{"id":28956,"date":"2019-12-16T15:55:00","date_gmt":"2019-12-16T20:55:00","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=28956"},"modified":"2024-05-20T15:37:48","modified_gmt":"2024-05-20T19:37:48","slug":"sharpe-ratio-quantinsti","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/sharpe-ratio-quantinsti\/","title":{"rendered":"Sharpe Ratio: Calculation, Application, Limitations"},"content":{"rendered":"\n

Excerpt<\/strong><\/em><\/p>\n\n\n\n

William Sharpe introduced a simple formula to help compare different portfolios and help us find the most feasible of them all. Let\u2019s understand its mechanism in this article. <\/p>\n\n\n\n

What is Sharpe Ratio?<\/strong><\/p>\n\n\n\n

Sharpe ratio is a measure for calculating risk-adjusted return. It is the ratio of the excess expected return of investment (over risk-free rate) per unit of volatility or standard deviation.<\/p>\n\n\n\n

Let us see the formula for Sharpe ratio which will make things much clearer. The sharpe ratio calculation is done in the following manner<\/p>\n\n\n\n

Sharpe Ratio = (Rx \u2013 Rf) \/ StdDev(x)<\/code><\/p>\n\n\n\n

Where,
x is the investment
Rx is the average rate of return of x
Rf is the risk-free rate of return
StdDev(x) is the standard deviation of Rx
<\/p>\n\n\n\n

Sharpe Ratio can be used in many different contexts such as performance measurement, risk management and to test market efficiency. When it comes to strategy performance measurement, as an industry standard, the Sharpe ratio is usually quoted as \u201cannualised Sharpe\u201d which is calculated based on the trading period for which the returns are measured. If there are N trading periods in a year, the annualised Sharpe is calculated as:<\/p>\n\n\n\n

Sharpe Ratio = \u221aN (E(Rx \u2013 Rf) \/ StdDev(x))<\/code><\/p>\n\n\n\n

\n\n\n\n

Sharpe ratio in Python<\/strong><\/p>\n\n\n\n

If you would like to find the Sharpe ratio on your own, you can try the following Python code:<\/p>\n\n\n\n

# Load the required modules and packages
\nimport numpy as np
\nimport pandas as pd
\nimport pandas_datareader as web
\n# Pull NIFTY data from Yahoo finance
\nNIFTY = web.DataReader(‘^NSEI’,data_source=’yahoo’,start=’6\/1\/2012′, end=’6\/1\/2016′)
\n# Compute the logarithmic returns using the closing price 
\nreturns = np.log(NIFTY[‘Close’] \/ NIFTY[‘Close’].shift(1))
\nvolatility = returns.std() * np.sqrt(252) 
\nsharpe_ratio = (returns.mean() – 0.05) \/ volatility
\nsharpe_ratio\n<\/p>\n\n\n\n

The above program gives the following output:<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Visit QuantInsti website to download the Python code:
https:\/\/blog.quantinsti.com\/sharpe-ratio-applications-algorithmic-trading\/<\/a> <\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Sharpe ratio is a measure for calculating risk-adjusted return. It is the ratio of the excess expected return of investment (over risk-free rate) per unit of volatility or standard deviation.<\/p>\n","protected":false},"author":186,"featured_media":28966,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":true,"footnotes":""},"categories":[339,343,349,338,350,341,344],"tags":[1225,1224,595,5545,860],"contributors-categories":[13654],"class_list":{"0":"post-28956","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-programing-languages","9":"category-python-development","10":"category-ibkr-quant-news","11":"category-quant-asia-pacific","12":"category-quant-development","13":"category-quant-regions","14":"tag-numpy","15":"tag-pandas","16":"tag-python","17":"tag-sharpe-ratio","18":"tag-volatility","19":"contributors-categories-quantinsti"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"\nSharpe Ratio: Calculation, Application, Limitations<\/title>\n<meta name=\"description\" content=\"QuantInsti dives into Sharpe ratio Calculation, Application, Limitations. 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