final-code-autoencoder-APT.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 139,
      "metadata": {
        "id": "6T5QoWRd0kfs"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import TensorDataset\n",
        "import datetime\n",
        "import gc  # garbage collect to reduce memory use\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import statsmodels.api as sm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "NPiL8h7X0kfu",
        "outputId": "d69b26ea-ce78-4b09-807b-401b53937f6f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Torch version:  2.3.0+cpu\n",
            "GPU Available: False\n"
          ]
        }
      ],
      "source": [
        "use_gpu = torch.cuda.is_available()\n",
        "device = torch.device(\"cuda:0\" if use_gpu else \"cpu\")\n",
        "print(\"Torch version: \", torch.__version__)\n",
        "print(\"GPU Available: {}\".format(use_gpu))\n",
        "\n",
        "seed = 1234\n",
        "np.random.seed(seed)\n",
        "torch.manual_seed(seed)\n",
        "torch.cuda.manual_seed(seed)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hdww5XNm0kfv"
      },
      "outputs": [],
      "source": [
        "class Autoencoder(nn.Module):\n",
        "    def __init__(self, input_dim, factor_dim):\n",
        "        super(Autoencoder, self).__init__()\n",
        "\n",
        "        # Beta side (1 hidden layer as an example)\n",
        "        self.beta_fc1 = nn.Linear(input_dim, 100, bias=False)\n",
        "        self.beta_bn1 = nn.BatchNorm1d(100)\n",
        "        self.beta_bn2 = nn.BatchNorm1d(100)\n",
        "        self.beta_fc2 = nn.Linear(100, factor_dim, bias=False)\n",
        "\n",
        "        self.beta_fc3 = nn.Linear(input_dim, 100, bias=False)\n",
        "        self.beta_fc4 = nn.Linear(100, factor_dim, bias=False)\n",
        "        self.layernorm1 = nn.LayerNorm(100, eps=1e-6)\n",
        "        self.layernorm2 = nn.LayerNorm(100, eps=1e-6)\n",
        "        self.dropout1 = nn.Dropout(0.1)\n",
        "        self.dropout2 = nn.Dropout(0.3)\n",
        "\n",
        "        # Factor side\n",
        "        self.factor_fc1 = nn.Linear(input_dim, factor_dim, bias=False)\n",
        "\n",
        "    def forward(self, firm_chars, portfolios):\n",
        "        # Beta side\n",
        "        x = F.relu(self.beta_bn1(self.beta_fc1(firm_chars)))\n",
        "\n",
        "        #x = self.dropout1(self.beta_fc2(x))\n",
        "\n",
        "        x = self.dropout1(self.beta_fc2(x))\n",
        "\n",
        "        x = torch.squeeze(x)\n",
        "\n",
        "        # Factor side\n",
        "        y = F.relu(self.beta_bn2(self.beta_fc3(portfolios)))\n",
        "\n",
        "        #y = self.dropout2(self.beta_fc4(y))\n",
        "        y = self.dropout2(self.beta_fc4(y))\n",
        "\n",
        "        y = torch.squeeze(y)\n",
        "\n",
        "        # print(y.shape, x.shape)\n",
        "\n",
        "        # Dot product\n",
        "        out = torch.sum(x * y, dim=1)\n",
        "\n",
        "        return x, y, out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mtuVjlq30kfv"
      },
      "outputs": [],
      "source": [
        "def train_ae(model, criterion, train_loader, optimizer, l1_lambda, device):\n",
        "    # activate the training mode\n",
        "    model.train()\n",
        "    torch.set_grad_enabled(True)\n",
        "    total_loss = 0\n",
        "\n",
        "    # iteration over the mini-batches\n",
        "    for batch_idx, (firm_chars, portfolios, target) in enumerate(train_loader):\n",
        "        # transfer the data on the chosen device\n",
        "        firm_chars, portfolios, target = firm_chars.to(device), portfolios.to(device), target.to(device)\n",
        "\n",
        "        # reinitialize the gradients to zero\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # forward propagation on the data\n",
        "        beta, factor, prediction = model(firm_chars, portfolios)\n",
        "\n",
        "        # compute the cost function w.r.t. the targets\n",
        "        loss = criterion(prediction, target)\n",
        "\n",
        "        # add l1 regularization to loss function\n",
        "        l1_norm = torch.linalg.norm(model.beta_fc1.weight, ord=1) + torch.linalg.norm(model.beta_fc2.weight, ord=1) + torch.linalg.norm(model.factor_fc1.weight, ord=1)\n",
        "        loss += l1_lambda * l1_norm\n",
        "\n",
        "        # execute the backpropagation\n",
        "        loss.backward()\n",
        "\n",
        "        # execute an optimization step\n",
        "        optimizer.step()\n",
        "\n",
        "        # accumulate the loss\n",
        "        total_loss += loss.item() * len(firm_chars)\n",
        "\n",
        "    # compute the average cost per epoch\n",
        "    mean_loss = total_loss / len(train_loader.dataset)\n",
        "\n",
        "    return mean_loss\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EjVixaSp0kfw"
      },
      "outputs": [],
      "source": [
        "def evaluate_ae(model, criterion, eval_loader, device):\n",
        "    # activate the evaluation mode\n",
        "    model.eval()\n",
        "    total_loss = 0\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for batch_idx, (firm_chars, portfolios, target) in enumerate(eval_loader):\n",
        "            # transfer the data on the chosen device\n",
        "            firm_chars, portfolios, target = firm_chars.to(device), portfolios.to(device), target.to(device)\n",
        "\n",
        "            # forward propagation on the data\n",
        "            beta, factor, prediction = model(firm_chars, portfolios)\n",
        "\n",
        "            # compute the cost function w.r.t. the targets\n",
        "            loss = criterion(prediction, target)\n",
        "\n",
        "            # accumulate the loss\n",
        "            total_loss += loss.item() * len(firm_chars)\n",
        "\n",
        "    # compute the average cost per epoch\n",
        "    mean_loss = total_loss / len(eval_loader.dataset)\n",
        "\n",
        "    return mean_loss\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M27HaXl20kfw"
      },
      "outputs": [],
      "source": [
        "def ae_learning_loop(ensemble, input_size, factor_size, batch_size, num_epoch,\n",
        "                     l1_lambda, learning_rate, train_dataset, val_dataset, test_dataset,\n",
        "                     patience, device):\n",
        "\n",
        "    output_name = 'best_model_ae.pth'\n",
        "    mean_pred = np.zeros((test_dataset[0].shape[0], ensemble))\n",
        "\n",
        "    for i in range(ensemble):\n",
        "        # accumulators of average costs obtained per epoch\n",
        "        train_losses = []\n",
        "        val_losses = []\n",
        "\n",
        "        # model definition\n",
        "        model = Autoencoder(input_size, factor_size)\n",
        "        model = model.to(device)\n",
        "\n",
        "        # loss function\n",
        "        criterion = nn.MSELoss()\n",
        "\n",
        "        # optimizer definition\n",
        "        optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
        "\n",
        "        # create mini-batches\n",
        "        train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
        "        val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=False)\n",
        "\n",
        "        # learning loop\n",
        "        min_val_loss = np.inf  # for early stopping, record the minimum validation loss\n",
        "        num_no_improve = 0  # record the number of epochs with no improvements\n",
        "\n",
        "        for epoch in range(num_epoch):\n",
        "            # train the model\n",
        "            train_loss = train_ae(model, criterion, train_loader, optimizer, l1_lambda, device)\n",
        "\n",
        "            # get the validation loss\n",
        "            val_loss = evaluate_ae(model, criterion, val_loader, device)\n",
        "\n",
        "            # Save the costs obtained\n",
        "            train_losses.append(train_loss)\n",
        "            val_losses.append(val_loss)\n",
        "\n",
        "            # early stopping\n",
        "            if val_loss < min_val_loss:\n",
        "                min_val_loss = val_loss\n",
        "                num_no_improve = 0\n",
        "\n",
        "                # save the best model\n",
        "                torch.save(model.state_dict(), output_name)\n",
        "            else:\n",
        "                num_no_improve += 1\n",
        "\n",
        "            if num_no_improve >= patience:\n",
        "                break\n",
        "\n",
        "        model.load_state_dict(torch.load(output_name))  # load the best model\n",
        "        model.eval()\n",
        "        firm_chars, portfolios = test_dataset[0], test_dataset[1]\n",
        "        beta, factor, prediction = model(firm_chars, portfolios)  # get the OOS prediction\n",
        "        mean_pred[:, i] = prediction.cpu().data.numpy()\n",
        "\n",
        "    # compute the mean predictions across ensembles\n",
        "    mean_pred = mean_pred.mean(axis=1)\n",
        "    return mean_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "-JAv8M930kfw",
        "outputId": "daed1d6b-42f9-4716-a91c-b6c9e36089d7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2024-06-14 10:56:05.397111\n"
          ]
        }
      ],
      "source": [
        " # for timing purpose\n",
        "print(datetime.datetime.now())\n",
        "\n",
        "# turn off Setting with Copy Warning\n",
        "pd.set_option('mode.chained_assignment', None)\n",
        "\n",
        "# read sample data\n",
        "file_path = './homework_sample_small.csv'\n",
        "raw = pd.read_csv(file_path, parse_dates=['date'], low_memory=False)\n",
        "raw = raw.drop(columns=['date'])\n",
        "raw['day'] = 1\n",
        "raw['date'] = pd.to_datetime(raw[['year', 'month', 'day']])  # first day of the return month\n",
        "raw = raw.drop(columns=['day'])\n",
        "raw = raw[raw['stock_exret'].notna()]\n",
        "gc.collect()\n",
        "\n",
        "# read list of predictors\n",
        "file_path = './factors_char_list1.csv'\n",
        "var_list = list(pd.read_csv(file_path)['variable'].values)\n",
        "binary_vars = ['convind', 'divi', 'divo', 'rd', 'securedind', 'sin']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "rjHOHsk10kfw"
      },
      "outputs": [],
      "source": [
        "raw.drop(['zero_trades_126d'],axis=1, inplace= True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [
        {
          "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>ret_eom</th>\n",
              "      <th>permno</th>\n",
              "      <th>stock_ret</th>\n",
              "      <th>mspread</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>sizeport</th>\n",
              "      <th>roeport</th>\n",
              "      <th>me</th>\n",
              "      <th>prc</th>\n",
              "      <th>...</th>\n",
              "      <th>betabab_1260d</th>\n",
              "      <th>rmax5_rvol_21d</th>\n",
              "      <th>age</th>\n",
              "      <th>qmj</th>\n",
              "      <th>qmj_prof</th>\n",
              "      <th>qmj_growth</th>\n",
              "      <th>qmj_safety</th>\n",
              "      <th>rf</th>\n",
              "      <th>stock_exret</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20050131</td>\n",
              "      <td>10012</td>\n",
              "      <td>-0.320388</td>\n",
              "      <td>0.031502</td>\n",
              "      <td>2005</td>\n",
              "      <td>1</td>\n",
              "      <td>S</td>\n",
              "      <td>V</td>\n",
              "      <td>24.443959</td>\n",
              "      <td>1.030000</td>\n",
              "      <td>...</td>\n",
              "      <td>3.252622</td>\n",
              "      <td>3.413290</td>\n",
              "      <td>240</td>\n",
              "      <td>-1.704654</td>\n",
              "      <td>-1.537990</td>\n",
              "      <td>-1.728815</td>\n",
              "      <td>-1.419387</td>\n",
              "      <td>0.0016</td>\n",
              "      <td>-0.321988</td>\n",
              "      <td>2005-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20050131</td>\n",
              "      <td>10025</td>\n",
              "      <td>0.277966</td>\n",
              "      <td>0.006849</td>\n",
              "      <td>2005</td>\n",
              "      <td>1</td>\n",
              "      <td>S</td>\n",
              "      <td>P</td>\n",
              "      <td>123.973750</td>\n",
              "      <td>14.750000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.355911</td>\n",
              "      <td>3.058774</td>\n",
              "      <td>240</td>\n",
              "      <td>-1.338785</td>\n",
              "      <td>-0.574028</td>\n",
              "      <td>-1.094585</td>\n",
              "      <td>-1.091335</td>\n",
              "      <td>0.0016</td>\n",
              "      <td>0.276366</td>\n",
              "      <td>2005-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20050131</td>\n",
              "      <td>10026</td>\n",
              "      <td>-0.015909</td>\n",
              "      <td>0.012113</td>\n",
              "      <td>2005</td>\n",
              "      <td>1</td>\n",
              "      <td>S</td>\n",
              "      <td>V</td>\n",
              "      <td>442.348649</td>\n",
              "      <td>49.029999</td>\n",
              "      <td>...</td>\n",
              "      <td>0.811736</td>\n",
              "      <td>0.902699</td>\n",
              "      <td>240</td>\n",
              "      <td>1.095448</td>\n",
              "      <td>1.392309</td>\n",
              "      <td>-0.813281</td>\n",
              "      <td>1.475873</td>\n",
              "      <td>0.0016</td>\n",
              "      <td>-0.017509</td>\n",
              "      <td>2005-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20050131</td>\n",
              "      <td>10028</td>\n",
              "      <td>-0.063830</td>\n",
              "      <td>0.015489</td>\n",
              "      <td>2005</td>\n",
              "      <td>1</td>\n",
              "      <td>S</td>\n",
              "      <td>V</td>\n",
              "      <td>13.854660</td>\n",
              "      <td>2.820000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.954925</td>\n",
              "      <td>1.518679</td>\n",
              "      <td>240</td>\n",
              "      <td>-0.159205</td>\n",
              "      <td>-0.354419</td>\n",
              "      <td>1.169657</td>\n",
              "      <td>-0.993571</td>\n",
              "      <td>0.0016</td>\n",
              "      <td>-0.065430</td>\n",
              "      <td>2005-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20050131</td>\n",
              "      <td>10032</td>\n",
              "      <td>-0.114527</td>\n",
              "      <td>0.010306</td>\n",
              "      <td>2005</td>\n",
              "      <td>1</td>\n",
              "      <td>S</td>\n",
              "      <td>V</td>\n",
              "      <td>562.019000</td>\n",
              "      <td>13.010000</td>\n",
              "      <td>...</td>\n",
              "      <td>2.608261</td>\n",
              "      <td>0.734373</td>\n",
              "      <td>228</td>\n",
              "      <td>-1.437156</td>\n",
              "      <td>-1.493778</td>\n",
              "      <td>-1.581260</td>\n",
              "      <td>-0.094867</td>\n",
              "      <td>0.0016</td>\n",
              "      <td>-0.116127</td>\n",
              "      <td>2005-01-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 158 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    ret_eom  permno  stock_ret   mspread  year  month sizeport roeport  \\\n",
              "0  20050131   10012  -0.320388  0.031502  2005      1        S       V   \n",
              "1  20050131   10025   0.277966  0.006849  2005      1        S       P   \n",
              "2  20050131   10026  -0.015909  0.012113  2005      1        S       V   \n",
              "3  20050131   10028  -0.063830  0.015489  2005      1        S       V   \n",
              "4  20050131   10032  -0.114527  0.010306  2005      1        S       V   \n",
              "\n",
              "           me        prc  ...  betabab_1260d  rmax5_rvol_21d  age       qmj  \\\n",
              "0   24.443959   1.030000  ...       3.252622        3.413290  240 -1.704654   \n",
              "1  123.973750  14.750000  ...       0.355911        3.058774  240 -1.338785   \n",
              "2  442.348649  49.029999  ...       0.811736        0.902699  240  1.095448   \n",
              "3   13.854660   2.820000  ...       0.954925        1.518679  240 -0.159205   \n",
              "4  562.019000  13.010000  ...       2.608261        0.734373  228 -1.437156   \n",
              "\n",
              "   qmj_prof  qmj_growth  qmj_safety      rf  stock_exret       date  \n",
              "0 -1.537990   -1.728815   -1.419387  0.0016    -0.321988 2005-01-01  \n",
              "1 -0.574028   -1.094585   -1.091335  0.0016     0.276366 2005-01-01  \n",
              "2  1.392309   -0.813281    1.475873  0.0016    -0.017509 2005-01-01  \n",
              "3 -0.354419    1.169657   -0.993571  0.0016    -0.065430 2005-01-01  \n",
              "4 -1.493778   -1.581260   -0.094867  0.0016    -0.116127 2005-01-01  \n",
              "\n",
              "[5 rows x 158 columns]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "raw.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "WTpFOwd40kfw"
      },
      "outputs": [],
      "source": [
        "var_list.remove('zero_trades_126d')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VPxDBnCu0kfx"
      },
      "outputs": [],
      "source": [
        "panel = raw.pivot_table(index = 'date', columns = 'permno', values = 'stock_exret', aggfunc = lambda x:x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OieEIgDG0kfx",
        "outputId": "79355d5a-6d60-43d4-97a7-1db1bb540971"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='date'>"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "np.invert(panel.isna()).sum(1).plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k7Riu2Hk0kfx",
        "outputId": "e19c9f53-2134-425c-a91c-6a3e4001367b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "e:\\Users\\tsong\\anaconda3\\Lib\\site-packages\\seaborn\\matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.\n",
            "  warnings.warn(msg)\n",
            "e:\\Users\\tsong\\anaconda3\\Lib\\site-packages\\seaborn\\matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.\n",
            "  warnings.warn(msg)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<seaborn.matrix.ClusterGrid at 0x1d802fa26d0>"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1000x1000 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.clustermap(raw[var_list+['stock_exret']].corr())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0QhFxxAm0kfx"
      },
      "outputs": [],
      "source": [
        "# transform each variable in each month\n",
        "monthly = raw.groupby('date')\n",
        "adj_raw = pd.DataFrame()\n",
        "for date, monthly_raw in monthly:\n",
        "    group = monthly_raw.copy()\n",
        "    # rank transform each variable to [-1, 1]\n",
        "    for var in var_list:\n",
        "        var_median = group[var].median(skipna=True)\n",
        "        group[var] = group[var].fillna(var_median)\n",
        "\n",
        "        if var in binary_vars:\n",
        "            pass\n",
        "        else:\n",
        "            group[var] = group[var].rank(method='dense') - 1\n",
        "            group_max = group[var].max()\n",
        "            if group_max > 0:\n",
        "                group[var] = (group[var] / group_max) * 2 - 1\n",
        "            else:\n",
        "                group[var] = 0  # in case of all missing values\n",
        "                print('Warning:', date, var, 'set to zero.')\n",
        "\n",
        "    # add the adjusted values\n",
        "    adj_raw = pd.concat([adj_raw,group],axis = 0)\n",
        "    gc.collect()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XGcAlIYJ0kfx",
        "outputId": "9039210c-42e5-4aa6-a909-0679bf31d2bb"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\tsong\\AppData\\Local\\Temp\\ipykernel_11488\\336586901.py:2: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  adj_raw['constant'] = 1\n"
          ]
        }
      ],
      "source": [
        "# add a constant to the list of characteristics\n",
        "adj_raw['constant'] = 1\n",
        "var_list.append('constant')\n",
        "port_list = [var + '_port' for var in var_list]\n",
        "\n",
        "# create managed portfolios for each month\n",
        "monthly_adj = adj_raw.groupby('date')\n",
        "data = pd.DataFrame()\n",
        "for date, group in monthly_adj:\n",
        "    expand = group.copy()\n",
        "    Zt = group[var_list].values\n",
        "    Rt = group['stock_exret'].values\n",
        "    Xt = LinearRegression(fit_intercept=False).fit(Zt, Rt).coef_\n",
        "    X_port = pd.DataFrame(data=[Xt], columns=port_list)\n",
        "    expand = pd.concat([expand.reset_index(drop=True), X_port.reset_index(drop=True)], axis=1)\n",
        "\n",
        "    # print(expand.shape)\n",
        "    # print(expand.head())\n",
        "\n",
        "\n",
        "    expand.ffill(inplace=True)\n",
        "    data = pd.concat([data,expand],axis=0)\n",
        "    gc.collect()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bIfhngtD0kfy",
        "outputId": "37a48fa3-6397-45c4-9810-86a2005c5faf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1, 147)"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_port.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mnh0xV090kfy",
        "outputId": "de934e4f-f13b-4777-b782-6b572fda7159"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(147,)"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Xt.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zJuhA61T0kfy",
        "outputId": "ec956d51-de54-4c1c-b26c-ba749fc93200"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1757, 147)"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Zt.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bUuteXNF0kfy",
        "outputId": "c61e6564-4d5f-4ab1-e47d-e72a23083225"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1, 147)"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_port.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VNuRXaWP0kfy"
      },
      "outputs": [],
      "source": [
        "# estimation with expanding window\n",
        "\n",
        "# initialize\n",
        "starting = pd.to_datetime('20050101', format=\"%Y%m%d\")\n",
        "counter = 0\n",
        "pred_out = pd.DataFrame()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2Ltgqj5Y0kfy",
        "outputId": "860e3e6e-7d5f-475d-af0d-1ffebd1479e9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2024-05-29 22:41:54.943476\n",
            "2024-05-29 22:51:14.808424\n",
            "2024-05-29 23:00:47.357275\n",
            "2024-05-29 23:11:30.926218\n"
          ]
        }
      ],
      "source": [
        "while (starting + pd.DateOffset(years= 12 + counter)) <= pd.to_datetime('20200101', format='%Y%m%d'):\n",
        "    cutoff = [starting, starting + pd.DateOffset(years=10 + counter),\n",
        "                starting + pd.DateOffset(years=12 + counter),\n",
        "                starting + pd.DateOffset(years=31 + counter)]\n",
        "\n",
        "    # cut the sample into training, validation, and testing sets\n",
        "    train = data[(data['date'] >= cutoff[0]) & (data['date'] < cutoff[1])]\n",
        "    validate = data[(data['date'] >= cutoff[1]) & (data['date'] < cutoff[2])]\n",
        "    test = data[(data['date'] >= cutoff[2]) & (data['date'] < cutoff[3])]\n",
        "\n",
        "\n",
        "\n",
        "    # get Xs and Ys and Zs\n",
        "    Z_train = train[var_list].values\n",
        "    X_train = train[port_list].values\n",
        "    Y_train = train['stock_exret'].values\n",
        "\n",
        "    Z_val = validate[var_list].values\n",
        "    X_val = validate[port_list].values\n",
        "    Y_val = validate['stock_exret'].values\n",
        "\n",
        "    Z_test = test[var_list].values\n",
        "    X_test = test[port_list].values\n",
        "    Y_test = test['stock_exret'].values\n",
        "\n",
        "    #print(Z_train)\n",
        "\n",
        "    # prepare output data\n",
        "    pred_batch = test[['year', 'month', 'date', 'permno', 'stock_exret']]\n",
        "\n",
        "    # load data into tensor datasets (firm_chars, portfolios, target)\n",
        "    train_dataset = TensorDataset(torch.from_numpy(Z_train).float(), torch.from_numpy(X_train).float(), torch.from_numpy(Y_train).float())\n",
        "    val_dataset = TensorDataset(torch.from_numpy(Z_val).float(), torch.from_numpy(X_val).float(), torch.from_numpy(Y_val).float())\n",
        "    Z_test = torch.from_numpy(Z_test).float()\n",
        "    Z_test = Z_test.to(device)\n",
        "    X_test = torch.from_numpy(X_test).float()\n",
        "    X_test = X_test.to(device)\n",
        "    test_dataset = (Z_test, X_test)\n",
        "\n",
        "    # define some general variables for nn\n",
        "    batch_size = 10000\n",
        "    num_epoch = 100\n",
        "    patience = 5\n",
        "    ensemble = 10\n",
        "    input_size = len(var_list)\n",
        "    factor_size = 15  # could be a hyperparameter\n",
        "    l1_lambda = 0.0001  # could be a hyperparameter\n",
        "    learning_rate = 0.001  # could be a hyperparameter\n",
        "\n",
        "    pred_batch['ae'] = ae_learning_loop(ensemble, input_size, factor_size, batch_size, num_epoch,\n",
        "                                        l1_lambda, learning_rate, train_dataset, val_dataset, test_dataset,\n",
        "                                        patience, device)\n",
        "\n",
        "    # add to the output data\n",
        "    pred_out = pd.concat([pred_out,pred_batch], axis = 0)\n",
        "\n",
        "    # go to the next year\n",
        "    counter += 1\n",
        "\n",
        "    # for timing purpose\n",
        "    print(datetime.datetime.now())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Rba816ke0kfz"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xn2-OISK0kfz",
        "outputId": "fa62a1a3-6d3d-4154-fa3f-42e206874a32"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.015764807417151583\n"
          ]
        }
      ],
      "source": [
        "# print the OOS Total R2\n",
        "yreal = pred_out['stock_exret'].values\n",
        "ypred = pred_out['ae'].values\n",
        "r2 = 1 - np.sum(np.square((yreal - ypred))) / np.sum(np.square(yreal))\n",
        "print(r2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 162,
      "metadata": {
        "id": "Jir99b0I0kfz",
        "outputId": "33bf1589-88d3-4ed4-bbc3-a9e4076ab5c5"
      },
      "outputs": [
        {
          "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>stock_exret</th>\n",
              "      <th>ae</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>stock_exret</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.214065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ae</th>\n",
              "      <td>0.214065</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             stock_exret        ae\n",
              "stock_exret     1.000000  0.214065\n",
              "ae              0.214065  1.000000"
            ]
          },
          "execution_count": 162,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pred_out[['stock_exret','ae']].corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8242dC3r0kfz",
        "outputId": "f91ef26d-8720-41b7-8c85-5d41e50ea64e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "pred_out['stock_exret'].hist(bins = 100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CmqiIWAX0kfz",
        "outputId": "b8e71666-c34f-48ed-9384-758268a85ea0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "pred_out['ae'].hist(bins = 100, alpha = 0.5, color = 'r')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gO5qt7d70kfz"
      },
      "outputs": [],
      "source": [
        "pred_out.to_csv('pred_out.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "pred_out = pd.read_csv('pred_out.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "T1C583x60kfz"
      },
      "outputs": [],
      "source": [
        "top_50 = pd.DataFrame()\n",
        "for d in pred_out['date'].unique():\n",
        "\n",
        "    top_50 = pd.concat([top_50, pred_out[pred_out.date == d].sort_values('ae', ascending = False).iloc[:1]],axis = 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "RQM4-RqF0kfz"
      },
      "outputs": [],
      "source": [
        "rnd = pd.DataFrame()\n",
        "for d in pred_out['date'].unique():\n",
        "\n",
        "    rnd = pd.concat([rnd, pred_out[pred_out.date == d].sort_values('ae',ascending = False).iloc[:100].sample(50, replace= False)],axis = 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "_ZJb41x50kfz"
      },
      "outputs": [],
      "source": [
        "rnd_50 = pd.DataFrame()\n",
        "for d in pred_out['date'].unique():\n",
        "\n",
        "    rnd_50 = pd.concat([rnd_50, pred_out[pred_out.date == d].sort_values('ae',ascending = False).iloc[:500].sample(50, replace= False)],axis = 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "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>Unnamed: 0</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>date</th>\n",
              "      <th>permno</th>\n",
              "      <th>stock_exret</th>\n",
              "      <th>ae</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>876</th>\n",
              "      <td>876</td>\n",
              "      <td>2017</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01</td>\n",
              "      <td>81566</td>\n",
              "      <td>-0.002622</td>\n",
              "      <td>0.006635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>570</th>\n",
              "      <td>570</td>\n",
              "      <td>2017</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01</td>\n",
              "      <td>25304</td>\n",
              "      <td>-0.018782</td>\n",
              "      <td>0.006316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>287</th>\n",
              "      <td>287</td>\n",
              "      <td>2017</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01</td>\n",
              "      <td>14451</td>\n",
              "      <td>-0.140342</td>\n",
              "      <td>0.007949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>841</th>\n",
              "      <td>841</td>\n",
              "      <td>2017</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01</td>\n",
              "      <td>80054</td>\n",
              "      <td>0.012571</td>\n",
              "      <td>0.006931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>319</th>\n",
              "      <td>319</td>\n",
              "      <td>2017</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01</td>\n",
              "      <td>14556</td>\n",
              "      <td>-0.142240</td>\n",
              "      <td>0.004925</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Unnamed: 0  year  month        date  permno  stock_exret        ae\n",
              "876         876  2017      1  2017-01-01   81566    -0.002622  0.006635\n",
              "570         570  2017      1  2017-01-01   25304    -0.018782  0.006316\n",
              "287         287  2017      1  2017-01-01   14451    -0.140342  0.007949\n",
              "841         841  2017      1  2017-01-01   80054     0.012571  0.006931\n",
              "319         319  2017      1  2017-01-01   14556    -0.142240  0.004925"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rnd_50.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "YTNdB0_X0kf0"
      },
      "outputs": [],
      "source": [
        "rnd['1+stock_exret'] = 1+rnd['stock_exret']\n",
        "rnd['1+ae'] = 1+rnd['ae']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "LaLuIoQB0kf0",
        "outputId": "ba20d15f-f89d-42ef-eb0a-3f549a9f6ebb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='date'>"
            ]
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "rnd.groupby('date')[['stock_exret','ae']].mean().cumsum().plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "4o2HiZGq0kf0",
        "outputId": "c41cc320-35bd-4a41-c5af-3c4b80db5317"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='date'>"
            ]
          },
          "execution_count": 74,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "rnd_50['1+stock_exret'] = 1+rnd_50['stock_exret']\n",
        "rnd_50['1+ae'] = 1+rnd_50['ae']\n",
        "\n",
        "rnd_50.groupby('date')[['stock_exret','ae']].mean().cumsum().plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pu2v2dml0kf0"
      },
      "outputs": [],
      "source": [
        "top_50['1+stock_exret'] = 1+top_50['stock_exret']\n",
        "top_50['1+ae'] = 1+top_50['ae']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tj3MJzwJ0kf0",
        "outputId": "932e4c88-6151-4718-d7e6-2e18997590a4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='date'>"
            ]
          },
          "execution_count": 99,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "top_50.groupby('date')[['stock_exret','ae']].mean().cumsum().plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "VSMAPRGj0kf0"
      },
      "outputs": [],
      "source": [
        "spy = pd.read_csv('E:/Final Paper/SPY.csv', parse_dates = ['timestamp'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "YRMhpDzZ0kf0"
      },
      "outputs": [],
      "source": [
        "spy.set_index('timestamp',inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "uWV23G0W0kf0"
      },
      "outputs": [],
      "source": [
        "spy = spy[spy.index.is_month_start]\n",
        "spy = spy.iloc[::-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "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>Unnamed: 0</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>adjusted_close</th>\n",
              "      <th>volume</th>\n",
              "      <th>dividend</th>\n",
              "      <th>split</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>timestamp</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>1999-11-01</th>\n",
              "      <td>6031</td>\n",
              "      <td>136.5000</td>\n",
              "      <td>137.000000</td>\n",
              "      <td>135.562500</td>\n",
              "      <td>135.562500</td>\n",
              "      <td>87.576805</td>\n",
              "      <td>4006500</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999-12-01</th>\n",
              "      <td>6010</td>\n",
              "      <td>139.3125</td>\n",
              "      <td>140.500000</td>\n",
              "      <td>139.000000</td>\n",
              "      <td>140.406204</td>\n",
              "      <td>90.705960</td>\n",
              "      <td>6980200</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-02-01</th>\n",
              "      <td>5968</td>\n",
              "      <td>139.7500</td>\n",
              "      <td>141.687500</td>\n",
              "      <td>138.531204</td>\n",
              "      <td>140.937500</td>\n",
              "      <td>91.270965</td>\n",
              "      <td>8419900</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-03-01</th>\n",
              "      <td>5948</td>\n",
              "      <td>137.6250</td>\n",
              "      <td>139.000000</td>\n",
              "      <td>137.218704</td>\n",
              "      <td>138.437500</td>\n",
              "      <td>89.651967</td>\n",
              "      <td>6868000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-05-01</th>\n",
              "      <td>5906</td>\n",
              "      <td>146.5625</td>\n",
              "      <td>148.484299</td>\n",
              "      <td>145.843704</td>\n",
              "      <td>147.062500</td>\n",
              "      <td>95.477856</td>\n",
              "      <td>7328300</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-03-01</th>\n",
              "      <td>162</td>\n",
              "      <td>395.4100</td>\n",
              "      <td>396.690000</td>\n",
              "      <td>393.380000</td>\n",
              "      <td>394.740000</td>\n",
              "      <td>390.366871</td>\n",
              "      <td>99706823</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-05-01</th>\n",
              "      <td>120</td>\n",
              "      <td>415.4700</td>\n",
              "      <td>417.620000</td>\n",
              "      <td>415.265000</td>\n",
              "      <td>415.510000</td>\n",
              "      <td>412.493758</td>\n",
              "      <td>62122254</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-06-01</th>\n",
              "      <td>98</td>\n",
              "      <td>418.0900</td>\n",
              "      <td>422.920000</td>\n",
              "      <td>416.790000</td>\n",
              "      <td>421.820000</td>\n",
              "      <td>418.757953</td>\n",
              "      <td>88865018</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-08-01</th>\n",
              "      <td>57</td>\n",
              "      <td>456.2700</td>\n",
              "      <td>457.250000</td>\n",
              "      <td>455.490000</td>\n",
              "      <td>456.480000</td>\n",
              "      <td>454.855818</td>\n",
              "      <td>55502199</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-09-01</th>\n",
              "      <td>34</td>\n",
              "      <td>453.1700</td>\n",
              "      <td>453.670000</td>\n",
              "      <td>449.680000</td>\n",
              "      <td>451.190000</td>\n",
              "      <td>449.584640</td>\n",
              "      <td>58944098</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>186 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Unnamed: 0      open        high         low       close  \\\n",
              "timestamp                                                              \n",
              "1999-11-01        6031  136.5000  137.000000  135.562500  135.562500   \n",
              "1999-12-01        6010  139.3125  140.500000  139.000000  140.406204   \n",
              "2000-02-01        5968  139.7500  141.687500  138.531204  140.937500   \n",
              "2000-03-01        5948  137.6250  139.000000  137.218704  138.437500   \n",
              "2000-05-01        5906  146.5625  148.484299  145.843704  147.062500   \n",
              "...                ...       ...         ...         ...         ...   \n",
              "2023-03-01         162  395.4100  396.690000  393.380000  394.740000   \n",
              "2023-05-01         120  415.4700  417.620000  415.265000  415.510000   \n",
              "2023-06-01          98  418.0900  422.920000  416.790000  421.820000   \n",
              "2023-08-01          57  456.2700  457.250000  455.490000  456.480000   \n",
              "2023-09-01          34  453.1700  453.670000  449.680000  451.190000   \n",
              "\n",
              "            adjusted_close    volume  dividend  split  \n",
              "timestamp                                              \n",
              "1999-11-01       87.576805   4006500       0.0    1.0  \n",
              "1999-12-01       90.705960   6980200       0.0    1.0  \n",
              "2000-02-01       91.270965   8419900       0.0    1.0  \n",
              "2000-03-01       89.651967   6868000       0.0    1.0  \n",
              "2000-05-01       95.477856   7328300       0.0    1.0  \n",
              "...                    ...       ...       ...    ...  \n",
              "2023-03-01      390.366871  99706823       0.0    1.0  \n",
              "2023-05-01      412.493758  62122254       0.0    1.0  \n",
              "2023-06-01      418.757953  88865018       0.0    1.0  \n",
              "2023-08-01      454.855818  55502199       0.0    1.0  \n",
              "2023-09-01      449.584640  58944098       0.0    1.0  \n",
              "\n",
              "[186 rows x 9 columns]"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "spy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {},
      "outputs": [],
      "source": [
        "spy = np.log(spy['adjusted_close']/spy['adjusted_close'].shift(1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "2UDyOw9V0kf0"
      },
      "outputs": [],
      "source": [
        "# spy = spy['adjusted_close']/spy['adjusted_close'].shift(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [],
      "source": [
        "rf = raw.groupby(['date'])['rf'].mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['ret_eom', 'permno', 'stock_ret', 'mspread', 'year', 'month',\n",
              "       'sizeport', 'roeport', 'me', 'prc',\n",
              "       ...\n",
              "       'betabab_1260d', 'rmax5_rvol_21d', 'age', 'qmj', 'qmj_prof',\n",
              "       'qmj_growth', 'qmj_safety', 'rf', 'stock_exret', 'date'],\n",
              "      dtype='object', length=158)"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "raw.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [],
      "source": [
        "ret = raw[['date','permno','stock_ret']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {},
      "outputs": [],
      "source": [
        "ret['date'] = ret['date'].astype('str')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 4200 entries, 876 to 394333\n",
            "Data columns (total 7 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   Unnamed: 0   4200 non-null   int64  \n",
            " 1   year         4200 non-null   int64  \n",
            " 2   month        4200 non-null   int64  \n",
            " 3   date         4200 non-null   object \n",
            " 4   permno       4200 non-null   int64  \n",
            " 5   stock_exret  4200 non-null   float64\n",
            " 6   ae           4200 non-null   float64\n",
            "dtypes: float64(2), int64(4), object(1)\n",
            "memory usage: 262.5+ KB\n"
          ]
        }
      ],
      "source": [
        "rnd_50.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 381355 entries, 0 to 381354\n",
            "Data columns (total 3 columns):\n",
            " #   Column     Non-Null Count   Dtype  \n",
            "---  ------     --------------   -----  \n",
            " 0   date       381355 non-null  object \n",
            " 1   permno     381355 non-null  int64  \n",
            " 2   stock_ret  381355 non-null  float64\n",
            "dtypes: float64(1), int64(1), object(1)\n",
            "memory usage: 8.7+ MB\n"
          ]
        }
      ],
      "source": [
        "ret.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {},
      "outputs": [],
      "source": [
        "rnd_50 = rnd_50.merge(ret, left_on=['date','permno'], right_on=['date','permno'], how='left')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {},
      "outputs": [],
      "source": [
        "top_50 = top_50.merge(ret, left_on=['date','permno'], right_on=['date','permno'], how='left')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {},
      "outputs": [],
      "source": [
        "rnd = rnd.merge(ret, left_on=['date','permno'], right_on=['date','permno'], how='left')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {},
      "outputs": [],
      "source": [
        "rnd_50['date'] = pd.to_datetime(rnd_50['date'], format = \"%Y-%m-%d\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {},
      "outputs": [],
      "source": [
        "top_50['date'] = pd.to_datetime(top_50['date'], format = \"%Y-%m-%d\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {},
      "outputs": [],
      "source": [
        "rnd['date'] = pd.to_datetime(rnd['date'], format = \"%Y-%m-%d\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "id": "zOosyN330kf1"
      },
      "outputs": [],
      "source": [
        "combined  = pd.concat([top_50.groupby('date')['stock_ret'].mean().rename('top50'),\n",
        "                       rnd.groupby('date')['stock_ret'].mean().rename('rand_50_out_of_top100'),\n",
        "                       rnd_50.groupby('date')['stock_ret'].mean().rename('rand_50_out_of_top500'),\n",
        "                       spy.rename('SPY'), rf],axis = 1).dropna()\n",
        "\n",
        "# combined['SPY'] = combined['adjusted_close'].cumprod()\n",
        "\n",
        "# combined[['top50', 'random_10', 'random_50','SPY']].plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## With Conditional Autoencoder, random stock picking from model recommendations superior to selective stock picking"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "id": "596YK2FU0kf1",
        "outputId": "87fb0ab1-5e7b-4424-b045-87ec4c1c5272"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 87,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "combined[['top50', 'rand_50_out_of_top100', 'rand_50_out_of_top500','SPY']].cumsum().plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 171,
      "metadata": {
        "id": "QUBhPxg60kf1"
      },
      "outputs": [],
      "source": [
        "def calculate_performance_statistics(returns):\n",
        "\n",
        "    statistics = {}\n",
        "\n",
        "    # Calculate maximum drawdown\n",
        "    def max_drawdown(cum_returns):\n",
        "        peak = cum_returns.cummax()\n",
        "        drawdown = cum_returns - peak\n",
        "\n",
        "        return drawdown.min()\n",
        "\n",
        "    def cal_alpha(endog,exog):\n",
        "\n",
        "        #exog = sm.add_constant(returns[['rf', 'SPY']])\n",
        "\n",
        "        ols = sm.OLS(endog = endog, exog = exog).fit(cov_type = 'HAC', cov_kwds = {'maxlags':3}, use_t = True)\n",
        "        alpha = float(ols.summary().tables[1].data[1][1])\n",
        "        return alpha\n",
        "\n",
        "\n",
        "    spy_returns  = returns['SPY']\n",
        "    sharpe_ratio_spy = ((spy_returns-returns['rf']).mean() / spy_returns.std()) * np.sqrt(12)\n",
        "    avg_return_spy = spy_returns.mean()\n",
        "    std_dev_spy = spy_returns.std()\n",
        "\n",
        "    #print(std_dev_spy)\n",
        "    max_drawdown_spy = max_drawdown(spy_returns.cumsum())\n",
        "    max_one_month_loss_spy = spy_returns.min()\n",
        "\n",
        "    statistics['SPY'] = {'Alpha':0, 'Sharpe Ratio':sharpe_ratio_spy, 'Average Return':avg_return_spy,\n",
        "                'Standard Deviation':std_dev_spy, 'Maximum Drawdown':max_drawdown_spy,'Maximum One-Month Loss':max_one_month_loss_spy}\n",
        "\n",
        "\n",
        "    def cal_strat(strat):\n",
        "        strategy_returns = returns[strat]\n",
        "        # Calculate Alpha (difference in average return)\n",
        "        strategy_cum_returns = returns[strat].cumsum()\n",
        "\n",
        "        strategy_extret = returns[strat] - returns['rf']\n",
        "\n",
        "        endog = strategy_extret\n",
        "        exog = sm.add_constant(combined['SPY']-combined['rf'])\n",
        "\n",
        "        alpha = cal_alpha(endog,exog)\n",
        "\n",
        "        # Calculate Sharpe Ratio (annualized)\n",
        "        sharpe_ratio_strategy = (strategy_extret.mean() / strategy_returns.std()) * np.sqrt(12)\n",
        "        # Calculate average return\n",
        "        avg_return_strategy = strategy_returns.mean()\n",
        "        # Calculate standard deviation\n",
        "        std_dev_strategy = strategy_returns.std()\n",
        "        max_drawdown_strategy = max_drawdown(strategy_cum_returns)\n",
        "        # Calculate maximum one-month loss\n",
        "        max_one_month_loss_strategy = strategy_returns.min()\n",
        "\n",
        "        return {'Alpha':alpha, 'Sharpe Ratio':sharpe_ratio_strategy, 'Average Return':avg_return_strategy,\n",
        "                'Standard Deviation':std_dev_strategy, 'Maximum Drawdown':max_drawdown_strategy,'Maximum One-Month Loss':max_one_month_loss_strategy }\n",
        "\n",
        "    for col in returns.columns:\n",
        "        if col != 'SPY' and col !='rf':\n",
        "            statistics[col] = cal_strat(col)\n",
        "\n",
        "    return pd.DataFrame(statistics)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 172,
      "metadata": {
        "id": "F7-QkvYI0kf1"
      },
      "outputs": [],
      "source": [
        "performance_statistics_multiple = calculate_performance_statistics(combined)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 173,
      "metadata": {
        "id": "aCNfkEmL0kf2",
        "outputId": "d788fdde-dcd1-4720-b88e-118e9ab0722e"
      },
      "outputs": [
        {
          "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>SPY</th>\n",
              "      <th>top50</th>\n",
              "      <th>rand_50_out_of_top100</th>\n",
              "      <th>rand_50_out_of_top500</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.010500</td>\n",
              "      <td>0.016500</td>\n",
              "      <td>0.009900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe Ratio</th>\n",
              "      <td>0.889835</td>\n",
              "      <td>-0.379357</td>\n",
              "      <td>0.228677</td>\n",
              "      <td>0.256173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Average Return</th>\n",
              "      <td>0.015536</td>\n",
              "      <td>-0.024422</td>\n",
              "      <td>0.009203</td>\n",
              "      <td>0.007762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Standard Deviation</th>\n",
              "      <td>0.055751</td>\n",
              "      <td>0.234102</td>\n",
              "      <td>0.121003</td>\n",
              "      <td>0.088536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Maximum Drawdown</th>\n",
              "      <td>-0.207094</td>\n",
              "      <td>-1.965012</td>\n",
              "      <td>-0.770806</td>\n",
              "      <td>-0.767849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Maximum One-Month Loss</th>\n",
              "      <td>-0.207094</td>\n",
              "      <td>-0.629804</td>\n",
              "      <td>-0.152343</td>\n",
              "      <td>-0.144858</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             SPY     top50  rand_50_out_of_top100  \\\n",
              "Alpha                   0.000000 -0.010500               0.016500   \n",
              "Sharpe Ratio            0.889835 -0.379357               0.228677   \n",
              "Average Return          0.015536 -0.024422               0.009203   \n",
              "Standard Deviation      0.055751  0.234102               0.121003   \n",
              "Maximum Drawdown       -0.207094 -1.965012              -0.770806   \n",
              "Maximum One-Month Loss -0.207094 -0.629804              -0.152343   \n",
              "\n",
              "                        rand_50_out_of_top500  \n",
              "Alpha                                0.009900  \n",
              "Sharpe Ratio                         0.256173  \n",
              "Average Return                       0.007762  \n",
              "Standard Deviation                   0.088536  \n",
              "Maximum Drawdown                    -0.767849  \n",
              "Maximum One-Month Loss              -0.144858  "
            ]
          },
          "execution_count": 173,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "performance_statistics_multiple"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 159,
      "metadata": {
        "id": "ObQF0K700kf2"
      },
      "outputs": [],
      "source": [
        "performance_statistics_multiple.to_excel('statistics.xlsx')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 186,
      "metadata": {
        "id": "NfqXKSQC0kf2"
      },
      "outputs": [],
      "source": [
        "top_50.pivot_table(index = 'permno', columns= 'date', values = 'stock_ret', aggfunc= lambda x:x).to_csv('top_50_CA.csv')\n",
        "rnd.pivot_table(index = 'permno', columns= 'date', values = 'stock_ret', aggfunc= lambda x:x).to_csv('rnd_50_CA_of_100.csv')\n",
        "rnd_50.pivot_table(index = 'permno', columns= 'date', values = 'stock_ret', aggfunc= lambda x:x).to_csv('rnd_50_CA_of_500.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gmG5PLBg0m59"
      },
      "source": [
        "# APT"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sdd7xw2n11sL",
        "outputId": "690d0e1e-0dfc-4b12-ced1-160a18b5d5c0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\tsong\\AppData\\Local\\Temp\\ipykernel_22180\\4124553741.py:45: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  adj_raw['constant'] = 1\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import gc\n",
        "import datetime\n",
        "\n",
        "# Load data\n",
        "file_path = 'E:/Final Paper/homework_sample_small.csv'\n",
        "raw = pd.read_csv(file_path, parse_dates=['date'], low_memory=False)\n",
        "raw = raw.drop(columns=['date'])\n",
        "raw['day'] = 1\n",
        "raw['date'] = pd.to_datetime(raw[['year', 'month', 'day']])\n",
        "raw = raw.drop(columns=['day'])\n",
        "raw = raw[raw['stock_exret'].notna()]\n",
        "gc.collect()\n",
        "\n",
        "file_path = 'E:/Final Paper/factors_char_list1.csv'\n",
        "var_list = list(pd.read_csv(file_path)['variable'].values)\n",
        "binary_vars = ['convind', 'divi', 'divo', 'rd', 'securedind', 'sin']\n",
        "\n",
        "raw.drop(['zero_trades_126d'], axis=1, inplace=True)\n",
        "var_list.remove('zero_trades_126d')\n",
        "\n",
        "panel = raw.pivot_table(index='date', columns='permno', values='stock_exret', aggfunc=lambda x: x)\n",
        "\n",
        "# Transform each variable in each month\n",
        "monthly = raw.groupby('date')\n",
        "adj_raw = pd.DataFrame()\n",
        "for date, monthly_raw in monthly:\n",
        "    group = monthly_raw.copy()\n",
        "    for var in var_list:\n",
        "        var_median = group[var].median(skipna=True)\n",
        "        group[var] = group[var].fillna(var_median)\n",
        "        if var not in binary_vars:\n",
        "            group[var] = group[var].rank(method='dense') - 1\n",
        "            group_max = group[var].max()\n",
        "            if group_max > 0:\n",
        "                group[var] = (group[var] / group_max) * 2 - 1\n",
        "            else:\n",
        "                group[var] = 0\n",
        "    adj_raw = pd.concat([adj_raw, group], axis=0)\n",
        "    gc.collect()\n",
        "\n",
        "adj_raw['constant'] = 1\n",
        "var_list.append('constant')\n",
        "port_list = [var + '_port' for var in var_list]\n",
        "\n",
        "monthly_adj = adj_raw.groupby('date')\n",
        "data = pd.DataFrame()\n",
        "for date, group in monthly_adj:\n",
        "    expand = group.copy()\n",
        "    Zt = group[var_list].values\n",
        "    Rt = group['stock_exret'].values\n",
        "    Xt = LinearRegression(fit_intercept=False).fit(Zt, Rt).coef_\n",
        "    X_port = pd.DataFrame(data=[Xt], columns=port_list)\n",
        "    expand = pd.concat([expand.reset_index(drop=True), X_port.reset_index(drop=True)], axis=1)\n",
        "    expand.ffill(inplace=True)\n",
        "    data = pd.concat([data, expand], axis=0)\n",
        "    gc.collect()\n",
        "\n",
        "# Function to train and evaluate the Linear Regression model\n",
        "def train_evaluate_lr(train_data, val_data, test_data, var_list):\n",
        "    Z_train = train_data[var_list].values\n",
        "    Y_train = train_data['stock_exret'].values\n",
        "    Z_val = val_data[var_list].values\n",
        "    Y_val = val_data['stock_exret'].values\n",
        "    Z_test = test_data[var_list].values\n",
        "    Y_test = test_data['stock_exret'].values\n",
        "\n",
        "    if len(Z_train) == 0 or len(Z_val) == 0 or len(Z_test) == 0:\n",
        "        print(\"Empty dataset encountered, skipping this iteration.\")\n",
        "        return np.zeros(len(Y_val)), np.zeros(len(Y_test))\n",
        "\n",
        "    lr_model = LinearRegression()\n",
        "    lr_model.fit(Z_train, Y_train)\n",
        "\n",
        "    val_pred = lr_model.predict(Z_val)\n",
        "    test_pred = lr_model.predict(Z_test)\n",
        "\n",
        "    val_rmse = np.sqrt(mean_squared_error(Y_val, val_pred))\n",
        "    test_rmse = np.sqrt(mean_squared_error(Y_test, test_pred))\n",
        "    print(f'Validation RMSE: {val_rmse}')\n",
        "    print(f'Testing RMSE: {test_rmse}')\n",
        "\n",
        "    return val_rmse, test_rmse, test_pred\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation RMSE: 0.19870639919822305\n",
            "Testing RMSE: 0.2544993361826387\n",
            "2024-06-10 00:18:46.305496\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\tsong\\AppData\\Local\\Temp\\ipykernel_22180\\3390097522.py:22: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  pred_batch_lr['lr'] = test_pred\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation RMSE: 0.20327931983334427\n",
            "Testing RMSE: 0.2626001248458009\n",
            "2024-06-10 00:18:49.871327\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\tsong\\AppData\\Local\\Temp\\ipykernel_22180\\3390097522.py:22: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  pred_batch_lr['lr'] = test_pred\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation RMSE: 0.1875979558217884\n",
            "Testing RMSE: 0.2750195075840338\n",
            "2024-06-10 00:18:53.370997\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\tsong\\AppData\\Local\\Temp\\ipykernel_22180\\3390097522.py:22: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  pred_batch_lr['lr'] = test_pred\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation RMSE: 0.20390554402396163\n",
            "Testing RMSE: 0.28539446249748457\n",
            "2024-06-10 00:18:56.810832\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\tsong\\AppData\\Local\\Temp\\ipykernel_22180\\3390097522.py:22: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  pred_batch_lr['lr'] = test_pred\n"
          ]
        }
      ],
      "source": [
        "# Initialize\n",
        "starting = pd.to_datetime('20050101', format=\"%Y%m%d\")\n",
        "counter = 0\n",
        "pred_out_lr = pd.DataFrame()\n",
        "val_rmse_list = []\n",
        "test_rmse_list = []\n",
        "\n",
        "while (starting + pd.DateOffset(years=12 + counter)) <= pd.to_datetime('20200101', format='%Y%m%d'):\n",
        "    cutoff = [starting, starting + pd.DateOffset(years=10 + counter),\n",
        "              starting + pd.DateOffset(years=12 + counter),\n",
        "              starting + pd.DateOffset(years=31 + counter)]\n",
        "\n",
        "    train = adj_raw[(adj_raw['date'] >= cutoff[0]) & (adj_raw['date'] < cutoff[1])]\n",
        "    validate = adj_raw[(adj_raw['date'] >= cutoff[1]) & (adj_raw['date'] < cutoff[2])]\n",
        "    test = adj_raw[(adj_raw['date'] >= cutoff[2]) & (adj_raw['date'] < cutoff[3])]\n",
        "\n",
        "    pred_batch_lr = test[['year', 'month', 'date', 'permno', 'stock_exret']]\n",
        "\n",
        "    val_rmse, test_rmse, test_pred = train_evaluate_lr(train, validate, test, var_list)\n",
        "    val_rmse_list.append(val_rmse)\n",
        "    test_rmse_list.append(test_rmse)\n",
        "    pred_batch_lr['lr'] = test_pred\n",
        "\n",
        "    pred_out_lr = pd.concat([pred_out_lr, pred_batch_lr], axis=0)\n",
        "\n",
        "    counter += 1\n",
        "    print(datetime.datetime.now())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "OOS Total R2 for Linear Regression: 0.0010408685609870627\n",
            "Average Validation RMSE: 0.19837230471932935\n",
            "Average Testing RMSE: 0.26937835777748953\n"
          ]
        }
      ],
      "source": [
        "# Print the OOS Total R2 for Linear Regression model\n",
        "yreal_lr = pred_out_lr['stock_exret'].values\n",
        "ypred_lr = pred_out_lr['lr'].values\n",
        "r2_lr = 1 - np.sum(np.square((yreal_lr - ypred_lr))) / np.sum(np.square(yreal_lr))\n",
        "print(f'OOS Total R2 for Linear Regression: {r2_lr}')\n",
        "\n",
        "# Print average RMSE\n",
        "avg_val_rmse = np.mean(val_rmse_list)\n",
        "avg_test_rmse = np.mean(test_rmse_list)\n",
        "print(f'Average Validation RMSE: {avg_val_rmse}')\n",
        "print(f'Average Testing RMSE: {avg_test_rmse}')\n",
        "\n",
        "# Save the predictions to CSV\n",
        "pred_out_lr.to_csv('pred_out_lr.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {},
      "outputs": [
        {
          "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>year</th>\n",
              "      <th>month</th>\n",
              "      <th>date</th>\n",
              "      <th>permno</th>\n",
              "      <th>stock_exret</th>\n",
              "      <th>lr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2017</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01</td>\n",
              "      <td>10028</td>\n",
              "      <td>-0.000400</td>\n",
              "      <td>0.005039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01</td>\n",
              "      <td>10044</td>\n",
              "      <td>0.075164</td>\n",
              "      <td>0.006732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2017</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01</td>\n",
              "      <td>10253</td>\n",
              "      <td>0.380981</td>\n",
              "      <td>0.045410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2017</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01</td>\n",
              "      <td>10258</td>\n",
              "      <td>-0.079496</td>\n",
              "      <td>0.012116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2017</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01</td>\n",
              "      <td>10259</td>\n",
              "      <td>0.016267</td>\n",
              "      <td>0.026944</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   year  month        date  permno  stock_exret        lr\n",
              "0  2017      1  2017-01-01   10028    -0.000400  0.005039\n",
              "1  2017      1  2017-01-01   10044     0.075164  0.006732\n",
              "2  2017      1  2017-01-01   10253     0.380981  0.045410\n",
              "3  2017      1  2017-01-01   10258    -0.079496  0.012116\n",
              "4  2017      1  2017-01-01   10259     0.016267  0.026944"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pred_out_lr = pd.read_csv('pred_out_lr.csv')\n",
        "pred_out_lr.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Right skewed extreme value in stock return"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "pred_out_lr['stock_exret'].hist(bins = 100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "pred_out_lr['lr'].hist(bins = 100, alpha = 0.5, color = 'g')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 163,
      "metadata": {},
      "outputs": [
        {
          "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>stock_exret</th>\n",
              "      <th>lr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>stock_exret</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.038031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lr</th>\n",
              "      <td>0.038031</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             stock_exret        lr\n",
              "stock_exret     1.000000  0.038031\n",
              "lr              0.038031  1.000000"
            ]
          },
          "execution_count": 163,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pred_out_lr[['stock_exret','lr']].corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {},
      "outputs": [],
      "source": [
        "top_50_APT = pd.DataFrame()\n",
        "for d in pred_out_lr['date'].unique():\n",
        "\n",
        "    top_50_APT = pd.concat([top_50_APT, pred_out_lr[pred_out_lr.date == d].sort_values('lr', ascending = False).iloc[:1]],axis = 0)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {},
      "outputs": [],
      "source": [
        "rnd_50_APT = pd.DataFrame()\n",
        "for d in pred_out_lr['date'].unique():\n",
        "\n",
        "    rnd_50_APT = pd.concat([rnd_50_APT, pred_out_lr[pred_out_lr.date == d].sort_values('lr',ascending = False).iloc[:500].sample(50, replace= False)],axis = 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {},
      "outputs": [],
      "source": [
        "top_50_APT = top_50_APT.merge(ret, left_on=['date','permno'], right_on=['date','permno'], how='left')\n",
        "\n",
        "top_50_APT['date'] = pd.to_datetime(top_50_APT['date'], format = \"%Y-%m-%d\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {},
      "outputs": [],
      "source": [
        "rnd_50_APT = rnd_50_APT.merge(ret, left_on=['date','permno'], right_on=['date','permno'], how='left')\n",
        "\n",
        "rnd_50_APT['date'] = pd.to_datetime(rnd_50_APT['date'], format = \"%Y-%m-%d\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 176,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 176,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "combined_APT  = pd.concat([top_50_APT.groupby('date')['stock_ret'].mean().rename('top50_APT'),\n",
        "                       rnd_50_APT.groupby('date')['stock_ret'].mean().rename('random_50_APT'),\n",
        "                       spy.rename('SPY'),rf],axis = 1).dropna()\n",
        "\n",
        "# combined_APT['SPY'] = combined_APT['adjusted_close'].cumprod()\n",
        "\n",
        "combined_APT[['top50_APT', 'random_50_APT','SPY']].cumsum().plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 177,
      "metadata": {},
      "outputs": [],
      "source": [
        "performance_statistics_APT = calculate_performance_statistics(combined_APT)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 178,
      "metadata": {},
      "outputs": [
        {
          "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>SPY</th>\n",
              "      <th>top50_APT</th>\n",
              "      <th>random_50_APT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.169100</td>\n",
              "      <td>0.004400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe Ratio</th>\n",
              "      <td>0.889835</td>\n",
              "      <td>0.440015</td>\n",
              "      <td>0.053511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Average Return</th>\n",
              "      <td>0.015536</td>\n",
              "      <td>0.092192</td>\n",
              "      <td>0.002642</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Standard Deviation</th>\n",
              "      <td>0.055751</td>\n",
              "      <td>0.716231</td>\n",
              "      <td>0.092389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Maximum Drawdown</th>\n",
              "      <td>-0.207094</td>\n",
              "      <td>-1.713433</td>\n",
              "      <td>-0.722321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Maximum One-Month Loss</th>\n",
              "      <td>-0.207094</td>\n",
              "      <td>-0.420732</td>\n",
              "      <td>-0.225596</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             SPY  top50_APT  random_50_APT\n",
              "Alpha                   0.000000   0.169100       0.004400\n",
              "Sharpe Ratio            0.889835   0.440015       0.053511\n",
              "Average Return          0.015536   0.092192       0.002642\n",
              "Standard Deviation      0.055751   0.716231       0.092389\n",
              "Maximum Drawdown       -0.207094  -1.713433      -0.722321\n",
              "Maximum One-Month Loss -0.207094  -0.420732      -0.225596"
            ]
          },
          "execution_count": 178,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "performance_statistics_APT"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## With APT, random stock picking is inferior to selective stock picking"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 183,
      "metadata": {},
      "outputs": [],
      "source": [
        "top_50_APT.pivot_table(index = 'permno', columns= 'date', values = 'stock_ret', aggfunc= lambda x:x).to_csv('top_50_APT.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 184,
      "metadata": {},
      "outputs": [],
      "source": [
        "rnd_50_APT.pivot_table(index = 'permno', columns= 'date', values = 'stock_ret', aggfunc= lambda x:x).to_csv('rnd_50_APT.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {},
      "outputs": [],
      "source": [
        "# def fibo(n):\n",
        "#     if n == 0 or n== 1:\n",
        "#         return 1\n",
        "#     else:\n",
        "#         return fibo(n-1)+fibo(n-2) \n",
        "       "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {},
      "outputs": [],
      "source": [
        "# for i in range(18):\n",
        "#     print(fibo(i))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "base",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}