clustering-based_stock_portfolio.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering-based Stock Portfolio\n",
    "1. [Data preprocessing](#01.-Data-Preprocessing)\n",
    "2. [Main](#02.-Main)\n",
    "3. [Analysis](#03.-Analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.preprocessing_utils import *\n",
    "from src.clustering import *\n",
    "from src.portfolio import *\n",
    "from functools import reduce\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn.apionly as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 01. Data Preprocessing\n",
    "### `scripts/01_data-preprocessing.R` $\\rightarrow$ `scripts/preprocess.py`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stock data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = os.path.join(\"data\", \"raw\")\n",
    "file_names = [\"asset\", \"asset-growth\", \"equity\", \"equity-turnover\",\n",
    "              \"leverage\", \"market-cap\", \"net-profit\", \"pcr\", \"per\",\n",
    "              \"stock-number\", \"stock-price\", \"trade-amount\", \"volatility\"]\n",
    "var_names = [\"asset\", \"asset_growth\", \"equity\", \"equity_turnover\",\n",
    "             \"leverage\", \"market_cap\", \"net_profit\", \"pcr\", \"per\",\n",
    "             \"stock_num\", \"price\", \"trade_amount\", \"volatility\"]\n",
    "extension = \".xls\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "asset, asset-growth, equity, equity-turnover, leverage, market-cap, net-profit, pcr, per, stock-number, stock-price, trade-amount, volatility, "
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>time</th>\n",
       "      <th>leverage</th>\n",
       "      <th>asset_growth</th>\n",
       "      <th>shares_turnover</th>\n",
       "      <th>roa</th>\n",
       "      <th>roe</th>\n",
       "      <th>size</th>\n",
       "      <th>pcr</th>\n",
       "      <th>per</th>\n",
       "      <th>equity_turnover</th>\n",
       "      <th>volatility</th>\n",
       "      <th>logret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000020</td>\n",
       "      <td>1997-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006314</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.850400e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.586667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.255281</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000020</td>\n",
       "      <td>1997-2</td>\n",
       "      <td>206.92</td>\n",
       "      <td>23.60</td>\n",
       "      <td>0.015739</td>\n",
       "      <td>0.002507</td>\n",
       "      <td>0.007696</td>\n",
       "      <td>1.104880e+11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.053333</td>\n",
       "      <td>2.22</td>\n",
       "      <td>62.004616</td>\n",
       "      <td>0.235505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000020</td>\n",
       "      <td>1997-3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.004035</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.087867e+11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.543333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.334550</td>\n",
       "      <td>-0.040738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000020</td>\n",
       "      <td>1997-4</td>\n",
       "      <td>324.23</td>\n",
       "      <td>12.41</td>\n",
       "      <td>0.004633</td>\n",
       "      <td>0.009214</td>\n",
       "      <td>0.039088</td>\n",
       "      <td>4.613333e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.346667</td>\n",
       "      <td>2.89</td>\n",
       "      <td>62.702979</td>\n",
       "      <td>-0.847219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000020</td>\n",
       "      <td>1998-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.012191</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.973333e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.726084</td>\n",
       "      <td>0.120535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     code    time  leverage  asset_growth  shares_turnover       roa  \\\n",
       "0  000020  1997-1       NaN           NaN         0.006314       NaN   \n",
       "1  000020  1997-2    206.92         23.60         0.015739  0.002507   \n",
       "2  000020  1997-3       NaN           NaN         0.004035       NaN   \n",
       "3  000020  1997-4    324.23         12.41         0.004633  0.009214   \n",
       "4  000020  1998-1       NaN           NaN         0.012191       NaN   \n",
       "\n",
       "        roe          size  pcr        per  equity_turnover  volatility  \\\n",
       "0       NaN  8.850400e+10  NaN  15.586667              NaN   60.255281   \n",
       "1  0.007696  1.104880e+11  NaN  11.053333             2.22   62.004616   \n",
       "2       NaN  1.087867e+11  NaN  10.543333              NaN   56.334550   \n",
       "3  0.039088  4.613333e+10  NaN   4.346667             2.89   62.702979   \n",
       "4       NaN  5.973333e+10  NaN   4.750000              NaN   73.726084   \n",
       "\n",
       "     logret  \n",
       "0       NaN  \n",
       "1  0.235505  \n",
       "2 -0.040738  \n",
       "3 -0.847219  \n",
       "4  0.120535  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_tbl = preprocess(path, file_names, var_names, extension=\".xls\")\n",
    "stock_tbl.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KOSPI index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unpack_df(df):\n",
    "    df = df.reset_index()\n",
    "    df[\"time\"] = df[['time', 'level_1']].astype(str).apply(lambda x: '-'.join(x), axis=1)\n",
    "    df = pd.concat([df.time, df.price], axis=1)\n",
    "    df.columns = [\"time\", \"logret\"]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>logret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1997-1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1997-2</td>\n",
       "      <td>0.082477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1997-3</td>\n",
       "      <td>-0.064037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1997-4</td>\n",
       "      <td>-0.499768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1998-1</td>\n",
       "      <td>0.247514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     time    logret\n",
       "0  1997-1       NaN\n",
       "1  1997-2  0.082477\n",
       "2  1997-3 -0.064037\n",
       "3  1997-4 -0.499768\n",
       "4  1998-1  0.247514"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kospi = pd.read_excel(\"data/raw/kospi-index.xlsx\", names=[\"time\", \"price\"])\n",
    "g = kospi.groupby([pd.DatetimeIndex(kospi.time).year, as_quarter(pd.DatetimeIndex(kospi.time).month)])\n",
    "g = np.log(g.mean()).diff()\n",
    "kospi = unpack_df(g)\n",
    "kospi.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Risk-free rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>r</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1997-1</td>\n",
       "      <td>0.119707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1997-2</td>\n",
       "      <td>0.118287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1997-3</td>\n",
       "      <td>0.119115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1997-4</td>\n",
       "      <td>0.145110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1998-1</td>\n",
       "      <td>0.204599</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     time         r\n",
       "0  1997-1  0.119707\n",
       "1  1997-2  0.118287\n",
       "2  1997-3  0.119115\n",
       "3  1997-4  0.145110\n",
       "4  1998-1  0.204599"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "risk_free = pd.read_excel(\"data/raw/cd-risk-free.xlsx\", names=[\"time\", \"r\"])\n",
    "risk_free.time = risk_free.time.str.replace(\"/\", \"-\").str.split().str[0]\n",
    "risk_free.r = np.log(1 + risk_free.r / 100)\n",
    "risk_free.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save processed data"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import os\n",
    "os.mkdir(\"data/processed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_tbl.to_csv(\"data/processed/stock2.csv\", index=False)\n",
    "kospi.to_csv(\"data/processed/kospi2.csv\", index=False)\n",
    "risk_free.to_csv(\"data/processed/risk_free2.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 02. Main\n",
    "### `scripts/02_main.R` $\\rightarrow$ `scripts/main.py`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_state = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_tbl = pd.read_csv(\"data/processed/stock.csv\")\n",
    "kospi = pd.read_csv(\"data/processed/kospi.csv\")\n",
    "risk_free = pd.read_csv(\"data/processed/risk_free.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "with_list = [\n",
    "    \"return\", \n",
    "    \"market_residual\", \n",
    "    \"factors\", \n",
    "    \"factors_residual\"\n",
    "]\n",
    "n_time_list = [\n",
    "    6, 8, \n",
    "    10, 12\n",
    "]\n",
    "method_list = [\n",
    "    \"GMV\", \"Tangency\"\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validation period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "from 2002-4 to 2005-3\n",
      "  with: return\n",
      "  n_time: 6\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:23<00:00,  1.91s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 6\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:22<00:00,  1.90s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 8\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:30<00:00,  2.50s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 8\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:29<00:00,  2.48s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 10\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:38<00:00,  3.12s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 10\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:37<00:00,  3.13s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 12\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:45<00:00,  3.86s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 12\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:44<00:00,  3.72s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 6\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:17<00:00,  6.56s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 6\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:27<00:00,  7.51s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 8\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:35<00:00,  7.20s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 8\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:25<00:00,  7.11s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 10\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:32<00:00,  7.88s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 10\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:32<00:00,  7.72s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 12\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:41<00:00,  8.46s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 12\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:40<00:00,  8.39s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 6\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:10<00:00,  1.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 6\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:10<00:00,  1.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 8\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:13<00:00,  1.14s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 8\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:13<00:00,  1.15s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 10\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:16<00:00,  1.48s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 10\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:16<00:00,  1.42s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 12\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:20<00:00,  1.77s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 12\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:20<00:00,  1.76s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 6\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:46<00:00,  8.82s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 6\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:46<00:00,  8.87s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 8\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:54<00:00,  9.57s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 8\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:55<00:00,  9.59s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 10\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [02:03<00:00, 10.32s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 10\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [02:03<00:00, 10.31s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 12\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [02:11<00:00, 10.97s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 12\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [02:14<00:00, 11.87s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "from 2005-4 to 2008-3\n",
      "  with: return\n",
      "  n_time: 6\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:25<00:00,  2.12s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 6\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:24<00:00,  2.05s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 8\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:33<00:00,  2.72s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 8\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:32<00:00,  2.72s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 10\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:40<00:00,  3.40s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 10\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:41<00:00,  3.48s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 12\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:49<00:00,  4.11s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 12\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:49<00:00,  4.13s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 6\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:23<00:00,  6.89s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 6\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:23<00:00,  6.92s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 8\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:31<00:00,  7.72s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 8\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:30<00:00,  7.56s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 10\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:40<00:00,  8.33s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 10\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:40<00:00,  8.30s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 12\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:48<00:00,  9.05s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 12\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:48<00:00,  9.06s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 6\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:12<00:00,  1.08s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 6\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:12<00:00,  1.07s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 8\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:15<00:00,  1.34s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 8\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:16<00:00,  1.35s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 10\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:19<00:00,  1.64s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 10\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:20<00:00,  1.64s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 12\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:22<00:00,  1.94s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 12\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:23<00:00,  1.95s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 6\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:46<00:00,  8.82s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 6\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:46<00:00,  8.86s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 8\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:54<00:00,  9.58s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 8\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:55<00:00,  9.61s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 10\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [02:03<00:00, 10.32s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 10\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [02:03<00:00, 10.28s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 12\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [02:12<00:00, 10.99s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 12\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [02:12<00:00, 11.02s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "from 2008-4 to 2011-3\n",
      "  with: return\n",
      "  n_time: 6\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:24<00:00,  2.05s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 6\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:24<00:00,  2.09s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 8\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:33<00:00,  2.75s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 8\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:32<00:00,  2.73s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 10\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:41<00:00,  3.41s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 10\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:41<00:00,  3.46s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 12\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:48<00:00,  4.08s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 12\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:49<00:00,  4.11s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 6\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:23<00:00,  6.95s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 6\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:23<00:00,  6.95s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 8\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:32<00:00,  7.72s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 8\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:31<00:00,  7.63s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 10\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:40<00:00,  8.35s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 10\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:41<00:00,  8.47s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 12\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:49<00:00,  9.10s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 12\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:49<00:00,  9.06s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 6\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:13<00:00,  1.14s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 6\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:12<00:00,  1.12s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 8\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:16<00:00,  1.42s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 8\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:16<00:00,  1.41s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 10\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:20<00:00,  1.80s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 10\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:20<00:00,  1.72s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 12\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:23<00:00,  2.01s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 12\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:23<00:00,  2.00s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 6\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:46<00:00,  8.84s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 6\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:46<00:00,  8.89s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 8\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:59<00:00,  9.68s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 8\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:55<00:00,  9.62s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 10\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [02:04<00:00, 10.30s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 10\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [02:03<00:00, 10.31s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 12\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [02:12<00:00, 10.99s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 12\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [02:12<00:00, 10.99s/it]\n",
      "C:\\Users\\user\\Desktop\\clustering-based-stock-portfolio\\src\\portfolio.py:96: RuntimeWarning: invalid value encountered in sqrt\n",
      "  pr_sd = np.diag(np.sqrt(pr_tbl.iloc[:, 2:].cov()))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "from 2011-4 to 2014-3\n",
      "  with: return\n",
      "  n_time: 6\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:25<00:00,  2.10s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 6\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:26<00:00,  2.21s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 8\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:33<00:00,  2.79s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 8\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:33<00:00,  2.78s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 10\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:41<00:00,  3.48s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 10\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:43<00:00,  3.51s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 12\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:49<00:00,  4.12s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 12\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:50<00:00,  4.19s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 6\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:23<00:00,  6.97s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 6\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:25<00:00,  7.04s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 8\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:32<00:00,  7.72s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 8\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:33<00:00,  7.87s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 10\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:40<00:00,  8.38s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 10\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:41<00:00,  8.41s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 12\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:52<00:00,  9.20s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 12\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:50<00:00,  9.15s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 6\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:14<00:00,  1.19s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 6\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:14<00:00,  1.20s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 8\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:19<00:00,  1.61s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 8\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:18<00:00,  1.55s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 10\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:22<00:00,  1.90s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 10\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:22<00:00,  1.89s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 12\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:26<00:00,  2.26s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 12\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:27<00:00,  2.37s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 6\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:46<00:00,  8.90s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 6\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:48<00:00,  8.88s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 8\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 8\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 10\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [02:03<00:00, 10.27s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 10\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [02:04<00:00, 10.28s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 12\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [02:12<00:00, 11.01s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 12\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [02:13<00:00, 11.22s/it]\n",
      "C:\\Users\\user\\Desktop\\clustering-based-stock-portfolio\\src\\portfolio.py:96: RuntimeWarning: invalid value encountered in sqrt\n",
      "  pr_sd = np.diag(np.sqrt(pr_tbl.iloc[:, 2:].cov()))\n"
     ]
    }
   ],
   "source": [
    "start_list = [\"2002-4\", \"2005-4\", \"2008-4\", \"2011-4\"]\n",
    "end_list = [\"2005-3\", \"2008-3\", \"2011-3\", \"2014-3\"]\n",
    "valid_res = []\n",
    "\n",
    "for st, en in zip(start_list, end_list):\n",
    "    valid_res.append(evaluate_portfolio(stock_tbl, kospi, risk_free, st, en,\n",
    "                                        with_list, n_time_list, method_list, \n",
    "                                        random_state=random_state))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>kospi</th>\n",
       "      <th>market_residual_6_GMV</th>\n",
       "      <th>market_residual_8_GMV</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>2002-4</td>\n",
       "      <td>-0.043557</td>\n",
       "      <td>-0.182029</td>\n",
       "      <td>-0.168141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003-1</td>\n",
       "      <td>-0.166381</td>\n",
       "      <td>0.150088</td>\n",
       "      <td>0.133076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003-2</td>\n",
       "      <td>0.110888</td>\n",
       "      <td>0.033397</td>\n",
       "      <td>0.123056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003-3</td>\n",
       "      <td>0.131688</td>\n",
       "      <td>0.120947</td>\n",
       "      <td>0.070731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003-4</td>\n",
       "      <td>0.096017</td>\n",
       "      <td>0.013463</td>\n",
       "      <td>0.015975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     time     kospi  market_residual_6_GMV  market_residual_8_GMV\n",
       "0  2002-4 -0.043557              -0.182029              -0.168141\n",
       "1  2003-1 -0.166381               0.150088               0.133076\n",
       "2  2003-2  0.110888               0.033397               0.123056\n",
       "3  2003-3  0.131688               0.120947               0.070731\n",
       "4  2003-4  0.096017               0.013463               0.015975"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_res[0][\"return\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>with</th>\n",
       "      <th>n_time</th>\n",
       "      <th>method</th>\n",
       "      <th>cumsum</th>\n",
       "      <th>sd</th>\n",
       "      <th>info_r</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>market_residual_6_GMV</th>\n",
       "      <td>market_residual</td>\n",
       "      <td>6</td>\n",
       "      <td>GMV</td>\n",
       "      <td>0.683359</td>\n",
       "      <td>0.095750</td>\n",
       "      <td>0.127044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>market_residual_8_GMV</th>\n",
       "      <td>market_residual</td>\n",
       "      <td>8</td>\n",
       "      <td>GMV</td>\n",
       "      <td>0.496945</td>\n",
       "      <td>0.098431</td>\n",
       "      <td>0.006877</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  with  n_time method    cumsum        sd  \\\n",
       "market_residual_6_GMV  market_residual       6    GMV  0.683359  0.095750   \n",
       "market_residual_8_GMV  market_residual       8    GMV  0.496945  0.098431   \n",
       "\n",
       "                         info_r  \n",
       "market_residual_6_GMV  0.127044  \n",
       "market_residual_8_GMV  0.006877  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_res[0][\"summary\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "from 2014-4 to 2017-3\n",
      "  with: return\n",
      "  n_time: 6\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:25<00:00,  2.13s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 6\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:26<00:00,  2.19s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 8\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:33<00:00,  2.80s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 8\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:34<00:00,  2.99s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 10\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:41<00:00,  3.47s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 10\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:41<00:00,  3.44s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 12\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:50<00:00,  4.24s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: return\n",
      "  n_time: 12\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:49<00:00,  4.16s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 6\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:24<00:00,  7.03s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 6\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:23<00:00,  6.96s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 8\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:33<00:00,  7.70s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 8\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:36<00:00,  8.39s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 10\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:41<00:00,  8.45s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 10\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:41<00:00,  8.49s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 12\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:50<00:00,  9.26s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: market_residual\n",
      "  n_time: 12\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:50<00:00,  9.32s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 6\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:15<00:00,  1.34s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 6\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:15<00:00,  1.35s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 8\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:19<00:00,  1.71s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 8\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:19<00:00,  1.72s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 10\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:24<00:00,  2.10s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 10\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:25<00:00,  2.14s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 12\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:28<00:00,  2.48s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors\n",
      "  n_time: 12\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:28<00:00,  2.48s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 6\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:47<00:00,  8.90s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 6\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:46<00:00,  8.90s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 8\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:55<00:00,  9.68s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 8\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [01:55<00:00,  9.68s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 10\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [02:05<00:00, 10.76s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 10\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [02:05<00:00, 10.38s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 12\n",
      "  method: GMV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [02:16<00:00, 11.57s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  with: factors_residual\n",
      "  n_time: 12\n",
      "  method: Tangency\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [02:13<00:00, 11.05s/it]\n",
      "C:\\Users\\user\\Desktop\\clustering-based-stock-portfolio\\src\\portfolio.py:96: RuntimeWarning: invalid value encountered in sqrt\n",
      "  pr_sd = np.diag(np.sqrt(pr_tbl.iloc[:, 2:].cov()))\n"
     ]
    }
   ],
   "source": [
    "start = \"2014-4\"\n",
    "end = \"2017-3\"\n",
    "\n",
    "test_res = evaluate_portfolio(stock_tbl, kospi, risk_free, start, end,\n",
    "                              with_list, n_time_list, method_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "outputs directory already exists\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    os.mkdir(\"outputs\")\n",
    "except OSError:\n",
    "    print(\"outputs directory already exists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"outputs/valid_res_list.pickle\", \"wb\") as wb:\n",
    "    pickle.dump(valid_res, wb)\n",
    "with open(\"outputs/test_res.pickle\", \"wb\") as wb:\n",
    "    pickle.dump(test_res, wb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2019, 5, 22, 21, 1, 12, 977914)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime\n",
    "datetime.datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 03. Analysis\n",
    "### `scripts/03_analysis.R` $\\rightarrow$ `scripts/analysis.py`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"outputs/valid_res_list.pickle\", \"rb\") as rb:\n",
    "    valid_res = pickle.load(rb)\n",
    "with open(\"outputs/test_res.pickle\", \"rb\") as rb:\n",
    "    test_res = pickle.load(rb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "kospi = pd.read_csv(\"data/processed/kospi.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>logret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1997-1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1997-2</td>\n",
       "      <td>0.078101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1997-3</td>\n",
       "      <td>-0.064037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1997-4</td>\n",
       "      <td>-0.499768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1998-1</td>\n",
       "      <td>0.247514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     time    logret\n",
       "0  1997-1       NaN\n",
       "1  1997-2  0.078101\n",
       "2  1997-3 -0.064037\n",
       "3  1997-4 -0.499768\n",
       "4  1998-1  0.247514"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kospi.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of `[\"result\"]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_model(valid_res_return):\n",
    "    valid_res_return = valid_res_return.loc[:, [\"time\", \"kospi\", \"factors_8_GMV\", \"factors_8_Tangency\"]]\n",
    "    valid_res_return.columns = [\"time\", \"kospi\", \"GMV\", \"Tangency\"]\n",
    "    return valid_res_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def summarize_sd(selected):\n",
    "    return selected.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1) Summary of validation results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "sd_summ = None\n",
    "for i in range(len(valid_res)):\n",
    "    newline = select_model(valid_res[i][\"return\"]).std()\n",
    "    sd_summ = pd.concat([sd_summ, newline], axis=1) if sd_summ is not None else newline\n",
    "sd_summ = sd_summ.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>kospi</th>\n",
       "      <th>GMV</th>\n",
       "      <th>Tangency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.100272</td>\n",
       "      <td>0.100403</td>\n",
       "      <td>0.124856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.104019</td>\n",
       "      <td>0.113553</td>\n",
       "      <td>0.152245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.127488</td>\n",
       "      <td>0.071997</td>\n",
       "      <td>0.074054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.036766</td>\n",
       "      <td>0.046499</td>\n",
       "      <td>0.238077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      kospi       GMV  Tangency\n",
       "0  0.100272  0.100403  0.124856\n",
       "1  0.104019  0.113553  0.152245\n",
       "0  0.127488  0.071997  0.074054\n",
       "0  0.036766  0.046499  0.238077"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd_summ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "sd_summ.to_csv(\"outputs/sd_summ2.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2) Summary of test result**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "kospi       0.044938\n",
       "GMV         0.045417\n",
       "Tangency    0.063051\n",
       "dtype: float64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summarize_sd(select_model(test_res[\"return\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of `[\"summary\"]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "def method_avg(x):\n",
    "    n = x.shape[0]\n",
    "    y = pd.DataFrame(x.loc[:, [\"cumsum\", \"sd\", \"info_r\"]] \\\n",
    "                      .to_numpy() \\\n",
    "                      .reshape(2, -1, order=\"F\") \\\n",
    "                      .mean(axis=0) \\\n",
    "                      .reshape(n//2, -1, order=\"F\") \\\n",
    "                      .round(3))\n",
    "    y.columns = [\"cumsum\", \"sd\", \"info_r\"]\n",
    "    \n",
    "    return pd.concat([x.loc[:, [\"with\", \"n_time\"]] \\\n",
    "                       .drop_duplicates() \\\n",
    "                       .reset_index(drop=True), y], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1) Summary of validation results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "summ_list = np.dstack(map(lambda x: x[\"summary\"].loc[:, [\"cumsum\", \"sd\", \"info_r\"]] \\\n",
    "                                                .to_numpy(), valid_res)).sum(axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "summ_list = summ_list / len(valid_res)\n",
    "summ_list = pd.DataFrame(summ_list, columns=[\"cumsum\", \"sd\", \"info_r\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "valid_summ = method_avg(pd.concat([valid_res[0][\"summary\"].loc[:, [\"with\", \"n_time\", \"method\"]].reset_index(drop=True), summ_list], axis=1))\n",
    "valid_summ[\"rank\"] = np.argsort(-valid_summ.info_r) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>with</th>\n",
       "      <th>n_time</th>\n",
       "      <th>cumsum</th>\n",
       "      <th>sd</th>\n",
       "      <th>info_r</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>factors</td>\n",
       "      <td>6</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.024</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>factors</td>\n",
       "      <td>8</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.016</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>factors</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.038</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>factors</td>\n",
       "      <td>12</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.002</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>factors_residual</td>\n",
       "      <td>6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.021</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>factors_residual</td>\n",
       "      <td>8</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.024</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>factors_residual</td>\n",
       "      <td>10</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.002</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>factors_residual</td>\n",
       "      <td>12</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.005</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>market_residual</td>\n",
       "      <td>6</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.019</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>market_residual</td>\n",
       "      <td>8</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.026</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>market_residual</td>\n",
       "      <td>10</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.016</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>market_residual</td>\n",
       "      <td>12</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.014</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>return</td>\n",
       "      <td>6</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.006</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>return</td>\n",
       "      <td>8</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.024</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>return</td>\n",
       "      <td>10</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.005</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>return</td>\n",
       "      <td>12</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.002</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                with  n_time  cumsum     sd  info_r  rank\n",
       "0            factors       6   0.100  0.027   0.024    10\n",
       "1            factors       8   0.096  0.029   0.016     1\n",
       "2            factors      10   0.000  0.038  -0.011     6\n",
       "3            factors      12   0.069  0.024   0.002    14\n",
       "4   factors_residual       6   0.098  0.027   0.021     5\n",
       "5   factors_residual       8   0.110  0.029   0.024     9\n",
       "6   factors_residual      10   0.036  0.035   0.002     2\n",
       "7   factors_residual      12   0.073  0.024   0.005    11\n",
       "8    market_residual       6   0.092  0.022   0.019    12\n",
       "9    market_residual       8   0.101  0.022   0.026    13\n",
       "10   market_residual      10   0.082  0.023   0.016     8\n",
       "11   market_residual      12   0.083  0.022   0.014    15\n",
       "12            return       6   0.146  0.052   0.006     4\n",
       "13            return       8   0.135  0.053   0.024     7\n",
       "14            return      10   0.062  0.045   0.005    16\n",
       "15            return      12   0.023  0.037   0.002     3"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_summ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2) Summary of test result**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_summ = method_avg(test_res[\"summary\"])\n",
    "test_summ[\"rank\"] = np.argsort(-test_summ.info_r) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>n_time</th>\n",
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       "      <th>sd</th>\n",
       "      <th>info_r</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>factors</td>\n",
       "      <td>6</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.037</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>factors</td>\n",
       "      <td>8</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.054</td>\n",
       "      <td>-0.055</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>factors</td>\n",
       "      <td>10</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.043</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>factors</td>\n",
       "      <td>12</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.050</td>\n",
       "      <td>-0.026</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>factors_residual</td>\n",
       "      <td>6</td>\n",
       "      <td>0.223</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.094</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>factors_residual</td>\n",
       "      <td>8</td>\n",
       "      <td>0.171</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.032</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>factors_residual</td>\n",
       "      <td>10</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.047</td>\n",
       "      <td>-0.038</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>factors_residual</td>\n",
       "      <td>12</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.050</td>\n",
       "      <td>-0.030</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>market_residual</td>\n",
       "      <td>6</td>\n",
       "      <td>0.279</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.148</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>market_residual</td>\n",
       "      <td>8</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.251</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>market_residual</td>\n",
       "      <td>10</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.257</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>market_residual</td>\n",
       "      <td>12</td>\n",
       "      <td>0.370</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.287</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>return</td>\n",
       "      <td>6</td>\n",
       "      <td>0.275</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.137</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>return</td>\n",
       "      <td>8</td>\n",
       "      <td>0.292</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.152</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>return</td>\n",
       "      <td>10</td>\n",
       "      <td>0.261</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.126</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>return</td>\n",
       "      <td>12</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.052</td>\n",
       "      <td>-0.045</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                with  n_time  cumsum     sd  info_r  rank\n",
       "0            factors       6   0.179  0.052   0.037    12\n",
       "1            factors       8   0.109  0.054  -0.055    11\n",
       "2            factors      10   0.178  0.054   0.043    10\n",
       "3            factors      12   0.132  0.050  -0.026    14\n",
       "4   factors_residual       6   0.223  0.054   0.094     9\n",
       "5   factors_residual       8   0.171  0.059   0.032    13\n",
       "6   factors_residual      10   0.128  0.047  -0.038    15\n",
       "7   factors_residual      12   0.127  0.050  -0.030     5\n",
       "8    market_residual       6   0.279  0.051   0.148     3\n",
       "9    market_residual       8   0.387  0.055   0.251     1\n",
       "10   market_residual      10   0.362  0.048   0.257     6\n",
       "11   market_residual      12   0.370  0.046   0.287     4\n",
       "12            return       6   0.275  0.065   0.137     8\n",
       "13            return       8   0.292  0.070   0.152     7\n",
       "14            return      10   0.261  0.071   0.126    16\n",
       "15            return      12   0.113  0.052  -0.045     2"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_summ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validation plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cumret_plot(model):\n",
    "    model = pd.melt(model, id_vars=['time'], value_vars=['kospi', \"GMV\", \"Tangency\"])\n",
    "    model.columns = [\"time\", \"asset\", \"logret\"]\n",
    "    model[\"asset\"] = model.asset.astype(\"category\")\n",
    "    for a in model.asset.unique():\n",
    "        model.loc[model.asset == a, \"cumret\"] = model.loc[model.asset == a, \"logret\"].cumsum().to_numpy()\n",
    "        #model.loc[model.asset == a, \"cumret_lin\"] = np.exp(model.loc[model.asset == a, \"cumret\"].to_numpy())\n",
    "    \n",
    "    fig = plt.figure(figsize=(12,6))\n",
    "    ax = sns.lineplot(x=\"time\", y=\"cumret\", hue=\"asset\",\n",
    "                      marker=\"+\", mec=None, dashes=False, data=model)\n",
    "    ax.xaxis.set_tick_params(rotation=90)\n",
    "    \n",
    "    plt.xlabel(\"Time\", size=16)\n",
    "    plt.ylabel(\"Cumulative return\", size=16)\n",
    "    plt.grid()\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = select_model(pd.concat([v[\"return\"] for v in valid_res]).reset_index(drop=True))\n",
    "valid_cumret_total_plot = cumret_plot(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "valid_cumret_part_plot = [cumret_plot(select_model(v[\"return\"])) for v in valid_res]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Save figures**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "figures saved\n"
     ]
    }
   ],
   "source": [
    "valid_cumret_total_plot[0].savefig(\"outputs/valid_plot_00(2).png\")\n",
    "[fig.savefig(f\"outputs/valid_plot_0{idx+1}(2).png\") for idx, (fig, ax) in enumerate(valid_cumret_part_plot)]\n",
    "\n",
    "print(\"figures saved\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_ret = test_res[\"return\"].loc[:, [\"time\", \"kospi\", \"factors_8_GMV\", \"factors_8_Tangency\"]]\n",
    "test_ret.columns = [\"time\", \"kospi\", \"GMV\", \"Tangency\"]\n",
    "test_ret.iloc[:, 1:] = test_ret.iloc[:, 1:].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>time</th>\n",
       "      <th>kospi</th>\n",
       "      <th>GMV</th>\n",
       "      <th>Tangency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-4</td>\n",
       "      <td>-0.050561</td>\n",
       "      <td>0.029067</td>\n",
       "      <td>0.017840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-1</td>\n",
       "      <td>-0.031081</td>\n",
       "      <td>0.057541</td>\n",
       "      <td>0.118789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-2</td>\n",
       "      <td>0.024265</td>\n",
       "      <td>-0.021340</td>\n",
       "      <td>0.167135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-3</td>\n",
       "      <td>-0.038072</td>\n",
       "      <td>0.005758</td>\n",
       "      <td>0.101273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-4</td>\n",
       "      <td>-0.029967</td>\n",
       "      <td>-0.015840</td>\n",
       "      <td>0.086028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-1</td>\n",
       "      <td>-0.056762</td>\n",
       "      <td>0.016858</td>\n",
       "      <td>0.064041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-2</td>\n",
       "      <td>-0.035809</td>\n",
       "      <td>0.046115</td>\n",
       "      <td>0.013034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-3</td>\n",
       "      <td>-0.011466</td>\n",
       "      <td>0.037461</td>\n",
       "      <td>-0.094428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-4</td>\n",
       "      <td>-0.024071</td>\n",
       "      <td>0.095484</td>\n",
       "      <td>-0.092357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-1</td>\n",
       "      <td>0.024783</td>\n",
       "      <td>0.188314</td>\n",
       "      <td>0.007016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017-2</td>\n",
       "      <td>0.119118</td>\n",
       "      <td>0.157476</td>\n",
       "      <td>-0.019079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2017-3</td>\n",
       "      <td>0.149714</td>\n",
       "      <td>0.203660</td>\n",
       "      <td>0.014659</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      time     kospi       GMV  Tangency\n",
       "0   2014-4 -0.050561  0.029067  0.017840\n",
       "1   2015-1 -0.031081  0.057541  0.118789\n",
       "2   2015-2  0.024265 -0.021340  0.167135\n",
       "3   2015-3 -0.038072  0.005758  0.101273\n",
       "4   2015-4 -0.029967 -0.015840  0.086028\n",
       "5   2016-1 -0.056762  0.016858  0.064041\n",
       "6   2016-2 -0.035809  0.046115  0.013034\n",
       "7   2016-3 -0.011466  0.037461 -0.094428\n",
       "8   2016-4 -0.024071  0.095484 -0.092357\n",
       "9   2017-1  0.024783  0.188314  0.007016\n",
       "10  2017-2  0.119118  0.157476 -0.019079\n",
       "11  2017-3  0.149714  0.203660  0.014659"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_cumret_plot = cumret_plot(select_model(test_res[\"return\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Save figures**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_cumret_plot[0].savefig(\"outputs/test-plot(2).png\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}