adaptivwealth: the new web app that I made to bring adaptive asset allocation to the masses



I recently finished the beta version of a web app I’ve been building, a web app that brings adaptive asset allocation to the masses.

What is adaptive asset allocation?

I’ve written about it in several previous posts. Essentially, it’s the idea that traditional Markowitz mean-variance asset allocation can be improved–generating portfolios that have better risk-adjusted performance–by making the models more adaptive to market changes.

What’s the point of the web app?

adaptivwealth’s goal is to make models that try to improve upon the weaknesses of traditional asset allocation more accessible to individual investors.

Asset allocation–allocating one’s money to different asset classes such as equities, bonds, and commodities–often produces more diversified portfolios than, for example, just picking stocks. Portfolios constructed using asset allocation can have decreased risk and increased returns (see the above screen shot of the performance of the Minimum Variance Portfolio vs. the performance of the S&P 500 for an example). A portfolio’s holdings can be optimized such that return is maximized given a level of risk. Asset allocation is powerful: the famous Brinson, Hood, and Beebower study showed that asset allocation is responsible for 91.5% of pension funds’ returns. Not stock selection, not market timing.

Asset allocation is traditionally not very accessible to individual investors. Individual investors have data, computation, knowledge, and/or time constraints that prevent them from running asset allocation algorithms to optimize their portfolios; asset allocation services are usually performed by financial advisers for individual investors, and large institutions like pension funds and hedge funds obviously have the resources to do it for themselves. Companies like are closing this gap, taking out the middle man, financial advisers, and lowering the costs of implementing asset allocation for the individual investor.

Companies like wealthfront implement traditional asset allocation algorithms. adaptivwealth differentiates itself by using models that try to improve upon the weaknesses of traditional asset allocation, and by making these models more accessible to individual investors. One approach to addressing the weakness of traditional asset allocation is by making the models more adaptive to market changes.

A call for help

adaptivwealth is still very rough around the edges, and I have a whole list of features that I want to implement, ideas for growth, etc. But I wanted to get a minimum viable product out there and collect feedback as quickly as possible. Let me know your thoughts! Questions, suggestions for features, advice, criticisms, anything and everything helps. Thank you.

Adaptive Asset Allocation: update to reflect investor data constraints

I realized that the portfolios presented so far would be pretty difficult for the individual retail investor to implement due to data constraints.

The problem

Say today is January 31, and the market has just closed. The adaptive asset allocation portfolios I constructed assume that the investor exists at the close of the last day of the month. Which is definitely reasonable assuming a brokerage account at a place like Interactive Brokers with market on close orders. However, the algorithms also assumed that we would enter the new positions on January 31. This could be possible if live streaming quotes were used, and the weights were calculated seconds before actual close and the positions entered right before close, but it’s definitely not possible for a normal retail investor to do.

The solution

So I decided to test the effect on returns of delaying the entry by one day; specifically, entering the new positions on the close of February 1 in the example above (and still exiting the positions on January 31). Again, this is a reasonable simulation for what a retail investor would actually do: he would exit his old positions on January 31, calculate new portfolio weights on February 1, and enter the new positions on the market close of February 1.


Sharpe Ratio drops from around 0.8 to 0.62. CAGR is only 7.6%. It’s interesting that performance deteriorates so much from delaying entry by just one day. Perhaps the performance decrease represents the costs of (unrealistically) both entering and exiting positions on the same day.

An interesting caveat

I wanted to test the next logical variation: what if we, instead of entering the new positions one day later, exited the old positions one day earlier? Using our example, the investor would exit his positions on January 30, calculate the new portfolio weights on January 31 (using data looking back from January 30, not 31), and then enter the new positions on February 1. Below are the results.



Both CAGR and Sharpe Ratio are higher than if we entered new positions one day late: CAGR is 2% higher, Sharpe Ratio is also 0.77 compared to 0.62. It seems we’re missing out on a lot more of the returns if we skip the first day of each month instead of the last day of each month. Is this evidence of the end of month/first of month effect (basically that the returns on the first day of a month are significantly higher than average)? Maybe, but for now, I need to move forward with my project. Creating the adaptive asset allocation algorithms is only the first part… more to come.