42 Exciting Python Project Ideas & Topics for Beginners [] | upGrad blog

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 python projects to build portfolio 64 bit of these randomly weighted portfolio and then finally extracting the details corresponding to the result with the highest value.

We will always experience some discrepancies however as we can never run enough simulated portfolios to replicate the exact portfo,io we are searching for…we can get close, but never exact. In this example we will create a portfolio of 5 stocks and runsimulated portfolios python projects to build portfolio 64 bit produce our results. We then download price data for the stocks we wish to include in our portfolio. 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.

The results will be produced by defining and running two functions shown below. The arguments we will provide are, the weights of the portfolio tp, the mean daily return of each of those constituents as calculated over the historic data that we downloaded earlierthe co-variance matrix of the constituents and finally the risk free interest rate. The risk free rate is required for the calculation of the Sharpe ratio and should be provided as an annualised rate.

In this example I have chosen to set the rate to zero, but the functionality is there to easily amend this for your bkt purposes. The second function deals with the overall creation of multiple randomly weighted portfolios, which are then passed to the function we just described above to calculate the required values we wish to record.

The values are then indeed recorded and once all portfolios have been simulated, python projects to build portfolio 64 bit results are stored python projects to build portfolio 64 bit and returned as a Pandas Portfplio. The values recorded are as previously mentioned, the annualised return, annualised standard deviation and annualised Sharpe ratio — we also store the weights of each stock in the portfolio that generated those values.

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 pyrhon free rate. We then call the required function and store the results in a variable so we can then extract and visualise them.

These are highlighted with a red star for the maximum Sharp ratio portfolio, and bkild green star for the minimum variance portfolio.

The data python projects to build portfolio 64 bit are coloured according to their respective Sharpe ratios, with blue signifying a higher value, and red a lower value. 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.

These are shown below firstly for the maximum Sharpe portfolio, and then for the minimum variance portfolio. The code is fairly brief but there are a couple of things worth mentioning.

Saying as we wish to maximise the Sharpe ration, Python Projects To Build Portfolio Keyword 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.

So firstly we define a function pojects similar to our earlier function that calculates and returns the negative Sharpe ratio of a portfolio. So the most simple way to achieve this is to create a lambda function that returns the sum of the portfolio weights, minus 1.

The constraint that this needs to sum to zero that the function needs to equate to zero by definition means that the weights must sum to 1. 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 weightings of each stock are not more than a couple of percent away between the two approaches…hopefully that indicates we did something right at least!

We can then just use the same approach to identify the minimum variance portfolio. This time there is no need to negate the output of our function as it is already a minimisation problem this time as opposed to the Sharpe porhfolio when we wanted projectts find the maximum.

Again we see the results are very close to those we were presented with when using the Monte Carlo approach. Great stuff so far! Now let us move on to the problem of identifying the portfolio weights that minimise the Value at Risk VaR. The logic is very similar to that followed when dealing with the first Monte Carlo problem above, so I will projevts to identify the changes bjild differences only rather than repeat myself too much.

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.

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. 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. The plot colours the data points according to the value of VaR for that portfolio.

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? The data points are still coloured according to their corresponding VaR value. 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 projectz that is to be expected considering the calculation method chosen for VaR.

From this we can see that VaR falls when portfolio returns increase and vice versa, whereas the Sharpe ratio increases as portfolio returns increase — so what minimises VaR in terms of returns actually maximises the Sharpe ratio. 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 Builr ratio.

Now we move onto the second approach to identify the minimum VaR portfolio. 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. We need a new function that calculates and returns just the VaR of a portfolio, this is defined first.

Nothing changes here from our original function that calculated VaR, only that we return a single VaR value rather than the three original values that previously included portfolio return and standard deviation. The constraints are the same, as are the bounds etc.

Once again we see the results are very close to those we were presented with when using the Monte Carlo approach, with the weights being within prpjects couple of percent of each other. Python projects to build portfolio 64 bit work, thanks!

Hi Scott, thanks for your comment. 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. Awesome work very well explained, thank you! Hi Chris, thanks for your comment also…I will make that the subject of my next post. 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.

Thank you very much for your quick answer. Very, very good s Just one small note — You did forget to include: pd. Hi jojo, apologies for the late reply… To assign sector constraints etc should be possible of course, it would depend on you having the data of pyhhon stock related to which sector.

If you have this data available I would promects happy to take a look and see if I can create what you have described.

If so, ping me a message here and I will lortfolio you my contact details to forward the data file on to. Is it something you would be particularly interested in seeing? Will be waiting for your reply. Apologies for the late reply… What was the error you are receiving?

The pandas data pythin is currently still working so you should be able to use it. First of all this code is awesome and works exactly the way I would want a portfolio optimization setup to work. Great work, appreciate your time to create. I know currently there is no dollars involved in terms of portfolio amount, but this is the piece I am looking to add on. Hi, great article, was python projects to build portfolio 64 bit how you would modify your code if you wanted to include buld positions.

I havnt tested for any bugs python projects to build portfolio 64 bit may introduce further down the line - but this solves the first problem at least!!!

After running the code, I printed out what those weights were, and they were different form the weights resulting from the minimum variance function. Given that I have certain benchmark returns and weights for the same stocks in my portfolio.

If python projects to build portfolio 64 bit 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? Which one are you trying yo implement please? Thank you S for another solid piece of financial code in Python! I really like your professional, storytelling-like approach for optimisation and previous topic. I have two questions for which your advice would be much appreciated: 1.

How can I provide my own historical data from a csv or spreadsheet file instead of reading from on online source? How will the return calculations and the correlation matrix take this into account? Impressive work! Thanks for the intellectually stimulating content. I remember python projects to build portfolio 64 bit now, deriving the formula for modern portfolio theory.

Thanks for the impressive work. I think you are right, it seems there is a small mistake regarding the annualization of the returns. Thanks for the great post! Excellent analysis. You obviously have a deep understanding of finance and programming. I am just starting with programming and I want to deepen my knowledge in data analysis and financial analysis.

This helped me a lot. Thank you so much for sharing it. Thank you for your time, Gus. 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. Hi Stuart, thank you for your comments. Regards, Gus. Hey Stuart, Hats off for this superb article. I just have a few issues when running the code. I am trying to do the exact same thing as you do in the first approach but with 24 different stocks.

I am not able to post a python projects to build portfolio 64 bit here so it might be difficult to illustrate, but basically my python projects to build portfolio 64 bit looks more like a circle with the different portfolio points. Any guess what the problem could be? Your help would mean a lot. If you would like to post your code here I am happy to take a look. If possible try to get it correctly formatted as python code by wrapping it with:.

Django is a fully featured Python web framework that can be used to build complex web applications. In this course, you’ll jump in and learn Django by example. You’ll follow the steps to create a fully functioning web application and, along the way, learn some of the most important features of the framework and how they work together. Mar 12,  · So, here are a few Python Projects for beginners can work on. Python Project Ideas: Beginners Level. This list of python project ideas for students is suited for beginners, and those just starting out with Python or Data Science in general. These python project ideas will get you going with all the practicalities you need to succeed in your career as a Python developer. porfolio_metrics = [portfolio_returns,portfolio_risk,sharpe_ratio_port, portfolio_weights] #from Python list we create a Pandas DataFrame portfolio_dfs = - ame(porfolio_metrics) portfolio_dfs = portfolio_dfs.T #Rename the columns: portfolio_- s = ['Port Returns','Port Risk','Sharpe Ratio','Portfolio Weights'] #convert from object to float the first three columns. for col in ['Port Returns', 'Port Risk', 'Sharpe Ratio']: portfolio_dfs[col] = portfolio.

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