Is Backtrader event-driven or vectorized?
I know there is already a similar question in the community: https://community.backtrader.com/topic/242/is-backtrader-event-driven-or-vectorized
It says Backtrader is both event-driven and vectorized. But I still have a question about it.
For example, the
SMAindicator's real logic is as the following(basicops.py#L341):
class Average(PeriodN): alias = ('ArithmeticMean', 'Mean',) lines = ('av',) def next(self): self.line = \ math.fsum(self.data.get(size=self.p.period)) / self.p.period def once(self, start, end): src = self.data.array dst = self.line.array period = self.p.period for i in range(start, end): dst[i] = math.fsum(src[i - period + 1:i + 1]) / period
once()function it calculates SMA in a for loop, which doesn't make use of Numpy vectorization at all.
To my understanding, here is a Pandas way to calculate SMA:
sma = df['close'].rolling(N).mean()
It will make use of the vectorization capability from Pandas&NumPy, this is the real vectorized version, which is much faster than for loop.
In summary, Backtrader seems like an event-driven backtesting framework.
Why is Backtrader both event-driven and vectorized? Thanks!
vladisld last edited by
Backtrader is not 'vectorized' in a classic definition of this term as you may see from the post you've mentioned
I guess the attempt to use the approach you've suggested was done in
numpylinesbranch of the original repository - but it was never merged back.
run-out last edited by
@vladisld I thought that by using the python array library, that many of the speed benefits were picked up similar to vectorizing, since the array library is just a thin wrapper for C? However, your point is valid, it is definitely not vectorized in the pandas/numpy sense of things.