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  1. {
  2. "nbformat": 4,
  3. "nbformat_minor": 0,
  4. "metadata": {
  5. "colab": {
  6. "provenance": [],
  7. "collapsed_sections": []
  8. },
  9. "kernelspec": {
  10. "name": "python3",
  11. "display_name": "Python 3"
  12. },
  13. "language_info": {
  14. "name": "python"
  15. }
  16. },
  17. "cells": [
  18. {
  19. "cell_type": "markdown",
  20. "source": [
  21. "[Keeping it Really Simple](https://www.reddit.com/r/RealDayTrading/comments/svn70b/keeping_it_really_simple/)\n",
  22. "\n",
  23. "3. HA continuation of at least 2 days\n",
  24. "4. Above or below all major SMAs (50/100/200-day)\n",
  25. "\n",
  26. "[HA continuation is at least 2 flat-topped/bottomed candles in a row prior to the current trading day](https://www.reddit.com/r/RealDayTrading/comments/x0i2do/weekly_lounge_informal_discussion_general_talk/imsh2hl/)\n",
  27. "\n",
  28. "Eventually I want to pull D1s for all stocks on the NYSE, NASDAQ, and AMEX and incorporate [u/lilsgymdan's criteria](https://www.reddit.com/r/RealDayTrading/comments/ua7rm4/these_trade_criteria_work_really_really_well/)"
  29. ],
  30. "metadata": {
  31. "id": "3c-Udtwdecef"
  32. }
  33. },
  34. {
  35. "cell_type": "code",
  36. "execution_count": 2,
  37. "metadata": {
  38. "id": "c6GNIptFcXkQ",
  39. "colab": {
  40. "base_uri": "https://localhost:8080/"
  41. },
  42. "outputId": "f30c0050-ed4d-4aa6-ad8b-9b2b0d5a13b5"
  43. },
  44. "outputs": [
  45. {
  46. "output_type": "stream",
  47. "name": "stdout",
  48. "text": [
  49. "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
  50. "Collecting yfinance\n",
  51. " Downloading yfinance-0.1.74-py2.py3-none-any.whl (27 kB)\n",
  52. "Collecting requests>=2.26\n",
  53. " Downloading requests-2.28.1-py3-none-any.whl (62 kB)\n",
  54. "\u001b[K |████████████████████████████████| 62 kB 1.2 MB/s \n",
  55. "\u001b[?25hRequirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.11)\n",
  56. "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.3.5)\n",
  57. "Requirement already satisfied: lxml>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from yfinance) (4.9.1)\n",
  58. "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.21.6)\n",
  59. "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2.8.2)\n",
  60. "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2022.2.1)\n",
  61. "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24.0->yfinance) (1.15.0)\n",
  62. "Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.1.1)\n",
  63. "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (1.24.3)\n",
  64. "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.10)\n",
  65. "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2022.6.15)\n",
  66. "Installing collected packages: requests, yfinance\n",
  67. " Attempting uninstall: requests\n",
  68. " Found existing installation: requests 2.23.0\n",
  69. " Uninstalling requests-2.23.0:\n",
  70. " Successfully uninstalled requests-2.23.0\n",
  71. "Successfully installed requests-2.28.1 yfinance-0.1.74\n"
  72. ]
  73. }
  74. ],
  75. "source": [
  76. "#https://blog.quantinsti.com/stock-market-data-analysis-python/\n",
  77. "!pip install yfinance"
  78. ]
  79. },
  80. {
  81. "cell_type": "code",
  82. "source": [
  83. "# import packages\n",
  84. "import yfinance as yf\n",
  85. "import pandas as pd\n",
  86. "# import numpy as np\n",
  87. "# import datetime as dt"
  88. ],
  89. "metadata": {
  90. "id": "Zoa2ux9FcaeI"
  91. },
  92. "execution_count": 3,
  93. "outputs": []
  94. },
  95. {
  96. "cell_type": "code",
  97. "source": [
  98. "# get SPY and SPDR sector ETFs D1 data\n",
  99. "etfs = ['SPY','XLK','XLC','XLY','XLP','XLV','XLF','XLE','XLI','XLB','XLU','XLRE']\n",
  100. "# etfs\n",
  101. "\n",
  102. "start_date = '2021-9-3'\n",
  103. "end_date = '2022-9-3'\n",
  104. "\n",
  105. "etf_data = yf.download(etfs,start_date,end_date) #auto_adjust=True would make the close adjusted for dividends, which we don't want. See SPY on 8/16/22 for example: bounced off the 200SMA unadjusted"
  106. ],
  107. "metadata": {
  108. "id": "YeXt3wjhcfg-",
  109. "colab": {
  110. "base_uri": "https://localhost:8080/"
  111. },
  112. "outputId": "716f0beb-409f-483b-fffe-8b044cd586e4"
  113. },
  114. "execution_count": 4,
  115. "outputs": [
  116. {
  117. "output_type": "stream",
  118. "name": "stdout",
  119. "text": [
  120. "[*********************100%***********************] 12 of 12 completed\n"
  121. ]
  122. }
  123. ]
  124. },
  125. {
  126. "cell_type": "code",
  127. "source": [
  128. "# etf_data.dtypes"
  129. ],
  130. "metadata": {
  131. "id": "tsmIvqSSN8Qe"
  132. },
  133. "execution_count": null,
  134. "outputs": []
  135. },
  136. {
  137. "cell_type": "code",
  138. "source": [
  139. "SPY_D1 = pd.DataFrame()\n",
  140. "\n",
  141. "SPY_D1['Close'] = etf_data['Close']['SPY']\n",
  142. "SPY_D1['sma50'] = SPY_D1['Close'].rolling(window = 50, min_periods = 1).mean()\n",
  143. "SPY_D1['sma100'] = SPY_D1['Close'].rolling(window = 100, min_periods = 1).mean()\n",
  144. "SPY_D1['sma200'] = SPY_D1['Close'].rolling(window = 200, min_periods = 1).mean()\n",
  145. "SPY_D1.tail()"
  146. ],
  147. "metadata": {
  148. "colab": {
  149. "base_uri": "https://localhost:8080/",
  150. "height": 238
  151. },
  152. "id": "Oo09C6mYLIM-",
  153. "outputId": "c18e1638-0459-4d89-e27e-f6227e4592b7"
  154. },
  155. "execution_count": null,
  156. "outputs": [
  157. {
  158. "output_type": "execute_result",
  159. "data": {
  160. "text/plain": [
  161. " Close sma50 sma100 sma200\n",
  162. "Date \n",
  163. "2022-08-29 402.630005 399.283201 405.922501 429.377851\n",
  164. "2022-08-30 398.209991 399.930201 405.439401 429.050051\n",
  165. "2022-08-31 395.179993 400.332401 404.903501 428.689601\n",
  166. "2022-09-01 396.420013 400.773000 404.392001 428.334552\n",
  167. "2022-09-02 392.239990 401.056600 403.915201 427.949352"
  168. ],
  169. "text/html": [
  170. "\n",
  171. " <div id=\"df-f4ef70aa-3092-4d55-a981-4d26634db27a\">\n",
  172. " <div class=\"colab-df-container\">\n",
  173. " <div>\n",
  174. "<style scoped>\n",
  175. " .dataframe tbody tr th:only-of-type {\n",
  176. " vertical-align: middle;\n",
  177. " }\n",
  178. "\n",
  179. " .dataframe tbody tr th {\n",
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  182. "\n",
  183. " .dataframe thead th {\n",
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  186. "</style>\n",
  187. "<table border=\"1\" class=\"dataframe\">\n",
  188. " <thead>\n",
  189. " <tr style=\"text-align: right;\">\n",
  190. " <th></th>\n",
  191. " <th>Close</th>\n",
  192. " <th>sma50</th>\n",
  193. " <th>sma100</th>\n",
  194. " <th>sma200</th>\n",
  195. " </tr>\n",
  196. " <tr>\n",
  197. " <th>Date</th>\n",
  198. " <th></th>\n",
  199. " <th></th>\n",
  200. " <th></th>\n",
  201. " <th></th>\n",
  202. " </tr>\n",
  203. " </thead>\n",
  204. " <tbody>\n",
  205. " <tr>\n",
  206. " <th>2022-08-29</th>\n",
  207. " <td>402.630005</td>\n",
  208. " <td>399.283201</td>\n",
  209. " <td>405.922501</td>\n",
  210. " <td>429.377851</td>\n",
  211. " </tr>\n",
  212. " <tr>\n",
  213. " <th>2022-08-30</th>\n",
  214. " <td>398.209991</td>\n",
  215. " <td>399.930201</td>\n",
  216. " <td>405.439401</td>\n",
  217. " <td>429.050051</td>\n",
  218. " </tr>\n",
  219. " <tr>\n",
  220. " <th>2022-08-31</th>\n",
  221. " <td>395.179993</td>\n",
  222. " <td>400.332401</td>\n",
  223. " <td>404.903501</td>\n",
  224. " <td>428.689601</td>\n",
  225. " </tr>\n",
  226. " <tr>\n",
  227. " <th>2022-09-01</th>\n",
  228. " <td>396.420013</td>\n",
  229. " <td>400.773000</td>\n",
  230. " <td>404.392001</td>\n",
  231. " <td>428.334552</td>\n",
  232. " </tr>\n",
  233. " <tr>\n",
  234. " <th>2022-09-02</th>\n",
  235. " <td>392.239990</td>\n",
  236. " <td>401.056600</td>\n",
  237. " <td>403.915201</td>\n",
  238. " <td>427.949352</td>\n",
  239. " </tr>\n",
  240. " </tbody>\n",
  241. "</table>\n",
  242. "</div>\n",
  243. " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f4ef70aa-3092-4d55-a981-4d26634db27a')\"\n",
  244. " title=\"Convert this dataframe to an interactive table.\"\n",
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  251. " </svg>\n",
  252. " </button>\n",
  253. " \n",
  254. " <style>\n",
  255. " .colab-df-container {\n",
  256. " display:flex;\n",
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  258. " gap: 12px;\n",
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  260. "\n",
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  295. " buttonEl.style.display =\n",
  296. " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
  297. "\n",
  298. " async function convertToInteractive(key) {\n",
  299. " const element = document.querySelector('#df-f4ef70aa-3092-4d55-a981-4d26634db27a');\n",
  300. " const dataTable =\n",
  301. " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
  302. " [key], {});\n",
  303. " if (!dataTable) return;\n",
  304. "\n",
  305. " const docLinkHtml = 'Like what you see? Visit the ' +\n",
  306. " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
  307. " + ' to learn more about interactive tables.';\n",
  308. " element.innerHTML = '';\n",
  309. " dataTable['output_type'] = 'display_data';\n",
  310. " await google.colab.output.renderOutput(dataTable, element);\n",
  311. " const docLink = document.createElement('div');\n",
  312. " docLink.innerHTML = docLinkHtml;\n",
  313. " element.appendChild(docLink);\n",
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  315. " </script>\n",
  316. " </div>\n",
  317. " </div>\n",
  318. " "
  319. ]
  320. },
  321. "metadata": {},
  322. "execution_count": 40
  323. }
  324. ]
  325. },
  326. {
  327. "cell_type": "code",
  328. "source": [
  329. "SPY_D1.head()"
  330. ],
  331. "metadata": {
  332. "colab": {
  333. "base_uri": "https://localhost:8080/",
  334. "height": 238
  335. },
  336. "id": "-OGBIKtoXhGu",
  337. "outputId": "e539660b-5589-45e9-db57-74075ab33631"
  338. },
  339. "execution_count": null,
  340. "outputs": [
  341. {
  342. "output_type": "execute_result",
  343. "data": {
  344. "text/plain": [
  345. " Close sma50 sma100 sma200\n",
  346. "Date \n",
  347. "2021-09-03 453.079987 453.079987 453.079987 453.079987\n",
  348. "2021-09-07 451.459991 452.269989 452.269989 452.269989\n",
  349. "2021-09-08 450.910004 451.816661 451.816661 451.816661\n",
  350. "2021-09-09 448.980011 451.107498 451.107498 451.107498\n",
  351. "2021-09-10 445.440002 449.973999 449.973999 449.973999"
  352. ],
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  354. "\n",
  355. " <div id=\"df-c6335dbd-71e3-49b0-8c3a-b3cb4d3a953a\">\n",
  356. " <div class=\"colab-df-container\">\n",
  357. " <div>\n",
  358. "<style scoped>\n",
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  372. " <thead>\n",
  373. " <tr style=\"text-align: right;\">\n",
  374. " <th></th>\n",
  375. " <th>Close</th>\n",
  376. " <th>sma50</th>\n",
  377. " <th>sma100</th>\n",
  378. " <th>sma200</th>\n",
  379. " </tr>\n",
  380. " <tr>\n",
  381. " <th>Date</th>\n",
  382. " <th></th>\n",
  383. " <th></th>\n",
  384. " <th></th>\n",
  385. " <th></th>\n",
  386. " </tr>\n",
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  388. " <tbody>\n",
  389. " <tr>\n",
  390. " <th>2021-09-03</th>\n",
  391. " <td>453.079987</td>\n",
  392. " <td>453.079987</td>\n",
  393. " <td>453.079987</td>\n",
  394. " <td>453.079987</td>\n",
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  396. " <tr>\n",
  397. " <th>2021-09-07</th>\n",
  398. " <td>451.459991</td>\n",
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  400. " <td>452.269989</td>\n",
  401. " <td>452.269989</td>\n",
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  404. " <th>2021-09-08</th>\n",
  405. " <td>450.910004</td>\n",
  406. " <td>451.816661</td>\n",
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  408. " <td>451.816661</td>\n",
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  410. " <tr>\n",
  411. " <th>2021-09-09</th>\n",
  412. " <td>448.980011</td>\n",
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  417. " <tr>\n",
  418. " <th>2021-09-10</th>\n",
  419. " <td>445.440002</td>\n",
  420. " <td>449.973999</td>\n",
  421. " <td>449.973999</td>\n",
  422. " <td>449.973999</td>\n",
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  453. " padding: 0 0 0 0;\n",
  454. " width: 32px;\n",
  455. " }\n",
  456. "\n",
  457. " .colab-df-convert:hover {\n",
  458. " background-color: #E2EBFA;\n",
  459. " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
  460. " fill: #174EA6;\n",
  461. " }\n",
  462. "\n",
  463. " [theme=dark] .colab-df-convert {\n",
  464. " background-color: #3B4455;\n",
  465. " fill: #D2E3FC;\n",
  466. " }\n",
  467. "\n",
  468. " [theme=dark] .colab-df-convert:hover {\n",
  469. " background-color: #434B5C;\n",
  470. " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
  471. " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
  472. " fill: #FFFFFF;\n",
  473. " }\n",
  474. " </style>\n",
  475. "\n",
  476. " <script>\n",
  477. " const buttonEl =\n",
  478. " document.querySelector('#df-c6335dbd-71e3-49b0-8c3a-b3cb4d3a953a button.colab-df-convert');\n",
  479. " buttonEl.style.display =\n",
  480. " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
  481. "\n",
  482. " async function convertToInteractive(key) {\n",
  483. " const element = document.querySelector('#df-c6335dbd-71e3-49b0-8c3a-b3cb4d3a953a');\n",
  484. " const dataTable =\n",
  485. " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
  486. " [key], {});\n",
  487. " if (!dataTable) return;\n",
  488. "\n",
  489. " const docLinkHtml = 'Like what you see? Visit the ' +\n",
  490. " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
  491. " + ' to learn more about interactive tables.';\n",
  492. " element.innerHTML = '';\n",
  493. " dataTable['output_type'] = 'display_data';\n",
  494. " await google.colab.output.renderOutput(dataTable, element);\n",
  495. " const docLink = document.createElement('div');\n",
  496. " docLink.innerHTML = docLinkHtml;\n",
  497. " element.appendChild(docLink);\n",
  498. " }\n",
  499. " </script>\n",
  500. " </div>\n",
  501. " </div>\n",
  502. " "
  503. ]
  504. },
  505. "metadata": {},
  506. "execution_count": 61
  507. }
  508. ]
  509. },
  510. {
  511. "cell_type": "code",
  512. "source": [
  513. "# get the stock tickers that make up S&P500\n",
  514. "tickers = pd.read_html(\n",
  515. " 'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')[0]\n",
  516. "\n",
  517. "# pd.set_option('display.max_rows', None)\n",
  518. "# tickers"
  519. ],
  520. "metadata": {
  521. "id": "j09lEw9-cfeA"
  522. },
  523. "execution_count": 5,
  524. "outputs": []
  525. },
  526. {
  527. "cell_type": "code",
  528. "source": [
  529. "# scrub tickers\n",
  530. "tickers.at[64, 'Symbol'] = 'BRK-B'\n",
  531. "tickers.at[80, 'Symbol'] = 'BF-B'"
  532. ],
  533. "metadata": {
  534. "id": "JD0MBIOEEB4l"
  535. },
  536. "execution_count": 6,
  537. "outputs": []
  538. },
  539. {
  540. "cell_type": "code",
  541. "source": [
  542. "# get the data for these tickers from yahoo finance\n",
  543. "\n",
  544. "start_date = '2021-9-3'\n",
  545. "end_date = '2022-9-3'\n",
  546. "\n",
  547. "ticker_data = yf.download(tickers.Symbol.to_list(),start_date,end_date)\n",
  548. "print(ticker_data['Close'])"
  549. ],
  550. "metadata": {
  551. "id": "YK-G8mYtdhMu",
  552. "colab": {
  553. "base_uri": "https://localhost:8080/"
  554. },
  555. "outputId": "efc54b69-bae1-4c7b-f1e2-f4417d783ffa"
  556. },
  557. "execution_count": 29,
  558. "outputs": [
  559. {
  560. "output_type": "stream",
  561. "name": "stdout",
  562. "text": [
  563. "[*********************100%***********************] 503 of 503 completed\n",
  564. " A AAL AAP AAPL ABBV \\\n",
  565. "Date \n",
  566. "2021-09-02 NaN NaN NaN NaN NaN \n",
  567. "2021-09-03 179.279999 19.370001 199.440002 154.300003 111.620003 \n",
  568. "2021-09-07 177.720001 19.530001 199.809998 156.690002 109.029999 \n",
  569. "2021-09-08 178.729996 19.129999 200.179993 155.110001 110.360001 \n",
  570. "2021-09-09 177.229996 20.200001 201.220001 154.070007 107.269997 \n",
  571. "... ... ... ... ... ... \n",
  572. "2022-08-29 128.110001 13.510000 170.539993 161.380005 135.710007 \n",
  573. "2022-08-30 128.279999 13.330000 170.470001 158.910004 135.550003 \n",
  574. "2022-08-31 128.250000 12.990000 168.639999 157.220001 134.460007 \n",
  575. "2022-09-01 128.929993 12.930000 172.149994 157.960007 138.449997 \n",
  576. "2022-09-02 128.009995 12.990000 171.729996 155.809998 136.279999 \n",
  577. "\n",
  578. " ABC ABMD ABT ACN ADBE ... \\\n",
  579. "Date ... \n",
  580. "2021-09-02 NaN NaN NaN NaN NaN ... \n",
  581. "2021-09-03 125.139999 372.690002 128.940002 343.329987 666.590027 ... \n",
  582. "2021-09-07 124.209999 376.200012 128.389999 341.489990 661.390015 ... \n",
  583. "2021-09-08 124.309998 374.790009 129.059998 340.320007 663.219971 ... \n",
  584. "2021-09-09 123.599998 365.839996 127.980003 341.100006 661.679993 ... \n",
  585. "... ... ... ... ... ... ... \n",
  586. "2022-08-29 147.490005 256.980011 101.839996 295.140015 375.260010 ... \n",
  587. "2022-08-30 145.429993 258.649994 102.199997 292.500000 375.070007 ... \n",
  588. "2022-08-31 146.559998 259.279999 102.650002 288.459991 373.440002 ... \n",
  589. "2022-09-01 147.610001 259.529999 104.839996 288.790009 370.529999 ... \n",
  590. "2022-09-02 147.710007 261.239990 102.500000 284.070007 368.140015 ... \n",
  591. "\n",
  592. " WYNN XEL XOM XRAY XYL \\\n",
  593. "Date \n",
  594. "2021-09-02 NaN NaN NaN NaN NaN \n",
  595. "2021-09-03 100.300003 69.800003 54.869999 62.430000 137.250000 \n",
  596. "2021-09-07 105.930000 67.900002 54.549999 62.169998 135.649994 \n",
  597. "2021-09-08 104.620003 69.309998 54.049999 62.310001 135.110001 \n",
  598. "2021-09-09 103.930000 69.269997 54.090000 62.459999 133.940002 \n",
  599. "... ... ... ... ... ... \n",
  600. "2022-08-29 62.410000 75.620003 100.120003 33.080002 92.050003 \n",
  601. "2022-08-30 60.209999 74.690002 96.309998 32.939999 91.349998 \n",
  602. "2022-08-31 60.590000 74.250000 95.589996 32.770000 91.099998 \n",
  603. "2022-09-01 59.180000 75.419998 93.870003 32.169998 92.089996 \n",
  604. "2022-09-02 59.180000 74.410004 95.589996 31.799999 91.209999 \n",
  605. "\n",
  606. " YUM ZBH ZBRA ZION ZTS \n",
  607. "Date \n",
  608. "2021-09-02 NaN NaN NaN NaN NaN \n",
  609. "2021-09-03 130.919998 142.466019 590.599976 57.119999 209.690002 \n",
  610. "2021-09-07 128.830002 142.349518 581.140015 57.500000 207.809998 \n",
  611. "2021-09-08 130.190002 143.184464 579.750000 56.500000 209.330002 \n",
  612. "2021-09-09 129.250000 140.135925 577.469971 57.560001 206.020004 \n",
  613. "... ... ... ... ... ... \n",
  614. "2022-08-29 112.209999 107.620003 307.899994 55.750000 157.869995 \n",
  615. "2022-08-30 110.779999 107.370003 302.859985 55.400002 157.080002 \n",
  616. "2022-08-31 111.239998 106.320000 301.640015 55.029999 156.529999 \n",
  617. "2022-09-01 114.260002 106.800003 309.739990 55.110001 159.410004 \n",
  618. "2022-09-02 112.739998 105.570000 297.600006 55.040001 156.979996 \n",
  619. "\n",
  620. "[253 rows x 503 columns]\n"
  621. ]
  622. }
  623. ]
  624. },
  625. {
  626. "cell_type": "code",
  627. "source": [
  628. "ticker_data.drop(index=('2021-09-02'), inplace=True) # not sure why the first row is always NA"
  629. ],
  630. "metadata": {
  631. "id": "FZ3qI1BQJdlz"
  632. },
  633. "execution_count": 30,
  634. "outputs": []
  635. },
  636. {
  637. "cell_type": "code",
  638. "source": [
  639. "# get HA candles\n",
  640. "# https://tlc.thinkorswim.com/center/howToTos/thinkManual/charts/Chart-Modes/Chart-Types/heikinashi\n",
  641. "\n",
  642. "ha_ticker_data = ticker_data.copy()\n",
  643. "\n",
  644. "ha_ticker_data['Close'] = (ticker_data['Open'] + ticker_data['High'] + ticker_data['Low'] + ticker_data['Close'])/4\n",
  645. "\n",
  646. "ha_ticker_data['Open'].iloc[0] = ticker_data['Open'].iloc[0]\n",
  647. "\n",
  648. "for i in range(1, len(ticker_data.index)):\n",
  649. " ha_ticker_data['Open'].iloc[i] = (ha_ticker_data['Open'].iloc[i-1] + ha_ticker_data['Close'].iloc[i-1])/2\n",
  650. "\n",
  651. "ha_ticker_data['High'] = pd.concat([ticker_data['High'], ha_ticker_data['Open'], ha_ticker_data['Close']]).max(level=0)\n",
  652. "ha_ticker_data['Low'] = pd.concat([ticker_data['Low'], ha_ticker_data['Open'], ha_ticker_data['Close']]).min(level=0)"
  653. ],
  654. "metadata": {
  655. "colab": {
  656. "base_uri": "https://localhost:8080/"
  657. },
  658. "id": "N6I2TgJs7UlW",
  659. "outputId": "424fe035-deb4-4ed7-b3c7-a8c3ae346205"
  660. },
  661. "execution_count": 38,
  662. "outputs": [
  663. {
  664. "output_type": "stream",
  665. "name": "stderr",
  666. "text": [
  667. "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
  668. "A value is trying to be set on a copy of a slice from a DataFrame\n",
  669. "\n",
  670. "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
  671. " self._setitem_single_block(indexer, value, name)\n",
  672. "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:723: SettingWithCopyWarning: \n",
  673. "A value is trying to be set on a copy of a slice from a DataFrame\n",
  674. "\n",
  675. "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
  676. " iloc._setitem_with_indexer(indexer, value, self.name)\n",
  677. "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.median(level=1) should use df.groupby(level=1).median().\n",
  678. " del sys.path[0]\n",
  679. "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:14: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.median(level=1) should use df.groupby(level=1).median().\n",
  680. " \n"
  681. ]
  682. }
  683. ]
  684. },
  685. {
  686. "cell_type": "code",
  687. "source": [],
  688. "metadata": {
  689. "id": "Aw6qbdU6X5gt"
  690. },
  691. "execution_count": 9,
  692. "outputs": []
  693. },
  694. {
  695. "cell_type": "code",
  696. "source": [
  697. "ticker_closes = ticker_data['Close']\n",
  698. "\n",
  699. "ticker_results = pd.DataFrame(index = ticker_closes.columns, \\\n",
  700. " columns = ['last_close', 'sma50', 'sma100', 'sma200', 'above_all', 'below_all', \\\n",
  701. " 'last_flat_bottom', 't1_flat_bottom', 'last_flat_top', 't1_flat_top', \\\n",
  702. " 'two_flat_bottom', 'two_flat_top', 'good_long', 'good_short'])\n",
  703. "\n",
  704. "ticker_results['last_close'] = ticker_closes.iloc[-1]\n",
  705. "\n",
  706. "ticker_results.head()"
  707. ],
  708. "metadata": {
  709. "colab": {
  710. "base_uri": "https://localhost:8080/",
  711. "height": 206
  712. },
  713. "id": "07yexkvAddJi",
  714. "outputId": "9476d0eb-11a9-40e6-8742-13a2f6c2a284"
  715. },
  716. "execution_count": 41,
  717. "outputs": [
  718. {
  719. "output_type": "execute_result",
  720. "data": {
  721. "text/plain": [
  722. " last_close sma50 sma100 sma200 above_all below_all last_flat_bottom \\\n",
  723. "A 128.009995 NaN NaN NaN NaN NaN NaN \n",
  724. "AAL 12.990000 NaN NaN NaN NaN NaN NaN \n",
  725. "AAP 171.729996 NaN NaN NaN NaN NaN NaN \n",
  726. "AAPL 155.809998 NaN NaN NaN NaN NaN NaN \n",
  727. "ABBV 136.279999 NaN NaN NaN NaN NaN NaN \n",
  728. "\n",
  729. " t1_flat_bottom last_flat_top t1_flat_top two_flat_bottom two_flat_top \\\n",
  730. "A NaN NaN NaN NaN NaN \n",
  731. "AAL NaN NaN NaN NaN NaN \n",
  732. "AAP NaN NaN NaN NaN NaN \n",
  733. "AAPL NaN NaN NaN NaN NaN \n",
  734. "ABBV NaN NaN NaN NaN NaN \n",
  735. "\n",
  736. " good_long good_short \n",
  737. "A NaN NaN \n",
  738. "AAL NaN NaN \n",
  739. "AAP NaN NaN \n",
  740. "AAPL NaN NaN \n",
  741. "ABBV NaN NaN "
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  748. "<style scoped>\n",
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  753. " .dataframe tbody tr th {\n",
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  765. " <th>last_close</th>\n",
  766. " <th>sma50</th>\n",
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  769. " <th>above_all</th>\n",
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  800. " <th>AAL</th>\n",
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  828. " <td>NaN</td>\n",
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  834. " <th>AAPL</th>\n",
  835. " <td>155.809998</td>\n",
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  895. " height: 32px;\n",
  896. " padding: 0 0 0 0;\n",
  897. " width: 32px;\n",
  898. " }\n",
  899. "\n",
  900. " .colab-df-convert:hover {\n",
  901. " background-color: #E2EBFA;\n",
  902. " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
  903. " fill: #174EA6;\n",
  904. " }\n",
  905. "\n",
  906. " [theme=dark] .colab-df-convert {\n",
  907. " background-color: #3B4455;\n",
  908. " fill: #D2E3FC;\n",
  909. " }\n",
  910. "\n",
  911. " [theme=dark] .colab-df-convert:hover {\n",
  912. " background-color: #434B5C;\n",
  913. " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
  914. " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
  915. " fill: #FFFFFF;\n",
  916. " }\n",
  917. " </style>\n",
  918. "\n",
  919. " <script>\n",
  920. " const buttonEl =\n",
  921. " document.querySelector('#df-735ccea8-9105-4c3a-bb04-114746405acf button.colab-df-convert');\n",
  922. " buttonEl.style.display =\n",
  923. " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
  924. "\n",
  925. " async function convertToInteractive(key) {\n",
  926. " const element = document.querySelector('#df-735ccea8-9105-4c3a-bb04-114746405acf');\n",
  927. " const dataTable =\n",
  928. " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
  929. " [key], {});\n",
  930. " if (!dataTable) return;\n",
  931. "\n",
  932. " const docLinkHtml = 'Like what you see? Visit the ' +\n",
  933. " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
  934. " + ' to learn more about interactive tables.';\n",
  935. " element.innerHTML = '';\n",
  936. " dataTable['output_type'] = 'display_data';\n",
  937. " await google.colab.output.renderOutput(dataTable, element);\n",
  938. " const docLink = document.createElement('div');\n",
  939. " docLink.innerHTML = docLinkHtml;\n",
  940. " element.appendChild(docLink);\n",
  941. " }\n",
  942. " </script>\n",
  943. " </div>\n",
  944. " </div>\n",
  945. " "
  946. ]
  947. },
  948. "metadata": {},
  949. "execution_count": 41
  950. }
  951. ]
  952. },
  953. {
  954. "cell_type": "code",
  955. "source": [
  956. "ticker = 'A'\n",
  957. "ha_ticker_data['Low'][ticker][-1]"
  958. ],
  959. "metadata": {
  960. "colab": {
  961. "base_uri": "https://localhost:8080/"
  962. },
  963. "id": "Ek6PrinpHR-Z",
  964. "outputId": "2480ebea-de0e-4e85-8f90-a9e6c3683bd4"
  965. },
  966. "execution_count": 47,
  967. "outputs": [
  968. {
  969. "output_type": "execute_result",
  970. "data": {
  971. "text/plain": [
  972. "127.12000274658203"
  973. ]
  974. },
  975. "metadata": {},
  976. "execution_count": 47
  977. }
  978. ]
  979. },
  980. {
  981. "cell_type": "code",
  982. "source": [
  983. "for ticker in ticker_closes.columns:\n",
  984. "\n",
  985. " ticker_results.loc[ticker, 'sma50'] = ticker_closes[ticker].rolling(window = 50, min_periods = 1).mean().tail(1)[0]\n",
  986. " ticker_results.loc[ticker, 'sma100'] = ticker_closes[ticker].rolling(window = 100, min_periods = 1).mean().tail(1)[0]\n",
  987. " ticker_results.loc[ticker, 'sma200'] = ticker_closes[ticker].rolling(window = 200, min_periods = 1).mean().tail(1)[0]\n",
  988. " ticker_results.loc[ticker, 'above_all'] = ticker_results.loc[ticker, 'last_close'] > ticker_results.loc[ticker, 'sma50'] and \\\n",
  989. " ticker_results.loc[ticker, 'last_close'] > ticker_results.loc[ticker, 'sma100'] and \\\n",
  990. " ticker_results.loc[ticker, 'last_close'] > ticker_results.loc[ticker, 'sma200']\n",
  991. " ticker_results.loc[ticker, 'below_all'] = ticker_results.loc[ticker, 'last_close'] < ticker_results.loc[ticker, 'sma50'] and \\\n",
  992. " ticker_results.loc[ticker, 'last_close'] < ticker_results.loc[ticker, 'sma100'] and \\\n",
  993. " ticker_results.loc[ticker, 'last_close'] < ticker_results.loc[ticker, 'sma200']\n",
  994. "\n",
  995. " ticker_results.loc[ticker, 'last_flat_bottom'] = ha_ticker_data['Low'][ticker][-1] == ha_ticker_data['Open'][ticker][-1]\n",
  996. " ticker_results.loc[ticker, 'last_flat_top'] = ha_ticker_data['High'][ticker][-1] == ha_ticker_data['Open'][ticker][-1]\n",
  997. "\n",
  998. " ticker_results.loc[ticker, 't1_flat_bottom'] = ha_ticker_data['Low'][ticker][-2] == ha_ticker_data['Open'][ticker][-2]\n",
  999. " ticker_results.loc[ticker, 't1_flat_top'] = ha_ticker_data['High'][ticker][-2] == ha_ticker_data['Open'][ticker][-2]\n",
  1000. "\n",
  1001. " ticker_results.loc[ticker, 'two_flat_bottom'] = ticker_results.loc[ticker, 't1_flat_bottom'] and ticker_results.loc[ticker, 'last_flat_bottom']\n",
  1002. " ticker_results.loc[ticker, 'two_flat_top'] = ticker_results.loc[ticker, 'last_flat_top'] and ticker_results.loc[ticker, 't1_flat_top']\n",
  1003. "\n",
  1004. " ticker_results.loc[ticker, 'good_long'] = ticker_results.loc[ticker, 'above_all'] and ticker_results.loc[ticker, 'two_flat_bottom']\n",
  1005. " ticker_results.loc[ticker, 'good_short'] = ticker_results.loc[ticker, 'below_all'] and ticker_results.loc[ticker, 'two_flat_top']"
  1006. ],
  1007. "metadata": {
  1008. "id": "trBorl18ZHBw"
  1009. },
  1010. "execution_count": 53,
  1011. "outputs": []
  1012. },
  1013. {
  1014. "cell_type": "code",
  1015. "source": [
  1016. "ticker_results"
  1017. ],
  1018. "metadata": {
  1019. "id": "CoDRholqsSVK",
  1020. "colab": {
  1021. "base_uri": "https://localhost:8080/",
  1022. "height": 424
  1023. },
  1024. "outputId": "fde710bf-32f1-4829-b789-e1120b96edf2"
  1025. },
  1026. "execution_count": 54,
  1027. "outputs": [
  1028. {
  1029. "output_type": "execute_result",
  1030. "data": {
  1031. "text/plain": [
  1032. " last_close sma50 sma100 sma200 above_all below_all \\\n",
  1033. "A 128.009995 127.3384 124.5744 133.724999 False False \n",
  1034. "AAL 12.990000 14.1182 15.4952 16.4178 False True \n",
  1035. "AAP 171.729996 187.9036 192.8611 208.2079 False True \n",
  1036. "AAPL 155.809998 156.845 153.2715 161.0885 False False \n",
  1037. "ABBV 136.279999 145.376 148.0243 144.24435 False True \n",
  1038. "... ... ... ... ... ... ... \n",
  1039. "YUM 112.739998 117.2044 116.6597 120.96335 False True \n",
  1040. "ZBH 105.570000 109.213401 113.8394 117.647967 False True \n",
  1041. "ZBRA 297.600006 318.251199 330.535999 414.611649 False True \n",
  1042. "ZION 55.040001 53.9468 55.1171 60.8519 False False \n",
  1043. "ZTS 156.979996 172.122399 171.1468 189.399199 False True \n",
  1044. "\n",
  1045. " last_flat_bottom t1_flat_bottom last_flat_top t1_flat_top \\\n",
  1046. "A False False False True \n",
  1047. "AAL False False False True \n",
  1048. "AAP False False False False \n",
  1049. "AAPL False False False True \n",
  1050. "ABBV False False False False \n",
  1051. "... ... ... ... ... \n",
  1052. "YUM True False False False \n",
  1053. "ZBH False False False True \n",
  1054. "ZBRA False False False False \n",
  1055. "ZION False False False True \n",
  1056. "ZTS False False False False \n",
  1057. "\n",
  1058. " two_flat_bottom two_flat_top good_long good_short \n",
  1059. "A False False False False \n",
  1060. "AAL False False False False \n",
  1061. "AAP False False False False \n",
  1062. "AAPL False False False False \n",
  1063. "ABBV False False False False \n",
  1064. "... ... ... ... ... \n",
  1065. "YUM False False False False \n",
  1066. "ZBH False False False False \n",
  1067. "ZBRA False False False False \n",
  1068. "ZION False False False False \n",
  1069. "ZTS False False False False \n",
  1070. "\n",
  1071. "[503 rows x 14 columns]"
  1072. ],
  1073. "text/html": [
  1074. "\n",
  1075. " <div id=\"df-f778fc03-ad9e-4ec3-98de-4b3a4289fd87\">\n",
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  1077. " <div>\n",
  1078. "<style scoped>\n",
  1079. " .dataframe tbody tr th:only-of-type {\n",
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  1091. "<table border=\"1\" class=\"dataframe\">\n",
  1092. " <thead>\n",
  1093. " <tr style=\"text-align: right;\">\n",
  1094. " <th></th>\n",
  1095. " <th>last_close</th>\n",
  1096. " <th>sma50</th>\n",
  1097. " <th>sma100</th>\n",
  1098. " <th>sma200</th>\n",
  1099. " <th>above_all</th>\n",
  1100. " <th>below_all</th>\n",
  1101. " <th>last_flat_bottom</th>\n",
  1102. " <th>t1_flat_bottom</th>\n",
  1103. " <th>last_flat_top</th>\n",
  1104. " <th>t1_flat_top</th>\n",
  1105. " <th>two_flat_bottom</th>\n",
  1106. " <th>two_flat_top</th>\n",
  1107. " <th>good_long</th>\n",
  1108. " <th>good_short</th>\n",
  1109. " </tr>\n",
  1110. " </thead>\n",
  1111. " <tbody>\n",
  1112. " <tr>\n",
  1113. " <th>A</th>\n",
  1114. " <td>128.009995</td>\n",
  1115. " <td>127.3384</td>\n",
  1116. " <td>124.5744</td>\n",
  1117. " <td>133.724999</td>\n",
  1118. " <td>False</td>\n",
  1119. " <td>False</td>\n",
  1120. " <td>False</td>\n",
  1121. " <td>False</td>\n",
  1122. " <td>False</td>\n",
  1123. " <td>True</td>\n",
  1124. " <td>False</td>\n",
  1125. " <td>False</td>\n",
  1126. " <td>False</td>\n",
  1127. " <td>False</td>\n",
  1128. " </tr>\n",
  1129. " <tr>\n",
  1130. " <th>AAL</th>\n",
  1131. " <td>12.990000</td>\n",
  1132. " <td>14.1182</td>\n",
  1133. " <td>15.4952</td>\n",
  1134. " <td>16.4178</td>\n",
  1135. " <td>False</td>\n",
  1136. " <td>True</td>\n",
  1137. " <td>False</td>\n",
  1138. " <td>False</td>\n",
  1139. " <td>False</td>\n",
  1140. " <td>True</td>\n",
  1141. " <td>False</td>\n",
  1142. " <td>False</td>\n",
  1143. " <td>False</td>\n",
  1144. " <td>False</td>\n",
  1145. " </tr>\n",
  1146. " <tr>\n",
  1147. " <th>AAP</th>\n",
  1148. " <td>171.729996</td>\n",
  1149. " <td>187.9036</td>\n",
  1150. " <td>192.8611</td>\n",
  1151. " <td>208.2079</td>\n",
  1152. " <td>False</td>\n",
  1153. " <td>True</td>\n",
  1154. " <td>False</td>\n",
  1155. " <td>False</td>\n",
  1156. " <td>False</td>\n",
  1157. " <td>False</td>\n",
  1158. " <td>False</td>\n",
  1159. " <td>False</td>\n",
  1160. " <td>False</td>\n",
  1161. " <td>False</td>\n",
  1162. " </tr>\n",
  1163. " <tr>\n",
  1164. " <th>AAPL</th>\n",
  1165. " <td>155.809998</td>\n",
  1166. " <td>156.845</td>\n",
  1167. " <td>153.2715</td>\n",
  1168. " <td>161.0885</td>\n",
  1169. " <td>False</td>\n",
  1170. " <td>False</td>\n",
  1171. " <td>False</td>\n",
  1172. " <td>False</td>\n",
  1173. " <td>False</td>\n",
  1174. " <td>True</td>\n",
  1175. " <td>False</td>\n",
  1176. " <td>False</td>\n",
  1177. " <td>False</td>\n",
  1178. " <td>False</td>\n",
  1179. " </tr>\n",
  1180. " <tr>\n",
  1181. " <th>ABBV</th>\n",
  1182. " <td>136.279999</td>\n",
  1183. " <td>145.376</td>\n",
  1184. " <td>148.0243</td>\n",
  1185. " <td>144.24435</td>\n",
  1186. " <td>False</td>\n",
  1187. " <td>True</td>\n",
  1188. " <td>False</td>\n",
  1189. " <td>False</td>\n",
  1190. " <td>False</td>\n",
  1191. " <td>False</td>\n",
  1192. " <td>False</td>\n",
  1193. " <td>False</td>\n",
  1194. " <td>False</td>\n",
  1195. " <td>False</td>\n",
  1196. " </tr>\n",
  1197. " <tr>\n",
  1198. " <th>...</th>\n",
  1199. " <td>...</td>\n",
  1200. " <td>...</td>\n",
  1201. " <td>...</td>\n",
  1202. " <td>...</td>\n",
  1203. " <td>...</td>\n",
  1204. " <td>...</td>\n",
  1205. " <td>...</td>\n",
  1206. " <td>...</td>\n",
  1207. " <td>...</td>\n",
  1208. " <td>...</td>\n",
  1209. " <td>...</td>\n",
  1210. " <td>...</td>\n",
  1211. " <td>...</td>\n",
  1212. " <td>...</td>\n",
  1213. " </tr>\n",
  1214. " <tr>\n",
  1215. " <th>YUM</th>\n",
  1216. " <td>112.739998</td>\n",
  1217. " <td>117.2044</td>\n",
  1218. " <td>116.6597</td>\n",
  1219. " <td>120.96335</td>\n",
  1220. " <td>False</td>\n",
  1221. " <td>True</td>\n",
  1222. " <td>True</td>\n",
  1223. " <td>False</td>\n",
  1224. " <td>False</td>\n",
  1225. " <td>False</td>\n",
  1226. " <td>False</td>\n",
  1227. " <td>False</td>\n",
  1228. " <td>False</td>\n",
  1229. " <td>False</td>\n",
  1230. " </tr>\n",
  1231. " <tr>\n",
  1232. " <th>ZBH</th>\n",
  1233. " <td>105.570000</td>\n",
  1234. " <td>109.213401</td>\n",
  1235. " <td>113.8394</td>\n",
  1236. " <td>117.647967</td>\n",
  1237. " <td>False</td>\n",
  1238. " <td>True</td>\n",
  1239. " <td>False</td>\n",
  1240. " <td>False</td>\n",
  1241. " <td>False</td>\n",
  1242. " <td>True</td>\n",
  1243. " <td>False</td>\n",
  1244. " <td>False</td>\n",
  1245. " <td>False</td>\n",
  1246. " <td>False</td>\n",
  1247. " </tr>\n",
  1248. " <tr>\n",
  1249. " <th>ZBRA</th>\n",
  1250. " <td>297.600006</td>\n",
  1251. " <td>318.251199</td>\n",
  1252. " <td>330.535999</td>\n",
  1253. " <td>414.611649</td>\n",
  1254. " <td>False</td>\n",
  1255. " <td>True</td>\n",
  1256. " <td>False</td>\n",
  1257. " <td>False</td>\n",
  1258. " <td>False</td>\n",
  1259. " <td>False</td>\n",
  1260. " <td>False</td>\n",
  1261. " <td>False</td>\n",
  1262. " <td>False</td>\n",
  1263. " <td>False</td>\n",
  1264. " </tr>\n",
  1265. " <tr>\n",
  1266. " <th>ZION</th>\n",
  1267. " <td>55.040001</td>\n",
  1268. " <td>53.9468</td>\n",
  1269. " <td>55.1171</td>\n",
  1270. " <td>60.8519</td>\n",
  1271. " <td>False</td>\n",
  1272. " <td>False</td>\n",
  1273. " <td>False</td>\n",
  1274. " <td>False</td>\n",
  1275. " <td>False</td>\n",
  1276. " <td>True</td>\n",
  1277. " <td>False</td>\n",
  1278. " <td>False</td>\n",
  1279. " <td>False</td>\n",
  1280. " <td>False</td>\n",
  1281. " </tr>\n",
  1282. " <tr>\n",
  1283. " <th>ZTS</th>\n",
  1284. " <td>156.979996</td>\n",
  1285. " <td>172.122399</td>\n",
  1286. " <td>171.1468</td>\n",
  1287. " <td>189.399199</td>\n",
  1288. " <td>False</td>\n",
  1289. " <td>True</td>\n",
  1290. " <td>False</td>\n",
  1291. " <td>False</td>\n",
  1292. " <td>False</td>\n",
  1293. " <td>False</td>\n",
  1294. " <td>False</td>\n",
  1295. " <td>False</td>\n",
  1296. " <td>False</td>\n",
  1297. " <td>False</td>\n",
  1298. " </tr>\n",
  1299. " </tbody>\n",
  1300. "</table>\n",
  1301. "<p>503 rows × 14 columns</p>\n",
  1302. "</div>\n",
  1303. " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f778fc03-ad9e-4ec3-98de-4b3a4289fd87')\"\n",
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  1364. "\n",
  1365. " const docLinkHtml = 'Like what you see? Visit the ' +\n",
  1366. " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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  1791. " <td>38.75215</td>\n",
  1792. " <td>False</td>\n",
  1793. " <td>True</td>\n",
  1794. " <td>False</td>\n",
  1795. " <td>False</td>\n",
  1796. " <td>True</td>\n",
  1797. " <td>True</td>\n",
  1798. " <td>False</td>\n",
  1799. " <td>True</td>\n",
  1800. " <td>False</td>\n",
  1801. " <td>True</td>\n",
  1802. " </tr>\n",
  1803. " <tr>\n",
  1804. " <th>FIS</th>\n",
  1805. " <td>89.970001</td>\n",
  1806. " <td>96.906</td>\n",
  1807. " <td>98.1052</td>\n",
  1808. " <td>101.71945</td>\n",
  1809. " <td>False</td>\n",
  1810. " <td>True</td>\n",
  1811. " <td>False</td>\n",
  1812. " <td>False</td>\n",
  1813. " <td>True</td>\n",
  1814. " <td>True</td>\n",
  1815. " <td>False</td>\n",
  1816. " <td>True</td>\n",
  1817. " <td>False</td>\n",
  1818. " <td>True</td>\n",
  1819. " </tr>\n",
  1820. " <tr>\n",
  1821. " <th>FLT</th>\n",
  1822. " <td>209.119995</td>\n",
  1823. " <td>220.1844</td>\n",
  1824. " <td>229.4393</td>\n",
  1825. " <td>231.01195</td>\n",
  1826. " <td>False</td>\n",
  1827. " <td>True</td>\n",
  1828. " <td>False</td>\n",
  1829. " <td>False</td>\n",
  1830. " <td>True</td>\n",
  1831. " <td>True</td>\n",
  1832. " <td>False</td>\n",
  1833. " <td>True</td>\n",
  1834. " <td>False</td>\n",
  1835. " <td>True</td>\n",
  1836. " </tr>\n",
  1837. " <tr>\n",
  1838. " <th>HPQ</th>\n",
  1839. " <td>27.639999</td>\n",
  1840. " <td>32.6776</td>\n",
  1841. " <td>34.7818</td>\n",
  1842. " <td>35.70045</td>\n",
  1843. " <td>False</td>\n",
  1844. " <td>True</td>\n",
  1845. " <td>False</td>\n",
  1846. " <td>False</td>\n",
  1847. " <td>True</td>\n",
  1848. " <td>True</td>\n",
  1849. " <td>False</td>\n",
  1850. " <td>True</td>\n",
  1851. " <td>False</td>\n",
  1852. " <td>True</td>\n",
  1853. " </tr>\n",
  1854. " <tr>\n",
  1855. " <th>HRL</th>\n",
  1856. " <td>46.389999</td>\n",
  1857. " <td>48.9204</td>\n",
  1858. " <td>49.3459</td>\n",
  1859. " <td>48.72335</td>\n",
  1860. " <td>False</td>\n",
  1861. " <td>True</td>\n",
  1862. " <td>False</td>\n",
  1863. " <td>False</td>\n",
  1864. " <td>True</td>\n",
  1865. " <td>True</td>\n",
  1866. " <td>False</td>\n",
  1867. " <td>True</td>\n",
  1868. " <td>False</td>\n",
  1869. " <td>True</td>\n",
  1870. " </tr>\n",
  1871. " <tr>\n",
  1872. " <th>HSIC</th>\n",
  1873. " <td>71.260002</td>\n",
  1874. " <td>76.1284</td>\n",
  1875. " <td>79.9454</td>\n",
  1876. " <td>79.9241</td>\n",
  1877. " <td>False</td>\n",
  1878. " <td>True</td>\n",
  1879. " <td>False</td>\n",
  1880. " <td>False</td>\n",
  1881. " <td>True</td>\n",
  1882. " <td>True</td>\n",
  1883. " <td>False</td>\n",
  1884. " <td>True</td>\n",
  1885. " <td>False</td>\n",
  1886. " <td>True</td>\n",
  1887. " </tr>\n",
  1888. " <tr>\n",
  1889. " <th>IQV</th>\n",
  1890. " <td>207.259995</td>\n",
  1891. " <td>225.362001</td>\n",
  1892. " <td>219.561001</td>\n",
  1893. " <td>233.43535</td>\n",
  1894. " <td>False</td>\n",
  1895. " <td>True</td>\n",
  1896. " <td>False</td>\n",
  1897. " <td>False</td>\n",
  1898. " <td>True</td>\n",
  1899. " <td>True</td>\n",
  1900. " <td>False</td>\n",
  1901. " <td>True</td>\n",
  1902. " <td>False</td>\n",
  1903. " <td>True</td>\n",
  1904. " </tr>\n",
  1905. " <tr>\n",
  1906. " <th>LYB</th>\n",
  1907. " <td>80.779999</td>\n",
  1908. " <td>87.351</td>\n",
  1909. " <td>96.7496</td>\n",
  1910. " <td>96.72625</td>\n",
  1911. " <td>False</td>\n",
  1912. " <td>True</td>\n",
  1913. " <td>False</td>\n",
  1914. " <td>False</td>\n",
  1915. " <td>True</td>\n",
  1916. " <td>True</td>\n",
  1917. " <td>False</td>\n",
  1918. " <td>True</td>\n",
  1919. " <td>False</td>\n",
  1920. " <td>True</td>\n",
  1921. " </tr>\n",
  1922. " <tr>\n",
  1923. " <th>MPWR</th>\n",
  1924. " <td>424.420013</td>\n",
  1925. " <td>455.526601</td>\n",
  1926. " <td>439.029101</td>\n",
  1927. " <td>450.458952</td>\n",
  1928. " <td>False</td>\n",
  1929. " <td>True</td>\n",
  1930. " <td>False</td>\n",
  1931. " <td>False</td>\n",
  1932. " <td>True</td>\n",
  1933. " <td>True</td>\n",
  1934. " <td>False</td>\n",
  1935. " <td>True</td>\n",
  1936. " <td>False</td>\n",
  1937. " <td>True</td>\n",
  1938. " </tr>\n",
  1939. " <tr>\n",
  1940. " <th>NSC</th>\n",
  1941. " <td>239.259995</td>\n",
  1942. " <td>242.808199</td>\n",
  1943. " <td>244.047299</td>\n",
  1944. " <td>260.42445</td>\n",
  1945. " <td>False</td>\n",
  1946. " <td>True</td>\n",
  1947. " <td>False</td>\n",
  1948. " <td>False</td>\n",
  1949. " <td>True</td>\n",
  1950. " <td>True</td>\n",
  1951. " <td>False</td>\n",
  1952. " <td>True</td>\n",
  1953. " <td>False</td>\n",
  1954. " <td>True</td>\n",
  1955. " </tr>\n",
  1956. " <tr>\n",
  1957. " <th>NVDA</th>\n",
  1958. " <td>136.470001</td>\n",
  1959. " <td>168.6804</td>\n",
  1960. " <td>175.922</td>\n",
  1961. " <td>221.70725</td>\n",
  1962. " <td>False</td>\n",
  1963. " <td>True</td>\n",
  1964. " <td>False</td>\n",
  1965. " <td>False</td>\n",
  1966. " <td>True</td>\n",
  1967. " <td>True</td>\n",
  1968. " <td>False</td>\n",
  1969. " <td>True</td>\n",
  1970. " <td>False</td>\n",
  1971. " <td>True</td>\n",
  1972. " </tr>\n",
  1973. " <tr>\n",
  1974. " <th>NWL</th>\n",
  1975. " <td>17.520000</td>\n",
  1976. " <td>19.792</td>\n",
  1977. " <td>20.5676</td>\n",
  1978. " <td>21.64345</td>\n",
  1979. " <td>False</td>\n",
  1980. " <td>True</td>\n",
  1981. " <td>False</td>\n",
  1982. " <td>False</td>\n",
  1983. " <td>True</td>\n",
  1984. " <td>True</td>\n",
  1985. " <td>False</td>\n",
  1986. " <td>True</td>\n",
  1987. " <td>False</td>\n",
  1988. " <td>True</td>\n",
  1989. " </tr>\n",
  1990. " <tr>\n",
  1991. " <th>PVH</th>\n",
  1992. " <td>54.580002</td>\n",
  1993. " <td>61.9398</td>\n",
  1994. " <td>65.6222</td>\n",
  1995. " <td>80.47085</td>\n",
  1996. " <td>False</td>\n",
  1997. " <td>True</td>\n",
  1998. " <td>False</td>\n",
  1999. " <td>False</td>\n",
  2000. " <td>True</td>\n",
  2001. " <td>True</td>\n",
  2002. " <td>False</td>\n",
  2003. " <td>True</td>\n",
  2004. " <td>False</td>\n",
  2005. " <td>True</td>\n",
  2006. " </tr>\n",
  2007. " <tr>\n",
  2008. " <th>RL</th>\n",
  2009. " <td>90.010002</td>\n",
  2010. " <td>95.2326</td>\n",
  2011. " <td>97.9254</td>\n",
  2012. " <td>107.68015</td>\n",
  2013. " <td>False</td>\n",
  2014. " <td>True</td>\n",
  2015. " <td>False</td>\n",
  2016. " <td>False</td>\n",
  2017. " <td>True</td>\n",
  2018. " <td>True</td>\n",
  2019. " <td>False</td>\n",
  2020. " <td>True</td>\n",
  2021. " <td>False</td>\n",
  2022. " <td>True</td>\n",
  2023. " </tr>\n",
  2024. " <tr>\n",
  2025. " <th>RTX</th>\n",
  2026. " <td>87.589996</td>\n",
  2027. " <td>93.15</td>\n",
  2028. " <td>94.4544</td>\n",
  2029. " <td>93.18995</td>\n",
  2030. " <td>False</td>\n",
  2031. " <td>True</td>\n",
  2032. " <td>False</td>\n",
  2033. " <td>False</td>\n",
  2034. " <td>True</td>\n",
  2035. " <td>True</td>\n",
  2036. " <td>False</td>\n",
  2037. " <td>True</td>\n",
  2038. " <td>False</td>\n",
  2039. " <td>True</td>\n",
  2040. " </tr>\n",
  2041. " <tr>\n",
  2042. " <th>SEDG</th>\n",
  2043. " <td>267.119995</td>\n",
  2044. " <td>296.899196</td>\n",
  2045. " <td>281.759498</td>\n",
  2046. " <td>285.52135</td>\n",
  2047. " <td>False</td>\n",
  2048. " <td>True</td>\n",
  2049. " <td>False</td>\n",
  2050. " <td>False</td>\n",
  2051. " <td>True</td>\n",
  2052. " <td>True</td>\n",
  2053. " <td>False</td>\n",
  2054. " <td>True</td>\n",
  2055. " <td>False</td>\n",
  2056. " <td>True</td>\n",
  2057. " </tr>\n",
  2058. " <tr>\n",
  2059. " <th>SWK</th>\n",
  2060. " <td>85.769997</td>\n",
  2061. " <td>102.1148</td>\n",
  2062. " <td>112.0977</td>\n",
  2063. " <td>141.003349</td>\n",
  2064. " <td>False</td>\n",
  2065. " <td>True</td>\n",
  2066. " <td>False</td>\n",
  2067. " <td>False</td>\n",
  2068. " <td>True</td>\n",
  2069. " <td>True</td>\n",
  2070. " <td>False</td>\n",
  2071. " <td>True</td>\n",
  2072. " <td>False</td>\n",
  2073. " <td>True</td>\n",
  2074. " </tr>\n",
  2075. " <tr>\n",
  2076. " <th>T</th>\n",
  2077. " <td>17.190001</td>\n",
  2078. " <td>19.1376</td>\n",
  2079. " <td>19.6063</td>\n",
  2080. " <td>18.968898</td>\n",
  2081. " <td>False</td>\n",
  2082. " <td>True</td>\n",
  2083. " <td>False</td>\n",
  2084. " <td>False</td>\n",
  2085. " <td>True</td>\n",
  2086. " <td>True</td>\n",
  2087. " <td>False</td>\n",
  2088. " <td>True</td>\n",
  2089. " <td>False</td>\n",
  2090. " <td>True</td>\n",
  2091. " </tr>\n",
  2092. " <tr>\n",
  2093. " <th>TTWO</th>\n",
  2094. " <td>118.629997</td>\n",
  2095. " <td>126.3804</td>\n",
  2096. " <td>125.426001</td>\n",
  2097. " <td>143.99845</td>\n",
  2098. " <td>False</td>\n",
  2099. " <td>True</td>\n",
  2100. " <td>False</td>\n",
  2101. " <td>False</td>\n",
  2102. " <td>True</td>\n",
  2103. " <td>True</td>\n",
  2104. " <td>False</td>\n",
  2105. " <td>True</td>\n",
  2106. " <td>False</td>\n",
  2107. " <td>True</td>\n",
  2108. " </tr>\n",
  2109. " <tr>\n",
  2110. " <th>TWTR</th>\n",
  2111. " <td>38.630001</td>\n",
  2112. " <td>40.088</td>\n",
  2113. " <td>41.3936</td>\n",
  2114. " <td>40.6522</td>\n",
  2115. " <td>False</td>\n",
  2116. " <td>True</td>\n",
  2117. " <td>False</td>\n",
  2118. " <td>False</td>\n",
  2119. " <td>True</td>\n",
  2120. " <td>True</td>\n",
  2121. " <td>False</td>\n",
  2122. " <td>True</td>\n",
  2123. " <td>False</td>\n",
  2124. " <td>True</td>\n",
  2125. " </tr>\n",
  2126. " <tr>\n",
  2127. " <th>VZ</th>\n",
  2128. " <td>41.299999</td>\n",
  2129. " <td>46.719</td>\n",
  2130. " <td>48.4743</td>\n",
  2131. " <td>50.5009</td>\n",
  2132. " <td>False</td>\n",
  2133. " <td>True</td>\n",
  2134. " <td>False</td>\n",
  2135. " <td>False</td>\n",
  2136. " <td>True</td>\n",
  2137. " <td>True</td>\n",
  2138. " <td>False</td>\n",
  2139. " <td>True</td>\n",
  2140. " <td>False</td>\n",
  2141. " <td>True</td>\n",
  2142. " </tr>\n",
  2143. " <tr>\n",
  2144. " <th>XRAY</th>\n",
  2145. " <td>31.799999</td>\n",
  2146. " <td>35.5014</td>\n",
  2147. " <td>37.6142</td>\n",
  2148. " <td>45.09095</td>\n",
  2149. " <td>False</td>\n",
  2150. " <td>True</td>\n",
  2151. " <td>False</td>\n",
  2152. " <td>False</td>\n",
  2153. " <td>True</td>\n",
  2154. " <td>True</td>\n",
  2155. " <td>False</td>\n",
  2156. " <td>True</td>\n",
  2157. " <td>False</td>\n",
  2158. " <td>True</td>\n",
  2159. " </tr>\n",
  2160. " </tbody>\n",
  2161. "</table>\n",
  2162. "</div>\n",
  2163. " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9637e530-e0a4-45bb-8b13-8abac86d7b02')\"\n",
  2164. " title=\"Convert this dataframe to an interactive table.\"\n",
  2165. " style=\"display:none;\">\n",
  2166. " \n",
  2167. " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
  2168. " width=\"24px\">\n",
  2169. " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
  2170. " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
  2171. " </svg>\n",
  2172. " </button>\n",
  2173. " \n",
  2174. " <style>\n",
  2175. " .colab-df-container {\n",
  2176. " display:flex;\n",
  2177. " flex-wrap:wrap;\n",
  2178. " gap: 12px;\n",
  2179. " }\n",
  2180. "\n",
  2181. " .colab-df-convert {\n",
  2182. " background-color: #E8F0FE;\n",
  2183. " border: none;\n",
  2184. " border-radius: 50%;\n",
  2185. " cursor: pointer;\n",
  2186. " display: none;\n",
  2187. " fill: #1967D2;\n",
  2188. " height: 32px;\n",
  2189. " padding: 0 0 0 0;\n",
  2190. " width: 32px;\n",
  2191. " }\n",
  2192. "\n",
  2193. " .colab-df-convert:hover {\n",
  2194. " background-color: #E2EBFA;\n",
  2195. " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
  2196. " fill: #174EA6;\n",
  2197. " }\n",
  2198. "\n",
  2199. " [theme=dark] .colab-df-convert {\n",
  2200. " background-color: #3B4455;\n",
  2201. " fill: #D2E3FC;\n",
  2202. " }\n",
  2203. "\n",
  2204. " [theme=dark] .colab-df-convert:hover {\n",
  2205. " background-color: #434B5C;\n",
  2206. " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
  2207. " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
  2208. " fill: #FFFFFF;\n",
  2209. " }\n",
  2210. " </style>\n",
  2211. "\n",
  2212. " <script>\n",
  2213. " const buttonEl =\n",
  2214. " document.querySelector('#df-9637e530-e0a4-45bb-8b13-8abac86d7b02 button.colab-df-convert');\n",
  2215. " buttonEl.style.display =\n",
  2216. " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
  2217. "\n",
  2218. " async function convertToInteractive(key) {\n",
  2219. " const element = document.querySelector('#df-9637e530-e0a4-45bb-8b13-8abac86d7b02');\n",
  2220. " const dataTable =\n",
  2221. " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
  2222. " [key], {});\n",
  2223. " if (!dataTable) return;\n",
  2224. "\n",
  2225. " const docLinkHtml = 'Like what you see? Visit the ' +\n",
  2226. " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
  2227. " + ' to learn more about interactive tables.';\n",
  2228. " element.innerHTML = '';\n",
  2229. " dataTable['output_type'] = 'display_data';\n",
  2230. " await google.colab.output.renderOutput(dataTable, element);\n",
  2231. " const docLink = document.createElement('div');\n",
  2232. " docLink.innerHTML = docLinkHtml;\n",
  2233. " element.appendChild(docLink);\n",
  2234. " }\n",
  2235. " </script>\n",
  2236. " </div>\n",
  2237. " </div>\n",
  2238. " "
  2239. ]
  2240. },
  2241. "metadata": {},
  2242. "execution_count": 56
  2243. }
  2244. ]
  2245. }
  2246. ]
  2247. }
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