打开股票量化的黑箱-自己动手实现一个印钞机6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 打开股票量化的黑箱(自己动手实现一个印钞机) 第六章\n",
    "### 作者：阿布🐶\n",
    "\n",
    "### 未经本人允许禁止转载"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  `非均衡胜负收益`带来的必然`非均衡胜负比例`，目标由`因子`的能力解决一部分，`模式识别`提升关键的一部分"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上一章使用gmm-hmm统计分析分类，进行模式识别，大体思路已经说明，这一章开始，具体完善这个方法，提高胜率\n",
    "这一章将引入裁判机制，主裁与辅助裁判"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import ZEnv\n",
    "import ZLog\n",
    "import ZCommonUtil\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "pd.options.display.max_columns = 100\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from UmpMain import UmpMainClass\n",
    "from UmpJump import UmpJumpClass\n",
    "from MlFiterJumpPd import MlFiterJumpPdClass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "依然是从加载数据开始"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4837, 31)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn = ZEnv.g_project_root + '/data/cache/golden_n6_test_abu'\n",
    "key = 'golden_n6_test_abu'\n",
    "orders_pd_test = ZCommonUtil.load_hdf5(fn, key)\n",
    "orders_pd_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42538, 31)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn =  ZEnv.g_project_root + '/data/cache/golden_n6_train_abu'\n",
    "key = 'golden_n6_train_abu'\n",
    "orders_pd_train = ZCommonUtil.load_hdf5(fn, key)\n",
    "orders_pd_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "和之前不一样的地方，使用全量测试集数据，没有分割测试集与训练集的数据为的是之后构建主裁将使用这份全量的数据作为训练集，不再切割的目的是担心gmm－hmm只是识别了这段时间内的特征，对同一时段内的不同股票交易可以生效，但是实盘就不管用了，这样的话，我们不切割数据，训练完整个模型之后再向前回溯一整年的数据作为测试数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(47374, 39)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn = ZEnv.g_project_root + '/data/cache/orders_pd_ump_hit_predict_abu'\n",
    "key = 'orders_pd_ump_hit_predict_abu'\n",
    "orders_pd_ump = ZCommonUtil.load_hdf5(fn, key)\n",
    "orders_pd_ump.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上一章只用到了UmpMainClass 也就是**主裁**\n",
    "引入辅助裁判概念，这里的实例就是跳空裁判\n",
    "\n",
    "对跳空的概念，请自行搜索，我这里用的实例基本上都是股票领域里非常常用的理念，\n",
    "对这里的跳空实现感兴趣的请参考文章的第一章关于跳空的示例，也可查看源代码TLineJump\n",
    "\n",
    "**jump ump 辅助裁决**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(5035, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>result</th>\n",
       "      <th>jump_power</th>\n",
       "      <th>diff_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.382554</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.084962</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>0</td>\n",
       "      <td>-3.415892</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>0</td>\n",
       "      <td>-3.236708</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.307611</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            result  jump_power  diff_days\n",
       "2015-07-28       1   -1.382554          6\n",
       "2015-07-28       0   -1.084962         21\n",
       "2015-07-28       0   -3.415892          5\n",
       "2015-07-28       0   -3.236708          4\n",
       "2015-07-28       0   -1.307611         13"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 只针对有21天内有跳空行为的交易，一下子就只剩下5035了，只简单跳空强度和跳空间隔天数两个特征组成\n",
    "ump_jump = UmpJumpClass(orders_pd_ump, MlFiterJumpPdClass, dd_threshold=21)\n",
    "ZLog.info(ump_jump.fiter.df.shape)\n",
    "ump_jump.fiter.df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "all fit order = (44906, 39)\n",
      "win rate = 0.500757137131\n",
      "profit_cg.sum() = 272.117613217\n",
      "win mean = 0.0743788075658 loss_mean = -0.0626291433739 \n"
     ]
    },
    {
     "data": {
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KqenyKJZEZH/gVeBRVf2biLRT1Z3uY/2BKcAjwImqOt7d/irwR1VdVNuxy8rK/emN6AVq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\nGBNkScEYY0yQJQVjjDFBlhSMMcYE/T/Qrc9JlPSpXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1219eb550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "all fit order = (5035, 39)\n",
      "win rate = 0.498907646475\n",
      "profit_cg.sum() = 20.2331512267\n",
      "win mean = 0.0673137719871 loss_mean = -0.0591414557032 \n"
     ]
    },
    {
     "data": {
      "image/png": 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ASQgAy9ftpuvBdauuCU1YMy8/hM4pFXtD1cS9l05k7da9pO/dz6BeVU9JrZRq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1252228d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 这个你应该熟悉了，看看这些数据的交易大体情况，第一个不过滤数据，即所有传进去的4w多条交易\n",
    "ump_jump.show_general()\n",
    "# 过滤数据，只统计符合条件的数据，即那5千多条的情况\n",
    "ump_jump.show_general(use_fiter=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**从上面可以发现跳空监控区，收益小于整个区间**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "死心不改的仍然使用svm对模型想要抽取特征，结果🙈"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(5035, 2)\n",
      "(4531, 2)\n",
      "(504, 2)\n",
      "accuracy = 0.56\n",
      "precision_score = 0.57\n",
      "recall_score = 0.54\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.55      0.59      0.57       247\n",
      "        1.0       0.57      0.54      0.56       257\n",
      "\n",
      "avg / total       0.56      0.56      0.56       504\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix  [[145 102]\n",
      " [119 138]]\n",
      "          Predicted\n",
      "         |  0  |  1  |\n",
      "         |-----|-----|\n",
      "       0 | 145 | 102 |\n",
      "Actual   |-----|-----|\n",
      "       1 | 119 | 138 |\n",
      "         |-----|-----|\n"
     ]
    }
   ],
   "source": [
    "ump_jump.fiter().estimator.svc()\n",
    "ump_jump.fiter().train_test_split_xy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 下面继续使用gmm-hmm方式进行特征抽取, 由于数据不多，从18个分类到42个分类，阀值还是选择65%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YRDqU1IhLv0sILtqoth7oE2VvIiJNddVVVyUmTpyYjLuPqEQaDI249HsdQThU\nKwY+j6QpEZEsam17CwCJZDKe0Av3GM5z99PD6VeA0cB7wBPA1e6+IJbmRETasHy68vl84H6C4x4z\nFQoiIvGIbY9BRETyky5wExGRNAoGERFJo2AQEZE0CgYREUmTT2clfcnMEsAfCJ5vWg6c4+7vpCz/\nJvC/4eTHwBkE91iqc52o6rr7ZjN7GVgbzn/X3c/ORk0z6w08yNYLBPcDfg7cFuW21lXX3W9tzrY2\nVDdcPhb4CVAB3Onuf2xonajqhvMj+dmGy88ELia4Zucud5+So23dpm5zt7VG/YOAX7v7YTXmHw9c\nQfBv9U53vz0b29vYmuH8rGxra5WXwQCcCHRw98HhD3xyOK/arcBod38nvLp6V6BfA+tEUtfMlgO4\n++FN2M56a7r7SuAwADMbBFxHEAoN9RlJXTPr0MxtrbduaBKwF8EDz183sweAwxtYJ6q65RDNz9bM\ntgeuIQjddcCzZvYscEADfUZVd2Uzt5Xw838GnAlsqDG/fdjHAcBGYJ6ZPQ4MqavXCGuug+Zva2uW\nr0NJQ4CnAdx9PjCweoGZ7UnwYOyfmNk/gB7uvrS+dSKu2x/obGbPmNmz4V/urNSs4XfA+e6ebMQ6\n2a7b3G3NpO5rQHdgu3A6V9tbW90of7a7A/9297Xh/9sFwMEZ9BlF3UFk52cLwcPpT6pl/l4Ej6hc\n5+5bgDnAsAZ6zXbNucBQsretrVa+BkMJW3fzACrMrLrXngT/gG4muNHeCDM7rIF1oqo7nOAb5iR3\nP5rgxoD3NbJug32Hu8OL3X1ZputEVLe525pJ3SXAy8Ai4Al3X5fBOlHVjfJnuxToa2alZtYJOALo\nlEGfUdTtDHxB83+2uPujBMNxDfW0AehKcPubZm1vI2quD2tmZVtbs3z9n7GO4C9MtQJ3rwpfrwaW\nuftb7l5B8G1jIMFfgLrWibKuA/cBhHsQq4GvZqlmtTMIhrEas04Udd+iedtab10z2wc4lmBocDeg\nt5mNIeKfbR11R9P87a2zprt/TnBMY3pY42XgUyLe1nrqLqX5P9uGeqp5P7Q19fUaUc3PiX5bW7x8\nDYZ5wCj4cox7Ucqyd4AuZrZ7OH0osBh4geAfd23rRFV3CXA24QFpM9uB4C/fR1mqWW2gu7/YyHWi\nqDue5m1rQ3XXEnxL3xQOc3wCdAvXifJnW1vd7jR/e+usaWbtgAHuPhQ4BfhG+P4X6ukzyrrZ+Nmm\nqnljuTdOnIPoAAABt0lEQVSAPcysm5kVEfz7eZHsbG9ja2Z7W1udvLwlRsqZCvuGs8YRHEDqHJ7J\nMBz4n3DZC+4+obZ13P2tHNQtBO4k+LZZRXD2zj+zWLMnwb2jBtS3TgTbWlvdZm1rhnXPI/iHuwl4\nGzgXqMzB9tZWN9Gc7c2g5pUEB1o3Av/r7o/k6GdbW91m/2xT6u8KPBAeUD4tpe6xwFUE/1/v8PQz\nzpq8vU2ombVtba3yMhhERCQ++TqUJCIiMVEwiIhIGgWDiIikUTCIiEgaBYOIiKRRMIiISBoFg7RY\nZjbMzJ6Puw+R1kbBIC2dLsQRybJ8ve22SKYS4V7DZ8DeBLdf/iHQN1x+S/U9+EUkM9pjkNYgCbzm\n7nsR3KG0h7sfABwJDI61M5EWSHsM0lrMD/9cDOxpZk8DTxI88U5EGkF7DNJabARw988InuZ3M2DA\nq2ZWUt+KIpJOwSCtSvhwoXvd/UngRwQPZ9k53q5EWhYFg7R0Nc9KegooM7MlwD+B6e6+JPdtibRc\nuu22iIik0R6DiIikUTCIiEgaBYOIiKRRMIiISBoFg4iIpFEwiIhIGgWDiIikUTCIiEia/wfA30I4\nskLtbgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13b989390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MIWt6QcBxHK666mrHfZMm7Wv+e/78YzF//rFJHlFK95VWkvLkGKls6LPDmIq+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1239463d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9/kvAp12m4R7HumiqC2QzrwPnALPN7FnP/6vILuejhvcQj/d/0Gjx2Sp95Ovt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\np7Gq52cP1INbHjmp7K8ZW3g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4B6COw09QL341tMD/B9QhyObfj/Ey/CSVH3D5PnIwX/Lr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123a5c190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+iy5Ht+fp415Ww8xsZ9cpnuZUZEuPAMvN7HzUUM1yvXZ5mCc8vu2RYzIze5vn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\nedQjehMyuqOQ8YxFDvBP6DFyOKogmyP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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12413a350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lcs</th>\n",
       "      <th>lrs</th>\n",
       "      <th>lps</th>\n",
       "      <th>lms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18_2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18_4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-0.003412</td>\n",
       "      <td>-0.001137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18_14</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>-0.233595</td>\n",
       "      <td>-0.058399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19_5</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>-0.265865</td>\n",
       "      <td>-0.033233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19_14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20_2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20_8</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>-0.265865</td>\n",
       "      <td>-0.033233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21_2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.011840</td>\n",
       "      <td>0.001973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21_12</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.294978</td>\n",
       "      <td>-0.098326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21_13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21_18</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>-0.921051</td>\n",
       "      <td>-0.038377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22_2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22_9</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-0.003412</td>\n",
       "      <td>-0.001137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22_10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101029</td>\n",
       "      <td>-0.101029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22_20</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>-0.233595</td>\n",
       "      <td>-0.058399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23_2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23_5</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-0.003412</td>\n",
       "      <td>-0.001137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23_12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>-0.233595</td>\n",
       "      <td>-0.058399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23_13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101029</td>\n",
       "      <td>-0.101029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24_3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24_6</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>-0.233595</td>\n",
       "      <td>-0.058399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24_18</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-0.003412</td>\n",
       "      <td>-0.001137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24_20</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-0.077712</td>\n",
       "      <td>-0.012952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24_21</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101029</td>\n",
       "      <td>-0.101029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25_4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.015253</td>\n",
       "      <td>0.005084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25_15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25_17</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-0.003412</td>\n",
       "      <td>-0.001137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25_19</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101029</td>\n",
       "      <td>-0.101029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25_21</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.294978</td>\n",
       "      <td>-0.098326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26_2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101029</td>\n",
       "      <td>-0.101029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37_36</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.248848</td>\n",
       "      <td>-0.248848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38_0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>-1.140307</td>\n",
       "      <td>-0.035635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38_3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.015253</td>\n",
       "      <td>0.005084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38_12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.028857</td>\n",
       "      <td>-0.028857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38_13</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-0.003412</td>\n",
       "      <td>-0.001137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38_17</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38_23</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101029</td>\n",
       "      <td>-0.101029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38_37</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.248848</td>\n",
       "      <td>-0.248848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39_2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009235</td>\n",
       "      <td>-0.009235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39_5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101029</td>\n",
       "      <td>-0.101029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39_12</th>\n",
       "      <td>40.0</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>-1.052118</td>\n",
       "      <td>-0.026303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39_13</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>-0.150335</td>\n",
       "      <td>-0.006264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39_17</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.015253</td>\n",
       "      <td>0.005084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39_18</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39_22</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.248848</td>\n",
       "      <td>-0.248848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39_32</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.046130</td>\n",
       "      <td>-0.023065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39_36</th>\n",
       "      <td>9.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-0.780621</td>\n",
       "      <td>-0.086736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40_3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-0.003412</td>\n",
       "      <td>-0.001137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40_11</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.015253</td>\n",
       "      <td>0.005084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40_16</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40_17</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.248848</td>\n",
       "      <td>-0.248848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40_18</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101029</td>\n",
       "      <td>-0.101029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40_19</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.046130</td>\n",
       "      <td>-0.023065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41_3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-0.003412</td>\n",
       "      <td>-0.001137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41_8</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0.724138</td>\n",
       "      <td>-0.679913</td>\n",
       "      <td>-0.023445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41_14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41_21</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.028857</td>\n",
       "      <td>-0.028857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41_25</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101029</td>\n",
       "      <td>-0.101029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41_30</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.015253</td>\n",
       "      <td>0.005084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41_38</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.248848</td>\n",
       "      <td>-0.248848</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        lcs       lrs       lps       lms\n",
       "18_2    1.0  1.000000 -0.059441 -0.059441\n",
       "18_4    3.0  0.666667 -0.003412 -0.001137\n",
       "18_14   4.0  0.750000 -0.233595 -0.058399\n",
       "19_5    8.0  0.750000 -0.265865 -0.033233\n",
       "19_14   1.0  1.000000 -0.059441 -0.059441\n",
       "20_2    1.0  1.000000 -0.059441 -0.059441\n",
       "20_8    8.0  0.750000 -0.265865 -0.033233\n",
       "21_2    6.0  0.666667  0.011840  0.001973\n",
       "21_12   3.0  1.000000 -0.294978 -0.098326\n",
       "21_13   1.0  1.000000 -0.059441 -0.059441\n",
       "21_18  24.0  0.708333 -0.921051 -0.038377\n",
       "22_2    1.0  1.000000 -0.059441 -0.059441\n",
       "22_9    3.0  0.666667 -0.003412 -0.001137\n",
       "22_10   1.0  1.000000 -0.101029 -0.101029\n",
       "22_20   4.0  0.750000 -0.233595 -0.058399\n",
       "23_2    1.0  1.000000 -0.059441 -0.059441\n",
       "23_5    3.0  0.666667 -0.003412 -0.001137\n",
       "23_12   4.0  0.750000 -0.233595 -0.058399\n",
       "23_13   1.0  1.000000 -0.101029 -0.101029\n",
       "24_3    1.0  1.000000 -0.059441 -0.059441\n",
       "24_6    4.0  0.750000 -0.233595 -0.058399\n",
       "24_18   3.0  0.666667 -0.003412 -0.001137\n",
       "24_20   6.0  0.666667 -0.077712 -0.012952\n",
       "24_21   1.0  1.000000 -0.101029 -0.101029\n",
       "25_4    3.0  0.666667  0.015253  0.005084\n",
       "25_15   1.0  1.000000 -0.059441 -0.059441\n",
       "25_17   3.0  0.666667 -0.003412 -0.001137\n",
       "25_19   1.0  1.000000 -0.101029 -0.101029\n",
       "25_21   3.0  1.000000 -0.294978 -0.098326\n",
       "26_2    1.0  1.000000 -0.101029 -0.101029\n",
       "...     ...       ...       ...       ...\n",
       "37_36   1.0  1.000000 -0.248848 -0.248848\n",
       "38_0   32.0  0.687500 -1.140307 -0.035635\n",
       "38_3    3.0  0.666667  0.015253  0.005084\n",
       "38_12   1.0  1.000000 -0.028857 -0.028857\n",
       "38_13   3.0  0.666667 -0.003412 -0.001137\n",
       "38_17   1.0  1.000000 -0.059441 -0.059441\n",
       "38_23   1.0  1.000000 -0.101029 -0.101029\n",
       "38_37   1.0  1.000000 -0.248848 -0.248848\n",
       "39_2    1.0  1.000000 -0.009235 -0.009235\n",
       "39_5    1.0  1.000000 -0.101029 -0.101029\n",
       "39_12  40.0  0.700000 -1.052118 -0.026303\n",
       "39_13  24.0  0.708333 -0.150335 -0.006264\n",
       "39_17   3.0  0.666667  0.015253  0.005084\n",
       "39_18   1.0  1.000000 -0.059441 -0.059441\n",
       "39_22   1.0  1.000000 -0.248848 -0.248848\n",
       "39_32   2.0  1.000000 -0.046130 -0.023065\n",
       "39_36   9.0  0.666667 -0.780621 -0.086736\n",
       "40_3    3.0  0.666667 -0.003412 -0.001137\n",
       "40_11   3.0  0.666667  0.015253  0.005084\n",
       "40_16   1.0  1.000000 -0.059441 -0.059441\n",
       "40_17   1.0  1.000000 -0.248848 -0.248848\n",
       "40_18   1.0  1.000000 -0.101029 -0.101029\n",
       "40_19   2.0  1.000000 -0.046130 -0.023065\n",
       "41_3    3.0  0.666667 -0.003412 -0.001137\n",
       "41_8   29.0  0.724138 -0.679913 -0.023445\n",
       "41_14   1.0  1.000000 -0.059441 -0.059441\n",
       "41_21   1.0  1.000000 -0.028857 -0.028857\n",
       "41_25   1.0  1.000000 -0.101029 -0.101029\n",
       "41_30   3.0  0.666667  0.015253  0.005084\n",
       "41_38   1.0  1.000000 -0.248848 -0.248848\n",
       "\n",
       "[128 rows x 4 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ump_jump.gmm_component_filter(p_ncs=np.arange(18, 42), threshold=0.65)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如下所示找个数量和失败率都很高的分类看看\n",
    "发现特征了吗？跳空缺口的能量大概在－1.6左右，跳空时间间隔大约是9-11\n",
    "这个类别里因子触发的70%交易都以失败告终，所以呢？拦截呗！（拦截这个词对我有种特殊的情怀，那段美好的青春岁月好怀念！👻）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>result</th>\n",
       "      <th>jump_power</th>\n",
       "      <th>diff_days</th>\n",
       "      <th>ind</th>\n",
       "      <th>ss</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-08-19</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.633778</td>\n",
       "      <td>11</td>\n",
       "      <td>50</td>\n",
       "      <td>18</td>\n",
       "      <td>-0.036806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-28</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.618578</td>\n",
       "      <td>11</td>\n",
       "      <td>119</td>\n",
       "      <td>18</td>\n",
       "      <td>0.007078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.664892</td>\n",
       "      <td>9</td>\n",
       "      <td>398</td>\n",
       "      <td>18</td>\n",
       "      <td>-0.025568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-11</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.588977</td>\n",
       "      <td>10</td>\n",
       "      <td>923</td>\n",
       "      <td>18</td>\n",
       "      <td>0.086466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-02</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.660151</td>\n",
       "      <td>9</td>\n",
       "      <td>1241</td>\n",
       "      <td>18</td>\n",
       "      <td>-0.032483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-22</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.620002</td>\n",
       "      <td>9</td>\n",
       "      <td>1511</td>\n",
       "      <td>18</td>\n",
       "      <td>-0.024310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-08</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.642379</td>\n",
       "      <td>9</td>\n",
       "      <td>1558</td>\n",
       "      <td>18</td>\n",
       "      <td>-0.021877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-25</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.699512</td>\n",
       "      <td>9</td>\n",
       "      <td>1586</td>\n",
       "      <td>18</td>\n",
       "      <td>-0.025143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-16</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.680736</td>\n",
       "      <td>9</td>\n",
       "      <td>1698</td>\n",
       "      <td>18</td>\n",
       "      <td>0.061564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-01</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.656696</td>\n",
       "      <td>10</td>\n",
       "      <td>1850</td>\n",
       "      <td>18</td>\n",
       "      <td>0.047325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-10</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.634521</td>\n",
       "      <td>9</td>\n",
       "      <td>2086</td>\n",
       "      <td>18</td>\n",
       "      <td>-0.007584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-24</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.664548</td>\n",
       "      <td>9</td>\n",
       "      <td>2117</td>\n",
       "      <td>18</td>\n",
       "      <td>-0.039683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-24</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.615231</td>\n",
       "      <td>11</td>\n",
       "      <td>2178</td>\n",
       "      <td>18</td>\n",
       "      <td>0.020121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-10</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.586630</td>\n",
       "      <td>11</td>\n",
       "      <td>2646</td>\n",
       "      <td>18</td>\n",
       "      <td>-0.033229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.605406</td>\n",
       "      <td>9</td>\n",
       "      <td>2913</td>\n",
       "      <td>18</td>\n",
       "      <td>0.035427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-19</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.646098</td>\n",
       "      <td>11</td>\n",
       "      <td>3057</td>\n",
       "      <td>18</td>\n",
       "      <td>-0.040881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-26</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.623594</td>\n",
       "      <td>10</td>\n",
       "      <td>3253</td>\n",
       "      <td>18</td>\n",
       "      <td>-0.096399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-06</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.599377</td>\n",
       "      <td>9</td>\n",
       "      <td>3285</td>\n",
       "      <td>18</td>\n",
       "      <td>-0.091164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-28</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.664107</td>\n",
       "      <td>11</td>\n",
       "      <td>3352</td>\n",
       "      <td>18</td>\n",
       "      <td>-0.054264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-03</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.659790</td>\n",
       "      <td>10</td>\n",
       "      <td>3383</td>\n",
       "      <td>18</td>\n",
       "      <td>-0.158246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-10</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.633026</td>\n",
       "      <td>11</td>\n",
       "      <td>3561</td>\n",
       "      <td>18</td>\n",
       "      <td>-0.468551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-10</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.671418</td>\n",
       "      <td>9</td>\n",
       "      <td>3958</td>\n",
       "      <td>18</td>\n",
       "      <td>-0.113089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-21</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.700286</td>\n",
       "      <td>9</td>\n",
       "      <td>4131</td>\n",
       "      <td>18</td>\n",
       "      <td>-0.025519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-29</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.672649</td>\n",
       "      <td>9</td>\n",
       "      <td>4526</td>\n",
       "      <td>18</td>\n",
       "      <td>0.115759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            result  jump_power  diff_days   ind  ss    profit\n",
       "2015-08-19       0   -1.633778         11    50  18 -0.036806\n",
       "2015-09-28       1   -1.618578         11   119  18  0.007078\n",
       "2016-01-19       0   -1.664892          9   398  18 -0.025568\n",
       "2014-08-11       1   -1.588977         10   923  18  0.086466\n",
       "2015-02-02       0   -1.660151          9  1241  18 -0.032483\n",
       "2015-05-22       0   -1.620002          9  1511  18 -0.024310\n",
       "2015-06-08       0   -1.642379          9  1558  18 -0.021877\n",
       "2015-06-25       0   -1.699512          9  1586  18 -0.025143\n",
       "2013-08-16       1   -1.680736          9  1698  18  0.061564\n",
       "2013-10-01       1   -1.656696         10  1850  18  0.047325\n",
       "2014-02-10       0   -1.634521          9  2086  18 -0.007584\n",
       "2014-02-24       0   -1.664548          9  2117  18 -0.039683\n",
       "2014-03-24       1   -1.615231         11  2178  18  0.020121\n",
       "2012-10-10       0   -1.586630         11  2646  18 -0.033229\n",
       "2012-12-31       1   -1.605406          9  2913  18  0.035427\n",
       "2013-02-19       0   -1.646098         11  3057  18 -0.040881\n",
       "2013-04-26       0   -1.623594         10  3253  18 -0.096399\n",
       "2013-05-06       0   -1.599377          9  3285  18 -0.091164\n",
       "2013-05-28       0   -1.664107         11  3352  18 -0.054264\n",
       "2013-06-03       0   -1.659790         10  3383  18 -0.158246\n",
       "2011-08-10       0   -1.633026         11  3561  18 -0.468551\n",
       "2011-11-10       0   -1.671418          9  3958  18 -0.113089\n",
       "2011-11-21       0   -1.700286          9  4131  18 -0.025519\n",
       "2011-12-29       1   -1.672649          9  4526  18  0.115759"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ump_jump.nts['21_18']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面统计一下跳空能量的分布情况，进一步总结特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NormaltestResult(statistic=11612.386867948926, pvalue=0.0)\n"
     ]
    },
    {
     "data": {
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jIr4NnAE8lJk/BDYB/cBfUD/RexDYDPzziOgDrgd+bwrPJ0maJi0d6Wfmi8Bbqus7gIsm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\nTPQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123946490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "jump_power show hist and qcuts\n",
      "(-1.0692, -1.000217]    504\n",
      "(-1.243, -1.149]        504\n",
      "(-1.669, -1.499]        504\n",
      "(-2.304, -1.932]        504\n",
      "[-123.0916, -3.0491]    504\n",
      "(-1.149, -1.0692]       503\n",
      "(-1.366, -1.243]        503\n",
      "(-1.499, -1.366]        503\n",
      "(-1.932, -1.669]        503\n",
      "(-3.0491, -2.304]       503\n",
      "Name: jump_power, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats as scs\n",
    "import MlFiterBinsCs\n",
    "ZLog.info(scs.normaltest(ump_jump.fiter.df.jump_power))\n",
    "MlFiterBinsCs.show_orders_hist(ump_jump.fiter.df, s_list = ['jump_power'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**如下jump power选取llps没有执行最优化求解，因为数据量太少，按照分类数目要求大于20, 其它0， 0， 0.65来跑跑看看先**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lcs</th>\n",
       "      <th>lrs</th>\n",
       "      <th>lps</th>\n",
       "      <th>lms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18_2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19_14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20_2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21_12</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.294978</td>\n",
       "      <td>-0.098326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21_13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21_18</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>-0.921051</td>\n",
       "      <td>-0.038377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22_2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22_10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101029</td>\n",
       "      <td>-0.101029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23_2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23_13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101029</td>\n",
       "      <td>-0.101029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24_3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24_21</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101029</td>\n",
       "      <td>-0.101029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25_15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25_19</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101029</td>\n",
       "      <td>-0.101029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25_21</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.294978</td>\n",
       "      <td>-0.098326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26_2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101029</td>\n",
       "      <td>-0.101029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26_12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26_17</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.294978</td>\n",
       "      <td>-0.098326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27_0</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>-0.745038</td>\n",
       "      <td>-0.021287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27_3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27_9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101029</td>\n",
       "      <td>-0.101029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27_23</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.294978</td>\n",
       "      <td>-0.098326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28_2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28_9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101029</td>\n",
       "      <td>-0.101029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28_21</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.248848</td>\n",
       "      <td>-0.248848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29_15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29_19</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101029</td>\n",
       "      <td>-0.101029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29_27</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.294978</td>\n",
       "      <td>-0.098326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30_3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30_11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101029</td>\n",
       "      <td>-0.101029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35_28</th>\n",
       "      <td>23.0</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>-0.215931</td>\n",
       "      <td>-0.009388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35_30</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.248848</td>\n",
       "      <td>-0.248848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36_8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101029</td>\n",
       "      <td>-0.101029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36_16</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.294978</td>\n",
       "      <td>-0.098326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36_18</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37_20</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37_21</th>\n",
       "      <td>40.0</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>-1.602485</td>\n",
       "      <td>-0.040062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37_24</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101029</td>\n",
       "      <td>-0.101029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37_36</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.248848</td>\n",
       "      <td>-0.248848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38_0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>-1.140307</td>\n",
       "      <td>-0.035635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38_12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.028857</td>\n",
       "      <td>-0.028857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38_17</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38_23</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101029</td>\n",
       "      <td>-0.101029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38_37</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.248848</td>\n",
       "      <td>-0.248848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39_2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009235</td>\n",
       "      <td>-0.009235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39_5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101029</td>\n",
       "      <td>-0.101029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39_12</th>\n",
       "      <td>40.0</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>-1.052118</td>\n",
       "      <td>-0.026303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39_13</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>-0.150335</td>\n",
       "      <td>-0.006264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39_18</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39_22</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.248848</td>\n",
       "      <td>-0.248848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39_32</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.046130</td>\n",
       "      <td>-0.023065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40_16</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40_17</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.248848</td>\n",
       "      <td>-0.248848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40_18</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101029</td>\n",
       "      <td>-0.101029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40_19</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.046130</td>\n",
       "      <td>-0.023065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41_8</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0.724138</td>\n",
       "      <td>-0.679913</td>\n",
       "      <td>-0.023445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41_14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059441</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41_21</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.028857</td>\n",
       "      <td>-0.028857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41_25</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101029</td>\n",
       "      <td>-0.101029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41_38</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.248848</td>\n",
       "      <td>-0.248848</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        lcs       lrs       lps       lms\n",
       "18_2    1.0  1.000000 -0.059441 -0.059441\n",
       "19_14   1.0  1.000000 -0.059441 -0.059441\n",
       "20_2    1.0  1.000000 -0.059441 -0.059441\n",
       "21_12   3.0  1.000000 -0.294978 -0.098326\n",
       "21_13   1.0  1.000000 -0.059441 -0.059441\n",
       "21_18  24.0  0.708333 -0.921051 -0.038377\n",
       "22_2    1.0  1.000000 -0.059441 -0.059441\n",
       "22_10   1.0  1.000000 -0.101029 -0.101029\n",
       "23_2    1.0  1.000000 -0.059441 -0.059441\n",
       "23_13   1.0  1.000000 -0.101029 -0.101029\n",
       "24_3    1.0  1.000000 -0.059441 -0.059441\n",
       "24_21   1.0  1.000000 -0.101029 -0.101029\n",
       "25_15   1.0  1.000000 -0.059441 -0.059441\n",
       "25_19   1.0  1.000000 -0.101029 -0.101029\n",
       "25_21   3.0  1.000000 -0.294978 -0.098326\n",
       "26_2    1.0  1.000000 -0.101029 -0.101029\n",
       "26_12   1.0  1.000000 -0.059441 -0.059441\n",
       "26_17   3.0  1.000000 -0.294978 -0.098326\n",
       "27_0   35.0  0.714286 -0.745038 -0.021287\n",
       "27_3    1.0  1.000000 -0.059441 -0.059441\n",
       "27_9    1.0  1.000000 -0.101029 -0.101029\n",
       "27_23   3.0  1.000000 -0.294978 -0.098326\n",
       "28_2    1.0  1.000000 -0.059441 -0.059441\n",
       "28_9    1.0  1.000000 -0.101029 -0.101029\n",
       "28_21   1.0  1.000000 -0.248848 -0.248848\n",
       "29_15   1.0  1.000000 -0.059441 -0.059441\n",
       "29_19   1.0  1.000000 -0.101029 -0.101029\n",
       "29_27   3.0  1.000000 -0.294978 -0.098326\n",
       "30_3    1.0  1.000000 -0.059441 -0.059441\n",
       "30_11   1.0  1.000000 -0.101029 -0.101029\n",
       "...     ...       ...       ...       ...\n",
       "35_28  23.0  0.652174 -0.215931 -0.009388\n",
       "35_30   1.0  1.000000 -0.248848 -0.248848\n",
       "36_8    1.0  1.000000 -0.101029 -0.101029\n",
       "36_16   3.0  1.000000 -0.294978 -0.098326\n",
       "36_18   1.0  1.000000 -0.059441 -0.059441\n",
       "37_20   1.0  1.000000 -0.059441 -0.059441\n",
       "37_21  40.0  0.700000 -1.602485 -0.040062\n",
       "37_24   1.0  1.000000 -0.101029 -0.101029\n",
       "37_36   1.0  1.000000 -0.248848 -0.248848\n",
       "38_0   32.0  0.687500 -1.140307 -0.035635\n",
       "38_12   1.0  1.000000 -0.028857 -0.028857\n",
       "38_17   1.0  1.000000 -0.059441 -0.059441\n",
       "38_23   1.0  1.000000 -0.101029 -0.101029\n",
       "38_37   1.0  1.000000 -0.248848 -0.248848\n",
       "39_2    1.0  1.000000 -0.009235 -0.009235\n",
       "39_5    1.0  1.000000 -0.101029 -0.101029\n",
       "39_12  40.0  0.700000 -1.052118 -0.026303\n",
       "39_13  24.0  0.708333 -0.150335 -0.006264\n",
       "39_18   1.0  1.000000 -0.059441 -0.059441\n",
       "39_22   1.0  1.000000 -0.248848 -0.248848\n",
       "39_32   2.0  1.000000 -0.046130 -0.023065\n",
       "40_16   1.0  1.000000 -0.059441 -0.059441\n",
       "40_17   1.0  1.000000 -0.248848 -0.248848\n",
       "40_18   1.0  1.000000 -0.101029 -0.101029\n",
       "40_19   2.0  1.000000 -0.046130 -0.023065\n",
       "41_8   29.0  0.724138 -0.679913 -0.023445\n",
       "41_14   1.0  1.000000 -0.059441 -0.059441\n",
       "41_21   1.0  1.000000 -0.028857 -0.028857\n",
       "41_25   1.0  1.000000 -0.101029 -0.101029\n",
       "41_38   1.0  1.000000 -0.248848 -0.248848\n",
       "\n",
       "[81 rows x 4 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llps = ump_jump.cprs[(ump_jump.cprs['lps'] <= 0) & (ump_jump.cprs['lms'] <= 0 ) \n",
    "                & (ump_jump.cprs['lrs'] >=0.65) & (  (ump_jump.cprs['lcs'] >= 20)\n",
    "                                                    | (ump_jump.cprs['lrs'] == 1) )]\n",
    "llps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我擦，预测能提高0.013"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "nts_pd.shape = (182, 6)\n",
      "nts_pd loss rate = 0.681318681319\n",
      "improved rate = 0.0131082423039\n",
      "predict win rate = 0.512015888779\n"
     ]
    },
    {
     "data": {
      "image/png": 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Goh6nFezYYvgx8CXgMasDEULEL88ZupKCwSDdfQHauwf4/iN7aGrtpXcgwKHa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\nMP8fkOng7wP3qfQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123cca910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ump_jump.choose_cprs_component(llps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本地序列化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ump_jump.dump_clf(llps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_____"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "好了，准备进入正题，做好准备，前方高能\n",
    "\n",
    "### 只有一个主裁是不够的，我们需要构造多个主裁，多个辅助裁判，还有边裁（边裁下一章介绍）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "deg ump 主裁，只使用全局最优llps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>result</th>\n",
       "      <th>deg_hisWindowPd</th>\n",
       "      <th>deg_windowPd</th>\n",
       "      <th>deg_60WindowPd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.256691</td>\n",
       "      <td>3.895622</td>\n",
       "      <td>5.363046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>0</td>\n",
       "      <td>15.908454</td>\n",
       "      <td>4.108007</td>\n",
       "      <td>4.199374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            result  deg_hisWindowPd  deg_windowPd  deg_60WindowPd\n",
       "2015-07-28       1        -1.256691      3.895622        5.363046\n",
       "2015-07-28       0        15.908454      4.108007        4.199374"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from MlFiterDegPd import MlFiterDegPdClass\n",
    "ump_deg = UmpMainClass(orders_pd_ump, MlFiterDegPdClass)\n",
    "ump_deg.fiter.df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ktwNZePs4Bg37LQCfTJ/C4Y1/6SG0aQCx+V9GlFhq4oJhHflm3EQWlO5DWz7h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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b01af90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OpRAGbS8IWkmhMITR0dGWCILuhT1QTJfgEe7+LChOTOCF2+9C3+i9eMPKARjE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\nBIKmDA0NB3Q8bK0q4FnT36n/zrds58PfeggXfe88TMM0Or5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1C7fpthvsCy+Z1ADAaiWC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z8XCxZ8jOPHj9rfD7V+CAzDYMiQ19GtW3eMGzcajzzyCEaMeAs6nc5hMuEnt0AvgMpIf0L4keww\ntCiRFBZj1oHUpn1rZeIsiX8fq9UsKpXURuoXkF+Mg+csEYKd2aMzK17IiLqdWroSWi3Gnu0Q+lb4\n6ywRKHM0pVEmq9WKs2czkZ6ejrNnM9GgQQMcOXIER48eRWpqe8ye/QHq1auvxlemcABNGQQRS5d+\njLCwMAwf/haysm5j5MjhyM/Px5YtOwHwKYW0tD8wevT4IFvqDIvFgk8+WYZdu37GwoUL0aABX5rj\nGBpWk9XtLekPgEiAJLj16MFqrRyI8jX1bFOf5+CbHc4EO36HDg8LmfpRplDK6XubRlDGMXEm/t25\ncweHD6chLS0dR44cQVFRIRo3boKUlBSkpLRDo0aJOHBgP/75z0W4du0KWrRoiRUrVgf0u5dl0JRB\niCIuLg56Pf8TxMTEwmKxoHHjRBw5ko42bVJw4MB+JCenBtlKeeh0OowYMQrdu/fE6NGj8PTT/TBk\nyBBbLwK5boHeLYBqkf5YVifa7ZSA44IRjhVHC8huVP1yL+V5f4HsRyD0StAO/GIX/OZANmts9wrv\nMFmtjuWSgqMZCNa//SpeRi4CCXHqy7GhFf/9nSMAcucQP58mkwknTx5HWlo60tLScOXKZVSuXNlW\n898NI0eOQUyMc1O4Tp26IDW1PTZt2oDISM/EQArfQCMEQURRURHmzXsfd+5kw2w2Y9Cg55GY2LTU\n9UMwmUxYvPhDHD6cho8+WoSaNWsBcN5huCLXebeL9Y30Jy0LDG60IHSkfkNHqEYr0qHS0L8YwSJA\nhlJpIF8BYHZxj/FwfDY5Drhx4wbS09OQlpaGEyeOg+M4NG/eAikpqUhObos6deqF/PxW1kCrDCg0\nwbFjhzFlygS89NIreO6550AY/uJwOZnUAHjYWQQ+HBtMpru3ZZuBkvrlz+1cFaE1z4HAWYrZvwXQ\ne8Ef/l4jtoRKuaTW2hJKHPRr167hypUrSE5OQUxMHAoLC3Hs2FE78z8r6zZq1aqN5GQ+9N+8eUu7\nlglF8EAdAgrNUFxcjAULZuPixQv48MMPwTAMTp06hbi4WLRo0dzFUa53sYFAaEULhB2gUCnBV0Yo\nI7CpKfUbHJ6DvC3eL4DKWf/KQ/+hVC7pyLlQK43gi0qi1WrB5MkTsW/fPkRERIBlWcTGxqNNm2Tb\n7j8V1apV88suisCAOgQUmqCkpATHjh3GmTOnsW/fHpw+fdI+eSUkJOD77793OIK159a1RihFCxxV\nDuUQyDJJMZyJZMETnHIXuQgk618OwW5rTOBvGsEb4p+4VDI3NxeHD/N5/8OH05GXl4cHH2yEatWq\n4ueff4bRWIzExKaYPXsBqlevoeI3plAb1CGg0ASzZ8/E9u0/2v+uWLESqlSpgsqVK+OFFwajZcs2\ntsk8NIR7AO2jBd5J/eqg0/G9AbRceFxFLoIXLhennCByCKQItNMUWhUAnqMozmW5yoh/rvT+k5NT\nkJzcFsnJbe0t0gEgOzsLK1b8Ezt2bMO4cZPQt+8zAf72FP6AOgQUmuDo0cNIS/sDjRolIimpGapU\nqQoA2L17FxYsmIOxY8ejZ8+eAJwn+mDn8wMRLfBV6pfvohj8yEWwwuWB5EuoY19oRVHEaQRxBMVV\nykSs+Oet3r875OTcQUJCRUoSDHFQh6CM4tSpk1i5cimWLv0E169fk+2BsGnT99i06Xvo9Xq8/PJQ\ndOrUJSi25ubexbRpk2Aw6DFr1mx7aZE0WhC8fD7gX7RAbalfX/ozBArORD/1bHEcN098CWJPIGzx\nBvKhe+1skRL/XEeaHJ0mtfT+KUovqA5BGcQ33/wHO3ZstdfkLl26CMOHv2XvgbBnz240a9YC69ev\nxb///RWMxmKMGPEa2rXrYNc+0BLx8QlYuvQTbN26Cc88MwDTps1Aly5dINQ6B1crAIBt56TMFl/Z\n60q/k7OiXzDHRT1b1Bg3ltVLbLFatW8iJa/qFzhblIwbqY44fvwYZs2ahdat26Bv3344f/5PWb3/\np54aQPX+KSSgDkEpRc2atTF37kLMmjUDAJCZmSHpgfDHHwfAMCxatGgNvV4PvT4GtWrVxvnz59Ck\nSdOg2f3440+hXbuOmDRpLLZv34qpU6cjMjJS0rte0LnXfvcn5MvFtljAstJSSZkjnRYyfxcFV7YE\ng3Phiy3KCWzejZujLRxngdmsvdSve1t8V130dtwI8S8/Pw9HjhzGmTNnEBUVhZ07f8KuXT8jNbU9\nXnjhZbz33jyq90/hFtQhKKXo2vUR3Lz5l/1vcXg6KioaBQUFKCwsRExMjP31yMgoFBQEvzdC5cpV\n8OmnX2LDhu/Qv39fzJ49FykpKV7t0AMFYfK1SshrYlIboK3Ur/O4BEf/X2qLtHGTIPXrnXKdP/bL\n2RIsop+zLcpUF70j/snr/aenp+Hs2bOIjo5BmzbJaNu2Hfr2fRZ79/6KVauW4+DB3xEZGRmyqqeh\nBI7j8NFH83H+/DmEhYVh4sRpdqE1ADhz5hSWLVsMgCdNz5gxCz//vANbt262p2TOnz+LTZt2IDo6\nxtVlQhbUISgjEO+kCwsLEBsbi+ho3jEQXi+UlQUNBhiGwYABz6Jz54cwbtwoNGvWHOPGjUdYWJh9\nEhVHC/R69VnuSvP+RBeAgGX1dnElrSCWtDWbpc2SghEtINck6nUWS4ns5wJdKikvCx3Mjpty3SWl\nrbiVaCU4Ev/c6f0PH/42GjVKdIqm9e8/EI888ig+//xTVKxYSZMxKO347bfdKCkpwcqVq3Hq1Eks\nW/Yx5s37yP7+Bx/MwezZH6BmzVrYsmUjbt78C71790Hv3n0AAIsWLcCTTz5dKp0BgDoEZQaNGyfi\n6NHDaN062d4DoWnTJKxatRwmkwlGoxFXrlyyNyEKFTzwQA2sWfMdvvrqC/Tv3xcLFnyAZs2ai9oq\nm21hWP8neX+kfsWVCHy0gAtKPl+sL69Va2WlrH+CYDQpcu4YaBalNLRPO+n1YTb9f6EHgOvPS4l/\nJpMJx497r/cvh4SEihgzZqJaX63M4/jxo2jfvhMAoFmz5sjIOGN/78qVy4iLq4C1a7/GhQt/olOn\nh1C7dh37+xkZp3Hp0sVSPd7UISgjeOut0ViwYLa9B8IjjzwKhmEwcOCzGDHiVXAcMGzYWyGZQ2QY\nBi+9NAQPP9wN48aNRpcuXfD22yNt3AcDrFbWPslznLLmN2rnr+Vz6NagaMtLd8Xqtlb2tVoCgN0W\ni8UMlkVQduhyRD+t0k6eiH8cxyEnJweRkVGIjY0Dw7DgOM6t3v/kydOp3r+GKCwskKRZdTq+4RXL\nssjNvYdTp45j7NiJqFGjJiZMeAdNmjRFcnJbAMCaNZ9jyJDXg2W6KqAOQSnGAw9Ux8qVfBvQ2rXr\nYNmyVU6f6dOnL/r06au1aT6hbt36WLv2e3z66XIMHDgACxcuRMOGjUTRAnEYVogWeKda55/Ur1w+\nn+OsQYsWSLsFkhy68miBmtUSQme84O/QXREg1eJ7kHuOlPx5djiBkydP4s0330B4eDiaNk1CXl4+\nsrOz7Hr/Tz3VH1OmvEv1/oOIqKhoFBYKaVbiDABAfHw8atasjTp16gIAOnToiIyMM0hObov8/Hxc\nvXoFbdqkBMVutUAdAoqQAsuyGD78bXTv3hNjx76Dxx9/Aq+99jpYlpVZcMwS4p8Ygcxfh260wH1r\n5UCx/sUIrXJJ9UiHjnl/JVETjuNw4cJ5HDrEE/8yMs4gNbUdTp8+haNHj6BWrTqYN28hWrZsrdZX\npvATLVu2wr59e/DIIz1w8uQJNGz4oP29GjVqoaioENevX0PNmrVw7NgR+2br6NHDSElpFyyzVQMV\nJqIIWZjNZixaNB/79u1F796P4dKly7hx4zomTJiAJk2aSD5LJuFA9qqXQyj1RHBsrUzs4d/zTyjJ\nFzj2aAimRLU36oLeEf8Eromc3n/Dhg/aRX8SE5tCr9fj/v37WLXqX9i4cQMSEipi06Ydgf3yZQS+\nVAAwDIM5c97FzZs3oNPpMWHCVPsO3901/vzzHABg8uSZyMw8g+LiYjz5ZF8cPpyGFSuWAgBatGiJ\nf/xjLADgm2/WwGAwYODA5wL19VUDVSqkKFXIzb2HlSuX4fjxo7h8+ZLkvWrVqmHu3Hlo1KgRAEG1\nLpjd+Xg7grv4KdvBatMgydEuLVv3erLFUV2QdwwEqV+lTZJc6f0L3f6kev9yOHs2A/n5+fYcNIV7\n/PrrL9i37zdMmTITp06dxFdffS6pABgyZLCkAqBVqza4fPkidu7cjvfem4dDhw5i48b1mD37gyB+\ni+CDKhVSlCpcunQRmzf/gKioaKSkpKJp02Zo1KgRDh06iNOnT6FChQRbm2B+pycl1mnPcOft0I5b\noFzrX5pOCUaDInG5pNoESF9sAViwrMFeKWK1mmC1On9OzPwH4Fbv/513xivW+xejceMmnj9EYYcv\nFQC8o24Bx3EoKMiHXh96pOpQAnUIShmOHEnH6tWrsHTpJ8E2JWBo1aoNfvzxZ8TGxkkIaY8++jcc\nOnQAf//73zFs2DD07z/AvuCQUDkvCGMNyi40EMqC/vZIEDcocqyJ1xKuCZCBExGSEv8IYdJ57PLy\n8rBz507Ur98AKSmptrK/w7J6/716PY5x46ZQvf8gwJcKgFq1auPGjRsYPHgA7t/PxYIFi4P4DUIf\n1CEohVB7Mhc3STp3LhOLFy+ETqeDwRCGadPeQ0JCguZNklyFW1NTO2D9+s2YM2cmtm3bivnzF6BS\npcpgGD0E3YLgifYA/kULlGgleBP6JzXxYoXD4MpCywn3qCMi5Cvx78yZDKxYsQImkwlhYWGIi4tH\nq1Zt0LZtK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Is5c97FkiUL0bPnY5rZqxTESWzQoCGuXLmCMWNGYseObXjzzX8E2TKK8gjq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125235d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7MusFddNvt29j36FNJb5fubGSfR/rd4j37V3k9x3E+69orNb2Rfo9VbZMvkjq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\nIhWlBC4iUlFK4CIiFaUELiJSUf8fYgfHe31OyPQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125bb0210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a7c6e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lcs</th>\n",
       "      <th>lrs</th>\n",
       "      <th>lps</th>\n",
       "      <th>lms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56_21</th>\n",
       "      <td>84.0</td>\n",
       "      <td>0.702381</td>\n",
       "      <td>-1.699820</td>\n",
       "      <td>-0.020236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57_27</th>\n",
       "      <td>86.0</td>\n",
       "      <td>0.697674</td>\n",
       "      <td>-1.591000</td>\n",
       "      <td>-0.018500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58_35</th>\n",
       "      <td>141.0</td>\n",
       "      <td>0.687943</td>\n",
       "      <td>-5.994469</td>\n",
       "      <td>-0.042514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61_17</th>\n",
       "      <td>137.0</td>\n",
       "      <td>0.693431</td>\n",
       "      <td>-5.788397</td>\n",
       "      <td>-0.042251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61_59</th>\n",
       "      <td>90.0</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>-1.401181</td>\n",
       "      <td>-0.015569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62_47</th>\n",
       "      <td>95.0</td>\n",
       "      <td>0.694737</td>\n",
       "      <td>-1.830953</td>\n",
       "      <td>-0.019273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63_31</th>\n",
       "      <td>155.0</td>\n",
       "      <td>0.683871</td>\n",
       "      <td>-6.826563</td>\n",
       "      <td>-0.044042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64_34</th>\n",
       "      <td>111.0</td>\n",
       "      <td>0.711712</td>\n",
       "      <td>-5.487420</td>\n",
       "      <td>-0.049436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64_41</th>\n",
       "      <td>57.0</td>\n",
       "      <td>0.701754</td>\n",
       "      <td>-3.558004</td>\n",
       "      <td>-0.062421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64_50</th>\n",
       "      <td>85.0</td>\n",
       "      <td>0.694118</td>\n",
       "      <td>-1.677678</td>\n",
       "      <td>-0.019737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65_15</th>\n",
       "      <td>145.0</td>\n",
       "      <td>0.703448</td>\n",
       "      <td>-5.934523</td>\n",
       "      <td>-0.040928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65_37</th>\n",
       "      <td>86.0</td>\n",
       "      <td>0.697674</td>\n",
       "      <td>-1.734857</td>\n",
       "      <td>-0.020173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66_4</th>\n",
       "      <td>88.0</td>\n",
       "      <td>0.693182</td>\n",
       "      <td>-1.715398</td>\n",
       "      <td>-0.019493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67_33</th>\n",
       "      <td>115.0</td>\n",
       "      <td>0.730435</td>\n",
       "      <td>-6.528349</td>\n",
       "      <td>-0.056768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67_36</th>\n",
       "      <td>77.0</td>\n",
       "      <td>0.688312</td>\n",
       "      <td>-1.016318</td>\n",
       "      <td>-0.013199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68_10</th>\n",
       "      <td>79.0</td>\n",
       "      <td>0.696203</td>\n",
       "      <td>-1.512461</td>\n",
       "      <td>-0.019145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68_44</th>\n",
       "      <td>137.0</td>\n",
       "      <td>0.693431</td>\n",
       "      <td>-6.297579</td>\n",
       "      <td>-0.045968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69_26</th>\n",
       "      <td>114.0</td>\n",
       "      <td>0.745614</td>\n",
       "      <td>-6.872898</td>\n",
       "      <td>-0.060289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69_38</th>\n",
       "      <td>82.0</td>\n",
       "      <td>0.682927</td>\n",
       "      <td>-1.584954</td>\n",
       "      <td>-0.019329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70_48</th>\n",
       "      <td>85.0</td>\n",
       "      <td>0.694118</td>\n",
       "      <td>-1.657861</td>\n",
       "      <td>-0.019504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70_62</th>\n",
       "      <td>103.0</td>\n",
       "      <td>0.728155</td>\n",
       "      <td>-5.746673</td>\n",
       "      <td>-0.055793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71_45</th>\n",
       "      <td>98.0</td>\n",
       "      <td>0.734694</td>\n",
       "      <td>-5.554473</td>\n",
       "      <td>-0.056678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72_54</th>\n",
       "      <td>126.0</td>\n",
       "      <td>0.706349</td>\n",
       "      <td>-6.144978</td>\n",
       "      <td>-0.048770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73_55</th>\n",
       "      <td>117.0</td>\n",
       "      <td>0.709402</td>\n",
       "      <td>-5.770077</td>\n",
       "      <td>-0.049317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73_68</th>\n",
       "      <td>73.0</td>\n",
       "      <td>0.684932</td>\n",
       "      <td>-1.130658</td>\n",
       "      <td>-0.015488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74_36</th>\n",
       "      <td>126.0</td>\n",
       "      <td>0.690476</td>\n",
       "      <td>-5.826468</td>\n",
       "      <td>-0.046242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75_43</th>\n",
       "      <td>79.0</td>\n",
       "      <td>0.683544</td>\n",
       "      <td>-1.393574</td>\n",
       "      <td>-0.017640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75_48</th>\n",
       "      <td>123.0</td>\n",
       "      <td>0.707317</td>\n",
       "      <td>-6.067599</td>\n",
       "      <td>-0.049330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76_41</th>\n",
       "      <td>85.0</td>\n",
       "      <td>0.694118</td>\n",
       "      <td>-1.257220</td>\n",
       "      <td>-0.014791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76_63</th>\n",
       "      <td>107.0</td>\n",
       "      <td>0.710280</td>\n",
       "      <td>-5.348693</td>\n",
       "      <td>-0.049988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77_36</th>\n",
       "      <td>135.0</td>\n",
       "      <td>0.696296</td>\n",
       "      <td>-5.407719</td>\n",
       "      <td>-0.040057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77_48</th>\n",
       "      <td>83.0</td>\n",
       "      <td>0.686747</td>\n",
       "      <td>-1.429252</td>\n",
       "      <td>-0.017220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78_35</th>\n",
       "      <td>70.0</td>\n",
       "      <td>0.685714</td>\n",
       "      <td>-1.308801</td>\n",
       "      <td>-0.018697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78_53</th>\n",
       "      <td>66.0</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>-3.242988</td>\n",
       "      <td>-0.049136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79_38</th>\n",
       "      <td>109.0</td>\n",
       "      <td>0.752294</td>\n",
       "      <td>-6.785169</td>\n",
       "      <td>-0.062249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80_46</th>\n",
       "      <td>120.0</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>-5.798165</td>\n",
       "      <td>-0.048318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81_66</th>\n",
       "      <td>102.0</td>\n",
       "      <td>0.735294</td>\n",
       "      <td>-6.190534</td>\n",
       "      <td>-0.060692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82_46</th>\n",
       "      <td>74.0</td>\n",
       "      <td>0.689189</td>\n",
       "      <td>-1.214181</td>\n",
       "      <td>-0.016408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82_71</th>\n",
       "      <td>53.0</td>\n",
       "      <td>0.698113</td>\n",
       "      <td>-2.419266</td>\n",
       "      <td>-0.045647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82_73</th>\n",
       "      <td>97.0</td>\n",
       "      <td>0.752577</td>\n",
       "      <td>-6.261241</td>\n",
       "      <td>-0.064549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83_33</th>\n",
       "      <td>113.0</td>\n",
       "      <td>0.707965</td>\n",
       "      <td>-3.611449</td>\n",
       "      <td>-0.031960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83_53</th>\n",
       "      <td>66.0</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>-0.477639</td>\n",
       "      <td>-0.007237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83_80</th>\n",
       "      <td>87.0</td>\n",
       "      <td>0.747126</td>\n",
       "      <td>-5.564710</td>\n",
       "      <td>-0.063962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84_48</th>\n",
       "      <td>71.0</td>\n",
       "      <td>0.704225</td>\n",
       "      <td>-1.782087</td>\n",
       "      <td>-0.025100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84_77</th>\n",
       "      <td>101.0</td>\n",
       "      <td>0.722772</td>\n",
       "      <td>-5.183489</td>\n",
       "      <td>-0.051322</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lcs       lrs       lps       lms\n",
       "56_21   84.0  0.702381 -1.699820 -0.020236\n",
       "57_27   86.0  0.697674 -1.591000 -0.018500\n",
       "58_35  141.0  0.687943 -5.994469 -0.042514\n",
       "61_17  137.0  0.693431 -5.788397 -0.042251\n",
       "61_59   90.0  0.700000 -1.401181 -0.015569\n",
       "62_47   95.0  0.694737 -1.830953 -0.019273\n",
       "63_31  155.0  0.683871 -6.826563 -0.044042\n",
       "64_34  111.0  0.711712 -5.487420 -0.049436\n",
       "64_41   57.0  0.701754 -3.558004 -0.062421\n",
       "64_50   85.0  0.694118 -1.677678 -0.019737\n",
       "65_15  145.0  0.703448 -5.934523 -0.040928\n",
       "65_37   86.0  0.697674 -1.734857 -0.020173\n",
       "66_4    88.0  0.693182 -1.715398 -0.019493\n",
       "67_33  115.0  0.730435 -6.528349 -0.056768\n",
       "67_36   77.0  0.688312 -1.016318 -0.013199\n",
       "68_10   79.0  0.696203 -1.512461 -0.019145\n",
       "68_44  137.0  0.693431 -6.297579 -0.045968\n",
       "69_26  114.0  0.745614 -6.872898 -0.060289\n",
       "69_38   82.0  0.682927 -1.584954 -0.019329\n",
       "70_48   85.0  0.694118 -1.657861 -0.019504\n",
       "70_62  103.0  0.728155 -5.746673 -0.055793\n",
       "71_45   98.0  0.734694 -5.554473 -0.056678\n",
       "72_54  126.0  0.706349 -6.144978 -0.048770\n",
       "73_55  117.0  0.709402 -5.770077 -0.049317\n",
       "73_68   73.0  0.684932 -1.130658 -0.015488\n",
       "74_36  126.0  0.690476 -5.826468 -0.046242\n",
       "75_43   79.0  0.683544 -1.393574 -0.017640\n",
       "75_48  123.0  0.707317 -6.067599 -0.049330\n",
       "76_41   85.0  0.694118 -1.257220 -0.014791\n",
       "76_63  107.0  0.710280 -5.348693 -0.049988\n",
       "77_36  135.0  0.696296 -5.407719 -0.040057\n",
       "77_48   83.0  0.686747 -1.429252 -0.017220\n",
       "78_35   70.0  0.685714 -1.308801 -0.018697\n",
       "78_53   66.0  0.681818 -3.242988 -0.049136\n",
       "79_38  109.0  0.752294 -6.785169 -0.062249\n",
       "80_46  120.0  0.708333 -5.798165 -0.048318\n",
       "81_66  102.0  0.735294 -6.190534 -0.060692\n",
       "82_46   74.0  0.689189 -1.214181 -0.016408\n",
       "82_71   53.0  0.698113 -2.419266 -0.045647\n",
       "82_73   97.0  0.752577 -6.261241 -0.064549\n",
       "83_33  113.0  0.707965 -3.611449 -0.031960\n",
       "83_53   66.0  0.681818 -0.477639 -0.007237\n",
       "83_80   87.0  0.747126 -5.564710 -0.063962\n",
       "84_48   71.0  0.704225 -1.782087 -0.025100\n",
       "84_77  101.0  0.722772 -5.183489 -0.051322"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ump_deg.gmm_component_filter(p_ncs=np.arange(18, 85), threshold=0.68)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "nts_pd.shape = (513, 7)\n",
      "nts_pd loss rate = 0.690058479532\n",
      "improved rate = 0.00434240413308\n",
      "predict win rate = 0.505099541264\n"
     ]
    },
    {
     "data": {
      "image/png": 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hkwLtAdx0wRgunpHGH17ZysY9Ba7uqledM7Ld/u6nny6flw6APSCAGxZkMmV0\nAl/uymPrviJWbM1lxVZjLemkuDBKKurIPlJCbX2Ta6T1YCanj4QQPi0+OpTf3DmbuKgQ17YLzhre\nbr8ZKpG7r5rAgmnDSXUrEACOtFhuXzyOh79zNj+6eSrnTR1GbGQwUzMTuP4CB+VVDazafqLPfxd/\nIGVRCOHzgoPsjB4WzWZdCEBUeHC7fWw2G7PGJTNrXHKnxwm0BzAhI54JGfGubaERIbz1+QH+88VB\n9h0rY86EZGaOTcJms3n/F/ED0lIQQvgF9wvJ3hQVHsydl40jJT6c7QeKeOqd3ew9Utonz+UPpKUg\nhPAL504dxu7DpcybmOL1Y88Ym8SMsUm8vfoQ7649THl1x2MbBgMpCkIIvxAaHMg9N07p0+eIdbtu\nMVjJ6SMhhGjD6YTi8jqqatvPwDrQSVEQQghTWLBx8uTQiQrue3Y9D722vcMBbwOZFAUhhDBFhhkz\nr3629TgNjS0cya90TasxWEhREEIIU0JMqOvniSPjsQHvrztsVTiWkAvNQghhSooLc/08b1IKTmB3\nTgk7DxVTU9fE5NFDBvyoZ2kpCCGEyX3AWkRoEBNHGoPc/vL6Dv7+7m5WZ+VZFVq/GdglTwgheig6\nPJj0iVEcyC2nobGFnYeKqT+D9aP9lbQUhBDCTet1heT4MKLDg/n2NZNYOCsNgLdW57DGbC3kFVdT\nXlVvWZx9RVoKQgjh5o9L57Xblp4cxdAh4eQV17Dsgz2EBtt55r/ZhATZ+f71k4mPDvWYsM+fSUtB\nCCG6EBkWxAPfmMM15rKfT7y9i8amFqpqG3ngxS386G9rLY7Qe6QoCCFEN11x9kgmZMS5bt+xeKyF\n0fQNS4uCUuoapdTLbrevVkodUEqtML/mWxmfEEK0NV0Zq7bdutDB/CnDXNsHyshny64pKKUeARYC\n2902Twd+orV+y5qohBDi9OZPGcq49DiS48MBmO5IZMu+QqpqGomOaL/Og7+xsqWwFljaZtt0YIlS\n6gul1J+VUnJ6SwjhU+wBAa6CALguMJdWDoyeSH3eUlBKLQHuAZyAzfx+h9b6DaXUeW12/wR4W2t9\nWCn1FHA38ERfxyiEED0VF20UhZLKOtJToiyOpvf6vChorZcBy7q5+3Na63Lz53eAa7t6QFycUbET\nE33jjyFxeJI4PEkcngZCHOnDYgFodNp6/fv4Qj58bZxCllJqrtb6BHAhsKWrB5SW1pCYGEVhYWXf\nR9cFiUMCQJt2AAATVElEQVTikDgGXxyBGBeYj54o79Vx+jsfnRUgXysKdwJvKaVqgGzgGYvjEUKI\n00oZYpytOF5YZXEk3mFpUdBarwJWud1eDiy3LiIhhDgz0eHBxEWFcDTf+laPN0jvHiGE6KURSZGU\nVTVQUd1gdSi9JkVBCCF6qbXX0c5DxRZH0ntSFIQQopfOciQCsGFPvsWR9J4UBSGE6KVhCRGEhdjZ\ndajE79d0lqIghBC9FGgPYPHsdABKK+ssjqZ3pCgIIYQXuK/vDNDS4uTz7bl+d/HZ18YpCCGEX2tx\nQn1jM0sfMnrbb8zO596vnGVxVN0nRUEIIbwgPtpYxvPlT/fx8qf7XNtPFFVbFVKPyOkjIYTwgszh\nMdx2iSKmzfTZocH+9dlbioIQQnjJgmnDefg7ZzM8IcK1raCslm37Ci2M6sxIURBCCC+y2Wz87Nbp\nLJg6jBvPz8Rmgw83HLU6rG7zr3aNEEL4gfDQQG5bZKzfvP1AEfuPlVFZ00BUuO+vzCYtBSGE6EPD\nEyNwAiUV/rEymxQFIYToQynm0p3Pf7iXqtpGi6PpmhQFIYToQxdOT+XcKUM5kl/JH1/ZRkWNbw9m\nk6IghBB9KMBm47ZFYzn/rOEcL6zi8X9n0dTc0un+9Q3NPPTaNl5feaAfozxFioIQQvSxAJuNWy92\nMHt8MgdzK1i1/USn+y59eBW7D5fyUR/2WGpu6bwoSVEQQoh+YLPZOH/acADKq7t30XnfsTKcTmeH\n93W2vSvZh0v43qNrOr1fioIQQviIlhbPN/oHX97Kcx/sZcmDK1i7M8+1/bkP9nDfMxuob2w+o+Pv\nOVzCo29m0djU+eOkKAghhI+oqW9qt22NWQz+8f4e17bVWXmcLKlh5dbcMzr+v1YeoKXFyXeundzp\nPlIUhBDCR1R30WW1bUviww1Hzmhq7uLyOpLjw5k8ekin+0hREEKIfhIQYAOgubnj6wG5XcyoeiC3\nHIDQYDsAlTWN/N/T6/lsy/EurzE0NjVTXddEbOTpR1VLURBCiH4SHxUCQHFF+9XZjhdUssztFFFH\nHn0zixVbj1Pf2MzIodHccuEYnBjTde88VHzax5ZVGS2KmIiQ0+4nRUEIIfpJbFQIgXYbhWXti8KK\nzceoqW9i3sQU17Y7Fo/lz9+a57pdW9/ES5/sIzTYzrXnjeLimWl888rxADz3wV7eW5tDSycthrKq\nejMGaSkIIYRPCLDZSImP4FhBFbtzSjzewFtPKV1wVqprW3REMPHRofzvVz1Xbvv5bTOYkBEPwNgR\ncZw9MYWm5hbeWp3Ds//NJrewqt1zl5sthdguWgoyS6oQQvSjC84azgsfax7613aGRIdgtwcwd0IK\n9kC7a59fL5nF++sOuy4IZw6Pcd331x+cS3joqbfu4CA7d14+nsqaBh55I4v1u/NZvzuflPhw4qJC\niAgL4rpzR1HqailIURBCCJ+xYNpwUhMj+SLrBJv2FFDf2Mw7a3KIdluxLS0pkruvmui6HRBg495b\nphEUFOBRENxFhQez9OoJ3PvkOgBOltRwsqQGgLzialfLou3KcG1JURBCiH6WmRpDZmoMt1w4hg3Z\n+by2Yr+ra2lYiL3Dx4xNj+vyuAkxYfztnnNdvZNanE6e+2AvX+46SW6h0bOpq5aCXFMQQgiLhIUE\nsmDacK46e6RrW0JMaK+PabPZsNls2AMCuPLsDMa5FRRpKQghhI8LDjrVOggK7Lil0FNJceH85JZp\nZB0spqC0hpCg0x9fioIQQlhs5tgksg6VcOXZ6X32HMZF685HMreSoiCEEBaLjgjmd986m8LCSqtD\nkWsKQgghTrGkpaCUigZeAqKBIOCHWusNSqk5wCNAI/Cp1vr/WRGfEEIMVla1FH4ILNdaLwDuAJ4w\ntz8J3Ky1ng/MVkpNsSg+IYQYlKy6pvAw0Lr0UBBQq5SKAoK11ofN7R8DFwE7+j88IYQYnPq8KCil\nlgD3AE7AZn6/Q2u9RSmVArwIfA/jVFKF20MrgZF0ITExymZ+93LkPSNxeJI4PEkcniQOT74QR58X\nBa31MmBZ2+1KqUnAK8CPtNZrzJZCtNsuUUBZX8cnhBDiFEuuKSilxgOvA1/RWn8CoLWuBOqVUiOV\nUjbgEmC1FfEJIcRgZdU1hd8BIcCjZgEo01pfAyzFaD0EAJ9orTdZFJ8QQgxKtq6WcBNCCDF4yOA1\nIYQQLlIUhBBCuEhREEII4SJFQQghhIvfFAWl1BBzziTLKaUmKKUszZ1SKlMpNcvKGFoppfpuvt/u\nx5CmlMrwgTjOMr9b9vpQSqUqpeZZHYf5/Ne1xmJhDBOUUt9RSiVbGYcZyxxfeR/rjF/0PlJKhQFv\nAo8DH2utLQlaKbUA+BHGqOwC4BGt9a5+jiEQ+DMwFcgFsoFntNYF/RmHGcsCjHy0YExL8mZ/x2Hm\n4xFgIlAKPK61XtGfMbjFcjHwLjBUa12mlLL192tVKRUCPAqkYIwDqunP53eL4zLgG8AR4Hda63wL\nYgjGeG2MBp7SWr/V3zG4xTIH+C1QCHwGvKa1rrIqntPxl5bCecA5wPnAiP58YnMcRatvAc9pra8E\nDgEL+zMW0yQgwpxM8E9AMsaYj37RJh/fwZim5GfANGBYf8XhHhKn8vEBcFe/PbFnLgDSMEbhP2ze\ntuL/qwEIA5qAu6HDOPtE6/OY338PvKW1/j7gUEol9UcMbZwDJAJfA3KVUouVUgn99eRt8n4L8BeM\n/5kajA9SPskni4JSaqJSaqz5sw0IxUhmDDDD/DTUH3HYgVHmz9OAKoxPxACZQE4/xTFRKTXOvDkb\n4x8e4Bgwvz9iMONwz8d4IB94D+PT4HwgSSnV5XxVXoijbT6KzJ/rgDFKqfP6+lRSm1y0LqqbAUwB\n5iul5mO8IfUp9/8V03AgEvgFMEUpdTn9UKzNfIw2b9qBp4BFSqnPgVuBZ5VSN/VDHO6vjSqgHKO1\n8H3gBuA3Sqm5/RCH++sjBSMn8cCrGP8r/1RKXdfXcfSETxYF4LuYn3LM5ne21vpFYC1Ga2H0aR7r\nTbcA/zHj2Ab8Smtdbd4XCezspzi+izHaG631UxgTCAKMMTbpY/0Uh3s+soGfaq1rgSUYBXIa8E4/\nxOGej2Va658opSKA6RjTp9xE37cYbgH+bcZQZ24LNU+frQY+BC7r4xjA7X/FFIbxN6rHOMX4AP3z\nf34LxiletNZNGJNb1gB3aa2/iTE9/r39EIf7e8dGoBnYrbX+Ksapzmzg3H6Iw/1/5STG6bzFwL1a\n66UYLeyf9EMcZ8znioJS6kpgHjBCKXWVufmQ+f1ljPP585VSsX0cRwJwBZCulPqBuTnXvG8CEK61\nPqCUmm2eW++rOFrzkeqWj9am5wKMUyYopW434+qrODrKRx2A1vpxrfVirfUfgPXmJ6O+isM9H1e2\nbtdaV2utf6C1/hOwAThh7u/1UyduuchQSn3f3DYKOEcp9R9ztxyguq9iMI/b0f+KAh4EngHex/jg\nktYXz+8Wh3s+7jE3r8IoBK3/u2uBtUqpyH7IR5pS6lpz85+A5wG01qUYZx3yzf37Kg73/5UfmZuf\nAOYCwebtFcA2pVRMX8TQG5ZfaFZKfQWjmbdda31UKXU+kIpxIferwDe01vVKqSCtdaNS6hLg68DP\ntdaHOj/yGceRgvEJYoPW+oh5GuQcYB3wOaBaWwlKqW9iXOfYB8wBfq+1XuWlOFrzsU1rfew0+YjA\naKKXYbwRbAR+6/aptbdxdCsf5ov6AuAocCdGC+rr3rrAegb5uBgjDycxrv08r7V+wUsxnC4XqzBy\nUaWUegJYqbV+Qyl1NkaHgHn9mIu7gLEYrelnzb/PUqBWa/28N2Iw4+huPlKARcAB4CsAWutveTGO\n7r42rgXOArYD/wP8VWv9rhfj6CofY7XWlUqppzA+1H4BXIixfsxt3orDW6zsNhemlHoM48UyGXhV\nKTVUa70S4xSAxjgfeIf5kGYArfXHGNNte7MgXA2sxPhD/kUptVBrnQOs1lofAJZj9OhAKRWE8SY4\nEyjRWi/yRkHoIB+vdZGPIRgv/AiM5Ux/7sWC0O18AOEYp/N+DRzUWt/mjTfBHuRjP8Z061dg5MNb\nBaGrXHzKqVx8xywINq31Wq313H7MRSXwNa11ltb6UbMg2DA6Rjzf2xjcYulOPh4xd7djXOP4KXDI\nWwWhB6+NHRittssxTnl6syB0Jx+Pmbv/HOO04iXAXl8sCAA4nc5+/XI4HCHm9xSHw7HcbfvDDofj\n/xwOR4p5O8jhcFzicDj+7XA4RprbbG7727wY068cDsdc8+evOhyOPzocjoVu90c5HI4jDodjhnn7\n+tY4LcrHaHPb2e758FZOzjQf7r+HRfnI7Oj14Y18nEEuzjJvB3rxddmTXIwyt9nbHMvK10aw1a+N\ntrmwIB/T3bZ57TXSF1/92lJQSv0QeFMp9TWMVdgOK6VuMO9+CqPpOwJAa92IUeF3AenmNtenrt58\nAlNKTVVKvaiU+oZSKg6jl0jr+elPgMMYa0SHm89VCfwT+KZ5+03z4lGv9CIfrdvWmsexa62dPc1J\nL/Lhupirta6nl3qRj1Rzm9M8TkBP89GLXPyPebup/VHPnBdeG83ux7P4tdHQk+duE0evXhtux+nx\na8N8fE/z8Y3WY3jrNdJX+u2aglLqeYzzf+9gnH6pwDj/Owb4ozYG+/wRCNFG3+bWxwV6M4nmi+ou\n4GmMbqXTMHosvAlcqbXOV0otwmhq/h9QpbX2ep9iyUe7OJ7H4nxILtrFIfnwjMMn8tHX+qWloJRK\nA2K11t/RWn8KNAJ7MS7E2Dj1qeJLoFi59QrQWjcpL/QScDtGCsZ51peBv2P0cc/DuHj6oLnPaozW\nia31j+qNGNxikXx4xmJpPiQX7WKQfHjG4DP56A/9UhS00Y/+P8qYkgAgCWMA1j6MwRzXK6WeA/4I\nfNa2aeeNi3VuxziB0R0MjC6dQeanifuB6Uqp/4cxDH0HZpdLb8XgdizJh2csluZDctEuBslHx8ew\nPB/9wtsXKRwOR0AX949yOBw73W7Hml8LHA5HUF/F0lFcDofjTYfDcXmbi0IXOxyOiyQfAzsfkgvJ\nh6/nw6ovr15TMC/itDaZJgI1uk3XUWX0J78Ao8r/FnhPa/2M2/32thfJzjCGEGCC1nqreTvIvPDU\ndr8wjK6E9wL3YEyo9lXtpW6d5nNIPjyfw9J8SC7axSD58Dy+z+TDSl49faS1blFKjVNKPQm8DXzV\nTLT7ebWFGL14fgk86v5HNY/R4z+q6WLgEaVUvFLqNuBDpdRSpdRMMw67ud8IjPOR72Ocm7zN239U\nyYcnH8iH5MKT5MOTz+TDSt5uKYwG/gE8hDGc/HaM+YI2ue3zG6BYa/2I27ZeTTFsvmgCWl8USqln\nMWYPPQa8hjHA5ULgJm12j1PGZFTXAvdrY5CJ10k+2sXV7/mQXLR7XsmH5/P6ZD4sdYbn2WwdnWNz\nOBzzHA7HhQ6HY4zD4fjcbfujDofjrw6HI7Yn5xB7cB4wyfw+0uFwrHc4HIvN23EOh+Nph8Nxrpef\nT/LhJ/mQXEg+fCkfvvx1pqePgsFo6rVuMJt4czFmAEwBNiilbjTv3oUxQdXEtgcyK3yP+vAqpexK\nqSVKqSnm7QCl1APAv5VS/wRiMYaXn20+pAVjCoTsnjzfaUg+PFmeD8lFu8dKPjwf6yv58FndKgpm\nIn8HvKWUGmNu+7lS6mZtjGRdDpRgTDq1GliqlPoXxiRRBzEnw3LXi9MB12OMHFQYE22BMQdQmNZ6\nPsYgl19izIEyWyn1N4xmaS5QobzTb1ny4RmDT+RDctEuFsmHZyyW58MfdOuagjL6CL+KMelWFcZg\nDTtwI7BUa12kjOmUx2GcE2wBFmqtn1BK/QQIBB7s5bm/WOAj4DjGucZdbvd9DWMitGqM/sHFGH/M\neRgzRv5Ba72/p8/dQSySD89YLM2H5KJdDJIPzxh8Jh/+oMuWgjK6ijUBmzCGlz+IsYpRJJCAMeNf\nq2iM0XwlQLIyVl2aAPytN39UUzlGxV6jtd6ljMXJH1LGClvlGM3PA9pYwCIamK+1fllrfZeXX+SS\nDzc+kg/JhSfJhyefyIe/6HbvI6XUNRiTP30GvIGxiMglGMsxjsEYYeha40AZvQmCtdZ7vBWsUmoS\nxvDyNRgvmC1a618qY1GLyzHOT44B1mmt7/PW83YSi+TDMxZL8yG5aBeD5MMzBp/Jh687k6JwI8aA\njZXAa1rrd5VSL2F8CtiqtV5t7meDvhvarZT6LcYiO2N0m77BSikHUKq1LuyL527zXJIPz+eyPB+S\ni3ZxSD484/CJfPi8M+iyFepwOD5zOBxj3bYN6ctuYp3EkexwOD51OByzzNtemau9B3FIPnwsH5IL\nyYc/5MPXv86kS2oSUApUK3Nkn9a6GDwqfJ9PE6u1zse4cPVX83av52rvIcmHJ8vzIbnwJPnw5EP5\n8GndLgpa66MYV+gbtZcW8OiFV4EnlNHH2JJuYpIPTz6UD8mFJ8mHJ8vz4ev6bZEdIYQQvu+MJ8RT\nSvXrEp6+TvLhSfJxiuTCk+TDP0hLQQghhItUbiGEEC5SFIQQQrhIURBCCOEiRUEIIYSLFAUhhBAu\n/x/fF9qlgVtYOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123b6fbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 只使用全局凸优化寻找最优\n",
    "brust_min = ump_deg.brust_min()\n",
    "llps = ump_deg.cprs[(ump_deg.cprs['lps'] <= brust_min[0]) & (ump_deg.cprs['lms'] <= brust_min[1] ) & \n",
    "                    (ump_deg.cprs['lrs'] >= brust_min[2])]\n",
    "ump_deg.choose_cprs_component(llps)\n",
    "ump_deg.dump_clf(llps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "wave ump第二主裁"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>result</th>\n",
       "      <th>wave_score1</th>\n",
       "      <th>wave_score2</th>\n",
       "      <th>wave_score3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>1</td>\n",
       "      <td>0.116619</td>\n",
       "      <td>-0.122372</td>\n",
       "      <td>-0.222840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>0</td>\n",
       "      <td>0.646118</td>\n",
       "      <td>0.731843</td>\n",
       "      <td>0.662114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            result  wave_score1  wave_score2  wave_score3\n",
       "2015-07-28       1     0.116619    -0.122372    -0.222840\n",
       "2015-07-28       0     0.646118     0.731843     0.662114"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from MlFiterWavePd import MlFiterWavePdClass\n",
    "ump_wave = UmpMainClass(orders_pd_ump, MlFiterWavePdClass)\n",
    "ump_wave.fiter.df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Hm/y+qyah6Yfo3zaFW68dxOYdBxj34+fgcrG/djRrXCdr787DJ290L1u5hhdm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\nHc4TTvnu46u4qpoqIsuBJO/6P1T1ptK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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b05d390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QPEoJg8bkMajt/GWEgZ9kch4gXYEyv56EKwzcYkqC6liWguDw4UN84hN/wV/8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\nhEGzz0VGdGSuoaKoVQsD4SEQ0N4eY3Z2caYMstV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xKToWzcZoZgQHD5+AoHCQkzzaFp8E\nUYxA71jplSmN4HQRASq046Z23Q1mzZuHPb1HUew/jrmlMumBsVH0JKNYsWKFGqpmJD/mXLg3RO55\nD4//+m6cLY2gaTYlYBJCsOfgXvQM9iGd6VLfFxcErJ/Vgm/++LtYddo/O5/ZIVx/9OhRnCSdANBu\n+cwacQJ7du3E0uUrSsewz+vrG0iVd46CJfL51VPQj6XBIWgAmUwGw8OTxSHQjBFl5zeIfmFi6ren\nnlzk4EwGHEe5JmD659gNaVV7hlOtbUi1tvmeW2bceJ6FkM216/5D2UtOX4cTQ0N4ae8+cIQg9ZpV\nWDF3juE9dFccgyAokKS8yRDFqk5hjI2Oor2/B01zOgyvt0dF9EoKZKJA4HnKtlcIOABtY0N46Jt3\nYPEFb8fiFdbSUCcUCnk08fbzlBR5DGazulc0h4Y5QeYGUixqou8sGSTsHBtr2WRlx6DBIWhARTqd\nweho+GWHlXei+hiVmXhW/8YoaAQh7FN/7anL49n2V9E3MohZonGH+Gp+J46ceVz93fosa6Q/K2rn\nxNrXrucNOW6vaGlrQ4sLjRK6QzUL+2RtxYXcNFNiOLBnJ1amkhgfG4UiSRAiUUTjMWTzBXQ2JTAw\nNoFZzUlwClENGgHw+rSIp++/G5mOf0RrW4crMZ+urm78nsSx2mYcT2c5vL7UWM5p/Ha7dNZZ0myM\nw1YYdBZasnPU6j9CUD9tmkJGOp3G8LDZIdDnLb2DNUwhRAYhUimHK5X+yaALp/nYAqgfJ4LjxBKB\niYefTmj1DnOXSuf5Ns81B/rV0M91xDTftZ7rqXNvb3n0Tvxz8XvYnd+jvrY9/zz+U/wltnzvJnV+\nrc8yg3F+7eY2yPlVFAUvPPw4dv7099j5o99h+68fxMiJE2ooOxZLgudFlXBWKEyoJESKagyB/WeZ\nIYpE4mCd8/L5LIrFvMWQupkLhXDYs28vkhPjyMhFiMODOLF/L1p5YO/gCTy5vwejEznVGdh28Aji\nsRgy8TjOmdeO5/74W+Tz44YOi07+CMdxaN14Pp4ZGDO8/vLQOOJrzoYoipb324HNgZaTp8aYzn9w\nyXk3jpXxfvCqo1Yo5NSxNHQIGlCRTmcwNuaPQ+C95Iz91HaiGuErbB+uEjFu6sM+5O+uZwJQ31/4\nIEAIwf97+UZ8+arbkHqcR5GTkbykHdd+7kaUF05iThdfg+dYw59+cDcWHJMxq6UNs9s6IeREPPHT\nh7Dg/5yDlrY20O511rwy27WHBef6fclFPtuIkWe2QcgXIAo8pGIRcZ5HvCmBoWwOkkKwZu5s/Pjp\nl3BSRwt4nkcmEcec9nYUZAUKIYjKNA2kT6Gw0k2778P6c9+El1ta8bPHH0J0YgyFeBJzN70JmzZu\n8jwPzBizOWDGmJIOWbqh+u9kpWNYhZaMCox0Pup7bWg4BC4hiiIkycmoaKgfHX939c/+EYzjEaSW\nfwNGVHJkr//ODbp3V36WjSTY8CHLMn5z279g3WAcS1OzMHjsKLYpu3Dq2jU4o3UxHn3kKax75wXq\n+40tjzVJYI4TStfjfQzmvLOiKDjWewQAMHvuPPA8D2vDngIkKa9LSZWfs55DB7GUFNC2fAXue/ll\nrG5pQlcmhX1Dw/jTvkN4w+JudDQlcfKsTgzncjghFTAhRJGQ83hp1x7wHIc+KYLe/QexYMkSWzEf\nO5y8dj1OXuvci8KrbLGdMdaO5eoQTiPxNA6zCqRex4DxX2rp0NYSdeMQ5HI53HLLpzE6OopIJIJP\nf/oWdHR0VP6gTxi/JEwIJUxjRA3odGHlu4c7x8O/sA/rmRBmGWW9RFXY/ysRV6eGI0sIwWBfP5Si\njLY5nRAEukgXCgWcODqI7b9+EG8szsbSznYABJlYE07CbPx5+7PY9PrXI3HcSo6027UzmW1Zll2V\nymXHxjG47yD4QhFFAch0z0EkE0fP7j3IvvwKukolfHuefhaJk1diwdKl6rkZh4Gx8el1KlAU2TFi\nMHL8OJYlYmhtSuKMJYtx7PBhHDjeg650Cq/tXoBkNAKB59CaSGBeJoV9Y1nwiow3LpoPgGA4X8SK\nlg68+PADGEpfillz5xjOL0kFdV6c0NtzGC8/9gg4jsfaN16A1jY65+y63MIqycwiF3kQovjq1eBf\n3MiqAgkA+Xy2VJkSRb1FDISbb77Z8Y/ZbMH5j1MM//M/P0cmk8GWLTeBEIKHH/6D2j+hWuTzeezZ\nsxtPPvlXHDiwD7/5za+xePFCtLbaSaqy/CjLkfKWPL+/xZOFYsPOXzNDMHnn0XaSiu6novtdb6jK\nzTcLU0Pd5dX6WqbiOZznV38uBuP82j3L3oRdgrmG430DGH12PzpP8Gg6LuHIq/twdKgPR7e/iomn\n9iM2kIf4whG0IYaUiRwWVTj0iXlk4zw619q3JeY4DjwvQBAiJd4JKRlmyfZ7PNTXj8HeYxgZOg5l\nx34sFJNohYhWiUP/3gMYKuYQ37kby1vbEI9GEY9GMTuRwFhPDwptrUgkk4Zz04oHXt0ly7JUkmC2\nplua0xnsevKv6GpOIjcygs5YBEvbWtCSiOOlvgGs7OxAVBTwRE8vFs3qRHZiAnOSCTRFIhjJFQBw\nEBUZc9LNeKa3D90nr4YgiFAUTfFRUZzP/+CPvof4Xx/AefE8VhRP4PlHfo/dx0fRtXwlZFkqzaO3\nvSebf/0YCFF0WgbunyFFocqR1NHwvrOn95qHLBfBGvURIqvfhemGpqbYLU5/q5sIwSWXXKp6gseO\nHUUqlarwifLI5XL45je/iueffxYHDx6ALNNdwp49uyEIAi666CIsXnwStB0uCyvWl8cYPtyWoAGN\nkL93eO+VAEz18sl8Lgfych8WJFoxfPgYouMy4oODEI6PojmTwrzuBZiYyKFvoohUqgnHx0fQkdKa\nI81JtuAvfb1Qli/D0QOHIGfzEJsTmLVgnuWaNcMkqz/1OgIT41n0P/MyZiOC2fEkXnrmWaTbOkCa\nWsCVSvO60m14/LGncc6KJZZr6c604PkdO9B61lmWv7Gx8LwAQohju+NEIoHxBSdhcPgoYjyPgqIg\nCQG9I6NIRiLgeQ55SULfWBYQRBBZwTNHjmHf8WGs6GzDotYWDOcLkEeGEW2OWc4vijHVITAz8J98\n5CFsGjmIeXNaS58B3jC/Dc/uegK7dyxH90mLqnqO2EcjkXhJgrlSm2MrgqxU4DgB0SidD0LC3GRM\nDqalQ7B16z24++6flMh2NIR+4403YeXKVbj22g9h7949+NrXnAU33ODEieO4777fIBKJYNWqU7B0\n6XJs2/YYrr/+BqxcuQrJZNKkhDc1F0/vCOc6pmoJWrAIm3fhfI4gulHWjrhaHYb292JRshXHD/Ri\nlhzHsdF+FA8PoZMkcOLEIA5kc1i4chnmNbVjtJBDMiJicHwE7U1pcAD2jvZhd1sWp8cTmNOTQzQS\nQf54Fgf3bces01ch0ZS0PS9VN4xhfHwEPM+BEAW9Tz6HVc20XO/E6AgWJTNII4KBw0fQ1j1f/Wyn\nzGM8l0NT3Kq2JxTK91mgzke0bLvjMy58O555+A848vBv0UWKODA4hDgHnLuoG0/1HMXAeBYd6RTa\nRQ7P5XP4u9NWARyH7T1HISvHsbSjFX0TE5Da9IqETLtBgChGLKQ/QRAx+sozmJdOWMa8ZlYLk8E4\nEAAAIABJREFUfrHtT+g+aVHF+1kO7LGmXAvRZ5vj6ssF9VwQlkpwQSmbdpiWDsHmzRdh8+aLbP/2\nT//0rzh4cD+uu+6j+NnPfu37HHPmzMVvf/swIpGoGma66qr3Y968+UgmzQtGLfLHtc5T+z+XN2Il\n0Oib4A1Bt6KebuCLCorFAhITBMcnTmBo9yGsSS1AIV9AjBcQy8dxeNc+FFNRYCSHRXNn42huBL3y\nGIis4GG+F2ecfyGWxbSoQSwSwbJIO/a8uBtdG+0q7IGj+w6A6z2ORAHIEQlHlXEsRAyyVADHCyAK\nAc/x9N/4BEAAwu5LJAJZsXZnJIRAjscsr5tRrqMg01RYd+75WLx6LQ7+4od4z+nrcKSvDwd6e7F8\n9iwMHO7Fprmz8NeDR7CwLYOJooRENIJ18+fgwV37sLSjFfsGjmPWWzSSoJkQadfqmCvmACLY2lpR\n1kt6+4XRmGuqh9Y2xzRqYVU9DEZQyDiOeuxjANSRDsF//dd/4Le/vR8AEI8nbFsse0UsFjfknNJp\nYwvkycHUehIr1/Y71fezedXnpcOItExv48fmV5tDGc5aFfr5Db++f7JAkhFkx8eRFGMYOzqIjniK\nXhfHQeEIYryAlryAOQu7sFcYw2N9u3BczuIwN4ZtrWM4+/or0F603ws1j8vI541kQ0II+g4cRvuR\nMSxKtGB2pgULWzqwrJjAWE8/AA5EkdGciGGgSFX6RAKDA6DM70Bf3kpi3HPiOOacYq8SaBfqZhUJ\nsVhSLYmUpAIKhSxkuYiWtjZ0vvVd+N2YjJ15Bf2pVvxsYBy7hQRePDGCRXNnY0VnOziOw1i+AFlR\nwAP48/7D+Gv/ME5asUo/AnZWw/kFQUQ0moQoxjDR1ELHafJJJwpFyK2dpc/YXl5V0GtI0JJAompI\nyLJkIn0HESEIIu0w9TEtIwR22Lz57fjCF27G1q33gBCCLVtuCvwc6XS67logu0XQu1KtPW1tHJzp\nUJ3RKKF0h85F89Gz9xnEZR6RIkF7IoW+8RFEwIEXBCgAWhMpHBocxqL1qzE+J47x7ha0tqSwpK0D\noydGERftl74YL6JQKCAWM+7aC4eOoaDEkeNFxEs7+qZ0GoXefoyOZ5FJp6DIEiLtGRwdOg4+EkEz\nL0BWJBwbH0Zk0Tz07zuEY8+9iJgkA8kYYt3zkV53GtIZO3JyedipLbL+ALPnz8e891yhCiydGWvC\nk398EBv79kAEMHF8AAlRRIwD8kUJw/kizlq0CMdXzfV4/gjO2HwxfvO9u3DRglK0hdDn+JdHJ3Du\nJW9j7/Z8fQyVDDHrZkgloY1yyKxXhBfFxzIjYWcM4FhTF3XjELS2tuGuu74R6jkyGWOEgOZdQz3l\nJIA98KxvAv3/TApJhw1/zpWeoV9/ZCYvEAQBHZtW4cV7HkN0YghxTsSB/CC6mtrREk1gVM5DyMt4\nLnsAs1+7FgvWLUMi2awy1DNtrRiQDyEJa+57mJcwx9SW+YU/PAry2CuY3TwL/UoRw3Fg6fo1iCfi\nENMpjAwPo7WlBTwnYNas2TgMgh45i1mF45CjIriFHRD39eHMtrnAGXORzxeQLxZwuEnE7AVdljF4\nAdspm/sDyLLREJ72ujfg0X/bjvPmtyMXS0CWChA4OpfRZBO2KxGsPP9Cw7Hd7Io7Zs3Civddg5/f\n/0vEh45CATDeOhsb/+FyRKNRg5aAP7jj5ZRrWhTE7n4m9DEA6sghqAVSqbRDyqBWHILg4bwrtTNS\nM3dX6hdBRVY0bYDGnBNCkEgmseLiN+DP/f+NFcU41sxagSMjQ9ivjCCRacKOsR6cfO07kMm0ACCG\nmn6e5yHPzWC8fwLJaALDg4MgYzmM5bPoX9CEwouvIHJ8ArykYOfunTgNGYgtczErkQbHcVCIgu2P\nP43V525Ccl4n9k4MgIwOIaoAEyIHbuVCrF60QDWGB598FstSLVSUaKAfRCGY1dmBWdkcDu8/gFxf\nH0RZgRyPoeuUkxGNMnVE97tSO4OozZeCWCyG9Nnn4+E//w5nzWlDIZ9Hz8AAHjzUj3kbz8Lqt2z2\nFakAgLkLujD3qo+q52LER3b9WjTQO2i3RPfvt+tNwKAoShVNpGYGh6DhEHhAJtOCkZETkzwKf0+i\nN8PEQMvPgMau3x2IKRXSiKxUi3LPrShy2HT1u/DEj3+H18gRzFmyABw4vHyiB21nn46Wlo7SDjqC\nYjGn1qMXChOYs2wh+mM9ePGR7ZhbjIGPiYi1ZzD/yCjG9g2h++SV4BM8Bnsn0BSNYqiYRVbmkWhK\ngucFLOSbcfRoL8bSMax507kghECSJHREIlAUBf09R1DM5ZBoaUYkm8eRbC/koWEsSGbA8zyO7NiD\nfiUHbt9urFm5CgAPZbyIVx78Izo3bUC6tc3XrlQvA1ws5gCgVKoYwYo1pyO7fBX+8KeHwMfySJ96\nFi5bs64CO9/bM6kP4ReLE6V5oQ6KVzlmbQzejbjRMWDzYN9EytUoGhyCBsxIpzPo6TloerXW7P/K\nMErEejVMBKyn/FQvP5tMGCMr5UoogUZkxR38cChisSTWf+Cd2PXcS9h7eAByjMPCt77WsNtlZWJ0\nt0jLBQuFCUiyhNVLVyARpaWAE+Pj4IdymBtPoqe3F6lEEtHRHFpnzQZPOGSlIrL9g8i0ZtAcieGx\ng/uw8b3vUM8RiURwon8QE68ewNx4ChExgr4jPTiwax9OS7ZgQTPNs/McjwXJDIZePYhZyxap4+R5\nHqe0duCl515C+pyzq5pLQRBRLNLvM22eRCsSYrEoXvuWza6eQa+7cz1omaAAQiRozYImwPNiyTGo\nvLZUm/tnGhKl38BxsC3ZdIcGh6ABE2jHQ3tS4WSR1oImotUuFDY9vljeIyuNEko3cP/cuoukLF9z\nKshprOrCeRcqihFwHA9JKqDYfxwil4QsF8HzInIj4+gsOQdkfBTKuARF4EEIwCsEHR1tkCQJxyZG\nIKfiWPSGM9CcTqvHlmUZuVcPoLtZa4Pc3pRCF0lCyUrgm3koRIFCFBwe7MPidAsKNk53Yixb6ptS\nnRHiOIAQDtFoUle377Z2H/C7Ozd+HupO3dwsyO1OPYjvEM/TyIW5ZNOtY9DgEDRgQSbTgtHRyS87\nnC5a8+4wdaIr/vP9LEowPaVMw4R1TgHWX8KKcJ5bRVHQs2M30DcMHjxIJonWpd0QxAhQ4EqpBAXg\nAUmWIAoi8hN5zE0mIDdFkS3mVUsQESNoj6fwOD+MM1atNJxn4FAP5iUylvO3zurE0IGDmKO0IVLq\nmjc8MY6W5hQEQiy5bR5c6bXqrpvt8O0qEmjtflEN49vLhwcHOw2Dyjv1IHbl2jGsTaSKFi2HMMWN\npgMaDoEH0LJDfy2QvaB8yN9spIJdRLXKialjqKuDfUqnurSKcY6rIU3VE6aqYNLBbc9jsZIEoi1U\nV18ScfDpXZDnNkMaVBAVqKFqak1hsP8wWmNNUOIRcOCwftVr8PhzT0EoKliVjOJEIYtXc0NYcuU7\nLOeRC0WL/gkhAKIRtMyZg+HWZijjEwDPofv0teh7dQdaBK4kg8uVDDOPbDKKOdGo2tjH/7wYd/ia\nqE9EJf6ZS/TMqI6Zb7z3bsSVjN8r9jnfQ7A9ht5Bchs5CdpBmqpoOAQeQIWJzCkDfUmY9ye3HrXm\npyYUnaNTXVrFGTNj0QD8Gn/2ntqVTvb3HsV8KQYhIuha2ALdTW3YPT6OQxEJC4txRCMlvf5ZKTy2\nfw/WnroaIwcHIMoFtCxfiI7587B/cABt6dlYmF6F2fPmWc6VbGvB2FAfmuPGcsb07A68crwfp7cv\nBdq1119uiiOeiKOlJMEuyxIOjA6jec2poc0HoCf+mUv03Of3vZ2Ps/xubfdsZ5CDjRDYjcs+clIo\nzYPeMaDre72vuQ2HwAOampqQzWZ9fbba3RMF65vQCEvbofwcm3fx0ymtMrmo5tk17vhqXzopDYyg\nKdZsKD9jiGSLmHPWaTh28DCUgRGAJyArZuPUc09Fz8HDGCoAHRMCFs1aBhCClZllODZ+AsmlVmcA\nAFo62nFw32EkFWN3xeHCBFrecDp29x9HbDwPDhzyySgWv/U8TGTHsWP/AfBFCTmRQ9vaU5Bua0Gx\nmK9qV+rms9YSPZbfj+i6E1ZrjJ0/X94gxwLZlbupDjBGTooGkSeWUqmGYDmd0HAIPIA1U6qEIMPR\nxuN6Gm5dwzvZj4NG+GsY/3Kop+6TRBRMlAVtjIpAxzxnYRewUPcZQtC1bBnmLu7G8NBx7Dl0DGJR\nAScqaF27xEAkNGP+ulOx69kXIR0aRDwWB2mOInpSJ7q6FwFLKPGQEAKxpJSYaW8DurrU8+pD6dp4\n/F+/m3uidwwYt0A7fzVOiTsj6pzKqO13laVOzCJPdANmTL/U61pclw7BgQP7cfXV78e99z6ISCQS\n8NGdHk4FRs15p89Wu4DW4km0z7sHegYPKo/VOVhsV2rt4z7TYU1XMZh309M3mtLSNRfHntmHziaj\nEZdkCaStyfYz+lx3W2cE6VZWwsghEok6VhQpioLeF3aiIy8gM28RxvI59HNZNLdoRMNyPVbsQukA\nSnX09o17goI1v58vXZMMSSq6qEiwg7cqBaMMsSYqRKtABB8aBvqNg/uxO4k8EaKobbDrFXXnEGSz\n4/jnf/46otHKHcS8QFEUHD58CJlMGt/5zr+it/cILr/8cixatKj0jvCIfpOF2pRSGg178GWU4Ts3\ntUEQ5C638zrdSye18Sabm3B8URuO7u1HZzIFjgNGJ7LojRexcIV9V0P1KKWQtiQVweaMhZLtSHhH\nX96FhUoCfJIawZZkM5qKEex/eQ9SZ663nqDCeYvFIg6+ugNydgLRTApdS5c5kv+s8Jd/Z04Jx3Gq\noE+lboK2Z69iC00NcgLFIo1WEEKq4DhUThk4j4OJPBVVgieTQ+a4WF1uMOrOIfjSl27D1Vdfgxtu\n+ETVx5IkCd/73r/hmWeexp49uzAxMQEA2Lt3D3iex/nnn69zCDiw+ufpt4DWHvoFo77KKCcX1XFV\nKOkufMJfbe/d7CULMdrRgj179yPCR9B0UicWzep0/XmWKoxGk+rO1WygZFlGdDgHvsnaHyGT55Ad\nG0ey2T4iYYcTg4M48qc/Y3myGU3xJoweHsArO/eg6/WvQ3Mqg0ikfA1/UHXzghABQFxVJNih2oon\nNgZaGqpxHJieRC3Avg88TzUpFEVGAM10pySmrUOwdes9uPvunxgeuNmz5+D889+MJUuWIohd4cjI\nMH760/8CIQSLFi3G0qXL8cILz+Gaaz6MU09djUwmU1qAGdkv7IWO7nanQ+c+PSobKT3hL2jjP33m\nySuC70AZxiinBhJNScxbtUTNEfsB27nakfAKBQlxW0eKoDmawMi4N4fg2LYncVprBwhRwHEc0skm\nbIjFsf3J7Vh6ztnI52tRw88iBjFDGJ/tkmnEwMkoB0cIpOeijZK8igoFIznMjiEgEolCUQgIqb/o\nADCNHYLNmy/C5s0XGV77u797F7ZuvQf33vtrDA4O4mMf+0d861vf8X2OtrZ23Hvvg4hGo2o71Guu\nuRrd3QuRyZgFSOp4NfWARs+EsKBVSxjTKnaY+mS/WkG79GoMpJEtr5HwJBSL1EBxnIIRuYBWG2b9\ncHECqYwzEdGMwf5+zCWcxUPjBQFtORmyTCAIPGS5iJ4D+zC2dz9EwkFONWPJutMRj8d9XKMZxvmy\nOkNy2d4AWoSi+meP4zgw+WmeF11pGDhdhx/onQoqh8xBdtLWmuaYtg6BHf77v/9H/f/FF78dX//6\nv1R9zFQqZfg9nU45dDysJ7jLu1eb7yeEyrPWJvQ3fRy28vNq/r1h/DVY+SjsX9AwGihKACy0RpEd\nHUc8ngDPUR6GLMsYaxbR4cFIT4yNo0XteAiDLYvzPGSZIJlMYse2v6Lj4CEsz2TAcRyU0XE8c889\n6H7zBWgyrVte4ZRyMFYkFMooDgYRobAew72GQfnr8DeO+kd9xj0AAO5KBL0inW5x7GdQf6Dzpy2s\nCgiRQYgEQoqgqRKmH6//8vKgu34BgAiOi4DjRHAcVWKrP6NV3e7D3bzqzyViZsy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8AAAg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1250d0610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tI9k6f8rC8RZnqMk83PLdiNr1IVa2tVaOySYrFg5G20zG9DkOtd1YlxnUD25l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\nuwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b018610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3WBx/QMveUyGEXVMyvBmtO78D7rcw/gj80J67w3T0XErmVtPV64Ht0HeBHjSd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\nz6OtNCn3ebSgLLIw4hznVqm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QkcYWaMdjCbnvH8TvAuyKlsXutC7RDQuPoHX7brSsRX3G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\nAYqI/DUfI2sKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12512e1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lcs</th>\n",
       "      <th>lrs</th>\n",
       "      <th>lps</th>\n",
       "      <th>lms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24_5</th>\n",
       "      <td>160.0</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>-3.223928</td>\n",
       "      <td>-0.020150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25_7</th>\n",
       "      <td>135.0</td>\n",
       "      <td>0.651852</td>\n",
       "      <td>-2.515814</td>\n",
       "      <td>-0.018636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26_15</th>\n",
       "      <td>110.0</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>-1.624467</td>\n",
       "      <td>-0.014768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27_26</th>\n",
       "      <td>120.0</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>-2.197939</td>\n",
       "      <td>-0.018316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28_24</th>\n",
       "      <td>142.0</td>\n",
       "      <td>0.640845</td>\n",
       "      <td>-3.833662</td>\n",
       "      <td>-0.026998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31_5</th>\n",
       "      <td>372.0</td>\n",
       "      <td>0.634409</td>\n",
       "      <td>-4.477736</td>\n",
       "      <td>-0.012037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31_12</th>\n",
       "      <td>138.0</td>\n",
       "      <td>0.637681</td>\n",
       "      <td>-4.040783</td>\n",
       "      <td>-0.029281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32_14</th>\n",
       "      <td>162.0</td>\n",
       "      <td>0.641975</td>\n",
       "      <td>-3.757361</td>\n",
       "      <td>-0.023194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33_5</th>\n",
       "      <td>124.0</td>\n",
       "      <td>0.645161</td>\n",
       "      <td>-5.155671</td>\n",
       "      <td>-0.041578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33_18</th>\n",
       "      <td>52.0</td>\n",
       "      <td>0.634615</td>\n",
       "      <td>0.401146</td>\n",
       "      <td>0.007714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34_11</th>\n",
       "      <td>108.0</td>\n",
       "      <td>0.685185</td>\n",
       "      <td>-4.270846</td>\n",
       "      <td>-0.039545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35_23</th>\n",
       "      <td>73.0</td>\n",
       "      <td>0.657534</td>\n",
       "      <td>-1.210312</td>\n",
       "      <td>-0.016580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36_28</th>\n",
       "      <td>73.0</td>\n",
       "      <td>0.671233</td>\n",
       "      <td>-2.032845</td>\n",
       "      <td>-0.027847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37_25</th>\n",
       "      <td>115.0</td>\n",
       "      <td>0.660870</td>\n",
       "      <td>-4.175807</td>\n",
       "      <td>-0.036311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38_7</th>\n",
       "      <td>122.0</td>\n",
       "      <td>0.647541</td>\n",
       "      <td>-3.728374</td>\n",
       "      <td>-0.030560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38_21</th>\n",
       "      <td>18.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.009932</td>\n",
       "      <td>0.056107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39_9</th>\n",
       "      <td>90.0</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>-1.914981</td>\n",
       "      <td>-0.021278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40_25</th>\n",
       "      <td>73.0</td>\n",
       "      <td>0.671233</td>\n",
       "      <td>-2.032845</td>\n",
       "      <td>-0.027847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41_10</th>\n",
       "      <td>346.0</td>\n",
       "      <td>0.632948</td>\n",
       "      <td>-5.712077</td>\n",
       "      <td>-0.016509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41_23</th>\n",
       "      <td>46.0</td>\n",
       "      <td>0.630435</td>\n",
       "      <td>-0.125099</td>\n",
       "      <td>-0.002720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41_31</th>\n",
       "      <td>129.0</td>\n",
       "      <td>0.651163</td>\n",
       "      <td>-2.841463</td>\n",
       "      <td>-0.022027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42_9</th>\n",
       "      <td>126.0</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>-4.609681</td>\n",
       "      <td>-0.036585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42_22</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>1.070885</td>\n",
       "      <td>0.048677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43_11</th>\n",
       "      <td>68.0</td>\n",
       "      <td>0.691176</td>\n",
       "      <td>-1.932722</td>\n",
       "      <td>-0.028422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44_28</th>\n",
       "      <td>42.0</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>-1.051805</td>\n",
       "      <td>-0.025043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44_31</th>\n",
       "      <td>114.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-4.074704</td>\n",
       "      <td>-0.035743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45_6</th>\n",
       "      <td>336.0</td>\n",
       "      <td>0.633929</td>\n",
       "      <td>-3.224770</td>\n",
       "      <td>-0.009598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45_13</th>\n",
       "      <td>66.0</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>-1.651001</td>\n",
       "      <td>-0.025015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46_10</th>\n",
       "      <td>75.0</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>-2.241460</td>\n",
       "      <td>-0.029886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47_11</th>\n",
       "      <td>84.0</td>\n",
       "      <td>0.690476</td>\n",
       "      <td>-3.868414</td>\n",
       "      <td>-0.046053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81_0</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>-0.499477</td>\n",
       "      <td>-0.009990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81_16</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>-0.190936</td>\n",
       "      <td>-0.019094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81_19</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.117092</td>\n",
       "      <td>-0.023418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81_21</th>\n",
       "      <td>37.0</td>\n",
       "      <td>0.729730</td>\n",
       "      <td>-3.473791</td>\n",
       "      <td>-0.093886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81_62</th>\n",
       "      <td>80.0</td>\n",
       "      <td>0.637500</td>\n",
       "      <td>-1.248253</td>\n",
       "      <td>-0.015603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81_72</th>\n",
       "      <td>239.0</td>\n",
       "      <td>0.648536</td>\n",
       "      <td>-2.384517</td>\n",
       "      <td>-0.009977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81_78</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>-0.162419</td>\n",
       "      <td>-0.023203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82_16</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>-0.310766</td>\n",
       "      <td>-0.028251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82_19</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.095641</td>\n",
       "      <td>-0.031880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82_21</th>\n",
       "      <td>37.0</td>\n",
       "      <td>0.729730</td>\n",
       "      <td>-3.473791</td>\n",
       "      <td>-0.093886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82_31</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.076625</td>\n",
       "      <td>0.007663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82_58</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>-0.292118</td>\n",
       "      <td>-0.014606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82_62</th>\n",
       "      <td>80.0</td>\n",
       "      <td>0.637500</td>\n",
       "      <td>-1.248253</td>\n",
       "      <td>-0.015603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82_72</th>\n",
       "      <td>238.0</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>-2.299922</td>\n",
       "      <td>-0.009664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82_78</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.015716</td>\n",
       "      <td>-0.015716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83_19</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.069436</td>\n",
       "      <td>-0.034718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83_21</th>\n",
       "      <td>37.0</td>\n",
       "      <td>0.729730</td>\n",
       "      <td>-3.473791</td>\n",
       "      <td>-0.093886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83_58</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>-0.132397</td>\n",
       "      <td>-0.006620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83_62</th>\n",
       "      <td>80.0</td>\n",
       "      <td>0.637500</td>\n",
       "      <td>-1.248253</td>\n",
       "      <td>-0.015603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83_70</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.135101</td>\n",
       "      <td>-0.045034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83_72</th>\n",
       "      <td>235.0</td>\n",
       "      <td>0.651064</td>\n",
       "      <td>-2.291549</td>\n",
       "      <td>-0.009751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83_75</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.086957</td>\n",
       "      <td>-0.086957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83_78</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.015716</td>\n",
       "      <td>-0.015716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84_4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.029706</td>\n",
       "      <td>0.009902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84_19</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.042480</td>\n",
       "      <td>-0.042480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84_21</th>\n",
       "      <td>37.0</td>\n",
       "      <td>0.729730</td>\n",
       "      <td>-3.473791</td>\n",
       "      <td>-0.093886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84_31</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.004204</td>\n",
       "      <td>-0.004204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84_62</th>\n",
       "      <td>80.0</td>\n",
       "      <td>0.637500</td>\n",
       "      <td>-1.248253</td>\n",
       "      <td>-0.015603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84_72</th>\n",
       "      <td>233.0</td>\n",
       "      <td>0.648069</td>\n",
       "      <td>-2.120741</td>\n",
       "      <td>-0.009102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84_75</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.086957</td>\n",
       "      <td>-0.086957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>163 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         lcs       lrs       lps       lms\n",
       "24_5   160.0  0.650000 -3.223928 -0.020150\n",
       "25_7   135.0  0.651852 -2.515814 -0.018636\n",
       "26_15  110.0  0.636364 -1.624467 -0.014768\n",
       "27_26  120.0  0.633333 -2.197939 -0.018316\n",
       "28_24  142.0  0.640845 -3.833662 -0.026998\n",
       "31_5   372.0  0.634409 -4.477736 -0.012037\n",
       "31_12  138.0  0.637681 -4.040783 -0.029281\n",
       "32_14  162.0  0.641975 -3.757361 -0.023194\n",
       "33_5   124.0  0.645161 -5.155671 -0.041578\n",
       "33_18   52.0  0.634615  0.401146  0.007714\n",
       "34_11  108.0  0.685185 -4.270846 -0.039545\n",
       "35_23   73.0  0.657534 -1.210312 -0.016580\n",
       "36_28   73.0  0.671233 -2.032845 -0.027847\n",
       "37_25  115.0  0.660870 -4.175807 -0.036311\n",
       "38_7   122.0  0.647541 -3.728374 -0.030560\n",
       "38_21   18.0  0.666667  1.009932  0.056107\n",
       "39_9    90.0  0.633333 -1.914981 -0.021278\n",
       "40_25   73.0  0.671233 -2.032845 -0.027847\n",
       "41_10  346.0  0.632948 -5.712077 -0.016509\n",
       "41_23   46.0  0.630435 -0.125099 -0.002720\n",
       "41_31  129.0  0.651163 -2.841463 -0.022027\n",
       "42_9   126.0  0.642857 -4.609681 -0.036585\n",
       "42_22   22.0  0.636364  1.070885  0.048677\n",
       "43_11   68.0  0.691176 -1.932722 -0.028422\n",
       "44_28   42.0  0.714286 -1.051805 -0.025043\n",
       "44_31  114.0  0.666667 -4.074704 -0.035743\n",
       "45_6   336.0  0.633929 -3.224770 -0.009598\n",
       "45_13   66.0  0.681818 -1.651001 -0.025015\n",
       "46_10   75.0  0.680000 -2.241460 -0.029886\n",
       "47_11   84.0  0.690476 -3.868414 -0.046053\n",
       "...      ...       ...       ...       ...\n",
       "81_0    50.0  0.680000 -0.499477 -0.009990\n",
       "81_16   10.0  0.700000 -0.190936 -0.019094\n",
       "81_19    5.0  1.000000 -0.117092 -0.023418\n",
       "81_21   37.0  0.729730 -3.473791 -0.093886\n",
       "81_62   80.0  0.637500 -1.248253 -0.015603\n",
       "81_72  239.0  0.648536 -2.384517 -0.009977\n",
       "81_78    7.0  0.714286 -0.162419 -0.023203\n",
       "82_16   11.0  0.727273 -0.310766 -0.028251\n",
       "82_19    3.0  1.000000 -0.095641 -0.031880\n",
       "82_21   37.0  0.729730 -3.473791 -0.093886\n",
       "82_31   10.0  0.700000  0.076625  0.007663\n",
       "82_58   20.0  0.700000 -0.292118 -0.014606\n",
       "82_62   80.0  0.637500 -1.248253 -0.015603\n",
       "82_72  238.0  0.647059 -2.299922 -0.009664\n",
       "82_78    1.0  1.000000 -0.015716 -0.015716\n",
       "83_19    2.0  1.000000 -0.069436 -0.034718\n",
       "83_21   37.0  0.729730 -3.473791 -0.093886\n",
       "83_58   20.0  0.650000 -0.132397 -0.006620\n",
       "83_62   80.0  0.637500 -1.248253 -0.015603\n",
       "83_70    3.0  1.000000 -0.135101 -0.045034\n",
       "83_72  235.0  0.651064 -2.291549 -0.009751\n",
       "83_75    1.0  1.000000 -0.086957 -0.086957\n",
       "83_78    1.0  1.000000 -0.015716 -0.015716\n",
       "84_4     3.0  0.666667  0.029706  0.009902\n",
       "84_19    1.0  1.000000 -0.042480 -0.042480\n",
       "84_21   37.0  0.729730 -3.473791 -0.093886\n",
       "84_31    1.0  1.000000 -0.004204 -0.004204\n",
       "84_62   80.0  0.637500 -1.248253 -0.015603\n",
       "84_72  233.0  0.648069 -2.120741 -0.009102\n",
       "84_75    1.0  1.000000 -0.086957 -0.086957\n",
       "\n",
       "[163 rows x 4 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ump_wave.gmm_component_filter(p_ncs=np.arange(18, 85), threshold=0.63)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "nts_pd.shape = (1126, 7)\n",
      "nts_pd loss rate = 0.639431616341\n",
      "improved rate = 0.00699238409121\n",
      "predict win rate = 0.507749521222\n"
     ]
    },
    {
     "data": {
      "image/png": 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J8DZwg83xBNUsm4dE/uSSsTx0y3Tr+Pc3TOUvPzqN2y8dx8yxffh/PjtkdUSs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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1235ffed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "brust_min = ump_wave.brust_min()\n",
    "llps = ump_wave.cprs[(ump_wave.cprs['lps'] <= brust_min[0]) & (ump_wave.cprs['lms'] <= brust_min[1] ) & \n",
    "                    (ump_wave.cprs['lrs'] >= brust_min[2])]\n",
    "ump_wave.choose_cprs_component(llps)\n",
    "ump_wave.dump_clf(llps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第三主裁 ump_main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>result</th>\n",
       "      <th>atr_std</th>\n",
       "      <th>deg_60WindowPd</th>\n",
       "      <th>wave_score1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>1</td>\n",
       "      <td>0.045208</td>\n",
       "      <td>5.363046</td>\n",
       "      <td>0.116619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>0</td>\n",
       "      <td>0.234763</td>\n",
       "      <td>4.199374</td>\n",
       "      <td>0.646118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            result   atr_std  deg_60WindowPd  wave_score1\n",
       "2015-07-28       1  0.045208        5.363046     0.116619\n",
       "2015-07-28       0  0.234763        4.199374     0.646118"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 构造方式换汤不换药，可以继续封装优化\n",
    "from MlFiterMainPd import MlFiterMainPdClass\n",
    "ump_main = UmpMainClass(orders_pd_ump, MlFiterMainPdClass)\n",
    "ump_main.fiter.df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JiaW2XbFqOn2v20bR5ZPufXcz75OpXNTtnirn4ffVIT/fT2ysc+zdO2uTWrdR\nlY/rto4dhY4dJdppGHNSsMJQSdnZ2Rw+nENqav2QhqBYsPx+rvr5IgAKC79jyoREhgz+e6ltG6Wd\nwY+b42jZ2nlFene6l7rJ4bl/0bfXL3nv5cV0uXgBGfvi2fvDCM4d0CosxzamRvumAm1P8CExrDBU\nwpzXXyT+0xepm5/Nl617c/lfx5f7dE9i8o9HPsfEeIhP/LHMtud2upjPPr+DDeveISbGR2b6YO4c\ncT179mRVOffatWtzw1VvsmXrJlrWS+ZCKX1k16ysLL7dvJHzO7QBalc5rjHmxGGFoYK2b9tG44+e\npldsBsTBBZs/4cNX/8GAO3533P0OZbQEdgCQn+8n91DL47YfPOAh8vJ+i8/no3bt2pWeT6E0MTEx\nnNHqzDK3r/p+A6M3r2LL+R1puGQuD/qTGHp+97DFN8YEE5FE4E0gFcgFfq6qO0u0GQQ8GlhcqqrH\nH3mzCuwOXgXt3rGN0wqPvq9QOwZiDu4rd79e5z/LpNf6M/2DDkx+42oG9nuy3H3i4uKoXTvyf63/\nc8Mqfhg6CG/zZuzr24P/7tse8RyMOcncASxR1YuBicCDxTeKSB3gaeBSVe0GbBGRBm4lY4Whgj5+\n5F5m7YCiie/mZsfTqOuAcvdr3Lg5QwePp8PpY6lLCnOmPcW+vbtdzrZycuOCO5KH449eJpu7+BM+\nmP0UM76aSDRm/zOmJlLVfwBjAounAftLNOkOrAaeE5GvgHRV3etWPnYpqQJ8Ph+tVi6lbw68dxji\nYmBZQm1+1b3PkTabvlvBphkvA9BqwO20anvOkW27dm5l08LruKH3Rvx+eHXKbHpfNZk6ydVjALsi\n/eJSWLphE7lntoIDGXTPyAVgyhcvE3/Be5zZ0kPG7kLe+exHrh/8+yhna8yJRURGAqOBoglb/MAI\nVV0qIrOA9kDJvzYb4sxw2QnIBuaKyAJV3ehGjlYYKqgQD428cNVhZ3lL6tFHTndt30r6+Fu5IXUT\nAJNfnUvCLz/mlOYtAVi99D1u7O38Hj0euKHvCiZ/M4WL+wWP+7Nr61aWPfsnamdlwoU96Xv3r90/\nsWJu6dmH+ksWsnTVLNrUTea6K64DYL93ER1bOvc66qbF8H2dZRHNy5iaQFXHAePK2NZPRASYArQu\ntmkvsFhVdwMEeg3nAFYYos3r9XJgwEBWTvmUdj6YFh9D6q1HRyVdNXcKN9bbdGT50nqbeWveZE4J\nzM7mrZVoe6ZgAAATv0lEQVRMzmFICNw22L0Ptq5fAcUKg8/n45vfjOSWNc68Ctvmz2JuYhJDf3tv\nBM7wqMu6dOUygidvL8gN/s+lMC+6M74ZU1OIyO+Bbao6ATgEFJRosgxoLyL1gUygK/Bft/KxewwV\ndO/ET/huzL946srrSHn9MwbfenSo7ZS0U9l+6OiPdGe2lzoNmx1Zvrj/bfxrUk82bIU1CnOmQ/+E\niaxbOf9Im927f6Lt+nVHlk/15ZO3conLZ3Usv9/PwYOZ+IrNVXp+ixEs+bA2P/2Yy5eTDrHkh3iW\n6fKI52ZMDTQOuElEvsC5+XwrgIiMFpHLAj2Fh4AZwALgfVVdV9bBqsp6DJVwyXXDueS64cesT4hN\n5uOZXqStD48HdJ2XM/vVPbK9Vq1aNG56Gbmr5hEXAzd1BI8nk7e+X8zZnZxJ7+vVS2VNWhPO3/49\nALl+yE8r/V0Dt6Tv/JFF/7yTFofXsTj+VE4Z+lfadrmIdnIB09f245ud6dTp1YQGQ5J4442POPes\nc8L6OK0xJxtV/Qk4ZkA0VX2+2Od3gXcjkY/1GMIo/du13La7gPzZkDcLRu4u4CddG9TmjLO7kuup\nx1mnOPcZ1qYn07hl5yPb4+PjSfvDX3jnzI580vg03hx4DX3veyii57Fs4uPcljyf/mn7GZaymh8/\nevzItvxUaHjBGdRukARAXmOvjXRqTA1jPYYwatmtJ/8gjuExeQCM9cfR/YJuQW3ObNuZBdvG8L2+\nisfjJ+b06+nV+aKgNh0HDKLjgEH4/f6o/CWekLc/6E+GxPyj72k09TVk9b79xNVPIOuz1cS+q6Tf\ntolWbTpEPE9jIqoiQ2Kc4KwwhNH2tau5izySAl+qd/nzmLZuDXJul6B23QYMhwHHXooqKVqXZ/Kb\nXcCezbNoWNtPfiHsST36yO3Nfa/nlamvseXz93hi49e0ji3kq8evZvWo/6NDr/5RydcYE15WGMKo\n4HBO0KhCtYGCnJxopVNp/W95kFlv1cazayXeBi0YOOzouwoej4fbBvycueP+TuvYQgAu8u3inSmv\nghUGY2oEKwxhdEaPi3neH8dvcS4lveCP56IeF7sa82BmBnPHPkpi1h4KW59H39tGV7mn4fF46Huj\n83hs8cdVi/MTHMNXg24+Z2Ud5M05L1GQlEODglO5rt9Iu7luTip28zmM1nwxi7Oz8njxELx4CNpk\n5bJuzmxXY876420MXzmeazd9Sr/PnuSLcc+Xv1MVeTwe8vvcxIaCBPx+mBnTjFOuvMP1uJHy75l/\nIWn4LlKvPcihS1by1sz/RTslYyLKegxhVLfJKRTGxHBuoXOJJcPrJbmxe4+aFhYW0mTHaryBP2bT\navnwrj/2nQefz4fP56NWrdB+3VlZB5k6617qpG6gIL8ZHc98jBantQlq0/+uB1nZ/gK++f47zu41\ngFNPb13G0dzj9/spKCgoc8jz1ctnsGv9M8TXyiKzsBeDr3mq3Jnf/H4/uQ334o1xhilJqB/H3tiy\nh0g3piaywhBGF10xhDfmfkn8h+/hAXKuupZbhlztWryYmBgyk9Ig2xmd1+eHnOSGQW3mvDoWpr1M\nnC+fvZ0v49IHny73ssjMrx7jqpHT8Xo9wGY+eu0hWpz20THtOvXoAz36HHuACPhm9kSyVvydxJgs\ndsVdzGV3vBRU+LKyssjYdD839t0MwP7MdXw+ozl9Bv7quMf1eDzEHDp6p8jv9xOTk+DOSRhTTVlh\nCCOPx8PNTz/P3J8NBL+fXgMGun5tuvldf2bCfx6hXtZudjVrT+9Rjx/ZtmHNClp/+jfOiXHuEfy0\n+GXmTzqHnlfddNxjxiduDxQFR1LKDldyr6x9+/ZSa93jDGufDkB27jtMmtSGAdf89kibXTt30ObU\nLUeWU1P8+PM2lTxUqQa1vIXPJryKv95hYtNTGdnrrirlu/n79ezZsZWzO3cnKSmpSscyJhKsMIRR\nYWEhH/5iBD+bOQmAD/pewdB/v0ZMTIxrMdt2603bbvPJz88/5pLK9g3fcjlHbxw3iikkZ9cP5R4z\nP6c1ubnziY/3OENj7D897HlXxU+7dtAqJf3IcmI8xGTuCmrT7NTmLFzWhratvgVgx+5Y4lM6hXT8\nTtKFTtKl1J9pSZmZmUx84X58fj/DRz9LSkrwSLmzJzxLq1XPcUFCFlM/7cA5v3mTJs1ahJSHMdFi\nN5/DaO77b3P9zEm08EALD9ww+xO+eneiqzELCwt56uaf8cLl5/D6mKC5PejQsx/Tah+dz3kBDWnR\ntW+5xxzY7zGmv3Uj09/vzLR3Lqd3txfCnndVtGx1Jgv3Hv2SX5eeRL0WwZPsJiQkcNq5/8fELy7h\n/bk9mbP59/TofWuF4oRSFCaMas8D3rd4KOZt3vxFew4cOHBke3Z2NknL/sP59bNomADD669m5QfP\nVSgHY6LBegxhlJd1kOIXChKBgkNVn6f5eJ7qcwaP1N9HWgos+Ool/rnzB375z7cAaJDWiCa/H8eb\n7/6TmMIC6ve/nnPOvbDcY8bFxTHk0qeBsh9XjabatWvT8ZrXmDDjb9T25hDX/BK69bzymHZNmrVh\n47R2FB7O4pReF4Q9jwnP38fv5QDxgX9FD7Y9wJi/38sv//QqAHl5udTxHA7aJzbwKLM5AS2OdgKR\nY4UhjC4ceh1vvj+Bm9avAuDNMzvQc+j1rsXLycmhm3cfaYHRr7vVha9XzQxqc0aHzpzRodSh36ud\n7Oxs5vzzCVLz9pHTvBO9bxlV5j2aps1b0fS2/5R5rMLCQqbdfz137J5DjAfmL/mItYyjXbfwvVfi\n9/nwFvsX5PWAh6Oj0darl8rshgM4P/99kmJhXkYaab2vCVt8Y9xihSGM6qbWp/vrH/Huq//B44Fu\nt9xJvfr1XYsXFxfHYV/wutwT+OrgjEdvZ8SWydTyQvry9/miIJ++t42u1LG2bt1Mzx/nExN4oKgH\nu3nnq0/CWhhuvPdZnv71DB5smwnAM98mc+PY4MtuVz7wPya/3xn/ob00H3QJHTp1DVt8Y9xihSHM\n6qelccn9f4hIrJiYGNY2aMs3Gd/Srg58vBfqXhPZCX3Cxe/3k/bDcmoF6lrjmEK83y2s9PFSUuqx\nq1YdwLnmX+iHvPjwPhGUmlqfa8eu4slnf40HuGnsWFJTg/8QiImJoe+wX4Y17olo55bNLH38Qert\nS2dvizMZ8LexJCYmlr+jiYpqUxhExAP8H86cpoeB21U1tOcLT2L3f7SId17+F69/PZPLH3iQARee\nmH+RejweDiU2gOztAPj9kJOUWunjNWzYkNWX3cuXk1+gUeFB5jXrzoDb7w9XukekptbnV2MmhP24\nNc2Sh37D8MVfAlC4djlvJSRy2dNjo5uUKVO1KQzAECBeVbuLyIXAc4F1phzDbh8Ft4+KdhpV1uyO\nJ3jz3w/TJOcnNqe1o8cvnqjS8frcdh+7rxjOwYwDXN7y9JDf/Dbhl7z96GPSMR5I3L41itlUXyLS\nBlgINFLVvBLbRgPDAD/wmar+ya08qtO/lJ7ANABVXSQiXcppb2qYdj360bbbAhITvXQ4XH77UKSl\npZGWlhaeg5lKO3ja6bDDuQC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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1235394d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HYZxDO69FIu/TueIJRvY+Akl6ABdnr8ImOjE0MR416vGNupIxjCnHcZGZnh5Y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\n0snw++vn7XHWWcOwZ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12479a510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QwnLA4vj3W0MIqw+NekKMPGPlCzGimozr6elpOlII4RjgX2Z2fAhhI+CHZrZN27UTQghR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\nBC6L/87FN5hXw53JyQ1krwe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      "text/plain": [
       "<matplotlib.figure.Figure at 0x123c90b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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69itsiYi0YW0kF83qub8bGy6OweYlx2MV9QfA46r6SHC3PTbEj92Nxozg21R1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\n7Z/YsPlJ7N6F87Ce9LFY5dk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      "text/plain": [
       "<matplotlib.figure.Figure at 0x123c90f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lcs</th>\n",
       "      <th>lrs</th>\n",
       "      <th>lps</th>\n",
       "      <th>lms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31_30</th>\n",
       "      <td>169.0</td>\n",
       "      <td>0.650888</td>\n",
       "      <td>-3.488626</td>\n",
       "      <td>-0.020643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33_11</th>\n",
       "      <td>49.0</td>\n",
       "      <td>0.673469</td>\n",
       "      <td>-2.314873</td>\n",
       "      <td>-0.047242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35_10</th>\n",
       "      <td>85.0</td>\n",
       "      <td>0.658824</td>\n",
       "      <td>-2.364326</td>\n",
       "      <td>-0.027816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39_33</th>\n",
       "      <td>100.0</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>-3.877226</td>\n",
       "      <td>-0.038772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42_20</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.659091</td>\n",
       "      <td>-2.604120</td>\n",
       "      <td>-0.059185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43_23</th>\n",
       "      <td>73.0</td>\n",
       "      <td>0.657534</td>\n",
       "      <td>-0.548995</td>\n",
       "      <td>-0.007520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45_29</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0.653846</td>\n",
       "      <td>-1.105545</td>\n",
       "      <td>-0.042521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46_11</th>\n",
       "      <td>90.0</td>\n",
       "      <td>0.655556</td>\n",
       "      <td>-2.752587</td>\n",
       "      <td>-0.030584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47_7</th>\n",
       "      <td>23.0</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>-0.536203</td>\n",
       "      <td>-0.023313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49_5</th>\n",
       "      <td>46.0</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>-2.461245</td>\n",
       "      <td>-0.053505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51_30</th>\n",
       "      <td>14.0</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>-2.291159</td>\n",
       "      <td>-0.163654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52_18</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-1.992503</td>\n",
       "      <td>-0.044278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53_30</th>\n",
       "      <td>21.0</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>-2.606842</td>\n",
       "      <td>-0.124135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54_5</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>-1.242849</td>\n",
       "      <td>-0.056493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55_10</th>\n",
       "      <td>42.0</td>\n",
       "      <td>0.738095</td>\n",
       "      <td>-2.028529</td>\n",
       "      <td>-0.048298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55_14</th>\n",
       "      <td>71.0</td>\n",
       "      <td>0.661972</td>\n",
       "      <td>0.153408</td>\n",
       "      <td>0.002161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56_7</th>\n",
       "      <td>25.0</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>-1.394074</td>\n",
       "      <td>-0.055763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56_24</th>\n",
       "      <td>17.0</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>-0.833099</td>\n",
       "      <td>-0.049006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56_50</th>\n",
       "      <td>59.0</td>\n",
       "      <td>0.661017</td>\n",
       "      <td>-1.069024</td>\n",
       "      <td>-0.018119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57_7</th>\n",
       "      <td>42.0</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>-1.574196</td>\n",
       "      <td>-0.037481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58_15</th>\n",
       "      <td>62.0</td>\n",
       "      <td>0.677419</td>\n",
       "      <td>-0.331972</td>\n",
       "      <td>-0.005354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59_44</th>\n",
       "      <td>34.0</td>\n",
       "      <td>0.735294</td>\n",
       "      <td>-1.886792</td>\n",
       "      <td>-0.055494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60_8</th>\n",
       "      <td>33.0</td>\n",
       "      <td>0.696970</td>\n",
       "      <td>-1.858589</td>\n",
       "      <td>-0.056321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61_17</th>\n",
       "      <td>28.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>-3.059575</td>\n",
       "      <td>-0.109271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61_26</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0.653846</td>\n",
       "      <td>-0.486007</td>\n",
       "      <td>-0.018693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62_9</th>\n",
       "      <td>51.0</td>\n",
       "      <td>0.725490</td>\n",
       "      <td>-2.412638</td>\n",
       "      <td>-0.047307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63_13</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>-1.847714</td>\n",
       "      <td>-0.153976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64_32</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>-2.936657</td>\n",
       "      <td>-0.058733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64_44</th>\n",
       "      <td>25.0</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>-1.977574</td>\n",
       "      <td>-0.079103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65_37</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>-1.026872</td>\n",
       "      <td>-0.051344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69_33</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>-0.757312</td>\n",
       "      <td>-0.108187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69_46</th>\n",
       "      <td>60.0</td>\n",
       "      <td>0.683333</td>\n",
       "      <td>-0.692344</td>\n",
       "      <td>-0.011539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70_22</th>\n",
       "      <td>53.0</td>\n",
       "      <td>0.716981</td>\n",
       "      <td>-3.815061</td>\n",
       "      <td>-0.071982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70_23</th>\n",
       "      <td>14.0</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>-0.735310</td>\n",
       "      <td>-0.052522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71_16</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-1.032935</td>\n",
       "      <td>-0.043039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71_21</th>\n",
       "      <td>69.0</td>\n",
       "      <td>0.681159</td>\n",
       "      <td>-1.465262</td>\n",
       "      <td>-0.021236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71_55</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>-1.425944</td>\n",
       "      <td>-0.118829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72_3</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0.655172</td>\n",
       "      <td>-0.642314</td>\n",
       "      <td>-0.022149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72_8</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>-0.757312</td>\n",
       "      <td>-0.108187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72_11</th>\n",
       "      <td>62.0</td>\n",
       "      <td>0.677419</td>\n",
       "      <td>-0.690655</td>\n",
       "      <td>-0.011140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72_59</th>\n",
       "      <td>78.0</td>\n",
       "      <td>0.653846</td>\n",
       "      <td>-0.769406</td>\n",
       "      <td>-0.009864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73_3</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>-0.936858</td>\n",
       "      <td>-0.021292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73_6</th>\n",
       "      <td>71.0</td>\n",
       "      <td>0.704225</td>\n",
       "      <td>-1.829152</td>\n",
       "      <td>-0.025763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73_19</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>-0.811367</td>\n",
       "      <td>-0.073761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73_33</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>-0.820737</td>\n",
       "      <td>-0.136790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74_15</th>\n",
       "      <td>51.0</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>-1.070951</td>\n",
       "      <td>-0.020999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74_27</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>-3.096087</td>\n",
       "      <td>-0.061922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75_9</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>-0.617400</td>\n",
       "      <td>-0.123480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75_54</th>\n",
       "      <td>9.0</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>-1.126203</td>\n",
       "      <td>-0.125134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76_3</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>-1.182426</td>\n",
       "      <td>-0.147803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76_34</th>\n",
       "      <td>37.0</td>\n",
       "      <td>0.810811</td>\n",
       "      <td>-4.498345</td>\n",
       "      <td>-0.121577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76_67</th>\n",
       "      <td>15.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-0.173521</td>\n",
       "      <td>-0.011568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77_45</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>-1.324581</td>\n",
       "      <td>-0.120416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77_54</th>\n",
       "      <td>62.0</td>\n",
       "      <td>0.677419</td>\n",
       "      <td>-0.645419</td>\n",
       "      <td>-0.010410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78_20</th>\n",
       "      <td>18.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-0.495147</td>\n",
       "      <td>-0.027508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78_22</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.704545</td>\n",
       "      <td>-2.978029</td>\n",
       "      <td>-0.067682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78_62</th>\n",
       "      <td>38.0</td>\n",
       "      <td>0.657895</td>\n",
       "      <td>-0.941355</td>\n",
       "      <td>-0.024773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78_66</th>\n",
       "      <td>112.0</td>\n",
       "      <td>0.669643</td>\n",
       "      <td>-1.630110</td>\n",
       "      <td>-0.014555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79_35</th>\n",
       "      <td>31.0</td>\n",
       "      <td>0.677419</td>\n",
       "      <td>-1.715791</td>\n",
       "      <td>-0.055348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79_48</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>-1.089348</td>\n",
       "      <td>-0.136168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         lcs       lrs       lps       lms\n",
       "31_30  169.0  0.650888 -3.488626 -0.020643\n",
       "33_11   49.0  0.673469 -2.314873 -0.047242\n",
       "35_10   85.0  0.658824 -2.364326 -0.027816\n",
       "39_33  100.0  0.680000 -3.877226 -0.038772\n",
       "42_20   44.0  0.659091 -2.604120 -0.059185\n",
       "43_23   73.0  0.657534 -0.548995 -0.007520\n",
       "45_29   26.0  0.653846 -1.105545 -0.042521\n",
       "46_11   90.0  0.655556 -2.752587 -0.030584\n",
       "47_7    23.0  0.695652 -0.536203 -0.023313\n",
       "49_5    46.0  0.652174 -2.461245 -0.053505\n",
       "51_30   14.0  0.857143 -2.291159 -0.163654\n",
       "52_18   45.0  0.666667 -1.992503 -0.044278\n",
       "53_30   21.0  0.761905 -2.606842 -0.124135\n",
       "54_5    22.0  0.681818 -1.242849 -0.056493\n",
       "55_10   42.0  0.738095 -2.028529 -0.048298\n",
       "55_14   71.0  0.661972  0.153408  0.002161\n",
       "56_7    25.0  0.680000 -1.394074 -0.055763\n",
       "56_24   17.0  0.764706 -0.833099 -0.049006\n",
       "56_50   59.0  0.661017 -1.069024 -0.018119\n",
       "57_7    42.0  0.714286 -1.574196 -0.037481\n",
       "58_15   62.0  0.677419 -0.331972 -0.005354\n",
       "59_44   34.0  0.735294 -1.886792 -0.055494\n",
       "60_8    33.0  0.696970 -1.858589 -0.056321\n",
       "61_17   28.0  0.750000 -3.059575 -0.109271\n",
       "61_26   26.0  0.653846 -0.486007 -0.018693\n",
       "62_9    51.0  0.725490 -2.412638 -0.047307\n",
       "63_13   12.0  0.750000 -1.847714 -0.153976\n",
       "64_32   50.0  0.680000 -2.936657 -0.058733\n",
       "64_44   25.0  0.760000 -1.977574 -0.079103\n",
       "65_37   20.0  0.750000 -1.026872 -0.051344\n",
       "...      ...       ...       ...       ...\n",
       "69_33    7.0  0.714286 -0.757312 -0.108187\n",
       "69_46   60.0  0.683333 -0.692344 -0.011539\n",
       "70_22   53.0  0.716981 -3.815061 -0.071982\n",
       "70_23   14.0  0.714286 -0.735310 -0.052522\n",
       "71_16   24.0  0.666667 -1.032935 -0.043039\n",
       "71_21   69.0  0.681159 -1.465262 -0.021236\n",
       "71_55   12.0  0.833333 -1.425944 -0.118829\n",
       "72_3    29.0  0.655172 -0.642314 -0.022149\n",
       "72_8     7.0  0.714286 -0.757312 -0.108187\n",
       "72_11   62.0  0.677419 -0.690655 -0.011140\n",
       "72_59   78.0  0.653846 -0.769406 -0.009864\n",
       "73_3    44.0  0.681818 -0.936858 -0.021292\n",
       "73_6    71.0  0.704225 -1.829152 -0.025763\n",
       "73_19   11.0  0.727273 -0.811367 -0.073761\n",
       "73_33    6.0  0.833333 -0.820737 -0.136790\n",
       "74_15   51.0  0.705882 -1.070951 -0.020999\n",
       "74_27   50.0  0.720000 -3.096087 -0.061922\n",
       "75_9     5.0  0.800000 -0.617400 -0.123480\n",
       "75_54    9.0  0.888889 -1.126203 -0.125134\n",
       "76_3     8.0  0.875000 -1.182426 -0.147803\n",
       "76_34   37.0  0.810811 -4.498345 -0.121577\n",
       "76_67   15.0  0.666667 -0.173521 -0.011568\n",
       "77_45   11.0  0.818182 -1.324581 -0.120416\n",
       "77_54   62.0  0.677419 -0.645419 -0.010410\n",
       "78_20   18.0  0.666667 -0.495147 -0.027508\n",
       "78_22   44.0  0.704545 -2.978029 -0.067682\n",
       "78_62   38.0  0.657895 -0.941355 -0.024773\n",
       "78_66  112.0  0.669643 -1.630110 -0.014555\n",
       "79_35   31.0  0.677419 -1.715791 -0.055348\n",
       "79_48    8.0  0.875000 -1.089348 -0.136168\n",
       "\n",
       "[67 rows x 4 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ump_main.gmm_component_filter(p_ncs=np.arange(18, 80), threshold=0.65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "nts_pd.shape = (804, 7)\n",
      "nts_pd loss rate = 0.661691542289\n",
      "improved rate = 0.00578987217744\n",
      "predict win rate = 0.506547009308\n"
     ]
    },
    {
     "data": {
      "image/png": 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l4rkZ3r0bOuL7WEsfQ0S4g8sXjOaiORm4PR5vX8RwMymcKq5mQmZir2MSnZOa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\n5ocAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b018dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "brust_min = ump_main.brust_min()\n",
    "llps = ump_main.cprs[(ump_main.cprs['lps'] <= brust_min[0]) & (ump_main.cprs['lms'] <= brust_min[1] ) & \n",
    "                    (ump_main.cprs['lrs'] >= brust_min[2])]\n",
    "ump_main.choose_cprs_component(llps)\n",
    "ump_main.dump_clf(llps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "所有主裁构建完成"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 感谢🙏您能有耐心看到这里\n",
    "## 如果有什么问题可以加阿布的微信 \n",
    "## 微信号：aaaabbbuu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image](./mmexport1475383814280.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
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