feature add: portfoliio optimization , 투자 전략 구현

@@ -5,18 +5,36 @@ import FinanceDataReader as fdr
 from scipy.optimize import minimize
 import json
 
+#소숫점 표현
+pd.options.display.float_format = '{:.3f}'.format
+np.set_printoptions(precision=3, suppress=True)
+
 class c_Models:
     #Input 값으로, 자산 list, 사용자 포트폴리오 비중, 시작일, 마지막일
     def __init__(self, assets, assets_w, start, end):
         self.result = None
         self.graph = None
     
+        stocks = pd.read_csv('stockcodename.csv', index_col=0)
+        symbol = ''
+        self.asset_name = assets[:]
+        for k in range(len(assets)):
+            for i in enumerate(stocks.Name):
+                if i[1] == assets[k]:
+                    assets[k] = (stocks.iloc[i[0]].Symbol)
+                    break
+
         data = pd.DataFrame()
         # 전체 자산 data들을 가지고 온 후, 정리함
+        
         for asset in assets: #total_list:
             tmp = fdr.DataReader(asset,start,end).Close
-            tmp.rename(columns={'Close': asset}, inplace=True)
-            data = pd.concat([data, tmp], axis=1)
+            if len(data) == 0 :
+                data = tmp
+            else:
+                data = pd.concat([data,tmp], axis=1)
+   
+        data.columns = self.asset_name
    
         if data.isnull().values.any() == True: #불러온 data에 오류가 있다면
             return "No Data",''
@@ -39,9 +57,9 @@ class c_Models:
         constraints = ({'type':'eq', 'fun':lambda x: np.sum(x)-1})
         bd = ((0,1),) * n_assets
         #cov = data.cov() * 12
-        
         gmv = minimize(fun, w0, method = 'SLSQP', constraints=constraints, bounds=bd)
-        return gmv.x
+        result = dict(zip(self.asset_name, np.round(gmv.x,3)))
+        return result
     
     #Max Sharp ratio : risk free rate은 0.8%로 지정했고, 
     def ms_opt(self):
@@ -51,7 +69,8 @@ class c_Models:
         bd = ((0,1),) * n_assets     
         constraints = ({'type': 'eq', 'fun': lambda x:  np.sum(x) - 1})
         maxsharp = minimize(fun, w0, method ='SLSQP', constraints=constraints, bounds=bd)
-        return maxsharp.x
+        result = dict(zip(self.asset_name, np.round(maxsharp.x,3)))
+        return result
     
     def rp_opt(self):
         def RC(cov, w):
@@ -80,5 +99,5 @@ class c_Models:
         bd = ((0,1),) * n_assets
 
         rp = minimize(RP_objective, w0,  constraints=constraints, bounds = bd, method='SLSQP')
-
-        return rp.x     #, RC(self.cov, rp.x)
+        result = dict(zip(self.asset_name, np.round(rp.x,3)))
+        return result     #, RC(self.cov, rp.x)
\ No newline at end of file

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,6 +66,180 @@
     "stocks = fdr.StockListing('KOSPI') # 코스피\n",
     "stocks.to_csv(\"stockcodename.csv\",mode='w', encoding='utf-8-sig')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "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",
+    "\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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'삼성전자': 0.727, 'LG전자': 0.0, '카카오': 0.273}"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#gmv 포트폴리오 -> 해당 종목을 각각 몇 퍼센트로 투자해야 위험이 제일 적은가\n",
+    "c_Models(['삼성전자','LG전자','카카오'],[0,0,0],'2015-01-01','2021-04-01').gmv_opt()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'삼성전자': 0.674, 'LG전자': 0.0, '카카오': 0.326}"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#maxsharp ratio -> 위험대비 수익률이 제일 좋은 포트폴리오 비중 , 즉 가성비가 좋다\n",
+    "c_Models(['삼성전자','LG전자','카카오'],[0,0,0],'2015-01-01','2021-04-01').ms_opt()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'삼성전자': 0.443, 'LG전자': 0.238, '카카오': 0.319}"
+      ]
+     },
+     "execution_count": 125,
+     "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()"
+   ]
   }
  ],
  "metadata": {