ratio_analysis.ipynb

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      "source": [
        "!pip install python-binance"
      ]
    },
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      "source": [
        "# -- Import --\n",
        "import pandas as pd\n",
        "from binance.client import Client\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set(rc={'figure.figsize':(15,8)})\n",
        "\n",
        "# -- Define Binance Client --\n",
        "client = Client()\n",
        "df_list = {}\n",
        "\n",
        "pair_list = [\"BTCUSDT\", \"ETHUSDT\", \"BNBUSDT\", \"ADAUSDT\", \"XRPUSDT\", \"LTCUSDT\", \"ETCUSDT\", \"BCHUSDT\", \"LINKUSDT\", \"SOLUSDT\"]\n",
        "start_date = \"01 january 2017\"\n",
        "time_interval = Client.KLINE_INTERVAL_1DAY\n",
        "\n",
        "# -- You can change the crypto pair ,the start date and the time interval below --\n",
        "for pair in pair_list:\n",
        "    # -- Load all price data from binance API --\n",
        "    klinesT = client.get_historical_klines(pair, time_interval, start_date)\n",
        "\n",
        "    # -- Define your dataset --\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",
        "    df['close'] = pd.to_numeric(df['close'])\n",
        "    df['high'] = pd.to_numeric(df['high'])\n",
        "    df['low'] = pd.to_numeric(df['low'])\n",
        "    df['open'] = pd.to_numeric(df['open'])\n",
        "\n",
        "    # -- Set the date to index --\n",
        "    df = df.set_index(df['timestamp'])\n",
        "    df.index = pd.to_datetime(df.index, unit='ms')\n",
        "\n",
        "    df.drop(\n",
        "        columns=df.columns.difference(['open','high','low','close','volume']), \n",
        "        inplace=True\n",
        "    )\n",
        "    \n",
        "    df[\"daily_return\"] = df[\"close\"] - df[\"open\"]\n",
        "    df[\"daily_return_pct\"] = (df[\"close\"] - df[\"open\"]) / df[\"open\"]\n",
        "    # df[\"ath\"] = df[\"close\"].cummax()\n",
        "    # df[\"drawdown\"] = (df[\"close\"] - df[\"ath\"]) / df[\"ath\"]\n",
        "    \n",
        "    df_list[pair] = df\n",
        "    \n",
        "df_list[\"BTCUSDT\"]"
      ],
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              "                open      high       low     close           volume  \\\n",
              "timestamp                                                             \n",
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              "2017-08-20   4120.98   4211.08   4032.62   4086.29     467.08302200   \n",
              "2017-08-21   4069.13   4119.62   3911.79   4016.00     691.74306000   \n",
              "...              ...       ...       ...       ...              ...   \n",
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              "2022-09-03  19950.98  20055.93  19652.72  19831.90  146639.03204000   \n",
              "2022-09-04  19832.45  20029.23  19583.10  20000.30  145588.77893000   \n",
              "2022-09-05  20000.30  20057.27  19633.83  19819.52  169745.09955000   \n",
              "\n",
              "            daily_return  daily_return_pct  \n",
              "timestamp                                   \n",
              "2017-08-17         23.60          0.005538  \n",
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              "2022-09-01         83.02          0.004141  \n",
              "2022-09-02       -180.78         -0.008979  \n",
              "2022-09-03       -119.08         -0.005969  \n",
              "2022-09-04        167.85          0.008463  \n",
              "2022-09-05       -180.78         -0.009039  \n",
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      "source": [
        "portfolio_repart_1 = {\n",
        "    \"BTCUSDT\": 0.5,\n",
        "    \"ETHUSDT\": 0.3,\n",
        "    \"BNBUSDT\": 0.2,\n",
        "}\n",
        "\n",
        "portfolio_repart_2 = {\n",
        "    \"BTCUSDT\": 0.1,\n",
        "    \"ETHUSDT\": 0.1,\n",
        "    \"BNBUSDT\": 0.1,\n",
        "    \"ADAUSDT\": 0.1,\n",
        "    \"XRPUSDT\": 0.1,\n",
        "    \"LTCUSDT\": 0.1,\n",
        "    \"ETCUSDT\": 0.1,\n",
        "    \"BCHUSDT\": 0.1,\n",
        "    \"LINKUSDT\": 0.1,\n",
        "    \"SOLUSDT\": 0.1,\n",
        "}\n",
        "\n",
        "portfolio_daily_return = 0\n",
        "for pair in portfolio_repart_1:\n",
        "    portfolio_daily_return = (df_list[pair][\"daily_return_pct\"] * portfolio_repart_1[pair]).add(portfolio_daily_return, fill_value=0)\n",
        "    \n",
        "portfolio1 = pd.DataFrame(portfolio_daily_return)\n",
        "portfolio1[\"close\"] = (portfolio1[\"daily_return_pct\"] + 1).cumprod()\n",
        "\n",
        "portfolio1[\"close\"].plot()\n",
        "\n",
        "df_list[\"portfolio1\"] = portfolio1\n",
        "\n",
        "portfolio_daily_return = 0\n",
        "for pair in portfolio_repart_2:\n",
        "    portfolio_daily_return = (df_list[pair][\"daily_return_pct\"] * portfolio_repart_2[pair]).add(portfolio_daily_return, fill_value=0)\n",
        "    \n",
        "portfolio2 = pd.DataFrame(portfolio_daily_return)\n",
        "portfolio2[\"close\"] = (portfolio2[\"daily_return_pct\"] + 1).cumprod()\n",
        "\n",
        "portfolio2[\"close\"].plot()\n",
        "\n",
        "df_list[\"portfolio2\"] = portfolio2\n",
        "\n",
        "portfolio2"
      ],
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              "            daily_return_pct      close\n",
              "timestamp                              \n",
              "2017-08-17          0.000843   1.000843\n",
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              "...                      ...        ...\n",
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              "2022-09-02         -0.000237   9.767244\n",
              "2022-09-03          0.001427   9.781182\n",
              "2022-09-04          0.018211   9.959310\n",
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              "      <th>2022-09-01</th>\n",
              "      <td>0.021217</td>\n",
              "      <td>9.769563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-09-02</th>\n",
              "      <td>-0.000237</td>\n",
              "      <td>9.767244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-09-03</th>\n",
              "      <td>0.001427</td>\n",
              "      <td>9.781182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-09-04</th>\n",
              "      <td>0.018211</td>\n",
              "      <td>9.959310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-09-05</th>\n",
              "      <td>0.009822</td>\n",
              "      <td>10.057129</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1846 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-46eab17b-f5a7-419e-b349-56e2506ad333')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\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",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      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",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-46eab17b-f5a7-419e-b349-56e2506ad333 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-46eab17b-f5a7-419e-b349-56e2506ad333');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# df = pd.read_csv(\"super_reversal_wallet.csv\")\n",
        "# df = df.set_index(df['day'])\n",
        "# df.index = pd.to_datetime(df.index)\n",
        "# del df[\"day\"]\n",
        "# df[\"close\"] = df[\"wallet\"]\n",
        "# df[\"daily_return\"] = df[\"close\"] - df[\"close\"].shift(1)\n",
        "# df[\"daily_return_pct\"] = (df[\"close\"] - df[\"close\"].shift(1)) / df[\"close\"].shift(1)\n",
        "# df_list[\"super_reversal\"] = df\n",
        "# df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "YwYwznH7gmfQ",
        "outputId": "8c90bae1-7a5a-4fa2-93b1-0374fcaf2efa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   wallet          close  daily_return  daily_return_pct\n",
              "day                                                                     \n",
              "2018-06-17    1000.000000    1000.000000           NaN               NaN\n",
              "2018-06-18    1000.453673    1000.453673      0.453673          0.000454\n",
              "2018-06-19     997.684103     997.684103     -2.769570         -0.002768\n",
              "2018-06-20     996.523334     996.523334     -1.160769         -0.001163\n",
              "2018-06-21     996.016249     996.016249     -0.507085         -0.000509\n",
              "...                   ...            ...           ...               ...\n",
              "2022-09-01  111015.696021  111015.696021    706.414644          0.006404\n",
              "2022-09-02  110070.174192  110070.174192   -945.521829         -0.008517\n",
              "2022-09-03  110046.721024  110046.721024    -23.453168         -0.000213\n",
              "2022-09-04  109900.799625  109900.799625   -145.921400         -0.001326\n",
              "2022-09-05  109034.391889  109034.391889   -866.407736         -0.007884\n",
              "\n",
              "[1542 rows x 4 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>wallet</th>\n",
              "      <th>close</th>\n",
              "      <th>daily_return</th>\n",
              "      <th>daily_return_pct</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>day</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-06-17</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-06-18</th>\n",
              "      <td>1000.453673</td>\n",
              "      <td>1000.453673</td>\n",
              "      <td>0.453673</td>\n",
              "      <td>0.000454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-06-19</th>\n",
              "      <td>997.684103</td>\n",
              "      <td>997.684103</td>\n",
              "      <td>-2.769570</td>\n",
              "      <td>-0.002768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-06-20</th>\n",
              "      <td>996.523334</td>\n",
              "      <td>996.523334</td>\n",
              "      <td>-1.160769</td>\n",
              "      <td>-0.001163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-06-21</th>\n",
              "      <td>996.016249</td>\n",
              "      <td>996.016249</td>\n",
              "      <td>-0.507085</td>\n",
              "      <td>-0.000509</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-09-01</th>\n",
              "      <td>111015.696021</td>\n",
              "      <td>111015.696021</td>\n",
              "      <td>706.414644</td>\n",
              "      <td>0.006404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-09-02</th>\n",
              "      <td>110070.174192</td>\n",
              "      <td>110070.174192</td>\n",
              "      <td>-945.521829</td>\n",
              "      <td>-0.008517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-09-03</th>\n",
              "      <td>110046.721024</td>\n",
              "      <td>110046.721024</td>\n",
              "      <td>-23.453168</td>\n",
              "      <td>-0.000213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-09-04</th>\n",
              "      <td>109900.799625</td>\n",
              "      <td>109900.799625</td>\n",
              "      <td>-145.921400</td>\n",
              "      <td>-0.001326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-09-05</th>\n",
              "      <td>109034.391889</td>\n",
              "      <td>109034.391889</td>\n",
              "      <td>-866.407736</td>\n",
              "      <td>-0.007884</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1542 rows × 4 columns</p>\n",
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              "                                                     [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1>Sharpe Ration</h1>"
      ],
      "metadata": {
        "id": "lhMZGuzdOkKW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"-- Sharpe Ratio --\")\n",
        "data = {}\n",
        "for asset in df_list:\n",
        "    df = df_list[asset]\n",
        "    sharpe_ratio = 365**0.5 * (df[\"daily_return_pct\"].mean() / df[\"daily_return_pct\"].std())\n",
        "    data[asset] = sharpe_ratio\n",
        "    print(f\"{asset}: {round(sharpe_ratio, 2)}\")\n",
        "\n",
        "keys = list(data.keys())\n",
        "vals = list(data.values())\n",
        "\n",
        "sns.barplot(x=keys, y=vals)\n",
        "print(\"\\n\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 778
        },
        "id": "MNjlKshuOo1w",
        "outputId": "b556cd59-32bd-4acd-9aa6-1bd26eaa82af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-- Sharpe Ratio --\n",
            "BTCUSDT: 0.79\n",
            "ETHUSDT: 0.83\n",
            "BNBUSDT: 1.49\n",
            "ADAUSDT: 0.7\n",
            "XRPUSDT: 0.48\n",
            "LTCUSDT: 0.22\n",
            "ETCUSDT: 0.73\n",
            "BCHUSDT: 0.39\n",
            "LINKUSDT: 1.19\n",
            "SOLUSDT: 1.52\n",
            "portfolio1: 1.1\n",
            "portfolio2: 0.99\n",
            "super_reversal: 2.68\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1>Sortino Ration</h1>"
      ],
      "metadata": {
        "id": "9gGJIINgPYhy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"-- Sortino Ratio --\")\n",
        "data = {}\n",
        "for asset in df_list:\n",
        "    df = df_list[asset]\n",
        "    sortino_ratio = 365**0.5 * (df[\"daily_return_pct\"].mean() / df[\"daily_return_pct\"][df[\"daily_return_pct\"] < 0].std())\n",
        "    data[asset] = sortino_ratio\n",
        "    print(f\"{asset}: {round(sortino_ratio, 2)}\")\n",
        "\n",
        "keys = list(data.keys())\n",
        "vals = list(data.values())\n",
        "\n",
        "sns.barplot(x=keys, y=vals)\n",
        "print(\"\\n\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 778
        },
        "id": "LRH44Et-Owc5",
        "outputId": "d9ab759a-014f-4d5f-bb2b-9792f8d4cd02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-- Sortino Ratio --\n",
            "BTCUSDT: 1.07\n",
            "ETHUSDT: 1.15\n",
            "BNBUSDT: 2.35\n",
            "ADAUSDT: 1.1\n",
            "XRPUSDT: 0.78\n",
            "LTCUSDT: 0.32\n",
            "ETCUSDT: 1.09\n",
            "BCHUSDT: 0.54\n",
            "LINKUSDT: 1.87\n",
            "SOLUSDT: 2.59\n",
            "portfolio1: 1.42\n",
            "portfolio2: 1.24\n",
            "super_reversal: 4.22\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1>Drawdown</h1>"
      ],
      "metadata": {
        "id": "Dll2TEMUPa9L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"-- Drawdown --\")\n",
        "data = {}\n",
        "for asset in df_list:\n",
        "    df = df_list[asset]\n",
        "    df[\"ath\"] = df[\"close\"].cummax()\n",
        "    df[\"drawdown\"] = (df[\"close\"] - df[\"ath\"]) / df[\"ath\"]\n",
        "    max_drawdown = abs(df[\"drawdown\"].min())\n",
        "    data[asset] = max_drawdown\n",
        "    print(f\"{asset}: {round(max_drawdown, 2)}\")\n",
        "\n",
        "keys = list(data.keys())\n",
        "vals = list(data.values())\n",
        "\n",
        "sns.barplot(x=keys, y=vals)\n",
        "print(\"\\n\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 778
        },
        "id": "pX9aKorvO8zT",
        "outputId": "c793a8ea-cdae-45ce-ed96-7c10b56b72ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-- Drawdown --\n",
            "BTCUSDT: 0.83\n",
            "ETHUSDT: 0.94\n",
            "BNBUSDT: 0.8\n",
            "ADAUSDT: 0.94\n",
            "XRPUSDT: 0.85\n",
            "LTCUSDT: 0.93\n",
            "ETCUSDT: 0.9\n",
            "BCHUSDT: 0.94\n",
            "LINKUSDT: 0.89\n",
            "SOLUSDT: 0.89\n",
            "portfolio1: 0.84\n",
            "portfolio2: 0.78\n",
            "super_reversal: 0.24\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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LDHZNMXv27HznO9/JNddcUwmAy2LatJlpaCgWuH9V2/Dvv//JUrXX/1Wr/4kxWN37nyzdGKzu/U9WvTFY3fufGAP9X737nxiDpe3/6qCqqsUXOtG1xGDXtWvXTJkyJfX19amurk59fX2mTp2arl27Vtq8//77mTRpUk477bQkyccff5yiKDJz5sxcddVVy1wcAAAAS7bEYNexY8f06NEjY8eOTU1NTcaOHZsePXo0moa54YYb5vnnn6/cvummm/LZZ5/loosuWjFVAwAAUNGkb8W84oorMmrUqPTq1SujRo3K0KFDkySDBg3KSy+9tEILBAAAYPGadI1dt27dMnr06AXuv+222xba/pxzzvliVQEAANBkTTpjBwAAwMpLsAMAACg5wQ4AAKDkBDsAAICSE+wAAABKTrADAAAoOcEOAACg5AQ7AACAkhPsAAAASk6wAwAAKDnBDgAAoOQEOwAAgJIT7AAAAEpOsAMAACg5wQ4AAKDkBDsAAICSE+wAAABKTrADAAAoOcEOAACg5AQ7AACAkhPsAAAASk6wAwAAKDnBDgAAoOQEOwAAgJIT7AAAAEpOsAMAACg5wQ4AAKDkBDsAAICSE+wAAABKTrADAAAoOcEOAACg5AQ7AACAkhPsAAAASk6wAwAAKDnBDgAAoOQEOwAAgJIT7AAAAEpOsAMAACg5wQ4AAKDkBDsAAICSE+wAAABKTrADAAAoOcEOAACg5AQ7AACAkhPsAAAASk6wAwAAKDnBDgAAoOQEOwAAgJIT7AAAAEpOsAMAACg5wQ4AAKDkBDsAAICSE+wAAABKTrADAAAoOcEOAACg5AQ7AACAkhPsAAAASk6wAwAAKDnBDgAAoOQEOwAAgJIT7AAAAEpOsAMAACg5wQ4AAKDkBDsAAICSE+wAAABKrmVzFwAAALA4HdZZI9WtV53oUl83J9M/+udyXeeqMzoAAMAqqbp1y0wZ8bvmLmO56XLuXst9naZiAgAAlJxgBwAAUHKCHQAAQMkJdgAAACUn2AEAAJScYAcAAFBygh0AAEDJCXYAAAAlJ9gBAACUnGAHAABQcoIdAABAyQl2AAAAJdeyKY0mTJiQiy++ODNmzMi6666b4cOHZ7PNNmvU5oc//GEeffTRVFVVpVWrVjn//POz9957r4iaAQAAmE+Tgt3ll1+egQMHpqamJg8//HCGDBmSu+66q1GbHXbYIaecckrWWGONvPrqqzn++OPzu9/9Lm3btl0hhQMAADDXEqdiTps2LePGjUufPn2SJH369Mm4ceMyffr0Ru323nvvrLHGGkmS7t27pyiKzJgxYwWUDAAAwPyWeMZu8uTJ6dKlS6qrq5Mk1dXV6dy5cyZPnpwOHTosdJmHHnoom2yySTbYYIOlKqZjx3ZL1b6sOnVq39wlNKvVvf+JMVjd+58YA/1fvfufGAP9X737nxiD1b3/yfIfgyZNxVwaf/jDH/KDH/wgt99++1IvO23azDQ0FAvcv6pt+Pff/2Sp2uv/qtX/xBis7v1Plm4MVvf+J6veGKzu/U+Mgf6v3v1PjMHq3v9kwTGoqmrxhU50LXEqZteuXTNlypTU19cnSerr6zN16tR07dp1gbZ/+ctf8u1vfzs//OEPs8UWWyxzUQAAADTdEoNdx44d06NHj4wdOzZJMnbs2PTo0WOBaZgvvvhizj///IwYMSLbbrvtiqkWAACABTTpd+yuuOKKjBo1Kr169cqoUaMydOjQJMmgQYPy0ksvJUmGDh2aWbNmZciQIampqUlNTU3Gjx+/4ioHAAAgSROvsevWrVtGjx69wP233XZb5f9jxoxZflUBAADQZE06YwcAAMDKS7ADAAAoOcEOAACg5AQ7AACAkhPsAAAASk6wAwAAKDnBDgAAoOQEOwAAgJIT7AAAAEpOsAMAACg5wQ4AAKDkBDsAAICSE+wAAABKTrADAAAoOcEOAACg5AQ7AACAkhPsAAAASk6wAwAAKDnBDgAAoOQEOwAAgJIT7AAAAEpOsAMAACg5wQ4AAKDkBDsAAICSE+wAAABKTrADAAAoOcEOAACg5AQ7AACAkhPsAAAASk6wAwAAKDnBDgAAoOQEOwAAgJIT7AAAAEpOsAMAACg5wQ4AAKDkBDsAAICSE+wAAABKTrADAAAoOcEOAACg5AQ7AACAkhPsAAAASk6wAwAAKDnBDgAAoOQEOwAAgJIT7AAAAEpOsAMAACg5wQ4AAKDkBDsAAICSE+wAAABKTrADAAAoOcEOAACg5AQ7AACAkhPsAAAASk6wAwAAKDnBDgAAoOQEOwAAgJIT7AAAAEpOsAMAACg5wQ4AAKDkBDsAAICSE+wAAABKTrADAAAoOcEOAACg5AQ7AACAkhPsAAAASk6wAwAAKDnBDgAAoOQEOwAAgJIT7AAAAEpOsAMAACg5wQ4AAKDkBDsAAICSE+wAAABKTrADAAAoOcEOAACg5AQ7AACAkhPsAAAASk6wAwAAKDnBDgAAoOSaFOwmTJiQAQMGpFevXhkwYEAmTpy4QJv6+voMHTo0Bx54YA466KCMHj16edcKAADAQjQp2F1++eUZOHBgnnjiiQwcODBDhgxZoM3Pf/7zTJo0Kb/85S/z05/+NDfddFPefvvt5V4wAAAAjbVcUoNp06Zl3LhxueOOO5Ikffr0yVVXXZXp06enQ4cOlXaPPvpojjrqqFRVVaVDhw458MAD8/jjj+fUU09tcjFVVS0W/Vj7tZq8npXd4vq5KNXtO66ASprHsvS/bbvOK6CS5rMsY9BurS4roJLmsSz9X3fNVaf/ydKPQec1111BlTSPZdkHOq/ZbgVU0jyWrf9rroBKms+yjEGnNVuvgEqax7L0f+01q1dAJc1jmd4LrLVqXUG0TO8H116994Gq9m1WQCXN5/NjsCxjMr8WRVEUi2vwt7/9LRdddFF+8YtfVO7r3bt3rrvuumy77baV+/r27Zurr746O+ywQ5Lktttuy5QpU3LZZZd9oQIBAABYvFXrow8AAIDV0BKDXdeuXTNlypTU19cnmfslKVOnTk3Xrl0XaPfuu+9Wbk+ePDkbbLDBci4XAACAz1tisOvYsWN69OiRsWPHJknGjh2bHj16NLq+LkkOOeSQjB49Og0NDZk+fXqefPLJ9OrVa8VUDQAAQMUSr7FLktdffz0XX3xxPv7446y99toZPnx4tthiiwwaNCjnnntutt9++9TX1+fKK6/M73//+yTJoEGDMmDAgBXeAQAAgNVdk4IdAAAAKy9fngIAAFBygh0AAEDJCXYAAAAlJ9gBAACUnGAHAABQci2bu4Cm2n///dO6deu0adMmtbW16dmzZ3bYYYf85Cc/STL3B9Hbtm2b9dZbL0ly5ZVXZu21187111+fV199Neuss05at26dU089NQceeGBOOOGEnHLKKdlvv/0qz3Huuedm3333zeGHH5533nknQ4cOzeTJk5MkrVq1yrXXXputttoqN910U+6555506dIl//znP9OuXbscdthhOf744/Pxxx/nG9/4RpLks88+y9SpU7PZZpslSfbdd9+cf/75X7j/87z//vvp1KlTZs+enYkTJ+bLX/5ykmSbbbbJN7/5zRxxxBF5/vnnK+0//fTT7LLLLhk/fnxlnbfccku22mqrSpvDDz88F110UXbfffe8+uqrufrqq/Pxxx9n9uzZWXvttTNy5Misv/76ufjii/PMM8+kQ4cO+eyzz9KxY8cMGDAg/fr1y/jx43PhhRcmST766KPMnDkzX/rSl5IkRx99dI477rhl7vu8bX/55Zfn5z//eQYPHpwbb7wxvXv3TpI88MAD+c1vfpMRI0bk7bffzsEHH1wZl7q6uhx77LE58cQTk6R0+8BHH32UvffeO0cffXQuu+yySn+HDRuWjTbaKLW1tWnVqlUOPvjgnHrqqWnbtm1l2d/+9rc57bTTMnLkyBx00EGV+8syBjNmzMhhhx2WkSNHZocddkiS3HLLLXn55Zez3377ZdiwYfnSl76U2bNnZ6ONNsrVV1+dTp06Vfr47rvvpl27dqmtrc2xxx6bk046Kc8//3yGDx+eBx54oPI8r732Ws4444w89dRTSZKf/OQnue+++9KiRYvU1dVlv/32y0UXXZQk6d69e+Vvp66uLttuu23OOuusbLnllvnRj36Uxx9/PEkyadKkdOjQIe3atavU3bVr16Xq//zm/d1OmTIl119/fZLkgw8+SENDQzp37pwkOfvss7PTTjvluuuuy5/+9Kesvfbaqa6uztFHH52jjz46F198cbbbbrscf/zxlfUOHz48a665Zs4555x89NFHGTp0aF577bW0aNEiVVVVufjii7PHHnsscZ876qijUldXt9Dj0jXXXLPM/Z7X96U5Dg4bNix33XVX7r///iRz99/tttsuF154YV555ZVSbf9FHQdbtWqVRx55JLfffntmzZqVNddcM5tuumm+/e1vZ8MNN0z37t3z5z//OWuttVZlXbvvvnvGjBmTjTbaqDTHgIW9Xs1f+0033ZSRI0fm/vvvz4477pgkuemmm/LZZ5/loosuWuDv/cUXX8zZZ5+doUOH5sMPP6y8bszz61//OrfffnvuvvvuFEWRESNG5Je//GWqq6szZ86cHHXUUTn55JMbvc40NDRk9uzZ6dmzZ84+++xssMEGGTp0aP785z8nmfvTURtttFFl/33ggQdSXV29VOOQJI899lj+9//+3ymKIrW1tdl2223z/e9/P3V1dbnhhhvy5JNPpmXLlmnbtm3OPvvsHHjggUmy0GPe58dxnpVxH1hennzyyXTu3LnyWpIkN954Y5544ol06NAh99xzzyKXnX+f+tWvfpX/+Z//qRwTFuWVV17J0KFD88orr2SfffZptJ81l3/1GNx///25++67K7dPPfXU1NTUfPGOrGYW9tq9gKIk9ttvv2L8+PFFURTFnDlzigEDBhS/+MUvKo9fdNFFxd133125PWXKlGLPPfcsHnzwwcp9U6dOrdw+/vjji6eeeqrRc5xzzjnFmDFjiqIoikGDBjVa33vvvVd88MEHRVEUxYgRI4prr7228tikSZOKAQMGFFdddVWj9T333HNF//79v1C/55m//5/31ltvFbvtttsS75s5c2ax1VZbLXad/fv3L5577rmiKIqid+/ejcZowoQJxcyZM4uiWHC8x40bVxx88MHF7bff3mh9Y8aMKc4555ymdnOhFrXtx4wZU+y3337FwQcfXMyePXuB5/v8GEybNq3YaaediqlTpxZFUb59YNSoUcXxxx9ffPWrXy1qa2sX6G9RFMUHH3xQnH766cXpp5++QL9OPPHEBe4v0xj83//7f4vevXsXtbW1xauvvlrstddexQcffNBoDBoaGopvfetbxZAhQxbax3fffbfYZZddildeeWWhdY0fP77Yb7/9iqIoihdeeKE46KCDio8++qgoirn73iuvvFJpu9VWW1X+Hurr64t77rmn6NmzZzFp0qRG61zYGH8RC/u7/fy2+Oyzz4qDDz64uOWWW4r6+vqiKIri448/Lu69996iKBb8+y2Korj22muLESNGFEVRFFdccUVx7bXXFg0NDUVRFMX06dOLd955pyiKpu9zCzsGfVFLexy84YYbimOPPbZ4//33i6KYu3888cQTxaRJk0q3/Rd1HLz//vuLf/u3fysmTJhQafvcc88VL7zwwgJ1zrPbbrsVb7311iLrWxmPAQvb9vPXPmLEiGK//fYrTjjhhMrj89c3fw3PPPNMsddeexXPP/98URQLf5166qmniuOPP74oiqJ49NFHi2OOOaaYNWtWURRFUVtbW/z9738vimLB/a62trb4z//8z2KfffYpPv744yX2YWlNmTKl2H333Yt33323KIq5+/TLL79cFEVRDB48uDjvvPMqdY4fP77Ye++9iz/84Q8LjMH8yrIPLA9z5sxZ6PFv++23L6ZNm7bE5T/f36Z47733ir/+9a/Fvffe+4XfDy0PzTEGzz33XPHhhx8WRVEUkydPbnQM+lepr6+vvKYtyZw5c1ZoLfPesy6thW23zyvlVMza2trU1tZm7bXXXmSbn/zkJ9l9993Tr1+/yn2dOnVqdHtx3nvvvXTp0qVyu0uXLnj2bwYAABSYSURBVOnYseNC22688ca5+uqrc++99+aTTz5pYi9Wfp8fg80226zRp77z69GjRy699NLcdtttKVbgTyN+fttvt9122XzzzfOzn/1sict++umnadWqVaMzWYuzsu0DY8aMyVlnnZXu3bvnV7/61ULbdOzYMcOHD8+zzz6bv//970mSDz/8MM8991y+//3v5y9/+Uvef//9Jj/nyjQGBx54YHr06JHrr78+F198cQYPHrxALS1atMiuu+5a+WT587p27ZrNN988EyZMWOLzTZkyJe3atcuaa66ZJKmurs7WW2+90LZVVVU59thjs9deey32k85/lbFjx2bdddfN6aefnqqquYf59u3b55hjjmnS8vO2e4sWLZIk6623XjbccMOFtl3YPrcy+PTTT3PHHXfku9/9btZff/0kc/ePgw8+OBtvvPESl1+Zt//8x8GRI0dm8ODBlTMhydwzcvN/Ev9FrEzHgCU5+OCDM2PGjPz3f//3Its89dRTueiii3LzzTdnt912a9J6p0yZkvXWWy+tW7dOkrRu3TpbbrnlQtu2bt065513Xrp06ZJHHnlk6TuxBB988EFatmyZddddN8ncfXqbbbbJO++8k8ceeyxXXHFF5YzgVlttlTPOOCMjR478Qs+5suwD3bt3z4gRI1JTU5NevXrliSeeqDz29NNPp1+/funbt29OOumkvPnmm0nmnqXs27dvBg8enJqamvz617/OU089lVtvvTU1NTV56KGHMnDgwNTW1uakk07K8OHDkyS33npr+vTpkz59+mTw4MH59NNPF6jngQceyLnnnlu5vahlunTpkh133LGy/6yOY7D77rtX9tkNNtgg66+/fgYPHpzevXvnsMMOy3nnnbfAuua//cADD+Tkk0/OGWeckd69e+fEE0/MlClTGj3vkUcemf79++eMM86ovM+56aabcu655+aUU05J79698/HHHy90XB944IF84xvfyDe/+c306dMnr732Wl544YWccMIJOfzww3P44YfnN7/5TZLk0ksvzY9//OPKsq+99loOOOCAFEWRmTNn5tJLL82RRx6Zvn375rvf/W7q6+uTzD0zfvXVV+foo4/OmWeemWnTpuUb3/hG+vbtm759+2bYsGFJkvHjx2fgwIHp379/evfunTvvvHNJu0UjpZmKmcydGtCmTZtMmjQpe+21V/baa69Fth03bly+9rWvLfNznXrqqbnwwguz7bbbZscdd0yvXr0W+0LZrVu3tG3bNhMmTFhuL6ifN6//ydw3GZ+fTvF5n3zySaNT3Q0NDUv1fGeccUaOO+647Lzzztlpp51y6KGHplu3botsv+OOO2batGmZPn36Ig/6y2ph235e/88///ycdtppCw3t88agvr4+EydOzAUXXJD27ds36TlXpn3g1VdfzYwZM/LVr34177//fsaMGZN/+7d/W2jbddZZJ5tuumn+/ve/58tf/nIeeeSR7Lvvvll//fVz0EEH5cEHH8xpp53WpOddmcYgSb7zne9kv/32y5577lmZfju/urq6PP300wt9LEn+8Y9/5I033kj37t2XGHC/9rWv5bbbbst+++2X3XbbLbvttlsOO+ywrLHGGotcZscdd8zvf//7pevUCvDyyy9/ofE/8cQTc+6552bs2LHZeeeds//++2ePPfZYZPvP73MrUlOPg6+//npat26dLbbYYpmeZ2Xc/p8/Dvbo0SPvvfdeZerhohxzzDGVgJ9kqd5wr2zHgMVp0aJF/uM//iM33njjQt8fvPnmm7ngggvy05/+dKn20969e+fee+/NwQcfnJ49e+arX/1qDj300LRsuei3UNtvv/0K+aBj6623zg477JB99903u+++e3bZZZfU1NTktddeyyabbFJ58zzPTjvtlB/84Adf6DlXpn2gqqoqDz/8cN54440ce+yx6dmzZ5LkwgsvzKhRo7Lllltm9OjRueCCCzJ69Ogkc4/7V155ZXbeeeckc6chzj+drV+/funevXvuu+++rLXWWvntb3+bRx55pHJ73gcB3/72txdZ17Iss7qOwfPPP58PPvggnTp1yqOPPppk7qUmi/rAep4//elPeeihh7LFFltk5MiRufrqqzNixIg8/PDDeeutt3L//fenqqoq99xzT6699tp8//vfTzJ32vUDDzyQDh06LHb9L7zwQh5++OFssskm+fjjj3PiiSfm1ltvTefOnTN16tQceeSRGTt2bPr375+rr746J510UpK5obB///5p0aJFrrnmmuy66665+uqr09DQkAsuuCBjxozJ0UcfnSR56623cs8996Rly5a58847s8kmm1SC20cffZQk+dKXvpQ777wzrVu3zqeffpqjjjoqe++992Lff8+vVGfs5m3A5557LrW1tUudYpti3ifUhx12WJ566qkMHDgw//znP3PSSSdl7Nixi112RZ6pSv5//x9++OElhrpk7if089o//PDDue+++5r0PPPGYNCgQXn88cdTU1OTd999N0cccUT++Mc/LnK5Fdn/xW377t27Z9ddd200f3ueeWMwduzY/OpXv8pdd92Vl156abHPtTLuAz/72c9SU1NTOePw4osvNvq0anF1zDvoJEn//v2btO+sjGOQJM8++2zatWuXN954I3V1dZX7n3nmmdTU1GSPPfbI9OnTFwi93/3ud1NTU5NLLrkkV155ZbbYYotKHxdlzTXXzE9/+tP88Ic/zHbbbZfRo0dnwIABjZ7381b0MWB5WVTf592/xx575Ne//nXOPPPMtGrVKt/61rdy6623Lnad/6q+L+1xcFHKuP0/fxxsav/vu+++Rq8FTflwa2U9BizJvvvumzZt2uSxxx5b4LFOnTqlW7duC7wWLmlf6Ny5c37xi1/kmmuuyWabbZZbbrklp59++nKtu6mqqqpy88035+67787uu++e3/72tznssMMyY8aM5f5cK+M+cNRRRyVJtthii2yzzTb561//mhdeeCFbb7115SzqEUcckVdeeSUzZ85Mkmy66aaVQNMUzz77bHr37p127dqlRYsWOfroo/Pss88u92WWVZnH4B//+EcuuuiiXH755Zk4cWKGDh2axx57rElnM7/yla9UPqg76qij8txzzyWZexb+mWeeSf/+/VNTU5N77rkn77zzTmW5r3/960sMdUmyyy67ZJNNNkmS/OUvf8nbb7+dQYMGpaamJoMGDUqLFi3y5ptvpmfPnvn0008zfvz4zJkzpxL25tXyf/7P/0lNTU369++fl19+udEMob59+1Y+ENpxxx3z9NNPZ/jw4fn1r39dmR0ya9asXHLJJenbt2+OPfbYTJ06Na+++uoS65+nVGfs5mnTpk323Xff/OY3v6lcmPt522yzzWLfwHfo0GGBA+GHH37YaOOvt9566d27d3r37p0NNtggY8eOTZ8+fRa6vjfeeCO1tbXL/Olwc1jYGMyYMaPR2bYuXbqkpqYmNTU1adOmTZ544onsuuuuC13fSy+9lI4dOy73s3Xzm3/bH3LIIZX7zzvvvBxzzDGLPRPVpUuX7LDDDnn++eez/fbbl2YfqKury9ixY9O6des8/PDDSZLZs2fngQceaDQ9Zp6PPvookyZNylZbbZW//e1v+cc//pFLL7208vjUqVPzpz/9KV/5yldKMwZJMn369AwbNiy33nprbr/99owYMSIXXHBBkmTPPffMiBEjMnPmzPz7v/97fvCDH1S+wCdJLrvsskZfDJAs+hgw//7bokWL7LDDDtlhhx1y3HHHZc8998zf//73bLvttgut8aWXXlrhZ6yaYtttt82YMWMW+fh666230L7PP0WxXbt2OeCAA3LAAQdku+22y49+9KNF/n3Nv8+tLLp165ba2tpMmDAhm2+++QKPl3n7z38c7NKlS1588cXFzmBZnDIdA5rqf/2v/5VLL7200WtEMjes33777Tn55JNz1VVX5Tvf+U6SuWPw4YcfNmr7+X2hZcuW6dmzZ3r27JkjjjgiX/va1xYbpl566aUcdthhy7FXjW211VbZaqutctxxx6V3796ZMWNGJk2alBkzZjQ6a/fXv/413bt3X+y6VsV9YH7z3jCvzlaGMZg4cWJOO+20DB06NPvss0++/vWv57nnnsvTTz+dG2+8Md/85jcbzSyrra1t0nqLosiZZ56ZI488cqGPL+oSosW1K4oi3bt3r3xB4+f169cvDz74YHbbbbd069at8gWBRVHk5ptvXuR0//m3w84775wHH3wwzzzzTB5++OHceuutuffee3PDDTekU6dOufbaa9OyZcuccsopTR6LpGRn7OZpaGjIH//4x0bXFHzewIED8+yzz+bnP/955b5p06bloYceSjJ3ms1DDz1UGaxXX301r7/+emXawG9+85vKY/X19Rk/fnw22mijhT7X22+/nUsvvTTHHnts5ZvPyuBrX/taRo8eXZn/+9vf/jZVVVWVTyyefPLJymO1tbV54403FjkGr776aoYNG5ZBgwat0JoXte033njj9OrVq9G858+bOXNmXn755cqyZdkHfvWrX2XzzTfP008/naeeeipPPfVUbr/99jz44IMLtJ0+fXouueSS7LHHHtlyyy0zZsyYnHrqqZXlnnrqqZxzzjmVN/1lGYMkGTp0aI4++uhsvfXWufTSSzN27NgFPrxp165dhg4dmnvvvTdTp05d7Po23XTTJKlcj1NfX5/777+/MoX79ddfz2uvvVZpP2HChMyePTsbbLDBAutqaGjI/fffn//+7//Oscce+4X6uTwceuihmT59ev7rv/6r8sn5zJkz89Of/jTJ3O3+2GOPVaZ+TJ48Ob/73e/y1a9+NUny+9//vvJJb1EUGTdu3CK3++f3uZXFWmutlW984xsZMmRIpk2blmRuX5588sm89dZbpd7+8x8HzzrrrFx77bWZNGlS5fE//vGPefHFF5u0rjIdA5qqZ8+e2WyzzRq9/s/Tvn373HHHHXnhhRdy1VVXJZk7bfIf//hH5RujZ82alQcffLCyL/ztb3/L22+/XVnHyy+/nHXWWWeh1/nX1dVl5MiRee+991ZIsJsyZUr+8pe/VG6/9957mT59enbfffcccsghueKKKyrb67XXXsstt9ySs88+e7HrLNM+MO+1a+LEiRk3blx22mmn7LTTTpWak+TBBx/MNttss8jnb9eu3WKnI++xxx557LHHMnPmzBRFkZ/97GfZc889F1vXsiyzrMo4Bm+99Vb+/d//PZdeemn22WefvPfee6murs6BBx6YwYMHZ/r06dl4440zfvz41NXVpa6urtH1g0ny5z//ORMnTqyMwbzXq/333z/33HNP5fWsrq5uqc5wLczOO++cN998s3JWMJk7pXPe62m/fv0yduzYjB49Oocffnilzf77759bb7218t55+vTpeeuttxb6HG+99VbatWuXQw89NIMHD87LL7+choaGfPLJJ9lggw3SsmXLvPbaa/mf//mfpaq9VGfs5l1fMHv27Hz5y1/ON7/5zUW27dKlS+6+++5cf/31+c///M+sueaaWXPNNSvB48gjj8y7776bI488MlVVVWnbtm1uvPHGys8lzPta4JYtW6a+vj7bbbddzjvvvMr6H3rooTz77LOVr/ft27dvTjjhhH9J/+f57ne/m+23336Z13fGGWfke9/7Xvr165eqqqqss846GTlyZFq1apUkefzxx3PdddelTZs2mTNnTvbcc89GP1Vw6623ZvTo0Zk1a1Y6dOiQ008/vclfTrO0FrbtPz8f+6yzzlog7Mx/nWFtbW169+5d+ernsuwDY8aMSd++fRvdt/POO6ehoSHvvvtunnnmmfTr1y+zZs1K69atc9BBB2XQoEGpra3NL37xi9x7772Nlu3Tp08OO+ywXHbZZaUZg0cffTQTJ06sfL3/OuuskyFDhuSSSy5Z4Gt/t9566xxyyCG57bbbGp2p/LxWrVpl5MiRueaaa3L99denoaEhu+66a84444wkc9/cDRs2LNOmTUubNm1SXV2d6667rtGn+PO+jKSuri7bbLNN7r333iZ9MccXdfLJJzf6mvSDDz648nebzP1U8O677851112XAw44IGuttVZatmyZgQMHJpn7Ru6YY47JCSeckBYtWqS6ujqXXXZZ5RP28ePH59prr628iG266aYZMmRIZf2L2uf+FZbmOPgf//EfufPOOyv7ZFEU6dmzZ3bbbbdSbv+FHQfXWWedtG3bNueee25mzZqVqqqqbL311k2+tqcsx4Bkwf3+89eTze/888+vTI/6vPbt2y9w5u6GG27IkCFDMmvWrDQ0NOSggw7KEUcckWTu2auhQ4dm5syZad26ddZYY4388Ic/rFy3OP+13PN+7uC+++5r8vXcS2POnDm56aab8s4776Rt27ZpaGjIt771rWyzzTa5/PLLc8MNN6R3795p1apV2rRpk0svvbTRl8S89tpr+frXv165veeee2bYsGGl2Qfq6+vTr1+//POf/8yVV15Z+Xv83ve+lwsuuCBz5sxJhw4dct111y1yHYcddlgGDx6cxx9/PCeffPIC71v22WefjB8/vvL3vd122+XMM89cbF2LW+btt9/OwIEDM2vWrNTW1ubrX/96zjnnnMqUytVhDK677rrMmDEjI0aMqMyuSeZ+ANfQ0JDTTjstu+yyS/bYY48ceuih6dy5c7beeutG18LvsssuGT58eN58882sv/76lf7169cvM2bMqLwXKIoixx577CK/7Kop1llnndx888257rrrMmzYsMyePTsbb7xxbrnllrRo0SIbbrhhttxyy/zhD3/IDTfcUFnukksuyXXXXVe5dKZVq1a55JJLFvq68Ic//CF33nlnqqqq0tDQkKFDh6aqqipnnnlmLrzwwvzsZz/L5ptvvshZcovSoijLRSEAAKyWFvabjKub1XUM5v+NYhavlFMxAQAA+P+csQMAAFZphx9+eOX6t3l23HHHXHnllc1U0fIn2AEAAJScqZgAAAAlJ9gBAACUnGAHAABQcoIdAABAyQl2AAAAJff/AOTOh6o4Y0N/AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"-- Calmar Ratio --\")\n",
        "data = {}\n",
        "for asset in df_list:\n",
        "    df = df_list[asset]\n",
        "    df[\"ath\"] = df[\"close\"].cummax()\n",
        "    df[\"drawdown\"] = (df[\"close\"] - df[\"ath\"]) / df[\"ath\"]\n",
        "    max_drawdown = abs(df[\"drawdown\"].min())\n",
        "    calmar_ratio = (df[\"daily_return_pct\"].mean()*365) / max_drawdown\n",
        "    data[asset] = calmar_ratio\n",
        "    print(f\"{asset}: {round(calmar_ratio, 2)}\")\n",
        "\n",
        "keys = list(data.keys())\n",
        "vals = list(data.values())\n",
        "\n",
        "sns.barplot(x=keys, y=vals)\n",
        "print(\"\\n\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 778
        },
        "id": "opsgDq8ARWf6",
        "outputId": "d24d17f4-e9ba-4b2c-ed9f-7a1baf7c5fd2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-- Calmar Ratio --\n",
            "BTCUSDT: 0.75\n",
            "ETHUSDT: 0.89\n",
            "BNBUSDT: 2.26\n",
            "ADAUSDT: 0.83\n",
            "XRPUSDT: 0.68\n",
            "LTCUSDT: 0.25\n",
            "ETCUSDT: 0.96\n",
            "BCHUSDT: 0.48\n",
            "LINKUSDT: 1.73\n",
            "SOLUSDT: 2.59\n",
            "portfolio1: 1.09\n",
            "portfolio2: 0.97\n",
            "super_reversal: 5.06\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}