{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys, json, collections\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FG（玻璃）数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80808, 127)\n"
     ]
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>MidPxChange_1</th>\n",
       "      <th>MidPxChange_5</th>\n",
       "      <th>MidPxChange_10</th>\n",
       "      <th>MidPxChange_20</th>\n",
       "      <th>MidPxChange_50</th>\n",
       "      <th>MidPxChange_100</th>\n",
       "      <th>MidPxChange_200</th>\n",
       "      <th>MidPxVolRatio_1</th>\n",
       "      <th>MidPxVolRatio_5</th>\n",
       "      <th>...</th>\n",
       "      <th>label1</th>\n",
       "      <th>label2</th>\n",
       "      <th>last</th>\n",
       "      <th>bid</th>\n",
       "      <th>ask</th>\n",
       "      <th>bid_vol</th>\n",
       "      <th>ask_vol</th>\n",
       "      <th>volume</th>\n",
       "      <th>turnover</th>\n",
       "      <th>predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-05-31 21:00:00.500</td>\n",
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       "      <td>-0.00303</td>\n",
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       "      <td>...</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-2.285714</td>\n",
       "      <td>1406.0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>...</td>\n",
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       "      <td>1406.0</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>1406.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>1102.0</td>\n",
       "      <td>3077.0</td>\n",
       "      <td>4332491.0</td>\n",
       "      <td>-0.709275</td>\n",
       "    </tr>\n",
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       "                Unnamed: 0  MidPxChange_1  MidPxChange_5  MidPxChange_10  \\\n",
       "0  2023-05-31 21:00:00.500           -1.0           -1.0            -1.0   \n",
       "1  2023-05-31 21:00:00.750            0.0           -1.0            -1.0   \n",
       "\n",
       "   MidPxChange_20  MidPxChange_50  MidPxChange_100  MidPxChange_200  \\\n",
       "0            -1.0            -1.0             -1.0             -1.0   \n",
       "1            -1.0            -1.0             -1.0             -1.0   \n",
       "\n",
       "   MidPxVolRatio_1  MidPxVolRatio_5  ...  label1    label2    last     bid  \\\n",
       "0         -0.00303        -0.003030  ...    -7.0 -2.285714  1406.0  1405.0   \n",
       "1          0.00000        -0.001105  ...    -7.0 -2.285714  1406.0  1405.0   \n",
       "\n",
       "      ask  bid_vol  ask_vol  volume   turnover   predict  \n",
       "0  1406.0    594.0     96.0  2502.0  3524194.0  0.245262  \n",
       "1  1406.0    460.0   1102.0  3077.0  4332491.0 -0.709275  \n",
       "\n",
       "[2 rows x 127 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filepath = '/Users/saidcalculationboy/Coding/GenWealth/data/generate_data/FG/2023-06-01.csv'\n",
    "FG_1_df = pd.read_csv(filepath)\n",
    "print(FG_1_df.shape)\n",
    "FG_1_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(84453, 127)\n"
     ]
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       "      <th>MidPxChange_1</th>\n",
       "      <th>MidPxChange_5</th>\n",
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       "      <th>MidPxChange_50</th>\n",
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       "      <th>MidPxVolRatio_5</th>\n",
       "      <th>...</th>\n",
       "      <th>label1</th>\n",
       "      <th>label2</th>\n",
       "      <th>last</th>\n",
       "      <th>bid</th>\n",
       "      <th>ask</th>\n",
       "      <th>bid_vol</th>\n",
       "      <th>ask_vol</th>\n",
       "      <th>volume</th>\n",
       "      <th>turnover</th>\n",
       "      <th>predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2023-05-31 21:00:01.000</td>\n",
       "      <td>0.5</td>\n",
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       "      <td>0.5</td>\n",
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       "      <td>4901.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>2023-05-31 21:00:01.250</td>\n",
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       "      <td>6.035714</td>\n",
       "      <td>4904.0</td>\n",
       "      <td>4902.0</td>\n",
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       "                Unnamed: 0  MidPxChange_1  MidPxChange_5  MidPxChange_10  \\\n",
       "0  2023-05-31 21:00:01.000            0.5            0.5             0.5   \n",
       "1  2023-05-31 21:00:01.250           -0.5            0.0             0.0   \n",
       "\n",
       "   MidPxChange_20  MidPxChange_50  MidPxChange_100  MidPxChange_200  \\\n",
       "0             0.5             0.5              0.5              0.5   \n",
       "1             0.0             0.0              0.0              0.0   \n",
       "\n",
       "   MidPxVolRatio_1  MidPxVolRatio_5  ...    label1    label2    last     bid  \\\n",
       "0         0.019231         0.019231  ...  5.142857  5.142857  4905.0  4901.0   \n",
       "1        -0.006173         0.000000  ...  6.035714  6.035714  4904.0  4902.0   \n",
       "\n",
       "      ask  bid_vol  ask_vol  volume   turnover   predict  \n",
       "0  4905.0      4.0      5.0   559.0  2744938.0 -0.014487  \n",
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       "\n",
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filepath = '/Users/saidcalculationboy/Coding/GenWealth/data/generate_data/sc/2023-06-01.csv'\n",
    "sc_1_df = pd.read_csv(filepath)\n",
    "print(sc_1_df.shape)\n",
    "sc_1_df.head(2)"
   ]
  }
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