Metadata-Version: 1.1
Name: stockbot
Version: 0.2.0
Summary: Stock market analysis library written in Python.
Home-page: https://github.com/ChrisPappalardo/stockbot
Author: Chris Pappalardo
Author-email: cpappala@yahoo.com
License: CC BY-NC-ND 4.0
Description: ===============================
        stockbot
        ===============================
        
        .. image:: https://img.shields.io/travis/ChrisPappalardo/stockbot.svg
                :target: https://travis-ci.org/ChrisPappalardo/stockbot
        
        .. image:: https://img.shields.io/pypi/v/stockbot.svg
                :target: https://pypi.python.org/pypi/stockbot
        
        
        Stock market analysis library written in Python.
        
        * Copyright (C) 2016 by Chris Pappalardo <cpappala@yahoo.com>
        * License: Creative Commons (CC) BY-NC-ND 4.0 https://creativecommons.org/licenses/by-nc-nd/4.0/
        * Documentation: https://stockbot-lib.readthedocs.org/
        
        Features
        --------
        
        * Market data sourcing from Yahoo!, CNBC, and zipline bundles
        * S&P500 stock listing scraper
        * ADX, DI, and Stochastic technical indicators implemented using TA-lib
        * Average Directional Movement Index (ADX) ranking for portfolios
        * Trending and oscillating instrument trading algorithms for zipline
        
        Installation
        ------------
        
        Install the latest package with::
        
          $ pip install stockbot
        
        The dependencies are not trivial and may not install properly on your system through pip.  We
        recommend developing and deploying your projects that use stockbot in containers with the necessary
        packages pre-installed.
        
        One way to do this is to first build the `quantopian/zipline` Docker image with the following command::
        
          $ docker build -t quantopian/zipline https://github.com/quantopian/zipline.git#1.0.2
        
        Using a docker-compose development configuration similar to the one contained in stockbot, you could
        then create a development container with::
        
          $ docker-compose -f docker-compose-dev.yml up
        
        Note that our docker configuration installs the latest zipline `quantopian-quandl` bundle in the project
        root.  This is necessary for the default stockbot configuration when using functions such as
        `get_zipline_dp` and `get_zipline_hist`.
        
        Usage
        -----
        
        Stockbot can provide you with a list of S&P500 stocks from `wikipedia`::
        
           >>> from stockbot.core import get_sp500_list
           >>> get_sp500_list()
           [u'MMM', u'ABT', u'ABBV', u'ACN', u'ATVI', u'AYI', u'ADBE', ... u'ZTS']
           
        To get a delayed quote from Yahoo! use `get_yahoo_quote`::
             
           >>> from stockbot.sources import get_yahoo_quote
           >>> get_yahoo_quote('YHOO')
           {'volume': 3405057, 'last': 41.0, 'symbol': 'YHOO', 'datetime': datetime.datetime(2016, 11, 22, 18, 0, tzinfo=<UTC>), 'high': 41.4, 'low': 40.83, 'open': 41.2, 'change': -0.11}
        
        Or a real-time quote from CNBC using `get_cnbc_quote`::
        
           >>> from stockbot.sources import get_cnbc_quote
           >>> next(get_cnbc_quote('YHOO'))
           {'volume': 3528566, 'last': 41.04, 'symbol': u'YHOO', 'datetime': datetime.datetime(2016, 11, 22, 21, 0, tzinfo=<UTC>), 'high': 41.395, 'low': 40.83, 'open': 41.2, 'change': -0.07}
        
        Note:: `get_cnbc_quote` returns a generator
        
        Stockbot returns quote data using a `dict` like object `stockbot.marketdata.MarketData` that performs
        certain data and datetime processing.
        
        Historical data can be obtained from Yahoo! using `get_yahoo_hist`::
             
           >>> from stockbot.sources import get_yahoo_hist
           >>> get_yahoo_hist('YHOO')
           {'high': 41.48, 'last': 41.110001, 'datetime': datetime.datetime(2016, 11, 21, 21, 0, tzinfo=<UTC>), 'volume': 11338000, 'low': 40.939999, 'close': 41.110001, 'open': 41.439999}
           
        Historical data can also be obtained from zipline bundles using the `get_zipline_hist` function::
        
           >>> from stockbot.sources import get_zipline_hist
           >>> get_zipline_hist('YHOO', 'close', 
           2016-01-04 00:00:00+00:00    31.41
           Freq: C, Name: Equity(3177 [YHOO]), dtype: float64
        
        Look up symbols with `stockbot.sources.get_symbol` which searches Yahoo! finance for the passed term.
        
        Zipline trading algorithms that utilize the Directional Movement technical indicator system are provided in 
        `stockbot.algo`.  For example, the following zipline trading algorithm would use ADX and DI to trade the
        top trending stocks and Stochastic Oscillators to trade the top oscillating stocks in the S&P 500 index::
        
           from logbook import Logger
           from stockbot.algo.core import (
               adx_init,
               trade_di,
               trade_so,
           )
           from stockbot.core import get_sp500_list
        
           def initialize(context):
               return adx_init(
                   context,
                   name='adx_di_so',
                   top_rank=5,
                   bot_rank=5,
                   di_window=14,
                   symbols=get_sp500_list(),
                   log=Logger('Stockbot'),
               )
        
           def handle_data(context, data):
               # increment counter and log datetime
               context.i += 1
               context.adx['log'].info('processing %s' % context.get_datetime())
        
               # trade trending S&P500 stocks using the DI system
               trade_di(
                   context,
                   data,
                   window=context.adx['di_window'],
                   portfolio=[i for (i, adx) in context.adx['top']],
                   capital_ppi=1.0/(len(context.adx['top'])+len(context.adx['bot'])),
                   log=context.adx['log'],
               )
        
               # trade oscillating S&P500 stocks using the SO system
               trade_so(
                   context,
                   data,
                   window=context.adx['di_window'],
                   portfolio=[i for (i, adx) in context.adx['bot']],
                   capital_ppi=1.0/(len(context.adx['top'])+len(context.adx['bot'])),
                   log=context.adx['log'],
               )
        
        To run this algorithm in a docker container, copy the code above into a file and issue the following::
        
          $ zipline run -f <file> --start <date> --end <date>
        
        Use the the `<YYYY-M-D>` format for dates.  Use `-o /path/file.pickle` to capture pickled results that
        can be used in python.
        
        
        
        
        History
        -------
        
        0.1.0 (2015-09-23)
        ---------------------
        
        * created basic market data sourcing from Yahoo! and CNBC
        
        0.2.0 (2016-11-22)
        ------------------
        
        * created S&P500 stock listing scraper
        * implemented TA-lib for technical analysis (ADX, DI, STOCH)
        * added zipline bundles to data sourcing
        * created zipline trading algorithms for trending and oscillating instruments
        
Keywords: stockbot
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: Other/Proprietary License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
