Great stuff so far! I do have a different question though, related to the individual stock weights. We can do that by optimising our portfolio. click here. Anyway, I started from scratch, and got (not null) values for VaR (results_frame). The “bounds” just specify that each individual stock weight must be between 0 and 1, with the “args” being the arguments that we want to pass to the function we are trying to minimise (calc_neg_sharpe) – that is all the arguments EXCEPT the weights vector which of course is the variable we are changing to optimise the output. You obviously have a deep understanding of finance and programming. By looking into the DataFrame, we see that each row represents a different portfolio. This module provides a set of functions for financial portfolio optimization, such as construction of Markowitz portfolios, minimum variance portfolios and tangency portfolios (i.e. In my experience, a VaR or CVaR portfolio optimization problem is usually best specified as minimizing the VaR or CVaR and then using a constraint for the expected return. portfolio risk) of the portfolio. See below a summary of the Python portfolio optimization process that we will follow: We will start by retrieving stock prices using a financial free API and creating a Pandas Dataframe with the daily stock returns. So that is to say we will be calculating the one-year 95% VaR, and attempting to minimise that value. I'm looking for advice as to what additional analyses or functions / features I should add. cme = pdr.get_data_stooq(‘CME’, start, end). This final VaR value has then been converted to an absolute value, as VaR is more often than not reported as a positive value (it also allows us to run the required “minimization” function when it is cast as a positive value). Hi people, I write this post to share a portfolio optimization library that I developed for Python called Riskfolio-Lib. Posted on November 7, 2020 by George Pipis in Data science | 0 Comments [This article was first published on Python â Predictive Hacks, and kindly contributed to python-bloggers]. This library allows to optimize portfolios using several criterions like variance, CVaR, CDaR, Omega ratio, risk parity, among others. You can provide your own risk-aversion level and compute the appropriate portfolio. In this installment I demonstrate the code and concepts required to build a Markowitz Optimal Portfolio in Python, including the calculation of the capital market line. By altering the variables a bit, you should be able to reuse the code to find the best portfolio using your favourite stocks. When quoting the official docs or referring to the actual function itself I shall use a “z” to fall in line. Based on what we learned, we should be able to get the Rp and Op of any portfolio. This includes quadratic programming as a special case for the risk-return optimization. Sure thing – it should be possible with the code below: and then change the code in the "simulate_random_portfolios" function so that instead of the lines: you have (for example - with 5 stocks that you want to sum to a weight of 1, with any individual stock being allowed to range from -1 to 1: You can ofcourse change the n,m,low, high arguments to fit your requirements. Again we see the results are very close to those we were presented with when using the Monte Carlo approach. The construction of long-only, long/short and market neutral portfolios is supported. What is the correlation between bitcoin and gold? Suppose that a portfolio contains different assets. In this article, we will show a very simplified version of the portfolio optimization problem, which can be cast into an LP framework and solved efficiently using simple Python scripting. I am also planning to do a couple of posts on environments used for coding so this will definitely be explained in there shortly also. portfolio weights) has the highest Sharpe Ratio? @2019 - All Rights Reserved PythonForFinance.net, Investment Portfolio Optimisation with Python – Revisited, https://docs.scipy.org/doc/scipy/reference/optimize.html), investment portfolio optimisation with python, Time Series Decomposition & Prediction in Python. Sounds like a nice idea to run some historical comparisons of the differing portfolio suggestions, see if the reality bares out the same as the theory. Given that I have certain benchmark returns and weights for the same stocks in my portfolio. It has been amended and added…thanks! The error message is telling you that you are trying to use a label based key but the method you are using only accepts an integer as an index key. That will set an upper bound of 8% on each holding. Portfolio optimization in finance is the technique of creating a portfolio of assets, for which your investment has the maximum return and minimum risk. It is time to take another step forward and learn portfolio optimization with Python. Portfolio Optimization in Python. Hi Cristovam apologies for the late reply, actually I havnt yet but it was something I’ve been thinking about doing. Congratulations for your work.Very inspiring. Also, portfolio managers of mutual funds typically have restrictions on the maximum permitted allocation to a single line. the negative Sharpe ratio, the variance and the Value at Risk). So the first thing to do is to get the stock prices programmatically using Python. Thank you very much for your quick answer. Is it possible to cap the weights at 8% so that no stock is attributed more than that and further that the excess weight is then evenly distributed to other stocks. Portfolio Optimization in Python. Then find a portfolio that maximizes returns based on the selected risk level. You can use this piece of code a modify accordingly: #set dates start = datetime.datetime(2018, 3, 1) end = datetime.datetime(2018, 12, 31), #fetch data cme = pdr.get_data_yahoo(‘CME’, start, end), you can also easily use data feed from stooq.com or stooq.pl – you will find more macro data there i guess. When we run the optimisation, we get the following results: When we compare this output with that from our Monte Carlo approach we can see that they are similar, but of course as explained above they will not be identical. The hierarchical_portfolio module seeks to implement one of the recent advances in portfolio optimisation â the application of hierarchical clustering models in allocation. Sir, I have just started my journey in Python, and i met with error in the first step, like pandas_datareader is not working anymore, so is there some other library for the getting the data from yahoo finance. The “fun” refers to the function defining the constraint, in our case the constraint that the sum of the stock weights must be 1. PyPortfolioOpt is a library that implements portfolio optimisation methods, including classical mean-variance optimisation techniques and Black-Litterman allocation, as well as more recent developments in the field like shrinkage and Hierarchical Risk Parity, along with some novel experimental features like exponentially-weighted covariance matrices. Hi, great article, was wondering how you would modify your code if you wanted to include short positions. Going foward, did you even tried implementing the Black-Litterman model using Python? For simplicity reasons we have assumed a Risk free rate of 0. Given a weight w of the portfolio, you can calculate the variance of the stocks by using the covariance matrix. Sanket Karve in Towards Data Science. In this example I have chosen 5 random stocks that I am sure most people will at least have heard of…Apple, Microsoft, Netflix, Amazon and Google. def calc_neg_sharpe(weights, mean_returns, cov, rf): portfolio_return = np.sum(mean_returns * weights) * 252 portfolio_std = np.sqrt(np.dot(weights.T, np.dot(cov, weights))) * np.sqrt(252) sharpe_ratio = (portfolio_return - rf) / portfolio_std return -sharpe_ratio constraints = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1}) def max_sharpe_ratio(mean_returns, cov, rf): num_assets = â¦ The higher of a return you want, the higher of a risk (variance) you will need to take on. The second function is pretty much analogous to the one used for the Sharpe optimisation with some slight changes to variable names, parameters and arguments passed of course. (I understand the “panda-restrictions” about the “i.loc”.) A blog about Python for Finance, programming and web development. Follow. Portfolio Optimization with Python using Efficient Frontier with Practical Examples by Shruti Dash | Portfolio optimization in finance is the technique of creating a portfolio of assets, for which your investment has the maximum return and minimum risk. I am trying to do the exact same thing as you do in the first approach but with 24 different stocks. wow i did not get any notification for you reply.. haha.. i just saw it. We will calculate portfolio â¦ After which, I would draw out an efficient frontier graph and pinpoint the Sharpe ratio for portfolio optimization. I am not able to post a picture here so it might be difficult to illustrate, but basically my graph looks more like a circle with the different portfolio points. the max you can allocate for each stock is 20%.. You look like a remarkable dad! They will allow us to find out which portfolio has the highest returns and Sharpe Ratio and minimum risk: Within seconds, our Python code returns the portfolio with the highest Sharpe Ratio as well as the portfolio with the minimum risk. These are highlighted with a red star for the maximum Sharp ratio portfolio, and a green star for the minimum variance portfolio. Anyway, it’s a great and inspiring article. Great work, thanks! We use cookies to ensure that we give you the best experience to our site. Hi Stuart, Thanks a lot, it worked! Portfolio Optimization in Python. In this post we will only show the code with minor explanations. If just considering one single stock I guess the risk and return would just be the historic CAGR and the annualised standard deviation of the stock returns no? We may have investors pursuing different objectives when optimizing their portfolio. The weights of the resulting minimum VaR portfolio is as shown below. 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