dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e2579468",
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "import pandas as pd\n",
    "from datetime import date, timedelta\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "316b96cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
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      ],
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       "                                 Open        High         Low       Close  \\\n",
       "Datetime                                                                    \n",
       "2023-03-20 09:30:00-04:00  277.359985  277.359985  277.359985  277.359985   \n",
       "2023-03-20 09:31:00-04:00  277.359985  277.359985  276.049988  276.480011   \n",
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       "2023-03-20 09:34:00-04:00  275.690002  275.750000  274.600006  274.640015   \n",
       "...                               ...         ...         ...         ...   \n",
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       "2023-03-28 15:59:00-04:00  275.190002  275.329987  275.170013  275.260010   \n",
       "\n",
       "                            Adj Close   Volume  \n",
       "Datetime                                        \n",
       "2023-03-20 09:30:00-04:00  277.359985  1502878  \n",
       "2023-03-20 09:31:00-04:00  276.480011   238223  \n",
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       "...                               ...      ...  \n",
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       "\n",
       "[2722 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = yf.download(tickers = \"MSFT\", period = \"7d\", interval = \"1m\")\n",
    "start_date = datetime.datetime(2023, 3, 22)\n",
    "end_date = datetime.datetime(2023, 3, 28)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8aecb353",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start_date: 2023-03-22 00:00:00\n",
      "end_date: 2023-03-28 00:00:00\n",
      "start_date_list:\n",
      "[datetime.datetime(2023, 3, 22, 0, 0), datetime.datetime(2023, 3, 29, 0, 0), datetime.datetime(2023, 4, 5, 0, 0), datetime.datetime(2023, 4, 12, 0, 0), datetime.datetime(2023, 4, 19, 0, 0)]\n",
      "end_date_list:\n",
      "[datetime.datetime(2023, 3, 28, 0, 0), datetime.datetime(2023, 4, 4, 0, 0), datetime.datetime(2023, 4, 11, 0, 0), datetime.datetime(2023, 4, 18, 0, 0), datetime.datetime(2023, 3, 28, 0, 0)]\n",
      "===========================================================\n",
      "\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Data size: 9330\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kayra\\AppData\\Local\\Temp\\ipykernel_24464\\3840562353.py:28: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_1min = df_1min.append(data)\n"
     ]
    }
   ],
   "source": [
    "# Specify start dates and end dates\n",
    "# end_date = date.today()\n",
    "# start_date = end_date - timedelta(days=29)\n",
    "\n",
    "print('start_date:', start_date)\n",
    "print('end_date:', end_date)\n",
    "\n",
    "# Start dates\n",
    "start_date_list = [start_date + timedelta(days=7*i) for i in range(5)]\n",
    "print('start_date_list:')\n",
    "print(start_date_list)\n",
    "\n",
    "# End dates\n",
    "end_date_list = [start_date_list[i+1] - timedelta(days=1) for i in range(4)] + [end_date]\n",
    "print('end_date_list:')\n",
    "print(end_date_list)\n",
    "\n",
    "print('===========================================================\\n')\n",
    "\n",
    "# Use for-loop to extract data\n",
    "df_1min = pd.DataFrame({})\n",
    "\n",
    "for idx in range(1):\n",
    "    data = yf.download(tickers=\"MSFT\", \n",
    "                       start=start_date_list[idx].strftime(\"%Y-%m-%d\"), \n",
    "                       end=end_date_list[idx].strftime(\"%Y-%m-%d\"), \n",
    "                       interval = \"1m\")\n",
    "    df_1min = df_1min.append(data)\n",
    "\n",
    "print(\"Data size: \" + str(df_1min.size))\n",
    "data\n",
    "\n",
    "# Create a test csv file given a raw path string\n",
    "df_1min.to_csv(r\"Datasets/tmp.csv\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "12d5a67e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start_date: 2023-01-13\n",
      "end_date: 2023-02-11\n",
      "start_date_list:\n",
      "[datetime.date(2023, 1, 13), datetime.date(2023, 1, 20), datetime.date(2023, 1, 27), datetime.date(2023, 2, 3), datetime.date(2023, 2, 10)]\n",
      "end_date_list:\n",
      "[datetime.date(2023, 1, 19), datetime.date(2023, 1, 26), datetime.date(2023, 2, 2), datetime.date(2023, 2, 9), datetime.date(2023, 2, 11)]\n",
      "===========================================================\n",
      "\n",
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kayra\\AppData\\Local\\Temp\\ipykernel_27416\\3677925876.py:28: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_1min = df_1min.append(data)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kayra\\AppData\\Local\\Temp\\ipykernel_27416\\3677925876.py:28: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_1min = df_1min.append(data)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kayra\\AppData\\Local\\Temp\\ipykernel_27416\\3677925876.py:28: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_1min = df_1min.append(data)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Data size: 37422\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kayra\\AppData\\Local\\Temp\\ipykernel_27416\\3677925876.py:28: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_1min = df_1min.append(data)\n",
      "C:\\Users\\kayra\\AppData\\Local\\Temp\\ipykernel_27416\\3677925876.py:28: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_1min = df_1min.append(data)\n"
     ]
    }
   ],
   "source": [
    "# Specify start dates and end dates\n",
    "end_date = date.today()\n",
    "start_date = end_date - timedelta(days=29)\n",
    "\n",
    "print('start_date:', start_date)\n",
    "print('end_date:', end_date)\n",
    "\n",
    "# Start dates\n",
    "start_date_list = [start_date + timedelta(days=7*i) for i in range(5)]\n",
    "print('start_date_list:')\n",
    "print(start_date_list)\n",
    "\n",
    "# End dates\n",
    "end_date_list = [start_date_list[i+1] - timedelta(days=1) for i in range(4)] + [end_date]\n",
    "print('end_date_list:')\n",
    "print(end_date_list)\n",
    "\n",
    "print('===========================================================\\n')\n",
    "\n",
    "# Use for-loop to extract data\n",
    "df_1min = pd.DataFrame({})\n",
    "\n",
    "for idx in range(5):\n",
    "    data = yf.download(tickers=\"MSFT\", \n",
    "                       start=start_date_list[idx].strftime(\"%Y-%m-%d\"), \n",
    "                       end=end_date_list[idx].strftime(\"%Y-%m-%d\"), \n",
    "                       interval = \"1m\")\n",
    "    df_1min = df_1min.append(data)\n",
    "\n",
    "print(\"Data size: \" + str(df_1min.size))\n",
    "\n",
    "# Create a test csv file given a raw path string\n",
    "df_1min.to_csv(r\"Datasets/MSFT_2023-01-13_2023-02-10.csv\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96bd0253",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update Dataset\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "78f43978",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Datasets/dataset (MSFT).csv\")\n",
    "df_2 = pd.read_csv(\"Predictions/Combined Model Price Prediction.csv\")\n",
    "\n",
    "dfloc = df.loc[5446:, 'Unnamed: 0']\n",
    "dfloc.reset_index(drop=True, inplace = True)\n",
    "df_2[\"Date\"] = dfloc\n",
    "df_2.to_csv (r'C:\\Users\\kayra\\OneDrive\\Desktop\\Stonks 2022\\Stocks-Prediction\\Predictions\\Combined Model Price Prediction Dates.csv', index = False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7d80ebb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kayra\\AppData\\Local\\Temp\\ipykernel_11296\\4138909008.py:9: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_1min = df_1min.append(data)\n"
     ]
    },
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-14 15:56:00-05:00</th>\n",
       "      <td>272.890015</td>\n",
       "      <td>272.945007</td>\n",
       "      <td>272.660004</td>\n",
       "      <td>272.679993</td>\n",
       "      <td>272.679993</td>\n",
       "      <td>131914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-14 15:57:00-05:00</th>\n",
       "      <td>272.670013</td>\n",
       "      <td>272.700012</td>\n",
       "      <td>272.359985</td>\n",
       "      <td>272.429993</td>\n",
       "      <td>272.429993</td>\n",
       "      <td>177605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-14 15:58:00-05:00</th>\n",
       "      <td>272.433105</td>\n",
       "      <td>272.450012</td>\n",
       "      <td>272.179993</td>\n",
       "      <td>272.315002</td>\n",
       "      <td>272.315002</td>\n",
       "      <td>245706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-14 15:59:00-05:00</th>\n",
       "      <td>272.320007</td>\n",
       "      <td>272.380005</td>\n",
       "      <td>271.920013</td>\n",
       "      <td>272.040009</td>\n",
       "      <td>272.040009</td>\n",
       "      <td>512459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>780 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Open        High         Low       Close  \\\n",
       "Datetime                                                                    \n",
       "2023-02-13 09:30:00-05:00  267.640015  268.339996  267.187500  267.429993   \n",
       "2023-02-13 09:31:00-05:00  267.429993  268.459991  267.149994  267.582001   \n",
       "2023-02-13 09:32:00-05:00  267.609985  269.000000  267.540009  268.779999   \n",
       "2023-02-13 09:33:00-05:00  268.769989  268.890015  268.010010  268.529999   \n",
       "2023-02-13 09:34:00-05:00  268.524994  269.535004  268.500000  269.089996   \n",
       "...                               ...         ...         ...         ...   \n",
       "2023-02-14 15:55:00-05:00  272.980011  273.220001  272.839996  272.894989   \n",
       "2023-02-14 15:56:00-05:00  272.890015  272.945007  272.660004  272.679993   \n",
       "2023-02-14 15:57:00-05:00  272.670013  272.700012  272.359985  272.429993   \n",
       "2023-02-14 15:58:00-05:00  272.433105  272.450012  272.179993  272.315002   \n",
       "2023-02-14 15:59:00-05:00  272.320007  272.380005  271.920013  272.040009   \n",
       "\n",
       "                            Adj Close   Volume  \n",
       "Datetime                                        \n",
       "2023-02-13 09:30:00-05:00  267.429993  1686911  \n",
       "2023-02-13 09:31:00-05:00  267.582001   303844  \n",
       "2023-02-13 09:32:00-05:00  268.779999   380392  \n",
       "2023-02-13 09:33:00-05:00  268.529999   239949  \n",
       "2023-02-13 09:34:00-05:00  269.089996   340502  \n",
       "...                               ...      ...  \n",
       "2023-02-14 15:55:00-05:00  272.894989   161513  \n",
       "2023-02-14 15:56:00-05:00  272.679993   131914  \n",
       "2023-02-14 15:57:00-05:00  272.429993   177605  \n",
       "2023-02-14 15:58:00-05:00  272.315002   245706  \n",
       "2023-02-14 15:59:00-05:00  272.040009   512459  \n",
       "\n",
       "[780 rows x 6 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_date = datetime.datetime(2023, 2, 13)\n",
    "end_date = datetime.datetime(2023, 2, 15)\n",
    "\n",
    "data = yf.download(tickers=\"MSFT\", \n",
    "                       start=start_date.strftime(\"%Y-%m-%d\"), \n",
    "                       end=end_date.strftime(\"%Y-%m-%d\"), \n",
    "                       interval = \"1m\")\n",
    "\n",
    "df_1min = df_1min.append(data)\n",
    "df_1min.to_csv(r\"Datasets/tmp.csv\") \n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b9879c66",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_real = pd.read_csv(\"Datasets/MSFT_2023-01-13.csv\")\n",
    "df_predicted = pd.read_csv(\"Predictions/3-13-2023 Combined Model Price Prediction.csv\")\n",
    "\n",
    "df_real = df_real[11000:]\n",
    "df_dates = df_real['Date']\n",
    "df_predicted['Date'] = df_dates.values\n",
    "df_predicted.to_csv(r\"Predictions/3-13-2023 Combined Model Price Prediction Dates.csv\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d8ff0373",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_predicted = pd.read_csv(\"Predictions/3-13-2023 Combined Model Price Prediction Dates.csv\")\n",
    "\n",
    "open_arr = df_predicted['Open'].to_numpy()\n",
    "high_arr = df_predicted['High'].to_numpy()\n",
    "low_arr = df_predicted['Low'].to_numpy()\n",
    "close_arr = df_predicted['Close'].to_numpy()\n",
    "\n",
    "open_arr = open_arr[1:]\n",
    "high_arr = high_arr[1:]\n",
    "low_arr = low_arr[1:]\n",
    "close_arr = close_arr[:-1]\n",
    "\n",
    "index = 0\n",
    "for o, c in zip(open_arr, close_arr):\n",
    "    tmp = o\n",
    "    open_arr[index] = close_arr[index]\n",
    "    close_arr[index] = tmp\n",
    "    index += 1\n",
    "    \n",
    "altered_df = pd.DataFrame({'Open':open_arr, 'High':high_arr, 'Low':low_arr,'Close':close_arr})\n",
    "df_dates = df_predicted['Date']\n",
    "df_dates = df_dates[1:]\n",
    "altered_df['Date'] = df_dates.values\n",
    "\n",
    "altered_df.to_csv(r\"Predictions/3-13-2023 Altered Predictions.csv\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7e21176f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% 71.72829792814058\n"
     ]
    }
   ],
   "source": [
    "# Test accuracy (green vs. red candle sticks)\n",
    "true_count = 0\n",
    "df_predicted = pd.read_csv(\"Predictions/3-13-2023 Altered Predictions.csv\")\n",
    "df_real = pd.read_csv(\"Datasets/MSFT_2023-01-13.csv\")\n",
    "df_real = df_real[11001:-1]\n",
    "df_predicted = df_predicted[1:]\n",
    "\n",
    "df_real_open = df_real['Open']\n",
    "df_real_close = df_real['Close']\n",
    "df_predicted_open = df_predicted['Open']\n",
    "df_predicted_close = df_predicted['Close']\n",
    "\n",
    "for real_open, real_close, predicted_open, predicted_close in zip(df_real_open, df_real_close, df_predicted_open, df_predicted_close):\n",
    "    if (predicted_open > predicted_close and real_open > real_close) or (predicted_close > predicted_open and real_close > real_open):\n",
    "        true_count += 1\n",
    "        \n",
    "print(\"%\", ((true_count / len(df_real)*100)))"
   ]
  }
 ],
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