added part of example to show how to get top n security in a industry

notebooks/Quick Start 4 - Step By Step Alpha Modeling.ipynb

@@ -23,6 +23,7 @@
     "from alphamind.data.dbmodel.models import IndexComponent\n",
     "from alphamind.data import neutralize\n",
     "from alphamind.portfolio.linearbuilder import linear_builder\n",
+    "from PyFin.api import *\n",
     "import sqlalchemy as sa\n",
     "from sqlalchemy import outerjoin, and_, select\n",
     "from matplotlib import rc\n",
@@ -90,7 +91,7 @@
       "2     6  0.4970\n",
       "3     8  0.0592\n",
       "4     9  0.0612\n",
-      "Wall time: 508 ms\n"
+      "Wall time: 857 ms\n"
      ]
     }
    ],
@@ -130,7 +131,7 @@
       "2     6           房地产\n",
       "3     8          机械设备\n",
       "4     9            综合\n",
-      "Wall time: 156 ms\n"
+      "Wall time: 422 ms\n"
      ]
     }
    ],
@@ -171,7 +172,7 @@
       "2     6       0.00039\n",
       "3     8       0.00071\n",
       "4     9       0.00075\n",
-      "Wall time: 154 ms\n"
+      "Wall time: 920 ms\n"
      ]
     }
    ],
@@ -245,7 +246,7 @@
       "2     6           -0.201598\n",
       "3     8           -0.396290\n",
       "4     9           -0.168950\n",
-      "Wall time: 389 ms\n"
+      "Wall time: 67.8 ms\n"
      ]
     }
    ],
@@ -263,6 +264,8 @@
     "## 4. 组合构建\n",
     "-----------------------------\n",
     "\n",
+    "### 4.1 使用因子值作为组合权重\n",
+    "\n",
     "使用alpha-mind中的线性优化器来做组合构建，在做组合构建的时候，我们已生成**主动权重**为目标（主动权重 = 组合权重 - 指数权重）。优化器，以最大化组合的预期收益为目标，同时达到以下的限制条件：\n",
     "\n",
     "* 最小主动权重-2%，同时保证不做空（所以单只股票最小主动权重为-2%与其指数权重负值中的较大值）；\n",
@@ -356,7 +359,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x242bf2b8470>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1a58b13c978>"
       ]
      },
      "execution_count": 12,
@@ -421,6 +424,129 @@
     "plt.ylim((-0.02, 0.025))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.2 使用每个行业中选择因子值最大的2只股票组成权重"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "oper = CSTopN('er', 2, groups='industry')\n",
+    "data = df[['code', 'neutralized_factor', 'industryName1']].set_index('code')\n",
+    "data.rename(columns={'neutralized_factor': 'er'}, inplace=True)\n",
+    "data['industry'] = pd.Categorical(data.industryName1).codes.astype(float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "oper.push(data.to_dict(orient='index'))\n",
+    "data['chosen'] = oper.value.to_pd_series()\n",
+    "data = data[data.chosen == True]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "所有的行业都选择了两只股票，如下图所示："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1a58b2d6da0>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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kSZI0OCY6kiRJkgbHREeSJEnS4JjoSJIkSRocEx1JkiRJg2OiI0mSJGlwTHQkSZIkDY6JjiRJkqTBMdGRJEmSNDgmOpIkSZIGZ9qJTpI1k2w7zo2RJEmSpD4sM9FJsm6STwLXAa+YYv1OSb6VZEGS45Os1C3fI8kPk1yV5Kj+N12SJEmSpjadFp1FwPHAYUtY/y7gSOB+wC7AU5IEOBnYH9gJODDJrjPfXEmSJElatmUmOlV1Y1WdAyycvC7JHGCrqvpcVd0GfBh4AvAg4LqquryqbgJO75ZLkiRJ0tjNdDKCTYGfjjy+BtgY2AxYMMXyO0hycJJLklxy/fXXz3BTJEmSJKmZaaKzGq1r24RFwG1LWX4HVXVSVc2rqnlz5syZ4aZIkiRJUjPTROfnwCYjjzcFrl7KckmSJEkauxklOlX1U+CmJHsmWRl4LvAx4GJguyTbJVkL2Bf4xIy3VpIkSZKmYZVlPSHJOsClwDrAGkn2BF4ObF1VxwIHAh8E7gWcUlVf6V53EHAmrRvbm6pqwRRvL0mSJEm9W2aiU1V/AOYuZf03gZ2nWP55wAuMSpIkSZp1Mx2jI0mSJEl/cUx0JEmSJA2OiY4kSZKkwTHRkSRJkjQ4JjqSJEmSBsdER5IkSdLgmOhIkiRJGhwTHUmSJEmDY6IjSZIkaXBMdCRJkiQNjomOJEmSpMEx0ZEkSZI0OCY6kiRJkgbHREeSJEnS4JjoSJIkSRocEx1JkiRJg2OiI0mSJGlwTHQkSZIkDY6JjiRJkqTBMdGRJEmSNDgmOpIkSZIGx0RHkiRJ0uCY6EiSJEkaHBMdSZIkSYNjoiNJkiRpcEx0JEmSJA2OiY4kSZKkwTHRkSRJkjQ4JjqSJEmSBsdER5IkSdLgmOhIkiRJGhwTHUmSJEmDY6IjSZIkaXBMdCRJkiQNjomOJEmSpMEx0ZEkSZI0ONNKdJI8I8lVSa5M8oKR5Xt1yyZuv03y5m7d+Unmj6xbeVwfQpIkSZJGrbKsJyRZBzgOeBhwG3BZkjOr6vqqOg+YO/LcC4HTRl6+Z1XN73eTJUmSJGnpptOiszdwQVVdW1W/AM4FHjP5SUm2B1atqm/3vI2SJEmSdJdMJ9HZDFgw8vgaYOMpnvcPwPtGHt8KnJfk0iQHTPXGSQ5OckmSS66//vrpbrMkSZIkLdUyu64BqwGLRh4vonVhu12S1YH9gZ0nllXV3t26HYBzkny9qq4YfV1VnQScBDBv3rxang8gSZIkSZNNp0Xn58AmI483Ba6e9JynAedX1Q2TX1xV3wMuBLZf3o2UJEmSpLtiOonO2cDeSTZMshGwG/CFSc+Z3G2NJHO7+y2AhwKXzXxzJUmSJGnZltl1raquS3IUcFG36HDg8Um2rqpjk9wP2Bz40qSXnpFkbeBm4DBnX5MkSZI0W6YzRoeqOgU4ZQnrfgJsM8Xynad4uiRJkiSN3bQuGCpJkiRJKxITHUmSJEmDY6IjSZIkaXBMdCRJkiQNjomOJEmSpMEx0ZEkSZI0OCY6kiRJkgbHREeSJEnS4JjoSJIkSRocEx1JkiRJg2OiI0mSJGlwTHQkSZIkDY6JjiRJkqTBMdGRJEmSNDgmOpIkSZIGx0RHkiRJ0uCY6EiSJEkaHBMdSZIkSYNjoiNJkiRpcEx0JEmSJA2OiY4kSZKkwTHRkSRJkjQ4JjqSJEmSBsdER5IkSdLgmOhIkiRJGhwTHUmSJEmDY6IjSZIkaXBMdCRJkiQNjomOJEmSpMEx0ZEkSZI0OCY6kiRJkgbHREeSJEnS4JjoSJIkSRocEx1JkiRJg2OiI0mSJGlwTHQkSZIkDc60Ep0kz0hyVZIrk7xg0rpTklzbrbsyyebd8j2S/LB73VHj2HhJkiRJmsoqy3pCknWA44CHAbcBlyU5s6quH3nac6rq/JHXBDgZ2A/4MXBpks9U1WV9brwkSZIkTWU6LTp7AxdU1bVV9QvgXOAxy3jNg4DrquryqroJOB14wsw2VZIkSZKmZzqJzmbAgpHH1wAbjzz+M/DBJN9Ncvg0XwNAkoOTXJLkkuuvv37yakmSJElaLsvsugasBiwaebyI1oUNgKr6B4AkmwFfTPKtZb1m5LUnAScBzJs3r+7qxkuSJEnSVKbTovNzYJORx5sCV09+UlVdDZwF7DTd10iSJEnSOEwn0Tkb2DvJhkk2AnYDvjCxMsnc7n592jicrwMXA9sl2S7JWsC+wCf63nhJkiRJmsoyu65V1XXd9NAXdYsOBx6fZOuqOhZ4R5IdgFuB46vqQoAkBwFn0rqxvamqFkzx9pIkSZLUu+mM0aGqTgFOWcK6Jy5h+eeBbZd3wyRJkiRpeU3rgqGSJEmStCIx0ZEkSZI0OCY6kiRJkgbHREeSJEnS4JjoSJIkSRocEx1JkiRJg2OiI0mSJGlwTHQkSZIkDY6JjiRJkqTBMdGRJEmSNDgmOpIkSZIGx0RHkiRJ0uCY6EiSJEkaHBMdSZIkSYNjoiNJkiRpcEx0JEmSJA2OiY4kSZKkwTHRkSRJkjQ4JjqSJEmSBsdER5IkSdLgmOhIkiRJGhwTHUmSJEmDY6IjSZIkaXBMdCRJkiQNjomOJEmSpMEx0ZEkSZI0OCY6kiRJkgbHREeSJEnS4JjoSJIkSRocEx1JkiRJg2OiI0mSJGlwTHQkSZIkDY6JjiRJkqTBMdGRJEmSNDgmOpIkSZIGx0RHkiRJ0uBMK9FJ8owkVyW5MskLJq17SZLvJ5mf5LQkq3TLz++WXdndVh7HB5AkSZKkyZaZ6CRZBzgOeGR3OybJnJGn/AF4ADAXuA/wtyPr9qyqud3ttv42W5IkSZKWbDotOnsDF1TVtVX1C+Bc4DETK6vq/VX1p6paCFwOrDeeTZUkSZKk6ZlOorMZsGDk8TXAxpOflOQewJOAM7tFtwLnJbk0yQEz3VBJkiRJmq5VpvGc1YBFI48XAXfohpZkJeBU4Piqmg9QVXt363YAzkny9aq6YtLrDgYOBth8882X8yNIkiRJ0h1Np0Xn58AmI483Ba6eeJAkwMnA96rqXZNfXFXfAy4Etp9i3UlVNa+q5s2ZM2fyakmSJElaLtNJdM4G9k6yYZKNgN2AL4ysfxfwi6p69eiLkszt7rcAHgpc1s8mS5IkSdLSLbPrWlVdl+Qo4KJu0eHA45NsDVwMvBD4SZJndOtfVVX/CZyRZG3gZuCwiS5tkiRJkjRu0xmjQ1WdApyyhNVTtgpV1c7Lt0mSJEmSNDPTumCoJEmSJK1ITHQkSZIkDY6JjiRJkqTBMdGRJEmSNDgmOpIkSZIGx0RHkiRJ0uCY6EiSJEkaHBMdSZIkSYNjoiNJkiRpcEx0JEmSJA2OiY4kSZKkwTHRkSRJkjQ4JjqSJEmSBsdER5IkSdLgmOhIkiRJGhwTHUmSJEmDY6IjSZIkaXBMdCRJkiQNjomOJEmSpMEx0ZEkSZI0OCY6kiRJkgbHREeSJEnS4JjoSJIkSRocEx1JkiRJg2OiI0mSJGlwTHQkSZIkDY6JjiRJkqTBMdGRJEmSNDgmOpIkSZIGx0RHkiRJ0uCY6EiSJEkaHBMdSZIkSYNjoiNJkiRpcEx0JEmSJA2OiY4kSZKkwTHRkSRJkjQ4JjqSJEmSBmdaiU6SZyS5KsmVSV4wad1OSb6VZEGS45Os1C3fI8kPu9cdNY6NlyRJkqSpLDPRSbIOcBzwyO52TJI5I095F3AkcD9gF+ApSQKcDOwP7AQcmGTXnrddkiRJkqY0nRadvYELquraqvoFcC7wGIAu4dmqqj5XVbcBHwaeADwIuK6qLq+qm4DTu+WSJEmSNHapqqU/ITkU2KCqjuoevxn4eVW9LckDgROq6hHduicCLwROAfavqud0y18EbFdVL5n03gcDB3cPtwOuWI7PsAHwq+V43fIynvH+UuMN+bMZz3jGu/viDfmzGc94xrv74i1vrC2qas6ynwarTOM5qwGLRh4vAm5bxrqlveZ2VXUScNJ0NnRJklxSVfNm8h7GM94Q4g35sxnPeMa7++IN+bMZz3jGu/vizUas6XRd+zmwycjjTYGrl7Fuaa+RJEmSpLGaTqJzNrB3kg2TbATsBnwBoKp+CtyUZM8kKwPPBT4GXAxsl2S7JGsB+wKfGMsnkCRJkqRJltl1raqu66aHvqhbdDjw+CRbV9WxwIHAB4F7AadU1VcAkhwEnEnrxvamqlowjg/ADLu+Gc94A4o35M9mPOMZ7+6LN+TPZjzjGe/uizf2WMucjECSJEmSVjTTumCoJEmSJK1ITHQkSZIkDY6JjiRJkqTBWeESnSTrJnl3kudNuj03yYPHEO9xSQ7o+32niLN6kudMWva+7v6QJI8dY+wndrPjTV7+8Z7jrJfkvpNuayV5S59xulgbLWP9v48h5h5J/jHJc5I8JcleSf4qyWZ9x1pC/O2TbNTNgNj3e8/6/pz0/od3v4P1xhlnUsyTx/S+ayc5vvv7+Ule3d1eNa7feZJ3d/cHjeP9J8XarbttPWn5M5O8YBzH6e79H5zk0eN47yliPXyKZW+Ypdi9HpeXEmdW9meSE7r7p3f3J42sOyjJhmOIuVKS9yZJ3+89jdirJPl8kguSvHYM7//AJGsk+bsp1h2bpNdyX5L/TrJxkvWTPLRb9lcTt55jPbu77d6VmdbtbockOSzJYT3Hu9/Ebz3JMUnW6P5eJclHktyjx1h7jPw9K7/xLtZTk7xo5PbgJI9I8tokH5uF+GM5z46azgVD/9K8FvglcB0wOpPCqsDpwBYzDdAlNl/uZorbAVgrydOAe9CuEXRhVd060ziTfBLYKsnzgT/TPs/9kmxJm577iT3HG7UrcEySrwDHVdVV3fL79vHmSTYBfgb8F/Bj4EnAZ7r7A4EH9BFnks8CSzvIPmYMMf8G+A6wMrAZcG9gPWCbJH+uqn37CpTk1JGHBbwcOBc4H5ib5CtVdWhf8Zjl/ZlkAfBN4IHA1sATgJ8Cr0xyCfDgqtq0x3jPm7wI+KvR5VV1Kv1YmcXHqYNoM1mGdjx+B/CgnuKM2rG7PxB43xjef9QHgR8B901yBPAC4Fra5zufNsvOA/sIlGQ/4OKquha4P/CAJLuOPqeq3tpHrEmOo11qgSSHdTHGkhQk+QGwT1Vd2S3q5bi8hFh3x/7cobs/hHZ5iu2TnAb8HfB82nmjb88A/lxVleTg0RXdhcx7kWTbJax6Me0c/3FamaZPxwH70MoPb6X91gp4P/DwLm6f5Zf7AnvRymRH0X4HnwT+m7afZ1wmG3EU7fImmwJbdrEeATyHNsPvW7tbXzajHTsvAuYAKyW5D3AKcEFV3dxjrH+nO6Ywxt/4FF4CnAj8AlgHeDLt3Ht534Fm+Tx7uxWqRSfJK4D5wMOr6mzgWcAzu9s9gLN6CnUg8NEkc2hf8v8FDgXWBv6adm2hvu0PnAZUVU0kNQuAVwHPqao/jyEmtIDHVNWutOTjnd0PGe6YSM7EKbTrKN27qg4Brhi5P4/2Ze/bst5zXDV5z6IlppsB51fVYVX1ZGC7nuPcH/gh8Pnu718DV1bVs6rqwbTEoE+zvT9/UlVPA64CTqZ9F1cGftQt/1HP8TLpBnAzsMakZX1ZJcnmwOq0E8zPgWuAL3XL+7ZKkmeM4X2ncg3wNdrnug/wZlpha03gjd36vvwjcEaSjYHtafvzN8ABwDe6+3EY/T78zRTL+rQu8OGJ2mT6Oy5PZVb3Z5LHAPeeouXoa8CbgKur6qaeY96LllRNFCZfAdxCK/z32iIAfAU4Evh/k25HdrHe1HO8yXatqr1oM+x+iFaJOhvlvh9X1cuBn/T8vr+kJR030MpHp9AqT9ekVaT0lsB1x8u9aMn+PrTk6gPAqcDbq+qNfcWaCDkp/uXd7dtJrkxyZs/xSHIGsBOtMuqltN//02ll4H/o1vUaktk9zwIrXovOI6rqqUme3HXxuGxk3cb0W/NzBHACMBf4F9qB4j0AXY3yuG0F3JP2w31fkqqqp/QZIMnnuONJc+JL9oExNOm/HDinS1Y3n7jvOcaoZRUGxlVYOBG4gPZ93C/Ja2jfn2/0HOdWWgHkOuDWqrotyc5dN5CjaCe0Ps32/lwryQ6038B7aLWUvwO2TbILrdKhN1X1wbRuXd9S7hR2AAAgAElEQVQDrgSeB3wZOHekJr1P69EKPJvQCj2jv7cjgRf1HK9otYU79/y+S4u3ErAh7f+2Bq2Qcj3Q63GM1iL2flqh/EPA14HfVdUFSW7oOdaEqb7v4zqmLKAl+++lXZR73GZzf86j1SKPdmcs4KPAt4CH9RksyWq0/fjPwDu7xddP1CIn+fs+4wELquoFPb/nEiU5j9ZD4nPAl0ZWTXw3ezsvdMnw25jd38KENWiF8NDOERN/r9pjjDm060OuT2t1nEOrUL+Fdkzr2+g+q6raZXRlkh/3HrDqad135jLgt7T9ekNVHdBVCFzcc7zZPs8CK16i856uP9/KwLNphccHApfSvnwvpe20maqqOj/JC+i6qSUZ/RL2PgaCOx8Yruri/AZ4SVX9agwxl3ZQD+1kMyNJdgfWqqork/yQlgTs19331pVr6tBZ0gmm1ySu6/N8ErARLfF4Au2g+G5aN6GP0JqHx+3btILJmcDren7vWdufnc/QWjk/STtO7cDiLlB/Q/uMvUmyJrBbVb2v6x/9amAPWreL3g7A3e/hBcBvquqQJDtVVd9JzWi8lWmFutuq6qVJdlvWa3q0Bi252ob225hHK5TcA+irlr6q6stJrqBVnNwCPA6Yk2RfWiF6HFZP8iXad3+n7u8dunuq6lE9xqrue/mIJP9N6+L8rm7dIuC8quqrT/+s7s+qelOSvbv7J3bdxjehnXv2pbW2/FOPIT8NHF1V315CXV7fBfR1kzx+5PEfaa3vV1TVbT3Hoqr2SnIurfvRkVM8ZWX6S3ZuoPU4ef/SNqmnWJP9itYyvBOtZ8N7aRUrT+sxxldpic4Pq+pdSbahlTG3AE5J8paq6nMMy7LOo30PlyDJzrTPdM9u0S7AL5M8hLY/z+g53qycZydboRKdqvpskgfQunlsSktytqL9mP5MSw76tCmtgHckrRVi56r6Nq3fad8+STuxXJXks8BC4H7AwcCpSZ42hnFB6/f8flNZE9gibQByVdXXktzQ3f9+zLE3YHzdSW5XVYuSvJH2v3o4ravON4DX05Kep9NaJJ495k3Zmva9eR2tf3vfAxpnZX92JmrMVgIeCdxGO9DfSCvc9XoCrao/Jjmqe/iOqvpu2gQdf+g5zpe7AtYRSf4DuLT7vY8+p+/xeAtH/u79ZLkUt9KOa98FtqX1rd+VdkzotTsSrb/+mt3f96AV6Nah5246aQPjV6e1oj6qW/alqnpUkot6TnAmexEtWdwR+HC37B60gmbfv/UtmYX92bmxu/9sF2NT4PiquijJS5JsVFW/6CnWzfTcGrwMH6G1Sk0cN1ehnXfvn+Ra4DVV1Vv3rrSJfTak/c+6Rfl3YOu0we6pqoVLfIO7oBuf8qMlJIz37SrGNu4j1ohVad+RiYRtAW3c3/G0Lp7f7jHWzbTf+jOTzO2WVVV9L8njgAuTfKPH/9//jvw91U7dcYplM/VWWktO0ZLHBwKfoo0bO5uexx3O1nl2shUq0encTBvk/Spgd9pJfDNaU9iM+353tVYbJHknrf/ns4Hzuvujkvy6qvqsYZrwZFpm/TPgatoP9hW0k8D/0LoTHNNzzJdPejxReOytMFtVX0iyI62Gbm6STwPrd/1N10/y5L5i3Tl0vXlJK9MG3fYZ7ErgFV2L4zxaLfZ/0mqc/lRVY01ykjyTxYNBj6HVON+jx8GSs7o/aX2EDwXeWlUP6t7/X2gDTw+nDdrvbdBp16Xl2K4me68kf0s72B+81Bcuhy7Z+Sdad6DXjGzDPWlN+X3Guo027u6AJP9JKwzMlkW0gslhtER1HVrXznVoJ9UZSfJE2viO19O6XuxMa0n9FPDYrpvE82caZ5J70r532yTZrqqu6Pn9l6iqbgG+kuT3VXXhxPIkvSSNs70/k6xCq4n/dZKJVoHTaQW+XbpC+wa0c29fv/VnAKcnuXFk2di6WVXV65KcUlXPT7ITcGRVHQDQPX57kn37Sj5orTj/C7wM+BMtOV6XVuFwNXCn2VV7cimt8uK93eN/7e77nonwR8CjaMeWiYkVJpK6HwDvWsLr7rKquiLJal0lxpG0ffv67v6NtO/S/D5iJbkfcHjXO+QAWrfmydszru/pXFqr7UtpLbhr08otR9LzRFGzeZ4dtUIlOml99regdU97MW2A9220L/pDaLMyvWKGYX5BOxgsqqpTk6xDa2n5XVU9M8kbkrykqv5jhnEmW4uW3DyaNkjyYNpA3s/Ruj19ped4VNXt/b27JsWn0wpa+1fV75J8tadQm9JmYLkF+Ez33pvRuu/clGSrnuLcbZJ8kXbCfCDtO7oB7Xv5U+B5aWOsHr+Ut7ir7klLplandRF6PK0G9u+B/66qsU1HPksmjwn4Ha3QdXNVXZj+xwq8k9atZFva5A4TMy+9Oq3KsqpqxgfjJOvS/mfrAd9OsgV3LGjtmeTcqvruTGNNsog25nCs04BPci5t3OQZtNrXf6HNtEiSV/dQuLuF9p3YoqoO7ArhT6NVFq2TNrVt312tfgQ8Ock3gdcnmY0WgmVVPL2ypzizuj+ramFXgNyR1gp+AO14eSStoH42rdtab7/1LubzaF1jJ3oUbNi1PoR2zu1NV3u9e5JX0o7Rl3Z/T/haj0kO3VjNjYDX0M5Df2Rx6+nf0ypNe9F1ezqF9huf6A53YdpkKn0MIZjKy2jdlveh9eY5nPa5Qps843BaBdmMJbk/rVwJreLt98DTkvyUdv59dlUd3UcsYE/aDJwvpH2uf0pyzuQnVVVfv/U7vfXI39+kjaN8JP2PLZ6V8+xkK0yi02W6B9L+ARvTZt74Da0b269otegnzjROVX01bTrN/+pqkc/jjgODXwd8NcmJPXclezctcbuBVkA4jXaS+T6tgPB12hekV2l9+I+n1ZSfCfxzVU10G5pxy06STWmzlBxAq1E6P8newFOBFye5Cjgryb2r6rczjTcausf3mo6n0KYAf3FVPQ4gycXAE2s8M+Z9qrtfD/hIVb09yXtoXda+mmTPqjq/x3izPevaDmkzwmzfJQfQaii365bvsOSXLpc30Lr+PZhWw3Ux8FjaCftLS3ndXbUpcCytpmwbWuFntK/+jd2y3hOdLkG8OsnXaSe20Pqhv6Wq+pwJ7c/AQ2nHq1No3UxWobW+fwE4rY/CXVWd2xW2/jvJ/rTP8izad+MsWit57zMVdW6tqr9NG/O0z8QmjSnWNyc9vsNvrao+0UeQu2l/PoZWyHsr8Mqqel5ardBvkpxNG9/5v0t9h7uoqn6f5HUsHqP67yzep0tstV5O59DGGt1C+x1sSzumXNtzHACSfIjWu+VPtAqGi2mfbWXaWIg7Xf9peXXjnB5JGzP5Adqx82O0Y9jE8aXod2KVY2ld+o/t3n9HWqJ1Tfd41yW/9C57EbBp2nV0FtLGVi2klTfPoscpoKvq/WmTXH2IVs79RHc7g/YdgvEdX0IrA65GO05vQGs5G8ekW7N1nr2jqlqhbrQannvTDh4n0q7tsRHwwp7jrEZrHQotCdlgZN3+o497jPmU7vaFLvbR3fK9gX8f4z594BKWn9jDe68DbNv9/VTgbybHBo6mTT3d52faFnjIFMs/STtIXdFzvHvQrvnwRlpN5Ja0mqUHjev/NsU2rE7rurYlMLfv/bmM9TP+rizj/Semu9xijPvubbQm+7d1y9akja3acwzx7knrnnDULH03jp5i2cq0ypR3z9I2rEsrPJw2hv/d62njR94yS5/llVMsu5mWCBw+5tgndvdb08bq7L6i7s/uOL1yd559bXfefUe37n7AIWOI+dHut3cmrTXu3d02vBHYZQzx3jby925d/K3HtD/XG/n76EnrHkG7xlV6jHef7n4b2sDyT3VxduqOn+8BVu4x3gbAySOPzwVe1v39TlpX575iHdF9T/5EazU9oru9gtbt/2Fj+P/NpV3LEFrr6rnAmt3jE2mJf5/xnkvr/rd6d/9MWhl74vainuOtTqvU2JlZOM/eHndcbzyWjV38o9qZdm2ZieX36nbgO3qK8whaLdaq3eOHTRwcuoP/qWP6fF+adP+niQM9sNIY4v31FMv+hnYxxr5jPWaqk0h3gjl2TPvzHd2BfeWRZWvSkuNeE52R93/syN/rjPz98Z7jPIBW0JnywNf9RlbrMd5htG6NT55i3X+MY18uYTvGUrgbef8TZulznNDdP727P2lk3UHAhj3H24XWbezRtNq0tWjdSc8H9hvTZ9yCkULe6HdpDLH+GXjkFMv/H2MoVNJqIJ/VfcZH0WrLn0zrWnPOGOKdOnI7rbt/ffd/vXRF3Z93x7mWVnjcGnh/9/g8WiXHc8cQa4kFb3pO+Lv3fPxSbo8FNuo53h9pXfu/ROsu92NaZeLJwBeBC4E1eop1X1oi9QVg027Z/3b329Au8t7nZ/sWrbX2RFqr4x7d7TW0SYa+0nO8n9Em5FhAG7LwI1pPmLfREuSz+v6+jMRe0rH60DHEeuNU5xxaa9J7gbX7jrnCdF3rnEnrM7kh3QwU3WD2N9EGLD60pzin0n6sT0i7Xs/rgZWTvJc2HqLPbh5L8zXaxaqeBfxd2jScfTZfbpjkRbRahHNpBdltgD8muaWq+pxx4/7AW5J8lzbe6PSq+jWtT+pYZl+rqhcneRUt2TmgW/ZH4JtjGOMx4RgW94M+h8V9fPu+0vFnaS1T2yX5KO0EM9G//HLawfnJtOuW9OEg2lixV9N1X0nymap6Ei1x7F2Sqa6QPJ/2e/8grTWwr1gTkwDsneS53LF70CLg69XvwPPZvhr8X9FO1j+idXd8UlXdv+taeh79z9oFrcvtNwHSJh85oqq+R2sR72VweXf8Cq2C5vJukOu7aInkbbQBw8f3EWsK96adD06lddG7mbZ/91nKa5bXzrRj2Km0WtiVaMnxq5Pc0leQu2F/zuq5tusGu5A7dgO/J+03WOl3hjeAXbtJRo7gjmO5TqZNo923hzP1tfGKxS0sW/cYbz6tF8Et1SZeeCRtCu+v0Qqtt9Jft+YbaN+JQ2lDC/5uZN2/0fZxn35PKxftQut18kHaOfWXtMSu73LLFVX1xCQfq6qnJzm+qj6UNj35sbQeMeOypGP102mJVp/2ABYleUZV/W0Xcw3a/r26qm5c6quXw4qW6Iz+YNIN6tuUdoGjy3o84P+0qg5Psipt7M//0L5op9IKV73ORAGQZKqDz0T/1vNoLQLj6KO5P61QvDqtqfRttBqF14wh1qtoYw92Bz6d5Hu0GfN6Lxhk8dXlTwXelOTVtMIItNrse/cdcyL0Ev7u+3/3k6p6YZINaE32z6SdxKCN9fpFVfWV5AD8uqr+J+0CqBPGdY2SCbNSuOtMTKW7CfB2WmHu592y1YHP0waJzlhm+Wrw3fjG82mFoInxCBt0YyGuSdL7xe/SZhHahlZIOI3W5eSoJJf3HOqltELIcVX1pSS/7JafmXbNmQvHceKc5Oqqeu2YY/yx2lSsN3cFELJ4Wt8+jy2zvT9n7VybNkXw+7hz4fvetNaO1WjH0bl3fvXyhwX+g/ZbOIPW5f6L3f3cJG+uqplOoHS7Ltk4cOpVdWrazHp9uoaWgDwsbea8HWiVNfvQPnefF2C9mdY69N2u8vdPtHLgK2gtO31NnjShqurmJG+i9Tx5Ke23dujI8l7jTbpfNcmnaLOpvodWQdXnOR2Y1WP1hIVV9cokKyeZGIZyMPDeqjp5HAFXtESnJv19QrWBhb0OVhyJ8zPaD3k/2uwQv6Vl8nuweIBYX46jtd6cCNwrbW5xaP0mb2V8tZLQZppbhzENdusmdbgX7cAxP8k9aDWfWwC/rR5nnRkxUegv2snmecDf0mb1WUgrUI7D5O/ouGyW5BBa8/pngYOq6sMASY6mDQ6dLeP6nLNVuKOq3tO9//NprSxvpyVVH5lY3iUGfcSd1avBs/hY9QDazEQLab+Jc7rCSa/7svt9v5HWCjgxUL6q6jlJnkq/NZO/rqrRCxv/uar+I+0aJR+i/+t4kHbF8FHj/J3TnQtW6vZr0mbIXKlbti6Lp9ftw2zvz1k711a7aPXetBaH0f/ZfNosbJfSulj3aQfauNtP0yZPupWWGHyaNnnNKT3HG7VpF2u05WEc0/jeg3YNw0NprXOn02brurSL18tkPFVVabagzcx3Nu2aRHsDH5xola+qqXoCLJeupeFTwIeqXeZgYqKq51XVBX3FWYKFtO6Uv++S9DfQEvHezPKxejTuy2jlsV1pv78Dqupr44gFK16iM+ofgeuSTMwWAS3r7sOaXYvAR2gFgnvSvgw/7/4+gp4Tnar6myQX0prv9qHVbN1EO1CcMKbWnEfRTlxPojUJb9wt+w2wcZLdq2pG00SmlUw3pvUxfVzaxcvOodUYfjttuu5/qapeE7mq+utJ27ETbRDvXy/hJTOS5AO0k+eWXeExk/7u2+9ohYMjaYkOaRfTXZ/WYtb3NZdu13Wx2hTYOe0q3LukTYncW1fHWS7c3UHXOrw38N4kW1TVvwMf6Os3WLN8Nfiq2gsgyTHABVV19uj6JHe6ZsMMfZBW6/mrJBt13aHuM9ItaoMeY62VZOuq+vHEgq4w8mJaoevxtLFIvUi79stZtGParbRa5nH7PK3r79m0Qd5ns3g63U/TWj/6Mqv7k1k+11bVLd254O+54wUa96C1QBzSV6zOQbRKt4kKt4ntuDLJTRMVOGPwLdrv7Ou0VvG5XSLwOVrLcZ++RutFsDrtGL1qd1uLNj62z4rMlWit0q+jtfiPXoi17/PsXFoX258Bu6XNvgatm/gRSRZVVZ8zyt0n7eLRm3T3G9GSKmif7aFJHlBV3+ox5qwdq7vf3X/QznUnVdWxadfVeThwZNq03S8dR1l3RUt0Rr/IH6BNy3o2MKcrEPWV6HyH1nw/4XraAWvCuE5un612Neirqmp3aLWHtXi6577dTKs1uJG2L2/rlk08Ppl2AFtu3Zf2BOCErkD3ENrF6CbGyLweOC9Jr8lckidW1e1XnK+q7yS5LMmDqqrvueGhdf8LrTb+RNoB+aEjf/d5ccuNaf2iP0srcO1C++6vSasl3ILWDfG6vmJ2cbelnbD/gXbs+Dytz/IXuvs+zWbhbtQXuvv1aOORjklycN+JOLN4NfgkW9K6JKwPPCvJ1SOr16H/gs/otWVuo/3WF9GS83DH6bRn6hzaNRjuDzyHVkP4QlrN5020gl1vBfOu0PbIJE+j/QYvAsZ60dCq2j3JV7v7L1fVowCSXFRVe/Ycblb3J3fPufaBtOutHNc9fhvtc11PDxeyneSwqnp0kvO4c8vfuFoC16dVBFV3u09V/XVaF9Yvsvhz9+FbVXVakk1oycf3aGWGbWktdH13EV9Uiy++fUWSI2gtf2tW1Tt7jrUbreLp47Rz0Hdo4yfPp50fJk/7PiNVtWOSQ6vqbUkeV1VfTHJEVyH2z7RxM313J5u1Y3VXBtuX1mr7mSQX0S6gewFwQdqFik+hzbDaqxUt0Rm9WNKtVXVC2kDsd9G6Rs14zECXYU61XyZqZIpW6OpNdwD6KvDZtH77785I//2J7jpVdW6fcWnzpO8IXEDbdw/vli0A9qKnqygnuS9t3x1CG9y2FfBv3UEK4MCek5w1gP261qMzaX15oc2D/6QkTwKoqtf3FZM289GfktxY3XUfJv3dYyi2pf2vDqDNorNXVwC6GLg47eKlr6LNnNSX0AokDwK2q6pvdTVaN03c9xhrtgt3dO+9OvDwJOvRDriHAC+gXTitrxizfjX4qppPu2jh42hdBA7stuWBtNmu+u76+/fAh5P8C3B9VX04yT9X1Ue6lrLerpdVVYfC7a2ZBwP/WFUfTfIg2jTrVyR5SJ+fsTtHTMxitynwwi7+LdXvhBVTGWvX2Nncn7N9rk27SPXraAnqD4HvJvl7WsXQrrQJO/pM4rqwOZhW2QXd/6z7nW+aZIOq6i25ShtIvhVt2vh7d/f3S/Jp2j7t89p/0M7jrwb+lTYZzkTvmtC67PU9bmailf9jtFbvqjYu6aNJLq2exukkWbXrZr9PVb01ycdp3d+fSOvK9m+08+uCnuKtT0swnkxLvN+U5NfA/t35/JddzL4rFGftWA1QVTekTa5wAfDNqloEtx8L/kz7/25T7cLMvVnREp3Lk7ysa/L6Y5JHdcuPp81r/t4eYqxKS5oOpP1Yz2TxhdL2oR2wPky/tYSLkuxOOzD8P9qUmyfTCua3P402A0jfQmuivYk7n0Sf21OMf+vebytaP9PQusi9mfblLlqBshdVdQtwUNpAt5fRuj+8mZ5rYCbZL8lLWXygh/F0WYNWg/wvtMLwm5M8FCDt6t7QLjL7kCW8dnlVtYv5bUUbqNj7xWuXFnsJf/cmbZanlWktRifTfudHLl6dZ1QPV2yuu+Fq8N33Y2XgLcCL0i5yCW1a2H26behNVf05yaG01szbj2FJ3ke7aHBv0rqTvIT2GT5DmznrUbTj+DG0ffrjJb/DcnkHrUvQzbRzzmNoM1kd0h1zPlpVfV94cuMkL6Z1a3kx7diyUZINq+qXy3jttM3y/pzVc21VXZ12UeXjaefSX9Mm+1mfVpD8MW1MzWV9xOtsRysMr067LtA9ab+7t9MKzW+gdcPvy3Npx49LaGMBL6YVyCf2cW/Suhx+itYNvZJ8AvjBxGraOX6/PmPSvhMvoE3bPT+LaxBfQpvg6KEThecZOijJQcB9k8yjJXF/R+tB8WxaxdTT6e+7sgWty+aOaRNTrU/7LWxA+y0E+F2S+1RVbz01ZvlYvRatfLQa7XuzVhZPGgWt0uijfSc5sAIlOl2N0ntY3KrzQVpWP/FF343+BvbtSLt4WWiF89fQarNeSiuU9D6Qvar+TGsi/XjX4nAQ8Ooxdlub8D7aj+wiJs0Y1mPt5GG0/fk7Wje2S2njEL5fVZ/uKcadVNVvaYXy/6J1Q3pnn7Vnk2L9Z5KLu3gfp/3/RvuW93mSuY7WonMibar1E2i1lBPdBG4BfpBk+6r6fk8xN0ibLayq6hldbc/2Pb33kkxVuNu478Jd5420A/Bf0QomD6adzM7rOQ7M/tXgH0X7bPfq4sLi72Of42VuV1XXdr+HiYHQVwNvrqoruu/OjKXN1HUUraC4Pq3weAaLP9vZtOsR/Xrqd1hu76iq73UtZP/aHWc+AXwiyYb0P5kE/P/2zjzervHq499fIkKNVSJFDaWhjeIdqqYoaqqi1RedjEWVok1VES8VU5pKBaWNEoLWUC9Vc4W0TUVFTaGImjWGNqHmEJHf+8d6du7O6c14n3Nvzs3z/XzuZ5+z9zl7nXPuHp71rLV+K66fEKm+FYPJmFrSzu85nKjLaebv2an3WkfB8+6Svg6Mt31oGuhVNY9ZshhqHEVE/35FpJNVQg+LE87VFEk9Mg3OIQq8DybuAe8QKbIziOsbRJpXLun6vwOftf1Kev6w7YOqjZLWBY5XFO7n+n67NBx/9wLYfjHd5/sQ4kodwvYIYESKeh9GOHNnSurtjCp5NXv3AdunSOowon/iTYoa0ZyZJ+3Zbvq1OrEacY3egJhQWJs4Xit6Ev2Jrs9oE2BmY66FHoXSjW2/lp6f33BSrUnk+3V4dkSh6tFID+Ik2ohomJb9YO9sJG1r+7ba8xWJ1KQXgSm2X8hkR8RB3Y8YdE0kGu5t7SbJCXYlCrWSj9keVlt3ru1sBeaKOp2q8HM123c0bN8WeCjX7I+kI4kcdtu+tGHbGba/l8NOw35nl6t7QxMGr9VAb4jt7yt6X5wK3OmkvJbRTj9i9ngGMbA7jWiae4RC6nMH2z/PaG8ZIqf8bKLW4tTq/6XoGTTG9vO57NXsLg38yPZhDesPsD0ys63lgQPr51xhwems33NRuNdKOohwML5r+5Da+uFE3cW02b55wewtQyigvU8MGl+kTbzl9eSYFwoz6eRr9VJExHhH4Es5yxZma7NVHB2AKmynkNqrqy7NAJ4gFB0OdTQ264idfeaweQYx+HmqIzYWBlKu5OwwMNF2tvClpP1sj6o9HwbcbDu3VHeXIOks29+RtAERLZ1GNKb7V23WK7fNnQglrbca1l9tO2vagKRrHeqAvYD3M87StWers4/N79n+t5oYRYHkhkTD0FM6ydY9tk/OYSvt90iiT8hGRF3JMEeB9HXE7OdVtkfnsjeHz/ET4n83yvZfM+1zWSIq8HEiPegOh5rjWsTM6NvAkU2IAHZbJG1v+9aGdbsRaVFjbZ+Z0daW7ax+1PbkFEHe33bW1MquQtJYp1rDJtqYo4iJpCG2c6ssFroRKX31AdtTFe1OjmwcX2S2N8s5YXtsbhs95v6ShYpnUk7fhUSR3TVEKH8gMXOxPjHD3VFOIAaqvRr+FiNC+lnFCLqQC4g0i02J3gXVchNCbrND0tLt8A2IwjtF87lXu4uTk/iPtBxK5EQfRszY35hyXpvBRsA4SeekwV1F9v4hxCwrxOzkbZLGSvqTQs3uqMy2RtK5x+YXIQbOknaRtKekPQmlncuAr3airay9EhInEgPIep3aB2lrjpodSSdIuiz9nUXUCI0h+rHkYhjR7G49ItVwT0nXEmkSQ4l6iNzpSN2dYyRtLenDkvqk6PFjhGjG5zPbupRIp9w6/V1CyNseRhS4D5r9WwvtcNNctufuE1RoYSS9IOkmSc+m5eNE9s1pilrO1XI6OZK2r/1tnVZfQgj+DCDGpNlpmRqdxDNEDvE7DqWNLQip2fFEUWhjx+MFZbLtCwEUzRdPt/2upDOTnWYMRLqC56v8z5TGNjgtq3Xb5TCSZl0FLCNpMKEPPwR4Km3D9utz2EWrMdFt6kU9HGITTemXYPs04qL0OeBcSfvnLFaskLRag926KmBvYiB0ekaTkzrj2KwhRZ+Zl4ii0+HEMbs+8CAhK9qKtqAm4KDIm68G/u+lWpN3chpLk1GTiCjSXqSi8jTTfJOk4zOam0j009iMyHvYgHAAABZOSURBVPFeifh+ZxNKklvnjPwtIlQy7v8kpHR/T6gvTWDWnPocPFevQUgpt/8gnNetcqd1dTaSHqVNRe4jDfcBEanAn8hpsoPbC4sWj9neSdJVtveQ9FPbv0wZFcPI3zD0fGISU4R8fT/g72kcg6StMtsDWs/RmUSoEW2ikGb9BDFQ2JlIWzswk516Pt8ngJ6pXmApR6f2phXQdzLtqVnV1+VSDro4Ldcjfs/biahZddE1oVffsij6JHxS0v60SYgeSNywjyZv7xAk3cys/6vqt7xIyqtlLWl3YoKhkm+fJd81TQLkDm131rFZsTZRG/O2pN1tX5Kc8t6NNUmtZEvSJwjH6Q7iGNmHSL+tk60eKPEL4A2it8Vz6XPUbebMl36QUAtaETg3PT7W9jGSvkNEAoujs+BMtL1nE/f/b71lbE8mVLRaHtszBVsUMvkDmm2yg9sLixaN99Zekn5LTDacR9R2Ts5o77naZGUVXVwupa+JEMzJTqs5OhC1OU8T6WrXEuH0ewklr28ScsVZUBSVr0o4UgOBkyTtmux2B9aRdAlxgPVLj9etrZtKKMF1CNu7QVzoiRvY4cT/7GfNrPPoTJx62aSnWyvUWvoTjemgTTUpF3Ny6kWbuk8OxhDH/HhJdwLrSbqB6EVxTRJCyD3T2ynHZo1BwEhJJwMoxAjesv3DjDa6wta7xIx8NXt8nKQnFZ2315G0g+3zMtucSigCXqOodxSzyq5nw/ZtkvoTIidVDdU2KapU9WLJeS4sanT6wDhFj08HDu5mkf6ZKHp1TW/C95Pa2gz827bMtgrdj+nA3rZfV9TCn0LeDKb2ridXEqlroklj61Z0dMYT2vS9aVOd6kWkK/R0dK7uKNUFYbW03yGEI9U3bcvp4XYlk4gBeA/iNx1MpCwMTttzOyFX274v1XPsD/wupVpNymynq7DtiyTdTXSfHpjS1lYjdONz8qHM+5stlZCCpGm2N0uPlyCiVbtI+jZ5m5NC5x+b3yD6IqxPRACuIAYNOxEps8NyFdB3pi3bT0p6rmH1c8TN67+BkyVtavvEHPbazPp2SQ8S95jOGGBNI7qyQ0iD3wyM6w6iMd2cDSRV/eFESM+uS9Tf3ibpa7af6LJPl5d6V/u+xPWtGdGyFSlOTWHeWDlNeq2aln2BK1JSiIBPS9rQ9oRM9to7Licxa5Pg7LSaozPB9qWSViUGPI8QF8V+xI/1wTm9eT44FsD2uakO6LtEYetytodksrEw8E41EJD0ThoUvWM7d4O9qo5je0mjiH5H3yPyv9ci/nctTYrmrC9pL2J2eSTwXKrreoZoVppTTaSx+L+6QDTlBifph8B9knomVcOqePhtYvb15+S9aXfasVljJULcQbY/l2wfRPy2Z0v6Skb1rs60BbPeQO5JM8lj0nF7m6Sf56jtknQc8JGZRttqHffr6L7nwCSifmQC0YPlZSKyf7Sk6c4o617Ii+3lFX3jXrU9Dqga3N5LDLoukLS13ULysA1I2julpP5WbWqSInqTfd72jRnN2XNoVispdwPPQgtju7+kgbaHS9rO9mhJR9seqhAEOdr2g3Pd0XyYbGfdIKKVQy9iXHhJRntA6zk6p0o6gVBjWZm2dAgBuwJ3dtSApCWB70uakmZVXwJes72fpLMlHWf71I7aWUjorbZ+LItL+hBNUOJTW9f59YlUw/eJDttps/Z2hq7zXYntAWnQuBwR7r2byG9dkVDp24CMjo7tvavH6Zjdg6i/2N32qynFLCe7EyHmGxUCEqOJlKBtCbWUIzLb65RjE0IwguhavgERXdlZ0s/S5nWA39XFF1rFVo1JRIQRSWNjoapm58/AARkFLH4NfDENXq2QIu+RbFeR96Uz2ULSxsQN8iCifvNW2tJFLyHuE4V5QNLiwD3p6WtA0yRlk739iUad9xGCKt8iBjqHElHPHQgxiVZ2cn5M1CBcQaRzjqJtMupuYGMgp6NTKMwT6Z46A9iFEMQZKulloqHuaEKM5NS0PRcr19KZV0jr/pnqVHsDX8toayYt4+iknOvfEh1qLekaQnEH4kfbEsgxW7EzcBXRgX1jQvL1aymU9yCwuqRvVDOVLc5ThIiDiBSai4D3Jd1DRCF+YvvPGexUXef7EwOrzYCHmLsUZityI5GS8DSwLLA5cDzhiGQlDZrPSTauBw6z/Wq1ObO5nsTx8nR6/D4RSf0AEZ3YhpBGzkV1bEIcm6OIY/Pe9BlyHZuk9MIfEoOOKsWwmsxYmojm/qTVbNVsXkk4qbMgqepEPVTS1213WH3N9uOSNieOhT6Eky8i9/tWItJybkft1PgUsESyNZWo01wJyN4Atbtje5qkzxD1f8cQAiSNaY85uQ74FuGk/oaQrr6S+P9Vk5i9iLTEVuVq2+PTtfoeN7nLPUV1rTDvrEGMzfpLWptIh9+FmJzdmThWXlXqX5nJ5hnAkrXHAH2S87M4EcXNTss0DFV4GisA37J9aooSnAU8nByfdYmIzurOoPutkLlcg7hBV6xMqFFg++L23teqpHTAo20fkZ5/ipCGPSqHxGc6kI+zvW6q7ziWmDF4gFCzu3WOO1jIkXQ0cYH437TqKNunSNoB+Atwt+11MtscRZwDjzNrzYqBM1zrwp3B1r7MPn+2KnTPFnKWtCHRb+Yvtm9q2DaG+M6H55SflTSAKIp82vYVtXVbEmmr2Tq0d6atriBdn3sTDR+zKg7Oxt6Jtk9M9XDHEAPoO4HfN0nkoVuToqnDgaG2708DoZOA85y5oV+atHyCmMC4kFCpPIu4/9zS5HTVboWkfrb/lh5/iYimXm/73bRuhO1vdeVnLCxcSDqC6GU1knA2dgHezTl+aMfmmcQY6T1JX69t6uH8Kqet4+hUSLrT9mYp2jIC+Kztf6VtdwH/YzvLbJ5qnYwlHUBEjL7S3ZRgUsjwd8DI+kGWToBnbXe4kF7SBGLmegrw8VT/JOAu4Ajb4ztqoyupHZdjbW9ZW25G5KD2sb1xZpuVjX8C44gI7XRihnu5Sjggk61niEEItBUNiohavU0otayZ0d6+RP3dDCK15RHbExX9go6x/ZlctpK9k4hI44pENOliIs3qQNvbSLrX9n+1mq2uIqVOHkjUN9aZAVzX6Lx2wE6/2W0izoerba+Xw9aigkLZsJFniLTji23/RzvbO2KvunbeQVxXqkHJEsAo2z+b/bsLdRRtDr5DROJGA9sR51s1jpk5pikU4N/GubOMX5pk7+PAybZ3r607jMjauNb2trlttkzqWh1JfYn0h/1qTk5f4P1cTk7NVj9ihulxYGd3EznkCkl9gMuJA+zStG4Vonv50kRKUg5esf1rSYsBP1T0IhpEzLi2tJPTQGNqwBDgEOIkzm8sZrAfJuo91rL9o7T+7sym/m77JEm32N5R0jmE4/pN4rttldkeRHH5t4nj82RJtwJfpTk9l7YgBghbpf1PZNbvlLOpZmfa6ipEqFbeAxxA1FxcSNRfnE2+tNVxRNpm43lXDZiHZrKzKPFJIppyMVH31wP4he0TlL+5bF0hrKftTWvbliVSnIujM+8sC/wAWAUYlOo1e0n6IPF/7Nmln65QiFYUH5f0B0JArJrw+xiRwpqdVnR0RKS0HAL8NOXsHwscTKYLolJ/C0J+9S7gUmLG9cRUq4PtE3LY6koUssD7Ew32Rqd1KxH9SU4mUoPezGTOALanK5oj/gW4zPYxmfa/sNBboRq0pEIu+G5H9/lm5EYvQzikPQiHY60UQVqV6Gqck2qWtcqd70/k2L7UsD23PRxNeicT8tJfaIItiEH5nkTd0SpEUWQ/QnbzUCKNtRVtdTXPEj2CHpb0lu17JeWsuXjG9uz6hhQWjKnp/zXV9iMAtctXtnNP0cz2YtqKkhsbEb8u6Y1c9hYR3gL2JeS5p6R1a9BWi9uniz5XYSFE0hBgTUmnEcdH9bhaYntQRnv9gSeJEpBDCDGjLxDtKcYTY5fstIyjk4r59gN62R6RVn9G0t7A7USRa65CvxvS8kDgRUIZ5ka6XyHfrcCIeg697clq61J7XEcNSPoYkfO5mKT1CfWu7Yho3C2SriXSDZuex99J9Aa+DqxORCK2n/PLFwxJexLd53ci0g4PA86yvauk0bYvb4ZdYjD+TeDDROHwDrkNKBqV7UE4cRX3EY7VRkAPSc/Y3i+j2XeJmrE+6fE/iKLod4nC9pwz2Z1pa2Giun4ennGfy6pNshdCkOBl4LFudE3pNCQtRZxfH4inWpKYSOmRIiwfyGjuCeJecHtKW+sv6XKiVuePxP2pOLHzh2y/r5Cqvy7dy5+0vQ/MVF0sFCpuSX/15xBjimbwKSI7ahlmndhYArjc9tvNMNoUudYm8Ulg7XbWVz/a27lkKG2PT+lUjxPqSG8Sg/5JtW3dgaeqwYCkpSQ9IOmztt9LRd45ZGBfIGaWNiIGqy/Y3tx2dUKNpkmSgp1MT0n/C0xLgg5/I2SXL0jpZTlnQnsQ5wPpwjCdcMYrmll4N40YmL9HTALkivjV2ZwY7EAMtn4JnEaksb1K9O/ZKLPNKcSEyYPEQPk2InVuSqpRe6VFbXUqkvoqhDlmeww6X+NVCHW3TYBN09/2hNM/WtKlkj6a0daiwC3AesRAZ/20vIWIpl5Hxh4Xtqc5VCKnEdfKPsBAYlJxAKHQ2VSJ627I/QCOHlxXEpNu9UFrd5usLXQMz8NfTmYQvRPrIl8iopDPKZrJZ6dlHB3bE2wfB7wnaVdJG0j6KVHnsSPxI30uh61UnA8xOzLd9umERPA1kjbJYWMh4UhJV0la1aFUtztwqKSRaWav91zeP1dsv5XUuO4lvPndJZ0qaR+FEtsMom9Rq1+ATTjGvVPEw45GW4PInEZme4bt44nZ7F8TspCXEZLo1wMfVagGNoNqMF4tJ87tDfOLQ9Hw3tqqpYkw925Ebcd3CEcrJyMJyezJRPPTqUTa6iOSGmefWslWZ/Mq4ahtCOzNrN8l+/eyPRj4aFpeTdSpHeroRTQUODPVBRbmAdsDCOGPAcBDtresrdvK9hlz2cWCcAHwVdvv2n6JSGU7HvgV0aV9lSbY7JbYPkLSr9LTcwjhn1MkXZDWNVveutBafI4ozdix9ri+bsfM9m4nHJuptXUmrgEjiEmr7LTiDcDE7OfVwGDb1wNIOp2o0bk5g40xkh4Chij690DcwAcC/yXpBdvN7C/QKdj+saTPAjdJGmz7GuB/JO1KRFpWymvOEyTtSMzOn0REyd4gGmn2JSIErcoM21emVLzDiWMU29en9IFmzCy/ARxF9D86DdjD9sFNsFOnOu7/mJbNHpSbmKEfSszw/otQP9wus50DiALsvYjfcy8inP4oUa+Ws+dTZ9rqVBy9eM6XdCBxrFwGvCzpz8AKaelcioCSjgMGSBpERHPuT48rxtue3v67C3OhqU6qpP8k6mAvBX6sUFrcn7h+3kAULe9i+4XctrsjqT5zMtGX5CpCeGCTlAr83woZb4h7e6GA7WMlbVHV4UjavHFdZnvPS1qOSO0fQtSork6MY0Qcpz1zpx23oqMj289K+hpwmaQ7bP/L9lMp/arDzY1sby5pG2Ig/hlCVvMN2iR1NySKv1se27dL2hr4jaSVbJ9n+7rk6N0saVnnkdNeVdIJwIW2j1Go9/RPJ9Mv5/LeVqBfiq5ASg9oeJ7TaaxwOhc2AI4Alleo2VXsW6kSZmCN9P97Ki2dliJq51af05sXkA2Bu2xPktSLSJ0ZTsz8HAScntneKYR6XR9iJuvDwPnEgCG389GZtrqCGbaPk/QEkVJ2cK7U4gZuI5Tr3iF+u37AnygNQzvChxWtBVZNSwF9JfVJKVG5eIS4v15PpFl9kDgnViDS1P8KLJfRXndnOeArRFrv/xGNzxe3/SVJFwHfblYNRKHb0BmZNRcRDUkPabDXA1ivGbWVrejo3Adg+28pn2+J2rYRhDpah7E9hojsHEBILB/sbiYtXWH7FUmfB86Q1COlRj1N5Grn4gBiIPJasnliSpFbw/azGe10FXPre9KMwugH0rJdpRKnJnGZ2Gcu2/+Q0RbEd1sTuCM9P5/UF4XIOV+5CfYmELPI30u2dyKiLY8SCo+rZJpd7kxbXcWNALYvUjQj3pYmzCQ7us6PTSlVZ0jalIi8H+vSaHJBOTEt62lOg8l8DUvRv+GSRhKzu6MJh+cEYtBzM3FuTMhpt7ti+2ZicnI68BNC0KWKvD9ApDgXR6fQSF246EdpeX+zjNl+S9K49LgxKNGUrJ6WaxhaKBQKhUKhUCgUCnOjZcQICoVCoVAoFAqFQmFeKY5OoVAoFAqFQqFQ6HYUR6dQKBQKM5G0mqRR8/H6/Wrytd0SSUtK6tfVn6NQKBQK80dxdAqFQqEwE9uTbO/XbDuSdpM0YD7f84ykqxvWjZK0V95PN3PfyybJ+H8AP2iGjUKhUCg0j+LoFAqFQqEr+AKwRuPKeWgevFGu5tDzwAzgp4Q6XqFQKBRajOLoFAqFQmEmktaU9ERKSfs/STdIelHSsNprvpFe8wDRC6Va/wdJW9T3kx5/WtJDKSJzkqRjiP43wyT9vGbzV8Btkp6rUsUkLS3p+dRLCeD7wFmS6q0FKvtfkTRR0rOShtc+x5PJzouSRkjaV9Jjkh6RtHbtdWMk/U3SbyQtZftN27cDpelooVAotCDF0SkUCoXC7NiU6Fa/PnCgpFUlrQscD2wGbEw0O50bg4BTba8JnGn7R8A1wPdtH5JesyZwHtFz50pg17R+J+AG2++l538heq4c246dp4lGs+sCe0has7bvXwAfA74IbGZ7XeAW2po/jwQG2u4HPEY3aQpdKBQKizLF0SkUCoXC7Bhje7Ltl4GHgdUJR+Q3tv9pexrRhX1u3AH8QNKOtl+ZzWum2B7raO52OW2Ozm7M2tQOwnHaV9I6DeufB44kum8vTVsz3edt32/7TeAhwskCuBtYTdIywBbAlZImEs1cV5+H71UoFAqFhZjFuvoDFAqFQmGh5d3a4/eAnsAS6XFF79rj6ek1AFWqGbZPl/Rn4DRJn7d9eDu23qy9/j5JH5K0CrARMLb+QtuvSRoEnEPqpi1pMWAccDQwinBUqnqfabW3v1/7XtXn7QG8aXu9dj5XoVAoFFqUEtEpFAqFwvxwF/AlSctLWgqoK549QzgmANtUKyX1s30H8F2gUlqbCqw4B/GBq4BhwM22ZzRutH0ZMVlX2VkeWAa4jnDGNpjXL2T7NeBFSV9On/ejkj4yr+8vFAqFwsJJiegUCoVCYZ6xPU7Sr4lUtheBMcAKafMw4ApJ2wCP1t42UNJOwOtExAWiDucqYC1geDumLk/7+PQcPs6hwIPpc02RdBVRpzMO+Ot8frV9gJGShgAvAV9OKW33Ew7UEpK2Ag6y/fv53HehUCgUugBFOnShUCgUCgsPkvoCf0yiAYVCoVAozDclda1QKBQKCyMDCVGBQqFQKBQWiJK6VigUCoWFCknjgVeJpqKFQqFQKCwQJXWtUCgUCoVCoVAodDtK6lqhUCgUCoVCoVDodhRHp1AoFAqFQqFQKHQ7iqNTKBQKhUKhUCgUuh3F0SkUCoVCoVAoFArdjuLoFAqFQqFQKBQKhW5HcXQKhUKhUCgUCoVCt+P/ASWlp0LeNTyDAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1008x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data.groupby('industryName1').count().plot.bar(y=['chosen'], figsize=(14, 7))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(56, 4)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "股票代码："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Int64Index([   333,    538,    581,    651,    661,    932,    938,   2008,\n",
+       "              2051,   2110,   2304,   2311,   2624,   2916,   2920, 600009,\n",
+       "            600036, 600038, 600104, 600188, 600260, 600298, 600309, 600340,\n",
+       "            600398, 600516, 600519, 600525, 600585, 600694, 600704, 600729,\n",
+       "            600754, 600801, 600835, 600893, 600900, 601021, 601088, 601100,\n",
+       "            601155, 601166, 601186, 601318, 601336, 601869, 601877, 601888,\n",
+       "            601992, 603225, 603260, 603444, 603568, 603816, 603833, 603877],\n",
+       "           dtype='int64', name='code')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.index"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,