{
    "info": {
        "author": "JoinQuant",
        "author_email": "xlx@joinquant.com",
        "bugtrack_url": null,
        "classifiers": [
            "Operating System :: Microsoft :: Windows",
            "Operating System :: Unix",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
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        "description": "\n\n# jqfactor_analyzer\n\n**\u805a\u5bbd\u5355\u56e0\u5b50\u5206\u6790\u5de5\u5177\u5f00\u6e90\u7248**\n\n---\n\n\u805a\u5bbd\u5355\u56e0\u5b50\u5206\u6790\u5de5\u5177\u5f00\u6e90\u7248\u662f\u63d0\u4f9b\u7ed9\u7528\u6237\u8fdb\u884c\u56e0\u5b50\u5206\u6790\u7684\u5de5\u5177\uff0c\u63d0\u4f9b\u4e86\u5305\u62ec\u8ba1\u7b97\u56e0\u5b50IC\u503c\uff0c\u56e0\u5b50\u6536\u76ca\uff0c\u56e0\u5b50\u6362\u624b\u7387\u7b49\u5404\u79cd\u8be6\u7ec6\u6307\u6807\uff0c\u7528\u6237\u53ef\u4ee5\u6309\u7167\u81ea\u5df1\u7684\u9700\u6c42\u67e5\u770b\u56e0\u5b50\u8be6\u60c5\u3002\n\n\u6b22\u8fce\u52a0\u5165jqfactor_analyzer\u4ea4\u6d41\u7fa4\uff0cQQ \u7fa4\u804a\u53f7\u7801\uff1a779882614\n\n\n## **\u5b89\u88c5**\n\n```bash\npip install jqfactor_analyzer\n```\n\n\n\n## **\u5347\u7ea7**\n\n```bash\npip install -U jqfactor_analyzer\n```\n\n\n\n## **\u5177\u4f53\u4f7f\u7528\u65b9\u6cd5**\n\n[analyze_factor](https://github.com/JoinQuant/jqfactor_analyzer/blob/master/docs/API%E6%96%87%E6%A1%A3.md): \u56e0\u5b50\u5206\u6790\u51fd\u6570\n\n\n\n## **\u4f7f\u7528\u793a\u4f8b**\n\n* ### \u793a\u4f8b\uff1a5\u65e5\u5e73\u5747\u6362\u624b\u7387\u56e0\u5b50\u5206\u6790\n\n```python\n# \u8f7d\u5165\u51fd\u6570\u5e93\nimport pandas as pd\nimport jqfactor_analyzer as ja\n\n# \u83b7\u53d6 jqdatasdk \u6388\u6743\uff0c\u8f93\u5165\u7528\u6237\u540d\u3001\u5bc6\u7801\uff0c\u7533\u8bf7\u5730\u5740\uff1ahttp://t.cn/EINDOxE\n# \u805a\u5bbd\u5b98\u7f51\u53ca\u91d1\u878d\u7ec8\u7aef\uff0c\u4f7f\u7528\u65b9\u6cd5\u53c2\u89c1\uff1ahttp://t.cn/EINcS4j\nimport jqdatasdk\njqdatasdk.auth('username', 'password')\n\n# \u83b7\u53d65\u65e5\u5e73\u5747\u6362\u624b\u7387\u56e0\u5b502018-01-01\u52302018-12-31\u4e4b\u95f4\u7684\u6570\u636e\uff08\u793a\u4f8b\u7528\u4ece\u5e93\u4e2d\u76f4\u63a5\u8c03\u53d6\uff09\n# \u805a\u5bbd\u56e0\u5b50\u5e93\u6570\u636e\u83b7\u53d6\u65b9\u6cd5\u5728\u4e0b\u65b9\nfrom jqfactor_analyzer.sample import VOL5\nfactor_data = VOL5\n\n# \u5bf9\u56e0\u5b50\u8fdb\u884c\u5206\u6790\nfar = ja.analyze_factor(\n    factor_data,  # factor_data \u4e3a\u56e0\u5b50\u503c\u7684 pandas.DataFrame\n    quantiles=10,\n    periods=(1, 10),\n    industry='jq_l1',\n    weight_method='avg',\n    max_loss=0.1\n)\n\n# \u83b7\u53d6\u6574\u7406\u540e\u7684\u56e0\u5b50\u7684IC\u503c\nfar.ic\n```\n\n\u7ed3\u679c\u5c55\u793a\uff1a\n\n![1](http://img0.ph.126.net/yJ8JpnMULEAqE4hzaGzMcg==/861876378788739324.png)\n\n```python\n# \u751f\u6210\u7edf\u8ba1\u56fe\u8868\nfar.create_full_tear_sheet(\n    demeaned=False, group_adjust=False, by_group=False,\n    turnover_periods=None, avgretplot=(5, 15), std_bar=False\n)\n```\n\n\u7ed3\u679c\u5c55\u793a\uff1a\n\n![2](https://image.joinquant.com/88e0de9b43424e3b7b1ab1fe48514625)\n\n## \u83b7\u53d6\u805a\u5bbd\u56e0\u5b50\u5e93\u6570\u636e\u7684\u65b9\u6cd5\n\n1. [\u805a\u5bbd\u56e0\u5b50\u5e93](https://www.joinquant.com/help/api/help?name=factor_values)\u5305\u542b\u6570\u767e\u4e2a\u8d28\u91cf\u3001\u60c5\u7eea\u3001\u98ce\u9669\u7b49\u5176\u4ed6\u7c7b\u76ee\u7684\u56e0\u5b50\n\n2. \u8fde\u63a5jqdatasdk\u83b7\u53d6\u6570\u636e\u5305\uff0c\u6570\u636e\u63a5\u53e3\u9700\u8c03\u7528\u805a\u5bbd [`jqdatasdk`](https://github.com/JoinQuant/jqdatasdk/blob/master/README.md) \u63a5\u53e3\u83b7\u53d6\u91d1\u878d\u6570\u636e([\u8bd5\u7528\u6ce8\u518c\u5730\u5740](http://t.cn/EINDOxE))\n\n   ```python\n   # \u83b7\u53d6\u56e0\u5b50\u6570\u636e\uff1a\u4ee55\u65e5\u5e73\u5747\u6362\u624b\u7387\u4e3a\u4f8b\uff0c\u8be5\u6570\u636e\u53ef\u4ee5\u76f4\u63a5\u7528\u4e8e\u56e0\u5b50\u5206\u6790\n   # \u5177\u4f53\u4f7f\u7528\u65b9\u6cd5\u53ef\u4ee5\u53c2\u7167jqdatasdk\u7684API\u6587\u6863\n   import jqdatasdk\n   jqdatasdk.auth('username', 'password')\n   # \u83b7\u53d6\u805a\u5bbd\u56e0\u5b50\u5e93\u4e2d\u7684VOL5\u6570\u636e\n   factor_data=jqdatasdk.get_factor_values(\n       securities=jqdatasdk.get_index_stocks('000300.XSHG'),\n       factors=['VOL5'],\n       start_date='2018-01-01',\n       end_date='2018-12-31')['VOL5']\n   ```\n\n\n\n## \u5c06\u81ea\u6709\u56e0\u5b50\u503c\u8f6c\u6362\u6210 DataFrame \u683c\u5f0f\u7684\u6570\u636e\n\n* index \u4e3a\u65e5\u671f\uff0c\u683c\u5f0f\u4e3a pandas \u65e5\u671f\u901a\u7528\u7684 DatetimeIndex\n\n* columns \u4e3a\u80a1\u7968\u4ee3\u7801\uff0c\u683c\u5f0f\u8981\u6c42\u7b26\u5408\u805a\u5bbd\u7684\u4ee3\u7801\u5b9a\u4e49\u89c4\u5219\uff08\u5982\uff1a\u5e73\u5b89\u94f6\u884c\u7684\u80a1\u7968\u4ee3\u7801\u4e3a 000001.XSHE\uff09\n  * \u5982\u679c\u662f\u6df1\u4ea4\u6240\u4e0a\u5e02\u7684\u80a1\u7968\uff0c\u5728\u80a1\u7968\u4ee3\u7801\u540e\u9762\u9700\u8981\u52a0\u5165.XSHE\n  * \u5982\u679c\u662f\u4e0a\u4ea4\u6240\u4e0a\u5e02\u7684\u80a1\u7968\uff0c\u5728\u80a1\u7968\u4ee3\u7801\u540e\u9762\u9700\u8981\u52a0\u5165.XSHG\n\n* \u5c06 pandas.DataFrame \u8f6c\u6362\u6210\u6ee1\u8db3\u683c\u5f0f\u8981\u6c42\u6570\u636e\u683c\u5f0f\n\n  \u9996\u5148\u8981\u4fdd\u8bc1 index \u4e3a `DatetimeIndex` \u683c\u5f0f\n\n  \u4e00\u822c\u662f\u901a\u8fc7 pandas \u63d0\u4f9b\u7684 [`pandas.to_datetime`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.html) \u51fd\u6570\u8fdb\u884c\u8f6c\u6362, \u5728\u8f6c\u6362\u524d\u5e94\u786e\u4fdd index \u4e2d\u7684\u503c\u90fd\u4e3a\u5408\u7406\u7684\u65e5\u671f\u683c\u5f0f, \u5982 `'2018-01-01'` / `'20180101'`, \u4e4b\u540e\u518d\u8c03\u7528 `pandas.to_datetime` \u8fdb\u884c\u8f6c\u6362\n\n  \u53e6\u5916\u5e94\u786e\u4fdd index \u7684\u65e5\u671f\u662f\u6309\u7167\u4ece\u5c0f\u5230\u5927\u7684\u987a\u5e8f\u6392\u5217\u7684, \u53ef\u4ee5\u901a\u8fc7 [`sort_index`](https://pandas.pydata.org/pandas-docs/version/0.23.3/generated/pandas.DataFrame.sort_index.html) \u8fdb\u884c\u6392\u5e8f\n\n  \u6700\u540e\u8bf7\u68c0\u67e5 columns \u4e2d\u7684\u80a1\u7968\u4ee3\u7801\u662f\u5426\u90fd\u6ee1\u8db3\u805a\u5bbd\u7684\u4ee3\u7801\u5b9a\u4e49\n\n  ```python\n  import pandas as pd\n\n  sample_data = pd.DataFrame(\n      [[0.84, 0.43, 2.33, 0.86, 0.96],\n       [1.06, 0.51, 2.60, 0.90, 1.09],\n       [1.12, 0.54, 2.68, 0.94, 1.12],\n       [1.07, 0.64, 2.65, 1.33, 1.15],\n       [1.21, 0.73, 2.97, 1.65, 1.19]],\n      index=['2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05', '2018-01-08'],\n      columns=['000001.XSHE', '000002.XSHE', '000063.XSHE', '000069.XSHE', '000100.XSHE']\n  )\n\n  print(sample_data)\n\n  factor_data = sample_data.copy()\n  # \u5c06 index \u8f6c\u6362\u4e3a DatetimeIndex\n  factor_data.index = pd.to_datetime(factor_data.index)\n  # \u5c06 DataFrame \u6309\u7167\u65e5\u671f\u987a\u5e8f\u6392\u5217\n  factor_data = factor_data.sort_index()\n  # \u68c0\u67e5 columns \u662f\u5426\u6ee1\u8db3\u805a\u5bbd\u80a1\u7968\u4ee3\u7801\u683c\u5f0f\n  if not sample_data.columns.astype(str).str.match('\\d{6}\\.XSH[EG]').all():\n      print(\"\u6709\u4e0d\u6ee1\u8db3\u805a\u5bbd\u80a1\u7968\u4ee3\u7801\u683c\u5f0f\u7684\u80a1\u7968\")\n      print(sample_data.columns[~sample_data.columns.astype(str).str.match('\\d{6}\\.XSH[EG]')])\n\n  print(factor_data)\n  ```\n\n* \u5c06\u952e\u4e3a\u65e5\u671f, \u503c\u4e3a\u5404\u80a1\u7968\u56e0\u5b50\u503c\u7684 `Series` \u7684 `dict` \u8f6c\u6362\u6210 `pandas.DataFrame`\n\n  \u53ef\u4ee5\u76f4\u63a5\u5229\u7528 `pandas.DataFrame` \u751f\u6210\n\n  ```python\n  sample_data = \\\n  {'2018-01-02': pd.Seris([0.84, 0.43, 2.33, 0.86, 0.96],\n                          index=['000001.XSHE', '000002.XSHE', '000063.XSHE', '000069.XSHE', '000100.XSHE']),\n   '2018-01-03': pd.Seris([1.06, 0.51, 2.60, 0.90, 1.09],\n                          index=['000001.XSHE', '000002.XSHE', '000063.XSHE', '000069.XSHE', '000100.XSHE']),\n   '2018-01-04': pd.Seris([1.12, 0.54, 2.68, 0.94, 1.12],\n                          index=['000001.XSHE', '000002.XSHE', '000063.XSHE', '000069.XSHE', '000100.XSHE']),\n   '2018-01-05': pd.Seris([1.07, 0.64, 2.65, 1.33, 1.15],\n                          index=['000001.XSHE', '000002.XSHE', '000063.XSHE', '000069.XSHE', '000100.XSHE']),\n   '2018-01-08': pd.Seris([1.21, 0.73, 2.97, 1.65, 1.19],\n                          index=['000001.XSHE', '000002.XSHE', '000063.XSHE', '000069.XSHE', '000100.XSHE'])}\n\n  import pandas as pd\n  # \u76f4\u63a5\u8c03\u7528 pd.DataFrame \u5c06 dict \u8f6c\u6362\u4e3a DataFrame\n  factor_data = pd.DataFrame(data).T\n\n  print(factor_data)\n\n  # \u4e4b\u540e\u8bf7\u6309\u7167 DataFrame \u7684\u65b9\u6cd5\u8f6c\u6362\u6210\u6ee1\u8db3\u683c\u5f0f\u8981\u6c42\u6570\u636e\u683c\u5f0f\n  ```\n\n\n",
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