Candle_Sticks_Predictions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "af2120a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import keras\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dropout\n",
    "from keras.layers import *\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.callbacks import EarlyStopping\n",
    "from datetime import date, timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5f88eac2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows and columns: (2333, 5)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
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       "      <td>229.93149</td>\n",
       "      <td>229.93149</td>\n",
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       "      <td>229.95550</td>\n",
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       "      <td>229.94392</td>\n",
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       "      <td>231.00873</td>\n",
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       "      <td>230.01222</td>\n",
       "      <td>230.01222</td>\n",
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       "    <tr>\n",
       "      <th>2320</th>\n",
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       "      <td>230.86769</td>\n",
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       "      <td>229.88654</td>\n",
       "      <td>229.88654</td>\n",
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       "      <td>230.87318</td>\n",
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       "      <td>229.89800</td>\n",
       "      <td>229.89800</td>\n",
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       "    <tr>\n",
       "      <th>2322</th>\n",
       "      <td>2322</td>\n",
       "      <td>230.71930</td>\n",
       "      <td>231.18670</td>\n",
       "      <td>229.75221</td>\n",
       "      <td>229.75221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2323</th>\n",
       "      <td>2323</td>\n",
       "      <td>230.73582</td>\n",
       "      <td>231.19736</td>\n",
       "      <td>229.75830</td>\n",
       "      <td>229.75830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2324</th>\n",
       "      <td>2324</td>\n",
       "      <td>230.80956</td>\n",
       "      <td>231.24936</td>\n",
       "      <td>229.81334</td>\n",
       "      <td>229.81334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2325</th>\n",
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       "      <td>231.00542</td>\n",
       "      <td>231.42519</td>\n",
       "      <td>229.99132</td>\n",
       "      <td>229.99132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2326</th>\n",
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       "      <td>230.88542</td>\n",
       "      <td>231.34157</td>\n",
       "      <td>229.88867</td>\n",
       "      <td>229.88867</td>\n",
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       "    <tr>\n",
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       "      <td>230.95435</td>\n",
       "      <td>231.41443</td>\n",
       "      <td>229.98439</td>\n",
       "      <td>229.98439</td>\n",
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       "    <tr>\n",
       "      <th>2329</th>\n",
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       "      <td>231.26785</td>\n",
       "      <td>229.82895</td>\n",
       "      <td>229.82895</td>\n",
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       "      <td>229.53477</td>\n",
       "      <td>229.53477</td>\n",
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       "      <td>229.38823</td>\n",
       "      <td>229.38823</td>\n",
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       "      <td>230.11935</td>\n",
       "      <td>230.63647</td>\n",
       "      <td>229.14973</td>\n",
       "      <td>229.14973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0       Open       High        Low      Close\n",
       "2313        2313  230.99556  231.45380  230.04018  230.04018\n",
       "2314        2314  231.00122  231.43726  230.02988  230.02988\n",
       "2315        2315  230.97928  231.40758  229.99214  229.99214\n",
       "2316        2316  230.92430  231.36057  229.93149  229.93149\n",
       "2317        2317  230.95305  231.38630  229.95550  229.95550\n",
       "2318        2318  230.94061  231.37491  229.94392  229.94392\n",
       "2319        2319  231.00873  231.43938  230.01222  230.01222\n",
       "2320        2320  230.86769  231.32074  229.88654  229.88654\n",
       "2321        2321  230.87318  231.33075  229.89800  229.89800\n",
       "2322        2322  230.71930  231.18670  229.75221  229.75221\n",
       "2323        2323  230.73582  231.19736  229.75830  229.75830\n",
       "2324        2324  230.80956  231.24936  229.81334  229.81334\n",
       "2325        2325  231.00542  231.42519  229.99132  229.99132\n",
       "2326        2326  230.88542  231.34157  229.88867  229.88867\n",
       "2327        2327  230.92912  231.40187  229.95085  229.95085\n",
       "2328        2328  230.95435  231.41443  229.98439  229.98439\n",
       "2329        2329  230.78568  231.26785  229.82895  229.82895\n",
       "2330        2330  230.46996  230.99084  229.53477  229.53477\n",
       "2331        2331  230.33377  230.84792  229.38823  229.38823\n",
       "2332        2332  230.11935  230.63647  229.14973  229.14973"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df1 = pd.read_csv(\"Predictions\\Open Price Prediction.csv\")\n",
    "# df2 = pd.read_csv(\"Predictions\\High Price Prediction.csv\")\n",
    "# df3 = pd.read_csv(\"Predictions\\Low Price Prediction.csv\")\n",
    "# df4 = pd.read_csv(\"Predictions\\Close Price Prediction.csv\")\n",
    "\n",
    "original_df = pd.read_csv(\"dataset (MSFT).csv\")\n",
    "\n",
    "# df = pd.concat([df1, df2, df3, df4], axis=1)\n",
    "df = pd.read_csv(\"Predictions\\Overall_Predictions.csv\")\n",
    "print(\"Number of rows and columns:\", df.shape)\n",
    "df.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4700bc78",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.tail(20)\n",
    "# print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6dacc0c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "\n",
    "#define width of candlestick elements\n",
    "width = .4\n",
    "width2 = .05\n",
    "\n",
    "#define up and down prices\n",
    "up = df[df.Close>=df.Open]\n",
    "down = df[df.Close<df.Open]\n",
    "\n",
    "#define colors to use\n",
    "col1 = 'green'\n",
    "col2 = 'red'\n",
    "\n",
    "#plot up prices\n",
    "plt.bar(up.index,up.Close-up.Open,width,bottom=up.Open,color=col1)\n",
    "plt.bar(up.index,up.High-up.Close,width2,bottom=up.Close,color=col1)\n",
    "plt.bar(up.index,up.Low-up.Open,width2,bottom=up.Open,color=col1)\n",
    "\n",
    "#plot down prices\n",
    "plt.bar(down.index,down.Close-down.Open,width,bottom=down.Open,color=col2)\n",
    "plt.bar(down.index,down.High-down.Open,width2,bottom=down.Open,color=col2)\n",
    "plt.bar(down.index,down.Low-down.Close,width2,bottom=down.Close,color=col2)\n",
    "\n",
    "#rotate x-axis tick labels\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "#display candlestick chart\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "38a41377",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_arr, actual_arr = [], []\n",
    "predicted_open = df['Open'].tolist()[-100:]\n",
    "predicted_close = df['Close'].tolist()[-100:]\n",
    "for o, c in zip(predicted_open, predicted_close):\n",
    "    predicted_arr.append(o - c)\n",
    "    \n",
    "# predicted_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4011d31e",
   "metadata": {},
   "outputs": [],
   "source": [
    "actual_open = original_df['Open'].tolist()[-100:]\n",
    "actual_close = original_df['Close'].tolist()[-100:]\n",
    "for o, c in zip(actual_open, actual_close):\n",
    "    actual_arr.append(o - c)\n",
    "    \n",
    "# actual_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b649a0a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_counter = 0\n",
    "for predicted, actual in zip(predicted_arr, actual_arr):\n",
    "    if predicted >= 0 and actual >= 0:\n",
    "        accuracy_counter += 1\n",
    "        \n",
    "accuracy_counter / 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4ed8062",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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