Mariner Backtesting - md.bar.daily()

Return a daily bars slice structure that goes back one or more market day(s).

Interface:

 daily(start=-1,
      end=None,
      include_empty=False)

Sample - Calling method

 md.bar.daily(start=-10)

Parameters:

Name	Type	Default	Information
start	integer or muts	-1	Offset. May be a negative integer as a reference to now (e.g. -10 ... 10 days ago), or timestamp to a specific date. Negative integer. This represents 'trading days back from now'. Timestamp. Usually computed by service.time() or service.time_interval().
end	integer or muts	None	Offset. May be a negative integer as a reference to now (e.g. -10 ... 10 days ago), a timestamp to a specific date, or None. A value of None indicates to include the most-recently-formed bar. Negative integer. This represents 'trading days back from now'. The default is None, which represents the most-recently formed daily-bar. Timestamp. Usually computed by service.time() or service.time_interval().
include_empty	boolean	False	If True, return bars for empty days instead of skipping over them, and check the "valid" property on the resulting bar slice to verify whether a bar is empty or not. False - don't return empty bar data True - return NaN if no trades happened in bar.

Returns:

Daily BarView - tuple of mostly arrays

Daily BarView Attributes:

Name	Type	Information
askvol	Array[integer]	items in the array - the volume of trades that occurred at the ask during timeframe
bidvol	Array[integer]	the volume of trades that occurred at the bid during timeframe
bvwap	Array[float]	the vwap for the bar
close	Array[float]	final, or closing price for the bar
closing_trade	Array[float]	closing price for the symbol's primary exchange
count	Array[integer]	number of items contributing to the bar
high	Array[float]	highest price for the bar
length	integer	time length for the bar
low	Array[float]	lowest price for the bar
open	Array[float]	first qualifying trade of the bar
opening_trade	Array[float]	opening price for the symbol's primary exchange
spread	Array[float]	average difference between the ask and the bid for the bar
symbol	string	symbol for the bar
timestamp	Array[integer]	timestamps for the beginning of each bar
valid	Array[bool]	whether or not bar is valid
volume	Array[integer]	volume for the bar
vwap	Array[float]	volume weighted average price(vwap) of the bar

Related Links:

There are a lot of bar examples under Knowledge.

Remarks:

Bars are formed - not forming. Forming data is part of L1.
Daily bar data is static information (it doesn't change during the trading day). You should only need to request a symbol's daily bar data once in a script (example - in on_start() or in a function )
When using this method, it may help to think of the daily bars as snapshots of trading data from previous days relative to the current simulation day.
By default, if there is no market data on a given day between start and end, the returned array will contain fewer items than expected. To retrieve the exact number of daily bars requested, set include_empty=True. When include_empty=True, the valid property (see below) provides a boolean which indicates if the bar was empty (valid == True means "not empty").
The returned slice can be accessed via .property, where property may be one of the following: symbol, length, timestamp, open, high, low, close, volume, vwap, bvwap, spread, bidvol, askvol, count, avgdelta, valid.
When using include_empty, be aware the max() and min() and the first item in the array is NaN can cause the return value to be NaN even if there are other values besides NaN. See http://stackoverflow.com/questions/4237914/python-max-min-builtin-functions-depend-on-parameter-order
At the moment one any given testing/trading day, 1 year of daily bars is available (starting 1/1/11).

Working Example:

 import itertools
from cloudquant.interfaces import Strategy


class DailyBarsExample(Strategy):
    @classmethod
    def is_symbol_qualified(cls, symbol, md, service, account):
        return symbol == 'TQQQ'


    def on_start(self, md, order, service, account):
        # get bars
        bars = md.bar.daily(start=-21)

        print '\nget the last high value'
        print 'bars.high[-1]\t-\t', bars.high[-1]

        print '\nget the first low value'
        print 'bars.low[0]\t-\t', bars.low[0]


        # print the bar data
        fields = bars._fields
        for key, value in enumerate(bars):
            print '\n\n', fields[key], '\n', value

        service.terminate()

Console

get the last high value
bars.high[-1]   -   104.449996948

get the first low value
bars.low[0] -   93.6299972534


symbol 
['AAPL' 'AAPL' 'AAPL' 'AAPL' 'AAPL' 'AAPL' 'AAPL' 'AAPL' 'AAPL' 'AAPL'
 'AAPL' 'AAPL' 'AAPL' 'AAPL' 'AAPL' 'AAPL' 'AAPL' 'AAPL' 'AAPL' 'AAPL'
 'AAPL']


length 
[23400 23400 23400 23400 23400 23400 23400 23400 23400 23400 23400 23400
 23400 23400 23400 23400 23400 23400 23400 23400 23400]


timestamp 
[1467207000000000 1467293400000000 1467379800000000 1467725400000000
 1467811800000000 1467898200000000 1467984600000000 1468243800000000
 1468330200000000 1468416600000000 1468503000000000 1468589400000000
 1468848600000000 1468935000000000 1469021400000000 1469107800000000
 1469194200000000 1469453400000000 1469539800000000 1469626200000000
 1469712600000000]


open 
[  93.97000122   94.44000244   95.48999786   95.38999939   94.59999847
   95.69999695   96.48999786   96.75         97.16999817   97.41000366
   97.38999939   98.91999817   98.69999695   99.55999756  100.
   99.83000183   99.26000214   98.25         96.81999969  104.26499939
  102.83000183]


high 
[  94.55000305   95.76999664   96.46499634   95.40000153   95.66000366
   96.5          96.88999939   97.65000153   97.69999695   97.66999817
   98.98000336   99.30000305  100.12999725  100.          100.45999908
  101.           99.30000305   98.83999634   97.97000122  104.34999847
  104.44999695]


low 
[  93.62999725   94.30000305   95.33000183   94.45999908   94.37000275
   95.62000275   96.05000305   96.73999786   97.12000275   96.83999634
   97.31999969   98.5          98.61000061   99.33999634   99.73500061
   99.12999725   98.30999756   96.91999817   96.41999817  102.75
  102.81999969]


close 
[  94.40000153   95.58000183   95.88999939   95.04000092   95.52999878
   95.94000244   96.68000031   96.98000336   97.41999817   96.87000275
   98.79000092   98.77999878   99.83000183   99.87000275   99.95999908
   99.40000153   98.66000366   97.33999634   96.66999817  103.02999878
  104.33999634]


volume 
[36532435 35953026 26007244 27724722 30951005 25141361 28929900 23795922
 24167463 25892533 38921985 30137795 36679958 23780554 26277068 32779214
 28378442 40373629 56270102 92346874 39869985]


vwap 
[  94.23366547   95.21818542   96.02340698   94.80203247   95.13683319
   96.06842804   96.63038635   97.26353455   97.40542603   97.18331146
   98.3677063    98.86615753   99.78375244   99.79320526   99.98825073
   99.9590683    98.6979599    97.68746948   97.06649017  103.53497314
  103.88442993]


spread 
[ 0.00982997  0.00990416  0.00992139  0.01000847  0.00990139  0.00993327
  0.00983547  0.00987558  0.00972609  0.00990837  0.00958601  0.01024598
  0.0097909   0.01007923  0.00989556  0.00984205  0.00994838  0.00992271
  0.0098503   0.01009011  0.00970959]


bidvol 
[11698070 14279332  9647840  9269781 11285611  8078345  9289119  7259073
  8155971 10761212 12907579  9155765 14177175  9121777  8901961 10286266
  8741883 13287066 13817396 30352321 11964169]


askvol 
[14018939 10348263  8302942  7677200 11576578  8758522  8333373  9335631
  8511582  7532046 15455691 12922405 11675214  7016688  9221600 10956753
 10764836 12235336 13000567 25665777 12841713]


count 
[216409 204314 154609 153279 187589 143924 168670 144022 151231 144845
 231502 157727 200056 134943 146681 169900 153518 224064 289094 484311
 216364]


valid 
[ True  True  True  True  True  True  True  True  True  True  True  True
  True  True  True  True  True  True  True  True  True]


closing_trade 
[  94.40000153   95.59999847   95.88999939   94.98999786   95.52999878
   95.94000244   96.68000031   96.98000336   97.41999817   96.87000275
   98.79000092   98.77999878   99.83000183   99.87000275   99.95999908
   99.43000031   98.66000366   97.33999634   96.66999817  102.94999695
  104.33999634]


opening_trade 
[  93.97000122   94.45999908   95.5          95.37999725   94.59999847
   95.69999695   96.48999786   96.76000214   97.16999817   97.41000366
   97.33000183   99.05999756   98.69999695   99.5         100.          100.
   99.26000214   98.19999695   96.83000183  104.25        102.83000183]