{ "metadata": { "name": "", "signature": "sha256:572d7fa8488445eb11225f6bd72f10e7ff71755a779652b82425d596b4469d36" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Prepare data for DAX30 index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are going to analyze some properties of the DAX index." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%load_ext autoreload\n", "%autoreload 2" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "import pandas.io.data as web\n", "import datetime\n", "import pandas.io.pytables\n", "import numpy as np\n", "\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "from collections import OrderedDict" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import display, HTML" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "HTML('')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "" ], "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "dax_symbols = pd.DataFrame({'Symbol': ['ADS.DE', 'ALV.DE', 'BAS.DE', 'BAYN.DE', 'BEI.DE', 'BMW.DE', 'CBK.DE', 'CON.DE', 'DAI.DE', 'DB1.DE'\n", " 'DBK.DE', 'DPW.DE', 'DTE.DE', 'EOAN.DE', 'FME.DE', 'FRE.DE', 'HEI.DE', 'HEN3.DE', 'IFX.DE', 'LHA.DE',\n", " 'LIN.DE', 'LXS.DE', 'MRK.DE', 'MUV2.DE', 'RWE.DE', 'SAP.DE', 'SDF.DE', 'SIE.DE', 'TKA.DE', 'VOW3.DE']})" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get further info on the DAX components" ] }, { "cell_type": "code", "collapsed": false, "input": [ "HTML('')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "" ], "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "dax_info = \"\"\"Name\tBranche\tLogo\tIndexgewichtung in %\t(Letzte) Aufnahme\tSitz\n", "Adidas\tBekleidung\tAdidas-group-logo-fr.svg\t1,92\t22. Juni 1998\tHerzogenaurach\n", "Allianz\tVersicherungen\tAllianz.svg\t6,90\t1. Juli 1988\tM\u00fcnchen\n", "BASF\tChemie\tBASF-Logo bw.svg\t9,82\t1. Juli 1988\tLudwigshafen am Rhein\n", "Bayer\tChemie und Pharma\tBayer-Logo.svg\t10,10\t1. Juli 1988\tLeverkusen\n", "Beiersdorf\tKonsumg\u00fcter\tBeiersdorf Logo.svg\t0,86\t22. Dez. 2008\tHamburg\n", "BMW\tAutomobilproduktion\tBMW.svg\t3,63\t1. Juli 1988\tM\u00fcnchen\n", "Commerzbank\tBanken\tCommerzbank (2009).svg\t1,34\t1. Juli 1988\tFrankfurt am Main\n", "Continental\tAutomobilzulieferer\tContinental AG logo.svg\t2,26\t24. Sep. 2012\tHannover\n", "Daimler\tAutomobilproduktion\tDaimler AG.svg\t8,54\t21. Dez. 19981\tStuttgart\n", "Deutsche Bank\tBanken\tDeutsche Bank logo without wordmark.svg\t3,50\t1. Juli 1988\tFrankfurt am Main\n", "Deutsche Boerse\tBoersen\tDeutsche Boerse Group Logo.svg\t1,25\t23. Dez. 2002\tFrankfurt am Main\n", "Deutsche Post\tTransport\tDeutsche Post.svg\t3,06\t19. M\u00e4r. 2001\tBonn\n", "Deutsche Telekom\tTelekommunikation\tTelekom Logo 2013.svg\t4,75\t18. Nov. 1996\tBonn\n", "E.ON\tEnergieversorgung\tEON Logo.svg\t3,52\t19. Juni 20001\tD\u00fcsseldorf\n", "Fresenius Medical Care\tMedizintechnik\tFresenius Medical Care 20xx logo.svg\t1,22\t20. Sep. 1999\tHof an der Saale\n", "Fresenius\tMedizintechnik\tFresenius.svg\t1,73\t23. M\u00e4r. 2009\tBad Homburg vor der H\u00f6he\n", "HeidelbergCement\tBaustoffe (Zement)\tHeidelbergCement Logo.svg\t1,08\t21. Juni 2010\tHeidelberg\n", "Henkel\tKonsumg\u00fcter\tHenkel-Logo.svg\t1,79\t1. Juli 1988\tD\u00fcsseldorf\n", "Infineon Technologies\tHalbleiter\tInfineon-Logo.svg\t1,25\t21. Sep. 2009\tNeubiberg\n", "K+S\tChemie\tKs logo.svg\t0,51\t22. Sep. 2008\tKassel\n", "Lanxess\tChemie\tLanXess-Logo.svg\t0,50\t24. Sep. 2012\tK\u00f6ln\n", "Linde\tIndustriegase und Anlagenbau\tTheLindeGroup-Logo.svg\t3,35\t1. Juli 1988\tM\u00fcnchen\n", "Lufthansa\tLuftfahrt\tLufthansa-Logo.svg\t0,88\t1. Juli 1988\tK\u00f6ln\n", "Merck\tChemie und Pharma\tMerck-Logo.svg\t1,01\t18. Juni 2007\tDarmstadt\n", "Munich Re\tVersicherungen\tM\u00fcnchener R\u00fcck logo.svg\t3,09\t23. Sep. 1996\tM\u00fcnchen\n", "RWE\tEnergieversorgung\tLogo RWE Claim.svg\t1,87\t1. Juli 1988\tEssen\n", "SAP\tStandardsoftware\tSAP 2011 logo.svg\t6,31\t18. Sep. 1995\tWalldorf (Baden)\n", "Siemens\tElektrotechnik\tSiemens-logo.svg\t9,42\t1. Juli 1988\tBerlin und M\u00fcnchen\n", "ThyssenKrupp\tStahl\tThyssenKruppLogo.svg\t1,10\t25. M\u00e4r. 1999\tDuisburg und Essen\n", "Volkswagen\tAutomobilproduktion\tVWAG-Logo.svg\t3,42\t1. Juli 1988\tWolfsburg\"\"\"\n", "\n", "dax_info_en = \"\"\"Logo\tCompany\tPrime Standard industry group\tTicker symbol\tIndex weighting (%)1\tEmployees\n", "Adidas Logo.svg\tAdidas\tClothing\tADS\t2.04\t86,824\n", "\tAllianz\tInsurance\tALV\t6.66\t151,340\n", "BASF-Logo bw.svg\tBASF\tChemicals\tBAS\t9.62\t111,141\n", "Bayer Logo.svg\tBayer\tPharmaceuticals and Chemicals\tBAYN\t7.55\t111,800\n", "Beiersdorf.svg\tBeiersdorf\tConsumer goods\tBEI\t0.86\t19,130\n", "BMW.svg\tBMW\tManufacturing\tBMW\t3.26\t102,007\n", "\tCommerzbank\tBanking\tCBK\t1.01\t56,221\n", "\tContinental\tManufacturing\tCON\t0.79\t170,000\n", "Daimler AG.svg\tDaimler\tManufacturing\tDAI\t5.77\t267,274\n", "Deutsche Bank logo without wordmark.svg\tDeutsche Bank\tBanking\tDBK\t5.05\t100,474\n", "\tDeutsche Boerse\tSecurities\tDB1\t1.47\t3,588\n", "\tDeutsche Lufthansa\tTransport Aviation\tLHA\t0.83\t118,088\n", "Deutsche Post DHL.svg\tDeutsche Post\tCommunications\tDPW\t1.90\t424,351\n", "Deutsche Telekom.svg\tDeutsche Telekom\tCommunications\tDTE\t5.38\t235,132\n", "Logo E.ON.svg\tE.ON\tEnergy\tEOAN\t6.24\t85,105\n", "Fresenius.svg\tFresenius\tMedical\tFRE\t1.63\t149,351\n", "\tFresenius Medical Care\tMedical\tFME\t2.16\t73,450\n", "HeidelbergCement.svg\tHeidelbergCement\tBuilding\tHEI\t0.86\t53,440\n", "Henkel-Logo.svg\tHenkel\tConsumer goods\tHEN3\t1.53\t47,753\n", "\tInfineon Technologies\tManufacturing\tIFX\t1.27\t26,658\n", "\tK+S\tChemicals\tSDF\t1.18\t15,170\n", "LanXess-Logo.svg\tLanxess\tChemicals\tLXS\t0.73\t14,650\n", "\tLinde\tIndustrial gases\tLIN\t3.81\t62,000\n", "\tMerck\tPharmaceuticals\tMRK\t0.97\t40,676\n", "\tMunich Re\tInsurance\tMUV2\t2.92\t46,915\n", "RWE AG.svg\tRWE\tEnergy\tRWE\t2.19\t70,860\n", "SAP 2011 logo.svg\tSAP\tIT\tSAP\t7.70\t61,344\n", "Siemens AG logo.svg\tSiemens\tIndustrial, electronics\tSIE\t9.96\t405,000\n", "ThyssenKruppLogo.jpg\tThyssenKrupp\tIndustrial, manufacturing\tTKA\t1.33\t180,050\n", "Volkswagen Group.svg\tVolkswagen Group\tManufacturing\tVOW3\t3.36\t549,763\"\"\"\n", "\n", "from StringIO import StringIO\n", "df_dax_info = pd.read_table(StringIO(dax_info_en))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 38 }, { "cell_type": "code", "collapsed": false, "input": [ "df_dax_info" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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LogoCompanyPrime Standard industry groupTicker symbolIndex weighting (%)1Employees
0 Adidas Logo.svg Adidas Clothing ADS 2.04 86,824
1 NaN Allianz Insurance ALV 6.66 151,340
2 BASF-Logo bw.svg BASF Chemicals BAS 9.62 111,141
3 Bayer Logo.svg Bayer Pharmaceuticals and Chemicals BAYN 7.55 111,800
4 Beiersdorf.svg Beiersdorf Consumer goods BEI 0.86 19,130
5 BMW.svg BMW Manufacturing BMW 3.26 102,007
6 NaN Commerzbank Banking CBK 1.01 56,221
7 NaN Continental Manufacturing CON 0.79 170,000
8 Daimler AG.svg Daimler Manufacturing DAI 5.77 267,274
9 Deutsche Bank logo without wordmark.svg Deutsche Bank Banking DBK 5.05 100,474
10 NaN Deutsche Boerse Securities DB1 1.47 3,588
11 NaN Deutsche Lufthansa Transport Aviation LHA 0.83 118,088
12 Deutsche Post DHL.svg Deutsche Post Communications DPW 1.90 424,351
13 Deutsche Telekom.svg Deutsche Telekom Communications DTE 5.38 235,132
14 Logo E.ON.svg E.ON Energy EOAN 6.24 85,105
15 Fresenius.svg Fresenius Medical FRE 1.63 149,351
16 NaN Fresenius Medical Care Medical FME 2.16 73,450
17 HeidelbergCement.svg HeidelbergCement Building HEI 0.86 53,440
18 Henkel-Logo.svg Henkel Consumer goods HEN3 1.53 47,753
19 NaN Infineon Technologies Manufacturing IFX 1.27 26,658
20 NaN K+S Chemicals SDF 1.18 15,170
21 LanXess-Logo.svg Lanxess Chemicals LXS 0.73 14,650
22 NaN Linde Industrial gases LIN 3.81 62,000
23 NaN Merck Pharmaceuticals MRK 0.97 40,676
24 NaN Munich Re Insurance MUV2 2.92 46,915
25 RWE AG.svg RWE Energy RWE 2.19 70,860
26 SAP 2011 logo.svg SAP IT SAP 7.70 61,344
27 Siemens AG logo.svg Siemens Industrial, electronics SIE 9.96 405,000
28 ThyssenKruppLogo.jpg ThyssenKrupp Industrial, manufacturing TKA 1.33 180,050
29 Volkswagen Group.svg Volkswagen Group Manufacturing VOW3 3.36 549,763
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 39, "text": [ " Logo Company \\\n", "0 Adidas Logo.svg Adidas \n", "1 NaN Allianz \n", "2 BASF-Logo bw.svg BASF \n", "3 Bayer Logo.svg Bayer \n", "4 Beiersdorf.svg Beiersdorf \n", "5 BMW.svg BMW \n", "6 NaN Commerzbank \n", "7 NaN Continental \n", "8 Daimler AG.svg Daimler \n", "9 Deutsche Bank logo without wordmark.svg Deutsche Bank \n", "10 NaN Deutsche Boerse \n", "11 NaN Deutsche Lufthansa \n", "12 Deutsche Post DHL.svg Deutsche Post \n", "13 Deutsche Telekom.svg Deutsche Telekom \n", "14 Logo E.ON.svg E.ON \n", "15 Fresenius.svg Fresenius \n", "16 NaN Fresenius Medical Care \n", "17 HeidelbergCement.svg HeidelbergCement \n", "18 Henkel-Logo.svg Henkel \n", "19 NaN Infineon Technologies \n", "20 NaN K+S \n", "21 LanXess-Logo.svg Lanxess \n", "22 NaN Linde \n", "23 NaN Merck \n", "24 NaN Munich Re \n", "25 RWE AG.svg RWE \n", "26 SAP 2011 logo.svg SAP \n", "27 Siemens AG logo.svg Siemens \n", "28 ThyssenKruppLogo.jpg ThyssenKrupp \n", "29 Volkswagen Group.svg Volkswagen Group \n", "\n", " Prime Standard industry group Ticker symbol Index weighting (%)1 \\\n", "0 Clothing ADS 2.04 \n", "1 Insurance ALV 6.66 \n", "2 Chemicals BAS 9.62 \n", "3 Pharmaceuticals and Chemicals BAYN 7.55 \n", "4 Consumer goods BEI 0.86 \n", "5 Manufacturing BMW 3.26 \n", "6 Banking CBK 1.01 \n", "7 Manufacturing CON 0.79 \n", "8 Manufacturing DAI 5.77 \n", "9 Banking DBK 5.05 \n", "10 Securities DB1 1.47 \n", "11 Transport Aviation LHA 0.83 \n", "12 Communications DPW 1.90 \n", "13 Communications DTE 5.38 \n", "14 Energy EOAN 6.24 \n", "15 Medical FRE 1.63 \n", "16 Medical FME 2.16 \n", "17 Building HEI 0.86 \n", "18 Consumer goods HEN3 1.53 \n", "19 Manufacturing IFX 1.27 \n", "20 Chemicals SDF 1.18 \n", "21 Chemicals LXS 0.73 \n", "22 Industrial gases LIN 3.81 \n", "23 Pharmaceuticals MRK 0.97 \n", "24 Insurance MUV2 2.92 \n", "25 Energy RWE 2.19 \n", "26 IT SAP 7.70 \n", "27 Industrial, electronics SIE 9.96 \n", "28 Industrial, manufacturing TKA 1.33 \n", "29 Manufacturing VOW3 3.36 \n", "\n", " Employees \n", "0 86,824 \n", "1 151,340 \n", "2 111,141 \n", "3 111,800 \n", "4 19,130 \n", "5 102,007 \n", "6 56,221 \n", "7 170,000 \n", "8 267,274 \n", "9 100,474 \n", "10 3,588 \n", "11 118,088 \n", "12 424,351 \n", "13 235,132 \n", "14 85,105 \n", "15 149,351 \n", "16 73,450 \n", "17 53,440 \n", "18 47,753 \n", "19 26,658 \n", "20 15,170 \n", "21 14,650 \n", "22 62,000 \n", "23 40,676 \n", "24 46,915 \n", "25 70,860 \n", "26 61,344 \n", "27 405,000 \n", "28 180,050 \n", "29 549,763 " ] } ], "prompt_number": 39 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copy the symbols table from the Yahoo! Finance page above and read them into a Pandas DataFrame" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df_dax_info['Symbol'] = df_dax_info['Ticker symbol'].map(lambda x: x + \".DE\")\n", "df_dax = pd.merge( df_dax_info, dax_symbols, left_on='Symbol', right_on='Symbol')\n", "\n", "with pd.io.pytables.get_store('dax30.h5') as store:\n", "# store.remove('dax')\n", " store.append('dax', df_dax)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [ "### NASDAQ100\n", "### http://finance.yahoo.com/q/cp?s=%5ENDX" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Retrieve the stock prices from Yahoo!" ] }, { "cell_type": "code", "collapsed": false, "input": [ "start = datetime.datetime(2000, 1, 1)\n", "end = datetime.datetime(2014, 1, 1)\n", "\n", "stx = web.DataReader(df_dax.Symbol.tolist(), data_source=\"yahoo\", start=start, end=end)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 41 }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fill in missing closing prices by copying the previous value" ] }, { "cell_type": "code", "collapsed": true, "input": [ "stx['Adj Close'] = stx.ix['Adj Close'].fillna(method='ffill', axis=0, limit=1)\n", "# stx['Close'] = stx.loc['Close'].fillna(method='ffill', axis=0, limit=3)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 42 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the daily returns.\n", "\n", "$$ r(t) = log\\left( \\frac{p(t)}{p(t-1)} \\right)$$" ] }, { "cell_type": "code", "collapsed": false, "input": [ "stx['Returns'] = np.log( stx.loc['Adj Close'] / stx.loc['Adj Close'].shift(1) )" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 43 }, { "cell_type": "code", "collapsed": false, "input": [ "stx.Returns.ix[datetime.datetime(2008,1,1):][stx.Returns['VOW3.DE'].isnull()]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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Date
2008-01-01 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000... 0.000000 NaN 0.000000 0.000000 NaN 0.000000 0.000000 0.000000 0.000000NaN
2008-01-02-0.024964-0.013828-0.004819-0.017357-0.011592 0.008921-0.014174-0.027002-0.027774-0.012551...-0.002602 NaN-0.009738-0.001339 NaN-0.018394 0.027944-0.014572 0.000651NaN
2008-03-24 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000... 0.000000 NaN 0.000000 0.000000 NaN 0.000000 0.000000 0.000000 0.000000NaN
2008-03-25 0.059161 0.063979 0.021526 0.042070 0.005485 0.034733 0.085254 0.051949 0.033861 0.017208... 0.016638 NaN 0.023906 0.037229 NaN 0.043075 0.086051 0.006614 0.008448NaN
2008-07-30 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN... NaN NaN NaN NaN NaN NaN NaN NaN NaNNaN
2008-07-31 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN... NaN NaN NaN NaN NaN NaN NaN NaN NaNNaN
2008-08-01 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN... NaN NaN NaN NaN NaN NaN NaN NaN NaNNaN
2008-08-04 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN... NaN NaN NaN NaN NaN NaN NaN NaN NaNNaN
2008-08-05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN... NaN NaN NaN NaN NaN NaN NaN NaN NaNNaN
2008-08-06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN... NaN NaN NaN NaN NaN NaN NaN NaN NaNNaN
2008-08-07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN... NaN NaN NaN NaN NaN NaN NaN NaN NaNNaN
2008-08-08 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN... NaN NaN NaN NaN NaN NaN NaN NaN NaNNaN
2008-08-11 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN... NaN NaN NaN NaN NaN NaN NaN NaN NaNNaN
2008-08-12 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN... NaN NaN NaN NaN NaN NaN NaN NaN NaNNaN
2008-08-13 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN... NaN NaN NaN NaN NaN NaN NaN NaN NaNNaN
2008-08-14 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN... NaN NaN NaN NaN NaN NaN NaN NaN NaNNaN
2008-08-15 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN... NaN NaN NaN NaN NaN NaN NaN NaN NaNNaN
2008-08-18 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN... NaN NaN NaN NaN NaN NaN NaN NaN NaNNaN
2008-12-25 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000NaN
2008-12-26 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000NaN
2008-12-29 0.008490 0.015703 0.032535 0.028312 0.016954 0.002039 0.028874-0.017362 0.028456 0.024853... 0.000735 0.040478 0.036978 0.003407 0.024533 0.004405 0.049660 0.016151 0.017072NaN
2008-12-30 0.028965 0.008009 0.031973 0.028596 0.006284 0.014156 0.021310-0.014465 0.040993 0.040779...-0.000735 0.055458 0.015596 0.023986 0.016446 0.041753 0.027268 0.019653 0.025074NaN
2008-12-31 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000NaN
2009-01-01 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000NaN
2009-01-02 0.024340 0.028185 0.005998 0.030385 0.006245 0.033567 0.018650 0.052944 0.038501-0.020705... 0.056607 0.027758 0.021146 0.013646 0.036144 0.026624 0.032468 0.052301 0.053766NaN
2009-01-05 0.002668-0.002751-0.011101 0.000258 0.030654-0.022087-0.031534 0.001684-0.040328-0.013692... 0.010363 0.012845 0.011478-0.014104 0.031566 0.013861 0.020986-0.010738-0.002410NaN
2009-01-06 0.046122-0.049828-0.003728 0.033231 0.012997-0.003480-0.024712-0.001010 0.032834 0.013692... 0.015684-0.009050 0.002138-0.013957-0.011004 0.010872 0.019524 0.022903 0.026779NaN
2009-01-07-0.012436-0.037478-0.037094-0.012048-0.026675 0.011881-0.048868-0.032868-0.003102-0.030804...-0.048342-0.033118 0.017287 0.006715-0.021167 0.004794 0.001288-0.019086-0.070546NaN
2009-01-08-0.012593-0.055807 0.006279 0.006544-0.037681-0.006418-0.148420 0.051224-0.011158-0.036248... 0.029388-0.015786 0.014584 0.007357-0.002627 0.016601-0.016353-0.040001-0.003788NaN
2009-01-09-0.031424 0.060042-0.035779 0.001755-0.013062 0.022044-0.117069-0.039459 0.003137-0.046946...-0.068488 0.000000-0.001725 0.022648-0.041944 0.010916-0.047979-0.042397-0.032136NaN
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252 rows \u00d7 28 columns

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\n", "... ... ... ... \n", "2009-10-09 0.003682 -0.003044 NaN \n", "2009-10-12 0.026117 0.011691 NaN \n", "2009-10-13 -0.012611 0.011556 NaN \n", "2009-10-14 0.050205 0.036763 NaN \n", "2009-10-15 -0.007963 0.013040 NaN \n", "2009-10-16 -0.028022 -0.022519 NaN \n", "2009-10-19 0.017540 0.008658 NaN \n", "2009-10-20 -0.007050 -0.010731 NaN \n", "2009-10-21 0.002297 -0.004575 NaN \n", "2009-10-22 -0.019961 -0.027468 NaN \n", "2009-10-23 -0.008134 0.004702 NaN \n", "2009-10-26 -0.013521 -0.016771 NaN \n", "2009-10-27 0.005869 -0.023696 NaN \n", "2009-10-28 -0.030452 -0.062764 NaN \n", "2009-10-29 0.022372 0.027514 NaN \n", "2009-10-30 -0.048541 -0.032253 NaN \n", "2009-11-02 0.004441 0.008986 NaN \n", "2009-11-03 -0.018670 -0.016137 NaN \n", "2009-11-04 0.012873 0.031091 NaN \n", "2009-11-05 0.012518 0.008774 NaN \n", "2009-11-06 -0.003067 0.010519 NaN \n", "2009-11-09 0.027643 0.025602 NaN \n", "2009-11-10 -0.001869 -0.009804 NaN \n", "2009-11-11 0.002056 0.019952 NaN \n", "2009-11-12 0.000560 -0.006165 NaN \n", "2009-11-13 0.001678 0.012292 NaN \n", "2013-12-25 0.000000 0.000000 NaN \n", "2013-12-26 0.000000 0.000000 NaN \n", "2013-12-27 0.011901 0.014455 NaN \n", "2014-01-01 0.000000 0.000000 NaN \n", "\n", "[252 rows x 28 columns]" ] } ], "prompt_number": 44 }, { "cell_type": "code", "collapsed": false, "input": [ "with pd.io.pytables.get_store('dax30.h5') as store:\n", " #store.remove('stx')\n", " store.append('stx', stx)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 45 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Explore the data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.html.widgets import interact, interactive, fixed\n", "from bokeh import mpl as mplbkh\n", "from bokeh.plotting import output_notebook, line, show\n", "output_notebook()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ " \n", " \n", " \n", "
\n", " \n", " BokehJS successfully loaded.\n", "
\n", "

Warning: BokehJS previously loaded

" ], "metadata": {}, "output_type": "display_data" } ], "prompt_number": 46 }, { "cell_type": "code", "collapsed": false, "input": [ "dfclose = stx['Close']\n", "dfclose.ix[pd.Timestamp('20080807'):pd.Timestamp('20080815')].isnull().any(axis=1) #.all(axis=1)\n", "# dfclose.fillna(method='ffill', limit=0).isnull().any(axis=1) #.plot(kind='area')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 47, "text": [ "Date\n", "2008-08-07 True\n", "2008-08-08 True\n", "2008-08-11 True\n", "2008-08-12 True\n", "2008-08-13 True\n", "2008-08-14 True\n", "2008-08-15 True\n", "dtype: bool" ] } ], "prompt_number": 47 }, { "cell_type": "markdown", "metadata": {}, "source": [ "For info on the difference between \"Close\" and \"Adj Close\", take a look at this [discussion thread](http://quant.stackexchange.com/questions/942/any-known-bugs-with-yahoo-finance-adjusted-close-data) or this [entry]()" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_close(stock):\n", " ig, axes = plt.subplots(nrows=2, ncols=1)\n", " stx.ix['Adj Close'][stock].plot(legend=True, ax=axes[0], x_compat=True, figsize=(14, 6))\n", " stx.ix['Close'][stock].plot(legend=True, ax=axes[0], x_compat=True, figsize=(14, 6))\n", " \n", " axes[0].set_title('Closing price')\n", " axes[0].set_ylabel('Price')\n", " stx.ix['Returns'][stock].plot(legend=False, ax=axes[1], x_compat=True, figsize=(14, 6))\n", " axes[1].set_title('Log returns')\n", "\n", "names_dict = OrderedDict(df_dax_info[['Symbol', 'Company']].set_index('Company').to_records() )\n", "interact(plot_close, stock=names_dict)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 48, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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xaXGmiI4OJju7+HQ3Q5yEk3l2u/P2Ol53jVBUW6vRBfsAqKg0k5VVxD1LHm22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\nNOsi4O3SEwfmUN1Kye/vprO74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wqCUKw65kYANwFoBPB/giD8xPM8B6AQgOA5bLkgCDOM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\nBpwM8k9CTDjqGpzBbgaDwWAwWimtQvhoFY1gMBitG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"text": [ "" ] } ], "prompt_number": 48 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Store the stock values in an HDFStore" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Checking that it can be retrieved." ] }, { "cell_type": "code", "collapsed": false, "input": [ "with pd.io.pytables.get_store('dax30.h5') as store:\n", " ret = store.select('stx').Returns\n", " stx = store.select('stx')\n", "\n", "# ret.plot(legend=False, figsize=(14,6))\n", "# ret.hist(bins=100)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/home/wiecki/envs/zipline_p14/lib/python2.7/site-packages/pandas/io/pytables.py:3535: DuplicateWarning: \n", "duplicate entries in table, taking most recently appended\n", "\n", " warnings.warn(duplicate_doc, DuplicateWarning)\n" ] } ], "prompt_number": 49 } ], "metadata": {} } ] }