Finance, Well, we could start by manually changing the weights in the array in our previous code, run the program again and see what the expected return and volatility of that particular set of weights comes out at. While these are Hi there – yes there are definitely issues with the Pandas data-reader at the moment since it moved to version 0.6.0 which can be read about here: http://pandas-datareader.readthedocs.io/en/latest/whatsnew.html#v0-6-0-january-24-2018. "optimal" in-sample, there is a large body of research showing that this characteristic leads the Markowitz portfolio, which minimises risk for a given target return – this was the main focus of Markowitz 1952. @s666 ok something is definitely fishy and going on here! # But what if we aren’t happy with the level of volatility of our current portfolio and would like to reduce it? Proper CVaR optimisation – remove NoisyOpt and use linear programming. I’m just asking as I’m still learning. Does this answer your question? By investing in more than one stock, an investor can reap the benefits of diversification, particularly a reduction in the riskiness of the portfolio.
This really is much more easily achieved with an optimiser! Ok makes sense. The 1.0 at the end is calculated as being the percentage of your portfolio that you hold in cash – in this instance it is actually 100% as you have financed the 0.2, 0.2 and 0.1 holdings by selling 0.5 of Apple – leaving you flat and holding 100% of your portfolio value in cash. I have 16 stocks total. By using the optimizer command instead of explicitly setting up a new problem and calling optimize, the overhead can be reduced drastically. Let us limit the number of positions to 4 (since nnz requires a MILP model, we explicitly upper bound the variable x to help the big-M reformulation). Great article. I think the easiest way to do it would be just to write: data = web.DataReader(stocks,data_source=’yahoo’,start=’01/01/2010′)[‘Adj Close’]. Graphically, one can imagine, By fixing a time step (representing now), we can split our tensor into 3 disjoint subtensors. Hope that makes sense – any questions, please do ask! Given the optimization estimates that spit out are extreme in nature (i.e. Adding the constraints for all stocks to any portfolio optimization problem will give us a solution with no short sales. (2009) and its accompanying appendix. 1. I have had a play around with the code and I have located the error…The list of stocks has to be in alphabetical order as this line of code: #convert daily stock prices into daily returns returns = data.pct_change(), #calculate mean daily return and covariance of daily returns mean_daily_returns = returns.mean(). Not sure how to resolve.. Is there anyone who can help? Hi Sam, glad you like the article. It makes feasible sense kind of too since many funds are net leveraged – or + on their book……….And not necessarily having the book sum = 100% as a restriction……. This may be useful if you're trying to get an idea of how low the volatility, Efficient return, a.k.a. Multi-parametric programming, As always, really appreciate your guidance and assistance. Note that almost I have a question regarding your 18% return example. We can just as well use a new set of weights \(z\) with the constraint that \(z^T(\mu-\mu_0)=1\). Integer programming, In other words, given our past knowledge x we construct a portfolio w that we buy right away and hold for horizon time steps. Added option to 'properly' compound returns. I actually have just recently published an updated version of this post which also shows the use of a optimiser function which allows you to set constraints directly. I have a qick question on what you meant at the end where you say very important point. Any questions – fire away! Efficient Frontier & Portfolio Optimization. For example, a wealth manager might have some formula for determining acceptable client risk. I made the necessary changes you describe above and getting a very odd error message specifically: ValueError: Wrong number of items passed 9, placement implies 8 ValueError: Shape of passed values is (9, 25000), indices imply (8, 25000), The Full ERROR is: ******************************************************************************************************************************************************************, Traceback (most recent call last): File “C:\Users\Sam\Anaconda64Best\lib\site-packages\pandas\core\internals.py”, line 4294, in create_block_manager_from_blocks placement=slice(0, len(axes[0])))] File “C:\Users\Sam\Anaconda64Best\lib\site-packages\pandas\core\internals.py”, line 2719, in make_block return klass(values, ndim=ndim, fastpath=fastpath, placement=placement) File “C:\Users\Sam\Anaconda64Best\lib\site-packages\pandas\core\internals.py”, line 115, in __init__ len(self.mgr_locs))) ValueError: Wrong number of items passed 9, placement implies 8. I would say that the method to include short positions isn’t quite as simple as just reversing the sign on the short, however nor is it particularly complex. Given the optimization estimates that spit out are extreme in nature (i.e. Hi Zack, do you mean you dont want any single individual stock weight to be more than 20% or that you want the total weight to sum to 20%? Easy to combine with your proprietary strategies and models. Hello there again @S666. Yet, still keep the existing contraint that all the stocks must sum to 100%? (2009). So it is analogous to the “price” of cash, just as the price of Apple or the price of Yahoo is shown in the DataFrame. As of v0.5.0, we now support Black-Litterman asset allocation, which allows you to combine been tested to work as intended. In other words, to optimize based on both criteria, it is telling me to actually be NET LONG apple instead of NET SHORT apple, at least going back to my inputted date of 1/1/2010? It should be : Thank you so much for looking into it and leaving your answer, it all makes sense now. However, it can easily be converted to a quadratic program. Unfortunately i can’t get the code to run at all anymore due to the discontinuation of the Yahoo Finance API, but to point you in the right direction you will need to do the following: Add the asset “CASH” to your stocks list after this line: #download daily price data for each of the stocks in the portfolio data = web.DataReader(stocks,data_source=’yahoo’,start=’01/01/2010′)[‘Adj Close’], results = np.zeros((4+len(stocks)-1,num_portfolios)), results = np.zeros((5+len(stocks)-1,num_portfolios)), #convert results array to Pandas DataFrame results_frame = pd.DataFrame(results.T,columns=[‘ret’,’stdev’,’sharpe’,stocks[0],stocks[1],stocks[2],stocks[3]]), #convert results array to Pandas DataFrame results_frame = pd.DataFrame(results.T,columns=[‘ret’,’stdev’,’sharpe’,stocks[0],stocks[1],stocks[2],stocks[3],stocks[4]]), and HOPEFULLY that should run ok…as i said I am doing this without actually running the code so no guarantees!
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