CSV Data Feed Development

backtrader already offers a Generic CSV Data feed and some specific CSV Data Feeds. Summarizing:

GenericCSVData
VisualChartCSVData
YahooFinanceData (for online downloads)
YahooFinanceCSVData (for already downloaded data)
BacktraderCSVData (in-house … for testing purposed, but can be used)

But even with that, the end user may wish to develop support for a specific CSV Data Feed.

The usual motto would be: “It’s easier said than done”. Actually the structure is meant to make it easy.

Steps:

Inherit from backtrader.CSVDataBase
Define any params if needed
Do any initialization in the start method
Do any clean-up in the stop method
Define a _loadline method where the actual work happens

This method receives a single argument: linetokens.

As the name suggests this contains the tokens after the current line has been splitten according to the separator parameter (inherited from the base class)

If after doing its work there is new data … fill up the corresponding lines and return True

If nothing is available and therefore the parsing has come to an end: return False

Returning False may not even be needed if the behind the scenes code which is reading the file lines finds out there are no more lines to parse.

Things which are already taken into account:

Opening the file (or receiving a file-like object)
Skipping the headers row if indicated as present
Reading the lines
Tokenizing the lines
Preloading support (to load the entire data feed at once in memory)

Usually an example is worth a thousand requirement descriptions. Let’s use a simplified version of the in-house defined CSV parsing code from BacktraderCSVData. This one needs no initialization or clean-up (this could be opening a socket and closing it later, for example).

Note

backtrader data feeds contain the usual industry standard feeds, which are the ones to be filled. Namely:

datetime
open
high
low
close
volume
openinterest

If your strategy/algorithm or simple data perusal only needs, for example the closing prices you can leave the others untouched (each iteration fills them automatically with a float(‘NaN’) value before the end user code has a chance to do anything.

In this example only a daily format is supported:

import itertools
...
import backtrader as bt

class MyCSVData(bt.CSVDataBase):

    def start(self):
        # Nothing to do for this data feed type
        pass

    def stop(self):
        # Nothing to do for this data feed type
        pass

    def _loadline(self, linetokens):
        i = itertools.count(0)

        dttxt = linetokens[next(i)]
        # Format is YYYY-MM-DD
        y = int(dttxt[0:4])
        m = int(dttxt[5:7])
        d = int(dttxt[8:10])

        dt = datetime.datetime(y, m, d)
        dtnum = date2num(dt)

        self.lines.datetime[0] = dtnum
        self.lines.open[0] = float(linetokens[next(i)])
        self.lines.high[0] = float(linetokens[next(i)])
        self.lines.low[0] = float(linetokens[next(i)])
        self.lines.close[0] = float(linetokens[next(i)])
        self.lines.volume[0] = float(linetokens[next(i)])
        self.lines.openinterest[0] = float(linetokens[next(i)])

        return True

The code expects all fields to be in place and be convertible to floats, except for the datetime which has a fixed YYYY-MM-DD format and can be parsed without using datetime.datetime.strptime.

More complex needs can be covered by adding just a few lines of code to account for null values, date format parsing. The GenericCSVData does that.

Caveat Emptor

Using the GenericCSVData existing feed and inheritance a lot can be acomplished in order to support formats.

Let’s add support for Sierra Chart daily format (which is always stored in CSV format).

Definition (by looking into one of the ‘.dly’ data files:

Fields: Date, Open, High, Low, Close, Volume, OpenInterest

The industry standard ones and the ones already supported by GenericCSVData in the same order (which is also industry standard)
Separator: ,
Date Format: YYYY/MM/DD

A parser for those files:

class SierraChartCSVData(backtrader.feeds.GenericCSVData):

    params = (('dtformat', '%Y/%m/%d'),)

The params definition simply redefines one of the existing parameters in the base class. In this case just the formatting string for dates needs a change.

Et voilá … the parser for Sierra Chart is finished.

Here below the parameters definition of GenericCSVData as a reminder:

class GenericCSVData(feed.CSVDataBase):
    params = (
        ('nullvalue', float('NaN')),
        ('dtformat', '%Y-%m-%d %H:%M:%S'),
        ('tmformat', '%H:%M:%S'),

        ('datetime', 0),
        ('time', -1),
        ('open', 1),
        ('high', 2),
        ('low', 3),
        ('close', 4),
        ('volume', 5),
        ('openinterest', 6),
    )