Portfolio Optimization with Daily/Weekly Rebalances?

Hi Folks,

I'm trying to optimize a portfolio of mutual funds, treasuries, and options strategies. I have used Portfolio Visualizer in the past for optimizing strategies that only require monthly rebalancing, but I am now trying to roll out a sub-strategy that requires more frequent rebalances to maximize risk adjusted returns. I will be managing various separately managed accounts, so aim to have allocations driven by different risk-return characteristics for each account.

The strategy: Sell 1 DTE SPX Put Credit Spreads, go Long SPY LEAPs, and use 7 DTE VIX Put Credit Spreads as a hedge. Integrate various equity, bond, and liquid alternative mutual funds into the portfolio as well for diversification. When using Portfolio Visualizer, I essentially packaged the options strategies into mutual fund data series with daily % return and uploaded to Portfolio Visualizer. Given the fact that the SPX Put Credit spreads go to zero fairly consistently, monthly rebalances are useless because as soon as there is a total loss, that strategy is dead for the month so the benefit of rebalancing is greatly reduced, especially because the returns on these SPX Put credit spreads are outsized following losses due to increases in volatility.

I have tested around with different long and short deltas for both the SPX and VIX Put credit spreads. What I am trying to do now is construct an efficient frontier with different weights in the SPX and Put Credit Spreads, in addition to LEAPs and Treasuries. SPX and VIX Put Credit Spreads would be rebalanced daily due to their extreme volatility (annualized standard deviations of 150+). Monthly rebalances for the treasuries and LEAPs since I use ladders for each (example: Buy more LEAPs if the portfolio of LEAPs has gone below the target allocation, or if they have outperformed, hold off on buying more, essentially letting the balance sheet of LEAPs run off. This is because the bid-ask spreads are large, so I want to avoid selling until they are closer to expiration with lower bid-ask spreads). I would also do monthly rebalances with a variety of equity, bond and liquid alternative mutual funds to avoid ST redemption fees and round trips. 

For the efficient frontier, each strategy would be treated as a mutual fund for rebalancing purposes as this is the only way I see to construct an efficient frontier using multiple sub-strategies. I wouldn't annualize returns and would instead leverage daily returns/correlations etc. I would be interested in constructing the equivalent of an efficient frontier but plotting Sortino Ratio/other risk-adjusted return metrics instead of Sharpe as well. It is also pertinent that I am able to include asset/group constraints as I am aware that any of the sub-strategies could drastically underperform. My assumption is that R, Python, or Excel would be my best bet. I think that I can build a workflow in Excel for this, but it would obviously be incredibly time consuming and take a lot of trial and error. I'm intermediate in Excel and very beginner in Python and R, but am a quick learner so feel I could follow a step by step guide and understand the rationale behind given inputs and outputs for each step.

Any help would be much appreciated!

Best,

Jack