Scraping
pam
2023-07-22
Instalación
install.packages("quantmod")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.3'
## (as 'lib' is unspecified)Ejecución de la libreria
library("quantmod")## Loading required package: xts## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric## Loading required package: TTR## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zooScraping a Apple
apple_df <- getSymbols('AAPL', src='yahoo', auto.assign=FALSE)apple_df## AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume
## 2007-01-03 3.081786 3.092143 2.925000 2.992857 1238319600
## 2007-01-04 3.001786 3.069643 2.993571 3.059286 847260400
## 2007-01-05 3.063214 3.078571 3.014286 3.037500 834741600
## 2007-01-08 3.070000 3.090357 3.045714 3.052500 797106800
## 2007-01-09 3.087500 3.320714 3.041071 3.306071 3349298400
## 2007-01-10 3.383929 3.492857 3.337500 3.464286 2952880000
## 2007-01-11 3.426429 3.456429 3.396429 3.421429 1440252800
## 2007-01-12 3.378214 3.395000 3.329643 3.379286 1312690400
## 2007-01-16 3.417143 3.473214 3.408929 3.467857 1244076400
## 2007-01-17 3.484286 3.485714 3.386429 3.391071 1646260000
## ...
## 2023-07-17 191.899994 194.320007 191.809998 193.990005 50520200
## 2023-07-18 193.350006 194.330002 192.419998 193.729996 48353800
## 2023-07-19 193.100006 198.229996 192.649994 195.100006 80507300
## 2023-07-20 195.089996 196.470001 192.500000 193.130005 59581200
## 2023-07-21 194.100006 194.970001 191.229996 191.940002 71917800
## 2023-07-24 193.410004 194.910004 192.250000 192.750000 45377800
## 2023-07-25 193.330002 194.440002 192.919998 193.619995 37283200
## 2023-07-26 193.669998 195.639999 193.320007 194.500000 47471900
## 2023-07-27 196.020004 197.199997 192.550003 193.220001 47460200
## 2023-07-28 194.669998 196.630005 194.139999 195.830002 48254600
## AAPL.Adjusted
## 2007-01-03 2.543757
## 2007-01-04 2.600218
## 2007-01-05 2.581702
## 2007-01-08 2.594450
## 2007-01-09 2.809970
## 2007-01-10 2.944445
## 2007-01-11 2.908019
## 2007-01-12 2.872200
## 2007-01-16 2.947480
## 2007-01-17 2.882216
## ...
## 2023-07-17 193.990005
## 2023-07-18 193.729996
## 2023-07-19 195.100006
## 2023-07-20 193.130005
## 2023-07-21 191.940002
## 2023-07-24 192.750000
## 2023-07-25 193.619995
## 2023-07-26 194.500000
## 2023-07-27 193.220001
## 2023-07-28 195.830002Grafica
chartSeries(apple_df, name="AAPL", subset="last 6 months", theme=chartTheme("white"))
La tendencia de esta grafica nos dice que es altista quiere decir que
tiende mas a subir
Scraping a Microsoft
apple_df <- getSymbols('MSFT', src='yahoo', auto.assign=FALSE)apple_df## MSFT.Open MSFT.High MSFT.Low MSFT.Close MSFT.Volume MSFT.Adjusted
## 2007-01-03 29.91 30.25 29.40 29.86 76935100 21.52599
## 2007-01-04 29.70 29.97 29.44 29.81 45774500 21.48994
## 2007-01-05 29.63 29.75 29.45 29.64 44607200 21.36738
## 2007-01-08 29.65 30.10 29.53 29.93 50220200 21.57645
## 2007-01-09 30.00 30.18 29.73 29.96 44636600 21.59807
## 2007-01-10 29.80 29.89 29.43 29.66 55017400 21.38181
## 2007-01-11 29.76 30.75 29.65 30.70 99464300 22.13154
## 2007-01-12 30.65 31.39 30.64 31.21 103972500 22.49919
## 2007-01-16 31.26 31.45 31.03 31.16 62379600 22.46315
## 2007-01-17 31.26 31.44 31.01 31.10 58519600 22.41990
## ...
## 2023-07-17 345.68 346.99 342.20 345.73 20363900 345.73001
## 2023-07-18 345.83 366.78 342.17 359.49 64872700 359.48999
## 2023-07-19 361.75 362.46 352.44 355.08 39732900 355.07999
## 2023-07-20 353.57 357.97 345.37 346.87 33778400 346.87000
## 2023-07-21 349.15 350.30 339.83 343.77 69368900 343.76999
## 2023-07-24 345.85 346.92 342.31 345.11 26678100 345.10999
## 2023-07-25 347.11 351.89 345.07 350.98 41637700 350.98001
## 2023-07-26 341.44 344.67 333.11 337.77 58383700 337.76999
## 2023-07-27 340.48 341.33 329.05 330.72 39635300 330.72000
## 2023-07-28 333.67 340.01 333.17 338.37 28463000 338.37000Grafica
chartSeries(apple_df, name="MSFT", subset="last 6 months", theme=chartTheme("white"))
Su tendencia igual es altista pero con mayor bajas
Scraping a Sony
apple_df <- getSymbols('SONY', src='yahoo', auto.assign=FALSE)apple_df## SONY.Open SONY.High SONY.Low SONY.Close SONY.Volume SONY.Adjusted
## 2007-01-03 42.90 43.37 42.73 42.91 1200600 42.34515
## 2007-01-04 43.18 43.88 43.12 43.80 1209600 43.22343
## 2007-01-05 43.98 45.60 43.96 44.80 3197500 44.21027
## 2007-01-08 44.81 45.33 44.43 44.81 2344300 44.22013
## 2007-01-09 46.25 47.00 45.80 46.40 1731000 45.78920
## 2007-01-10 45.65 45.95 45.38 45.78 826200 45.17737
## 2007-01-11 45.27 45.99 45.26 45.65 958400 45.04908
## 2007-01-12 46.50 47.85 46.40 47.68 4216400 47.05235
## 2007-01-16 47.22 47.32 46.78 47.00 1498500 46.38131
## 2007-01-17 47.12 47.90 47.00 47.54 1656400 46.91420
## ...
## 2023-07-17 92.53 94.29 92.37 93.57 769700 93.57000
## 2023-07-18 94.86 95.42 94.58 95.10 967100 95.10000
## 2023-07-19 94.76 95.00 94.13 94.57 539100 94.57000
## 2023-07-20 93.29 93.86 93.18 93.25 650700 93.25000
## 2023-07-21 93.82 93.96 93.28 93.39 425700 93.39000
## 2023-07-24 93.39 93.92 93.26 93.66 386200 93.66000
## 2023-07-25 93.03 93.79 92.80 93.69 437300 93.69000
## 2023-07-26 93.39 94.07 93.20 93.74 374700 93.74000
## 2023-07-27 94.70 95.19 93.59 93.76 686100 93.76000
## 2023-07-28 93.77 94.17 93.40 93.47 679800 93.47000Grafica
chartSeries(apple_df, name="SONY", subset="last 6 months", theme=chartTheme("white"))
La tendencia de esta grafica es muy volatil ya que sus velitas suben y bajan
Scraping a Tesla
apple_df <- getSymbols('TSLA', src='yahoo', auto.assign=FALSE)apple_df## TSLA.Open TSLA.High TSLA.Low TSLA.Close TSLA.Volume
## 2010-06-29 1.266667 1.666667 1.169333 1.592667 281494500
## 2010-06-30 1.719333 2.028000 1.553333 1.588667 257806500
## 2010-07-01 1.666667 1.728000 1.351333 1.464000 123282000
## 2010-07-02 1.533333 1.540000 1.247333 1.280000 77097000
## 2010-07-06 1.333333 1.333333 1.055333 1.074000 103003500
## 2010-07-07 1.093333 1.108667 0.998667 1.053333 103825500
## 2010-07-08 1.076000 1.168000 1.038000 1.164000 115671000
## 2010-07-09 1.172000 1.193333 1.103333 1.160000 60759000
## 2010-07-12 1.196667 1.204667 1.133333 1.136667 33037500
## 2010-07-13 1.159333 1.242667 1.126667 1.209333 40201500
## ...
## 2023-07-17 286.630005 292.230011 283.570007 290.380005 131569600
## 2023-07-18 290.149994 295.260010 286.010010 293.339996 112434700
## 2023-07-19 296.040009 299.290009 289.519989 291.260010 142355400
## 2023-07-20 279.559998 280.929993 261.200012 262.899994 175158300
## 2023-07-21 268.000000 268.000000 255.800003 260.019989 161050100
## 2023-07-24 255.850006 269.850006 254.119995 269.059998 136508500
## 2023-07-25 272.380005 272.899994 265.000000 265.279999 112757300
## 2023-07-26 263.250000 268.040009 261.750000 264.350006 95856200
## 2023-07-27 268.309998 269.130005 255.300003 255.710007 103697300
## 2023-07-28 259.859985 267.250000 258.230011 266.440002 111149300
## TSLA.Adjusted
## 2010-06-29 1.592667
## 2010-06-30 1.588667
## 2010-07-01 1.464000
## 2010-07-02 1.280000
## 2010-07-06 1.074000
## 2010-07-07 1.053333
## 2010-07-08 1.164000
## 2010-07-09 1.160000
## 2010-07-12 1.136667
## 2010-07-13 1.209333
## ...
## 2023-07-17 290.380005
## 2023-07-18 293.339996
## 2023-07-19 291.260010
## 2023-07-20 262.899994
## 2023-07-21 260.019989
## 2023-07-24 269.059998
## 2023-07-25 265.279999
## 2023-07-26 264.350006
## 2023-07-27 255.710007
## 2023-07-28 266.440002Grafica
chartSeries(apple_df, name="TSLA", subset="last 6 months", theme=chartTheme("white"))
La tendecia de esta grafica esta llegando a su punto maximo que en algun momento su tendencia puede bajar mas