MultiPeriodOptimization Behavior Documentation about cvxportfolio HOT 10 OPEN

EDIT: I hadn't read your comment well. If you do nothing it simply carries over the value for the day (no look-ahead).

Hello, I also need to improve the docs on this. There are two ways to do it. One is to build a MPO policy by providing a list of objectives and a list of lists of constraints:

mpo_policy = cvx.MultiPeriodOptimization(
    objective = [cvx.ReturnsForecast(signal_today_dataframe) - gamma_risk * cvx.FullCovariance() - ...,
                cvx.ReturnsForecast(signal_tomorrow_dataframe) - gamma_risk * cvx.FullCovariance() - ...]
    constraints = [[cvx.LeverageLimit(3)],
                [cvx.LeverageLimit(3)]]
)

so the planning_horizon argument is unused. In this way you can change all the terms you want for each MPO step.

The other, in case you simply have slow and fast signals, is to use the decay argument of ReturnsForecast

mpo_policy = cvx.MultiPeriodOptimization(
    objective = cvx.ReturnsForecast(fast_signal_dataframe, decay = .2)
              + cvx.ReturnsForecast(slow_signal_dataframe, decay = .8)
              - gamma_risk * cvx.FullCovariance() , 
    constraints = [cvx.LeverageLimit(3)],
    planning_horizon = 5,
)

Let me know if this answers your question.

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Thank you that clarifies perfectly. Very helpful.

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I need to improve the docs for this; it might take a while so at least if someone comes here to ask something similar they find this.

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One more question about the indexing of signal_today_dataframe and signal_tomorrow_dataframe in

mpo_policy = cvx.MultiPeriodOptimization(
    objective = [cvx.ReturnsForecast(signal_today_dataframe) - gamma_risk * cvx.FullCovariance() - ...,
                cvx.ReturnsForecast(signal_tomorrow_dataframe) - gamma_risk * cvx.FullCovariance() - ...]
    constraints = [[cvx.LeverageLimit(3)],
                [cvx.LeverageLimit(3)]]
)

Should the index be the dates of the prediction or the date it is predicting?
Ie lets say I have a dataframe with:
t symbol pred_{t+1} pred_{t+2} pred_{t+3}

Is signal_tomorrow_dataframe going to be
t pred_{t+2} (so the index is the date the prediction was made)
or should it be:
t+1 pred_{t+2}
Or even
t pred_t

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The timestamp refers to the time of each period in the back-test, say 9:30am EST on a Monday. Then signal today, at timestamp 9:30am Monday, is the prediction of the return from 9:30am Monday to 9:30am Tuesday, signal tomorrow, at timestamp 9:30am Monday, is prediction of return from Tuesday to Wednesday, .... The time convention is the one defined in the paper, section 2. In practice you can assume that signal for today is built knowing all data up to the open price of today (and the open-to-open total return from yesterday open to today open). Does it make sense? I should definitely make these things clearer in the docs. The policy objects receive a view (past_returns) of the open-to-open total returns up to the open of the day, as a dataframe. ReturnsForecast without arguments simply computes a .mean() of that, so each day it does the full mean of all past returns for each name. In the user provided forecasters example you see how you can use the same model to do arbitrary forecasting.

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so if I have a dataframe of signal_day_after_tomorrow (indices are the date the signals are created) I should shift it forward by two when I feed it into the objective?

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I think in your formalism (comment before) it's t+1 pred t+1 for signal today, and so on. The timestamp in the signal dataframe is such that the prediction at that timestamp is done using all data up to the price at that timestamp. For signals for the future it's the same, but you predict a future quantity.

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I'm still not 100% sure, but it sounds like basically if I got signal1 and and signal2 with index being the index outputted by a regression (so the date at which we predict target return of next day for signal1, and then that target shifted back by 1 for signal2); the correct way to use the optimizer would be something like:

mpo_policy = cvx.MultiPeriodOptimization(
    objective = [cvx.ReturnsForecast(signal1.shift(1)) - gamma_risk * cvx.FullCovariance() - ...,
                cvx.ReturnsForecast(signal2.shift(2)) - gamma_risk * cvx.FullCovariance() - ...]
    constraints = [[cvx.LeverageLimit(3)],
                [cvx.LeverageLimit(3)]]
)

I suppose what I'm confused about is whether it would instead be

mpo_policy = cvx.MultiPeriodOptimization(
    objective = [cvx.ReturnsForecast(signal1) - gamma_risk * cvx.FullCovariance() - ...,
                cvx.ReturnsForecast(signal2.shift(1)) - gamma_risk * cvx.FullCovariance() - ...]
    constraints = [[cvx.LeverageLimit(3)],
                [cvx.LeverageLimit(3)]]
)

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You've got to think about the way data is consumed by your machine learning model that produces the signal. That's why you can take the user provided forecasters example as a starting point https://github.com/cvxgrp/cvxportfolio/blob/master/examples/user_provided_forecasters.py .

The line of your signal that has timestamp t, must be built with data that was available at time t. That's the case for all forecasted quantities: returns for the period, for the next period, volumes, risk model parameters, .... (If you don't have that property you're doing look-ahead and any analysis is invalid.)

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An improved explanation of the multi-period optimization model was just merged in master, can be seen on the development version of the docs https://www.cvxportfolio.com/en/master/manual.html#multi-period-optimization

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