├── AppleStockAnalysis.ipynb
├── BitcoinPredictionRNN.ipynb
├── Bitcoinprediction.py
├── CryptoScraper.ipynb
├── LICENSE
├── MLPresentation.pdf
├── Machine_Learning_Project_Report.pdf
├── PredictStockPricesRNN.py
├── README.md
└── RedditScraper.ipynb


/AppleStockAnalysis.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |   "nbformat": 4,
   3 |   "nbformat_minor": 0,
   4 |   "metadata": {
   5 |     "colab": {
   6 |       "name": "AppleStockAnalysis.ipynb",
   7 |       "provenance": [],
   8 |       "collapsed_sections": [],
   9 |       "include_colab_link": true
  10 |     },
  11 |     "kernelspec": {
  12 |       "name": "python3",
  13 |       "display_name": "Python 3"
  14 |     }
  15 |   },
  16 |   "cells": [
  17 |     {
  18 |       "cell_type": "markdown",
  19 |       "metadata": {
  20 |         "id": "view-in-github",
  21 |         "colab_type": "text"
  22 |       },
  23 |       "source": [
  24 |         "<a href=\"https://colab.research.google.com/github/manthanthakker/BitcoinPrediction/blob/master/AppleStockAnalysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
  25 |       ]
  26 |     },
  27 |     {
  28 |       "cell_type": "markdown",
  29 |       "metadata": {
  30 |         "id": "8yDlnoA-xbTy",
  31 |         "colab_type": "text"
  32 |       },
  33 |       "source": [
  34 |         "# Apple Stock prediction Recurrent Neural Network\n",
  35 |         "\n",
  36 |         "Please Note, this notebook is only for exploration purpose and is meant to explore application of LSTMs into financial markets. Please don't trade or make decisions based on these analysis. "
  37 |       ]
  38 |     },
  39 |     {
  40 |       "cell_type": "code",
  41 |       "metadata": {
  42 |         "id": "NEG0TzxOShBM",
  43 |         "colab_type": "code",
  44 |         "colab": {}
  45 |       },
  46 |       "source": [
  47 |         "!pip install yfinance\n",
  48 |         "!conda install -c pytorch pytorch-nightly-cpu -y \n",
  49 |         "!conda install -c fastai torchvision-nightly-cpu -y \n",
  50 |         "!conda install -c fastai fastai -y \n",
  51 |         "!pip install tensorflow\n",
  52 |         "!pip install fastai\n",
  53 |         "!conda install nb_conda -y\n",
  54 |         "!pip install dataclasses\n",
  55 |         "!pip install fastai==0.7.0\n",
  56 |         "!pip install keras\n",
  57 |         "!pip install --upgrade pip\n",
  58 |         "!conda remove seaborn -y\n",
  59 |         "!pip uninstall seaborn \n",
  60 |         "!pip3 --no-cache-dir install seaborn "
  61 |       ],
  62 |       "execution_count": 0,
  63 |       "outputs": []
  64 |     },
  65 |     {
  66 |       "cell_type": "code",
  67 |       "metadata": {
  68 |         "id": "1YjrU5aqSiIr",
  69 |         "colab_type": "code",
  70 |         "colab": {
  71 |           "base_uri": "https://localhost:8080/",
  72 |           "height": 34
  73 |         },
  74 |         "outputId": "9ed3e1fe-9864-4742-849b-542d13275169"
  75 |       },
  76 |       "source": [
  77 |         "import yfinance as yf\n",
  78 |         "import matplotlib.pyplot as plt\n",
  79 |         "import numpy as np\n",
  80 |         "import pandas as pd\n",
  81 |         "import seaborn; seaborn.set()\n",
  82 |         "import datetime\n",
  83 |         "import pandas as pd\n",
  84 |         "from keras.models import Sequential\n",
  85 |         "from keras.layers import Dense, Activation\n",
  86 |         "import seaborn as sns\n",
  87 |         "sns.set()\n",
  88 |         "%load_ext autoreload\n",
  89 |         "%autoreload 2\n",
  90 |         "%matplotlib inline\n",
  91 |         "from fastai.imports import *\n",
  92 |         "from fastai.structured import *\n",
  93 |         "from pandas_summary import DataFrameSummary\n",
  94 |         "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
  95 |         "from IPython.display import display\n",
  96 |         "from sklearn import metrics"
  97 |       ],
  98 |       "execution_count": 85,
  99 |       "outputs": [
 100 |         {
 101 |           "output_type": "stream",
 102 |           "text": [
 103 |             "Using TensorFlow backend.\n"
 104 |           ],
 105 |           "name": "stderr"
 106 |         }
 107 |       ]
 108 |     },
 109 |     {
 110 |       "cell_type": "markdown",
 111 |       "metadata": {
 112 |         "id": "MBPNT_XqZIcj",
 113 |         "colab_type": "text"
 114 |       },
 115 |       "source": [
 116 |         "### We will do this in 2 steps:\n",
 117 |         "\n",
 118 |         "\n",
 119 |         "1.   Fundamental analysis using Yahoo Finance API\n",
 120 |         "2.   Applying Recurrent Neural Network\n",
 121 |         "\n"
 122 |       ]
 123 |     },
 124 |     {
 125 |       "cell_type": "markdown",
 126 |       "metadata": {
 127 |         "id": "F1mFDySKydB1",
 128 |         "colab_type": "text"
 129 |       },
 130 |       "source": [
 131 |         "## Fundamental analysis"
 132 |       ]
 133 |     },
 134 |     {
 135 |       "cell_type": "code",
 136 |       "metadata": {
 137 |         "id": "5Tssn1a4Wz_9",
 138 |         "colab_type": "code",
 139 |         "outputId": "754fdb1c-6851-4efd-9f93-a9f0240ff51e",
 140 |         "colab": {
 141 |           "base_uri": "https://localhost:8080/",
 142 |           "height": 1000
 143 |         }
 144 |       },
 145 |       "source": [
 146 |         "apple=yf.Ticker(\"AAPL\")\n",
 147 |         "pd.DataFrame(list(apple.info.items()))\n",
 148 |         "#print(apple.actions)"
 149 |       ],
 150 |       "execution_count": 86,
 151 |       "outputs": [
 152 |         {
 153 |           "output_type": "execute_result",
 154 |           "data": {
 155 |             "text/html": [
 156 |               "<div>\n",
 157 |               "<style scoped>\n",
 158 |               "    .dataframe tbody tr th:only-of-type {\n",
 159 |               "        vertical-align: middle;\n",
 160 |               "    }\n",
 161 |               "\n",
 162 |               "    .dataframe tbody tr th {\n",
 163 |               "        vertical-align: top;\n",
 164 |               "    }\n",
 165 |               "\n",
 166 |               "    .dataframe thead th {\n",
 167 |               "        text-align: right;\n",
 168 |               "    }\n",
 169 |               "</style>\n",
 170 |               "<table border=\"1\" class=\"dataframe\">\n",
 171 |               "  <thead>\n",
 172 |               "    <tr style=\"text-align: right;\">\n",
 173 |               "      <th></th>\n",
 174 |               "      <th>0</th>\n",
 175 |               "      <th>1</th>\n",
 176 |               "    </tr>\n",
 177 |               "  </thead>\n",
 178 |               "  <tbody>\n",
 179 |               "    <tr>\n",
 180 |               "      <th>0</th>\n",
 181 |               "      <td>language</td>\n",
 182 |               "      <td>en-US</td>\n",
 183 |               "    </tr>\n",
 184 |               "    <tr>\n",
 185 |               "      <th>1</th>\n",
 186 |               "      <td>region</td>\n",
 187 |               "      <td>US</td>\n",
 188 |               "    </tr>\n",
 189 |               "    <tr>\n",
 190 |               "      <th>2</th>\n",
 191 |               "      <td>quoteType</td>\n",
 192 |               "      <td>EQUITY</td>\n",
 193 |               "    </tr>\n",
 194 |               "    <tr>\n",
 195 |               "      <th>3</th>\n",
 196 |               "      <td>quoteSourceName</td>\n",
 197 |               "      <td>Delayed Quote</td>\n",
 198 |               "    </tr>\n",
 199 |               "    <tr>\n",
 200 |               "      <th>4</th>\n",
 201 |               "      <td>currency</td>\n",
 202 |               "      <td>USD</td>\n",
 203 |               "    </tr>\n",
 204 |               "    <tr>\n",
 205 |               "      <th>5</th>\n",
 206 |               "      <td>priceHint</td>\n",
 207 |               "      <td>2</td>\n",
 208 |               "    </tr>\n",
 209 |               "    <tr>\n",
 210 |               "      <th>6</th>\n",
 211 |               "      <td>shortName</td>\n",
 212 |               "      <td>Apple Inc.</td>\n",
 213 |               "    </tr>\n",
 214 |               "    <tr>\n",
 215 |               "      <th>7</th>\n",
 216 |               "      <td>market</td>\n",
 217 |               "      <td>us_market</td>\n",
 218 |               "    </tr>\n",
 219 |               "    <tr>\n",
 220 |               "      <th>8</th>\n",
 221 |               "      <td>regularMarketPrice</td>\n",
 222 |               "      <td>236.41</td>\n",
 223 |               "    </tr>\n",
 224 |               "    <tr>\n",
 225 |               "      <th>9</th>\n",
 226 |               "      <td>regularMarketTime</td>\n",
 227 |               "      <td>1571428801</td>\n",
 228 |               "    </tr>\n",
 229 |               "    <tr>\n",
 230 |               "      <th>10</th>\n",
 231 |               "      <td>regularMarketChange</td>\n",
 232 |               "      <td>1.13</td>\n",
 233 |               "    </tr>\n",
 234 |               "    <tr>\n",
 235 |               "      <th>11</th>\n",
 236 |               "      <td>regularMarketOpen</td>\n",
 237 |               "      <td>234.59</td>\n",
 238 |               "    </tr>\n",
 239 |               "    <tr>\n",
 240 |               "      <th>12</th>\n",
 241 |               "      <td>regularMarketDayHigh</td>\n",
 242 |               "      <td>237.58</td>\n",
 243 |               "    </tr>\n",
 244 |               "    <tr>\n",
 245 |               "      <th>13</th>\n",
 246 |               "      <td>regularMarketDayLow</td>\n",
 247 |               "      <td>234.29</td>\n",
 248 |               "    </tr>\n",
 249 |               "    <tr>\n",
 250 |               "      <th>14</th>\n",
 251 |               "      <td>regularMarketVolume</td>\n",
 252 |               "      <td>21772221</td>\n",
 253 |               "    </tr>\n",
 254 |               "    <tr>\n",
 255 |               "      <th>15</th>\n",
 256 |               "      <td>exchangeDataDelayedBy</td>\n",
 257 |               "      <td>0</td>\n",
 258 |               "    </tr>\n",
 259 |               "    <tr>\n",
 260 |               "      <th>16</th>\n",
 261 |               "      <td>marketState</td>\n",
 262 |               "      <td>CLOSED</td>\n",
 263 |               "    </tr>\n",
 264 |               "    <tr>\n",
 265 |               "      <th>17</th>\n",
 266 |               "      <td>exchange</td>\n",
 267 |               "      <td>NMS</td>\n",
 268 |               "    </tr>\n",
 269 |               "    <tr>\n",
 270 |               "      <th>18</th>\n",
 271 |               "      <td>twoHundredDayAverageChangePercent</td>\n",
 272 |               "      <td>0.153417</td>\n",
 273 |               "    </tr>\n",
 274 |               "    <tr>\n",
 275 |               "      <th>19</th>\n",
 276 |               "      <td>marketCap</td>\n",
 277 |               "      <td>1068384059392</td>\n",
 278 |               "    </tr>\n",
 279 |               "    <tr>\n",
 280 |               "      <th>20</th>\n",
 281 |               "      <td>forwardPE</td>\n",
 282 |               "      <td>18.5274</td>\n",
 283 |               "    </tr>\n",
 284 |               "    <tr>\n",
 285 |               "      <th>21</th>\n",
 286 |               "      <td>priceToBook</td>\n",
 287 |               "      <td>11.1064</td>\n",
 288 |               "    </tr>\n",
 289 |               "    <tr>\n",
 290 |               "      <th>22</th>\n",
 291 |               "      <td>sourceInterval</td>\n",
 292 |               "      <td>15</td>\n",
 293 |               "    </tr>\n",
 294 |               "    <tr>\n",
 295 |               "      <th>23</th>\n",
 296 |               "      <td>exchangeTimezoneName</td>\n",
 297 |               "      <td>America/New_York</td>\n",
 298 |               "    </tr>\n",
 299 |               "    <tr>\n",
 300 |               "      <th>24</th>\n",
 301 |               "      <td>exchangeTimezoneShortName</td>\n",
 302 |               "      <td>EDT</td>\n",
 303 |               "    </tr>\n",
 304 |               "    <tr>\n",
 305 |               "      <th>25</th>\n",
 306 |               "      <td>gmtOffSetMilliseconds</td>\n",
 307 |               "      <td>-14400000</td>\n",
 308 |               "    </tr>\n",
 309 |               "    <tr>\n",
 310 |               "      <th>26</th>\n",
 311 |               "      <td>esgPopulated</td>\n",
 312 |               "      <td>False</td>\n",
 313 |               "    </tr>\n",
 314 |               "    <tr>\n",
 315 |               "      <th>27</th>\n",
 316 |               "      <td>tradeable</td>\n",
 317 |               "      <td>True</td>\n",
 318 |               "    </tr>\n",
 319 |               "    <tr>\n",
 320 |               "      <th>28</th>\n",
 321 |               "      <td>triggerable</td>\n",
 322 |               "      <td>True</td>\n",
 323 |               "    </tr>\n",
 324 |               "    <tr>\n",
 325 |               "      <th>29</th>\n",
 326 |               "      <td>messageBoardId</td>\n",
 327 |               "      <td>finmb_24937</td>\n",
 328 |               "    </tr>\n",
 329 |               "    <tr>\n",
 330 |               "      <th>...</th>\n",
 331 |               "      <td>...</td>\n",
 332 |               "      <td>...</td>\n",
 333 |               "    </tr>\n",
 334 |               "    <tr>\n",
 335 |               "      <th>40</th>\n",
 336 |               "      <td>financialCurrency</td>\n",
 337 |               "      <td>USD</td>\n",
 338 |               "    </tr>\n",
 339 |               "    <tr>\n",
 340 |               "      <th>41</th>\n",
 341 |               "      <td>postMarketPrice</td>\n",
 342 |               "      <td>236.21</td>\n",
 343 |               "    </tr>\n",
 344 |               "    <tr>\n",
 345 |               "      <th>42</th>\n",
 346 |               "      <td>postMarketChange</td>\n",
 347 |               "      <td>-0.199997</td>\n",
 348 |               "    </tr>\n",
 349 |               "    <tr>\n",
 350 |               "      <th>43</th>\n",
 351 |               "      <td>regularMarketChangePercent</td>\n",
 352 |               "      <td>0.480281</td>\n",
 353 |               "    </tr>\n",
 354 |               "    <tr>\n",
 355 |               "      <th>44</th>\n",
 356 |               "      <td>regularMarketDayRange</td>\n",
 357 |               "      <td>234.29 - 237.58</td>\n",
 358 |               "    </tr>\n",
 359 |               "    <tr>\n",
 360 |               "      <th>45</th>\n",
 361 |               "      <td>bid</td>\n",
 362 |               "      <td>236.21</td>\n",
 363 |               "    </tr>\n",
 364 |               "    <tr>\n",
 365 |               "      <th>46</th>\n",
 366 |               "      <td>ask</td>\n",
 367 |               "      <td>236.37</td>\n",
 368 |               "    </tr>\n",
 369 |               "    <tr>\n",
 370 |               "      <th>47</th>\n",
 371 |               "      <td>bidSize</td>\n",
 372 |               "      <td>14</td>\n",
 373 |               "    </tr>\n",
 374 |               "    <tr>\n",
 375 |               "      <th>48</th>\n",
 376 |               "      <td>askSize</td>\n",
 377 |               "      <td>9</td>\n",
 378 |               "    </tr>\n",
 379 |               "    <tr>\n",
 380 |               "      <th>49</th>\n",
 381 |               "      <td>trailingAnnualDividendRate</td>\n",
 382 |               "      <td>2.96</td>\n",
 383 |               "    </tr>\n",
 384 |               "    <tr>\n",
 385 |               "      <th>50</th>\n",
 386 |               "      <td>trailingAnnualDividendYield</td>\n",
 387 |               "      <td>0.0125808</td>\n",
 388 |               "    </tr>\n",
 389 |               "    <tr>\n",
 390 |               "      <th>51</th>\n",
 391 |               "      <td>epsTrailingTwelveMonths</td>\n",
 392 |               "      <td>11.777</td>\n",
 393 |               "    </tr>\n",
 394 |               "    <tr>\n",
 395 |               "      <th>52</th>\n",
 396 |               "      <td>epsForward</td>\n",
 397 |               "      <td>12.76</td>\n",
 398 |               "    </tr>\n",
 399 |               "    <tr>\n",
 400 |               "      <th>53</th>\n",
 401 |               "      <td>fullExchangeName</td>\n",
 402 |               "      <td>NasdaqGS</td>\n",
 403 |               "    </tr>\n",
 404 |               "    <tr>\n",
 405 |               "      <th>54</th>\n",
 406 |               "      <td>longName</td>\n",
 407 |               "      <td>Apple Inc.</td>\n",
 408 |               "    </tr>\n",
 409 |               "    <tr>\n",
 410 |               "      <th>55</th>\n",
 411 |               "      <td>averageDailyVolume3Month</td>\n",
 412 |               "      <td>28456101</td>\n",
 413 |               "    </tr>\n",
 414 |               "    <tr>\n",
 415 |               "      <th>56</th>\n",
 416 |               "      <td>averageDailyVolume10Day</td>\n",
 417 |               "      <td>25089957</td>\n",
 418 |               "    </tr>\n",
 419 |               "    <tr>\n",
 420 |               "      <th>57</th>\n",
 421 |               "      <td>fiftyTwoWeekLowChange</td>\n",
 422 |               "      <td>94.41</td>\n",
 423 |               "    </tr>\n",
 424 |               "    <tr>\n",
 425 |               "      <th>58</th>\n",
 426 |               "      <td>fiftyTwoWeekLowChangePercent</td>\n",
 427 |               "      <td>0.664859</td>\n",
 428 |               "    </tr>\n",
 429 |               "    <tr>\n",
 430 |               "      <th>59</th>\n",
 431 |               "      <td>fiftyTwoWeekRange</td>\n",
 432 |               "      <td>142.0 - 238.13</td>\n",
 433 |               "    </tr>\n",
 434 |               "    <tr>\n",
 435 |               "      <th>60</th>\n",
 436 |               "      <td>fiftyTwoWeekHighChange</td>\n",
 437 |               "      <td>-1.72</td>\n",
 438 |               "    </tr>\n",
 439 |               "    <tr>\n",
 440 |               "      <th>61</th>\n",
 441 |               "      <td>fiftyTwoWeekHighChangePercent</td>\n",
 442 |               "      <td>-0.00722295</td>\n",
 443 |               "    </tr>\n",
 444 |               "    <tr>\n",
 445 |               "      <th>62</th>\n",
 446 |               "      <td>fiftyTwoWeekLow</td>\n",
 447 |               "      <td>142</td>\n",
 448 |               "    </tr>\n",
 449 |               "    <tr>\n",
 450 |               "      <th>63</th>\n",
 451 |               "      <td>fiftyTwoWeekHigh</td>\n",
 452 |               "      <td>238.13</td>\n",
 453 |               "    </tr>\n",
 454 |               "    <tr>\n",
 455 |               "      <th>64</th>\n",
 456 |               "      <td>dividendDate</td>\n",
 457 |               "      <td>1565827200</td>\n",
 458 |               "    </tr>\n",
 459 |               "    <tr>\n",
 460 |               "      <th>65</th>\n",
 461 |               "      <td>earningsTimestamp</td>\n",
 462 |               "      <td>1572480000</td>\n",
 463 |               "    </tr>\n",
 464 |               "    <tr>\n",
 465 |               "      <th>66</th>\n",
 466 |               "      <td>earningsTimestampStart</td>\n",
 467 |               "      <td>1572480000</td>\n",
 468 |               "    </tr>\n",
 469 |               "    <tr>\n",
 470 |               "      <th>67</th>\n",
 471 |               "      <td>earningsTimestampEnd</td>\n",
 472 |               "      <td>1572480000</td>\n",
 473 |               "    </tr>\n",
 474 |               "    <tr>\n",
 475 |               "      <th>68</th>\n",
 476 |               "      <td>trailingPE</td>\n",
 477 |               "      <td>20.0739</td>\n",
 478 |               "    </tr>\n",
 479 |               "    <tr>\n",
 480 |               "      <th>69</th>\n",
 481 |               "      <td>symbol</td>\n",
 482 |               "      <td>AAPL</td>\n",
 483 |               "    </tr>\n",
 484 |               "  </tbody>\n",
 485 |               "</table>\n",
 486 |               "<p>70 rows × 2 columns</p>\n",
 487 |               "</div>"
 488 |             ],
 489 |             "text/plain": [
 490 |               "                                    0                 1\n",
 491 |               "0                            language             en-US\n",
 492 |               "1                              region                US\n",
 493 |               "2                           quoteType            EQUITY\n",
 494 |               "3                     quoteSourceName     Delayed Quote\n",
 495 |               "4                            currency               USD\n",
 496 |               "5                           priceHint                 2\n",
 497 |               "6                           shortName        Apple Inc.\n",
 498 |               "7                              market         us_market\n",
 499 |               "8                  regularMarketPrice            236.41\n",
 500 |               "9                   regularMarketTime        1571428801\n",
 501 |               "10                regularMarketChange              1.13\n",
 502 |               "11                  regularMarketOpen            234.59\n",
 503 |               "12               regularMarketDayHigh            237.58\n",
 504 |               "13                regularMarketDayLow            234.29\n",
 505 |               "14                regularMarketVolume          21772221\n",
 506 |               "15              exchangeDataDelayedBy                 0\n",
 507 |               "16                        marketState            CLOSED\n",
 508 |               "17                           exchange               NMS\n",
 509 |               "18  twoHundredDayAverageChangePercent          0.153417\n",
 510 |               "19                          marketCap     1068384059392\n",
 511 |               "20                          forwardPE           18.5274\n",
 512 |               "21                        priceToBook           11.1064\n",
 513 |               "22                     sourceInterval                15\n",
 514 |               "23               exchangeTimezoneName  America/New_York\n",
 515 |               "24          exchangeTimezoneShortName               EDT\n",
 516 |               "25              gmtOffSetMilliseconds         -14400000\n",
 517 |               "26                       esgPopulated             False\n",
 518 |               "27                          tradeable              True\n",
 519 |               "28                        triggerable              True\n",
 520 |               "29                     messageBoardId       finmb_24937\n",
 521 |               "..                                ...               ...\n",
 522 |               "40                  financialCurrency               USD\n",
 523 |               "41                    postMarketPrice            236.21\n",
 524 |               "42                   postMarketChange         -0.199997\n",
 525 |               "43         regularMarketChangePercent          0.480281\n",
 526 |               "44              regularMarketDayRange   234.29 - 237.58\n",
 527 |               "45                                bid            236.21\n",
 528 |               "46                                ask            236.37\n",
 529 |               "47                            bidSize                14\n",
 530 |               "48                            askSize                 9\n",
 531 |               "49         trailingAnnualDividendRate              2.96\n",
 532 |               "50        trailingAnnualDividendYield         0.0125808\n",
 533 |               "51            epsTrailingTwelveMonths            11.777\n",
 534 |               "52                         epsForward             12.76\n",
 535 |               "53                   fullExchangeName          NasdaqGS\n",
 536 |               "54                           longName        Apple Inc.\n",
 537 |               "55           averageDailyVolume3Month          28456101\n",
 538 |               "56            averageDailyVolume10Day          25089957\n",
 539 |               "57              fiftyTwoWeekLowChange             94.41\n",
 540 |               "58       fiftyTwoWeekLowChangePercent          0.664859\n",
 541 |               "59                  fiftyTwoWeekRange    142.0 - 238.13\n",
 542 |               "60             fiftyTwoWeekHighChange             -1.72\n",
 543 |               "61      fiftyTwoWeekHighChangePercent       -0.00722295\n",
 544 |               "62                    fiftyTwoWeekLow               142\n",
 545 |               "63                   fiftyTwoWeekHigh            238.13\n",
 546 |               "64                       dividendDate        1565827200\n",
 547 |               "65                  earningsTimestamp        1572480000\n",
 548 |               "66             earningsTimestampStart        1572480000\n",
 549 |               "67               earningsTimestampEnd        1572480000\n",
 550 |               "68                         trailingPE           20.0739\n",
 551 |               "69                             symbol              AAPL\n",
 552 |               "\n",
 553 |               "[70 rows x 2 columns]"
 554 |             ]
 555 |           },
 556 |           "metadata": {
 557 |             "tags": []
 558 |           },
 559 |           "execution_count": 86
 560 |         }
 561 |       ]
 562 |     },
 563 |     {
 564 |       "cell_type": "markdown",
 565 |       "metadata": {
 566 |         "id": "YKh9oWzubCfv",
 567 |         "colab_type": "text"
 568 |       },
 569 |       "source": [
 570 |         "So two important things, price of the stock is **236 dollars**. The regular market change is **234.29 - 237.58**. So If one is enter to buy apple stock once can target for 234 $ for that day.\n",
 571 |         "Looking at the 52 week range: **142.0 - 238.13**, the stock is very volatile and looks inflated at this price and can likely go back down. The fifty day average is **222.745**, so may be one **should wait** to drop a bit and then buy. "
 572 |       ]
 573 |     },
 574 |     {
 575 |       "cell_type": "code",
 576 |       "metadata": {
 577 |         "id": "xbHH847Pcklp",
 578 |         "colab_type": "code",
 579 |         "outputId": "f2cc65c0-8c5a-48da-dbbd-2b86754d5e08",
 580 |         "colab": {
 581 |           "base_uri": "https://localhost:8080/",
 582 |           "height": 187
 583 |         }
 584 |       },
 585 |       "source": [
 586 |         "print('Total Dividends:')\n",
 587 |         "print(apple.actions['Dividends'].sum())\n",
 588 |         "\n",
 589 |         "print('Total Stock splits since inception')\n",
 590 |         "print(apple.actions['Stock Splits'][apple.actions['Stock Splits']!=0])\n",
 591 |         "apple.actions['Stock Splits'][apple.actions['Stock Splits']!=0].product()\n"
 592 |       ],
 593 |       "execution_count": 87,
 594 |       "outputs": [
 595 |         {
 596 |           "output_type": "stream",
 597 |           "text": [
 598 |             "Total Dividends:\n",
 599 |             "16.234360000000002\n",
 600 |             "Total Stock splits since inception\n",
 601 |             "Date\n",
 602 |             "1987-06-16    2.0\n",
 603 |             "2000-06-21    2.0\n",
 604 |             "2005-02-28    2.0\n",
 605 |             "2014-06-09    7.0\n",
 606 |             "Name: Stock Splits, dtype: float64\n"
 607 |           ],
 608 |           "name": "stdout"
 609 |         },
 610 |         {
 611 |           "output_type": "execute_result",
 612 |           "data": {
 613 |             "text/plain": [
 614 |               "56.0"
 615 |             ]
 616 |           },
 617 |           "metadata": {
 618 |             "tags": []
 619 |           },
 620 |           "execution_count": 87
 621 |         }
 622 |       ]
 623 |     },
 624 |     {
 625 |       "cell_type": "markdown",
 626 |       "metadata": {
 627 |         "id": "mQUePSFgdLlw",
 628 |         "colab_type": "text"
 629 |       },
 630 |       "source": [
 631 |         "Since, inception Apple has given **16.23 dollars** of dividend to their shareholders. Since incpetion, if you had 1 apple stock it has split **4 times** and today you would be having **56 stocks for 1 stock**. "
 632 |       ]
 633 |     },
 634 |     {
 635 |       "cell_type": "code",
 636 |       "metadata": {
 637 |         "id": "Kr4jbySrfCLP",
 638 |         "colab_type": "code",
 639 |         "outputId": "3a2b771d-62ef-4980-fb16-293e5760b103",
 640 |         "colab": {
 641 |           "base_uri": "https://localhost:8080/",
 642 |           "height": 102
 643 |         }
 644 |       },
 645 |       "source": [
 646 |         "stockpriceAtInception=apple.history(period=\"MAX\")['Close'][0]\n",
 647 |         "latestStockPrice=apple.history(period=\"MAX\")['Close'][-1:]\n",
 648 |         "stockSplit=apple.actions['Stock Splits'][apple.actions['Stock Splits']!=0].product()\n",
 649 |         "\n",
 650 |         "profitSinceInception=(latestStockPrice-stockpriceAtInception)*stockSplit\n",
 651 |         "profitPercentageSinceInception=profitSinceInception/stockpriceAtInception*100\n",
 652 |         "print(\"Stock Price at inception was\",stockpriceAtInception)\n",
 653 |         "print(\"Stock Price today is\",latestStockPrice.to_string(index=False))\n",
 654 |         "print(\"This is\",profitPercentageSinceInception.to_string(index=False),\"percetage of profit.\")"
 655 |       ],
 656 |       "execution_count": 122,
 657 |       "outputs": [
 658 |         {
 659 |           "output_type": "stream",
 660 |           "text": [
 661 |             "Stock Price at inception was 0.4089711308479309\n",
 662 |             "Stock Price today is Date\n",
 663 |             " 236.410004\n",
 664 |             "This is Date\n",
 665 |             " 32315.380781 percetage of profit.\n"
 666 |           ],
 667 |           "name": "stdout"
 668 |         }
 669 |       ]
 670 |     },
 671 |     {
 672 |       "cell_type": "markdown",
 673 |       "metadata": {
 674 |         "id": "opdBudb2mYb6",
 675 |         "colab_type": "text"
 676 |       },
 677 |       "source": [
 678 |         "If you purchased 1 stock since inception for 0.4 today, you would have 3231538.0781 percetage of profit! Wow!"
 679 |       ]
 680 |     },
 681 |     {
 682 |       "cell_type": "markdown",
 683 |       "metadata": {
 684 |         "id": "0sWlCovPfWob",
 685 |         "colab_type": "text"
 686 |       },
 687 |       "source": [
 688 |         "Okay, now the fundamental analysis is done lets try to predict how is the stock going to behave in the coming days. "
 689 |       ]
 690 |     },
 691 |     {
 692 |       "cell_type": "code",
 693 |       "metadata": {
 694 |         "id": "P9X9HToHfnm7",
 695 |         "colab_type": "code",
 696 |         "colab": {
 697 |           "base_uri": "https://localhost:8080/",
 698 |           "height": 309
 699 |         },
 700 |         "outputId": "60c4a01f-1cb1-4714-c382-b4cb858b3e2d"
 701 |       },
 702 |       "source": [
 703 |         "history=apple.history(period=\"MAX\")['Close']\n",
 704 |         "import seaborn as sns; sns.set()\n",
 705 |         "plt.title('Analysis since inception')\n",
 706 |         "history.plot()"
 707 |       ],
 708 |       "execution_count": 137,
 709 |       "outputs": [
 710 |         {
 711 |           "output_type": "execute_result",
 712 |           "data": {
 713 |             "text/plain": [
 714 |               "<matplotlib.axes._subplots.AxesSubplot at 0x7f68d1d06588>"
 715 |             ]
 716 |           },
 717 |           "metadata": {
 718 |             "tags": []
 719 |           },
 720 |           "execution_count": 137
 721 |         },
 722 |         {
 723 |           "output_type": "display_data",
 724 |           "data": {
 725 |             "image/png": 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AVqxYwbBhw6qdJ4QQwnGaAqPRYH8GoPTuIB9P5/T6U+1WTpw4wdKlS2nXrh3jxo0DoFWr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\nioAQQjRhUgSEEKIJ+/+nQbXZ0WAPwwAAAABJRU5ErkJggg==\n",
 726 |             "text/plain": [
 727 |               "<Figure size 432x288 with 1 Axes>"
 728 |             ]
 729 |           },
 730 |           "metadata": {
 731 |             "tags": []
 732 |           }
 733 |         }
 734 |       ]
 735 |     },
 736 |     {
 737 |       "cell_type": "markdown",
 738 |       "metadata": {
 739 |         "id": "rHuRYaKJnFnK",
 740 |         "colab_type": "text"
 741 |       },
 742 |       "source": [
 743 |         "So the apple stock price has gained maximum profits since 1998. If you look on the web, that is when Apple Ipods was released and it was spectacular and apple started becoming the industry trend setter. "
 744 |       ]
 745 |     },
 746 |     {
 747 |       "cell_type": "code",
 748 |       "metadata": {
 749 |         "id": "U9ssXxYPg-Iw",
 750 |         "colab_type": "code",
 751 |         "colab": {
 752 |           "base_uri": "https://localhost:8080/",
 753 |           "height": 318
 754 |         },
 755 |         "outputId": "3452598a-9c57-4576-af46-ba2b6a10375b"
 756 |       },
 757 |       "source": [
 758 |         "history=apple.history(period=\"5y\")['Close']\n",
 759 |         "import seaborn as sns; sns.set()\n",
 760 |         "plt.title('5 years price analysis')\n",
 761 |         "history.plot()"
 762 |       ],
 763 |       "execution_count": 138,
 764 |       "outputs": [
 765 |         {
 766 |           "output_type": "execute_result",
 767 |           "data": {
 768 |             "text/plain": [
 769 |               "<matplotlib.axes._subplots.AxesSubplot at 0x7f68cf37ccf8>"
 770 |             ]
 771 |           },
 772 |           "metadata": {
 773 |             "tags": []
 774 |           },
 775 |           "execution_count": 138
 776 |         },
 777 |         {
 778 |           "output_type": "display_data",
 779 |           "data": {
 780 |             "image/png": 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8rtfrkZKSgkmTJrlMMIIgWg+D0YRz6YXIyC136nx+RdBctR0agcRF3c4sWJrc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\n5uN4EDDGmI/jQcAYYz6OBwFjjPm4fwBYY7DCOGVjhwAAAABJRU5ErkJggg==\n",
 781 |             "text/plain": [
 782 |               "<Figure size 432x288 with 1 Axes>"
 783 |             ]
 784 |           },
 785 |           "metadata": {
 786 |             "tags": []
 787 |           }
 788 |         }
 789 |       ]
 790 |     },
 791 |     {
 792 |       "cell_type": "code",
 793 |       "metadata": {
 794 |         "id": "qopwnL1moYTe",
 795 |         "colab_type": "code",
 796 |         "colab": {
 797 |           "base_uri": "https://localhost:8080/",
 798 |           "height": 318
 799 |         },
 800 |         "outputId": "4f1a264e-c465-495f-a17f-d872d1953809"
 801 |       },
 802 |       "source": [
 803 |         "history=apple.history(period=\"2y\")['Close']\n",
 804 |         "import seaborn as sns; sns.set()\n",
 805 |         "plt.title('2 years price analysis')\n",
 806 |         "history.plot()"
 807 |       ],
 808 |       "execution_count": 140,
 809 |       "outputs": [
 810 |         {
 811 |           "output_type": "execute_result",
 812 |           "data": {
 813 |             "text/plain": [
 814 |               "<matplotlib.axes._subplots.AxesSubplot at 0x7f68cf15d898>"
 815 |             ]
 816 |           },
 817 |           "metadata": {
 818 |             "tags": []
 819 |           },
 820 |           "execution_count": 140
 821 |         },
 822 |         {
 823 |           "output_type": "display_data",
 824 |           "data": {
 825 |             "image/png": 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h6WdLwOVmYWsXezhaWOejAOQyBrFa38EvShnTpSVAUZQYExAI1wSxziBKgBCx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\n/Q9/dUYZ4olQQQAAAABJRU5ErkJggg==\n",
 826 |             "text/plain": [
 827 |               "<Figure size 432x288 with 1 Axes>"
 828 |             ]
 829 |           },
 830 |           "metadata": {
 831 |             "tags": []
 832 |           }
 833 |         }
 834 |       ]
 835 |     },
 836 |     {
 837 |       "cell_type": "code",
 838 |       "metadata": {
 839 |         "id": "Qtdfk9RDoiZg",
 840 |         "colab_type": "code",
 841 |         "colab": {
 842 |           "base_uri": "https://localhost:8080/",
 843 |           "height": 318
 844 |         },
 845 |         "outputId": "9f30fd22-43e1-4992-f3f4-317e3c945e5c"
 846 |       },
 847 |       "source": [
 848 |         "history=apple.history(period=\"6mo\")['Close']\n",
 849 |         "import seaborn as sns; sns.set()\n",
 850 |         "plt.title('6 month price analysis')\n",
 851 |         "history.plot()"
 852 |       ],
 853 |       "execution_count": 145,
 854 |       "outputs": [
 855 |         {
 856 |           "output_type": "execute_result",
 857 |           "data": {
 858 |             "text/plain": [
 859 |               "<matplotlib.axes._subplots.AxesSubplot at 0x7f68cefa3160>"
 860 |             ]
 861 |           },
 862 |           "metadata": {
 863 |             "tags": []
 864 |           },
 865 |           "execution_count": 145
 866 |         },
 867 |         {
 868 |           "output_type": "display_data",
 869 |           "data": {
 870 |             "image/png": 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AEOyvISosgLzzVQDdnk9TTQMOp5voiLa7Xpaa6vhkbwEZafEoTleLNDjtnp5B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\n",
 871 |             "text/plain": [
 872 |               "<Figure size 432x288 with 1 Axes>"
 873 |             ]
 874 |           },
 875 |           "metadata": {
 876 |             "tags": []
 877 |           }
 878 |         }
 879 |       ]
 880 |     },
 881 |     {
 882 |       "cell_type": "markdown",
 883 |       "metadata": {
 884 |         "id": "mKv6Rsvjq8OP",
 885 |         "colab_type": "text"
 886 |       },
 887 |       "source": [
 888 |         "#Recurrent Neural network \n",
 889 |         "RNNs to capture time series data and predict stock price for the next few day"
 890 |       ]
 891 |     },
 892 |     {
 893 |       "cell_type": "code",
 894 |       "metadata": {
 895 |         "id": "bBasfZzdrL86",
 896 |         "colab_type": "code",
 897 |         "colab": {
 898 |           "base_uri": "https://localhost:8080/",
 899 |           "height": 1000
 900 |         },
 901 |         "outputId": "d0757f6d-ce96-4f04-84c0-a682955e5b97"
 902 |       },
 903 |       "source": [
 904 |         "import numpy\n",
 905 |         "import matplotlib.pyplot as plt\n",
 906 |         "from pandas import read_csv\n",
 907 |         "import math\n",
 908 |         "from keras.models import Sequential\n",
 909 |         "from keras.layers import Dense\n",
 910 |         "from keras.layers import LSTM,GRU\n",
 911 |         "from sklearn.preprocessing import MinMaxScaler,RobustScaler,StandardScaler\n",
 912 |         "from sklearn.metrics import mean_squared_error\n",
 913 |         "from pandas import Series\n",
 914 |         "\n",
 915 |         "data=apple.history(period=\"3y\")['Close']\n",
 916 |         "\n",
 917 |         "\n",
 918 |         "#data=data.set_index(pd.DatetimeIndex(data['Date']))['Close']\n",
 919 |         "def difference(dataset, interval=1):\n",
 920 |         "\tdiff = list()\n",
 921 |         "\tfor i in range(interval, len(dataset)):\n",
 922 |         "\t\tvalue = dataset[i] - dataset[i - interval]\n",
 923 |         "\t\tdiff.append(value)\n",
 924 |         "\treturn Series(diff)\n",
 925 |         "\n",
 926 |         "look_back=7\n",
 927 |         "#data=difference(data,look_back)\n",
 928 |         "\n",
 929 |         "\n",
 930 |         "#def project(data,look_back):\n",
 931 |         "    #convert an array of values into a dataset matrix\n",
 932 |         "    \n",
 933 |         "\n",
 934 |         "def create_dataset(dataset, look_back=1):\n",
 935 |         "    dataX, dataY = [], []\n",
 936 |         "    for i in range(len(dataset)-look_back-1):\n",
 937 |         "        #takes \n",
 938 |         "        a = dataset[i:(i+look_back), 0]\n",
 939 |         "        dataX.append(a)\n",
 940 |         "        dataY.append(dataset[i+look_back, 0])\n",
 941 |         "    return numpy.array(dataX), numpy.array(dataY)\n",
 942 |         "\n",
 943 |         "# fix random seed for reproducibility\n",
 944 |         "numpy.random.seed(0)\n",
 945 |         "\n",
 946 |         "# load the dataset\n",
 947 |         "dataframe = data\n",
 948 |         "dataset = dataframe.values\n",
 949 |         "dataset = dataset.astype('float64').reshape(-1, 1)\n",
 950 |         "\n",
 951 |         "# normalize the dataset\n",
 952 |         "scaler = MinMaxScaler()\n",
 953 |         "#scaler=RobustScaler()\n",
 954 |         "#scaler=StandardScaler()\n",
 955 |         "dataset = scaler.fit_transform(dataset)\n",
 956 |         "\n",
 957 |         "# split into train and test sets\n",
 958 |         "train_size = int(len(dataset) * 0.67)\n",
 959 |         "test_size = len(dataset) - train_size\n",
 960 |         "train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]\n",
 961 |         "\n",
 962 |         "# reshape into X=t and Y=t+1\n",
 963 |         "#look_back = 10\n",
 964 |         "trainX, trainY = create_dataset(train, look_back)\n",
 965 |         "testX, testY = create_dataset(test, look_back)\n",
 966 |         "\n",
 967 |         "# reshape input to be [samples, time steps, features]\n",
 968 |         "trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\n",
 969 |         "testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))\n",
 970 |         "# create and fit the LSTM network\n",
 971 |         "from keras.layers import Activation, Dense,Dropout\n",
 972 |         "model = Sequential()\n",
 973 |         "\n",
 974 |         "model.add(LSTM(256, return_sequences=True,input_shape=(1, look_back)))\n",
 975 |         "model.add(LSTM(256, return_sequences=True,input_shape=(1, look_back)))\n",
 976 |         "#model.add(LSTM(256, return_sequences=True,input_shape=(1, look_back)))\n",
 977 |         "model.add(LSTM(256))\n",
 978 |         "#model.add(LSTM(100, input_shape=(1, look_back)))\n",
 979 |         "\n",
 980 |         "model.add(Dense(1))\n",
 981 |         "import keras\n",
 982 |         "from keras import optimizers\n",
 983 |         "\n",
 984 |         "#keras.optimizers.Adam(lr=0.01, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False, clipnorm=1)\n",
 985 |         "\n",
 986 |         "model.compile(loss='mean_squared_error', optimizer='adam')\n",
 987 |         "model.fit(trainX, trainY, epochs=50, verbose=1,shuffle=False,batch_size=50)\n",
 988 |         "# make predictions\n",
 989 |         "trainPredict = model.predict(trainX)\n",
 990 |         "testPredict = model.predict(testX)\n",
 991 |         "# invert predictions\n",
 992 |         "trainPredict = scaler.inverse_transform(trainPredict)\n",
 993 |         "trainY = scaler.inverse_transform([trainY])\n",
 994 |         "testPredict = scaler.inverse_transform(testPredict)\n",
 995 |         "testY = scaler.inverse_transform([testY])\n",
 996 |         "# calculate root mean squared error\n",
 997 |         "trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))\n",
 998 |         "print('Train Score: %.2f RMSE' % (trainScore))\n",
 999 |         "testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))\n",
1000 |         "print('Test Score: %.2f RMSE' % (testScore))\n",
1001 |         "predictions = numpy.empty_like(dataset)\n",
1002 |         "predictions[:, :] = numpy.nan\n",
1003 |         "predictions[look_back:len(trainPredict)+look_back, :] = trainPredict\n",
1004 |         "predictions[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict\n",
1005 |         "#data=pd.DataFrame(numpy.concatenate((trainPredict[0:len(trainPredict)-look_back-1],testPredict[0:len(testPredict)-look_back-1])),columns=[\"predicted\"])\n",
1006 |         "#print('one',data.count())\n",
1007 |         "#print('two',dataframe.count())\n",
1008 |         "predictionsDF=pd.DataFrame(predictions,columns=[\"predicted\"],index=dataframe.index)\n",
1009 |         "ans=pd.concat([dataframe,predictionsDF],axis=1)\n",
1010 |         "print( ans,[look_back,trainScore,testScore])"
1011 |       ],
1012 |       "execution_count": 159,
1013 |       "outputs": [
1014 |         {
1015 |           "output_type": "stream",
1016 |           "text": [
1017 |             "Epoch 1/50\n",
1018 |             "496/496 [==============================] - 5s 11ms/step - loss: 0.1539\n",
1019 |             "Epoch 2/50\n",
1020 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0041\n",
1021 |             "Epoch 3/50\n",
1022 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0258\n",
1023 |             "Epoch 4/50\n",
1024 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0086\n",
1025 |             "Epoch 5/50\n",
1026 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0087\n",
1027 |             "Epoch 6/50\n",
1028 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0056\n",
1029 |             "Epoch 7/50\n",
1030 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0039\n",
1031 |             "Epoch 8/50\n",
1032 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0031\n",
1033 |             "Epoch 9/50\n",
1034 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0032\n",
1035 |             "Epoch 10/50\n",
1036 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0052\n",
1037 |             "Epoch 11/50\n",
1038 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0052\n",
1039 |             "Epoch 12/50\n",
1040 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0103\n",
1041 |             "Epoch 13/50\n",
1042 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0098\n",
1043 |             "Epoch 14/50\n",
1044 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0197\n",
1045 |             "Epoch 15/50\n",
1046 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0134\n",
1047 |             "Epoch 16/50\n",
1048 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0280\n",
1049 |             "Epoch 17/50\n",
1050 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0088\n",
1051 |             "Epoch 18/50\n",
1052 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0128\n",
1053 |             "Epoch 19/50\n",
1054 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0035\n",
1055 |             "Epoch 20/50\n",
1056 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0037\n",
1057 |             "Epoch 21/50\n",
1058 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0017\n",
1059 |             "Epoch 22/50\n",
1060 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0015\n",
1061 |             "Epoch 23/50\n",
1062 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0012\n",
1063 |             "Epoch 24/50\n",
1064 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0012\n",
1065 |             "Epoch 25/50\n",
1066 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0012\n",
1067 |             "Epoch 26/50\n",
1068 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0014\n",
1069 |             "Epoch 27/50\n",
1070 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0013\n",
1071 |             "Epoch 28/50\n",
1072 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0016\n",
1073 |             "Epoch 29/50\n",
1074 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0016\n",
1075 |             "Epoch 30/50\n",
1076 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0021\n",
1077 |             "Epoch 31/50\n",
1078 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0021\n",
1079 |             "Epoch 32/50\n",
1080 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0028\n",
1081 |             "Epoch 33/50\n",
1082 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0029\n",
1083 |             "Epoch 34/50\n",
1084 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0041\n",
1085 |             "Epoch 35/50\n",
1086 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0039\n",
1087 |             "Epoch 36/50\n",
1088 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0057\n",
1089 |             "Epoch 37/50\n",
1090 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0050\n",
1091 |             "Epoch 38/50\n",
1092 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0074\n",
1093 |             "Epoch 39/50\n",
1094 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0057\n",
1095 |             "Epoch 40/50\n",
1096 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0082\n",
1097 |             "Epoch 41/50\n",
1098 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0056\n",
1099 |             "Epoch 42/50\n",
1100 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0074\n",
1101 |             "Epoch 43/50\n",
1102 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0047\n",
1103 |             "Epoch 44/50\n",
1104 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0057\n",
1105 |             "Epoch 45/50\n",
1106 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0037\n",
1107 |             "Epoch 46/50\n",
1108 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0041\n",
1109 |             "Epoch 47/50\n",
1110 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0030\n",
1111 |             "Epoch 48/50\n",
1112 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0032\n",
1113 |             "Epoch 49/50\n",
1114 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0025\n",
1115 |             "Epoch 50/50\n",
1116 |             "496/496 [==============================] - 1s 1ms/step - loss: 0.0027\n",
1117 |             "Train Score: 4.26 RMSE\n",
1118 |             "Test Score: 6.51 RMSE\n",
1119 |             "                 Close   predicted\n",
1120 |             "Date                              \n",
1121 |             "2016-10-21  111.147491         NaN\n",
1122 |             "2016-10-24  112.148384         NaN\n",
1123 |             "2016-10-25  112.720345         NaN\n",
1124 |             "2016-10-26  110.184723         NaN\n",
1125 |             "2016-10-27  109.126633         NaN\n",
1126 |             "2016-10-28  108.402168         NaN\n",
1127 |             "2016-10-31  108.230583         NaN\n",
1128 |             "2016-11-01  106.276443  111.018829\n",
1129 |             "2016-11-02  106.371758  110.281784\n",
1130 |             "2016-11-03  105.231598  109.543221\n",
1131 |             "2016-11-04  104.283051  108.522545\n",
1132 |             "2016-11-07  105.787323  107.737976\n",
1133 |             "2016-11-08  106.410103  107.361107\n",
1134 |             "2016-11-09  106.237648  107.135780\n",
1135 |             "2016-11-10  103.277008  106.972916\n",
1136 |             "2016-11-11  103.890221  106.498917\n",
1137 |             "2016-11-14  101.284096  106.184479\n",
1138 |             "2016-11-15  102.625488  105.460014\n",
1139 |             "2016-11-16  105.384895  105.269196\n",
1140 |             "2016-11-17  105.346558  105.287186\n",
1141 |             "2016-11-18  105.451973  105.247276\n",
1142 |             "2016-11-21  107.052055  105.317947\n",
1143 |             "2016-11-22  107.119118  105.877716\n",
1144 |             "2016-11-23  106.572975  106.313782\n",
1145 |             "2016-11-25  107.109535  106.974609\n",
1146 |             "2016-11-28  106.898750  107.526741\n",
1147 |             "2016-11-29  106.793365  107.680824\n",
1148 |             "2016-11-30  105.892700  107.859009\n",
1149 |             "2016-12-01  104.905830  107.847054\n",
1150 |             "2016-12-02  105.298676  107.513321\n",
1151 |             "...                ...         ...\n",
1152 |             "2019-09-09  214.169998  205.542816\n",
1153 |             "2019-09-10  216.699997  206.734802\n",
1154 |             "2019-09-11  223.589996  207.771317\n",
1155 |             "2019-09-12  223.089996  209.858673\n",
1156 |             "2019-09-13  218.750000  211.992386\n",
1157 |             "2019-09-16  219.899994  213.104614\n",
1158 |             "2019-09-17  220.699997  213.852753\n",
1159 |             "2019-09-18  222.770004  214.549957\n",
1160 |             "2019-09-19  220.960007  215.600220\n",
1161 |             "2019-09-20  217.729996  216.026169\n",
1162 |             "2019-09-23  218.720001  215.283585\n",
1163 |             "2019-09-24  217.679993  214.765121\n",
1164 |             "2019-09-25  221.029999  214.467484\n",
1165 |             "2019-09-26  219.889999  214.731934\n",
1166 |             "2019-09-27  218.820007  214.612671\n",
1167 |             "2019-09-30  223.970001  214.232010\n",
1168 |             "2019-10-01  224.589996  214.792908\n",
1169 |             "2019-10-02  218.960007  215.612198\n",
1170 |             "2019-10-03  220.820007  215.589096\n",
1171 |             "2019-10-04  227.009995  215.963852\n",
1172 |             "2019-10-07  227.059998  216.730896\n",
1173 |             "2019-10-08  224.399994  217.663986\n",
1174 |             "2019-10-09  227.029999  218.378906\n",
1175 |             "2019-10-10  230.089996  218.831711\n",
1176 |             "2019-10-11  236.210007  219.549927\n",
1177 |             "2019-10-14  235.869995  221.819977\n",
1178 |             "2019-10-15  235.320007  223.642380\n",
1179 |             "2019-10-16  234.369995  224.751572\n",
1180 |             "2019-10-17  235.279999  225.541077\n",
1181 |             "2019-10-18  236.410004         NaN\n",
1182 |             "\n",
1183 |             "[753 rows x 2 columns] [7, 4.256240830438758, 6.506423928549112]\n"
1184 |           ],
1185 |           "name": "stdout"
1186 |         }
1187 |       ]
1188 |     },
1189 |     {
1190 |       "cell_type": "markdown",
1191 |       "metadata": {
1192 |         "id": "VAtgdnszusJS",
1193 |         "colab_type": "text"
1194 |       },
1195 |       "source": [
1196 |         "The above is a LSTM RNN to predict the stock price of Apple for past X runs. It has been trained on last 67% of the runs and tested on the new runs. "
1197 |       ]
1198 |     },
1199 |     {
1200 |       "cell_type": "markdown",
1201 |       "metadata": {
1202 |         "id": "UmOzkfp7vGxv",
1203 |         "colab_type": "text"
1204 |       },
1205 |       "source": [
1206 |         "# Observations\n",
1207 |         "### Lookback \n",
1208 |         "The lookback parameter is nothing but how much past data will the model be given to predict todays price, one can play around and see the accuracy of the model. for various dates. \n",
1209 |         "My observation is if you want to predict short term price say, for the next week or so, lookback < 5-20 gives good performance. That seems pretty obvious too, looks like the model is doing nothing but weighted sum of past runs. \n",
1210 |         "The intresting thing is, the stock price over a longer run, say next 3 months  you need to give past 200-300 days of data and it will actually give you the stock price based on what its value should be. (Its an algorithm purely based on time series and thats why it doesnt take into account any sentiments etc) Why is this important? Say there was a rumour about Apple that some analyst downgraded stocks rating, investers would start selling but the value of the company is unchanged, so the stocks are bound to rise if you take into account the long run. Adding to that this model can be used to leverage many temporal patterns such as stock rises during christmas because may be the sales for iphone is always high. Etc. This is a temporal pattern and having lookback to 365 captures such details. \n",
1211 |         "\n",
1212 |         "### MoreFeatures\n",
1213 |         "I think adding earnings date, iphone sales date and apple launch date can help in analysing more about the common patterns to build strategies to profit from the stock price. \n",
1214 |         "\n"
1215 |       ]
1216 |     }
1217 |   ]
1218 | }


--------------------------------------------------------------------------------
/Bitcoinprediction.py:
--------------------------------------------------------------------------------
  1 | #!/usr/bin/env python3
  2 | """Download Bitcoin prices and forecast using three models."""
  3 | 
  4 | import pandas as pd
  5 | import numpy as np
  6 | import yfinance as yf
  7 | from sklearn.model_selection import train_test_split
  8 | from sklearn.ensemble import RandomForestRegressor
  9 | from sklearn.metrics import mean_squared_error
 10 | from tensorflow.keras.models import Sequential
 11 | from tensorflow.keras.layers import LSTM, Dense
 12 | from sklearn.preprocessing import MinMaxScaler
 13 | from statsmodels.tsa.arima.model import ARIMA
 14 | 
 15 | 
 16 | def download_data(ticker="BTC-USD", period="5y"):
 17 |     """Download historical data from Yahoo Finance."""
 18 |     print(f"Downloading {ticker} data for {period}...")
 19 |     df = yf.download(ticker, period=period)
 20 |     print("Data downloaded:\n", df.head())
 21 |     return df
 22 | 
 23 | 
 24 | def prepare_random_forest_data(df):
 25 |     """Create features and next-day target for RandomForest."""
 26 |     print("Preparing data for RandomForest...")
 27 |     df = df.copy()
 28 |     df["target"] = df["Close"].shift(-1)
 29 |     df = df.dropna()
 30 |     features = ["Open", "High", "Low", "Close", "Volume"]
 31 |     X = df[features]
 32 |     y = df["target"]
 33 |     X_train, X_test, y_train, y_test = train_test_split(
 34 |         X, y, test_size=0.2, shuffle=False
 35 |     )
 36 |     test_dates = y_test.index
 37 |     return X_train, X_test, y_train, y_test, test_dates
 38 | 
 39 | 
 40 | def run_random_forest(X_train, X_test, y_train, y_test):
 41 |     print("\n=== Training RandomForestRegressor ===")
 42 |     model = RandomForestRegressor(n_estimators=100, random_state=0)
 43 |     model.fit(X_train, y_train)
 44 |     preds = model.predict(X_test)
 45 |     rmse = mean_squared_error(y_test, preds) ** 0.5
 46 |     print(f"Random Forest RMSE: {rmse:.2f}")
 47 |     print("RandomForest training complete!")
 48 |     return model, preds, rmse
 49 | 
 50 | 
 51 | def prepare_lstm_data(df, look_back=3):
 52 |     """Prepare sequences for LSTM model."""
 53 |     print("Preparing data for LSTM...")
 54 |     values = df[["Close"]].values.astype("float32")
 55 |     scaler = MinMaxScaler()
 56 |     scaled = scaler.fit_transform(values)
 57 | 
 58 |     X, y = [], []
 59 |     for i in range(len(scaled) - look_back - 1):
 60 |         X.append(scaled[i:(i + look_back), 0])
 61 |         y.append(scaled[i + look_back, 0])
 62 |     X = np.array(X)
 63 |     y = np.array(y)
 64 | 
 65 |     train_size = int(len(X) * 0.67)
 66 |     X_train, X_test = X[:train_size], X[train_size:]
 67 |     y_train, y_test = y[:train_size], y[train_size:]
 68 | 
 69 |     # reshape input to be [samples, time steps, features]
 70 |     X_train = X_train.reshape((X_train.shape[0], 1, X_train.shape[1]))
 71 |     X_test = X_test.reshape((X_test.shape[0], 1, X_test.shape[1]))
 72 |     return X_train, X_test, y_train, y_test, scaler
 73 | 
 74 | 
 75 | def run_lstm(X_train, X_test, y_train, y_test, scaler):
 76 |     print("\n=== Training LSTM ===")
 77 |     model = Sequential()
 78 |     model.add(LSTM(256, return_sequences=True, input_shape=(1, X_train.shape[2])))
 79 |     model.add(LSTM(256))
 80 |     model.add(Dense(1))
 81 |     model.compile(loss="mean_squared_error", optimizer="adam")
 82 |     model.fit(X_train, y_train, epochs=50, batch_size=50, verbose=1, shuffle=False)
 83 | 
 84 |     train_pred = model.predict(X_train)
 85 |     test_pred = model.predict(X_test)
 86 |     train_rmse = mean_squared_error(y_train, train_pred) ** 0.5
 87 |     test_rmse = mean_squared_error(y_test, test_pred) ** 0.5
 88 |     print(f"LSTM Train RMSE: {train_rmse:.4f}")
 89 |     print(f"LSTM Test RMSE: {test_rmse:.4f}")
 90 |     print("LSTM training complete!")
 91 |     return model, test_pred.flatten(), test_rmse
 92 | 
 93 | 
 94 | def prepare_arima_data(series, test_size=0.2):
 95 |     """Split series for ARIMA training and testing."""
 96 |     print("Preparing data for ARIMA...")
 97 |     split = int(len(series) * (1 - test_size))
 98 |     train, test = series[:split], series[split:]
 99 |     return train, test
100 | 
101 | 
102 | def run_arima(train_series, test_series):
103 |     print("\n=== Training ARIMA(5,1,0) ===")
104 |     model = ARIMA(train_series, order=(5, 1, 0))
105 |     model_fit = model.fit()
106 |     preds = model_fit.forecast(steps=len(test_series))
107 |     rmse = mean_squared_error(test_series, preds) ** 0.5
108 |     print(f"ARIMA RMSE: {rmse:.2f}")
109 |     print("ARIMA training complete!")
110 |     return model_fit, preds, rmse
111 | 
112 | 
113 | def main():
114 |     print("Starting Bitcoin prediction pipeline...")
115 |     df = download_data()
116 | 
117 |     # Random Forest
118 |     X_train, X_test, y_train, y_test, test_dates = prepare_random_forest_data(df)
119 |     rf_model, rf_preds, rf_rmse = run_random_forest(X_train, X_test, y_train, y_test)
120 | 
121 |     # LSTM
122 |     X_train_lstm, X_test_lstm, y_train_lstm, y_test_lstm, scaler = prepare_lstm_data(df)
123 |     lstm_model, lstm_preds_scaled, lstm_rmse = run_lstm(
124 |         X_train_lstm, X_test_lstm, y_train_lstm, y_test_lstm, scaler
125 |     )
126 |     lstm_preds_all = scaler.inverse_transform(lstm_preds_scaled.reshape(-1, 1)).flatten()
127 | 
128 |     # Align LSTM output length with RandomForest test set
129 |     align_len = min(len(test_dates), len(lstm_preds_all))
130 |     lstm_preds = lstm_preds_all[-align_len:]
131 |     test_dates = test_dates[-align_len:]
132 |     y_test = y_test[-align_len:]
133 | 
134 |     # ARIMA
135 |     train_series, test_series = prepare_arima_data(df["Close"])
136 |     arima_model, arima_preds, arima_rmse = run_arima(train_series, test_series)
137 | 
138 |     # Assemble comparison table
139 |     compare_df = pd.DataFrame({
140 |         "Date": test_dates,
141 |         "Actual": y_test.values,
142 |         "RandomForest": rf_preds[-align_len:],
143 |         "LSTM": lstm_preds,
144 |         "ARIMA": arima_preds.values[-align_len:],
145 |     })
146 | 
147 |     print("\n=== Prediction Comparison (last 5 points) ===")
148 |     print(compare_df.tail(5).to_string(index=False))
149 | 
150 |     print("\n=== RMSE Report ===")
151 |     print(f"Random Forest RMSE: {rf_rmse:.2f}")
152 |     print(f"LSTM Test RMSE: {lstm_rmse:.2f}")
153 |     print(f"ARIMA RMSE: {arima_rmse:.2f}")
154 |     print("Pipeline complete!")
155 | 
156 | 
157 | if __name__ == "__main__":
158 |     main()
159 | 


--------------------------------------------------------------------------------
/CryptoScraper.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |   "nbformat": 4,
   3 |   "nbformat_minor": 0,
   4 |   "metadata": {
   5 |     "colab": {
   6 |       "name": "CryptoScraper.ipynb",
   7 |       "provenance": [],
   8 |       "authorship_tag": "ABX9TyMNxduQpnBeKaAzQpIgRPDZ",
   9 |       "include_colab_link": true
  10 |     },
  11 |     "kernelspec": {
  12 |       "name": "python3",
  13 |       "display_name": "Python 3"
  14 |     }
  15 |   },
  16 |   "cells": [
  17 |     {
  18 |       "cell_type": "markdown",
  19 |       "metadata": {
  20 |         "id": "view-in-github",
  21 |         "colab_type": "text"
  22 |       },
  23 |       "source": [
  24 |         "<a href=\"https://colab.research.google.com/github/manthanthakker/BitcoinPrediction/blob/master/CryptoScraper.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
  25 |       ]
  26 |     },
  27 |     {
  28 |       "cell_type": "markdown",
  29 |       "metadata": {
  30 |         "id": "zXrwcuzGkirp",
  31 |         "colab_type": "text"
  32 |       },
  33 |       "source": [
  34 |         "# **Scrape CryptoCurrency**\n",
  35 |         "\n",
  36 |         "*   For Frequency (Daily, Hourly, Minute)\n",
  37 |         "*   For all cryptocurrencies\n",
  38 |         "\n",
  39 |         "\n",
  40 |         "\n"
  41 |       ]
  42 |     },
  43 |     {
  44 |       "cell_type": "markdown",
  45 |       "metadata": {
  46 |         "id": "D-53FVt4hDim",
  47 |         "colab_type": "text"
  48 |       },
  49 |       "source": [
  50 |         "Scrape BitCoinData RealTime using this API https://min-api.cryptocompare.com/\n",
  51 |         "\n",
  52 |         "1. Create a trial account\n",
  53 |         "2. Create a free token and fill it here\n",
  54 |         "3. Fetch Latest Bitcoin data"
  55 |       ]
  56 |     },
  57 |     {
  58 |       "cell_type": "markdown",
  59 |       "metadata": {
  60 |         "id": "FJ0hmhsnhe7A",
  61 |         "colab_type": "text"
  62 |       },
  63 |       "source": [
  64 |         "# Bitcoin Scraper "
  65 |       ]
  66 |     },
  67 |     {
  68 |       "cell_type": "code",
  69 |       "metadata": {
  70 |         "id": "KWZg0nQthAiT",
  71 |         "colab_type": "code",
  72 |         "colab": {}
  73 |       },
  74 |       "source": [
  75 |         "from datetime import datetime\n",
  76 |         "import pandas as pd\n",
  77 |         "import requests\n",
  78 |         "\n",
  79 |         "\n",
  80 |         "def convertTimeStampToUTC(data):\n",
  81 |         "  from datetime import datetime\n",
  82 |         "  return datetime.fromtimestamp(data)\n",
  83 |         "  \n",
  84 |         "def getURL(apiKey,cryptoSymbol,frequency,timestamp,MaxRecordsToGetOnEachCall):\n",
  85 |         "  API_KEY='&api_key='+str(apiKey)\n",
  86 |         "  limit='&limit='+str(MaxRecordsToGetOnEachCall)\n",
  87 |         "  cryptoHeader='fsym='+str(cryptoSymbol)\n",
  88 |         "  ouputCurrency='&tsym=USD'\n",
  89 |         "\n",
  90 |         "  if(timestamp==\"\"):\n",
  91 |         "    url='https://min-api.cryptocompare.com/data/v2/histo'+frequency+'?'+cryptoHeader\n",
  92 |         "  else:  \n",
  93 |         "    url='https://min-api.cryptocompare.com/data/v2/histo'+frequency+'?'+cryptoHeader+'&toTs='+str(timestamp)\n",
  94 |         "\n",
  95 |         "  return url+ouputCurrency+API_KEY+limit  \n",
  96 |         "\n",
  97 |         "\n",
  98 |         "def getCryptoData(apikey,crypto,frequency,numOfTimesToHitApi,MaxRecordsToGetOnEachCall):\n",
  99 |         "  numOfTimesToHitApi=5\n",
 100 |         "  data=pd.DataFrame()\n",
 101 |         "  nextTimeStamp=0\n",
 102 |         "\n",
 103 |         "  for i in range(0,numOfTimesToHitApi):\n",
 104 |         "    if(nextTimeStamp==0):\n",
 105 |         "      url=getURL(apikey,crypto,frequency,\"\",MaxRecordsToGetOnEachCall)\n",
 106 |         "    else:\n",
 107 |         "      url=getURL(apikey,crypto,frequency,nextTimeStamp,MaxRecordsToGetOnEachCall)\n",
 108 |         "\n",
 109 |         "    response = requests.get(url).json()\n",
 110 |         "    partialData=pd.DataFrame(response.get('Data').get('Data'))\n",
 111 |         "    nextTimeStamp=partialData['time'].min()-1\n",
 112 |         "    data=pd.concat([data,partialData])\n",
 113 |         "\n",
 114 |         "\n",
 115 |         "  data=data.sort_values(by=['time'],ignore_index=True)\n",
 116 |         "  data['timeUTC']=data['time'].apply(convertTimeStampToUTC)\n",
 117 |         "  data=data.reset_index(drop=True)\n",
 118 |         "  return data\n"
 119 |       ],
 120 |       "execution_count": 0,
 121 |       "outputs": []
 122 |     },
 123 |     {
 124 |       "cell_type": "code",
 125 |       "metadata": {
 126 |         "id": "oxxYi4oohqJl",
 127 |         "colab_type": "code",
 128 |         "colab": {}
 129 |       },
 130 |       "source": [
 131 |         ""
 132 |       ],
 133 |       "execution_count": 0,
 134 |       "outputs": []
 135 |     },
 136 |     {
 137 |       "cell_type": "markdown",
 138 |       "metadata": {
 139 |         "id": "hv3i969ohsnH",
 140 |         "colab_type": "text"
 141 |       },
 142 |       "source": [
 143 |         "# Invoke BitCoin Scraper"
 144 |       ]
 145 |     },
 146 |     {
 147 |       "cell_type": "code",
 148 |       "metadata": {
 149 |         "id": "PWIk1Vm6hr64",
 150 |         "colab_type": "code",
 151 |         "colab": {
 152 |           "base_uri": "https://localhost:8080/",
 153 |           "height": 413
 154 |         },
 155 |         "outputId": "dc087da5-102e-43fb-98df-2b85366e81a8"
 156 |       },
 157 |       "source": [
 158 |         "## Parameters To Scrape Any Bitcoin Data\n",
 159 |         "\n",
 160 |         "## API_KEY: Create a trial account on https://min-api.cryptocompare.com/ and create a API_KEY (Its free)\n",
 161 |         "## Valid Values for crypto: BTC | ETH | XRP | BCH | BSV | LTC | EOS | XMR | ADA and Many More\n",
 162 |         "## numOfTimesToHitApi: For Trial Account this is somwhere 1000 per second, which is sufficient here\n",
 163 |         "## MaxRecordsToGetOnEachCall: Max is 2000\n",
 164 |         "## frequency : Valid Options are  day|hour|minute\n",
 165 |         "\n",
 166 |         "API_KEY=\"\"\n",
 167 |         "crypto=\"BTC\"\n",
 168 |         "numOfTimesToHitApi=5\n",
 169 |         "MaxRecordsToGetOnEachCall=200\n",
 170 |         "frequency=\"hour\"\n",
 171 |         "\n",
 172 |         "cryptoData=getCryptoData(API_KEY,crypto,frequency,numOfTimesToHitApi,MaxRecordsToGetOnEachCall)\n",
 173 |         "cryptoData"
 174 |       ],
 175 |       "execution_count": 2,
 176 |       "outputs": [
 177 |         {
 178 |           "output_type": "execute_result",
 179 |           "data": {
 180 |             "text/html": [
 181 |               "<div>\n",
 182 |               "<style scoped>\n",
 183 |               "    .dataframe tbody tr th:only-of-type {\n",
 184 |               "        vertical-align: middle;\n",
 185 |               "    }\n",
 186 |               "\n",
 187 |               "    .dataframe tbody tr th {\n",
 188 |               "        vertical-align: top;\n",
 189 |               "    }\n",
 190 |               "\n",
 191 |               "    .dataframe thead th {\n",
 192 |               "        text-align: right;\n",
 193 |               "    }\n",
 194 |               "</style>\n",
 195 |               "<table border=\"1\" class=\"dataframe\">\n",
 196 |               "  <thead>\n",
 197 |               "    <tr style=\"text-align: right;\">\n",
 198 |               "      <th></th>\n",
 199 |               "      <th>time</th>\n",
 200 |               "      <th>high</th>\n",
 201 |               "      <th>low</th>\n",
 202 |               "      <th>open</th>\n",
 203 |               "      <th>volumefrom</th>\n",
 204 |               "      <th>volumeto</th>\n",
 205 |               "      <th>close</th>\n",
 206 |               "      <th>conversionType</th>\n",
 207 |               "      <th>conversionSymbol</th>\n",
 208 |               "      <th>timeUTC</th>\n",
 209 |               "    </tr>\n",
 210 |               "  </thead>\n",
 211 |               "  <tbody>\n",
 212 |               "    <tr>\n",
 213 |               "      <th>0</th>\n",
 214 |               "      <td>1583985600</td>\n",
 215 |               "      <td>7671.97</td>\n",
 216 |               "      <td>7574.94</td>\n",
 217 |               "      <td>7671.97</td>\n",
 218 |               "      <td>3575.29</td>\n",
 219 |               "      <td>27208739.92</td>\n",
 220 |               "      <td>7634.05</td>\n",
 221 |               "      <td>direct</td>\n",
 222 |               "      <td></td>\n",
 223 |               "      <td>2020-03-12 04:00:00</td>\n",
 224 |               "    </tr>\n",
 225 |               "    <tr>\n",
 226 |               "      <th>1</th>\n",
 227 |               "      <td>1583989200</td>\n",
 228 |               "      <td>7664.82</td>\n",
 229 |               "      <td>7609.07</td>\n",
 230 |               "      <td>7634.05</td>\n",
 231 |               "      <td>1549.63</td>\n",
 232 |               "      <td>11831969.34</td>\n",
 233 |               "      <td>7653.71</td>\n",
 234 |               "      <td>direct</td>\n",
 235 |               "      <td></td>\n",
 236 |               "      <td>2020-03-12 05:00:00</td>\n",
 237 |               "    </tr>\n",
 238 |               "    <tr>\n",
 239 |               "      <th>2</th>\n",
 240 |               "      <td>1583992800</td>\n",
 241 |               "      <td>7656.60</td>\n",
 242 |               "      <td>7470.01</td>\n",
 243 |               "      <td>7653.71</td>\n",
 244 |               "      <td>6044.18</td>\n",
 245 |               "      <td>45503478.49</td>\n",
 246 |               "      <td>7510.10</td>\n",
 247 |               "      <td>direct</td>\n",
 248 |               "      <td></td>\n",
 249 |               "      <td>2020-03-12 06:00:00</td>\n",
 250 |               "    </tr>\n",
 251 |               "    <tr>\n",
 252 |               "      <th>3</th>\n",
 253 |               "      <td>1583996400</td>\n",
 254 |               "      <td>7510.64</td>\n",
 255 |               "      <td>7368.71</td>\n",
 256 |               "      <td>7510.10</td>\n",
 257 |               "      <td>8287.60</td>\n",
 258 |               "      <td>61461972.46</td>\n",
 259 |               "      <td>7396.02</td>\n",
 260 |               "      <td>direct</td>\n",
 261 |               "      <td></td>\n",
 262 |               "      <td>2020-03-12 07:00:00</td>\n",
 263 |               "    </tr>\n",
 264 |               "    <tr>\n",
 265 |               "      <th>4</th>\n",
 266 |               "      <td>1584000000</td>\n",
 267 |               "      <td>7466.43</td>\n",
 268 |               "      <td>7321.57</td>\n",
 269 |               "      <td>7396.02</td>\n",
 270 |               "      <td>6152.71</td>\n",
 271 |               "      <td>45465855.44</td>\n",
 272 |               "      <td>7383.65</td>\n",
 273 |               "      <td>direct</td>\n",
 274 |               "      <td></td>\n",
 275 |               "      <td>2020-03-12 08:00:00</td>\n",
 276 |               "    </tr>\n",
 277 |               "    <tr>\n",
 278 |               "      <th>...</th>\n",
 279 |               "      <td>...</td>\n",
 280 |               "      <td>...</td>\n",
 281 |               "      <td>...</td>\n",
 282 |               "      <td>...</td>\n",
 283 |               "      <td>...</td>\n",
 284 |               "      <td>...</td>\n",
 285 |               "      <td>...</td>\n",
 286 |               "      <td>...</td>\n",
 287 |               "      <td>...</td>\n",
 288 |               "      <td>...</td>\n",
 289 |               "    </tr>\n",
 290 |               "    <tr>\n",
 291 |               "      <th>1000</th>\n",
 292 |               "      <td>1587585600</td>\n",
 293 |               "      <td>7123.76</td>\n",
 294 |               "      <td>7094.55</td>\n",
 295 |               "      <td>7098.28</td>\n",
 296 |               "      <td>1009.65</td>\n",
 297 |               "      <td>7182051.74</td>\n",
 298 |               "      <td>7119.91</td>\n",
 299 |               "      <td>direct</td>\n",
 300 |               "      <td></td>\n",
 301 |               "      <td>2020-04-22 20:00:00</td>\n",
 302 |               "    </tr>\n",
 303 |               "    <tr>\n",
 304 |               "      <th>1001</th>\n",
 305 |               "      <td>1587589200</td>\n",
 306 |               "      <td>7140.16</td>\n",
 307 |               "      <td>7100.17</td>\n",
 308 |               "      <td>7119.91</td>\n",
 309 |               "      <td>1127.31</td>\n",
 310 |               "      <td>8033871.97</td>\n",
 311 |               "      <td>7102.78</td>\n",
 312 |               "      <td>direct</td>\n",
 313 |               "      <td></td>\n",
 314 |               "      <td>2020-04-22 21:00:00</td>\n",
 315 |               "    </tr>\n",
 316 |               "    <tr>\n",
 317 |               "      <th>1002</th>\n",
 318 |               "      <td>1587592800</td>\n",
 319 |               "      <td>7124.71</td>\n",
 320 |               "      <td>7096.36</td>\n",
 321 |               "      <td>7102.78</td>\n",
 322 |               "      <td>1100.92</td>\n",
 323 |               "      <td>7832794.37</td>\n",
 324 |               "      <td>7120.72</td>\n",
 325 |               "      <td>direct</td>\n",
 326 |               "      <td></td>\n",
 327 |               "      <td>2020-04-22 22:00:00</td>\n",
 328 |               "    </tr>\n",
 329 |               "    <tr>\n",
 330 |               "      <th>1003</th>\n",
 331 |               "      <td>1587596400</td>\n",
 332 |               "      <td>7135.41</td>\n",
 333 |               "      <td>7113.24</td>\n",
 334 |               "      <td>7120.72</td>\n",
 335 |               "      <td>1193.30</td>\n",
 336 |               "      <td>8502019.00</td>\n",
 337 |               "      <td>7135.41</td>\n",
 338 |               "      <td>direct</td>\n",
 339 |               "      <td></td>\n",
 340 |               "      <td>2020-04-22 23:00:00</td>\n",
 341 |               "    </tr>\n",
 342 |               "    <tr>\n",
 343 |               "      <th>1004</th>\n",
 344 |               "      <td>1587600000</td>\n",
 345 |               "      <td>7152.44</td>\n",
 346 |               "      <td>7110.82</td>\n",
 347 |               "      <td>7135.41</td>\n",
 348 |               "      <td>1375.44</td>\n",
 349 |               "      <td>9805176.61</td>\n",
 350 |               "      <td>7125.60</td>\n",
 351 |               "      <td>direct</td>\n",
 352 |               "      <td></td>\n",
 353 |               "      <td>2020-04-23 00:00:00</td>\n",
 354 |               "    </tr>\n",
 355 |               "  </tbody>\n",
 356 |               "</table>\n",
 357 |               "<p>1005 rows × 10 columns</p>\n",
 358 |               "</div>"
 359 |             ],
 360 |             "text/plain": [
 361 |               "            time     high  ...  conversionSymbol             timeUTC\n",
 362 |               "0     1583985600  7671.97  ...                   2020-03-12 04:00:00\n",
 363 |               "1     1583989200  7664.82  ...                   2020-03-12 05:00:00\n",
 364 |               "2     1583992800  7656.60  ...                   2020-03-12 06:00:00\n",
 365 |               "3     1583996400  7510.64  ...                   2020-03-12 07:00:00\n",
 366 |               "4     1584000000  7466.43  ...                   2020-03-12 08:00:00\n",
 367 |               "...          ...      ...  ...               ...                 ...\n",
 368 |               "1000  1587585600  7123.76  ...                   2020-04-22 20:00:00\n",
 369 |               "1001  1587589200  7140.16  ...                   2020-04-22 21:00:00\n",
 370 |               "1002  1587592800  7124.71  ...                   2020-04-22 22:00:00\n",
 371 |               "1003  1587596400  7135.41  ...                   2020-04-22 23:00:00\n",
 372 |               "1004  1587600000  7152.44  ...                   2020-04-23 00:00:00\n",
 373 |               "\n",
 374 |               "[1005 rows x 10 columns]"
 375 |             ]
 376 |           },
 377 |           "metadata": {
 378 |             "tags": []
 379 |           },
 380 |           "execution_count": 2
 381 |         }
 382 |       ]
 383 |     },
 384 |     {
 385 |       "cell_type": "markdown",
 386 |       "metadata": {
 387 |         "id": "vu9Whyf_jfss",
 388 |         "colab_type": "text"
 389 |       },
 390 |       "source": [
 391 |         "\n",
 392 |         "Get Daily Bitcoin Rate\n"
 393 |       ]
 394 |     },
 395 |     {
 396 |       "cell_type": "code",
 397 |       "metadata": {
 398 |         "id": "l_X1CkVbh1Mv",
 399 |         "colab_type": "code",
 400 |         "colab": {
 401 |           "base_uri": "https://localhost:8080/",
 402 |           "height": 413
 403 |         },
 404 |         "outputId": "1f8adef1-3990-4cb7-f7fe-b28ed62e1db7"
 405 |       },
 406 |       "source": [
 407 |         "API_KEY=\"\"\n",
 408 |         "crypto=\"BTC\"\n",
 409 |         "numOfTimesToHitApi=5\n",
 410 |         "MaxRecordsToGetOnEachCall=200\n",
 411 |         "frequency=\"day\"\n",
 412 |         "\n",
 413 |         "cryptoData=getCryptoData(API_KEY,crypto,frequency,numOfTimesToHitApi,MaxRecordsToGetOnEachCall)\n",
 414 |         "cryptoData"
 415 |       ],
 416 |       "execution_count": 4,
 417 |       "outputs": [
 418 |         {
 419 |           "output_type": "execute_result",
 420 |           "data": {
 421 |             "text/html": [
 422 |               "<div>\n",
 423 |               "<style scoped>\n",
 424 |               "    .dataframe tbody tr th:only-of-type {\n",
 425 |               "        vertical-align: middle;\n",
 426 |               "    }\n",
 427 |               "\n",
 428 |               "    .dataframe tbody tr th {\n",
 429 |               "        vertical-align: top;\n",
 430 |               "    }\n",
 431 |               "\n",
 432 |               "    .dataframe thead th {\n",
 433 |               "        text-align: right;\n",
 434 |               "    }\n",
 435 |               "</style>\n",
 436 |               "<table border=\"1\" class=\"dataframe\">\n",
 437 |               "  <thead>\n",
 438 |               "    <tr style=\"text-align: right;\">\n",
 439 |               "      <th></th>\n",
 440 |               "      <th>time</th>\n",
 441 |               "      <th>high</th>\n",
 442 |               "      <th>low</th>\n",
 443 |               "      <th>open</th>\n",
 444 |               "      <th>volumefrom</th>\n",
 445 |               "      <th>volumeto</th>\n",
 446 |               "      <th>close</th>\n",
 447 |               "      <th>conversionType</th>\n",
 448 |               "      <th>conversionSymbol</th>\n",
 449 |               "      <th>timeUTC</th>\n",
 450 |               "    </tr>\n",
 451 |               "  </thead>\n",
 452 |               "  <tbody>\n",
 453 |               "    <tr>\n",
 454 |               "      <th>0</th>\n",
 455 |               "      <td>1500854400</td>\n",
 456 |               "      <td>2798.89</td>\n",
 457 |               "      <td>2715.69</td>\n",
 458 |               "      <td>2756.61</td>\n",
 459 |               "      <td>83009.25</td>\n",
 460 |               "      <td>2.290474e+08</td>\n",
 461 |               "      <td>2763.42</td>\n",
 462 |               "      <td>direct</td>\n",
 463 |               "      <td></td>\n",
 464 |               "      <td>2017-07-24</td>\n",
 465 |               "    </tr>\n",
 466 |               "    <tr>\n",
 467 |               "      <th>1</th>\n",
 468 |               "      <td>1500940800</td>\n",
 469 |               "      <td>2779.08</td>\n",
 470 |               "      <td>2472.62</td>\n",
 471 |               "      <td>2763.42</td>\n",
 472 |               "      <td>205883.15</td>\n",
 473 |               "      <td>5.347654e+08</td>\n",
 474 |               "      <td>2582.58</td>\n",
 475 |               "      <td>direct</td>\n",
 476 |               "      <td></td>\n",
 477 |               "      <td>2017-07-25</td>\n",
 478 |               "    </tr>\n",
 479 |               "    <tr>\n",
 480 |               "      <th>2</th>\n",
 481 |               "      <td>1501027200</td>\n",
 482 |               "      <td>2631.73</td>\n",
 483 |               "      <td>2441.54</td>\n",
 484 |               "      <td>2582.58</td>\n",
 485 |               "      <td>135502.93</td>\n",
 486 |               "      <td>3.404652e+08</td>\n",
 487 |               "      <td>2559.21</td>\n",
 488 |               "      <td>direct</td>\n",
 489 |               "      <td></td>\n",
 490 |               "      <td>2017-07-26</td>\n",
 491 |               "    </tr>\n",
 492 |               "    <tr>\n",
 493 |               "      <th>3</th>\n",
 494 |               "      <td>1501113600</td>\n",
 495 |               "      <td>2712.92</td>\n",
 496 |               "      <td>2547.74</td>\n",
 497 |               "      <td>2559.21</td>\n",
 498 |               "      <td>103832.54</td>\n",
 499 |               "      <td>2.698809e+08</td>\n",
 500 |               "      <td>2691.88</td>\n",
 501 |               "      <td>direct</td>\n",
 502 |               "      <td></td>\n",
 503 |               "      <td>2017-07-27</td>\n",
 504 |               "    </tr>\n",
 505 |               "    <tr>\n",
 506 |               "      <th>4</th>\n",
 507 |               "      <td>1501200000</td>\n",
 508 |               "      <td>2843.78</td>\n",
 509 |               "      <td>2683.59</td>\n",
 510 |               "      <td>2691.88</td>\n",
 511 |               "      <td>138904.86</td>\n",
 512 |               "      <td>3.834798e+08</td>\n",
 513 |               "      <td>2806.75</td>\n",
 514 |               "      <td>direct</td>\n",
 515 |               "      <td></td>\n",
 516 |               "      <td>2017-07-28</td>\n",
 517 |               "    </tr>\n",
 518 |               "    <tr>\n",
 519 |               "      <th>...</th>\n",
 520 |               "      <td>...</td>\n",
 521 |               "      <td>...</td>\n",
 522 |               "      <td>...</td>\n",
 523 |               "      <td>...</td>\n",
 524 |               "      <td>...</td>\n",
 525 |               "      <td>...</td>\n",
 526 |               "      <td>...</td>\n",
 527 |               "      <td>...</td>\n",
 528 |               "      <td>...</td>\n",
 529 |               "      <td>...</td>\n",
 530 |               "    </tr>\n",
 531 |               "    <tr>\n",
 532 |               "      <th>1000</th>\n",
 533 |               "      <td>1587254400</td>\n",
 534 |               "      <td>7276.84</td>\n",
 535 |               "      <td>7072.33</td>\n",
 536 |               "      <td>7264.29</td>\n",
 537 |               "      <td>29493.83</td>\n",
 538 |               "      <td>2.114224e+08</td>\n",
 539 |               "      <td>7132.88</td>\n",
 540 |               "      <td>direct</td>\n",
 541 |               "      <td></td>\n",
 542 |               "      <td>2020-04-19</td>\n",
 543 |               "    </tr>\n",
 544 |               "    <tr>\n",
 545 |               "      <th>1001</th>\n",
 546 |               "      <td>1587340800</td>\n",
 547 |               "      <td>7225.23</td>\n",
 548 |               "      <td>6770.07</td>\n",
 549 |               "      <td>7132.88</td>\n",
 550 |               "      <td>64707.00</td>\n",
 551 |               "      <td>4.523198e+08</td>\n",
 552 |               "      <td>6842.45</td>\n",
 553 |               "      <td>direct</td>\n",
 554 |               "      <td></td>\n",
 555 |               "      <td>2020-04-20</td>\n",
 556 |               "    </tr>\n",
 557 |               "    <tr>\n",
 558 |               "      <th>1002</th>\n",
 559 |               "      <td>1587427200</td>\n",
 560 |               "      <td>6948.78</td>\n",
 561 |               "      <td>6779.51</td>\n",
 562 |               "      <td>6842.45</td>\n",
 563 |               "      <td>44531.91</td>\n",
 564 |               "      <td>3.058443e+08</td>\n",
 565 |               "      <td>6855.11</td>\n",
 566 |               "      <td>direct</td>\n",
 567 |               "      <td></td>\n",
 568 |               "      <td>2020-04-21</td>\n",
 569 |               "    </tr>\n",
 570 |               "    <tr>\n",
 571 |               "      <th>1003</th>\n",
 572 |               "      <td>1587513600</td>\n",
 573 |               "      <td>7165.43</td>\n",
 574 |               "      <td>6831.44</td>\n",
 575 |               "      <td>6855.11</td>\n",
 576 |               "      <td>43780.72</td>\n",
 577 |               "      <td>3.082545e+08</td>\n",
 578 |               "      <td>7135.41</td>\n",
 579 |               "      <td>direct</td>\n",
 580 |               "      <td></td>\n",
 581 |               "      <td>2020-04-22</td>\n",
 582 |               "    </tr>\n",
 583 |               "    <tr>\n",
 584 |               "      <th>1004</th>\n",
 585 |               "      <td>1587600000</td>\n",
 586 |               "      <td>7152.44</td>\n",
 587 |               "      <td>7110.82</td>\n",
 588 |               "      <td>7135.41</td>\n",
 589 |               "      <td>1478.46</td>\n",
 590 |               "      <td>1.053967e+07</td>\n",
 591 |               "      <td>7129.93</td>\n",
 592 |               "      <td>direct</td>\n",
 593 |               "      <td></td>\n",
 594 |               "      <td>2020-04-23</td>\n",
 595 |               "    </tr>\n",
 596 |               "  </tbody>\n",
 597 |               "</table>\n",
 598 |               "<p>1005 rows × 10 columns</p>\n",
 599 |               "</div>"
 600 |             ],
 601 |             "text/plain": [
 602 |               "            time     high      low  ...  conversionType  conversionSymbol    timeUTC\n",
 603 |               "0     1500854400  2798.89  2715.69  ...          direct                   2017-07-24\n",
 604 |               "1     1500940800  2779.08  2472.62  ...          direct                   2017-07-25\n",
 605 |               "2     1501027200  2631.73  2441.54  ...          direct                   2017-07-26\n",
 606 |               "3     1501113600  2712.92  2547.74  ...          direct                   2017-07-27\n",
 607 |               "4     1501200000  2843.78  2683.59  ...          direct                   2017-07-28\n",
 608 |               "...          ...      ...      ...  ...             ...               ...        ...\n",
 609 |               "1000  1587254400  7276.84  7072.33  ...          direct                   2020-04-19\n",
 610 |               "1001  1587340800  7225.23  6770.07  ...          direct                   2020-04-20\n",
 611 |               "1002  1587427200  6948.78  6779.51  ...          direct                   2020-04-21\n",
 612 |               "1003  1587513600  7165.43  6831.44  ...          direct                   2020-04-22\n",
 613 |               "1004  1587600000  7152.44  7110.82  ...          direct                   2020-04-23\n",
 614 |               "\n",
 615 |               "[1005 rows x 10 columns]"
 616 |             ]
 617 |           },
 618 |           "metadata": {
 619 |             "tags": []
 620 |           },
 621 |           "execution_count": 4
 622 |         }
 623 |       ]
 624 |     },
 625 |     {
 626 |       "cell_type": "markdown",
 627 |       "metadata": {
 628 |         "id": "vhtZO9GxjhMW",
 629 |         "colab_type": "text"
 630 |       },
 631 |       "source": [
 632 |         "Get Every Minute of Bitcoin Price"
 633 |       ]
 634 |     },
 635 |     {
 636 |       "cell_type": "code",
 637 |       "metadata": {
 638 |         "id": "4ovlVZudjhYn",
 639 |         "colab_type": "code",
 640 |         "colab": {
 641 |           "base_uri": "https://localhost:8080/",
 642 |           "height": 413
 643 |         },
 644 |         "outputId": "5184315e-41b7-4e00-ad38-b4cc66db4a03"
 645 |       },
 646 |       "source": [
 647 |         "API_KEY=\"\"\n",
 648 |         "crypto=\"BTC\"\n",
 649 |         "numOfTimesToHitApi=5\n",
 650 |         "MaxRecordsToGetOnEachCall=200\n",
 651 |         "frequency=\"minute\"\n",
 652 |         "\n",
 653 |         "cryptoData=getCryptoData(API_KEY,crypto,frequency,numOfTimesToHitApi,MaxRecordsToGetOnEachCall)\n",
 654 |         "cryptoData"
 655 |       ],
 656 |       "execution_count": 5,
 657 |       "outputs": [
 658 |         {
 659 |           "output_type": "execute_result",
 660 |           "data": {
 661 |             "text/html": [
 662 |               "<div>\n",
 663 |               "<style scoped>\n",
 664 |               "    .dataframe tbody tr th:only-of-type {\n",
 665 |               "        vertical-align: middle;\n",
 666 |               "    }\n",
 667 |               "\n",
 668 |               "    .dataframe tbody tr th {\n",
 669 |               "        vertical-align: top;\n",
 670 |               "    }\n",
 671 |               "\n",
 672 |               "    .dataframe thead th {\n",
 673 |               "        text-align: right;\n",
 674 |               "    }\n",
 675 |               "</style>\n",
 676 |               "<table border=\"1\" class=\"dataframe\">\n",
 677 |               "  <thead>\n",
 678 |               "    <tr style=\"text-align: right;\">\n",
 679 |               "      <th></th>\n",
 680 |               "      <th>time</th>\n",
 681 |               "      <th>high</th>\n",
 682 |               "      <th>low</th>\n",
 683 |               "      <th>open</th>\n",
 684 |               "      <th>volumefrom</th>\n",
 685 |               "      <th>volumeto</th>\n",
 686 |               "      <th>close</th>\n",
 687 |               "      <th>conversionType</th>\n",
 688 |               "      <th>conversionSymbol</th>\n",
 689 |               "      <th>timeUTC</th>\n",
 690 |               "    </tr>\n",
 691 |               "  </thead>\n",
 692 |               "  <tbody>\n",
 693 |               "    <tr>\n",
 694 |               "      <th>0</th>\n",
 695 |               "      <td>1587543480</td>\n",
 696 |               "      <td>6956.05</td>\n",
 697 |               "      <td>6951.09</td>\n",
 698 |               "      <td>6951.09</td>\n",
 699 |               "      <td>23.080</td>\n",
 700 |               "      <td>160433.10</td>\n",
 701 |               "      <td>6955.98</td>\n",
 702 |               "      <td>direct</td>\n",
 703 |               "      <td></td>\n",
 704 |               "      <td>2020-04-22 08:18:00</td>\n",
 705 |               "    </tr>\n",
 706 |               "    <tr>\n",
 707 |               "      <th>1</th>\n",
 708 |               "      <td>1587543540</td>\n",
 709 |               "      <td>6956.71</td>\n",
 710 |               "      <td>6955.71</td>\n",
 711 |               "      <td>6955.98</td>\n",
 712 |               "      <td>4.895</td>\n",
 713 |               "      <td>34046.80</td>\n",
 714 |               "      <td>6955.88</td>\n",
 715 |               "      <td>direct</td>\n",
 716 |               "      <td></td>\n",
 717 |               "      <td>2020-04-22 08:19:00</td>\n",
 718 |               "    </tr>\n",
 719 |               "    <tr>\n",
 720 |               "      <th>2</th>\n",
 721 |               "      <td>1587543600</td>\n",
 722 |               "      <td>6956.38</td>\n",
 723 |               "      <td>6954.24</td>\n",
 724 |               "      <td>6955.88</td>\n",
 725 |               "      <td>37.710</td>\n",
 726 |               "      <td>262219.93</td>\n",
 727 |               "      <td>6954.75</td>\n",
 728 |               "      <td>direct</td>\n",
 729 |               "      <td></td>\n",
 730 |               "      <td>2020-04-22 08:20:00</td>\n",
 731 |               "    </tr>\n",
 732 |               "    <tr>\n",
 733 |               "      <th>3</th>\n",
 734 |               "      <td>1587543660</td>\n",
 735 |               "      <td>6954.76</td>\n",
 736 |               "      <td>6954.44</td>\n",
 737 |               "      <td>6954.75</td>\n",
 738 |               "      <td>3.223</td>\n",
 739 |               "      <td>22407.70</td>\n",
 740 |               "      <td>6954.44</td>\n",
 741 |               "      <td>direct</td>\n",
 742 |               "      <td></td>\n",
 743 |               "      <td>2020-04-22 08:21:00</td>\n",
 744 |               "    </tr>\n",
 745 |               "    <tr>\n",
 746 |               "      <th>4</th>\n",
 747 |               "      <td>1587543720</td>\n",
 748 |               "      <td>6956.87</td>\n",
 749 |               "      <td>6954.25</td>\n",
 750 |               "      <td>6954.44</td>\n",
 751 |               "      <td>10.950</td>\n",
 752 |               "      <td>76207.62</td>\n",
 753 |               "      <td>6955.57</td>\n",
 754 |               "      <td>direct</td>\n",
 755 |               "      <td></td>\n",
 756 |               "      <td>2020-04-22 08:22:00</td>\n",
 757 |               "    </tr>\n",
 758 |               "    <tr>\n",
 759 |               "      <th>...</th>\n",
 760 |               "      <td>...</td>\n",
 761 |               "      <td>...</td>\n",
 762 |               "      <td>...</td>\n",
 763 |               "      <td>...</td>\n",
 764 |               "      <td>...</td>\n",
 765 |               "      <td>...</td>\n",
 766 |               "      <td>...</td>\n",
 767 |               "      <td>...</td>\n",
 768 |               "      <td>...</td>\n",
 769 |               "      <td>...</td>\n",
 770 |               "    </tr>\n",
 771 |               "    <tr>\n",
 772 |               "      <th>1000</th>\n",
 773 |               "      <td>1587603480</td>\n",
 774 |               "      <td>7129.56</td>\n",
 775 |               "      <td>7128.66</td>\n",
 776 |               "      <td>7129.28</td>\n",
 777 |               "      <td>10.740</td>\n",
 778 |               "      <td>76532.65</td>\n",
 779 |               "      <td>7129.17</td>\n",
 780 |               "      <td>direct</td>\n",
 781 |               "      <td></td>\n",
 782 |               "      <td>2020-04-23 00:58:00</td>\n",
 783 |               "    </tr>\n",
 784 |               "    <tr>\n",
 785 |               "      <th>1001</th>\n",
 786 |               "      <td>1587603540</td>\n",
 787 |               "      <td>7129.45</td>\n",
 788 |               "      <td>7126.76</td>\n",
 789 |               "      <td>7129.17</td>\n",
 790 |               "      <td>26.470</td>\n",
 791 |               "      <td>188599.65</td>\n",
 792 |               "      <td>7126.88</td>\n",
 793 |               "      <td>direct</td>\n",
 794 |               "      <td></td>\n",
 795 |               "      <td>2020-04-23 00:59:00</td>\n",
 796 |               "    </tr>\n",
 797 |               "    <tr>\n",
 798 |               "      <th>1002</th>\n",
 799 |               "      <td>1587603600</td>\n",
 800 |               "      <td>7127.24</td>\n",
 801 |               "      <td>7125.59</td>\n",
 802 |               "      <td>7126.88</td>\n",
 803 |               "      <td>24.010</td>\n",
 804 |               "      <td>171058.44</td>\n",
 805 |               "      <td>7125.59</td>\n",
 806 |               "      <td>direct</td>\n",
 807 |               "      <td></td>\n",
 808 |               "      <td>2020-04-23 01:00:00</td>\n",
 809 |               "    </tr>\n",
 810 |               "    <tr>\n",
 811 |               "      <th>1003</th>\n",
 812 |               "      <td>1587603660</td>\n",
 813 |               "      <td>7129.79</td>\n",
 814 |               "      <td>7123.11</td>\n",
 815 |               "      <td>7125.59</td>\n",
 816 |               "      <td>9.072</td>\n",
 817 |               "      <td>64643.10</td>\n",
 818 |               "      <td>7129.20</td>\n",
 819 |               "      <td>direct</td>\n",
 820 |               "      <td></td>\n",
 821 |               "      <td>2020-04-23 01:01:00</td>\n",
 822 |               "    </tr>\n",
 823 |               "    <tr>\n",
 824 |               "      <th>1004</th>\n",
 825 |               "      <td>1587603720</td>\n",
 826 |               "      <td>7129.30</td>\n",
 827 |               "      <td>7129.20</td>\n",
 828 |               "      <td>7129.20</td>\n",
 829 |               "      <td>0.000</td>\n",
 830 |               "      <td>0.00</td>\n",
 831 |               "      <td>7129.30</td>\n",
 832 |               "      <td>direct</td>\n",
 833 |               "      <td></td>\n",
 834 |               "      <td>2020-04-23 01:02:00</td>\n",
 835 |               "    </tr>\n",
 836 |               "  </tbody>\n",
 837 |               "</table>\n",
 838 |               "<p>1005 rows × 10 columns</p>\n",
 839 |               "</div>"
 840 |             ],
 841 |             "text/plain": [
 842 |               "            time     high  ...  conversionSymbol             timeUTC\n",
 843 |               "0     1587543480  6956.05  ...                   2020-04-22 08:18:00\n",
 844 |               "1     1587543540  6956.71  ...                   2020-04-22 08:19:00\n",
 845 |               "2     1587543600  6956.38  ...                   2020-04-22 08:20:00\n",
 846 |               "3     1587543660  6954.76  ...                   2020-04-22 08:21:00\n",
 847 |               "4     1587543720  6956.87  ...                   2020-04-22 08:22:00\n",
 848 |               "...          ...      ...  ...               ...                 ...\n",
 849 |               "1000  1587603480  7129.56  ...                   2020-04-23 00:58:00\n",
 850 |               "1001  1587603540  7129.45  ...                   2020-04-23 00:59:00\n",
 851 |               "1002  1587603600  7127.24  ...                   2020-04-23 01:00:00\n",
 852 |               "1003  1587603660  7129.79  ...                   2020-04-23 01:01:00\n",
 853 |               "1004  1587603720  7129.30  ...                   2020-04-23 01:02:00\n",
 854 |               "\n",
 855 |               "[1005 rows x 10 columns]"
 856 |             ]
 857 |           },
 858 |           "metadata": {
 859 |             "tags": []
 860 |           },
 861 |           "execution_count": 5
 862 |         }
 863 |       ]
 864 |     },
 865 |     {
 866 |       "cell_type": "markdown",
 867 |       "metadata": {
 868 |         "id": "SzJvZDYRjhmV",
 869 |         "colab_type": "text"
 870 |       },
 871 |       "source": [
 872 |         "Get Ethereum Price, frequency daily"
 873 |       ]
 874 |     },
 875 |     {
 876 |       "cell_type": "code",
 877 |       "metadata": {
 878 |         "id": "q3ERdzMMjhyH",
 879 |         "colab_type": "code",
 880 |         "colab": {
 881 |           "base_uri": "https://localhost:8080/",
 882 |           "height": 413
 883 |         },
 884 |         "outputId": "d288fd2f-7115-4c12-c5d7-55c7d41db8d3"
 885 |       },
 886 |       "source": [
 887 |         "API_KEY=\"\"\n",
 888 |         "crypto=\"ETH\"\n",
 889 |         "numOfTimesToHitApi=5\n",
 890 |         "MaxRecordsToGetOnEachCall=200\n",
 891 |         "frequency=\"day\"\n",
 892 |         "\n",
 893 |         "cryptoData=getCryptoData(API_KEY,crypto,frequency,numOfTimesToHitApi,MaxRecordsToGetOnEachCall)\n",
 894 |         "cryptoData"
 895 |       ],
 896 |       "execution_count": 6,
 897 |       "outputs": [
 898 |         {
 899 |           "output_type": "execute_result",
 900 |           "data": {
 901 |             "text/html": [
 902 |               "<div>\n",
 903 |               "<style scoped>\n",
 904 |               "    .dataframe tbody tr th:only-of-type {\n",
 905 |               "        vertical-align: middle;\n",
 906 |               "    }\n",
 907 |               "\n",
 908 |               "    .dataframe tbody tr th {\n",
 909 |               "        vertical-align: top;\n",
 910 |               "    }\n",
 911 |               "\n",
 912 |               "    .dataframe thead th {\n",
 913 |               "        text-align: right;\n",
 914 |               "    }\n",
 915 |               "</style>\n",
 916 |               "<table border=\"1\" class=\"dataframe\">\n",
 917 |               "  <thead>\n",
 918 |               "    <tr style=\"text-align: right;\">\n",
 919 |               "      <th></th>\n",
 920 |               "      <th>time</th>\n",
 921 |               "      <th>high</th>\n",
 922 |               "      <th>low</th>\n",
 923 |               "      <th>open</th>\n",
 924 |               "      <th>volumefrom</th>\n",
 925 |               "      <th>volumeto</th>\n",
 926 |               "      <th>close</th>\n",
 927 |               "      <th>conversionType</th>\n",
 928 |               "      <th>conversionSymbol</th>\n",
 929 |               "      <th>timeUTC</th>\n",
 930 |               "    </tr>\n",
 931 |               "  </thead>\n",
 932 |               "  <tbody>\n",
 933 |               "    <tr>\n",
 934 |               "      <th>0</th>\n",
 935 |               "      <td>1500854400</td>\n",
 936 |               "      <td>229.56</td>\n",
 937 |               "      <td>224.04</td>\n",
 938 |               "      <td>228.32</td>\n",
 939 |               "      <td>397799.34</td>\n",
 940 |               "      <td>9.016589e+07</td>\n",
 941 |               "      <td>225.48</td>\n",
 942 |               "      <td>direct</td>\n",
 943 |               "      <td></td>\n",
 944 |               "      <td>2017-07-24</td>\n",
 945 |               "    </tr>\n",
 946 |               "    <tr>\n",
 947 |               "      <th>1</th>\n",
 948 |               "      <td>1500940800</td>\n",
 949 |               "      <td>225.98</td>\n",
 950 |               "      <td>192.64</td>\n",
 951 |               "      <td>225.48</td>\n",
 952 |               "      <td>1382784.11</td>\n",
 953 |               "      <td>2.853748e+08</td>\n",
 954 |               "      <td>203.59</td>\n",
 955 |               "      <td>direct</td>\n",
 956 |               "      <td></td>\n",
 957 |               "      <td>2017-07-25</td>\n",
 958 |               "    </tr>\n",
 959 |               "    <tr>\n",
 960 |               "      <th>2</th>\n",
 961 |               "      <td>1501027200</td>\n",
 962 |               "      <td>209.29</td>\n",
 963 |               "      <td>193.10</td>\n",
 964 |               "      <td>203.59</td>\n",
 965 |               "      <td>809346.64</td>\n",
 966 |               "      <td>1.616593e+08</td>\n",
 967 |               "      <td>202.88</td>\n",
 968 |               "      <td>direct</td>\n",
 969 |               "      <td></td>\n",
 970 |               "      <td>2017-07-26</td>\n",
 971 |               "    </tr>\n",
 972 |               "    <tr>\n",
 973 |               "      <th>3</th>\n",
 974 |               "      <td>1501113600</td>\n",
 975 |               "      <td>205.68</td>\n",
 976 |               "      <td>198.93</td>\n",
 977 |               "      <td>202.88</td>\n",
 978 |               "      <td>504777.47</td>\n",
 979 |               "      <td>1.020094e+08</td>\n",
 980 |               "      <td>202.93</td>\n",
 981 |               "      <td>direct</td>\n",
 982 |               "      <td></td>\n",
 983 |               "      <td>2017-07-27</td>\n",
 984 |               "    </tr>\n",
 985 |               "    <tr>\n",
 986 |               "      <th>4</th>\n",
 987 |               "      <td>1501200000</td>\n",
 988 |               "      <td>203.93</td>\n",
 989 |               "      <td>189.77</td>\n",
 990 |               "      <td>202.93</td>\n",
 991 |               "      <td>631780.70</td>\n",
 992 |               "      <td>1.234255e+08</td>\n",
 993 |               "      <td>191.21</td>\n",
 994 |               "      <td>direct</td>\n",
 995 |               "      <td></td>\n",
 996 |               "      <td>2017-07-28</td>\n",
 997 |               "    </tr>\n",
 998 |               "    <tr>\n",
 999 |               "      <th>...</th>\n",
1000 |               "      <td>...</td>\n",
1001 |               "      <td>...</td>\n",
1002 |               "      <td>...</td>\n",
1003 |               "      <td>...</td>\n",
1004 |               "      <td>...</td>\n",
1005 |               "      <td>...</td>\n",
1006 |               "      <td>...</td>\n",
1007 |               "      <td>...</td>\n",
1008 |               "      <td>...</td>\n",
1009 |               "      <td>...</td>\n",
1010 |               "    </tr>\n",
1011 |               "    <tr>\n",
1012 |               "      <th>1000</th>\n",
1013 |               "      <td>1587254400</td>\n",
1014 |               "      <td>185.31</td>\n",
1015 |               "      <td>178.32</td>\n",
1016 |               "      <td>187.85</td>\n",
1017 |               "      <td>1929266.85</td>\n",
1018 |               "      <td>3.478839e+08</td>\n",
1019 |               "      <td>180.32</td>\n",
1020 |               "      <td>multiply</td>\n",
1021 |               "      <td>BTC</td>\n",
1022 |               "      <td>2020-04-19</td>\n",
1023 |               "    </tr>\n",
1024 |               "    <tr>\n",
1025 |               "      <th>1001</th>\n",
1026 |               "      <td>1587340800</td>\n",
1027 |               "      <td>176.88</td>\n",
1028 |               "      <td>169.08</td>\n",
1029 |               "      <td>180.32</td>\n",
1030 |               "      <td>2362597.95</td>\n",
1031 |               "      <td>4.031790e+08</td>\n",
1032 |               "      <td>170.65</td>\n",
1033 |               "      <td>multiply</td>\n",
1034 |               "      <td>BTC</td>\n",
1035 |               "      <td>2020-04-20</td>\n",
1036 |               "    </tr>\n",
1037 |               "    <tr>\n",
1038 |               "      <th>1002</th>\n",
1039 |               "      <td>1587427200</td>\n",
1040 |               "      <td>173.37</td>\n",
1041 |               "      <td>170.14</td>\n",
1042 |               "      <td>170.65</td>\n",
1043 |               "      <td>1526448.92</td>\n",
1044 |               "      <td>2.610762e+08</td>\n",
1045 |               "      <td>171.03</td>\n",
1046 |               "      <td>multiply</td>\n",
1047 |               "      <td>BTC</td>\n",
1048 |               "      <td>2020-04-21</td>\n",
1049 |               "    </tr>\n",
1050 |               "    <tr>\n",
1051 |               "      <th>1003</th>\n",
1052 |               "      <td>1587513600</td>\n",
1053 |               "      <td>183.59</td>\n",
1054 |               "      <td>177.67</td>\n",
1055 |               "      <td>171.03</td>\n",
1056 |               "      <td>1563640.09</td>\n",
1057 |               "      <td>2.861825e+08</td>\n",
1058 |               "      <td>183.02</td>\n",
1059 |               "      <td>multiply</td>\n",
1060 |               "      <td>BTC</td>\n",
1061 |               "      <td>2020-04-22</td>\n",
1062 |               "    </tr>\n",
1063 |               "    <tr>\n",
1064 |               "      <th>1004</th>\n",
1065 |               "      <td>1587600000</td>\n",
1066 |               "      <td>183.65</td>\n",
1067 |               "      <td>181.76</td>\n",
1068 |               "      <td>183.04</td>\n",
1069 |               "      <td>14310.17</td>\n",
1070 |               "      <td>2.614673e+06</td>\n",
1071 |               "      <td>182.49</td>\n",
1072 |               "      <td>direct</td>\n",
1073 |               "      <td></td>\n",
1074 |               "      <td>2020-04-23</td>\n",
1075 |               "    </tr>\n",
1076 |               "  </tbody>\n",
1077 |               "</table>\n",
1078 |               "<p>1005 rows × 10 columns</p>\n",
1079 |               "</div>"
1080 |             ],
1081 |             "text/plain": [
1082 |               "            time    high     low  ...  conversionType  conversionSymbol    timeUTC\n",
1083 |               "0     1500854400  229.56  224.04  ...          direct                   2017-07-24\n",
1084 |               "1     1500940800  225.98  192.64  ...          direct                   2017-07-25\n",
1085 |               "2     1501027200  209.29  193.10  ...          direct                   2017-07-26\n",
1086 |               "3     1501113600  205.68  198.93  ...          direct                   2017-07-27\n",
1087 |               "4     1501200000  203.93  189.77  ...          direct                   2017-07-28\n",
1088 |               "...          ...     ...     ...  ...             ...               ...        ...\n",
1089 |               "1000  1587254400  185.31  178.32  ...        multiply               BTC 2020-04-19\n",
1090 |               "1001  1587340800  176.88  169.08  ...        multiply               BTC 2020-04-20\n",
1091 |               "1002  1587427200  173.37  170.14  ...        multiply               BTC 2020-04-21\n",
1092 |               "1003  1587513600  183.59  177.67  ...        multiply               BTC 2020-04-22\n",
1093 |               "1004  1587600000  183.65  181.76  ...          direct                   2020-04-23\n",
1094 |               "\n",
1095 |               "[1005 rows x 10 columns]"
1096 |             ]
1097 |           },
1098 |           "metadata": {
1099 |             "tags": []
1100 |           },
1101 |           "execution_count": 6
1102 |         }
1103 |       ]
1104 |     },
1105 |     {
1106 |       "cell_type": "code",
1107 |       "metadata": {
1108 |         "id": "TNb-HFryj-Q8",
1109 |         "colab_type": "code",
1110 |         "colab": {}
1111 |       },
1112 |       "source": [
1113 |         "|"
1114 |       ],
1115 |       "execution_count": 0,
1116 |       "outputs": []
1117 |     }
1118 |   ]
1119 | }


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/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2019 Manthan Thakker
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


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/MLPresentation.pdf:
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https://raw.githubusercontent.com/manthanthakker/BitcoinPrediction/aa7828be14955b64b0a3c8edc57b646ba1376505/MLPresentation.pdf


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/Machine_Learning_Project_Report.pdf:
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https://raw.githubusercontent.com/manthanthakker/BitcoinPrediction/aa7828be14955b64b0a3c8edc57b646ba1376505/Machine_Learning_Project_Report.pdf


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/PredictStockPricesRNN.py:
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 1 | import pandas as pd
 2 | import numpy as np
 3 | import tensorflow as tf
 4 | import yfinance as yf
 5 | 
 6 | # function to download and preprocess real-time data for Microsoft stock
 7 | def get_data():
 8 |   # use Yahoo Finance API to download real-time data for Microsoft stock
 9 |   msft = yf.Ticker("MSFT")
10 |   df = msft.history(period="1d", interval="1m")
11 | 
12 |   # clean and format data
13 |   df = df.dropna()
14 |   df = df[['Close']]
15 |   df = df.reset_index(drop=True)
16 | 
17 |   # normalize data
18 |   df['Close'] = df['Close'] / df['Close'].iloc[0]
19 | 
20 |   return df
21 | 
22 | # function to split data into training and testing sets
23 | def split_data(df, train_frac=0.8):
24 |   # split data into training and testing sets
25 |   train_size = int(len(df) * train_frac)
26 |   train_data = df.iloc[:train_size]
27 |   test_data = df.iloc[train_size:]
28 | 
29 |   # convert data to numpy arrays
30 |   X_train = np.array(train_data['Close'])
31 |   X_test = np.array(test_data['Close'])
32 | 
33 |   return (X_train, X_test)
34 | 
35 | # function to create and train RNN
36 | def train_rnn(X_train, X_test):
37 |   # create RNN model
38 |   model = tf.keras.Sequential()
39 |   model.add(tf.keras.layers.LSTM(units=50, input_shape=(1, 1)))
40 |   model.add(tf.keras.layers.Dense(units=1))
41 |   model.compile(loss='mean_squared_error', optimizer='adam')
42 | 
43 |   # reshape data for use with RNN
44 |   X_train = X_train.reshape((X_train.shape[0], 1, 1))
45 |   X_test = X_test.reshape((X_test.shape[0], 1, 1))
46 | 
47 |   # check shapes of input data
48 |   print(f"X_train shape: {X_train.shape}")
49 |   print(f"X_test shape: {X_test.shape}")
50 | 
51 |   # train RNN
52 |   model.fit(X_train, X_train, epochs=100, batch_size=1, verbose=2)
53 | 
54 |   return model
55 | 
56 | # function to evaluate RNN
57 | def evaluate_rnn(model, X_test):
58 |   # make predictions with RNN
59 |   predictions = model.predict(X_test)
60 | 
61 |   # calculate mean squared error
62 |   mse = np.mean((predictions - X_test)**2)
63 | 
64 |   return mse
65 | 
66 | # function to make predictions with RNN
67 | def predict_with_rnn(model, X_test):
68 |   # make predictions with RNN
69 |   predictions = model.predict(X_test)
70 | 
71 |   return predictions
72 | 
73 | def main():
74 |   # download and preprocess data
75 |   df = get_data()
76 | 
77 |   # split data into training and testing sets
78 |   X_train, X_test = split_data(df)
79 | 
80 |   # create and train RNN
81 |   model = train_rnn(X_train, X_test)
82 | 
83 |   # evaluate RNN
84 |   mse = evaluate_rnn(model, X_test)
85 |   print(f"Mean Squared Error: {mse}")
86 | 
87 |   # make predictions with RNN
88 |   predictions = predict_with_rnn(model, X_test)
89 |   print(predictions)
90 | 
91 | # call main function
92 | if __name__ == "__main__":
93 |   main()
94 | 


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/README.md:
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 1 | # BitcoinPrediction
 2 | 
 3 | ### CryptoCurrency prediction using Deep Recurrent Neural Networks
 4 | This repository contains various Machine learning models used in industry to predict stock prices and cryptocurrency in finance industry. 
 5 | 
 6 |   - Fundamental analysis of the stock price using Yahoo Finance
 7 |   - Data Visualization using Seaborn
 8 |   - ARIMA model to capture the trends,seasonality, forecast the prices and use as a baseline
 9 |   - Simpler machine learning models (Random Forest, Regression etc)
10 |   - Recurrent Neural Networks / Long Short Term Memory Networks
11 | 
12 | Each model is compared against each other to highlight pros and cons of each model. 
13 | 
14 | ### Install
15 | 
16 | This project requires **Python** and the following Python libraries installed:
17 | 
18 | - [NumPy](http://www.numpy.org/)
19 | - [Pandas](http://pandas.pydata.org/)
20 | - [matplotlib](http://matplotlib.org/)
21 | - [scikit-learn](http://scikit-learn.org/stable/)
22 | - [fastai]
23 | - [pytorch]
24 | 
25 | You will also need to have software installed to run and execute a [Jupyter Notebook](http://ipython.org/notebook.html)
26 | 
27 | If you do not have Python installed yet, it is highly recommended that you install the [Anaconda](http://continuum.io/downloads) distribution of Python, which already has the above packages and more included. 
28 | 
29 | ### Code
30 | 
31 | The source code is divided into multiple sections following the machine learning design pattern of : Data Exploration, Training, Testing and Hyperparameter Optimization.
32 | You can view the precompiled version of the notebook or you can rerun the entire notebook. The datasets are made available on public S3 Buckets. 
33 | Running the notebook, will automatically download the datasets for you. 
34 | 
35 | ### Run
36 | 
37 | In a terminal or command window, navigate to the top-level project directory `boston_housing/` (that contains this README) and run one of the following commands:
38 | 
39 | ```bash
40 | ipython notebook BitcoinPredictionRNN.ipynb
41 | ```  
42 | or
43 | ```bash
44 | jupyter notebook BitcoinPredictionRNN.ipynb
45 | ```
46 | 
47 | You can also run the entire training and evaluation pipeline from the command
48 | line:
49 | 
50 | ```bash
51 | python bitcoin_prediction_pipeline.py
52 | ```
53 | 
54 | The script downloads the latest Bitcoin price data and trains Random Forest,
55 | LSTM and ARIMA models. At the end it prints a comparison table of predictions
56 | and a summary of RMSE values.
57 | 
58 | 
59 | This will open the Jupyter Notebook software and project file in your browser.
60 | 
61 | ### Data
62 | BitCoin Price Data from Jan 2015- August 2018. The prices are as per coinbase cryptoexchange. There were many missing values and forward strategy was used to fill these missing values. 
63 | 
64 | **Features**
65 | BitCoin Price Data from Jan 2015- August 2018 
66 | 
67 | **Target Variable**
68 |  `Close Price`: Close price of Bitcoin for each day
69 | 


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