backtester in regimes.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "C4_1_구간조절_백테스팅_정리의_사본.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/FinancialEngineerLab/finefinance/blob/master/backtester%20in%20regimes.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T-tkuLCmJGJA"
      },
      "source": [
        "#from google.colab import files\n",
        "#uploaded = files.upload()\n",
        "#for fn in uploaded.keys():\n",
        "#  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "#      name=fn, length=len(uploaded[fn])))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KW-Mt33ZrfYT",
        "cellView": "both"
      },
      "source": [
        "!pip install -U finance_datareader\n",
        "!wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz\n",
        "!tar -xzvf ta-lib-0.4.0-src.tar.gz\n",
        "%cd ta-lib\n",
        "!./configure --prefix=/usr\n",
        "!make\n",
        "!make install\n",
        "!pip install Ta-Lib"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nPZmOTKn5CKq"
      },
      "source": [
        "import FinanceDataReader as fdr\n",
        "import talib\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import datetime\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "besarZhFXsD2",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 77
        },
        "outputId": "b35ec39f-e40a-49ba-e1f0-50741417853a"
      },
      "source": [
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-fb293de0-a83f-4fdf-b180-ae71686527ec\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-fb293de0-a83f-4fdf-b180-ae71686527ec\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y9GCfwaqPP4M"
      },
      "source": [
        "## Data Preprocessing ##\n",
        "\n",
        "#인덱스 설정 함수\n",
        "def price_indexr(df):\n",
        "    df.index=df.date;df=df.iloc[:,1:]\n",
        "    return df\n",
        "\n",
        "def finance_indexr(df):\n",
        "    df.index=df.date;df=df.iloc[:,1:]\n",
        "    df0=df.iloc[43:,:]\n",
        "    df0.index=df.index[:-43]\n",
        "    return df0\n",
        "  \n",
        "  \n",
        "#대차대조표\n",
        "asset0=pd.ExcelFile(\"daily_asset_131230191230.xlsx\").parse(1)\n",
        "debt0=pd.ExcelFile(\"daily_debt_131230191230.xlsx\").parse(1)\n",
        "equity0=pd.ExcelFile(\"daily_equity_131230191230.xlsx\").parse(1)\n",
        "asset=finance_indexr(asset0);debt=finance_indexr(debt0);equity=finance_indexr(equity0)\n",
        "\n",
        "#손익계산서\n",
        "sales0=pd.ExcelFile(\"daily_sales_131230191230.xlsx\").parse(1)\n",
        "grossprofit0=pd.ExcelFile(\"daily_grossprofit_131230191230.xlsx\").parse(1)\n",
        "profit0=pd.ExcelFile(\"daily_profit_131230191230.xlsx\").parse(1)\n",
        "earning0=pd.ExcelFile(\"daily_earning_131230191230.xlsx\").parse(1)\n",
        "\n",
        "#영업현금흐름\n",
        "opercash0=pd.ExcelFile(\"daily_opercash_131230191230.xlsx\").parse(1)\n",
        "sales=finance_indexr(sales0);grossprofit=finance_indexr(grossprofit0);profit=finance_indexr(profit0);earning=finance_indexr(earning0)\n",
        "opercash=finance_indexr(opercash0)\n",
        "\n",
        "#주가배수\n",
        "pbr0=pd.ExcelFile(\"daily_pbr_131230191230.xlsx\").parse(1)\n",
        "per0=pd.ExcelFile(\"daily_per_131230191230.xlsx\").parse(1)\n",
        "pcr0=pd.ExcelFile(\"daily_pcr_131230191230.xlsx\").parse(1)\n",
        "psr0=pd.ExcelFile(\"daily_psr_131230191230.xlsx\").parse(1)\n",
        "per=price_indexr(per0);pbr=price_indexr(pbr0);pcr=price_indexr(pcr0);psr=price_indexr(psr0)\n",
        "\n",
        "mktcap0=pd.ExcelFile(\"daily_mktcap_131230191230.xlsx\").parse(1)\n",
        "mktcap=price_indexr(mktcap0)\n",
        "\n",
        "tickerlist=mktcap.columns\n",
        "start='2012-01-01'; end = str(datetime.date.today())\n",
        "data = fdr.DataReader(mktcap.columns[0], start, end)\n",
        "Open0 = data[\"Open\"]\n",
        "High0 = data[\"High\"]\n",
        "Low0 = data[\"Low\"]\n",
        "Close0 = data[\"Close\"]\n",
        "Volume0 = data[\"Volume\"]\n",
        "\n",
        "for i in range(len(tickerlist)-1):\n",
        "    ii=i+1\n",
        "    print(ii)\n",
        "    temp_data = fdr.DataReader(mktcap.columns[ii], start, end)\n",
        "    Open0=pd.concat((Open0,temp_data[\"Open\"]),axis=1)\n",
        "    High0=pd.concat((High0,temp_data[\"High\"]),axis=1)\n",
        "    Low0=pd.concat((Low0,temp_data[\"Low\"]),axis=1)\n",
        "    Close0=pd.concat((Close0,temp_data[\"Close\"]),axis=1)\n",
        "    Volume0=pd.concat((Volume0,temp_data[\"Volume\"]),axis=1)\n",
        "    \n",
        "Open=Open0[::-1];High=High0[::-1];Low=Low0[::-1];Close=Close0[::-1];Volume=Volume0[::-1]\n",
        "Close.columns=mktcap.columns\n",
        "Open=pd.DataFrame(np.where(Open>0,Open,Close));Open.index=Close.index;Open.columns=mktcap.columns;\n",
        "High=pd.DataFrame(np.where(High>0,High,Close));High.index=Close.index;High.columns=mktcap.columns;\n",
        "Low=pd.DataFrame(np.where(Low>0,Low,Close));Low.index=Close.index;Low.columns=mktcap.columns;\n",
        "Volume=pd.DataFrame(np.where(Volume>0,Volume,Close));Volume.index=Close.index;Volume.columns=mktcap.columns;\n",
        "\n",
        "amount=np.multiply(np.add(np.add(Close,Low),High)/3,Volume)\n",
        "\n",
        "#영업일 뽑기\n",
        "workday=pd.DataFrame(Close.iloc[:,0:1]*0+1);workday.columns=[\"wd\",]\n",
        "workday_L=pd.DataFrame(workday)\n",
        "workday_S=workday_L.loc[workday_L.index>pd.to_datetime('2015-01-01')]\n",
        "\n",
        "\n",
        "#주가배수 추가\n",
        "def adder(df):\n",
        "  update_days=list(workday_S.index).index(per0.index[0])\n",
        "  multiple=np.divide(Close.iloc[:update_days,:],Close.iloc[update_days:update_days+1,:])\n",
        "  add=np.multiply(multiple,df.iloc[0,:])\n",
        "  \n",
        "  return pd.concat((add,df),axis=0)\n",
        "\n",
        "per=adder(per);pbr=adder(pbr);pcr=adder(pcr);psr=adder(psr);mktcap=adder(mktcap)\n",
        "\n",
        "#종가\n",
        "ends1=pd.DataFrame(Close)\n",
        "ends2=pd.DataFrame(Close)\n",
        "\n",
        "\n",
        "#Kospi200 뽑기\n",
        "start0='2015-01-02'\n",
        "kospi_bool_0=(price_indexr(pd.ExcelFile(\"kospi_bool.xlsx\").parse(0)))\n",
        "kospi_bool_1=kospi_bool_0[asset.columns]\n",
        "kospi_bool_2=pd.DataFrame(kospi_bool_1.iloc[:list(kospi_bool_1.index).index(pd.to_datetime(start0+' 00:00:00'))+1,:])\n",
        "\n",
        "#주가배수 추가#### 추후 실험해야함\n",
        "def adder(df):\n",
        "  update_days=list(workday_S.index).index(kospi_bool_2.index[0])\n",
        "  multiple=np.divide(Close.iloc[:update_days,:],Close.iloc[update_days:update_days+1,:])\n",
        "  add=np.multiply(multiple,df.iloc[0,:])\n",
        "  return pd.concat((add,df),axis=0)\n",
        "kospi_bool=(adder(kospi_bool_2)>0)+0\n",
        "\n",
        "\n",
        "#벤치마크\n",
        "kospi200_bench=pd.DataFrame(fdr.DataReader('KS200', '2015')[\"Open\"][::-1])\n",
        "kospi200_bench.columns=[\"Kospi200\"]\n",
        "kospi200_bench\n",
        "\n",
        "#재무데이터 전처리 함수\n",
        "def finance_rank(df0):\n",
        "    df=pd.DataFrame(df0)\n",
        "    mod_df=df.fillna(method=\"backfill\").loc[workday_S.index].fillna(method=\"backfill\")*kospi_bool.loc[workday_S.index]*(amount.loc[workday_S.index]>10**9/2)\n",
        "    #na 값이 선택되지 않게금 매우 낮은 값을 줌\n",
        "    mod_df_na=np.where(mod_df>-900000000000,mod_df,-900000000000)\n",
        "    #ranking을 매긴다/높은 roe가 1등,17년 12월 31일 제거\n",
        "    mod_df_ranked=len(df0.columns)-np.argsort(np.argsort(mod_df_na))\n",
        "    df1=pd.DataFrame(mod_df_ranked)\n",
        "    df1.index=mod_df.index;df1.columns=mod_df.columns\n",
        "    return df1\n",
        "\n",
        "#가격배수 전처리 함수\n",
        "def priceratio_rank(df0):    \n",
        "    df=pd.DataFrame(df0).loc[workday_S.index]*kospi_bool.loc[workday_S.index]*(amount.loc[workday_S.index]>10**9/2)\n",
        "    #na, -값을 처리한다\n",
        "    mod_df=np.where(df>0,df,900000000000)\n",
        "    #ranking을 매긴다/작은 per가 1등\n",
        "    mod_df_ranked=np.argsort(np.argsort(mod_df))+1\n",
        "    df1=pd.DataFrame(mod_df_ranked)\n",
        "    df1.index=df.index;df1.columns=df.columns\n",
        "    return df1\n",
        "\n",
        "#결손처리\n",
        "release_date=asset['005930'].dropna().index\n",
        "\n",
        "def filler_data(df0):\n",
        "    df1=df0.loc[release_date]\n",
        "    df2=df1.fillna(method=\"backfill\")\n",
        "    return pd.concat((df0.iloc[:,:1],df2),axis=1).iloc[:,1:][::-1]\n",
        "\n",
        "  #성장률 계산\n",
        "  \n",
        "release_date=asset['005930'].dropna().index\n",
        "def growth_rater(df0,reg):\n",
        "    df1=df0.loc[release_date]\n",
        "    recent=df1.iloc[:-reg,:]\n",
        "    prev=df1.iloc[reg:,:]\n",
        "    rate=np.divide(recent,prev)\n",
        "    gr=rate.fillna(method=\"backfill\")\n",
        "    return pd.concat((df0.iloc[:,:1],gr),axis=1).iloc[:,1:][::-1]\n",
        "\n",
        "  \n",
        "#재무데이터 전처리 함수\n",
        "def finance_rank_1(df0,ext_filt):\n",
        "    df=pd.DataFrame(df0)\n",
        "    mod_df=df.fillna(method=\"backfill\").loc[workday_S.index].fillna(method=\"backfill\")*kospi_bool.loc[workday_S.index]*(amount.loc[workday_S.index]>10**9/2)*ext_filt.loc[workday_S.index]\n",
        "    #na 값이 선택되지 않게금 매우 낮은 값을 줌\n",
        "    mod_df_na=np.where(mod_df>-9000000000000,mod_df,-9000000000000)\n",
        "    #ranking을 매긴다/높은 roe가 1등,17년 12월 31일 제거\n",
        "    mod_df_ranked=len(df0.columns)-np.argsort(np.argsort(mod_df_na))\n",
        "    df1=pd.DataFrame(mod_df_ranked)\n",
        "    df1.index=mod_df.index;df1.columns=mod_df.columns\n",
        "    return df1\n",
        "\n",
        "#가격배수 전처리 함수\n",
        "def priceratio_rank_1(df0,ext_filt):    \n",
        "    df=pd.DataFrame(df0).loc[workday_S.index]*kospi_bool.loc[workday_S.index]*(amount.loc[workday_S.index]>10**9/2)*ext_filt.loc[workday_S.index]\n",
        "    #na, -값을 처리한다\n",
        "    mod_df=np.where(df>0,df,9000000000000)\n",
        "    #ranking을 매긴다/작은 per가 1등\n",
        "    mod_df_ranked=np.argsort(np.argsort(mod_df))+1\n",
        "    df1=pd.DataFrame(mod_df_ranked)\n",
        "    df1.index=df.index;df1.columns=df.columns\n",
        "    return df1\n",
        "  \n",
        "def plot_multi(data, cols=None, spacing=.1, **kwargs):\n",
        "\n",
        "    from pandas import plotting\n",
        "\n",
        "    # Get default color style from pandas - can be changed to any other color list\n",
        "    if cols is None: cols = data.columns\n",
        "    if len(cols) == 0: return\n",
        "    colors = getattr(getattr(plotting, '_style'), '_get_standard_colors')(num_colors=len(cols))\n",
        "\n",
        "    # First axis\n",
        "    ax = data.loc[:, cols[0]].plot(label=cols[0], color=colors[0], **kwargs)\n",
        "    ax.set_ylabel(ylabel=cols[0])\n",
        "    lines, labels = ax.get_legend_handles_labels()\n",
        "\n",
        "    for n in range(1, len(cols)):\n",
        "        # Multiple y-axes\n",
        "        ax_new = ax.twinx()\n",
        "        ax_new.spines['right'].set_position(('axes', 1 + spacing * (n - 1)))\n",
        "        data.loc[:, cols[n]].plot(ax=ax_new, label=cols[n], color=colors[n % len(colors)])\n",
        "        ax_new.set_ylabel(ylabel=cols[n])\n",
        "\n",
        "        # Proper legend position\n",
        "        line, label = ax_new.get_legend_handles_labels()\n",
        "        lines += line\n",
        "        labels += label\n",
        "\n",
        "    ax.legend(lines, labels, loc=0)\n",
        "    return ax\n",
        "\n",
        "#스캔 시작은 위부터\n",
        "def long_short_scanner(long_sign,short_sign):\n",
        "    position_sign0=pd.DataFrame(long_sign.iloc[0:1,:])\n",
        "    print(position_sign0)\n",
        "    for i in range(len(long_sign)):\n",
        "        j=i+1\n",
        "        position_sign_temp=np.add((np.add(-1*short_sign.iloc[j:j+1,:],position_sign0[i:i+1])>0),long_sign.iloc[j:j+1,:])>0\n",
        "        position_sign0=pd.concat((pd.DataFrame(position_sign0),pd.DataFrame(position_sign_temp)))\n",
        "        print(\"position\")\n",
        "        print(position_sign_temp)\n",
        "    return position_sign0\n",
        "\n",
        "asset1=filler_data(asset)\n",
        "equity1=filler_data(equity)\n",
        "debt1=filler_data(debt)\n",
        "sales1=filler_data(sales)\n",
        "grossprofit1=filler_data(grossprofit)\n",
        "profit1=filler_data(profit)\n",
        "earning1=filler_data(earning)\n",
        "opercash1=filler_data(opercash)\n",
        "\n",
        "amount_cut=10**9\n",
        "\n",
        "#재무데이터 전처리 함수\n",
        "def finance_rank(df0):\n",
        "    df=pd.DataFrame(df0)\n",
        "    mod_df=df.fillna(method=\"backfill\").loc[workday_S.index].fillna(method=\"backfill\")*kospi_bool.loc[workday_S.index]*(amount.loc[workday_S.index]>amount_cut)\n",
        "    #na 값이 선택되지 않게금 매우 낮은 값을 줌\n",
        "    mod_df_na=np.where(mod_df>-900000000000,mod_df,-900000000000)\n",
        "    #ranking을 매긴다/높은 roe가 1등,17년 12월 31일 제거\n",
        "    mod_df_ranked=len(df0.columns)-np.argsort(np.argsort(mod_df_na))\n",
        "    df1=pd.DataFrame(mod_df_ranked)\n",
        "    df1.index=mod_df.index;df1.columns=mod_df.columns\n",
        "    return df1\n",
        "\n",
        "#가격배수 전처리 함수\n",
        "def priceratio_rank(df0):    \n",
        "    df=pd.DataFrame(df0).loc[workday_S.index]*kospi_bool.loc[workday_S.index]*(amount.loc[workday_S.index]>amount_cut)\n",
        "    #na, -값을 처리한다\n",
        "    mod_df=np.where(df>0,df,900000000000)\n",
        "    #ranking을 매긴다/작은 per가 1등\n",
        "    mod_df_ranked=np.argsort(np.argsort(mod_df))+1\n",
        "    df1=pd.DataFrame(mod_df_ranked)\n",
        "    df1.index=df.index;df1.columns=df.columns\n",
        "    return df1\n",
        "  \n",
        "   \n",
        "#재무데이터 전처리 함수\n",
        "def finance_rank_1(df0,ext_filt):\n",
        "    df=pd.DataFrame(df0)\n",
        "    mod_df=df.fillna(method=\"backfill\").loc[workday_S.index].fillna(method=\"backfill\")*kospi_bool.loc[workday_S.index]*(amount.loc[workday_S.index]>amount_cut)*ext_filt.loc[workday_S.index]\n",
        "    #na 값이 선택되지 않게금 매우 낮은 값을 줌\n",
        "    mod_df_na=np.where(mod_df>-9000000000000,mod_df,-9000000000000)\n",
        "    #ranking을 매긴다/높은 roe가 1등,17년 12월 31일 제거\n",
        "    mod_df_ranked=len(df0.columns)-np.argsort(np.argsort(mod_df_na))\n",
        "    df1=pd.DataFrame(mod_df_ranked)\n",
        "    df1.index=mod_df.index;df1.columns=mod_df.columns\n",
        "    return df1\n",
        "\n",
        "#가격배수 전처리 함수\n",
        "def priceratio_rank_1(df0,ext_filt):    \n",
        "    df=pd.DataFrame(df0).loc[workday_S.index]*kospi_bool.loc[workday_S.index]*(amount.loc[workday_S.index]>amount_cut)*ext_filt.loc[workday_S.index]\n",
        "    #na, -값을 처리한다\n",
        "    mod_df=np.where(df>0,df,9000000000000)\n",
        "    #ranking을 매긴다/작은 per가 1등\n",
        "    mod_df_ranked=np.argsort(np.argsort(mod_df))+1\n",
        "    df1=pd.DataFrame(mod_df_ranked)\n",
        "    df1.index=df.index;df1.columns=df.columns\n",
        "    return df1\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ew5ci3mNhcI2"
      },
      "source": [
        "#amount\n",
        "amount0=np.multiply(np.add(np.add(Close,Low),High)/3,Volume)\n",
        "amount=np.multiply((amount0)[::-1].ewm(span=60).mean()[::-1],((amount0>0)+0))\n",
        "amount_cut=10**9\n",
        "\n",
        "#재무데이터 전처리 함수\n",
        "def finance_rank(df0):\n",
        "    df=pd.DataFrame(df0)\n",
        "    mod_df=df.fillna(method=\"backfill\").loc[workday_S.index].fillna(method=\"backfill\")*kospi_bool.loc[workday_S.index]*(amount.loc[workday_S.index]>amount_cut)\n",
        "    #na 값이 선택되지 않게금 매우 낮은 값을 줌\n",
        "    mod_df_na=np.where(mod_df>-900000000000,mod_df,-900000000000)\n",
        "    #ranking을 매긴다/높은 roe가 1등,17년 12월 31일 제거\n",
        "    mod_df_ranked=len(df0.columns)-np.argsort(np.argsort(mod_df_na))\n",
        "    df1=pd.DataFrame(mod_df_ranked)\n",
        "    df1.index=mod_df.index;df1.columns=mod_df.columns\n",
        "    return df1\n",
        "\n",
        "#가격배수 전처리 함수\n",
        "def priceratio_rank(df0):    \n",
        "    df=pd.DataFrame(df0).loc[workday_S.index]*kospi_bool.loc[workday_S.index]*(amount.loc[workday_S.index]>amount_cut)\n",
        "    #na, -값을 처리한다\n",
        "    mod_df=np.where(df>0,df,900000000000)\n",
        "    #ranking을 매긴다/작은 per가 1등\n",
        "    mod_df_ranked=np.argsort(np.argsort(mod_df))+1\n",
        "    df1=pd.DataFrame(mod_df_ranked)\n",
        "    df1.index=df.index;df1.columns=df.columns\n",
        "    return df1\n",
        "  \n",
        "   \n",
        "#재무데이터 전처리 함수\n",
        "def finance_rank_1(df0,ext_filt):\n",
        "    df=pd.DataFrame(df0)\n",
        "    mod_df=df.fillna(method=\"backfill\").loc[workday_S.index].fillna(method=\"backfill\")*kospi_bool.loc[workday_S.index]*(amount.loc[workday_S.index]>amount_cut)*ext_filt.loc[workday_S.index]\n",
        "    #na 값이 선택되지 않게금 매우 낮은 값을 줌\n",
        "    mod_df_na=np.where(mod_df>-9000000000000,mod_df,-9000000000000)\n",
        "    #ranking을 매긴다/높은 roe가 1등,17년 12월 31일 제거\n",
        "    mod_df_ranked=len(df0.columns)-np.argsort(np.argsort(mod_df_na))\n",
        "    df1=pd.DataFrame(mod_df_ranked)\n",
        "    df1.index=mod_df.index;df1.columns=mod_df.columns\n",
        "    return df1\n",
        "\n",
        "#가격배수 전처리 함수\n",
        "def priceratio_rank_1(df0,ext_filt):    \n",
        "    df=pd.DataFrame(df0).loc[workday_S.index]*kospi_bool.loc[workday_S.index]*(amount.loc[workday_S.index]>amount_cut)*ext_filt.loc[workday_S.index]\n",
        "    #na, -값을 처리한다\n",
        "    mod_df=np.where(df>0,df,9000000000000)\n",
        "    #ranking을 매긴다/작은 per가 1등\n",
        "    mod_df_ranked=np.argsort(np.argsort(mod_df))+1\n",
        "    df1=pd.DataFrame(mod_df_ranked)\n",
        "    df1.index=df.index;df1.columns=df.columns\n",
        "    return df1\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dKgc3_goQRV0"
      },
      "source": [
        "def signal_int_word(inte):\n",
        "  if inte == 1:\n",
        "    result=\"BUY\"\n",
        "  if inte == 0:\n",
        "    result=\"HOLD\"\n",
        "  if inte == -1:\n",
        "    result=\"SELL\"\n",
        "  return result\n",
        "\n",
        "def TA_1(kospi200_high,kospi200_low,kospi200_close,kospi200_volume):\n",
        "  result=pd.DataFrame(np.ones((1,15)))\n",
        "  result.columns=[\"Signal\",\"MA\",\"MA signal\",\"MACD\",\"MACD signal\",\"RSI\",\"RSI signal\",\"ADX\",\"ADX signal\",\"CCI\",\"CCI signal\",\"ROC\",\"ROC signal\",\"ATR\",\"ATR signal\"]\n",
        "  #5일 이동평균\n",
        "  ta_ma=talib.MA(kospi200_close, timeperiod=5, matype=0)\n",
        "  #1. 종가 MA5의 dead cross SELL\n",
        "  #2. 종가 MA5의 golden cross long\n",
        "  MA_signal_0=kospi200_close-ta_ma\n",
        "  #돌파여부\n",
        "  cross_bool=MA_signal_0[-1]*MA_signal_0[-2]\n",
        "  MA_signal=0\n",
        "  if (cross_bool<0) and (MA_signal_0[-1] < 0):\n",
        "    MA_signal=-1\n",
        "  if (cross_bool<0) and (MA_signal_0[-1] > 0):\n",
        "    MA_signal=+1\n",
        "  \n",
        "  result.iloc[0,1]=ta_ma[-1]\n",
        "  result.iloc[0,2]=signal_int_word(MA_signal)\n",
        "    \n",
        "  #12MA,26MA 이용 MACD\n",
        "  ta_macd=talib.MACD(kospi200_close)\n",
        "  #2. ta_macd[2]의\n",
        "  #2-1. 0 하방 돌파 후 추세 강화 시 -1\n",
        "  #2-2. 0 하방 돌파 후 추세 약화시 0\n",
        "  #2-3. 0 상방 돌파 후 추세 강화시 +1\n",
        "  #2-4. 0 상방 돌파 후 추세 약화 시 0\n",
        "  #돌파여부\n",
        "  cross_bool=ta_macd[2][-1]*ta_macd[2][-2]\n",
        "  macd_signal=0\n",
        "  if (cross_bool<0) and (ta_macd[2][-1] < 0):\n",
        "    macd_signal=-1\n",
        "  if (cross_bool<0) and (ta_macd[2][-1] > 0):\n",
        "    macd_signal=+1\n",
        "\n",
        "  result.iloc[0,3]=ta_macd[-1][-1]\n",
        "  result.iloc[0,4]=signal_int_word(macd_signal)\n",
        "  \n",
        "  #14일 RSI\n",
        "  ta_RSI=talib.RSI(kospi200_close)\n",
        "  #30, 70 상방돌파 +1 하방돌파 -1\n",
        "  rsi_signal=0\n",
        "  \n",
        "  cross30_bool=(ta_RSI[-1]-30)*(ta_RSI[-2]-30)\n",
        "  cross70_bool=(ta_RSI[-1]-70)*(ta_RSI[-2]-70)\n",
        "\n",
        "  if (cross30_bool < 0 ) and (ta_RSI[-1] > 30) :\n",
        "    rsi_signal=rsi_signal + 1\n",
        "  if (cross70_bool < 0 ) and (ta_RSI[-1] < 70) :\n",
        "    rsi_signal=rsi_signal - 1\n",
        "\n",
        "  \n",
        "  result.iloc[0,5]=ta_RSI[-1]\n",
        "  result.iloc[0,6]=signal_int_word(rsi_signal)    \n",
        "   \n",
        "  #14일 adx\n",
        "  ta_adx=talib.ADX(kospi200_high,kospi200_low,kospi200_close)\n",
        "  ta_pdi=talib.PLUS_DI(kospi200_high,kospi200_low,kospi200_close)\n",
        "  ta_mdi=talib.MINUS_DI(kospi200_high,kospi200_low,kospi200_close)\n",
        "  adx_signal=0\n",
        "  if (ta_adx[-1]>ta_adx[-2]) and (ta_pdi[-1]>ta_mdi[-1]) and (ta_pdi[-2]<ta_mdi[-2]):\n",
        "    adx_signal=adx_signal+1\n",
        "  if (ta_adx[-1]<ta_adx[-2]) and (ta_pdi[-1]<ta_mdi[-1]) and (ta_pdi[-2]>ta_mdi[-2]):\n",
        "    adx_signal=adx_signal-1\n",
        "\n",
        "  result.iloc[0,7]=ta_adx[-1]\n",
        "  result.iloc[0,8]=signal_int_word(adx_signal)    \n",
        "\n",
        "  #14일 cci\n",
        "  ta_cci=talib.CCI(kospi200_high,kospi200_low,kospi200_close)\n",
        "  #0하방 돌파시 sell, 상방 돌파시 buy\n",
        "  #돌파여부\n",
        "  cross_bool=ta_cci[-1]*ta_cci[-2]\n",
        "  cci_signal=0\n",
        "  if ((cross_bool<0) and (ta_cci[-1] < 0)) or ta_cci[-1]>100:\n",
        "    cci_signal=-1\n",
        "  if ((cross_bool<0) and (ta_cci[-1] > 0)) or ta_cci[-1]<-100:\n",
        "    cci_signal=+1\n",
        "\n",
        "  result.iloc[0,9]=ta_cci[-1]\n",
        "  result.iloc[0,10]=signal_int_word(cci_signal)    \n",
        "    \n",
        "  #14일 roc\n",
        "  ta_roc=talib.ROC(kospi200_close, timeperiod=10)\n",
        "  #0하방 돌파시 sell, 상방 돌파시 buy\n",
        "  #돌파여부\n",
        "  cross_bool=(ta_roc[-1]-100)*(ta_roc[-2]-100)\n",
        "  roc_signal=0\n",
        "  if (cross_bool<0) and (ta_roc[-1] < 100):\n",
        "    roc_signal=-1\n",
        "  if (cross_bool<0) and (ta_roc[-1] > 100):\n",
        "    roc_signal=+1\n",
        "\n",
        "  result.iloc[0,11]=ta_roc[-1]\n",
        "  result.iloc[0,12]=signal_int_word(roc_signal)    \n",
        "\n",
        "  #14일 ATR\n",
        "  ta_atr=talib.ATR(kospi200_high,kospi200_low,kospi200_close)\n",
        "  #ta_atr.plot()\n",
        "  #100영업일 기준 상위 30 이상이면 Sell, 하위 30이하면 buy신호를 준다.\n",
        "  atr_signal=0\n",
        "  if ta_atr[-1] > np.percentile(ta_atr[-100:].dropna(),70) :\n",
        "    atr_signal=-1\n",
        "  if ta_atr[-1] < np.percentile(ta_atr[-100:].dropna(),30) :\n",
        "    atr_signal=+1\n",
        "  \n",
        "  result.iloc[0,13]=ta_atr[-1]\n",
        "  result.iloc[0,14]=signal_int_word(atr_signal)    \n",
        "  \n",
        "  signal=(np.sum(result==\"BUY\",axis=1)-np.sum(result==\"SELL\",axis=1))[0]\n",
        "  signal_word = \"HOLD\"\n",
        "  if signal < -1:\n",
        "    signal_word = \"SELL\"\n",
        "  if signal < -4:\n",
        "    signal_word = \"STRONG SELL\"\n",
        "  if signal > 1:\n",
        "    signal_word = \"BUY\"\n",
        "  if signal > 4:\n",
        "    signal_word = \"STRONG BUY\"\n",
        "  \n",
        "  result.iloc[0,0]=signal_word\n",
        "  \n",
        "  return result"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xa-2xMVLHgcr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        },
        "outputId": "91cc1611-bc9d-4a74-baae-28b79b3e4a84"
      },
      "source": [
        "#######################Key in#################\n",
        "\n",
        "book_size=10**9\n",
        "pick=10\n",
        "tax_expense=0.0030\n",
        "\n",
        "#######기본 파일 제작#############\n",
        "#종가\n",
        "ends1=pd.DataFrame(np.array(Close))\n",
        "ends2=pd.DataFrame(np.array(Close))\n",
        "ends1.index=Close.index;ends1.columns=Close.columns\n",
        "ends2.index=Close.index;ends2.columns=Close.columns\n",
        "\n",
        "#측정일\n",
        "out_end=Close.iloc[:len(workday_S),:]\n",
        "#기준일 5y\n",
        "in_end_3y=ends1.iloc[740:740+len(workday_S),:]\n",
        "in_end_1y=ends1.iloc[250:250+len(workday_S),:]\n",
        "in_end_05y=ends1.iloc[125:125+len(workday_S),:]\n",
        "\n",
        "#in_end_05y=ends2.iloc[list(ends2.index).index(pd.to_datetime('2017-07-02'))-len(out_end)+1:list(ends2.index).index(pd.to_datetime('2017-07-02'))+1,:]\n",
        "#5y ret\n",
        "ret_3y=(np.divide(out_end,in_end_3y))-1\n",
        "ret_1y=(np.divide(out_end,in_end_1y))-1\n",
        "ret_05y=(np.divide(out_end,in_end_05y))-1\n",
        "\n",
        "#ret_05y=np.divide(out_end.fillna(method=\"backfill\"),in_end_05y.fillna(method=\"backfill\"))-1\n",
        "#vol 필터\n",
        "long_rets=np.divide(ends1.iloc[:-1,:],ends1.iloc[1:,:])-1\n",
        "vols_1y=pd.DataFrame(ends1+0).iloc[:len(workday_S),:]\n",
        "vols_3y=pd.DataFrame(ends1+0).iloc[:len(workday_S),:]\n",
        "#250영업일 vol \n",
        "for i in range(len(workday_S)):\n",
        "#    vols_05y.iloc[i,:]=np.std(long_rets.iloc[i:i+126,:],axis=0)\n",
        "    vols_1y.iloc[i,:]=np.std(long_rets.iloc[i:i+250,:],axis=0)\n",
        "    vols_3y.iloc[i,:]=np.std(long_rets.iloc[i:i+740,:],axis=0)\n",
        "\n",
        "ma_25d=pd.DataFrame(ends2).iloc[:len(workday_S),:]\n",
        "ma_90d=pd.DataFrame(ends2).iloc[:len(workday_S),:]\n",
        "#250영업일 vol \n",
        "for i in range(len(workday_S)):\n",
        "#    vols_05y.iloc[i,:]=np.std(long_rets.iloc[i:i+126,:],axis=0)\n",
        "    ma_25d.iloc[i,:]=np.nanmean(ends2.iloc[i:i+25,:],axis=0)\n",
        "    ma_90d.iloc[i,:]=np.nanmean(ends2.iloc[i:i+75,:],axis=0)\n",
        "\n",
        "ma25_filter=ma_25d<ends2.iloc[:len(workday_S),:]\n",
        "ma90_filter=ma_90d<ends2.iloc[:len(workday_S),:]\n",
        "\n",
        "\n",
        "p_s_ratio=profit1/sales1\n",
        "e_s_ratio=earning1/sales1\n",
        "roe=earning1/equity1\n",
        "roa=earning1/asset1\n",
        "\n",
        "sales_gr=growth_rater(sales,1)\n",
        "grossprofit_gr=growth_rater(grossprofit,1)*(((grossprofit)>0)+0)\n",
        "profit_gr=growth_rater(profit,1)*(((profit)>0)+0)\n",
        "earning_gr=growth_rater(earning,1)*(((earning)>0)+0)\n",
        "opercash_gr=growth_rater(opercash,1)*(((opercash)>0)+0)\n",
        "equity_gr=growth_rater(equity,1)\n",
        "\n",
        "sales_gr_y=growth_rater(sales,4)\n",
        "grossprofit_gr_y=growth_rater(grossprofit,4)*(((grossprofit)>0)+0)\n",
        "profit_gr_y=growth_rater(profit,4)*(((profit)>0)+0)\n",
        "earning_gr_y=growth_rater(earning,4)*(((earning)>0)+0)\n",
        "opercash_gr_y=growth_rater(opercash,4)*(((opercash)>0)+0)\n",
        "equity_gr_y=growth_rater(equity,4)\n",
        "\n",
        "\n",
        "debt_ratio_rev=equity1/debt1\n",
        "asset_turnover_ratio=sales1/asset1\n",
        "equity_turnover_ratio=sales1/equity1\n",
        "\n",
        "mktcap_H_filter=finance_rank(mktcap)<=pick\n",
        "mktcap_L_filter=priceratio_rank(mktcap)<=pick\n",
        "\n",
        "\n",
        "p_s_r_filter=finance_rank(p_s_ratio)<=pick;e_s_r_filter=finance_rank(e_s_ratio)<=pick;\n",
        "roe_filter=finance_rank(roe)<=pick;roa_filter=finance_rank(roa)<=pick\n",
        "\n",
        "debt_ratio_filter=finance_rank(debt_ratio_rev)<=pick;a_turnover_filter=finance_rank(asset_turnover_ratio)<=pick\n",
        "e_turnover_filter=finance_rank(equity_turnover_ratio)<=pick\n",
        "\n",
        "sales_gr_filter=finance_rank(sales_gr)<=pick;gp_gr_filter=finance_rank(grossprofit_gr)<=pick\n",
        "profit_gr_filter=finance_rank(profit_gr)<=pick;earning_gr_filter=finance_rank(earning_gr)<=pick;oc_gr_filter=finance_rank(opercash_gr)<=pick\n",
        "equity_gr_filter=finance_rank(equity_gr)<=pick\n",
        "\n",
        "sales_gr_y_filter=finance_rank(sales_gr_y)<=pick;gp_gr_y_filter=finance_rank(grossprofit_gr_y)<=pick\n",
        "profit_gr_y_filter=finance_rank(profit_gr_y)<=pick;earning_gr_y_filter=finance_rank(earning_gr_y)<=pick;oc_gr_filter=finance_rank(opercash_gr_y)<=pick\n",
        "equity_gr_y_filter=finance_rank(equity_gr_y)<=pick\n",
        "\n",
        "pbr_filter=priceratio_rank(pbr)<=pick;per_filter=priceratio_rank(per)<=pick\n",
        "pcr_filter=priceratio_rank(pcr)<=pick;psr_filter=priceratio_rank(psr)<=pick\n",
        "\n",
        "vol_1y_filter=priceratio_rank(vols_1y)<=pick\n",
        "vol_3y_filter=priceratio_rank(vols_3y)<=pick\n",
        "\n",
        "ret_1y_filter=finance_rank(ret_1y)<=pick\n",
        "ret_3y_filter=finance_rank(ret_3y)<=pick\n",
        "ret_05y_filter=finance_rank(ret_05y)<=pick\n",
        "\n",
        "filter_list=[kospi_bool\\\n",
        "             ,p_s_r_filter,e_s_r_filter,roe_filter,roa_filter\\\n",
        "             ,debt_ratio_filter\\\n",
        "             ,a_turnover_filter,e_turnover_filter\\\n",
        "             ,sales_gr_filter,gp_gr_filter,profit_gr_filter,earning_gr_filter,oc_gr_filter,equity_gr_filter\\\n",
        "             ,pbr_filter,per_filter,pcr_filter,psr_filter\\\n",
        "             ,mktcap_H_filter,mktcap_L_filter\\\n",
        "             ,vol_1y_filter,vol_3y_filter\\\n",
        "             ,ret_05y_filter,ret_1y_filter,ret_3y_filter]\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:42: RuntimeWarning: Mean of empty slice\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:43: RuntimeWarning: Mean of empty slice\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: FutureWarning: \n",
            "Passing list-likes to .loc or [] with any missing label will raise\n",
            "KeyError in the future, you can use .reindex() as an alternative.\n",
            "\n",
            "See the documentation here:\n",
            "https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nPER_PSR=priceratio_rank_1(per,finance_rank(p_s_ratio)<=45)\\nPER_ESR=priceratio_rank_1(per,finance_rank(e_s_ratio)<=45)\\nPER_ROE=priceratio_rank_1(per,finance_rank(roe)<=45)\\nPER_ROA=priceratio_rank_1(per,finance_rank(roa)<=45)\\nPER_SGR=priceratio_rank_1(per,finance_rank(sales_gr)<=45)\\nPER_GPR=priceratio_rank_1(per,finance_rank(grossprofit_gr)<=45)\\nPER_PGR=priceratio_rank_1(per,finance_rank(profit_gr)<=45)\\nPER_EGR=priceratio_rank_1(per,finance_rank(earning_gr)<=45)\\nPER_EQGR=priceratio_rank_1(per,finance_rank(equity_gr)<=45)\\nPER_OCGR=priceratio_rank_1(per,finance_rank(opercash_gr)<=45)\\nPER_DR=priceratio_rank_1(per,finance_rank(debt_ratio_rev)<=45)\\nPER_AT=priceratio_rank_1(per,finance_rank(asset_turnover_ratio)<=45)\\nPER_ET=priceratio_rank_1(per,finance_rank(equity_turnover_ratio)<=45)\\nPER_BF=priceratio_rank_1(per,finance_rank(mktcap)<=45)\\nPER_SF=priceratio_rank_1(per,priceratio_rank(mktcap)<=45)\\n\\nPER_pbr=priceratio_rank_1(per,priceratio_rank(pbr)<=45)\\nPER_pcr=priceratio_rank_1(per,priceratio_rank(pcr)<=45)\\nPER_psr=priceratio_rank_1(per,priceratio_rank(psr)<=45)\\n\\nPER_LV1=priceratio_rank_1(per,priceratio_rank(vols_1y)<=45)\\nPER_LV3=priceratio_rank_1(per,priceratio_rank(vols_3y)<=45)\\n\\nPER_RT05=priceratio_rank_1(per,finance_rank(ret_05y)<=45)\\nPER_RT1=priceratio_rank_1(per,finance_rank(ret_1y)<=45)\\nPER_RT3=priceratio_rank_1(per,finance_rank(ret_3y)<=45)\\n\\n\\nLV1_PGR=priceratio_rank_1(vols_1y,finance_rank(profit_gr)<=45)\\nPER_LV13_PGR=priceratio_rank_1(per,np.multiply(np.multiply(priceratio_rank(vols_1y)<=95,priceratio_rank(vols_3y)<=95),finance_rank(profit_gr)<=95))\\nEGR_ET=finance_rank_1(per,finance_rank(equity_turnover_ratio)<=45)\\nPER_ET_EGR=priceratio_rank_1(per,np.multiply(finance_rank(equity_turnover_ratio)<=95,finance_rank(equity_gr)<=95))\\n\\nPER_PSR_filter=PER_PSR<=pick\\nPER_ESR_filter=PER_ESR<=pick\\nPER_ROE_filter=PER_ROE<=pick\\nPER_ROA_filter=PER_ROA<=pick\\nPER_SGR_filter=PER_SGR<=pick\\nPER_GPR_filter=PER_GPR<=pick\\nPER_PGR_filter=PER_PGR<=pick\\nPER_EGR_filter=PER_EGR<=pick\\nPER_EQGR_filter=PER_EQGR<=pick\\nPER_OCGR_filter=PER_OCGR<=pick\\nPER_DR_filter=PER_DR<=pick\\nPER_AT_filter=PER_AT<=pick\\nPER_ET_filter=PER_ET<=pick\\nPER_BF_filter=PER_BF<=pick\\nPER_SF_filter=PER_SF<=pick\\nPER_pbr_filter=PER_pbr<=pick\\nPER_pcr_filter=PER_pcr<=pick\\nPER_psr_filter=PER_psr<=pick\\nPER_LV1_filter=PER_LV1<=pick\\nPER_LV3_filter=PER_LV3<=pick\\nPER_RT05_filter=PER_RT05<=pick\\nPER_RT1_filter=PER_RT1<=pick\\nPER_RT3_filter=PER_RT3<=pick\\n\\nLV1_PGR_filter=LV1_PGR<=pick\\nPER_LV13_PGR_filter=PER_LV13_PGR<=pick\\nEGR_ET_filter=EGR_ET<=pick\\nPER_ET_EGR_filter=PER_ET_EGR<=pick\\n############################################################\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "spFV439P8it6"
      },
      "source": [
        "cash_ratio=1-kospi200_bench.loc[workday_S.index]/340\n",
        "cash_ratio=cash_ratio*0\n",
        "\n",
        "#특정일에서 특정일 까지의 함수 마지막날이 최신일 경우 테스트 불가\n",
        "def backtester3(filter_list_0,start_day,end_day):\n",
        "    result_by_filter=[]\n",
        "    W_seed=mktcap.loc[workday_S.index[end_day:start_day]]\n",
        "    open_prices=Open.loc[workday_S.index[end_day:start_day]]\n",
        "    for z in range(len(filter_list_0)):\n",
        "        ex_filter=filter_list_0[z].loc[workday_S.index[end_day:start_day]]\n",
        "        W_pick=ex_filter*W_seed    \n",
        "        W_=np.multiply(np.multiply(W_pick,1/pd.DataFrame(np.sum(W_pick,axis=1))),pd.DataFrame(1-cash_ratio.loc[workday_S.index[end_day:start_day]]))            \n",
        "#        W_equal=np.multiply((W_>0),1/pd.DataFrame(np.sum(W_>0,axis=1)))\n",
        "#        port_size=np.add(W_,W_equal)/2*book_size\n",
        "        port_size=W_*book_size\n",
        "        ###########################\n",
        "        ####방식\n",
        "        #종가를 기준으로 포트폴리오 비중을 계산한다\n",
        "        #계산된 포트폴리오 비중에 맞아지게끔 그 다음 시가에 포트폴리오를 조정한다/다시말해 거래는 시가에만 이루어진다\n",
        "        #거래가 시가에만 이루어짐으로, PNL은 시가부터 시가까지로 계산된단다\n",
        "        #시가에 구성된 포트폴리오의 비중은 다음 시가까지 바뀌지 않으므로, 0일 시가에 구성된 포트는 1일 시가까지의 가격차이*주식수로 구할 수 있다.\n",
        "        #0일 포트폴리오에 구성에 쓰여진 비용은 0일 pnl에 부과하여 손익을 구하며, 1일 시가에 포트폴리오 비중을 조절하며 사용된 비용은 1일 pnl에 부과한다\n",
        "        #일간 트레이딩은 해당 코드의 타임 프레임을 좁혀서 사용한다\n",
        "        #ii일 종가시점\n",
        "        ii=0\n",
        "        jj=ii+1\n",
        "        #시가에 진입한 주식수, 당일 pnl, 수익률, 세금\n",
        "        volume=[];pnl=[];ret=[];tax=[];pnl_tax=[]\n",
        "        #jj일 전일 종가기준 진입 대금\n",
        "        temp_in_amount=port_size.iloc[-jj,:]\n",
        "        #jj일 시가 진입 가격\n",
        "        temp_in_price=open_prices.iloc[-jj-1,:]\n",
        "        #jj일 시가 진입 수량\n",
        "        temp_in_volume=np.round(temp_in_amount/temp_in_price,0)\n",
        "        #jj일 시가 진입 대금\n",
        "        temp_in_real_amount=temp_in_price*temp_in_volume\n",
        "        #jj일 시가 진입 세금\n",
        "        temp_tax=temp_in_real_amount*0\n",
        "        #jj일+1 시가 기준 가격\n",
        "        temp_out_price=open_prices.iloc[-jj-2,:]\n",
        "        #jj일+1 시가 기준 대금\n",
        "        temp_out_amount=temp_out_price*temp_in_volume\n",
        "        #jj일 손익\n",
        "        temp_pnl=temp_out_amount-temp_in_real_amount\n",
        "        #jj일 수익률\n",
        "        temp_ret=(temp_out_price/temp_in_price-1)*(temp_in_amount>0)\n",
        "        #리스트에 담기\n",
        "        pnl+=[temp_pnl];ret+=[temp_ret];tax+=[temp_tax];volume+=[temp_in_volume]\n",
        "        pnl_tax+=[temp_pnl-temp_tax]\n",
        "        \n",
        "        for i in range(len(open_prices)-3):    \n",
        "            ii=i+1\n",
        "            print(ii)\n",
        "            jj=ii+1\n",
        "            #jj일 전일 종가기준 진입 대금\n",
        "            temp_in_amount=port_size.iloc[-jj,:]\n",
        "            #jj일 시가 진입 가격\n",
        "            temp_in_price=open_prices.iloc[-jj-1,:]\n",
        "            #jj일 시가 진입 수량\n",
        "            temp_in_volume=np.round(temp_in_amount/temp_in_price,0)\n",
        "            #jj일 시가 진입 대금\n",
        "            temp_in_real_amount=temp_in_price*temp_in_volume\n",
        "            #jj일 시가 진입/청산 세금\n",
        "            ##volume변동\n",
        "            ##volume변동\n",
        "            volume_diff=(temp_in_volume-volume[ii-1])\n",
        "            temp_tax=np.subtract(np.abs(volume_diff*temp_in_price),(volume_diff*temp_in_price))/2*tax_expense\n",
        "            #jj일 종가 기준 가격\n",
        "            temp_out_price=open_prices.iloc[-jj-2,:]\n",
        "            #jj일 종가 기준 대금\n",
        "            temp_out_amount=temp_out_price*temp_in_volume\n",
        "            #jj일 손익\n",
        "            temp_pnl=temp_out_amount-temp_in_real_amount\n",
        "            #jj일 수익률\n",
        "            temp_ret=(temp_out_price/temp_in_price-1)*(temp_in_volume>0)\n",
        "            #리스트에 담기\n",
        "            pnl+=[temp_pnl];ret+=[temp_ret];tax+=[temp_tax];volume+=[temp_in_volume]\n",
        "            pnl_tax+=[temp_pnl-temp_tax]\n",
        "        \n",
        "        df_pnl=pd.DataFrame(pnl[0])\n",
        "        df_ret=pd.DataFrame(ret[0])\n",
        "        df_volume=pd.DataFrame(volume[0])\n",
        "        df_pnl_tax=pd.DataFrame(pnl_tax[0])\n",
        "        for i in range(len(pnl)-1):\n",
        "            df_pnl=pd.concat((df_pnl,pd.DataFrame(pnl[i+1])),axis=1)\n",
        "            df_ret=pd.concat((df_ret,pd.DataFrame(ret[i+1])),axis=1)\n",
        "            df_volume=pd.concat((df_volume,pd.DataFrame(volume[i+1])),axis=1)\n",
        "            df_pnl_tax=pd.concat((df_pnl_tax,pd.DataFrame(pnl_tax[i+1])),axis=1)\n",
        "        df_pnl.columns=workday_S.index[1+end_day:start_day-1][::-1]\n",
        "        df_ret.columns=workday_S.index[1+end_day:start_day-1][::-1]\n",
        "        df_volume.columns=workday_S.index[1+end_day:start_day-1][::-1]\n",
        "        df_pnl_tax.columns=workday_S.index[1+end_day:start_day-1][::-1]\n",
        "        result_by_filter += [(df_pnl,df_ret,df_volume,df_pnl_tax)]\n",
        "\n",
        "    return (result_by_filter)\n",
        "#pnl을 뽑는 보조함수\n",
        "def backtest_concat2(btst0,start_day):    \n",
        "    result_pnl0=pd.DataFrame(np.append(book_size,book_size+np.cumsum(np.sum(btst0[0][-1]))))\n",
        "    result_pnl0.index=workday_S.index[::-1][-start_day:-1]\n",
        "    for i in range(len(btst0)-1):\n",
        "        j=i+1\n",
        "        result_pnl_temp=pd.DataFrame(np.append(book_size,book_size+np.cumsum(np.sum(btst0[j][-1]))))\n",
        "        result_pnl_temp.index=workday_S.index[::-1][-start_day:-1]\n",
        "        result_pnl0=pd.concat((result_pnl0,result_pnl_temp),axis=1)\n",
        "    return result_pnl0\n",
        "  \n",
        "#pnl을 뽑는 보조함수\n",
        "def backtest_concat2_0(btst0,start_day):    \n",
        "    result_pnl0=pd.DataFrame(np.append(book_size,book_size+np.cumsum(np.sum(btst0[0][0]))))\n",
        "    result_pnl0.index=workday_S.index[::-1][-start_day:-1]\n",
        "    for i in range(len(btst0)-1):\n",
        "        j=i+1\n",
        "        result_pnl_temp=pd.DataFrame(np.append(book_size,book_size+np.cumsum(np.sum(btst0[j][0]))))\n",
        "        result_pnl_temp.index=workday_S.index[::-1][-start_day:-1]\n",
        "        result_pnl0=pd.concat((result_pnl0,result_pnl_temp),axis=1)\n",
        "    return result_pnl0\n",
        "\n",
        "#pnl을 뽑는 보조함수\n",
        "def backtest_concat3(btst0,start_day,end_day):    \n",
        "    result_pnl0=pd.DataFrame(np.append(book_size,book_size+np.cumsum(np.sum(btst0[0][-1]))))\n",
        "    result_pnl0.index=workday_S.index[::-1][-start_day:-end_day-1]\n",
        "    for i in range(len(btst0)-1):\n",
        "        j=i+1\n",
        "        result_pnl_temp=pd.DataFrame(np.append(book_size,book_size+np.cumsum(np.sum(btst0[j][-1]))))\n",
        "        result_pnl_temp.index=workday_S.index[::-1][-start_day:-end_day-1]\n",
        "        result_pnl0=pd.concat((result_pnl0,result_pnl_temp),axis=1)\n",
        "    return result_pnl0\n",
        "\n",
        "def filter_list_drawer_per(start_day, end_day):\n",
        "  period_1_0=backtester3(per_filter_list,start_day,end_day)\n",
        "  period_1_0_result=backtest_concat3(period_1_0,start_day,end_day)\n",
        "  period_1_0_result.columns=[\"Same delta\",\n",
        "                \"PER\",\n",
        "                 \"PER_PSR\",\n",
        "                 \"PER_ESR\",\n",
        "                 \"PER_ROE\",\n",
        "                 \"PER_ROA\",\n",
        "                 \"PER_SGR\",\n",
        "                 \"PER_GPR\",\n",
        "                 \"PER_PGR\",\n",
        "                 \"PER_EGR\",\n",
        "                 \"PER_EQGR\",\n",
        "                 \"PER_OCGR\",\n",
        "                 \"PER_DR\",\n",
        "                 \"PER_AT\",\n",
        "                 \"PER_ET\",\n",
        "                 \"PER_BF\",\n",
        "                 \"PER_SF\",\n",
        "                 \"PER_pbr\",\n",
        "                 \"PER_pcr\",\n",
        "                 \"PER_psr\",\n",
        "                 \"PER_LV1\",\n",
        "                 \"PER_LV3\",\n",
        "                 \"PER_RT05\",\n",
        "                 \"PER_RT1\",\n",
        "                 \"PER_RT3\"]\n",
        "\n",
        "                 \n",
        "  benchmark=kospi200_bench.loc[workday_S.index[end_day:start_day][::-1]]\n",
        "  benchmark1=pd.DataFrame(benchmark.iloc[1:]/benchmark.iloc[1]*book_size)\n",
        "  benchmark1.index=benchmark.index[:-1]\n",
        "\n",
        "  for i in range(np.shape(period_1_0_result)[1]):\n",
        "    pd.concat((pd.DataFrame(benchmark1[::-1]),period_1_0_result.iloc[:,i:i+1]),axis=1).plot()\n",
        "    \n",
        "  p_1_bar=((pd.concat((pd.DataFrame(benchmark1[::-1]),period_1_0_result.iloc[:,1:]),axis=1)).iloc[-1,:]-book_size)\n",
        "  \n",
        "  return(p_1_bar)\n",
        "\n",
        "  \n",
        "def filter_list_drawer(start_day, end_day):\n",
        "  period_1_0=backtester3(filter_list,start_day,end_day)\n",
        "  period_1_0_result=backtest_concat3(period_1_0,start_day,end_day)\n",
        "  period_1_0_result.columns=[\"Same delta\"\\\n",
        "               ,\"Profit/Sales\",\"Earnings/sales\",\"ROE\",\"ROA\"\\\n",
        "               ,\"Debt_ratio\"\\\n",
        "               ,\"Asset turnover\",\"Equity turnover\"\\\n",
        "               ,\"Sales growth rate\",\"Gross profit growth rate\",\"Profit growth rate\",\"Earnings growth rate\",\"Operating CF growth rate\",\"Equity growth rate\"\\\n",
        "               ,\"PBR\",\"PER\",\"PCR\",\"PSR\"\n",
        "               ,\"mktcap_H_filter\",\"mktcap_L_filter\"\n",
        "               ,\"Low Vol 1y\",\"Low Vol 3y\"\n",
        "               ,\"05y Momentum\",\"1y Momentum\",\"3y Momentum\"]\n",
        "\n",
        "  benchmark=kospi200_bench.loc[workday_S.index[end_day:start_day][::-1]]\n",
        "  benchmark1=pd.DataFrame(benchmark.iloc[1:]/benchmark.iloc[1]*book_size)\n",
        "  benchmark1.index=benchmark.index[:-1]\n",
        "\n",
        "  for i in range(np.shape(period_1_0_result)[1]):\n",
        "    pd.concat((pd.DataFrame(benchmark1[::-1]),period_1_0_result.iloc[:,i:i+1]),axis=1).plot()\n",
        "    \n",
        "  p_1_bar=((pd.concat((pd.DataFrame(benchmark1[::-1]),period_1_0_result.iloc[:,1:]),axis=1)).iloc[-1,:]-book_size)\n",
        "  \n",
        "  return(p_1_bar)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XVmKrIa7OJsg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "outputId": "c3ba2eca-f2cd-490c-b514-f6c465b9647a"
      },
      "source": [
        "#합성 필터 제작\n",
        "\n",
        "PGR_PSR=finance_rank_1(profit_gr,finance_rank(p_s_ratio)<=45)\n",
        "PGR_ESR=finance_rank_1(profit_gr,finance_rank(e_s_ratio)<=45)\n",
        "PGR_ROE=finance_rank_1(profit_gr,finance_rank(roe)<=45)\n",
        "PGR_ROA=finance_rank_1(profit_gr,finance_rank(roa)<=45)\n",
        "PGR_SGR=finance_rank_1(profit_gr,finance_rank(sales_gr)<=45)\n",
        "PGR_GPR=finance_rank_1(profit_gr,finance_rank(grossprofit_gr)<=45)\n",
        "PGR_PGR=finance_rank_1(profit_gr,finance_rank(profit_gr)<=45)\n",
        "PGR_EGR=finance_rank_1(profit_gr,finance_rank(earning_gr)<=45)\n",
        "PGR_EQGR=finance_rank_1(profit_gr,finance_rank(equity_gr)<=45)\n",
        "PGR_OCGR=finance_rank_1(profit_gr,finance_rank(opercash_gr)<=45)\n",
        "PGR_DR=finance_rank_1(profit_gr,finance_rank(debt_ratio_rev)<=45)\n",
        "PGR_AT=finance_rank_1(profit_gr,finance_rank(asset_turnover_ratio)<=45)\n",
        "PGR_ET=finance_rank_1(profit_gr,finance_rank(equity_turnover_ratio)<=45)\n",
        "PGR_BF=finance_rank_1(profit_gr,finance_rank(mktcap)<=45)\n",
        "PGR_SF=finance_rank_1(profit_gr,priceratio_rank(mktcap)<=45)\n",
        "\n",
        "PGR_pbr=finance_rank_1(profit_gr,priceratio_rank(pbr)<=45)\n",
        "PGR_pcr=finance_rank_1(profit_gr,priceratio_rank(pcr)<=45)\n",
        "PGR_psr=finance_rank_1(profit_gr,priceratio_rank(psr)<=45)\n",
        "PGR_per=finance_rank_1(profit_gr,priceratio_rank(per)<=45)\n",
        "\n",
        "PGR_LV1=finance_rank_1(profit_gr,priceratio_rank(vols_1y)<=45)\n",
        "PGR_LV3=finance_rank_1(profit_gr,priceratio_rank(vols_3y)<=45)\n",
        "\n",
        "PGR_RT05=finance_rank_1(profit_gr,finance_rank(ret_05y)<=45)\n",
        "PGR_RT1=finance_rank_1(profit_gr,finance_rank(ret_1y)<=45)\n",
        "PGR_RT3=finance_rank_1(profit_gr,finance_rank(ret_3y)<=45)\n",
        "\n",
        "PGR_PSR_filter=PGR_PSR<=pick\n",
        "PGR_ESR_filter=PGR_ESR<=pick\n",
        "PGR_ROE_filter=PGR_ROE<=pick\n",
        "PGR_ROA_filter=PGR_ROA<=pick\n",
        "PGR_SGR_filter=PGR_SGR<=pick\n",
        "PGR_GPR_filter=PGR_GPR<=pick\n",
        "PGR_PGR_filter=PGR_PGR<=pick\n",
        "PGR_EGR_filter=PGR_EGR<=pick\n",
        "PGR_EQGR_filter=PGR_EQGR<=pick\n",
        "PGR_OCGR_filter=PGR_OCGR<=pick\n",
        "PGR_DR_filter=PGR_DR<=pick\n",
        "PGR_AT_filter=PGR_AT<=pick\n",
        "PGR_ET_filter=PGR_ET<=pick\n",
        "PGR_BF_filter=PGR_BF<=pick\n",
        "PGR_SF_filter=PGR_SF<=pick\n",
        "PGR_pbr_filter=PGR_pbr<=pick\n",
        "PGR_pcr_filter=PGR_pcr<=pick\n",
        "PGR_psr_filter=PGR_psr<=pick\n",
        "PGR_per_filter=PGR_per<=pick\n",
        "PGR_LV1_filter=PGR_LV1<=pick\n",
        "PGR_LV3_filter=PGR_LV3<=pick\n",
        "PGR_RT05_filter=PGR_RT05<=pick\n",
        "PGR_RT1_filter=PGR_RT1<=pick\n",
        "PGR_RT3_filter=PGR_RT3<=pick\n",
        "\n",
        "\n",
        "PGR_filter_list=[kospi_bool,profit_gr_filter,\n",
        "                 PGR_PSR_filter,\n",
        "                 PGR_ESR_filter,\n",
        "                 PGR_ROE_filter,\n",
        "                 PGR_ROA_filter,\n",
        "                 PGR_SGR_filter,\n",
        "                 PGR_EGR_filter,\n",
        "                 PGR_GPR_filter,\n",
        "                 PGR_EQGR_filter,\n",
        "                 PGR_OCGR_filter,\n",
        "                 PGR_DR_filter,\n",
        "                 PGR_AT_filter,\n",
        "                 PGR_ET_filter,\n",
        "                 PGR_BF_filter,\n",
        "                 PGR_SF_filter,\n",
        "                 PGR_pbr_filter,\n",
        "                 PGR_pcr_filter,\n",
        "                 PGR_psr_filter,\n",
        "                 PGR_per_filter,\n",
        "                 PGR_LV1_filter,\n",
        "                 PGR_LV3_filter,\n",
        "                 PGR_RT05_filter,\n",
        "                 PGR_RT1_filter,\n",
        "                 PGR_RT3_filter]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: FutureWarning: \n",
            "Passing list-likes to .loc or [] with any missing label will raise\n",
            "KeyError in the future, you can use .reindex() as an alternative.\n",
            "\n",
            "See the documentation here:\n",
            "https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:32: FutureWarning: \n",
            "Passing list-likes to .loc or [] with any missing label will raise\n",
            "KeyError in the future, you can use .reindex() as an alternative.\n",
            "\n",
            "See the documentation here:\n",
            "https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zmLQQEVhcQGN"
      },
      "source": [
        "#합성필터 백테스트 후 그리기 함수\n",
        "\n",
        "def filter_list_drawer_PGR(start_day, end_day):\n",
        "  period_1_0=backtester3(PGR_filter_list,start_day,end_day)\n",
        "  period_1_0_result=backtest_concat3(period_1_0,start_day,end_day)\n",
        "  period_1_0_result.columns=[\"Same delta\",\n",
        "                 \"PGR\",\n",
        "                 \"PGR_PSR\",\n",
        "                 \"PGR_ESR\",\n",
        "                 \"PGR_ROE\",\n",
        "                 \"PGR_ROA\",\n",
        "                 \"PGR_SGR\",\n",
        "                 \"PGR_EGR\",\n",
        "                 \"PGR_GPR\",\n",
        "                 \"PGR_EQGR\",\n",
        "                 \"PGR_OCGR\",\n",
        "                 \"PGR_DR\",\n",
        "                 \"PGR_AT\",\n",
        "                 \"PGR_ET\",\n",
        "                 \"PGR_BF\",\n",
        "                 \"PGR_SF\",\n",
        "                 \"PGR_pbr\",\n",
        "                 \"PGR_pcr\",\n",
        "                 \"PGR_psr\",\n",
        "                 \"PGR_per\",\n",
        "                 \"PGR_LV1\",\n",
        "                 \"PGR_LV3\",\n",
        "                 \"PGR_RT05\",\n",
        "                 \"PGR_RT1\",\n",
        "                 \"PGR_RT3\"]\n",
        "\n",
        "  benchmark=kospi200_bench.loc[workday_S.index[end_day:start_day][::-1]]\n",
        "  benchmark1=pd.DataFrame(benchmark.iloc[1:]/benchmark.iloc[1]*book_size)\n",
        "  benchmark1.index=benchmark.index[:-1]\n",
        "\n",
        "  for i in range(np.shape(period_1_0_result)[1]):\n",
        "    pd.concat((pd.DataFrame(benchmark1[::-1]),period_1_0_result.iloc[:,i:i+1]),axis=1).plot()\n",
        "    \n",
        "  p_1_bar=((pd.concat((pd.DataFrame(benchmark1[::-1]),period_1_0_result.iloc[:,1:]),axis=1)).iloc[-1,:]-book_size)\n",
        "  \n",
        "  return(p_1_bar)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3_ia0Ch1S9aJ"
      },
      "source": [
        "import pandas as pd\n",
        "from datetime import datetime\n",
        "from pandas_datareader import data\n",
        "\n",
        "##******##\n",
        "#구간 시작일 종료일 입력\n",
        "start_list=[datetime(2015,1,7),\tdatetime(2015,4,24),\tdatetime(2015,8,27),\tdatetime(2015,12,24),\tdatetime(2016,2,15),\tdatetime(2016,4,28),\tdatetime(2016,5,23),\tdatetime(2016,6,10),\tdatetime(2016,6,29),\tdatetime(2016,9,19),\tdatetime(2016,10,6),\tdatetime(2016,11,22),\tdatetime(2017,7,27),\tdatetime(2017,8,14),\tdatetime(2017,8,25),\tdatetime(2017,9,5),\tdatetime(2017,11,3),\tdatetime(2017,12,28),\tdatetime(2018,1,30),\tdatetime(2018,2,12)]\n",
        "end_list=[datetime(2015,4,23),\tdatetime(2015,8,26),\tdatetime(2015,11,4),\tdatetime(2016,1,21),\tdatetime(2016,3,31),\tdatetime(2016,5,20),\tdatetime(2016,6,9),\tdatetime(2016,6,28),\tdatetime(2016,9,7),\tdatetime(2016,10,5),\tdatetime(2016,11,21),\tdatetime(2017,7,26),\tdatetime(2017,8,11),\tdatetime(2017,8,23),\tdatetime(2017,9,4),\tdatetime(2017,11,2),\tdatetime(2017,12,27),\tdatetime(2018,1,29),\tdatetime(2018,2,9),\tdatetime(2018,6,7)]\n",
        "\n",
        "#구간별 백테스트\n",
        "p1_filter =filter_list_drawer(list(workday_S.index).index(start_list[0])+1, list(workday_S.index).index(end_list[0]))\n",
        "p2_filter =filter_list_drawer(list(workday_S.index).index(start_list[1])+1, list(workday_S.index).index(end_list[1]))\n",
        "p3_filter =filter_list_drawer(list(workday_S.index).index(start_list[2])+1, list(workday_S.index).index(end_list[2]))\n",
        "p4_filter =filter_list_drawer(list(workday_S.index).index(start_list[3])+1, list(workday_S.index).index(end_list[3]))\n",
        "p5_filter =filter_list_drawer(list(workday_S.index).index(start_list[4])+1, list(workday_S.index).index(end_list[4]))\n",
        "p6_filter =filter_list_drawer(list(workday_S.index).index(start_list[5])+1, list(workday_S.index).index(end_list[5]))\n",
        "p7_filter =filter_list_drawer(list(workday_S.index).index(start_list[6])+1, list(workday_S.index).index(end_list[6]))\n",
        "p8_filter =filter_list_drawer(list(workday_S.index).index(start_list[7])+1, list(workday_S.index).index(end_list[7]))\n",
        "p9_filter =filter_list_drawer(list(workday_S.index).index(start_list[8])+1, list(workday_S.index).index(end_list[8]))\n",
        "p10_filter=filter_list_drawer(list(workday_S.index).index(start_list[9])+1, list(workday_S.index).index(end_list[9]))\n",
        "p11_filter=filter_list_drawer(list(workday_S.index).index(start_list[10])+1, list(workday_S.index).index(end_list[10]))\n",
        "p12_filter=filter_list_drawer(list(workday_S.index).index(start_list[11])+1, list(workday_S.index).index(end_list[11]))\n",
        "p13_filter=filter_list_drawer(list(workday_S.index).index(start_list[12])+1, list(workday_S.index).index(end_list[12]))\n",
        "p14_filter=filter_list_drawer(list(workday_S.index).index(start_list[13])+1, list(workday_S.index).index(end_list[13]))\n",
        "p15_filter=filter_list_drawer(list(workday_S.index).index(start_list[14])+1, list(workday_S.index).index(end_list[14]))\n",
        "p16_filter=filter_list_drawer(list(workday_S.index).index(start_list[15])+1, list(workday_S.index).index(end_list[15]))\n",
        "p17_filter=filter_list_drawer(list(workday_S.index).index(start_list[16])+1, list(workday_S.index).index(end_list[16]))\n",
        "p18_filter=filter_list_drawer(list(workday_S.index).index(start_list[17])+1, list(workday_S.index).index(end_list[17]))\n",
        "p19_filter=filter_list_drawer(list(workday_S.index).index(start_list[18])+1, list(workday_S.index).index(end_list[18]))\n",
        "p20_filter=filter_list_drawer(list(workday_S.index).index(start_list[19])+1, list(workday_S.index).index(end_list[19]))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-9SdabLic9wH"
      },
      "source": [
        "import pandas as pd\n",
        "from datetime import datetime\n",
        "from pandas_datareader import data\n",
        "\n",
        "### ***ㅠ수정요망 ###\n",
        "start_list=[datetime(2015,1,7),\tdatetime(2015,4,24),\tdatetime(2015,8,27),\tdatetime(2015,12,24),\tdatetime(2016,2,15),\tdatetime(2016,4,28),\tdatetime(2016,5,23),\tdatetime(2016,6,10),\tdatetime(2016,6,29),\tdatetime(2016,9,19),\tdatetime(2016,10,6),\tdatetime(2016,11,22),\tdatetime(2017,7,27),\tdatetime(2017,8,14),\tdatetime(2017,8,25),\tdatetime(2017,9,5),\tdatetime(2017,11,3),\tdatetime(2017,12,28),\tdatetime(2018,1,30),\tdatetime(2018,2,12)]\n",
        "end_list=[datetime(2015,4,23),\tdatetime(2015,8,26),\tdatetime(2015,11,4),\tdatetime(2016,1,21),\tdatetime(2016,3,31),\tdatetime(2016,5,20),\tdatetime(2016,6,9),\tdatetime(2016,6,28),\tdatetime(2016,9,7),\tdatetime(2016,10,5),\tdatetime(2016,11,21),\tdatetime(2017,7,26),\tdatetime(2017,8,11),\tdatetime(2017,8,23),\tdatetime(2017,9,4),\tdatetime(2017,11,2),\tdatetime(2017,12,27),\tdatetime(2018,1,29),\tdatetime(2018,2,9),\tdatetime(2018,6,7)]\n",
        "\n",
        "\n",
        "#합성필터 백테스트\n",
        "p1_filter_PGR =filter_list_drawer_PGR(list(workday_S.index).index(start_list[0])+1, list(workday_S.index).index(end_list[0]))\n",
        "p2_filter_PGR =filter_list_drawer_PGR(list(workday_S.index).index(start_list[1])+1, list(workday_S.index).index(end_list[1]))\n",
        "p3_filter_PGR =filter_list_drawer_PGR(list(workday_S.index).index(start_list[2])+1, list(workday_S.index).index(end_list[2]))\n",
        "p4_filter_PGR =filter_list_drawer_PGR(list(workday_S.index).index(start_list[3])+1, list(workday_S.index).index(end_list[3]))\n",
        "p5_filter_PGR =filter_list_drawer_PGR(list(workday_S.index).index(start_list[4])+1, list(workday_S.index).index(end_list[4]))\n",
        "p6_filter_PGR =filter_list_drawer_PGR(list(workday_S.index).index(start_list[5])+1, list(workday_S.index).index(end_list[5]))\n",
        "p7_filter_PGR =filter_list_drawer_PGR(list(workday_S.index).index(start_list[6])+1, list(workday_S.index).index(end_list[6]))\n",
        "p8_filter_PGR =filter_list_drawer_PGR(list(workday_S.index).index(start_list[7])+1, list(workday_S.index).index(end_list[7]))\n",
        "p9_filter_PGR =filter_list_drawer_PGR(list(workday_S.index).index(start_list[8])+1, list(workday_S.index).index(end_list[8]))\n",
        "p10_filter_PGR=filter_list_drawer_PGR(list(workday_S.index).index(start_list[9])+1, list(workday_S.index).index(end_list[9]))\n",
        "p11_filter_PGR=filter_list_drawer_PGR(list(workday_S.index).index(start_list[10])+1, list(workday_S.index).index(end_list[10]))\n",
        "p12_filter_PGR=filter_list_drawer_PGR(list(workday_S.index).index(start_list[11])+1, list(workday_S.index).index(end_list[11]))\n",
        "p13_filter_PGR=filter_list_drawer_PGR(list(workday_S.index).index(start_list[12])+1, list(workday_S.index).index(end_list[12]))\n",
        "p14_filter_PGR=filter_list_drawer_PGR(list(workday_S.index).index(start_list[13])+1, list(workday_S.index).index(end_list[13]))\n",
        "p15_filter_PGR=filter_list_drawer_PGR(list(workday_S.index).index(start_list[14])+1, list(workday_S.index).index(end_list[14]))\n",
        "p16_filter_PGR=filter_list_drawer_PGR(list(workday_S.index).index(start_list[15])+1, list(workday_S.index).index(end_list[15]))\n",
        "p17_filter_PGR=filter_list_drawer_PGR(list(workday_S.index).index(start_list[16])+1, list(workday_S.index).index(end_list[16]))\n",
        "p18_filter_PGR=filter_list_drawer_PGR(list(workday_S.index).index(start_list[17])+1, list(workday_S.index).index(end_list[17]))\n",
        "p19_filter_PGR=filter_list_drawer_PGR(list(workday_S.index).index(start_list[18])+1, list(workday_S.index).index(end_list[18]))\n",
        "p20_filter_PGR=filter_list_drawer_PGR(list(workday_S.index).index(start_list[19])+1, list(workday_S.index).index(end_list[19]))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mk-mAhQJl5P1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c4019be0-3854-48b6-d2ee-af22f22348ec"
      },
      "source": [
        "#필터 백테스트 비주얼라이제이션\n",
        "\n",
        "bars=pd.concat((p1_filter,p2_filter,p3_filter,p4_filter,p5_filter,p6_filter,p7_filter,p8_filter,p9_filter\n",
        "                ,p10_filter,p11_filter,p12_filter,p13_filter,p14_filter,p15_filter,p16_filter,p17_filter,p18_filter\n",
        "                ,p19_filter,p20_filter,p21_filter,p22_filter,p23_filter,p24_filter,p25_filter,p26_filter,p27_filter\n",
        "                ,p28_filter,p29_filter,p30_filter,p31_filter,p32_filter,p33_filter,p34_filter,p35_filter),axis=1)\n",
        "bars.columns=[\"Period 1\",\"Period 2\",\"Period 3\",\"Period 4\",\"Period 5\",\"Period 6\",\"Period 7\",\"Period 8\",\"Period 9\"\n",
        "              ,\"Period 10\",\"Period 11\",\"Period 12\",\"Period 13\",\"Period 14\",\"Period 15\",\"Period 16\",\"Period 17\",\"Period 18\"\n",
        "              ,\"Period 19\",\"Period 20]\n",
        "bars.plot.bar(figsize=(40,30),fontsize=12)\n",
        "\n",
        "\n",
        "bars_up=bars[[\"Period 1\",\"Period 3\",\"Period 5\",\"Period 7\",\"Period 9\",\"Period 10\",\"Period 12\",\"Period 14\",\"Period 16\",\"Period 18\"]]\n",
        "bars_up.plot.bar(figsize=(60,12),fontsize=12)\n",
        "plt.savefig(\"bars_up.png\", bbox_inches = \"tight\")\n",
        "files.download(\"bars_up.png\") \n",
        "\n",
        "bars_side=bars[[\"Period 20\"]]\n",
        "bars_side.plot.bar(figsize=(60,12),fontsize=12)\n",
        "plt.savefig(\"bars_side.png\", bbox_inches = \"tight\")\n",
        "files.download(\"bars_side.png\") \n",
        "\n",
        "bars_down=bars[[\"Period 2\",\"Period 4\",\"Period 6\",\"Period 8\",\"Period 11\",\"Period 13\",\"Period 15\",\"Period 17\",\"Period 19\"]]\n",
        "bars_down.plot.bar(figsize=(60,12),fontsize=12)\n",
        "plt.savefig(\"bars_down.png\", bbox_inches = \"tight\")\n",
        "files.download(\"bars_down.png\") \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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3bh5//PFJn93w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Kq6eZLb5OI9NhC7QVV1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\nY1dUZYXVgNm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/tn39uISLI8l/Ae4H3sLWm7xvBfZr+7KR2RZBkuuBp7e9PsmRwGfaOupr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\n3WTxjSRJkiRJkiRJkiRJkrSb/j/qFxeDDoo9JgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 2880x2160 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OxsbR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\nkiRJutaS7AVc0fYbo7NIWhxJzgR2ant5klOA2wMXA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            "text/plain": [
              "<Figure size 4320x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
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d9jya+ojolbpYjCyMGkfa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\nkiRJkiRJkiRJkiRJkiRtdv4/4LZDKgHlm/YAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 4320x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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e794mR8v1DP9ws9nCQfrqr3tc4hSZKkx6Yk0xvd7gIurKpftsij8WBdLDqbnRpI8nHgecCRdFMh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\n+7anj0un0ZJ8B7hz21PmxnZl+iF2p3HJN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            "text/plain": [
              "<Figure size 4320x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9p9eTpgcqZkG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        },
        "outputId": "3430b67d-2b5b-4929-d90b-52005fda3eef"
      },
      "source": [
        "#우량필터 선정\n",
        "up_rank=pd.DataFrame(len(bars_up)-np.argsort(np.argsort(np.mean(bars_up,axis=1),axis=0)))\n",
        "side_rank=pd.DataFrame(len(bars_side)-np.argsort(np.argsort(np.mean(bars_side,axis=1),axis=0)))\n",
        "down_rank=pd.DataFrame(len(bars_down)-np.argsort(np.argsort(np.mean(bars_down,axis=1),axis=0)))\n",
        "\n",
        "ranks=pd.concat((up_rank,side_rank,down_rank),axis=1)\n",
        "ranks=pd.concat((ranks,np.sum(ranks,axis=1)),axis=1)\n",
        "ranks=pd.concat((ranks,1+np.argsort(np.argsort(ranks.iloc[:,-1]))),axis=1)\n",
        "ranks.columns=[\"ups\",\"sides\",\"downs\",\"sum\",\"total rank\"]\n",
        "ranks.sort_values(\"total rank\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>ups</th>\n",
              "      <th>sides</th>\n",
              "      <th>downs</th>\n",
              "      <th>sum</th>\n",
              "      <th>total rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Profit growth rate</th>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Debt_ratio</th>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>22</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kospi200</th>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>23</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PBR</th>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>23</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Earnings growth rate</th>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PER</th>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>13</td>\n",
              "      <td>29</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Low Vol 3y</th>\n",
              "      <td>7</td>\n",
              "      <td>20</td>\n",
              "      <td>4</td>\n",
              "      <td>31</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Profit/Sales</th>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>21</td>\n",
              "      <td>33</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ROA</th>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>18</td>\n",
              "      <td>35</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mktcap_H_filter</th>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>22</td>\n",
              "      <td>36</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Operating CF growth rate</th>\n",
              "      <td>13</td>\n",
              "      <td>18</td>\n",
              "      <td>5</td>\n",
              "      <td>36</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Equity turnover</th>\n",
              "      <td>5</td>\n",
              "      <td>13</td>\n",
              "      <td>20</td>\n",
              "      <td>38</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mktcap_L_filter</th>\n",
              "      <td>16</td>\n",
              "      <td>5</td>\n",
              "      <td>17</td>\n",
              "      <td>38</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Earnings/sales</th>\n",
              "      <td>10</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gross profit growth rate</th>\n",
              "      <td>19</td>\n",
              "      <td>17</td>\n",
              "      <td>6</td>\n",
              "      <td>42</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Low Vol 1y</th>\n",
              "      <td>23</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>43</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PSR</th>\n",
              "      <td>14</td>\n",
              "      <td>7</td>\n",
              "      <td>24</td>\n",
              "      <td>45</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Equity growth rate</th>\n",
              "      <td>17</td>\n",
              "      <td>15</td>\n",
              "      <td>14</td>\n",
              "      <td>46</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PCR</th>\n",
              "      <td>24</td>\n",
              "      <td>16</td>\n",
              "      <td>9</td>\n",
              "      <td>49</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3y Momentum</th>\n",
              "      <td>22</td>\n",
              "      <td>25</td>\n",
              "      <td>2</td>\n",
              "      <td>49</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>05y Momentum</th>\n",
              "      <td>25</td>\n",
              "      <td>1</td>\n",
              "      <td>25</td>\n",
              "      <td>51</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sales growth rate</th>\n",
              "      <td>20</td>\n",
              "      <td>21</td>\n",
              "      <td>11</td>\n",
              "      <td>52</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ROE</th>\n",
              "      <td>15</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>54</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1y Momentum</th>\n",
              "      <td>21</td>\n",
              "      <td>24</td>\n",
              "      <td>12</td>\n",
              "      <td>57</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Asset turnover</th>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>23</td>\n",
              "      <td>63</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          ups  sides  downs  sum  total rank\n",
              "Profit growth rate          6      2      7   15           1\n",
              "Debt_ratio                 11      3      8   22           2\n",
              "Kospi200                    3     10     10   23           3\n",
              "PBR                         9     11      3   23           4\n",
              "Earnings growth rate        1      6     19   26           5\n",
              "PER                        12      4     13   29           6\n",
              "Low Vol 3y                  7     20      4   31           7\n",
              "Profit/Sales                4      8     21   33           8\n",
              "ROA                         8      9     18   35           9\n",
              "mktcap_H_filter             2     12     22   36          10\n",
              "Operating CF growth rate   13     18      5   36          11\n",
              "Equity turnover             5     13     20   38          12\n",
              "mktcap_L_filter            16      5     17   38          13\n",
              "Earnings/sales             10     14     15   39          14\n",
              "Gross profit growth rate   19     17      6   42          15\n",
              "Low Vol 1y                 23     19      1   43          16\n",
              "PSR                        14      7     24   45          17\n",
              "Equity growth rate         17     15     14   46          18\n",
              "PCR                        24     16      9   49          19\n",
              "3y Momentum                22     25      2   49          20\n",
              "05y Momentum               25      1     25   51          21\n",
              "Sales growth rate          20     21     11   52          22\n",
              "ROE                        15     23     16   54          23\n",
              "1y Momentum                21     24     12   57          24\n",
              "Asset turnover             18     22     23   63          25"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "frG7SBWCj6eF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4ab4efb9-5948-4154-fd1a-a0f11fd58267"
      },
      "source": [
        "#합성 필터 비주얼라이제이션\n",
        "\n",
        "bars_PGR=pd.concat((p1_filter_PGR\n",
        "                    ,p2_filter_PGR\n",
        "                    ,p3_filter_PGR\n",
        "                    ,p4_filter_PGR\n",
        "                    ,p5_filter_PGR\n",
        "                    ,p6_filter_PGR\n",
        "                    ,p7_filter_PGR\n",
        "                    ,p8_filter_PGR\n",
        "                    ,p9_filter_PGR\n",
        "                    ,p10_filter_PGR\n",
        "                    ,p11_filter_PGR\n",
        "                    ,p12_filter_PGR\n",
        "                    ,p13_filter_PGR\n",
        "                    ,p14_filter_PGR\n",
        "                    ,p15_filter_PGR\n",
        "                    ,p16_filter_PGR\n",
        "                    ,p17_filter_PGR\n",
        "                    ,p18_filter_PGR\n",
        "                    ,p19_filter_PGR\n",
        "                    ,p20_filter_PGR),axis=1)\n",
        "bars_PGR.columns=[\"Period 1\",\"Period 2\",\"Period 3\",\"Period 4\",\"Period 5\",\"Period 6\",\"Period 7\",\"Period 8\",\"Period 9\"\n",
        "              ,\"Period 10\",\"Period 11\",\"Period 12\",\"Period 13\",\"Period 14\",\"Period 15\",\"Period 16\",\"Period 17\",\"Period 18\",\"Period 19\",\"Period 20\"]\n",
        "bars_PGR.plot.bar(figsize=(60,30),fontsize=12)\n",
        "\n",
        "\n",
        "bars_PGR_up=bars_PGR[[\"Period 1\",\"Period 3\",\"Period 5\",\"Period 7\",\"Period 9\",\"Period 10\",\"Period 12\",\"Period 14\",\"Period 16\",\"Period 18\"]]\n",
        "bars_PGR_up.plot.bar(figsize=(60,12),fontsize=12)\n",
        "plt.savefig(\"bars_PGR_up.png\", bbox_inches = \"tight\")\n",
        "files.download(\"bars_PGR_up.png\") \n",
        "\n",
        "bars_PGR_side=bars_PGR[[\"Period 20\"]]\n",
        "bars_PGR_side.plot.bar(figsize=(60,12),fontsize=12)\n",
        "plt.savefig(\"bars_PGR_side.png\", bbox_inches = \"tight\")\n",
        "files.download(\"bars_PGR_side.png\") \n",
        "\n",
        "bars_PGR_down=bars_PGR[[\"Period 2\",\"Period 4\",\"Period 6\",\"Period 8\",\"Period 11\",\"Period 13\",\"Period 15\",\"Period 17\",\"Period 19\"]]\n",
        "bars_PGR_down.plot.bar(figsize=(60,12),fontsize=12)\n",
        "plt.savefig(\"bars_PGR_down.png\", bbox_inches = \"tight\")\n",
        "files.download(\"bars_PGR_down.png\") \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5a36a05e48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
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oDMyL5gV2cEJWzyb2rDL7kqxWmT3OKrOLpi74Os4j7KvNVb/iXoU1dbFYB1XVxze2r7i1ne6+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\n5IIRzciMGF5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            "text/plain": [
              "<Figure size 4320x2160 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNSBKAZzl0NU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        },
        "outputId": "738c8d59-887b-403a-cd8d-b4ffdd78bce2"
      },
      "source": [
        "#합성 필터 성과표\n",
        "\n",
        "bars_PGR_up_outperform=(np.subtract(bars_PGR_up,bars_up.iloc[:1,:])>0)+0\n",
        "bars_PGR_up_outperform=pd.concat((bars_PGR_up_outperform,np.sum(bars_PGR_up_outperform,axis=1)),axis=1)\n",
        "bars_PGR_up_outperform.columns=list(bars_PGR_up_outperform.columns)[:-1]+[\"SUM\"]\n",
        "bars_PGR_up_outperform.sort_values(\"SUM\", ascending = False)\n",
        "\n",
        "bars_PGR_side_outperform=(np.subtract(bars_PGR_side,bars_side.iloc[:1,:])>0)+0\n",
        "bars_PGR_side_outperform=pd.concat((bars_PGR_side_outperform,np.sum(bars_PGR_side_outperform,axis=1)),axis=1)\n",
        "bars_PGR_side_outperform.columns=list(bars_PGR_side_outperform.columns)[:-1]+[\"SUM\"]\n",
        "bars_PGR_side_outperform.sort_values(\"SUM\", ascending = False)\n",
        "\n",
        "bars_PGR_down_outperform=(np.subtract(bars_PGR_down,bars_down.iloc[:1,:])>0)+0\n",
        "bars_PGR_down_outperform=pd.concat((bars_PGR_down_outperform,np.sum(bars_PGR_down_outperform,axis=1)),axis=1)\n",
        "bars_PGR_down_outperform.columns=list(bars_PGR_down_outperform.columns)[:-1]+[\"SUM\"]\n",
        "bars_PGR_down_outperform.sort_values(\"SUM\", ascending = False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Period 2</th>\n",
              "      <th>Period 4</th>\n",
              "      <th>Period 6</th>\n",
              "      <th>Period 8</th>\n",
              "      <th>Period 11</th>\n",
              "      <th>Period 13</th>\n",
              "      <th>Period 15</th>\n",
              "      <th>Period 17</th>\n",
              "      <th>Period 19</th>\n",
              "      <th>Period 21</th>\n",
              "      <th>Period 23</th>\n",
              "      <th>Period 25</th>\n",
              "      <th>Period 27</th>\n",
              "      <th>Period 29</th>\n",
              "      <th>Period 31</th>\n",
              "      <th>Period 34</th>\n",
              "      <th>SUM</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PGR_LV3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PGR_SGR</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PGR_LV1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PGR_SF</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PGR_AT</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PGR_pbr</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PGR_ROE</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PGR_EGR</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PGR_GPR</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PGR_EQGR</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PGR_DR</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PGR_psr</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PGR_RT1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PGR_RT05</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PGR_per</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PGR_pcr</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PGR_RT3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PGR_BF</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PGR_ET</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PGR_OCGR</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PGR_ROA</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PGR_ESR</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PGR_PSR</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PGR</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kospi200</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Period 2  Period 4  Period 6  ...  Period 31  Period 34  SUM\n",
              "PGR_LV3          1         0         1  ...          1          1   14\n",
              "PGR_SGR          1         0         1  ...          1          1   14\n",
              "PGR_LV1          1         0         1  ...          1          1   14\n",
              "PGR_SF           1         0         1  ...          1          1   14\n",
              "PGR_AT           1         0         1  ...          1          1   13\n",
              "PGR_pbr          1         0         0  ...          1          1   13\n",
              "PGR_ROE          1         0         0  ...          1          1   13\n",
              "PGR_EGR          1         0         0  ...          1          1   13\n",
              "PGR_GPR          1         0         1  ...          1          1   13\n",
              "PGR_EQGR         1         0         1  ...          1          1   13\n",
              "PGR_DR           1         0         1  ...          1          1   13\n",
              "PGR_psr          1         0         0  ...          1          1   13\n",
              "PGR_RT1          1         0         0  ...          1          1   12\n",
              "PGR_RT05         1         0         0  ...          1          1   12\n",
              "PGR_per          1         0         0  ...          1          1   12\n",
              "PGR_pcr          1         0         0  ...          1          1   12\n",
              "PGR_RT3          1         0         0  ...          1          1   12\n",
              "PGR_BF           1         0         0  ...          1          1   12\n",
              "PGR_ET           1         0         0  ...          1          1   12\n",
              "PGR_OCGR         1         0         0  ...          1          1   12\n",
              "PGR_ROA          1         0         0  ...          1          1   12\n",
              "PGR_ESR          1         0         0  ...          1          1   12\n",
              "PGR_PSR          1         0         0  ...          1          1   12\n",
              "PGR              1         0         0  ...          0          1    9\n",
              "Kospi200         0         0         0  ...          0          0    0\n",
              "\n",
              "[25 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oDcTTToewYoK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "73579cf9-8e44-4ed1-9033-f1a73c9d3030"
      },
      "source": [
        "#선정 종목 표시\n",
        "\n",
        "df_krx = fdr.StockListing('KRX')\n",
        "df_krx.index=df_krx.iloc[:,0];df_krx=df_krx.iloc[:,1:]\n",
        "\n",
        "up_filter_select=[]\n",
        "side_filter_select=[PGR_AT_filter,PGR_PSR_filter,PGR_ESR_filter,PGR_ROA_filter,PGR_SGR_filter,PGR_GPR_filter]\n",
        "down_filter_select=[PGR_LV3_filter,PGR_SF_filter,PGR_per_filter,PGR_ESR_filter,PGR_PSR_filter,PGR_SGR_filter,PGR_EGR_filter,PGR_LV1_filter,profit_gr_filter,PGR_OCGR_filter,PGR_DR_filter,PGR_AT_filter,PGR_EQGR_filter,PGR_psr_filter]\n",
        "\n",
        "\n",
        "top_filter00=up_filter_select+side_filter_select+down_filter_select\n",
        "top_filter0=top_filter00[0].iloc[:1,:]+0\n",
        "if len(top_filter00)>1:\n",
        "  for i in range(len(top_filter00)-1):\n",
        "    top_filter0=pd.concat((top_filter0,top_filter00[i+1].iloc[:1,:]),axis=0)+0\n",
        "else:\n",
        "  top_filter0=top_filter0=top_filter00[0].iloc[:1,:]+0\n",
        "#top_filter0=pd.concat((per_filter.iloc[:1,:],PER_PSR_filter.iloc[:1,:],PER_ESR_filter.iloc[:1,:],PER_BF_filter.iloc[:1,:],PER_RT3_filter.iloc[:1,:],PER_ROA_filter.iloc[:1,:],PER_DR_filter.iloc[:1,:],PER_EGR_filter.iloc[:1,:],PER_AT_filter.iloc[:1,:],PER_PGR_filter.iloc[:1,:],PER_ET_filter.iloc[:1,:]),axis=0)+0\n",
        "top_filter1=pd.concat((top_filter0,pd.DataFrame(np.sum(top_filter0.iloc[:,:],axis=0)).T))\n",
        "top_filter2=pd.concat((top_filter1,mktcap.iloc[:1,:]),axis=0)\n",
        "top_filter2.index=np.arange(len(top_filter2.index))\n",
        "top_filter=(top_filter2.T).sort_values(top_filter2.index[-2], ascending = False)\n",
        "blacklist=[]\n",
        "today_top=(top_filter.drop(blacklist)).iloc[:]\n",
        "today_top_result0=pd.concat((df_krx.loc[(today_top.index)[:]],today_top.iloc[:,-2:]),axis=1)\n",
        "today_top_result=pd.concat(((((Close[today_top_result0.index]).iloc[:1,:]).T),today_top_result0),axis=1)\n",
        "today_top_result.iloc[:20]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:23: FutureWarning: \n",
            "Passing list-likes to .loc or [] with any missing label will raise\n",
            "KeyError in the future, you can use .reindex() as an alternative.\n",
            "\n",
            "See the documentation here:\n",
            "https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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",
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              "    }\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>2020-01-20 00:00:00</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sector</th>\n",
              "      <th>Industry</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>170900</th>\n",
              "      <td>99200.0</td>\n",
              "      <td>동아에스티</td>\n",
              "      <td>의약품 제조업</td>\n",
              "      <td>스티렌, 자이데나</td>\n",
              "      <td>14.0</td>\n",
              "      <td>8.376317e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>060980</th>\n",
              "      <td>44150.0</td>\n",
              "      <td>한라홀딩스</td>\n",
              "      <td>기타 금융업</td>\n",
              "      <td>지주회사 및 경영컨설팅 서비스업</td>\n",
              "      <td>13.0</td>\n",
              "      <td>4.623419e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>251270</th>\n",
              "      <td>94600.0</td>\n",
              "      <td>넷마블</td>\n",
              "      <td>소프트웨어 개발 및 공급업</td>\n",
              "      <td>온라인·모바일 게임 소프트웨어 개발 및 공급</td>\n",
              "      <td>12.0</td>\n",
              "      <td>8.111436e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271560</th>\n",
              "      <td>113000.0</td>\n",
              "      <td>오리온</td>\n",
              "      <td>기타 식품 제조업</td>\n",
              "      <td>스낵 등 과자류</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.467583e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>049770</th>\n",
              "      <td>226500.0</td>\n",
              "      <td>동원F&amp;B</td>\n",
              "      <td>기타 식품 제조업</td>\n",
              "      <td>참치캔,음료,냉동식품 제조,판매</td>\n",
              "      <td>10.0</td>\n",
              "      <td>8.740916e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>006840</th>\n",
              "      <td>34300.0</td>\n",
              "      <td>AK홀딩스</td>\n",
              "      <td>기타 금융업</td>\n",
              "      <td>지주사업</td>\n",
              "      <td>8.0</td>\n",
              "      <td>4.543913e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>018250</th>\n",
              "      <td>31100.0</td>\n",
              "      <td>애경산업</td>\n",
              "      <td>기타 화학제품 제조업</td>\n",
              "      <td>Age 20's,루나, 트리오, 2080치약, 순샘, 제트 등</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8.213490e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>010780</th>\n",
              "      <td>30350.0</td>\n",
              "      <td>아이에스동서</td>\n",
              "      <td>내화, 비내화 요업제품 제조업</td>\n",
              "      <td>타일,위생도기,콘크리트파일,P.C관,침목 제조,도매</td>\n",
              "      <td>7.0</td>\n",
              "      <td>9.375906e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>069260</th>\n",
              "      <td>19700.0</td>\n",
              "      <td>휴켐스</td>\n",
              "      <td>기타 화학제품 제조업</td>\n",
              "      <td>화합물,화학제품 제조</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8.053082e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>000080</th>\n",
              "      <td>32050.0</td>\n",
              "      <td>하이트진로</td>\n",
              "      <td>알코올음료 제조업</td>\n",
              "      <td>소주</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.247782e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>001740</th>\n",
              "      <td>5340.0</td>\n",
              "      <td>SK네트웍스</td>\n",
              "      <td>기타 전문 도매업</td>\n",
              "      <td>종합무역업(전자전기제품,섬유,에너지화학제품,철강금속제품),의류,수입산합판,MDF판매...</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.325322e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>093370</th>\n",
              "      <td>8750.0</td>\n",
              "      <td>후성</td>\n",
              "      <td>기초 화학물질 제조업</td>\n",
              "      <td>불소화물 제조,도매</td>\n",
              "      <td>6.0</td>\n",
              "      <td>8.103097e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161390</th>\n",
              "      <td>30750.0</td>\n",
              "      <td>한국타이어앤테크놀로지</td>\n",
              "      <td>고무제품 제조업</td>\n",
              "      <td>타이어 제조</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.809158e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105630</th>\n",
              "      <td>16800.0</td>\n",
              "      <td>한세실업</td>\n",
              "      <td>봉제의복 제조업</td>\n",
              "      <td>의류 완제품</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.720000e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>004370</th>\n",
              "      <td>224000.0</td>\n",
              "      <td>농심</td>\n",
              "      <td>기타 식품 제조업</td>\n",
              "      <td>라면,스낵,유지 제조,도매,수출입</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.362512e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>097950</th>\n",
              "      <td>249500.0</td>\n",
              "      <td>CJ제일제당</td>\n",
              "      <td>기타 식품 제조업</td>\n",
              "      <td>설탕,소맥분,조미식품,육가공식품(돈육,돼지비계,돼지식용설육),대두가공식품,사료,의약...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.756019e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>011070</th>\n",
              "      <td>147500.0</td>\n",
              "      <td>LG이노텍</td>\n",
              "      <td>전자부품 제조업</td>\n",
              "      <td>기타 전자부품 제조업</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.490898e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>020000</th>\n",
              "      <td>30850.0</td>\n",
              "      <td>한섬</td>\n",
              "      <td>봉제의복 제조업</td>\n",
              "      <td>여성의류,금속 액세서리,신발,가방,선글라스 제조,판매</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.598355e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>004990</th>\n",
              "      <td>37750.0</td>\n",
              "      <td>롯데지주</td>\n",
              "      <td>기타 금융업</td>\n",
              "      <td>지주회사</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.960324e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>010120</th>\n",
              "      <td>53800.0</td>\n",
              "      <td>LS산전</td>\n",
              "      <td>전동기, 발전기 및 전기 변환 · 공급 · 제어 장치 제조업</td>\n",
              "      <td>고압기기,저압기기,변압기,배전반,PLC,인버터,빌딩설비,공조기,건축배관,자동화기기 ...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.614000e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>035420</th>\n",
              "      <td>187500.0</td>\n",
              "      <td>NAVER</td>\n",
              "      <td>자료처리, 호스팅, 포털 및 기타 인터넷 정보매개 서비스업</td>\n",
              "      <td>포털 서비스 및 온라인 광고</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.090251e+13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>003550</th>\n",
              "      <td>73700.0</td>\n",
              "      <td>LG</td>\n",
              "      <td>기타 금융업</td>\n",
              "      <td>지주회사</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.271746e+13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>069960</th>\n",
              "      <td>86500.0</td>\n",
              "      <td>현대백화점</td>\n",
              "      <td>종합 소매업</td>\n",
              "      <td>백화점</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.024311e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>007070</th>\n",
              "      <td>39300.0</td>\n",
              "      <td>GS리테일</td>\n",
              "      <td>종합 소매업</td>\n",
              "      <td>체인화 편의점,슈퍼마켓,마트,전자상거래/부동산(빌딩) 임대</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.026100e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115390</th>\n",
              "      <td>13750.0</td>\n",
              "      <td>락앤락</td>\n",
              "      <td>플라스틱제품 제조업</td>\n",
              "      <td>플라스틱 밀폐용기</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.450658e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>088350</th>\n",
              "      <td>2200.0</td>\n",
              "      <td>한화생명</td>\n",
              "      <td>보험업</td>\n",
              "      <td>생명보험</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.910766e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>006280</th>\n",
              "      <td>123000.0</td>\n",
              "      <td>녹십자</td>\n",
              "      <td>의약품 제조업</td>\n",
              "      <td>알부민외 제조,판매</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.437444e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192400</th>\n",
              "      <td>114500.0</td>\n",
              "      <td>쿠쿠홀딩스</td>\n",
              "      <td>기타 금융업</td>\n",
              "      <td>주방용 전기기기 제조업</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.143740e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>030200</th>\n",
              "      <td>26100.0</td>\n",
              "      <td>케이티</td>\n",
              "      <td>전기 통신업</td>\n",
              "      <td>유무선통신사업,공중전기통신사업,인터넷,전자상거래,네트워크(전용회선,데이터통신,초고속...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.815018e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>029780</th>\n",
              "      <td>38050.0</td>\n",
              "      <td>삼성카드</td>\n",
              "      <td>기타 금융업</td>\n",
              "      <td>신용카드업,상품신용판매,현금서비스,카드론,리스,기업대출,여행업무,통신판매</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.408431e+12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        2020-01-20 00:00:00         Name  ...    20            21\n",
              "170900              99200.0        동아에스티  ...  14.0  8.376317e+11\n",
              "060980              44150.0        한라홀딩스  ...  13.0  4.623419e+11\n",
              "251270              94600.0          넷마블  ...  12.0  8.111436e+12\n",
              "271560             113000.0          오리온  ...  10.0  4.467583e+12\n",
              "049770             226500.0        동원F&B  ...  10.0  8.740916e+11\n",
              "006840              34300.0        AK홀딩스  ...   8.0  4.543913e+11\n",
              "018250              31100.0         애경산업  ...   7.0  8.213490e+11\n",
              "010780              30350.0       아이에스동서  ...   7.0  9.375906e+11\n",
              "069260              19700.0          휴켐스  ...   7.0  8.053082e+11\n",
              "000080              32050.0        하이트진로  ...   6.0  2.247782e+12\n",
              "001740               5340.0       SK네트웍스  ...   6.0  1.325322e+12\n",
              "093370               8750.0           후성  ...   6.0  8.103097e+11\n",
              "161390              30750.0  한국타이어앤테크놀로지  ...   6.0  3.809158e+12\n",
              "105630              16800.0         한세실업  ...   6.0  6.720000e+11\n",
              "004370             224000.0           농심  ...   6.0  1.362512e+12\n",
              "097950             249500.0       CJ제일제당  ...   5.0  3.756019e+12\n",
              "011070             147500.0        LG이노텍  ...   5.0  3.490898e+12\n",
              "020000              30850.0           한섬  ...   5.0  7.598355e+11\n",
              "004990              37750.0         롯데지주  ...   4.0  3.960324e+12\n",
              "010120              53800.0         LS산전  ...   4.0  1.614000e+12\n",
              "035420             187500.0        NAVER  ...   4.0  3.090251e+13\n",
              "003550              73700.0           LG  ...   4.0  1.271746e+13\n",
              "069960              86500.0        현대백화점  ...   3.0  2.024311e+12\n",
              "007070              39300.0        GS리테일  ...   3.0  3.026100e+12\n",
              "115390              13750.0          락앤락  ...   3.0  7.450658e+11\n",
              "088350               2200.0         한화생명  ...   3.0  1.910766e+12\n",
              "006280             123000.0          녹십자  ...   3.0  1.437444e+12\n",
              "192400             114500.0        쿠쿠홀딩스  ...   2.0  8.143740e+11\n",
              "030200              26100.0          케이티  ...   2.0  6.815018e+12\n",
              "029780              38050.0         삼성카드  ...   2.0  4.408431e+12\n",
              "\n",
              "[30 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    }
  ]
}