from pynance import *


>>> # Save all financial ratio data from quandl.
>>> tickers = ['DD','AA','MMM']
>>> dm = DataManager(tickers)
>>> dm.save_companies()
>>> 
>>> # save a csv named by date with company:ratio as the rows:cols for each date
>>> dm.save_date_csv()
>>>
>>> # Save a csv named by the ratio with ticker:date as the rows:cols
>>> dm.save_field_csv()
>>>
>>>
>>> # Make a portfolio of historical data
>>> P = Portfolio(['MMM', 'DD', 'AA'], '2014-02-01','2014-03-01') # datetime objects work too
>>> P.prices
               AA     DD     MMM
Date                            
2014-02-28  11.74  66.62  134.73
2014-02-27  12.03  66.00  134.34
2014-02-26  12.05  65.51  132.86
2014-02-25  11.62  64.92  132.93
2014-02-24  11.77  64.58  132.20
2014-02-21  11.73  64.87  131.57
2014-02-20  11.78  65.35  131.56
2014-02-19  11.76  64.26  130.56
2014-02-18  11.40  64.71  131.80
2014-02-14  11.37  64.50  132.12
2014-02-13  11.40  63.98  130.14
2014-02-12  11.27  63.51  130.44
2014-02-11  11.33  63.84  130.12
2014-02-10  11.06  63.01  128.85
2014-02-07  11.19  63.01  129.48
2014-02-06  11.05  62.52  128.06
2014-02-05  11.04  61.47  126.53
2014-02-04  11.42  61.18  125.89
2014-02-03  11.20  59.57  123.09

>>>
>>> P.volumes
                  AA        DD      MMM
Date                                   
2014-02-28  19015500   4689300  3179400
2014-02-27  15466500   5119900  3124000
2014-02-26  32810600   4002700  2771100
2014-02-25  21331000   3684800  2863100
2014-02-24  16780900   5521100  2454300
2014-02-21  20272300   4887100  2574300
2014-02-20  22289600   5277400  2155800
2014-02-19  33420700   3778000  2719400
2014-02-18  14298100   4897000  2876500
2014-02-14  11462800   3564300  3030000
2014-02-13  11953500   2964400  2850600
2014-02-12  16103000   3886100  2100600
2014-02-11  20566000   4536500  2604000
2014-02-10  22175600   4315900  3317400
2014-02-07  15312300   4627200  3263200
2014-02-06  22107800   4938300  3828400
2014-02-05  36519500   6049100  4960000
2014-02-04  19897100  15052700  7412500
2014-02-03  26572300   5928600  4245100

>>> # Current quotes from yahoo
>>> P.quotes
                                       AA       DD      MMM
avg_daily_volume              2.73167e+07  4479600  2905540
book_value                          9.839   17.252   26.386
change                                0.1      0.4     0.69
dividend_per_share                   0.12      1.8     2.76
dividend_yield                       1.01      2.7     2.08
earnings_per_share                  -2.14    5.182     6.72
ebitda                             2.556B   5.869B   8.037B
fifty_day_moving_avg              11.6891  64.8429  131.265
fifty_two_week_high                 12.38    67.95   140.43
fifty_two_week_low                   7.63    48.21   102.89
market_cap                        12.943B  62.138B  88.218B
one_yr_target_price                  9.97    67.43   141.12
pct_change_from_200_day_MA        +20.70%   +8.42%   +3.82%
pct_change_from_50_day_MA          +2.75%   +3.30%   +1.41%
price                               12.01    66.98   133.12
price_book_ratio                     1.21     3.86     5.02
price_earnings_growth_ratio          1.05     1.92     1.79
price_earnings_ratio                  N/A    12.85    19.71
price_sales_ratio                    0.56     1.72     2.84
revenue                           23.032B  35.935B  30.871B
short_ratio                           5.7        6      1.9
stock_exchange                       NYSE     NYSE     NYSE
two_hundred_day_moving_avg         9.9507  61.7791   128.22
volume                       3.242145e+07  6827455  5920375

>>> # Get data about each sector, this can also get data per industry
>>> sector_data()

                 1-Day Price Chg % Debt to Equity Div. Yield %   Market Cap  
Basic Materials             24.348         64.748        3.186   238848.55B   
Conglomerates                0.343         39.905        4.262     3740.09B   
Consumer Goods              20.333         103.46        2.283  2112765.28B   
Financial                   25.711        159.778         2.16   201348.83B   
Healthcare                  -0.882         70.265        2.495    87071.67B   
Industrial Goods            45.861        163.004         2.16   264448.05B   
Services                   114.914        120.456        1.085   151329.32B   
Technology                       1          44.66        3.387  1012212.01B   
Utilities                    0.429        147.769         3.36    21777.19B   

                 Net Profit Margin (mrq)     P/E  
Basic Materials                    5.972  15.153   
Conglomerates                        7.8    15.5   
Consumer Goods                      5.62  17.053   
Financial                         15.462  17.931   
Healthcare                        13.197  13.326   
Industrial Goods                    8.81  21.699   
Services                           7.476  19.993   
Technology                         6.439  25.909   
Utilities                          6.026  24.572   

                 Price To Free Cash Flow (mrq) Price to Book   ROE %  
Basic Materials                        -20.554         2.399  12.171  
Conglomerates                               -6          6.42    23.9  
Consumer Goods                          -4.837         1.003   13.12  
Financial                                4.681         1.703   9.307  
Healthcare                              -3.197        19.746   13.66  
Industrial Goods                        33.965        17.325    14.4  
Services                                -8.127        -2.347  33.085  
Technology                            -113.482         2.638   8.867  
Utilities                              -89.242         2.577   7.235