pystocklib
Python package for Stock Market analysis. (Historical Data, News analysis, visual, Empirical Mode Decomposition)
Features
- Historical Data
- load_csv
- get_date, get_open, get_high, get_low, get_close, get_adj, get_volume
- retrieve_col_data
- create_csv
- moving_average
- high_minus_low
- standard_deviation
- info_plot
- News analysis
- extract_news
- get_sentiment
- get_news
- get_result
- visual
- plot_fig
- save_fig
- Empirical Mode Decomposition (EMD)
- get_trend
- get_modes
- save_figure
How to use
Get the historical data
hist = HistoricalData('AAPL', from_date=[2005, 1, 1], to_date=[2018, 3, 1])
price = hist.get_high()
# or use: hist.retrieve_col_data('Open') 'Date', 'Open', 'High', 'Low', 'Adj', 'Close', 'Volume'
Output
Date Open High Low Close Adj Close Volume
0 2007-01-03 12.327143 12.368571 11.700000 10.812462 11.971429 309579900
1 2007-01-04 12.007143 12.278571 11.974286 11.052453 12.237143 211815100
2 2007-01-05 12.252857 12.314285 12.057143 10.973743 12.150000 208685400
3 2007-01-08 12.280000 12.361428 12.182858 11.027935 12.210000 199276700
4 2007-01-09 12.350000 13.282857 12.164286 11.944029 13.224286 837324600
5 2007-01-10 13.535714 13.971429 13.350000 12.515617 13.857142 738220000
6 2007-01-11 13.705714 13.825714 13.585714 12.360788 13.685715 360063200
7 2007-01-12 13.512857 13.580000 13.318571 12.208535 13.517143 328172600
Plot & Generate CSV file
hist.create_csv()
plot(price)
Output
Load data from csv file
hist = HistoricalData()
hist.load_csv('AAPL')
hist.info_plot('Close')
Output
Apply EMD & show the figure
emd = EMD(price)
emd.save_figure('AAPL-trend', type='trend') # type => trend, all, modes, ds
Output
Calc The SD
sdv = hist.standard_deviation('Open')
print(sdv)
Output
if 0 < sdv < 25
then it will be considered as 'SAFE'
Otherwise
it's 'RISKY'
Gather News & analysis it
news = News('Apple')
result = news.get_result()
Output
{
'news': [
{
'text': 'Apple May Be Working on High-End Headphones and a Cheaper MacBook Air',
'a': 'http://fortune.com/2018/03/10/apple-headphones-macbook-air/',
'website': 'fortune.com',
'sentiment': 0.6
},
{
'text': "Apple's December 2016 Quarter Seems To Have Confused A Lot Of People",
'a': 'https://www.forbes.com/sites/chuckjones/2018/apples-14-week-december-2016/',
'website': 'forbes.com',
'sentiment': -0.4
}
...
...
...
],
'sentiment': 77.4
}
Credit
License
MIT License Copyright (c) 2018 mohabmes