{"id":46889,"date":"2020-05-29T09:55:00","date_gmt":"2020-05-29T13:55:00","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=46889"},"modified":"2022-11-21T09:45:37","modified_gmt":"2022-11-21T14:45:37","slug":"python-itertools-tutorial-part-ii","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/python-itertools-tutorial-part-ii\/","title":{"rendered":"Python Itertools Tutorial \u2013 Part II"},"content":{"rendered":"\n

See Part I<\/a> in this series to get familiar with itertools.<\/em><\/p>\n\n\n\n

Terminating iterators<\/strong><\/p>\n\n\n\n

In contrast to the infinite iterators, this type of iterator does not keep going endlessly. Terminating iterators produce a short output and are used for fast processing of the elements in a collection. Let\u2019s go through a few of them now.<\/p>\n\n\n\n

The accumulate() iterator<\/strong><\/p>\n\n\n\n

This iterator can be used to perform algebraic operations on the elements of a collection. For example, let\u2019s say we have the daily percentage returns of the closing price of Tesla, Inc. (TSLA) and we want to see how it adds up. Well we will use the accumulate function.<\/p>\n\n\n\n

Since we need the data of a stock, we will import yahoo finance libraries and retrieve the data of Tesla Inc. for this example.<\/p>\n\n\n\n

The code is as follows:<\/p>\n\n\n\n

\n# Importing libraries
\nimport yfinance as yf
\nimport pandas as pd

\n\n# Import Tesla data
\ntesla = yf.download(‘TSLA’,’2020-03-01′, ‘2020-03-30’)
\ntesla[‘daily_returns’] = tesla[‘Close’].pct_change()\n\n<\/p>\n\n\n\n

Now, we will use the accumulate function.<\/p>\n\n\n\n

\n# accumulate function
\ntesla[‘daily_returns’].dropna(inplace=True)
\nresult = itertools.accumulate(tesla[‘daily_returns’], operator.add)
\nfor each in result:
\n print(each)\n\n<\/p>\n\n\n\n

In the next installment, the author will discuss<\/em> the chain() iterator<\/em>.<\/p>\n\n\n\n

Visit https:\/\/www.quantinsti.com\/<\/a> for ready-to-use Python functions as applied in trading and data analysis.<\/p>\n","protected":false},"excerpt":{"rendered":"

QuantInsti explores the accumulate() iterator, which is used to perform algebraic operations. Code along using Python yfinance and pandas.<\/p>\n","protected":false},"author":91,"featured_media":21196,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[343,349,338,350,341,344],"tags":[851,5615,806,7460,7691,1224,595,6674],"contributors-categories":[13654],"class_list":{"0":"post-46889","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-programing-languages","8":"category-python-development","9":"category-ibkr-quant-news","10":"category-quant-asia-pacific","11":"category-quant-development","12":"category-quant-regions","13":"tag-algo-trading","14":"tag-argument","15":"tag-data-science","16":"tag-iterator","17":"tag-lambda-machine-learning","18":"tag-pandas","19":"tag-python","20":"tag-yfinance","21":"contributors-categories-quantinsti"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"\nPython Itertools Tutorial \u2013 Part II | IBKR Quant<\/title>\n<meta name=\"description\" content=\"QuantInsti explores the accumulate() iterator, which is used to perform algebraic operations. 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