Let’s transform the data a little bit to make it easier to work with. You obviously have a deep understanding of finance and programming. Thank you very much for publishing this! How will the return calculations and the correlation matrix take this into account? The values are then indeed recorded and once all portfolios have been simulated, the results are stored in and returned as a Pandas DataFrame. I started by declaring my parameters and sets, including my risk threshold, my stock portfolio, the expected return of my stock portfolio, and covariance matrix estimated using the shrinkage estimator of Ledoit and Wolf(2003). I second Scott, it would be interesting to see a backtest of the various optimizations 😉 and may I aks you what matplotlib theme do you use? let’s say that one instrument starts only in 2010 while another starts in 2005. Let me run through each entry and hopefully clarify them somewhat: Firstly, as we will be using the ‘SLSQP’ method in our “minimize” function (which stands for Sequential Least Squares Programming), the constraints argument must be in the format of a list of dictionaries, containing the fields “type” and “fun”, with the optional fields “jac” and “args”. I havnt tested for any bugs this may introduce further down the line - but this solves the first problem at least!!! In my previous post, we learned how to calculate portfolio returns and portfolio risk using Python. Any guess what the problem could be? Thank you for your time, Gus. Will be waiting for your reply. Note: this page is part of the documentation for version 3 of Plotly.py, which is not the most recent version . Which one are you trying yo implement please? Apologies for the late reply… What was the error you are receiving? Indra A. As always we begin by importing the required modules. 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. Having our portfolio weights, we can move on to calculate the annualised portfolio returns, risk and Sharpe Ratio. In the mean time, if you have any questions about the package, or portfolio optimisation in general, please let me know. I just have a few issues when running the code. 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. A portfolio is a vector w with the balances of each stock. We will then show how you can create a simple backtest that rebalances its portfolio in a Markowitz-optimal way. Thank you very much for your quick answer. Cheers, Youri. Portfolio Optimization in Python. Riskfolio-Lib is a library for making quantitative strategic asset allocation or portfolio optimization in Python. Programming: Create The Fictional Portfolio. The plot colours the data points according to the value of VaR for that portfolio. Excellent analysis. Now we quickly calculate the mean returns and co-variance matrix of our list of stocks, set the number of portfolios we wish to simulate and finally we set the desired value of the risk free rate. Given a weight w of the portfolio, you can calculate the variance of the stocks by using the covariance matrix. 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. Based on what we learned, we should be able to get the Rp and Op of any portfolio. The hierarchical_portfolio module seeks to implement one of the recent advances in portfolio optimisation – the application of hierarchical clustering models in allocation. 428 4 4 silver badges 13 13 bronze badges $\endgroup$ add a comment | 2 Answers Active Oldest Votes. Now I want to show the daily simple returns which is... Optimize The 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. 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. Portfolio Optimization in Python. 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 = … This is going to illustrate how to implement the Mean-Variance portfolio theory (aka the markowitz model) in python to minimize the variance of your portfolio given a set target average return. Medium is an open platform where 170 million readers come to … The constraints remain the same, so we just adapt the “max_sharpe_ratio” function above, rename it to “min_variance” and change the “args” variable to hold the correct arguments that we need to pass to our new “calc_portfolio_std” that we are minimising. save_weights_to_file() saves the weights to csv, json, or txt. The arguments we will provide are, the weights of the portfolio constituents, the mean daily return of each of those constituents (as calculated over the historic data that we downloaded earlier), the co-variance matrix of the constituents and finally the risk free interest rate. Portfolio Optimization with Python and SciPy. 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. Everything runs fine except for the fact that my graph looks off and it doesn’t have the typical minimum variance frontier. Portfolio optimization is the process to identify the best possible portfolio from a set of portfolios. This includes quadratic programming as a special case for the risk-return optimization. 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. Thank you very much for taking the time to help out. We start again by creating our two functions – but this time instead of one that returns portfolio return, volatility and Sharpe ratio, it returns the parametric portfolio VaR to a confidence level determined by the value of the “alpha” argument (confidence level will be 1 – alpha), and to a time scale determined by the “days” argument. set_weights() creates self.weights (np.ndarray) from a weights dict; clean_weights() rounds the weights and clips near-zeros. While convex optimization can be used for many purposes, I think we're best suited to use it in the algorithm for portfolio management. It fails there with the following error code: “/home/ni/.local/lib/python3.6/site-packages/pandas/core/indexing.py”, line 1493, in _getitem_axis raise TypeError(“Cannot index by location index with a non-integer key”) Have you, or any of the people on this forum, had this issue? Portfolio optimization is the process of selecting the best portfolio (asset distribution), out of the set of all portfolios being considered, according to some objective. In this post I am going to be looking at portfolio optimisation methods, touching on both the use of Monte Carlo, “brute force” style optimisation and then the use of Scipy’s “optimize” function for “minimizing (or maximizing) objective functions, possibly subject to constraints”, as it states in the official docs (https://docs.scipy.org/doc/scipy/reference/optimize.html). I know this question has been asked under a different article of yours, but I couldn’t find the answer yet. For this tutorial, we will build a portfolio that minimizes the risk. If you would like to post your code here I am happy to take a look. 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. I’m done creating the fictional portfolio. I could run some “walk forward” optimisation, running the analysis each month and then holding that optimal portfolio for the following month so there is no “look forward bias” as it were. My guess is that it was due to the fact that too many ‘Adj. As next steps, it will be interested to know if we could achieve a similar return lowering the risk. Posted on November 7, 2020 by George Pipis in Data science | 0 Comments [This article was first published on Python – Predictive Hacks, and kindly contributed to python-bloggers]. This method assigns equal weights to all components. Another approach to find the best possible portfolio is to use the Sharpe Ratio. This is a mathematical framework for assembling a portfolio of assets such that the expected return is maximized for a given level of risk. Again we see the results are very close to those we were presented with when using the Monte Carlo approach. Optimizing Portfolios with Modern Portfolio Theory Using Python MPT and some basic Python implementations for tracking risk, performance, and optimizing your portfolio. Beginner’s Guide to Portfolio Optimization with Python from Scratch. Now we just take a look at the stock weightings that made up those two portfolios, along with the annualised return, annualised standard deviation and annualised Sharpe ratio. I also hold an MSc in Data Science and a BA in Economics. The cost of being wrong due to underestimating VaR and that due to overestimating VaR is almost never symmetric – there is almost always a higher cost to an underestimation. If you are unfamiliar with the calculation, feel free to have a look at my previous post where portfolio risk calculation is explained in details. Now you might notice at this point that the results of the minimum VaR portfolio simulations look pretty similar to those of the maximum Sharpe ratio portfolio but that is to be expected considering the calculation method chosen for VaR. Hello Stuart, I’m trying to follow this amazing investment tutorial/Python-code, and in my PC (Linux/Python 3.6.9), it runs well till it reaches the “localization of the portfolio with minimum VaR” (after the random portfolios simulation). This part of the code is exactly the same that I used in my previous article. Saying as we wish to maximise the Sharpe ration, this may seem like a bit of a problem at first glance, but it is easily solved by realising that the maximisation of the Sharpe ratio is analogous to the minimisation of the negative Sharpe ratio – that is literally just the Sharpe ratio value with a minus sign stuck at the front. The way this needs to be entered is sort of a bit “back to front”. Anyway, I started from scratch, and got (not null) values for VaR (results_frame). The Overflow Blog Failing over with falling over. It has been amended and added…thanks! For example, given w = [0.2, 0.3, 0.4, 0.1], will say that we have 20% in the first stock, 30% in the second, 40% in the third, and 10% in the final stock. Introduction In this post you will learn about the basic idea behind Markowitz portfolio optimization as well as how to do it in Python. 5/31/2018 Written by DD. (You can report issue about the content on this page here) Want to share your content on python-bloggers? Efficient return, a.k.a. The construction of long-only, long/short and market neutral portfolios is supported. Automating Portfolio Optimization using Python. Now we move onto the second approach to identify the minimum VaR portfolio. Michael Michael. Great stuff so far! Portfolio Optimization using SAS and Python. In this example we will create a portfolio of 5 stocks and run 100,000 simulated portfolios to produce our results. Enjoyable course. Hi, Is it possible to include dividends on returns? Impressive work! 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. Minimize the Risk of the Portfolio. Browse other questions tagged python pandas optimization scipy portfolio or ask your own question. Portfolio optimization could be done in python using the cvxopt package which covers convex optimization. Learn more. The last element in the Sharpe Ratio is the Risk free rate (Rf). Investor’s Portfolio Optimization using Python with Practical Examples. If possible try to get it correctly formatted as python code by wrapping it with: at the start and end – NOTE: DONT include the underscores at the start and end of each line -I have just added them to allow the actual wrappers to be visible and not changed into HTML themselves…. Hi Stuart, Thanks a lot, it worked! After which, I would draw out an efficient frontier graph and pinpoint the Sharpe ratio for portfolio optimization. Suppose that a portfolio contains different assets. It’s always nice to have things suggested by readers, so many thanks for that. The rate of return of asset is a random variable with expected value .The problem is to find what fraction to invest in each asset in order to minimize risk, subject to a specified minimum expected rate of return.. Let denote the covariance matrix of rates of asset returns.. Thanks for the impressive work. Second, I wanted to know how difficult it would be to implement a $ value of the capital and constrain it such that it has to chose funds with a minimum fund amount (i.e. The “type” can be either “eq” or “ineq” referring to “equality” or “inequality” respectively. Hey Stuart, Hats off for this superb article. It is built on top of cvxpy and closely integrated with pandas data structures. Again the code is rather similar to the optimisation code used to calculate the maximum Sharpe and minimum variance portfolios, again with some minor tweaking. I am going to use the five... Financial Calculations. But how do we define the best portfolio? The results will be produced by defining and running two functions (shown below). PyPortfolioOpt is a library that implements portfolio optimisation methods, including classical efficient frontier 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. 32% bitcoin and 68% gold . In this post we will demonstrate how to use python to calculate the optimal portfolio and visualize the efficient frontier. The pandas data reader is currently still working so you should be able to use it. 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. 1- When calling the ‘calc_portfolio_std’ function in sco.minimize, where are the “weights” variables being passed on from? Portfolio optimization is a mathematically intensive process that can be accomplished with a variety of optimization functions that are freely available in Python. I started by declaring my parameters and sets, including my risk threshold, my stock portfolio, the expected return of my stock portfolio, and covariance matrix estimated using the shrinkage estimator of Ledoit and Wolf(2003). In terms of the theme I used, it wasn’t a mtplotlib theme per se, but rather a Jupiter Notebook theme using the following package; https://github.com/dunovank/jupyter-themes. Saying as we are looking for the minimum VaR and the maximum Sharpe, it makes sense that they will be be achieved with “similar” portfolios. 2- If I wanted to add a portfolio tracking error constraint to the minimum variance function, how can I incorporate that in the code? Portfolio optimization python github Posted on 09.06.2020 09.06.2020 GitHub is home to over 40 million developers working together to host and review code, manage projects, and … This time we plot the results of each portfolio with annualised return remaining on the y-axis but the x-axis this time representing the portfolio VaR (rather than standard deviation). Hopefully that makes sense – let me know if you cant resolve it 😉, Hi Stuart, thank you for your comments. portfolio weights) has the highest Sharpe Ratio? hello, for the MC optimization is it possible to apply other constraints such as sector constraints for a portfolio that has 100+ plus names? The Overflow Blog Podcast 284: pros and cons of the SPA I can’t find how to tel to the program that weights can take value between -1;1 Can You help me ? df = data.set_index ('date') table = df.pivot (columns='ticker') # By specifying col … The logic is very similar to that followed when dealing with the first Monte Carlo problem above, so I will try to identify the changes and differences only rather than repeat myself too much. We will calculate portfolio … If you like the content of the blog and want to support it, enroll in my latest Udemy course: Financial Analysis with Python – Analysing Balance Sheet. Similar variables are defined as before this time with the addition of “days” and “alpha”. Similarly, an increase in portfolio standard deviation increases VaR but decreases the Sharpe ratio – so what maximises VaR in terms of portfolio standard deviation actually minimises the Sharpe ratio. So there you have it, two approaches(Monte Carlo “brute force” and use of Scipy’s “minimize” function) to optimise a portfolio of stocks based on minimising different cost functions ( i.e. I’ll get on to this as soon as I have a free moment. After running the code, I printed out what those weights were, and they were different form the weights resulting from the minimum variance function. I know currently there is no dollars involved in terms of portfolio amount, but this is the piece I am looking to add on. We can find the answer to that questions by transforming our data into a Pandas DataFrame and performing some basic queries. data.head () data.info () By looking at the info () of data, it seems like the “date” column is already in datetime format. Thanks for the great post! Such an allocation would give an average return of about 20%. Close’ values were missing (probably because I didn’t choose the correct ticker), which I then replaced using a simple Forward Fill. wow i did not get any notification for you reply.. haha.. i just saw it. Indra A. This library allows to optimize portfolios using several criterions like variance, CVaR, CDaR, Omega ratio, risk parity, among others. That is exactly what we cover in my next post, portfolio optimization with Python. Data Analysis with Pandas and Customised Visuals with... Trading Strategy Performance Report in Python – Part... Trading Strategy Performance Report in Python – Part... https://github.com/dunovank/jupyter-themes. So far so good it seems…what happens if we plot the location of the minimum VaR portfolio on a chart with the y-axis as return and the x-axis as standard deviation as before? Some of key functionality that Riskfolio-Lib offers: If you have this data available I would be happy to take a look and see if I can create what you have described. The random weightings that we create in this example will be bound by the constraint that they must be between zero and one for each of the individual stocks, and also that all the weights must sum to one to represent an investment of 100% of our theoretical capital. The “minimum variance portfolio” is just what it sounds like, the portfolio with the lowest recorded variance (which also, by definition displays the lowest recorded standard deviation or “volatility”). We need a new function that calculates and returns just the VaR of a portfolio, this is defined first. With this approach we try to discover the optimal weights by simply creating a large number of random portfolios, all with varying combinations of constituent stock weightings, calculating and recording the Sharpe ratio of each of these randomly weighted portfolio and then finally extracting the details corresponding to the result with the highest value. Feel free to have a look at it! So the first thing to do is to get the stock prices programmatically using Python. I think you are right, it seems there is a small mistake regarding the annualization of the returns. Algorithmic Portfolio Optimization in Python. Thinking about managing your own stock portfolio? 5/31/2018 Written by DD. It’s almost the same code as above although this time we need to define a function to calculate and return the volatility of a portfolio, and use it as the function we wish the minimise (“calc_portfolio_std”). I am a current PhD Computer Science candidate, a CFA Charterholder (CFAI) and Certified Financial Risk Manager (GARP) with over 16 years experience as a financial derivatives trader in London. A simple python project where we use price data from the NASDAQ website to help optimize our portfolio of stocks using modern portfolio theory. Therefore, I will not go into the details on how to do this part since you can refer to my previous post. 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