From 01dc653359f2d833260dc564b6fbaaa51ee9c682 Mon Sep 17 00:00:00 2001
From: unknown <shen.lan123@gmail.com>
Date: Fri, 24 Sep 2021 17:07:37 +0800
Subject: [PATCH] reset folder

---
 ...6\350\257\201\347\240\224\347\251\266.pdf" |  Bin 0 -> 1604089 bytes
 .../FFScore.ipynb"                            | 2944 +++++++++++++++++
 ...1\350\202\241\346\263\225\345\210\231.pdf" |  Bin
 ...350\202\241\346\263\225\345\210\231.ipynb" |    0
 ...4\350\277\233\351\230\266\347\211\210.pdf" |  Bin
 ...345\255\220\346\250\241\345\236\213.ipynb" |    0
 ...7\345\233\240\345\255\220\357\274\237.pdf" |  Bin
 ...351\207\217\345\233\240\345\255\220.ipynb" |    0
 ...1\345\273\211\345\245\275\357\274\237.pdf" |  Bin
 ...347\272\277\345\233\240\345\255\220.ipynb" |    0
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 ...351\207\217\345\233\240\345\255\220.ipynb" |    0
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 .../py/CalFunc.py"                            |    0
 ...345\215\226\345\216\213\345\212\233.ipynb" |    0
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 .../py/BuildPeriodicDate.py"                  |    0
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 ...345\274\272\347\255\226\347\225\245.ipynb" |    0
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 ...36\346\265\213\346\250\241\345\235\227.py" |  184 +-
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 ...345\255\220\346\236\204\345\273\272.ipynb" |    0
 .../\346\225\260\346\215\256\351\233\206.md"  |    4 +-
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 ...350\227\217\347\273\223\346\236\204.ipynb" |    0
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 ...6\213\357\274\210DFQ~2020\357\274\211.pdf" |  Bin
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 ...351\242\235\346\224\266\347\233\212.ipynb" |    0
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 ...32\204 2.0 \347\211\210\346\234\254.ipynb" |    0
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 ...7\345\220\210\346\246\202\347\216\207.pdf" |  Bin
 ...4\347\224\250\346\216\242\350\256\250.pdf" |  Bin
 ...e Probability of Backtest Overfitting.pdf" |  Bin
 ...346\236\220\346\241\206\346\236\266.ipynb" |    0
 ...347\206\212\346\214\207\346\240\207.ipynb" |    0
 ...3\347\206\212\346\214\207\346\240\207.pdf" |  Bin
 ...6\344\270\216\346\224\271\350\277\233.pdf" |  Bin
 .../py/RSRS.ipynb"                            |    0
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 ...345\256\232\345\274\272\345\220\227.ipynb" |    0
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 ...345\200\274\345\272\224\347\224\250.ipynb" |    0
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 ...346\236\220\346\213\251\346\227\266.ipynb" |    0
 ...46\263\242\345\210\206\346\236\220_svm.py" |  830 ++---
 ...57\347\211\271\345\217\230\346\215\242.py" |  482 +--
 ...\346\230\216\346\226\207\346\241\243.docx" |  Bin
 ...346\225\243\346\214\207\346\240\207.ipynb" |    0
 ...2\346\234\254\347\224\250\346\263\225.pdf" |  Bin
 ...347\216\207\345\233\240\345\255\220.ipynb" |    0
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 ...347\237\251\346\213\251\346\227\266.ipynb" |    0
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 ...1\346\227\266\347\255\226\347\225\245.pdf" |  Bin
 ... Time-varying Sharpe Ratio to Chinese.pdf" |  Bin
 .../py/Tsharpe.ipynb"                         |    0
 .../sharpe2-1997.pdf"                         |  Bin
 .../tvsharpe.pdf"                             |  Bin
 .../varcov jf94-1994.pdf"                     |  Bin
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 ...347\276\244\346\225\210\345\272\224.ipynb" |    0
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 .../py/DE_algorithm.py"                       |    0
 ...345\214\226\347\256\227\346\263\225.ipynb" |    0
 80 files changed, 3694 insertions(+), 750 deletions(-)
 create mode 100644 "A-\351\207\217\345\214\226\345\237\272\346\234\254\351\235\242/\345\215\216\346\263\260FFScore/20170209-\345\215\216\346\263\260\350\257\201\345\210\270-\345\215\216\346\263\260\344\273\267\345\200\274\351\200\211\350\202\241\344\271\213FFScore\346\250\241\345\236\213\357\274\232\346\257\224\344\271\224\346\226\257\345\237\272\351\200\211\350\202\241\346\250\241\345\236\213A\350\202\241\345\256\236\350\257\201\347\240\224\347\251\266.pdf"
 create mode 100644 "A-\351\207\217\345\214\226\345\237\272\346\234\254\351\235\242/\345\215\216\346\263\260FFScore/FFScore.ipynb"
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 rename "DE\347\256\227\346\263\225\344\270\213\347\232\204\347\273\204\345\220\210\344\274\230\345\214\226/20191018-\346\265\231\345\225\206\350\257\201\345\210\270-\344\272\272\345\267\245\346\231\272\350\203\275\347\263\273\345\210\227\357\274\210\344\272\214\357\274\211\357\274\232\344\272\272\345\267\245\346\231\272\350\203\275\345\206\215\345\207\272\345\217\221\357\274\214\346\254\241\344\274\230\347\220\206\350\256\272\344\270\213\347\232\204\347\273\204\345\220\210\351\205\215\347\275\256\344\270\216\347\255\226\347\225\245\346\236\204\345\273\272.pdf" => "D-\347\273\204\345\220\210\344\274\230\345\214\226/DE\347\256\227\346\263\225\344\270\213\347\232\204\347\273\204\345\220\210\344\274\230\345\214\226/20191018-\346\265\231\345\225\206\350\257\201\345\210\270-\344\272\272\345\267\245\346\231\272\350\203\275\347\263\273\345\210\227\357\274\210\344\272\214\357\274\211\357\274\232\344\272\272\345\267\245\346\231\272\350\203\275\345\206\215\345\207\272\345\217\221\357\274\214\346\254\241\344\274\230\347\220\206\350\256\272\344\270\213\347\232\204\347\273\204\345\220\210\351\205\215\347\275\256\344\270\216\347\255\226\347\225\245\346\236\204\345\273\272.pdf" (100%)
 rename "DE\347\256\227\346\263\225\344\270\213\347\232\204\347\273\204\345\220\210\344\274\230\345\214\226/20191101-\346\265\231\345\225\206\350\257\201\345\210\270-FOF\347\273\204\345\220\210\347\263\273\345\210\227\357\274\210\344\270\200\357\274\211\357\274\232\345\233\236\346\222\244\346\234\200\345\260\217\347\233\256\346\240\207\344\270\213\347\232\204\345\201\217\345\200\272FOF\347\273\204\345\220\210\346\236\204\345\273\272\344\273\245\357\274\214\344\270\200\345\256\266\345\205\254\345\213\237\344\272\247\345\223\201\344\270\272\344\276\213.pdf" => "D-\347\273\204\345\220\210\344\274\230\345\214\226/DE\347\256\227\346\263\225\344\270\213\347\232\204\347\273\204\345\220\210\344\274\230\345\214\226/20191101-\346\265\231\345\225\206\350\257\201\345\210\270-FOF\347\273\204\345\220\210\347\263\273\345\210\227\357\274\210\344\270\200\357\274\211\357\274\232\345\233\236\346\222\244\346\234\200\345\260\217\347\233\256\346\240\207\344\270\213\347\232\204\345\201\217\345\200\272FOF\347\273\204\345\220\210\346\236\204\345\273\272\344\273\245\357\274\214\344\270\200\345\256\266\345\205\254\345\213\237\344\272\247\345\223\201\344\270\272\344\276\213.pdf" (100%)
 rename "DE\347\256\227\346\263\225\344\270\213\347\232\204\347\273\204\345\220\210\344\274\230\345\214\226/py/DE_algorithm.py" => "D-\347\273\204\345\220\210\344\274\230\345\214\226/DE\347\256\227\346\263\225\344\270\213\347\232\204\347\273\204\345\220\210\344\274\230\345\214\226/py/DE_algorithm.py" (100%)
 rename "DE\347\256\227\346\263\225\344\270\213\347\232\204\347\273\204\345\220\210\344\274\230\345\214\226/py/\345\267\256\345\210\206\350\277\233\345\214\226\347\256\227\346\263\225.ipynb" => "D-\347\273\204\345\220\210\344\274\230\345\214\226/DE\347\256\227\346\263\225\344\270\213\347\232\204\347\273\204\345\220\210\344\274\230\345\214\226/py/\345\267\256\345\210\206\350\277\233\345\214\226\347\256\227\346\263\225.ipynb" (100%)

diff --git "a/A-\351\207\217\345\214\226\345\237\272\346\234\254\351\235\242/\345\215\216\346\263\260FFScore/20170209-\345\215\216\346\263\260\350\257\201\345\210\270-\345\215\216\346\263\260\344\273\267\345\200\274\351\200\211\350\202\241\344\271\213FFScore\346\250\241\345\236\213\357\274\232\346\257\224\344\271\224\346\226\257\345\237\272\351\200\211\350\202\241\346\250\241\345\236\213A\350\202\241\345\256\236\350\257\201\347\240\224\347\251\266.pdf" "b/A-\351\207\217\345\214\226\345\237\272\346\234\254\351\235\242/\345\215\216\346\263\260FFScore/20170209-\345\215\216\346\263\260\350\257\201\345\210\270-\345\215\216\346\263\260\344\273\267\345\200\274\351\200\211\350\202\241\344\271\213FFScore\346\250\241\345\236\213\357\274\232\346\257\224\344\271\224\346\226\257\345\237\272\351\200\211\350\202\241\346\250\241\345\236\213A\350\202\241\345\256\236\350\257\201\347\240\224\347\251\266.pdf"
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diff --git "a/A-\351\207\217\345\214\226\345\237\272\346\234\254\351\235\242/\345\215\216\346\263\260FFScore/FFScore.ipynb" "b/A-\351\207\217\345\214\226\345\237\272\346\234\254\351\235\242/\345\215\216\346\263\260FFScore/FFScore.ipynb"
new file mode 100644
index 0000000..2eec6d9
--- /dev/null
+++ "b/A-\351\207\217\345\214\226\345\237\272\346\234\254\351\235\242/\345\215\216\346\263\260FFScore/FFScore.ipynb"
@@ -0,0 +1,2944 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 前言\n",
+    "\n",
+    "**比乔斯基选股模型**是斯坦福大学商学院教授约瑟夫·比乔斯基的基于市净率、以 历史财务数据为衡量标准的价值选股模型。该选股模型在低市净率选股的 基础上，通过代表盈利水平、财务杠杆及流动性和运营效率的9个财务指标对股票进行优劣筛选。\n",
+    "\n",
+    "底层逻辑:比乔斯基认为购买低市净率的上市公司股票即股价和每 股净资产比值相对低的股票是具有安全边际的。但美股中低市净率股票的上市公司更容易陷入财务困境直至破产和退市，于是考虑通过上市公司财务报表分析方法来避免低市净率的投资陷阱。其经典论文《Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers》就提出并验证了 FScore 财务分析方法在美国股票市场可以显著提高低市净率股票组合收益率。\n",
+    "\n",
+    "## 低市净率比乔斯基选股量化标准\n",
+    "\n",
+    "比乔斯基低市净率FScore的9指标模型股票池为市净率为正值且位于全市场的前20%(PB因子升序排序分五组的第一组)；比乔斯基9指标模型选股标准如下： \n",
+    "\n",
+    "**盈利水平**\n",
+    "\n",
+    "代表了盈利水平的指标为资产收益率(ROA)、经营现金流量(CFO)、资产收益率变化($\\Delta ROA$)、与自然增长性(ACCRUAL)。一家企业的收益率与现金流指标展现了一家企业累积资本的能力。比乔斯基也提到低市净率的上市公司有历史收益不佳的问题，如果在最新财报期能够看到盈利能力并呈现现金净流入，即收益增长趋势，则可以说明这家公司开始 具备改善未来盈利水平的能力。此外，自然增长性表现了收益与现金流的关系。如果现金的流入能够超越收益，则说明了企业在未来的增长有更好的基础。\n",
+    "\n",
+    "1. 资产收益率(ROA:净利润\\*2/(期初总资产+期末总资产),原本使用的EBIT \\* 2/(期初总资产+期末总资产))为正则为1否则为0； \n",
+    "2. 经营现金流量(CFO:经营活动产生的现金流量净额除以资产总值)为正则为1否则为0； \n",
+    "3. 资产收益率同比($\\Delta ROA$)增加为正则为1否则为0；\n",
+    "4. 收益自然增长率(ACCRUAL:CFO减去ROA的差值)为正则为1否则为0；\n",
+    "\n",
+    "**财务杠杆和流动性**\n",
+    "\n",
+    "代表了财务杠杆及流动性的指标为杠杆变化($\\Delta LEVER$)、流动性变化($\\Delta LIQUID$)）、与是否发行普通股权(EQ_OFFER)。正如我们分析得出的结论，低市净率的公司往往面临 着财务困境，所以通常会有着杠杆偏高，流动性偏弱，以及需要通过增资扩股来进行外部 融资的需求。我们可以将杠杆的减少、流动性的增强，以及没有扩充股权的需求来作为一家企业在财务困境方面是否有好转的信号\n",
+    "\n",
+    "5. 长期资产负债率同比($\\Delta LEVER$)减少,为负时为1否则为0；\n",
+    "6. 流动比率同比($\\Delta LIQUID$)增加正则为1否则为0；\n",
+    "7. 股本等于或者小于同比的股本(EQ_OFFER)正则为1否则为0；\n",
+    "\n",
+    "**运营效率**\n",
+    "\n",
+    "代表了运营效率的指标为毛利润率变化($\\Delta MARGIN$)与资产周转率变化($\\Delta TURN$)。毛利润的增加意味着公司的成本降低或者产品售价提高。资产周转率增加则反映了公司运营 周转效率的提升。因此，这两个指标有效的表示了企业在运营方面的能力提高，从而为其未来生存竞争力与公司的成长提供有力的支持。\n",
+    "\n",
+    "8. 毛利率同比增加($\\Delta MARGIN$)正则为1否则为0；\n",
+    "9. 资产周转率同比增加($\\Delta TURN$)正则为1否则为0\n",
+    "\n",
+    "FScore为以上九个参数之和:\n",
+    "$$FScore = ROA + \\Delta ROA + CFO + ACCRUAL + \\Delta MARGIN + \\Delta TURN + \\Delta LEVER + \\Delta LIQUID + EQ_OFFER$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import (List,Tuple,Dict,Callable,Union)\n",
+    "\n",
+    "import datetime as dt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import empyrical as ep\n",
+    "\n",
+    "import graphviz\n",
+    "from sklearn.tree import export_graphviz\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "from tqdm import tqdm_notebook\n",
+    "\n",
+    "import alphalens as al\n",
+    "\n",
+    "from jqdata import *\n",
+    "from jqfactor import (calc_factors,Factor)\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
+    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "code_folding": [
+     2,
+     23,
+     124,
+     148,
+     175,
+     209
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "'''股票池筛选'''\n",
+    "# 获取年末季末时点\n",
+    "def get_trade_period(start_date: str, end_date: str, freq: str = 'ME') -> list:\n",
+    "    '''\n",
+    "    start_date/end_date:str YYYY-MM-DD\n",
+    "    freq:M月，Q季,Y年 默认ME E代表期末 S代表期初\n",
+    "    ================\n",
+    "    return  list[datetime.date]\n",
+    "    '''\n",
+    "    days = pd.Index(pd.to_datetime(get_trade_days(start_date, end_date)))\n",
+    "    idx_df = days.to_frame()\n",
+    "\n",
+    "    if freq[-1] == 'E':\n",
+    "        day_range = idx_df.resample(freq[0]).last()\n",
+    "    else:\n",
+    "        day_range = idx_df.resample(freq[0]).first()\n",
+    "\n",
+    "    day_range = day_range[0].dt.date\n",
+    "\n",
+    "    return day_range.dropna().values.tolist()\n",
+    "\n",
+    "\n",
+    "# 筛选股票池\n",
+    "class Filter_Stocks(object):\n",
+    "    '''\n",
+    "    获取某日的成分股股票\n",
+    "    1. 过滤st\n",
+    "    2. 过滤上市不足N个月\n",
+    "    3. 过滤当月交易不超过N日的股票\n",
+    "    ---------------\n",
+    "    输入参数：\n",
+    "        index_symbol:指数代码,A等于全市场,\n",
+    "        watch_date:日期\n",
+    "    '''\n",
+    "    \n",
+    "    def __init__(self,symbol:str,watch_date:str)->None:\n",
+    "        \n",
+    "        if isinstance(watch_date,str):\n",
+    "            \n",
+    "            self.watch_date = pd.to_datetime(watch_date).date()\n",
+    "            \n",
+    "        else:\n",
+    "            \n",
+    "            self.watch_date = watch_date\n",
+    "            \n",
+    "        self.symbol = symbol\n",
+    "        self.get_index_component_stocks()\n",
+    "        \n",
+    "    def get_index_component_stocks(self)->list:\n",
+    "        \n",
+    "        '''获取指数成分股'''\n",
+    "        \n",
+    "        if self.symbol == 'A':\n",
+    "            \n",
+    "            wd:pd.DataFrame = get_all_securities(types=['stock'],date=self.watch_date)\n",
+    "            self.securities:List = wd.query('end_date != \"2200-01-01\"').index.tolist()\n",
+    "        else:\n",
+    "            \n",
+    "            self.securities:List = get_index_stocks(self.symbol,self.watch_date)\n",
+    "    \n",
+    "    def filter_paused(self,paused_N:int=1,threshold:int=None)->list:\n",
+    "        \n",
+    "        '''过滤停牌股\n",
+    "        -----\n",
+    "        输入:\n",
+    "            paused_N:默认为1即查询当日不停牌\n",
+    "            threshold:在过paused_N日内停牌数量小于threshold\n",
+    "        '''\n",
+    "        \n",
+    "        if (threshold is not None) and (threshold > paused_N):\n",
+    "            raise ValueError(f'参数threshold天数不能大于paused_N天数')\n",
+    "            \n",
+    "        \n",
+    "        paused = get_price(self.securities,end_date=self.watch_date,count=paused_N,fields='paused',panel=False)\n",
+    "        paused = paused.pivot(index='time',columns='code')['paused']\n",
+    "        \n",
+    "        # 如果threhold不为None 获取过去paused_N内停牌数少于threshodl天数的股票\n",
+    "        if threshold:\n",
+    "            \n",
+    "            sum_paused_day = paused.sum()\n",
+    "            self.securities = sum_paused_day[sum_paused_day < threshold].index.tolist()\n",
+    "        \n",
+    "        else:\n",
+    "            \n",
+    "            paused_ser = paused.iloc[-1]\n",
+    "            self.securities = paused_ser[paused_ser == 0].index.tolist()\n",
+    "    \n",
+    "    def filter_st(self)->list:\n",
+    "        \n",
+    "        '''过滤ST'''\n",
+    "              \n",
+    "        extras_ser = get_extras('is_st',self.securities,end_date=self.watch_date,count=1).iloc[-1]\n",
+    "        \n",
+    "        self.securities = extras_ser[extras_ser == False].index.tolist()\n",
+    "    \n",
+    "    def filter_ipodate(self,threshold:int=180)->list:\n",
+    "        \n",
+    "        '''\n",
+    "        过滤上市天数不足以threshold天的股票\n",
+    "        -----\n",
+    "        输入：\n",
+    "            threhold:默认为180日\n",
+    "        '''\n",
+    "        \n",
+    "        def _check_ipodate(code:str,watch_date:dt.date)->bool:\n",
+    "            \n",
+    "            code_info = get_security_info(code)\n",
+    "            \n",
+    "            if (code_info is not None) and ((watch_date - code_info.start_date).days > threshold):\n",
+    "                \n",
+    "                return True\n",
+    "            \n",
+    "            else:\n",
+    "                \n",
+    "                return False\n",
+    "\n",
+    "        self.securities = [code for code in self.securities if _check_ipodate(code,self.watch_date)]\n",
+    "    \n",
+    "    def filter_industry(self,industry:Union[List,str],level:str='sw_l1',method:str='industry_name')->list:\n",
+    "        '''过略行业'''\n",
+    "        ind = get_stock_ind(self.securities,self.watch_date,level,method)\n",
+    "        target = ind.to_frame('industry').query('industry != @industry')\n",
+    "        self.securities = target.index.tolist()\n",
+    "        \n",
+    "def get_stock_ind(securities:list,watch_date:str,level:str='sw_l1',method:str='industry_code')->pd.Series:\n",
+    "    \n",
+    "    '''\n",
+    "    获取行业\n",
+    "    --------\n",
+    "        securities:股票列表\n",
+    "        watch_date:查询日期\n",
+    "        level:查询股票所属行业级别\n",
+    "        method:返回行业名称or代码\n",
+    "    '''\n",
+    "    \n",
+    "    indusrty_dict = get_industry(securities, watch_date)\n",
+    "\n",
+    "    indusrty_ser = pd.Series({k: v.get(level, {method: np.nan})[\n",
+    "                             method] for k, v in indusrty_dict.items()})\n",
+    "    \n",
+    "    indusrty_ser.name = method.upper()\n",
+    "    \n",
+    "    return indusrty_ser\n",
+    "\n",
+    "'''风险指标'''\n",
+    "# 风险指标\n",
+    "\n",
+    "\n",
+    "def Strategy_performance(return_df: pd.DataFrame,\n",
+    "                         periods='daily') -> pd.DataFrame:\n",
+    "    '''计算风险指标 默认为月度:月度调仓'''\n",
+    "\n",
+    "    ser: pd.DataFrame = pd.DataFrame()\n",
+    "    ser['年化收益率'] = ep.annual_return(return_df, period=periods)\n",
+    "    ser['累计收益'] = ep.cum_returns(return_df).iloc[-1]\n",
+    "    ser['波动率'] = return_df.apply(\n",
+    "        lambda x: ep.annual_volatility(x, period=periods))\n",
+    "    ser['夏普'] = return_df.apply(ep.sharpe_ratio, period=periods)\n",
+    "    ser['最大回撤'] = return_df.apply(lambda x: ep.max_drawdown(x))\n",
+    "    \n",
+    "    if 'benchmark' in return_df.columns:\n",
+    "\n",
+    "        select_col = [col for col in return_df.columns if col != 'benchmark']\n",
+    "\n",
+    "        ser['IR'] = return_df[select_col].apply(\n",
+    "            lambda x: information_ratio(x, return_df['benchmark']))\n",
+    "        ser['Alpha'] = return_df[select_col].apply(\n",
+    "            lambda x: ep.alpha(x, return_df['benchmark'], period=periods))\n",
+    "        \n",
+    "        ser['超额收益'] = ser['年化收益率'] - ser.loc[\n",
+    "            'benchmark', '年化收益率']  #计算相对年化波动率\n",
+    "        \n",
+    "    return ser.T\n",
+    "\n",
+    "\n",
+    "def information_ratio(returns, factor_returns):\n",
+    "    \"\"\"\n",
+    "    Determines the Information ratio of a strategy.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    returns : :py:class:`pandas.Series` or pd.DataFrame\n",
+    "        Daily returns of the strategy, noncumulative.\n",
+    "        See full explanation in :func:`~empyrical.stats.cum_returns`.\n",
+    "    factor_returns: :class:`float` / :py:class:`pandas.Series`\n",
+    "        Benchmark return to compare returns against.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    :class:`float`\n",
+    "        The information ratio.\n",
+    "\n",
+    "    Note\n",
+    "    -----\n",
+    "    See https://en.wikipedia.org/wiki/information_ratio for more details.\n",
+    "\n",
+    "    \"\"\"\n",
+    "    if len(returns) < 2:\n",
+    "        return np.nan\n",
+    "\n",
+    "    active_return = _adjust_returns(returns, factor_returns)\n",
+    "    tracking_error = np.std(active_return, ddof=1)\n",
+    "    if np.isnan(tracking_error):\n",
+    "        return 0.0\n",
+    "    if tracking_error == 0:\n",
+    "        return np.nan\n",
+    "    return np.mean(active_return) / tracking_error\n",
+    "\n",
+    "\n",
+    "def _adjust_returns(returns, adjustment_factor):\n",
+    "    \"\"\"\n",
+    "    Returns a new :py:class:`pandas.Series` adjusted by adjustment_factor.\n",
+    "    Optimizes for the case of adjustment_factor being 0.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    returns : :py:class:`pandas.Series`\n",
+    "    adjustment_factor : :py:class:`pandas.Series` / :class:`float`\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    :py:class:`pandas.Series`\n",
+    "    \"\"\"\n",
+    "    if isinstance(adjustment_factor, (float, int)) and adjustment_factor == 0:\n",
+    "        return returns.copy()\n",
+    "    return returns - adjustment_factor\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "code_folding": [
+     1,
+     7,
+     94
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# 标记得分\n",
+    "def sign(ser: pd.Series) -> pd.Series:\n",
+    "    '''标记分数,正数为1,负数为0'''\n",
+    "    \n",
+    "    return ser.apply(lambda x: np.where(x > 0, 1, 0))\n",
+    "\n",
+    "\n",
+    "class FScore(Factor):\n",
+    "    '''\n",
+    "    FScore原始模型\n",
+    "    '''\n",
+    "    name = 'FScore'\n",
+    "    max_window = 1\n",
+    "\n",
+    "    watch_date = None\n",
+    "    # paidin_capital 实收资本 股本变化会反应在该科目中\n",
+    "    dependencies = [\n",
+    "        'roa', 'roa_4', 'net_operate_cash_flow', 'total_assets',\n",
+    "        'total_assets_1', 'total_assets_4', 'total_assets_5',\n",
+    "        'operating_revenue', 'operating_revenue_4',\n",
+    "        'total_non_current_assets', 'total_non_current_liability',\n",
+    "        'total_non_current_assets_4', 'total_non_current_liability_4',\n",
+    "        'total_current_assets', 'total_current_liability',\n",
+    "        'total_current_assets_4', 'total_current_liability_4',\n",
+    "        'gross_profit_margin', 'gross_profit_margin_4', 'paidin_capital',\n",
+    "        'paidin_capital_4'\n",
+    "    ]\n",
+    "\n",
+    "    def calc(self, data: Dict) -> None:\n",
+    "\n",
+    "        roa: pd.DataFrame = data['roa'] # 单位为百分号\n",
+    "\n",
+    "        cfo: pd.DataFrame = data['net_operate_cash_flow'] / \\\n",
+    "            data['total_assets']\n",
+    "\n",
+    "        delta_roa: pd.DataFrame = roa / data['roa_4'] - 1\n",
+    "\n",
+    "        accrual: pd.DataFrame = cfo - roa * 0.01\n",
+    "\n",
+    "        # 杠杆变化\n",
+    "        ## 变化为负数时为1，否则为0 取相反\n",
+    "        leveler: pd.DataFrame = data['total_non_current_liability'] / \\\n",
+    "            data['total_non_current_assets']\n",
+    "\n",
+    "        leveler1: pd.DataFrame = data['total_non_current_liability_4'] / \\\n",
+    "            data['total_non_current_assets_4']\n",
+    "\n",
+    "        delta_leveler: pd.DataFrame = -(leveler / leveler1 - 1)\n",
+    "\n",
+    "        # 流动性变化\n",
+    "        liquid: pd.DataFrame = data['total_current_assets'] / \\\n",
+    "            data['total_current_liability']\n",
+    "\n",
+    "        liquid_1: pd.DataFrame = data['total_current_assets_4'] / \\\n",
+    "            data['total_current_liability_4']\n",
+    "\n",
+    "        delta_liquid: pd.DataFrame = liquid / liquid_1 - 1\n",
+    "\n",
+    "        # 毛利率变化\n",
+    "        delta_margin: pd.DataFrame = data['gross_profit_margin'] / \\\n",
+    "            data['gross_profit_margin_4'] - 1\n",
+    "\n",
+    "        # 是否发行普通股权\n",
+    "        eq_offser: pd.DataFrame = data['paidin_capital'] / data[\n",
+    "            'paidin_capital_4'] - 1\n",
+    "\n",
+    "        # 总资产周转率\n",
+    "        total_asset_turnover_rate: pd.DataFrame = data[\n",
+    "            'operating_revenue'] / (data['total_assets'] +\n",
+    "                                          data['total_assets_1']).mean()\n",
+    "\n",
+    "        total_asset_turnover_rate_1: pd.DataFrame = data[\n",
+    "            'operating_revenue_4'] / (data['total_assets_4'] +\n",
+    "                                            data['total_assets_5']).mean()\n",
+    "\n",
+    "        # 总资产周转率同比\n",
+    "        delta_turn: pd.DataFrame = total_asset_turnover_rate / \\\n",
+    "            total_asset_turnover_rate_1 - 1\n",
+    "\n",
+    "        indicator_tuple: Tuple = (roa, cfo, delta_roa, accrual, delta_leveler,\n",
+    "                                  delta_liquid, delta_margin, delta_turn,\n",
+    "                                  eq_offser)\n",
+    "\n",
+    "        # 储存计算FFscore所需原始数据\n",
+    "        self.basic: pd.DataFrame = pd.concat(indicator_tuple).T.replace([-np.inf,np.inf],np.nan)\n",
+    "\n",
+    "        self.basic.columns = [\n",
+    "            'ROA', 'CFO', 'DELTA_ROA', 'ACCRUAL', 'DELTA_LEVELER',\n",
+    "            'DLTA_LIQUID', 'DELTA_MARGIN', 'DELTA_TURN', 'EQ_OFFSER'\n",
+    "        ]\n",
+    "        \n",
+    "        self.fscore: pd.Series = self.basic.apply(sign).sum(axis=1)\n",
+    "\n",
+    "\n",
+    "class FFScore(Factor):\n",
+    "\n",
+    "    name = 'FFScore'\n",
+    "    max_window = 1\n",
+    "\n",
+    "    watch_date = None\n",
+    "\n",
+    "    dependencies = [\n",
+    "        'roe', 'roe_4', 'total_assets', 'total_assets_1', 'total_assets_4',\n",
+    "        'total_assets_5', 'operating_revenue', 'operating_revenue_4',\n",
+    "        'total_non_current_assets', 'total_non_current_assets_4',\n",
+    "        'total_non_current_liability', 'total_non_current_liability_4',\n",
+    "        'total_current_assets', 'total_current_assets_1',\n",
+    "        'total_current_assets_4', 'total_current_assets_5',\n",
+    "        'total_operating_revenue', 'total_operating_revenue_4'\n",
+    "    ]\n",
+    "\n",
+    "    def calc(self, data: Dict) -> None:\n",
+    "\n",
+    "        roe: pd.DataFrame = data['roe']\n",
+    "\n",
+    "        delta_roe: pd.DataFrame = roe / data['roe_4'] - 1\n",
+    "\n",
+    "        # 杠杆变化\n",
+    "        ## 变化为负数时为1，否则为0 取相反\n",
+    "        lever: pd.DataFrame = data['total_non_current_liability'] / \\\n",
+    "            data['total_non_current_assets']\n",
+    "\n",
+    "        lever1: pd.DataFrame = data['total_non_current_liability_4'] / \\\n",
+    "            data['total_non_current_assets_4']\n",
+    "\n",
+    "        delta_lever: pd.DataFrame = -(lever / lever1 - 1)\n",
+    "\n",
+    "        # 流动资产周转率\n",
+    "        caturn_1 = data['operating_revenue'] / (\n",
+    "            data['total_current_assets'] +\n",
+    "            data['total_current_assets_1']).mean()\n",
+    "        caturn_2 = data['operating_revenue_4'] / (\n",
+    "            data['total_current_assets_4'] +\n",
+    "            data['total_current_assets_5']).mean()\n",
+    "        \n",
+    "        # 流动资产周转率同比\n",
+    "        delata_caturn = caturn_1 / caturn_2 - 1\n",
+    "\n",
+    "        # 总资产周转率\n",
+    "        total_asset_turnover_rate: pd.DataFrame = data[\n",
+    "            'total_operating_revenue'] / (data['total_assets'] +\n",
+    "                                          data['total_assets_1']).mean()\n",
+    "\n",
+    "        total_asset_turnover_rate_1: pd.DataFrame = data[\n",
+    "            'total_operating_revenue_4'] / (data['total_assets_4'] +\n",
+    "                                            data['total_assets_5']).mean()\n",
+    "\n",
+    "        # 总资产周转率同比\n",
+    "        delta_turn: pd.DataFrame = total_asset_turnover_rate / \\\n",
+    "            total_asset_turnover_rate_1 - 1\n",
+    "\n",
+    "        indicator_tuple: Tuple = (roe, delta_roe, delata_caturn, delta_turn,\n",
+    "                                  delta_lever)\n",
+    "\n",
+    "        # 储存计算FFscore所需原始数据\n",
+    "        self.basic: pd.DataFrame = pd.concat(indicator_tuple).T.replace([-np.inf,np.inf],np.nan)\n",
+    "\n",
+    "        self.basic.columns = [\n",
+    "            'ROE', 'DELTA_ROE', 'DELTA_CATURN', 'DELTA_TURN', 'DELTA_LEVER'\n",
+    "        ]\n",
+    "\n",
+    "        self.ffscore: pd.Series = self.basic.apply(sign).sum(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "code_folding": [
+     1,
+     38,
+     51
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# 因子获取\n",
+    "def get_FFScore(\n",
+    "    symbol: str,\n",
+    "    factor: Factor,\n",
+    "    periods: List,\n",
+    "    filter_industry: Union[List,\n",
+    "                           str] = None) -> Tuple[pd.Series, pd.DataFrame]:\n",
+    "    '''\n",
+    "    获取FFScore得分\n",
+    "    ------\n",
+    "    输入参数：\n",
+    "        symbol:输入A表示全A股票池或者输入指数代码\n",
+    "        factor:不同的ffscore模型\n",
+    "        periods:计算得分的时间序列\n",
+    "        filter_industry:传入需要过滤的行业\n",
+    "    ------\n",
+    "    return 最终得分,财务数据\n",
+    "    '''\n",
+    "    for trade in tqdm_notebook(periods, desc='FFScore因子获取'):\n",
+    "\n",
+    "        # 获取股票池\n",
+    "        stock_pool_func = Filter_Stocks(symbol, trade)\n",
+    "        stock_pool_func.filter_paused(22, 21)  # 过滤22日停牌超过21日的股票\n",
+    "        stock_pool_func.filter_st()  # 过滤st\n",
+    "        stock_pool_func.filter_ipodate(180)  # 过滤次新\n",
+    "        # 是否过滤行业\n",
+    "        if filter_industry:\n",
+    "\n",
+    "            stock_pool_func.filter_industry(filter_industry)\n",
+    "\n",
+    "        my_factor = factor()\n",
+    "        my_factor.watch_date = trade\n",
+    "        calc_factors(stock_pool_func.securities,[my_factor],start_date=trade,end_date=trade)\n",
+    "        \n",
+    "        yield my_factor\n",
+    "\n",
+    "\n",
+    "\n",
+    "def get_factor_price(security: Union[List, str],\n",
+    "                     periods: List) -> pd.DataFrame:\n",
+    "    '''获取对应频率的收盘价'''\n",
+    "    for trade in tqdm_notebook(periods, desc='获取收盘价数据'):\n",
+    "\n",
+    "        yield get_price(security,\n",
+    "                        end_date=trade,\n",
+    "                        count=1,\n",
+    "                        fields='close',\n",
+    "                        fq = 'post',\n",
+    "                        panel=False)\n",
+    "\n",
+    "\n",
+    "def get_factor_pb_ratio(securities: Union[list, str],\n",
+    "                        periods: list) -> pd.DataFrame:\n",
+    "    '''获取PB数据'''\n",
+    "    for trade in tqdm_notebook(periods, desc='获取PB数据'):\n",
+    "\n",
+    "        yield get_valuation(securities,\n",
+    "                            end_date=trade,\n",
+    "                            fields=['pb_ratio'],\n",
+    "                            count=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 设置时间范围\n",
+    "startDt, endDt = '2014-01-01', '2020-12-31'\n",
+    "# 每月初\n",
+    "periods: List = get_trade_period(startDt, endDt, 'M')\n",
+    "\n",
+    "# 获取FFScore得分\n",
+    "data_list = list(get_FFScore('A', FScore, periods))\n",
+    "\n",
+    "# 因子值\n",
+    "FScore_factor: pd.Series = pd.concat(\n",
+    "    {pd.to_datetime(f.watch_date): f.fscore\n",
+    "     for f in data_list},\n",
+    "    names=['date', 'asset'])\n",
+    "\n",
+    "# 基础数据\n",
+    "basic_df: pd.DataFrame = pd.concat({f.watch_date: f.basic\n",
+    "                                    for f in data_list},\n",
+    "                                   names=['date', 'asset'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 储存数据\n",
+    "# FScore_factor.to_frame('FScore').to_csv('FScore.csv')\n",
+    "# basic_df.to_csv('basic.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 读取\n",
+    "FScore_factor = pd.read_csv('FScore.csv', \n",
+    "                             index_col=[0,1], \n",
+    "                             parse_dates=[0])\n",
+    "\n",
+    "basic_df = pd.read_csv('basic.csv',\n",
+    "                       index_col=[0,1], \n",
+    "                       parse_dates=[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1dc43f4844dd4b10a5d40fe53beb6d85",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, description='获取收盘价数据', max=132, style=ProgressStyle(description_width='ini…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "676b6af4d95c41ca8fcb8c012b85ac46",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, description='获取PB数据', max=84, style=ProgressStyle(description_width='initi…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 时间范围\n",
+    "price_periods = get_trade_period('2010-01-01', '2020-12-31', 'M')\n",
+    "\n",
+    "# 股票池\n",
+    "securities = FScore_factor.index.levels[1].tolist()\n",
+    "\n",
+    "# 获取收盘价\n",
+    "price_list = list(get_factor_price(securities, price_periods))\n",
+    "\n",
+    "# 获取PB 这里用的时间段与因子的一致\n",
+    "pb_list = list(get_factor_pb_ratio(securities,periods))\n",
+    "\n",
+    "pb_df = pd.concat(pb_list)\n",
+    "\n",
+    "price_df = pd.concat(price_list)\n",
+    "\n",
+    "pivot_price = pd.pivot_table(price_df,\n",
+    "                             index='time',\n",
+    "                             columns='code',\n",
+    "                             values='close')\n",
+    "\n",
+    "pivot_pb = pd.pivot_table(pb_df,\n",
+    "                         index='day',\n",
+    "                         columns='code',\n",
+    "                         values='pb_ratio')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 市净率与长期市场表现的关系\n",
+    "\n",
+    "我们首先考察不同市净率的股票在市场中的表现。在剔除负市净率的股票后，按照市净率 的从低到高对 A 股市场中的股票进行分层，按照每层 20%均分为 5 层。其中第一层为市 净率前 20%的股票，第二层为市净率 20%~40%的股票，第三层为市净率 40%~60%的股票，第四层为市净率 60%~80%的股票，第五层为市净率 80%~100%的股票。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 过滤PB小于0的\n",
+    "filter_pb = pivot_pb[pivot_pb > 0]\n",
+    "\n",
+    "filter_pb.index = pd.to_datetime(filter_pb.index)\n",
+    "\n",
+    "pb_ser = filter_pb.stack()\n",
+    "\n",
+    "pb_ser.index.names = ['date','asset']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dropped 1.4% entries from factor data (1.4% after in forward returns computation and 0.0% in binning phase). Set max_loss=0 to see potentially suppressed Exceptions.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 获取A股中的PB因子\n",
+    "pb_factor = al.utils.get_clean_factor_and_forward_returns(pb_ser,\n",
+    "                                                           pivot_price,\n",
+    "                                                           quantiles = 5,\n",
+    "                                                           periods=(1,))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "PB因子从高到底排列的各个分层,其总收益率分别为199.38%,215.36%,194.41%,171.36%,107.48%。可以看到PB因子除第1组与第二组之间在分层上有所重叠外，其余分组上分层效果较为明显。由此可以看出PB是一个较好的选股因子。尤其是市净率前20%的股票在各方面指上均明显领先其他各层股票。这与比乔斯基结论一致，符合低 PB股票池优于市场表现的结论。后续我们将以市净率前 20%的股票作为投资组合基准，借鉴比乔斯基方法对 A股市场进行实证研究。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pb_ret = pd.pivot_table(pb_factor.reset_index(),index='date',columns='factor_quantile',values=1)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(18, 6))\n",
+    "ax.set_title('PB因子分组收益')\n",
+    "ax.yaxis.set_major_formatter(\n",
+    "    mpl.ticker.FuncFormatter(lambda x, pos: '%.2f%%' % (x * 100)))\n",
+    "\n",
+    "color_map = ['#5D91A7', '#00516C', '#6BCFF6', '#00A4DC', '#6DBBBF', '#008982']\n",
+    "ep.cum_returns(pb_ret).plot(ax=ax,color=color_map);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "</style>  \n",
+       "<table id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494\" > \n",
+       "<thead>    <tr> \n",
+       "        <th class=\"index_name level0\" >factor_quantile</th> \n",
+       "        <th class=\"col_heading level0 col0\" >1</th> \n",
+       "        <th class=\"col_heading level0 col1\" >2</th> \n",
+       "        <th class=\"col_heading level0 col2\" >3</th> \n",
+       "        <th class=\"col_heading level0 col3\" >4</th> \n",
+       "        <th class=\"col_heading level0 col4\" >5</th> \n",
+       "    </tr></thead> \n",
+       "<tbody>    <tr> \n",
+       "        <th id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494level0_row0\" class=\"row_heading level0 row0\" >年化收益率</th> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row0_col0\" class=\"data row0 col0\" >21.88%</td> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row0_col1\" class=\"data row0 col1\" >21.20%</td> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row0_col2\" class=\"data row0 col2\" >21.46%</td> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row0_col3\" class=\"data row0 col3\" >14.44%</td> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row0_col4\" class=\"data row0 col4\" >5.25%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494level0_row1\" class=\"row_heading level0 row1\" >累计收益</th> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row1_col0\" class=\"data row1 col0\" >293.05%</td> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row1_col1\" class=\"data row1 col1\" >278.09%</td> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row1_col2\" class=\"data row1 col2\" >283.75%</td> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row1_col3\" class=\"data row1 col3\" >154.16%</td> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row1_col4\" class=\"data row1 col4\" >42.50%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494level0_row2\" class=\"row_heading level0 row2\" >波动率</th> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row2_col0\" class=\"data row2 col0\" >27.42%</td> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row2_col1\" class=\"data row2 col1\" >30.85%</td> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row2_col2\" class=\"data row2 col2\" >32.85%</td> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row2_col3\" class=\"data row2 col3\" >33.71%</td> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row2_col4\" class=\"data row2 col4\" >34.43%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494level0_row3\" class=\"row_heading level0 row3\" >夏普</th> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row3_col0\" class=\"data row3 col0\" >85.93%</td> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row3_col1\" class=\"data row3 col1\" >77.52%</td> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row3_col2\" class=\"data row3 col2\" >75.23%</td> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row3_col3\" class=\"data row3 col3\" >56.55%</td> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row3_col4\" class=\"data row3 col4\" >31.69%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494level0_row4\" class=\"row_heading level0 row4\" >最大回撤</th> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row4_col0\" class=\"data row4 col0\" >-32.13%</td> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row4_col1\" class=\"data row4 col1\" >-39.53%</td> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row4_col2\" class=\"data row4 col2\" >-38.37%</td> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row4_col3\" class=\"data row4 col3\" >-54.10%</td> \n",
+       "        <td id=\"T_9c99dd86_1c38_11ec_a568_c6008c580494row4_col4\" class=\"data row4 col4\" >-70.21%</td> \n",
+       "    </tr></tbody> \n",
+       "</table> "
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fb7978e82e8>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Strategy_performance(pb_ret,'monthly').style.format('{:.2%}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# FScore财务模型\n",
+    "\n",
+    "## 以与收益率相关性最高的财务指标筛选为核心\n",
+    "\n",
+    "比乔斯基在其模型中选用了代表盈利水平、财务杠杆及流动性和运营效率的 9 个财务指标 对股票进行优劣筛选。首先将这三个领域共九个指标采用 0-1 度量（即参数为 1 或者 0），\n",
+    "使得股票的得分为 0 至 9 分。分数越高时，代表该股票的投资价值越高。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "FScore分值分布,可以看到绝大多数股票得分为3至4分"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.title('FScore值分布情况')\n",
+    "FScore_factor['FScore'].hist();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "原始财务指标的统计信息如下"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "</style>  \n",
+       "<table id=\"T_a110b88a_1c38_11ec_a568_c6008c580494\" > \n",
+       "<thead>    <tr> \n",
+       "        <th class=\"blank level0\" ></th> \n",
+       "        <th class=\"col_heading level0 col0\" >count</th> \n",
+       "        <th class=\"col_heading level0 col1\" >mean</th> \n",
+       "        <th class=\"col_heading level0 col2\" >std</th> \n",
+       "        <th class=\"col_heading level0 col3\" >min</th> \n",
+       "        <th class=\"col_heading level0 col4\" >25%</th> \n",
+       "        <th class=\"col_heading level0 col5\" >50%</th> \n",
+       "        <th class=\"col_heading level0 col6\" >75%</th> \n",
+       "        <th class=\"col_heading level0 col7\" >max</th> \n",
+       "    </tr></thead> \n",
+       "<tbody>    <tr> \n",
+       "        <th id=\"T_a110b88a_1c38_11ec_a568_c6008c580494level0_row0\" class=\"row_heading level0 row0\" >ROA</th> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row0_col0\" class=\"data row0 col0\" >243391.0000</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row0_col1\" class=\"data row0 col1\" >1.0684</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row0_col2\" class=\"data row0 col2\" >1.9380</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row0_col3\" class=\"data row0 col3\" >-124.7700</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row0_col4\" class=\"data row0 col4\" >0.2300</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row0_col5\" class=\"data row0 col5\" >0.8400</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row0_col6\" class=\"data row0 col6\" >1.7400</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row0_col7\" class=\"data row0 col7\" >114.0100</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_a110b88a_1c38_11ec_a568_c6008c580494level0_row1\" class=\"row_heading level0 row1\" >CFO</th> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row1_col0\" class=\"data row1 col0\" >242799.0000</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row1_col1\" class=\"data row1 col1\" >0.0056</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row1_col2\" class=\"data row1 col2\" >0.0394</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row1_col3\" class=\"data row1 col3\" >-1.0898</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row1_col4\" class=\"data row1 col4\" >-0.0113</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row1_col5\" class=\"data row1 col5\" >0.0065</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row1_col6\" class=\"data row1 col6\" >0.0242</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row1_col7\" class=\"data row1 col7\" >0.7908</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_a110b88a_1c38_11ec_a568_c6008c580494level0_row2\" class=\"row_heading level0 row2\" >DELTA_ROA</th> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row2_col0\" class=\"data row2 col0\" >232114.0000</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row2_col1\" class=\"data row2 col1\" >-0.1687</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row2_col2\" class=\"data row2 col2\" >19.7152</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row2_col3\" class=\"data row2 col3\" >-2149.8000</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row2_col4\" class=\"data row2 col4\" >-0.5625</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row2_col5\" class=\"data row2 col5\" >-0.1147</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row2_col6\" class=\"data row2 col6\" >0.2418</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row2_col7\" class=\"data row2 col7\" >2292.6364</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_a110b88a_1c38_11ec_a568_c6008c580494level0_row3\" class=\"row_heading level0 row3\" >ACCRUAL</th> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row3_col0\" class=\"data row3 col0\" >242764.0000</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row3_col1\" class=\"data row3 col1\" >-0.0051</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row3_col2\" class=\"data row3 col2\" >0.0393</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row3_col3\" class=\"data row3 col3\" >-1.2975</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row3_col4\" class=\"data row3 col4\" >-0.0209</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row3_col5\" class=\"data row3 col5\" >-0.0026</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row3_col6\" class=\"data row3 col6\" >0.0130</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row3_col7\" class=\"data row3 col7\" >1.2546</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_a110b88a_1c38_11ec_a568_c6008c580494level0_row4\" class=\"row_heading level0 row4\" >DELTA_LEVELER</th> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row4_col0\" class=\"data row4 col0\" >216819.0000</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row4_col1\" class=\"data row4 col1\" >-117.5187</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row4_col2\" class=\"data row4 col2\" >21233.5567</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row4_col3\" class=\"data row4 col3\" >-4896347.5711</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row4_col4\" class=\"data row4 col4\" >-0.3860</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row4_col5\" class=\"data row4 col5\" >0.0355</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row4_col6\" class=\"data row4 col6\" >0.2732</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row4_col7\" class=\"data row4 col7\" >2239.4868</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_a110b88a_1c38_11ec_a568_c6008c580494level0_row5\" class=\"row_heading level0 row5\" >DLTA_LIQUID</th> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row5_col0\" class=\"data row5 col0\" >228151.0000</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row5_col1\" class=\"data row5 col1\" >0.0552</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row5_col2\" class=\"data row5 col2\" >1.5493</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row5_col3\" class=\"data row5 col3\" >-67.6335</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row5_col4\" class=\"data row5 col4\" >-0.1781</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row5_col5\" class=\"data row5 col5\" >-0.0299</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row5_col6\" class=\"data row5 col6\" >0.1178</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row5_col7\" class=\"data row5 col7\" >264.4299</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_a110b88a_1c38_11ec_a568_c6008c580494level0_row6\" class=\"row_heading level0 row6\" >DELTA_MARGIN</th> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row6_col0\" class=\"data row6 col0\" >228051.0000</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row6_col1\" class=\"data row6 col1\" >0.2942</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row6_col2\" class=\"data row6 col2\" >53.6886</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row6_col3\" class=\"data row6 col3\" >-1542.3056</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row6_col4\" class=\"data row6 col4\" >-0.1368</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row6_col5\" class=\"data row6 col5\" >-0.0068</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row6_col6\" class=\"data row6 col6\" >0.1228</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row6_col7\" class=\"data row6 col7\" >10209.5201</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_a110b88a_1c38_11ec_a568_c6008c580494level0_row7\" class=\"row_heading level0 row7\" >DELTA_TURN</th> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row7_col0\" class=\"data row7 col0\" >229540.0000</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row7_col1\" class=\"data row7 col1\" >7864.0876</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row7_col2\" class=\"data row7 col2\" >3767598.7658</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row7_col3\" class=\"data row7 col3\" >-20129.0717</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row7_col4\" class=\"data row7 col4\" >-0.1762</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row7_col5\" class=\"data row7 col5\" >-0.0245</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row7_col6\" class=\"data row7 col6\" >0.1340</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row7_col7\" class=\"data row7 col7\" >1805069110.1253</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_a110b88a_1c38_11ec_a568_c6008c580494level0_row8\" class=\"row_heading level0 row8\" >EQ_OFFSER</th> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row8_col0\" class=\"data row8 col0\" >232508.0000</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row8_col1\" class=\"data row8 col1\" >0.2016</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row8_col2\" class=\"data row8 col2\" >0.6812</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row8_col3\" class=\"data row8 col3\" >-0.8999</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row8_col4\" class=\"data row8 col4\" >0.0000</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row8_col5\" class=\"data row8 col5\" >0.0000</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row8_col6\" class=\"data row8 col6\" >0.1236</td> \n",
+       "        <td id=\"T_a110b88a_1c38_11ec_a568_c6008c580494row8_col7\" class=\"data row8 col7\" >34.8357</td> \n",
+       "    </tr></tbody> \n",
+       "</table> "
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fb796b38438>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "basic_df.describe().T.style.format('{:.4f}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "测试实施细节:\n",
+    "\n",
+    "1. 选股范围为全A,等权重，不考虑交易费用\n",
+    "2. 样本：2012-11-01至2021-01-31期间的所有A股上市公司。将每期FFScore按【0-4】、【5-9】进行分组，月度调仓。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dropped 2.1% entries from factor data (1.5% after in forward returns computation and 0.6% in binning phase). Set max_loss=0 to see potentially suppressed Exceptions.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 将FFScore按0-1分和8至9分进行分组\n",
+    "factor_data = al.utils.get_clean_factor_and_forward_returns(FScore_factor,\n",
+    "                                                           pivot_price,\n",
+    "                                                           quantiles = None,\n",
+    "                                                           bins=[0,1,7,9],\n",
+    "                                                           periods=(1,))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "532358c6ed1e4bbc99d844fe0d830f39",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, description='获取收盘价数据', max=132, style=ProgressStyle(description_width='ini…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "benchmark = list(get_factor_price('000300.XSHG', price_periods))\n",
+    "benchmark = pd.concat(benchmark)\n",
+    "benchmark_ret = benchmark['close'].pct_change()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ret = pd.pivot_table(factor_data.reindex(),\n",
+    "                     index='date',\n",
+    "                     columns='factor_quantile',\n",
+    "                     values=1)\n",
+    "\n",
+    "ret.rename(columns={1.0: 'FScore低分组',2.0:'中间组', 3.0: 'FScore高分组'}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "仅使用FScore财务模型进行选股,高分组累计收益332.06%,年化收益20.07%,夏普及回撤均优于低分组及中间组。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(18, 6))\n",
+    "ax.set_title('FScore累计收益')\n",
+    "ax.yaxis.set_major_formatter(\n",
+    "    mpl.ticker.FuncFormatter(lambda x, pos: '%.2f%%' % (x * 100)))\n",
+    "\n",
+    "ep.cum_returns(ret).plot(ax=ax,color=['#00516C', '#6BCFF6'])\n",
+    "ep.cum_returns(benchmark_ret.reindex(ret.index)).plot(ax=ax,\n",
+    "                                   label='HS300',\n",
+    "                                   color='darkgray',\n",
+    "                                   ls='--')\n",
+    "ax.axhline(0, color='black', lw=1)\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "</style>  \n",
+       "<table id=\"T_a9d2ac26_1c38_11ec_a568_c6008c580494\" > \n",
+       "<thead>    <tr> \n",
+       "        <th class=\"index_name level0\" >factor_quantile</th> \n",
+       "        <th class=\"col_heading level0 col0\" >FScore低分组</th> \n",
+       "        <th class=\"col_heading level0 col1\" >中间组</th> \n",
+       "        <th class=\"col_heading level0 col2\" >FScore高分组</th> \n",
+       "    </tr></thead> \n",
+       "<tbody>    <tr> \n",
+       "        <th id=\"T_a9d2ac26_1c38_11ec_a568_c6008c580494level0_row0\" class=\"row_heading level0 row0\" >年化收益率</th> \n",
+       "        <td id=\"T_a9d2ac26_1c38_11ec_a568_c6008c580494row0_col0\" class=\"data row0 col0\" >7.84%</td> \n",
+       "        <td id=\"T_a9d2ac26_1c38_11ec_a568_c6008c580494row0_col1\" class=\"data row0 col1\" >12.77%</td> \n",
+       "        <td id=\"T_a9d2ac26_1c38_11ec_a568_c6008c580494row0_col2\" class=\"data row0 col2\" >22.99%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_a9d2ac26_1c38_11ec_a568_c6008c580494level0_row1\" class=\"row_heading level0 row1\" >累计收益</th> \n",
+       "        <td id=\"T_a9d2ac26_1c38_11ec_a568_c6008c580494row1_col0\" class=\"data row1 col0\" >68.52%</td> \n",
+       "        <td id=\"T_a9d2ac26_1c38_11ec_a568_c6008c580494row1_col1\" class=\"data row1 col1\" >129.56%</td> \n",
+       "        <td id=\"T_a9d2ac26_1c38_11ec_a568_c6008c580494row1_col2\" class=\"data row1 col2\" >318.33%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_a9d2ac26_1c38_11ec_a568_c6008c580494level0_row2\" class=\"row_heading level0 row2\" >波动率</th> \n",
+       "        <td id=\"T_a9d2ac26_1c38_11ec_a568_c6008c580494row2_col0\" class=\"data row2 col0\" >35.13%</td> \n",
+       "        <td id=\"T_a9d2ac26_1c38_11ec_a568_c6008c580494row2_col1\" class=\"data row2 col1\" >31.33%</td> \n",
+       "        <td id=\"T_a9d2ac26_1c38_11ec_a568_c6008c580494row2_col2\" class=\"data row2 col2\" >29.22%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_a9d2ac26_1c38_11ec_a568_c6008c580494level0_row3\" class=\"row_heading level0 row3\" >夏普</th> \n",
+       "        <td id=\"T_a9d2ac26_1c38_11ec_a568_c6008c580494row3_col0\" class=\"data row3 col0\" >38.57%</td> \n",
+       "        <td id=\"T_a9d2ac26_1c38_11ec_a568_c6008c580494row3_col1\" class=\"data row3 col1\" >53.87%</td> \n",
+       "        <td id=\"T_a9d2ac26_1c38_11ec_a568_c6008c580494row3_col2\" class=\"data row3 col2\" >85.52%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_a9d2ac26_1c38_11ec_a568_c6008c580494level0_row4\" class=\"row_heading level0 row4\" >最大回撤</th> \n",
+       "        <td id=\"T_a9d2ac26_1c38_11ec_a568_c6008c580494row4_col0\" class=\"data row4 col0\" >-63.72%</td> \n",
+       "        <td id=\"T_a9d2ac26_1c38_11ec_a568_c6008c580494row4_col1\" class=\"data row4 col1\" >-53.89%</td> \n",
+       "        <td id=\"T_a9d2ac26_1c38_11ec_a568_c6008c580494row4_col2\" class=\"data row4 col2\" >-38.97%</td> \n",
+       "    </tr></tbody> \n",
+       "</table> "
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fb7948ee780>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Strategy_performance(ret,'monthly').style.format('{:.2%}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 比乔斯基9指标模型策略回测结果分析\n",
+    "\n",
+    "用比乔斯基 FScore 模型中的 9 个选股标准对低 PB 股票进行筛选进行历史回测，同 时比较低市净率策略与 FScore 模型之间的差别。\n",
+    "\n",
+    "FScore 策略与低市净率策略的年化收益率分别为14.65%和14.69%；A股市场中比乔斯基FScore策略并未跑赢作为基准的低市净率策略，更高的波动率对整体收益带来了更多不确定性，因此可知**原始比乔斯基模型在A股市场并不完全有效**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = factor_data.copy()\n",
+    "df['pb_group'] = pb_factor['factor_quantile']\n",
+    "\n",
+    "cross_df = df.query('pb_group == 1 and factor_quantile == 3')\n",
+    "cross_ret = pd.pivot_table(cross_df.reset_index(),index='date',columns='factor_quantile',values=1)\n",
+    "\n",
+    "fih,ax = plt.subplots(figsize=(18,6))\n",
+    "ax.set_title('比乔斯基9指标模型策略')\n",
+    "\n",
+    "all_ret = pd.concat((cross_ret[3],ret['FScore高分组'],pb_ret[1],benchmark_ret.reindex(cross_ret.index)),axis=1)\n",
+    "all_ret.columns = ['比乔斯基9指标模型策略','FScore高分组','PB因子(前20%)','benchmark']\n",
+    "\n",
+    "ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda x,pos:'%.2f%%'%(x*100)))\n",
+    "ep.cum_returns(all_ret).plot(ax=ax,color=color_map[:3] + ['darkgray'])\n",
+    "ax.legend();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "</style>  \n",
+       "<table id=\"T_c26aade2_1c38_11ec_a568_c6008c580494\" > \n",
+       "<thead>    <tr> \n",
+       "        <th class=\"blank level0\" ></th> \n",
+       "        <th class=\"col_heading level0 col0\" >比乔斯基9指标模型策略</th> \n",
+       "        <th class=\"col_heading level0 col1\" >FScore高分组</th> \n",
+       "        <th class=\"col_heading level0 col2\" >PB因子(前20%)</th> \n",
+       "        <th class=\"col_heading level0 col3\" >benchmark</th> \n",
+       "    </tr></thead> \n",
+       "<tbody>    <tr> \n",
+       "        <th id=\"T_c26aade2_1c38_11ec_a568_c6008c580494level0_row0\" class=\"row_heading level0 row0\" >年化收益率</th> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row0_col0\" class=\"data row0 col0\" >24.70%</td> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row0_col1\" class=\"data row0 col1\" >22.99%</td> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row0_col2\" class=\"data row0 col2\" >21.88%</td> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row0_col3\" class=\"data row0 col3\" >10.15%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_c26aade2_1c38_11ec_a568_c6008c580494level0_row1\" class=\"row_heading level0 row1\" >累计收益</th> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row1_col0\" class=\"data row1 col0\" >360.37%</td> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row1_col1\" class=\"data row1 col1\" >318.33%</td> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row1_col2\" class=\"data row1 col2\" >293.05%</td> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row1_col3\" class=\"data row1 col3\" >95.17%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_c26aade2_1c38_11ec_a568_c6008c580494level0_row2\" class=\"row_heading level0 row2\" >波动率</th> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row2_col0\" class=\"data row2 col0\" >27.15%</td> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row2_col1\" class=\"data row2 col1\" >29.22%</td> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row2_col2\" class=\"data row2 col2\" >27.42%</td> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row2_col3\" class=\"data row2 col3\" >24.81%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_c26aade2_1c38_11ec_a568_c6008c580494level0_row3\" class=\"row_heading level0 row3\" >夏普</th> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row3_col0\" class=\"data row3 col0\" >95.40%</td> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row3_col1\" class=\"data row3 col1\" >85.52%</td> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row3_col2\" class=\"data row3 col2\" >85.93%</td> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row3_col3\" class=\"data row3 col3\" >51.08%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_c26aade2_1c38_11ec_a568_c6008c580494level0_row4\" class=\"row_heading level0 row4\" >最大回撤</th> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row4_col0\" class=\"data row4 col0\" >-40.30%</td> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row4_col1\" class=\"data row4 col1\" >-38.97%</td> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row4_col2\" class=\"data row4 col2\" >-32.13%</td> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row4_col3\" class=\"data row4 col3\" >-42.85%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_c26aade2_1c38_11ec_a568_c6008c580494level0_row5\" class=\"row_heading level0 row5\" >IR</th> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row5_col0\" class=\"data row5 col0\" >11.61%</td> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row5_col1\" class=\"data row5 col1\" >10.36%</td> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row5_col2\" class=\"data row5 col2\" >9.69%</td> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row5_col3\" class=\"data row5 col3\" >nan%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_c26aade2_1c38_11ec_a568_c6008c580494level0_row6\" class=\"row_heading level0 row6\" >Alpha</th> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row6_col0\" class=\"data row6 col0\" >23.10%</td> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row6_col1\" class=\"data row6 col1\" >21.97%</td> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row6_col2\" class=\"data row6 col2\" >20.31%</td> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row6_col3\" class=\"data row6 col3\" >nan%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_c26aade2_1c38_11ec_a568_c6008c580494level0_row7\" class=\"row_heading level0 row7\" >超额收益</th> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row7_col0\" class=\"data row7 col0\" >14.55%</td> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row7_col1\" class=\"data row7 col1\" >12.84%</td> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row7_col2\" class=\"data row7 col2\" >11.73%</td> \n",
+       "        <td id=\"T_c26aade2_1c38_11ec_a568_c6008c580494row7_col3\" class=\"data row7 col3\" >0.00%</td> \n",
+       "    </tr></tbody> \n",
+       "</table> "
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fb794181630>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Strategy_performance(all_ret,'monthly').style.format('{:.2%}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cross_df.groupby(level='date')['factor'].count().plot(title='持仓数量',figsize=(18,4));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 中国版Fscore财务模型\n",
+    "\n",
+    "A股市场相对于美国市场的波动更高、以及9个财务指标来进行筛选高分股票数量偏少导致的投资收益的风险增大的原因，同时可能还由于财务报告编制方法、中美财务指标的计算原则、以及 A股市场投资者对财务指标变化的理解有所不同，我们尝试进一步优化原有的比乔斯基低市净率9指标FScore选股策略，并提出了更适合中国A股市场的Five FScore（FFScore）低市净率选股模型。\n",
+    "\n",
+    "**华泰低市净率 FFScore 模型财务指标的获取与计算方法**\n",
+    "\n",
+    "**盈利水平**\n",
+    "\n",
+    "1. 资产收益率(ROE)：收益率为正数时ROE=1，否则为0。\n",
+    "2. 资产收益率变化($\\Delta ROE$)：当期最新可得财务报告的ROE同比的变化。 变化为正数时$\\Delta ROE$=1，否则为0。\n",
+    "\n",
+    "**财务杠杆和流动性**\n",
+    "\n",
+    "3. 杠杆变化($\\Delta LEVER$)：杠杆通过非流动负债合计除以非流动资产合计计 算，杠杆变化为当期最新可得财务报告的杠杆同比的变化。变化为负数时$\\Delta LEVER$=1，否则为0。\n",
+    "\n",
+    "**运营效率**\n",
+    "\n",
+    "从财务分析的角度考虑，销售毛利率增量($\\Delta MARGIN$)中毛利润除以营业收入的构成， 这与$\\Delta ROE$指标的选取要素净利润类似，均含有利润相关的指标。一定程度上和盈利水平指标重复。在5指标模型中已经分析得到$\\Delta MARGIN$与$\\Delta ROE$指标的相关度达到了0.64892。因此即使在使用单个指标时效果优异，组合后的效果并不明显。类似的，我们选取净利率($\\Delta NETMARGIN$)后发现，组合的效果并不理想。因此我们选择加入了流动 资产周转率变化($\\Delta CATURN$)指标，通过主营业务收入与流动资产的比例来反映流动资产的周转速度，来衡量企业在生产运营上对流动资产的利用效率。\n",
+    "\n",
+    "4. 流动资产周转率变化($\\Delta CATURN$)： 流动资产周转率变化为当期最新可 得财务报告的资产周转率同比的变化。变化为正数时$\\Delta CATURN$ =1，否则为0。\n",
+    "5. 资产周转率变化($\\Delta TURN$)： 资产周转率通过总资产周转率除以平均资 产总值计算，资产周转率变化为当期最新可得财务报告的资产周转率同比的变化。变化为正数时$\\Delta TURN$ =1，否则为0\n",
+    "\n",
+    "因为只有五个财务指标故名字为Five_FScore(简称:FFScore)：\n",
+    "\n",
+    "$Five\\_FScore = ROE + \\Delta ROE + \\Delta CATURN + \\Delta TURN + \\Delta LEVER$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b5ee54a5700c4b49a1030cb24447729c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, description='FFScore因子获取', max=84, style=ProgressStyle(description_width='…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 设置时间范围\n",
+    "startDt, endDt = '2014-01-01', '2020-12-31'\n",
+    "# 每月初\n",
+    "periods: List = get_trade_period(startDt, endDt, 'M')\n",
+    "\n",
+    "# 获取FFScore得分\n",
+    "data_list1 = list(get_FFScore('A', FFScore, periods))\n",
+    "\n",
+    "# 因子值\n",
+    "FFScore_factor: pd.Series = pd.concat(\n",
+    "    {pd.to_datetime(f.watch_date): f.ffscore\n",
+    "     for f in data_list1},\n",
+    "    names=['date', 'asset'])\n",
+    "\n",
+    "# 基础数据\n",
+    "basic_df1: pd.DataFrame = pd.concat({f.watch_date: f.basic\n",
+    "                                    for f in data_list1},\n",
+    "                                   names=['date', 'asset'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 储存\n",
+    "FFScore_factor.to_frame('FFScore').to_csv('FFScore.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 读取\n",
+    "FFScore_factor = pd.read_csv('FFScore.csv', \n",
+    "                             index_col=[0,1], \n",
+    "                             parse_dates=[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.title('FFScore值分布情况')\n",
+    "FFScore_factor['FFScore'].hist();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dropped 5.0% entries from factor data (1.5% after in forward returns computation and 3.6% in binning phase). Set max_loss=0 to see potentially suppressed Exceptions.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 将FFScore按0-5分和5至8分进行分组\n",
+    "factor_data1 = al.utils.get_clean_factor_and_forward_returns(FFScore_factor,\n",
+    "                                                           pivot_price,\n",
+    "                                                           quantiles = None,\n",
+    "                                                           bins=[0,3,5],\n",
+    "                                                           periods=(1,))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ret1 = pd.pivot_table(factor_data1.reindex(),\n",
+    "                     index='date',\n",
+    "                     columns='factor_quantile',\n",
+    "                     values=1)\n",
+    "\n",
+    "ret1.rename(columns={1.0: 'FFScore低分组', 2.0: 'FFScore高分组'}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "近FFScore财务模型选股,高分组比FScore略有下降"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(18, 6))\n",
+    "ax.set_title('FFScore累计收益')\n",
+    "ax.yaxis.set_major_formatter(\n",
+    "    mpl.ticker.FuncFormatter(lambda x, pos: '%.2f%%' % (x * 100)))\n",
+    "\n",
+    "ep.cum_returns(ret1).plot(ax=ax,color=['#00516C', '#6BCFF6'])\n",
+    "ep.cum_returns(benchmark_ret.reindex(ret1.index)).plot(ax=ax,\n",
+    "                                   label='HS300',\n",
+    "                                   color='darkgray',\n",
+    "                                   ls='--')\n",
+    "ax.axhline(0, color='black', lw=1)\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "</style>  \n",
+       "<table id=\"T_95a80bf4_1c3a_11ec_a568_c6008c580494\" > \n",
+       "<thead>    <tr> \n",
+       "        <th class=\"index_name level0\" >factor_quantile</th> \n",
+       "        <th class=\"col_heading level0 col0\" >FFScore低分组</th> \n",
+       "        <th class=\"col_heading level0 col1\" >FFScore高分组</th> \n",
+       "    </tr></thead> \n",
+       "<tbody>    <tr> \n",
+       "        <th id=\"T_95a80bf4_1c3a_11ec_a568_c6008c580494level0_row0\" class=\"row_heading level0 row0\" >年化收益率</th> \n",
+       "        <td id=\"T_95a80bf4_1c3a_11ec_a568_c6008c580494row0_col0\" class=\"data row0 col0\" >10.63%</td> \n",
+       "        <td id=\"T_95a80bf4_1c3a_11ec_a568_c6008c580494row0_col1\" class=\"data row0 col1\" >18.65%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_95a80bf4_1c3a_11ec_a568_c6008c580494level0_row1\" class=\"row_heading level0 row1\" >累计收益</th> \n",
+       "        <td id=\"T_95a80bf4_1c3a_11ec_a568_c6008c580494row1_col0\" class=\"data row1 col0\" >101.17%</td> \n",
+       "        <td id=\"T_95a80bf4_1c3a_11ec_a568_c6008c580494row1_col1\" class=\"data row1 col1\" >226.31%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_95a80bf4_1c3a_11ec_a568_c6008c580494level0_row2\" class=\"row_heading level0 row2\" >波动率</th> \n",
+       "        <td id=\"T_95a80bf4_1c3a_11ec_a568_c6008c580494row2_col0\" class=\"data row2 col0\" >31.30%</td> \n",
+       "        <td id=\"T_95a80bf4_1c3a_11ec_a568_c6008c580494row2_col1\" class=\"data row2 col1\" >32.03%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_95a80bf4_1c3a_11ec_a568_c6008c580494level0_row3\" class=\"row_heading level0 row3\" >夏普</th> \n",
+       "        <td id=\"T_95a80bf4_1c3a_11ec_a568_c6008c580494row3_col0\" class=\"data row3 col0\" >47.77%</td> \n",
+       "        <td id=\"T_95a80bf4_1c3a_11ec_a568_c6008c580494row3_col1\" class=\"data row3 col1\" >69.28%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_95a80bf4_1c3a_11ec_a568_c6008c580494level0_row4\" class=\"row_heading level0 row4\" >最大回撤</th> \n",
+       "        <td id=\"T_95a80bf4_1c3a_11ec_a568_c6008c580494row4_col0\" class=\"data row4 col0\" >-56.81%</td> \n",
+       "        <td id=\"T_95a80bf4_1c3a_11ec_a568_c6008c580494row4_col1\" class=\"data row4 col1\" >-46.11%</td> \n",
+       "    </tr></tbody> \n",
+       "</table> "
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fb78afad278>"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Strategy_performance(ret1,'monthly').style.format('{:.2%}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "但相较于FScore选股策略来说结合低市盈率后收益从14.69%提升至18.09%(原始财务模型策略为14.65%)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = factor_data1.copy()\n",
+    "df['pb_group'] = pb_factor['factor_quantile']\n",
+    "\n",
+    "cross_df1 = df.query('pb_group == 1 and factor_quantile == 2').copy()\n",
+    "cross_ret1 = pd.pivot_table(cross_df1.reset_index(),index='date',columns='factor_quantile',values=1)\n",
+    "\n",
+    "fih,ax = plt.subplots(figsize=(18,6))\n",
+    "ax.set_title('FFSCore指标模型策略')\n",
+    "\n",
+    "all_ret = pd.concat((cross_ret1[2],ret1['FFScore高分组'],pb_ret[1],benchmark_ret.reindex(cross_ret1.index)),axis=1)\n",
+    "all_ret.columns = ['FFSCore指标模型策略','FFScore高分组','PB因子(前20%)','benchmark']\n",
+    "\n",
+    "ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda x,pos:'%.2f%%'%(x*100)))\n",
+    "ep.cum_returns(all_ret).plot(ax=ax,color=color_map[:3] + ['darkgray'])\n",
+    "ax.legend();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "</style>  \n",
+       "<table id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494\" > \n",
+       "<thead>    <tr> \n",
+       "        <th class=\"blank level0\" ></th> \n",
+       "        <th class=\"col_heading level0 col0\" >FFSCore指标模型策略</th> \n",
+       "        <th class=\"col_heading level0 col1\" >FFScore高分组</th> \n",
+       "        <th class=\"col_heading level0 col2\" >PB因子(前20%)</th> \n",
+       "        <th class=\"col_heading level0 col3\" >benchmark</th> \n",
+       "    </tr></thead> \n",
+       "<tbody>    <tr> \n",
+       "        <th id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494level0_row0\" class=\"row_heading level0 row0\" >年化收益率</th> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row0_col0\" class=\"data row0 col0\" >24.42%</td> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row0_col1\" class=\"data row0 col1\" >18.65%</td> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row0_col2\" class=\"data row0 col2\" >21.88%</td> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row0_col3\" class=\"data row0 col3\" >10.15%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494level0_row1\" class=\"row_heading level0 row1\" >累计收益</th> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row1_col0\" class=\"data row1 col0\" >353.33%</td> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row1_col1\" class=\"data row1 col1\" >226.31%</td> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row1_col2\" class=\"data row1 col2\" >293.05%</td> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row1_col3\" class=\"data row1 col3\" >95.17%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494level0_row2\" class=\"row_heading level0 row2\" >波动率</th> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row2_col0\" class=\"data row2 col0\" >28.86%</td> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row2_col1\" class=\"data row2 col1\" >32.03%</td> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row2_col2\" class=\"data row2 col2\" >27.42%</td> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row2_col3\" class=\"data row2 col3\" >24.81%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494level0_row3\" class=\"row_heading level0 row3\" >夏普</th> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row3_col0\" class=\"data row3 col0\" >90.34%</td> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row3_col1\" class=\"data row3 col1\" >69.28%</td> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row3_col2\" class=\"data row3 col2\" >85.93%</td> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row3_col3\" class=\"data row3 col3\" >51.08%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494level0_row4\" class=\"row_heading level0 row4\" >最大回撤</th> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row4_col0\" class=\"data row4 col0\" >-34.13%</td> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row4_col1\" class=\"data row4 col1\" >-46.11%</td> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row4_col2\" class=\"data row4 col2\" >-32.13%</td> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row4_col3\" class=\"data row4 col3\" >-42.85%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494level0_row5\" class=\"row_heading level0 row5\" >IR</th> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row5_col0\" class=\"data row5 col0\" >11.58%</td> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row5_col1\" class=\"data row5 col1\" >7.59%</td> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row5_col2\" class=\"data row5 col2\" >9.69%</td> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row5_col3\" class=\"data row5 col3\" >nan%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494level0_row6\" class=\"row_heading level0 row6\" >Alpha</th> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row6_col0\" class=\"data row6 col0\" >22.64%</td> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row6_col1\" class=\"data row6 col1\" >18.76%</td> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row6_col2\" class=\"data row6 col2\" >20.31%</td> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row6_col3\" class=\"data row6 col3\" >nan%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494level0_row7\" class=\"row_heading level0 row7\" >超额收益</th> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row7_col0\" class=\"data row7 col0\" >14.27%</td> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row7_col1\" class=\"data row7 col1\" >8.50%</td> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row7_col2\" class=\"data row7 col2\" >11.73%</td> \n",
+       "        <td id=\"T_a016f7ee_1c3a_11ec_a568_c6008c580494row7_col3\" class=\"data row7 col3\" >0.00%</td> \n",
+       "    </tr></tbody> \n",
+       "</table> "
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fb78aa77710>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Strategy_performance(all_ret,'monthly').style.format('{:.2%}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 309,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cross_df1.groupby(level='date')['factor'].count().plot(title='持仓数量',figsize=(18,4));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 策略组合的行业分布"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cross_df1['industry'] = cross_df1.groupby(\n",
+    "    level='date').apply(lambda x: get_stock_ind(\n",
+    "        x.index.get_level_values(1).values.tolist(), x.name, 'sw_l1',\n",
+    "        'industry_name'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cross_df1.groupby('industry')[1].count().sort_values(ascending=False).plot.bar(\n",
+    "    color='r', figsize=(18, 5), title='各行业入选次数');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,ax = plt.subplots(figsize=(18,5))\n",
+    "\n",
+    "ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda x,pos:'{:.2%}'.format(x * 100)))\n",
+    "cross_df1.groupby('industry')[1].mean().sort_values(ascending=False).plot.bar(\n",
+    "    color='r', figsize=(18, 5),title='每期收益贡献',ax=ax)\n",
+    "\n",
+    "plt.axhline(0);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 使用随机森林算法分析特征"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "metadata": {
+    "code_folding": [
+     1
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# 涉及到的13个财务指标获取\n",
+    "class F13_Score(Factor):\n",
+    "    \n",
+    "    '''获取FScore及FFScore中提到的因子'''\n",
+    "    name = 'F15_Score'\n",
+    "    max_window = 1\n",
+    "    watch_date = None\n",
+    "    \n",
+    "    dependencies = ['roa', 'roa_4', 'net_operate_cash_flow', 'total_assets',\n",
+    "        'total_assets_1', 'total_assets_4', 'total_assets_5',\n",
+    "        'operating_revenue', 'operating_revenue_4','pb_ratio',\n",
+    "        'total_current_assets','total_current_assets_1','total_current_assets_4','total_current_assets_5',\n",
+    "        'total_non_current_assets', 'total_non_current_liability',\n",
+    "        'total_non_current_assets_4', 'total_non_current_liability_4',\n",
+    "        'total_current_assets', 'total_current_liability',\n",
+    "        'total_current_assets_4', 'total_current_liability_4',\n",
+    "        'gross_profit_margin', 'gross_profit_margin_4', 'paidin_capital',\n",
+    "        'paidin_capital_4','roe', 'roe_4','total_profit','total_profit_4','financial_expense','financial_expense_4']\n",
+    "    \n",
+    "    def calc(self,data:dict)->None:\n",
+    "        \n",
+    "        roe: pd.DataFrame = data['roe']\n",
+    "        \n",
+    "        roa1: pd.DataFrame = data['roa'] # 单位为百分号\n",
+    "        \n",
+    "        # 息税前利润（EBIT）除以资产总值\n",
+    "        # 利息支出项大多数情况时缺失的所以这里用财务费用代替\n",
+    "        roa2 = data['total_profit'] + data['financial_expense'] / (\n",
+    "            data['total_current_assets'] +\n",
+    "            data['total_current_assets_1']).mean()\n",
+    "        \n",
+    "        roa2_4 = data['total_profit_4'] + data['financial_expense_4'] / (\n",
+    "            data['total_current_assets_4'] +\n",
+    "            data['total_current_assets_5']).mean()\n",
+    "        \n",
+    "        delta_roa2 = roa2 / roa2_4 - 1\n",
+    "        \n",
+    "        cfo: pd.DataFrame = data['net_operate_cash_flow'] / \\\n",
+    "            data['total_assets']\n",
+    "\n",
+    "        delta_roa1: pd.DataFrame = roa1 / data['roa_4'] - 1\n",
+    "        \n",
+    "        delta_roe: pd.DataFrame = roe / data['roe_4'] - 1\n",
+    "            \n",
+    "        accrual: pd.DataFrame = cfo - roa1 * 0.01\n",
+    "\n",
+    "        # 杠杆变化\n",
+    "        ## 变化为负数时为1，否则为0 取相反\n",
+    "        leveler: pd.DataFrame = data['total_non_current_liability'] / \\\n",
+    "            data['total_non_current_assets']\n",
+    "\n",
+    "        leveler1: pd.DataFrame = data['total_non_current_liability_4'] / \\\n",
+    "            data['total_non_current_assets_4']\n",
+    "\n",
+    "        delta_leveler: pd.DataFrame = -(leveler / leveler1 - 1)\n",
+    "\n",
+    "        # 流动性变化\n",
+    "        liquid: pd.DataFrame = data['total_current_assets'] / \\\n",
+    "            data['total_current_liability']\n",
+    "\n",
+    "        liquid_1: pd.DataFrame = data['total_current_assets_4'] / \\\n",
+    "            data['total_current_liability_4']\n",
+    "\n",
+    "        delta_liquid: pd.DataFrame = liquid / liquid_1 - 1\n",
+    "\n",
+    "        # 毛利率变化\n",
+    "        delta_margin: pd.DataFrame = data['gross_profit_margin'] / \\\n",
+    "            data['gross_profit_margin_4'] - 1\n",
+    "\n",
+    "        # 是否发行普通股权\n",
+    "        eq_offser: pd.DataFrame = data['paidin_capital'] / data[\n",
+    "            'paidin_capital_4'] - 1\n",
+    "        \n",
+    "        # 流动资产周转率\n",
+    "        caturn_1 = data['operating_revenue'] / (\n",
+    "            data['total_current_assets'] +\n",
+    "            data['total_current_assets_1']).mean()\n",
+    "        caturn_2 = data['operating_revenue_4'] / (\n",
+    "            data['total_current_assets_4'] +\n",
+    "            data['total_current_assets_5']).mean()\n",
+    "        \n",
+    "        # 流动资产周转率同比\n",
+    "        delata_caturn = caturn_1 / caturn_2 - 1\n",
+    "        \n",
+    "        # 总资产周转率\n",
+    "        total_asset_turnover_rate: pd.DataFrame = data[\n",
+    "            'operating_revenue'] / (data['total_assets'] +\n",
+    "                                          data['total_assets_1']).mean()\n",
+    "\n",
+    "        total_asset_turnover_rate_1: pd.DataFrame = data[\n",
+    "            'operating_revenue_4'] / (data['total_assets_4'] +\n",
+    "                                            data['total_assets_5']).mean()\n",
+    "\n",
+    "        # 总资产周转率同比\n",
+    "        delta_turn: pd.DataFrame = total_asset_turnover_rate / \\\n",
+    "            total_asset_turnover_rate_1 - 1\n",
+    "        \n",
+    "        indicator_tuple: Tuple = (roe, delta_roe,roa1,delta_roa1,roa2,delta_roa2, accrual, delta_leveler,\n",
+    "                                  delta_liquid, delta_margin,eq_offser,delata_caturn, delta_turn,data['pb_ratio'])\n",
+    "\n",
+    "        # 储存计算FFscore所需原始数据\n",
+    "        self.basic: pd.DataFrame = pd.concat(indicator_tuple).T.replace([-np.inf,np.inf],np.nan)\n",
+    "\n",
+    "        self.basic.columns = [\n",
+    "            'ROE', 'DELTA_ROE','ROA1','DELTA_ROA1','ROA2','DELTA_ROA2','ACCRUAL', 'DELTA_LEVER', \n",
+    "            'DLTA_LIQUID', 'DELTA_MARGIN', 'EQ_OFFSER', 'DELTA_CATURN', 'DELTA_TURN','PB_RATIO']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24e57a630811404189bc4d994f423a57",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, description='FFScore因子获取', max=132, style=ProgressStyle(description_width=…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 设置时间范围\n",
+    "startDt, endDt = '2010-01-01', '2020-12-31'\n",
+    "\n",
+    "# 每月初\n",
+    "periods: List = get_trade_period(startDt, endDt, 'M')\n",
+    "    \n",
+    "all_fundamentals_list = list(get_FFScore('A', F13_Score, periods))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_fundamentals: pd.DataFrame = pd.concat({f.watch_date: f.basic\n",
+    "                                    for f in all_fundamentals_list},\n",
+    "                                   names=['date', 'asset'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all_fundamentals.to_csv('all_fundamentals.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_fundamentals = pd.read_csv('all_fundamentals.csv',index_col=[0,1],parse_dates=[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "仅杠杆比率的缺失率高于10%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ROE             0.000255\n",
+       "DELTA_ROE       0.055107\n",
+       "ROA1            0.000255\n",
+       "DELTA_ROA1      0.056428\n",
+       "ROA2            0.021327\n",
+       "DELTA_ROA2      0.087180\n",
+       "ACCRUAL         0.002111\n",
+       "DELTA_LEVER     0.135790\n",
+       "DLTA_LIQUID     0.071811\n",
+       "DELTA_MARGIN    0.070257\n",
+       "EQ_OFFSER       0.054317\n",
+       "DELTA_CATURN    0.087437\n",
+       "DELTA_TURN      0.070023\n",
+       "PB_RATIO        0.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_fundamentals.isna().sum() / len(all_fundamentals)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2bf5b129d274d98be2bd585d64f7c99",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, description='获取收盘价数据', max=133, style=ProgressStyle(description_width='ini…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "price_periods = get_trade_period('2010-01-01', '2021-01-31', 'M')\n",
+    "\n",
+    "# 股票池\n",
+    "securities = all_fundamentals.index.levels[1].tolist()\n",
+    "\n",
+    "# 获取收盘价\n",
+    "price_list = list(get_factor_price(securities, price_periods))\n",
+    "\n",
+    "price_df = pd.concat(price_list)\n",
+    "\n",
+    "pivot_price = pd.pivot_table(price_df,\n",
+    "                             index='time',\n",
+    "                             columns='code',\n",
+    "                             values='close')\n",
+    "\n",
+    "next_ret = pivot_price.pct_change().shift(-1)\n",
+    "next_ret = next_ret.iloc[:-1].stack()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "仅使用2010至2013年的数据进行特征分析"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 训练集 使用2010-01-01至2013-12-02的数据进行训练\n",
+    "X_train_time = price_periods[:48]  # 2013-12-02\n",
+    "\n",
+    "X_train = all_fundamentals.loc[X_train_time]\n",
+    "X_train = X_train.iloc[:, :-1]\n",
+    "y_train = next_ret.loc[X_train_time]\n",
+    "y_train = y_train.reindex(X_train.index)\n",
+    "\n",
+    "# 测试集\n",
+    "X_test = all_fundamentals.loc[price_periods[48:]]\n",
+    "X_test_fundamentals = X_test.iloc[:,:-1]\n",
+    "X_test_pb = X_test.iloc[:,-1]\n",
+    "y_test = next_ret.reindex(X_test.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
+       "            max_depth=10, max_features='auto', max_leaf_nodes=8,\n",
+       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "            min_samples_leaf=1, min_samples_split=2,\n",
+       "            min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=-1,\n",
+       "            oob_score=False, random_state=42, verbose=0, warm_start=False)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rnd_clf = RandomForestClassifier(n_estimators=200,\n",
+    "                                 max_leaf_nodes=8,\n",
+    "                                 max_depth=10,\n",
+    "                                 n_jobs=-1,\n",
+    "                                 random_state=42,\n",
+    "                                 criterion='entropy')\n",
+    "\n",
+    "rnd_clf.fit(X_train.fillna(0).values, sign(y_train).values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<!-- Generated by graphviz version 2.38.0 (20140413.2041)\n",
+       " -->\n",
+       "<!-- Title: Tree Pages: 1 -->\n",
+       "<svg width=\"884pt\" height=\"477pt\"\n",
+       " viewBox=\"0.00 0.00 884.00 477.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 473)\">\n",
+       "<title>Tree</title>\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-473 880,-473 880,4 -4,4\"/>\n",
+       "<!-- 0 -->\n",
+       "<g id=\"node1\" class=\"node\"><title>0</title>\n",
+       "<path fill=\"#e58139\" fill-opacity=\"0.047059\" stroke=\"black\" d=\"M431.5,-469C431.5,-469 300.5,-469 300.5,-469 294.5,-469 288.5,-463 288.5,-457 288.5,-457 288.5,-413 288.5,-413 288.5,-407 294.5,-401 300.5,-401 300.5,-401 431.5,-401 431.5,-401 437.5,-401 443.5,-407 443.5,-413 443.5,-413 443.5,-457 443.5,-457 443.5,-463 437.5,-469 431.5,-469\"/>\n",
+       "<text text-anchor=\"start\" x=\"296.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">DELTA_ROA2 ≤ 0.077</text>\n",
+       "<text text-anchor=\"start\" x=\"326\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 1.0</text>\n",
+       "<text text-anchor=\"start\" x=\"313.5\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 59625</text>\n",
+       "<text text-anchor=\"start\" x=\"296.5\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [48283, 46071]</text>\n",
+       "</g>\n",
+       "<!-- 1 -->\n",
+       "<g id=\"node2\" class=\"node\"><title>1</title>\n",
+       "<path fill=\"#e58139\" fill-opacity=\"0.098039\" stroke=\"black\" d=\"M345.5,-365C345.5,-365 214.5,-365 214.5,-365 208.5,-365 202.5,-359 202.5,-353 202.5,-353 202.5,-309 202.5,-309 202.5,-303 208.5,-297 214.5,-297 214.5,-297 345.5,-297 345.5,-297 351.5,-297 357.5,-303 357.5,-309 357.5,-309 357.5,-353 357.5,-353 357.5,-359 351.5,-365 345.5,-365\"/>\n",
+       "<text text-anchor=\"start\" x=\"217.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">ROA2 ≤ 85415584.0</text>\n",
+       "<text text-anchor=\"start\" x=\"232.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.998</text>\n",
+       "<text text-anchor=\"start\" x=\"227.5\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 35271</text>\n",
+       "<text text-anchor=\"start\" x=\"210.5\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [29360, 26516]</text>\n",
+       "</g>\n",
+       "<!-- 0&#45;&gt;1 -->\n",
+       "<g id=\"edge1\" class=\"edge\"><title>0&#45;&gt;1</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M338.078,-400.884C330.568,-391.976 322.355,-382.235 314.532,-372.957\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"317.198,-370.689 308.076,-365.299 311.846,-375.201 317.198,-370.689\"/>\n",
+       "<text text-anchor=\"middle\" x=\"305.956\" y=\"-386.509\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">True</text>\n",
+       "</g>\n",
+       "<!-- 2 -->\n",
+       "<g id=\"node7\" class=\"node\"><title>2</title>\n",
+       "<path fill=\"#399de5\" fill-opacity=\"0.031373\" stroke=\"black\" d=\"M518.5,-365C518.5,-365 387.5,-365 387.5,-365 381.5,-365 375.5,-359 375.5,-353 375.5,-353 375.5,-309 375.5,-309 375.5,-303 381.5,-297 387.5,-297 387.5,-297 518.5,-297 518.5,-297 524.5,-297 530.5,-303 530.5,-309 530.5,-309 530.5,-353 530.5,-353 530.5,-359 524.5,-365 518.5,-365\"/>\n",
+       "<text text-anchor=\"start\" x=\"409\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">ROA1 ≤ 1.055</text>\n",
+       "<text text-anchor=\"start\" x=\"413\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 1.0</text>\n",
+       "<text text-anchor=\"start\" x=\"400.5\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 24354</text>\n",
+       "<text text-anchor=\"start\" x=\"383.5\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [18923, 19555]</text>\n",
+       "</g>\n",
+       "<!-- 0&#45;&gt;2 -->\n",
+       "<g id=\"edge6\" class=\"edge\"><title>0&#45;&gt;2</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M394.246,-400.884C401.844,-391.976 410.152,-382.235 418.066,-372.957\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"420.771,-375.179 424.598,-365.299 415.445,-370.636 420.771,-375.179\"/>\n",
+       "<text text-anchor=\"middle\" x=\"426.576\" y=\"-386.521\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">False</text>\n",
+       "</g>\n",
+       "<!-- 3 -->\n",
+       "<g id=\"node3\" class=\"node\"><title>3</title>\n",
+       "<path fill=\"#e58139\" fill-opacity=\"0.070588\" stroke=\"black\" d=\"M184,-261C184,-261 42,-261 42,-261 36,-261 30,-255 30,-249 30,-249 30,-205 30,-205 30,-199 36,-193 42,-193 42,-193 184,-193 184,-193 190,-193 196,-199 196,-205 196,-205 196,-249 196,-249 196,-255 190,-261 184,-261\"/>\n",
+       "<text text-anchor=\"start\" x=\"38\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">DELTA_LEVER ≤ &#45;4.473</text>\n",
+       "<text text-anchor=\"start\" x=\"65.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.999</text>\n",
+       "<text text-anchor=\"start\" x=\"60.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 29816</text>\n",
+       "<text text-anchor=\"start\" x=\"43.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [24501, 22758]</text>\n",
+       "</g>\n",
+       "<!-- 1&#45;&gt;3 -->\n",
+       "<g id=\"edge2\" class=\"edge\"><title>1&#45;&gt;3</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M225.78,-296.884C209.949,-287.214 192.511,-276.563 176.177,-266.587\"/>\n",
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+       "<g id=\"node6\" class=\"node\"><title>4</title>\n",
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+       "<text text-anchor=\"start\" x=\"236.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.988</text>\n",
+       "<text text-anchor=\"start\" x=\"235.5\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5455</text>\n",
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+       "</g>\n",
+       "<!-- 1&#45;&gt;4 -->\n",
+       "<g id=\"edge5\" class=\"edge\"><title>1&#45;&gt;4</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M281.299,-296.884C281.717,-286.216 282.182,-274.352 282.607,-263.519\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"286.105,-263.649 282.999,-253.52 279.11,-263.375 286.105,-263.649\"/>\n",
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+       "<!-- 13 -->\n",
+       "<g id=\"node4\" class=\"node\"><title>13</title>\n",
+       "<path fill=\"#399de5\" fill-opacity=\"0.101961\" stroke=\"black\" d=\"M128,-149.5C128,-149.5 12,-149.5 12,-149.5 6,-149.5 0,-143.5 0,-137.5 0,-137.5 0,-108.5 0,-108.5 0,-102.5 6,-96.5 12,-96.5 12,-96.5 128,-96.5 128,-96.5 134,-96.5 140,-102.5 140,-108.5 140,-108.5 140,-137.5 140,-137.5 140,-143.5 134,-149.5 128,-149.5\"/>\n",
+       "<text text-anchor=\"start\" x=\"22.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.998</text>\n",
+       "<text text-anchor=\"start\" x=\"21.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1537</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1199, 1337]</text>\n",
+       "</g>\n",
+       "<!-- 3&#45;&gt;13 -->\n",
+       "<g id=\"edge3\" class=\"edge\"><title>3&#45;&gt;13</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M99.0391,-192.884C94.4491,-181.996 89.3342,-169.863 84.6916,-158.85\"/>\n",
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+       "<g id=\"node5\" class=\"node\"><title>14</title>\n",
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+       "</g>\n",
+       "<!-- 3&#45;&gt;14 -->\n",
+       "<g id=\"edge4\" class=\"edge\"><title>3&#45;&gt;14</title>\n",
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+       "<!-- 5 -->\n",
+       "<g id=\"node8\" class=\"node\"><title>5</title>\n",
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+       "</g>\n",
+       "<!-- 2&#45;&gt;5 -->\n",
+       "<g id=\"edge7\" class=\"edge\"><title>2&#45;&gt;5</title>\n",
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+       "<!-- 7 -->\n",
+       "<g id=\"node10\" class=\"node\"><title>7</title>\n",
+       "<path fill=\"#e58139\" fill-opacity=\"0.047059\" stroke=\"black\" d=\"M547.5,-157C547.5,-157 422.5,-157 422.5,-157 416.5,-157 410.5,-151 410.5,-145 410.5,-145 410.5,-101 410.5,-101 410.5,-95 416.5,-89 422.5,-89 422.5,-89 547.5,-89 547.5,-89 553.5,-89 559.5,-95 559.5,-101 559.5,-101 559.5,-145 559.5,-145 559.5,-151 553.5,-157 547.5,-157\"/>\n",
+       "<text text-anchor=\"start\" x=\"418.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">ROA2 ≤ 650786432.0</text>\n",
+       "<text text-anchor=\"start\" x=\"445\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 1.0</text>\n",
+       "<text text-anchor=\"start\" x=\"436.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3570</text>\n",
+       "<text text-anchor=\"start\" x=\"423\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2908, 2768]</text>\n",
+       "</g>\n",
+       "<!-- 6&#45;&gt;7 -->\n",
+       "<g id=\"edge9\" class=\"edge\"><title>6&#45;&gt;7</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M572.793,-192.884C560.873,-183.531 547.781,-173.259 535.43,-163.568\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"537.468,-160.719 527.44,-157.299 533.147,-166.226 537.468,-160.719\"/>\n",
+       "</g>\n",
+       "<!-- 8 -->\n",
+       "<g id=\"node13\" class=\"node\"><title>8</title>\n",
+       "<path fill=\"#399de5\" fill-opacity=\"0.125490\" stroke=\"black\" d=\"M714.5,-157C714.5,-157 589.5,-157 589.5,-157 583.5,-157 577.5,-151 577.5,-145 577.5,-145 577.5,-101 577.5,-101 577.5,-95 583.5,-89 589.5,-89 589.5,-89 714.5,-89 714.5,-89 720.5,-89 726.5,-95 726.5,-101 726.5,-101 726.5,-145 726.5,-145 726.5,-151 720.5,-157 714.5,-157\"/>\n",
+       "<text text-anchor=\"start\" x=\"585.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">ROA2 ≤ 121342256.0</text>\n",
+       "<text text-anchor=\"start\" x=\"604.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.997</text>\n",
+       "<text text-anchor=\"start\" x=\"599.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 11099</text>\n",
+       "<text text-anchor=\"start\" x=\"590\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [8224, 9391]</text>\n",
+       "</g>\n",
+       "<!-- 6&#45;&gt;8 -->\n",
+       "<g id=\"edge12\" class=\"edge\"><title>6&#45;&gt;8</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M627.013,-192.884C630.018,-184.6 633.284,-175.595 636.434,-166.911\"/>\n",
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+       "</g>\n",
+       "<!-- 11 -->\n",
+       "<g id=\"node11\" class=\"node\"><title>11</title>\n",
+       "<path fill=\"#e58139\" fill-opacity=\"0.007843\" stroke=\"black\" d=\"M396,-53C396,-53 280,-53 280,-53 274,-53 268,-47 268,-41 268,-41 268,-12 268,-12 268,-6 274,-0 280,-0 280,-0 396,-0 396,-0 402,-0 408,-6 408,-12 408,-12 408,-41 408,-41 408,-47 402,-53 396,-53\"/>\n",
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+       "<text text-anchor=\"start\" x=\"289.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3243</text>\n",
+       "<text text-anchor=\"start\" x=\"276\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2595, 2571]</text>\n",
+       "</g>\n",
+       "<!-- 7&#45;&gt;11 -->\n",
+       "<g id=\"edge10\" class=\"edge\"><title>7&#45;&gt;11</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M433.587,-88.9485C418.36,-79.1601 401.74,-68.4756 386.691,-58.8015\"/>\n",
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+       "</g>\n",
+       "<!-- 12 -->\n",
+       "<g id=\"node12\" class=\"node\"><title>12</title>\n",
+       "<path fill=\"#e58139\" fill-opacity=\"0.372549\" stroke=\"black\" d=\"M539.5,-53C539.5,-53 438.5,-53 438.5,-53 432.5,-53 426.5,-47 426.5,-41 426.5,-41 426.5,-12 426.5,-12 426.5,-6 432.5,-0 438.5,-0 438.5,-0 539.5,-0 539.5,-0 545.5,-0 551.5,-6 551.5,-12 551.5,-12 551.5,-41 551.5,-41 551.5,-47 545.5,-53 539.5,-53\"/>\n",
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+       "<text text-anchor=\"start\" x=\"444\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 327</text>\n",
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+       "</g>\n",
+       "<!-- 7&#45;&gt;12 -->\n",
+       "<g id=\"edge11\" class=\"edge\"><title>7&#45;&gt;12</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M486.399,-88.9485C486.748,-80.7153 487.123,-71.848 487.477,-63.4814\"/>\n",
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+       "<!-- 9 -->\n",
+       "<g id=\"node14\" class=\"node\"><title>9</title>\n",
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+       "</g>\n",
+       "<!-- 8&#45;&gt;9 -->\n",
+       "<g id=\"edge13\" class=\"edge\"><title>8&#45;&gt;9</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M650.601,-88.9485C650.252,-80.7153 649.877,-71.848 649.523,-63.4814\"/>\n",
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+       "</g>\n",
+       "<!-- 10 -->\n",
+       "<g id=\"node15\" class=\"node\"><title>10</title>\n",
+       "<path fill=\"#399de5\" fill-opacity=\"0.003922\" stroke=\"black\" d=\"M864,-53C864,-53 748,-53 748,-53 742,-53 736,-47 736,-41 736,-41 736,-12 736,-12 736,-6 742,-0 748,-0 748,-0 864,-0 864,-0 870,-0 876,-6 876,-12 876,-12 876,-41 876,-41 876,-47 870,-53 864,-53\"/>\n",
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+       "</g>\n",
+       "<!-- 8&#45;&gt;10 -->\n",
+       "<g id=\"edge14\" class=\"edge\"><title>8&#45;&gt;10</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M705.862,-88.9485C721.962,-79.0686 739.551,-68.2758 755.432,-58.5305\"/>\n",
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+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.files.Source at 0x7f5fcb27af98>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dot_data = export_graphviz(rnd_clf.estimators_[-1],\n",
+    "                           out_file=None,\n",
+    "                           feature_names=X_train.columns.tolist(),\n",
+    "                           filled=True,\n",
+    "                           rounded=True,\n",
+    "                           special_characters=True)\n",
+    "\n",
+    "graph = graphviz.Source(dot_data)\n",
+    "\n",
+    "graph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "下图可以看到关键特征从$\\Delta ROE$至$\\Delta ROA1$时重要性已经大于80%,$\\Delta ROA1$与$\\Delta ROE$相关系数为0.49这里我们试着将DELTA_ROA1剔除,构建以$\\Delta ROE,ROA2,\\Delta MARGIN,\\Delta CATURN,Delta ROA2,\\Delta LIQUID$为基础的6因子财务打分模型。\n",
+    "\n",
+    "**盈利水平**\n",
+    "\n",
+    "1. $\\Delta ROE$\n",
+    "2. ROA2(息税前利润（EBIT）除以资产总值)\n",
+    "3. $\\Delta ROA2$\n",
+    "\n",
+    "**财务杠杆及流动性**\n",
+    "\n",
+    "4. $\\Delta LIQUID$\n",
+    "5. $\\Delta CATURN$\n",
+    "\n",
+    "**运营效率**\n",
+    "\n",
+    "6. $\\Delta MAGRIN$\n",
+    "\n",
+    "$SFScore = \\Delta ROE + ROA2 + \\Delta ROA2 + \\Delta LIQUID + \\Delta CATURN + \\Delta MAGRIN$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "code_folding": [
+     0
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 特征重要性\n",
+    "feature_score = pd.Series(index=X_train.columns.tolist(),\n",
+    "                          data=rnd_clf.feature_importances_).sort_values(ascending=False)\n",
+    "\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "\n",
+    "ax.set_title('特征重要度')\n",
+    "ax.yaxis.set_major_formatter(\n",
+    "    mpl.ticker.FuncFormatter(lambda x, pos: '{:.2%}'.format(x)))\n",
+    "\n",
+    "feature_score.plot.bar(ax=ax,color='#3EB2E0')\n",
+    "feature_score.cumsum().plot.line(ax=ax,rot=90)\n",
+    "plt.axhline(0.8,color='black',ls='--');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "code_folding": [
+     0
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 因子每期截面均值\n",
+    "corr = X_train.groupby(level='date').corr()\n",
+    "for i, (trade, df) in enumerate(corr.groupby(level='date'), start=1):\n",
+    "\n",
+    "    if i == 1:\n",
+    "        corr_mean = df.xs(trade)\n",
+    "    else:\n",
+    "        corr_mean += df.xs(trade)\n",
+    "\n",
+    "corr_mean /= i\n",
+    "\n",
+    "mask = np.zeros_like(corr_mean)\n",
+    "mask[np.triu_indices_from(mask)] = True\n",
+    "\n",
+    "f, ax = plt.subplots(figsize=(8, 8))\n",
+    "ax.set_title('因子截面相关系数均值')\n",
+    "ax = sns.heatmap(corr_mean, mask=mask, square=True, annot=True, fmt='.2f')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sfscore = sign(X_test_fundamentals[[\n",
+    "    'DELTA_ROE', 'ROA2', 'DELTA_MARGIN', 'DELTA_CATURN', 'DELTA_ROA2',\n",
+    "    'DLTA_LIQUID'\n",
+    "]]).sum(axis=1)\n",
+    "\n",
+    "sfscore.name = 'SFScore'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb78a8326a0>"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sfscore.hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dropped 4.2% entries from factor data (0.0% after in forward returns computation and 4.2% in binning phase). Set max_loss=0 to see potentially suppressed Exceptions.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 将FFScore按0-5分和5至8分进行分组\n",
+    "factor_data2 = al.utils.get_clean_factor_and_forward_returns(sfscore,\n",
+    "                                                           pivot_price,\n",
+    "                                                           quantiles = None,\n",
+    "                                                           bins=[0,4,6],\n",
+    "                                                           periods=(1,))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ret2 = pd.pivot_table(factor_data2.reindex(),\n",
+    "                     index='date',\n",
+    "                     columns='factor_quantile',\n",
+    "                     values=1)\n",
+    "\n",
+    "ret2.rename(columns={1.0: 'SFScore低分组', 2.0: 'SFScore高分组'}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(18, 6))\n",
+    "ax.set_title('SFScore累计收益')\n",
+    "ax.yaxis.set_major_formatter(\n",
+    "    mpl.ticker.FuncFormatter(lambda x, pos: '%.2f%%' % (x * 100)))\n",
+    "\n",
+    "ep.cum_returns(ret2).plot(ax=ax,color=['#00516C', '#6BCFF6'])\n",
+    "ep.cum_returns(benchmark_ret.reindex(ret2.index)).plot(ax=ax,\n",
+    "                                   label='HS300',\n",
+    "                                   color='darkgray',\n",
+    "                                   ls='--')\n",
+    "ax.axhline(0, color='black', lw=1)\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "</style>  \n",
+       "<table id=\"T_557920b2_1c3b_11ec_a568_c6008c580494\" > \n",
+       "<thead>    <tr> \n",
+       "        <th class=\"index_name level0\" >factor_quantile</th> \n",
+       "        <th class=\"col_heading level0 col0\" >SFScore低分组</th> \n",
+       "        <th class=\"col_heading level0 col1\" >SFScore高分组</th> \n",
+       "    </tr></thead> \n",
+       "<tbody>    <tr> \n",
+       "        <th id=\"T_557920b2_1c3b_11ec_a568_c6008c580494level0_row0\" class=\"row_heading level0 row0\" >年化收益率</th> \n",
+       "        <td id=\"T_557920b2_1c3b_11ec_a568_c6008c580494row0_col0\" class=\"data row0 col0\" >10.50%</td> \n",
+       "        <td id=\"T_557920b2_1c3b_11ec_a568_c6008c580494row0_col1\" class=\"data row0 col1\" >19.44%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_557920b2_1c3b_11ec_a568_c6008c580494level0_row1\" class=\"row_heading level0 row1\" >累计收益</th> \n",
+       "        <td id=\"T_557920b2_1c3b_11ec_a568_c6008c580494row1_col0\" class=\"data row1 col0\" >101.18%</td> \n",
+       "        <td id=\"T_557920b2_1c3b_11ec_a568_c6008c580494row1_col1\" class=\"data row1 col1\" >246.82%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_557920b2_1c3b_11ec_a568_c6008c580494level0_row2\" class=\"row_heading level0 row2\" >波动率</th> \n",
+       "        <td id=\"T_557920b2_1c3b_11ec_a568_c6008c580494row2_col0\" class=\"data row2 col0\" >31.58%</td> \n",
+       "        <td id=\"T_557920b2_1c3b_11ec_a568_c6008c580494row2_col1\" class=\"data row2 col1\" >30.71%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_557920b2_1c3b_11ec_a568_c6008c580494level0_row3\" class=\"row_heading level0 row3\" >夏普</th> \n",
+       "        <td id=\"T_557920b2_1c3b_11ec_a568_c6008c580494row3_col0\" class=\"data row3 col0\" >47.21%</td> \n",
+       "        <td id=\"T_557920b2_1c3b_11ec_a568_c6008c580494row3_col1\" class=\"data row3 col1\" >73.18%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_557920b2_1c3b_11ec_a568_c6008c580494level0_row4\" class=\"row_heading level0 row4\" >最大回撤</th> \n",
+       "        <td id=\"T_557920b2_1c3b_11ec_a568_c6008c580494row4_col0\" class=\"data row4 col0\" >-56.66%</td> \n",
+       "        <td id=\"T_557920b2_1c3b_11ec_a568_c6008c580494row4_col1\" class=\"data row4 col1\" >-42.03%</td> \n",
+       "    </tr></tbody> \n",
+       "</table> "
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fb78c263c50>"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Strategy_performance(ret2,'monthly').style.format('{:.2%}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fb78c271a58>"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = factor_data2.copy()\n",
+    "df['pb_group'] = pb_factor['factor_quantile']\n",
+    "\n",
+    "cross_df2 = df.query('pb_group == 1 and factor_quantile == 2').copy()\n",
+    "cross_ret2 = pd.pivot_table(cross_df2.reset_index(),\n",
+    "                            index='date',\n",
+    "                            columns='factor_quantile',\n",
+    "                            values=1)\n",
+    "\n",
+    "fih, ax = plt.subplots(figsize=(18, 6))\n",
+    "ax.set_title('SFSCore指标模型策略')\n",
+    "\n",
+    "all_ret = pd.concat((cross_ret2[2], ret2['SFScore高分组'], pb_ret[1],\n",
+    "                     benchmark_ret.reindex(cross_ret2.index)),\n",
+    "                    axis=1)\n",
+    "all_ret.columns = ['SFSCore指标模型策略', 'SFScore高分组', 'PB因子(前20%)', 'benchmark']\n",
+    "\n",
+    "ax.yaxis.set_major_formatter(\n",
+    "    mpl.ticker.FuncFormatter(lambda x, pos: '%.2f%%' % (x * 100)))\n",
+    "ep.cum_returns(all_ret).plot(ax=ax, color=color_map[:3] + ['darkgray'])\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "</style>  \n",
+       "<table id=\"T_4217e754_1c3d_11ec_a568_c6008c580494\" > \n",
+       "<thead>    <tr> \n",
+       "        <th class=\"blank level0\" ></th> \n",
+       "        <th class=\"col_heading level0 col0\" >SFSCore指标模型策略</th> \n",
+       "        <th class=\"col_heading level0 col1\" >SFScore高分组</th> \n",
+       "        <th class=\"col_heading level0 col2\" >PB因子(前20%)</th> \n",
+       "        <th class=\"col_heading level0 col3\" >benchmark</th> \n",
+       "    </tr></thead> \n",
+       "<tbody>    <tr> \n",
+       "        <th id=\"T_4217e754_1c3d_11ec_a568_c6008c580494level0_row0\" class=\"row_heading level0 row0\" >年化收益率</th> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row0_col0\" class=\"data row0 col0\" >24.80%</td> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row0_col1\" class=\"data row0 col1\" >19.44%</td> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row0_col2\" class=\"data row0 col2\" >21.60%</td> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row0_col3\" class=\"data row0 col3\" >10.02%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_4217e754_1c3d_11ec_a568_c6008c580494level0_row1\" class=\"row_heading level0 row1\" >累计收益</th> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row1_col0\" class=\"data row1 col0\" >371.53%</td> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row1_col1\" class=\"data row1 col1\" >246.82%</td> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row1_col2\" class=\"data row1 col2\" >293.05%</td> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row1_col3\" class=\"data row1 col3\" >95.17%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_4217e754_1c3d_11ec_a568_c6008c580494level0_row2\" class=\"row_heading level0 row2\" >波动率</th> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row2_col0\" class=\"data row2 col0\" >28.43%</td> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row2_col1\" class=\"data row2 col1\" >30.71%</td> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row2_col2\" class=\"data row2 col2\" >27.42%</td> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row2_col3\" class=\"data row2 col3\" >24.81%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_4217e754_1c3d_11ec_a568_c6008c580494level0_row3\" class=\"row_heading level0 row3\" >夏普</th> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row3_col0\" class=\"data row3 col0\" >93.35%</td> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row3_col1\" class=\"data row3 col1\" >73.18%</td> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row3_col2\" class=\"data row3 col2\" >85.93%</td> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row3_col3\" class=\"data row3 col3\" >51.08%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_4217e754_1c3d_11ec_a568_c6008c580494level0_row4\" class=\"row_heading level0 row4\" >最大回撤</th> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row4_col0\" class=\"data row4 col0\" >-34.30%</td> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row4_col1\" class=\"data row4 col1\" >-42.03%</td> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row4_col2\" class=\"data row4 col2\" >-32.13%</td> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row4_col3\" class=\"data row4 col3\" >-42.85%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_4217e754_1c3d_11ec_a568_c6008c580494level0_row5\" class=\"row_heading level0 row5\" >IR</th> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row5_col0\" class=\"data row5 col0\" >12.12%</td> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row5_col1\" class=\"data row5 col1\" >8.24%</td> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row5_col2\" class=\"data row5 col2\" >9.69%</td> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row5_col3\" class=\"data row5 col3\" >nan%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_4217e754_1c3d_11ec_a568_c6008c580494level0_row6\" class=\"row_heading level0 row6\" >Alpha</th> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row6_col0\" class=\"data row6 col0\" >23.10%</td> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row6_col1\" class=\"data row6 col1\" >19.48%</td> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row6_col2\" class=\"data row6 col2\" >20.31%</td> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row6_col3\" class=\"data row6 col3\" >nan%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_4217e754_1c3d_11ec_a568_c6008c580494level0_row7\" class=\"row_heading level0 row7\" >超额收益</th> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row7_col0\" class=\"data row7 col0\" >14.78%</td> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row7_col1\" class=\"data row7 col1\" >9.42%</td> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row7_col2\" class=\"data row7 col2\" >11.57%</td> \n",
+       "        <td id=\"T_4217e754_1c3d_11ec_a568_c6008c580494row7_col3\" class=\"data row7 col3\" >0.00%</td> \n",
+       "    </tr></tbody> \n",
+       "</table> "
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fb78b9a6a90>"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Strategy_performance(all_ret,'monthly').style.format('{:.2%}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 横向比较"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以看到从原始模型至改进模型,单独使用模型进行选股的选股能力是依次减弱的，由原始的22.68%减弱至19.44%。但对低PB组合的增强能力是依次增加的由24.7%增加至25.13%(相较于单独的PB因子选股收益的21.6%来说)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "</style>  \n",
+       "<table id=\"T_cd17d1de_1c3d_11ec_a568_c6008c580494\" > \n",
+       "<thead>    <tr> \n",
+       "        <th class=\"blank level0\" ></th> \n",
+       "        <th class=\"col_heading level0 col0\" >FScore高分组</th> \n",
+       "        <th class=\"col_heading level0 col1\" >FFScore高分组</th> \n",
+       "        <th class=\"col_heading level0 col2\" >SFScore高分组</th> \n",
+       "    </tr></thead> \n",
+       "<tbody>    <tr> \n",
+       "        <th id=\"T_cd17d1de_1c3d_11ec_a568_c6008c580494level0_row0\" class=\"row_heading level0 row0\" >年化收益率</th> \n",
+       "        <td id=\"T_cd17d1de_1c3d_11ec_a568_c6008c580494row0_col0\" class=\"data row0 col0\" >22.68%</td> \n",
+       "        <td id=\"T_cd17d1de_1c3d_11ec_a568_c6008c580494row0_col1\" class=\"data row0 col1\" >18.41%</td> \n",
+       "        <td id=\"T_cd17d1de_1c3d_11ec_a568_c6008c580494row0_col2\" class=\"data row0 col2\" >19.44%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_cd17d1de_1c3d_11ec_a568_c6008c580494level0_row1\" class=\"row_heading level0 row1\" >累计收益</th> \n",
+       "        <td id=\"T_cd17d1de_1c3d_11ec_a568_c6008c580494row1_col0\" class=\"data row1 col0\" >318.33%</td> \n",
+       "        <td id=\"T_cd17d1de_1c3d_11ec_a568_c6008c580494row1_col1\" class=\"data row1 col1\" >226.31%</td> \n",
+       "        <td id=\"T_cd17d1de_1c3d_11ec_a568_c6008c580494row1_col2\" class=\"data row1 col2\" >246.82%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_cd17d1de_1c3d_11ec_a568_c6008c580494level0_row2\" class=\"row_heading level0 row2\" >波动率</th> \n",
+       "        <td id=\"T_cd17d1de_1c3d_11ec_a568_c6008c580494row2_col0\" class=\"data row2 col0\" >29.22%</td> \n",
+       "        <td id=\"T_cd17d1de_1c3d_11ec_a568_c6008c580494row2_col1\" class=\"data row2 col1\" >32.03%</td> \n",
+       "        <td id=\"T_cd17d1de_1c3d_11ec_a568_c6008c580494row2_col2\" class=\"data row2 col2\" >30.71%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_cd17d1de_1c3d_11ec_a568_c6008c580494level0_row3\" class=\"row_heading level0 row3\" >夏普</th> \n",
+       "        <td id=\"T_cd17d1de_1c3d_11ec_a568_c6008c580494row3_col0\" class=\"data row3 col0\" >85.52%</td> \n",
+       "        <td id=\"T_cd17d1de_1c3d_11ec_a568_c6008c580494row3_col1\" class=\"data row3 col1\" >69.28%</td> \n",
+       "        <td id=\"T_cd17d1de_1c3d_11ec_a568_c6008c580494row3_col2\" class=\"data row3 col2\" >73.18%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_cd17d1de_1c3d_11ec_a568_c6008c580494level0_row4\" class=\"row_heading level0 row4\" >最大回撤</th> \n",
+       "        <td id=\"T_cd17d1de_1c3d_11ec_a568_c6008c580494row4_col0\" class=\"data row4 col0\" >-38.97%</td> \n",
+       "        <td id=\"T_cd17d1de_1c3d_11ec_a568_c6008c580494row4_col1\" class=\"data row4 col1\" >-46.11%</td> \n",
+       "        <td id=\"T_cd17d1de_1c3d_11ec_a568_c6008c580494row4_col2\" class=\"data row4 col2\" >-42.03%</td> \n",
+       "    </tr></tbody> \n",
+       "</table> "
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fb78bc92f98>"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_ret_df = pd.concat((ret['FScore高分组'],ret1['FFScore高分组'],ret2['SFScore高分组']),axis=1)\n",
+    "\n",
+    "Strategy_performance(score_ret_df,'monthly').style.format('{:.2%}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "</style>  \n",
+       "<table id=\"T_7873c598_1c3d_11ec_a568_c6008c580494\" > \n",
+       "<thead>    <tr> \n",
+       "        <th class=\"blank level0\" ></th> \n",
+       "        <th class=\"col_heading level0 col0\" >FScore指标模型策略</th> \n",
+       "        <th class=\"col_heading level0 col1\" >FFScore指标模型策略</th> \n",
+       "        <th class=\"col_heading level0 col2\" >SFSCore指标模型策略</th> \n",
+       "    </tr></thead> \n",
+       "<tbody>    <tr> \n",
+       "        <th id=\"T_7873c598_1c3d_11ec_a568_c6008c580494level0_row0\" class=\"row_heading level0 row0\" >年化收益率</th> \n",
+       "        <td id=\"T_7873c598_1c3d_11ec_a568_c6008c580494row0_col0\" class=\"data row0 col0\" >24.70%</td> \n",
+       "        <td id=\"T_7873c598_1c3d_11ec_a568_c6008c580494row0_col1\" class=\"data row0 col1\" >24.42%</td> \n",
+       "        <td id=\"T_7873c598_1c3d_11ec_a568_c6008c580494row0_col2\" class=\"data row0 col2\" >25.13%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_7873c598_1c3d_11ec_a568_c6008c580494level0_row1\" class=\"row_heading level0 row1\" >累计收益</th> \n",
+       "        <td id=\"T_7873c598_1c3d_11ec_a568_c6008c580494row1_col0\" class=\"data row1 col0\" >360.37%</td> \n",
+       "        <td id=\"T_7873c598_1c3d_11ec_a568_c6008c580494row1_col1\" class=\"data row1 col1\" >353.33%</td> \n",
+       "        <td id=\"T_7873c598_1c3d_11ec_a568_c6008c580494row1_col2\" class=\"data row1 col2\" >371.53%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_7873c598_1c3d_11ec_a568_c6008c580494level0_row2\" class=\"row_heading level0 row2\" >波动率</th> \n",
+       "        <td id=\"T_7873c598_1c3d_11ec_a568_c6008c580494row2_col0\" class=\"data row2 col0\" >27.15%</td> \n",
+       "        <td id=\"T_7873c598_1c3d_11ec_a568_c6008c580494row2_col1\" class=\"data row2 col1\" >28.86%</td> \n",
+       "        <td id=\"T_7873c598_1c3d_11ec_a568_c6008c580494row2_col2\" class=\"data row2 col2\" >28.43%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_7873c598_1c3d_11ec_a568_c6008c580494level0_row3\" class=\"row_heading level0 row3\" >夏普</th> \n",
+       "        <td id=\"T_7873c598_1c3d_11ec_a568_c6008c580494row3_col0\" class=\"data row3 col0\" >95.40%</td> \n",
+       "        <td id=\"T_7873c598_1c3d_11ec_a568_c6008c580494row3_col1\" class=\"data row3 col1\" >90.34%</td> \n",
+       "        <td id=\"T_7873c598_1c3d_11ec_a568_c6008c580494row3_col2\" class=\"data row3 col2\" >93.35%</td> \n",
+       "    </tr>    <tr> \n",
+       "        <th id=\"T_7873c598_1c3d_11ec_a568_c6008c580494level0_row4\" class=\"row_heading level0 row4\" >最大回撤</th> \n",
+       "        <td id=\"T_7873c598_1c3d_11ec_a568_c6008c580494row4_col0\" class=\"data row4 col0\" >-40.30%</td> \n",
+       "        <td id=\"T_7873c598_1c3d_11ec_a568_c6008c580494row4_col1\" class=\"data row4 col1\" >-34.13%</td> \n",
+       "        <td id=\"T_7873c598_1c3d_11ec_a568_c6008c580494row4_col2\" class=\"data row4 col2\" >-34.30%</td> \n",
+       "    </tr></tbody> \n",
+       "</table> "
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fb78bc92e80>"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cross_ret_df = pd.concat((cross_ret[3], cross_ret1[2], cross_ret2[2]), axis=1)\n",
+    "cross_ret_df.columns = ['FScore指标模型策略', 'FFScore指标模型策略', 'SFSCore指标模型策略']\n",
+    "\n",
+    "Strategy_performance(cross_ret_df, 'monthly').style.format('{:.2%}')"
+   ]
+  }
+ ],
+ "metadata": {
+  "hide_input": true,
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "MarkDown菜单",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/\347\224\263\344\270\207\345\244\247\345\270\210\347\263\273\345\210\227\345\215\201\344\270\211/20151019-\347\224\263\344\270\207\345\256\217\346\272\220-\347\224\263\344\270\207\345\244\247\345\270\210\347\263\273\345\210\227.\344\273\267\345\200\274\346\212\225\350\265\204\347\257\207\344\271\213\345\215\201\344\270\211\357\274\232\347\275\227\344\274\257.\347\221\236\345\205\213\350\266\205\351\242\235\347\216\260\351\207\221\346\265\201\351\200\211\350\202\241\346\263\225\345\210\231.pdf" "b/A-\351\207\217\345\214\226\345\237\272\346\234\254\351\235\242/\347\224\263\344\270\207\345\244\247\345\270\210\347\263\273\345\210\227\345\215\201\344\270\211/20151019-\347\224\263\344\270\207\345\256\217\346\272\220-\347\224\263\344\270\207\345\244\247\345\270\210\347\263\273\345\210\227.\344\273\267\345\200\274\346\212\225\350\265\204\347\257\207\344\271\213\345\215\201\344\270\211\357\274\232\347\275\227\344\274\257.\347\221\236\345\205\213\350\266\205\351\242\235\347\216\260\351\207\221\346\265\201\351\200\211\350\202\241\346\263\225\345\210\231.pdf"
similarity index 100%
rename from "\347\224\263\344\270\207\345\244\247\345\270\210\347\263\273\345\210\227\345\215\201\344\270\211/20151019-\347\224\263\344\270\207\345\256\217\346\272\220-\347\224\263\344\270\207\345\244\247\345\270\210\347\263\273\345\210\227.\344\273\267\345\200\274\346\212\225\350\265\204\347\257\207\344\271\213\345\215\201\344\270\211\357\274\232\347\275\227\344\274\257.\347\221\236\345\205\213\350\266\205\351\242\235\347\216\260\351\207\221\346\265\201\351\200\211\350\202\241\346\263\225\345\210\231.pdf"
rename to "A-\351\207\217\345\214\226\345\237\272\346\234\254\351\235\242/\347\224\263\344\270\207\345\244\247\345\270\210\347\263\273\345\210\227\345\215\201\344\270\211/20151019-\347\224\263\344\270\207\345\256\217\346\272\220-\347\224\263\344\270\207\345\244\247\345\270\210\347\263\273\345\210\227.\344\273\267\345\200\274\346\212\225\350\265\204\347\257\207\344\271\213\345\215\201\344\270\211\357\274\232\347\275\227\344\274\257.\347\221\236\345\205\213\350\266\205\351\242\235\347\216\260\351\207\221\346\265\201\351\200\211\350\202\241\346\263\225\345\210\231.pdf"
diff --git "a/\347\224\263\344\270\207\345\244\247\345\270\210\347\263\273\345\210\227\345\215\201\344\270\211/py/\347\263\273\345\210\227\345\215\201\344\270\211\357\274\232\347\275\227\344\274\257\302\267\347\221\236\345\205\213\350\266\205\351\242\235\347\216\260\351\207\221\346\265\201\351\200\211\350\202\241\346\263\225\345\210\231.ipynb" "b/A-\351\207\217\345\214\226\345\237\272\346\234\254\351\235\242/\347\224\263\344\270\207\345\244\247\345\270\210\347\263\273\345\210\227\345\215\201\344\270\211/py/\347\263\273\345\210\227\345\215\201\344\270\211\357\274\232\347\275\227\344\274\257\302\267\347\221\236\345\205\213\350\266\205\351\242\235\347\216\260\351\207\221\346\265\201\351\200\211\350\202\241\346\263\225\345\210\231.ipynb"
similarity index 100%
rename from "\347\224\263\344\270\207\345\244\247\345\270\210\347\263\273\345\210\227\345\215\201\344\270\211/py/\347\263\273\345\210\227\345\215\201\344\270\211\357\274\232\347\275\227\344\274\257\302\267\347\221\236\345\205\213\350\266\205\351\242\235\347\216\260\351\207\221\346\265\201\351\200\211\350\202\241\346\263\225\345\210\231.ipynb"
rename to "A-\351\207\217\345\214\226\345\237\272\346\234\254\351\235\242/\347\224\263\344\270\207\345\244\247\345\270\210\347\263\273\345\210\227\345\215\201\344\270\211/py/\347\263\273\345\210\227\345\215\201\344\270\211\357\274\232\347\275\227\344\274\257\302\267\347\221\236\345\205\213\350\266\205\351\242\235\347\216\260\351\207\221\346\265\201\351\200\211\350\202\241\346\263\225\345\210\231.ipynb"
diff --git "a/APM\345\233\240\345\255\220\346\250\241\345\236\213/20200307-\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2105\357\274\211\357\274\232APM\345\233\240\345\255\220\346\250\241\345\236\213\347\232\204\350\277\233\351\230\266\347\211\210.pdf" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/APM\345\233\240\345\255\220\346\250\241\345\236\213/20200307-\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2105\357\274\211\357\274\232APM\345\233\240\345\255\220\346\250\241\345\236\213\347\232\204\350\277\233\351\230\266\347\211\210.pdf"
similarity index 100%
rename from "APM\345\233\240\345\255\220\346\250\241\345\236\213/20200307-\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2105\357\274\211\357\274\232APM\345\233\240\345\255\220\346\250\241\345\236\213\347\232\204\350\277\233\351\230\266\347\211\210.pdf"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/APM\345\233\240\345\255\220\346\250\241\345\236\213/20200307-\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2105\357\274\211\357\274\232APM\345\233\240\345\255\220\346\250\241\345\236\213\347\232\204\350\277\233\351\230\266\347\211\210.pdf"
diff --git "a/APM\345\233\240\345\255\220\346\250\241\345\236\213/py/APM\345\233\240\345\255\220\346\250\241\345\236\213.ipynb" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/APM\345\233\240\345\255\220\346\250\241\345\236\213/py/APM\345\233\240\345\255\220\346\250\241\345\236\213.ipynb"
similarity index 100%
rename from "APM\345\233\240\345\255\220\346\250\241\345\236\213/py/APM\345\233\240\345\255\220\346\250\241\345\236\213.ipynb"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/APM\345\233\240\345\255\220\346\250\241\345\236\213/py/APM\345\233\240\345\255\220\346\250\241\345\236\213.ipynb"
diff --git "a/A\350\202\241\345\270\202\345\234\272\344\270\255\345\246\202\344\275\225\346\236\204\351\200\240\345\212\250\351\207\217\345\233\240\345\255\220\357\274\237/20200721-\345\274\200\346\272\220\350\257\201\345\210\270-\345\274\200\346\272\220\351\207\217\345\214\226\350\257\204\350\256\272\357\274\2103\357\274\211\357\274\232A\350\202\241\345\270\202\345\234\272\344\270\255\345\246\202\344\275\225\346\236\204\351\200\240\345\212\250\351\207\217\345\233\240\345\255\220\357\274\237.pdf" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/A\350\202\241\345\270\202\345\234\272\344\270\255\345\246\202\344\275\225\346\236\204\351\200\240\345\212\250\351\207\217\345\233\240\345\255\220\357\274\237/20200721-\345\274\200\346\272\220\350\257\201\345\210\270-\345\274\200\346\272\220\351\207\217\345\214\226\350\257\204\350\256\272\357\274\2103\357\274\211\357\274\232A\350\202\241\345\270\202\345\234\272\344\270\255\345\246\202\344\275\225\346\236\204\351\200\240\345\212\250\351\207\217\345\233\240\345\255\220\357\274\237.pdf"
similarity index 100%
rename from "A\350\202\241\345\270\202\345\234\272\344\270\255\345\246\202\344\275\225\346\236\204\351\200\240\345\212\250\351\207\217\345\233\240\345\255\220\357\274\237/20200721-\345\274\200\346\272\220\350\257\201\345\210\270-\345\274\200\346\272\220\351\207\217\345\214\226\350\257\204\350\256\272\357\274\2103\357\274\211\357\274\232A\350\202\241\345\270\202\345\234\272\344\270\255\345\246\202\344\275\225\346\236\204\351\200\240\345\212\250\351\207\217\345\233\240\345\255\220\357\274\237.pdf"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/A\350\202\241\345\270\202\345\234\272\344\270\255\345\246\202\344\275\225\346\236\204\351\200\240\345\212\250\351\207\217\345\233\240\345\255\220\357\274\237/20200721-\345\274\200\346\272\220\350\257\201\345\210\270-\345\274\200\346\272\220\351\207\217\345\214\226\350\257\204\350\256\272\357\274\2103\357\274\211\357\274\232A\350\202\241\345\270\202\345\234\272\344\270\255\345\246\202\344\275\225\346\236\204\351\200\240\345\212\250\351\207\217\345\233\240\345\255\220\357\274\237.pdf"
diff --git "a/A\350\202\241\345\270\202\345\234\272\344\270\255\345\246\202\344\275\225\346\236\204\351\200\240\345\212\250\351\207\217\345\233\240\345\255\220\357\274\237/notebook/A\350\202\241\345\270\202\345\234\272\344\270\255\345\246\202\344\275\225\346\236\204\351\200\240\345\212\250\351\207\217\345\233\240\345\255\220.ipynb" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/A\350\202\241\345\270\202\345\234\272\344\270\255\345\246\202\344\275\225\346\236\204\351\200\240\345\212\250\351\207\217\345\233\240\345\255\220\357\274\237/notebook/A\350\202\241\345\270\202\345\234\272\344\270\255\345\246\202\344\275\225\346\236\204\351\200\240\345\212\250\351\207\217\345\233\240\345\255\220.ipynb"
similarity index 100%
rename from "A\350\202\241\345\270\202\345\234\272\344\270\255\345\246\202\344\275\225\346\236\204\351\200\240\345\212\250\351\207\217\345\233\240\345\255\220\357\274\237/notebook/A\350\202\241\345\270\202\345\234\272\344\270\255\345\246\202\344\275\225\346\236\204\351\200\240\345\212\250\351\207\217\345\233\240\345\255\220.ipynb"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/A\350\202\241\345\270\202\345\234\272\344\270\255\345\246\202\344\275\225\346\236\204\351\200\240\345\212\250\351\207\217\345\233\240\345\255\220\357\274\237/notebook/A\350\202\241\345\270\202\345\234\272\344\270\255\345\246\202\344\275\225\346\236\204\351\200\240\345\212\250\351\207\217\345\233\240\345\255\220.ipynb"
diff --git "a/\344\270\212\344\270\213\345\275\261\347\272\277\345\233\240\345\255\220/20200619-\344\270\234\345\220\264\350\257\201\345\210\270-\342\200\234\346\212\200\346\234\257\345\210\206\346\236\220\346\213\245\346\212\261\351\200\211\350\202\241\345\233\240\345\255\220\342\200\235\347\263\273\345\210\227\347\240\224\347\251\266\357\274\210\344\272\214\357\274\211\357\274\232\344\270\212\344\270\213\345\275\261\347\272\277\357\274\214\350\234\241\347\203\233\345\245\275\350\277\230\346\230\257\345\250\201\345\273\211\345\245\275\357\274\237.pdf" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\344\270\212\344\270\213\345\275\261\347\272\277\345\233\240\345\255\220/20200619-\344\270\234\345\220\264\350\257\201\345\210\270-\342\200\234\346\212\200\346\234\257\345\210\206\346\236\220\346\213\245\346\212\261\351\200\211\350\202\241\345\233\240\345\255\220\342\200\235\347\263\273\345\210\227\347\240\224\347\251\266\357\274\210\344\272\214\357\274\211\357\274\232\344\270\212\344\270\213\345\275\261\347\272\277\357\274\214\350\234\241\347\203\233\345\245\275\350\277\230\346\230\257\345\250\201\345\273\211\345\245\275\357\274\237.pdf"
similarity index 100%
rename from "\344\270\212\344\270\213\345\275\261\347\272\277\345\233\240\345\255\220/20200619-\344\270\234\345\220\264\350\257\201\345\210\270-\342\200\234\346\212\200\346\234\257\345\210\206\346\236\220\346\213\245\346\212\261\351\200\211\350\202\241\345\233\240\345\255\220\342\200\235\347\263\273\345\210\227\347\240\224\347\251\266\357\274\210\344\272\214\357\274\211\357\274\232\344\270\212\344\270\213\345\275\261\347\272\277\357\274\214\350\234\241\347\203\233\345\245\275\350\277\230\346\230\257\345\250\201\345\273\211\345\245\275\357\274\237.pdf"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\344\270\212\344\270\213\345\275\261\347\272\277\345\233\240\345\255\220/20200619-\344\270\234\345\220\264\350\257\201\345\210\270-\342\200\234\346\212\200\346\234\257\345\210\206\346\236\220\346\213\245\346\212\261\351\200\211\350\202\241\345\233\240\345\255\220\342\200\235\347\263\273\345\210\227\347\240\224\347\251\266\357\274\210\344\272\214\357\274\211\357\274\232\344\270\212\344\270\213\345\275\261\347\272\277\357\274\214\350\234\241\347\203\233\345\245\275\350\277\230\346\230\257\345\250\201\345\273\211\345\245\275\357\274\237.pdf"
diff --git "a/\344\270\212\344\270\213\345\275\261\347\272\277\345\233\240\345\255\220/py/\344\270\212\344\270\213\345\274\225\347\272\277\345\233\240\345\255\220.ipynb" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\344\270\212\344\270\213\345\275\261\347\272\277\345\233\240\345\255\220/py/\344\270\212\344\270\213\345\274\225\347\272\277\345\233\240\345\255\220.ipynb"
similarity index 100%
rename from "\344\270\212\344\270\213\345\275\261\347\272\277\345\233\240\345\255\220/py/\344\270\212\344\270\213\345\274\225\347\272\277\345\233\240\345\255\220.ipynb"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\344\270\212\344\270\213\345\275\261\347\272\277\345\233\240\345\255\220/py/\344\270\212\344\270\213\345\274\225\347\272\277\345\233\240\345\255\220.ipynb"
diff --git "a/\345\206\215\350\256\272\345\212\250\351\207\217\345\233\240\345\255\220/20190615-\345\205\211\345\244\247\350\257\201\345\210\270-\345\244\232\345\233\240\345\255\220\347\263\273\345\210\227\346\212\245\345\221\212\344\271\213\344\272\214\345\215\201\344\272\214\357\274\232\345\206\215\350\256\272\345\212\250\351\207\217\345\233\240\345\255\220.pdf" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\206\215\350\256\272\345\212\250\351\207\217\345\233\240\345\255\220/20190615-\345\205\211\345\244\247\350\257\201\345\210\270-\345\244\232\345\233\240\345\255\220\347\263\273\345\210\227\346\212\245\345\221\212\344\271\213\344\272\214\345\215\201\344\272\214\357\274\232\345\206\215\350\256\272\345\212\250\351\207\217\345\233\240\345\255\220.pdf"
similarity index 100%
rename from "\345\206\215\350\256\272\345\212\250\351\207\217\345\233\240\345\255\220/20190615-\345\205\211\345\244\247\350\257\201\345\210\270-\345\244\232\345\233\240\345\255\220\347\263\273\345\210\227\346\212\245\345\221\212\344\271\213\344\272\214\345\215\201\344\272\214\357\274\232\345\206\215\350\256\272\345\212\250\351\207\217\345\233\240\345\255\220.pdf"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\206\215\350\256\272\345\212\250\351\207\217\345\233\240\345\255\220/20190615-\345\205\211\345\244\247\350\257\201\345\210\270-\345\244\232\345\233\240\345\255\220\347\263\273\345\210\227\346\212\245\345\221\212\344\271\213\344\272\214\345\215\201\344\272\214\357\274\232\345\206\215\350\256\272\345\212\250\351\207\217\345\233\240\345\255\220.pdf"
diff --git "a/\345\206\215\350\256\272\345\212\250\351\207\217\345\233\240\345\255\220/py/\345\206\215\350\256\272\345\212\250\351\207\217\345\233\240\345\255\220.ipynb" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\206\215\350\256\272\345\212\250\351\207\217\345\233\240\345\255\220/py/\345\206\215\350\256\272\345\212\250\351\207\217\345\233\240\345\255\220.ipynb"
similarity index 100%
rename from "\345\206\215\350\256\272\345\212\250\351\207\217\345\233\240\345\255\220/py/\345\206\215\350\256\272\345\212\250\351\207\217\345\233\240\345\255\220.ipynb"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\206\215\350\256\272\345\212\250\351\207\217\345\233\240\345\255\220/py/\345\206\215\350\256\272\345\212\250\351\207\217\345\233\240\345\255\220.ipynb"
diff --git "a/\345\211\224\351\231\244\350\267\250\346\234\237\346\210\252\351\235\242\347\233\270\345\205\263\346\200\247\347\232\204\347\272\257\347\234\237\346\263\242\345\212\250\347\216\207\345\233\240\345\255\220/20200528-\344\270\234\345\220\264\350\257\201\345\210\270-\342\200\234\346\263\242\345\212\250\347\216\207\351\200\211\350\202\241\345\233\240\345\255\220\342\200\235\347\263\273\345\210\227\347\240\224\347\251\266\357\274\210\344\270\200\357\274\211\357\274\232\345\257\273\346\211\276\347\211\271\350\264\250\346\263\242\345\212\250\347\216\207\344\270\255\347\232\204\347\272\257\347\234\237\344\277\241\346\201\257\357\274\214\345\211\224\351\231\244\350\267\250\346\234\237\346\210\252\351\235\242\347\233\270\345\205\263\346\200\247\347\232\204\347\272\257\347\234\237\346\263\242\345\212\250\347\216\207\345\233\240\345\255\220.pdf" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\211\224\351\231\244\350\267\250\346\234\237\346\210\252\351\235\242\347\233\270\345\205\263\346\200\247\347\232\204\347\272\257\347\234\237\346\263\242\345\212\250\347\216\207\345\233\240\345\255\220/20200528-\344\270\234\345\220\264\350\257\201\345\210\270-\342\200\234\346\263\242\345\212\250\347\216\207\351\200\211\350\202\241\345\233\240\345\255\220\342\200\235\347\263\273\345\210\227\347\240\224\347\251\266\357\274\210\344\270\200\357\274\211\357\274\232\345\257\273\346\211\276\347\211\271\350\264\250\346\263\242\345\212\250\347\216\207\344\270\255\347\232\204\347\272\257\347\234\237\344\277\241\346\201\257\357\274\214\345\211\224\351\231\244\350\267\250\346\234\237\346\210\252\351\235\242\347\233\270\345\205\263\346\200\247\347\232\204\347\272\257\347\234\237\346\263\242\345\212\250\347\216\207\345\233\240\345\255\220.pdf"
similarity index 100%
rename from "\345\211\224\351\231\244\350\267\250\346\234\237\346\210\252\351\235\242\347\233\270\345\205\263\346\200\247\347\232\204\347\272\257\347\234\237\346\263\242\345\212\250\347\216\207\345\233\240\345\255\220/20200528-\344\270\234\345\220\264\350\257\201\345\210\270-\342\200\234\346\263\242\345\212\250\347\216\207\351\200\211\350\202\241\345\233\240\345\255\220\342\200\235\347\263\273\345\210\227\347\240\224\347\251\266\357\274\210\344\270\200\357\274\211\357\274\232\345\257\273\346\211\276\347\211\271\350\264\250\346\263\242\345\212\250\347\216\207\344\270\255\347\232\204\347\272\257\347\234\237\344\277\241\346\201\257\357\274\214\345\211\224\351\231\244\350\267\250\346\234\237\346\210\252\351\235\242\347\233\270\345\205\263\346\200\247\347\232\204\347\272\257\347\234\237\346\263\242\345\212\250\347\216\207\345\233\240\345\255\220.pdf"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\211\224\351\231\244\350\267\250\346\234\237\346\210\252\351\235\242\347\233\270\345\205\263\346\200\247\347\232\204\347\272\257\347\234\237\346\263\242\345\212\250\347\216\207\345\233\240\345\255\220/20200528-\344\270\234\345\220\264\350\257\201\345\210\270-\342\200\234\346\263\242\345\212\250\347\216\207\351\200\211\350\202\241\345\233\240\345\255\220\342\200\235\347\263\273\345\210\227\347\240\224\347\251\266\357\274\210\344\270\200\357\274\211\357\274\232\345\257\273\346\211\276\347\211\271\350\264\250\346\263\242\345\212\250\347\216\207\344\270\255\347\232\204\347\272\257\347\234\237\344\277\241\346\201\257\357\274\214\345\211\224\351\231\244\350\267\250\346\234\237\346\210\252\351\235\242\347\233\270\345\205\263\346\200\247\347\232\204\347\272\257\347\234\237\346\263\242\345\212\250\347\216\207\345\233\240\345\255\220.pdf"
diff --git "a/\345\211\224\351\231\244\350\267\250\346\234\237\346\210\252\351\235\242\347\233\270\345\205\263\346\200\247\347\232\204\347\272\257\347\234\237\346\263\242\345\212\250\347\216\207\345\233\240\345\255\220/py/\346\263\242\345\212\250\347\216\207\351\200\211\350\202\241\345\233\240\345\255\220_\347\211\271\350\264\250\346\263\242\345\212\250\347\216\207.ipynb" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\211\224\351\231\244\350\267\250\346\234\237\346\210\252\351\235\242\347\233\270\345\205\263\346\200\247\347\232\204\347\272\257\347\234\237\346\263\242\345\212\250\347\216\207\345\233\240\345\255\220/py/\346\263\242\345\212\250\347\216\207\351\200\211\350\202\241\345\233\240\345\255\220_\347\211\271\350\264\250\346\263\242\345\212\250\347\216\207.ipynb"
similarity index 100%
rename from "\345\211\224\351\231\244\350\267\250\346\234\237\346\210\252\351\235\242\347\233\270\345\205\263\346\200\247\347\232\204\347\272\257\347\234\237\346\263\242\345\212\250\347\216\207\345\233\240\345\255\220/py/\346\263\242\345\212\250\347\216\207\351\200\211\350\202\241\345\233\240\345\255\220_\347\211\271\350\264\250\346\263\242\345\212\250\347\216\207.ipynb"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\211\224\351\231\244\350\267\250\346\234\237\346\210\252\351\235\242\347\233\270\345\205\263\346\200\247\347\232\204\347\272\257\347\234\237\346\263\242\345\212\250\347\216\207\345\233\240\345\255\220/py/\346\263\242\345\212\250\347\216\207\351\200\211\350\202\241\345\233\240\345\255\220_\347\211\271\350\264\250\346\263\242\345\212\250\347\216\207.ipynb"
diff --git "a/\345\237\272\344\272\216\351\207\217\344\273\267\345\205\263\347\263\273\345\272\246\351\207\217\350\202\241\347\245\250\347\232\204\344\271\260\345\215\226\345\216\213\345\212\233/20191029-\344\270\234\346\226\271\350\257\201\345\210\270- \345\233\240\345\255\220\351\200\211\350\202\241\347\263\273\345\210\227\347\240\224\347\251\266\345\205\255\345\215\201\357\274\232\345\237\272\344\272\216\351\207\217\344\273\267\345\205\263\347\263\273\345\272\246\351\207\217\350\202\241\347\245\250\347\232\204\344\271\260\345\215\226\345\216\213\345\212\233.pdf" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\237\272\344\272\216\351\207\217\344\273\267\345\205\263\347\263\273\345\272\246\351\207\217\350\202\241\347\245\250\347\232\204\344\271\260\345\215\226\345\216\213\345\212\233/20191029-\344\270\234\346\226\271\350\257\201\345\210\270- \345\233\240\345\255\220\351\200\211\350\202\241\347\263\273\345\210\227\347\240\224\347\251\266\345\205\255\345\215\201\357\274\232\345\237\272\344\272\216\351\207\217\344\273\267\345\205\263\347\263\273\345\272\246\351\207\217\350\202\241\347\245\250\347\232\204\344\271\260\345\215\226\345\216\213\345\212\233.pdf"
similarity index 100%
rename from "\345\237\272\344\272\216\351\207\217\344\273\267\345\205\263\347\263\273\345\272\246\351\207\217\350\202\241\347\245\250\347\232\204\344\271\260\345\215\226\345\216\213\345\212\233/20191029-\344\270\234\346\226\271\350\257\201\345\210\270- \345\233\240\345\255\220\351\200\211\350\202\241\347\263\273\345\210\227\347\240\224\347\251\266\345\205\255\345\215\201\357\274\232\345\237\272\344\272\216\351\207\217\344\273\267\345\205\263\347\263\273\345\272\246\351\207\217\350\202\241\347\245\250\347\232\204\344\271\260\345\215\226\345\216\213\345\212\233.pdf"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\237\272\344\272\216\351\207\217\344\273\267\345\205\263\347\263\273\345\272\246\351\207\217\350\202\241\347\245\250\347\232\204\344\271\260\345\215\226\345\216\213\345\212\233/20191029-\344\270\234\346\226\271\350\257\201\345\210\270- \345\233\240\345\255\220\351\200\211\350\202\241\347\263\273\345\210\227\347\240\224\347\251\266\345\205\255\345\215\201\357\274\232\345\237\272\344\272\216\351\207\217\344\273\267\345\205\263\347\263\273\345\272\246\351\207\217\350\202\241\347\245\250\347\232\204\344\271\260\345\215\226\345\216\213\345\212\233.pdf"
diff --git "a/\345\237\272\344\272\216\351\207\217\344\273\267\345\205\263\347\263\273\345\272\246\351\207\217\350\202\241\347\245\250\347\232\204\344\271\260\345\215\226\345\216\213\345\212\233/py/CalFunc.py" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\237\272\344\272\216\351\207\217\344\273\267\345\205\263\347\263\273\345\272\246\351\207\217\350\202\241\347\245\250\347\232\204\344\271\260\345\215\226\345\216\213\345\212\233/py/CalFunc.py"
similarity index 100%
rename from "\345\237\272\344\272\216\351\207\217\344\273\267\345\205\263\347\263\273\345\272\246\351\207\217\350\202\241\347\245\250\347\232\204\344\271\260\345\215\226\345\216\213\345\212\233/py/CalFunc.py"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\237\272\344\272\216\351\207\217\344\273\267\345\205\263\347\263\273\345\272\246\351\207\217\350\202\241\347\245\250\347\232\204\344\271\260\345\215\226\345\216\213\345\212\233/py/CalFunc.py"
diff --git "a/\345\237\272\344\272\216\351\207\217\344\273\267\345\205\263\347\263\273\345\272\246\351\207\217\350\202\241\347\245\250\347\232\204\344\271\260\345\215\226\345\216\213\345\212\233/py/\345\237\272\344\272\216\351\207\217\344\273\267\345\205\263\347\263\273\345\272\246\351\207\217\350\202\241\347\245\250\347\232\204\344\271\260\345\215\226\345\216\213\345\212\233.ipynb" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\237\272\344\272\216\351\207\217\344\273\267\345\205\263\347\263\273\345\272\246\351\207\217\350\202\241\347\245\250\347\232\204\344\271\260\345\215\226\345\216\213\345\212\233/py/\345\237\272\344\272\216\351\207\217\344\273\267\345\205\263\347\263\273\345\272\246\351\207\217\350\202\241\347\245\250\347\232\204\344\271\260\345\215\226\345\216\213\345\212\233.ipynb"
similarity index 100%
rename from "\345\237\272\344\272\216\351\207\217\344\273\267\345\205\263\347\263\273\345\272\246\351\207\217\350\202\241\347\245\250\347\232\204\344\271\260\345\215\226\345\216\213\345\212\233/py/\345\237\272\344\272\216\351\207\217\344\273\267\345\205\263\347\263\273\345\272\246\351\207\217\350\202\241\347\245\250\347\232\204\344\271\260\345\215\226\345\216\213\345\212\233.ipynb"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\237\272\344\272\216\351\207\217\344\273\267\345\205\263\347\263\273\345\272\246\351\207\217\350\202\241\347\245\250\347\232\204\344\271\260\345\215\226\345\216\213\345\212\233/py/\345\237\272\344\272\216\351\207\217\344\273\267\345\205\263\347\263\273\345\272\246\351\207\217\350\202\241\347\245\250\347\232\204\344\271\260\345\215\226\345\216\213\345\212\233.ipynb"
diff --git "a/\345\237\272\351\207\221\351\207\215\344\273\223\350\266\205\351\205\215\345\233\240\345\255\220\345\217\212\345\205\266\345\257\271\346\214\207\346\225\260\345\242\236\345\274\272\347\273\204\345\220\210\347\232\204\345\275\261\345\223\215/20200707-\346\265\267\351\200\232\350\257\201\345\210\270-\351\200\211\350\202\241\345\233\240\345\255\220\347\263\273\345\210\227\347\240\224\347\251\266\357\274\210\345\205\255\345\215\201\345\205\253\357\274\211\357\274\232\345\237\272\351\207\221\351\207\215\344\273\223\350\266\205\351\205\215\345\233\240\345\255\220\345\217\212\345\205\266\345\257\271\346\214\207\346\225\260\345\242\236\345\274\272\347\273\204\345\220\210\347\232\204\345\275\261\345\223\215.pdf" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\237\272\351\207\221\351\207\215\344\273\223\350\266\205\351\205\215\345\233\240\345\255\220\345\217\212\345\205\266\345\257\271\346\214\207\346\225\260\345\242\236\345\274\272\347\273\204\345\220\210\347\232\204\345\275\261\345\223\215/20200707-\346\265\267\351\200\232\350\257\201\345\210\270-\351\200\211\350\202\241\345\233\240\345\255\220\347\263\273\345\210\227\347\240\224\347\251\266\357\274\210\345\205\255\345\215\201\345\205\253\357\274\211\357\274\232\345\237\272\351\207\221\351\207\215\344\273\223\350\266\205\351\205\215\345\233\240\345\255\220\345\217\212\345\205\266\345\257\271\346\214\207\346\225\260\345\242\236\345\274\272\347\273\204\345\220\210\347\232\204\345\275\261\345\223\215.pdf"
similarity index 100%
rename from "\345\237\272\351\207\221\351\207\215\344\273\223\350\266\205\351\205\215\345\233\240\345\255\220\345\217\212\345\205\266\345\257\271\346\214\207\346\225\260\345\242\236\345\274\272\347\273\204\345\220\210\347\232\204\345\275\261\345\223\215/20200707-\346\265\267\351\200\232\350\257\201\345\210\270-\351\200\211\350\202\241\345\233\240\345\255\220\347\263\273\345\210\227\347\240\224\347\251\266\357\274\210\345\205\255\345\215\201\345\205\253\357\274\211\357\274\232\345\237\272\351\207\221\351\207\215\344\273\223\350\266\205\351\205\215\345\233\240\345\255\220\345\217\212\345\205\266\345\257\271\346\214\207\346\225\260\345\242\236\345\274\272\347\273\204\345\220\210\347\232\204\345\275\261\345\223\215.pdf"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\237\272\351\207\221\351\207\215\344\273\223\350\266\205\351\205\215\345\233\240\345\255\220\345\217\212\345\205\266\345\257\271\346\214\207\346\225\260\345\242\236\345\274\272\347\273\204\345\220\210\347\232\204\345\275\261\345\223\215/20200707-\346\265\267\351\200\232\350\257\201\345\210\270-\351\200\211\350\202\241\345\233\240\345\255\220\347\263\273\345\210\227\347\240\224\347\251\266\357\274\210\345\205\255\345\215\201\345\205\253\357\274\211\357\274\232\345\237\272\351\207\221\351\207\215\344\273\223\350\266\205\351\205\215\345\233\240\345\255\220\345\217\212\345\205\266\345\257\271\346\214\207\346\225\260\345\242\236\345\274\272\347\273\204\345\220\210\347\232\204\345\275\261\345\223\215.pdf"
diff --git "a/\345\237\272\351\207\221\351\207\215\344\273\223\350\266\205\351\205\215\345\233\240\345\255\220\345\217\212\345\205\266\345\257\271\346\214\207\346\225\260\345\242\236\345\274\272\347\273\204\345\220\210\347\232\204\345\275\261\345\223\215/py/\345\237\272\351\207\221\346\214\201\350\202\241\346\257\224\345\210\227.ipynb" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\237\272\351\207\221\351\207\215\344\273\223\350\266\205\351\205\215\345\233\240\345\255\220\345\217\212\345\205\266\345\257\271\346\214\207\346\225\260\345\242\236\345\274\272\347\273\204\345\220\210\347\232\204\345\275\261\345\223\215/py/\345\237\272\351\207\221\346\214\201\350\202\241\346\257\224\345\210\227.ipynb"
similarity index 100%
rename from "\345\237\272\351\207\221\351\207\215\344\273\223\350\266\205\351\205\215\345\233\240\345\255\220\345\217\212\345\205\266\345\257\271\346\214\207\346\225\260\345\242\236\345\274\272\347\273\204\345\220\210\347\232\204\345\275\261\345\223\215/py/\345\237\272\351\207\221\346\214\201\350\202\241\346\257\224\345\210\227.ipynb"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\237\272\351\207\221\351\207\215\344\273\223\350\266\205\351\205\215\345\233\240\345\255\220\345\217\212\345\205\266\345\257\271\346\214\207\346\225\260\345\242\236\345\274\272\347\273\204\345\220\210\347\232\204\345\275\261\345\223\215/py/\345\237\272\351\207\221\346\214\201\350\202\241\346\257\224\345\210\227.ipynb"
diff --git "a/\345\244\204\347\275\256\346\225\210\345\272\224\345\233\240\345\255\220/20170707-\345\271\277\345\217\221\350\257\201\345\210\270-\350\241\214\344\270\272\351\207\221\350\236\215\345\233\240\345\255\220\347\240\224\347\251\266\344\271\213\344\270\200\357\274\232\350\265\204\346\234\254\345\210\251\345\276\227\347\252\201\345\207\272\351\207\217CGO\344\270\216\351\243\216\351\231\251\345\201\217\345\245\275.pdf" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\244\204\347\275\256\346\225\210\345\272\224\345\233\240\345\255\220/20170707-\345\271\277\345\217\221\350\257\201\345\210\270-\350\241\214\344\270\272\351\207\221\350\236\215\345\233\240\345\255\220\347\240\224\347\251\266\344\271\213\344\270\200\357\274\232\350\265\204\346\234\254\345\210\251\345\276\227\347\252\201\345\207\272\351\207\217CGO\344\270\216\351\243\216\351\231\251\345\201\217\345\245\275.pdf"
similarity index 100%
rename from "\345\244\204\347\275\256\346\225\210\345\272\224\345\233\240\345\255\220/20170707-\345\271\277\345\217\221\350\257\201\345\210\270-\350\241\214\344\270\272\351\207\221\350\236\215\345\233\240\345\255\220\347\240\224\347\251\266\344\271\213\344\270\200\357\274\232\350\265\204\346\234\254\345\210\251\345\276\227\347\252\201\345\207\272\351\207\217CGO\344\270\216\351\243\216\351\231\251\345\201\217\345\245\275.pdf"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\244\204\347\275\256\346\225\210\345\272\224\345\233\240\345\255\220/20170707-\345\271\277\345\217\221\350\257\201\345\210\270-\350\241\214\344\270\272\351\207\221\350\236\215\345\233\240\345\255\220\347\240\224\347\251\266\344\271\213\344\270\200\357\274\232\350\265\204\346\234\254\345\210\251\345\276\227\347\252\201\345\207\272\351\207\217CGO\344\270\216\351\243\216\351\231\251\345\201\217\345\245\275.pdf"
diff --git "a/\345\244\204\347\275\256\346\225\210\345\272\224\345\233\240\345\255\220/20190531-\345\233\275\344\277\241\350\257\201\345\210\270-\350\241\214\344\270\272\351\207\221\350\236\215\345\255\246\347\263\273\345\210\227\344\271\213\344\272\214\357\274\232\345\244\204\347\275\256\346\225\210\345\272\224\344\270\216\346\226\260\345\242\236\344\277\241\346\201\257\345\217\202\344\270\216\345\256\232\344\273\267\347\232\204\345\217\215\345\272\224\350\277\237\346\273\236.pdf" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\244\204\347\275\256\346\225\210\345\272\224\345\233\240\345\255\220/20190531-\345\233\275\344\277\241\350\257\201\345\210\270-\350\241\214\344\270\272\351\207\221\350\236\215\345\255\246\347\263\273\345\210\227\344\271\213\344\272\214\357\274\232\345\244\204\347\275\256\346\225\210\345\272\224\344\270\216\346\226\260\345\242\236\344\277\241\346\201\257\345\217\202\344\270\216\345\256\232\344\273\267\347\232\204\345\217\215\345\272\224\350\277\237\346\273\236.pdf"
similarity index 100%
rename from "\345\244\204\347\275\256\346\225\210\345\272\224\345\233\240\345\255\220/20190531-\345\233\275\344\277\241\350\257\201\345\210\270-\350\241\214\344\270\272\351\207\221\350\236\215\345\255\246\347\263\273\345\210\227\344\271\213\344\272\214\357\274\232\345\244\204\347\275\256\346\225\210\345\272\224\344\270\216\346\226\260\345\242\236\344\277\241\346\201\257\345\217\202\344\270\216\345\256\232\344\273\267\347\232\204\345\217\215\345\272\224\350\277\237\346\273\236.pdf"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\244\204\347\275\256\346\225\210\345\272\224\345\233\240\345\255\220/20190531-\345\233\275\344\277\241\350\257\201\345\210\270-\350\241\214\344\270\272\351\207\221\350\236\215\345\255\246\347\263\273\345\210\227\344\271\213\344\272\214\357\274\232\345\244\204\347\275\256\346\225\210\345\272\224\344\270\216\346\226\260\345\242\236\344\277\241\346\201\257\345\217\202\344\270\216\345\256\232\344\273\267\347\232\204\345\217\215\345\272\224\350\277\237\346\273\236.pdf"
diff --git "a/\345\244\204\347\275\256\346\225\210\345\272\224\345\233\240\345\255\220/py/BuildPeriodicDate.py" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\244\204\347\275\256\346\225\210\345\272\224\345\233\240\345\255\220/py/BuildPeriodicDate.py"
similarity index 100%
rename from "\345\244\204\347\275\256\346\225\210\345\272\224\345\233\240\345\255\220/py/BuildPeriodicDate.py"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\244\204\347\275\256\346\225\210\345\272\224\345\233\240\345\255\220/py/BuildPeriodicDate.py"
diff --git "a/\345\244\204\347\275\256\346\225\210\345\272\224\345\233\240\345\255\220/py/\350\265\204\346\234\254\345\210\251\345\276\227\347\252\201\345\207\272\351\207\217CGO\344\270\216\351\243\216\351\231\251\345\201\217\345\245\275_\351\207\215\347\275\256.ipynb" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\244\204\347\275\256\346\225\210\345\272\224\345\233\240\345\255\220/py/\350\265\204\346\234\254\345\210\251\345\276\227\347\252\201\345\207\272\351\207\217CGO\344\270\216\351\243\216\351\231\251\345\201\217\345\245\275_\351\207\215\347\275\256.ipynb"
similarity index 100%
rename from "\345\244\204\347\275\256\346\225\210\345\272\224\345\233\240\345\255\220/py/\350\265\204\346\234\254\345\210\251\345\276\227\347\252\201\345\207\272\351\207\217CGO\344\270\216\351\243\216\351\231\251\345\201\217\345\245\275_\351\207\215\347\275\256.ipynb"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\244\204\347\275\256\346\225\210\345\272\224\345\233\240\345\255\220/py/\350\265\204\346\234\254\345\210\251\345\276\227\347\252\201\345\207\272\351\207\217CGO\344\270\216\351\243\216\351\231\251\345\201\217\345\245\275_\351\207\215\347\275\256.ipynb"
diff --git "a/\345\244\232\345\233\240\345\255\220\346\214\207\346\225\260\345\242\236\345\274\272/20180705-\345\244\251\351\243\216\350\257\201\345\210\270-\351\207\221\345\267\245\344\270\223\351\242\230\346\212\245\345\221\212\357\274\232\345\237\272\344\272\216\350\207\252\351\200\202\345\272\224\351\243\216\351\231\251\346\216\247\345\210\266\347\232\204\346\214\207\346\225\260\345\242\236\345\274\272\347\255\226\347\225\245.pdf" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\244\232\345\233\240\345\255\220\346\214\207\346\225\260\345\242\236\345\274\272/20180705-\345\244\251\351\243\216\350\257\201\345\210\270-\351\207\221\345\267\245\344\270\223\351\242\230\346\212\245\345\221\212\357\274\232\345\237\272\344\272\216\350\207\252\351\200\202\345\272\224\351\243\216\351\231\251\346\216\247\345\210\266\347\232\204\346\214\207\346\225\260\345\242\236\345\274\272\347\255\226\347\225\245.pdf"
similarity index 100%
rename from "\345\244\232\345\233\240\345\255\220\346\214\207\346\225\260\345\242\236\345\274\272/20180705-\345\244\251\351\243\216\350\257\201\345\210\270-\351\207\221\345\267\245\344\270\223\351\242\230\346\212\245\345\221\212\357\274\232\345\237\272\344\272\216\350\207\252\351\200\202\345\272\224\351\243\216\351\231\251\346\216\247\345\210\266\347\232\204\346\214\207\346\225\260\345\242\236\345\274\272\347\255\226\347\225\245.pdf"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\244\232\345\233\240\345\255\220\346\214\207\346\225\260\345\242\236\345\274\272/20180705-\345\244\251\351\243\216\350\257\201\345\210\270-\351\207\221\345\267\245\344\270\223\351\242\230\346\212\245\345\221\212\357\274\232\345\237\272\344\272\216\350\207\252\351\200\202\345\272\224\351\243\216\351\231\251\346\216\247\345\210\266\347\232\204\346\214\207\346\225\260\345\242\236\345\274\272\347\255\226\347\225\245.pdf"
diff --git "a/\345\244\232\345\233\240\345\255\220\346\214\207\346\225\260\345\242\236\345\274\272/py/\345\237\272\344\272\216\350\207\252\351\200\202\345\272\224\351\243\216\351\231\251\346\216\247\345\210\266\347\232\204\346\214\207\346\225\260\345\242\236\345\274\272\347\255\226\347\225\245.ipynb" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\244\232\345\233\240\345\255\220\346\214\207\346\225\260\345\242\236\345\274\272/py/\345\237\272\344\272\216\350\207\252\351\200\202\345\272\224\351\243\216\351\231\251\346\216\247\345\210\266\347\232\204\346\214\207\346\225\260\345\242\236\345\274\272\347\255\226\347\225\245.ipynb"
similarity index 100%
rename from "\345\244\232\345\233\240\345\255\220\346\214\207\346\225\260\345\242\236\345\274\272/py/\345\237\272\344\272\216\350\207\252\351\200\202\345\272\224\351\243\216\351\231\251\346\216\247\345\210\266\347\232\204\346\214\207\346\225\260\345\242\236\345\274\272\347\255\226\347\225\245.ipynb"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\244\232\345\233\240\345\255\220\346\214\207\346\225\260\345\242\236\345\274\272/py/\345\237\272\344\272\216\350\207\252\351\200\202\345\272\224\351\243\216\351\231\251\346\216\247\345\210\266\347\232\204\346\214\207\346\225\260\345\242\236\345\274\272\347\255\226\347\225\245.ipynb"
diff --git "a/\345\244\232\345\233\240\345\255\220\346\214\207\346\225\260\345\242\236\345\274\272/\343\200\220\345\215\216\346\263\260\351\207\221\345\267\245\343\200\221\346\214\207\346\225\260\345\242\236\345\274\272\346\226\271\346\263\225\346\261\207\346\200\273\345\217\212\345\256\236\344\276\21320180531.pdf" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\244\232\345\233\240\345\255\220\346\214\207\346\225\260\345\242\236\345\274\272/\343\200\220\345\215\216\346\263\260\351\207\221\345\267\245\343\200\221\346\214\207\346\225\260\345\242\236\345\274\272\346\226\271\346\263\225\346\261\207\346\200\273\345\217\212\345\256\236\344\276\21320180531.pdf"
similarity index 100%
rename from "\345\244\232\345\233\240\345\255\220\346\214\207\346\225\260\345\242\236\345\274\272/\343\200\220\345\215\216\346\263\260\351\207\221\345\267\245\343\200\221\346\214\207\346\225\260\345\242\236\345\274\272\346\226\271\346\263\225\346\261\207\346\200\273\345\217\212\345\256\236\344\276\21320180531.pdf"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\244\232\345\233\240\345\255\220\346\214\207\346\225\260\345\242\236\345\274\272/\343\200\220\345\215\216\346\263\260\351\207\221\345\267\245\343\200\221\346\214\207\346\225\260\345\242\236\345\274\272\346\226\271\346\263\225\346\261\207\346\200\273\345\217\212\345\256\236\344\276\21320180531.pdf"
diff --git "a/\345\244\232\345\233\240\345\255\220\346\214\207\346\225\260\345\242\236\345\274\272/\345\233\236\346\265\213\346\250\241\345\235\227.py" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\244\232\345\233\240\345\255\220\346\214\207\346\225\260\345\242\236\345\274\272/\345\233\236\346\265\213\346\250\241\345\235\227.py"
similarity index 96%
rename from "\345\244\232\345\233\240\345\255\220\346\214\207\346\225\260\345\242\236\345\274\272/\345\233\236\346\265\213\346\250\241\345\235\227.py"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\244\232\345\233\240\345\255\220\346\214\207\346\225\260\345\242\236\345\274\272/\345\233\236\346\265\213\346\250\241\345\235\227.py"
index 1107c77..212b0e6 100644
--- "a/\345\244\232\345\233\240\345\255\220\346\214\207\346\225\260\345\242\236\345\274\272/\345\233\236\346\265\213\346\250\241\345\235\227.py"
+++ "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\244\232\345\233\240\345\255\220\346\214\207\346\225\260\345\242\236\345\274\272/\345\233\236\346\265\213\346\250\241\345\235\227.py"
@@ -1,92 +1,92 @@
-'''
-Author: Hugo
-Date: 2020-06-10 20:52:43
-LastEditTime: 2020-11-02 10:08:02
-LastEditors: Please set LastEditors
-Description: 读取指数增强研究文档生成的result_df文件进行下单
-'''
-
-
-from jqdata import *
-import pandas as pd
-import numpy as np
-from six import BytesIO  # 文件读取
-
-enable_profile()  # 开启性能分析
-
-
-def initialize(context):
-
-    set_params()
-    set_variables()
-    set_backtest()
-
-    run_monthly(Trade, -1, time='open', reference_security='000300.XSHG')
-
-
-def set_params():
-
-    g.result_df = pd.read_csv(
-        BytesIO(read_file('result_df.csv')), index_col=[0],)
-
-
-def set_variables():
-
-    pass
-
-
-def set_backtest():
-
-    set_option("avoid_future_data", True)  # 避免数据
-    set_option("use_real_price", True)  # 真实价格交易
-    set_benchmark('000300.XSHG')  # 设置基准
-    #log.set_level("order", "debuge")
-    log.set_level('order', 'error')
-
-
-# 每日盘前运行
-def before_trading_start(context):
-
-    # 手续费设置
-    # 将滑点设置为0
-    set_slippage(FixedSlippage(0))
-
-    # 根据不同的时间段设置手续费
-    dt = context.current_dt
-
-    if dt > datetime.datetime(2013, 1, 1):
-        set_commission(PerTrade(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
-
-    elif dt > datetime.datetime(2011, 1, 1):
-        set_commission(PerTrade(buy_cost=0.001, sell_cost=0.002, min_cost=5))
-
-    elif dt > datetime.datetime(2009, 1, 1):
-        set_commission(PerTrade(buy_cost=0.002, sell_cost=0.003, min_cost=5))
-
-    else:
-        set_commission(PerTrade(buy_cost=0.003, sell_cost=0.004, min_cost=5))
-
-
-def Trade(context):
-
-    bar_time = context.current_dt.strftime('%Y-%m-%d')
-    log.info('%s启动' % bar_time)
-
-    if bar_time in g.result_df.index:
-        print('存在')
-        target_slice = g.result_df.loc[bar_time]
-        BuyStock(context, target_slice)
-
-
-def BuyStock(context, target_slice: pd.DataFrame):
-
-    order_dict = target_slice.set_index('code')['w'].to_dict()
-
-    for hold in context.portfolio.long_positions:
-        if hold not in order_dict:
-            order_target(hold, 0)
-
-    totalasset = context.portfolio.total_value
-    for buy_stock, pre in order_dict.items():
-
-        order_target_value(buy_stock, pre * totalasset)
+'''
+Author: Hugo
+Date: 2020-06-10 20:52:43
+LastEditTime: 2020-11-02 10:08:02
+LastEditors: Please set LastEditors
+Description: 读取指数增强研究文档生成的result_df文件进行下单
+'''
+
+
+from jqdata import *
+import pandas as pd
+import numpy as np
+from six import BytesIO  # 文件读取
+
+enable_profile()  # 开启性能分析
+
+
+def initialize(context):
+
+    set_params()
+    set_variables()
+    set_backtest()
+
+    run_monthly(Trade, -1, time='open', reference_security='000300.XSHG')
+
+
+def set_params():
+
+    g.result_df = pd.read_csv(
+        BytesIO(read_file('result_df.csv')), index_col=[0],)
+
+
+def set_variables():
+
+    pass
+
+
+def set_backtest():
+
+    set_option("avoid_future_data", True)  # 避免数据
+    set_option("use_real_price", True)  # 真实价格交易
+    set_benchmark('000300.XSHG')  # 设置基准
+    #log.set_level("order", "debuge")
+    log.set_level('order', 'error')
+
+
+# 每日盘前运行
+def before_trading_start(context):
+
+    # 手续费设置
+    # 将滑点设置为0
+    set_slippage(FixedSlippage(0))
+
+    # 根据不同的时间段设置手续费
+    dt = context.current_dt
+
+    if dt > datetime.datetime(2013, 1, 1):
+        set_commission(PerTrade(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
+
+    elif dt > datetime.datetime(2011, 1, 1):
+        set_commission(PerTrade(buy_cost=0.001, sell_cost=0.002, min_cost=5))
+
+    elif dt > datetime.datetime(2009, 1, 1):
+        set_commission(PerTrade(buy_cost=0.002, sell_cost=0.003, min_cost=5))
+
+    else:
+        set_commission(PerTrade(buy_cost=0.003, sell_cost=0.004, min_cost=5))
+
+
+def Trade(context):
+
+    bar_time = context.current_dt.strftime('%Y-%m-%d')
+    log.info('%s启动' % bar_time)
+
+    if bar_time in g.result_df.index:
+        print('存在')
+        target_slice = g.result_df.loc[bar_time]
+        BuyStock(context, target_slice)
+
+
+def BuyStock(context, target_slice: pd.DataFrame):
+
+    order_dict = target_slice.set_index('code')['w'].to_dict()
+
+    for hold in context.portfolio.long_positions:
+        if hold not in order_dict:
+            order_target(hold, 0)
+
+    totalasset = context.portfolio.total_value
+    for buy_stock, pre in order_dict.items():
+
+        order_target_value(buy_stock, pre * totalasset)
diff --git "a/\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2101\357\274\211\357\274\232A\350\202\241\345\217\215\350\275\254\344\271\213\345\212\233\347\232\204\345\276\256\350\247\202\346\235\245\346\272\220/20191223-\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2101\357\274\211\357\274\232A\350\202\241\345\217\215\350\275\254\344\271\213\345\212\233\347\232\204\345\276\256\350\247\202\346\235\245\346\272\220.pdf" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2101\357\274\211\357\274\232A\350\202\241\345\217\215\350\275\254\344\271\213\345\212\233\347\232\204\345\276\256\350\247\202\346\235\245\346\272\220/20191223-\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2101\357\274\211\357\274\232A\350\202\241\345\217\215\350\275\254\344\271\213\345\212\233\347\232\204\345\276\256\350\247\202\346\235\245\346\272\220.pdf"
similarity index 100%
rename from "\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2101\357\274\211\357\274\232A\350\202\241\345\217\215\350\275\254\344\271\213\345\212\233\347\232\204\345\276\256\350\247\202\346\235\245\346\272\220/20191223-\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2101\357\274\211\357\274\232A\350\202\241\345\217\215\350\275\254\344\271\213\345\212\233\347\232\204\345\276\256\350\247\202\346\235\245\346\272\220.pdf"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2101\357\274\211\357\274\232A\350\202\241\345\217\215\350\275\254\344\271\213\345\212\233\347\232\204\345\276\256\350\247\202\346\235\245\346\272\220/20191223-\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2101\357\274\211\357\274\232A\350\202\241\345\217\215\350\275\254\344\271\213\345\212\233\347\232\204\345\276\256\350\247\202\346\235\245\346\272\220.pdf"
diff --git "a/\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2101\357\274\211\357\274\232A\350\202\241\345\217\215\350\275\254\344\271\213\345\212\233\347\232\204\345\276\256\350\247\202\346\235\245\346\272\220/py/W\345\233\240\345\255\220\346\236\204\345\273\272.ipynb" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2101\357\274\211\357\274\232A\350\202\241\345\217\215\350\275\254\344\271\213\345\212\233\347\232\204\345\276\256\350\247\202\346\235\245\346\272\220/py/W\345\233\240\345\255\220\346\236\204\345\273\272.ipynb"
similarity index 100%
rename from "\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2101\357\274\211\357\274\232A\350\202\241\345\217\215\350\275\254\344\271\213\345\212\233\347\232\204\345\276\256\350\247\202\346\235\245\346\272\220/py/W\345\233\240\345\255\220\346\236\204\345\273\272.ipynb"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2101\357\274\211\357\274\232A\350\202\241\345\217\215\350\275\254\344\271\213\345\212\233\347\232\204\345\276\256\350\247\202\346\235\245\346\272\220/py/W\345\233\240\345\255\220\346\236\204\345\273\272.ipynb"
diff --git "a/\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2101\357\274\211\357\274\232A\350\202\241\345\217\215\350\275\254\344\271\213\345\212\233\347\232\204\345\276\256\350\247\202\346\235\245\346\272\220/py/\346\225\260\346\215\256\351\233\206.md" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2101\357\274\211\357\274\232A\350\202\241\345\217\215\350\275\254\344\271\213\345\212\233\347\232\204\345\276\256\350\247\202\346\235\245\346\272\220/py/\346\225\260\346\215\256\351\233\206.md"
similarity index 97%
rename from "\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2101\357\274\211\357\274\232A\350\202\241\345\217\215\350\275\254\344\271\213\345\212\233\347\232\204\345\276\256\350\247\202\346\235\245\346\272\220/py/\346\225\260\346\215\256\351\233\206.md"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2101\357\274\211\357\274\232A\350\202\241\345\217\215\350\275\254\344\271\213\345\212\233\347\232\204\345\276\256\350\247\202\346\235\245\346\272\220/py/\346\225\260\346\215\256\351\233\206.md"
index 9ed46e6..e6dcc8f 100644
--- "a/\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2101\357\274\211\357\274\232A\350\202\241\345\217\215\350\275\254\344\271\213\345\212\233\347\232\204\345\276\256\350\247\202\346\235\245\346\272\220/py/\346\225\260\346\215\256\351\233\206.md"
+++ "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2101\357\274\211\357\274\232A\350\202\241\345\217\215\350\275\254\344\271\213\345\212\233\347\232\204\345\276\256\350\247\202\346\235\245\346\272\220/py/\346\225\260\346\215\256\351\233\206.md"
@@ -1,3 +1,3 @@
-
-链接：https://pan.baidu.com/s/1SkH3f6r16ICD6mPqR5nz_Q 
+
+链接：https://pan.baidu.com/s/1SkH3f6r16ICD6mPqR5nz_Q 
 提取码：odjq
\ No newline at end of file
diff --git "a/\346\214\257\345\271\205\345\233\240\345\255\220\347\232\204\351\232\220\350\227\217\347\273\223\346\236\204/20200516-\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2107\357\274\211\357\274\232\346\214\257\345\271\205\345\233\240\345\255\220\347\232\204\351\232\220\350\227\217\347\273\223\346\236\204.pdf" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\346\214\257\345\271\205\345\233\240\345\255\220\347\232\204\351\232\220\350\227\217\347\273\223\346\236\204/20200516-\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2107\357\274\211\357\274\232\346\214\257\345\271\205\345\233\240\345\255\220\347\232\204\351\232\220\350\227\217\347\273\223\346\236\204.pdf"
similarity index 100%
rename from "\346\214\257\345\271\205\345\233\240\345\255\220\347\232\204\351\232\220\350\227\217\347\273\223\346\236\204/20200516-\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2107\357\274\211\357\274\232\346\214\257\345\271\205\345\233\240\345\255\220\347\232\204\351\232\220\350\227\217\347\273\223\346\236\204.pdf"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\346\214\257\345\271\205\345\233\240\345\255\220\347\232\204\351\232\220\350\227\217\347\273\223\346\236\204/20200516-\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2107\357\274\211\357\274\232\346\214\257\345\271\205\345\233\240\345\255\220\347\232\204\351\232\220\350\227\217\347\273\223\346\236\204.pdf"
diff --git "a/\346\214\257\345\271\205\345\233\240\345\255\220\347\232\204\351\232\220\350\227\217\347\273\223\346\236\204/notebook/\346\214\257\345\271\205\345\233\240\345\255\220\347\232\204\351\232\220\350\227\217\347\273\223\346\236\204.ipynb" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\346\214\257\345\271\205\345\233\240\345\255\220\347\232\204\351\232\220\350\227\217\347\273\223\346\236\204/notebook/\346\214\257\345\271\205\345\233\240\345\255\220\347\232\204\351\232\220\350\227\217\347\273\223\346\236\204.ipynb"
similarity index 100%
rename from "\346\214\257\345\271\205\345\233\240\345\255\220\347\232\204\351\232\220\350\227\217\347\273\223\346\236\204/notebook/\346\214\257\345\271\205\345\233\240\345\255\220\347\232\204\351\232\220\350\227\217\347\273\223\346\236\204.ipynb"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\346\214\257\345\271\205\345\233\240\345\255\220\347\232\204\351\232\220\350\227\217\347\273\223\346\236\204/notebook/\346\214\257\345\271\205\345\233\240\345\255\220\347\232\204\351\232\220\350\227\217\347\273\223\346\236\204.ipynb"
diff --git "a/\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212/20190115-\344\270\234\346\226\271\350\257\201\345\210\270-\345\233\240\345\255\220\351\200\211\350\202\241\347\263\273\345\210\227\344\271\213\344\272\224\345\215\201\357\274\232A\350\202\241\350\241\214\344\270\232\345\206\205\351\200\211\350\202\241\345\210\206\346\236\220\346\200\273\347\273\223.pdf" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212/20190115-\344\270\234\346\226\271\350\257\201\345\210\270-\345\233\240\345\255\220\351\200\211\350\202\241\347\263\273\345\210\227\344\271\213\344\272\224\345\215\201\357\274\232A\350\202\241\350\241\214\344\270\232\345\206\205\351\200\211\350\202\241\345\210\206\346\236\220\346\200\273\347\273\223.pdf"
similarity index 100%
rename from "\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212/20190115-\344\270\234\346\226\271\350\257\201\345\210\270-\345\233\240\345\255\220\351\200\211\350\202\241\347\263\273\345\210\227\344\271\213\344\272\224\345\215\201\357\274\232A\350\202\241\350\241\214\344\270\232\345\206\205\351\200\211\350\202\241\345\210\206\346\236\220\346\200\273\347\273\223.pdf"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212/20190115-\344\270\234\346\226\271\350\257\201\345\210\270-\345\233\240\345\255\220\351\200\211\350\202\241\347\263\273\345\210\227\344\271\213\344\272\224\345\215\201\357\274\232A\350\202\241\350\241\214\344\270\232\345\206\205\351\200\211\350\202\241\345\210\206\346\236\220\346\200\273\347\273\223.pdf"
diff --git "a/\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212/20191127-\344\270\234\346\226\271\350\257\201\345\210\270-\343\200\212\345\233\240\345\255\220\351\200\211\350\202\241\347\263\273\345\210\227\347\240\224\347\251\266\344\271\213\345\205\255\345\215\201\344\272\214\343\200\213\357\274\232\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212.pdf" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212/20191127-\344\270\234\346\226\271\350\257\201\345\210\270-\343\200\212\345\233\240\345\255\220\351\200\211\350\202\241\347\263\273\345\210\227\347\240\224\347\251\266\344\271\213\345\205\255\345\215\201\344\272\214\343\200\213\357\274\232\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212.pdf"
similarity index 100%
rename from "\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212/20191127-\344\270\234\346\226\271\350\257\201\345\210\270-\343\200\212\345\233\240\345\255\220\351\200\211\350\202\241\347\263\273\345\210\227\347\240\224\347\251\266\344\271\213\345\205\255\345\215\201\344\272\214\343\200\213\357\274\232\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212.pdf"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212/20191127-\344\270\234\346\226\271\350\257\201\345\210\270-\343\200\212\345\233\240\345\255\220\351\200\211\350\202\241\347\263\273\345\210\227\347\240\224\347\251\266\344\271\213\345\205\255\345\215\201\344\272\214\343\200\213\357\274\232\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212.pdf"
diff --git "a/\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212/20200528-\344\270\234\346\226\271\350\257\201\345\210\270-\351\207\221\350\236\215\345\267\245\347\250\213\344\270\223\351\242\230\346\212\245\345\221\212\357\274\232\344\270\234\346\226\271A\350\202\241\345\233\240\345\255\220\351\243\216\351\231\251\346\250\241\345\236\213\357\274\210DFQ~2020\357\274\211.pdf" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212/20200528-\344\270\234\346\226\271\350\257\201\345\210\270-\351\207\221\350\236\215\345\267\245\347\250\213\344\270\223\351\242\230\346\212\245\345\221\212\357\274\232\344\270\234\346\226\271A\350\202\241\345\233\240\345\255\220\351\243\216\351\231\251\346\250\241\345\236\213\357\274\210DFQ~2020\357\274\211.pdf"
similarity index 100%
rename from "\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212/20200528-\344\270\234\346\226\271\350\257\201\345\210\270-\351\207\221\350\236\215\345\267\245\347\250\213\344\270\223\351\242\230\346\212\245\345\221\212\357\274\232\344\270\234\346\226\271A\350\202\241\345\233\240\345\255\220\351\243\216\351\231\251\346\250\241\345\236\213\357\274\210DFQ~2020\357\274\211.pdf"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212/20200528-\344\270\234\346\226\271\350\257\201\345\210\270-\351\207\221\350\236\215\345\267\245\347\250\213\344\270\223\351\242\230\346\212\245\345\221\212\357\274\232\344\270\234\346\226\271A\350\202\241\345\233\240\345\255\220\351\243\216\351\231\251\346\250\241\345\236\213\357\274\210DFQ~2020\357\274\211.pdf"
diff --git "a/\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212/20200707-\346\265\267\351\200\232\350\257\201\345\210\270-\351\200\211\350\202\241\345\233\240\345\255\220\347\263\273\345\210\227\347\240\224\347\251\266\357\274\210\345\205\255\345\215\201\345\205\253\357\274\211\357\274\232\345\237\272\351\207\221\351\207\215\344\273\223\350\266\205\351\205\215\345\233\240\345\255\220\345\217\212\345\205\266\345\257\271\346\214\207\346\225\260\345\242\236\345\274\272\347\273\204\345\220\210\347\232\204\345\275\261\345\223\215.pdf" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212/20200707-\346\265\267\351\200\232\350\257\201\345\210\270-\351\200\211\350\202\241\345\233\240\345\255\220\347\263\273\345\210\227\347\240\224\347\251\266\357\274\210\345\205\255\345\215\201\345\205\253\357\274\211\357\274\232\345\237\272\351\207\221\351\207\215\344\273\223\350\266\205\351\205\215\345\233\240\345\255\220\345\217\212\345\205\266\345\257\271\346\214\207\346\225\260\345\242\236\345\274\272\347\273\204\345\220\210\347\232\204\345\275\261\345\223\215.pdf"
similarity index 100%
rename from "\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212/20200707-\346\265\267\351\200\232\350\257\201\345\210\270-\351\200\211\350\202\241\345\233\240\345\255\220\347\263\273\345\210\227\347\240\224\347\251\266\357\274\210\345\205\255\345\215\201\345\205\253\357\274\211\357\274\232\345\237\272\351\207\221\351\207\215\344\273\223\350\266\205\351\205\215\345\233\240\345\255\220\345\217\212\345\205\266\345\257\271\346\214\207\346\225\260\345\242\236\345\274\272\347\273\204\345\220\210\347\232\204\345\275\261\345\223\215.pdf"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212/20200707-\346\265\267\351\200\232\350\257\201\345\210\270-\351\200\211\350\202\241\345\233\240\345\255\220\347\263\273\345\210\227\347\240\224\347\251\266\357\274\210\345\205\255\345\215\201\345\205\253\357\274\211\357\274\232\345\237\272\351\207\221\351\207\215\344\273\223\350\266\205\351\205\215\345\233\240\345\255\220\345\217\212\345\205\266\345\257\271\346\214\207\346\225\260\345\242\236\345\274\272\347\273\204\345\220\210\347\232\204\345\275\261\345\223\215.pdf"
diff --git "a/\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212/py/\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212.ipynb" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212/py/\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212.ipynb"
similarity index 100%
rename from "\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212/py/\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212.ipynb"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212/py/\346\235\245\350\207\252\344\274\230\347\247\200\345\237\272\351\207\221\347\273\217\347\220\206\347\232\204\350\266\205\351\242\235\346\224\266\347\233\212.ipynb"
diff --git "a/\350\201\252\346\230\216\351\222\261\345\233\240\345\255\220\346\250\241\345\236\213\347\232\2042.0\347\211\210\346\234\254/20200209-\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2103\357\274\211\357\274\232\350\201\252\346\230\216\351\222\261\345\233\240\345\255\220\346\250\241\345\236\213\347\232\2042.0\347\211\210\346\234\254.pdf" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\350\201\252\346\230\216\351\222\261\345\233\240\345\255\220\346\250\241\345\236\213\347\232\2042.0\347\211\210\346\234\254/20200209-\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2103\357\274\211\357\274\232\350\201\252\346\230\216\351\222\261\345\233\240\345\255\220\346\250\241\345\236\213\347\232\2042.0\347\211\210\346\234\254.pdf"
similarity index 100%
rename from "\350\201\252\346\230\216\351\222\261\345\233\240\345\255\220\346\250\241\345\236\213\347\232\2042.0\347\211\210\346\234\254/20200209-\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2103\357\274\211\357\274\232\350\201\252\346\230\216\351\222\261\345\233\240\345\255\220\346\250\241\345\236\213\347\232\2042.0\347\211\210\346\234\254.pdf"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\350\201\252\346\230\216\351\222\261\345\233\240\345\255\220\346\250\241\345\236\213\347\232\2042.0\347\211\210\346\234\254/20200209-\345\274\200\346\272\220\350\257\201\345\210\270-\345\270\202\345\234\272\345\276\256\350\247\202\347\273\223\346\236\204\347\240\224\347\251\266\347\263\273\345\210\227\357\274\2103\357\274\211\357\274\232\350\201\252\346\230\216\351\222\261\345\233\240\345\255\220\346\250\241\345\236\213\347\232\2042.0\347\211\210\346\234\254.pdf"
diff --git "a/\350\201\252\346\230\216\351\222\261\345\233\240\345\255\220\346\250\241\345\236\213\347\232\2042.0\347\211\210\346\234\254/notebook/\350\201\252\346\230\216\351\222\261\345\233\240\345\255\220\346\250\241\345\236\213\347\232\204 2.0 \347\211\210\346\234\254.ipynb" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\350\201\252\346\230\216\351\222\261\345\233\240\345\255\220\346\250\241\345\236\213\347\232\2042.0\347\211\210\346\234\254/notebook/\350\201\252\346\230\216\351\222\261\345\233\240\345\255\220\346\250\241\345\236\213\347\232\204 2.0 \347\211\210\346\234\254.ipynb"
similarity index 100%
rename from "\350\201\252\346\230\216\351\222\261\345\233\240\345\255\220\346\250\241\345\236\213\347\232\2042.0\347\211\210\346\234\254/notebook/\350\201\252\346\230\216\351\222\261\345\233\240\345\255\220\346\250\241\345\236\213\347\232\204 2.0 \347\211\210\346\234\254.ipynb"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\350\201\252\346\230\216\351\222\261\345\233\240\345\255\220\346\250\241\345\236\213\347\232\2042.0\347\211\210\346\234\254/notebook/\350\201\252\346\230\216\351\222\261\345\233\240\345\255\220\346\250\241\345\236\213\347\232\204 2.0 \347\211\210\346\234\254.ipynb"
diff --git "a/\351\253\230\350\264\250\351\207\217\345\212\250\351\207\217\345\233\240\345\255\220\351\200\211\350\202\241/\351\253\230\350\264\250\351\207\217\345\212\250\351\207\217\351\200\211\350\202\241.ipynb" "b/B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\351\253\230\350\264\250\351\207\217\345\212\250\351\207\217\345\233\240\345\255\220\351\200\211\350\202\241/\351\253\230\350\264\250\351\207\217\345\212\250\351\207\217\351\200\211\350\202\241.ipynb"
similarity index 100%
rename from "\351\253\230\350\264\250\351\207\217\345\212\250\351\207\217\345\233\240\345\255\220\351\200\211\350\202\241/\351\253\230\350\264\250\351\207\217\345\212\250\351\207\217\351\200\211\350\202\241.ipynb"
rename to "B-\345\233\240\345\255\220\346\236\204\345\273\272\347\261\273/\351\253\230\350\264\250\351\207\217\345\212\250\351\207\217\345\233\240\345\255\220\351\200\211\350\202\241/\351\253\230\350\264\250\351\207\217\345\212\250\351\207\217\351\200\211\350\202\241.ipynb"
diff --git "a/CSVC\346\241\206\346\236\266\345\217\212\347\206\212\347\211\233\346\214\207\346\240\207/20190617-\345\215\216\346\263\260\350\257\201\345\210\270-\345\215\216\346\263\260\344\272\272\345\267\245\346\231\272\350\203\275\347\263\273\345\210\227\344\271\213\344\272\214\345\215\201\344\272\214\357\274\232\345\237\272\344\272\216CSCV\346\241\206\346\236\266\347\232\204\345\233\236\346\265\213\350\277\207\346\213\237\345\220\210\346\246\202\347\216\207.pdf" "b/C-\346\213\251\346\227\266\347\261\273/CSVC\346\241\206\346\236\266\345\217\212\347\206\212\347\211\233\346\214\207\346\240\207/20190617-\345\215\216\346\263\260\350\257\201\345\210\270-\345\215\216\346\263\260\344\272\272\345\267\245\346\231\272\350\203\275\347\263\273\345\210\227\344\271\213\344\272\214\345\215\201\344\272\214\357\274\232\345\237\272\344\272\216CSCV\346\241\206\346\236\266\347\232\204\345\233\236\346\265\213\350\277\207\346\213\237\345\220\210\346\246\202\347\216\207.pdf"
similarity index 100%
rename from "CSVC\346\241\206\346\236\266\345\217\212\347\206\212\347\211\233\346\214\207\346\240\207/20190617-\345\215\216\346\263\260\350\257\201\345\210\270-\345\215\216\346\263\260\344\272\272\345\267\245\346\231\272\350\203\275\347\263\273\345\210\227\344\271\213\344\272\214\345\215\201\344\272\214\357\274\232\345\237\272\344\272\216CSCV\346\241\206\346\236\266\347\232\204\345\233\236\346\265\213\350\277\207\346\213\237\345\220\210\346\246\202\347\216\207.pdf"
rename to "C-\346\213\251\346\227\266\347\261\273/CSVC\346\241\206\346\236\266\345\217\212\347\206\212\347\211\233\346\214\207\346\240\207/20190617-\345\215\216\346\263\260\350\257\201\345\210\270-\345\215\216\346\263\260\344\272\272\345\267\245\346\231\272\350\203\275\347\263\273\345\210\227\344\271\213\344\272\214\345\215\201\344\272\214\357\274\232\345\237\272\344\272\216CSCV\346\241\206\346\236\266\347\232\204\345\233\236\346\265\213\350\277\207\346\213\237\345\220\210\346\246\202\347\216\207.pdf"
diff --git "a/CSVC\346\241\206\346\236\266\345\217\212\347\206\212\347\211\233\346\214\207\346\240\207/20200407-\345\215\216\346\263\260\350\257\201\345\210\270-\345\215\216\346\263\260\351\207\221\345\267\245\351\207\217\345\214\226\346\213\251\346\227\266\347\263\273\345\210\227\357\274\232\347\211\233\347\206\212\346\214\207\346\240\207\345\234\250\346\213\251\346\227\266\350\275\256\345\212\250\344\270\255\347\232\204\345\272\224\347\224\250\346\216\242\350\256\250.pdf" "b/C-\346\213\251\346\227\266\347\261\273/CSVC\346\241\206\346\236\266\345\217\212\347\206\212\347\211\233\346\214\207\346\240\207/20200407-\345\215\216\346\263\260\350\257\201\345\210\270-\345\215\216\346\263\260\351\207\221\345\267\245\351\207\217\345\214\226\346\213\251\346\227\266\347\263\273\345\210\227\357\274\232\347\211\233\347\206\212\346\214\207\346\240\207\345\234\250\346\213\251\346\227\266\350\275\256\345\212\250\344\270\255\347\232\204\345\272\224\347\224\250\346\216\242\350\256\250.pdf"
similarity index 100%
rename from "CSVC\346\241\206\346\236\266\345\217\212\347\206\212\347\211\233\346\214\207\346\240\207/20200407-\345\215\216\346\263\260\350\257\201\345\210\270-\345\215\216\346\263\260\351\207\221\345\267\245\351\207\217\345\214\226\346\213\251\346\227\266\347\263\273\345\210\227\357\274\232\347\211\233\347\206\212\346\214\207\346\240\207\345\234\250\346\213\251\346\227\266\350\275\256\345\212\250\344\270\255\347\232\204\345\272\224\347\224\250\346\216\242\350\256\250.pdf"
rename to "C-\346\213\251\346\227\266\347\261\273/CSVC\346\241\206\346\236\266\345\217\212\347\206\212\347\211\233\346\214\207\346\240\207/20200407-\345\215\216\346\263\260\350\257\201\345\210\270-\345\215\216\346\263\260\351\207\221\345\267\245\351\207\217\345\214\226\346\213\251\346\227\266\347\263\273\345\210\227\357\274\232\347\211\233\347\206\212\346\214\207\346\240\207\345\234\250\346\213\251\346\227\266\350\275\256\345\212\250\344\270\255\347\232\204\345\272\224\347\224\250\346\216\242\350\256\250.pdf"
diff --git "a/CSVC\346\241\206\346\236\266\345\217\212\347\206\212\347\211\233\346\214\207\346\240\207/The Probability of Backtest Overfitting.pdf" "b/C-\346\213\251\346\227\266\347\261\273/CSVC\346\241\206\346\236\266\345\217\212\347\206\212\347\211\233\346\214\207\346\240\207/The Probability of Backtest Overfitting.pdf"
similarity index 100%
rename from "CSVC\346\241\206\346\236\266\345\217\212\347\206\212\347\211\233\346\214\207\346\240\207/The Probability of Backtest Overfitting.pdf"
rename to "C-\346\213\251\346\227\266\347\261\273/CSVC\346\241\206\346\236\266\345\217\212\347\206\212\347\211\233\346\214\207\346\240\207/The Probability of Backtest Overfitting.pdf"
diff --git "a/CSVC\346\241\206\346\236\266\345\217\212\347\206\212\347\211\233\346\214\207\346\240\207/py/CSCV\345\233\236\346\265\213\350\277\207\346\213\237\345\220\210\346\246\202\347\216\207\345\210\206\346\236\220\346\241\206\346\236\266.ipynb" "b/C-\346\213\251\346\227\266\347\261\273/CSVC\346\241\206\346\236\266\345\217\212\347\206\212\347\211\233\346\214\207\346\240\207/py/CSCV\345\233\236\346\265\213\350\277\207\346\213\237\345\220\210\346\246\202\347\216\207\345\210\206\346\236\220\346\241\206\346\236\266.ipynb"
similarity index 100%
rename from "CSVC\346\241\206\346\236\266\345\217\212\347\206\212\347\211\233\346\214\207\346\240\207/py/CSCV\345\233\236\346\265\213\350\277\207\346\213\237\345\220\210\346\246\202\347\216\207\345\210\206\346\236\220\346\241\206\346\236\266.ipynb"
rename to "C-\346\213\251\346\227\266\347\261\273/CSVC\346\241\206\346\236\266\345\217\212\347\206\212\347\211\233\346\214\207\346\240\207/py/CSCV\345\233\236\346\265\213\350\277\207\346\213\237\345\220\210\346\246\202\347\216\207\345\210\206\346\236\220\346\241\206\346\236\266.ipynb"
diff --git "a/CSVC\346\241\206\346\236\266\345\217\212\347\206\212\347\211\233\346\214\207\346\240\207/py/\346\263\242\345\212\250\347\216\207\345\222\214\346\215\242\346\211\213\347\216\207\346\236\204\345\273\272\347\211\233\347\206\212\346\214\207\346\240\207.ipynb" "b/C-\346\213\251\346\227\266\347\261\273/CSVC\346\241\206\346\236\266\345\217\212\347\206\212\347\211\233\346\214\207\346\240\207/py/\346\263\242\345\212\250\347\216\207\345\222\214\346\215\242\346\211\213\347\216\207\346\236\204\345\273\272\347\211\233\347\206\212\346\214\207\346\240\207.ipynb"
similarity index 100%
rename from "CSVC\346\241\206\346\236\266\345\217\212\347\206\212\347\211\233\346\214\207\346\240\207/py/\346\263\242\345\212\250\347\216\207\345\222\214\346\215\242\346\211\213\347\216\207\346\236\204\345\273\272\347\211\233\347\206\212\346\214\207\346\240\207.ipynb"
rename to "C-\346\213\251\346\227\266\347\261\273/CSVC\346\241\206\346\236\266\345\217\212\347\206\212\347\211\233\346\214\207\346\240\207/py/\346\263\242\345\212\250\347\216\207\345\222\214\346\215\242\346\211\213\347\216\207\346\236\204\345\273\272\347\211\233\347\206\212\346\214\207\346\240\207.ipynb"
diff --git "a/CSVC\346\241\206\346\236\266\345\217\212\347\206\212\347\211\233\346\214\207\346\240\207/\346\213\251\346\227\266-20190927-\345\215\216\346\263\260\350\257\201\345\210\270-\345\215\216\346\263\260\351\207\221\345\267\245\351\207\217\345\214\226\346\213\251\346\227\266\347\263\273\345\210\227\357\274\232\346\263\242\345\212\250\347\216\207\344\270\216\346\215\242\346\211\213\347\216\207\346\236\204\351\200\240\347\211\233\347\206\212\346\214\207\346\240\207.pdf" "b/C-\346\213\251\346\227\266\347\261\273/CSVC\346\241\206\346\236\266\345\217\212\347\206\212\347\211\233\346\214\207\346\240\207/\346\213\251\346\227\266-20190927-\345\215\216\346\263\260\350\257\201\345\210\270-\345\215\216\346\263\260\351\207\221\345\267\245\351\207\217\345\214\226\346\213\251\346\227\266\347\263\273\345\210\227\357\274\232\346\263\242\345\212\250\347\216\207\344\270\216\346\215\242\346\211\213\347\216\207\346\236\204\351\200\240\347\211\233\347\206\212\346\214\207\346\240\207.pdf"
similarity index 100%
rename from "CSVC\346\241\206\346\236\266\345\217\212\347\206\212\347\211\233\346\214\207\346\240\207/\346\213\251\346\227\266-20190927-\345\215\216\346\263\260\350\257\201\345\210\270-\345\215\216\346\263\260\351\207\221\345\267\245\351\207\217\345\214\226\346\213\251\346\227\266\347\263\273\345\210\227\357\274\232\346\263\242\345\212\250\347\216\207\344\270\216\346\215\242\346\211\213\347\216\207\346\236\204\351\200\240\347\211\233\347\206\212\346\214\207\346\240\207.pdf"
rename to "C-\346\213\251\346\227\266\347\261\273/CSVC\346\241\206\346\236\266\345\217\212\347\206\212\347\211\233\346\214\207\346\240\207/\346\213\251\346\227\266-20190927-\345\215\216\346\263\260\350\257\201\345\210\270-\345\215\216\346\263\260\351\207\221\345\267\245\351\207\217\345\214\226\346\213\251\346\227\266\347\263\273\345\210\227\357\274\232\346\263\242\345\212\250\347\216\207\344\270\216\346\215\242\346\211\213\347\216\207\346\236\204\351\200\240\347\211\233\347\206\212\346\214\207\346\240\207.pdf"
diff --git "a/RSRS\346\213\251\346\227\266\346\214\207\346\240\207/20191117-\345\205\211\345\244\247\350\257\201\345\210\270-\346\212\200\346\234\257\346\214\207\346\240\207\347\263\273\345\210\227\346\212\245\345\221\212\344\271\213\345\205\255\357\274\232RSRS\346\213\251\346\227\266~\345\233\236\351\241\276\344\270\216\346\224\271\350\277\233.pdf" "b/C-\346\213\251\346\227\266\347\261\273/RSRS\346\213\251\346\227\266\346\214\207\346\240\207/20191117-\345\205\211\345\244\247\350\257\201\345\210\270-\346\212\200\346\234\257\346\214\207\346\240\207\347\263\273\345\210\227\346\212\245\345\221\212\344\271\213\345\205\255\357\274\232RSRS\346\213\251\346\227\266~\345\233\236\351\241\276\344\270\216\346\224\271\350\277\233.pdf"
similarity index 100%
rename from "RSRS\346\213\251\346\227\266\346\214\207\346\240\207/20191117-\345\205\211\345\244\247\350\257\201\345\210\270-\346\212\200\346\234\257\346\214\207\346\240\207\347\263\273\345\210\227\346\212\245\345\221\212\344\271\213\345\205\255\357\274\232RSRS\346\213\251\346\227\266~\345\233\236\351\241\276\344\270\216\346\224\271\350\277\233.pdf"
rename to "C-\346\213\251\346\227\266\347\261\273/RSRS\346\213\251\346\227\266\346\214\207\346\240\207/20191117-\345\205\211\345\244\247\350\257\201\345\210\270-\346\212\200\346\234\257\346\214\207\346\240\207\347\263\273\345\210\227\346\212\245\345\221\212\344\271\213\345\205\255\357\274\232RSRS\346\213\251\346\227\266~\345\233\236\351\241\276\344\270\216\346\224\271\350\277\233.pdf"
diff --git "a/RSRS\346\213\251\346\227\266\346\214\207\346\240\207/py/RSRS.ipynb" "b/C-\346\213\251\346\227\266\347\261\273/RSRS\346\213\251\346\227\266\346\214\207\346\240\207/py/RSRS.ipynb"
similarity index 100%
rename from "RSRS\346\213\251\346\227\266\346\214\207\346\240\207/py/RSRS.ipynb"
rename to "C-\346\213\251\346\227\266\347\261\273/RSRS\346\213\251\346\227\266\346\214\207\346\240\207/py/RSRS.ipynb"
diff --git "a/RSRS\346\213\251\346\227\266\346\214\207\346\240\207/py/RSRS\346\224\271\350\277\233.ipynb" "b/C-\346\213\251\346\227\266\347\261\273/RSRS\346\213\251\346\227\266\346\214\207\346\240\207/py/RSRS\346\224\271\350\277\233.ipynb"
similarity index 100%
rename from "RSRS\346\213\251\346\227\266\346\214\207\346\240\207/py/RSRS\346\224\271\350\277\233.ipynb"
rename to "C-\346\213\251\346\227\266\347\261\273/RSRS\346\213\251\346\227\266\346\214\207\346\240\207/py/RSRS\346\224\271\350\277\233.ipynb"
diff --git "a/RSRS\346\213\251\346\227\266\346\214\207\346\240\207/\346\213\251\346\227\266-20170501-\345\205\211\345\244\247\350\257\201\345\210\270-\346\213\251\346\227\266\347\263\273\345\210\227\346\212\245\345\221\212\344\271\213\344\270\200\357\274\232\345\237\272\344\272\216\351\230\273\345\212\233\346\224\257\346\222\221\347\233\270\345\257\271\345\274\272\345\272\246\357\274\210RSRS\357\274\211\347\232\204\345\270\202\345\234\272\346\213\251\346\227\266.pdf" "b/C-\346\213\251\346\227\266\347\261\273/RSRS\346\213\251\346\227\266\346\214\207\346\240\207/\346\213\251\346\227\266-20170501-\345\205\211\345\244\247\350\257\201\345\210\270-\346\213\251\346\227\266\347\263\273\345\210\227\346\212\245\345\221\212\344\271\213\344\270\200\357\274\232\345\237\272\344\272\216\351\230\273\345\212\233\346\224\257\346\222\221\347\233\270\345\257\271\345\274\272\345\272\246\357\274\210RSRS\357\274\211\347\232\204\345\270\202\345\234\272\346\213\251\346\227\266.pdf"
similarity index 100%
rename from "RSRS\346\213\251\346\227\266\346\214\207\346\240\207/\346\213\251\346\227\266-20170501-\345\205\211\345\244\247\350\257\201\345\210\270-\346\213\251\346\227\266\347\263\273\345\210\227\346\212\245\345\221\212\344\271\213\344\270\200\357\274\232\345\237\272\344\272\216\351\230\273\345\212\233\346\224\257\346\222\221\347\233\270\345\257\271\345\274\272\345\272\246\357\274\210RSRS\357\274\211\347\232\204\345\270\202\345\234\272\346\213\251\346\227\266.pdf"
rename to "C-\346\213\251\346\227\266\347\261\273/RSRS\346\213\251\346\227\266\346\214\207\346\240\207/\346\213\251\346\227\266-20170501-\345\205\211\345\244\247\350\257\201\345\210\270-\346\213\251\346\227\266\347\263\273\345\210\227\346\212\245\345\221\212\344\271\213\344\270\200\357\274\232\345\237\272\344\272\216\351\230\273\345\212\233\346\224\257\346\222\221\347\233\270\345\257\271\345\274\272\345\272\246\357\274\210RSRS\357\274\211\347\232\204\345\270\202\345\234\272\346\213\251\346\227\266.pdf"
diff --git "a/\344\275\216\345\273\266\350\277\237\350\266\213\345\212\277\347\272\277\344\270\216\344\272\244\346\230\223\346\213\251\346\227\266/20170303-\345\271\277\345\217\221\350\257\201\345\210\270-\344\275\216\345\273\266\350\277\237\350\266\213\345\212\277\347\272\277\344\270\216\344\272\244\346\230\223\346\213\251\346\227\266.pdf" "b/C-\346\213\251\346\227\266\347\261\273/\344\275\216\345\273\266\350\277\237\350\266\213\345\212\277\347\272\277\344\270\216\344\272\244\346\230\223\346\213\251\346\227\266/20170303-\345\271\277\345\217\221\350\257\201\345\210\270-\344\275\216\345\273\266\350\277\237\350\266\213\345\212\277\347\272\277\344\270\216\344\272\244\346\230\223\346\213\251\346\227\266.pdf"
similarity index 100%
rename from "\344\275\216\345\273\266\350\277\237\350\266\213\345\212\277\347\272\277\344\270\216\344\272\244\346\230\223\346\213\251\346\227\266/20170303-\345\271\277\345\217\221\350\257\201\345\210\270-\344\275\216\345\273\266\350\277\237\350\266\213\345\212\277\347\272\277\344\270\216\344\272\244\346\230\223\346\213\251\346\227\266.pdf"
rename to "C-\346\213\251\346\227\266\347\261\273/\344\275\216\345\273\266\350\277\237\350\266\213\345\212\277\347\272\277\344\270\216\344\272\244\346\230\223\346\213\251\346\227\266/20170303-\345\271\277\345\217\221\350\257\201\345\210\270-\344\275\216\345\273\266\350\277\237\350\266\213\345\212\277\347\272\277\344\270\216\344\272\244\346\230\223\346\213\251\346\227\266.pdf"
diff --git "a/\344\275\216\345\273\266\350\277\237\350\266\213\345\212\277\347\272\277\344\270\216\344\272\244\346\230\223\346\213\251\346\227\266/py/\344\275\216\345\273\266\350\277\237\350\266\213\345\212\277\347\272\277\344\270\216\344\272\244\346\230\223\346\213\251\346\227\266.ipynb" "b/C-\346\213\251\346\227\266\347\261\273/\344\275\216\345\273\266\350\277\237\350\266\213\345\212\277\347\272\277\344\270\216\344\272\244\346\230\223\346\213\251\346\227\266/py/\344\275\216\345\273\266\350\277\237\350\266\213\345\212\277\347\272\277\344\270\216\344\272\244\346\230\223\346\213\251\346\227\266.ipynb"
similarity index 100%
rename from "\344\275\216\345\273\266\350\277\237\350\266\213\345\212\277\347\272\277\344\270\216\344\272\244\346\230\223\346\213\251\346\227\266/py/\344\275\216\345\273\266\350\277\237\350\266\213\345\212\277\347\272\277\344\270\216\344\272\244\346\230\223\346\213\251\346\227\266.ipynb"
rename to "C-\346\213\251\346\227\266\347\261\273/\344\275\216\345\273\266\350\277\237\350\266\213\345\212\277\347\272\277\344\270\216\344\272\244\346\230\223\346\213\251\346\227\266/py/\344\275\216\345\273\266\350\277\237\350\266\213\345\212\277\347\272\277\344\270\216\344\272\244\346\230\223\346\213\251\346\227\266.ipynb"
diff --git "a/\345\214\227\345\220\221\350\265\204\351\207\221\344\272\244\346\230\223\350\203\275\345\212\233\344\270\200\345\256\232\345\274\272\345\220\227/20200624-\345\256\211\344\277\241\350\257\201\345\210\270-\351\207\221\350\236\215\345\267\245\347\250\213\344\270\273\351\242\230\346\212\245\345\221\212\357\274\232\345\214\227\345\220\221\350\265\204\351\207\221\344\272\244\346\230\223\350\203\275\345\212\233\344\270\200\345\256\232\345\274\272\345\220\227.pdf" "b/C-\346\213\251\346\227\266\347\261\273/\345\214\227\345\220\221\350\265\204\351\207\221\344\272\244\346\230\223\350\203\275\345\212\233\344\270\200\345\256\232\345\274\272\345\220\227/20200624-\345\256\211\344\277\241\350\257\201\345\210\270-\351\207\221\350\236\215\345\267\245\347\250\213\344\270\273\351\242\230\346\212\245\345\221\212\357\274\232\345\214\227\345\220\221\350\265\204\351\207\221\344\272\244\346\230\223\350\203\275\345\212\233\344\270\200\345\256\232\345\274\272\345\220\227.pdf"
similarity index 100%
rename from "\345\214\227\345\220\221\350\265\204\351\207\221\344\272\244\346\230\223\350\203\275\345\212\233\344\270\200\345\256\232\345\274\272\345\220\227/20200624-\345\256\211\344\277\241\350\257\201\345\210\270-\351\207\221\350\236\215\345\267\245\347\250\213\344\270\273\351\242\230\346\212\245\345\221\212\357\274\232\345\214\227\345\220\221\350\265\204\351\207\221\344\272\244\346\230\223\350\203\275\345\212\233\344\270\200\345\256\232\345\274\272\345\220\227.pdf"
rename to "C-\346\213\251\346\227\266\347\261\273/\345\214\227\345\220\221\350\265\204\351\207\221\344\272\244\346\230\223\350\203\275\345\212\233\344\270\200\345\256\232\345\274\272\345\220\227/20200624-\345\256\211\344\277\241\350\257\201\345\210\270-\351\207\221\350\236\215\345\267\245\347\250\213\344\270\273\351\242\230\346\212\245\345\221\212\357\274\232\345\214\227\345\220\221\350\265\204\351\207\221\344\272\244\346\230\223\350\203\275\345\212\233\344\270\200\345\256\232\345\274\272\345\220\227.pdf"
diff --git "a/\345\214\227\345\220\221\350\265\204\351\207\221\344\272\244\346\230\223\350\203\275\345\212\233\344\270\200\345\256\232\345\274\272\345\220\227/py/\345\214\227\345\220\221\350\265\204\351\207\221\344\272\244\346\230\223\350\203\275\345\212\233\344\270\200\345\256\232\345\274\272\345\220\227.ipynb" "b/C-\346\213\251\346\227\266\347\261\273/\345\214\227\345\220\221\350\265\204\351\207\221\344\272\244\346\230\223\350\203\275\345\212\233\344\270\200\345\256\232\345\274\272\345\220\227/py/\345\214\227\345\220\221\350\265\204\351\207\221\344\272\244\346\230\223\350\203\275\345\212\233\344\270\200\345\256\232\345\274\272\345\220\227.ipynb"
similarity index 100%
rename from "\345\214\227\345\220\221\350\265\204\351\207\221\344\272\244\346\230\223\350\203\275\345\212\233\344\270\200\345\256\232\345\274\272\345\220\227/py/\345\214\227\345\220\221\350\265\204\351\207\221\344\272\244\346\230\223\350\203\275\345\212\233\344\270\200\345\256\232\345\274\272\345\220\227.ipynb"
rename to "C-\346\213\251\346\227\266\347\261\273/\345\214\227\345\220\221\350\265\204\351\207\221\344\272\244\346\230\223\350\203\275\345\212\233\344\270\200\345\256\232\345\274\272\345\220\227/py/\345\214\227\345\220\221\350\265\204\351\207\221\344\272\244\346\230\223\350\203\275\345\212\233\344\270\200\345\256\232\345\274\272\345\220\227.ipynb"
diff --git "a/\345\237\272\344\272\216\347\233\270\345\257\271\345\274\272\345\274\261\344\270\213\345\215\225\345\220\221\346\263\242\345\212\250\345\267\256\345\200\274\345\272\224\347\224\250/20151022-\345\233\275\344\277\241\350\257\201\345\210\270-\345\270\202\345\234\272\346\263\242\345\212\250\347\216\207\347\240\224\347\251\266\357\274\232\345\237\272\344\272\216\347\233\270\345\257\271\345\274\272\345\274\261\344\270\213\345\215\225\345\220\221\346\263\242\345\212\250\345\267\256\345\200\274\345\272\224\347\224\250.pdf" "b/C-\346\213\251\346\227\266\347\261\273/\345\237\272\344\272\216\347\233\270\345\257\271\345\274\272\345\274\261\344\270\213\345\215\225\345\220\221\346\263\242\345\212\250\345\267\256\345\200\274\345\272\224\347\224\250/20151022-\345\233\275\344\277\241\350\257\201\345\210\270-\345\270\202\345\234\272\346\263\242\345\212\250\347\216\207\347\240\224\347\251\266\357\274\232\345\237\272\344\272\216\347\233\270\345\257\271\345\274\272\345\274\261\344\270\213\345\215\225\345\220\221\346\263\242\345\212\250\345\267\256\345\200\274\345\272\224\347\224\250.pdf"
similarity index 100%
rename from "\345\237\272\344\272\216\347\233\270\345\257\271\345\274\272\345\274\261\344\270\213\345\215\225\345\220\221\346\263\242\345\212\250\345\267\256\345\200\274\345\272\224\347\224\250/20151022-\345\233\275\344\277\241\350\257\201\345\210\270-\345\270\202\345\234\272\346\263\242\345\212\250\347\216\207\347\240\224\347\251\266\357\274\232\345\237\272\344\272\216\347\233\270\345\257\271\345\274\272\345\274\261\344\270\213\345\215\225\345\220\221\346\263\242\345\212\250\345\267\256\345\200\274\345\272\224\347\224\250.pdf"
rename to "C-\346\213\251\346\227\266\347\261\273/\345\237\272\344\272\216\347\233\270\345\257\271\345\274\272\345\274\261\344\270\213\345\215\225\345\220\221\346\263\242\345\212\250\345\267\256\345\200\274\345\272\224\347\224\250/20151022-\345\233\275\344\277\241\350\257\201\345\210\270-\345\270\202\345\234\272\346\263\242\345\212\250\347\216\207\347\240\224\347\251\266\357\274\232\345\237\272\344\272\216\347\233\270\345\257\271\345\274\272\345\274\261\344\270\213\345\215\225\345\220\221\346\263\242\345\212\250\345\267\256\345\200\274\345\272\224\347\224\250.pdf"
diff --git "a/\345\237\272\344\272\216\347\233\270\345\257\271\345\274\272\345\274\261\344\270\213\345\215\225\345\220\221\346\263\242\345\212\250\345\267\256\345\200\274\345\272\224\347\224\250/py/\345\237\272\344\272\216\347\233\270\345\257\271\345\274\272\345\274\261\344\270\213\345\215\225\345\220\221\346\263\242\345\212\250\345\267\256\345\200\274\345\272\224\347\224\250.ipynb" "b/C-\346\213\251\346\227\266\347\261\273/\345\237\272\344\272\216\347\233\270\345\257\271\345\274\272\345\274\261\344\270\213\345\215\225\345\220\221\346\263\242\345\212\250\345\267\256\345\200\274\345\272\224\347\224\250/py/\345\237\272\344\272\216\347\233\270\345\257\271\345\274\272\345\274\261\344\270\213\345\215\225\345\220\221\346\263\242\345\212\250\345\267\256\345\200\274\345\272\224\347\224\250.ipynb"
similarity index 100%
rename from "\345\237\272\344\272\216\347\233\270\345\257\271\345\274\272\345\274\261\344\270\213\345\215\225\345\220\221\346\263\242\345\212\250\345\267\256\345\200\274\345\272\224\347\224\250/py/\345\237\272\344\272\216\347\233\270\345\257\271\345\274\272\345\274\261\344\270\213\345\215\225\345\220\221\346\263\242\345\212\250\345\267\256\345\200\274\345\272\224\347\224\250.ipynb"
rename to "C-\346\213\251\346\227\266\347\261\273/\345\237\272\344\272\216\347\233\270\345\257\271\345\274\272\345\274\261\344\270\213\345\215\225\345\220\221\346\263\242\345\212\250\345\267\256\345\200\274\345\272\224\347\224\250/py/\345\237\272\344\272\216\347\233\270\345\257\271\345\274\272\345\274\261\344\270\213\345\215\225\345\220\221\346\263\242\345\212\250\345\267\256\345\200\274\345\272\224\347\224\250.ipynb"
diff --git "a/\345\260\217\346\263\242\345\210\206\346\236\220/20100621-\345\233\275\344\277\241\350\257\201\345\210\270-\345\237\272\344\272\216\345\260\217\346\263\242\345\210\206\346\236\220\345\222\214\346\224\257\346\214\201\345\220\221\351\207\217\346\234\272\347\232\204\346\214\207\346\225\260\351\242\204\346\265\213\346\250\241\345\236\213.pdf" "b/C-\346\213\251\346\227\266\347\261\273/\345\260\217\346\263\242\345\210\206\346\236\220/20100621-\345\233\275\344\277\241\350\257\201\345\210\270-\345\237\272\344\272\216\345\260\217\346\263\242\345\210\206\346\236\220\345\222\214\346\224\257\346\214\201\345\220\221\351\207\217\346\234\272\347\232\204\346\214\207\346\225\260\351\242\204\346\265\213\346\250\241\345\236\213.pdf"
similarity index 100%
rename from "\345\260\217\346\263\242\345\210\206\346\236\220/20100621-\345\233\275\344\277\241\350\257\201\345\210\270-\345\237\272\344\272\216\345\260\217\346\263\242\345\210\206\346\236\220\345\222\214\346\224\257\346\214\201\345\220\221\351\207\217\346\234\272\347\232\204\346\214\207\346\225\260\351\242\204\346\265\213\346\250\241\345\236\213.pdf"
rename to "C-\346\213\251\346\227\266\347\261\273/\345\260\217\346\263\242\345\210\206\346\236\220/20100621-\345\233\275\344\277\241\350\257\201\345\210\270-\345\237\272\344\272\216\345\260\217\346\263\242\345\210\206\346\236\220\345\222\214\346\224\257\346\214\201\345\220\221\351\207\217\346\234\272\347\232\204\346\214\207\346\225\260\351\242\204\346\265\213\346\250\241\345\236\213.pdf"
diff --git "a/\345\260\217\346\263\242\345\210\206\346\236\220/20120220-\345\271\263\345\256\211\350\257\201\345\210\270-\351\207\217\345\214\226\346\213\251\346\227\266\351\200\211\350\202\241\347\263\273\345\210\227\346\212\245\345\221\212\344\272\214\357\274\232\346\260\264\350\207\264\346\270\205\345\210\231\351\261\274\350\207\252\347\216\260_\345\260\217\346\263\242\345\210\206\346\236\220\344\270\216\346\224\257\346\214\201\345\220\221\351\207\217\346\234\272\346\213\251\346\227\266\347\240\224\347\251\266.pdf" "b/C-\346\213\251\346\227\266\347\261\273/\345\260\217\346\263\242\345\210\206\346\236\220/20120220-\345\271\263\345\256\211\350\257\201\345\210\270-\351\207\217\345\214\226\346\213\251\346\227\266\351\200\211\350\202\241\347\263\273\345\210\227\346\212\245\345\221\212\344\272\214\357\274\232\346\260\264\350\207\264\346\270\205\345\210\231\351\261\274\350\207\252\347\216\260_\345\260\217\346\263\242\345\210\206\346\236\220\344\270\216\346\224\257\346\214\201\345\220\221\351\207\217\346\234\272\346\213\251\346\227\266\347\240\224\347\251\266.pdf"
similarity index 100%
rename from "\345\260\217\346\263\242\345\210\206\346\236\220/20120220-\345\271\263\345\256\211\350\257\201\345\210\270-\351\207\217\345\214\226\346\213\251\346\227\266\351\200\211\350\202\241\347\263\273\345\210\227\346\212\245\345\221\212\344\272\214\357\274\232\346\260\264\350\207\264\346\270\205\345\210\231\351\261\274\350\207\252\347\216\260_\345\260\217\346\263\242\345\210\206\346\236\220\344\270\216\346\224\257\346\214\201\345\220\221\351\207\217\346\234\272\346\213\251\346\227\266\347\240\224\347\251\266.pdf"
rename to "C-\346\213\251\346\227\266\347\261\273/\345\260\217\346\263\242\345\210\206\346\236\220/20120220-\345\271\263\345\256\211\350\257\201\345\210\270-\351\207\217\345\214\226\346\213\251\346\227\266\351\200\211\350\202\241\347\263\273\345\210\227\346\212\245\345\221\212\344\272\214\357\274\232\346\260\264\350\207\264\346\270\205\345\210\231\351\261\274\350\207\252\347\216\260_\345\260\217\346\263\242\345\210\206\346\236\220\344\270\216\346\224\257\346\214\201\345\220\221\351\207\217\346\234\272\346\213\251\346\227\266\347\240\224\347\251\266.pdf"
diff --git "a/\345\260\217\346\263\242\345\210\206\346\236\220/py/\345\260\217\346\263\242\345\210\206\346\236\220\346\213\251\346\227\266.ipynb" "b/C-\346\213\251\346\227\266\347\261\273/\345\260\217\346\263\242\345\210\206\346\236\220/py/\345\260\217\346\263\242\345\210\206\346\236\220\346\213\251\346\227\266.ipynb"
similarity index 100%
rename from "\345\260\217\346\263\242\345\210\206\346\236\220/py/\345\260\217\346\263\242\345\210\206\346\236\220\346\213\251\346\227\266.ipynb"
rename to "C-\346\213\251\346\227\266\347\261\273/\345\260\217\346\263\242\345\210\206\346\236\220/py/\345\260\217\346\263\242\345\210\206\346\236\220\346\213\251\346\227\266.ipynb"
diff --git "a/\345\260\217\346\263\242\345\210\206\346\236\220/\345\233\236\346\265\213\346\250\241\345\235\227/\345\260\217\346\263\242\345\210\206\346\236\220_svm.py" "b/C-\346\213\251\346\227\266\347\261\273/\345\260\217\346\263\242\345\210\206\346\236\220/\345\233\236\346\265\213\346\250\241\345\235\227/\345\260\217\346\263\242\345\210\206\346\236\220_svm.py"
similarity index 96%
rename from "\345\260\217\346\263\242\345\210\206\346\236\220/\345\233\236\346\265\213\346\250\241\345\235\227/\345\260\217\346\263\242\345\210\206\346\236\220_svm.py"
rename to "C-\346\213\251\346\227\266\347\261\273/\345\260\217\346\263\242\345\210\206\346\236\220/\345\233\236\346\265\213\346\250\241\345\235\227/\345\260\217\346\263\242\345\210\206\346\236\220_svm.py"
index 3017711..a4ac176 100644
--- "a/\345\260\217\346\263\242\345\210\206\346\236\220/\345\233\236\346\265\213\346\250\241\345\235\227/\345\260\217\346\263\242\345\210\206\346\236\220_svm.py"
+++ "b/C-\346\213\251\346\227\266\347\261\273/\345\260\217\346\263\242\345\210\206\346\236\220/\345\233\236\346\265\213\346\250\241\345\235\227/\345\260\217\346\263\242\345\210\206\346\236\220_svm.py"
@@ -1,416 +1,416 @@
-# 常用计算
-import talib
-import pandas as pd
-import numpy as np
-from sklearn import preprocessing
-from sklearn import svm   
-
-from scipy import stats 
-import scipy.fftpack as fftpack # 希尔伯特变换
-
-import pywt # 小波分析
-from typing import List
-
-# 时间操作
-import datetime as dt
-from dateutil.parser import parse
-
-# 数据获取
-from jqdata import *
-import tushare as ts
-
-# 研究文档数据读取
-from six import BytesIO  # 读取研究环境文件用
-
-enable_profile()  # 开启性能分析
-
-########################  初始化  #############################
-
-
-def initialize(context):
-    
-    set_params()
-    set_variables()
-    set_backtest()  # 设置回测条件
-   
-
-
-# 设置策参数
-def set_params():
-    
-   
-   g.M = 5 # 计算指标的窗口
-   g.N = 50 # 滚动学习计算窗口
-   g.index_code = '000016.XSHG'
-   g.etf_code = '000016.XSHG'#'510300.XSHG'
-   
-   
-# 中间变量
-def set_variables():
-    pass
-
-
-# 设置回测条件
-def set_backtest():
-
-    set_option("avoid_future_data", True)  # 避免未来数据
-    set_option('use_real_price', True)  #用真实价格交易
-    set_benchmark('000300.XSHG')  # 设置基准收益
-    log.set_level('order', 'debug')
-
-
-# 每日盘前运行
-def before_trading_start(context):
-
-    # 设置手续费
-    set_slip_fee(context)
-
-
-# 根据不同的时间段设置滑点与手续费
-def set_slip_fee(context):
-    # 将滑点设置为0
-    set_slippage(FixedSlippage(0))
-    # 根据不同的时间段设置手续费
-    dt = context.current_dt
-
-    if dt > datetime.datetime(2013, 1, 1):
-        set_commission(PerTrade(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
-
-    elif dt > datetime.datetime(2011, 1, 1):
-        set_commission(PerTrade(buy_cost=0.001, sell_cost=0.002, min_cost=5))
-
-    elif dt > datetime.datetime(2009, 1, 1):
-        set_commission(PerTrade(buy_cost=0.002, sell_cost=0.003, min_cost=5))
-
-    else:
-        set_commission(PerTrade(buy_cost=0.003, sell_cost=0.004, min_cost=5))
-        
-        
-        
-################################################## 信号获取 #########################################################
-# 信号去噪
-class DenoisingThreshold(object):
-    '''
-    获取小波去噪的阈值
-    1. CalSqtwolog 固定阈值准则(sqtwolog)
-    2. CalRigrsure 无偏风险估计准则（rigrsure）
-    3. CalMinmaxi 极小极大准则（ minimaxi）
-    4. CalHeursure
-    
-    参考：https://wenku.baidu.com/view/63d62a818762caaedd33d463.html
-    
-    对股票价格等数据而言，其信号频率较少地与噪声重叠因此可以选用sqtwolog和heursure准则，使去噪效果更明显。 
-    但对收益率这样的高频数据，尽量采用保守的 rigrsure 或 minimaxi 准则来确定阈值，以保留较多的信号。
-    '''
-    
-
-    def __init__(self,signal: np.array):
-
-        self.signal = signal
-
-        self.N = len(signal)
-
-    # 固定阈值准则(sqtwolog)
-    @property
-    def CalSqtwolog(self) -> float:
-
-        return np.sqrt(2 * np.log(self.N))
-
-   
-    # 无偏风险估计准则（rigrsure）
-    @property
-    def CalRigrsure(self)->float:
-
-        N = self.N
-        signal = np.abs(self.signal)
-        signal = np.sort(signal)
-        signal = np.power(signal, 2)
-
-        risk_j = np.zeros(N)
-
-        for j in range(N):
-
-            if j == 0:
-                risk_j[j] = 1 + signal[N - 1]
-            else:
-                risk_j[j] = (N - 2 * j + (N - j) *
-                             (signal[N - j]) + np.sum(signal[:j])) / N
-
-        k = risk_j.argmin()
-
-        return np.sqrt(signal[k])
-
-    # 极小极大准则（ minimaxi）
-    @property
-    def CalMinmaxi(self)->float:
-        
-        if self.N > 32:
-            # N>32 可以使用minmaxi阈值 反之则为0
-            return 0.3936 + 0.1829 * (np.log(self.N) / np.log(2))
-        
-        else:
-            
-            return 0
-
-    @property
-    def GetCrit(self)->float:
-
-        return np.sqrt(np.power(np.log(self.N) / np.log(2), 3) * 1 / self.N)
-
-    @property
-    def GetEta(self)->float:
-
-        return (np.sum(np.abs(self.signal)**2) - self.N) / self.N
-    
-    #混合准则（heursure）
-    @property
-    def CalHeursure(self):
-
-        if self.GetCrit > self.GetEta:
-
-            #print('推荐使用sqtwolog阈值')
-            return self.CalSqtwolog
-            
-        else:
-
-            #print('推荐使用 Min(sqtwolog阈值,rigrsure阈值)')
-            return min(self.CalRigrsure,self.CalSqtwolog)
-            
-class wavelet_svm_model(object):
-    '''对数据进行建模预测
-    --------------------
-    输入参数：
-
-        data:必须包含OHLC money及预测字段Y(ovo标记) 其余字段为训练数据
-        M:train数据的滚动计算窗口
-        window:滚动窗口 即T至T-window日 预测T-1至T-window日数据 预测T日数据
-        wavelet\wavelet_mode:同pywt.wavedec的参数
-        filter_num:需要过滤小波的细节组 比如(3,4)对三至四组进行过滤 为空则是1-4组全过滤 
-        whether_wave_process:是否使用小波处理
-    --------------------
-    方法：
-        wave_process:过滤阈值 采用 混合准则（heursure）
-        preprocess:生成训练用字段
-        rolling_svm:使用svm滚动训练
-    '''
-
-    def __init__(self,
-                 Data: pd.DataFrame,
-                 M: int,
-                 window: int,
-                 wavelet: str,
-                 wavelet_mode: str,
-                 filter_num=None,
-                 whether_wave_process: bool = False):
-
-        self.Data = Data
-        self.__M = M
-        self.__window = window
-        self.__wavelet = wavelet
-        self.__wavelet_mode = wavelet_mode
-        self.__filter_num = filter_num
-        self.__whether_wave_process = whether_wave_process
-
-        self.__train_col = [col for col in self.Data.columns if col != 'Y'
-                           ]  # 训练的字段
-
-        self.train_df = pd.DataFrame()  # 储存训练数据
-        self.predict_df = Data[['Y']].copy()  # 储存预测数据及真实Y
-
-    def wave_process(self):
-        '''对数据进行小波处理(可选)'''
-        
-        if self.__filter_num:
-            
-            a = self.__filter_num[0]
-            b = self.__filter_num[1]
-            
-            #self.__filter_num = range(a,b + 1)
-            
-        else:
-            a = 1
-            b = 5
-            #self.__filter_num = range(1,5)
-
-    
-        Data = self.Data.copy()  # 复制
-        
-        for col in self.__train_col:
-
-            #res1 = pywt.wavedec(
-            #    data[col].values, wavelet=self.__wavelet, mode=self.__wavelet_mode, level=4)
-
-            #for j in self.__filter_num:
-
-            #    threshold = DenoisingThreshold(res1[j]).CalHeursure
-            #    res1[j] = pywt.threshold(res1[j], threshold, 'soft')
-            
-            denosed_ser = wave_transform(Data[col], 
-                           wavelet=self.__wavelet, 
-                           wavelet_mode=self.__wavelet_mode, 
-                           level=4,
-                           n=a,
-                           m=b)
-            
-            Data[col] = denosed_ser
-
-        self.train_df = Data
-
-    def preprocess(self):
-        '''生成相应的特征'''
-
-        if self.__whether_wave_process:
-
-            self.wave_process()  # 小波处理
-
-            Data = self.train_df
-
-        else:
-
-            Data = self.Data.copy()
-
-        Data['近M日最高价'] = Data['high'].rolling(self.__M).max()
-        Data['近M日最低价'] = Data['low'].rolling(self.__M).min()
-        Data['成交额占比'] = Data['money'] / Data['money'].rolling(self.__M).sum()
-        Data['近M日涨跌幅'] = Data['close'].pct_change(self.__M)
-        Data['近M日均价'] = Data['close'].rolling(self.__M).mean()
-        
-        # 上面新增了需要训练用的字段 这里更新字段
-        self.__train_col = [col for col in Data.columns if col not in  self.__train_col + ['Y']]
-        self.train_df = Data[self.__train_col]
-        self.train_df = self.train_df.iloc[self.__M:]
-    
-    
-    def standardization(self):
-        '''对所有特征进行标准化处理'''
-
-        Data = preprocessing.scale(self.train_df[self.__train_col])
-        Data = pd.DataFrame(
-            Data, index=self.train_df.index, columns=self.__train_col)
-        Data['Y'] = self.predict_df['Y']
-        self.train_df = Data
-
-    def rolling_svm(self):
-        
-        '''利用SVM模型进行建模预测'''
-        predict_ser = rolling_apply(self.train_df, self.model_fit,
-                                    self.__window)
-        
-        self.predict_df['predict'] = predict_ser
-        
-        self.predict_df = self.predict_df.iloc[self.__window + self.__M:]
-
-    def model_fit(self, df: pd.DataFrame) -> pd.Series:
-        
-        idx = df.index[-1]
-
-        train_x = df[self.__train_col].iloc[:-1]
-        train_y = df['Y'].shift(-1).iloc[:-1]  # 对需要预测的y进行滞后一期处理
-
-        test_x = df[self.__train_col].iloc[-1:]
-
-        model = svm.SVC(gamma=0.001)
-
-        model.fit(train_x, train_y)
-
-        return pd.Series(model.predict(test_x), index=[idx])
-        
-# 定义rolling_apply理论上应该比for循环快
-# pandas.rolling.apply不支持多列
-def rolling_apply(df, func, win_size) -> pd.Series:
-
-    iidx = np.arange(len(df))
-
-    shape = (iidx.size - win_size + 1, win_size)
-
-    strides = (iidx.strides[0], iidx.strides[0])
-
-    res = np.lib.stride_tricks.as_strided(
-        iidx, shape=shape, strides=strides, writeable=True)
-
-    # 这里注意func返回的需要为df或者ser
-    return pd.concat((func(df.iloc[r]) for r in res), axis=0)  # concat可能会有点慢
-    
-# 小波变换
-def wave_transform(data_ser:pd.Series,wavelet:str,wavelet_mode:str,level:int,n:int,m:int)->pd.Series:
-    '''
-    参数：
-        data_ser:pd.Series
-        wavelet\wavelet_mode\level：同pywt.wavedec
-        n,m:需要过了的层级范围
-    '''
-    res1 = pywt.wavedec(
-                    data_ser.values, wavelet=wavelet, mode=wavelet_mode, level=level)
-
-    for j in range(n,m+1):
-
-        threshold = DenoisingThreshold(res1[j]).CalHeursure
-        res1[j] = pywt.threshold(res1[j], threshold, 'soft')
-    
-    # 有时候重构后的长度不等 
-    n = len(data_ser)
-    
-    return pd.Series(pywt.waverec(res1, wavelet)[-n:],index=data_ser.index)
-    
-    
-################################### 数据获取 ####################################################
-def GetSingal(context)->int:
-    
-    price_df = attribute_history(g.index_code, count=g.M + g.N + 25, unit='1d',
-            fields=['open', 'close', 'high', 'low', 'pre_close', 'money'])
-            
-    price_df['pct_chg'] = price_df['close'] / price_df['pre_close'] - 1
-
-    
-    price_df['MACD'] = talib.MACD(price_df['close'],fastperiod=6,slowperiod=12,signalperiod=9)[0]
-    price_df['RSI'] = talib.RSI(price_df['close'],timeperiod=14)        
-    # 标记
-    price_df['Y'] = np.sign(price_df['pct_chg'])
-    
-    price_df = price_df.dropna()
-
-    # 对昨日收盘价去噪
-    wsm = wavelet_svm_model(price_df,g.M,g.N,'db4','sym',True)
-    # 计算训练字段
-    wsm.preprocess()
-    # 标准化
-    wsm.standardization()
-    # 滚动训练
-    wsm.rolling_svm()
-            
-    
-    return wsm.predict_df['predict'].iloc[-1] # 1为持仓 其他空仓
-    
-#################################  交易  ############################################
-
-def handle_data(context,data):
-    
-    if GetSingal(context) == 1:
-        
-        BuyStock(context)
-        
-    else:
-        
-        SellStock(context)
-
-
-#################################  下单  ############################################        
-def BuyStock(context):
-    
-    
-    if g.etf_code in context.portfolio.long_positions:
-        
-        log.info('继续持有%s'%get_security_info(g.etf_code).display_name)
-        
-    else:
-        
-        log.info('买入%s'%get_security_info(g.etf_code).display_name)
-        order_target_value(g.etf_code,context.portfolio.total_value)
-        
-def SellStock(context):
-    
-    log.info('卖出%s'%get_security_info(g.etf_code).display_name)
-    for hold in context.portfolio.long_positions:
-        
-        order_target(hold,0)
+# 常用计算
+import talib
+import pandas as pd
+import numpy as np
+from sklearn import preprocessing
+from sklearn import svm   
+
+from scipy import stats 
+import scipy.fftpack as fftpack # 希尔伯特变换
+
+import pywt # 小波分析
+from typing import List
+
+# 时间操作
+import datetime as dt
+from dateutil.parser import parse
+
+# 数据获取
+from jqdata import *
+import tushare as ts
+
+# 研究文档数据读取
+from six import BytesIO  # 读取研究环境文件用
+
+enable_profile()  # 开启性能分析
+
+########################  初始化  #############################
+
+
+def initialize(context):
+    
+    set_params()
+    set_variables()
+    set_backtest()  # 设置回测条件
+   
+
+
+# 设置策参数
+def set_params():
+    
+   
+   g.M = 5 # 计算指标的窗口
+   g.N = 50 # 滚动学习计算窗口
+   g.index_code = '000016.XSHG'
+   g.etf_code = '000016.XSHG'#'510300.XSHG'
+   
+   
+# 中间变量
+def set_variables():
+    pass
+
+
+# 设置回测条件
+def set_backtest():
+
+    set_option("avoid_future_data", True)  # 避免未来数据
+    set_option('use_real_price', True)  #用真实价格交易
+    set_benchmark('000300.XSHG')  # 设置基准收益
+    log.set_level('order', 'debug')
+
+
+# 每日盘前运行
+def before_trading_start(context):
+
+    # 设置手续费
+    set_slip_fee(context)
+
+
+# 根据不同的时间段设置滑点与手续费
+def set_slip_fee(context):
+    # 将滑点设置为0
+    set_slippage(FixedSlippage(0))
+    # 根据不同的时间段设置手续费
+    dt = context.current_dt
+
+    if dt > datetime.datetime(2013, 1, 1):
+        set_commission(PerTrade(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
+
+    elif dt > datetime.datetime(2011, 1, 1):
+        set_commission(PerTrade(buy_cost=0.001, sell_cost=0.002, min_cost=5))
+
+    elif dt > datetime.datetime(2009, 1, 1):
+        set_commission(PerTrade(buy_cost=0.002, sell_cost=0.003, min_cost=5))
+
+    else:
+        set_commission(PerTrade(buy_cost=0.003, sell_cost=0.004, min_cost=5))
+        
+        
+        
+################################################## 信号获取 #########################################################
+# 信号去噪
+class DenoisingThreshold(object):
+    '''
+    获取小波去噪的阈值
+    1. CalSqtwolog 固定阈值准则(sqtwolog)
+    2. CalRigrsure 无偏风险估计准则（rigrsure）
+    3. CalMinmaxi 极小极大准则（ minimaxi）
+    4. CalHeursure
+    
+    参考：https://wenku.baidu.com/view/63d62a818762caaedd33d463.html
+    
+    对股票价格等数据而言，其信号频率较少地与噪声重叠因此可以选用sqtwolog和heursure准则，使去噪效果更明显。 
+    但对收益率这样的高频数据，尽量采用保守的 rigrsure 或 minimaxi 准则来确定阈值，以保留较多的信号。
+    '''
+    
+
+    def __init__(self,signal: np.array):
+
+        self.signal = signal
+
+        self.N = len(signal)
+
+    # 固定阈值准则(sqtwolog)
+    @property
+    def CalSqtwolog(self) -> float:
+
+        return np.sqrt(2 * np.log(self.N))
+
+   
+    # 无偏风险估计准则（rigrsure）
+    @property
+    def CalRigrsure(self)->float:
+
+        N = self.N
+        signal = np.abs(self.signal)
+        signal = np.sort(signal)
+        signal = np.power(signal, 2)
+
+        risk_j = np.zeros(N)
+
+        for j in range(N):
+
+            if j == 0:
+                risk_j[j] = 1 + signal[N - 1]
+            else:
+                risk_j[j] = (N - 2 * j + (N - j) *
+                             (signal[N - j]) + np.sum(signal[:j])) / N
+
+        k = risk_j.argmin()
+
+        return np.sqrt(signal[k])
+
+    # 极小极大准则（ minimaxi）
+    @property
+    def CalMinmaxi(self)->float:
+        
+        if self.N > 32:
+            # N>32 可以使用minmaxi阈值 反之则为0
+            return 0.3936 + 0.1829 * (np.log(self.N) / np.log(2))
+        
+        else:
+            
+            return 0
+
+    @property
+    def GetCrit(self)->float:
+
+        return np.sqrt(np.power(np.log(self.N) / np.log(2), 3) * 1 / self.N)
+
+    @property
+    def GetEta(self)->float:
+
+        return (np.sum(np.abs(self.signal)**2) - self.N) / self.N
+    
+    #混合准则（heursure）
+    @property
+    def CalHeursure(self):
+
+        if self.GetCrit > self.GetEta:
+
+            #print('推荐使用sqtwolog阈值')
+            return self.CalSqtwolog
+            
+        else:
+
+            #print('推荐使用 Min(sqtwolog阈值,rigrsure阈值)')
+            return min(self.CalRigrsure,self.CalSqtwolog)
+            
+class wavelet_svm_model(object):
+    '''对数据进行建模预测
+    --------------------
+    输入参数：
+
+        data:必须包含OHLC money及预测字段Y(ovo标记) 其余字段为训练数据
+        M:train数据的滚动计算窗口
+        window:滚动窗口 即T至T-window日 预测T-1至T-window日数据 预测T日数据
+        wavelet\wavelet_mode:同pywt.wavedec的参数
+        filter_num:需要过滤小波的细节组 比如(3,4)对三至四组进行过滤 为空则是1-4组全过滤 
+        whether_wave_process:是否使用小波处理
+    --------------------
+    方法：
+        wave_process:过滤阈值 采用 混合准则（heursure）
+        preprocess:生成训练用字段
+        rolling_svm:使用svm滚动训练
+    '''
+
+    def __init__(self,
+                 Data: pd.DataFrame,
+                 M: int,
+                 window: int,
+                 wavelet: str,
+                 wavelet_mode: str,
+                 filter_num=None,
+                 whether_wave_process: bool = False):
+
+        self.Data = Data
+        self.__M = M
+        self.__window = window
+        self.__wavelet = wavelet
+        self.__wavelet_mode = wavelet_mode
+        self.__filter_num = filter_num
+        self.__whether_wave_process = whether_wave_process
+
+        self.__train_col = [col for col in self.Data.columns if col != 'Y'
+                           ]  # 训练的字段
+
+        self.train_df = pd.DataFrame()  # 储存训练数据
+        self.predict_df = Data[['Y']].copy()  # 储存预测数据及真实Y
+
+    def wave_process(self):
+        '''对数据进行小波处理(可选)'''
+        
+        if self.__filter_num:
+            
+            a = self.__filter_num[0]
+            b = self.__filter_num[1]
+            
+            #self.__filter_num = range(a,b + 1)
+            
+        else:
+            a = 1
+            b = 5
+            #self.__filter_num = range(1,5)
+
+    
+        Data = self.Data.copy()  # 复制
+        
+        for col in self.__train_col:
+
+            #res1 = pywt.wavedec(
+            #    data[col].values, wavelet=self.__wavelet, mode=self.__wavelet_mode, level=4)
+
+            #for j in self.__filter_num:
+
+            #    threshold = DenoisingThreshold(res1[j]).CalHeursure
+            #    res1[j] = pywt.threshold(res1[j], threshold, 'soft')
+            
+            denosed_ser = wave_transform(Data[col], 
+                           wavelet=self.__wavelet, 
+                           wavelet_mode=self.__wavelet_mode, 
+                           level=4,
+                           n=a,
+                           m=b)
+            
+            Data[col] = denosed_ser
+
+        self.train_df = Data
+
+    def preprocess(self):
+        '''生成相应的特征'''
+
+        if self.__whether_wave_process:
+
+            self.wave_process()  # 小波处理
+
+            Data = self.train_df
+
+        else:
+
+            Data = self.Data.copy()
+
+        Data['近M日最高价'] = Data['high'].rolling(self.__M).max()
+        Data['近M日最低价'] = Data['low'].rolling(self.__M).min()
+        Data['成交额占比'] = Data['money'] / Data['money'].rolling(self.__M).sum()
+        Data['近M日涨跌幅'] = Data['close'].pct_change(self.__M)
+        Data['近M日均价'] = Data['close'].rolling(self.__M).mean()
+        
+        # 上面新增了需要训练用的字段 这里更新字段
+        self.__train_col = [col for col in Data.columns if col not in  self.__train_col + ['Y']]
+        self.train_df = Data[self.__train_col]
+        self.train_df = self.train_df.iloc[self.__M:]
+    
+    
+    def standardization(self):
+        '''对所有特征进行标准化处理'''
+
+        Data = preprocessing.scale(self.train_df[self.__train_col])
+        Data = pd.DataFrame(
+            Data, index=self.train_df.index, columns=self.__train_col)
+        Data['Y'] = self.predict_df['Y']
+        self.train_df = Data
+
+    def rolling_svm(self):
+        
+        '''利用SVM模型进行建模预测'''
+        predict_ser = rolling_apply(self.train_df, self.model_fit,
+                                    self.__window)
+        
+        self.predict_df['predict'] = predict_ser
+        
+        self.predict_df = self.predict_df.iloc[self.__window + self.__M:]
+
+    def model_fit(self, df: pd.DataFrame) -> pd.Series:
+        
+        idx = df.index[-1]
+
+        train_x = df[self.__train_col].iloc[:-1]
+        train_y = df['Y'].shift(-1).iloc[:-1]  # 对需要预测的y进行滞后一期处理
+
+        test_x = df[self.__train_col].iloc[-1:]
+
+        model = svm.SVC(gamma=0.001)
+
+        model.fit(train_x, train_y)
+
+        return pd.Series(model.predict(test_x), index=[idx])
+        
+# 定义rolling_apply理论上应该比for循环快
+# pandas.rolling.apply不支持多列
+def rolling_apply(df, func, win_size) -> pd.Series:
+
+    iidx = np.arange(len(df))
+
+    shape = (iidx.size - win_size + 1, win_size)
+
+    strides = (iidx.strides[0], iidx.strides[0])
+
+    res = np.lib.stride_tricks.as_strided(
+        iidx, shape=shape, strides=strides, writeable=True)
+
+    # 这里注意func返回的需要为df或者ser
+    return pd.concat((func(df.iloc[r]) for r in res), axis=0)  # concat可能会有点慢
+    
+# 小波变换
+def wave_transform(data_ser:pd.Series,wavelet:str,wavelet_mode:str,level:int,n:int,m:int)->pd.Series:
+    '''
+    参数：
+        data_ser:pd.Series
+        wavelet\wavelet_mode\level：同pywt.wavedec
+        n,m:需要过了的层级范围
+    '''
+    res1 = pywt.wavedec(
+                    data_ser.values, wavelet=wavelet, mode=wavelet_mode, level=level)
+
+    for j in range(n,m+1):
+
+        threshold = DenoisingThreshold(res1[j]).CalHeursure
+        res1[j] = pywt.threshold(res1[j], threshold, 'soft')
+    
+    # 有时候重构后的长度不等 
+    n = len(data_ser)
+    
+    return pd.Series(pywt.waverec(res1, wavelet)[-n:],index=data_ser.index)
+    
+    
+################################### 数据获取 ####################################################
+def GetSingal(context)->int:
+    
+    price_df = attribute_history(g.index_code, count=g.M + g.N + 25, unit='1d',
+            fields=['open', 'close', 'high', 'low', 'pre_close', 'money'])
+            
+    price_df['pct_chg'] = price_df['close'] / price_df['pre_close'] - 1
+
+    
+    price_df['MACD'] = talib.MACD(price_df['close'],fastperiod=6,slowperiod=12,signalperiod=9)[0]
+    price_df['RSI'] = talib.RSI(price_df['close'],timeperiod=14)        
+    # 标记
+    price_df['Y'] = np.sign(price_df['pct_chg'])
+    
+    price_df = price_df.dropna()
+
+    # 对昨日收盘价去噪
+    wsm = wavelet_svm_model(price_df,g.M,g.N,'db4','sym',True)
+    # 计算训练字段
+    wsm.preprocess()
+    # 标准化
+    wsm.standardization()
+    # 滚动训练
+    wsm.rolling_svm()
+            
+    
+    return wsm.predict_df['predict'].iloc[-1] # 1为持仓 其他空仓
+    
+#################################  交易  ############################################
+
+def handle_data(context,data):
+    
+    if GetSingal(context) == 1:
+        
+        BuyStock(context)
+        
+    else:
+        
+        SellStock(context)
+
+
+#################################  下单  ############################################        
+def BuyStock(context):
+    
+    
+    if g.etf_code in context.portfolio.long_positions:
+        
+        log.info('继续持有%s'%get_security_info(g.etf_code).display_name)
+        
+    else:
+        
+        log.info('买入%s'%get_security_info(g.etf_code).display_name)
+        order_target_value(g.etf_code,context.portfolio.total_value)
+        
+def SellStock(context):
+    
+    log.info('卖出%s'%get_security_info(g.etf_code).display_name)
+    for hold in context.portfolio.long_positions:
+        
+        order_target(hold,0)
         
\ No newline at end of file
diff --git "a/\345\260\217\346\263\242\345\210\206\346\236\220/\345\233\236\346\265\213\346\250\241\345\235\227/\345\260\217\346\263\242\345\210\206\346\236\220_\345\270\214\344\274\257\347\211\271\345\217\230\346\215\242.py" "b/C-\346\213\251\346\227\266\347\261\273/\345\260\217\346\263\242\345\210\206\346\236\220/\345\233\236\346\265\213\346\250\241\345\235\227/\345\260\217\346\263\242\345\210\206\346\236\220_\345\270\214\344\274\257\347\211\271\345\217\230\346\215\242.py"
similarity index 96%
rename from "\345\260\217\346\263\242\345\210\206\346\236\220/\345\233\236\346\265\213\346\250\241\345\235\227/\345\260\217\346\263\242\345\210\206\346\236\220_\345\270\214\344\274\257\347\211\271\345\217\230\346\215\242.py"
rename to "C-\346\213\251\346\227\266\347\261\273/\345\260\217\346\263\242\345\210\206\346\236\220/\345\233\236\346\265\213\346\250\241\345\235\227/\345\260\217\346\263\242\345\210\206\346\236\220_\345\270\214\344\274\257\347\211\271\345\217\230\346\215\242.py"
index ab8bc84..fb4f426 100644
--- "a/\345\260\217\346\263\242\345\210\206\346\236\220/\345\233\236\346\265\213\346\250\241\345\235\227/\345\260\217\346\263\242\345\210\206\346\236\220_\345\270\214\344\274\257\347\211\271\345\217\230\346\215\242.py"
+++ "b/C-\346\213\251\346\227\266\347\261\273/\345\260\217\346\263\242\345\210\206\346\236\220/\345\233\236\346\265\213\346\250\241\345\235\227/\345\260\217\346\263\242\345\210\206\346\236\220_\345\270\214\344\274\257\347\211\271\345\217\230\346\215\242.py"
@@ -1,242 +1,242 @@
-# 常用计算
-import talib
-import pandas as pd
-import numpy as np
-
-from scipy import stats 
-import scipy.fftpack as fftpack # 希尔伯特变换
-
-import pywt # 小波分析
-from typing import List
-
-# 时间操作
-import datetime as dt
-from dateutil.parser import parse
-
-# 数据获取
-from jqdata import *
-import tushare as ts
-
-# 研究文档数据读取
-from six import BytesIO  # 读取研究环境文件用
-
-enable_profile()  # 开启性能分析
-
-########################  初始化  #############################
-
-
-def initialize(context):
-    
-    set_params()
-    set_variables()
-    set_backtest()  # 设置回测条件
-   
-
-
-# 设置策参数
-def set_params():
-    
-   g.N = 20 # 计算窗口
-   g.index_code = '000300.XSHG'
-   g.etf_code = '510300.XSHG'
-   
-   
-# 中间变量
-def set_variables():
-    pass
-
-
-# 设置回测条件
-def set_backtest():
-
-    set_option("avoid_future_data", True)  # 避免未来数据
-    set_option('use_real_price', True)  #用真实价格交易
-    set_benchmark('000300.XSHG')  # 设置基准收益
-    log.set_level('order', 'debug')
-
-
-# 每日盘前运行
-def before_trading_start(context):
-
-    # 设置手续费
-    set_slip_fee(context)
-
-
-# 根据不同的时间段设置滑点与手续费
-def set_slip_fee(context):
-    # 将滑点设置为0
-    set_slippage(FixedSlippage(0))
-    # 根据不同的时间段设置手续费
-    dt = context.current_dt
-
-    if dt > datetime.datetime(2013, 1, 1):
-        set_commission(PerTrade(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
-
-    elif dt > datetime.datetime(2011, 1, 1):
-        set_commission(PerTrade(buy_cost=0.001, sell_cost=0.002, min_cost=5))
-
-    elif dt > datetime.datetime(2009, 1, 1):
-        set_commission(PerTrade(buy_cost=0.002, sell_cost=0.003, min_cost=5))
-
-    else:
-        set_commission(PerTrade(buy_cost=0.003, sell_cost=0.004, min_cost=5))
-        
-        
-        
-################################################## 信号获取 #########################################################
-# 信号去噪
-class DenoisingThreshold(object):
-    '''
-    获取小波去噪的阈值
-    1. CalSqtwolog 固定阈值准则(sqtwolog)
-    2. CalRigrsure 无偏风险估计准则（rigrsure）
-    3. CalMinmaxi 极小极大准则（ minimaxi）
-    4. CalHeursure
-    
-    参考：https://wenku.baidu.com/view/63d62a818762caaedd33d463.html
-    
-    对股票价格等数据而言，其信号频率较少地与噪声重叠因此可以选用sqtwolog和heursure准则，使去噪效果更明显。 
-    但对收益率这样的高频数据，尽量采用保守的 rigrsure 或 minimaxi 准则来确定阈值，以保留较多的信号。
-    '''
-    
-
-    def __init__(self,signal: np.array):
-
-        self.signal = signal
-
-        self.N = len(signal)
-
-    # 固定阈值准则(sqtwolog)
-    @property
-    def CalSqtwolog(self) -> float:
-
-        return np.sqrt(2 * np.log(self.N))
-
-   
-    # 无偏风险估计准则（rigrsure）
-    @property
-    def CalRigrsure(self)->float:
-
-        N = self.N
-        signal = np.abs(self.signal)
-        signal = np.sort(signal)
-        signal = np.power(signal, 2)
-
-        risk_j = np.zeros(N)
-
-        for j in range(N):
-
-            if j == 0:
-                risk_j[j] = 1 + signal[N - 1]
-            else:
-                risk_j[j] = (N - 2 * j + (N - j) *
-                             (signal[N - j]) + np.sum(signal[:j])) / N
-
-        k = risk_j.argmin()
-
-        return np.sqrt(signal[k])
-
-    # 极小极大准则（ minimaxi）
-    @property
-    def CalMinmaxi(self)->float:
-        
-        if self.N > 32:
-            # N>32 可以使用minmaxi阈值 反之则为0
-            return 0.3936 + 0.1829 * (np.log(self.N) / np.log(2))
-        
-        else:
-            
-            return 0
-
-    @property
-    def GetCrit(self)->float:
-
-        return np.sqrt(np.power(np.log(self.N) / np.log(2), 3) * 1 / self.N)
-
-    @property
-    def GetEta(self)->float:
-
-        return (np.sum(np.abs(self.signal)**2) - self.N) / self.N
-    
-    #混合准则（heursure）
-    @property
-    def CalHeursure(self):
-
-        if self.GetCrit > self.GetEta:
-
-            #print('推荐使用sqtwolog阈值')
-            return self.CalSqtwolog
-            
-        else:
-
-            #print('推荐使用 Min(sqtwolog阈值,rigrsure阈值)')
-            return min(self.CalRigrsure,self.CalSqtwolog)
-            
-# 小波变换
-def wave_transform(data_ser:pd.Series,wavelet:str,wavelet_mode:str,level:int,n:int,m:int)->pd.Series:
-    '''
-    参数：
-        data_ser:pd.Series
-        wavelet\wavelet_mode\level：同pywt.wavedec
-        n,m:需要过了的层级范围
-    '''
-    res1 = pywt.wavedec(
-                    data_ser.values, wavelet=wavelet, mode=wavelet_mode, level=level)
-
-    for j in range(n,m+1):
-
-        threshold = DenoisingThreshold(res1[j]).CalHeursure
-        res1[j] = pywt.threshold(res1[j], threshold, 'soft')
-    
-    # 有时候重构后的长度不等 
-    n = len(data_ser)
-    
-    return pd.Series(pywt.waverec(res1, wavelet)[-n:],index=data_ser.index)
-    
-def GetSingal(context)->int:
-    
-    price_df = history(count=g.N + 1, unit='1d', field='close', security_list=g.index_code)
-    
-    # 对昨日收盘价去噪
-    denoised_price = wave_transform(price_df[g.index_code],'db4','sym',4,1,4)
-    
-    diff_price = denoised_price.diff()
-    diff_price = diff_price.dropna()
-    
-    # 希尔伯特周期 滚动防止 前视偏差 
-    hilbert = fftpack.hilbert(diff_price)
-    
-    return np.sign(hilbert)[-1] # 1为持仓 其他空仓
-    
-#################################  交易  ############################################
-
-def handle_data(context,data):
-    
-    if GetSingal(context) == 1:
-        
-        BuyStock(context)
-        
-    else:
-        
-        SellStock(context)
-
-
-#################################  下单  ############################################        
-def BuyStock(context):
-    
-    if g.etf_code in context.portfolio.long_positions:
-        
-        log.info('继续持有%s'%get_security_info(g.etf_code).display_name)
-        
-    else:
-        
-        log.info('买入%s'%get_security_info(g.etf_code).display_name)
-        order_target_value(g.etf_code,context.portfolio.total_value)
-        
-def SellStock(context):
-    
-    log.info('卖出%s'%get_security_info(g.etf_code).display_name)
-    for hold in context.portfolio.long_positions:
-        
-        order_target(hold,0)
+# 常用计算
+import talib
+import pandas as pd
+import numpy as np
+
+from scipy import stats 
+import scipy.fftpack as fftpack # 希尔伯特变换
+
+import pywt # 小波分析
+from typing import List
+
+# 时间操作
+import datetime as dt
+from dateutil.parser import parse
+
+# 数据获取
+from jqdata import *
+import tushare as ts
+
+# 研究文档数据读取
+from six import BytesIO  # 读取研究环境文件用
+
+enable_profile()  # 开启性能分析
+
+########################  初始化  #############################
+
+
+def initialize(context):
+    
+    set_params()
+    set_variables()
+    set_backtest()  # 设置回测条件
+   
+
+
+# 设置策参数
+def set_params():
+    
+   g.N = 20 # 计算窗口
+   g.index_code = '000300.XSHG'
+   g.etf_code = '510300.XSHG'
+   
+   
+# 中间变量
+def set_variables():
+    pass
+
+
+# 设置回测条件
+def set_backtest():
+
+    set_option("avoid_future_data", True)  # 避免未来数据
+    set_option('use_real_price', True)  #用真实价格交易
+    set_benchmark('000300.XSHG')  # 设置基准收益
+    log.set_level('order', 'debug')
+
+
+# 每日盘前运行
+def before_trading_start(context):
+
+    # 设置手续费
+    set_slip_fee(context)
+
+
+# 根据不同的时间段设置滑点与手续费
+def set_slip_fee(context):
+    # 将滑点设置为0
+    set_slippage(FixedSlippage(0))
+    # 根据不同的时间段设置手续费
+    dt = context.current_dt
+
+    if dt > datetime.datetime(2013, 1, 1):
+        set_commission(PerTrade(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
+
+    elif dt > datetime.datetime(2011, 1, 1):
+        set_commission(PerTrade(buy_cost=0.001, sell_cost=0.002, min_cost=5))
+
+    elif dt > datetime.datetime(2009, 1, 1):
+        set_commission(PerTrade(buy_cost=0.002, sell_cost=0.003, min_cost=5))
+
+    else:
+        set_commission(PerTrade(buy_cost=0.003, sell_cost=0.004, min_cost=5))
+        
+        
+        
+################################################## 信号获取 #########################################################
+# 信号去噪
+class DenoisingThreshold(object):
+    '''
+    获取小波去噪的阈值
+    1. CalSqtwolog 固定阈值准则(sqtwolog)
+    2. CalRigrsure 无偏风险估计准则（rigrsure）
+    3. CalMinmaxi 极小极大准则（ minimaxi）
+    4. CalHeursure
+    
+    参考：https://wenku.baidu.com/view/63d62a818762caaedd33d463.html
+    
+    对股票价格等数据而言，其信号频率较少地与噪声重叠因此可以选用sqtwolog和heursure准则，使去噪效果更明显。 
+    但对收益率这样的高频数据，尽量采用保守的 rigrsure 或 minimaxi 准则来确定阈值，以保留较多的信号。
+    '''
+    
+
+    def __init__(self,signal: np.array):
+
+        self.signal = signal
+
+        self.N = len(signal)
+
+    # 固定阈值准则(sqtwolog)
+    @property
+    def CalSqtwolog(self) -> float:
+
+        return np.sqrt(2 * np.log(self.N))
+
+   
+    # 无偏风险估计准则（rigrsure）
+    @property
+    def CalRigrsure(self)->float:
+
+        N = self.N
+        signal = np.abs(self.signal)
+        signal = np.sort(signal)
+        signal = np.power(signal, 2)
+
+        risk_j = np.zeros(N)
+
+        for j in range(N):
+
+            if j == 0:
+                risk_j[j] = 1 + signal[N - 1]
+            else:
+                risk_j[j] = (N - 2 * j + (N - j) *
+                             (signal[N - j]) + np.sum(signal[:j])) / N
+
+        k = risk_j.argmin()
+
+        return np.sqrt(signal[k])
+
+    # 极小极大准则（ minimaxi）
+    @property
+    def CalMinmaxi(self)->float:
+        
+        if self.N > 32:
+            # N>32 可以使用minmaxi阈值 反之则为0
+            return 0.3936 + 0.1829 * (np.log(self.N) / np.log(2))
+        
+        else:
+            
+            return 0
+
+    @property
+    def GetCrit(self)->float:
+
+        return np.sqrt(np.power(np.log(self.N) / np.log(2), 3) * 1 / self.N)
+
+    @property
+    def GetEta(self)->float:
+
+        return (np.sum(np.abs(self.signal)**2) - self.N) / self.N
+    
+    #混合准则（heursure）
+    @property
+    def CalHeursure(self):
+
+        if self.GetCrit > self.GetEta:
+
+            #print('推荐使用sqtwolog阈值')
+            return self.CalSqtwolog
+            
+        else:
+
+            #print('推荐使用 Min(sqtwolog阈值,rigrsure阈值)')
+            return min(self.CalRigrsure,self.CalSqtwolog)
+            
+# 小波变换
+def wave_transform(data_ser:pd.Series,wavelet:str,wavelet_mode:str,level:int,n:int,m:int)->pd.Series:
+    '''
+    参数：
+        data_ser:pd.Series
+        wavelet\wavelet_mode\level：同pywt.wavedec
+        n,m:需要过了的层级范围
+    '''
+    res1 = pywt.wavedec(
+                    data_ser.values, wavelet=wavelet, mode=wavelet_mode, level=level)
+
+    for j in range(n,m+1):
+
+        threshold = DenoisingThreshold(res1[j]).CalHeursure
+        res1[j] = pywt.threshold(res1[j], threshold, 'soft')
+    
+    # 有时候重构后的长度不等 
+    n = len(data_ser)
+    
+    return pd.Series(pywt.waverec(res1, wavelet)[-n:],index=data_ser.index)
+    
+def GetSingal(context)->int:
+    
+    price_df = history(count=g.N + 1, unit='1d', field='close', security_list=g.index_code)
+    
+    # 对昨日收盘价去噪
+    denoised_price = wave_transform(price_df[g.index_code],'db4','sym',4,1,4)
+    
+    diff_price = denoised_price.diff()
+    diff_price = diff_price.dropna()
+    
+    # 希尔伯特周期 滚动防止 前视偏差 
+    hilbert = fftpack.hilbert(diff_price)
+    
+    return np.sign(hilbert)[-1] # 1为持仓 其他空仓
+    
+#################################  交易  ############################################
+
+def handle_data(context,data):
+    
+    if GetSingal(context) == 1:
+        
+        BuyStock(context)
+        
+    else:
+        
+        SellStock(context)
+
+
+#################################  下单  ############################################        
+def BuyStock(context):
+    
+    if g.etf_code in context.portfolio.long_positions:
+        
+        log.info('继续持有%s'%get_security_info(g.etf_code).display_name)
+        
+    else:
+        
+        log.info('买入%s'%get_security_info(g.etf_code).display_name)
+        order_target_value(g.etf_code,context.portfolio.total_value)
+        
+def SellStock(context):
+    
+    log.info('卖出%s'%get_security_info(g.etf_code).display_name)
+    for hold in context.portfolio.long_positions:
+        
+        order_target(hold,0)
         
\ No newline at end of file
diff --git "a/\345\260\217\346\263\242\345\210\206\346\236\220/\345\233\236\346\265\213\346\250\241\345\235\227/\350\257\264\346\230\216\346\226\207\346\241\243.docx" "b/C-\346\213\251\346\227\266\347\261\273/\345\260\217\346\263\242\345\210\206\346\236\220/\345\233\236\346\265\213\346\250\241\345\235\227/\350\257\264\346\230\216\346\226\207\346\241\243.docx"
similarity index 100%
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rename to "C-\346\213\251\346\227\266\347\261\273/\345\260\217\346\263\242\345\210\206\346\236\220/\345\233\236\346\265\213\346\250\241\345\235\227/\350\257\264\346\230\216\346\226\207\346\241\243.docx"
diff --git "a/\346\211\251\346\225\243\346\214\207\346\240\207/py/\346\211\251\346\225\243\346\214\207\346\240\207.ipynb" "b/C-\346\213\251\346\227\266\347\261\273/\346\211\251\346\225\243\346\214\207\346\240\207/py/\346\211\251\346\225\243\346\214\207\346\240\207.ipynb"
similarity index 100%
rename from "\346\211\251\346\225\243\346\214\207\346\240\207/py/\346\211\251\346\225\243\346\214\207\346\240\207.ipynb"
rename to "C-\346\213\251\346\227\266\347\261\273/\346\211\251\346\225\243\346\214\207\346\240\207/py/\346\211\251\346\225\243\346\214\207\346\240\207.ipynb"
diff --git "a/\346\211\251\346\225\243\346\214\207\346\240\207/\346\213\251\346\227\266-20190924-\344\270\234\345\214\227\350\257\201\345\210\270-\351\207\221\350\236\215\345\267\245\347\250\213\347\240\224\347\251\266\346\212\245\345\221\212\357\274\232\346\211\251\346\225\243\346\214\207\346\240\207\346\213\251\346\227\266\347\240\224\347\251\266\344\271\213\344\270\200\357\274\214\345\237\272\346\234\254\347\224\250\346\263\225.pdf" "b/C-\346\213\251\346\227\266\347\261\273/\346\211\251\346\225\243\346\214\207\346\240\207/\346\213\251\346\227\266-20190924-\344\270\234\345\214\227\350\257\201\345\210\270-\351\207\221\350\236\215\345\267\245\347\250\213\347\240\224\347\251\266\346\212\245\345\221\212\357\274\232\346\211\251\346\225\243\346\214\207\346\240\207\346\213\251\346\227\266\347\240\224\347\251\266\344\271\213\344\270\200\357\274\214\345\237\272\346\234\254\347\224\250\346\263\225.pdf"
similarity index 100%
rename from "\346\211\251\346\225\243\346\214\207\346\240\207/\346\213\251\346\227\266-20190924-\344\270\234\345\214\227\350\257\201\345\210\270-\351\207\221\350\236\215\345\267\245\347\250\213\347\240\224\347\251\266\346\212\245\345\221\212\357\274\232\346\211\251\346\225\243\346\214\207\346\240\207\346\213\251\346\227\266\347\240\224\347\251\266\344\271\213\344\270\200\357\274\214\345\237\272\346\234\254\347\224\250\346\263\225.pdf"
rename to "C-\346\213\251\346\227\266\347\261\273/\346\211\251\346\225\243\346\214\207\346\240\207/\346\213\251\346\227\266-20190924-\344\270\234\345\214\227\350\257\201\345\210\270-\351\207\221\350\236\215\345\267\245\347\250\213\347\240\224\347\251\266\346\212\245\345\221\212\357\274\232\346\211\251\346\225\243\346\214\207\346\240\207\346\213\251\346\227\266\347\240\224\347\251\266\344\271\213\344\270\200\357\274\214\345\237\272\346\234\254\347\224\250\346\263\225.pdf"
diff --git "a/\346\213\251\346\227\266\350\247\206\350\247\222\344\270\213\347\232\204\346\263\242\345\212\250\347\216\207\345\233\240\345\255\220.ipynb" "b/C-\346\213\251\346\227\266\347\261\273/\346\213\251\346\227\266\350\247\206\350\247\222\344\270\213\347\232\204\346\263\242\345\212\250\347\216\207\345\233\240\345\255\220.ipynb"
similarity index 100%
rename from "\346\213\251\346\227\266\350\247\206\350\247\222\344\270\213\347\232\204\346\263\242\345\212\250\347\216\207\345\233\240\345\255\220.ipynb"
rename to "C-\346\213\251\346\227\266\347\261\273/\346\213\251\346\227\266\350\247\206\350\247\222\344\270\213\347\232\204\346\263\242\345\212\250\347\216\207\345\233\240\345\255\220.ipynb"
diff --git "a/\346\214\207\346\225\260\351\253\230\351\230\266\347\237\251\346\213\251\346\227\266/20150520-\345\271\277\345\217\221\350\257\201\345\210\270-\344\272\244\346\230\223\346\200\247\346\213\251\346\227\266\347\255\226\347\225\245\347\240\224\347\251\266\344\271\213\345\205\253\357\274\232\346\214\207\346\225\260\351\253\230\351\230\266\347\237\251\346\213\251\346\227\266\347\255\226\347\225\245.pdf" "b/C-\346\213\251\346\227\266\347\261\273/\346\214\207\346\225\260\351\253\230\351\230\266\347\237\251\346\213\251\346\227\266/20150520-\345\271\277\345\217\221\350\257\201\345\210\270-\344\272\244\346\230\223\346\200\247\346\213\251\346\227\266\347\255\226\347\225\245\347\240\224\347\251\266\344\271\213\345\205\253\357\274\232\346\214\207\346\225\260\351\253\230\351\230\266\347\237\251\346\213\251\346\227\266\347\255\226\347\225\245.pdf"
similarity index 100%
rename from "\346\214\207\346\225\260\351\253\230\351\230\266\347\237\251\346\213\251\346\227\266/20150520-\345\271\277\345\217\221\350\257\201\345\210\270-\344\272\244\346\230\223\346\200\247\346\213\251\346\227\266\347\255\226\347\225\245\347\240\224\347\251\266\344\271\213\345\205\253\357\274\232\346\214\207\346\225\260\351\253\230\351\230\266\347\237\251\346\213\251\346\227\266\347\255\226\347\225\245.pdf"
rename to "C-\346\213\251\346\227\266\347\261\273/\346\214\207\346\225\260\351\253\230\351\230\266\347\237\251\346\213\251\346\227\266/20150520-\345\271\277\345\217\221\350\257\201\345\210\270-\344\272\244\346\230\223\346\200\247\346\213\251\346\227\266\347\255\226\347\225\245\347\240\224\347\251\266\344\271\213\345\205\253\357\274\232\346\214\207\346\225\260\351\253\230\351\230\266\347\237\251\346\213\251\346\227\266\347\255\226\347\225\245.pdf"
diff --git "a/\346\214\207\346\225\260\351\253\230\351\230\266\347\237\251\346\213\251\346\227\266/py/\346\214\207\346\225\260\351\253\230\351\230\266\347\237\251\346\213\251\346\227\266.ipynb" "b/C-\346\213\251\346\227\266\347\261\273/\346\214\207\346\225\260\351\253\230\351\230\266\347\237\251\346\213\251\346\227\266/py/\346\214\207\346\225\260\351\253\230\351\230\266\347\237\251\346\213\251\346\227\266.ipynb"
similarity index 100%
rename from "\346\214\207\346\225\260\351\253\230\351\230\266\347\237\251\346\213\251\346\227\266/py/\346\214\207\346\225\260\351\253\230\351\230\266\347\237\251\346\213\251\346\227\266.ipynb"
rename to "C-\346\213\251\346\227\266\347\261\273/\346\214\207\346\225\260\351\253\230\351\230\266\347\237\251\346\213\251\346\227\266/py/\346\214\207\346\225\260\351\253\230\351\230\266\347\237\251\346\213\251\346\227\266.ipynb"
diff --git "a/\346\227\266\345\217\230\345\244\217\346\231\256/20101028-\345\233\275\346\265\267\350\257\201\345\210\270-\346\226\260\351\207\217\345\214\226\346\213\251\346\227\266\346\214\207\346\240\207\344\271\213\344\272\214\357\274\232\346\227\266\345\217\230\345\244\217\346\231\256\346\257\224\347\216\207\346\212\212\346\217\241\351\225\277\344\270\255\347\237\255\350\266\213\345\212\277.pdf" "b/C-\346\213\251\346\227\266\347\261\273/\346\227\266\345\217\230\345\244\217\346\231\256/20101028-\345\233\275\346\265\267\350\257\201\345\210\270-\346\226\260\351\207\217\345\214\226\346\213\251\346\227\266\346\214\207\346\240\207\344\271\213\344\272\214\357\274\232\346\227\266\345\217\230\345\244\217\346\231\256\346\257\224\347\216\207\346\212\212\346\217\241\351\225\277\344\270\255\347\237\255\350\266\213\345\212\277.pdf"
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rename from "\346\227\266\345\217\230\345\244\217\346\231\256/20101028-\345\233\275\346\265\267\350\257\201\345\210\270-\346\226\260\351\207\217\345\214\226\346\213\251\346\227\266\346\214\207\346\240\207\344\271\213\344\272\214\357\274\232\346\227\266\345\217\230\345\244\217\346\231\256\346\257\224\347\216\207\346\212\212\346\217\241\351\225\277\344\270\255\347\237\255\350\266\213\345\212\277.pdf"
rename to "C-\346\213\251\346\227\266\347\261\273/\346\227\266\345\217\230\345\244\217\346\231\256/20101028-\345\233\275\346\265\267\350\257\201\345\210\270-\346\226\260\351\207\217\345\214\226\346\213\251\346\227\266\346\214\207\346\240\207\344\271\213\344\272\214\357\274\232\346\227\266\345\217\230\345\244\217\346\231\256\346\257\224\347\216\207\346\212\212\346\217\241\351\225\277\344\270\255\347\237\255\350\266\213\345\212\277.pdf"
diff --git "a/\346\227\266\345\217\230\345\244\217\346\231\256/20120726-\345\233\275\344\277\241\350\257\201\345\210\270-\346\227\266\345\217\230\345\244\217\346\231\256\347\216\207\347\232\204\346\213\251\346\227\266\347\255\226\347\225\245.pdf" "b/C-\346\213\251\346\227\266\347\261\273/\346\227\266\345\217\230\345\244\217\346\231\256/20120726-\345\233\275\344\277\241\350\257\201\345\210\270-\346\227\266\345\217\230\345\244\217\346\231\256\347\216\207\347\232\204\346\213\251\346\227\266\347\255\226\347\225\245.pdf"
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rename to "C-\346\213\251\346\227\266\347\261\273/\346\227\266\345\217\230\345\244\217\346\231\256/20120726-\345\233\275\344\277\241\350\257\201\345\210\270-\346\227\266\345\217\230\345\244\217\346\231\256\347\216\207\347\232\204\346\213\251\346\227\266\347\255\226\347\225\245.pdf"
diff --git "a/\346\227\266\345\217\230\345\244\217\346\231\256/The Applicability of Time-varying Sharpe Ratio to Chinese.pdf" "b/C-\346\213\251\346\227\266\347\261\273/\346\227\266\345\217\230\345\244\217\346\231\256/The Applicability of Time-varying Sharpe Ratio to Chinese.pdf"
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rename from "\346\227\266\345\217\230\345\244\217\346\231\256/The Applicability of Time-varying Sharpe Ratio to Chinese.pdf"
rename to "C-\346\213\251\346\227\266\347\261\273/\346\227\266\345\217\230\345\244\217\346\231\256/The Applicability of Time-varying Sharpe Ratio to Chinese.pdf"
diff --git "a/\346\227\266\345\217\230\345\244\217\346\231\256/py/Tsharpe.ipynb" "b/C-\346\213\251\346\227\266\347\261\273/\346\227\266\345\217\230\345\244\217\346\231\256/py/Tsharpe.ipynb"
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diff --git "a/\346\227\266\345\217\230\345\244\217\346\231\256/sharpe2-1997.pdf" "b/C-\346\213\251\346\227\266\347\261\273/\346\227\266\345\217\230\345\244\217\346\231\256/sharpe2-1997.pdf"
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diff --git "a/\346\227\266\345\217\230\345\244\217\346\231\256/tvsharpe.pdf" "b/C-\346\213\251\346\227\266\347\261\273/\346\227\266\345\217\230\345\244\217\346\231\256/tvsharpe.pdf"
similarity index 100%
rename from "\346\227\266\345\217\230\345\244\217\346\231\256/tvsharpe.pdf"
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diff --git "a/\346\227\266\345\217\230\345\244\217\346\231\256/varcov jf94-1994.pdf" "b/C-\346\213\251\346\227\266\347\261\273/\346\227\266\345\217\230\345\244\217\346\231\256/varcov jf94-1994.pdf"
similarity index 100%
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diff --git "a/\347\276\212\347\276\244\346\225\210\345\272\224/20181128-\345\233\275\346\263\260\345\220\233\345\256\211-\346\225\260\351\207\217\345\214\226\344\270\223\351\242\230\344\271\213\344\270\200\347\231\276\344\272\214\345\215\201\344\272\214\357\274\232\345\237\272\344\272\216CCK\346\250\241\345\236\213\347\232\204\350\202\241\347\245\250\345\270\202\345\234\272\347\276\212\347\276\244\346\225\210\345\272\224\347\240\224\347\251\266.pdf" "b/C-\346\213\251\346\227\266\347\261\273/\347\276\212\347\276\244\346\225\210\345\272\224/20181128-\345\233\275\346\263\260\345\220\233\345\256\211-\346\225\260\351\207\217\345\214\226\344\270\223\351\242\230\344\271\213\344\270\200\347\231\276\344\272\214\345\215\201\344\272\214\357\274\232\345\237\272\344\272\216CCK\346\250\241\345\236\213\347\232\204\350\202\241\347\245\250\345\270\202\345\234\272\347\276\212\347\276\244\346\225\210\345\272\224\347\240\224\347\251\266.pdf"
similarity index 100%
rename from "\347\276\212\347\276\244\346\225\210\345\272\224/20181128-\345\233\275\346\263\260\345\220\233\345\256\211-\346\225\260\351\207\217\345\214\226\344\270\223\351\242\230\344\271\213\344\270\200\347\231\276\344\272\214\345\215\201\344\272\214\357\274\232\345\237\272\344\272\216CCK\346\250\241\345\236\213\347\232\204\350\202\241\347\245\250\345\270\202\345\234\272\347\276\212\347\276\244\346\225\210\345\272\224\347\240\224\347\251\266.pdf"
rename to "C-\346\213\251\346\227\266\347\261\273/\347\276\212\347\276\244\346\225\210\345\272\224/20181128-\345\233\275\346\263\260\345\220\233\345\256\211-\346\225\260\351\207\217\345\214\226\344\270\223\351\242\230\344\271\213\344\270\200\347\231\276\344\272\214\345\215\201\344\272\214\357\274\232\345\237\272\344\272\216CCK\346\250\241\345\236\213\347\232\204\350\202\241\347\245\250\345\270\202\345\234\272\347\276\212\347\276\244\346\225\210\345\272\224\347\240\224\347\251\266.pdf"
diff --git "a/\347\276\212\347\276\244\346\225\210\345\272\224/py/\347\276\212\347\276\244\346\225\210\345\272\224.ipynb" "b/C-\346\213\251\346\227\266\347\261\273/\347\276\212\347\276\244\346\225\210\345\272\224/py/\347\276\212\347\276\244\346\225\210\345\272\224.ipynb"
similarity index 100%
rename from "\347\276\212\347\276\244\346\225\210\345\272\224/py/\347\276\212\347\276\244\346\225\210\345\272\224.ipynb"
rename to "C-\346\213\251\346\227\266\347\261\273/\347\276\212\347\276\244\346\225\210\345\272\224/py/\347\276\212\347\276\244\346\225\210\345\272\224.ipynb"
diff --git "a/DE\347\256\227\346\263\225\344\270\213\347\232\204\347\273\204\345\220\210\344\274\230\345\214\226/20191018-\346\265\231\345\225\206\350\257\201\345\210\270-\344\272\272\345\267\245\346\231\272\350\203\275\347\263\273\345\210\227\357\274\210\344\272\214\357\274\211\357\274\232\344\272\272\345\267\245\346\231\272\350\203\275\345\206\215\345\207\272\345\217\221\357\274\214\346\254\241\344\274\230\347\220\206\350\256\272\344\270\213\347\232\204\347\273\204\345\220\210\351\205\215\347\275\256\344\270\216\347\255\226\347\225\245\346\236\204\345\273\272.pdf" "b/D-\347\273\204\345\220\210\344\274\230\345\214\226/DE\347\256\227\346\263\225\344\270\213\347\232\204\347\273\204\345\220\210\344\274\230\345\214\226/20191018-\346\265\231\345\225\206\350\257\201\345\210\270-\344\272\272\345\267\245\346\231\272\350\203\275\347\263\273\345\210\227\357\274\210\344\272\214\357\274\211\357\274\232\344\272\272\345\267\245\346\231\272\350\203\275\345\206\215\345\207\272\345\217\221\357\274\214\346\254\241\344\274\230\347\220\206\350\256\272\344\270\213\347\232\204\347\273\204\345\220\210\351\205\215\347\275\256\344\270\216\347\255\226\347\225\245\346\236\204\345\273\272.pdf"
similarity index 100%
rename from "DE\347\256\227\346\263\225\344\270\213\347\232\204\347\273\204\345\220\210\344\274\230\345\214\226/20191018-\346\265\231\345\225\206\350\257\201\345\210\270-\344\272\272\345\267\245\346\231\272\350\203\275\347\263\273\345\210\227\357\274\210\344\272\214\357\274\211\357\274\232\344\272\272\345\267\245\346\231\272\350\203\275\345\206\215\345\207\272\345\217\221\357\274\214\346\254\241\344\274\230\347\220\206\350\256\272\344\270\213\347\232\204\347\273\204\345\220\210\351\205\215\347\275\256\344\270\216\347\255\226\347\225\245\346\236\204\345\273\272.pdf"
rename to "D-\347\273\204\345\220\210\344\274\230\345\214\226/DE\347\256\227\346\263\225\344\270\213\347\232\204\347\273\204\345\220\210\344\274\230\345\214\226/20191018-\346\265\231\345\225\206\350\257\201\345\210\270-\344\272\272\345\267\245\346\231\272\350\203\275\347\263\273\345\210\227\357\274\210\344\272\214\357\274\211\357\274\232\344\272\272\345\267\245\346\231\272\350\203\275\345\206\215\345\207\272\345\217\221\357\274\214\346\254\241\344\274\230\347\220\206\350\256\272\344\270\213\347\232\204\347\273\204\345\220\210\351\205\215\347\275\256\344\270\216\347\255\226\347\225\245\346\236\204\345\273\272.pdf"
diff --git "a/DE\347\256\227\346\263\225\344\270\213\347\232\204\347\273\204\345\220\210\344\274\230\345\214\226/20191101-\346\265\231\345\225\206\350\257\201\345\210\270-FOF\347\273\204\345\220\210\347\263\273\345\210\227\357\274\210\344\270\200\357\274\211\357\274\232\345\233\236\346\222\244\346\234\200\345\260\217\347\233\256\346\240\207\344\270\213\347\232\204\345\201\217\345\200\272FOF\347\273\204\345\220\210\346\236\204\345\273\272\344\273\245\357\274\214\344\270\200\345\256\266\345\205\254\345\213\237\344\272\247\345\223\201\344\270\272\344\276\213.pdf" "b/D-\347\273\204\345\220\210\344\274\230\345\214\226/DE\347\256\227\346\263\225\344\270\213\347\232\204\347\273\204\345\220\210\344\274\230\345\214\226/20191101-\346\265\231\345\225\206\350\257\201\345\210\270-FOF\347\273\204\345\220\210\347\263\273\345\210\227\357\274\210\344\270\200\357\274\211\357\274\232\345\233\236\346\222\244\346\234\200\345\260\217\347\233\256\346\240\207\344\270\213\347\232\204\345\201\217\345\200\272FOF\347\273\204\345\220\210\346\236\204\345\273\272\344\273\245\357\274\214\344\270\200\345\256\266\345\205\254\345\213\237\344\272\247\345\223\201\344\270\272\344\276\213.pdf"
similarity index 100%
rename from "DE\347\256\227\346\263\225\344\270\213\347\232\204\347\273\204\345\220\210\344\274\230\345\214\226/20191101-\346\265\231\345\225\206\350\257\201\345\210\270-FOF\347\273\204\345\220\210\347\263\273\345\210\227\357\274\210\344\270\200\357\274\211\357\274\232\345\233\236\346\222\244\346\234\200\345\260\217\347\233\256\346\240\207\344\270\213\347\232\204\345\201\217\345\200\272FOF\347\273\204\345\220\210\346\236\204\345\273\272\344\273\245\357\274\214\344\270\200\345\256\266\345\205\254\345\213\237\344\272\247\345\223\201\344\270\272\344\276\213.pdf"
rename to "D-\347\273\204\345\220\210\344\274\230\345\214\226/DE\347\256\227\346\263\225\344\270\213\347\232\204\347\273\204\345\220\210\344\274\230\345\214\226/20191101-\346\265\231\345\225\206\350\257\201\345\210\270-FOF\347\273\204\345\220\210\347\263\273\345\210\227\357\274\210\344\270\200\357\274\211\357\274\232\345\233\236\346\222\244\346\234\200\345\260\217\347\233\256\346\240\207\344\270\213\347\232\204\345\201\217\345\200\272FOF\347\273\204\345\220\210\346\236\204\345\273\272\344\273\245\357\274\214\344\270\200\345\256\266\345\205\254\345\213\237\344\272\247\345\223\201\344\270\272\344\276\213.pdf"
diff --git "a/DE\347\256\227\346\263\225\344\270\213\347\232\204\347\273\204\345\220\210\344\274\230\345\214\226/py/DE_algorithm.py" "b/D-\347\273\204\345\220\210\344\274\230\345\214\226/DE\347\256\227\346\263\225\344\270\213\347\232\204\347\273\204\345\220\210\344\274\230\345\214\226/py/DE_algorithm.py"
similarity index 100%
rename from "DE\347\256\227\346\263\225\344\270\213\347\232\204\347\273\204\345\220\210\344\274\230\345\214\226/py/DE_algorithm.py"
rename to "D-\347\273\204\345\220\210\344\274\230\345\214\226/DE\347\256\227\346\263\225\344\270\213\347\232\204\347\273\204\345\220\210\344\274\230\345\214\226/py/DE_algorithm.py"
diff --git "a/DE\347\256\227\346\263\225\344\270\213\347\232\204\347\273\204\345\220\210\344\274\230\345\214\226/py/\345\267\256\345\210\206\350\277\233\345\214\226\347\256\227\346\263\225.ipynb" "b/D-\347\273\204\345\220\210\344\274\230\345\214\226/DE\347\256\227\346\263\225\344\270\213\347\232\204\347\273\204\345\220\210\344\274\230\345\214\226/py/\345\267\256\345\210\206\350\277\233\345\214\226\347\256\227\346\263\225.ipynb"
similarity index 100%
rename from "DE\347\256\227\346\263\225\344\270\213\347\232\204\347\273\204\345\220\210\344\274\230\345\214\226/py/\345\267\256\345\210\206\350\277\233\345\214\226\347\256\227\346\263\225.ipynb"
rename to "D-\347\273\204\345\220\210\344\274\230\345\214\226/DE\347\256\227\346\263\225\344\270\213\347\232\204\347\273\204\345\220\210\344\274\230\345\214\226/py/\345\267\256\345\210\206\350\277\233\345\214\226\347\256\227\346\263\225.ipynb"