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        "author": "baoshare",
        "author_email": "baoshare@163.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Environment :: Console",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Software Development :: Libraries"
        ],
        "description": "\nbaoshare\n===============\n    \n* It's easy to use because most of the data returned are pandas DataFrame objects\n* We have our own data server, efficient and stable operation\n* Free china stock market data\n* Friendly to machine learning and data mining\n\nTarget Users\n--------------\n\n* China Financial Market Analyst\n* Financial data analysis enthusiasts\n* Quanters who are interested in china stock market\n\nInstallation\n--------------\n\n    pip install baoshare\n\nUpgrade\n---------------\n\n    pip install baoshare --upgrade\n\nQuick Start\n--------------\n\n::\n\n    import baostock as bs\n    import pandas as pd\n    \n    # \u767b\u9646\u7cfb\u7edf\n    lg = bs.login(user_id=\"anonymous\", password=\"123456\")\n    # \u663e\u793a\u767b\u9646\u8fd4\u56de\u4fe1\u606f\n    print(lg.error_code)\n    print(lg.error_msg)\n    # \u8be6\u7ec6\u6307\u6807\u53c2\u6570\uff0c\u53c2\u89c1\u201c\u5386\u53f2\u884c\u60c5\u6307\u6807\u53c2\u6570\u201d\u7ae0\u8282\n    rs = bs.query_history_k_data(\"sh.601398\",\n        \"date,code,open,high,low,close,volume,amount,adjustflag\",\n        start_date='2017-01-01', end_date='2017-01-31', \n        frequency=\"d\", adjustflag=\"3\")\n    print(rs.error_code)\n    print(rs.error_msg)\n    # \u83b7\u53d6\u5177\u4f53\u7684\u4fe1\u606f\n    result = pd.DataFrame(columns=[\"date\",\"code\",\"open\",\"high\",\"low\",\"close\",\"volume\",\"amount\",\"adjustflag\"])\n    while (rs.error_code == '0') & rs.next():\n        # \u5206\u9875\u67e5\u8be2\uff0c\u5c06\u6bcf\u9875\u4fe1\u606f\u5408\u5e76\u5728\u4e00\u8d77\n        result = result.append(rs.get_row_data(), ignore_index=True)\n    result.to_csv(\"D:\\history_k_data.csv\", index=False)\n    print(result)\n    # \u767b\u51fa\u7cfb\u7edf\n    bs.logout()\n\nreturn::\n\n              date       code    open    high     low   close preclose     volume\n    0   2017-01-03  sh.601398  4.4000  4.4300  4.3900  4.4300   4.4100  104161632   \n    1   2017-01-04  sh.601398  4.4200  4.4400  4.4100  4.4300   4.4300  118923425   \n    2   2017-01-05  sh.601398  4.4300  4.4500  4.4200  4.4400   4.4300   87356137   \n    3   2017-01-06  sh.601398  4.4400  4.4500  4.4300  4.4400   4.4400   87008191   \n    4   2017-01-09  sh.601398  4.4500  4.4800  4.4300  4.4600   4.4400  117454094   \n    5   2017-01-10  sh.601398  4.4500  4.4700  4.4400  4.4600   4.4600   63663257   \n    6   2017-01-11  sh.601398  4.4600  4.4800  4.4500  4.4700   4.4600   52395427   \n    7   2017-01-12  sh.601398  4.4600  4.4700  4.4400  4.4700   4.4700   62166279    \n    \n                 amount adjustflag      turn tradestatus  \n    0    460087744.0000          3  0.038634           1  \n    1    526408816.0000          3  0.044109           1  \n    2    387580736.0000          3  0.032401           1  \n    3    386138112.0000          3  0.032272           1  \n    4    523539392.0000          3  0.043564           1  \n    5    283646224.0000          3  0.023613           1  \n    6    233898107.0000          3  0.019434           1  \n    7    277258304.0000          3  0.023058           1  \n\n",
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