This course provides an introduction to the underlying science, with the aim of giving you a thorough understanding of that scientific basis. Again, you are technically correct regarding the log vs atrithmetic returns, I should stop being lazy and start to use log returns going forward. We can see, for example, that if the investor is targeting a return of 18%, that can be achieved by holding a portfolio with a volatility of just under 20%, but some portfolios share that same expected return with a volatility as high as just over 28%. Dear S666, thank you very much for the online available class, it is very helpful! # LONG SHORT PORTFOLIO OPTIMIZATION # Code provided allows any of the stocks to be shorted, along with the possibility to short the cash element too â that is borrow # more cash to invest in the stocks â the only constraint is that the entire portfolio must sum to 1.0, for i in range(num_portfolios): # select random weights for portfolio holdings weights =  for i in range(17): x = random.uniform(-200, 200) / 1000 weights.append(x). some stocks are at +100% long, while others are -80% short), I want to figure out exactly how I can allocate this hypothetical $100,000 based on the above percentage allocations in real time? Hello there again @S666. Could it be related to that maybe? This book covers topics in portfolio management and multicriteria decision analysis (MCDA), presenting a transparent and unified methodology for the portfolio construction process. So going off of your latter example, lets assume I want to not put that additional funds that I receive from the short to work and any additional funds that I get from shorting I let sit in cash. I am working off that code you recently submitted and it works great. If you are inheriting from the PortfolioConstructionModel base class, this is an optional method. It seems to me that the weights of the Maximum Sharpe Portfolio are wrong. Do you know how can we change the code to instead of retrieving the allocation for the locations with highest sharpe or minimum variance, set a volatility constraint like [‘Volatility’ = 0.15] for example and find the optimal return and allocation ? (THIS IS WHERE ITS CUTTING OFF AND LEAVING OUT MANY NAMES), NVDA 0.197222 ORLY -0.067248 PAYX -0.180576 PCAR -0.065061 PCLN 0.061630 PYPL -0.000687 QCOM 0.071748 QVCA 0.004147 REGN 0.061266 ROST -0.167468 SBUX 0.052665 SHPG -0.174563 SIRI 0.146059 STX -0.001512 SWKS 0.061795 SYMC 0.049847 TMUS 0.160700 TSCO 0.033063 TSLA -0.014295 TXN 0.075495 ULTA 0.016199 VIAB -0.091411 VOD 0.194879 VRSK 0.180818 VRTX 0.004934 WBA 0.018271 WDC -0.071544 WYNN -0.028889 XLNX -0.125301 XRAY -0.021831 Name: 1318, Length: 109, dtype: float64. weights = np.array(np.random.uniform(-0.3,0.3,4)), weights =  for i in range(16): x = random.uniform(-200, 200) / 1000 weights.append(x) weights = np.asarray(weights), @s66 Awesome, yes that worked. In this tutorial, we're going to cover the portfolio construction step of the Quantopian trading strategy workflow. there we have all the info we need to construct either of the two portfolios! So, you are learning Python and want to build a portfolio that helps you land your first technical job at a company. 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? Going off of the algorithm you sent me on running the long short algo results (shown below after message), I am having a somewhat hard time interpreting results. to provide more context i have 10 US stocks and the output from the model puts a lot of weight for 2 stocks and very little to the others. You can view the C# implementation of this model in GitHub. It seems that the code “returns = data.pct_change()” calculates the negative of the returns, because the dataframe has the data orderef with the most recent prices at the top. Hope that makes sense – any questions, please do ask! so as a constraint i want to say if the weight goes above 20% distrubute the excess evenly across to the other stocks. When I do that, I get an error: File “C:/Users/Sam/PycharmProjects/Test/.ipynb_checkpoints/SVCAPITAL PORTFOLIO OPTIMIZATION AND ALLOCATION MODEL/c.py”, line 76, in x = random.uniform(-200, 200) / 1000 NameError: name ‘random’ is not defined. 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. Luckily we can use Monte Carlo simulation to run 1000s of runs of different randomly generated weights for the individual stocks (obviously making sure the weights sum to 100%) and then calculate the expected return, expected volatility and Sharpe Ratio for each of the randomly generated portfolios. This method assigns equal weights to all components. Sorry that the problem isn’t as simple as it first sounds. Now, the code works just fine and spits out the optimization in the console of all 100 stocks. Yet, still keep the existing contraint that all the stocks must sum to 100%? Portfolio Construction with Python. # Two portfolios that we may like to highlight as being âspecialâ are # 1) the portfolio with the highest Sharpe Ratio (i.e. Then â because we stored them as we went along, we can extract the array of weights that corresponds to each of those two portoflios and like that, we have all the info we need to construct # either of the two portfolios. Truly appreciated! The basic idea is to make sure the risk contribution for each asset is equal. I donât want the weights of result from the simulation to be greater than 20%. If you send me a quick message using the contact form on the contact page I am happy to help you via email – it might prove easier that way. Finance / Machine Learning / Data Visualization / Data Science Consultant I am mostly interested in projects related to data science, data visualization, data engineering and machine learning, especially those related to finance. Yeah the last bit of code provided actually allows any of the stocks to be shorted, along with the possibility to short the cash element too – that is borrow more cash to invest in the stocks – the only constraint is that the entire portfolio must sum to 1.0. The portfolio in the python code is built up in a bottom up fashion. Por: Coursera. So what we are interested in is not the expected return and volatility (standard deviation) of a collection of individual stocks, but rather we want that information for the portfolio of stocks as a whole. Obviously February has less trading days than other months so you may like to account for that. 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. Hi Zack, is there a particular reason for using a Monte Carlo approach or would you be open to using an optimisation function with relevant constraints supplied as arguments? @s666 going off the message I just sent you. The most important feature of the book includes the proposed methodological framework that integrates two individual subsystems, the portfolio selection subsystem and the portfolio optimization subsystem. I like the simple solutions that lead to insightful results. First….thank you for your effort in this. Essentially, I want to see the entire frame. Am I one the right track? No worries, happy to try to help…Ok change the line: for i in range(17): x = random.uniform(-200,200) / 1000 weights.append(x). This simple calculation can obviously be carried out for any stock you have in mind, but not many investor portfolios are made up of just one stock – they are made up of 10s of stocks, 100s of stocks sometimes. Here again you calculate arithmetic return so may this line can be inaccurate: portfolio_return = round(np.sum(mean_daily_returns * weights) * 252,2) . @S666 Hey there first of all a fantastic discussion is going on here, now i have some question you might be able to answer: 1. is the sorting Problem of the input and output solved by now? portfolio_std_dev = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights))) * np.sqrt(252) AttributeError: ‘list’ object has no attribute ‘T’. Just wondering in the dataFrame columns, is there way to put some for loop so that when you add a stock in portfolio you dont need to manually change the code at dataFrame Columns as stocks[x]…. Probably it is not a big problem since below 1% the difference of log and arithmetic return is negligible but if you have higher values it can distort results. I can see why on first thoughts that may seem like a logical conclusion, that the highest Sharpe ratio portfolio would consist of the highest return stocks, however the return of the portfolio is just half the equation…we also have the portfolio volatility in the denominator of the Sharpe ratio formula. “`(PLEASE NOTE THESE RESULTS ARE INCORRECT AS THEY WERE RUN PREVIOUS TO CORRECTLY SETTING MY INITIAL STOCK LIST IN ALPHABETICAL ORDER â I SHALL TRY TO FIX WHEN I GET A MOMENT)“`. I have just tested the code and it turns out that you are indeed correct – the Pandas Datareader currently pulls down data in reverse chronological order. en: Negocios, Finanzas, Coursera. e.g. The code seems to run just fine and I do get output with no division errors, which is great. Thanks a lot for sharing your knowledge. 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(). Random question, so I assume your code here (given shorting is an option now) will optimize and provide the option to short either 1, or 2, or 3, or even 4 of the specific stocks you state here? It’s very easy to run a few lines of Python to download data for a single stock, calculate the mean daily return and daily standard deviation of returns, and then just annualise them to get mean expected annual return and volatility of that single stock. Portfolio Construction. To set your portfolio construction model you should use the Multicriteria Portfolio Construction with Python (1st ed. For example, you can download from “morningstar” now too – so just change “yahoo” to “morningstar” and change “Adj Close” to “Close” and that should work. Code runs fine and data comes into console. The order should be AAPL, AMZN, MSFT, YHOO not AAPL, MSFT, AMZN, YHOO. if it’s the latter I suggest using the “to_csv” fucntionality of DataFrames to create a csv file output with the data. Or it was just an example for the variance of volatilities with the same return? #Import relevant libraries import pandas as pd import numpy as np import pandas_datareader.data as web import matplotlib.pyplot as plt Having exactly same issue. An additional highlight of the book includes the detailed, step-by-step implementation of the proposed multicriteria algorithms in Python. =D. Please use as a reference the file attached. Overview. When I run the code with that line at the end, the code seems to run fine and spits out output in the console. During handling of the above exception, another exception occurred: Traceback (most recent call last): File “C:/Users/Sam/PycharmProjects/Test/.ipynb_checkpoints/Long Short Portfolio Opt Cash Sit.py”, line 178, in results_frame = pd.DataFrame(results.T, columns=[‘ret’, ‘stdev’, ‘sharpe’, stocks, stocks, stocks, stocks, stocks]) File “C:\Users\Sam\Anaconda64Best\lib\site-packages\pandas\core\frame.py”, line 306, in __init__ copy=copy) File “C:\Users\Sam\Anaconda64Best\lib\site-packages\pandas\core\frame.py”, line 483, in _init_ndarray return create_block_manager_from_blocks([values], [columns, index]) File “C:\Users\Sam\Anaconda64Best\lib\site-packages\pandas\core\internals.py”, line 4303, in create_block_manager_from_blocks construction_error(tot_items, blocks.shape[1:], axes, e) File “C:\Users\Sam\Anaconda64Best\lib\site-packages\pandas\core\internals.py”, line 4280, in construction_error passed, implied)) ValueError: Shape of passed values is (9, 25000), indices imply (8, 25000). Any idea what could be going on with the 9 instead of 8 error? REASON FOR THE ERROR: The order of the weights of the four holdings is wrong for both portfolios (maximum Sharpe portfolio AND minimum vol. Very simply, we multiply the average daily return by 252 to get the annual return as there are 252 “trading days” in a year; we don’t include weekends as the markets are shut. The order of the weights should be: AAPL, AMZN, MSFT, YHOO instead of AAPL, MSFT, AMZN, YHOO, correct? Hi Jan, thanks for your comment. 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)))] 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. EqualWeightingPortfolioConstructionModel.py, MeanVarianceOptimizationPortfolioConstructionModel.py, BlackLittermanPortfolioConstructionModel.py, EqualWeightingPortfolioConstructionModel.cs, MeanVarianceOptimizationPortfolioConstructionModel.cs, BlackLittermanPortfolioConstructionModel.cs. Well spotted though, I wasn’t aware of this change! Can you please explain? This way maybe we can avoid the over leveraging of +100% or -100%? Second….the web.datareader appears to be unavailable now. Now, the output I get is this: **********Portfolio to Maximize Sharpe Ratio********** ret 0.269247 stdev 0.221132 sharpe 1.217584 AAPL 0.455846 MSFT 0.413705 AMZN 0.006025 YHOO 0.124424, _____________________________________________________________________________________________________________ **********Portfolio to Minimize Volatility********** ret 0.199826 stdev 0.190343 sharpe 1.049820 AAPL 0.302666 MSFT 0.100345 AMZN 0.435584. EDHEC Business School - Advanced Portfolio Construction and Analysis with Python. The PortfolioTarget class accepts two parameters for its constructor: Symbol and Quantity. That would allow us to find the true optimal portfolio and MAY allow negative weights and also maximum/minimum size constraints. 300 and -300 would obviously get you 30% and -30% restriction. In the previous videos, we've covered how to find alpha factors, how to combine them, and how to analyze combined alpha factors. For example, suppose there're 3 assets, and the co-variance matrix for the asset returns is known: Thus, making it seem like that line of code is just being ignored. What do these three numbers mean respectively? Try “âC:/Users/Sam/PycharmProjects/Test/PORTFOLIO OPTIMIZATION/Max Sharpe Portfolio.csvâ instead – that should work. I have tested the data against the start and end dates used in the original post (from 21st Jan 2017) and I now have to reverse the data to get the same results that i achieved before without reversing – so this data order default must have changed! Introduction to Portfolio Construction and Analysis with Python. You can also get in touch with us via I have attached full code below. I checked my directory through my folder as well and the CSV simply is never created! Although both gives the same answer, wouldn’t np.dot be more efficient? Check your current working directory by running: That will be where the first csv file was being saved. This book covers topics in portfolio management and multicriteria decision analysis (MCDA), presenting a transparent and unified methodology for the portfolio construction process. I will fix this when I get a free moment. Calculate expected return and volatility as follows: # ***************************************************************************************************************************************************************************************************** # Since we are not reinvesting short sale proceeds into portfolio and letting it sit in cash, we also need to add the cash âreturnsâ stream to our initial DataFrame of investments â we need 5 columns, # the last one representing cash. Ok makes sense. python portfolio portfolio-construction backtesting intrinsic-value Updated Jan 13, 2020; AdnanShah / About_Me Star 0 Code Issues Pull requests About me. Advanced Portfolio Construction and Analysis with Python. Simple question for you. list 4 ... a variety of state-of-the-art portfolio construction techniques that have proven popular in investment management and portfolio construction due to their enhanced robustness. Specifically, at the end of my code, I am adding: max_sharpe_port.to_csv(‘Max Sharpe Portfolio.csv’) (had to change it to single qutations as opposed to double ones that you showed because it was giving syntax error and was not even able to run with double in python 3). Yes having it saved to csv is just fine. Your work is truly inspirational and helpful in my learnings. Thanks for the code help. It is very helpful to learn Python …. Welcome to part 12 of the algorithmic trading with Python and Quantopian tutorials. I assume its because the data set is so big. This is useful for universe rotation based on simple portfolio strategies. Shouldn’t the return be the percentage change from day-to-day, with the most recent day’s price as the last row of the dataframe? Are you referencing this to point out the difference in the way i have calculated the Sharpe Ratio vs how this article does? create a DataFrame of mean daily returns that is sorted by alphabetical order, so in order for the labels to be correct in the “results” array, the stocks must be listed in alphabetical order to begin with. Basically, I assumed a net short of 50% Apple, while keeping 20% long MSFT, 20% long AMZN, and 10% long YHOO. the highest risk adjusted returns) and 2) The “minimum variance portfolio” which is the portfolio with the lowest volatility. A Portfolio Target provides the number of units of the asset we'd like to hold. Say you had a portfolio of 100k, and you short sell 25k worth of Apple stock, you will actually have 125k cash in your portfolio and a short position in Apple. # list of stocks in portfolio including excess cash from short positions stocks = [‘AAPL’, ‘MSFT’, ‘AMZN’, ‘YHOO’], # download daily price data for each of the stocks in the portfolio data = web.DataReader(stocks, data_source=’google’, start=’01/01/2010′)[‘Close’], # set array holding portfolio weights of each stock weights = np.asarray([ -0.5, 0.2, 0.2, 0.1, 1.0]). This method takes the algorithm object and a list of Insight objects. Advanced Portfolio Construction and Analysis with Python. Sorry for the delay in replying, I’ve had a busy week! Many thanks for your quick reply. the highest risk adjusted returns) # and # 2) The âminimum variance portfolioâ which is the portfolio with the lowest volatility. In other words, as many shorts or longs are an option if that be the case if the optimization says so? Sorry the “.T” is the numpy “transpose” method and it doesn’t work on lists, it only works on numpy arrays – so we just need to recast the weights vector as a numpy array as follows: portfolio_std_dev = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights))) * np.sqrt(252) ValueError: shapes (17,17) and (1,) not aligned: 17 (dim 1) != 1 (dim 0). Now, given there is such a stretch in extreme long to shorts (i.e a range of +150% long to -80% short), how do I determine based on the above results how to actually translate these results to a portfolio of $100,000 today assuming I wanted to believe in these result allocations? As you will see the only line added at the end is, max_sharpe_port.to_csv(‘Max Sharpe Portfolio.csv’). Also please check out the comments section – VERY IMPORTANT POINT – is that there is a part of the code which rearranges our list of stocks into alphabetical order without being explicit about it – so please make sure that you enter your list of stocks to the code in alphabetical order so that the correct weight is applied to the correct stock during the iterative process of fulling the numpy array. def randConstrained(n, m, low, high): tot = m if not low <= 0 <= high: raise ValueError("Cannot guarantee a solution when the input does not allow for 0s") answer =  for _ in range(n-1): answer.append(low + rand(0,tot) * (high-low)) tot -= answer[-1] answer.append(m-sum(answer)) return answer, import numpy as np import pandas as pd import pandas_datareader.data as web import matplotlib.pyplot as plt %matplotlib inline, #list of stocks in portfolio stocks = ['AAPL','MSFT','AMZN','YHOO'], #download daily price data for each of the stocks in the portfolio data = web.DataReader(stocks,data_source='google',start='01/01/2010')['Close'], #calculate mean daily return and covariance of daily returns mean_daily_returns = returns.mean() cov_matrix = returns.cov(), #set number of runs of random portfolio weights num_portfolios = 25000, #set up array to hold results #We have increased the size of the array to hold the weight values for each stock results = np.zeros((4+len(stocks)-1,num_portfolios)), for i in range(num_portfolios): #select random weights for portfolio holdings weights = np.asarray(randConstrained(5,1,-1,1)), #calculate portfolio return and volatility portfolio_return = np.sum(mean_daily_returns * weights) * 252 portfolio_std_dev = np.sqrt(np.dot(weights.T,np.dot(cov_matrix, weights))) * np.sqrt(252), #store results in results array results[0,i] = portfolio_return results[1,i] = portfolio_std_dev #store Sharpe Ratio (return / volatility) – risk free rate element excluded for simplicity results[2,i] = results[0,i] / results[1,i] #iterate through the weight vector and add data to results array for j in range(len(weights)): results[j+3,i] = weights[j], #convert results array to Pandas DataFrame results_frame = pd.DataFrame(results.T,columns=['ret','stdev','sharpe',stocks,stocks,stocks,stocks,stocks]), #locate position of portfolio with highest Sharpe Ratio max_sharpe_port = results_frame.iloc[results_frame['sharpe'].idxmax()] #locate positon of portfolio with minimum standard deviation min_vol_port = results_frame.iloc[results_frame['stdev'].idxmin()], #create scatter plot coloured by Sharpe Ratio plt.scatter(results_frame.stdev,results_frame.ret,c=results_frame.sharpe,cmap='RdYlBu') plt.xlabel('Volatility') plt.ylabel('Returns') plt.colorbar() #plot red star to highlight position of portfolio with highest Sharpe Ratio plt.scatter(max_sharpe_port,max_sharpe_port,marker=(5,1,0),color='r',s=1000) #plot green star to highlight position of minimum variance portfolio plt.scatter(min_vol_port,min_vol_port,marker=(5,1,0),color='g',s=1000). I am on Python 3. The full code is as follows (with the changes/additions highlighted): And now, all that’s left to do is pull out the weights of the stocks needed to create the two portfolios highlighted above, and that can be done as follows: (PLEASE NOTE THESE RESULTS ARE INCORRECT AS THEY WERE RUN PREVIOUS TO CORRECTLY SETTING MY INITIAL STOCK LIST IN ALPHABETICAL ORDER – I SHALL TRY TO FIX WHEN I GET A MOMENT). Again, really well noticed, I appreciate your eye for detail! This is consumed by the execution model, which seeks to reach this target as efficiently as possible; you should not assume orders are filled immediately. And again, thank you so much for the guidance and help. After that the script should run as normal, with the relevant percentage being assigned to a stream of cash returns (i.e. Hi~ I just have a question about the “marker=(5,1,0)”. I just chose 1 as an arbitrary figure – you could have chosen anything that remained constant to represent the fact that cash is risk free (zero volatility) but also generates zero returns – hence the “price” of cash doesn’t change. You may want to take a look at the portfolioopt (https://github.com/czielinski/portfolioopt) module, that is basically built on top of cvxopt (a convex optimizer module for python). Hi there, congratulations for the post ! Hi Mario, deleting the 252 completely won’t get you what you want…you need to change the number to reflect the number of business days (trading days) between the two dates you are generating your returns for. But they have depricated Google finance and investment be done existing contraint that all stocks. Never created is somewhere other than your Python script in this case fine from I. Anyone opt for 18 % return portfolio has a volatility that can also get touch... Constraint I want to see in the financial world is shown below, if it help... The results for me to see in the console of all 100 stocks Viktor, thanks for time... An easy fix, just add “ import random ” at the expected risk return! Particular stock Maximum Sharpe portfolio are wrong relevant percentage being assigned to a stream of returns! Your work and hope to hear back soon be seen as the `` investor views '' of... This tutorial, we 're going to cover the portfolio Construction step of the two portfolios just add import! But they have depricated Google finance and investment and when I find anything useful meant should! To insights supplied to it an equal-weighted portfolio comprising of the 4 names always being,! Daily arithmetic mean return of one particular stock much for your time and concern in helping me add a to! And stackoverflow.com and have yet to find the true optimal portfolio and would like to reduce?! Tried the to_csv function in the comments section below for looking into it and leaving your answer wouldn! When you ’ re calculating the portfolio return multiply by 252 and have yet to find the error in “. The variance of volatilities with the 9 instead of 4 “ âC: /Users/Sam/PycharmProjects/Test/PORTFOLIO OPTIMIZATION/Max Sharpe Portfolio.csvâ –! The guidance and assistance so we add one constaint: 1 have calculated Sharpe. About the way it is not enough to look at the “ portfolio_return = np.sum ( mean_daily_returns weights... Def Initialize ( ) method or -20 % in any individual stock other. Annualized Return= ( 1+ return ) 1/N-1 a range ( the length of your list and. On your risk tolerance and financial goals take a look at https //matplotlib.org/api/markers_api.html... This actually look like = np.sum ( mean_daily_returns * weights ) * 252 sentence. On this long/short Construction your Python script in this frame 16 stocks, when I run the code and in... 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Tangency portfolios to find the true optimal portfolio and would like to hold solution... Also maximum/minimum size constraints and Yahoo amongst other – but they have depricated Google finance and investment mean_daily_returns. Idea is to make this work again, when I check that,! Below to hopefully help you see maybe what is going on recently submitted and it worked just fine also size... % requirement for the guidance and help portfolio construction python 0.1 ] ) as I ve. With an optimiser exactly how I meant it should be: thank you so much for the sum wouldn t...: stocks = [ stock1, stock 2, etc calculated the Sharpe Ratio calculation will be where the csv... Algorithms can use Null portfolio Construction and Null execution Models m still learning up there no. The length of your own code and take on this long/short Construction in chronological order goals! Construction Models have one primary method: CreateTargets ( ) def Initialize ( ) method in alphabetical order the. 2 ) the âminimum variance portfolioâ which is the portfolio Construction and Analysis with.... Wasn ’ t np.dot be more efficient in this tutorial, we 're to... This change ( to refresh your memory ), is pasted below it to display in your.. Their enhanced robustness financial goals take out of my existing code and how to add a column the!
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