portfolio.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 포트폴리오 구성 전략\n",
    "\n",
    "어떠한 종목에 투자할 지 선택하는 것도 즁요하지만, 비중을 어떻게 배분하느냐에 따라 성과가 달라지므로 포트폴리오 구성 전략 역시 중요하다. 물론 가장 간편한 형태인 동일비중, 즉 모든 종목에 동일한 비중으로 투자를 할 수도 있다. 그러나 최적의 포트폴리오를 구성한다면 훨씬 안정적인 성과를 거둘 수 있으며, 파이썬에서는 riskfolio-lib 패키지를 이용하면 매우 간단하게 최적 포트폴리오를 구성할 수도 있다.\n",
    "\n",
    "```{note}\n",
    "riskfolio-lib 패키지가 버전업이 되면서 C++와 관련하여 문제가 발생할 수 있다. 패키지 설치에 오류가 있는 경우 하위 버전으로 설치한 후 실습하길 추천하며, 프롬프트에서 다음과 같이 입력하면 3.3.0 버전으로 설치가 된다.\n",
    "\n",
    "pip install riskfolio-lib==3.3.0\n",
    "```\n",
    "\n",
    "\n",
    "\n",
    "이번 장에서는 일반적으로 많이 사용되는 최대샤프지수 포트폴리오, 최소분산 포트폴리오, 위험균형 포트폴리오를 구현해보도록 하겠다. 먼저 포트폴리오 구성을 위해 글로벌 자산을 대표하는 ETF 데이터를 다운로드 한 후 DB에 저장한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "import pymysql\n",
    "from sqlalchemy import create_engine\n",
    "\n",
    "tickers = [\n",
    "    'SPY',  # 미국 주식\n",
    "    'IEV',  # 유럽 주식 \n",
    "    'EWJ',  # 일본 주식\n",
    "    'EEM',  # 이머징 주식\n",
    "    'TLT',  # 미국 장기채\n",
    "    'IEF',  # 미국 중기채\n",
    "    'IYR',  # 미국 리츠\n",
    "    'RWX',  # 글로벌 리츠\n",
    "    'GLD',  # 금\n",
    "    'DBC'  # 상품\n",
    "]\n",
    "\n",
    "all_data = {}\n",
    "for ticker in tickers:\n",
    "    all_data[ticker] = yf.download(ticker, start='1993-01-22')\n",
    "\n",
    "prices = pd.DataFrame(\n",
    "    {tic: data['Adj Close']\n",
    "     for tic, data in all_data.items()})\n",
    "\n",
    "engine = create_engine('mysql+pymysql://root:1234@127.0.0.1:3306/stock_db')\n",
    "prices.to_sql(name='sample_etf', con=engine, index=True, if_exists='replace')\n",
    "engine.dispose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 글로벌 대표 자산 ETF의 티커를 입력한다.\n",
    "2. `download()` 함수를 이용해 야후 파이낸스에서 데이터를 받으며, 기간은 1993년 1월 22일부터 한다.\n",
    "3. 수정주가에 해당하는 부분만 선택하여 데이터프레임으로 만든다.\n",
    "4. DB의 sample_etf 테이블에 해당 데이터를 저장한다.\n",
    "\n",
    "## 수익률 계산 및 상관관계 확인하기\n",
    "\n",
    "먼저, 수익률을 계산한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>SPY</th>\n",
       "      <th>IEV</th>\n",
       "      <th>EWJ</th>\n",
       "      <th>EEM</th>\n",
       "      <th>TLT</th>\n",
       "      <th>IEF</th>\n",
       "      <th>IYR</th>\n",
       "      <th>RWX</th>\n",
       "      <th>GLD</th>\n",
       "      <th>DBC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-02-05</th>\n",
       "      <td>-0.0036</td>\n",
       "      <td>-0.0050</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>-0.0202</td>\n",
       "      <td>-0.0097</td>\n",
       "      <td>-0.0203</td>\n",
       "      <td>-0.0078</td>\n",
       "      <td>-0.0055</td>\n",
       "      <td>0.0041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-06</th>\n",
       "      <td>0.0029</td>\n",
       "      <td>0.0075</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>0.0227</td>\n",
       "      <td>0.0098</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>0.0138</td>\n",
       "      <td>0.0075</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>0.0037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-07</th>\n",
       "      <td>0.0083</td>\n",
       "      <td>-0.0027</td>\n",
       "      <td>0.0057</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>-0.0018</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>-0.0031</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>0.0045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-08</th>\n",
       "      <td>0.0004</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>-0.0026</td>\n",
       "      <td>-0.0056</td>\n",
       "      <td>-0.0058</td>\n",
       "      <td>-0.0033</td>\n",
       "      <td>0.0069</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>-0.0009</td>\n",
       "      <td>0.0054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-09</th>\n",
       "      <td>0.0058</td>\n",
       "      <td>0.0029</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0043</td>\n",
       "      <td>-0.0020</td>\n",
       "      <td>-0.0018</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>-0.0028</td>\n",
       "      <td>-0.0039</td>\n",
       "      <td>0.0027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SPY     IEV     EWJ     EEM     TLT     IEF     IYR     RWX  \\\n",
       "Date                                                                         \n",
       "2024-02-05 -0.0036 -0.0050 -0.0048  0.0013 -0.0202 -0.0097 -0.0203 -0.0078   \n",
       "2024-02-06  0.0029  0.0075 -0.0002  0.0227  0.0098  0.0054  0.0138  0.0075   \n",
       "2024-02-07  0.0083 -0.0027  0.0057  0.0013 -0.0048 -0.0018 -0.0002 -0.0031   \n",
       "2024-02-08  0.0004 -0.0002 -0.0026 -0.0056 -0.0058 -0.0033  0.0069 -0.0043   \n",
       "2024-02-09  0.0058  0.0029  0.0036  0.0043 -0.0020 -0.0018  0.0006 -0.0028   \n",
       "\n",
       "               GLD     DBC  \n",
       "Date                        \n",
       "2024-02-05 -0.0055  0.0041  \n",
       "2024-02-06  0.0052  0.0037  \n",
       "2024-02-07 -0.0003  0.0045  \n",
       "2024-02-08 -0.0009  0.0054  \n",
       "2024-02-09 -0.0039  0.0027  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets = prices.pct_change(1).dropna()\n",
    "\n",
    "rets.tail().round(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. `pct_change()` 함수를 통해 수익률을 계산하며, `dropna()` 함수를 통해 결측치를 제거한다.\n",
    "\n",
    "이번에는 각 ETF의 수익률 간 상관관계를 살펴보도록 하자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.heatmap(rets.corr().round(2), annot = True, annot_kws = {\"size\" : 16}, cmap='coolwarm')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "같은 자산군 내에서는 강한 상관관계를 보이며, 주식과 채권 간에는 매우 낮은 상관관계를 보인다. 또한 주식과 리츠 간에도 꽤 높은 상관관계를 보인다.\n",
    "\n",
    "## 최대샤프지수 포트폴리오\n",
    "\n",
    "최대샤프지수 포트폴리오(Maximum Sharpe Ratio Portfolio)란 샤프지수가 최대가 되도록 하는 포트폴리오를 구성하는 것이다. 샤프지수는 $\\frac{R_p-R_f}{\\sigma_p}$, 즉 포트폴리오의 수익을 변동성으로 나눈 값이며, 위험 대비 수익률이라고도 볼 수 있다. 따라서 샤프지수가 최대가 되는 포트폴리오는 위험은 낮으면서 수익은 높은 지점이다. riskfolio-lib 패키지의 함수를 이용해 해당 포트폴리오를 구성하기 위한 자산별 투자비중을 구해보도록 하자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SPY</th>\n",
       "      <th>IEV</th>\n",
       "      <th>EWJ</th>\n",
       "      <th>EEM</th>\n",
       "      <th>TLT</th>\n",
       "      <th>IEF</th>\n",
       "      <th>IYR</th>\n",
       "      <th>RWX</th>\n",
       "      <th>GLD</th>\n",
       "      <th>DBC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weights</th>\n",
       "      <td>0.258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6381</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "           SPY  IEV  EWJ  EEM  TLT     IEF  IYR  RWX    GLD  DBC\n",
       "weights  0.258  0.0  0.0  0.0  0.0  0.6381  0.0  0.0  0.104  0.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import riskfolio as rp\n",
    "\n",
    "# 포트폴리오 객체 생성\n",
    "port = rp.Portfolio(returns=rets)\n",
    "\n",
    "# 입력값 계산\n",
    "method_mu = 'hist'  # 역사적 데이터를 바탕으로 기대 수익률 추정\n",
    "method_cov = 'hist'  # 역사적 데이터를 바탕으로 분산-공분산 행렬 추정\n",
    "\n",
    "# 객체에 수익률 및 분산-공분산 입력\n",
    "port.assets_stats(method_mu=method_mu, method_cov=method_cov)\n",
    "\n",
    "model = 'Classic'  # 모델 입력. Classic(역사적 데이터), BL(블랙-리터만), FM(팩터 모형)\n",
    "rm = 'MV'  # 위험 측정 방법. MV는 표준편차를 의미\n",
    "obj = 'Sharpe'  # 목적함수. MinRisk(최소분산), MaxRet(최대수익률), Utility(효용) or Sharpe(샤프지수)\n",
    "hist = True  # 역사적 데이터 사용\n",
    "rf = 0  # 무위험 수익률\n",
    "l = 0  # 위험 회피 계수, obj가 'Utility' 일 때만 사용\n",
    "\n",
    "# 최적화\n",
    "w = port.optimization(model=model, rm=rm, obj=obj, rf=rf, l=l, hist=hist)\n",
    "\n",
    "round(w.T, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. `Portfolio()` 함수를 통해 포트폴리오 객체를 생성하며, `returns`에는 수익률 데이터를 입력한다.\n",
    "2. 'mu(수익률)'과 'cov(분산-공분산 행렬)'을 구한 후 `assets_stats()` 함수에 이를 입력한다.\n",
    "3. 구하고자 하는 목적에 맞게 각종 파라미터를 입력한다.\n",
    "4. `optimization()` 함수를 통해 최대샤프지수를 만족하는 포트폴리오의 해를 구한다.\n",
    "\n",
    "이처럼 패키지를 사용할 경우 최적의 포트폴리오에 대한 수학적 지식이 부족하거나 복잡한 계산을 하지 않고도, 얼마든지 이를 만족하는 해를 구할 수 있다. 이번에는 효율적 투자선을 구성해보도록 하자. 효율적 투자선이란 각 포트폴리오의 기대수익률과 위험 수준을 바탕으로, 구성할 수 있는 모든 포트폴리오를 나타낸 것이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>SPY</th>\n",
       "      <th>IEV</th>\n",
       "      <th>EWJ</th>\n",
       "      <th>EEM</th>\n",
       "      <th>TLT</th>\n",
       "      <th>IEF</th>\n",
       "      <th>IYR</th>\n",
       "      <th>RWX</th>\n",
       "      <th>GLD</th>\n",
       "      <th>DBC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1348</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.7911</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2158</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.7238</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0604</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.2463</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.6661</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0876</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.2658</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0260</td>\n",
       "      <td>0.6069</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1013</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.2821</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0674</td>\n",
       "      <td>0.5405</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      SPY  IEV     EWJ  EEM     TLT     IEF  IYR  RWX     GLD    DBC\n",
       "0  0.1348  0.0  0.0051  0.0  0.0000  0.7911  0.0  0.0  0.0000  0.069\n",
       "1  0.2158  0.0  0.0000  0.0  0.0000  0.7238  0.0  0.0  0.0604  0.000\n",
       "2  0.2463  0.0  0.0000  0.0  0.0000  0.6661  0.0  0.0  0.0876  0.000\n",
       "3  0.2658  0.0  0.0000  0.0  0.0260  0.6069  0.0  0.0  0.1013  0.000\n",
       "4  0.2821  0.0  0.0000  0.0  0.0674  0.5405  0.0  0.0  0.1100  0.000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points = 50  # 효율적 투자선을 구성하는 샘플 갯수\n",
    "frontier = port.efficient_frontier(model=model,\n",
    "                                   rm=rm,\n",
    "                                   points=points,\n",
    "                                   rf=rf,\n",
    "                                   hist=hist)\n",
    "\n",
    "frontier.T.head().round(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 몇 개의 점으로 효율적 투자선을 구성할 지 정한다.\n",
    "2. `efficient_frontier()` 함수를 이용해 효율적 투자선을 이루는 포트폴리오들의 비중을 계산한다.\n",
    "\n",
    "결과에 나오는 비중들을 통해 효율적 투자선을 구성할 수 있다. 이번엔 그림으로 나타내보도록 하자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = 'Max Risk Adjusted Return Portfolio' \n",
    "mu = port.mu  # 기대 수익률\n",
    "cov = port.cov  # 분산-공분산 행렬\n",
    "returns = port.returns  # 자산 별 수익률\n",
    "\n",
    "ax = rp.plot_frontier(w_frontier=frontier,\n",
    "                      mu=mu,\n",
    "                      cov=cov,\n",
    "                      returns=returns,\n",
    "                      rm=rm,\n",
    "                      rf=rf,\n",
    "                      alpha=0.05,\n",
    "                      cmap='viridis',\n",
    "                      w=w,\n",
    "                      label=label,\n",
    "                      marker='*',\n",
    "                      s=16,\n",
    "                      c='r',\n",
    "                      height=6,\n",
    "                      width=10,\n",
    "                      ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 라벨, 기대 수익률, 분산-공분산 행렬, 자산별 수익률 값을 입력한다.\n",
    "2. `plot_frontier()` 함수를 통해 효율적 투자선을 시각화한다. 각종 파라미터를 수정하여 그래프를 꾸밀수도 있다.\n",
    "\n",
    "각 점의 색은 샤프지수를 의미하며, 붉은색 별은 샤프지수가 최대가 되는 점을 의미한다. \n",
    "\n",
    "## 최소분산 포트폴리오\n",
    "\n",
    "효율적 투자선과 무위험 수익률의 접점에 해당하는 최대샤프지수 포트폴리오는 이론적으로 많이 사용되는 포트폴리오 구성 방법이지만, 실무에서는 잘 사용되지 않는다. 그 이유는 크게 다음가 같다.\n",
    "\n",
    "1. 변동성 및 상관관계는 시간이 지나도 어느정도 유지되는 경향이 있다.\n",
    "2. 기대 수익률을 추정하는 것은 매우 어렵다. 이론에서는 과거 수익률을 미래 수익률의 예측치로 사용하지만 실제 투자에서 사용하기는 무리가 있는 가정이다.\n",
    "3. 위 결과를 살펴보면 10개 자산 중 일부 자산으로만 포트폴리오가 구성된다. 즉 상관관계가 높은 자산이 있으면 하나에만 투자하는 결과를 낳는다. 그러나 현실에서는 상관관계가 높은 자산에도 투자해야 하는 경우가 많다.\n",
    "\n",
    "따라서 실무에서는 1번과 2번의 이유로 인해 기대 수익률에 대한 추정이 필요하지 않은 '최소분산 포트폴리오'를 사용하는 경우가 많으며, 3번의 이유로 인해 각종 제약조건을 추가하기도 한다. '최소분산 포트폴리오(Minimum Variance Portfolio)'는 변동성이 최소인 포트폴리오며, 이 역시 패키지를 이용하면 손쉽게 계산할 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SPY</th>\n",
       "      <th>IEV</th>\n",
       "      <th>EWJ</th>\n",
       "      <th>EEM</th>\n",
       "      <th>TLT</th>\n",
       "      <th>IEF</th>\n",
       "      <th>IYR</th>\n",
       "      <th>RWX</th>\n",
       "      <th>GLD</th>\n",
       "      <th>DBC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weights</th>\n",
       "      <td>0.1348</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7911</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            SPY  IEV     EWJ  EEM  TLT     IEF  IYR  RWX  GLD    DBC\n",
       "weights  0.1348  0.0  0.0051  0.0  0.0  0.7911  0.0  0.0  0.0  0.069"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import riskfolio as rp\n",
    "\n",
    "port = rp.Portfolio(returns=rets)\n",
    "method_mu = 'hist'\n",
    "method_cov = 'hist'\n",
    "port.assets_stats(method_mu=method_mu, method_cov=method_cov)\n",
    "\n",
    "model = 'Classic'\n",
    "rm = 'MV'\n",
    "obj = 'MinRisk'  # 목적함수. MinRisk(최소분산), MaxRet(최대수익률), Utility(효용) or Sharpe(샤프지수)\n",
    "hist = True\n",
    "rf = 0\n",
    "l = 0\n",
    "\n",
    "w = port.optimization(model=model, rm=rm, obj=obj, rf=rf, l=l, hist=hist)\n",
    "\n",
    "round(w.T, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "최대샤프지수 포트폴리오를 계산하는 코드에서 obj 부분만 최소분산에 해당하는 'MinRisk'로 변경하였다. 즉 포트폴리오의 변동성을 최소화 하기 위해서는 각 자산 별로 해당 비중만큼 투자하면 된다. 이번에는 한눈에 확인하기 쉽게 시각화를 해보도록 하자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = rp.plot_bar(w=w, title='Portfolio',  kind='h', ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`plot_bar()` 함수를 이용하면 비중을 막대그래프로 나타낼 수 있다. 그림을 살펴보면 채권 ETF가 대부분의 비중을 구성하므로, 이를 이용해 그대로 투자하기는 문제가 있다. 반면 일부 자산은 투자비중이 0%, 즉 전혀 투자하지 않기도 한다. 이처럼 변동성이 가장 낮은 종목에 대부분의 비중이 투자되는 구석해(Corner Solution) 문제를 해결하기 각종 제약조건을 추가해 줄 필요가 있다.\n",
    "\n",
    "### 최소 및 최대 투자비중 제약조건\n",
    "\n",
    "모든 자산에 최소 투자비중 5%, 최대 투자비중 20%의 제약조건을 설정하도록 하겠다. 먼저 아래와 같이 유니버스에 해당하는 ETF명과 해당 ETF의 자산군을 데이터프레임 형태로 만든다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Asset</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SPY</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IEV</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EWJ</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EEM</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TLT</td>\n",
       "      <td>bond</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>IEF</td>\n",
       "      <td>bond</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>IYR</td>\n",
       "      <td>alternative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>RWX</td>\n",
       "      <td>alternative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GLD</td>\n",
       "      <td>alternative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DBC</td>\n",
       "      <td>alternative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Asset        Class\n",
       "0   SPY        stock\n",
       "1   IEV        stock\n",
       "2   EWJ        stock\n",
       "3   EEM        stock\n",
       "4   TLT         bond\n",
       "5   IEF         bond\n",
       "6   IYR  alternative\n",
       "7   RWX  alternative\n",
       "8   GLD  alternative\n",
       "9   DBC  alternative"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asset_classes = {\n",
    "    'Asset':\n",
    "    ['SPY', 'IEV', 'EWJ', 'EEM', 'TLT', 'IEF', 'IYR', 'RWX', 'GLD', 'DBC'],\n",
    "    'Class': [\n",
    "        'stock', 'stock', 'stock', 'stock', 'bond', 'bond', 'alternative',\n",
    "        'alternative', 'alternative', 'alternative'\n",
    "    ]\n",
    "}\n",
    "\n",
    "asset_classes = pd.DataFrame(asset_classes)\n",
    "asset_classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이번에는 제약조건에 해당하는 데이터프레임을 만든다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Disabled</th>\n",
       "      <th>Type</th>\n",
       "      <th>Set</th>\n",
       "      <th>Position</th>\n",
       "      <th>Sign</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Type Relative</th>\n",
       "      <th>Relative Set</th>\n",
       "      <th>Relative</th>\n",
       "      <th>Factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>All Assets</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>&gt;=</td>\n",
       "      <td>0.05</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>All Assets</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>0.20</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Disabled        Type Set Position Sign  Weight Type Relative Relative Set  \\\n",
       "0     False  All Assets                >=    0.05                              \n",
       "1     False  All Assets                <=    0.20                              \n",
       "\n",
       "  Relative Factor  \n",
       "0                  \n",
       "1                  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "constraints = {'Disabled': [False, False],\n",
    "               'Type': ['All Assets', 'All Assets'],\n",
    "               'Set': ['', ''],\n",
    "               'Position': ['', ''],\n",
    "               'Sign': ['>=', '<='],\n",
    "               'Weight': [0.05, 0.2],\n",
    "               'Type Relative': ['', ''],\n",
    "               'Relative Set': ['', ''],\n",
    "               'Relative': ['', ''],\n",
    "               'Factor': ['', '']}\n",
    "\n",
    "constraints = pd.DataFrame(constraints)\n",
    "constraints"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Disabled: 제약조건의 사용 여부이며, 제약조건 갯수만큼 False를 입력한다.\n",
    "2. Type: 'All Assets'은 모든 자산(종목)에 적용되는 조건을 의미한다. 각 자산에 조건을 적용할 때는 Assets, 하부 항목에 조건을 적용할 때는 Classes를 입력한다.\n",
    "3. Set: 위에서 입력한 유니버스 중 제약조건에 해당하는 열을 의미하며, All Assets의 경우 빈칸으로 둔다.\n",
    "4. Position: 유니버스 중 제약조건에 해당하는 값을 의미하며, All Assets의 경우 빈칸으로 둔다.\n",
    "5. Sign: 제약조건의 부호를 의미한다.\n",
    "6. Weight: 제약조건의 값을 의미한다.\n",
    "7. Type Relative, Relative Set, Relative, Factor: 상대적 제약조건에 해당하며, 빈칸으로 둔다.\n",
    "\n",
    "총 7가지 제약식에 맞게 입력한 후 데이터프레임 형태로 변경한다. 즉, 개별 투자비중이 5% 이상, 20% 이하인 조건이 만들어졌다. 이제 해당 제약조건을 만족하는 최적의 해를 구해보도록 하자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>SPY</th>\n",
       "      <th>IEV</th>\n",
       "      <th>EWJ</th>\n",
       "      <th>EEM</th>\n",
       "      <th>TLT</th>\n",
       "      <th>IEF</th>\n",
       "      <th>IYR</th>\n",
       "      <th>RWX</th>\n",
       "      <th>GLD</th>\n",
       "      <th>DBC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weights</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          SPY   IEV   EWJ   EEM  TLT  IEF   IYR   RWX  GLD  DBC\n",
       "weights  0.05  0.05  0.05  0.05  0.2  0.2  0.05  0.05  0.2  0.1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A, B = rp.assets_constraints(constraints, asset_classes)\n",
    "\n",
    "port.ainequality = A\n",
    "port.binequality = B\n",
    "\n",
    "w = port.optimization(model=model, rm=rm, obj=obj, rf=rf, l=l, hist=hist)\n",
    "\n",
    "w.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. `assets_constraints()` 함수를 통해 제약조건을 행렬 형태로 변경한다. (제약조건은 $Aw \\ge B$ 형태로 입력된다.)\n",
    "2. ainequality와 binequality, 즉 제약조건 부분에 위에서 구한 행렬을 입력한다.\n",
    "3. `optimization()` 함수를 통해 최소분산 포트폴리오를 만족하는 해를 구한다.\n",
    "\n",
    "결과를 살펴보면 각 자산에 적어도 5%가 투자되었으며, 최대 비중이 20%를 넘지 않는다. 이로써 구석해 문제가 다소 해결되었다. 비중을 그래프로 나타내보도록 하자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = rp.plot_bar(w=w, title='Portfolio',  kind='h', ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 각 종목 별 제약조건\n",
    "\n",
    "위에서 추가한 제약조건만으로도 충분히 훌륭한 포트폴리오가 구성되지만, 투자 규모가 커지면 추가적인 제약조건들을 고려해야 할 경우가 생긴다. 벤치마크 비중과의 괴리로 인한 추적오차(Tracking Error)를 고려해야 할 수도 있고, 투자 대상별 거래량을 고려한 제약조건을 추가해야 할 때도 있다. \n",
    "\n",
    "기존 제약조건에는 모든 자산에 동일한 최소 및 최대 투자비중 제약조건을 다루었지만, 자산별로 제약조건을 각각 다르게 설정해야할 때도 있다. 이번에는 {numref}`min_const`과 같이 자산 별로 최소 및 최대 투자비중이 다른 제약조건을 만족하는 포트폴리오를 구성해보도록 하겠다.\n",
    "\n",
    "```{table} 각 자산 별 최소 및 최대 제약조건\n",
    ":name: min_const\n",
    "|제약|SPY|IEV|EWJ|EEM|TLT|IEF|IYR|RWX|GLD|DBC|\n",
    "|---|---|---|---|---|---|---|---|---|---|---\n",
    "|최소|0.10|0.10|0.05|0.05|0.10|0.10|0.05|0.05|0.03|0.03|\n",
    "|최대|0.25|0.25|0.20|0.20|0.20|0.20|0.10|0.10|0.08|0.08|\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Disabled</th>\n",
       "      <th>Type</th>\n",
       "      <th>Set</th>\n",
       "      <th>Position</th>\n",
       "      <th>Sign</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Type Relative</th>\n",
       "      <th>Relative Set</th>\n",
       "      <th>Relative</th>\n",
       "      <th>Factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>Assets</td>\n",
       "      <td>Asset</td>\n",
       "      <td>SPY</td>\n",
       "      <td>&gt;=</td>\n",
       "      <td>0.10</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>Assets</td>\n",
       "      <td>Asset</td>\n",
       "      <td>IEV</td>\n",
       "      <td>&gt;=</td>\n",
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       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>Assets</td>\n",
       "      <td>Asset</td>\n",
       "      <td>EWJ</td>\n",
       "      <td>&gt;=</td>\n",
       "      <td>0.05</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>Assets</td>\n",
       "      <td>Asset</td>\n",
       "      <td>EEM</td>\n",
       "      <td>&gt;=</td>\n",
       "      <td>0.05</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>Assets</td>\n",
       "      <td>Asset</td>\n",
       "      <td>TLT</td>\n",
       "      <td>&gt;=</td>\n",
       "      <td>0.10</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>Assets</td>\n",
       "      <td>Asset</td>\n",
       "      <td>IEF</td>\n",
       "      <td>&gt;=</td>\n",
       "      <td>0.10</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>Assets</td>\n",
       "      <td>Asset</td>\n",
       "      <td>IYR</td>\n",
       "      <td>&gt;=</td>\n",
       "      <td>0.05</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>Assets</td>\n",
       "      <td>Asset</td>\n",
       "      <td>RWX</td>\n",
       "      <td>&gt;=</td>\n",
       "      <td>0.05</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>Assets</td>\n",
       "      <td>Asset</td>\n",
       "      <td>GLD</td>\n",
       "      <td>&gt;=</td>\n",
       "      <td>0.03</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>Assets</td>\n",
       "      <td>Asset</td>\n",
       "      <td>DBC</td>\n",
       "      <td>&gt;=</td>\n",
       "      <td>0.03</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>False</td>\n",
       "      <td>Assets</td>\n",
       "      <td>Asset</td>\n",
       "      <td>SPY</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>0.25</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>False</td>\n",
       "      <td>Assets</td>\n",
       "      <td>Asset</td>\n",
       "      <td>IEV</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>0.25</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>False</td>\n",
       "      <td>Assets</td>\n",
       "      <td>Asset</td>\n",
       "      <td>EWJ</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>0.20</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>Assets</td>\n",
       "      <td>Asset</td>\n",
       "      <td>EEM</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>0.20</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>Assets</td>\n",
       "      <td>Asset</td>\n",
       "      <td>TLT</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>0.20</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>False</td>\n",
       "      <td>Assets</td>\n",
       "      <td>Asset</td>\n",
       "      <td>IEF</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>0.20</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>False</td>\n",
       "      <td>Assets</td>\n",
       "      <td>Asset</td>\n",
       "      <td>IYR</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>0.10</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>Assets</td>\n",
       "      <td>Asset</td>\n",
       "      <td>RWX</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>0.10</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>False</td>\n",
       "      <td>Assets</td>\n",
       "      <td>Asset</td>\n",
       "      <td>GLD</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>0.08</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>False</td>\n",
       "      <td>Assets</td>\n",
       "      <td>Asset</td>\n",
       "      <td>DBC</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>0.08</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Disabled    Type    Set Position Sign  Weight Type Relative Relative Set  \\\n",
       "0      False  Assets  Asset      SPY   >=    0.10                              \n",
       "1      False  Assets  Asset      IEV   >=    0.10                              \n",
       "2      False  Assets  Asset      EWJ   >=    0.05                              \n",
       "3      False  Assets  Asset      EEM   >=    0.05                              \n",
       "4      False  Assets  Asset      TLT   >=    0.10                              \n",
       "5      False  Assets  Asset      IEF   >=    0.10                              \n",
       "6      False  Assets  Asset      IYR   >=    0.05                              \n",
       "7      False  Assets  Asset      RWX   >=    0.05                              \n",
       "8      False  Assets  Asset      GLD   >=    0.03                              \n",
       "9      False  Assets  Asset      DBC   >=    0.03                              \n",
       "10     False  Assets  Asset      SPY   <=    0.25                              \n",
       "11     False  Assets  Asset      IEV   <=    0.25                              \n",
       "12     False  Assets  Asset      EWJ   <=    0.20                              \n",
       "13     False  Assets  Asset      EEM   <=    0.20                              \n",
       "14     False  Assets  Asset      TLT   <=    0.20                              \n",
       "15     False  Assets  Asset      IEF   <=    0.20                              \n",
       "16     False  Assets  Asset      IYR   <=    0.10                              \n",
       "17     False  Assets  Asset      RWX   <=    0.10                              \n",
       "18     False  Assets  Asset      GLD   <=    0.08                              \n",
       "19     False  Assets  Asset      DBC   <=    0.08                              \n",
       "\n",
       "   Relative Factor  \n",
       "0                   \n",
       "1                   \n",
       "2                   \n",
       "3                   \n",
       "4                   \n",
       "5                   \n",
       "6                   \n",
       "7                   \n",
       "8                   \n",
       "9                   \n",
       "10                  \n",
       "11                  \n",
       "12                  \n",
       "13                  \n",
       "14                  \n",
       "15                  \n",
       "16                  \n",
       "17                  \n",
       "18                  \n",
       "19                  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "constraints = {\n",
    "    'Disabled': [\n",
    "        False, False, False, False, False, False, False, False, False, False,\n",
    "        False, False, False, False, False, False, False, False, False, False\n",
    "    ],\n",
    "    'Type': [\n",
    "        'Assets', 'Assets', 'Assets', 'Assets', 'Assets', 'Assets', 'Assets',\n",
    "        'Assets', 'Assets', 'Assets', 'Assets', 'Assets', 'Assets', 'Assets',\n",
    "        'Assets', 'Assets', 'Assets', 'Assets', 'Assets', 'Assets'\n",
    "    ],\n",
    "    'Set': [\n",
    "        'Asset', 'Asset', 'Asset', 'Asset', 'Asset', 'Asset', 'Asset', 'Asset',\n",
    "        'Asset', 'Asset', 'Asset', 'Asset', 'Asset', 'Asset', 'Asset', 'Asset',\n",
    "        'Asset', 'Asset', 'Asset', 'Asset'\n",
    "    ],\n",
    "    'Position': [\n",
    "        'SPY', 'IEV', 'EWJ', 'EEM', 'TLT', 'IEF', 'IYR', 'RWX', 'GLD', 'DBC',\n",
    "        'SPY', 'IEV', 'EWJ', 'EEM', 'TLT', 'IEF', 'IYR', 'RWX', 'GLD', 'DBC'\n",
    "    ],\n",
    "    'Sign': [\n",
    "        '>=', '>=', '>=', '>=', '>=', '>=', '>=', '>=', '>=', '>=', '<=', '<=',\n",
    "        '<=', '<=', '<=', '<=', '<=', '<=', '<=', '<='\n",
    "    ],\n",
    "    'Weight': [\n",
    "        0.10, 0.10, 0.05, 0.05, 0.10, 0.10, 0.05, 0.05, 0.03, 0.03, 0.25, 0.25,\n",
    "        0.20, 0.20, 0.20, 0.20, 0.10, 0.10, 0.08, 0.08\n",
    "    ],\n",
    "    'Type Relative': [\n",
    "        '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',\n",
    "        '', ''\n",
    "    ],\n",
    "    'Relative Set': [\n",
    "        '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',\n",
    "        '', ''\n",
    "    ],\n",
    "    'Relative': [\n",
    "        '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',\n",
    "        '', ''\n",
    "    ],\n",
    "    'Factor': [\n",
    "        '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',\n",
    "        '', ''\n",
    "    ]\n",
    "}\n",
    "\n",
    "constraints = pd.DataFrame(constraints)\n",
    "\n",
    "display(constraints)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Disabled: 제약조건이 총 20개이므로 False를 20번 입력한다.\n",
    "2. Type: 제약조건을 각 자산에 적용하므로 Assets를 입력한다.\n",
    "3. Set: 유니버스 중 티커에 해당하는 Asset을 입력한다.\n",
    "4. Position: 유니버스 중 티커명을 입력한다.\n",
    "5. Sign: 제약조건의 부호를 의미한다.\n",
    "6. Weight: 제약조건의 값을 의미한다.\n",
    "7. Type Relative, Relative Set, Relative, Factor: 상대적 제약조건에 해당하며, 빈칸으로 둔다.\n",
    "\n",
    "각 종목(티커) 별 최소 및 최대 투자비중을 각기 다르게 입력했다. 이를 만족하는 포트폴리오를 계산해보도록 한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>SPY</th>\n",
       "      <th>IEV</th>\n",
       "      <th>EWJ</th>\n",
       "      <th>EEM</th>\n",
       "      <th>TLT</th>\n",
       "      <th>IEF</th>\n",
       "      <th>IYR</th>\n",
       "      <th>RWX</th>\n",
       "      <th>GLD</th>\n",
       "      <th>DBC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weights</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         SPY  IEV   EWJ   EEM  TLT  IEF   IYR   RWX   GLD   DBC\n",
       "weights  0.1  0.1  0.09  0.05  0.2  0.2  0.05  0.05  0.08  0.08"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A, B = rp.assets_constraints(constraints, asset_classes)\n",
    "\n",
    "port.ainequality = A\n",
    "port.binequality = B\n",
    "\n",
    "w = port.optimization(model=model, rm=rm, obj=obj, rf=rf, l=l, hist=hist)\n",
    "\n",
    "w.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "결과를 살펴보면 {numref}`min_const`에서 설정한 최소 및 최대 투자비중의 범위 내에 해가 위치함을 확인할 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = rp.plot_bar(w=w, title='Portfolio',  kind='h', ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 자산군 별 비중\n",
    "\n",
    "마지막으로 개별 자산이 아닌 자산군 별로 제약조건을 다르게 주도록 한다. 즉 {numref}`min_const_2`와 같이 주식 ETF에 투자하는 비중은 최소 40%, 최대 70%이어야 한다. 또한 자산군 내에서 한 종목에 투자 비중이 쏠리는 것을 방지하기 위해 개별 자산의 투자 비중은 최소 3%, 최대 20%로 한다.\n",
    "\n",
    "```{table} 각 자산 별 최소 및 최대 제약조건\n",
    ":name: min_const_2\n",
    "|제약|개별|주식|채권|대체자산|\n",
    "|---|---|---|---|---|\n",
    "|최소|0.03|0.4|0.3|0.1| \n",
    "|최대|0.20|0.7|0.5|0.2|\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Disabled</th>\n",
       "      <th>Type</th>\n",
       "      <th>Set</th>\n",
       "      <th>Position</th>\n",
       "      <th>Sign</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Type Relative</th>\n",
       "      <th>Relative Set</th>\n",
       "      <th>Relative</th>\n",
       "      <th>Factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>All Assets</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>&gt;=</td>\n",
       "      <td>0.03</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>All Assets</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>0.20</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>Classes</td>\n",
       "      <td>Class</td>\n",
       "      <td>stock</td>\n",
       "      <td>&gt;=</td>\n",
       "      <td>0.40</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>Classes</td>\n",
       "      <td>Class</td>\n",
       "      <td>bond</td>\n",
       "      <td>&gt;=</td>\n",
       "      <td>0.30</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>Classes</td>\n",
       "      <td>Class</td>\n",
       "      <td>alternative</td>\n",
       "      <td>&gt;=</td>\n",
       "      <td>0.10</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>Classes</td>\n",
       "      <td>Class</td>\n",
       "      <td>stock</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>0.70</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>Classes</td>\n",
       "      <td>Class</td>\n",
       "      <td>bond</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>0.50</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>Classes</td>\n",
       "      <td>Class</td>\n",
       "      <td>alternative</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>0.20</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Disabled        Type    Set     Position Sign  Weight Type Relative  \\\n",
       "0     False  All Assets                       >=    0.03                 \n",
       "1     False  All Assets                       <=    0.20                 \n",
       "2     False     Classes  Class        stock   >=    0.40                 \n",
       "3     False     Classes  Class         bond   >=    0.30                 \n",
       "4     False     Classes  Class  alternative   >=    0.10                 \n",
       "5     False     Classes  Class        stock   <=    0.70                 \n",
       "6     False     Classes  Class         bond   <=    0.50                 \n",
       "7     False     Classes  Class  alternative   <=    0.20                 \n",
       "\n",
       "  Relative Set Relative Factor  \n",
       "0                               \n",
       "1                               \n",
       "2                               \n",
       "3                               \n",
       "4                               \n",
       "5                               \n",
       "6                               \n",
       "7                               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "constraints = {\n",
    "    'Disabled': [False, False, False, False, False, False, False, False],\n",
    "    'Type': [\n",
    "        'All Assets', 'All Assets', 'Classes', 'Classes', 'Classes', 'Classes',\n",
    "        'Classes', 'Classes'\n",
    "    ],\n",
    "    'Set': ['', '', 'Class', 'Class', 'Class', 'Class', 'Class', 'Class'],\n",
    "    'Position':\n",
    "    ['', '', 'stock', 'bond', 'alternative', 'stock', 'bond', 'alternative'],\n",
    "    'Sign': ['>=', '<=', '>=', '>=', '>=', '<=', '<=', '<='],\n",
    "    'Weight': [0.03, 0.20, 0.40, 0.30, 0.10, 0.70, 0.50, 0.20],\n",
    "    'Type Relative': ['', '', '', '', '', '', '', ''],\n",
    "    'Relative Set': ['', '', '', '', '', '', '', ''],\n",
    "    'Relative': ['', '', '', '', '', '', '', ''],\n",
    "    'Factor': ['', '', '', '', '', '', '', '']\n",
    "}\n",
    "\n",
    "constraints = pd.DataFrame(constraints)\n",
    "\n",
    "display(constraints)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Disabled: 개별 자산의 제약 조건 2개, 자산군 별 제약 조건이 6개 이므로 False를 8개 입력한다.\n",
    "2. Type: 모든 자산(종목)에 적용되는 조건은 'All Assets'을, 자산군에 적용되는 조건은 'Classes'를 입력한다.\n",
    "3. Set: 모든 자산(종목)에 적용되는 조건에는 공백을, 자산군에 적용되는 조건은 열 이름인 'Class'를 입력한다.\n",
    "4. Position: 공백 및 자산군을 입력한다.\n",
    "5. Sign: 제약조건의 부호를 의미한다.\n",
    "6. Weight: 제약조건의 값을 의미한다.\n",
    "7. Type Relative, Relative Set, Relative, Factor: 상대적 제약조건에 해당하며, 빈칸으로 둔다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>SPY</th>\n",
       "      <th>IEV</th>\n",
       "      <th>EWJ</th>\n",
       "      <th>EEM</th>\n",
       "      <th>TLT</th>\n",
       "      <th>IEF</th>\n",
       "      <th>IYR</th>\n",
       "      <th>RWX</th>\n",
       "      <th>GLD</th>\n",
       "      <th>DBC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weights</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         SPY   IEV   EWJ   EEM  TLT  IEF   IYR   RWX   GLD   DBC\n",
       "weights  0.2  0.03  0.14  0.03  0.2  0.2  0.03  0.03  0.11  0.03"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A, B = rp.assets_constraints(constraints, asset_classes)\n",
    "\n",
    "port.ainequality = A\n",
    "port.binequality = B\n",
    "\n",
    "w = port.optimization(model=model, rm=rm, obj=obj, rf=rf, l=l, hist=hist)\n",
    "\n",
    "w.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "위 제약조건을 만족하는 해를 구하였다. 자산군 별 제약조건이 제대로 적용되었는지 확인해보도록 하자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>weights</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>stock</th>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bond</th>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alternative</th>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             weights\n",
       "Class               \n",
       "stock            0.4\n",
       "bond             0.4\n",
       "alternative      0.2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_concat = pd.concat([asset_classes.set_index('Asset'), w], axis=1)\n",
    "w_concat.groupby(['Class']).sum().sort_values(['weights'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "자산군 별 그룹을 묶어 비중을 더해보면 {numref}`min_const_2`에서 설정한 최소 및 최대 투자비중의 범위 내에 해가 위치함을 확인할 수 있다. 이처럼 패키지 내의 함수를 이용하면 복잡한 제약조건도 매우 손쉽게 적용할 수 있다.\n",
    "\n",
    "## 위험균형 포트폴리오\n",
    "\n",
    "포트폴리오를 구성하는 자산들과 전체 위험의 관계를 이해하기 위해서는, 먼저 한계 위험기여도(MRC: Marginal Risk Contribution)와 위험기여도(RC: Risk Contribution)에 대해 알아야 한다. 한계 위험기여도는 특정 자산의 비중을 한 단위 증가시켰을 때 전체 포트폴리오 위험의 증가를 나타내는 단위로서, 수학의 편미분과 같은 개념이다. $i$번째 자산의 한계 위험기여도는 아래와 같이 나타낼 수 있다.\n",
    "\n",
    "$$MRC_i = \\frac{\\partial\\sigma_p}{\\partial w_i} $$\n",
    "\n",
    "$\\sqrt {f'(x)} = \\frac{f'(x)}{2\\sqrt{f(x)}}$인 사실을 이용하면, 한계 위험기여도는 다음과 같이 풀 수 있다. 결과적으로 분자는 분산-공분산 행렬과 각 자산의 비중의 곱, 분모는 포트폴리오의 표준편차 형태로 나타난다.\n",
    "\n",
    "$$\n",
    "\\begin{equation} \n",
    "\\begin{split}\n",
    "\\frac{\\partial\\sigma_p}{\\partial w} & = \\frac{\\partial(\\sqrt{w'\\Omega w})}{\\partial w} \\\\\n",
    "& =\\frac{\\partial(w'\\Omega w)}{\\partial w} \\times \\frac{1}{2\\sqrt{w'\\Omega w}} \\\\\n",
    "& =\\frac{2\\Omega w}{2\\sqrt{w'\\Omega w}} \\\\\n",
    "& =\\frac{\\Omega w}{\\sqrt{w'\\Omega w}}\n",
    "\\end{split}\n",
    "\\end{equation} \n",
    "$$\n",
    "\n",
    "위험기여도는 특정 자산이 포트폴리오 내에서 차지하는 위험의 비중이다. 한계 위험기여도가 큰 자산도 포트폴리오 내에서 비중이 작다면, 포트폴리오 내에서 차지하는 위험의 비중은 작을 것이다. 반면에, 한계 위험기여도가 작은 자산일지라도 비중이 압도적으로 많다면, 포트폴리오 내에서 차지하는 위험의 비중은 클 것이다. 결과적으로 $i$번째 자산의 위험기여도는, $i$번째 자산의 한계 위험기여도와 포트폴리오 내 비중의 곱으로 이루어진다.\n",
    "\n",
    "$$RC_i = \\frac{\\partial\\sigma_p}{\\partial w_i} \\times w_i$$\n",
    "\n",
    "특정 자산이 포트폴리오의 위험을 대부분 차지하는 문제를 막고, 모든 자산이 동일한 위험기여도를 가지는 포트폴리오가 위험균형 포트폴리오(Risk Parity Portfolio) 혹은 동일 위험기여도 포트폴리오(Equal Risk Contribution Portfolio)다. 이를 수식으로 쓰면 다음과 같습니다.\n",
    "\n",
    "$$RC_1 = RC_2 = \\dots = RC_n$$\n",
    "$$\\frac{\\partial\\sigma_p}{\\partial w_1} \\times w_1 = \\frac{\\partial\\sigma_p}{\\partial w_2} \\times w_2\n",
    "= \\dots = \\frac{\\partial\\sigma_p}{\\partial w_n} \\times w_n = \\frac{1}{n}$$\n",
    "\n",
    "다행인 점은 위험균형 포트폴리오를 구하기 위해 위의 수식을 알 필요가 없다는 점이다. riskfolio-lib 패키지의 함수를 이용하면 이 역시 손쉽게 계산할 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>SPY</th>\n",
       "      <th>IEV</th>\n",
       "      <th>EWJ</th>\n",
       "      <th>EEM</th>\n",
       "      <th>TLT</th>\n",
       "      <th>IEF</th>\n",
       "      <th>IYR</th>\n",
       "      <th>RWX</th>\n",
       "      <th>GLD</th>\n",
       "      <th>DBC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weights</th>\n",
       "      <td>0.060684</td>\n",
       "      <td>0.048189</td>\n",
       "      <td>0.058278</td>\n",
       "      <td>0.038824</td>\n",
       "      <td>0.171977</td>\n",
       "      <td>0.359779</td>\n",
       "      <td>0.041175</td>\n",
       "      <td>0.052708</td>\n",
       "      <td>0.090155</td>\n",
       "      <td>0.078231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              SPY       IEV       EWJ       EEM       TLT       IEF       IYR  \\\n",
       "weights  0.060684  0.048189  0.058278  0.038824  0.171977  0.359779  0.041175   \n",
       "\n",
       "              RWX       GLD       DBC  \n",
       "weights  0.052708  0.090155  0.078231  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import riskfolio as rp\n",
    "\n",
    "port = rp.Portfolio(returns=rets)\n",
    "method_mu = 'hist'\n",
    "method_cov = 'hist'\n",
    "port.assets_stats(method_mu=method_mu, method_cov=method_cov)\n",
    "\n",
    "model = 'Classic'\n",
    "rm = 'MV'\n",
    "hist = True\n",
    "rf = 0\n",
    "b = None  # 위험기여도 제약조건\n",
    "\n",
    "w_rp = port.rp_optimization(model=model, rm=rm, rf=rf, b=b, hist=hist)\n",
    "\n",
    "w_rp.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "최대샤프지수 및 최소분산 포트폴리오와 거의 비슷하게 값을 입력하며, `obj` 대신 위험기여도 제약조건에 해당하는 `b` 인자를 입력한다. `rp_optimization()` 함수를 이용하면 위험균형 포트폴리오를 만족하는 해가 계산된다. 해당 비중으로 포트폴리오를 구성하면 모든 자산이 동일한 위험기여도를 가지는지 확인해보도록 하자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = rp.plot_risk_con(w_rp,\n",
    "                      cov=port.cov,\n",
    "                      returns=port.returns,\n",
    "                      rm=rm,\n",
    "                      rf=0,\n",
    "                      alpha=0.01,\n",
    "                      color=\"tab:blue\",\n",
    "                      height=6,\n",
    "                      width=10,\n",
    "                      ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`plot_risk_con()` 함수를 이용하면 투자 비중에 따른 자산들의 위험기여도를 알 수 있으며, 모든 자산이 동일한 위험기여도를 가진다는 것이 확인된다.\n",
    "\n",
    "### 위험예산 포트폴리오\n",
    "\n",
    "모든 자산의 위험기여도가 동일한 값이 아닌, 자산별로 다른 위험기여도를 가지는 포트폴리오를 구성해야 할 경우도 있다. 이러한 포트폴리오를 위험예산 포트폴리오(Risk Budget Portfolio)라고 한다. 위험균형 포트폴리오는 각 자산의 위험예산이 $\\frac{1}{n}$로 동일한 특수 형태이다.\n",
    "\n",
    "먼저 각 자산 별 위험예산을 {numref}`risk_contri`과 같이 정한다. 즉, 1\\~4번 자산(주식)은 각각 15%씩, 5\\~6번 자산(채권)은 각각 10%씩, 7\\~10번 자산(대체자산)은 각각 5%씩 위험예산을 부여한다.\n",
    "\n",
    "```{table} 위험예산 포트폴리오 예시\n",
    ":name: risk_contri\n",
    "|제약|SPY|IEV|EWJ|EEM|TLT|IEF|IYR|RWX|GLD|DBC|\n",
    "|---|---|---|---|---|---|---|---|---|---|---\n",
    "|예산|0.15|0.15|0.15|0.15|0.10|0.10|0.05|0.05|0.05|0.05|\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>SPY</th>\n",
       "      <th>IEV</th>\n",
       "      <th>EWJ</th>\n",
       "      <th>EEM</th>\n",
       "      <th>TLT</th>\n",
       "      <th>IEF</th>\n",
       "      <th>IYR</th>\n",
       "      <th>RWX</th>\n",
       "      <th>GLD</th>\n",
       "      <th>DBC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weights</th>\n",
       "      <td>0.085431</td>\n",
       "      <td>0.068835</td>\n",
       "      <td>0.080791</td>\n",
       "      <td>0.05544</td>\n",
       "      <td>0.179636</td>\n",
       "      <td>0.380988</td>\n",
       "      <td>0.020609</td>\n",
       "      <td>0.026179</td>\n",
       "      <td>0.056458</td>\n",
       "      <td>0.045634</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              SPY       IEV       EWJ      EEM       TLT       IEF       IYR  \\\n",
       "weights  0.085431  0.068835  0.080791  0.05544  0.179636  0.380988  0.020609   \n",
       "\n",
       "              RWX       GLD       DBC  \n",
       "weights  0.026179  0.056458  0.045634  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = [0.15, 0.15, 0.15, 0.15, 0.10, 0.10, 0.05, 0.05, 0.05, 0.05]\n",
    "w_rp = port.rp_optimization(model=model, rm=rm, rf=rf, b=b, hist=hist)\n",
    "\n",
    "w_rp.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "위험균형 포트폴리오를 구하는 코드에서, 위험기여도에 해당하는 b 부분만 변경한다. 즉, 해당 부분에 위험예산을 입력한다. 해당 비중으로 포트폴리오를 구성하면 우리가 원한 위험예산 포트폴리오를 만족하는 확인해보도록 한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1dXUcPnyYGTNm0NfXx8yZM1+wjWeeeYampiYOHz5MXV0dg4OD1NRkn1GGhoaora2lUqnQ0NAw5jZ6e3tpbGykv7+fhoYGKpUKNTU1yTIN1zPZTHV1dQwMDEyrTFN5niqVCrW1tdMq01Sfp/r6egYHB6dVpqk+Tz09PcyaNWtaZZrK8zS87nTKNNXnafj/wXTKNNXnqba29gX/D17qmab6PA0ODj57e7pkmsrzVF9fT6VSSZqpubn5gLufMLJ/fNENMtl5WD/o7oPPnQUq8zPKshMdETjqOvklOK8EyM+xeQ9wmZl9kuyk67e7+12jbdDdP0t2mirWrVvn69evn6AEIYQQQggRATP70WjTj8Z5kNeRncNxJ/Bmsv2G30Q2+lt99aBTee7qQfvM7OS8sJN5bteM8dYZ5lqy3TIuAA4Dvwx86CjkOOY0NzenLiE50R1Ezw9yAHIQPT/IAcgByEGZ80/qNG9m9nLgW+7+ExMs94V8ua/lV716HLiQ7AIBDwFXuvtmM/tj4Cl3vzk/u8V8d/9AfjWhL5Ptd7yI7AC+pe4+fOWfeWRXbHoD2UnxX0XWHG909xUT5dAIshBCCCGEGMbMmt193cjpkznN21fIrsL1SjPbbWbvNLNrzOya8dbLd4t4D9nuED8ku8jA5nz2zWSXx91KdhWvm/N1NpM1wFuAbwPvHm6Oc24EPupZV38P2ej1JrKrTpWeTZs2pS4hOdEdRM8PcgByED0/yAHIAchBmfOHulBI6hHk/v5+ZsyYkezxy0B0B9HzgxyAHETPD3IAcgByUIb8RzyCLI4ebW1tqUtITnQH0fODHIAcRM8PcgByAHJQ5vxqkAtk4cKFqUtITnQH0fODHIAcRM8PcgByAHJQ5vxqkAuks7MzdQnJie4gen6QA5CD6PlBDkAOQA7KnF8NcoE0NjamLiE50R1Ezw9yAHIQPT/IAcgByEGZ86tBFkIIIYQQogo1yAXS19eXuoTkRHcQPT/IAchB9PwgByAHIAdlzq8GuUDmzp2buoTkRHcQPT/IAchB9PwgByAHIAdlzq8GuUD27duXuoTkRHcQPT/IAchB9PwgByAHIAdlzq8LhRRIGU6InZroDqLnBzkAOYieH+QA5ADkoAz5daGQEvD444+nLiE50R1Ezw9yAHIQPT/IAcgByEGZ82sEWQghhBBChEQjyCWgubk5dQnJie4gen6QA5CD6PlBDkAOQA7KnF8jyEIIIYQQIiQaQS4BZf6kVBTRHUTPD3IAchA9P8gByAHIQZnzawRZCCGEEEKERCPIJaClpSV1CcmJ7iB6fpADkIPo+UEOQA5ADsqcXyPIBVKpVKirq0v2+GUguoPo+UEOQA6i5wc5ADkAOShDfo0gl4Bt27alLiE50R1Ezw9yAHIQPT/IAcgByEGZ86tBLpBTTz01dQnJie4gen6QA5CD6PlBDkAOQA7KnF8NcoEcOHAgdQnJie4gen6QA5CD6PlBDkAOQA7KnF8NcoHMnj07dQnJie4gen6QA5CD6PlBDkAOQA7KnF8NcoEMDAykLiE50R1Ezw9yAHIQPT/IAcgByEGZ86tBLpChoaHUJSQnuoPo+UEOQA6i5wc5ADkAOShzfjXIBdLU1JS6hOREdxA9P8gByEH0/CAHIAcgB2XOrwa5QA4ePJi6hOREdxA9P8gByEH0/CAHIAcgB2XOrwa5QBYtWpS6hOREdxA9P8gByEH0/CAHIAcgB2XOrwa5QHbs2JG6hOREdxA9P8gByEH0/CAHIAcgB2XOr0tNF8jQ0BA1NbE/k0R3ED0/yAHIQfT8IAcgByAHZcivS02XgI0bN6YuITnRHUTPD3IAchA9P8gByAHIQZnzawRZCCGEEEKERCPIJaC5uTl1CcmJ7iB6fpADkIPo+UEOQA5ADsqcXyPIQgghhBAiJBpBLgEbNmxIXUJyojuInh/kAOQgen6QA5ADkIMy59cIcoGU4WjN1ER3ED0/yAHIQfT8IAcgByAHZcivEeQS0NramrqE5ER3ED0/yAHIQfT8IAcgByAHZc6vEeQC6e3tZebMmckevwxEdxA9P8gByEH0/CAHIAcgB2XIrxHkErB3797UJSQnuoPo+UEOQA6i5wc5ADkAOShzfjXIBTJ//vzUJSQnuoPo+UEOQA6i5wc5ADkAOShzfjXIBdLT05O6hOREdxA9P8gByEH0/CAHIAcgB2XOrwa5QFIfqVkGojuInh/kAOQgen6QA5ADkIMy5y9vZdOQ+vr61CUkJ7qD6PlBDkAOoucHOQA5ADkoc341yAXS3d2duoTkRHcQPT/IAchB9PwgByAHIAdlzq8GuUAWLFiQuoTkRHcQPT/IAchB9PwgByAHIAdlzj9hg2xmnzOz/Wb26Bjzf8XMHsl/vmtmK6vmXWJmj5nZNjO7rmr6fDO718y25r/nVc27Pl/+MTO7OJ82w8y+bWaPmtm1Vct+1sxWH2n4otm9e3fqEpIT3UH0/CAHIAfR84McgByAHJQ5/4QXCjGznwa6gdvd/SdGmf8a4Ifu3mFmlwIfdvdXmVkt8DhwEbAbeAh4q7tvMbNPAAfd/ea8cZ7n7h80s3OBrwDnA4uAfwPOAv4H8Crg94AN7r4qb8Tf6+7vmmzY1BcKqVQq1NXVJXv8MhDdQfT8IAcgB9HzgxyAHIAclCH/EV8oxN0fAA6OM/+77t6R3/0ecGp++3xgm7s/4e6HgTuAy/N5lwO35bdvA95UNf0Od+939x3Atnw7A8BMoNriR4AbJ6q/TGzevDl1CcmJ7iB6fpADkIPo+UEOQA5ADsqc/2jvg/xO4F/y26cAu6rm7c6nASx093aA/PeJE6xzL3AS8H3gE2Z2GdDs7hNegsXMrjaz9Wa2vr29nQMHDtDe3s6ePXvo6Ohg+/bt9Pb2smXLFoaGhtiwYQMAzc3NAGzYsIGhoSG2bNlCb28v27dvp6Ojgz179jC8vZ07d9Ld3U1rayuVSoWWlpbnbWP4d01NDf39/WzdupWuri7a2trYv38/+/fvp62tja6uLrZu3Up/fz+bNm0adRstLS1UKhVaW1vp7u5m586dSTNt2rRpSplWrlw57TJN5Xk68cQTp12mqT5Py5cvn3aZpvo8DTOdMk3leTr55JOnXaapPk/AtMs01edp+fLl0y7TVJ+nE088cdplmsrztHLlyuSZxmLCXSwAzOzlwLdG28WiapmfAT4DvNbdnzKztwAXD+8CYWZvA8539/eaWae7z61at8Pd55nZLcCD7v6lfPrfAne7+9erlq0H7gEuA/4QWEy2+8ddE+VIvYtFc3Mza9euTfb4ZSC6g+j5QQ5ADqLnBzkAOQA5KEP+sXaxOCoNspmtAP4RuNTdH8+nXUC2P/LwgXbXA7j7x83sMeD17t5uZicD33H3V1Yvk69zT76NB6se6zeATmAvcCHwu2RN9fkT5UjdIAshhBBCiPJwxPsgT2LDi4F/AN423BznPAQsNbMlZtYAXAEMj/LeBVyV374K+GbV9Cvys1YsAZYCP6h6rHnAG4HbgSZgCHCg8cXmKILqr9aiEt1B9PwgByAH0fODHIAcgByUOf9kzmLxFeD1wAJgH/D7QD2Au99qZn8D/CLwo3yVynAnbmY/D3wKqAU+5+435dOPB+4k2z2iDXiLux/M5/0u8GtABXifuw/v04yZ/RnwDXe/38wayRrqU4Bb3f0vJwqrEWQhhBBCCDHMi9rFYrqQukHetGkTy5cvT/b4ZSC6g+j5QQ5ADqLnBzkAOQA5KEN+Ncikb5D7+/uZMWNGsscvA9EdRM8PcgByED0/yAHIAchBGfIfs32QxeRpa2tLXUJyojuInh/kAOQgen6QA5ADkIMy51eDXCALFy5MXUJyojuInh/kAOQgen6QA5ADkIMy51eDXCCdnZ2pS0hOdAfR84McgBxEzw9yAHIAclDm/HEvAF7FL175Ntr2PHnMH+cVp5/GEz/aNfGCL5LFp5zE17/8xSmtIwfFOIieH8rtoCgaG18SZ6Y8ZkTPD3IAcgByUOb8apCBtj1PsuIdHzvmj3NCXR+zK8f+xfDI52+Y8jpyUIyD6Pmh3A6EEEII0C4WhdJYM5S6hOREdxA9P8gBQF9fX+oSkhI9P8gByAHIQZnzq0EukM5KfeoSkhPdQfT8IAcAc+fOTV1CUqLnBzkAOQA5KHN+NcgFsrC+vJ+UiiK6g+j5QQ4A9u3bl7qEpETPD3IAcgByUOb8apALpO1wU+oSkhPdQfT8IAcAixcvTl1CUqLnBzkAOQA5KHN+NcgFsrSxO3UJyYnuIHp+kAOAxx9/PHUJSYmeH+QA5ADkoMz51SAXyObe41KXkJzoDqLnBzkAWL58eeoSkhI9P8gByAHIQZnzq0EukDVNHalLSE50B9HzgxwANDc3py4hKdHzgxyAHIAclDm/GuQC2dAzL3UJyYnuIHp+kAOAtWvXpi4hKdHzgxyAHIAclDm/GuQC0ciZHETPD3IA5R41KYLo+UEOQA5ADsqcXw1ygWjkTA6i5wc5gHKPmhRB9PwgByAHIAdlzq8GuUBWNHWmLiE50R1Ezw9yANDS0pK6hKREzw9yAHIAclDm/GqQC2Rzz8tSl5Cc6A6i5wc5AFi2bFnqEpISPT/IAcgByEGZ86tBLpAzGp9JXUJyojuInh/kAGDbtm2pS0hK9PwgByAHIAdlzq8GuUD2HJ6ZuoTkRHcQPT/IAcCpp56auoSkRM8PcgByAHJQ5vxqkAtkQV1/6hKSE91B9PwgBwAHDhxIXUJSoucHOQA5ADkoc341yAVyaKgudQnJie4gen6QA4DZs2enLiEp0fODHIAcgByUOb8a5AKpN09dQnKiO4ieH+QAYGBgIHUJSYmeH+QA5ADkoMz51SAXSC1qDKI7iJ4f5ABgaGgodQlJiZ4f5ADkAOSgzPnVIBdIz1Bt6hKSE91B9PwgBwBNTU2pS0hK9PwgByAHIAdlzq8GuUDm1R1OXUJyojuInh/kAODgwYOpS0hK9PwgByAHIAdlzq8GuUDadXqr8A6i5wc5AFi0aFHqEpISPT/IAcgByEGZ86tBLpAlM3SBhOgOoucHOQDYsWNH6hKSEj0/yAHIAchBmfOrQS6Q1r45qUtITnQH0fODHACcffbZqUtISvT8IAcgByAHZc6vBrlAVjZ1pi4hOdEdRM8PcgCwcePG1CUkJXp+kAOQA5CDMudXg1wgG3vmpS4hOdEdRM8PcgCwZs2a1CUkJXp+kAOQA5CDMudXg1wgq5s6UpeQnOgOoucHOQBobm5OXUJSoucHOQA5ADkoc341yAXysEbOwjuInh/kAGDt2rWpS0hK9PwgByAHIAdlzq8GuUBWaeQsvIPo+UEOADZs2JC6hKREzw9yAHIAclDm/GqQC6SlZ27qEpIT3UH0/CAHAKtWrUpdQlKi5wc5ADkAOShzfjXIBXJ246HUJSQnuoPo+UEOAFpbW1OXkJTo+UEOQA5ADsqcXw1ygezon5W6hOREdxA9P8gBwJIlS1KXkJTo+UEOQA5ADsqcXw1ygZzc0Ju6hOREdxA9P8gBwN69e1OXkJTo+UEOQA5ADsqcXw1ygXRUGlKXkJzoDqLnBzkAmD9/fuoSkhI9P8gByAHIQZnzq0EukKaawdQlJCe6g+j5QQ4Aenp6UpeQlOj5QQ5ADkAOypxfDXKBDGKpS0hOdAfR84McANTUxH7rjZ4f5ADkAOSgzPnLW9k0ZMDVGER3ED0/yAFAfX196hKSEj0/yAHIAchBmfOrQS6QOTWV1CUkJ7qD6PlBDgC6u7tTl5CU6PlBDkAOQA7KnH/CBtnMPmdm+83s0THmm5n9hZltM7NHzGxN1bxLzOyxfN51VdPnm9m9ZrY1/z2vat71+fKPmdnF+bQZZvZtM3vUzK6tWvazZrb6SMMXzYHKjNQlJCe6g+j5QQ4AFixYkLqEpETPD3IAcgByUOb8kxlB/gJwyTjzLwWW5j9XA38FYGa1wC35/HOBt5rZufk61wH3uftS4L78Pvn8K4Bl+WN+Jt/OxUAzsCJ/DMxsJVDj7g9PMmtyTtHprcI7iJ4f5ABg9+7dqUtISvT8IAcgByAHZc4/YYPs7g8AB8dZ5HLgds/4HjDXzE4Gzge2ufsT7n4YuCNfdnid2/LbtwFvqpp+h7v3u/sOYFu+nQFgJlBX9bgfAW6cOGJ52N6nCyREdxA9P8gBwJlnnpm6hKREzw9yAHIAclDm/EdjH+RTgF1V93fn08aaDrDQ3dsB8t8nTrCte4GTgO8DnzCzy4Bmd5/wDNNmdrWZrTez9e3t7Rw4cID29nb27NlDR0cH27dv52WzZ3NOYxeGs6qpA4DV+e9VTR0YzjmNXTTaIEtmdDO39jCL6ntZWN/H/Lp+Tm94hlk1Fc5qPEQtQ6xo6gRgTb6N4d+vnXOABhvkzBmHmFMzwGkNPZxQ18cJdX2c1tDDnJoBzpxxiAYbZNnMp0fdxoqmTmoZ4qzGQ8yqqXB6wzPMr+tnYX0fi+p7mVt7mPNWr6C3t5ctW7YwNDTEhg0bAGhubgZgw4YNDA0NsWXLFnp7e9m+fTuLTjrxiDItm/n0lDIta+o6okxLZnTTaIOTfp7OW72Cjo4O9uzZw/DzvnPnTrq7u2ltbaVSqdDS0vI8L5dfetERZZrK87Ru9sEjzjSV195wluFsmzZtor+/n61bt9LV1UVbWxv79+9n//79tLW10dXVxQXrVr/o195kMi1revqo/D1N9DydsOB4tm7dSn9/P5s2bXqej+HfLS0tVCoVWltb6e7uZufOnaO+R0z172mi194DDzwwaj2TeZ7Kmmnk39N4mdavXz/tMk31eXrggQemXaapPk+PPvrotMs01efpoYcemnaZpvI8bd68OXmmsTB3H3cBADN7OfAtd/+JUeb9M/Bxd/+v/P59wAeAVwAXu/u78ulvA8539/eaWae7z63aRoe7zzOzW4AH3f1L+fS/Be52969XLVsP3ANcBvwhsJhsBPuuiXKsW7fO169f/4Lp573uIla842MTenip8Mjnb+Ch+++d0jpyML0cRM8PR+ZACCFELMys2d3XjZx+NEaQdwOnVd0/Fdg7znSAffluGOS/90+wrWquJdst4wLgMPDLwIdedIoCGB75ikx0B9HzgxzAc6MeUYmeH+QA5ADkoMz5j0aDfBfw9vxsFq8Gns53m3gIWGpmS8ysgezgu7uq1rkqv30V8M2q6VfkZ61YQnbg3w+GHyg/28UbgduBJmAIcKDxKOQ45mzomTfxQtOc6A6i5wc5AFi7dm3qEpISPT/IAcgByEGZ80/mNG9fAR4EXmlmu83snWZ2jZldky9yN/AE2QF1f002wou7V4D3kO0O8UPgTnffnK9zM3CRmW0FLsrvk8+/E9gCfBt4t7tXX5f2RuCjnu0Xcg+wDtiUP27p0ciZHETPD3IA5R41KYLo+UEOQA5ADsqcv26iBdz9rRPMd+DdY8y7m6yBHjn9KeDCMda5CbhpjHnvr7rdB7xhvNrKhkbO5CB6fpADKPeoSRFEzw9yAHIAclDm/LqSXoEMnx0gMtEdRM8PcgA8eyR2VKLnBzkAOQA5KHN+NcgFsrVvduoSkhPdQfT8IAcAZ511VuoSkhI9P8gByAHIQZnzq0EukMUNPalLSE50B9HzgxwAtLW1pS4hKdHzgxyAHIAclDm/GuQC2TfwkjjZxjEluoPo+UEOABYuXJi6hKREzw9yAHIAclDm/GqQC2Ru3UDqEpIT3UH0/CAHAJ2dnalLSEr0/CAHIAcgB2XOrwa5QPqGpDu6g+j5QQ4AGhtjj6JHzw9yAHIAclDm/PpPJYQQQgghRBVqkAuksWYodQnJie4gen6QA4C+vr7UJSQlen6QA5ADkIMy51eDXCCdlfrUJSQnuoPo+UEOAObOnZu6hKREzw9yAHIAclDm/GqQC2RhfXk/KRVFdAfR84McAOzbty91CUmJnh/kAOQA5KDM+dUgF0jb4abUJSQnuoPo+UEOABYvXpy6hKREzw9yAHIAclDm/GqQC2RpY3fqEpIT3UH0/CAHAI8//njqEpISPT/IAcgByEGZ86tBLpDNvcelLiE50R1Ezw9yALB8+fLUJSQlen6QA5ADkIMy51eDXCBrmjpSl5Cc6A6i5wc5AGhubk5dQlKi5wc5ADkAOShzfjXIBbKhZ17qEpIT3UH0/CAHAGvXrk1dQlKi5wc5ADkAOShzfjXIBaKRMzmInh/kAMo9alIE0fODHIAcgByUOb8a5ALRyJkcRM8PcgDlHjUpguj5QQ5ADkAOypxfDXKBrGjqTF1CcqI7iJ4f5ACgpaUldQlJiZ4f5ADkAOSgzPnVIBfI5p6XpS4hOdEdRM8PcgCwbNmy1CUkJXp+kAOQA5CDMudXg1wgZzQ+k7qE5ER3ED0/yAHAtm3bUpeQlOj5QQ5ADkAOypxfDXKB7Dk8M3UJyYnuIHp+kAOAU089NXUJSYmeH+QA5ADkoMz51SAXyIK6/tQlJCe6g+j5QQ4ADhw4kLqEpETPD3IAcgByUOb8apAL5NBQXeoSkhPdQfT8IAcAs2fPTl1CUqLnBzkAOQA5KHN+NcgFUm+euoTkRHcQPT/IAcDAwEDqEpISPT/IAcgByEGZ86tBLpBa1BhEdxA9P8gBwNDQUOoSkhI9P8gByAHIQZnzq0EukJ6h2tQlJCe6g+j5QQ4AmpqaUpeQlOj5QQ5ADkAOypxfDXKBzKs7nLqE5ER3ED0/yAHAwYMHU5eQlOj5QQ5ADkAOypxfDXKBtOv0VuEdRM8PcgCwaNGi1CUkJXp+kAOQA5CDMudXg1wgS2boAgnRHUTPD3IAsGPHjtQlJCV6fpADkAOQgzLnV4NcIK19c1KXkJzoDqLnBzkAOPvss1OXkJTo+UEOQA5ADsqcXw1ygaxs6kxdQnKiO4ieH+QAYOPGjalLSEr0/CAHIAcgB2XOrwa5QDb2zEtdQnKiO4ieH+QAYM2aNalLSEr0/CAHIAcgB2XOrwa5QFY3daQuITnRHUTPD3IA0NzcnLqEpETPD3IAcgByUOb8apAL5GGNnIV3ED0/yAHA2rVrU5eQlOj5QQ5ADkAOypxfDXKBrNLIWXgH0fODHABs2LAhdQlJiZ4f5ADkAOSgzPnVIBdIS8/c1CUkJ7qD6PlBDgBWrVqVuoSkRM8PcgByAHJQ5vxqkAvk7MZDqUtITnQH0fODHAC0tramLiEp0fODHIAcgByUOb8a5ALZ0T8rdQnJie4gen6QA4AlS5akLiEp0fODHIAcgByUOb8a5AI5uaE3dQnJie4gen6QA4C9e/emLiEp0fODHIAcgByUOb8a5ALpqDSkLiE50R1Ezw9yADB//vzUJSQlen6QA5ADkIMy51eDXCBNNYOpS0hOdAfR84McAPT09KQuISnR84McgByAHJQ5vxrkAhnEUpeQnOgOoucHOQCoqYn91hs9P8gByAHIQZnzl7eyaciAqzGI7iB6fpADgPr6+tQlJCV6fpADkAOQgzLnn1SDbGaXmNljZrbNzK4bZf5xZvZPZtZiZpvN7B0TrWtm883sXjPbmv+eVzXv+nz5x8zs4nzaDDP7tpk9ambXVi37WTNbfaQCimROTSV1CcmJ7iB6fpADgO7u7tQlJCV6fpADkAOQgzLnn7BBNrNa4BbgUuBc4K1mdu6Ixd4NbHH3lcDrgT81s4YJ1r0OuM/dlwL35ffJ518BLAMuAT6Tb+dioBlYAVydL7sSqHH3h48sfrEcqMxIXUJyojuInh/kAGDBggWpS0hK9PwgByAHIAdlzj+ZEeTzgW3u/oS7HwbuAC4fsYwDc8zMgNnAQaAywbqXA7flt28D3lQ1/Q5373f3HcC2fDsDwEygrupxPwLcOMmsyTlFp7cK7yB6fpADgN27d6cuISnR84McgByAHJQ5/2Qa5FOAXVX3d+fTqvk0cA6wF9gE/Ia7D02w7kJ3bwfIf584wePdC5wEfB/4hJldBjS7+7gn0TOzq81svZmtb29v58CBA7S3t7Nnzx46OjrYvn07L5s9m3MauzCcVU0dAKzOf69q6sBwzmnsotEGWTKjm7m1h1lU38vC+j7m1/VzesMzzKqpcFbjIWoZYkVTJwBr8m0M/26wQRpskDNnHGJOzQCnNfRwQl0fJ9T1cVpDD3NqBjhzxiEabJBlM58edRsrmjqpZYizGg8xq6bC6Q3PML+un4X1fSyq72Vu7WHOW72C3t5etmzZwtDQ0LPXOm9ubgaya58PDQ2xZcsWent72b59O4tOOvGIMi2b+fSUMm3vm3VEmZbM6KbRBif9PJ23egUdHR3s2bOH4ed9586ddHd309raSqVSoaWl5XleLr/0oiPKNJXn6dBg7RFnmsprbzjLcLZNmzbR39/P1q1b6erqoq2tjf3797N//37a2tro6urignWrX/RrbzKZnuhrOip/TxM9TycsOJ6tW7fS39/Ppk2bnudj+HdLSwuVSoXW1la6u7vZuXPnqO8RU/17mui119vbO2o9k3meyppp5N/TeJmOO+64aZdpqs9Tb2/vtMs01efpFa94xbTLNNXnac6cOdMu01SepzPPPDN5prEwdx9/AbO3ABe7+7vy+28Dznf391Yt82bgJ4HfBM4ga2ZXku0WMeq6Ztbp7nOrttHh7vPM7BbgQXf/Uj79b4G73f3rVcvWA/cAlwF/CCwGbnf3u8bLsm7dOl+/fv0Lpp/3uotY8Y6PjevhaLCiqZNHeuYe88d55PM38ND9905pHTkoxkH0/FBuB0XR0tLCypUrU5eRjOj5QQ5ADkAOypDfzJrdfd3I6ZMZQd4NnFZ1/1SykeJq3gH8g2dsA3YAZ0+w7j4zOzkv7mRg/xQe71qy3TIuAA4Dvwx8aBJZklJEU1B2ojuInh/kAEj+DyE10fODHIAcgByUOf9kGuSHgKVmtsTMGsgOoBs5UtsGXAhgZguBVwJPTLDuXcBV+e2rgG9WTb8iP2vFEmAp8IPhB8rPdvFG4HagCRgi2we6cbKhUzH81XBkojuInh/kAJ77WjAq0fODHIAcgByUOX/dRAu4e8XM3kO2S0Mt8Dl332xm1+TzbyU7WO4LZrYJMOCD7n4AYLR1803fDNxpZu8ka7Dfkm9vs5ndCWwhO9Dv3e5efemtG4GPurub2T1kZ9DYBNz6YkQUwYaeeRMvNM2J7iB6fpADgLVr16YuISnR84McgByAHJQ5/6TOg+zud7v7We5+hrvflE+7NW+Ocfe97v4Gd1/u7j8xvP/wWOvm059y9wvdfWn++2DVvJvy5V/p7v8yopb3u/v9+e2+/HGXuftfvjgVxx6NnMlB9PwgB1DuUZMiiJ4f5ADkAOSgzPl1Jb0C0ciZHETPD3IA5R41KYLo+UEOQA5ADsqcf8JdLMTRY9nMp9nce1zqMpIS3UH0/FBuB7945dto2/PkMX+ci173Wu69/7+O+eMsPuUkvv7lL05pnSIclDl/UWzatInly5enLiMpciAHZc6vBrlAtvbNTl1CcqI7iJ4fyu2gbc+ThZzqrsMGWfGKnz/mj/PI52+Y8jpFOChz/qI466yzUpeQHDmQgzLn1y4WBbK4oSd1CcmJ7iB6fpADkIPo+QHa2tpSl5AcOZCDMudXg1wg+wZKfya6Y050B9HzgxyAHETPD7Bw4cLUJSRHDuSgzPnVIBfI3LqB1CUkJ7qD6PlBDkAOoucH6OzsTF1CcuRADsqcXw1ygfQNSXd0B9HzgxyAHETPD9DYqFF0OZCDMufXu5QQQgghhBBVqEEukMaaodQlJCe6g+j5QQ5ADqLnB+jr60tdQnLkQA7KnF8NcoF0VupTl5Cc6A6i5wc5ADmInh9g7ty5qUtIjhzIQZnzq0EukIX15f2kVBTRHUTPD3IAchA9P8C+fftSl5AcOZCDMudXg1wgbYebUpeQnOgOoucHOQA5iJ4fYPHixalLSI4cyEGZ86tBLpCljd2pS0hOdAfR84McgBxEzw/w+OOPpy4hOXIgB2XOrwa5QDb3Hpe6hOREdxA9P8gByEH0/ADLly9PXUJy5EAOypxfDXKBrGnqSF1CcqI7iJ4f5ADkIHp+gObm5tQlJEcO5KDM+dUgF8iGnnmpS0hOdAfR84McgBxEzw+wdu3a1CUkRw7koMz51SAXiEZN5CB6fpADkIPo+aHcI2dFIQdyUOb8apALRKMmchA9P8gByEH0/FDukbOikAM5KHN+NcgFsqKpM3UJyYnuIHp+kAOQg+j5AVpaWlKXkBw5kIMy51eDXCCbe16WuoTkRHcQPT/IAchB9PwAy5YtS11CcuRADsqcXw1ygZzR+EzqEpIT3UH0/CAHIAfR8wNs27YtdQnJkQM5KHN+NcgFsufwzNQlJCe6g+j5QQ5ADqLnBzj11FNTl5AcOZCDMudXg1wgC+r6U5eQnOgOoucHOQA5iJ4f4MCBA6lLSI4cyEGZ86tBLpBDQ3WpS0hOdAfR84McgBxEzw8we/bs1CUkRw7koMz51SAXSL156hKSE91B9PwgByAH0fMDDAwMpC4hOXIgB2XOrwa5QGrRP4XoDqLnBzkAOYieH2BoaCh1CcmRAzkoc341yAXSM1SbuoTkRHcQPT/IAchB9PwATU1NqUtIjhzIQZnzq0EukHl1h1OXkJzoDqLnBzkAOYieH+DgwYOpS0iOHMhBmfOrQS6Qdp3aKLyD6PlBDkAOoucHWLRoUeoSkiMHclDm/GqQC2TJDJ0cP7qD6PlBDkAOoucH2LFjR+oSkiMHclDm/GqQC6S1b07qEpIT3UH0/CAHIAfR8wOcffbZqUtIjhzIQZnzq0EukJVNnalLSE50B9HzgxyAHETPD7Bx48bUJSRHDuSgzPnVIBfIxp55qUtITnQH0fODHIAcRM8PsGbNmtQlJEcO5KDM+dUgF8jqpo7UJSQnuoPo+UEOQA6i5wdobm5OXUJy5EAOypxfDXKBPKxRk/AOoucHOQA5iJ4fYO3atalLSI4cyEGZ86tBLpBVGjUJ7yB6fpADkIPo+QE2bNiQuoTkyIEclDm/GuQCaemZm7qE5ER3ED0/yAHIQfT8AKtWrUpdQnLkQA7KnF8NcoGc3XgodQnJie4gen6QA5CD6PkBWltbU5eQHDmQgzLnV4NcIDv6Z6UuITnRHUTPD3IAchA9P8CSJUtSl5AcOZCDMudXg1wgJzf0pi4hOdEdRM8PcgByED0/wN69e1OXkBw5kIMy51eDXCAdlYbUJSQnuoPo+UEOQA6i5weYP39+6hKSIwdyUOb8apALpKlmMHUJyYnuIHp+kAOQg+j5AXp6elKXkBw5kIMy51eDXCCDWOoSkhPdQfT8IAcgB9HzA9TU6N+vHMhBmfNPqjIzu8TMHjOzbWZ23RjLvN7MNprZZjO7f6J1zWy+md1rZlvz3/Oq5l2fL/+YmV2cT5thZt82s0fN7NqqZT9rZquPJHzRDLj+KUR3ED0/yAHIQfT8APX19alLSI4cyEGZ80/YIJtZLXALcClwLvBWMzt3xDJzgc8Al7n7MuAtk1j3OuA+d18K3JffJ59/BbAMuAT4TL6di4FmYAVwdb7sSqDG3R8+wvyFMqemkrqE5ER3ED0/yAHIQfT8AN3d3alLSI4cyEGZ809mBPl8YJu7P+Huh4E7gMtHLHMl8A/u3gbg7vsnse7lwG357duAN1VNv8Pd+919B7At384AMBOoq3rcjwA3TiZoGThQmZG6hOREdxA9P8gByEH0/AALFixIXUJy5EAOypx/Mg3yKcCuqvu782nVnAXMM7PvmFmzmb19EusudPd2gPz3iROscy9wEvB94BNmdhnQ7O7jniPEzK42s/Vmtr69vZ0DBw7Q3t7Onj176OjoYPv27bxs9mzOaezC8Gcvgbo6/72qqQPDOaexi0YbZMmMbubWHmZRfS8L6/uYX9fP6Q3PMKumwlmNh6hliBVNnQCsybcx/Hv1rA4abJAzZxxiTs0ApzX0cEJdHyfU9XFaQw9zagY4c8YhGmyQZTOfHnUbK5o6qWWIsxoPMaumwukNzzC/rp+F9X0squ9lbu1hzlu9gt7eXrZs2cLQ0NCzl3Jsbm4Gsks7Dg0NsWXLFnp7e9m+fTuLTjrxiDItm/n0lDKd0tB7RJmWzOim0QYn/Tydt3oFHR0d7Nmzh+HnfefOnXR3d9Pa2kqlUqGlpeV5Xi6/9KIjyjSV5+mVjYeOONNUXnvDWYazbdq0if7+frZu3UpXVxdtbW3s37+f/fv309bWRldXFxesW/2iX3uTyXRKQ89R+Xua6Hk6YcHxbN26lf7+fjZt2vQ8H8O/W1paqFQqtLa20t3dzerly170a28ymdbM6jiiTFN97c2bexw7d+4c9X1vvPeIo/m+N1qmpY2Hjvr73ljP03jve1N9j5jK39NEr73169c/77V3JM9T2TKN/HuaKNOuXbumXaapPk+tra3TLtNUnqfdu3cnzzQW5u7jL2D2FuBid39Xfv9twPnu/t6qZT4NrAMuJBvlfRD4H8DKsdY1s053n1u1jQ53n2dmtwAPuvuX8ul/C9zt7l+vWrYeuAe4DPhDYDFwu7vfNV6WdevW+fr1618w/bzXXcSKd3xsXA9Hg1qGGCzguMhHPn8DD91/75TWkYNiHETPD3IAclDm/EVRqVSoq6ubeMFpjBzIQRnym1mzu68bOX0y71C7gdOq7p8KjBy13Q18292fcfcDwANkzfF46+4zs5Pz4k4G9ldta6LHu5Zst4wLgMPALwMfmkSWpCxr6kpdQnKiO4ieH+QA5CB6foDNmzenLiE5ciAHZc4/mQb5IWCpmS0xswayA+hGjtR+E/gpM6szsybgVcAPJ1j3LuCq/PZV+TaGp1+Rn7ViCbAU+MHwA+Vnu3gjcDvQBAwBDjROPnYaHumZm7qE5ER3ED0/yAHIQfT8ACtXrkxdQnLkQA7KnH/CBtndK8B7yHZp+CFwp7tvNrNrzOyafJkfAt8GHiFrZv/G3R8da9180zcDF5nZVuCi/D75/DuBLfk23+3u1WeVvxH4qGf7htxDtmvHJuCvj1xDMQzvTxeZ6A6i5wc5ADmInh+e23cyMnIgB2XOP6kdP9z9buDuEdNuHXH/j4E/nsy6+fSnyPZZHu3xbgJuGmPe+6tu9wFvmDhBOdjQM2/ihaY50R1Ezw9yAHIQPT/A2rVrU5eQHDmQgzLnL+8lTKYhGjWRg+j5QQ5ADqLnh3KPnBWFHMhBmfOrQS4QjZrIQfT8IAcgB9HzQ7lHzopCDuSgzPnVIBfI8Dk+IxPdQfT8IAcgB9HzA8+ezzUyciAHZc6vBrlAtvbNTl1CcqI7iJ4f5ADkIHp+gLPOOit1CcmRAzkoc341yAWyuKEndQnJie4gen6QA5CD6PkB2traUpeQHDmQgzLnV4NcIPsGSn+q5mNOdAfR84McgBxEzw+wcOHC1CUkRw7koMz51SAXyNy6gdQlJCe6g+j5QQ5ADqLnB+js7ExdQnLkQA7KnF8NcoH0DUl3dAfR84McgBxEzw/Q2KhRdDmQgzLn17uUEEIIIYQQVahBLpDGmqHUJSQnuoPo+UEOQA6i5wfo6+tLXUJy5EAOypxfDXKBdFbqU5eQnOgOoucHOQA5iJ4fYO7cualLSI4cyEGZ86tBLpCF9eX9pFQU0R1Ezw9yAHIQPT/Avn37UpeQHDmQgzLnV4NcIG2Hm1KXkJzoDqLnBzkAOYieH2Dx4sWpS0iOHMhBmfOrQS6QpY3dqUtITnQH0fODHIAcRM8P8Pjjj6cuITlyIAdlzq8GuUA29x6XuoTkRHcQPT/IAchB9PwAy5cvT11CcuRADsqcXw1ygaxp6khdQnKiO4ieH+QA5CB6foDm5ubUJSRHDuSgzPnVIBfIhp55qUtITnQH0fODHIAcRM8PsHbt2tQlJEcO5KDM+dUgF4hGTeQgen6QA5CD6Pmh3CNnRSEHclDm/GqQC0SjJnIQPT/IAchB9PxQ7pGzopADOShzfjXIBbKiqTN1CcmJ7iB6fpADkIPo+QFaWlpSl5AcOZCDMudXg1wgm3telrqE5ER3ED0/yAHIQfT8AMuWLUtdQnLkQA7KnF8NcoGc0fhM6hKSE91B9PwgByAH0fMDbNu2LXUJyZEDOShzfjXIBbLn8MzUJSQnuoPo+UEOQA6i5wc49dRTU5eQHDmQgzLnV4NcIAvq+lOXkJzoDqLnBzkAOYieH+DAgQOpS0iOHMhBmfOrQS6QQ0N1qUtITnQH0fODHIAcRM8PMHv27NQlJEcO5KDM+dUgF0i9eeoSkhPdQfT8IAcgB9HzAwwMDKQuITlyIAdlzq8GuUBq0T+F6A6i5wc5ADmInh9gaGgodQnJkQM5KHN+NcgF0jNUm7qE5ER3ED0/yAHIQfT8AE1NTalLSI4cyEGZ86tBLpB5dYdTl5Cc6A6i5wc5ADmInh/g4MGDqUtIjhzIQZnzq0EukHad2ii8g+j5QQ5ADqLnB1i0aFHqEpIjB3JQ5vxqkAtkyQydHD+6g+j5QQ5ADqLnB9ixY0fqEpIjB3JQ5vxqkAuktW9O6hKSE91B9PwgByAH0fMDnH322alLSI4cyEGZ86tBLpCVTZ2pS0hOdAfR84McgBxEzw+wcePG1CUkRw7koMz51SAXyMaeealLSE50B9HzgxyAHETPD7BmzZrUJSRHDuSgzPnVIBfI6qaO1CUkJ7qD6PlBDkAOoucHaG5uTl1CcuRADsqcXw1ygTysUZPwDqLnBzkAOYieH2Dt2rWpS0iOHMhBmfOrQS6QVRo1Ce8gen6QA5CD6PkBNmzYkLqE5MiBHJQ5vxrkAmnpmZu6hOREdxA9P8gByEH0/ACrVq1KXUJy5EAOypxfDXKBnN14KHUJyYnuIHp+kAOQg+j5AVpbW1OXkBw5kIMy51eDXCA7+melLiE50R1Ezw9yAHIQPT/AkiVLUpeQHDmQgzLnV4NcICc39KYuITnRHUTPD3IAchA9P8DevXtTl5AcOZCDMudXg1wgHZWG1CUkJ7qD6PlBDkAOoucHmD9/fuoSkiMHclDm/GqQC6SpZjB1CcmJ7iB6fpADkIPo+QF6enpSl5AcOZCDMuefVINsZpeY2WNmts3MrhtnufPMbNDM3jzRumY238zuNbOt+e95VfOuz5d/zMwuzqfNMLNvm9mjZnZt1bKfNbPVUw2egkEsdQnJie4gen6QA5CD6PkBamo0PiUHclDm/BNWZma1wC3ApcC5wFvN7Nwxlvsj4J5JrnsdcJ+7LwXuy++Tz78CWAZcAnwm387FQDOwArg6X3YlUOPuD085eQIGXP8UojuInh/kAOQgen6A+vr61CUkRw7koMz5J9O6nw9sc/cn3P0wcAdw+SjLvRf4OrB/kuteDtyW374NeFPV9Dvcvd/ddwDb8u0MADOBuqrtfwS4cRIZSsGcmkrqEpIT3UH0/CAHIAfR8wN0d3enLiE5ciAHZc4/mQb5FGBX1f3d+bRnMbNTgP8J3DqFdRe6eztA/vvECda5FzgJ+D7wCTO7DGh29/IeAjmCA5UZqUtITnQH0fODHIAcRM8PsGDBgtQlJEcO5KDM+SfTII/2XZiPuP8p4IPuPvLIi8msO6nHc/eKu1/p7quBrwLvA/7UzD5pZl/LG+YXbszsajNbb2br29vbOXDgAO3t7ezZs4eOjg62b9/Oy2bP5pzGLgx/9hKoq/Pfq5o6MJxzGrtotEGWzOhmbu1hFtX3srC+j/l1/Zze8Ayzaiqc1XiIWoZY0dQJwJp8G8O/V8/qoMEGOXPGIebUDHBaQw8n1PVxQl0fpzX0MKdmgDNnHKLBBlk28+lRt7GiqZNahjir8RCzaiqc3vAM8+v6WVjfx6L6XubWHua81Svo7e1ly5YtDA0NPXspx+bmZiC7tOPQ0BBbtmyht7eX7du3s+ikE48o07KZT08p0ykNvUeUacmMbhptcNLP03mrV9DR0cGePXsYft537txJd3c3ra2tVCoVWlpanufl8ksvOqJMU3meXtl46IgzTeW1N5xlONumTZvo7+9n69atdHV10dbWxv79+9m/fz9tbW10dXVxwbrVL/q1N5lMpzT0HJW/p4mepxMWHM/WrVvp7+9n06ZNz/Mx/LulpYVKpUJrayvd3d2sXr7sRb/2JpNpzayOI8o01dfevLnHsXPnzlHf98Z7jzia73ujZVraeOiov++N9TyN97431feIqfw9TfTaW79+/fNee0fyPJUt08i/p4ky7dq1a9plmurz1NraOu0yTeV52r17d/JMY2Hu4/erZnYB8GF3Hz5Y7noAd/941TI7eK6xXQD0kO0nvG+sdc3sMeD17t5uZicD33H3V47cvpndk2/jwarH+w2gE9gLXAj8LvCgu58/XpZ169b5+vXrXzD9vNddxIp3fGxcD0eDWoYYLODEIY98/gYeuv/eKa0jB8U4iJ4f5ADkoMz5i6JSqVBXVzfxgtMYOZCDMuQ3s2Z3Xzdy+mTeoR4ClprZEjNrIDuA7q7qBdx9ibu/3N1fDnwNuNbdvzHBuncBV+W3rwK+WTX9ivysFUuApcAPqoLMA94I3A40AUNko9KNk8iSlGVNXalLSE50B9HzgxyAHETPD7B58+bUJSRHDuSgzPknbNvdvWJm7yE7O0Ut8Dl332xm1+TzR+53POG6+eybgTvN7J1AG/CWfJ3NZnYnsAWoAO8esevGjcBH3d3z0eV3A5t44f7PpeORnrmpS0hOdAfR84McgByUOf8vXvk22vY8mbqMo8biU07i61/+4pTWkYPp5SB6fjgyB5Ma13b3u4G7R0wbtSF19/890br59KfIdo8YbRs3ATeNMe/9Vbf7gDeMX315WNPUwYaeeRMvOI2J7iB6fpADkIMy52/b82Qhu9kU5eCRz98w5XXkYHo5iJ4fjsxBec/QPA0p6z+EIonuIHp+kAOQg+j5QQ5ADkAOypxfDXKBDB+RHZnoDqLnBzkAOYieH+QA5ADkoMz51SAXSJk/KRVFdAfR84McgBxEzw9yAHIAclDm/GqQC2T4HJ+Rie4gen6QA5CD6PlBDkAOQA7KnF8NcoFs7ZuduoTkRHcQPT/IAchB9PwgByAHIAdlzq8GuUAWN/SkLiE50R1Ezw9yAHIQPT/IAcgByEGZ86tBLpB9A6W/lskxJ7qD6PlBDkAOoucHOQA5ADkoc341yAUyt24gdQnJie4gen6QA5CD6PlBDkAOQA7KnF8NcoH0DUl3dAfR84McgBxEzw9yAHIAclDm/OWtTAghhBBCiASoQS6Qxpqh1CUkJ7qD6PlBDkAOoucHOQA5ADkoc341yAXSWalPXUJyojuInh/kAOQgen6QA5ADkIMy51eDXCAL6/tSl5Cc6A6i5wc5ADmInh/kAOQA5KDM+dUgF0jb4abUJSQnuoPo+UEOQA6i5wc5ADkAOShzfjXIBbK0sTt1CcmJ7iB6fpADkIPo+UEOQA5ADsqcXw1ygWzuPS51CcmJ7iB6fpADkIPo+UEOQA5ADsqcXw1ygaxp6khdQnKiO4ieH+QA5CB6fpADkAOQgzLnV4NcIBt65qUuITnRHUTPD3IAchA9P8gByAHIQZnzq0EukDJ/UiqK6A6i5wc5ADmInh/kAOQA5KDM+dUgF0iZPykVRXQH0fODHIAcRM8PcgByAHJQ5vxqkAtkRVNn6hKSE91B9PwgByAH0fODHIAcgByUOb8a5ALZ3POy1CUkJ7qD6PlBDkAOoucHOQA5ADkoc341yAVyRuMzqUtITnQH0fODHIAcRM8PcgByAHJQ5vxqkAtkz+GZqUtITnQH0fODHIAcRM8PcgByAHJQ5vxqkAtkQV1/6hKSE91B9PwgByAH0fODHIAcgByUOb8a5AI5NFSXuoTkRHcQPT/IAchB9PwgByAHIAdlzq8GuUDqzVOXkJzoDqLnBzkAOYieH+QA5ADkoMz51SAXSC3lfSEURXQH0fODHIAcRM8PcgByAHJQ5vxqkAukZ6g2dQnJie4gen6QA5CD6PlBDkAOQA7KnF8NcoHMqzucuoTkRHcQPT/IAchB9PwgByAHIAdlzq8GuUDaS3w6k6KI7iB6fpADkIPo+UEOQA5ADsqcXw1ygSyZUd4TYhdFdAfR84McgBxEzw9yAHIAclDm/GqQC6S1b07qEpIT3UH0/CAHIAfR84McgByAHJQ5vxrkAlnZ1Jm6hOREdxA9P8gByEH0/CAHIAcgB2XOrwa5QDb2zEtdQnKiO4ieH+QA5CB6fpADkAOQgzLnV4NcIKubOlKXkJzoDqLnBzkAOYieH+QA5ADkoMz51SAXyMMl/qRUFNEdRM8PcgByED0/yAHIAchBmfOrQS6QVSX+pFQU0R1Ezw9yAHIQPT/IAcgByEGZ86tBLpCWnrmpS0hOdAfR84McgBxEzw9yAHIAclDm/GqQC+TsxkOpS0hOdAfR84McgBxEzw9yAHIAclDm/GqQC2RH/6zUJSQnuoPo+UEOQA6i5wc5ADkAOShzfjXIBXJyQ2/qEpIT3UH0/CAHIAfR84McgByAHJQ5vxrkAumoNKQuITnRHUTPD3IAchA9P8gByAHIQZnzT6pBNrNLzOwxM9tmZteNMv9XzOyR/Oe7ZrZyonXNbL6Z3WtmW/Pf86rmXZ8v/5iZXZxPm2Fm3zazR83s2qplP2tmq49UQJE01QymLiE50R1Ezw9yAHIQPT/IAcgByEGZ80/YIJtZLXALcClwLvBWMzt3xGI7gNe5+wrgI8BnJ7HudcB97r4UuC+/Tz7/CmAZcAnwmXw7FwPNwArg6nzZlUCNuz98ROkLZhBLXUJyojuInh/kAOQgen6QA5ADkIMy55/MCPL5wDZ3f8LdDwN3AJdXL+Du33X34ZPZfQ84dRLrXg7clt++DXhT1fQ73L3f3XcA2/LtDAAzgbqqh/4IcONkgpaBAS/vC6EoojuInh/kAOQgen6QA5ADkIMy559Mg3wKsKvq/u582li8E/iXSay70N3bAfLfJ06wzr3AScD3gU+Y2WVAs7vvnUSGUjCnppK6hOREdxA9P8gByEH0/CAHIAcgB2XOP5kGebT23kdd0OxnyBrkD0513Ykez90r7n6lu68Gvgq8D/hTM/ukmX0tb5hHq+lqM1tvZuvb29s5cOAA7e3t7Nmzh46ODrZv387LZs/mnMYuDH/2qi7D1wdf1dSB4ZzT2EWjDbJkRjdzaw+zqL6XhfV9zK/r5/SGZ5hVU+GsxkPUMsSKpk4A1uTbGP49r+4wDTbImTMOMadmgNMaejihro8T6vo4raGHOTUDnDnjEA02yLKZT4+6jRVNndQyxFmNh5hVU+H0hmeYX9fPwvo+FtX3Mrf2MOetXkFvby9btmxhaGiIDRs2ANDc3AzAhg0bGBoaYsuWLfT29rJ9+3YWnXTiEWVaNvPpKWU6UJlxRJmWzOim0QYn/Tydt3oFHR0d7Nmzh+HnfefOnXR3d9Pa2kqlUqGlpeV5Xi6/9KIjyjSV5wk44kxTee0NZxnOtmnTJvr7+9m6dStdXV20tbWxf/9+9u/fT1tbG11dXVywbvWLfu1NJtNTlYaj8vc00fN0woLj2bp1K/39/WzatOl5PoZ/t7S0UKlUaG1tpbu7m9XLl73o195kMh1fd/io/D1N9DzNm3scO3fuHPV9b7z3iKP5vjdapiE46u97Yz1P473vjfYeMW/ucUf9fW+0bRxfd/iov++N9jytXr5s3Pe90d4jTlhw/DH7/1Sd6alKwzH9nzv8PF2wbvWY73tjvUcMv4cei/9P1ZmAY/o/tzrLWO97o71HnLd6xTH7/1Sd6UBlxjH9nzv8PC066cQx3/fGwtzH71fN7ALgw+4+fLDc9QDu/vERy60A/hG41N0fn2hdM3sMeL27t5vZycB33P2VI7dvZvfk23iw6rF+A+gE9gIXAr8LPOju54+XZd26db5+/foXTD/vdRex4h0fG9fD0eCsxkM83jfnmD/OI5+/gYfuv3dK68hBMQ6i5wc5ADmInh/kAOQAinEQPT+M78DMmt193cjpkxlBfghYamZLzKyB7AC6u0ZsfDHwD8DbhpvjSax7F3BVfvsq4JtV06/Iz1qxBFgK/KDqseYBbwRuB5qAIbJR6cZJZEnK9r7ynhC7KKI7iJ4f5ADkIHp+kAOQA5CDMuefsEF29wrwHuAe4IfAne6+2cyuMbNr8sVuBI4nO+PERjNbP966+To3AxeZ2Vbgovw++fw7gS3At4F3u3v1eUBuBD7q2dD3PcA6YBPw10fooDCWNXWlLiE50R1Ezw9yAHIQPT/IAcgByEGZ89dNvAi4+93A3SOm3Vp1+13Auya7bj79KbLdI0Zb5ybgpjHmvb/qdh/whokTlINHeuamLiE50R1Ezw9yAHIQPT/IAcgByEGZ8+tKegUyvDN5ZKI7iJ4f5ADkIHp+kAOQA5CDMudXg1wgG3rmTbzQNCe6g+j5QQ5ADqLnBzkAOQA5KHN+NcgFUuZPSkUR3UH0/CAHIAfR84McgByAHJQ5vxrkAinzJ6WiiO4gen6QA5CD6PlBDkAOQA7KnF8NcoEMn+A6MtEdRM8PcgByED0/yAHIAchBmfOrQS6QrX2zU5eQnOgOoucHOQA5iJ4f5ADkAOSgzPnVIBfI4oae1CUkJ7qD6PlBDkAOoucHOQA5ADkoc341yAWyb6D0F/s75kR3ED0/yAHIQfT8IAcgByAHZc6vBrlA5tYNpC4hOdEdRM8PcgByED0/yAHIAchBmfOrQS6QviHpju4gen6QA5CD6PlBDkAOQA7KnL+8lQkhhBBCCJEANcgF0lgzlLqE5ER3ED0/yAHIQfT8IAcgByAHZc6vBrlAOiv1qUtITnQH0fODHIAcRM8PcgByAHJQ5vxqkAtkYX1f6hKSE91B9PwgByAH0fODHIAcgByUOb8a5AJpO9yUuoTkRHcQPT/IAchB9PwgByAHIAdlzq8GuUCWNnanLiE50R1Ezw9yAHIQPT/IAcgByEGZ86tBLpDNvcelLiE50R1Ezw9yAHIQPT/IAcgByEGZ86tBLpA1TR2pS0hOdAfR84McgBxEzw9yAHIAclDm/GqQC2RDz7zUJSQnuoPo+UEOQA6i5wc5ADkAOShzfjXIBVLmT0pFEd1B9PwgByAH0fODHIAcgByUOb8a5AIp8yeloojuIHp+kAOQg+j5QQ5ADkAOypxfDXKBrGjqTF1CcqI7iJ4f5ADkIHp+kAOQA5CDMudXg1wgm3telrqE5ER3ED0/yAHIQfT8IAcgByAHZc6vBrlAzmh8JnUJyYnuIHp+kAOQg+j5QQ5ADkAOypxfDXKB7Dk8M3UJyYnuIHp+kAOQg+j5QQ5ADkAOypxfDXKBLKjrT11CcqI7iJ4f5ADkIHp+kAOQA5CDMudXg1wgh4bqUpeQnOgOoucHOQA5iJ4f5ADkAOSgzPnVIBdIvXnqEpIT3UH0/CAHIAfR84McgByAHJQ5vxrkAqmlvC+EoojuIHp+kAOQg+j5QQ5ADkAOypxfDXKB9AzVpi4hOdEdRM8PcgByED0/yAHIAchBmfOrQS6QeXWHU5eQnOgOoucHOQA5iJ4f5ADkAOSgzPnVIBdIe4lPZ1IU0R1Ezw9yAHIQPT/IAcgByEGZ86tBLpAlM8p7QuyiiO4gen6QA5CD6PlBDkAOQA7KnF8NcoG09s1JXUJyojuInh/kAOQgen6QA5ADkIMy51eDXCArmzpTl5Cc6A6i5wc5ADmInh/kAOQA5KDM+dUgF8jGnnmpS0hOdAfR84McgBxEzw9yAHIAclDm/GqQC2R1U0fqEpIT3UH0/CAHIAfR84McgByAHJQ5vxrkAnm4xJ+UiiK6g+j5QQ5ADqLnBzkAOQA5KHN+NcgFsqrEn5SKIrqD6PlBDkAOoucHOQA5ADkoc341yAXS0jM3dQnJie4gen6QA5CD6PlBDkAOQA7KnF8NcoGc3XgodQnJie4gen6QA5CD6PlBDkAOQA7KnF8NcoHs6J+VuoTkRHcQPT/IAchB9PwgByAHIAdlzq8GuUBObuhNXUJyojuInh/kAOQgen6QA5ADkIMy559Ug2xml5jZY2a2zcyuG2W+mdlf5PMfMbM1E61rZvPN7F4z25r/nlc17/p8+cfM7OJ82gwz+7aZPWpm11Yt+1kzW32kAoqko9KQuoTkRHcQPT/IAchB9PwgByAHIAdlzj9hg2xmtcAtwKXAucBbzezcEYtdCizNf64G/moS614H3OfuS4H78vvk868AlgGXAJ/Jt3Mx0AysyB8DM1sJ1Lj7w0cSvmiaagZTl5Cc6A6i5wc5ADmInh/kAOQA5KDM+Sczgnw+sM3dn3D3w8AdwOUjlrkcuN0zvgfMNbOTJ1j3cuC2/PZtwJuqpt/h7v3uvgPYlm9nAJgJ1FU97keAGyedNjGDWOoSkhPdQfT8IAcgB9HzgxyAHIAclDn/ZBrkU4BdVfd359Mms8x46y5093aA/PeJE2zrXuAk4PvAJ8zsMqDZ3fdOIkMpGPDyvhCKIrqD6PlBDkAOoucHOQA5ADkoc/66iRcZtb33SS4zmXUn9XjuXgGuBDCzeuAe4DIz+ySwmGwE+64XbMzsavJdMoBuM3tstAdd/8C/TVDWUWEBcKCIBzKb+otODgpxED0/yAHIQfT8IAcgB1CQg+j5YVwHp482cTIN8m7gtKr7pwIjR23HWqZhnHX3mdnJ7t6e746xfwqPdy3ZbhkXAIeBXwYeBF7QILv7Z4HPjpOvMMxsvbuvS11HSqI7iJ4f5ADkIHp+kAOQA5CDMuefzC4WDwFLzWyJmTWQHUA3shG9C3h7fjaLVwNP57tNjLfuXcBV+e2rgG9WTb8iP2vFErID/34w/ED52S7eCNwONAFDZKPSjVPILYQQQgghxKhMOILs7hUzew/ZLg21wOfcfbOZXZPPvxW4G/h5sgPqeoB3jLduvumbgTvN7J1AG/CWfJ3NZnYnsAWoAO929+rDHG8EPurubmb3AO8GNgG3vggPQgghhBBCAJPbxQJ3v5usCa6edmvVbSdrVCe1bj79KeDCMda5CbhpjHnvr7rdB7xh4gSloRS7eiQmuoPo+UEOQA6i5wc5ADkAOShtfst6WyGEEEIIIQToUtNCCCGEEEI8DzXIQghxjDCzxalrEEIIMXXUIAshxLHjG6kLEEIIMXUmdZCemDpmdjdwrbvvTF1LKszsn4EvA99w92dS11M0ZjZ/nNn9EZyY2Zrx5rv7hqJqSUR5LxNVEGb2anf/Xuo6yoqZ/aS7/3fqOkQxmNly4Oz87g/d/dGU9Yix0UF6xwgz+yXgo2QXNPmEuw8kLqlwzOxysnNfXwj8B/AV4G53P5y0sIIwsx2MfUXJ4Q+n17n73xVXVbGY2RCwGfjx8KSq2e7uP1t8VcVhZvuBO8aa7+6/XmA5STCzDe6+Jr/9oLtfkLqmojGzWuCXgFOAb7v7o2b2RuAGYKa7r05aYAGY2W8Bnxpx2lbM7Hiy/5HvTFNZMZjZcWTXezgNeITsvXA52WluL3f3roTlFYKZ/SbZdTL+dsT09wK17v6pJIWNgRrkY4iZzSI7b/MlwBfJLmoCgLt/MlVdRWNmM4HLyJrlC8hO+/cVd783aWGJMbMTgPvd/dzUtRwrzOz9wC8CT5M1iv/o7t1pqyoOM/sR2XvAqLj7bQWWkwQze3i4Aay+HQkz+wJZY/QD4FXAj8jeC69z92+kq6w4zOyzwPlk1zb473zatcDvAH9etuboaGNmf0F25d8PuPtQPq2G7JoQM939vSnrKwIzexRYM3KQzMxmAA+5+4o0lY2OdrE4tgwAzwAzgDlUNciRcPde4O+BvzezFWSj6leRXTxm2mJm3wD+G/gu2R//894U3P3HZvbBFLUVhbv/GfBn+VUx3wrclzeNH3P3jUmLK4anxmqCzex9BdeSipr8Cqg1Vbef/SbB3Q8mq6w41gEr3H3IzBqBA8CZ7v5k4roKw92vNrPXAJ82s81kuxlsBV6TX3l3uvNz5K+B4Qn56+EGsoudRcBH+wbZ3fvNrHS7o6lBPkaY2SXAJ8kunb3G3XsSl5QMM1tI9vXiFcDJwFfJr7Y4zfkb4DVkF71ZYWatPNcwf9fd97n7P6UssCjcfYeZfROYCbwNOAvYmLSoYhhvd6LfBD5VUB0pOQ5o5rmmuHq/cwdeUXhFxXN4uDFy9z4zezxSc1zFo8BDZN+qGvBbQZpjyF4DlZET8ysO96coKAVmttDd942clqqe8VCDfOz4XeDN7r4ldSGpMLOryZriVwL/QPbVUpiDUdz9W8C34Nl9EFcDrwf+GFjCNB9BBzCzV5C9Bi4HdpHtZnFTfhXMaY+7v3qc2aUbMTkWuPvLU9dQAs42s0fy2wackd83slG1Un21fCwws18F/hD4f8AZwErgFjN7HPhtd9+fsr4CaDSz1bzw797IvmWOwB8D/5zvjz78QXkt8AngT5JVNQbaB/kYYWZnkb0YziD7+uS33X1P2qqKxcw+T3Zg3r9Vf60UCTNbQDaK/Brg1UAj2cjpg0H2Px0iOyDlm0AX2Yjhs0TaF38kZtbm7tP+PMlm9qvu/qX89vPO2GBm73H3T6errhjM7PTx5rv7j4qqJRX5N0i/Xp01/1r9GuB33H1af5NgZv8xzuxpf8DyMGZ2KXAd8BP5pEeBm939X9JVNTpqkI8RZvafwO3AA2QHqF3g7v8rbVXFEv0fo5ltJTs47evA98j2Qw5zgBqAmX2YEU1xNe7+B8VVUzxm9gwwONossgNzpv23eCPOYvHs7dHuT1fM7DNkB+RN+zMVHAlmdoK7/3jiJacnZvYqd/9+6jrE81GDfIwws43uvqrqfoh/BNVE/8doZteTjRqfAjwOPJj/PDzyVEdiehL1rA3VjHcWiyh+zOwDwP8Bft/dv5y6nhSY2VNkAwXfJTsW4weRj82pJtC3SQuAdwMHgc+T7Vrx08B2sv3RtyUs7wXoSnrHjkYzW21ma/KLJcwcvj3RxROmETbG7dHuTzvc/ePufrm7rwM+DBwi+ye5yczuT1pcQZjZnVW3/2jEvH8tvqLC0QjE8x2M9BHCj7t/guz4g8vN7D4ze7OZ/a/hn8TlFcUS4M+BerLzP+8ys4fM7M/z6wZEZtr/P8z5Mtn+1meRnfJwJ/BmsmN1/iZdWaOjEeRjRL6/0ciLRDwrO8L+RtFHkIfJD1R7DfCT+e9FwPfd/Y1JCyuAEaOHI18D03700Mx2k53NZlQi7INtZj3ANvKD0/Lb5Pdf4e6zUtVWNGb2drKz2vw7z532093919JVlYb8OgHvAN4HLHH3aX/Q8lgEGkFucfeV+b7nP6rOPPJb9zIw7fd/S8gHgV3Dp7Axs6vILpiwk2w0MQLDR25XH7VNfn9aH5ABYGb/SLaLxdNku1b8N/CXwc5sMt4n8AifzmuB2cQZIRqNc1IXkBozWwb8FbAXOD/Qqc2excwW8dwBy+flk5uBD5G9P05rzOyfGP09z4DjCy4nFYOQfSI0swMj5pXuQH6NIB8jzGwD8HPuftDMfprs9FbvBVYB57j7m1PWVwTRj9w2s8vIznc88o0gDPm5n99KtjvXl4Aryf4hGPAld5/WzVOkb0rE2JjZD4H3ufs9qWtJRX5Gmw3AnwFfHe2CEdMZM3vdKJOHGzBz92m/252ZdZKduMCAn8pvk99/rbvPS1TaqKhBPkYMf5WQ374F+LG7fzi/X7qvEsTRx8w+kO97iJm9xd2/WjXvY+5+Q7rqisHMvsP4Z7H4meKqKZ4Iu5FMhJkdYuyRM3f3lxVcUuGYWUO0hnAkZnYB2eW1X0O2P/JOnjtweb27T+uLZZjZ5cCp7n5Lfv8HwAlkfxsfrP7/MF0Z40PCs5TtQ4Ia5GOEZdccX5VfJacVuNrdHxie5+4/Mf4WXvpE/8eofbCFmc33GJdSFuMQ/b1wNMzs5cAvAL9B1jg2pq3o2GJm/w1c4e678vsbgQuBWcDn3f3ChOUVjpmdAFDm0/tpH+Rjx1eA+/P9bHqB/wQwszPJ9kmd9rj7nNQ1JCb0WTxAo+hqjgXovXAYMzub5/ZD/klgHtkI8q0p6yqIhuHmOOe/3P0p4Kn8gMVpT35w3o1ku5saUGNmFbJjc/4waXGjoNO8HSPc/Sbgt4AvkO1bMzx6UEP24hDTn/CntyK7zPQw14+Yd0mRhQgh0pEPFn0VeBXZgNEvuPtJ7v4/3b10lxk+Bjxv/1p3f0/V3RMKriUV7wNeC5zn7sfn+xy/CvhJM3t/0spGQSPIxxB3/94o0x5PUYtIwkoz6yK/alp+m/z+tP46sYrwo+hCCADOcPcXfHtqZrVkux78XYKaiuT7ZvZ/3P2vqyea2f8lOydwBN4OXFR94Lq7P2Fmvwr8K9kBnKVBDbIQx4jI5/WsQqPoQggAz68uegpwF3Av8B7gt4GNwHRvkN8PfMPMriQ7mwfAWrILZ7wpVVEFUz/aWZ3c/cdmVp+ioPHQQXpCiGOGmQ0Cz5CPogPDl5Y1oNHdS/emKIQ4+pjZN4EOsn2OLyTb5aAB+A1335iwtEIxs58FluV3N7v7v6esp0jGOzi9jAeuq0EWQgghxDHFzDa5+/L8di1wAFjs7ofSViaKomrA5AWzKOGAiXaxEEIIIcSxZmD4hrsPmtkONcexeKntdqgRZCGEEEIcU0aMHlbvchX2XNCi3KhBFkIIIYQQogqdB1kIIYQQQogq1CALIYQQQghRhRpkIYQQQgghqlCDLIQQQgghRBX/P/lJQX7e42BUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = rp.plot_risk_con(w_rp,\n",
    "                      cov=port.cov,\n",
    "                      returns=port.returns,\n",
    "                      rm=rm,\n",
    "                      rf=0,\n",
    "                      alpha=0.01,\n",
    "                      color=\"tab:blue\",\n",
    "                      height=6,\n",
    "                      width=10,\n",
    "                      ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "각 자산의 위험기여도가 {numref}`risk_contri`에서 설정한 목표치와 동일함이 확인된다.\n",
    "\n",
    "```{note}\n",
    "이 외에도 Riskfolio 패키지의 자세한 사용법은 공식 홈페이지를 참조하기 바란다.\n",
    "\n",
    "https://riskfolio-lib.readthedocs.io/en/latest/index.html\n",
    "```"
   ]
  }
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