Indicateurs_volatilité.ipynb

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        "<a href=\"https://colab.research.google.com/github/CryptoRobotFr/General-code-trading-bot/blob/main/Indicateurs_volatilit%C3%A9.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "!pip install ta python-binance"
      ],
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        "id": "Qt1hF5MCZ3fY"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "import pandas as pd\n",
        "from binance.client import Client\n",
        "import ta\n",
        "import numpy as np\n",
        "\n",
        "# Variables pour les données\n",
        "client = Client()\n",
        "pair_symbol = \"BTCUSDT\"\n",
        "time_interval = Client.KLINE_INTERVAL_1DAY\n",
        "start_date = \"01 january 2017\"\n",
        "\n",
        "# Récupération des données\n",
        "klinesT = client.get_historical_klines(pair_symbol, time_interval, start_date)\n",
        "\n",
        "# Créer un tableau grâce aux données\n",
        "df = pd.DataFrame(klinesT, columns = ['timestamp', 'open', 'high', 'low', 'close', 'volume', 'close_time', 'quote_av', 'trades', 'tb_base_av', 'tb_quote_av', 'ignore' ])\n",
        "\n",
        "# Supprime les colonnes inutiles\n",
        "df.drop(columns = df.columns.difference(['timestamp','open','high','low','close','volume']), inplace=True)\n",
        "\n",
        "# Convertit les colonnes en numéric\n",
        "for col in df.columns:\n",
        "    df[col] = pd.to_numeric(df[col])\n",
        "\n",
        "# Convertit les dates dans un format lisible\n",
        "df = df.set_index(df['timestamp'])\n",
        "df.index = pd.to_datetime(df.index, unit='ms')\n",
        "del df['timestamp']\n",
        "\n",
        "# Affiche le tableau\n",
        "df"
      ],
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              "                open      high       low     close        volume\n",
              "timestamp                                                       \n",
              "2017-08-17   4261.48   4485.39   4200.74   4285.08    795.150377\n",
              "2017-08-18   4285.08   4371.52   3938.77   4108.37   1199.888264\n",
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              "2017-08-21   4069.13   4119.62   3911.79   4016.00    691.743060\n",
              "...              ...       ...       ...       ...           ...\n",
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    {
      "cell_type": "code",
      "source": [
        "E#Court code pour définir les différents indicateurs de volatilité à implementer dans vos stratégies de trading\n",
        "\n",
        "# Kaufman ou KAMA\n",
        "df[\"KAMA\"] = ta.momentum.kama(df['close'], window=10, pow1=2, pow2=30)\n",
        "# ------------------------- #\n",
        "\n",
        "\n",
        "# ATR\n",
        "df[\"ATR\"] = ta.volatility.average_true_range(df['high'],df['low'],df['close'],window=2)\n",
        "# ------------------------- #\n",
        "\n",
        "\n",
        "# Les bandes de Bollingers\n",
        "df[\"BOL_HIGH\"] = ta.volatility.bollinger_hband(df['close'],window=20R,window_dev=2)\n",
        "df[\"BOL_LOW\"] = ta.volatility.bollinger_lband(df['close'],window=20,window_dev=2)\n",
        "df[\"BOL_MEDIUM\"] = ta.volatility.bollinger_mavg(df['close'],window=20)\n",
        "df[\"BOL_GAP\"] = ta.volatility.bollinger_wband(df['close'],window=20,window_dev=2)\n",
        "# Return binaire 0 ou 1 \n",
        "df[\"BOL_HIGHER\"] = ta.volatility.bollinger_hband_indicator(df['close'],window=20,window_dev=2)\n",
        "df[\"BOL_LOWER\"] = ta.volatility.bollinger_lband_indicator(df['close'],window=20,window_dev=2)\n",
        "# ------------------------- #\n",
        "\n",
        "\n",
        "# Le canal de Donchian\n",
        "df[\"DON_HIGH\"] = ta.volatility.donchian_channel_hband(df['high'],df['low'],df['close'],window=20,offset=0)\n",
        "df[\"DON_LOW\"] = ta.volatility.donchian_channel_lband(df['high'],df['low'],df['close'],window=20,offset=0)\n",
        "df[\"DON_MEDIUM\"] = ta.volatility.donchian_channel_mband(df['high'],df['low'],df['close'],window=20,offset=0)\n",
        "# ------------------------- #\n",
        "\n",
        "\n",
        "# Le canal de Keltner \n",
        "df[\"KEL_HIGH\"] = ta.volatility.keltner_channel_hband(df['high'],df['low'],df['close'],window=20,window_atr=10)\n",
        "df[\"KEL_LOW\"] = ta.volatility.keltner_channel_lband(df['high'],df['low'],df['close'],window=20,window_atr=10)\n",
        "df[\"KEL_MEDIUM\"] = ta.volatility.keltner_channel_mband(df['high'],df['low'],df['close'],window=20,window_atr=10)\n",
        "# Return binaire 0 ou 1 \n",
        "df[\"KEL_HIGHER\"] = ta.volatility.keltner_channel_hband_indicator(df['high'],df['low'],df['close'],window=20,window_atr=10)\n",
        "df[\"KEL_LOWER\"] = ta.volatility.keltner_channel_lband_indicator(df['high'],df['low'],df['close'],window=20,window_atr=10)\n",
        "# ------------------------- #\n",
        "\n",
        "\n",
        "# Chopiness\n",
        "def chop(high, low, close, window=14):\n",
        "    ''' Choppiness indicator\n",
        "    '''\n",
        "    tr1 = pd.DataFrame(high - low).rename(columns={0: 'tr1'})\n",
        "    tr2 = pd.DataFrame(abs(high - close.shift(1))\n",
        "                       ).rename(columns={0: 'tr2'})\n",
        "    tr3 = pd.DataFrame(abs(low - close.shift(1))\n",
        "                       ).rename(columns={0: 'tr3'})\n",
        "    frames = [tr1, tr2, tr3]\n",
        "    tr = pd.concat(frames, axis=1, join='inner').dropna().max(axis=1)\n",
        "    atr = tr.rolling(1).mean()\n",
        "    highh = high.rolling(window).max()\n",
        "    lowl = low.rolling(window).min()\n",
        "    chop_serie = 100 * np.log10((atr.rolling(window).sum()) /\n",
        "                          (highh - lowl)) / np.log10(window)\n",
        "    return pd.Series(chop_serie, name=\"CHOP\")\n",
        "\n",
        "df[\"CHOP\"] = chop(df['high'],df['low'],df['close'],window=14)\n",
        "# ------------------------- #\n",
        "\n",
        "df"
      ],
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              "      <td>358.700000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4590.345000</td>\n",
              "      <td>3872.945000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-08-19</th>\n",
              "      <td>4108.37</td>\n",
              "      <td>4184.69</td>\n",
              "      <td>3850.00</td>\n",
              "      <td>4139.98</td>\n",
              "      <td>381.309763</td>\n",
              "      <td>NaN</td>\n",
              "      <td>346.695000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4524.534444</td>\n",
              "      <td>3823.141111</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-08-20</th>\n",
              "      <td>4120.98</td>\n",
              "      <td>4211.08</td>\n",
              "      <td>4032.62</td>\n",
              "      <td>4086.29</td>\n",
              "      <td>467.083022</td>\n",
              "      <td>NaN</td>\n",
              "      <td>262.577500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4465.515000</td>\n",
              "      <td>3850.240000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-08-21</th>\n",
              "      <td>4069.13</td>\n",
              "      <td>4119.62</td>\n",
              "      <td>3911.79</td>\n",
              "      <td>4016.00</td>\n",
              "      <td>691.743060</td>\n",
              "      <td>NaN</td>\n",
              "      <td>235.203750</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4417.138667</td>\n",
              "      <td>3841.786667</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-26</th>\n",
              "      <td>39219.16</td>\n",
              "      <td>40348.45</td>\n",
              "      <td>38573.18</td>\n",
              "      <td>39116.72</td>\n",
              "      <td>29361.256800</td>\n",
              "      <td>39884.168418</td>\n",
              "      <td>2268.595010</td>\n",
              "      <td>46032.307699</td>\n",
              "      <td>36113.548301</td>\n",
              "      <td>41072.9280</td>\n",
              "      <td>24.149141</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45821.0</td>\n",
              "      <td>34322.28</td>\n",
              "      <td>40071.64</td>\n",
              "      <td>43320.046667</td>\n",
              "      <td>38958.671667</td>\n",
              "      <td>41139.359167</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.329925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-27</th>\n",
              "      <td>39116.73</td>\n",
              "      <td>39855.70</td>\n",
              "      <td>37000.00</td>\n",
              "      <td>37699.07</td>\n",
              "      <td>46229.447190</td>\n",
              "      <td>39753.245883</td>\n",
              "      <td>2562.147505</td>\n",
              "      <td>45762.240189</td>\n",
              "      <td>35769.523811</td>\n",
              "      <td>40765.8820</td>\n",
              "      <td>24.512450</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45821.0</td>\n",
              "      <td>34322.28</td>\n",
              "      <td>40071.64</td>\n",
              "      <td>43062.906333</td>\n",
              "      <td>38701.426333</td>\n",
              "      <td>40882.166333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>42.716844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-28</th>\n",
              "      <td>37699.08</td>\n",
              "      <td>44225.84</td>\n",
              "      <td>37450.17</td>\n",
              "      <td>43160.00</td>\n",
              "      <td>73945.638580</td>\n",
              "      <td>39887.329572</td>\n",
              "      <td>4668.908753</td>\n",
              "      <td>45616.042591</td>\n",
              "      <td>35827.422409</td>\n",
              "      <td>40721.7325</td>\n",
              "      <td>24.037828</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45821.0</td>\n",
              "      <td>34322.28</td>\n",
              "      <td>40071.64</td>\n",
              "      <td>43137.640167</td>\n",
              "      <td>38381.193167</td>\n",
              "      <td>40759.416667</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48.676201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-03-01</th>\n",
              "      <td>43160.00</td>\n",
              "      <td>44949.00</td>\n",
              "      <td>42809.98</td>\n",
              "      <td>44421.20</td>\n",
              "      <td>61743.098730</td>\n",
              "      <td>40134.233733</td>\n",
              "      <td>3403.964376</td>\n",
              "      <td>45625.739691</td>\n",
              "      <td>35822.573309</td>\n",
              "      <td>40724.1565</td>\n",
              "      <td>24.072116</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45821.0</td>\n",
              "      <td>34322.28</td>\n",
              "      <td>40071.64</td>\n",
              "      <td>43158.712833</td>\n",
              "      <td>38356.471833</td>\n",
              "      <td>40757.592333</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>47.778131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-03-02</th>\n",
              "      <td>44421.20</td>\n",
              "      <td>44867.05</td>\n",
              "      <td>43334.09</td>\n",
              "      <td>44636.71</td>\n",
              "      <td>33568.406380</td>\n",
              "      <td>40634.018041</td>\n",
              "      <td>2468.462188</td>\n",
              "      <td>45834.744051</td>\n",
              "      <td>35727.695949</td>\n",
              "      <td>40781.2200</td>\n",
              "      <td>24.783584</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>44949.0</td>\n",
              "      <td>34322.28</td>\n",
              "      <td>39635.64</td>\n",
              "      <td>43108.801333</td>\n",
              "      <td>38417.963333</td>\n",
              "      <td>40763.382333</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48.070050</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1659 rows × 22 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d86a650b-d20a-4c09-83aa-d8700ffa0a06')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "          document.querySelector('#df-d86a650b-d20a-4c09-83aa-d8700ffa0a06 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d86a650b-d20a-4c09-83aa-d8700ffa0a06');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                open      high       low  ...  KEL_HIGHER  KEL_LOWER       CHOP\n",
              "timestamp                                 ...                                  \n",
              "2017-08-17   4261.48   4485.39   4200.74  ...         0.0        0.0        NaN\n",
              "2017-08-18   4285.08   4371.52   3938.77  ...         0.0        0.0        NaN\n",
              "2017-08-19   4108.37   4184.69   3850.00  ...         0.0        0.0        NaN\n",
              "2017-08-20   4120.98   4211.08   4032.62  ...         0.0        0.0        NaN\n",
              "2017-08-21   4069.13   4119.62   3911.79  ...         0.0        0.0        NaN\n",
              "...              ...       ...       ...  ...         ...        ...        ...\n",
              "2022-02-26  39219.16  40348.45  38573.18  ...         0.0        0.0  40.329925\n",
              "2022-02-27  39116.73  39855.70  37000.00  ...         0.0        1.0  42.716844\n",
              "2022-02-28  37699.08  44225.84  37450.17  ...         1.0        0.0  48.676201\n",
              "2022-03-01  43160.00  44949.00  42809.98  ...         1.0        0.0  47.778131\n",
              "2022-03-02  44421.20  44867.05  43334.09  ...         1.0        0.0  48.070050\n",
              "\n",
              "[1659 rows x 22 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    }
  ]
}