{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "75629605-1f71-4f90-b46b-243052fc8a05",
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.qlib_scripts.dump_bin import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "18a16bed-cda3-472d-86c8-35c674219c56",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "784cb0ea-1699-4261-9c4a-dd75af648b07",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tinysoft = pd.read_csv('tinysoft-data/行情数据/20210101-20220531/k-daily/NE830799.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4b10c52d-70cc-4a4b-963d-874444b83e06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['StockID',\n",
       " 'date',\n",
       " 'close',\n",
       " 'vol',\n",
       " 'low',\n",
       " 'yclose',\n",
       " 'cjbs',\n",
       " 'amount',\n",
       " 'high',\n",
       " 'open',\n",
       " 'IsST',\n",
       " 'Factor',\n",
       " 'PctChg',\n",
       " 'FloatShares']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tinysoft.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "841bf388-96f3-4732-8a92-c3740af96b15",
   "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>StockID</th>\n",
       "      <th>date</th>\n",
       "      <th>close</th>\n",
       "      <th>vol</th>\n",
       "      <th>low</th>\n",
       "      <th>yclose</th>\n",
       "      <th>cjbs</th>\n",
       "      <th>amount</th>\n",
       "      <th>high</th>\n",
       "      <th>open</th>\n",
       "      <th>IsST</th>\n",
       "      <th>Factor</th>\n",
       "      <th>PctChg</th>\n",
       "      <th>FloatShares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NE830799</td>\n",
       "      <td>20210104</td>\n",
       "      <td>22.98</td>\n",
       "      <td>208835.0</td>\n",
       "      <td>22.90</td>\n",
       "      <td>23.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5082436.35</td>\n",
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       "      <td>23.00</td>\n",
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       "      <td>0.104658</td>\n",
       "      <td>-0.086957</td>\n",
       "      <td>117222839.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NE830799</td>\n",
       "      <td>20210105</td>\n",
       "      <td>23.12</td>\n",
       "      <td>59729.0</td>\n",
       "      <td>22.91</td>\n",
       "      <td>22.98</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1374072.92</td>\n",
       "      <td>23.13</td>\n",
       "      <td>23.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.104658</td>\n",
       "      <td>0.609225</td>\n",
       "      <td>117222839.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NE830799</td>\n",
       "      <td>20210106</td>\n",
       "      <td>23.29</td>\n",
       "      <td>32594.0</td>\n",
       "      <td>23.00</td>\n",
       "      <td>23.12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>753744.84</td>\n",
       "      <td>23.31</td>\n",
       "      <td>23.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.104658</td>\n",
       "      <td>0.735294</td>\n",
       "      <td>117222839.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NE830799</td>\n",
       "      <td>20210107</td>\n",
       "      <td>22.90</td>\n",
       "      <td>91805.0</td>\n",
       "      <td>22.90</td>\n",
       "      <td>23.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2119783.36</td>\n",
       "      <td>23.30</td>\n",
       "      <td>23.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.104658</td>\n",
       "      <td>-1.674538</td>\n",
       "      <td>117222839.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NE830799</td>\n",
       "      <td>20210108</td>\n",
       "      <td>23.33</td>\n",
       "      <td>202341.0</td>\n",
       "      <td>22.56</td>\n",
       "      <td>22.90</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4662152.94</td>\n",
       "      <td>23.94</td>\n",
       "      <td>23.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.104658</td>\n",
       "      <td>1.877729</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>NE830799</td>\n",
       "      <td>20220525</td>\n",
       "      <td>11.33</td>\n",
       "      <td>48637.0</td>\n",
       "      <td>11.12</td>\n",
       "      <td>11.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>548426.46</td>\n",
       "      <td>11.61</td>\n",
       "      <td>11.61</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.054229</td>\n",
       "      <td>1.160714</td>\n",
       "      <td>117222839.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>NE830799</td>\n",
       "      <td>20220526</td>\n",
       "      <td>11.55</td>\n",
       "      <td>103856.0</td>\n",
       "      <td>11.30</td>\n",
       "      <td>11.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1200104.83</td>\n",
       "      <td>11.65</td>\n",
       "      <td>11.62</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.054229</td>\n",
       "      <td>1.941748</td>\n",
       "      <td>117222839.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>NE830799</td>\n",
       "      <td>20220527</td>\n",
       "      <td>11.78</td>\n",
       "      <td>119210.0</td>\n",
       "      <td>11.53</td>\n",
       "      <td>11.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1402552.58</td>\n",
       "      <td>11.88</td>\n",
       "      <td>11.68</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.054229</td>\n",
       "      <td>1.991342</td>\n",
       "      <td>117222839.0</td>\n",
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       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>NE830799</td>\n",
       "      <td>20220530</td>\n",
       "      <td>11.74</td>\n",
       "      <td>122101.0</td>\n",
       "      <td>11.44</td>\n",
       "      <td>11.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1429026.66</td>\n",
       "      <td>11.91</td>\n",
       "      <td>11.91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.054229</td>\n",
       "      <td>-0.339559</td>\n",
       "      <td>117222839.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>NE830799</td>\n",
       "      <td>20220531</td>\n",
       "      <td>11.79</td>\n",
       "      <td>116001.0</td>\n",
       "      <td>11.70</td>\n",
       "      <td>11.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1366318.64</td>\n",
       "      <td>11.84</td>\n",
       "      <td>11.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.054229</td>\n",
       "      <td>0.425894</td>\n",
       "      <td>117222839.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>339 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      StockID      date  close       vol    low  yclose  cjbs      amount  \\\n",
       "0    NE830799  20210104  22.98  208835.0  22.90   23.00   0.0  5082436.35   \n",
       "1    NE830799  20210105  23.12   59729.0  22.91   22.98   0.0  1374072.92   \n",
       "2    NE830799  20210106  23.29   32594.0  23.00   23.12   0.0   753744.84   \n",
       "3    NE830799  20210107  22.90   91805.0  22.90   23.29   0.0  2119783.36   \n",
       "4    NE830799  20210108  23.33  202341.0  22.56   22.90   0.0  4662152.94   \n",
       "..        ...       ...    ...       ...    ...     ...   ...         ...   \n",
       "334  NE830799  20220525  11.33   48637.0  11.12   11.20   0.0   548426.46   \n",
       "335  NE830799  20220526  11.55  103856.0  11.30   11.33   0.0  1200104.83   \n",
       "336  NE830799  20220527  11.78  119210.0  11.53   11.55   0.0  1402552.58   \n",
       "337  NE830799  20220530  11.74  122101.0  11.44   11.78   0.0  1429026.66   \n",
       "338  NE830799  20220531  11.79  116001.0  11.70   11.74   0.0  1366318.64   \n",
       "\n",
       "      high   open  IsST    Factor    PctChg  FloatShares  \n",
       "0    23.07  23.00   0.0  0.104658 -0.086957  117222839.0  \n",
       "1    23.13  23.13   0.0  0.104658  0.609225  117222839.0  \n",
       "2    23.31  23.03   0.0  0.104658  0.735294  117222839.0  \n",
       "3    23.30  23.30   0.0  0.104658 -1.674538  117222839.0  \n",
       "4    23.94  23.00   0.0  0.104658  1.877729  117222839.0  \n",
       "..     ...    ...   ...       ...       ...          ...  \n",
       "334  11.61  11.61   0.0  0.054229  1.160714  117222839.0  \n",
       "335  11.65  11.62   0.0  0.054229  1.941748  117222839.0  \n",
       "336  11.88  11.68   0.0  0.054229  1.991342  117222839.0  \n",
       "337  11.91  11.91   0.0  0.054229 -0.339559  117222839.0  \n",
       "338  11.84  11.74   0.0  0.054229  0.425894  117222839.0  \n",
       "\n",
       "[339 rows x 14 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float_cols = list(set(df_tinysoft.columns.tolist()) - set(['StockID', 'date']))\n",
    "df_tinysoft[float_cols] = df_tinysoft[float_cols].astype('float')\n",
    "df_tinysoft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a07ef854-5810-4ca2-b4fc-4f130dd95423",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tinysoft.rename(columns={'vol':'volume', 'Factor':'factor'}, inplace=True)\n",
    "df_tinysoft['date'] = pd.to_datetime(df_tinysoft['date'].astype(str), format='%Y%m%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1d3d8626-2eba-40e9-9cff-796c534e5f08",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tinysoft.drop('StockID', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8b35bc1c-c667-41da-8163-713482fc654f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>low</th>\n",
       "      <th>yclose</th>\n",
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       "      <th>0</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>22.98</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>23.12</td>\n",
       "      <td>59729.0</td>\n",
       "      <td>22.91</td>\n",
       "      <td>22.98</td>\n",
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       "      <td>1374072.92</td>\n",
       "      <td>23.13</td>\n",
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       "      <td>117222839.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-06</td>\n",
       "      <td>23.29</td>\n",
       "      <td>32594.0</td>\n",
       "      <td>23.00</td>\n",
       "      <td>23.12</td>\n",
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       "      <td>23.31</td>\n",
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       "      <td>117222839.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-07</td>\n",
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       "      <td>22.90</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-08</td>\n",
       "      <td>23.33</td>\n",
       "      <td>202341.0</td>\n",
       "      <td>22.56</td>\n",
       "      <td>22.90</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4662152.94</td>\n",
       "      <td>23.94</td>\n",
       "      <td>23.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.104658</td>\n",
       "      <td>1.877729</td>\n",
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       "      <td>2022-05-25</td>\n",
       "      <td>11.33</td>\n",
       "      <td>48637.0</td>\n",
       "      <td>11.12</td>\n",
       "      <td>11.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>548426.46</td>\n",
       "      <td>11.61</td>\n",
       "      <td>11.61</td>\n",
       "      <td>0</td>\n",
       "      <td>0.054229</td>\n",
       "      <td>1.160714</td>\n",
       "      <td>117222839.0</td>\n",
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       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>2022-05-26</td>\n",
       "      <td>11.55</td>\n",
       "      <td>103856.0</td>\n",
       "      <td>11.30</td>\n",
       "      <td>11.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1200104.83</td>\n",
       "      <td>11.65</td>\n",
       "      <td>11.62</td>\n",
       "      <td>0</td>\n",
       "      <td>0.054229</td>\n",
       "      <td>1.941748</td>\n",
       "      <td>117222839.0</td>\n",
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       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>2022-05-27</td>\n",
       "      <td>11.78</td>\n",
       "      <td>119210.0</td>\n",
       "      <td>11.53</td>\n",
       "      <td>11.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1402552.58</td>\n",
       "      <td>11.88</td>\n",
       "      <td>11.68</td>\n",
       "      <td>0</td>\n",
       "      <td>0.054229</td>\n",
       "      <td>1.991342</td>\n",
       "      <td>117222839.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>2022-05-30</td>\n",
       "      <td>11.74</td>\n",
       "      <td>122101.0</td>\n",
       "      <td>11.44</td>\n",
       "      <td>11.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1429026.66</td>\n",
       "      <td>11.91</td>\n",
       "      <td>11.91</td>\n",
       "      <td>0</td>\n",
       "      <td>0.054229</td>\n",
       "      <td>-0.339559</td>\n",
       "      <td>117222839.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>2022-05-31</td>\n",
       "      <td>11.79</td>\n",
       "      <td>116001.0</td>\n",
       "      <td>11.70</td>\n",
       "      <td>11.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1366318.64</td>\n",
       "      <td>11.84</td>\n",
       "      <td>11.74</td>\n",
       "      <td>0</td>\n",
       "      <td>0.054229</td>\n",
       "      <td>0.425894</td>\n",
       "      <td>117222839.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>339 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  close    volume    low  yclose  cjbs      amount   high  \\\n",
       "0   2021-01-04  22.98  208835.0  22.90   23.00   0.0  5082436.35  23.07   \n",
       "1   2021-01-05  23.12   59729.0  22.91   22.98   0.0  1374072.92  23.13   \n",
       "2   2021-01-06  23.29   32594.0  23.00   23.12   0.0   753744.84  23.31   \n",
       "3   2021-01-07  22.90   91805.0  22.90   23.29   0.0  2119783.36  23.30   \n",
       "4   2021-01-08  23.33  202341.0  22.56   22.90   0.0  4662152.94  23.94   \n",
       "..         ...    ...       ...    ...     ...   ...         ...    ...   \n",
       "334 2022-05-25  11.33   48637.0  11.12   11.20   0.0   548426.46  11.61   \n",
       "335 2022-05-26  11.55  103856.0  11.30   11.33   0.0  1200104.83  11.65   \n",
       "336 2022-05-27  11.78  119210.0  11.53   11.55   0.0  1402552.58  11.88   \n",
       "337 2022-05-30  11.74  122101.0  11.44   11.78   0.0  1429026.66  11.91   \n",
       "338 2022-05-31  11.79  116001.0  11.70   11.74   0.0  1366318.64  11.84   \n",
       "\n",
       "      open  IsST    factor    PctChg  FloatShares  \n",
       "0    23.00     0  0.104658 -0.086957  117222839.0  \n",
       "1    23.13     0  0.104658  0.609225  117222839.0  \n",
       "2    23.03     0  0.104658  0.735294  117222839.0  \n",
       "3    23.30     0  0.104658 -1.674538  117222839.0  \n",
       "4    23.00     0  0.104658  1.877729  117222839.0  \n",
       "..     ...   ...       ...       ...          ...  \n",
       "334  11.61     0  0.054229  1.160714  117222839.0  \n",
       "335  11.62     0  0.054229  1.941748  117222839.0  \n",
       "336  11.68     0  0.054229  1.991342  117222839.0  \n",
       "337  11.91     0  0.054229 -0.339559  117222839.0  \n",
       "338  11.74     0  0.054229  0.425894  117222839.0  \n",
       "\n",
       "[339 rows x 13 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tinysoft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "42011d0c-bc95-4ff5-a96b-c4af09375d4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "path = Path('qlib-data/source/market/20210101-20220531/k-daily/')\n",
    "path.mkdir(parents=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5584b39b-6c92-4b73-ac00-5e424050b654",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tinysoft.to_csv('qlib-data/source/market/20210101-20220531/k-daily/NE830799.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "09e91453-df05-4a8c-8d09-ff07a7083819",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the directory for the target\n",
    "# no need for the date range in this context\n",
    "path = Path('qlib-data/target/market/k-daily/')\n",
    "path.mkdir(parents=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d30df9ef-30bc-4a2d-80af-34603a803aef",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = DumpDataAll(\n",
    "    csv_path='qlib-data/source/market/20210101-20220531/k-daily/',\n",
    "    qlib_dir='qlib-data/target/market/k-daily/',\n",
    "    max_workers=4,\n",
    "    symbol_field_name='StockID'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "31b2ecdb-f242-495e-93e7-7218c3ea0fc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-22 11:01:12.794 | INFO     | src.qlib_scripts.dump_bin:_get_all_date:272 - start get all date......\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  1.82it/s]\n",
      "2022-06-22 11:01:13.343 | INFO     | src.qlib_scripts.dump_bin:_get_all_date:291 - end of get all date.\n",
      "\n",
      "2022-06-22 11:01:13.343 | INFO     | src.qlib_scripts.dump_bin:_dump_calendars:294 - start dump calendars......\n",
      "2022-06-22 11:01:13.343 | INFO     | src.qlib_scripts.dump_bin:_dump_calendars:297 - end of calendars dump.\n",
      "\n",
      "2022-06-22 11:01:13.343 | INFO     | src.qlib_scripts.dump_bin:_dump_instruments:300 - start dump instruments......\n",
      "2022-06-22 11:01:13.343 | INFO     | src.qlib_scripts.dump_bin:_dump_instruments:302 - end of instruments dump.\n",
      "\n",
      "2022-06-22 11:01:13.343 | INFO     | src.qlib_scripts.dump_bin:_dump_features:305 - start dump features......\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  1.78it/s]\n",
      "2022-06-22 11:01:13.905 | INFO     | src.qlib_scripts.dump_bin:_dump_features:312 - end of features dump.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "d()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "21fc1650-c20c-4b34-ae94-1abfaa23ed70",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('qlib-data/source/market/k-daily/NE430198.csv')\n",
    "df['date'] = pd.to_datetime(df['date'], format=\"%Y-%m-%d\")\n",
    "# should not set index, otherwise it is hard to do division with dis-alignment\n",
    "# df.set_index('date', inplace=True)\n",
    "df = df[['date', 'close', 'yclose', 'factor']]\n",
    "df.sort_values('date', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d73b686b-a2c1-495a-abe4-921673623985",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
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       "      <th>factor</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2011-01-04</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>2011-01-05</td>\n",
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       "      <th>84</th>\n",
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       "      <th>2766</th>\n",
       "      <td>2022-05-25</td>\n",
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       "      <td>5.70</td>\n",
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       "    <tr>\n",
       "      <th>2767</th>\n",
       "      <td>2022-05-26</td>\n",
       "      <td>5.77</td>\n",
       "      <td>5.72</td>\n",
       "      <td>0.318452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2768</th>\n",
       "      <td>2022-05-27</td>\n",
       "      <td>5.43</td>\n",
       "      <td>5.47</td>\n",
       "      <td>0.301895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2769</th>\n",
       "      <td>2022-05-30</td>\n",
       "      <td>5.49</td>\n",
       "      <td>5.43</td>\n",
       "      <td>0.301895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2770</th>\n",
       "      <td>2022-05-31</td>\n",
       "      <td>5.50</td>\n",
       "      <td>5.49</td>\n",
       "      <td>0.301895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2771 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  close  yclose    factor\n",
       "82   2011-01-04    NaN     NaN  1.000000\n",
       "83   2011-01-05    NaN     NaN  1.000000\n",
       "84   2011-01-06    NaN     NaN  1.000000\n",
       "85   2011-01-07    NaN     NaN  1.000000\n",
       "86   2011-01-10    NaN     NaN  1.000000\n",
       "...         ...    ...     ...       ...\n",
       "2766 2022-05-25   5.72    5.70  0.318452\n",
       "2767 2022-05-26   5.77    5.72  0.318452\n",
       "2768 2022-05-27   5.43    5.47  0.301895\n",
       "2769 2022-05-30   5.49    5.43  0.301895\n",
       "2770 2022-05-31   5.50    5.49  0.301895\n",
       "\n",
       "[2771 rows x 4 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "875c8c7a-af6e-4939-bbf1-35198b6e73ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['yclose_t_by_close_t1'] = 1.\n",
    "df = df[~df['close'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "0b62eb6a-a6b0-45d3-864a-f791ad433a99",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-04-22</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-06-18</td>\n",
       "      <td>8.00</td>\n",
       "      <td>19.75</td>\n",
       "      <td>0.861317</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-06-19</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.861317</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-06-23</td>\n",
       "      <td>15.88</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.861317</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>2766</th>\n",
       "      <td>2022-05-25</td>\n",
       "      <td>5.72</td>\n",
       "      <td>5.70</td>\n",
       "      <td>0.318452</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2767</th>\n",
       "      <td>2022-05-26</td>\n",
       "      <td>5.77</td>\n",
       "      <td>5.72</td>\n",
       "      <td>0.318452</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2768</th>\n",
       "      <td>2022-05-27</td>\n",
       "      <td>5.43</td>\n",
       "      <td>5.47</td>\n",
       "      <td>0.301895</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2769</th>\n",
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       "      <td>5.49</td>\n",
       "      <td>5.43</td>\n",
       "      <td>0.301895</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2770</th>\n",
       "      <td>2022-05-31</td>\n",
       "      <td>5.50</td>\n",
       "      <td>5.49</td>\n",
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       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1032 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  close  yclose    factor  yclose_t_by_close_t1\n",
       "0    2015-04-10  22.93    8.00  1.000000                   1.0\n",
       "1    2015-04-22  20.00   22.93  1.000000                   1.0\n",
       "2    2015-06-18   8.00   19.75  0.861317                   1.0\n",
       "3    2015-06-19   8.00    8.00  0.861317                   1.0\n",
       "4    2015-06-23  15.88    8.00  0.861317                   1.0\n",
       "...         ...    ...     ...       ...                   ...\n",
       "2766 2022-05-25   5.72    5.70  0.318452                   1.0\n",
       "2767 2022-05-26   5.77    5.72  0.318452                   1.0\n",
       "2768 2022-05-27   5.43    5.47  0.301895                   1.0\n",
       "2769 2022-05-30   5.49    5.43  0.301895                   1.0\n",
       "2770 2022-05-31   5.50    5.49  0.301895                   1.0\n",
       "\n",
       "[1032 rows x 5 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "0ec6b9c1-c736-4a25-a5cb-f9b60508a7eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1.000000\n",
       "1       0.987500\n",
       "2       1.000000\n",
       "3       1.000000\n",
       "4       1.000000\n",
       "          ...   \n",
       "1026    1.000000\n",
       "1027    1.000000\n",
       "1028    0.948007\n",
       "1029    1.000000\n",
       "1030    1.000000\n",
       "Length: 1031, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['yclose'].iloc[1:].reset_index(drop=True) / df['close'].iloc[:-1].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "8d4df052-2567-4017-b88c-55c1c62864a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['yclose_t_by_close_t1'].copy(deep=True).iloc[1:] = \\\n",
    "    df['yclose'].iloc[1:].reset_index(drop=True) / \\\n",
    "    df['close'].iloc[:-1].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "c135882d-c6bc-44a5-9cfb-688b954ffc2a",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>close</th>\n",
       "      <th>yclose</th>\n",
       "      <th>factor</th>\n",
       "      <th>yclose_t_by_close_t1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-04-10</td>\n",
       "      <td>22.93</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-04-22</td>\n",
       "      <td>20.00</td>\n",
       "      <td>22.93</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-06-18</td>\n",
       "      <td>8.00</td>\n",
       "      <td>19.75</td>\n",
       "      <td>0.861317</td>\n",
       "      <td>0.987500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-06-19</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.861317</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-06-23</td>\n",
       "      <td>15.88</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.861317</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2766</th>\n",
       "      <td>2022-05-25</td>\n",
       "      <td>5.72</td>\n",
       "      <td>5.70</td>\n",
       "      <td>0.318452</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2767</th>\n",
       "      <td>2022-05-26</td>\n",
       "      <td>5.77</td>\n",
       "      <td>5.72</td>\n",
       "      <td>0.318452</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2768</th>\n",
       "      <td>2022-05-27</td>\n",
       "      <td>5.43</td>\n",
       "      <td>5.47</td>\n",
       "      <td>0.301895</td>\n",
       "      <td>0.948007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2769</th>\n",
       "      <td>2022-05-30</td>\n",
       "      <td>5.49</td>\n",
       "      <td>5.43</td>\n",
       "      <td>0.301895</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2770</th>\n",
       "      <td>2022-05-31</td>\n",
       "      <td>5.50</td>\n",
       "      <td>5.49</td>\n",
       "      <td>0.301895</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1032 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  close  yclose    factor  yclose_t_by_close_t1\n",
       "0    2015-04-10  22.93    8.00  1.000000              1.000000\n",
       "1    2015-04-22  20.00   22.93  1.000000              1.000000\n",
       "2    2015-06-18   8.00   19.75  0.861317              0.987500\n",
       "3    2015-06-19   8.00    8.00  0.861317              1.000000\n",
       "4    2015-06-23  15.88    8.00  0.861317              1.000000\n",
       "...         ...    ...     ...       ...                   ...\n",
       "2766 2022-05-25   5.72    5.70  0.318452              1.000000\n",
       "2767 2022-05-26   5.77    5.72  0.318452              1.000000\n",
       "2768 2022-05-27   5.43    5.47  0.301895              0.948007\n",
       "2769 2022-05-30   5.49    5.43  0.301895              1.000000\n",
       "2770 2022-05-31   5.50    5.49  0.301895              1.000000\n",
       "\n",
       "[1032 rows x 5 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "ad4ffd06-4f88-4b0f-97d9-f02b20b6ede3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['adjFactor'] = df['yclose_t_by_close_t1'].cumprod()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "ca05ce7c-c6c9-425e-af48-25827fa9d176",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>close</th>\n",
       "      <th>yclose</th>\n",
       "      <th>factor</th>\n",
       "      <th>yclose_t_by_close_t1</th>\n",
       "      <th>adjFactor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-04-10</td>\n",
       "      <td>22.93</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-04-22</td>\n",
       "      <td>20.00</td>\n",
       "      <td>22.93</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-06-18</td>\n",
       "      <td>8.00</td>\n",
       "      <td>19.75</td>\n",
       "      <td>0.861317</td>\n",
       "      <td>0.987500</td>\n",
       "      <td>0.987500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-06-19</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.861317</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.987500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-06-23</td>\n",
       "      <td>15.88</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.861317</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.987500</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2766</th>\n",
       "      <td>2022-05-25</td>\n",
       "      <td>5.72</td>\n",
       "      <td>5.70</td>\n",
       "      <td>0.318452</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.294950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2767</th>\n",
       "      <td>2022-05-26</td>\n",
       "      <td>5.77</td>\n",
       "      <td>5.72</td>\n",
       "      <td>0.318452</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.294950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2768</th>\n",
       "      <td>2022-05-27</td>\n",
       "      <td>5.43</td>\n",
       "      <td>5.47</td>\n",
       "      <td>0.301895</td>\n",
       "      <td>0.948007</td>\n",
       "      <td>0.279615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2769</th>\n",
       "      <td>2022-05-30</td>\n",
       "      <td>5.49</td>\n",
       "      <td>5.43</td>\n",
       "      <td>0.301895</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.279615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2770</th>\n",
       "      <td>2022-05-31</td>\n",
       "      <td>5.50</td>\n",
       "      <td>5.49</td>\n",
       "      <td>0.301895</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.279615</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1032 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  close  yclose    factor  yclose_t_by_close_t1  adjFactor\n",
       "0    2015-04-10  22.93    8.00  1.000000              1.000000   1.000000\n",
       "1    2015-04-22  20.00   22.93  1.000000              1.000000   1.000000\n",
       "2    2015-06-18   8.00   19.75  0.861317              0.987500   0.987500\n",
       "3    2015-06-19   8.00    8.00  0.861317              1.000000   0.987500\n",
       "4    2015-06-23  15.88    8.00  0.861317              1.000000   0.987500\n",
       "...         ...    ...     ...       ...                   ...        ...\n",
       "2766 2022-05-25   5.72    5.70  0.318452              1.000000   0.294950\n",
       "2767 2022-05-26   5.77    5.72  0.318452              1.000000   0.294950\n",
       "2768 2022-05-27   5.43    5.47  0.301895              0.948007   0.279615\n",
       "2769 2022-05-30   5.49    5.43  0.301895              1.000000   0.279615\n",
       "2770 2022-05-31   5.50    5.49  0.301895              1.000000   0.279615\n",
       "\n",
       "[1032 rows x 6 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "e444db45-e4ab-4633-9ae7-15f7b5d9444b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.core.common import SettingWithCopyWarning\n",
    "import warnings\n",
    "warnings.simplefilter('error', SettingWithCopyWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d0c8d58-36e4-4eb1-a1c1-a4cdc9de659d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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