【乐学偶得出品】2.Tech【技术分析指标之MACD】.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "乐学偶得版权所有  公众号：乐学Fintech  主讲人：William LX_LAB教研团队提供技术支持 正版视频： 网易云课堂：乐学偶得"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "前置知识：Moving Average(rolling average/ running average)\n",
    "之前我们指导了SMA/CMA/WMA/EMA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.通过tushare获取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tushare as ts\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts.set_token(\"e72c3c4a969e6d0daf858fd37b04c73f0e94a35b7915c2ee3513aab9\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pro = ts.pro_api()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_whole = pro.daily(ts_code=\"000001.SZ\",start_date=\"20181001\",end_date=\"20191001\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20190930</td>\n",
       "      <td>15.75</td>\n",
       "      <td>15.89</td>\n",
       "      <td>15.57</td>\n",
       "      <td>15.59</td>\n",
       "      <td>15.90</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-1.9497</td>\n",
       "      <td>1046363.17</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20190927</td>\n",
       "      <td>15.85</td>\n",
       "      <td>16.00</td>\n",
       "      <td>15.70</td>\n",
       "      <td>15.90</td>\n",
       "      <td>15.71</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.2094</td>\n",
       "      <td>1007706.27</td>\n",
       "      <td>1598567.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20190926</td>\n",
       "      <td>15.86</td>\n",
       "      <td>16.04</td>\n",
       "      <td>15.66</td>\n",
       "      <td>15.71</td>\n",
       "      <td>15.75</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.2540</td>\n",
       "      <td>1686592.58</td>\n",
       "      <td>2673096.951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20190925</td>\n",
       "      <td>15.20</td>\n",
       "      <td>15.85</td>\n",
       "      <td>15.19</td>\n",
       "      <td>15.75</td>\n",
       "      <td>15.18</td>\n",
       "      <td>0.57</td>\n",
       "      <td>3.7549</td>\n",
       "      <td>1938892.72</td>\n",
       "      <td>3024598.930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20190924</td>\n",
       "      <td>15.47</td>\n",
       "      <td>15.54</td>\n",
       "      <td>15.18</td>\n",
       "      <td>15.18</td>\n",
       "      <td>15.38</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-1.3004</td>\n",
       "      <td>1129103.24</td>\n",
       "      <td>1726238.272</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>238</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20181012</td>\n",
       "      <td>9.97</td>\n",
       "      <td>10.34</td>\n",
       "      <td>9.87</td>\n",
       "      <td>10.30</td>\n",
       "      <td>9.86</td>\n",
       "      <td>0.44</td>\n",
       "      <td>4.4625</td>\n",
       "      <td>1516810.28</td>\n",
       "      <td>1532651.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>239</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20181011</td>\n",
       "      <td>10.05</td>\n",
       "      <td>10.16</td>\n",
       "      <td>9.70</td>\n",
       "      <td>9.86</td>\n",
       "      <td>10.45</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-5.6459</td>\n",
       "      <td>1995143.83</td>\n",
       "      <td>1994186.611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>240</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20181010</td>\n",
       "      <td>10.54</td>\n",
       "      <td>10.66</td>\n",
       "      <td>10.38</td>\n",
       "      <td>10.45</td>\n",
       "      <td>10.56</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-1.0417</td>\n",
       "      <td>995200.08</td>\n",
       "      <td>1045666.180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>241</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20181009</td>\n",
       "      <td>10.46</td>\n",
       "      <td>10.70</td>\n",
       "      <td>10.39</td>\n",
       "      <td>10.56</td>\n",
       "      <td>10.45</td>\n",
       "      <td>0.11</td>\n",
       "      <td>1.0526</td>\n",
       "      <td>1064084.26</td>\n",
       "      <td>1117946.550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>242</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20181008</td>\n",
       "      <td>10.70</td>\n",
       "      <td>10.79</td>\n",
       "      <td>10.45</td>\n",
       "      <td>10.45</td>\n",
       "      <td>11.05</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-5.4299</td>\n",
       "      <td>1686358.52</td>\n",
       "      <td>1793455.283</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>243 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
       "0    000001.SZ   20190930  15.75  15.89  15.57  15.59      15.90   -0.31   \n",
       "1    000001.SZ   20190927  15.85  16.00  15.70  15.90      15.71    0.19   \n",
       "2    000001.SZ   20190926  15.86  16.04  15.66  15.71      15.75   -0.04   \n",
       "3    000001.SZ   20190925  15.20  15.85  15.19  15.75      15.18    0.57   \n",
       "4    000001.SZ   20190924  15.47  15.54  15.18  15.18      15.38   -0.20   \n",
       "..         ...        ...    ...    ...    ...    ...        ...     ...   \n",
       "238  000001.SZ   20181012   9.97  10.34   9.87  10.30       9.86    0.44   \n",
       "239  000001.SZ   20181011  10.05  10.16   9.70   9.86      10.45   -0.59   \n",
       "240  000001.SZ   20181010  10.54  10.66  10.38  10.45      10.56   -0.11   \n",
       "241  000001.SZ   20181009  10.46  10.70  10.39  10.56      10.45    0.11   \n",
       "242  000001.SZ   20181008  10.70  10.79  10.45  10.45      11.05   -0.60   \n",
       "\n",
       "     pct_chg         vol       amount  \n",
       "0    -1.9497  1046363.17  1645828.526  \n",
       "1     1.2094  1007706.27  1598567.052  \n",
       "2    -0.2540  1686592.58  2673096.951  \n",
       "3     3.7549  1938892.72  3024598.930  \n",
       "4    -1.3004  1129103.24  1726238.272  \n",
       "..       ...         ...          ...  \n",
       "238   4.4625  1516810.28  1532651.400  \n",
       "239  -5.6459  1995143.83  1994186.611  \n",
       "240  -1.0417   995200.08  1045666.180  \n",
       "241   1.0526  1064084.26  1117946.550  \n",
       "242  -5.4299  1686358.52  1793455.283  \n",
       "\n",
       "[243 rows x 11 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_whole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_whole[\"trade_date\"]=pd.to_datetime(df_whole[\"trade_date\"],format=\"%Y%m%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2019-09-30\n",
       "1     2019-09-27\n",
       "2     2019-09-26\n",
       "3     2019-09-25\n",
       "4     2019-09-24\n",
       "         ...    \n",
       "238   2018-10-12\n",
       "239   2018-10-11\n",
       "240   2018-10-10\n",
       "241   2018-10-09\n",
       "242   2018-10-08\n",
       "Name: trade_date, Length: 243, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_whole[\"trade_date\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_whole.set_index(\"trade_date\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>15.75</td>\n",
       "      <td>15.89</td>\n",
       "      <td>15.57</td>\n",
       "      <td>15.59</td>\n",
       "      <td>15.90</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-1.9497</td>\n",
       "      <td>1046363.17</td>\n",
       "      <td>1645828.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-09-27</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>15.85</td>\n",
       "      <td>16.00</td>\n",
       "      <td>15.70</td>\n",
       "      <td>15.90</td>\n",
       "      <td>15.71</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.2094</td>\n",
       "      <td>1007706.27</td>\n",
       "      <td>1598567.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-09-26</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>15.86</td>\n",
       "      <td>16.04</td>\n",
       "      <td>15.66</td>\n",
       "      <td>15.71</td>\n",
       "      <td>15.75</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.2540</td>\n",
       "      <td>1686592.58</td>\n",
       "      <td>2673096.951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-09-25</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>15.20</td>\n",
       "      <td>15.85</td>\n",
       "      <td>15.19</td>\n",
       "      <td>15.75</td>\n",
       "      <td>15.18</td>\n",
       "      <td>0.57</td>\n",
       "      <td>3.7549</td>\n",
       "      <td>1938892.72</td>\n",
       "      <td>3024598.930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-09-24</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>15.47</td>\n",
       "      <td>15.54</td>\n",
       "      <td>15.18</td>\n",
       "      <td>15.18</td>\n",
       "      <td>15.38</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-1.3004</td>\n",
       "      <td>1129103.24</td>\n",
       "      <td>1726238.272</td>\n",
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       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>2018-10-12</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>9.97</td>\n",
       "      <td>10.34</td>\n",
       "      <td>9.87</td>\n",
       "      <td>10.30</td>\n",
       "      <td>9.86</td>\n",
       "      <td>0.44</td>\n",
       "      <td>4.4625</td>\n",
       "      <td>1516810.28</td>\n",
       "      <td>1532651.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>10.05</td>\n",
       "      <td>10.16</td>\n",
       "      <td>9.70</td>\n",
       "      <td>9.86</td>\n",
       "      <td>10.45</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-5.6459</td>\n",
       "      <td>1995143.83</td>\n",
       "      <td>1994186.611</td>\n",
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       "    <tr>\n",
       "      <td>2018-10-10</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>10.54</td>\n",
       "      <td>10.66</td>\n",
       "      <td>10.38</td>\n",
       "      <td>10.45</td>\n",
       "      <td>10.56</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-1.0417</td>\n",
       "      <td>995200.08</td>\n",
       "      <td>1045666.180</td>\n",
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       "    <tr>\n",
       "      <td>2018-10-09</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>10.46</td>\n",
       "      <td>10.70</td>\n",
       "      <td>10.39</td>\n",
       "      <td>10.56</td>\n",
       "      <td>10.45</td>\n",
       "      <td>0.11</td>\n",
       "      <td>1.0526</td>\n",
       "      <td>1064084.26</td>\n",
       "      <td>1117946.550</td>\n",
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       "    <tr>\n",
       "      <td>2018-10-08</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>10.70</td>\n",
       "      <td>10.79</td>\n",
       "      <td>10.45</td>\n",
       "      <td>10.45</td>\n",
       "      <td>11.05</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-5.4299</td>\n",
       "      <td>1686358.52</td>\n",
       "      <td>1793455.283</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>243 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              ts_code   open   high    low  close  pre_close  change  pct_chg  \\\n",
       "trade_date                                                                      \n",
       "2019-09-30  000001.SZ  15.75  15.89  15.57  15.59      15.90   -0.31  -1.9497   \n",
       "2019-09-27  000001.SZ  15.85  16.00  15.70  15.90      15.71    0.19   1.2094   \n",
       "2019-09-26  000001.SZ  15.86  16.04  15.66  15.71      15.75   -0.04  -0.2540   \n",
       "2019-09-25  000001.SZ  15.20  15.85  15.19  15.75      15.18    0.57   3.7549   \n",
       "2019-09-24  000001.SZ  15.47  15.54  15.18  15.18      15.38   -0.20  -1.3004   \n",
       "...               ...    ...    ...    ...    ...        ...     ...      ...   \n",
       "2018-10-12  000001.SZ   9.97  10.34   9.87  10.30       9.86    0.44   4.4625   \n",
       "2018-10-11  000001.SZ  10.05  10.16   9.70   9.86      10.45   -0.59  -5.6459   \n",
       "2018-10-10  000001.SZ  10.54  10.66  10.38  10.45      10.56   -0.11  -1.0417   \n",
       "2018-10-09  000001.SZ  10.46  10.70  10.39  10.56      10.45    0.11   1.0526   \n",
       "2018-10-08  000001.SZ  10.70  10.79  10.45  10.45      11.05   -0.60  -5.4299   \n",
       "\n",
       "                   vol       amount  \n",
       "trade_date                           \n",
       "2019-09-30  1046363.17  1645828.526  \n",
       "2019-09-27  1007706.27  1598567.052  \n",
       "2019-09-26  1686592.58  2673096.951  \n",
       "2019-09-25  1938892.72  3024598.930  \n",
       "2019-09-24  1129103.24  1726238.272  \n",
       "...                ...          ...  \n",
       "2018-10-12  1516810.28  1532651.400  \n",
       "2018-10-11  1995143.83  1994186.611  \n",
       "2018-10-10   995200.08  1045666.180  \n",
       "2018-10-09  1064084.26  1117946.550  \n",
       "2018-10-08  1686358.52  1793455.283  \n",
       "\n",
       "[243 rows x 10 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_whole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_whole=df_whole.sort_index(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <td>2018-10-08</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>10.70</td>\n",
       "      <td>10.79</td>\n",
       "      <td>10.45</td>\n",
       "      <td>10.45</td>\n",
       "      <td>11.05</td>\n",
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       "      <td>1686358.52</td>\n",
       "      <td>1793455.283</td>\n",
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       "    <tr>\n",
       "      <td>2018-10-09</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>10.46</td>\n",
       "      <td>10.70</td>\n",
       "      <td>10.39</td>\n",
       "      <td>10.56</td>\n",
       "      <td>10.45</td>\n",
       "      <td>0.11</td>\n",
       "      <td>1.0526</td>\n",
       "      <td>1064084.26</td>\n",
       "      <td>1117946.550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-10-10</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>10.54</td>\n",
       "      <td>10.66</td>\n",
       "      <td>10.38</td>\n",
       "      <td>10.45</td>\n",
       "      <td>10.56</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-1.0417</td>\n",
       "      <td>995200.08</td>\n",
       "      <td>1045666.180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>10.05</td>\n",
       "      <td>10.16</td>\n",
       "      <td>9.70</td>\n",
       "      <td>9.86</td>\n",
       "      <td>10.45</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-5.6459</td>\n",
       "      <td>1995143.83</td>\n",
       "      <td>1994186.611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-10-12</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>9.97</td>\n",
       "      <td>10.34</td>\n",
       "      <td>9.87</td>\n",
       "      <td>10.30</td>\n",
       "      <td>9.86</td>\n",
       "      <td>0.44</td>\n",
       "      <td>4.4625</td>\n",
       "      <td>1516810.28</td>\n",
       "      <td>1532651.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-09-24</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>15.47</td>\n",
       "      <td>15.54</td>\n",
       "      <td>15.18</td>\n",
       "      <td>15.18</td>\n",
       "      <td>15.38</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-1.3004</td>\n",
       "      <td>1129103.24</td>\n",
       "      <td>1726238.272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-09-25</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>15.20</td>\n",
       "      <td>15.85</td>\n",
       "      <td>15.19</td>\n",
       "      <td>15.75</td>\n",
       "      <td>15.18</td>\n",
       "      <td>0.57</td>\n",
       "      <td>3.7549</td>\n",
       "      <td>1938892.72</td>\n",
       "      <td>3024598.930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-09-26</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>15.86</td>\n",
       "      <td>16.04</td>\n",
       "      <td>15.66</td>\n",
       "      <td>15.71</td>\n",
       "      <td>15.75</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.2540</td>\n",
       "      <td>1686592.58</td>\n",
       "      <td>2673096.951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-09-27</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>15.85</td>\n",
       "      <td>16.00</td>\n",
       "      <td>15.70</td>\n",
       "      <td>15.90</td>\n",
       "      <td>15.71</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.2094</td>\n",
       "      <td>1007706.27</td>\n",
       "      <td>1598567.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>15.75</td>\n",
       "      <td>15.89</td>\n",
       "      <td>15.57</td>\n",
       "      <td>15.59</td>\n",
       "      <td>15.90</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-1.9497</td>\n",
       "      <td>1046363.17</td>\n",
       "      <td>1645828.526</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>243 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              ts_code   open   high    low  close  pre_close  change  pct_chg  \\\n",
       "trade_date                                                                      \n",
       "2018-10-08  000001.SZ  10.70  10.79  10.45  10.45      11.05   -0.60  -5.4299   \n",
       "2018-10-09  000001.SZ  10.46  10.70  10.39  10.56      10.45    0.11   1.0526   \n",
       "2018-10-10  000001.SZ  10.54  10.66  10.38  10.45      10.56   -0.11  -1.0417   \n",
       "2018-10-11  000001.SZ  10.05  10.16   9.70   9.86      10.45   -0.59  -5.6459   \n",
       "2018-10-12  000001.SZ   9.97  10.34   9.87  10.30       9.86    0.44   4.4625   \n",
       "...               ...    ...    ...    ...    ...        ...     ...      ...   \n",
       "2019-09-24  000001.SZ  15.47  15.54  15.18  15.18      15.38   -0.20  -1.3004   \n",
       "2019-09-25  000001.SZ  15.20  15.85  15.19  15.75      15.18    0.57   3.7549   \n",
       "2019-09-26  000001.SZ  15.86  16.04  15.66  15.71      15.75   -0.04  -0.2540   \n",
       "2019-09-27  000001.SZ  15.85  16.00  15.70  15.90      15.71    0.19   1.2094   \n",
       "2019-09-30  000001.SZ  15.75  15.89  15.57  15.59      15.90   -0.31  -1.9497   \n",
       "\n",
       "                   vol       amount  \n",
       "trade_date                           \n",
       "2018-10-08  1686358.52  1793455.283  \n",
       "2018-10-09  1064084.26  1117946.550  \n",
       "2018-10-10   995200.08  1045666.180  \n",
       "2018-10-11  1995143.83  1994186.611  \n",
       "2018-10-12  1516810.28  1532651.400  \n",
       "...                ...          ...  \n",
       "2019-09-24  1129103.24  1726238.272  \n",
       "2019-09-25  1938892.72  3024598.930  \n",
       "2019-09-26  1686592.58  2673096.951  \n",
       "2019-09-27  1007706.27  1598567.052  \n",
       "2019-09-30  1046363.17  1645828.526  \n",
       "\n",
       "[243 rows x 10 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_whole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_close=df_whole[\"close\"]\n",
    "type(df_close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trade_date\n",
       "2018-10-08          NaN\n",
       "2018-10-09          NaN\n",
       "2018-10-10          NaN\n",
       "2018-10-11          NaN\n",
       "2018-10-12          NaN\n",
       "                ...    \n",
       "2019-09-24    14.634667\n",
       "2019-09-25    14.663333\n",
       "2019-09-26    14.688000\n",
       "2019-09-27    14.720000\n",
       "2019-09-30    14.743000\n",
       "Name: close, Length: 243, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_30_mavg=df_close.rolling(window=30).mean()\n",
    "df_30_mavg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = \"fake_price\"\n",
    "series = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 , 10.0,11.0,12.0,13.0,14.0,15.0,16.0,17.0,1888.0,19.0,20.0,21.0,22.0,23.0,24.0,25.0,26.0,27.0,28.0,29.0,30.0],name=name).to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>fake_price</th>\n",
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       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
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       "      <td>7</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9.0</td>\n",
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       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>12.0</td>\n",
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       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>17.0</td>\n",
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       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1888.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fake_price\n",
       "0          1.0\n",
       "1          2.0\n",
       "2          3.0\n",
       "3          4.0\n",
       "4          5.0\n",
       "5          6.0\n",
       "6          7.0\n",
       "7          8.0\n",
       "8          9.0\n",
       "9         10.0\n",
       "10        11.0\n",
       "11        12.0\n",
       "12        13.0\n",
       "13        14.0\n",
       "14        15.0\n",
       "15        16.0\n",
       "16        17.0\n",
       "17      1888.0\n",
       "18        19.0\n",
       "19        20.0\n",
       "20        21.0\n",
       "21        22.0\n",
       "22        23.0\n",
       "23        24.0\n",
       "24        25.0\n",
       "25        26.0\n",
       "26        27.0\n",
       "27        28.0\n",
       "28        29.0\n",
       "29        30.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "series[name + \"_ewma_unadj\"]= np.nan\n",
    "series.loc[0,name + \"_ewma_unadj\"]=series[name].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "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>fake_price</th>\n",
       "      <th>fake_price_ewma_unadj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1888.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fake_price  fake_price_ewma_unadj\n",
       "0          1.0                    1.0\n",
       "1          2.0                    NaN\n",
       "2          3.0                    NaN\n",
       "3          4.0                    NaN\n",
       "4          5.0                    NaN\n",
       "5          6.0                    NaN\n",
       "6          7.0                    NaN\n",
       "7          8.0                    NaN\n",
       "8          9.0                    NaN\n",
       "9         10.0                    NaN\n",
       "10        11.0                    NaN\n",
       "11        12.0                    NaN\n",
       "12        13.0                    NaN\n",
       "13        14.0                    NaN\n",
       "14        15.0                    NaN\n",
       "15        16.0                    NaN\n",
       "16        17.0                    NaN\n",
       "17      1888.0                    NaN\n",
       "18        19.0                    NaN\n",
       "19        20.0                    NaN\n",
       "20        21.0                    NaN\n",
       "21        22.0                    NaN\n",
       "22        23.0                    NaN\n",
       "23        24.0                    NaN\n",
       "24        25.0                    NaN\n",
       "25        26.0                    NaN\n",
       "26        27.0                    NaN\n",
       "27        28.0                    NaN\n",
       "28        29.0                    NaN\n",
       "29        30.0                    NaN"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "series[name + \"_ewma_adj\"]= np.nan\n",
    "series.loc[0,name + \"_ewma_adj\"]=series[name].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fake_price</th>\n",
       "      <th>fake_price_ewma_unadj</th>\n",
       "      <th>fake_price_ewma_adj</th>\n",
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       "      <td>9</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>11.0</td>\n",
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       "      <td>13</td>\n",
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       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>16.0</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1888.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>18</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>19</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>25</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>27</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fake_price  fake_price_ewma_unadj  fake_price_ewma_adj\n",
       "0          1.0                    1.0                  1.0\n",
       "1          2.0                    NaN                  NaN\n",
       "2          3.0                    NaN                  NaN\n",
       "3          4.0                    NaN                  NaN\n",
       "4          5.0                    NaN                  NaN\n",
       "5          6.0                    NaN                  NaN\n",
       "6          7.0                    NaN                  NaN\n",
       "7          8.0                    NaN                  NaN\n",
       "8          9.0                    NaN                  NaN\n",
       "9         10.0                    NaN                  NaN\n",
       "10        11.0                    NaN                  NaN\n",
       "11        12.0                    NaN                  NaN\n",
       "12        13.0                    NaN                  NaN\n",
       "13        14.0                    NaN                  NaN\n",
       "14        15.0                    NaN                  NaN\n",
       "15        16.0                    NaN                  NaN\n",
       "16        17.0                    NaN                  NaN\n",
       "17      1888.0                    NaN                  NaN\n",
       "18        19.0                    NaN                  NaN\n",
       "19        20.0                    NaN                  NaN\n",
       "20        21.0                    NaN                  NaN\n",
       "21        22.0                    NaN                  NaN\n",
       "22        23.0                    NaN                  NaN\n",
       "23        24.0                    NaN                  NaN\n",
       "24        25.0                    NaN                  NaN\n",
       "25        26.0                    NaN                  NaN\n",
       "26        27.0                    NaN                  NaN\n",
       "27        28.0                    NaN                  NaN\n",
       "28        29.0                    NaN                  NaN\n",
       "29        30.0                    NaN                  NaN"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "period = 5\n",
    "alpha = 2/(1+period)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(1,len(series)):\n",
    "    series.loc[i,name+\"_ewma_unadj\"]=(1-alpha)*series.loc[i-1,name+\"_ewma_unadj\"] + \\\n",
    "    alpha * series.loc[i ,name]\n",
    "    \n",
    "    ajusted_weights = np.array([(1-alpha)**(i-t) for t in range(i+1)])\n",
    "    series.loc[i,name+\"_ewma_adj\"] =np.sum(series.iloc[0:i+1][name].values * ajusted_weights)\\\n",
    "    / ajusted_weights.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fake_price</th>\n",
       "      <th>fake_price_ewma_unadj</th>\n",
       "      <th>fake_price_ewma_adj</th>\n",
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       "  </thead>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
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       "      <td>1.888889</td>\n",
       "      <td>2.263158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.592593</td>\n",
       "      <td>2.984615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>3.758294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.263374</td>\n",
       "      <td>4.577444</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.175583</td>\n",
       "      <td>5.435163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.117055</td>\n",
       "      <td>6.324822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.078037</td>\n",
       "      <td>7.240363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.052025</td>\n",
       "      <td>8.176476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.034683</td>\n",
       "      <td>9.128659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.023122</td>\n",
       "      <td>10.093207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.015415</td>\n",
       "      <td>11.067142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.010276</td>\n",
       "      <td>12.048122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.006851</td>\n",
       "      <td>13.034333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.004567</td>\n",
       "      <td>14.024396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.003045</td>\n",
       "      <td>15.017272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1888.0</td>\n",
       "      <td>639.335363</td>\n",
       "      <td>639.767579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>19.0</td>\n",
       "      <td>432.556909</td>\n",
       "      <td>432.751669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>20.0</td>\n",
       "      <td>295.037939</td>\n",
       "      <td>295.126391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>21.0</td>\n",
       "      <td>203.691959</td>\n",
       "      <td>203.732604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>22.0</td>\n",
       "      <td>143.127973</td>\n",
       "      <td>143.146972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>23.0</td>\n",
       "      <td>103.085315</td>\n",
       "      <td>103.094412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>24.0</td>\n",
       "      <td>76.723544</td>\n",
       "      <td>76.728042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>25.0</td>\n",
       "      <td>59.482362</td>\n",
       "      <td>59.484678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>26.0</td>\n",
       "      <td>48.321575</td>\n",
       "      <td>48.322824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>27.0</td>\n",
       "      <td>41.214383</td>\n",
       "      <td>41.215091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>28.0</td>\n",
       "      <td>36.809589</td>\n",
       "      <td>36.810009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34.206393</td>\n",
       "      <td>34.206652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>30.0</td>\n",
       "      <td>32.804262</td>\n",
       "      <td>32.804428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fake_price  fake_price_ewma_unadj  fake_price_ewma_adj\n",
       "0          1.0               1.000000             1.000000\n",
       "1          2.0               1.333333             1.600000\n",
       "2          3.0               1.888889             2.263158\n",
       "3          4.0               2.592593             2.984615\n",
       "4          5.0               3.395062             3.758294\n",
       "5          6.0               4.263374             4.577444\n",
       "6          7.0               5.175583             5.435163\n",
       "7          8.0               6.117055             6.324822\n",
       "8          9.0               7.078037             7.240363\n",
       "9         10.0               8.052025             8.176476\n",
       "10        11.0               9.034683             9.128659\n",
       "11        12.0              10.023122            10.093207\n",
       "12        13.0              11.015415            11.067142\n",
       "13        14.0              12.010276            12.048122\n",
       "14        15.0              13.006851            13.034333\n",
       "15        16.0              14.004567            14.024396\n",
       "16        17.0              15.003045            15.017272\n",
       "17      1888.0             639.335363           639.767579\n",
       "18        19.0             432.556909           432.751669\n",
       "19        20.0             295.037939           295.126391\n",
       "20        21.0             203.691959           203.732604\n",
       "21        22.0             143.127973           143.146972\n",
       "22        23.0             103.085315           103.094412\n",
       "23        24.0              76.723544            76.728042\n",
       "24        25.0              59.482362            59.484678\n",
       "25        26.0              48.321575            48.322824\n",
       "26        27.0              41.214383            41.215091\n",
       "27        28.0              36.809589            36.810009\n",
       "28        29.0              34.206393            34.206652\n",
       "29        30.0              32.804262            32.804428"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "unjusted_calculated_by_pandas = series[name].ewm(span=period,adjust = False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "当参数adjusted= False的情况下，我们自己构建的计算EWM函数与直接调用pandas里面的EWM功能之差为 ： 0.0\n"
     ]
    }
   ],
   "source": [
    "print(\"当参数adjusted= False的情况下，我们自己构建的计算EWM函数与直接调用pandas里面的EWM功能之差为 ：\" ,\n",
    "      np.sum(series[name+\"_ewma_unadj\"] -unjusted_calculated_by_pandas))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "justed_calculated_by_pandas = series[name].ewm(span=period,adjust = True).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "当参数adjusted= True的情况下，我们自己构建的计算EWM函数与直接调用pandas里面的EWM功能之差为 ： 2.318145675417327e-13\n"
     ]
    }
   ],
   "source": [
    "print(\"当参数adjusted= True的情况下，我们自己构建的计算EWM函数与直接调用pandas里面的EWM功能之差为 ：\" ,\n",
    "      np.sum(series[name+\"_ewma_adj\"] -justed_calculated_by_pandas))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1.000000\n",
       "1       1.600000\n",
       "2       2.263158\n",
       "3       2.984615\n",
       "4       3.758294\n",
       "5       4.577444\n",
       "6       5.435163\n",
       "7       6.324822\n",
       "8       7.240363\n",
       "9       8.176476\n",
       "10      9.128659\n",
       "11     10.093207\n",
       "12     11.067142\n",
       "13     12.048122\n",
       "14     13.034333\n",
       "15     14.024396\n",
       "16     15.017272\n",
       "17    639.767579\n",
       "18    432.751669\n",
       "19    295.126391\n",
       "20    203.732604\n",
       "21    143.146972\n",
       "22    103.094412\n",
       "23     76.728042\n",
       "24     59.484678\n",
       "25     48.322824\n",
       "26     41.215091\n",
       "27     36.810009\n",
       "28     34.206652\n",
       "29     32.804428\n",
       "Name: fake_price, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "justed_calculated_by_pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# import talib\n",
    "# import numpy as np\n",
    "# talib.EMA(np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 , 10.0,11.0,12.0,13.0,14.0,15.0,16.0,17.0,1888.0,19.0,20.0,21.0,22.0,23.0,24.0,25.0,26.0,27.0,28.0,29.0,30.0]),5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>fake_price</th>\n",
       "      <th>fake_price_ewma_unadj</th>\n",
       "      <th>fake_price_ewma_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.888889</td>\n",
       "      <td>2.263158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.592593</td>\n",
       "      <td>2.984615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.395062</td>\n",
       "      <td>3.758294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.263374</td>\n",
       "      <td>4.577444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.175583</td>\n",
       "      <td>5.435163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.117055</td>\n",
       "      <td>6.324822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.078037</td>\n",
       "      <td>7.240363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.052025</td>\n",
       "      <td>8.176476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.034683</td>\n",
       "      <td>9.128659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.023122</td>\n",
       "      <td>10.093207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.015415</td>\n",
       "      <td>11.067142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.010276</td>\n",
       "      <td>12.048122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.006851</td>\n",
       "      <td>13.034333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.004567</td>\n",
       "      <td>14.024396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.003045</td>\n",
       "      <td>15.017272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1888.0</td>\n",
       "      <td>639.335363</td>\n",
       "      <td>639.767579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>19.0</td>\n",
       "      <td>432.556909</td>\n",
       "      <td>432.751669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>20.0</td>\n",
       "      <td>295.037939</td>\n",
       "      <td>295.126391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>21.0</td>\n",
       "      <td>203.691959</td>\n",
       "      <td>203.732604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>22.0</td>\n",
       "      <td>143.127973</td>\n",
       "      <td>143.146972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>23.0</td>\n",
       "      <td>103.085315</td>\n",
       "      <td>103.094412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>24.0</td>\n",
       "      <td>76.723544</td>\n",
       "      <td>76.728042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>25.0</td>\n",
       "      <td>59.482362</td>\n",
       "      <td>59.484678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>26.0</td>\n",
       "      <td>48.321575</td>\n",
       "      <td>48.322824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>27.0</td>\n",
       "      <td>41.214383</td>\n",
       "      <td>41.215091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>28.0</td>\n",
       "      <td>36.809589</td>\n",
       "      <td>36.810009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34.206393</td>\n",
       "      <td>34.206652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>30.0</td>\n",
       "      <td>32.804262</td>\n",
       "      <td>32.804428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fake_price  fake_price_ewma_unadj  fake_price_ewma_adj\n",
       "0          1.0               1.000000             1.000000\n",
       "1          2.0               1.333333             1.600000\n",
       "2          3.0               1.888889             2.263158\n",
       "3          4.0               2.592593             2.984615\n",
       "4          5.0               3.395062             3.758294\n",
       "5          6.0               4.263374             4.577444\n",
       "6          7.0               5.175583             5.435163\n",
       "7          8.0               6.117055             6.324822\n",
       "8          9.0               7.078037             7.240363\n",
       "9         10.0               8.052025             8.176476\n",
       "10        11.0               9.034683             9.128659\n",
       "11        12.0              10.023122            10.093207\n",
       "12        13.0              11.015415            11.067142\n",
       "13        14.0              12.010276            12.048122\n",
       "14        15.0              13.006851            13.034333\n",
       "15        16.0              14.004567            14.024396\n",
       "16        17.0              15.003045            15.017272\n",
       "17      1888.0             639.335363           639.767579\n",
       "18        19.0             432.556909           432.751669\n",
       "19        20.0             295.037939           295.126391\n",
       "20        21.0             203.691959           203.732604\n",
       "21        22.0             143.127973           143.146972\n",
       "22        23.0             103.085315           103.094412\n",
       "23        24.0              76.723544            76.728042\n",
       "24        25.0              59.482362            59.484678\n",
       "25        26.0              48.321575            48.322824\n",
       "26        27.0              41.214383            41.215091\n",
       "27        28.0              36.809589            36.810009\n",
       "28        29.0              34.206393            34.206652\n",
       "29        30.0              32.804262            32.804428"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "series[\"ema5_adj\"]= pd.Series.ewm(series[\"fake_price\"],span=5 , adjust = True).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fake_price</th>\n",
       "      <th>fake_price_ewma_unadj</th>\n",
       "      <th>fake_price_ewma_adj</th>\n",
       "      <th>ema5_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.888889</td>\n",
       "      <td>2.263158</td>\n",
       "      <td>2.263158</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.592593</td>\n",
       "      <td>2.984615</td>\n",
       "      <td>2.984615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.395062</td>\n",
       "      <td>3.758294</td>\n",
       "      <td>3.758294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.263374</td>\n",
       "      <td>4.577444</td>\n",
       "      <td>4.577444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.175583</td>\n",
       "      <td>5.435163</td>\n",
       "      <td>5.435163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.117055</td>\n",
       "      <td>6.324822</td>\n",
       "      <td>6.324822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.078037</td>\n",
       "      <td>7.240363</td>\n",
       "      <td>7.240363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.052025</td>\n",
       "      <td>8.176476</td>\n",
       "      <td>8.176476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.034683</td>\n",
       "      <td>9.128659</td>\n",
       "      <td>9.128659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.023122</td>\n",
       "      <td>10.093207</td>\n",
       "      <td>10.093207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.015415</td>\n",
       "      <td>11.067142</td>\n",
       "      <td>11.067142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.010276</td>\n",
       "      <td>12.048122</td>\n",
       "      <td>12.048122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.006851</td>\n",
       "      <td>13.034333</td>\n",
       "      <td>13.034333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.004567</td>\n",
       "      <td>14.024396</td>\n",
       "      <td>14.024396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.003045</td>\n",
       "      <td>15.017272</td>\n",
       "      <td>15.017272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1888.0</td>\n",
       "      <td>639.335363</td>\n",
       "      <td>639.767579</td>\n",
       "      <td>639.767579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>19.0</td>\n",
       "      <td>432.556909</td>\n",
       "      <td>432.751669</td>\n",
       "      <td>432.751669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>20.0</td>\n",
       "      <td>295.037939</td>\n",
       "      <td>295.126391</td>\n",
       "      <td>295.126391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>21.0</td>\n",
       "      <td>203.691959</td>\n",
       "      <td>203.732604</td>\n",
       "      <td>203.732604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>22.0</td>\n",
       "      <td>143.127973</td>\n",
       "      <td>143.146972</td>\n",
       "      <td>143.146972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>23.0</td>\n",
       "      <td>103.085315</td>\n",
       "      <td>103.094412</td>\n",
       "      <td>103.094412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>24.0</td>\n",
       "      <td>76.723544</td>\n",
       "      <td>76.728042</td>\n",
       "      <td>76.728042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>25.0</td>\n",
       "      <td>59.482362</td>\n",
       "      <td>59.484678</td>\n",
       "      <td>59.484678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>26.0</td>\n",
       "      <td>48.321575</td>\n",
       "      <td>48.322824</td>\n",
       "      <td>48.322824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>27.0</td>\n",
       "      <td>41.214383</td>\n",
       "      <td>41.215091</td>\n",
       "      <td>41.215091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>28.0</td>\n",
       "      <td>36.809589</td>\n",
       "      <td>36.810009</td>\n",
       "      <td>36.810009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34.206393</td>\n",
       "      <td>34.206652</td>\n",
       "      <td>34.206652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>30.0</td>\n",
       "      <td>32.804262</td>\n",
       "      <td>32.804428</td>\n",
       "      <td>32.804428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fake_price  fake_price_ewma_unadj  fake_price_ewma_adj    ema5_adj\n",
       "0          1.0               1.000000             1.000000    1.000000\n",
       "1          2.0               1.333333             1.600000    1.600000\n",
       "2          3.0               1.888889             2.263158    2.263158\n",
       "3          4.0               2.592593             2.984615    2.984615\n",
       "4          5.0               3.395062             3.758294    3.758294\n",
       "5          6.0               4.263374             4.577444    4.577444\n",
       "6          7.0               5.175583             5.435163    5.435163\n",
       "7          8.0               6.117055             6.324822    6.324822\n",
       "8          9.0               7.078037             7.240363    7.240363\n",
       "9         10.0               8.052025             8.176476    8.176476\n",
       "10        11.0               9.034683             9.128659    9.128659\n",
       "11        12.0              10.023122            10.093207   10.093207\n",
       "12        13.0              11.015415            11.067142   11.067142\n",
       "13        14.0              12.010276            12.048122   12.048122\n",
       "14        15.0              13.006851            13.034333   13.034333\n",
       "15        16.0              14.004567            14.024396   14.024396\n",
       "16        17.0              15.003045            15.017272   15.017272\n",
       "17      1888.0             639.335363           639.767579  639.767579\n",
       "18        19.0             432.556909           432.751669  432.751669\n",
       "19        20.0             295.037939           295.126391  295.126391\n",
       "20        21.0             203.691959           203.732604  203.732604\n",
       "21        22.0             143.127973           143.146972  143.146972\n",
       "22        23.0             103.085315           103.094412  103.094412\n",
       "23        24.0              76.723544            76.728042   76.728042\n",
       "24        25.0              59.482362            59.484678   59.484678\n",
       "25        26.0              48.321575            48.322824   48.322824\n",
       "26        27.0              41.214383            41.215091   41.215091\n",
       "27        28.0              36.809589            36.810009   36.810009\n",
       "28        29.0              34.206393            34.206652   34.206652\n",
       "29        30.0              32.804262            32.804428   32.804428"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "series[\"ema5_unadj\"]= pd.Series.ewm(series[\"fake_price\"],span=5 , adjust = False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "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>fake_price</th>\n",
       "      <th>fake_price_ewma_unadj</th>\n",
       "      <th>fake_price_ewma_adj</th>\n",
       "      <th>ema5_adj</th>\n",
       "      <th>ema5_unadj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.888889</td>\n",
       "      <td>2.263158</td>\n",
       "      <td>2.263158</td>\n",
       "      <td>1.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.592593</td>\n",
       "      <td>2.984615</td>\n",
       "      <td>2.984615</td>\n",
       "      <td>2.592593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.395062</td>\n",
       "      <td>3.758294</td>\n",
       "      <td>3.758294</td>\n",
       "      <td>3.395062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.263374</td>\n",
       "      <td>4.577444</td>\n",
       "      <td>4.577444</td>\n",
       "      <td>4.263374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.175583</td>\n",
       "      <td>5.435163</td>\n",
       "      <td>5.435163</td>\n",
       "      <td>5.175583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.117055</td>\n",
       "      <td>6.324822</td>\n",
       "      <td>6.324822</td>\n",
       "      <td>6.117055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.078037</td>\n",
       "      <td>7.240363</td>\n",
       "      <td>7.240363</td>\n",
       "      <td>7.078037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.052025</td>\n",
       "      <td>8.176476</td>\n",
       "      <td>8.176476</td>\n",
       "      <td>8.052025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.034683</td>\n",
       "      <td>9.128659</td>\n",
       "      <td>9.128659</td>\n",
       "      <td>9.034683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.023122</td>\n",
       "      <td>10.093207</td>\n",
       "      <td>10.093207</td>\n",
       "      <td>10.023122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.015415</td>\n",
       "      <td>11.067142</td>\n",
       "      <td>11.067142</td>\n",
       "      <td>11.015415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.010276</td>\n",
       "      <td>12.048122</td>\n",
       "      <td>12.048122</td>\n",
       "      <td>12.010276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.006851</td>\n",
       "      <td>13.034333</td>\n",
       "      <td>13.034333</td>\n",
       "      <td>13.006851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.004567</td>\n",
       "      <td>14.024396</td>\n",
       "      <td>14.024396</td>\n",
       "      <td>14.004567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.003045</td>\n",
       "      <td>15.017272</td>\n",
       "      <td>15.017272</td>\n",
       "      <td>15.003045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1888.0</td>\n",
       "      <td>639.335363</td>\n",
       "      <td>639.767579</td>\n",
       "      <td>639.767579</td>\n",
       "      <td>639.335363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>19.0</td>\n",
       "      <td>432.556909</td>\n",
       "      <td>432.751669</td>\n",
       "      <td>432.751669</td>\n",
       "      <td>432.556909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>20.0</td>\n",
       "      <td>295.037939</td>\n",
       "      <td>295.126391</td>\n",
       "      <td>295.126391</td>\n",
       "      <td>295.037939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>21.0</td>\n",
       "      <td>203.691959</td>\n",
       "      <td>203.732604</td>\n",
       "      <td>203.732604</td>\n",
       "      <td>203.691959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>22.0</td>\n",
       "      <td>143.127973</td>\n",
       "      <td>143.146972</td>\n",
       "      <td>143.146972</td>\n",
       "      <td>143.127973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>23.0</td>\n",
       "      <td>103.085315</td>\n",
       "      <td>103.094412</td>\n",
       "      <td>103.094412</td>\n",
       "      <td>103.085315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>24.0</td>\n",
       "      <td>76.723544</td>\n",
       "      <td>76.728042</td>\n",
       "      <td>76.728042</td>\n",
       "      <td>76.723544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>25.0</td>\n",
       "      <td>59.482362</td>\n",
       "      <td>59.484678</td>\n",
       "      <td>59.484678</td>\n",
       "      <td>59.482362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>26.0</td>\n",
       "      <td>48.321575</td>\n",
       "      <td>48.322824</td>\n",
       "      <td>48.322824</td>\n",
       "      <td>48.321575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>27.0</td>\n",
       "      <td>41.214383</td>\n",
       "      <td>41.215091</td>\n",
       "      <td>41.215091</td>\n",
       "      <td>41.214383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>28.0</td>\n",
       "      <td>36.809589</td>\n",
       "      <td>36.810009</td>\n",
       "      <td>36.810009</td>\n",
       "      <td>36.809589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34.206393</td>\n",
       "      <td>34.206652</td>\n",
       "      <td>34.206652</td>\n",
       "      <td>34.206393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>30.0</td>\n",
       "      <td>32.804262</td>\n",
       "      <td>32.804428</td>\n",
       "      <td>32.804428</td>\n",
       "      <td>32.804262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fake_price  fake_price_ewma_unadj  fake_price_ewma_adj    ema5_adj  \\\n",
       "0          1.0               1.000000             1.000000    1.000000   \n",
       "1          2.0               1.333333             1.600000    1.600000   \n",
       "2          3.0               1.888889             2.263158    2.263158   \n",
       "3          4.0               2.592593             2.984615    2.984615   \n",
       "4          5.0               3.395062             3.758294    3.758294   \n",
       "5          6.0               4.263374             4.577444    4.577444   \n",
       "6          7.0               5.175583             5.435163    5.435163   \n",
       "7          8.0               6.117055             6.324822    6.324822   \n",
       "8          9.0               7.078037             7.240363    7.240363   \n",
       "9         10.0               8.052025             8.176476    8.176476   \n",
       "10        11.0               9.034683             9.128659    9.128659   \n",
       "11        12.0              10.023122            10.093207   10.093207   \n",
       "12        13.0              11.015415            11.067142   11.067142   \n",
       "13        14.0              12.010276            12.048122   12.048122   \n",
       "14        15.0              13.006851            13.034333   13.034333   \n",
       "15        16.0              14.004567            14.024396   14.024396   \n",
       "16        17.0              15.003045            15.017272   15.017272   \n",
       "17      1888.0             639.335363           639.767579  639.767579   \n",
       "18        19.0             432.556909           432.751669  432.751669   \n",
       "19        20.0             295.037939           295.126391  295.126391   \n",
       "20        21.0             203.691959           203.732604  203.732604   \n",
       "21        22.0             143.127973           143.146972  143.146972   \n",
       "22        23.0             103.085315           103.094412  103.094412   \n",
       "23        24.0              76.723544            76.728042   76.728042   \n",
       "24        25.0              59.482362            59.484678   59.484678   \n",
       "25        26.0              48.321575            48.322824   48.322824   \n",
       "26        27.0              41.214383            41.215091   41.215091   \n",
       "27        28.0              36.809589            36.810009   36.810009   \n",
       "28        29.0              34.206393            34.206652   34.206652   \n",
       "29        30.0              32.804262            32.804428   32.804428   \n",
       "\n",
       "    ema5_unadj  \n",
       "0     1.000000  \n",
       "1     1.333333  \n",
       "2     1.888889  \n",
       "3     2.592593  \n",
       "4     3.395062  \n",
       "5     4.263374  \n",
       "6     5.175583  \n",
       "7     6.117055  \n",
       "8     7.078037  \n",
       "9     8.052025  \n",
       "10    9.034683  \n",
       "11   10.023122  \n",
       "12   11.015415  \n",
       "13   12.010276  \n",
       "14   13.006851  \n",
       "15   14.004567  \n",
       "16   15.003045  \n",
       "17  639.335363  \n",
       "18  432.556909  \n",
       "19  295.037939  \n",
       "20  203.691959  \n",
       "21  143.127973  \n",
       "22  103.085315  \n",
       "23   76.723544  \n",
       "24   59.482362  \n",
       "25   48.321575  \n",
       "26   41.214383  \n",
       "27   36.809589  \n",
       "28   34.206393  \n",
       "29   32.804262  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "series[\"ema12_adj\"]= pd.Series.ewm(series[\"fake_price\"],span=12 , adjust = True).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "series[\"ema26_adj\"]= pd.Series.ewm(series[\"fake_price\"],span=26 , adjust = True).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# series[\"ema9_adj\"]= pd.Series.ewm(series[\"fake_price\"],span=9 , adjust = True).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fake_price</th>\n",
       "      <th>fake_price_ewma_unadj</th>\n",
       "      <th>fake_price_ewma_adj</th>\n",
       "      <th>ema5_adj</th>\n",
       "      <th>ema5_unadj</th>\n",
       "      <th>ema12_adj</th>\n",
       "      <th>ema26_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.541667</td>\n",
       "      <td>1.519231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.888889</td>\n",
       "      <td>2.263158</td>\n",
       "      <td>2.263158</td>\n",
       "      <td>1.888889</td>\n",
       "      <td>2.110855</td>\n",
       "      <td>2.051257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.592593</td>\n",
       "      <td>2.984615</td>\n",
       "      <td>2.984615</td>\n",
       "      <td>2.592593</td>\n",
       "      <td>2.707184</td>\n",
       "      <td>2.596040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.395062</td>\n",
       "      <td>3.758294</td>\n",
       "      <td>3.758294</td>\n",
       "      <td>3.395062</td>\n",
       "      <td>3.330134</td>\n",
       "      <td>3.153528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.263374</td>\n",
       "      <td>4.577444</td>\n",
       "      <td>4.577444</td>\n",
       "      <td>4.263374</td>\n",
       "      <td>3.979052</td>\n",
       "      <td>3.723654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.175583</td>\n",
       "      <td>5.435163</td>\n",
       "      <td>5.435163</td>\n",
       "      <td>5.175583</td>\n",
       "      <td>4.653166</td>\n",
       "      <td>4.306335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.117055</td>\n",
       "      <td>6.324822</td>\n",
       "      <td>6.324822</td>\n",
       "      <td>6.117055</td>\n",
       "      <td>5.351599</td>\n",
       "      <td>4.901476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.078037</td>\n",
       "      <td>7.240363</td>\n",
       "      <td>7.240363</td>\n",
       "      <td>7.078037</td>\n",
       "      <td>6.073382</td>\n",
       "      <td>5.508967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.052025</td>\n",
       "      <td>8.176476</td>\n",
       "      <td>8.176476</td>\n",
       "      <td>8.052025</td>\n",
       "      <td>6.817475</td>\n",
       "      <td>6.128686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.034683</td>\n",
       "      <td>9.128659</td>\n",
       "      <td>9.128659</td>\n",
       "      <td>9.034683</td>\n",
       "      <td>7.582776</td>\n",
       "      <td>6.760497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.023122</td>\n",
       "      <td>10.093207</td>\n",
       "      <td>10.093207</td>\n",
       "      <td>10.023122</td>\n",
       "      <td>8.368144</td>\n",
       "      <td>7.404253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.015415</td>\n",
       "      <td>11.067142</td>\n",
       "      <td>11.067142</td>\n",
       "      <td>11.015415</td>\n",
       "      <td>9.172410</td>\n",
       "      <td>8.059793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.010276</td>\n",
       "      <td>12.048122</td>\n",
       "      <td>12.048122</td>\n",
       "      <td>12.010276</td>\n",
       "      <td>9.994395</td>\n",
       "      <td>8.726949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.006851</td>\n",
       "      <td>13.034333</td>\n",
       "      <td>13.034333</td>\n",
       "      <td>13.006851</td>\n",
       "      <td>10.832919</td>\n",
       "      <td>9.405540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.004567</td>\n",
       "      <td>14.024396</td>\n",
       "      <td>14.024396</td>\n",
       "      <td>14.004567</td>\n",
       "      <td>11.686820</td>\n",
       "      <td>10.095374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.003045</td>\n",
       "      <td>15.017272</td>\n",
       "      <td>15.017272</td>\n",
       "      <td>15.003045</td>\n",
       "      <td>12.554958</td>\n",
       "      <td>10.796254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1888.0</td>\n",
       "      <td>639.335363</td>\n",
       "      <td>639.767579</td>\n",
       "      <td>639.767579</td>\n",
       "      <td>639.335363</td>\n",
       "      <td>316.092174</td>\n",
       "      <td>196.260675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>19.0</td>\n",
       "      <td>432.556909</td>\n",
       "      <td>432.751669</td>\n",
       "      <td>432.751669</td>\n",
       "      <td>432.556909</td>\n",
       "      <td>268.390080</td>\n",
       "      <td>179.170181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>20.0</td>\n",
       "      <td>295.037939</td>\n",
       "      <td>295.126391</td>\n",
       "      <td>295.126391</td>\n",
       "      <td>295.037939</td>\n",
       "      <td>228.773861</td>\n",
       "      <td>164.159223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>21.0</td>\n",
       "      <td>203.691959</td>\n",
       "      <td>203.732604</td>\n",
       "      <td>203.732604</td>\n",
       "      <td>203.691959</td>\n",
       "      <td>195.821643</td>\n",
       "      <td>150.925975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>22.0</td>\n",
       "      <td>143.127973</td>\n",
       "      <td>143.146972</td>\n",
       "      <td>143.146972</td>\n",
       "      <td>143.127973</td>\n",
       "      <td>168.384468</td>\n",
       "      <td>139.223309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>23.0</td>\n",
       "      <td>103.085315</td>\n",
       "      <td>103.094412</td>\n",
       "      <td>103.094412</td>\n",
       "      <td>103.085315</td>\n",
       "      <td>145.527448</td>\n",
       "      <td>128.846916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>24.0</td>\n",
       "      <td>76.723544</td>\n",
       "      <td>76.728042</td>\n",
       "      <td>76.728042</td>\n",
       "      <td>76.723544</td>\n",
       "      <td>126.485378</td>\n",
       "      <td>119.626411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>25.0</td>\n",
       "      <td>59.482362</td>\n",
       "      <td>59.484678</td>\n",
       "      <td>59.484678</td>\n",
       "      <td>59.482362</td>\n",
       "      <td>110.628774</td>\n",
       "      <td>111.418553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>26.0</td>\n",
       "      <td>48.321575</td>\n",
       "      <td>48.322824</td>\n",
       "      <td>48.322824</td>\n",
       "      <td>48.321575</td>\n",
       "      <td>97.437580</td>\n",
       "      <td>104.102048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>27.0</td>\n",
       "      <td>41.214383</td>\n",
       "      <td>41.215091</td>\n",
       "      <td>41.215091</td>\n",
       "      <td>41.214383</td>\n",
       "      <td>86.480574</td>\n",
       "      <td>97.573497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>28.0</td>\n",
       "      <td>36.809589</td>\n",
       "      <td>36.810009</td>\n",
       "      <td>36.810009</td>\n",
       "      <td>36.809589</td>\n",
       "      <td>77.399086</td>\n",
       "      <td>91.744210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34.206393</td>\n",
       "      <td>34.206652</td>\n",
       "      <td>34.206652</td>\n",
       "      <td>34.206393</td>\n",
       "      <td>69.894000</td>\n",
       "      <td>86.537688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>30.0</td>\n",
       "      <td>32.804262</td>\n",
       "      <td>32.804428</td>\n",
       "      <td>32.804428</td>\n",
       "      <td>32.804262</td>\n",
       "      <td>63.715311</td>\n",
       "      <td>81.887597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fake_price  fake_price_ewma_unadj  fake_price_ewma_adj    ema5_adj  \\\n",
       "0          1.0               1.000000             1.000000    1.000000   \n",
       "1          2.0               1.333333             1.600000    1.600000   \n",
       "2          3.0               1.888889             2.263158    2.263158   \n",
       "3          4.0               2.592593             2.984615    2.984615   \n",
       "4          5.0               3.395062             3.758294    3.758294   \n",
       "5          6.0               4.263374             4.577444    4.577444   \n",
       "6          7.0               5.175583             5.435163    5.435163   \n",
       "7          8.0               6.117055             6.324822    6.324822   \n",
       "8          9.0               7.078037             7.240363    7.240363   \n",
       "9         10.0               8.052025             8.176476    8.176476   \n",
       "10        11.0               9.034683             9.128659    9.128659   \n",
       "11        12.0              10.023122            10.093207   10.093207   \n",
       "12        13.0              11.015415            11.067142   11.067142   \n",
       "13        14.0              12.010276            12.048122   12.048122   \n",
       "14        15.0              13.006851            13.034333   13.034333   \n",
       "15        16.0              14.004567            14.024396   14.024396   \n",
       "16        17.0              15.003045            15.017272   15.017272   \n",
       "17      1888.0             639.335363           639.767579  639.767579   \n",
       "18        19.0             432.556909           432.751669  432.751669   \n",
       "19        20.0             295.037939           295.126391  295.126391   \n",
       "20        21.0             203.691959           203.732604  203.732604   \n",
       "21        22.0             143.127973           143.146972  143.146972   \n",
       "22        23.0             103.085315           103.094412  103.094412   \n",
       "23        24.0              76.723544            76.728042   76.728042   \n",
       "24        25.0              59.482362            59.484678   59.484678   \n",
       "25        26.0              48.321575            48.322824   48.322824   \n",
       "26        27.0              41.214383            41.215091   41.215091   \n",
       "27        28.0              36.809589            36.810009   36.810009   \n",
       "28        29.0              34.206393            34.206652   34.206652   \n",
       "29        30.0              32.804262            32.804428   32.804428   \n",
       "\n",
       "    ema5_unadj   ema12_adj   ema26_adj  \n",
       "0     1.000000    1.000000    1.000000  \n",
       "1     1.333333    1.541667    1.519231  \n",
       "2     1.888889    2.110855    2.051257  \n",
       "3     2.592593    2.707184    2.596040  \n",
       "4     3.395062    3.330134    3.153528  \n",
       "5     4.263374    3.979052    3.723654  \n",
       "6     5.175583    4.653166    4.306335  \n",
       "7     6.117055    5.351599    4.901476  \n",
       "8     7.078037    6.073382    5.508967  \n",
       "9     8.052025    6.817475    6.128686  \n",
       "10    9.034683    7.582776    6.760497  \n",
       "11   10.023122    8.368144    7.404253  \n",
       "12   11.015415    9.172410    8.059793  \n",
       "13   12.010276    9.994395    8.726949  \n",
       "14   13.006851   10.832919    9.405540  \n",
       "15   14.004567   11.686820   10.095374  \n",
       "16   15.003045   12.554958   10.796254  \n",
       "17  639.335363  316.092174  196.260675  \n",
       "18  432.556909  268.390080  179.170181  \n",
       "19  295.037939  228.773861  164.159223  \n",
       "20  203.691959  195.821643  150.925975  \n",
       "21  143.127973  168.384468  139.223309  \n",
       "22  103.085315  145.527448  128.846916  \n",
       "23   76.723544  126.485378  119.626411  \n",
       "24   59.482362  110.628774  111.418553  \n",
       "25   48.321575   97.437580  104.102048  \n",
       "26   41.214383   86.480574   97.573497  \n",
       "27   36.809589   77.399086   91.744210  \n",
       "28   34.206393   69.894000   86.537688  \n",
       "29   32.804262   63.715311   81.887597  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "12_ema = fast (快线) ， 26_ema slow(慢线)\n",
    "1）MACD Line(MACD线)  12ema-26 ema\n",
    "2）Signal Line（信号线） =  9ema of the MACD Line(信号线)\n",
    "3）Histogram(柱状图) = MACD Line - Signal Line\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "LXOD_MACD=series[\"ema12_adj\"]-series[\"ema26_adj\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(LXOD_MACD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.000000\n",
       "1       0.022436\n",
       "2       0.059598\n",
       "3       0.111144\n",
       "4       0.176605\n",
       "5       0.255398\n",
       "6       0.346831\n",
       "7       0.450122\n",
       "8       0.564415\n",
       "9       0.688789\n",
       "10      0.822279\n",
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       "12      1.112617\n",
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       "dtype: float64"
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     "execution_count": 41,
     "metadata": {},
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   "source": [
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   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "LXOD_Signal_Line=LXOD_MACD.ewm(span=9,adjust=True).mean()"
   ]
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   "execution_count": 43,
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   "outputs": [
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  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "Histogram=LXOD_MACD-LXOD_Signal_Line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "type(Histogram)"
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  {
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   "execution_count": 46,
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   "outputs": [
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       "              ts_code   open   high    low  close  pre_close  change  pct_chg  \\\n",
       "trade_date                                                                      \n",
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       "\n",
       "                   vol       amount  \n",
       "trade_date                           \n",
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    "df_whole.head()"
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  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_whole[\"ema12_adj\"]=pd.Series.ewm(df_whole[\"close\"],span=12 , adjust = True).mean()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
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       "              ts_code   open   high    low  close  pre_close  change  pct_chg  \\\n",
       "trade_date                                                                      \n",
       "2018-10-08  000001.SZ  10.70  10.79  10.45  10.45      11.05   -0.60  -5.4299   \n",
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       "\n",
       "                   vol       amount  ema12_adj  \n",
       "trade_date                                      \n",
       "2018-10-08  1686358.52  1793455.283  10.450000  \n",
       "2018-10-09  1064084.26  1117946.550  10.509583  \n",
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     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_whole[\"ema26_adj\"]=pd.Series.ewm(df_whole[\"close\"],span=26 , adjust = True).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
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       "              ts_code   open   high    low  close  pre_close  change  pct_chg  \\\n",
       "trade_date                                                                      \n",
       "2018-10-08  000001.SZ  10.70  10.79  10.45  10.45      11.05   -0.60  -5.4299   \n",
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       "2018-10-12  000001.SZ   9.97  10.34   9.87  10.30       9.86    0.44   4.4625   \n",
       "\n",
       "                   vol       amount  ema12_adj  ema26_adj  \n",
       "trade_date                                                 \n",
       "2018-10-08  1686358.52  1793455.283  10.450000  10.450000  \n",
       "2018-10-09  1064084.26  1117946.550  10.509583  10.507115  \n",
       "2018-10-10   995200.08  1045666.180  10.486328  10.486594  \n",
       "2018-10-11  1995143.83  1994186.611  10.288621  10.311426  \n",
       "2018-10-12  1516810.28  1532651.400  10.291712  10.308776  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_whole.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "LXOD_MACD=df_whole[\"ema12_adj\"]-df_whole[\"ema26_adj\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trade_date\n",
       "2018-10-08    0.000000\n",
       "2018-10-09    0.002468\n",
       "2018-10-10   -0.000266\n",
       "2018-10-11   -0.022805\n",
       "2018-10-12   -0.017064\n",
       "                ...   \n",
       "2019-09-24    0.200633\n",
       "2019-09-25    0.259148\n",
       "2019-09-26    0.298848\n",
       "2019-09-27    0.341704\n",
       "2019-09-30    0.346656\n",
       "Length: 243, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LXOD_MACD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_whole[\"signal_line\"]=pd.Series.ewm(LXOD_MACD,span=9 , adjust = True).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
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       "      <td>2018-10-08</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>10.70</td>\n",
       "      <td>10.79</td>\n",
       "      <td>10.45</td>\n",
       "      <td>10.45</td>\n",
       "      <td>11.05</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-5.4299</td>\n",
       "      <td>1686358.52</td>\n",
       "      <td>1793455.283</td>\n",
       "      <td>10.450000</td>\n",
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       "    <tr>\n",
       "      <td>2018-10-09</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>10.46</td>\n",
       "      <td>10.70</td>\n",
       "      <td>10.39</td>\n",
       "      <td>10.56</td>\n",
       "      <td>10.45</td>\n",
       "      <td>0.11</td>\n",
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       "      <td>1064084.26</td>\n",
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       "      <td>10.54</td>\n",
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       "      <td>10.38</td>\n",
       "      <td>10.45</td>\n",
       "      <td>10.56</td>\n",
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       "      <td>2018-10-11</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>10.05</td>\n",
       "      <td>10.16</td>\n",
       "      <td>9.70</td>\n",
       "      <td>9.86</td>\n",
       "      <td>10.45</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-5.6459</td>\n",
       "      <td>1995143.83</td>\n",
       "      <td>1994186.611</td>\n",
       "      <td>10.288621</td>\n",
       "      <td>10.311426</td>\n",
       "      <td>-0.007263</td>\n",
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       "    <tr>\n",
       "      <td>2018-10-12</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>9.97</td>\n",
       "      <td>10.34</td>\n",
       "      <td>9.87</td>\n",
       "      <td>10.30</td>\n",
       "      <td>9.86</td>\n",
       "      <td>0.44</td>\n",
       "      <td>4.4625</td>\n",
       "      <td>1516810.28</td>\n",
       "      <td>1532651.400</td>\n",
       "      <td>10.291712</td>\n",
       "      <td>10.308776</td>\n",
       "      <td>-0.010178</td>\n",
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       "      <td>2019-09-24</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>15.47</td>\n",
       "      <td>15.54</td>\n",
       "      <td>15.18</td>\n",
       "      <td>15.18</td>\n",
       "      <td>15.38</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-1.3004</td>\n",
       "      <td>1129103.24</td>\n",
       "      <td>1726238.272</td>\n",
       "      <td>14.830177</td>\n",
       "      <td>14.629544</td>\n",
       "      <td>0.127791</td>\n",
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       "    <tr>\n",
       "      <td>2019-09-25</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>15.20</td>\n",
       "      <td>15.85</td>\n",
       "      <td>15.19</td>\n",
       "      <td>15.75</td>\n",
       "      <td>15.18</td>\n",
       "      <td>0.57</td>\n",
       "      <td>3.7549</td>\n",
       "      <td>1938892.72</td>\n",
       "      <td>3024598.930</td>\n",
       "      <td>14.971689</td>\n",
       "      <td>14.712541</td>\n",
       "      <td>0.154062</td>\n",
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       "    <tr>\n",
       "      <td>2019-09-26</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>15.86</td>\n",
       "      <td>16.04</td>\n",
       "      <td>15.66</td>\n",
       "      <td>15.71</td>\n",
       "      <td>15.75</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.2540</td>\n",
       "      <td>1686592.58</td>\n",
       "      <td>2673096.951</td>\n",
       "      <td>15.085275</td>\n",
       "      <td>14.786427</td>\n",
       "      <td>0.183019</td>\n",
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       "    <tr>\n",
       "      <td>2019-09-27</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>15.85</td>\n",
       "      <td>16.00</td>\n",
       "      <td>15.70</td>\n",
       "      <td>15.90</td>\n",
       "      <td>15.71</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.2094</td>\n",
       "      <td>1007706.27</td>\n",
       "      <td>1598567.052</td>\n",
       "      <td>15.210617</td>\n",
       "      <td>14.868914</td>\n",
       "      <td>0.214756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>15.75</td>\n",
       "      <td>15.89</td>\n",
       "      <td>15.57</td>\n",
       "      <td>15.59</td>\n",
       "      <td>15.90</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-1.9497</td>\n",
       "      <td>1046363.17</td>\n",
       "      <td>1645828.526</td>\n",
       "      <td>15.268984</td>\n",
       "      <td>14.922328</td>\n",
       "      <td>0.241136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>243 rows × 13 columns</p>\n",
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      ],
      "text/plain": [
       "              ts_code   open   high    low  close  pre_close  change  pct_chg  \\\n",
       "trade_date                                                                      \n",
       "2018-10-08  000001.SZ  10.70  10.79  10.45  10.45      11.05   -0.60  -5.4299   \n",
       "2018-10-09  000001.SZ  10.46  10.70  10.39  10.56      10.45    0.11   1.0526   \n",
       "2018-10-10  000001.SZ  10.54  10.66  10.38  10.45      10.56   -0.11  -1.0417   \n",
       "2018-10-11  000001.SZ  10.05  10.16   9.70   9.86      10.45   -0.59  -5.6459   \n",
       "2018-10-12  000001.SZ   9.97  10.34   9.87  10.30       9.86    0.44   4.4625   \n",
       "...               ...    ...    ...    ...    ...        ...     ...      ...   \n",
       "2019-09-24  000001.SZ  15.47  15.54  15.18  15.18      15.38   -0.20  -1.3004   \n",
       "2019-09-25  000001.SZ  15.20  15.85  15.19  15.75      15.18    0.57   3.7549   \n",
       "2019-09-26  000001.SZ  15.86  16.04  15.66  15.71      15.75   -0.04  -0.2540   \n",
       "2019-09-27  000001.SZ  15.85  16.00  15.70  15.90      15.71    0.19   1.2094   \n",
       "2019-09-30  000001.SZ  15.75  15.89  15.57  15.59      15.90   -0.31  -1.9497   \n",
       "\n",
       "                   vol       amount  ema12_adj  ema26_adj  signal_line  \n",
       "trade_date                                                              \n",
       "2018-10-08  1686358.52  1793455.283  10.450000  10.450000     0.000000  \n",
       "2018-10-09  1064084.26  1117946.550  10.509583  10.507115     0.001371  \n",
       "2018-10-10   995200.08  1045666.180  10.486328  10.486594     0.000700  \n",
       "2018-10-11  1995143.83  1994186.611  10.288621  10.311426    -0.007263  \n",
       "2018-10-12  1516810.28  1532651.400  10.291712  10.308776    -0.010178  \n",
       "...                ...          ...        ...        ...          ...  \n",
       "2019-09-24  1129103.24  1726238.272  14.830177  14.629544     0.127791  \n",
       "2019-09-25  1938892.72  3024598.930  14.971689  14.712541     0.154062  \n",
       "2019-09-26  1686592.58  2673096.951  15.085275  14.786427     0.183019  \n",
       "2019-09-27  1007706.27  1598567.052  15.210617  14.868914     0.214756  \n",
       "2019-09-30  1046363.17  1645828.526  15.268984  14.922328     0.241136  \n",
       "\n",
       "[243 rows x 13 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_whole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_whole[\"LXOD_MACD\"]=LXOD_MACD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
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       "      <td>10.70</td>\n",
       "      <td>10.79</td>\n",
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       "      <td>10.45</td>\n",
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       "      <td>10.46</td>\n",
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       "      <td>10.39</td>\n",
       "      <td>10.56</td>\n",
       "      <td>10.45</td>\n",
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       "      <td>10.54</td>\n",
       "      <td>10.66</td>\n",
       "      <td>10.38</td>\n",
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       "      <td>000001.SZ</td>\n",
       "      <td>10.05</td>\n",
       "      <td>10.16</td>\n",
       "      <td>9.70</td>\n",
       "      <td>9.86</td>\n",
       "      <td>10.45</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-5.6459</td>\n",
       "      <td>1995143.83</td>\n",
       "      <td>1994186.611</td>\n",
       "      <td>10.288621</td>\n",
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       "      <td>2018-10-12</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>9.97</td>\n",
       "      <td>10.34</td>\n",
       "      <td>9.87</td>\n",
       "      <td>10.30</td>\n",
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      "text/plain": [
       "              ts_code   open   high    low  close  pre_close  change  pct_chg  \\\n",
       "trade_date                                                                      \n",
       "2018-10-08  000001.SZ  10.70  10.79  10.45  10.45      11.05   -0.60  -5.4299   \n",
       "2018-10-09  000001.SZ  10.46  10.70  10.39  10.56      10.45    0.11   1.0526   \n",
       "2018-10-10  000001.SZ  10.54  10.66  10.38  10.45      10.56   -0.11  -1.0417   \n",
       "2018-10-11  000001.SZ  10.05  10.16   9.70   9.86      10.45   -0.59  -5.6459   \n",
       "2018-10-12  000001.SZ   9.97  10.34   9.87  10.30       9.86    0.44   4.4625   \n",
       "\n",
       "                   vol       amount  ema12_adj  ema26_adj  signal_line  \\\n",
       "trade_date                                                               \n",
       "2018-10-08  1686358.52  1793455.283  10.450000  10.450000     0.000000   \n",
       "2018-10-09  1064084.26  1117946.550  10.509583  10.507115     0.001371   \n",
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       "2018-10-12  1516810.28  1532651.400  10.291712  10.308776    -0.010178   \n",
       "\n",
       "            LXOD_MACD  \n",
       "trade_date             \n",
       "2018-10-08   0.000000  \n",
       "2018-10-09   0.002468  \n",
       "2018-10-10  -0.000266  \n",
       "2018-10-11  -0.022805  \n",
       "2018-10-12  -0.017064  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_whole.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Histogram(柱状图) = MACD Line - Signal Line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_whole[\"Histo\"]=df_whole[\"LXOD_MACD\"]-df_whole[\"signal_line\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>ema12_adj</th>\n",
       "      <th>ema26_adj</th>\n",
       "      <th>signal_line</th>\n",
       "      <th>LXOD_MACD</th>\n",
       "      <th>Histo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_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",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2018-10-08</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>10.70</td>\n",
       "      <td>10.79</td>\n",
       "      <td>10.45</td>\n",
       "      <td>10.45</td>\n",
       "      <td>11.05</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-5.4299</td>\n",
       "      <td>1686358.52</td>\n",
       "      <td>1793455.283</td>\n",
       "      <td>10.450000</td>\n",
       "      <td>10.450000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <td>2018-10-09</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>10.46</td>\n",
       "      <td>10.70</td>\n",
       "      <td>10.39</td>\n",
       "      <td>10.56</td>\n",
       "      <td>10.45</td>\n",
       "      <td>0.11</td>\n",
       "      <td>1.0526</td>\n",
       "      <td>1064084.26</td>\n",
       "      <td>1117946.550</td>\n",
       "      <td>10.509583</td>\n",
       "      <td>10.507115</td>\n",
       "      <td>0.001371</td>\n",
       "      <td>0.002468</td>\n",
       "      <td>0.001097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-10-10</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>10.54</td>\n",
       "      <td>10.66</td>\n",
       "      <td>10.38</td>\n",
       "      <td>10.45</td>\n",
       "      <td>10.56</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-1.0417</td>\n",
       "      <td>995200.08</td>\n",
       "      <td>1045666.180</td>\n",
       "      <td>10.486328</td>\n",
       "      <td>10.486594</td>\n",
       "      <td>0.000700</td>\n",
       "      <td>-0.000266</td>\n",
       "      <td>-0.000966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>10.05</td>\n",
       "      <td>10.16</td>\n",
       "      <td>9.70</td>\n",
       "      <td>9.86</td>\n",
       "      <td>10.45</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-5.6459</td>\n",
       "      <td>1995143.83</td>\n",
       "      <td>1994186.611</td>\n",
       "      <td>10.288621</td>\n",
       "      <td>10.311426</td>\n",
       "      <td>-0.007263</td>\n",
       "      <td>-0.022805</td>\n",
       "      <td>-0.015543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-10-12</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>9.97</td>\n",
       "      <td>10.34</td>\n",
       "      <td>9.87</td>\n",
       "      <td>10.30</td>\n",
       "      <td>9.86</td>\n",
       "      <td>0.44</td>\n",
       "      <td>4.4625</td>\n",
       "      <td>1516810.28</td>\n",
       "      <td>1532651.400</td>\n",
       "      <td>10.291712</td>\n",
       "      <td>10.308776</td>\n",
       "      <td>-0.010178</td>\n",
       "      <td>-0.017064</td>\n",
       "      <td>-0.006886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ts_code   open   high    low  close  pre_close  change  pct_chg  \\\n",
       "trade_date                                                                      \n",
       "2018-10-08  000001.SZ  10.70  10.79  10.45  10.45      11.05   -0.60  -5.4299   \n",
       "2018-10-09  000001.SZ  10.46  10.70  10.39  10.56      10.45    0.11   1.0526   \n",
       "2018-10-10  000001.SZ  10.54  10.66  10.38  10.45      10.56   -0.11  -1.0417   \n",
       "2018-10-11  000001.SZ  10.05  10.16   9.70   9.86      10.45   -0.59  -5.6459   \n",
       "2018-10-12  000001.SZ   9.97  10.34   9.87  10.30       9.86    0.44   4.4625   \n",
       "\n",
       "                   vol       amount  ema12_adj  ema26_adj  signal_line  \\\n",
       "trade_date                                                               \n",
       "2018-10-08  1686358.52  1793455.283  10.450000  10.450000     0.000000   \n",
       "2018-10-09  1064084.26  1117946.550  10.509583  10.507115     0.001371   \n",
       "2018-10-10   995200.08  1045666.180  10.486328  10.486594     0.000700   \n",
       "2018-10-11  1995143.83  1994186.611  10.288621  10.311426    -0.007263   \n",
       "2018-10-12  1516810.28  1532651.400  10.291712  10.308776    -0.010178   \n",
       "\n",
       "            LXOD_MACD     Histo  \n",
       "trade_date                       \n",
       "2018-10-08   0.000000  0.000000  \n",
       "2018-10-09   0.002468  0.001097  \n",
       "2018-10-10  -0.000266 -0.000966  \n",
       "2018-10-11  -0.022805 -0.015543  \n",
       "2018-10-12  -0.017064 -0.006886  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_whole.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# %matplotlib\n",
    "\n",
    "# df_whole[\"ema12_adj\"].plot()\n",
    "# df_whole[\"ema26_adj\"].plot()\n",
    "# df_whole[\"close\"].plot()\n",
    "# df_whole[\"signal_line\"].plot()\n",
    "\n",
    "# plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: MacOSX\n"
     ]
    }
   ],
   "source": [
    "%matplotlib\n",
    "ax1= plt.subplot2grid((9,10),(0,0),rowspan = 7,colspan=10)\n",
    "ax2= plt.subplot2grid((9,10),(7,0),rowspan = 7,colspan=10,sharex=ax1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/william/opt/anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/converter.py:103: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fced8a3ac10>]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax1.plot(df_whole.index,df_whole[\"close\"],label=\"close price\")\n",
    "ax1.plot(df_whole.index,df_whole[\"ema12_adj\"],label=\"ema12\")\n",
    "ax1.plot(df_whole.index,df_whole[\"ema26_adj\"],label=\"ema26\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fced899c1d0>]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax2.bar(df_whole.index,df_whole[\"Histo\"],label=\"MACD_HISTO\")\n",
    "ax2.plot(df_whole.index,df_whole[\"signal_line\"],label=\"signal line\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax1.legend()\n",
    "ax2.legend(loc=\"upper left\")\n",
    "\n",
    "from matplotlib.widgets import Cursor\n",
    "\n",
    "cursor = Cursor(ax1,useblit=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不要禁锢于这些说法！这些只是前人的知识，与经验而谈的指标\n",
    "\n",
    "*概念辨析：\n",
    "\n",
    "1.\"signal_line\"（信号线）/DEA（离差平均值）= ema（dif,9）\n",
    "2.MACD/ DIF(离差值)=ema(close,12)-ema(close,26)\n",
    "3.Histogram/Bar = (DIF - DEA)*2\n",
    "\n",
    "*国内外经常会混淆的说法\n",
    "\n",
    "快线/慢线\n",
    "\n",
    "1.ema(close,12)比 ema(close,26)要快\n",
    "2.DIF比DEA要快\n",
    "\n",
    "\n",
    "tips:试试用pure python 实现以上功能，可以用apply(),lambda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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