randomforestclasssifier.ipynb

{
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   "cell_type": "code",
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   "id": "80a3ec26-9626-4046-bb7c-f34ed855b8d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix"
   ]
  },
  {
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   "execution_count": 17,
   "id": "bb51a3ce-a0a2-41b3-91ad-783913948c34",
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     "text": [
      "(14304, 20)\n"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker</th>\n",
       "      <th>purchase_volume</th>\n",
       "      <th>purchase_speculation</th>\n",
       "      <th>purchase_count</th>\n",
       "      <th>purchase_count_individual</th>\n",
       "      <th>purchase_days_ago</th>\n",
       "      <th>purchase_owner</th>\n",
       "      <th>purchase_confidence</th>\n",
       "      <th>sale_volume</th>\n",
       "      <th>sale_speculation</th>\n",
       "      <th>sale_count</th>\n",
       "      <th>sale_count_individual</th>\n",
       "      <th>sale_days_ago</th>\n",
       "      <th>sale_owner</th>\n",
       "      <th>sale_confidence</th>\n",
       "      <th>date</th>\n",
       "      <th>volume_net</th>\n",
       "      <th>score</th>\n",
       "      <th>price_change</th>\n",
       "      <th>greater_than</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DD</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>['Thomas R Carper']</td>\n",
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       "      <td>2013-01-01</td>\n",
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       "      <td>1.04</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DD</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>75.0</td>\n",
       "      <td>['Thomas R Carper']</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2013-03-02</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>47.0</td>\n",
       "      <td>['Thomas R Carper']</td>\n",
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       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-05-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DD</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>62.0</td>\n",
       "      <td>['Thomas R Carper']</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2013-06-30</td>\n",
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       "      <td>1.65</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DD</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>77.0</td>\n",
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       "      <td>2013-08-29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ticker  purchase_volume  purchase_speculation  purchase_count  \\\n",
       "0     DD              2.0                     0               2   \n",
       "1     DD              1.0                     0               1   \n",
       "2     DD              1.0                     0               1   \n",
       "3     DD              2.0                     0               2   \n",
       "4     DD              1.0                     0               1   \n",
       "\n",
       "   purchase_count_individual  purchase_days_ago       purchase_owner  \\\n",
       "0                          1               62.5  ['Thomas R Carper']   \n",
       "1                          1               75.0  ['Thomas R Carper']   \n",
       "2                          1               47.0  ['Thomas R Carper']   \n",
       "3                          1               62.0  ['Thomas R Carper']   \n",
       "4                          1               77.0  ['Thomas R Carper']   \n",
       "\n",
       "   purchase_confidence  sale_volume  sale_speculation  sale_count  \\\n",
       "0                0.261          0.0                 0           0   \n",
       "1                0.261          0.0                 0           0   \n",
       "2                0.414          0.0                 0           0   \n",
       "3                0.572          0.0                 0           0   \n",
       "4                0.572          0.0                 0           0   \n",
       "\n",
       "   sale_count_individual  sale_days_ago sale_owner  sale_confidence  \\\n",
       "0                      0           -1.0         []              0.0   \n",
       "1                      0           -1.0         []              0.0   \n",
       "2                      0           -1.0         []              0.0   \n",
       "3                      0           -1.0         []              0.0   \n",
       "4                      0           -1.0         []              0.0   \n",
       "\n",
       "         date  volume_net  score  price_change  greater_than  \n",
       "0  2013-01-01         2.0   1.04          1.42             1  \n",
       "1  2013-03-02         1.0   0.52          1.59             1  \n",
       "2  2013-05-01         1.0   0.83          1.52             1  \n",
       "3  2013-06-30         2.0   2.29          1.65             1  \n",
       "4  2013-08-29         1.0   1.14          1.47             1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/training_data/stock_metrics.csv') #\n",
    "df['greater_than'] = df['price_change'].apply(lambda x: 1 if x > 1.2 else 0)\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "17dcac99-7990-46e7-89e5-83b4eabe8967",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>purchase_volume</th>\n",
       "      <th>purchase_count</th>\n",
       "      <th>purchase_count_individual</th>\n",
       "      <th>purchase_days_ago</th>\n",
       "      <th>purchase_confidence</th>\n",
       "      <th>sale_volume</th>\n",
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      "text/plain": [
       "       purchase_volume  purchase_count  purchase_count_individual  \\\n",
       "13634              1.0               1                          1   \n",
       "8992               0.0               0                          0   \n",
       "8603               2.0               2                          1   \n",
       "10144              1.0               1                          1   \n",
       "3468               0.0               0                          0   \n",
       "\n",
       "       purchase_days_ago  purchase_confidence  sale_volume  sale_count  \\\n",
       "13634               93.0             1.154507          5.0           4   \n",
       "8992                -1.0             0.000000          2.0           2   \n",
       "8603               120.0             1.399048          0.0           0   \n",
       "10144               79.0             1.377273          0.0           0   \n",
       "3468                -1.0             0.000000          1.0           1   \n",
       "\n",
       "       sale_count_individual  sale_days_ago  sale_confidence  volume_net  \\\n",
       "13634                      3          65.25        -0.100667        -4.0   \n",
       "8992                       2         105.00        -0.123709        -2.0   \n",
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       "10144                      0          -1.00         0.000000         1.0   \n",
       "3468                       1          66.00         0.144062        -1.0   \n",
       "\n",
       "       score  \n",
       "13634   1.10  \n",
       "8992    0.07  \n",
       "8603    6.04  \n",
       "10144   1.09  \n",
       "3468   -0.14  "
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     "execution_count": 18,
     "metadata": {},
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   ],
   "source": [
    "# Define features and target\n",
    "x = df.drop(columns=['date', 'purchase_owner', 'sale_owner', 'sale_speculation', 'purchase_speculation', 'price_change', 'greater_than'])\n",
    "y = df['greater_than']\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n",
    "\n",
    "tickers = x_test['ticker']\n",
    "x_train = x_train.drop(columns=['ticker'])\n",
    "x_test = x_test.drop(columns=['ticker'])\n",
    "\n",
    "\n",
    "x_train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6ef23e84-ddd3-48ea-9c0c-aa9d3d74d791",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.70\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.88      0.79      1857\n",
      "           1       0.62      0.36      0.46      1004\n",
      "\n",
      "    accuracy                           0.70      2861\n",
      "   macro avg       0.67      0.62      0.63      2861\n",
      "weighted avg       0.69      0.70      0.68      2861\n",
      "\n",
      "Confusion Matrix:\n",
      "[[1635  222]\n",
      " [ 638  366]]\n"
     ]
    }
   ],
   "source": [
    "# Initialize and train the XGBoost classifier\n",
    "xgb_classifier = XGBClassifier(random_state=42)\n",
    "xgb_classifier.fit(x_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "y_pred = xgb_classifier.predict(x_test)\n",
    "\n",
    "# Evaluate the model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy:.2f}')\n",
    "print('Classification Report:')\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('Confusion Matrix:')\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  }
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