   timeperiod= n )\n",
        "Df['ADX'] = ta.ADX(Df['High'].shift(1), \n",
        "                   Df['Low'].shift(1),\n",
        "                   Df['Close'].shift(1),\n",
        "                   timeperiod= n )\n",
        "Df['SAR'] = ta.SAR(Df['High'].shift(1), \n",
        "                   Df['Low'].shift(1),\n",
        "                   0.02, 0.2)\n",
        "\n",
        "Df['OO'] = Df['Open'] - Df['Open'].shift(1)\n",
        "Df['OC'] = Df['Open'] - Df['Close'].shift(1)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5RMpsH120JzL",
        "colab_type": "text"
      },
      "source": [
        "* [RSI](https://ko.wikipedia.org/wiki/RSI_(투자지표)) : Relative Strength Index : 가격의 상승압력과 하락압력 간의 상대적인 강도\n",
        "* [SMA](https://medium.com/@igniter.yoo/기술적-분석의-기초-basics-of-technical-analysis-10-이동-평균-46b0d8ddf576) : Simple Moving Average : 단순이동평균, 모든 종가를 더해 총일로 나눈 값 \n",
        "* [ADX](http://blog.naver.com/PostView.nhn?blogId=liebeljd&logNo=60107680352&parentCategoryNo=&categoryNo=273&viewDate=&isShowPopularPosts=true&from=search) : Average directional movement index : 평균 방향 지수, 추세의 강도를 보여줌 \n",
        "* [SAR](https://academy.binance.com/ko/economics/a-brief-guide-to-the-parabolic-sar-indicator) : Stop and Reverse Index : 파라볼릭 SAR, 시장 추세와 잠재적 전환 시점을 파악하기 위해 사용되는 지표 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uf7d8P0_xZXE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "split = int(0.8 * len(Df))\n",
        "\n",
        "Df['Ret'] = np.log(Df['Close'].shift(-5)/Df['Close']) # Return : 향후 5일 뒤의 로그 수익률 \n",
        "Df['Signal'] = 0\n",
        "Df.loc[Df['Ret'] > Df['Ret'][:split].quantile(0.66),\n",
        "       'Signal'] = 1\n",
        "Df.loc[Df['Ret'] < Df['Ret'][:split].quantile(0.33),\n",
        "       'Signal'] = -1\n",
        "       "
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y0kM-rfWvMRf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "3c9b55c7-4c5f-44f6-d456-e375bc61ba53"
      },
      "source": [
        "Df.head()"
      ],
      "execution_count": 53,
      "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>Close</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Change</th>\n",
              "      <th>RSI</th>\n",
              "      <th>SMA</th>\n",
              "      <th>ADX</th>\n",
              "      <th>SAR</th>\n",
              "      <th>OO</th>\n",
              "      <th>OC</th>\n",
              "      <th>Ret</th>\n",
              "      <th>Signal</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></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>2015-01-02</th>\n",
              "      <td>114.08</td>\n",
              "      <td>112.49</td>\n",
              "      <td>114.80</td>\n",
              "      <td>112.32</td>\n",
              "      <td>7110000.0</td>\n",
              "      <td>0.0044</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.027494</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-05</th>\n",
              "      <td>115.80</td>\n",
              "      <td>114.78</td>\n",
              "      <td>116.00</td>\n",
              "      <td>114.73</td>\n",
              "      <td>8180000.0</td>\n",
              "      <td>0.0151</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.29</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.023555</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-06</th>\n",
              "      <td>117.12</td>\n",
              "      <td>116.22</td>\n",
              "      <td>117.50</td>\n",
              "      <td>115.80</td>\n",
              "      <td>11240000.0</td>\n",
              "      <td>0.0114</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112.320000</td>\n",
              "      <td>1.44</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.008841</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-07</th>\n",
              "      <td>116.43</td>\n",
              "      <td>116.47</td>\n",
              "      <td>116.88</td>\n",
              "      <td>116.17</td>\n",
              "      <td>6430000.0</td>\n",
              "      <td>-0.0059</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112.393600</td>\n",
              "      <td>0.25</td>\n",
              "      <td>-0.65</td>\n",
              "      <td>0.013140</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-08</th>\n",
              "      <td>115.94</td>\n",
              "      <td>116.45</td>\n",
              "      <td>116.87</td>\n",
              "      <td>115.85</td>\n",
              "      <td>7030000.0</td>\n",
              "      <td>-0.0042</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112.597856</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.042222</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Close    Open    High     Low  ...    OO    OC       Ret  Signal\n",
              "Date                                        ...                              \n",
              "2015-01-02  114.08  112.49  114.80  112.32  ...   NaN   NaN  0.027494       1\n",
              "2015-01-05  115.80  114.78  116.00  114.73  ...  2.29  0.70  0.023555       1\n",
              "2015-01-06  117.12  116.22  117.50  115.80  ...  1.44  0.42  0.008841       1\n",
              "2015-01-07  116.43  116.47  116.88  116.17  ...  0.25 -0.65  0.013140       1\n",
              "2015-01-08  115.94  116.45  116.87  115.85  ... -0.02  0.02  0.042222       1\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6FzkMViBoai_",
        "colab_type": "text"
      },
      "source": [
        "로그수익률의 상위 2/3보다 크면 1을 주고 1/3보다 작으면 -1을 준다. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GUxCgc205i8y",
        "colab_type": "text"
      },
      "source": [
        "[로그 수익률](https://insightcampus.co.kr/pt06/) : 금융 분야에서 주로 사용되는 수익률"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hK450TOD48bh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Df = Df.dropna()\n",
        "X = Df.drop(['Open','High','Low','Close','Volume','Change','Ret','Signal'],\n",
        "            axis=1)\n",
        "y = Df['Signal']"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RN1i7U6P5t3q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = X[:split]\n",
        "y_train = y[:split]\n",
        "\n",
        "X_test = X[split:]\n",
        "y_test = y[split:]"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zN8-cu0dphWS",
        "colab_type": "text"
      },
      "source": [
        "#### 정규화\n",
        "\n",
        "각 Feature 들의 범위가 다양할 경우 모델 트레이닝 하는 게 어려움.   \n",
        "이를 해결하기 위해 \"정규화\" 처리"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3o_BrS-ZpMES",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "21134b64-2d5f-43c6-fc34-ce3bebb337b4"
      },
      "source": [
        "scaler = StandardScaler()\n",
        "scaler.fit(X_train)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StandardScaler(copy=True, with_mean=True, with_std=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wywztIWnp1Du",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = SVC()"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E1ZDOAXbp-rf",
        "colab_type": "text"
      },
      "source": [
        "#### Soft Vector Machine\n",
        "\n",
        "SVC 모델은 다양한 hyper parameter 들이 존재.  \n",
        "파라메터들을 최적화하는 과정이 필요.  \n",
        "CloseVaildation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "36qn9-Qsp-Rk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fa56cceb-aaf9-4da6-a293-c3b82efc6657"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "params = {'C' : np.arange(1,30)}\n",
        "#rcv = RandomizedSearchCV(model, params, cv = 10)\n",
        "rcv = GridSearchCV(model, params, cv = 10 )\n",
        "rcv.fit(scaler.transform(X_train), y_train)\n",
        "rcv.best_params_['C']"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CI7Sp9Azvq5s",
        "colab_type": "text"
      },
      "source": [
        "HyperParameter C 의 값은 15"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KJuT8rJmrMrA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = SVC(C=15)"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B5xXIFxovxPg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "0a993706-5114-49c9-ca2a-111135376801"
      },
      "source": [
        "model.fit(scaler.transform(X_train), y_train)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=15, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xsxeO3Bpv6xH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predict = model.predict(scaler.transform(X_test)) # 모델 예측"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gf6oErq9wC3l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Df['Signal'][split:] = predict"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Z4Y9AkAwuXO",
        "colab_type": "text"
      },
      "source": [
        "Trading Model의 경우 정확도 보다는 예측값을 바탕으로 수익 가능성에 대한 결과를 확인하는 것이 더 중요.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bviGml8WwN1C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Df['Strategy_Return'] = Df.Signal * Df.Ret"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BwseZqZNwcY9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Df['GLD_Return'] = np.log(Df['Close'].shift(-1)/Df['Close'])"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6j4P9o6zxXY5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "ad0668b4-c6f1-4ddd-d948-5aa8dc8037f2"
      },
      "source": [
        "plt.figure(figsize = (10,5))\n",
        "plt.plot(Df['Strategy_Return'][split:].cumsum(), label ='Strategy')\n",
        "plt.plot(Df['GLD_Return'][split:].cumsum(), label = 'GLD')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SkaPH-3aycOb",
        "colab_type": "text"
      },
      "source": [
        "주황색 선은 GLD의 로그수익률, 파란색 선은 전략에 의한 로그수익률.  \n",
        "전략에 의한 수익률이 최종적으로 높다는 것을 알 수 있음.  \n",
        "하지만 이 전략이 정말 유용한 전략인지는 매우 다양한 지표들을 통해 추가 확인이 필요함.  \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BVlzKYaYzPzm",
        "colab_type": "text"
      },
      "source": [
        "#### 통계량 함수"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xjSL_EOTySA5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_stats(s, n = 252):\n",
        "    s = s.dropna()\n",
        "    win = len(s[s>0])\n",
        "    loss = len(s[s<0])\n",
        "    mean_win = round(s[s>0].mean(), 3)\n",
        "    mean_loss = round(s[s<0].mean(), 3)\n",
        "    max_loss = round(s.min(), 3)\n",
        "    Win_Ratio = round(win/len(s), 3)\n",
        "    PnL_Ratio = round(mean_win/abs(mean_loss), 3)\n",
        "    SR = round(s.mean()/ np.std(s)*np.sqrt(n), 3)\n",
        "\n",
        "    print('Trades : ', len(s),\n",
        "          '\\n Win : ', win,\n",
        "          '\\n Loss : ', loss,\n",
        "          '\\n WinRatio : ', Win_Ratio,\n",
        "          '\\n PnLRatio : ', PnL_Ratio,\n",
        "          '\\n SharpsRatio : ', SR,\n",
        "          '\\n MaxLoss : ', max_loss,)"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sgpDrE8P0U-g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "9e21c20a-535d-4c61-a63d-8e9eee130f87"
      },
      "source": [
        "get_stats(Df.Strategy_Return[split:])"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Trades :  126 \n",
            " Win :  55 \n",
            " Loss :  38 \n",
            " WinRatio :  0.437 \n",
            " PnLRatio :  1.083 \n",
            " SharpsRatio :  2.502 \n",
            " MaxLoss :  -0.03\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kALT1urf1Lir",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " "
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}