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# More Backtrader way to calculate covariance matrix

• I am trying to calculate the covariance matrix of a multiple asset portfolio at every period with N lookback period. My current implementation uses a counter to wait out the lookback period and then runs all calculations in the `def next()` method.

`````` def next(self):
if (self.counter < self.p.rperiod):
self.counter += 1
else:
# stack the closes together into a numpy array for ease of calculation
closes = np.stack(([d.close.get(0,self.p.rperiod).tolist() for d in self.datas]))
rets = np.diff(np.log(closes))
# covariance matrix. will be shoved somewhere else for records
cov = np.cov(rets)
``````

While it 'works', what is the more backtrader way of doing the same thing?

For example, there's the `PctChange` indicator for calculating percentage change that is defined at `__init__`.

``````def __init__(self):
rets = [bt.ind.PctChange(d, period=self.p.rperiod) for d in self.datas]
``````

But how do I access the value of N periods of indicators, run calculations, and then construct cross-asset indicators? And would it be possible to have, say, an indicator that contains multidimensional matrices?

Much thanks!

• My current implementation uses a counter to wait out the lookback period and then runs all calculations in the def next() method.

You should be using the `addminperiod` API or subclassing from the indicator `PeriodN`.

While it 'works', what is the more backtrader way of doing the same thing?

You calculate something in `next` using `numpy`. What would you be looking for?

But how do I access the value of N periods of indicators, run calculations, and then construct cross-asset indicators?

You are already using `get`

And would it be possible to have, say, an indicator that contains multidimensional matrices?

Not a clue what you mean. Indicator have `lines`. You may of course store anything in a member attribute.

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