Positive_Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "import yfinance as yf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import project_functions2 as pf\n",
    "import classifier_functions as cf\n",
    "import multi_model_builder as mmb\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.dummy import DummyClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_list = ['AMZN', 'AAPL', 'FB','GOOGL', 'MSFT', 'TSLA']\n",
    "stock_objects = {}\n",
    "for stock in stock_list:\n",
    "    stock_objects[stock] = yf.Ticker(stock)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_dfs = {}\n",
    "for key in stock_objects:\n",
    "    stock_dfs[key] = stock_objects[key].history(period='max')\n",
    "\n",
    "for key in stock_dfs:\n",
    "    stock_dfs[key] = pf.date_time_prep(stock_dfs[key]) \n",
    "    stock_dfs[key] = pf.rolling_aves(stock_dfs[key])\n",
    "    stock_dfs[key] = pf.classifier_setup(stock_dfs[key], 5)\n",
    "\n",
    "combine_df = pf.combiner(stock_dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3853881278538813"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, y_train, X_test, y_test = pf.multi_stock_train_test_split(combine_df, 365, stock_dfs)\n",
    "scaler = MinMaxScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "dummy_majority = DummyClassifier(strategy = 'most_frequent').fit(X_train_scaled, y_train)\n",
    "1 - dummy_majority.score(X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_reg_df = cf.train_multiple_log_regression(X_train_scaled, y_train, X_test_scaled, y_test, 0.0000000000001, 10000, alpha=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>C</th>\n",
       "      <th>Training Error</th>\n",
       "      <th>Test Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000e-13</td>\n",
       "      <td>0.456024</td>\n",
       "      <td>0.385388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000e-12</td>\n",
       "      <td>0.456024</td>\n",
       "      <td>0.385388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000000e-11</td>\n",
       "      <td>0.456024</td>\n",
       "      <td>0.385388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000000e-10</td>\n",
       "      <td>0.456024</td>\n",
       "      <td>0.385388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>0.456024</td>\n",
       "      <td>0.385388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>0.456024</td>\n",
       "      <td>0.385388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000000e-07</td>\n",
       "      <td>0.456024</td>\n",
       "      <td>0.385388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.000000e-06</td>\n",
       "      <td>0.456024</td>\n",
       "      <td>0.385388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.000000e-05</td>\n",
       "      <td>0.456024</td>\n",
       "      <td>0.385388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.456024</td>\n",
       "      <td>0.385388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.456024</td>\n",
       "      <td>0.385388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>0.453544</td>\n",
       "      <td>0.385388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.000000e-01</td>\n",
       "      <td>0.454109</td>\n",
       "      <td>0.386301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.453356</td>\n",
       "      <td>0.392694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>0.454799</td>\n",
       "      <td>0.409589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>0.454642</td>\n",
       "      <td>0.404110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>0.454768</td>\n",
       "      <td>0.409589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.000000e+04</td>\n",
       "      <td>0.455270</td>\n",
       "      <td>0.411416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               C  Training Error  Test Error\n",
       "0   1.000000e-13        0.456024    0.385388\n",
       "1   1.000000e-12        0.456024    0.385388\n",
       "2   1.000000e-11        0.456024    0.385388\n",
       "3   1.000000e-10        0.456024    0.385388\n",
       "4   1.000000e-09        0.456024    0.385388\n",
       "5   1.000000e-08        0.456024    0.385388\n",
       "6   1.000000e-07        0.456024    0.385388\n",
       "7   1.000000e-06        0.456024    0.385388\n",
       "8   1.000000e-05        0.456024    0.385388\n",
       "9   1.000000e-04        0.456024    0.385388\n",
       "10  1.000000e-03        0.456024    0.385388\n",
       "11  1.000000e-02        0.453544    0.385388\n",
       "12  1.000000e-01        0.454109    0.386301\n",
       "13  1.000000e+00        0.453356    0.392694\n",
       "14  1.000000e+01        0.454799    0.409589\n",
       "15  1.000000e+02        0.454642    0.404110\n",
       "16  1.000000e+03        0.454768    0.409589\n",
       "17  1.000000e+04        0.455270    0.411416"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "svm_df = cf.train_multiple_SVM(X_train_scaled, y_train, X_test_scaled, y_test, 0.0000001, 0.01, alpha=10, kernel='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>C</th>\n",
       "      <th>Training Error</th>\n",
       "      <th>Test Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000e-07</td>\n",
       "      <td>0.456024</td>\n",
       "      <td>0.385388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000e-06</td>\n",
       "      <td>0.456024</td>\n",
       "      <td>0.385388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000000e-05</td>\n",
       "      <td>0.456024</td>\n",
       "      <td>0.385388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000000e-04</td>\n",
       "      <td>0.456024</td>\n",
       "      <td>0.385388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.456024</td>\n",
       "      <td>0.385388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>0.456024</td>\n",
       "      <td>0.385388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              C  Training Error  Test Error\n",
       "0  1.000000e-07        0.456024    0.385388\n",
       "1  1.000000e-06        0.456024    0.385388\n",
       "2  1.000000e-05        0.456024    0.385388\n",
       "3  1.000000e-04        0.456024    0.385388\n",
       "4  1.000000e-03        0.456024    0.385388\n",
       "5  1.000000e-02        0.456024    0.385388"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gaus_svm_df = cf.train_multiple_SVM(X_train_scaled, y_train, X_test_scaled, y_test, 0.00001, 100, alpha=10, kernel='rbf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>C</th>\n",
       "      <th>Training Error</th>\n",
       "      <th>Test Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.456024</td>\n",
       "      <td>0.385388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00010</td>\n",
       "      <td>0.456024</td>\n",
       "      <td>0.385388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00100</td>\n",
       "      <td>0.456024</td>\n",
       "      <td>0.385388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.456024</td>\n",
       "      <td>0.385388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.456024</td>\n",
       "      <td>0.385388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.448522</td>\n",
       "      <td>0.413242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10.00000</td>\n",
       "      <td>0.424917</td>\n",
       "      <td>0.439726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100.00000</td>\n",
       "      <td>0.381537</td>\n",
       "      <td>0.493607</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           C  Training Error  Test Error\n",
       "0    0.00001        0.456024    0.385388\n",
       "1    0.00010        0.456024    0.385388\n",
       "2    0.00100        0.456024    0.385388\n",
       "3    0.01000        0.456024    0.385388\n",
       "4    0.10000        0.456024    0.385388\n",
       "5    1.00000        0.448522    0.413242\n",
       "6   10.00000        0.424917    0.439726\n",
       "7  100.00000        0.381537    0.493607"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gaus_svm_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_df = mmb.train_multiple_random_forests(combine_df, 365, stock_dfs, 10, 500, 5, 50, forest_step=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "re_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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