Stock analysis tool suite for individual investors.
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LICENSE
README.md
__init__.py
index.py
strategy.py
symbol.py
utils.py
xbrl.py
xbrl_fundamentals.py

README.md

stock_analysis

This is a suite of basic stock analysis methods collected from Internet. Due to my limited understanding of stocks and financial analysis, there's no guarantee for the correctness of technical/fundamental analysis implementations.

This library is implemented based on pandas and numpy. And it also requires the following libraries:

  • pandas_datareader for downloading history data from Yahoo Finance.
  • bs4 for BeautifulSoup
  • multiprocessing for multiprocessing
  • yahoo_finance for downloading stock statistics from YQL
  • selenium to download financial data from Google Finance

File organization:

  • symbol.py: class for a stock symbol(equity).
  • index.py: classes for a stock exchange index.
  • strategy.py: colleciton of stock analysis strategies.
  • utils.py: misc functions.

The basic usage of this library is:

from stock_analysis import *
nasdaq = NASDAQ()   # define an index
nasdaq.get_financials() # download financial data from Google Finance, a bit slow
nasdaq.get_stats() # compute equity statistic features

# for value analysis
nasdaq_value1 = value_analysis(nasdaq)
nasdaq_value2 = value_ranking(nasdaq)
# for growth analysis
nasdaq_growth = fast_grow_stocks(nasdaq)
# combination of growth and value analysis
stocks = grow_and_value(nasdaq)

# Or ranking based on other attribtues
rank_tags_hybrid = {'EarningsYield':True, 'ReturnOnCapital':True, 'EPSGrowth':True, 'AvgQuarterlyReturn':True,'PriceIn52weekRange':False}
nasdaq_hybrid = ranking(nasdaq, tags=rank_tags_hybrid)

For addtional explanation of the code, please refer to My First Taste of Computational Stock Analysis.

Any questions/suggestions please send e-mail to bonny95@gmail.com.