foxrelax
Project description
- it’s easy to use because most of the data returned are pandas DataFrame objects
- we have our own data server, efficient and stable operation
- free stock market data
- friendly to machine learning and data mining
Target Users
- learners of financial data analysis with pandas/NumPy
- financial market analyst
- financial data analysis enthusiasts
- quanters who are interested in stock market
Installation
pip install foxrelaxUpgrade
pip install foxrelax –upgradeQuick Start
import foxrelax as relax
resp = relax.stock_daily(symbol='000049.SZ',
start_date='2018-01-01',
end_date='2019-11-28')
print('code={0} message={1}'.format(resp.code, resp.message))
print(resp.result)return:
code=0 message=success
symbol trade_date open high low close pre_close \
0 000049.SZ 2018-01-02 39.91 40.18 39.22 39.78 39.59
1 000049.SZ 2018-01-03 39.90 40.59 39.71 40.56 39.78
2 000049.SZ 2018-01-04 41.61 43.18 41.61 42.65 40.56
3 000049.SZ 2018-01-05 42.00 42.45 41.51 42.07 42.65
4 000049.SZ 2018-01-08 42.08 42.38 41.32 41.49 42.07
5 000049.SZ 2018-01-09 41.50 41.60 40.60 40.80 41.49
...
458 000049.SZ 2019-11-21 36.53 36.77 36.00 36.50 36.81
459 000049.SZ 2019-11-22 36.44 37.80 36.30 36.68 36.50
460 000049.SZ 2019-11-25 36.30 36.48 34.93 35.16 36.68
461 000049.SZ 2019-11-26 35.28 35.89 35.00 35.76 35.16
462 000049.SZ 2019-11-27 35.69 37.27 35.40 36.72 35.76
463 000049.SZ 2019-11-28 36.72 38.48 36.50 37.49 36.72
price_change pct_change volume money
0 0.19 0.4800 3197416 126769960
1 0.78 1.9600 3913276 157416236
2 2.09 5.1500 8300770 352188889
3 -0.58 -1.3600 4260338 178822929
4 -0.58 -1.3800 3569519 148331795
5 -0.69 -1.6600 3138719 128433609
...
458 -0.31 -0.8422 4079903 148147523
459 0.18 0.4932 8250452 306343236
460 -1.52 -4.1439 6989292 247534854
461 0.60 1.7065 4304988 153468524
462 0.96 2.6846 7701535 281834519
463 0.77 2.0969 10463935 394639040
