The 'DollarNeutral' and 'NetGroupExposure' ARE dollar weighted. Whenever 'weight' is used it is the 'price x shares' or total dollar value of a position. It is NOT the weight of quantity of shares.

Assume one has a total portfolio value of $10,000. An order is placed with targets 'weights' of .50 AAPL and .50 IBM. This results in (approximately) $5,000 AAPL and $5,000 IBM. It does NOT result in an equal number of shares of each. One reason it's approximate is that one cannot order fractional shares. Additionally, it's approximate because the target shares are calculated at the current market price. The actual price (and therefore position value) may change as orders are filled and/or the value of a stock increases or decreases.

So yes, your definitions of 'DollarNeutral' and 'NetGroupExposure' are correct and that IS how they are implemented.

Dan,
Thanks for the explanation. So Dollar Neutral is really -tol <= ∑PiWi <= tol, where tol might be 0.01. In your example, the sum of IBM and AAPL's target weights should add up to the tolerance i.e. -0.01 <= (0.5 - 0.5) <= 0.01

@John Smith

Correct.

Note the optimize function will generate orders which meet the constraints. It assumes the last minute close price when calculating the orders. After filling the actual orders however, the weights may not be within the constraints.

Hi Dan, was going through the other available constraints and looks like it's the same treatment for :
MaxGrossLeverage : sum (abs(weight) * Price) <= % of portfolio value
PositionConcentration : each stock's (price * weight) in range (min, max)
BTW, are you a member of the Quantopian team?

@John Smith

Correct again. The 'weight' in the examples given above are all the total dollar weight, and again, not the share qty weight. Note that there isn't a 'MaxGrossLeverage' constraint. It's named 'MaxGrossExposure'. I often find it helpful to test out various functionality by writing small algorithms and logging the results. Attached is an algorithm with a MaxGrossExposure constraint varying between 1 and 2, and a DollarNeutral constraint of .1. Check out the custom data graph. It records those two metrics. Also check the logs.

Nope. Not a member of the Quantopian staff just an avid supporter.

Good luck.

And then also in case one could ever think of a reason to dynamically modify the values we set, for MaxGrossExposure, PositionConcentration etc, or just for our own educational value to see what happens, this is a small edit that varies DollarNeutral() using a context variable to illustrate.

Thanks guys. Curious which python function does QT use for the objective functions with constraints. I have tried scipy.optimize.minimize and CVXOPT.lp but the weights results do not match against QTs weights results.
Also for CVXOPT functions, how do you specify that you want to work with absolute weights since weights are not an input you provide in the constraints.