16_dual_momentum.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import time\n",
    "from tabulate import tabulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 44 ms, sys: 0 ns, total: 44 ms\n",
      "Wall time: 46.5 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "code_list = ['W993008','W993009','W993010','W993023']\n",
    "data = []\n",
    "for code in code_list:\n",
    "    tmp = pd.read_csv('../data/csv_day/%s.csv'%code, index_col=False).sort_values(by=['Date'],ascending=True).reset_index(drop=True)\n",
    "    tmp.index = pd.to_datetime(tmp['Date'])\n",
    "    tmp = tmp.drop(['Date'], axis=1)\n",
    "    #tmp['code'] = code\n",
    "    data.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.concat(data, keys=code_list)\n",
    "data.loc[code_list] = np.array(list(map(lambda x:float(str(x[0]).replace(',','')), data.loc[code_list].values))).reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "cur_date = data.loc[code_list[0]].index[120]\n",
    "prev_date = data.loc[code_list[0]].index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      Index\n",
      "        Date               \n",
      "W993008 2001-12-07  1142.80\n",
      "W993009 2001-12-07  1171.36\n",
      "W993010 2001-12-07  1087.73\n",
      "W993023 2001-12-07  1153.74\n",
      "                     Index\n",
      "        Date              \n",
      "W993008 2001-06-15  1000.0\n",
      "W993009 2001-06-15  1000.0\n",
      "W993010 2001-06-15  1000.0\n",
      "W993023 2001-06-15  1000.0\n"
     ]
    }
   ],
   "source": [
    "print(data.loc[(code_list,cur_date),:])\n",
    "print(data.loc[(code_list,prev_date),:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_result(res):\n",
    "    # plot result\n",
    "    res = pd.DataFrame(res, columns=[\"Date\", \"balance\"])\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.plot(res[\"Date\"], res[\"balance\"])\n",
    "\n",
    "    import matplotlib.dates as mdates\n",
    "    years = mdates.YearLocator()   # every year\n",
    "    months = mdates.MonthLocator()  # every month\n",
    "    yearsFmt = mdates.DateFormatter('%02Y')\n",
    "\n",
    "    # format the ticks\n",
    "    ax.xaxis.set_major_locator(years)\n",
    "    ax.xaxis.set_major_formatter(yearsFmt)\n",
    "    ax.xaxis.set_minor_locator(months)\n",
    "\n",
    "    # round to nearest years...\n",
    "    datemin = np.datetime64(res.iloc[0,0], 'Y')\n",
    "    datemax = np.datetime64(res.iloc[-1,0], 'Y') + np.timedelta64(1, 'Y')\n",
    "    ax.set_xlim(datemin, datemax)\n",
    "    ax.set_yscale(\"log\")\n",
    "\n",
    "    fig.autofmt_xdate()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlen = len(data.loc[code_list[0]])\n",
    "balance_list = []\n",
    "profit_list = []\n",
    "best_balance = prev_balance = balance = 1e4\n",
    "year_balance = [balance]\n",
    "mdd = 1\n",
    "prev_year = 2016\n",
    "for i in range(0,nlen):\n",
    "    if i < 21*6 or nlen < i+21:\n",
    "        continue\n",
    "    cur_date = data.loc[code_list[0]].index[i]\n",
    "    past_6m_date = data.loc[code_list[0]].index[i-21*6]\n",
    "    yesterday = data.loc[code_list[0]].index[i-1]\n",
    "    \n",
    "    # 월 1회 리밸런싱\n",
    "    if i%21 == 0:\n",
    "        # 최근 6개월간 가장 많이 오른 지수에 투자\n",
    "        div = np.divide(data.loc[(code_list,cur_date),'Index'], data.loc[(code_list,past_6m_date),'Index']).values\n",
    "        top = np.argmax(div)\n",
    "        # 네 개 지수의 6개월 수익이 예금금리 이하로 감소하면 모든 ETF 매도 후 현금 보유\n",
    "        if data.loc[(code_list[top],cur_date),'Index']/data.loc[(code_list[top],past_6m_date),'Index'] < 0.015:\n",
    "            top = -1\n",
    "    \n",
    "    if top >= 0:\n",
    "        amt = balance/data.loc[(code_list[top],yesterday),'Index']\n",
    "        balance += (data.loc[(code_list[top],cur_date),'Index']-data.loc[(code_list[top],yesterday),'Index'])*amt\n",
    "    \n",
    "    if cur_date.year != prev_year:\n",
    "        year_balance.append(balance)\n",
    "        prev_year = cur_date.year\n",
    "    profit_list.append(balance/prev_balance-1)\n",
    "    balance_list.append([cur_date, balance])\n",
    "    prev_balance = balance\n",
    "    best_balance = max(best_balance, balance)\n",
    "    mdd = min(mdd, balance/best_balance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  초기자산    최종자산  Return      CAGR(%)    표준편차(%)    최고의해    최악의해    MDD    샤프지수\n",
      "----------  ----------  --------  ---------  -------------  ----------  ----------  -----  ----------\n",
      "     10000     92477.9  x9          12.2595        0.26381       76.15      -32.78     53        0.76\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdb63d28748>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "CAGR = (int(balance/1e4)**(1/len(year_balance))-1)*100\n",
    "MDD = int((1-mdd)*100)\n",
    "year_profit = np.divide(year_balance[1:],year_balance[:-1])\n",
    "best_profit = max(year_profit)*100-100\n",
    "worst_profit = min(year_profit)*100-100\n",
    "best_profit = int(best_profit*100)/100\n",
    "worst_profit = int(worst_profit*100)/100\n",
    "SR = np.mean(profit_list)/np.std(profit_list)*np.sqrt(252)\n",
    "SR = int(100*SR)/100\n",
    "returns = 'x'+str(int(balance/1e4))\n",
    "headers = ['초기자산', '최종자산', 'Return', 'CAGR(%)', '표준편차(%)', '최고의해', '최악의해', 'MDD', '샤프지수']\n",
    "results = [1e4, balance, returns, CAGR, np.std(year_profit), best_profit, worst_profit, MDD, SR]\n",
    "print(tabulate([results], headers=headers))\n",
    "plot_result(balance_list)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}