bigfang/corr.ipynb

## corr.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 发帖量和上证指数的相关性\n",
    "\n",
    "验证发帖量和回帖量之间是否有关，验证发帖/回帖量是否和上证指数相关\n",
    "\n",
    "\n",
    "#### 发帖与回帖\n",
    "\n",
    "发帖和回帖强相关简直是一定的，不过还是用统计方法验证一下\n",
    "\n",
    "取出所有的主题贴和回复"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "from model import Posts, Replies\n",
    "from peewee import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = (Posts.select(fn.Count(Posts.id).alias('pcount'), fn.strftime('%Y%m%d', Posts.updated_at).alias('date'))\n",
    "     .where(SQL('date').is_null(False))\n",
    "     .group_by(SQL('date'))\n",
    "     .order_by(SQL('date')))\n",
    "r = (Replies.select(fn.Count(Replies.id).alias('rcount'), fn.strftime('%Y%m%d', Replies.updated_at).alias('date'))\n",
    "     .group_by(SQL('date'))\n",
    "     .order_by(SQL('date')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "pdf = pd.DataFrame(list(p.dicts()))\n",
    "rdf = pd.DataFrame(list(r.dicts()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "按照天数统计发帖回帖数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>pcount</th>\n",
       "      <th>rcount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>2017-12-27</td>\n",
       "      <td>152</td>\n",
       "      <td>1649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>2017-12-28</td>\n",
       "      <td>147</td>\n",
       "      <td>1663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>2017-12-29</td>\n",
       "      <td>145</td>\n",
       "      <td>1680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>91</td>\n",
       "      <td>1212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>72</td>\n",
       "      <td>942</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  pcount  rcount\n",
       "1973 2017-12-27     152    1649\n",
       "1974 2017-12-28     147    1663\n",
       "1975 2017-12-29     145    1680\n",
       "1976 2017-12-30      91    1212\n",
       "1977 2017-12-31      72     942"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(pdf, rdf, on='date')\n",
    "df.date = pd.to_datetime(df.date)\n",
    "\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在做相关性分析之前先做一下正态性检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(KstestResult(statistic=0.94792732002350932, pvalue=0.0),\n",
       " KstestResult(statistic=0.99410005141225266, pvalue=0.0))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "normp = stats.kstest(df['pcount'], 'norm')\n",
    "normr = stats.kstest(df['rcount'], 'norm')\n",
    "normp, normr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "完全不是正态分布啊，那么画图看一下长啥样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f4a2080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "f, axes = plt.subplots(1, 2, figsize=(9, 4), sharex=False)\n",
    "sns.despine(left=True)\n",
    "\n",
    "sns.distplot(df['pcount'], color=\"r\", ax=axes[0])\n",
    "sns.distplot(df['rcount'], color=\"g\", ax=axes[1])\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "既然不是正态分布，就不能采用`皮尔森系数`，这里选用`spearman系数`（我也不知道为啥要用这个，我们民科就是这样的啦）`pandas`直接提供了计算相关系数矩阵的方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pcount</th>\n",
       "      <th>rcount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pcount</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.973157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rcount</th>\n",
       "      <td>0.973157</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          pcount    rcount\n",
       "pcount  1.000000  0.973157\n",
       "rcount  0.973157  1.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr('spearman')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不过`pandas`似乎没有提供显著性检验的计算方法，所以这里用scipy再算一遍"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.97315673412260795, 0.0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r, p = stats.spearmanr(df['pcount'], df['rcount'])\n",
    "r, p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> spearman系数0.97，P值为0，这结果漂亮的不可能是实力派\n",
    "\n",
    "相关性用散点图可以直观的展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1104eb470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x='pcount', y='rcount', figsize=(6,6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里用`seaborn`和`matplotlib` 做个对比，`seaborn`比较强悍的一点就是连回归线都做出来了，还顺便画出了两个变量的分布情况。而同样地图形在plt里的代码量不敢想象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1170065f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot('pcount', 'rcount', data=df, kind=\"reg\", size=8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了图形好看，这里按周统计一下发帖数量。\n",
    "再引入一个回帖/发帖的比率`ratio`（作为民科，对比率有种莫名的执着），画出历史数据的折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1171c9588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfg = df.groupby(df.date.dt.to_period('W')).sum()\n",
    "dfg['ratio'] = dfg.rcount / dfg.pcount\n",
    "\n",
    "dfg.plot(xticks=dfg.index, subplots=True, figsize=(15,9))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 很明显发帖回帖图象非常相似，显然相关度是很高的。不过这个比率并不固定，从图象上来看，似乎<del>发帖量到达阶段高峰时，比率会有一个谷值，发帖量到达低谷时，比率会有一个峰值</del>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 指数与发帖\n",
    "\n",
    "利用`tushare`获得上证指数的历史记录，包括了开盘，收盘，最高，最低，成交量，均价，涨跌幅等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>close</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>price_change</th>\n",
       "      <th>p_change</th>\n",
       "      <th>ma5</th>\n",
       "      <th>ma10</th>\n",
       "      <th>ma20</th>\n",
       "      <th>v_ma5</th>\n",
       "      <th>v_ma10</th>\n",
       "      <th>v_ma20</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-02-12/2018-02-18</th>\n",
       "      <td>3128.37</td>\n",
       "      <td>3219.22</td>\n",
       "      <td>3199.16</td>\n",
       "      <td>3113.61</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.31</td>\n",
       "      <td>2.21</td>\n",
       "      <td>3367.416</td>\n",
       "      <td>3352.814</td>\n",
       "      <td>3360.591</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2248586.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-05/2018-02-11</th>\n",
       "      <td>3411.67</td>\n",
       "      <td>3487.72</td>\n",
       "      <td>3129.85</td>\n",
       "      <td>3062.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-332.23</td>\n",
       "      <td>-9.60</td>\n",
       "      <td>3413.372</td>\n",
       "      <td>3361.897</td>\n",
       "      <td>3368.259</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2723750.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-29/2018-02-04</th>\n",
       "      <td>3563.64</td>\n",
       "      <td>3587.03</td>\n",
       "      <td>3462.08</td>\n",
       "      <td>3388.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-96.05</td>\n",
       "      <td>-2.70</td>\n",
       "      <td>3465.752</td>\n",
       "      <td>3380.674</td>\n",
       "      <td>3379.447</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3287523.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-22/2018-01-28</th>\n",
       "      <td>3476.99</td>\n",
       "      <td>3574.90</td>\n",
       "      <td>3558.13</td>\n",
       "      <td>3475.67</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.27</td>\n",
       "      <td>2.02</td>\n",
       "      <td>3434.770</td>\n",
       "      <td>3369.848</td>\n",
       "      <td>3374.606</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3853820.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-15/2018-01-21</th>\n",
       "      <td>3428.95</td>\n",
       "      <td>3498.43</td>\n",
       "      <td>3487.86</td>\n",
       "      <td>3401.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58.92</td>\n",
       "      <td>1.72</td>\n",
       "      <td>3382.556</td>\n",
       "      <td>3352.326</td>\n",
       "      <td>3365.055</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4474194.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08/2018-01-14</th>\n",
       "      <td>3391.55</td>\n",
       "      <td>3435.42</td>\n",
       "      <td>3428.94</td>\n",
       "      <td>3384.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.19</td>\n",
       "      <td>1.10</td>\n",
       "      <td>3338.212</td>\n",
       "      <td>3346.807</td>\n",
       "      <td>3357.238</td>\n",
       "      <td>0.0</td>\n",
       "      <td>530644.7</td>\n",
       "      <td>4935094.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01/2018-01-07</th>\n",
       "      <td>3314.03</td>\n",
       "      <td>3402.07</td>\n",
       "      <td>3391.75</td>\n",
       "      <td>3314.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.58</td>\n",
       "      <td>2.56</td>\n",
       "      <td>3310.422</td>\n",
       "      <td>3341.087</td>\n",
       "      <td>3349.227</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1412637.3</td>\n",
       "      <td>5407215.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-25/2017-12-31</th>\n",
       "      <td>3296.21</td>\n",
       "      <td>3312.30</td>\n",
       "      <td>3307.17</td>\n",
       "      <td>3263.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.11</td>\n",
       "      <td>0.31</td>\n",
       "      <td>3295.596</td>\n",
       "      <td>3343.593</td>\n",
       "      <td>3340.066</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2160538.1</td>\n",
       "      <td>6018446.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-18/2017-12-24</th>\n",
       "      <td>3268.03</td>\n",
       "      <td>3309.22</td>\n",
       "      <td>3297.06</td>\n",
       "      <td>3254.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.92</td>\n",
       "      <td>0.95</td>\n",
       "      <td>3304.926</td>\n",
       "      <td>3350.741</td>\n",
       "      <td>3337.812</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2903128.6</td>\n",
       "      <td>6648020.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-11/2017-12-17</th>\n",
       "      <td>3290.49</td>\n",
       "      <td>3322.67</td>\n",
       "      <td>3266.14</td>\n",
       "      <td>3259.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-23.85</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>3322.096</td>\n",
       "      <td>3360.087</td>\n",
       "      <td>3335.621</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3757529.4</td>\n",
       "      <td>7179463.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-04/2017-12-10</th>\n",
       "      <td>3310.38</td>\n",
       "      <td>3324.00</td>\n",
       "      <td>3289.99</td>\n",
       "      <td>3254.61</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-27.63</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>3355.402</td>\n",
       "      <td>3368.367</td>\n",
       "      <td>3334.213</td>\n",
       "      <td>1061289.4</td>\n",
       "      <td>4497173.0</td>\n",
       "      <td>7763143.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-27/2017-12-03</th>\n",
       "      <td>3346.66</td>\n",
       "      <td>3347.05</td>\n",
       "      <td>3317.62</td>\n",
       "      <td>3300.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-36.20</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>3371.752</td>\n",
       "      <td>3374.621</td>\n",
       "      <td>3330.835</td>\n",
       "      <td>2825274.6</td>\n",
       "      <td>5447501.7</td>\n",
       "      <td>8227692.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          open     high    close      low  volume  \\\n",
       "date                                                                \n",
       "2018-02-12/2018-02-18  3128.37  3219.22  3199.16  3113.61     0.0   \n",
       "2018-02-05/2018-02-11  3411.67  3487.72  3129.85  3062.74     0.0   \n",
       "2018-01-29/2018-02-04  3563.64  3587.03  3462.08  3388.86     0.0   \n",
       "2018-01-22/2018-01-28  3476.99  3574.90  3558.13  3475.67     0.0   \n",
       "2018-01-15/2018-01-21  3428.95  3498.43  3487.86  3401.96     0.0   \n",
       "2018-01-08/2018-01-14  3391.55  3435.42  3428.94  3384.56     0.0   \n",
       "2018-01-01/2018-01-07  3314.03  3402.07  3391.75  3314.03     0.0   \n",
       "2017-12-25/2017-12-31  3296.21  3312.30  3307.17  3263.73     0.0   \n",
       "2017-12-18/2017-12-24  3268.03  3309.22  3297.06  3254.18     0.0   \n",
       "2017-12-11/2017-12-17  3290.49  3322.67  3266.14  3259.39     0.0   \n",
       "2017-12-04/2017-12-10  3310.38  3324.00  3289.99  3254.61     0.0   \n",
       "2017-11-27/2017-12-03  3346.66  3347.05  3317.62  3300.78     0.0   \n",
       "\n",
       "                       price_change  p_change       ma5      ma10      ma20  \\\n",
       "date                                                                          \n",
       "2018-02-12/2018-02-18         69.31      2.21  3367.416  3352.814  3360.591   \n",
       "2018-02-05/2018-02-11       -332.23     -9.60  3413.372  3361.897  3368.259   \n",
       "2018-01-29/2018-02-04        -96.05     -2.70  3465.752  3380.674  3379.447   \n",
       "2018-01-22/2018-01-28         70.27      2.02  3434.770  3369.848  3374.606   \n",
       "2018-01-15/2018-01-21         58.92      1.72  3382.556  3352.326  3365.055   \n",
       "2018-01-08/2018-01-14         37.19      1.10  3338.212  3346.807  3357.238   \n",
       "2018-01-01/2018-01-07         84.58      2.56  3310.422  3341.087  3349.227   \n",
       "2017-12-25/2017-12-31         10.11      0.31  3295.596  3343.593  3340.066   \n",
       "2017-12-18/2017-12-24         30.92      0.95  3304.926  3350.741  3337.812   \n",
       "2017-12-11/2017-12-17        -23.85     -0.72  3322.096  3360.087  3335.621   \n",
       "2017-12-04/2017-12-10        -27.63     -0.83  3355.402  3368.367  3334.213   \n",
       "2017-11-27/2017-12-03        -36.20     -1.08  3371.752  3374.621  3330.835   \n",
       "\n",
       "                           v_ma5     v_ma10      v_ma20  \n",
       "date                                                     \n",
       "2018-02-12/2018-02-18        0.0        0.0  2248586.50  \n",
       "2018-02-05/2018-02-11        0.0        0.0  2723750.85  \n",
       "2018-01-29/2018-02-04        0.0        0.0  3287523.00  \n",
       "2018-01-22/2018-01-28        0.0        0.0  3853820.75  \n",
       "2018-01-15/2018-01-21        0.0        0.0  4474194.10  \n",
       "2018-01-08/2018-01-14        0.0   530644.7  4935094.65  \n",
       "2018-01-01/2018-01-07        0.0  1412637.3  5407215.60  \n",
       "2017-12-25/2017-12-31        0.0  2160538.1  6018446.80  \n",
       "2017-12-18/2017-12-24        0.0  2903128.6  6648020.80  \n",
       "2017-12-11/2017-12-17        0.0  3757529.4  7179463.35  \n",
       "2017-12-04/2017-12-10  1061289.4  4497173.0  7763143.75  \n",
       "2017-11-27/2017-12-03  2825274.6  5447501.7  8227692.00  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tushare as ts\n",
    "\n",
    "adf = ts.get_hist_data('sh', ktype='W')\n",
    "adf.index = pd.to_datetime(adf.index).to_period('W')\n",
    "adf.head(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "合并两表再算相关性矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>close</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>price_change</th>\n",
       "      <th>p_change</th>\n",
       "      <th>ma5</th>\n",
       "      <th>ma10</th>\n",
       "      <th>ma20</th>\n",
       "      <th>v_ma5</th>\n",
       "      <th>v_ma10</th>\n",
       "      <th>v_ma20</th>\n",
       "      <th>pcount</th>\n",
       "      <th>rcount</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-07-16/2012-07-22</th>\n",
       "      <td>2187.887</td>\n",
       "      <td>2197.790</td>\n",
       "      <td>2168.638</td>\n",
       "      <td>2138.792</td>\n",
       "      <td>3329094.75</td>\n",
       "      <td>-17.257</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>2212.884</td>\n",
       "      <td>2270.423</td>\n",
       "      <td>2323.832</td>\n",
       "      <td>2986436.40</td>\n",
       "      <td>3446727.50</td>\n",
       "      <td>3896096.48</td>\n",
       "      <td>13</td>\n",
       "      <td>26</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-09/2012-07-15</th>\n",
       "      <td>2210.714</td>\n",
       "      <td>2216.697</td>\n",
       "      <td>2185.895</td>\n",
       "      <td>2152.675</td>\n",
       "      <td>3324005.50</td>\n",
       "      <td>-37.684</td>\n",
       "      <td>-1.70</td>\n",
       "      <td>2240.526</td>\n",
       "      <td>2293.057</td>\n",
       "      <td>2338.435</td>\n",
       "      <td>3006448.65</td>\n",
       "      <td>3581968.33</td>\n",
       "      <td>4004783.69</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>1.304348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-02/2012-07-08</th>\n",
       "      <td>2234.320</td>\n",
       "      <td>2244.832</td>\n",
       "      <td>2223.579</td>\n",
       "      <td>2185.522</td>\n",
       "      <td>3146987.25</td>\n",
       "      <td>-1.852</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>2259.637</td>\n",
       "      <td>2319.669</td>\n",
       "      <td>2351.121</td>\n",
       "      <td>3027540.75</td>\n",
       "      <td>3582027.15</td>\n",
       "      <td>4061328.31</td>\n",
       "      <td>26</td>\n",
       "      <td>53</td>\n",
       "      <td>2.038462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-25/2012-07-01</th>\n",
       "      <td>2253.514</td>\n",
       "      <td>2253.514</td>\n",
       "      <td>2225.431</td>\n",
       "      <td>2188.716</td>\n",
       "      <td>2793829.50</td>\n",
       "      <td>-35.446</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>2289.608</td>\n",
       "      <td>2336.943</td>\n",
       "      <td>2357.802</td>\n",
       "      <td>3311826.90</td>\n",
       "      <td>3861689.13</td>\n",
       "      <td>4116594.48</td>\n",
       "      <td>22</td>\n",
       "      <td>50</td>\n",
       "      <td>2.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-18/2012-06-24</th>\n",
       "      <td>2313.530</td>\n",
       "      <td>2325.052</td>\n",
       "      <td>2260.877</td>\n",
       "      <td>2253.789</td>\n",
       "      <td>2338265.00</td>\n",
       "      <td>-45.973</td>\n",
       "      <td>-1.99</td>\n",
       "      <td>2311.233</td>\n",
       "      <td>2355.086</td>\n",
       "      <td>2364.129</td>\n",
       "      <td>3531186.50</td>\n",
       "      <td>4070724.33</td>\n",
       "      <td>4189257.93</td>\n",
       "      <td>32</td>\n",
       "      <td>56</td>\n",
       "      <td>1.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           open      high     close       low      volume  \\\n",
       "date                                                                        \n",
       "2012-07-16/2012-07-22  2187.887  2197.790  2168.638  2138.792  3329094.75   \n",
       "2012-07-09/2012-07-15  2210.714  2216.697  2185.895  2152.675  3324005.50   \n",
       "2012-07-02/2012-07-08  2234.320  2244.832  2223.579  2185.522  3146987.25   \n",
       "2012-06-25/2012-07-01  2253.514  2253.514  2225.431  2188.716  2793829.50   \n",
       "2012-06-18/2012-06-24  2313.530  2325.052  2260.877  2253.789  2338265.00   \n",
       "\n",
       "                       price_change  p_change       ma5      ma10      ma20  \\\n",
       "date                                                                          \n",
       "2012-07-16/2012-07-22       -17.257     -0.79  2212.884  2270.423  2323.832   \n",
       "2012-07-09/2012-07-15       -37.684     -1.70  2240.526  2293.057  2338.435   \n",
       "2012-07-02/2012-07-08        -1.852     -0.08  2259.637  2319.669  2351.121   \n",
       "2012-06-25/2012-07-01       -35.446     -1.57  2289.608  2336.943  2357.802   \n",
       "2012-06-18/2012-06-24       -45.973     -1.99  2311.233  2355.086  2364.129   \n",
       "\n",
       "                            v_ma5      v_ma10      v_ma20  pcount  rcount  \\\n",
       "date                                                                        \n",
       "2012-07-16/2012-07-22  2986436.40  3446727.50  3896096.48      13      26   \n",
       "2012-07-09/2012-07-15  3006448.65  3581968.33  4004783.69      23      30   \n",
       "2012-07-02/2012-07-08  3027540.75  3582027.15  4061328.31      26      53   \n",
       "2012-06-25/2012-07-01  3311826.90  3861689.13  4116594.48      22      50   \n",
       "2012-06-18/2012-06-24  3531186.50  4070724.33  4189257.93      32      56   \n",
       "\n",
       "                          ratio  \n",
       "date                             \n",
       "2012-07-16/2012-07-22  2.000000  \n",
       "2012-07-09/2012-07-15  1.304348  \n",
       "2012-07-02/2012-07-08  2.038462  \n",
       "2012-06-25/2012-07-01  2.272727  \n",
       "2012-06-18/2012-06-24  1.750000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndf = pd.merge(adf, dfg, left_index=True, right_index=True)\n",
    "ndf.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "计算相关性之前，需要先检验数据是否服从正态分布，为了方便，直接做图查看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11736f518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\", palette=\"muted\", color_codes=True)\n",
    "\n",
    "f, axes = plt.subplots(2, 2, figsize=(7, 7), sharex=False)\n",
    "sns.despine(left=True)\n",
    "\n",
    "sns.distplot(ndf['rcount'], color=\"m\", ax=axes[0, 0])\n",
    "sns.distplot(ndf['pcount'], color=\"r\", ax=axes[0, 1])\n",
    "sns.distplot(ndf['open'], color=\"g\", ax=axes[1, 0])\n",
    "sns.distplot(ndf['ma10'], color=\"b\", ax=axes[1, 1])\n",
    "\n",
    "plt.setp(axes, xticks=[], yticks=[])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 很明显不服从正态分布，根本不用计算就能看出来，不过我比较惊奇的一点是，这四张图形是如此的相似，横向相似我能理解，可是纵向也相似就有点出乎意料了，不知道是巧合还是必然\n",
    "\n",
    "所以再一次选用`spearman系数`计算相关系数矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>close</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>price_change</th>\n",
       "      <th>p_change</th>\n",
       "      <th>ma5</th>\n",
       "      <th>ma10</th>\n",
       "      <th>ma20</th>\n",
       "      <th>v_ma5</th>\n",
       "      <th>v_ma10</th>\n",
       "      <th>v_ma20</th>\n",
       "      <th>pcount</th>\n",
       "      <th>rcount</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>open</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.992907</td>\n",
       "      <td>0.983927</td>\n",
       "      <td>0.987793</td>\n",
       "      <td>0.540368</td>\n",
       "      <td>0.043531</td>\n",
       "      <td>0.013520</td>\n",
       "      <td>0.983385</td>\n",
       "      <td>0.946717</td>\n",
       "      <td>0.888500</td>\n",
       "      <td>0.621266</td>\n",
       "      <td>0.674273</td>\n",
       "      <td>0.757040</td>\n",
       "      <td>0.767686</td>\n",
       "      <td>0.742914</td>\n",
       "      <td>0.512298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>0.992907</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.992535</td>\n",
       "      <td>0.987050</td>\n",
       "      <td>0.574416</td>\n",
       "      <td>0.112346</td>\n",
       "      <td>0.084672</td>\n",
       "      <td>0.980020</td>\n",
       "      <td>0.941812</td>\n",
       "      <td>0.881847</td>\n",
       "      <td>0.640411</td>\n",
       "      <td>0.689029</td>\n",
       "      <td>0.769704</td>\n",
       "      <td>0.763765</td>\n",
       "      <td>0.736227</td>\n",
       "      <td>0.504322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>0.983927</td>\n",
       "      <td>0.992535</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.990629</td>\n",
       "      <td>0.563766</td>\n",
       "      <td>0.184517</td>\n",
       "      <td>0.158415</td>\n",
       "      <td>0.970638</td>\n",
       "      <td>0.930829</td>\n",
       "      <td>0.871132</td>\n",
       "      <td>0.627201</td>\n",
       "      <td>0.673168</td>\n",
       "      <td>0.756056</td>\n",
       "      <td>0.757437</td>\n",
       "      <td>0.732242</td>\n",
       "      <td>0.519117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>0.987793</td>\n",
       "      <td>0.987050</td>\n",
       "      <td>0.990629</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.529481</td>\n",
       "      <td>0.122860</td>\n",
       "      <td>0.094312</td>\n",
       "      <td>0.968579</td>\n",
       "      <td>0.929813</td>\n",
       "      <td>0.869717</td>\n",
       "      <td>0.602662</td>\n",
       "      <td>0.652581</td>\n",
       "      <td>0.736244</td>\n",
       "      <td>0.756533</td>\n",
       "      <td>0.735054</td>\n",
       "      <td>0.545519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volume</th>\n",
       "      <td>0.540368</td>\n",
       "      <td>0.574416</td>\n",
       "      <td>0.563766</td>\n",
       "      <td>0.529481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.262555</td>\n",
       "      <td>0.248231</td>\n",
       "      <td>0.512120</td>\n",
       "      <td>0.437417</td>\n",
       "      <td>0.376935</td>\n",
       "      <td>0.891485</td>\n",
       "      <td>0.816348</td>\n",
       "      <td>0.760469</td>\n",
       "      <td>0.521389</td>\n",
       "      <td>0.472182</td>\n",
       "      <td>0.271777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_change</th>\n",
       "      <td>0.043531</td>\n",
       "      <td>0.112346</td>\n",
       "      <td>0.184517</td>\n",
       "      <td>0.122860</td>\n",
       "      <td>0.262555</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.993533</td>\n",
       "      <td>0.048163</td>\n",
       "      <td>0.031486</td>\n",
       "      <td>0.021582</td>\n",
       "      <td>0.173219</td>\n",
       "      <td>0.131493</td>\n",
       "      <td>0.134625</td>\n",
       "      <td>0.048361</td>\n",
       "      <td>0.036301</td>\n",
       "      <td>0.056228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_change</th>\n",
       "      <td>0.013520</td>\n",
       "      <td>0.084672</td>\n",
       "      <td>0.158415</td>\n",
       "      <td>0.094312</td>\n",
       "      <td>0.248231</td>\n",
       "      <td>0.993533</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020373</td>\n",
       "      <td>0.005294</td>\n",
       "      <td>-0.002630</td>\n",
       "      <td>0.155993</td>\n",
       "      <td>0.112074</td>\n",
       "      <td>0.113261</td>\n",
       "      <td>0.030729</td>\n",
       "      <td>0.021426</td>\n",
       "      <td>0.047754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ma5</th>\n",
       "      <td>0.983385</td>\n",
       "      <td>0.980020</td>\n",
       "      <td>0.970638</td>\n",
       "      <td>0.968579</td>\n",
       "      <td>0.512120</td>\n",
       "      <td>0.048163</td>\n",
       "      <td>0.020373</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.974054</td>\n",
       "      <td>0.912965</td>\n",
       "      <td>0.609359</td>\n",
       "      <td>0.672813</td>\n",
       "      <td>0.761291</td>\n",
       "      <td>0.776990</td>\n",
       "      <td>0.753241</td>\n",
       "      <td>0.495512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ma10</th>\n",
       "      <td>0.946717</td>\n",
       "      <td>0.941812</td>\n",
       "      <td>0.930829</td>\n",
       "      <td>0.929813</td>\n",
       "      <td>0.437417</td>\n",
       "      <td>0.031486</td>\n",
       "      <td>0.005294</td>\n",
       "      <td>0.974054</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.956987</td>\n",
       "      <td>0.546804</td>\n",
       "      <td>0.642030</td>\n",
       "      <td>0.750374</td>\n",
       "      <td>0.781936</td>\n",
       "      <td>0.761933</td>\n",
       "      <td>0.493230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ma20</th>\n",
       "      <td>0.888500</td>\n",
       "      <td>0.881847</td>\n",
       "      <td>0.871132</td>\n",
       "      <td>0.869717</td>\n",
       "      <td>0.376935</td>\n",
       "      <td>0.021582</td>\n",
       "      <td>-0.002630</td>\n",
       "      <td>0.912965</td>\n",
       "      <td>0.956987</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.464919</td>\n",
       "      <td>0.550992</td>\n",
       "      <td>0.695997</td>\n",
       "      <td>0.784537</td>\n",
       "      <td>0.766501</td>\n",
       "      <td>0.490181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_ma5</th>\n",
       "      <td>0.621266</td>\n",
       "      <td>0.640411</td>\n",
       "      <td>0.627201</td>\n",
       "      <td>0.602662</td>\n",
       "      <td>0.891485</td>\n",
       "      <td>0.173219</td>\n",
       "      <td>0.155993</td>\n",
       "      <td>0.609359</td>\n",
       "      <td>0.546804</td>\n",
       "      <td>0.464919</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.950925</td>\n",
       "      <td>0.867207</td>\n",
       "      <td>0.560007</td>\n",
       "      <td>0.515180</td>\n",
       "      <td>0.330782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_ma10</th>\n",
       "      <td>0.674273</td>\n",
       "      <td>0.689029</td>\n",
       "      <td>0.673168</td>\n",
       "      <td>0.652581</td>\n",
       "      <td>0.816348</td>\n",
       "      <td>0.131493</td>\n",
       "      <td>0.112074</td>\n",
       "      <td>0.672813</td>\n",
       "      <td>0.642030</td>\n",
       "      <td>0.550992</td>\n",
       "      <td>0.950925</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.926622</td>\n",
       "      <td>0.616271</td>\n",
       "      <td>0.576468</td>\n",
       "      <td>0.383076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_ma20</th>\n",
       "      <td>0.757040</td>\n",
       "      <td>0.769704</td>\n",
       "      <td>0.756056</td>\n",
       "      <td>0.736244</td>\n",
       "      <td>0.760469</td>\n",
       "      <td>0.134625</td>\n",
       "      <td>0.113261</td>\n",
       "      <td>0.761291</td>\n",
       "      <td>0.750374</td>\n",
       "      <td>0.695997</td>\n",
       "      <td>0.867207</td>\n",
       "      <td>0.926622</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.733913</td>\n",
       "      <td>0.694009</td>\n",
       "      <td>0.466891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pcount</th>\n",
       "      <td>0.767686</td>\n",
       "      <td>0.763765</td>\n",
       "      <td>0.757437</td>\n",
       "      <td>0.756533</td>\n",
       "      <td>0.521389</td>\n",
       "      <td>0.048361</td>\n",
       "      <td>0.030729</td>\n",
       "      <td>0.776990</td>\n",
       "      <td>0.781936</td>\n",
       "      <td>0.784537</td>\n",
       "      <td>0.560007</td>\n",
       "      <td>0.616271</td>\n",
       "      <td>0.733913</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.980763</td>\n",
       "      <td>0.633489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rcount</th>\n",
       "      <td>0.742914</td>\n",
       "      <td>0.736227</td>\n",
       "      <td>0.732242</td>\n",
       "      <td>0.735054</td>\n",
       "      <td>0.472182</td>\n",
       "      <td>0.036301</td>\n",
       "      <td>0.021426</td>\n",
       "      <td>0.753241</td>\n",
       "      <td>0.761933</td>\n",
       "      <td>0.766501</td>\n",
       "      <td>0.515180</td>\n",
       "      <td>0.576468</td>\n",
       "      <td>0.694009</td>\n",
       "      <td>0.980763</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.723773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratio</th>\n",
       "      <td>0.512298</td>\n",
       "      <td>0.504322</td>\n",
       "      <td>0.519117</td>\n",
       "      <td>0.545519</td>\n",
       "      <td>0.271777</td>\n",
       "      <td>0.056228</td>\n",
       "      <td>0.047754</td>\n",
       "      <td>0.495512</td>\n",
       "      <td>0.493230</td>\n",
       "      <td>0.490181</td>\n",
       "      <td>0.330782</td>\n",
       "      <td>0.383076</td>\n",
       "      <td>0.466891</td>\n",
       "      <td>0.633489</td>\n",
       "      <td>0.723773</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  open      high     close       low    volume  price_change  \\\n",
       "open          1.000000  0.992907  0.983927  0.987793  0.540368      0.043531   \n",
       "high          0.992907  1.000000  0.992535  0.987050  0.574416      0.112346   \n",
       "close         0.983927  0.992535  1.000000  0.990629  0.563766      0.184517   \n",
       "low           0.987793  0.987050  0.990629  1.000000  0.529481      0.122860   \n",
       "volume        0.540368  0.574416  0.563766  0.529481  1.000000      0.262555   \n",
       "price_change  0.043531  0.112346  0.184517  0.122860  0.262555      1.000000   \n",
       "p_change      0.013520  0.084672  0.158415  0.094312  0.248231      0.993533   \n",
       "ma5           0.983385  0.980020  0.970638  0.968579  0.512120      0.048163   \n",
       "ma10          0.946717  0.941812  0.930829  0.929813  0.437417      0.031486   \n",
       "ma20          0.888500  0.881847  0.871132  0.869717  0.376935      0.021582   \n",
       "v_ma5         0.621266  0.640411  0.627201  0.602662  0.891485      0.173219   \n",
       "v_ma10        0.674273  0.689029  0.673168  0.652581  0.816348      0.131493   \n",
       "v_ma20        0.757040  0.769704  0.756056  0.736244  0.760469      0.134625   \n",
       "pcount        0.767686  0.763765  0.757437  0.756533  0.521389      0.048361   \n",
       "rcount        0.742914  0.736227  0.732242  0.735054  0.472182      0.036301   \n",
       "ratio         0.512298  0.504322  0.519117  0.545519  0.271777      0.056228   \n",
       "\n",
       "              p_change       ma5      ma10      ma20     v_ma5    v_ma10  \\\n",
       "open          0.013520  0.983385  0.946717  0.888500  0.621266  0.674273   \n",
       "high          0.084672  0.980020  0.941812  0.881847  0.640411  0.689029   \n",
       "close         0.158415  0.970638  0.930829  0.871132  0.627201  0.673168   \n",
       "low           0.094312  0.968579  0.929813  0.869717  0.602662  0.652581   \n",
       "volume        0.248231  0.512120  0.437417  0.376935  0.891485  0.816348   \n",
       "price_change  0.993533  0.048163  0.031486  0.021582  0.173219  0.131493   \n",
       "p_change      1.000000  0.020373  0.005294 -0.002630  0.155993  0.112074   \n",
       "ma5           0.020373  1.000000  0.974054  0.912965  0.609359  0.672813   \n",
       "ma10          0.005294  0.974054  1.000000  0.956987  0.546804  0.642030   \n",
       "ma20         -0.002630  0.912965  0.956987  1.000000  0.464919  0.550992   \n",
       "v_ma5         0.155993  0.609359  0.546804  0.464919  1.000000  0.950925   \n",
       "v_ma10        0.112074  0.672813  0.642030  0.550992  0.950925  1.000000   \n",
       "v_ma20        0.113261  0.761291  0.750374  0.695997  0.867207  0.926622   \n",
       "pcount        0.030729  0.776990  0.781936  0.784537  0.560007  0.616271   \n",
       "rcount        0.021426  0.753241  0.761933  0.766501  0.515180  0.576468   \n",
       "ratio         0.047754  0.495512  0.493230  0.490181  0.330782  0.383076   \n",
       "\n",
       "                v_ma20    pcount    rcount     ratio  \n",
       "open          0.757040  0.767686  0.742914  0.512298  \n",
       "high          0.769704  0.763765  0.736227  0.504322  \n",
       "close         0.756056  0.757437  0.732242  0.519117  \n",
       "low           0.736244  0.756533  0.735054  0.545519  \n",
       "volume        0.760469  0.521389  0.472182  0.271777  \n",
       "price_change  0.134625  0.048361  0.036301  0.056228  \n",
       "p_change      0.113261  0.030729  0.021426  0.047754  \n",
       "ma5           0.761291  0.776990  0.753241  0.495512  \n",
       "ma10          0.750374  0.781936  0.761933  0.493230  \n",
       "ma20          0.695997  0.784537  0.766501  0.490181  \n",
       "v_ma5         0.867207  0.560007  0.515180  0.330782  \n",
       "v_ma10        0.926622  0.616271  0.576468  0.383076  \n",
       "v_ma20        1.000000  0.733913  0.694009  0.466891  \n",
       "pcount        0.733913  1.000000  0.980763  0.633489  \n",
       "rcount        0.694009  0.980763  1.000000  0.723773  \n",
       "ratio         0.466891  0.633489  0.723773  1.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = ndf.corr('spearman')\n",
    "corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由该表可以看出发帖量回帖量与20日均价相关性最大\n",
    "\n",
    "可以用热力图显示，这样更明显一些"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118aeb080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"darkgrid\")\n",
    "\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmin=.5, vmax=.82, center=.72,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 红色越深表示相关系数越接近1\n",
    "\n",
    "同样地，算一下P值，看看显著性，这里以20日均价和发帖量为例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.78453685256941941, 4.6579624282353562e-60)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r, p = stats.spearmanr(ndf['ma20'], ndf['pcount'])\n",
    "r, p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据好到反正我是不信的，最后再用`seaborn`里的散点图看一下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119b67278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot('ma20', 'pcount', data=ndf, kind=\"reg\", size=8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 很明显，在ma20接近2000的地方，发帖量也接近于0，且数据量非常大，发帖量小的原因应该是在论坛建立之初，人气比较低所致。可是却暗合了指数在2000多点徘徊许久的事实。\n",
    "> 虽然直觉上觉得数据的分布对相关分析产生了影响，但是受限于我的数学知识，无力。。。。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}