Installation

1. Install R.

2. Install RStudio.

3. Open RStudio and install devtools package by running install.packages( 'devtools' ). Depending on your platform for devtools to work on Windows install Rtools, on OSX install Xcode, on Linux install r-devel or r-base-dev.

4. Install QuantTools package by running:

   install.packages( QuantTools )
   # or Install QuantTools development version
   # devtools::install_bitbucket( 'quanttools/QuantTools' )

5. Make sure Rcpp package is installed and works. Running these lines should give you 4:

   library( QuantTools )
   Rcpp::evalCpp( code = '2 + 2' )

Congratulations, you are setup now! In case you have any trouble installing the components drop some comments.

Settings

QuantTools package different settings which are very useful if you plan to build backtesting and market data storage system. For complete list of settings run ?QuantTools_settings in RStudio.

Get Market Data

library( QuantTools )
from = '2016-01-01'
to   = '2016-06-01'
symbol = 'AAPL'

get_finam_data( symbol, from, to )

##            date   open   high    low  close  volume
##   1: 2016-01-04 102.48 105.36 102.00 105.34 3891905
##   2: 2016-01-05 105.75 105.78 102.42 102.71 3225980
##  ---                                               
## 103: 2016-06-01  99.00  99.54  98.33  98.43 2299445
## 104: 2016-06-02  97.67  97.84  96.63  97.71 2592315

get_google_data( symbol, from, to )

##            date   open   high    low  close   volume
##   1: 2016-01-04 102.61 105.37 102.00 105.35 67281190
##   2: 2016-01-05 105.75 105.85 102.41 102.71 55790992
##  ---                                                
## 103: 2016-05-31  99.60 100.40  98.82  99.86 42307212
## 104: 2016-06-01  99.02  99.54  98.33  98.46 29173285

get_yahoo_data( symbol, from, to )

##            date   open   high    low  close   volume adj close split_coeff
##   1: 2016-01-04 102.61 105.37 102.00 105.35 67649400 103.58618           1
##   2: 2016-01-05 105.75 105.85 102.41 102.71 55791000 100.99038           1
##  ---                                                                      
## 103: 2016-05-31  99.60 100.40  98.82  99.86 42307200  99.32195           1
## 104: 2016-06-01  99.02  99.54  98.33  98.46 29173300  97.92949           1

get_iqfeed_data( symbol, from, to )

##            date   open    high    low  close
##   1: 2016-01-04 102.61 105.368 102.00 105.35
##   2: 2016-01-05 105.75 105.850 102.41 102.71
##  ---                                        
## 103: 2016-05-31  99.60 100.400  98.82  99.86
## 104: 2016-06-01  99.02  99.540  98.33  98.46

get_finam_data( symbol, from, to, period = 'hour' )

##                     time   open   high    low   close volume
##   1: 2016-01-04 09:00:00 102.48 103.69 102.00 102.700 657731
##   2: 2016-01-04 10:00:00 102.68 103.93 102.44 103.345 918556
##  ---                                                        
## 697: 2016-06-01 14:00:00  98.99  99.18  98.90  99.005 248585
## 698: 2016-06-01 15:00:00  99.01  99.03  98.33  98.430 556923

get_iqfeed_data( symbol, from, to, period = 'hour' )

##                      time   open   high    low  close volume
##    1: 2016-01-04 05:00:00 105.45 105.45 103.40 103.40  35521
##    2: 2016-01-04 06:00:00 103.55 103.77 103.48 103.68   7043
##   ---                                                       
## 1634: 2016-05-31 19:00:00  99.86  99.91  99.85  99.85   4950
## 1635: 2016-05-31 20:00:00  99.85  99.95  99.81  99.95   9200

# most recent non weekend date
from = format( Sys.Date() - switch( format( Sys.Date(), '%u' ), '6' = 1, '7' = 2, 0 ) )

get_finam_data( symbol, from, to = from, period = 'tick' )

##                       time   price volume
##     1: 2016-10-10 09:30:00 115.080    100
##     2: 2016-10-10 09:30:00 115.090    100
##    ---                                   
## 15622: 2016-10-10 15:09:00 116.435    100
## 15623: 2016-10-10 15:09:03 116.440    100

get_iqfeed_data( symbol, from, to = from, period = 'tick' )

##                        time  price volume     size    bid    ask tick_id basis_for_last trade_market_center trade_conditions
##      1: 2016-10-10 04:00:00 114.15     50       50 113.86 113.93    1032              O                  11             8717
##      2: 2016-10-10 04:00:00 114.25    100      150 113.86 113.93    1036              E                  11             173D
##     ---                                                                                                                     
## 164099: 2016-10-10 15:09:40 116.44    103 28967078 116.43 116.44 1320337              C                  19               01
## 164100: 2016-10-10 15:09:40 116.43    103 28967181 116.43 116.44 1320338              C                  19               01

Store Market Data

Tick market data is a lot of information and when downlading from internet it takes not only time but some data providers allows you to download only several month of data. If you want to test your strategies for a longer period you have to store data locally. It is a big problem managing and storing tick market data. QuantTools makes this process easily controlled. You just setup storage parameters and ready to go. The parameters are where, since what date and which symbols you would like to be stored. Any time you can add more symbols and if they are not present in a storage QuantTools tries to get the data from specified start date. Here is an example script which do all that:

symbols = c( 'GAZP', 'SBER', 'VTBR' )
path = paste( path.expand('~') , 'Market Data', 'finam', sep = '/' )
start_date = '2016-01-01'

settings = list(
  finam_storage = path,
  finam_storage_from = start_date,
  finam_symbols = symbols
)
QuantTools_settings( settings )

store_finam_data()

symbols = c( 'AAPL','IBM','PG','WFC','YHOO','GBPUSD.FXCM','EURUSD.FXCM' )
path = paste( path.expand('~') , 'Market Data', 'iqfeed', sep = '/' )
start_date = '2016-01-01'

settings = list(
  iqfeed_storage = path,
  iqfeed_storage_from = start_date,
  iqfeed_symbols = symbols
)
QuantTools_settings( settings )

store_iqfeed_data()

get_iqfeed_data( symbol = 'AAPL', from = '2016-09-08', to = '2016-09-12', period = 'tick', local = T )

Plot Time Series Data

QuantTools provides plot_ts function to plot time series data without weekend, holidays and overnight gaps. Lets visualize some data:

## get data
aapl = get_finam_data( 'AAPL', '2016-01-01', '2016-09-01', period = '15min' )
## add some indicators
aapl[, sma := sma( close, 50 ) ]
aapl[, ema := ema( close, 50 ) ]
## plot
plot_ts( aapl )

To plot smaller intervals use %bw% function:

## plot selected month
plot_ts( aapl[ time %bw% '2016-05' ] )
plot_ts( aapl[ , .( time, sma, ema ) ], col = c( 'red', 'blue' ), add = T )

## plot selected date
plot_ts( aapl[ time %bw% '2016-05-09' ] )
plot_ts( aapl[ , .( time, sma, ema ) ], col = c( 'red', 'blue' ), add = T )

## plot time interval
library( fasttime )
plot_ts( aapl[ time %bw% fastPOSIXct( paste( '2016-05-09', c( '12:00', '13:30') ) ) ] )
plot_ts( aapl[ , .( time, sma, ema ) ], col = c( 'red', 'blue' ), add = T )

Note: for a list of available parameters see ?plot_ts

Back Testing

QuantTools allows you to write trading models using its C++ API. It is a little more code so it has its own section. Please see Examples on top of this site.