{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import FinanceDataReader as fdr\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def basicinform(input):\n",
    "    stocks = pd.read_csv('stockcodename.csv',index_col=0)\n",
    "    symbol = ''\n",
    "    for i in enumerate(stocks.Name) :\n",
    "        if i[1] == input:\n",
    "            symbol = (stocks.iloc[i[0]].Symbol)\n",
    "            break\n",
    "    df = fdr.DataReader(symbol)\n",
    "    ror_df = df.Close.pct_change()\n",
    "    volume = df.Volume.iloc[-1]\n",
    "    price = df.Close.iloc[-1]\n",
    "    ror = ror_df[-1]\n",
    "    #print(\"현재가: \", price)\n",
    "    #print(\"거래량: \", volume)\n",
    "    #print(\"전일 대비 수익률:\", ror)\n",
    "    #display(df)\n",
    "    value = {\"현재가\": price ,\n",
    "              \"거래랑\": volume ,\n",
    "              \"전일 대비 수익률:\" : ror\n",
    "            }\n",
    "    return value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(basicinform('삼성전자'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stocks = fdr.StockListing('KOSPI') # 코스피\n",
    "stocks.to_csv(\"stockcodename.csv\",mode='w', encoding='utf-8-sig')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import FinanceDataReader as fdr\n",
    "from scipy.optimize import minimize\n",
    "import json\n",
    "from datetime import date\n",
    "import math\n",
    "import itertools as it\n",
    "import operator\n",
    "from datetime import datetime\n",
    "from scipy import stats\n",
    "from scipy.stats import norm\n",
    "from dateutil import rrule\n",
    "from calendar import monthrange\n",
    "from dateutil.relativedelta import relativedelta\n",
    "from ast import literal_eval\n",
    "\n",
    "#소숫점 표현\n",
    "pd.options.display.float_format = '{:.3f}'.format\n",
    "np.set_printoptions(precision=3, suppress=True)\n",
    "\n",
    "class c_Models:\n",
    "    #Input 값으로, 자산 list, 사용자 포트폴리오 비중, 시작일, 마지막일\n",
    "    def __init__(self, assets, assets_w, start, end):\n",
    "        self.result = None\n",
    "        self.graph = None\n",
    "    \n",
    "        stocks = pd.read_csv('stockcodename.csv', index_col=0)\n",
    "        symbol = ''\n",
    "        self.asset_name = assets[:]\n",
    "        for k in range(len(assets)):\n",
    "            for i in enumerate(stocks.Name):\n",
    "                if i[1] == assets[k]:\n",
    "                    assets[k] = (stocks.iloc[i[0]].Symbol)\n",
    "                    break\n",
    "\n",
    "        data = pd.DataFrame()\n",
    "        # 전체 자산 data들을 가지고 온 후, 정리함\n",
    "        \n",
    "        for asset in assets: #total_list:\n",
    "            tmp = fdr.DataReader(asset,start,end).Close\n",
    "            if len(data) == 0 :\n",
    "                data = tmp\n",
    "            else:\n",
    "                data = pd.concat([data,tmp], axis=1)\n",
    "   \n",
    "        data.columns = self.asset_name\n",
    "   \n",
    "        if data.isnull().values.any() == True: #불러온 data에 오류가 있다면\n",
    "            return \"No Data\",''\n",
    "\n",
    "        else:\n",
    "            data = data.resample('M').mean() #일별 데이터를 월별 데이터로 만들어줌\n",
    "            data = data.pct_change() #월별 주가 데이터를 이용해 수익률 데이터로 변환\n",
    "            data.dropna(inplace=True) #결측치 제외(첫 row)\n",
    "\n",
    "            self.data = data\n",
    "            self.assets_w = assets_w\n",
    "            self.mu = data.mean() * 12\n",
    "            self.cov = data.cov() * 12\n",
    "\n",
    "    #GMV 최적화 : 제약 조건은 비중합=1, 공매도 불가능\n",
    "    def gmv_opt(self):\n",
    "        n_assets = len(self.data.columns)\n",
    "        w0 = np.ones(n_assets) / n_assets\n",
    "        fun = lambda w: np.dot(w.T, np.dot(self.cov, w))\n",
    "        constraints = ({'type':'eq', 'fun':lambda x: np.sum(x)-1})\n",
    "        bd = ((0,1),) * n_assets\n",
    "        #cov = data.cov() * 12\n",
    "        gmv = minimize(fun, w0, method = 'SLSQP', constraints=constraints, bounds=bd)\n",
    "        result = dict(zip(self.asset_name, np.round(gmv.x,3)))\n",
    "        return result\n",
    "    \n",
    "    #Max Sharp ratio : risk free rate은 0.8%로 지정했고, \n",
    "    def ms_opt(self):\n",
    "        n_assets = len(self.data.columns)\n",
    "        w0 = np.ones(n_assets) / n_assets\n",
    "        fun = lambda w: -(np.dot(w.T, self.mu) - 0.008) / np.sqrt(np.dot(w.T, np.dot(self.cov, w)))\n",
    "        bd = ((0,1),) * n_assets     \n",
    "        constraints = ({'type': 'eq', 'fun': lambda x:  np.sum(x) - 1})\n",
    "        maxsharp = minimize(fun, w0, method ='SLSQP', constraints=constraints, bounds=bd)\n",
    "        result = dict(zip(self.asset_name, np.round(maxsharp.x,3)))\n",
    "        return result\n",
    "    \n",
    "    def rp_opt(self):\n",
    "        def RC(cov, w):\n",
    "            pfo_std = np.sqrt(np.dot(w.T, np.dot(self.cov, w)))\n",
    "            mrc = 1/pfo_std * (np.dot(self.cov, w))\n",
    "            rc = mrc * w\n",
    "            rc = rc / rc.sum()\n",
    "            return rc\n",
    "        \n",
    "        \n",
    "        def RP_objective(x):\n",
    "            pfo_std = np.sqrt(np.dot(x.T, np.dot(self.cov, x)))\n",
    "            mrc = 1/pfo_std * (np.dot(self.cov, x))\n",
    "            rc = mrc * x\n",
    "            rc = rc / rc.sum()\n",
    "\n",
    "            a = np.reshape(rc, (len(rc),1))\n",
    "            differs = a - a.T\n",
    "            objective = np.sum(np.square(differs))\n",
    "\n",
    "            return objective    \n",
    "        \n",
    "        n_assets = len(self.data.columns)\n",
    "        w0 = np.ones(n_assets) / n_assets\n",
    "        constraints = [{'type':'eq', 'fun': lambda x: np.sum(x) -1}]\n",
    "        bd = ((0,1),) * n_assets\n",
    "\n",
    "        rp = minimize(RP_objective, w0,  constraints=constraints, bounds = bd, method='SLSQP')\n",
    "        result = dict(zip(self.asset_name, np.round(rp.x,3)))\n",
    "        return result     #, RC(self.cov, rp.x)\n",
    "\n",
    "    def plotting(self):\n",
    "        wt_gmv = np.asarray(list(self.gmv_opt().values()))\n",
    "        wt_ms = np.asarray(list(self.ms_opt().values()))\n",
    "        wt_rp = np.asarray(list(self.rp_opt().values()))\n",
    "        \n",
    "        ret_gmv = np.dot(wt_gmv, self.mu)\n",
    "        ret_ms = np.dot(wt_ms, self.mu)\n",
    "        ret_rp = np.dot(wt_rp, self.mu)\n",
    "        vol_gmv = np.sqrt(np.dot(wt_gmv.T, np.dot(self.cov, wt_gmv)))\n",
    "        vol_ms = np.sqrt(np.dot(wt_ms.T, np.dot(self.cov, wt_ms)))\n",
    "        vol_rp = np.sqrt(np.dot(wt_rp.T, np.dot(self.cov, wt_rp)))\n",
    "        \n",
    "        wt_gmv = wt_gmv.tolist()\n",
    "        wt_ms = wt_ms.tolist()\n",
    "        wt_rp = wt_rp.tolist()\n",
    "        \n",
    "        user_ret = np.dot(self.assets_w, self.mu)\n",
    "        user_risk = np.sqrt(np.dot(self.assets_w, np.dot(self.cov, self.assets_w)))\n",
    "\n",
    "        weights = {'gmv': wt_gmv, \"ms\" : wt_ms, \"rp\": wt_rp}\n",
    "        \n",
    "        #rec_rs = recommended_asset()\n",
    "\n",
    "        trets = np.linspace(ret_gmv, max(self.mu), 30) # 30개 짜르기 \n",
    "        tvols = []\n",
    "        \n",
    "        efpoints = dict()\n",
    "        for i, tret in enumerate(trets): #이 개별 return마다 최소 risk 찾기\n",
    "            n_assets = len(self.data.columns)\n",
    "            w0 = np.ones(n_assets) / n_assets\n",
    "            fun = lambda w: np.dot(w.T ,np.dot(self.cov, w))\n",
    "            constraints = [{'type': 'eq', 'fun': lambda x: np.sum(x) - 1},\n",
    "                           {'type': 'ineq', 'fun': lambda x: np.dot(x, self.mu) - tret}]\n",
    "                           #{'type': 'ineq', 'fun': lambda x: x}]\n",
    "            bd = ((0,1),) * n_assets\n",
    "\n",
    "            minvol = minimize(fun, w0, method='SLSQP',bounds = bd, constraints=constraints)\n",
    "            tvols.append(np.sqrt(np.dot(minvol.x, np.dot(self.cov, minvol.x))))\n",
    "            \n",
    "            pnumber = '{}point'.format(i+1)\n",
    "            efpoints[pnumber] = minvol.x.tolist()\n",
    "        \n",
    "        if self.data.shape[0] <= 1:\n",
    "            error = '기간에러'\n",
    "            return error,1,1\n",
    "        else:\n",
    "            ret_vol = {\"GMV\": [vol_gmv, ret_gmv],\"MaxSharp\": [vol_ms, ret_ms],\"RiskParity\": [vol_rp, ret_rp], \"Trets\" : trets.tolist(), \"Tvols\": tvols, \"User\" : [user_risk,user_ret]} #, \"Recommended\" : rec_rs}        \n",
    "            return ret_vol, json.dumps(efpoints), json.dumps(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class back_test:\n",
    "    # 단순 일별수익률의 평균을 *365하여 연간수익률을 산출\n",
    "    def __init__(self):\n",
    "        self.test = 0\n",
    "        \n",
    "    def Arithmetic_Mean_Annual(self,ret):\n",
    "        month_return =  np.mean(ret)\n",
    "        return (month_return*252)\n",
    "\n",
    "    # 기간중 투자했을때 하락할 수 있는 비율\n",
    "    def dd(self,ret):\n",
    "        cum_ret = (1 + ret).cumprod()\n",
    "        max_drawdown = 0\n",
    "        max_ret = 1\n",
    "        dd_list = []\n",
    "        c = 0\n",
    "        for ix_ret in cum_ret.values:\n",
    "            if max_ret < ix_ret:\n",
    "                max_ret = ix_ret\n",
    "            dd_list.append((ix_ret - max_ret) / max_ret) \n",
    "            c= c+1\n",
    "        return dd_list\n",
    "    \n",
    "    # 기간중 투자했을때 최고로 많이 하락할 수 있는 비율\n",
    "    def mdd(self,ret):\n",
    "        \n",
    "        cum_ret = (1 + ret).cumprod()\n",
    "        max_drawdown = 0\n",
    "        max_ret = 1\n",
    "        for ix_ret in cum_ret.values:\n",
    "            if max_drawdown > (ix_ret - max_ret) / max_ret:\n",
    "                max_drawdown = (ix_ret - max_ret) / max_ret\n",
    "            if max_ret < ix_ret:\n",
    "                max_ret = ix_ret\n",
    "\n",
    "        return abs(max_drawdown)\n",
    "\n",
    "    # 포트폴리오 수익률에서 무위험 수익률을 제한 후 이를 포트폴리오의 표준편차로 나눠 산출한 값, 즉 위험대비 얼마나 수익이 좋은지의 척도\n",
    "    def sharpe_ratio(self,ret, rf=0.008, num_of_date=252):\n",
    "        \n",
    "        return ((np.mean(ret - (rf / num_of_date))) / (np.std(ret))) * np.sqrt(num_of_date)\n",
    "    \n",
    "    # 설정한 confidence level에 따른(95%) 확률로 발생할 수 있는 손실액의 최대 액수\n",
    "    def value_at_risk(self,ret, para_or_hist=\"para\", confidence_level=0.95):\n",
    "        \n",
    "        vol = np.std(ret)\n",
    "        if para_or_hist == \"para\":\n",
    "            VaR = np.mean(ret) - vol * norm.ppf(confidence_level)\n",
    "        else:\n",
    "            print('error')\n",
    "\n",
    "        return VaR\n",
    "    \n",
    "    # 전체 투자기간에서 상승한 ( ret > 0 ) 기간의 비율\n",
    "    def winning_rate(self,ret):\n",
    "        var_winning_rate = np.sum(ret > 0) / len(ret)\n",
    "        return var_winning_rate    \n",
    "    \n",
    "    # 상승한날의 평균상승값을 하락한날의 평균하락값으로 나눈 비율\n",
    "    def profit_loss_ratio(self,ret):\n",
    "\n",
    "        if np.sum(ret > 0) == 0:\n",
    "            var_profit_loss_ratio = 0\n",
    "        elif np.sum(ret < 0) == 0:\n",
    "            var_profit_loss_ratio = np.inf\n",
    "        else:\n",
    "            win_mean = np.mean(ret[ret > 0])\n",
    "            loss_mean = np.mean(ret[ret < 0])\n",
    "            var_profit_loss_ratio = win_mean / loss_mean\n",
    "        return abs(var_profit_loss_ratio)\n",
    "\n",
    "    # 데이터 취합하는 코드 \n",
    "    #임시로 5가지 데이터 예시를 활용해 코드작성\n",
    "    # 선택한 종목의 이름과 비중, 투자기간을 input 값으로 받음       \n",
    "    \n",
    "    def backtest_data(self, assets,weight,start_data_1, end_data_1,start_amount,rebalancing_month, interval, opt_option):\n",
    "        # input으로 받는 assetnames 입력\n",
    "        a = assets\n",
    "        stock_num = len(a)\n",
    "        # input으로 받는 assetweights 입력\n",
    "        rebal_month = int(rebalancing_month)\n",
    "        # input으로 받는 rebalancing_month를 입력\n",
    "        # 나타내는 데이터 간격을 표시\n",
    "\n",
    "        # weight 간격  \n",
    "        b = list(map(float, weight))\n",
    "        \n",
    "\n",
    "        # input으로 받는 from_period와 to_period 입력\n",
    "        stock_return = pd.date_range(start=start_data_1, end=end_data_1)\n",
    "        stock_return = pd.DataFrame(stock_return)\n",
    "        stock_return.columns = ['Date']\n",
    "\n",
    "        stocks = pd.read_csv('stockcodename.csv', index_col=0)\n",
    "        symbol = ''\n",
    "        asset_name = assets[:]\n",
    "        for k in range(len(assets)):\n",
    "            for i in enumerate(stocks.Name):\n",
    "                if i[1] == assets[k]:\n",
    "                    assets[k] = (stocks.iloc[i[0]].Symbol)\n",
    "                    break\n",
    "        \n",
    "        # input으로 받는 from_period와 to_period 입력\n",
    "        stock_return = pd.date_range(start=start_data_1, end=end_data_1)\n",
    "        stock_return = pd.DataFrame(stock_return)\n",
    "        stock_return.columns = ['Date']\n",
    "        \n",
    "\n",
    "        for asset in assets: #total_list:\n",
    "            tmp = fdr.DataReader(asset,start_data_1,end_data_1)\n",
    "            tmp.insert(1,\"Date\",tmp.index.copy(),True)\n",
    "            tmp = tmp[['Date','Change']]\n",
    "            tmp.columns = ['Date',asset]\n",
    "            tmp = tmp.reset_index(drop=True)\n",
    "            stock_return = pd.merge(stock_return,tmp,how='inner', on='Date')\n",
    "\n",
    "        stock_return = stock_return.dropna(axis=0)\n",
    "\n",
    "        #print(stock_return)\n",
    "        if opt_option == 'basic' :\n",
    "\n",
    "            # 투자비중으로 이루어진 dataframe 만들기\n",
    "\n",
    "            start_datetime = stock_return.iloc[0,0]\n",
    "            end_datetime = stock_return.iloc[-1,0]\n",
    "            diff_months_list = list(rrule.rrule(rrule.MONTHLY, dtstart=start_datetime, until=end_datetime))\n",
    "            month_gap = len(diff_months_list)\n",
    "            rebal_roof = month_gap//rebal_month\n",
    "            rebal_weight = pd.DataFrame()\n",
    "\n",
    "            for i in range(rebal_roof+1):\n",
    "                # 데이터로부터 리밸런싱기간만큼 가져오기\n",
    "                filtered_df =stock_return.loc[stock_return[\"Date\"].between(start_datetime, \n",
    "                                                                         start_datetime + relativedelta(months=rebal_month)+relativedelta(days = -1))]\n",
    "                # 리밸런싱 기간의 누적수익률 산출\n",
    "                for j in range(stock_num):\n",
    "                    filtered_df.iloc[:,j+1] = (1 + filtered_df.iloc[:,j+1]).cumprod()\n",
    "                # 해당 누적수익률에 initial 투자비중을 곱해준다 \n",
    "                for j in range(stock_num):\n",
    "                    filtered_df.iloc[:,j+1] = filtered_df.iloc[:,j+1]*float(b[j])\n",
    "                # 이후 각각의 종목의 비중을 계산해서 산출한다\n",
    "                filtered_df['total_value'] = filtered_df.sum(axis=1)\n",
    "                for j in range(stock_num):\n",
    "                    filtered_df.iloc[:,j+1] = filtered_df.iloc[:,j+1]/filtered_df['total_value']\n",
    "\n",
    "                rebal_weight = pd.concat([rebal_weight,filtered_df])\n",
    "                start_datetime = start_datetime + relativedelta(months=rebal_month)\n",
    "\n",
    "                #final_day = monthrange(start_datetime.year, start_datetime.month)\n",
    "\n",
    "            stock_weight = rebal_weight.iloc[:,:-1]\n",
    "            #print(stock_weight)\n",
    "            '''\n",
    "            stock_weight = stock_return.Date\n",
    "            stock_weight = pd.DataFrame(stock_weight)\n",
    "            c = 0\n",
    "            for stockweight in b:\n",
    "                stock_weight[a[c]] = float(stockweight)\n",
    "                c = c + 1\n",
    "            #print(stock_weight)\n",
    "            '''\n",
    "        else :\n",
    "            # 포트폴리오 최적화 코드를 통한 리벨런싱 이중 리스트 weight 산출\n",
    "            # 1. 입력 받은 start ~ end 날짜를 리밸런싱 기간으로 쪼개기   \n",
    "            opt_start_datetime = stock_return.iloc[0,0]\n",
    "            opt_end_datetime = stock_return.iloc[-1,0]\n",
    "            opt_diff_months_list = list(rrule.rrule(rrule.MONTHLY, dtstart=opt_start_datetime, until=opt_end_datetime))\n",
    "            opt_month_gap = len(opt_diff_months_list)\n",
    "            opt_rebal_roof = opt_month_gap//rebal_month\n",
    "            opt_rebal_weight = pd.DataFrame()\n",
    "            #opt_array = [[0]*stock_num]*(opt_rebal_roof+1)\n",
    "\n",
    "            for i in range(opt_rebal_roof+1):\n",
    "                opt_df = stock_return.loc[stock_return[\"Date\"].between(opt_start_datetime,opt_start_datetime + relativedelta(months=rebal_month)+relativedelta(days = -1))]\n",
    "                # 최적화 코드에서 기간마다의 가중치를 가져온다\n",
    "                c_m = c_Models(a,b,opt_df.iat[0,0]- relativedelta(months=3),opt_df.iat[-1,0])\n",
    "                ret_vol, efpoints, weights = c_m.plotting()\n",
    "                weights = literal_eval(weights)\n",
    "                weights = weights.get(opt_option)\n",
    "                ##print(weights)\n",
    "                # 리밸런싱 기간의 누적수익률 산출\n",
    "                for j in range(stock_num):\n",
    "                    opt_df.iloc[:,j+1] = (1 + opt_df.iloc[:,j+1]).cumprod()\n",
    "                # 해당 누적수익률에 initial 투자비중을 곱해준다 \n",
    "                for j in range(stock_num):\n",
    "                    opt_df.iloc[:,j+1] = opt_df.iloc[:,j+1]*float(weights[j])\n",
    "                # 이후 각각의 종목의 비중을 계산해서 산출한다\n",
    "                opt_df['total_value'] = opt_df.sum(axis=1)\n",
    "                for j in range(stock_num):\n",
    "                    opt_df.iloc[:,j+1] = opt_df.iloc[:,j+1]/opt_df['total_value']\n",
    "\n",
    "                # 이후 각각의 종목의 비중을 계산해서 산출한다\n",
    "                #print(opt_df)\n",
    "                opt_rebal_weight = pd.concat([opt_rebal_weight,opt_df])\n",
    "                opt_start_datetime = opt_start_datetime + relativedelta(months=rebal_month)\n",
    "                #리밸런싱으로 start 기간이 고객이 원하는 end 기간보다 커지게 되면 종료 \n",
    "                if opt_start_datetime > stock_return.iloc[-1,0]:    # i가 100일 때\n",
    "                    break    \n",
    "            stock_weight = opt_rebal_weight.iloc[:,:-1]\n",
    "            ##print(stock_weight)\n",
    "        # 수익률 데이터와 투자비중을 곱한 하나의 데이터 생성 \n",
    "        pfo_return = stock_weight.Date\n",
    "        pfo_return = pd.DataFrame(pfo_return)\n",
    "        # weight 와 return의 날짜 맞춰주기 \n",
    "        #pfo_return = pfo_return[0:len(stock_weight)]\n",
    "        pfo_return = pd.merge(pfo_return, stock_return, left_on='Date', right_on='Date', how='left')\n",
    "        pfo_return['mean_return'] = 0\n",
    "        ##print(pfo_return)\n",
    "        for i in range(0,len(pfo_return)):\n",
    "            return_result = list(pfo_return.iloc[i,1:1+stock_num])\n",
    "            return_weight = list(stock_weight.iloc[i,1:1+stock_num])\n",
    "            pfo_return.iloc[i,1+stock_num]  = np.dot(return_result,return_weight)\n",
    "            #rint(pfo_return)\n",
    "        pfo_return['acc_return'] = [x+1 for x in pfo_return['mean_return']]\n",
    "        pfo_return['acc_return'] = list(it.accumulate(pfo_return['acc_return'], operator.mul))\n",
    "        pfo_return['acc_return'] = [x-1 for x in pfo_return['acc_return']]\n",
    "        pfo_return['final_balance'] = float(start_amount) + float(start_amount)*pfo_return['acc_return']\n",
    "        pfo_return['Drawdown_list'] = back_test.dd(input,pfo_return['mean_return'])\n",
    "        pfo_return = pfo_return.set_index('Date') \n",
    "        #print(pfo_return)\n",
    "        \n",
    "        \n",
    "        ### 벤치마크 데이터 로드 및 전처리\n",
    "        \n",
    "        tiker_list = ['KS11','US500'] \n",
    "        bench_list = [fdr.DataReader(ticker, start_data_1,  end_data_1)['Change'] for ticker in tiker_list]\n",
    "        bench = pd.concat(bench_list, axis=1)\n",
    "        bench.columns = ['KOSPI', 'S&P500']\n",
    "        bench['KOSPI'] = bench['KOSPI'].fillna(0)\n",
    "        bench['S&P500'] = bench['S&P500'].fillna(0)\n",
    "        #bench = bench.dropna()\n",
    "        \n",
    "        # 벤치마크 누적수익률, DD 값 \n",
    "        \n",
    "        bench['KOSPI_acc'] = [x+1 for x in bench['KOSPI']]\n",
    "        bench['KOSPI_acc'] = list(it.accumulate(bench['KOSPI_acc'], operator.mul))\n",
    "        bench['KOSPI_acc'] = [x-1 for x in bench['KOSPI_acc']]\n",
    "        bench['KOSPI_balance'] = float(start_amount) + float(start_amount)*bench['KOSPI_acc']\n",
    "        bench['KOSPI_Drawdown'] = back_test.dd(input,bench['KOSPI'])\n",
    "        bench['S&P500_acc'] = [x+1 for x in bench['S&P500']]\n",
    "        bench['S&P500_acc'] = list(it.accumulate(bench['S&P500_acc'], operator.mul))\n",
    "        bench['S&P500_acc'] = [x-1 for x in bench['S&P500_acc']]\n",
    "        bench['S&P500_balance'] = float(start_amount) + float(start_amount)*bench['S&P500_acc']\n",
    "        bench['S&P500_Drawdown'] = back_test.dd(input,bench['S&P500'])\n",
    "        \n",
    "        if interval == 'monthly' or interval == 'weekly' :\n",
    "            if interval == 'monthly' :\n",
    "                inter = 'M'\n",
    "            if interval == 'weekly' :\n",
    "                inter = 'W'\n",
    "            pfo_return_interval = pfo_return.resample(inter).last()\n",
    "            pfo_return_first = pd.DataFrame(pfo_return.iloc[0]).transpose()\n",
    "            pfo_return_interval = pd.concat([pfo_return_first, pfo_return_interval])\n",
    "            pfo_return_interval['mean_return'] = pfo_return_interval['final_balance'].pct_change()\n",
    "            pfo_return_interval = pfo_return_interval.dropna()\n",
    "            \n",
    "            # 월별 간격으로 만들어주기, 여기서는 return과 value만 monthly로 산출함 나머지값은 daily\n",
    "            bench_interval = bench.resample(inter).last()\n",
    "            #bench_ex['KOSPI'] = bench_ex['final_balance'].pct_change()\n",
    "            bench_first = pd.DataFrame(bench.iloc[0]).transpose()\n",
    "            bench_interval = pd.concat([bench_first, bench_interval])\n",
    "            bench_interval['KOSPI'] = bench_interval['KOSPI_balance'].pct_change()\n",
    "            bench_interval['S&P500'] = bench_interval['S&P500_balance'].pct_change()\n",
    "            bench_interval = bench_interval.dropna()\n",
    "            \n",
    "            # 날짜타입 열로 만들기 및 str 타입으로 전처리 \n",
    "            pfo_return = pfo_return.rename_axis('Date').reset_index()\n",
    "            pfo_return['Date'] =  pd.to_datetime(pfo_return['Date'], format='%d/%m/%Y').dt.date\n",
    "            pfo_return['Date'] = list(map(str, pfo_return['Date']))\n",
    "            \n",
    "            pfo_return_interval = pfo_return_interval.rename_axis('Date').reset_index()\n",
    "            pfo_return_interval['Date'] =  pd.to_datetime(pfo_return_interval['Date'], format='%d/%m/%Y').dt.date\n",
    "            pfo_return_interval['Date'] = list(map(str, pfo_return_interval['Date']))\n",
    "            \n",
    "            bench = bench.rename_axis('Date').reset_index()\n",
    "            bench['Date'] =  pd.to_datetime(bench['Date'], format='%d/%m/%Y').dt.date\n",
    "            bench['Date'] = list(map(str, bench['Date']))        \n",
    "           \n",
    "            bench_interval = bench_interval.rename_axis('Date').reset_index()\n",
    "            bench_interval['Date'] =  pd.to_datetime(bench_interval['Date'], format='%d/%m/%Y').dt.date\n",
    "            bench_interval['Date'] = list(map(str, bench_interval['Date']))       \n",
    "            \n",
    "            backtest_return = {\n",
    "                 'pfo_return': [\n",
    "                         {\n",
    "                         'Date': list(pfo_return_interval['Date']),\n",
    "                         'mean_return': list(pfo_return_interval['mean_return']),                 \n",
    "                         'acc_return ratio': list(pfo_return_interval['acc_return']),\n",
    "                         'final_balance': list(pfo_return_interval['final_balance']),\n",
    "                         'Drawdown_list' : list(pfo_return_interval['Drawdown_list'])\n",
    "                          }\n",
    "                 ],         \n",
    "                 'bench': [\n",
    "                         {\n",
    "                         'Date': list(bench_interval['Date']),\n",
    "                         'KOSPI_return': list(bench_interval['KOSPI']),              \n",
    "                         'S&P500_return': list(bench_interval['S&P500']),\n",
    "                         'KOSPI_acc_return': list(bench_interval['KOSPI_acc']),\n",
    "                         'KOSPI_balance' : list(bench_interval['KOSPI_balance']),                 \n",
    "                         'KOSPI_Drawdown': list(bench_interval['KOSPI_Drawdown']),\n",
    "                         'S&P500_acc_return': list(bench_interval['S&P500_acc']),\n",
    "                         'S&P500_balance' : list(bench_interval['S&P500_balance']),                 \n",
    "                         'S&P500_Drawdown': list(bench_interval['S&P500_Drawdown'])\n",
    "                          }\n",
    "                 ],    \n",
    "                 'indicator': [\n",
    "                         {\n",
    "                         'Mean': back_test.Arithmetic_Mean_Annual(input,pfo_return['mean_return']),\n",
    "                         'Std': pfo_return['mean_return'].std() * np.sqrt(365),                 \n",
    "                         'Sharpe ratio': back_test.sharpe_ratio(input,pfo_return['mean_return']),\n",
    "                         'VaR': back_test.value_at_risk(input,pfo_return['mean_return']),\n",
    "                         'MDD': back_test.mdd(input,pfo_return['mean_return']),\n",
    "                         'Winning ratio': back_test.winning_rate(input,pfo_return['mean_return']),\n",
    "                         'Gain/Loss Ratio': back_test.profit_loss_ratio(input,pfo_return['mean_return'])\n",
    "                          }\n",
    "                 ],    \n",
    "                 'KOSPI_indicator': [\n",
    "                         {\n",
    "                         'Mean': back_test.Arithmetic_Mean_Annual(input,bench['KOSPI']),\n",
    "                         'Std': bench['KOSPI'].std() * np.sqrt(365),                 \n",
    "                         'Sharpe ratio': back_test.sharpe_ratio(input,bench['KOSPI']),\n",
    "                         'VaR': back_test.value_at_risk(input,bench['KOSPI']),\n",
    "                         'MDD': back_test.mdd(input,bench['KOSPI']),\n",
    "                         'Winning ratio': back_test.winning_rate(input,bench['KOSPI']),\n",
    "                         'Gain/Loss Ratio': back_test.profit_loss_ratio(input,bench['KOSPI'])\n",
    "                          }\n",
    "                 ],    \n",
    "                 'S&P500_indicator': [\n",
    "                         {\n",
    "                         'Mean': back_test.Arithmetic_Mean_Annual(input,bench['S&P500']),\n",
    "                         'Std': bench['S&P500'].std() * np.sqrt(365),                 \n",
    "                         'Sharpe ratio': back_test.sharpe_ratio(input,bench['S&P500']),\n",
    "                        'VaR': back_test.value_at_risk(input,bench['S&P500']),\n",
    "                         'MDD': back_test.mdd(input,bench['S&P500']),\n",
    "                         'Winning ratio': back_test.winning_rate(input,bench['S&P500']),\n",
    "                         'Gain/Loss Ratio': back_test.profit_loss_ratio(input,bench['S&P500'])\n",
    "                          }\n",
    "                 ]\n",
    "             } \n",
    "            \n",
    "        else :\n",
    "            # 날짜타입 열로 만들기 및 str 타입으로 전처리 \n",
    "            pfo_return = pfo_return.rename_axis('Date').reset_index()\n",
    "            pfo_return['Date'] =  pd.to_datetime(pfo_return['Date'], format='%d/%m/%Y').dt.date\n",
    "            pfo_return['Date'] = list(map(str, pfo_return['Date']))\n",
    "            \n",
    "            bench = bench.rename_axis('Date').reset_index()\n",
    "            bench['Date'] =  pd.to_datetime(bench['Date'], format='%d/%m/%Y').dt.date\n",
    "            bench['Date'] = list(map(str, bench['Date']))\n",
    "            backtest_return = {\n",
    "                 'pfo_return': [\n",
    "                         {\n",
    "                         'Date': list(pfo_return['Date']),\n",
    "                         'mean_return': list(pfo_return['mean_return']),                 \n",
    "                         'acc_return ratio': list(pfo_return['acc_return']),\n",
    "                         'final_balance': list(pfo_return['final_balance']),\n",
    "                         'Drawdown_list' : list(pfo_return['Drawdown_list'])\n",
    "                          }\n",
    "                 ],         \n",
    "                 'bench': [\n",
    "                         {\n",
    "                         'Date': list(bench['Date']),\n",
    "                         'KOSPI_return': list(bench['KOSPI']),              \n",
    "                         'S&P500_return': list(bench['S&P500']),\n",
    "                         'KOSPI_acc_return': list(bench['KOSPI_acc']),\n",
    "                         'KOSPI_balance' : list(bench['KOSPI_balance']),                 \n",
    "                         'KOSPI_Drawdown': list(bench['KOSPI_Drawdown']),\n",
    "                         'S&P500_acc_return': list(bench['S&P500_acc']),\n",
    "                         'S&P500_balance' : list(bench['S&P500_balance']),                 \n",
    "                         'S&P500_Drawdown': list(bench['S&P500_Drawdown'])\n",
    "                          }\n",
    "                 ],    \n",
    "                 'indicator': [\n",
    "                         {\n",
    "                         'Mean': back_test.Arithmetic_Mean_Annual(input,pfo_return['mean_return']),\n",
    "                         'Std': pfo_return['mean_return'].std() * np.sqrt(365),                 \n",
    "                         'Sharpe ratio': back_test.sharpe_ratio(input,pfo_return['mean_return']),\n",
    "                         'VaR': back_test.value_at_risk(input,pfo_return['mean_return']),\n",
    "                         'MDD': back_test.mdd(input,pfo_return['mean_return']),\n",
    "                         'Winning ratio': back_test.winning_rate(input,pfo_return['mean_return']),\n",
    "                         'Gain/Loss Ratio': back_test.profit_loss_ratio(input,pfo_return['mean_return'])\n",
    "                          }\n",
    "                 ],    \n",
    "                 'KOSPI_indicator': [\n",
    "                         {\n",
    "                         'Mean': back_test.Arithmetic_Mean_Annual(input,bench['KOSPI']),\n",
    "                         'Std': bench['KOSPI'].std() * np.sqrt(365),                 \n",
    "                         'Sharpe ratio': back_test.sharpe_ratio(input,bench['KOSPI']),\n",
    "                         'VaR': back_test.value_at_risk(input,bench['KOSPI']),\n",
    "                         'MDD': back_test.mdd(input,bench['KOSPI']),\n",
    "                         'Winning ratio': back_test.winning_rate(input,bench['KOSPI']),\n",
    "                         'Gain/Loss Ratio': back_test.profit_loss_ratio(input,bench['KOSPI'])\n",
    "                          }\n",
    "                 ],    \n",
    "                 'S&P500_indicator': [\n",
    "                         {\n",
    "                         'Mean': back_test.Arithmetic_Mean_Annual(input,bench['S&P500']),\n",
    "                         'Std': bench['S&P500'].std() * np.sqrt(365),                 \n",
    "                         'Sharpe ratio': back_test.sharpe_ratio(input,bench['S&P500']),\n",
    "                        'VaR': back_test.value_at_risk(input,bench['S&P500']),\n",
    "                         'MDD': back_test.mdd(input,bench['S&P500']),\n",
    "                         'Winning ratio': back_test.winning_rate(input,bench['S&P500']),\n",
    "                         'Gain/Loss Ratio': back_test.profit_loss_ratio(input,bench['S&P500'])\n",
    "                          }\n",
    "                 ]\n",
    "             }  \n",
    "\n",
    "        \n",
    "\n",
    "        return backtest_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#gmv 포트폴리오 -> 해당 종목을 각각 몇 퍼센트로 투자해야 위험이 제일 적은가\n",
    "c_Models(['삼성전자','LG전자','카카오'],[0.2,0.5,0.3],'2015-01-01','2021-04-01').plotting()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'삼성전자': 0.727, 'LG전자': 0.0, '카카오': 0.273}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_Models(['삼성전자','LG전자','카카오'],[0,0,0],'2015-01-01','2021-04-01').gmv_opt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'삼성전자': 0.674, 'LG전자': 0.0, '카카오': 0.326}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_Models(['삼성전자','LG전자','카카오'],[0,0,0],'2015-01-01','2021-04-01').ms_opt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'삼성전자': 0.443, 'LG전자': 0.238, '카카오': 0.319}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#risk parity -> 포트폴리오에 대한 자산 위험 비중을 동일하게 조정, 즉 삼전,lg,카카오의 포트폴리오 위험 기여도를 0.33으로 하게 만드는 비중\n",
    "c_Models(['삼성전자','LG전자','카카오'],[0,0,0],'2015-01-01','2021-04-01').rp_opt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:494: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\n",
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:188: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    }
   ],
   "source": [
    "#def backtest_data(self,assets,weight,start_data_1, end_data_1,start_amount,rebalancing_month, interval, opt_option):\n",
    "data = back_test().backtest_data(['삼성전자','LG전자'],[0.9,0.1],'2010-01-01', '2021-01-02',10000000,3, 'monthly', 'gmv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'pfo_return': [{'Date': ['2010-01-31',\n",
       "    '2010-02-28',\n",
       "    '2010-03-31',\n",
       "    '2010-04-30',\n",
       "    '2010-05-31',\n",
       "    '2010-06-30',\n",
       "    '2010-07-31',\n",
       "    '2010-08-31',\n",
       "    '2010-09-30',\n",
       "    '2010-10-31',\n",
       "    '2010-11-30',\n",
       "    '2010-12-31',\n",
       "    '2011-01-31',\n",
       "    '2011-02-28',\n",
       "    '2011-03-31',\n",
       "    '2011-04-30',\n",
       "    '2011-05-31',\n",
       "    '2011-06-30',\n",
       "    '2011-07-31',\n",
       "    '2011-08-31',\n",
       "    '2011-09-30',\n",
       "    '2011-10-31',\n",
       "    '2011-11-30',\n",
       "    '2011-12-31',\n",
       "    '2012-01-31',\n",
       "    '2012-02-29',\n",
       "    '2012-03-31',\n",
       "    '2012-04-30',\n",
       "    '2012-05-31',\n",
       "    '2012-06-30',\n",
       "    '2012-07-31',\n",
       "    '2012-08-31',\n",
       "    '2012-09-30',\n",
       "    '2012-10-31',\n",
       "    '2012-11-30',\n",
       "    '2012-12-31',\n",
       "    '2013-01-31',\n",
       "    '2013-02-28',\n",
       "    '2013-03-31',\n",
       "    '2013-04-30',\n",
       "    '2013-05-31',\n",
       "    '2013-06-30',\n",
       "    '2013-07-31',\n",
       "    '2013-08-31',\n",
       "    '2013-09-30',\n",
       "    '2013-10-31',\n",
       "    '2013-11-30',\n",
       "    '2013-12-31',\n",
       "    '2014-01-31',\n",
       "    '2014-02-28',\n",
       "    '2014-03-31',\n",
       "    '2014-04-30',\n",
       "    '2014-05-31',\n",
       "    '2014-06-30',\n",
       "    '2014-07-31',\n",
       "    '2014-08-31',\n",
       "    '2014-09-30',\n",
       "    '2014-10-31',\n",
       "    '2014-11-30',\n",
       "    '2014-12-31',\n",
       "    '2015-01-31',\n",
       "    '2015-02-28',\n",
       "    '2015-03-31',\n",
       "    '2015-04-30',\n",
       "    '2015-05-31',\n",
       "    '2015-06-30',\n",
       "    '2015-07-31',\n",
       "    '2015-08-31',\n",
       "    '2015-09-30',\n",
       "    '2015-10-31',\n",
       "    '2015-11-30',\n",
       "    '2015-12-31',\n",
       "    '2016-01-31',\n",
       "    '2016-02-29',\n",
       "    '2016-03-31',\n",
       "    '2016-04-30',\n",
       "    '2016-05-31',\n",
       "    '2016-06-30',\n",
       "    '2016-07-31',\n",
       "    '2016-08-31',\n",
       "    '2016-09-30',\n",
       "    '2016-10-31',\n",
       "    '2016-11-30',\n",
       "    '2016-12-31',\n",
       "    '2017-01-31',\n",
       "    '2017-02-28',\n",
       "    '2017-03-31',\n",
       "    '2017-04-30',\n",
       "    '2017-05-31',\n",
       "    '2017-06-30',\n",
       "    '2017-07-31',\n",
       "    '2017-08-31',\n",
       "    '2017-09-30',\n",
       "    '2017-10-31',\n",
       "    '2017-11-30',\n",
       "    '2017-12-31',\n",
       "    '2018-01-31',\n",
       "    '2018-02-28',\n",
       "    '2018-03-31',\n",
       "    '2018-04-30',\n",
       "    '2018-05-31',\n",
       "    '2018-06-30',\n",
       "    '2018-07-31',\n",
       "    '2018-08-31',\n",
       "    '2018-09-30',\n",
       "    '2018-10-31',\n",
       "    '2018-11-30',\n",
       "    '2018-12-31',\n",
       "    '2019-01-31',\n",
       "    '2019-02-28',\n",
       "    '2019-03-31',\n",
       "    '2019-04-30',\n",
       "    '2019-05-31',\n",
       "    '2019-06-30',\n",
       "    '2019-07-31',\n",
       "    '2019-08-31',\n",
       "    '2019-09-30',\n",
       "    '2019-10-31',\n",
       "    '2019-11-30',\n",
       "    '2019-12-31',\n",
       "    '2020-01-31',\n",
       "    '2020-02-29',\n",
       "    '2020-03-31',\n",
       "    '2020-04-30',\n",
       "    '2020-05-31',\n",
       "    '2020-06-30',\n",
       "    '2020-07-31',\n",
       "    '2020-08-31',\n",
       "    '2020-09-30',\n",
       "    '2020-10-31',\n",
       "    '2020-11-30',\n",
       "    '2020-12-31'],\n",
       "   'mean_return': [-0.05067854441549435,\n",
       "    -0.04392728728061679,\n",
       "    0.09608616909605039,\n",
       "    0.04224004683675231,\n",
       "    -0.09349880594338011,\n",
       "    -0.013304369076572953,\n",
       "    0.049223594176685204,\n",
       "    -0.06455863090060376,\n",
       "    0.025910073719470406,\n",
       "    0.006800406553817195,\n",
       "    0.06191113306131535,\n",
       "    0.1483376513043253,\n",
       "    0.039085886857121555,\n",
       "    -0.059123343527013095,\n",
       "    0.009750812567713707,\n",
       "    -0.041845493562231995,\n",
       "    0.010078387458006821,\n",
       "    -0.08425720620842636,\n",
       "    0.02179176755447987,\n",
       "    -0.1184834123222751,\n",
       "    0.12903225806451557,\n",
       "    0.15238095238095206,\n",
       "    0.037190082644627864,\n",
       "    0.05378486055776932,\n",
       "    0.05540454593420452,\n",
       "    0.08461117503885873,\n",
       "    0.04817377650237287,\n",
       "    0.06168751591343846,\n",
       "    -0.12024654885956909,\n",
       "    -0.015023161011744612,\n",
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       "    0.08906160524237716,\n",
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       "    0.05303008690584776,\n",
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       "    -0.036368465091615954,\n",
       "    0.1545463117851742,\n",
       "    0.28197138103272],\n",
       "   'acc_return ratio': [-0.03398208329183405,\n",
       "    -0.07641662983729647,\n",
       "    0.012326958042457203,\n",
       "    0.055087696164277666,\n",
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       "    0.1034918180350517,\n",
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       "    -0.08062943715780757,\n",
       "    0.037999022563765195,\n",
       "    0.19617030219252918,\n",
       "    0.24065597458811894,\n",
       "    0.30738448318150424,\n",
       "    0.3798195268336002,\n",
       "    0.49656767834055304,\n",
       "    0.5686629951976061,\n",
       "    0.6654299186766806,\n",
       "    0.46516771858833694,\n",
       "    0.44315626804277386,\n",
       "    0.5790944753827074,\n",
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       "    0.6501965970674413,\n",
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       "    0.886154787720195,\n",
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       "    0.6508045462987992,\n",
       "    0.6716790404494786,\n",
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       "    0.5623760539727451,\n",
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       "    0.5474163878442484,\n",
       "    0.57489734350529,\n",
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       "    0.3872949595618602,\n",
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       "    0.3636934994796943,\n",
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       "    0.28999028593478204,\n",
       "    0.23112960244591552,\n",
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       "    0.4137912689007668,\n",
       "    0.35835299490044226,\n",
       "    0.39330978528380856,\n",
       "    0.4503438983192052,\n",
       "    0.5295449162993744,\n",
       "    0.685140903360292,\n",
       "    0.6496640242257852,\n",
       "    0.7738971670766384,\n",
       "    0.9217026418675796,\n",
       "    0.9251480970748727,\n",
       "    1.0474617569337692,\n",
       "    1.0274615792190342,\n",
       "    1.0089111737966694,\n",
       "    1.2014447621554991,\n",
       "    1.4243073715977124,\n",
       "    1.3631606646209455,\n",
       "    1.6991385744316378,\n",
       "    1.6674798560810555,\n",
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       "    1.8334869062637278,\n",
       "    2.143311518280636,\n",
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       "    3.106964873725878],\n",
       "   'final_balance': [9660179.16708166,\n",
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       "   'Drawdown_list': [-0.07754960145733585,\n",
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       "    0.028447559374776388,\n",
       "    -0.0015617132306704473,\n",
       "    -0.08400223672351914,\n",
       "    -0.1252088201212309,\n",
       "    0.12699900554878818,\n",
       "    0.045155761009440853,\n",
       "    0.018497998093304435,\n",
       "    0.054955774700922166,\n",
       "    0.06986338946459902,\n",
       "    -0.0392825158376523,\n",
       "    -0.027751758824443473,\n",
       "    0.1073443140695245,\n",
       "    0.0370325982869566],\n",
       "   'KOSPI_acc_return': [-0.047804175008100636,\n",
       "    -0.052389703776282714,\n",
       "    0.0057419630002457644,\n",
       "    0.03476455433433401,\n",
       "    -0.024769011917288086,\n",
       "    0.00919084737456255,\n",
       "    0.04542542170191699,\n",
       "    0.035708862439566946,\n",
       "    0.11303435836276865,\n",
       "    0.11899991429405388,\n",
       "    0.1319998872680177,\n",
       "    0.2189634726444909,\n",
       "    0.23021632116880286,\n",
       "    0.152687142339077,\n",
       "    0.25234454872687384,\n",
       "    0.3036237603560379,\n",
       "    0.27369280875884683,\n",
       "    0.2490863581431797,\n",
       "    0.2683639679937784,\n",
       "    0.11789440328988543,\n",
       "    0.051947584461169205,\n",
       "    0.13463108261467438,\n",
       "    0.09805302443237651,\n",
       "    0.08507188885049377,\n",
       "    0.16234372297756194,\n",
       "    0.20665418761105192,\n",
       "    0.19739381205732287,\n",
       "    0.17850235070752785,\n",
       "    0.09607704438330522,\n",
       "    0.10217798167012138,\n",
       "    0.11877595691014586,\n",
       "    0.13250113051374868,\n",
       "    0.18669949378862194,\n",
       "    0.13638200101372,\n",
       "    0.14871172834064006,\n",
       "    0.18682625447143986,\n",
       "    0.16598383879455758,\n",
       "    0.2045000404931614,\n",
       "    0.19148373200455993,\n",
       "    0.16709219069678882,\n",
       "    0.18914978038563723,\n",
       "    0.10764837517060544,\n",
       "    0.13745671002173165,\n",
       "    0.14506085898523735,\n",
       "    0.18681397694225188,\n",
       "    0.20658622238073132,\n",
       "    0.21554563279799188,\n",
       "    0.19552465050821066,\n",
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       "    0.1770053325266676,\n",
       "    0.1802687396032474,\n",
       "    0.16598889780675719,\n",
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       "    0.20061885779395494,\n",
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       "    0.21289171515508176,\n",
       "    0.26414880214680037,\n",
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       "    0.16664067854941123,\n",
       "    0.20623077974246984,\n",
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       "    0.16554450151015576,\n",
       "    0.13616197594406998,\n",
       "    0.13895947320463287,\n",
       "    0.18582090756995662,\n",
       "    0.18467990725749917,\n",
       "    0.1785238596337988,\n",
       "    0.17106823931384874,\n",
       "    0.19836912452098976,\n",
       "    0.20924520407180047,\n",
       "    0.21463648899326704,\n",
       "    0.19351975141786348,\n",
       "    0.17901537136565415,\n",
       "    0.20454621751718682,\n",
       "    0.22902768827347653,\n",
       "    0.24318620429930538,\n",
       "    0.2838256646155968,\n",
       "    0.31071285036044305,\n",
       "    0.3949986316766454,\n",
       "    0.42152134185259094,\n",
       "    0.42796583687462975,\n",
       "    0.4044622519823411,\n",
       "    0.42301343881701214,\n",
       "    0.49939887872820354,\n",
       "    0.47134122964221015,\n",
       "    0.4658961878267909,\n",
       "    0.5248128900399656,\n",
       "    0.4422873087981507,\n",
       "    0.4527195939615605,\n",
       "    0.49420879236882587,\n",
       "    0.4395174322430637,\n",
       "    0.3820435747899402,\n",
       "    0.36374420739590096,\n",
       "    0.3804938967285565,\n",
       "    0.39283251224731885,\n",
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       "    0.20388957163111687,\n",
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       "   'KOSPI_balance': [9521958.249918994,\n",
       "    9476102.962237174,\n",
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       "    15429249.908649597,\n",
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       "   'KOSPI_Drawdown': [-0.06952292537600757,\n",
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       "    0.0],\n",
       "   'S&P500_acc_return': [-0.03699647011622831,\n",
       "    -0.009445260990927795,\n",
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       "    0.22158892024810584,\n",
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       "    0.2613969032144281,\n",
       "    0.2918862889464864,\n",
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       "    0.8693578910095783,\n",
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       " 'indicator': [{'Mean': 0.1642931796250566,\n",
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       "   'VaR': -0.026041138050481374,\n",
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       "   'Gain/Loss Ratio': 1.0584216709276888}],\n",
       " 'KOSPI_indicator': [{'Mean': 0.060352718344440656,\n",
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       "   'VaR': -0.016377429535298044,\n",
       "   'MDD': 0.4381875851692738,\n",
       "   'Winning ratio': 0.5015783935461242,\n",
       "   'Gain/Loss Ratio': 0.9515509731489774}],\n",
       " 'S&P500_indicator': [{'Mean': 0.1221903893370747,\n",
       "   'Std': 0.20753149406227922,\n",
       "   'Sharpe ratio': 0.6623200182189786,\n",
       "   'VaR': -0.017379526723227807,\n",
       "   'MDD': 0.33925448191870333,\n",
       "   'Winning ratio': 0.5289372150122764,\n",
       "   'Gain/Loss Ratio': 0.9437313817386278}]}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#data['pfo_return'][0]['mean_return']\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "\n",
    "x = data['pfo_return'][0]['Date']\n",
    "y = data['pfo_return'][0]['acc_return ratio']\n",
    "y2 = data['bench'][0]['KOSPI_acc_return']\n",
    "y3 = data['bench'][0]['S&P500_acc_return']\n",
    "x_ticks = []\n",
    "for i,j in enumerate(x):\n",
    "    if (i % 6) == 0:\n",
    "        x_ticks.append(j)\n",
    "    else:\n",
    "        x_ticks.append('')\n",
    "x_ticks[-1]= x[-1]\n",
    "plt.figure(figsize=(10,5))\n",
    "ax=plt.gca()\n",
    "ax.xaxis.set_major_locator(ticker.MultipleLocator(12))\n",
    "plt.plot(x,y,label = 'gmv result')\n",
    "plt.plot(x,y2 ,label = 'kospi result')\n",
    "plt.plot(x,y3, label = 's&p500 result')\n",
    "plt.xticks(x_ticks,rotation=60)\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Return')\n",
    "plt.title('result')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = fdr.DataReader('005930')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c_m = c_Models(['삼성전자','LG전자','카카오'],[0,0,0],'2015-01-01','2021-04-01')\n",
    "ret_vol, efpoints, weights = c_m.plotting()\n",
    "print(weights)\n",
    "weights = literal_eval(weights)\n",
    "weights = weights.get('ms')\n",
    "print(weights)"
   ]
  }
 ],
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