Candle_Sticks_Predictions.ipynb

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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kayra\\anaconda3\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.1\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "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": 2,
   "id": "15a70bca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<details>\n",
       "<summary>Click to view session information</summary>\n",
       "<pre>\n",
       "-----\n",
       "keras               2.9.0\n",
       "matplotlib          3.5.2\n",
       "numpy               1.23.1\n",
       "pandas              1.4.4\n",
       "session_info        1.0.0\n",
       "sklearn             1.0.2\n",
       "tensorflow          2.9.1\n",
       "-----\n",
       "</pre>\n",
       "<details>\n",
       "<summary>Click to view modules imported as dependencies</summary>\n",
       "<pre>\n",
       "OpenSSL                                     22.0.0\n",
       "PIL                                         9.2.0\n",
       "aa8f2297d25b4dc6fd3d98411eb3ba53823c4f42    NA\n",
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       "asttokens                                   NA\n",
       "astunparse                                  1.6.3\n",
       "backcall                                    0.2.0\n",
       "bcrypt                                      3.2.0\n",
       "beta_ufunc                                  NA\n",
       "binom_ufunc                                 NA\n",
       "boto3                                       1.24.28\n",
       "botocore                                    1.27.28\n",
       "bottleneck                                  1.3.5\n",
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       "certifi                                     2022.09.24\n",
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       "chardet                                     4.0.0\n",
       "charset_normalizer                          2.0.4\n",
       "colorama                                    0.4.5\n",
       "cryptography                                37.0.1\n",
       "cycler                                      0.10.0\n",
       "cython_runtime                              NA\n",
       "dateutil                                    2.8.2\n",
       "debugpy                                     1.5.1\n",
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       "flatbuffers                                 2.0\n",
       "fsspec                                      2022.7.1\n",
       "gast                                        0.5.3\n",
       "google                                      NA\n",
       "h5py                                        3.7.0\n",
       "idna                                        3.3\n",
       "importlib_metadata                          NA\n",
       "ipykernel                                   6.15.2\n",
       "ipython_genutils                            0.2.0\n",
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       "jmespath                                    0.10.0\n",
       "joblib                                      1.1.0\n",
       "jupyter_server                              1.18.1\n",
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       "lz4                                         3.1.3\n",
       "mkl                                         2.4.0\n",
       "mpl_toolkits                                NA\n",
       "nbinom_ufunc                                NA\n",
       "nt                                          NA\n",
       "ntsecuritycon                               NA\n",
       "numexpr                                     2.8.3\n",
       "opt_einsum                                  v3.3.0\n",
       "packaging                                   21.3\n",
       "parso                                       0.8.3\n",
       "pickleshare                                 0.7.5\n",
       "pkg_resources                               NA\n",
       "prompt_toolkit                              3.0.20\n",
       "psutil                                      5.9.0\n",
       "pure_eval                                   0.2.2\n",
       "pydev_ipython                               NA\n",
       "pydevconsole                                NA\n",
       "pydevd                                      2.6.0\n",
       "pydevd_concurrency_analyser                 NA\n",
       "pydevd_file_utils                           NA\n",
       "pydevd_plugins                              NA\n",
       "pydevd_tracing                              NA\n",
       "pygments                                    2.11.2\n",
       "pyparsing                                   3.0.9\n",
       "pythoncom                                   NA\n",
       "pytz                                        2022.1\n",
       "pywintypes                                  NA\n",
       "requests                                    2.28.1\n",
       "scipy                                       1.7.3\n",
       "setuptools                                  63.4.1\n",
       "six                                         1.16.0\n",
       "snappy                                      NA\n",
       "socks                                       1.7.1\n",
       "sphinxcontrib                               NA\n",
       "stack_data                                  0.2.0\n",
       "tblib                                       1.7.0\n",
       "tensorboard                                 2.9.0\n",
       "termcolor                                   NA\n",
       "threadpoolctl                               2.2.0\n",
       "tornado                                     6.1\n",
       "traitlets                                   5.1.1\n",
       "typing_extensions                           NA\n",
       "urllib3                                     1.26.11\n",
       "wcwidth                                     0.2.5\n",
       "win32api                                    NA\n",
       "win32com                                    NA\n",
       "win32security                               NA\n",
       "wrapt                                       1.12.1\n",
       "zipp                                        NA\n",
       "zmq                                         23.2.0\n",
       "zope                                        NA\n",
       "</pre>\n",
       "</details> <!-- seems like this ends pre, so might as well be explicit -->\n",
       "<pre>\n",
       "-----\n",
       "IPython             8.4.0\n",
       "jupyter_client      6.1.12\n",
       "jupyter_core        4.11.1\n",
       "jupyterlab          3.4.4\n",
       "notebook            6.4.12\n",
       "-----\n",
       "Python 3.9.12 (main, Apr  4 2022, 05:22:27) [MSC v.1916 64 bit (AMD64)]\n",
       "Windows-10-10.0.19044-SP0\n",
       "-----\n",
       "Session information updated at 2022-12-28 23:26\n",
       "</pre>\n",
       "</details>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import session_info\n",
    "session_info.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5f88eac2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows and columns: (2333, 4)\n"
     ]
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    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2313</th>\n",
       "      <td>230.34784</td>\n",
       "      <td>230.50710</td>\n",
       "      <td>230.23982</td>\n",
       "      <td>230.37935</td>\n",
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       "    <tr>\n",
       "      <th>2314</th>\n",
       "      <td>230.33110</td>\n",
       "      <td>230.49048</td>\n",
       "      <td>230.22318</td>\n",
       "      <td>230.36273</td>\n",
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       "    <tr>\n",
       "      <th>2315</th>\n",
       "      <td>230.27754</td>\n",
       "      <td>230.43704</td>\n",
       "      <td>230.16975</td>\n",
       "      <td>230.30940</td>\n",
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       "    <tr>\n",
       "      <th>2316</th>\n",
       "      <td>230.27698</td>\n",
       "      <td>230.43658</td>\n",
       "      <td>230.16919</td>\n",
       "      <td>230.30885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2317</th>\n",
       "      <td>230.25781</td>\n",
       "      <td>230.41747</td>\n",
       "      <td>230.15004</td>\n",
       "      <td>230.28975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2318</th>\n",
       "      <td>230.29134</td>\n",
       "      <td>230.45096</td>\n",
       "      <td>230.18347</td>\n",
       "      <td>230.32312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2319</th>\n",
       "      <td>230.23079</td>\n",
       "      <td>230.39047</td>\n",
       "      <td>230.12308</td>\n",
       "      <td>230.26279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2320</th>\n",
       "      <td>230.21143</td>\n",
       "      <td>230.37125</td>\n",
       "      <td>230.10371</td>\n",
       "      <td>230.24352</td>\n",
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       "    <tr>\n",
       "      <th>2321</th>\n",
       "      <td>230.11536</td>\n",
       "      <td>230.27538</td>\n",
       "      <td>230.00784</td>\n",
       "      <td>230.14783</td>\n",
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       "    <tr>\n",
       "      <th>2322</th>\n",
       "      <td>230.07007</td>\n",
       "      <td>230.23036</td>\n",
       "      <td>229.96260</td>\n",
       "      <td>230.10275</td>\n",
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       "    <tr>\n",
       "      <th>2323</th>\n",
       "      <td>230.07477</td>\n",
       "      <td>230.23521</td>\n",
       "      <td>229.96724</td>\n",
       "      <td>230.10745</td>\n",
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       "    <tr>\n",
       "      <th>2324</th>\n",
       "      <td>230.20206</td>\n",
       "      <td>230.36229</td>\n",
       "      <td>230.09418</td>\n",
       "      <td>230.23418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2325</th>\n",
       "      <td>230.20862</td>\n",
       "      <td>230.36862</td>\n",
       "      <td>230.10074</td>\n",
       "      <td>230.24062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2326</th>\n",
       "      <td>230.25320</td>\n",
       "      <td>230.41304</td>\n",
       "      <td>230.14520</td>\n",
       "      <td>230.28497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2327</th>\n",
       "      <td>230.26202</td>\n",
       "      <td>230.42178</td>\n",
       "      <td>230.15404</td>\n",
       "      <td>230.29376</td>\n",
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       "    <tr>\n",
       "      <th>2328</th>\n",
       "      <td>230.17294</td>\n",
       "      <td>230.33281</td>\n",
       "      <td>230.06519</td>\n",
       "      <td>230.20508</td>\n",
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       "    <tr>\n",
       "      <th>2329</th>\n",
       "      <td>229.99861</td>\n",
       "      <td>230.15892</td>\n",
       "      <td>229.89125</td>\n",
       "      <td>230.03154</td>\n",
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       "    <tr>\n",
       "      <th>2330</th>\n",
       "      <td>229.84834</td>\n",
       "      <td>230.00930</td>\n",
       "      <td>229.74127</td>\n",
       "      <td>229.88197</td>\n",
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       "    <tr>\n",
       "      <th>2331</th>\n",
       "      <td>229.65579</td>\n",
       "      <td>229.81741</td>\n",
       "      <td>229.54907</td>\n",
       "      <td>229.69026</td>\n",
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       "    <tr>\n",
       "      <th>2332</th>\n",
       "      <td>229.51352</td>\n",
       "      <td>229.67583</td>\n",
       "      <td>229.40700</td>\n",
       "      <td>229.54863</td>\n",
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      ],
      "text/plain": [
       "           Open       High        Low      Close\n",
       "2313  230.34784  230.50710  230.23982  230.37935\n",
       "2314  230.33110  230.49048  230.22318  230.36273\n",
       "2315  230.27754  230.43704  230.16975  230.30940\n",
       "2316  230.27698  230.43658  230.16919  230.30885\n",
       "2317  230.25781  230.41747  230.15004  230.28975\n",
       "2318  230.29134  230.45096  230.18347  230.32312\n",
       "2319  230.23079  230.39047  230.12308  230.26279\n",
       "2320  230.21143  230.37125  230.10371  230.24352\n",
       "2321  230.11536  230.27538  230.00784  230.14783\n",
       "2322  230.07007  230.23036  229.96260  230.10275\n",
       "2323  230.07477  230.23521  229.96724  230.10745\n",
       "2324  230.20206  230.36229  230.09418  230.23418\n",
       "2325  230.20862  230.36862  230.10074  230.24062\n",
       "2326  230.25320  230.41304  230.14520  230.28497\n",
       "2327  230.26202  230.42178  230.15404  230.29376\n",
       "2328  230.17294  230.33281  230.06519  230.20508\n",
       "2329  229.99861  230.15892  229.89125  230.03154\n",
       "2330  229.84834  230.00930  229.74127  229.88197\n",
       "2331  229.65579  229.81741  229.54907  229.69026\n",
       "2332  229.51352  229.67583  229.40700  229.54863"
      ]
     },
     "execution_count": 18,
     "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(\"Datasets/dataset (MSFT).csv\")\n",
    "\n",
    "# df = pd.concat([df1, df2, df3, df4], axis=1)\n",
    "df = pd.read_csv(\"Predictions\\Combined Model Price Prediction.csv\")\n",
    "print(\"Number of rows and columns:\", df.shape)\n",
    "df.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4700bc78",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.tail(20)\n",
    "# print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 6,
   "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": 7,
   "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": 8,
   "id": "b649a0a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 8,
     "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": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}