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The whale portfolios and algorithmic portfolio CSV files contain daily returns, but the S&P 500 CSV file contains closing prices. If any columns have dollar signs or characters other than numeric values, remove those characters and convert the data types as needed.
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Sp500_history.csv: Contains historical closing prices of the S&P 500 Index. Whale_returns.csv: Contains returns of some famous "whale" investors' portfolios.Īlgo_returns.csv: Contains returns from the in-house trading algorithms from Harold's company. Be sure to convert the dates to a DateTimeIndex. Use Pandas to read the following CSV files as a DataFrame.
#Python exponentially weighted standard deviation code#
Use the Whale Analysis Starter Code to complete the following steps: The CSV files include whale portfolio returns, algorithmic trading portfolio returns, and S&P 500 historical prices. You will then use this analysis to create a custom portfolio of stocks and compare its performance to that of the other portfolios, as well as the larger market ( S&P 500 Index).įor this homework assignment, you have three main tasks:įile: Whale Analysis Starter Code Prepare the Dataįirst, read and clean several CSV files for analysis. You will be given the historical daily returns of several portfolios: some from the firm's algorithmic portfolios, some that represent the portfolios of famous "whale" investors like Warren Buffett, and some from the big hedge and mutual funds. You need to create a tool (an analysis notebook) that analyzes and visualizes the major metrics of the portfolios across all of these areas, and determine which portfolio outperformed the others.
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You just learned these quantitative analysis techniques with Python and Pandas, so Harold has come to you with a challenge-to help him determine which portfolio is performing the best across multiple areas: volatility, returns, risk, and Sharpe ratios. Some of the investment managers love them, some hate them, but they all think their way is best. Harold's company has been investing in algorithmic trading strategies. #PLEASE IGNORE FIRST IPYNB I ACCIDENTLY UPLOADED TWICE, PROPER FILE IS IN STARTERCODE>whale_analysis.pynb
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Variance = np.Unit 4 Homework Assignment: A Whale Off the Port(folio) how many samples in each group).Īverage = np.average(values, weights=weights) Values, weights - Numpy ndarrays with the same shape.Īssumes that weights contains only integers (e.g. Return the weighted average and weighted sample standard deviation. Or modifying the answer by as follows: def weighted_sample_avg_std(values, weights): Var = (lhs_numerator - rhs_numerator) / denominator Applied StatisticsĪnd Probability for Engineers, Enhanced eText. Where X is the quantity each person in group i has,Īnd n is the number of people in group i. Just in case you're interested in the relation between the standard error and the standard deviation: The standard error is (for ddof = 0) calculated as the weighted standard deviation divided by the square root of the sum of the weights minus 1 ( corresponding source for statsmodels version 0.9 on GitHub): standard_error = standard_deviation / sqrt(sum(weights) - 1)Ī follow-up to "sample" or "unbiased" standard deviation in the " frequency weights" sense since "weighted sample standard deviation python" Google search leads to this post: def frequency_sample_std_dev(X, n): std_mean the standard error of weighted mean: > weighted_stats.std_mean var the weighted variance: > weighted_stats.var std the weighted standard deviation: > weighted_stats.std You initialize the class (note that you have to pass in the correction factor, the delta degrees of freedom at this point): weighted_stats = DescrStatsW(array, weights=weights, ddof=0) There is a class in statsmodels that makes it easy to calculate weighted statistics: .Īssuming this dataset and weights: import numpy as npįrom import DescrStatsW