解决方案B-ABU量化系统.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ABU 量化系统  简介（版本 0.1）\n",
    "\n",
    "* #### __author__ = 'BBFamily'\n",
    "* ####  __email__ = 'bbfamily@126.com'\n",
    "* ####  __weixin__ = 'aaaabbbuu'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第八部分 解决方案B"
   ]
  },
  {
   "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": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import ZEnv\n",
    "import ZLog\n",
    "import ZCommonUtil\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from UmpMain import UmpMainClass\n",
    "from UmpJump import UmpJumpClass\n",
    "from MlFiterJumpPd import MlFiterJumpPdClass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4837, 31)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn = './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": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42538, 31)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn = './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": [
    "**使用全量测试集数据**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(47374, 39)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn = './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": [
    "**jump ump 辅助裁决**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ump_jump = UmpJumpClass(orders_pd_ump, MlFiterJumpPdClass, dd_threshold=21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ZLog.info(ump_jump.fiter.df.shape)\n",
    "ump_jump.fiter.df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": {
      "image/png": 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BsMUWbj9cqKimJjs64sAHDWA6kvFBhw8awA8dPmgAf3REKVpHkUyX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e94ba10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ump_jump.show_general()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**跳空监控区，收益小于整个区间**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "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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DrgeGAwOBn4vIFsmqchzHKW0aVY5CRIYCF6jqPtH2eEBV9fKM4zxH4TiOUwdK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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e345090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ump_jump.show_general(use_fiter=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**make a joke**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
      "       loss       0.55      0.59      0.57       247\n",
      "        win       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": [
    "**p_ncs的选择要依据数据集的大小**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fbf21d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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41RBSOWS32w2fz9cQfRIqvUc4LrdxEM/zOR0FaYxGjyEoNI5Tto8bbrNwwIAB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\nxbn8vTt37sS8efMwatQoPP300/jtt98QHByMsWPH8jV1coISY/N3hBK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z9zC9DnfnI5DB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x134178e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Xdrqq3iAiL3U9TsAe+L2AYq71F6r6sIiMxQzwRV7+/3Jdj3H9vcDLvhbrKe6N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\np1xGVPXr2AjnEM+jE3s7MK0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      "text/plain": [
       "<matplotlib.figure.Figure at 0x13471e4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hdsx3HAVc5P9TGXZQ3JT/0ffTtfot4P9h/mkVdsP+IUUjL3UD3+a/eym6SFP6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\n+rMzVPV2rK4Ox2xpap38+jI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      "text/plain": [
       "<matplotlib.figure.Figure at 0x13471eed0>"
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     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
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       "<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",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "ump_jump.gmm_component_filter(p_ncs=np.arange(18, 42), threshold=0.65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>2014-01-27</th>\n",
       "      <td>0</td>\n",
       "      <td>-85.133358</td>\n",
       "      <td>14</td>\n",
       "      <td>2062</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.059441</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            result  jump_power  diff_days   ind  ss    profit\n",
       "2014-01-27       0  -85.133358         14  2062  15 -0.059441"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ump_jump.nts['25_15']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**跳空能量向下两倍以上，10天左右的认为不应买入**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>2015-08-10</th>\n",
       "      <td>0</td>\n",
       "      <td>-2.706233</td>\n",
       "      <td>11</td>\n",
       "      <td>41</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.176608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-01</th>\n",
       "      <td>0</td>\n",
       "      <td>-2.702703</td>\n",
       "      <td>9</td>\n",
       "      <td>143</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.053364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-23</th>\n",
       "      <td>1</td>\n",
       "      <td>-2.418221</td>\n",
       "      <td>10</td>\n",
       "      <td>170</td>\n",
       "      <td>12</td>\n",
       "      <td>0.062206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-16</th>\n",
       "      <td>1</td>\n",
       "      <td>-3.402683</td>\n",
       "      <td>11</td>\n",
       "      <td>249</td>\n",
       "      <td>12</td>\n",
       "      <td>0.113208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-17</th>\n",
       "      <td>0</td>\n",
       "      <td>-3.153450</td>\n",
       "      <td>10</td>\n",
       "      <td>262</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.032323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-25</th>\n",
       "      <td>0</td>\n",
       "      <td>-3.251595</td>\n",
       "      <td>12</td>\n",
       "      <td>283</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.128647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>0</td>\n",
       "      <td>-3.497082</td>\n",
       "      <td>10</td>\n",
       "      <td>354</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.013350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-15</th>\n",
       "      <td>0</td>\n",
       "      <td>-2.571623</td>\n",
       "      <td>9</td>\n",
       "      <td>578</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.007959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-16</th>\n",
       "      <td>0</td>\n",
       "      <td>-3.490188</td>\n",
       "      <td>10</td>\n",
       "      <td>672</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.017408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-03</th>\n",
       "      <td>0</td>\n",
       "      <td>-2.728997</td>\n",
       "      <td>10</td>\n",
       "      <td>1016</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.003032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-08</th>\n",
       "      <td>1</td>\n",
       "      <td>-3.083089</td>\n",
       "      <td>12</td>\n",
       "      <td>1020</td>\n",
       "      <td>12</td>\n",
       "      <td>0.049996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-05</th>\n",
       "      <td>1</td>\n",
       "      <td>-2.483176</td>\n",
       "      <td>10</td>\n",
       "      <td>1122</td>\n",
       "      <td>12</td>\n",
       "      <td>0.060298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30</th>\n",
       "      <td>0</td>\n",
       "      <td>-2.631553</td>\n",
       "      <td>10</td>\n",
       "      <td>1193</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.027207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-19</th>\n",
       "      <td>0</td>\n",
       "      <td>-2.951475</td>\n",
       "      <td>11</td>\n",
       "      <td>1493</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.044630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-18</th>\n",
       "      <td>0</td>\n",
       "      <td>-2.629061</td>\n",
       "      <td>9</td>\n",
       "      <td>1580</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.026883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-27</th>\n",
       "      <td>1</td>\n",
       "      <td>-3.107009</td>\n",
       "      <td>12</td>\n",
       "      <td>1762</td>\n",
       "      <td>12</td>\n",
       "      <td>0.096847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-02</th>\n",
       "      <td>1</td>\n",
       "      <td>-2.795049</td>\n",
       "      <td>10</td>\n",
       "      <td>1960</td>\n",
       "      <td>12</td>\n",
       "      <td>0.031864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-11</th>\n",
       "      <td>0</td>\n",
       "      <td>-2.657454</td>\n",
       "      <td>11</td>\n",
       "      <td>1989</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.017341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-12</th>\n",
       "      <td>0</td>\n",
       "      <td>-3.151759</td>\n",
       "      <td>12</td>\n",
       "      <td>2141</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.030650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-14</th>\n",
       "      <td>0</td>\n",
       "      <td>-3.117394</td>\n",
       "      <td>11</td>\n",
       "      <td>2277</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.000439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-23</th>\n",
       "      <td>1</td>\n",
       "      <td>-2.646682</td>\n",
       "      <td>11</td>\n",
       "      <td>2336</td>\n",
       "      <td>12</td>\n",
       "      <td>0.040926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-08-24</th>\n",
       "      <td>1</td>\n",
       "      <td>-3.501264</td>\n",
       "      <td>10</td>\n",
       "      <td>2500</td>\n",
       "      <td>12</td>\n",
       "      <td>0.025974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-20</th>\n",
       "      <td>0</td>\n",
       "      <td>-2.593575</td>\n",
       "      <td>9</td>\n",
       "      <td>2575</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.010300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-22</th>\n",
       "      <td>0</td>\n",
       "      <td>-2.853089</td>\n",
       "      <td>10</td>\n",
       "      <td>2690</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.010843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-03</th>\n",
       "      <td>0</td>\n",
       "      <td>-2.946918</td>\n",
       "      <td>11</td>\n",
       "      <td>2829</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.134032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-14</th>\n",
       "      <td>0</td>\n",
       "      <td>-2.873770</td>\n",
       "      <td>9</td>\n",
       "      <td>3152</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.080194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-22</th>\n",
       "      <td>0</td>\n",
       "      <td>-2.482930</td>\n",
       "      <td>10</td>\n",
       "      <td>3169</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.026621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-23</th>\n",
       "      <td>0</td>\n",
       "      <td>-2.795248</td>\n",
       "      <td>9</td>\n",
       "      <td>3342</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.046241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-30</th>\n",
       "      <td>0</td>\n",
       "      <td>-3.409071</td>\n",
       "      <td>11</td>\n",
       "      <td>3633</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.086524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-16</th>\n",
       "      <td>0</td>\n",
       "      <td>-2.776829</td>\n",
       "      <td>9</td>\n",
       "      <td>3785</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.172645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-07</th>\n",
       "      <td>0</td>\n",
       "      <td>-2.599565</td>\n",
       "      <td>9</td>\n",
       "      <td>3838</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.160468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-28</th>\n",
       "      <td>0</td>\n",
       "      <td>-2.456538</td>\n",
       "      <td>10</td>\n",
       "      <td>3871</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.067081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-14</th>\n",
       "      <td>1</td>\n",
       "      <td>-3.023274</td>\n",
       "      <td>10</td>\n",
       "      <td>3972</td>\n",
       "      <td>12</td>\n",
       "      <td>0.023094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-14</th>\n",
       "      <td>0</td>\n",
       "      <td>-2.891566</td>\n",
       "      <td>10</td>\n",
       "      <td>3973</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.095696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-14</th>\n",
       "      <td>0</td>\n",
       "      <td>-3.050215</td>\n",
       "      <td>10</td>\n",
       "      <td>3974</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.037638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-22</th>\n",
       "      <td>1</td>\n",
       "      <td>-3.099930</td>\n",
       "      <td>12</td>\n",
       "      <td>4243</td>\n",
       "      <td>12</td>\n",
       "      <td>0.035945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-23</th>\n",
       "      <td>1</td>\n",
       "      <td>-2.819676</td>\n",
       "      <td>10</td>\n",
       "      <td>4509</td>\n",
       "      <td>12</td>\n",
       "      <td>0.016492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-02</th>\n",
       "      <td>0</td>\n",
       "      <td>-2.681171</td>\n",
       "      <td>9</td>\n",
       "      <td>4859</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.124722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-10</th>\n",
       "      <td>1</td>\n",
       "      <td>-2.707060</td>\n",
       "      <td>11</td>\n",
       "      <td>4872</td>\n",
       "      <td>12</td>\n",
       "      <td>0.034586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-05-16</th>\n",
       "      <td>0</td>\n",
       "      <td>-3.198386</td>\n",
       "      <td>11</td>\n",
       "      <td>4962</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.010707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            result  jump_power  diff_days   ind  ss    profit\n",
       "2015-08-10       0   -2.706233         11    41  12 -0.176608\n",
       "2015-10-01       0   -2.702703          9   143  12 -0.053364\n",
       "2015-10-23       1   -2.418221         10   170  12  0.062206\n",
       "2015-11-16       1   -3.402683         11   249  12  0.113208\n",
       "2015-11-17       0   -3.153450         10   262  12 -0.032323\n",
       "2015-11-25       0   -3.251595         12   283  12 -0.128647\n",
       "2016-01-04       0   -3.497082         10   354  12 -0.013350\n",
       "2016-04-15       0   -2.571623          9   578  12 -0.007959\n",
       "2016-05-16       0   -3.490188         10   672  12 -0.017408\n",
       "2014-10-03       0   -2.728997         10  1016  12 -0.003032\n",
       "2014-10-08       1   -3.083089         12  1020  12  0.049996\n",
       "2014-12-05       1   -2.483176         10  1122  12  0.060298\n",
       "2014-12-30       0   -2.631553         10  1193  12 -0.027207\n",
       "2015-05-19       0   -2.951475         11  1493  12 -0.044630\n",
       "2015-06-18       0   -2.629061          9  1580  12 -0.026883\n",
       "2013-08-27       1   -3.107009         12  1762  12  0.096847\n",
       "2013-12-02       1   -2.795049         10  1960  12  0.031864\n",
       "2013-12-11       0   -2.657454         11  1989  12 -0.017341\n",
       "2014-03-12       0   -3.151759         12  2141  12 -0.030650\n",
       "2014-05-14       0   -3.117394         11  2277  12 -0.000439\n",
       "2014-06-23       1   -2.646682         11  2336  12  0.040926\n",
       "2012-08-24       1   -3.501264         10  2500  12  0.025974\n",
       "2012-09-20       0   -2.593575          9  2575  12 -0.010300\n",
       "2012-10-22       0   -2.853089         10  2690  12 -0.010843\n",
       "2012-12-03       0   -2.946918         11  2829  12 -0.134032\n",
       "2013-03-14       0   -2.873770          9  3152  12 -0.080194\n",
       "2013-03-22       0   -2.482930         10  3169  12 -0.026621\n",
       "2013-05-23       0   -2.795248          9  3342  12 -0.046241\n",
       "2011-08-30       0   -3.409071         11  3633  12 -0.086524\n",
       "2011-09-16       0   -2.776829          9  3785  12 -0.172645\n",
       "2011-10-07       0   -2.599565          9  3838  12 -0.160468\n",
       "2011-10-28       0   -2.456538         10  3871  12 -0.067081\n",
       "2011-11-14       1   -3.023274         10  3972  12  0.023094\n",
       "2011-11-14       0   -2.891566         10  3973  12 -0.095696\n",
       "2011-11-14       0   -3.050215         10  3974  12 -0.037638\n",
       "2011-11-22       1   -3.099930         12  4243  12  0.035945\n",
       "2011-12-23       1   -2.819676         10  4509  12  0.016492\n",
       "2012-04-02       0   -2.681171          9  4859  12 -0.124722\n",
       "2012-04-10       1   -2.707060         11  4872  12  0.034586\n",
       "2012-05-16       0   -3.198386         11  4962  12 -0.010707"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ii = ump_jump.nts['39_12']\n",
    "ii"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NormaltestResult(statistic=11612.386867948926, pvalue=0.0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f6de610>"
      ]
     },
     "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": [
    "**jump power选取llps没有执行最优化求解，因为数据量不够，按照0， 0， 0.65来办**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 17,
     "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": "code",
   "execution_count": 18,
   "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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zs2EhhPXR5UOAJcBNwBVm1p8C/pPMzEe1gvUJ8D7Qy8y2LWTrovrfCf53OhcY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f9bbc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ump_jump.choose_cprs_component(llps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ump_jump.dump_clf(llps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from MlFiterDegPd import MlFiterDegPdClass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**deg ump 主裁，只使用全局最优llps**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8//vBnMCscogQL/bTei1zaXl3797NqlWr2L9//1GP3bRpE0mpp0P8yf4diY1I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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126dce750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VK1Zg+fLlkGUZn332GebMmYOjjjoKHo8HuVwO4XC4+oAW0I4eAie/Exl+KuvMA/xaDyNe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\nAiIiIgITAiIiIgITAiIiIgLwX6rel8ytAFwHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121085610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x121314610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x121314650>"
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     "metadata": {},
     "output_type": "display_data"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lcs</th>\n",
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       "      <td>-0.020173</td>\n",
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       "      <td>-0.019493</td>\n",
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       "      <td>-0.056768</td>\n",
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       "      <td>-0.013199</td>\n",
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       "      <td>-0.019145</td>\n",
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       "      <td>-0.045968</td>\n",
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       "      <td>-6.872898</td>\n",
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       "    </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 = UmpMainClass(orders_pd_ump, MlFiterDegPdClass)\n",
    "ump_deg.gmm_component_filter(p_ncs=np.arange(18, 85), threshold=0.68)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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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cdoGZzU5u3y/b85Q21aLtq0lxVKU4qlIcVSmOqgoljoJLFMDjwLbuPgCYDVwA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\nA3WObxluA9pPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f99f7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": [
    "**wave ump第二主裁**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wUTdXOc1bquDCZ1Rr5lB/GOicTf0yYFkemx912QbZyfMGI6r6i6o+ab+KrIMA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f9cb1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Vg2e32yMC2ra9t4nZQ/urYgDAbDKyZOp4fr1pM7PnzUv6OHVEZ4RBZ+lMlUGd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\n9AkEAklGOZtgDaAS7QDh+LUaA5CcViGuQzrHQCuHQChMlG/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4MOm8rDEIBoN8ZFCOuA7H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IkfpXaNUXIc26OAJVomEomMHWk542AVJsUck3xZZ+XimHMIHn74YTz22GMwGo04/fTTcd999yk+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\nA6rQS4MkXQtLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fb94510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Y1iNdqmchvQS/yJ5lh8q3XMP6qoZxNPBQLXa3ZQuLdBAU4CKkf3dtjLFHBwH/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120853110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AR48+CIKgdkbFccFx/G8QBEHjGRU9+OiZB0EQNJ5R0YMPgiAIGk8Y+CrE9FEQBGOVUTFF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\nTW/k8j+TpTELW2gGWxDMZUp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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1340bf690>"
      ]
     },
     "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from MlFiterWavePd import MlFiterWavePdClass\n",
    "ump_wave = UmpMainClass(orders_pd_ump, MlFiterWavePdClass)\n",
    "ump_wave.gmm_component_filter(p_ncs=np.arange(18, 85), threshold=0.63)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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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0yZqgEucLgScK55zLogYN4PTT4aab4IsvbEny8nLYccfksuVx44nCOeeyrGVL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x131101e90>"
      ]
     },
     "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": [
    "**第三主裁**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8885jwdxfCUrdQ9j1VwIgFeKQXj3Yu+YQANvm7qN+nfreaKYxpvhedg49JQIX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\nNqoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x131d1d2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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O8xg9JB8FfTPw5sr/YcDAgZDJZB22KZPJUDhoDDZu34NZU/LAMPjhhe3A+i3n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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x131d1d850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9cRZ3gZm9y93PBm4ys4OT7DTiQrnAzA529xvMbGaSdRV3NGQtyleX\nXvRwYn34YXd/j8XLdm8n9juOcve9+yB7LbE38S/ERboV7vHYiUuBS0cxvV70qMqjbrh+23k0ZHVt\nPyoj8mtJu9sMnji5ltigmNutLMlfSGwMtTxl0o2sph5LiN31xQyeAFmS/t/QTpbi78LWI9Ra6Y2z\nuMVplLtof7LjbtIJlWHEHQ3ZkPJ10Q560WNDOV+qT4AMW5b91vYZ7ND6faQjmV4velTlUTdcv+08\nGrK6tnf3UXHkO1TJiPXgl3YrS/K9iLXHlqdMhiMbpn47Ac8ZjqyX9MYyLp1Pdgx5aH63cUdD1mO9\nDyffwyrSqToBMmxZF+Ufk/R6ybeXcE2UdfMZ8c1OIYQQI4seYyuEEA1HjlwIIRqOHLkQQjQcOXIh\nhGg4cuRCCNFw/j9LfLwl2Tjr4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1208ac9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lNFYg3bTKEUJ0lnYPVP8T+AyQb71McfdBAHdfZmY7t5nHuGQ0Dm11MCxE79LyzN3M3goM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\nuvujDdZrf8LoUJQ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1340c4c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "  <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",
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       "    <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",
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       "    <tr>\n",
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       "      <td>73.0</td>\n",
       "      <td>0.657534</td>\n",
       "      <td>-0.548995</td>\n",
       "      <td>-0.007520</td>\n",
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       "    <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",
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       "    <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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from MlFiterMainPd import MlFiterMainPdClass\n",
    "ump_main = UmpMainClass(orders_pd_ump, MlFiterMainPdClass)\n",
    "ump_main.gmm_component_filter(p_ncs=np.arange(18, 80), threshold=0.65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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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BvHl+uVmzzBLFHnskLzdv7mtKVSUEmDq1/jFWp+AShZmNNrOZZjbDzCaa2dop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\nImn9f52/dj/oYaiNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12085cb90>"
      ]
     },
     "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": "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}