estudo_imab.ipynb

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      "name": "Untitled0.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyN/fY3v3pWVoG5ADyM5m+mK",
      "include_colab_link": true
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      "name": "python"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Zegoverno/Maua_IoT_Vivo/blob/master/estudo_imab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ESTUDO POSIÇÃO EM IMAB EM RELAÇÃO AO CDI E OPERACIONALIZAÇÃO DA MESMA ESTRATÉGIA ALANCADA EM 3X"
      ],
      "metadata": {
        "id": "4Eb6tYkymHQp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade zenfin\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tabulate as tab\n",
        "import math\n",
        "from zenfin import (\n",
        "    utils as u,\n",
        "    stats as s,\n",
        "    reports as r\n",
        ")"
      ],
      "metadata": {
        "id": "IjXWitL1yocm",
        "outputId": "4d5d8004-7880-43f6-b74b-9da8534a312d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: zenfin in /usr/local/lib/python3.7/dist-packages (0.5.23)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url = 'https://raw.githubusercontent.com/teracapital/tera/main/precos.csv?token=ABXSK4FJ7G3BNTOWZOQ25X3BZIORQ'\n",
        "df_copy = pd.read_csv(url)"
      ],
      "metadata": {
        "id": "9DMS90iqePof"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df_copy.copy()\n",
        "df = df.dropna()\n",
        "df[\"Date\"] = pd.to_datetime(df['Date'])\n",
        "df = df.set_index('Date')\n",
        "df = df.sort_index()"
      ],
      "metadata": {
        "id": "Y3YZSDpqko2U"
      },
      "execution_count": 4,
      "outputs": []
    },
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      "source": [
        "df"
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      "execution_count": 5,
      "outputs": [
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              "                  B5       B5+         B        DI     IHFA\n",
              "Date                                                       \n",
              "2007-09-28  1485.017  1724.495  1543.469  1.000418   982.51\n",
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              "...              ...       ...       ...       ...      ...\n",
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              "2021-12-08  7273.688  9598.791  8090.664  1.000293  4019.49\n",
              "\n",
              "[3564 rows x 5 columns]"
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    {
      "cell_type": "code",
      "source": [
        "df['DIr'] = df['DI'] -1\n",
        "df['DI'] = np.power(1+df['DIr'], 252) -1\n"
      ],
      "metadata": {
        "id": "yoygRAvfk6nl"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for c in df:\n",
        "  if 'DI' not in c:\n",
        "    df[c+\"r\"] = u.to_returns(df[c])"
      ],
      "metadata": {
        "id": "bDQUDdFXlaDv"
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      "execution_count": 7,
      "outputs": []
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              "      <td>0.076499</td>\n",
              "      <td>4019.49</td>\n",
              "      <td>0.000293</td>\n",
              "      <td>-0.000531</td>\n",
              "      <td>0.003986</td>\n",
              "      <td>0.001705</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-08</th>\n",
              "      <td>7273.688</td>\n",
              "      <td>9598.791</td>\n",
              "      <td>8090.664</td>\n",
              "      <td>0.076499</td>\n",
              "      <td>4019.49</td>\n",
              "      <td>0.000293</td>\n",
              "      <td>0.001398</td>\n",
              "      <td>0.005908</td>\n",
              "      <td>0.003635</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3564 rows × 10 columns</p>\n",
              "</div>\n",
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              "        buttonEl.style.display =\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<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",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                  B5       B5+         B  ...      B5+r        Br     IHFAr\n",
              "Date                                      ...                              \n",
              "2007-09-28  1485.017  1724.495  1543.469  ...  0.000000  0.000000  0.000000\n",
              "2007-10-01  1487.414  1729.508  1546.812  ...  0.002907  0.002166  0.001598\n",
              "2007-10-02  1487.609  1731.424  1547.659  ...  0.001108  0.000548 -0.000030\n",
              "2007-10-03  1487.339  1730.447  1547.125  ... -0.000564 -0.000345 -0.000569\n",
              "2007-10-04  1487.192  1728.644  1546.347  ... -0.001042 -0.000503  0.000102\n",
              "...              ...       ...       ...  ...       ...       ...       ...\n",
              "2021-12-02  7245.944  9432.687  8005.522  ...  0.004230  0.003087  0.000000\n",
              "2021-12-03  7275.176  9517.631  8057.475  ...  0.009005  0.006490  0.005760\n",
              "2021-12-06  7267.395  9504.533  8047.633  ... -0.001376 -0.001221  0.004262\n",
              "2021-12-07  7263.536  9542.419  8061.357  ...  0.003986  0.001705  0.000000\n",
              "2021-12-08  7273.688  9598.791  8090.664  ...  0.005908  0.003635  0.000000\n",
              "\n",
              "[3564 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def percent_string(number, dec):\n",
        "  return str(round(number*100, dec))+'%'"
      ],
      "metadata": {
        "id": "t-UM96Rz0lDb"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "table=[]\n",
        "date_beg = df.index.min().strftime('%d-%m-%y')\n",
        "date_end = df.index.max().strftime('%d-%m-%y')\n",
        "header = [date_beg+' to '+date_end]\n",
        "for c in df:\n",
        "  if \"r\" in c:\n",
        "    header.append(c.strip('r'))\n",
        "line = ['HPR']\n",
        "for c in df:\n",
        "  if \"r\" in c:\n",
        "    line.append(s.total_return(df[c]))\n",
        "table.append(line)"
      ],
      "metadata": {
        "id": "G9cnkFQtm9Ot"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(tab.tabulate(table, headers=header))"
      ],
      "metadata": {
        "id": "SXscH3vjRnpR",
        "outputId": "50993d35-ebdc-4f20-e621-15a9abfce073",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "28-09-07 to 08-12-21         DI       B5      B5+        B     IHFA\n",
            "----------------------  -------  -------  -------  -------  -------\n",
            "HPR                     2.44033  3.89805  4.56615  4.24187  3.09104\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in dir(s):\n",
        "  if \"__\" not in i: \n",
        "    print(i)"
      ],
      "metadata": {
        "id": "WtgiO6S0Rvw_",
        "outputId": "7aaa122c-a29c-4177-9af3-3bd88a32b032",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "adjusted_sortino\n",
            "alpha\n",
            "autocorr_penalty\n",
            "avg_loss\n",
            "avg_return\n",
            "avg_win\n",
            "best\n",
            "beta\n",
            "cagr\n",
            "common_sense_ratio\n",
            "conditional_value_at_risk\n",
            "consecutive_losses\n",
            "consecutive_wins\n",
            "cpc_index\n",
            "expected_return\n",
            "exponential_stdev\n",
            "gain_to_pain_ratio\n",
            "implied_volatility\n",
            "information_ratio\n",
            "kelly_criterion\n",
            "kurtosis\n",
            "linregress\n",
            "max_drawdown\n",
            "norm\n",
            "np\n",
            "omega\n",
            "outlier_loss_ratio\n",
            "outlier_win_ratio\n",
            "outliers\n",
            "payoff_ratio\n",
            "pd\n",
            "profit_factor\n",
            "profit_ratio\n",
            "r_squared\n",
            "rar\n",
            "recovery_factor\n",
            "risk_of_ruin\n",
            "risk_return_ratio\n",
            "rolling_greeks\n",
            "rolling_returns\n",
            "rolling_sharpe\n",
            "rolling_sortino\n",
            "rolling_volatility\n",
            "serenity_index\n",
            "sharpe\n",
            "skew\n",
            "sortino\n",
            "sqrt\n",
            "tail_ratio\n",
            "total_return\n",
            "ulcer_index\n",
            "ulcer_performance_index\n",
            "utils\n",
            "value_at_risk\n",
            "volatility\n",
            "win_rate\n",
            "worst\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dr = pd.DataFrame()\n",
        "dr[['B5', 'B', 'B5+', 'DI', 'IHFA']] = df[['B5r', 'Br', 'B5+r', 'DIr', 'IHFAr']]\n",
        "rf = df['DI']"
      ],
      "metadata": {
        "id": "1Td_6xlhWAu0"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dr, rf"
      ],
      "metadata": {
        "id": "WIdf9QKu8x6y",
        "outputId": "95a6faa2-4fc0-45d0-c255-0ea6d7b1249f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(                  B5         B       B5+        DI      IHFA\n",
              " Date                                                        \n",
              " 2007-09-28       NaN       NaN       NaN  0.000418       NaN\n",
              " 2007-10-01  0.001614  0.002166  0.002907  0.000418  0.001598\n",
              " 2007-10-02  0.000131  0.000548  0.001108  0.000418 -0.000030\n",
              " 2007-10-03 -0.000181 -0.000345 -0.000564  0.000418 -0.000569\n",
              " 2007-10-04 -0.000099 -0.000503 -0.001042  0.000418  0.000102\n",
              " ...              ...       ...       ...       ...       ...\n",
              " 2021-12-02  0.001974  0.003087  0.004230  0.000293  0.000000\n",
              " 2021-12-03  0.004034  0.006490  0.009005  0.000293  0.005760\n",
              " 2021-12-06 -0.001070 -0.001221 -0.001376  0.000293  0.004262\n",
              " 2021-12-07 -0.000531  0.001705  0.003986  0.000293  0.000000\n",
              " 2021-12-08  0.001398  0.003635  0.005908  0.000293  0.000000\n",
              " \n",
              " [3564 rows x 5 columns], Date\n",
              " 2007-09-28    0.111199\n",
              " 2007-10-01    0.111199\n",
              " 2007-10-02    0.111199\n",
              " 2007-10-03    0.111099\n",
              " 2007-10-04    0.111099\n",
              "                 ...   \n",
              " 2021-12-02    0.076499\n",
              " 2021-12-03    0.076499\n",
              " 2021-12-06    0.076499\n",
              " 2021-12-07    0.076499\n",
              " 2021-12-08    0.076499\n",
              " Name: DI, Length: 3564, dtype: float64)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def rar(returns, rf=0., periods=252):\n",
        "    \"\"\"\n",
        "    Calculates the risk-adjusted return of access returns\n",
        "    (CAGR / exposure. takes time into account.)\n",
        "    \"\"\"\n",
        "    excess_returns = u.to_excess_returns(returns, rf, periods)\n",
        "    return s.cagr(excess_returns) / u.exposure(excess_returns)"
      ],
      "metadata": {
        "id": "sMpy-DGzVQhM"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "s.serenity_index(dr.drop(['DI'], axis=1), rf)"
      ],
      "metadata": {
        "id": "q1HRh-v2KV7Y",
        "outputId": "7c1c10cc-510c-462b-b70d-c3e78e590fd4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "B5      437.960906\n",
              "B       218.874877\n",
              "B5+     173.581739\n",
              "IHFA    147.138461\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def beta(returns, benchmark ):\n",
        "  \"\"\"Calculates beta of the portfolio\"\"\"\n",
        "  # find covariance\n",
        "  frames = [returns, benchmark]\n",
        "  df = pd.concat(frames, axis=1)\n",
        "  matrix = df.cov()\n",
        "  res = {}\n",
        "  for c in returns:\n",
        "    res[c] = matrix[c].iloc[-1]/ matrix.iloc[-1,-1]\n",
        "  return pd.DataFrame(res, index=[0])\n",
        "\n",
        "def alpha(returns, benchmark, periods=252):\n",
        "  \"\"\"Calculates alpha of the portfolio\"\"\"\n",
        "\n",
        "  # find beta\n",
        "  _beta = beta(returns, benchmark)\n",
        "  # calculates measures now\n",
        "  alpha = returns.mean() - _beta * benchmark.mean()\n",
        "  return alpha * periods\n",
        "\n",
        "def rolling_greeks(returns, benchmark, periods=252):\n",
        "  \"\"\"Calculates rolling alpha and beta of the portfolio\"\"\"\n",
        "  frames = [returns, benchmark]\n",
        "  df = pd.concat(frames, axis=1)\n",
        "  df.columns = [*df.columns[:-1], 'benchmark']\n",
        "  df = df.fillna(0)\n",
        "  corr = df.rolling(int(periods)).corr().unstack()['benchmark'].drop('benchmark', axis=1)\n",
        "  std = df.rolling(int(periods)).std()\n",
        "  beta = pd.DataFrame()\n",
        "  alpha = pd.DataFrame()\n",
        "  for c in returns:\n",
        "    beta[c] = corr[c] * std[c] / std['benchmark']\n",
        "    alpha[c] = df[c].mean() - beta[c] * df['benchmark'].mean()\n",
        "  return pd.concat([beta,alpha],axis=1,keys=['beta','alpha']).swaplevel(0,1,axis=1).sort_index(axis=1).fillna(0)"
      ],
      "metadata": {
        "id": "wqASmbiLhPKR"
      },
      "execution_count": 377,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rolling_greeks(dr[['B5']], dr['IHFA'])"
      ],
      "metadata": {
        "id": "6ajJqqOWx-kX",
        "outputId": "705cfab5-8bcd-4f63-e99c-d0b1b3f57c36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        }
      },
      "execution_count": 378,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-9b54cd35-4e38-4203-bc7c-145bdf27a707\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">B5</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>alpha</th>\n",
              "      <th>beta</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2007-09-28</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-10-01</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-10-02</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-10-03</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-10-04</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-02</th>\n",
              "      <td>0.000364</td>\n",
              "      <td>0.208730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-03</th>\n",
              "      <td>0.000363</td>\n",
              "      <td>0.211847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-06</th>\n",
              "      <td>0.000365</td>\n",
              "      <td>0.207853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-07</th>\n",
              "      <td>0.000365</td>\n",
              "      <td>0.206161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-08</th>\n",
              "      <td>0.000365</td>\n",
              "      <td>0.206524</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3564 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9b54cd35-4e38-4203-bc7c-145bdf27a707')\"\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",
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              "  </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-9b54cd35-4e38-4203-bc7c-145bdf27a707 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-9b54cd35-4e38-4203-bc7c-145bdf27a707');\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",
              "  "
            ],
            "text/plain": [
              "                  B5          \n",
              "               alpha      beta\n",
              "Date                          \n",
              "2007-09-28  0.000000  0.000000\n",
              "2007-10-01  0.000000  0.000000\n",
              "2007-10-02  0.000000  0.000000\n",
              "2007-10-03  0.000000  0.000000\n",
              "2007-10-04  0.000000  0.000000\n",
              "...              ...       ...\n",
              "2021-12-02  0.000364  0.208730\n",
              "2021-12-03  0.000363  0.211847\n",
              "2021-12-06  0.000365  0.207853\n",
              "2021-12-07  0.000365  0.206161\n",
              "2021-12-08  0.000365  0.206524\n",
              "\n",
              "[3564 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 378
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Fokl41BA3toi"
      },
      "execution_count": 347,
      "outputs": []
    }
  ]
}