Scratching_ADF_KPSS.ipynb

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      "collapsed_sections": [
        "vIN3SeTaNko3",
        "R1v6sYTUQTli"
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      "name": "python3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# ADF"
      ],
      "metadata": {
        "id": "80g6AiRwu-B5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rbsRFaqYu4_c"
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      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Trail"
      ],
      "metadata": {
        "id": "vIN3SeTaNko3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.stattools import adfuller\n",
        "from statsmodels.tsa.stattools import grangercausalitytests\n",
        "import statsmodels.tsa.stattools as ts\n",
        "data.index[window+33]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v4XkI5kry_VW",
        "outputId": "8d04a709-f0b5-4a9e-a3f2-2d30dc4b7515"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2021-02-18 00:00:00')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def unit_root(b):\n",
        "  a = np.average(data[stock2][window+8-window:window+8] - b*data[stock1][window+8-window:window+8])\n",
        "  fair_value = a + b*data[stock1][window+8-window:window+8]\n",
        "  diff = np.array(fair_value - data[stock2][window+8-window:window+8])\n",
        "  diff_diff = diff[1:] - diff[:-1]\n",
        "  reg = sm.OLS(diff_diff, diff[:-1])\n",
        "  res = reg.fit()\n",
        "  return res.params[0]/res.bse[0]"
      ],
      "metadata": {
        "id": "5nc3jdbW2ra-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "b = data[stock2][window+33]/data[stock1][window+33]\n",
        "a = np.average(data[stock2][window+33-window:window+33] - b*data[stock1][window+33-window:window+33])\n",
        "fair_value = a + b * data[stock1][window+33-window:window+33] # input stock1\n",
        "diff = np.array(fair_value - data[stock2][window+33-window:window+33])\n",
        "diff_diff = diff[1:] - diff[:-1]\n",
        "reg = sm.OLS(diff_diff, diff[:-1]) #sm.add_constant(diff[:-1]))\n",
        "res = reg.fit()\n",
        "t_ss = res.params[0]/res.bse[0]\n",
        "###\n",
        "res0 = spop.minimize(unit_root, b, method='Nelder-Mead')\n",
        "t_opt = res0.fun\n",
        "b_opt = float(res0.x)\n",
        "a_opt = np.average(data[stock2][window+33-window:window+33] - b_opt*data[stock1][window+33-window:window+33])\n",
        "\n",
        "#ADF1 = adfuller(data[stock2][window+33-window:window+33]/data[stock1][window+33-window:window+33])\n",
        "\n",
        "fair_value_opt = a_opt + b_opt*data[stock1][window+33]\n",
        "\n",
        "resid = data[stock2][window+33-window:window+33] - fair_value_opt\n",
        "ADF = adfuller(resid)\n",
        "\n",
        "ADF[0]< DF_threshold,t_opt < DF_threshold,ADF ,t_opt ,t_ss,data[stock2][window+33-window:window+33],'\\n',ts.coint(data[stock2][window+33-window:window+33],data[stock1][window+33-window:window+33]),'\\n',fair_value_opt,fair_value,a_opt,a,b_opt,b,b*data[stock1][window+33-window:window+33],'\\n',diff_diff,diff"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hMXADiDa9wKK",
        "outputId": "91bfc9e8-f810-4f32-e607-d4403d389bd9"
      },
      "execution_count": null,
      "outputs": [
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              "         1.45900618e+00,  1.49583509e+00,  1.63952849e+00,  2.05052250e+00,\n",
              "         1.32336791e+00,  1.59455034e+00,  3.41734375e-02,  3.23602976e-01,\n",
              "         1.45926908e-01, -8.58289931e-01, -2.74838676e+00, -1.48796013e+00,\n",
              "        -1.84734300e+00, -1.89658265e+00, -1.82397850e+00, -2.55999671e+00,\n",
              "        -1.82724844e+00, -2.04824130e+00, -8.97957860e-01, -9.35493171e-01,\n",
              "        -9.65305848e-01, -1.16777180e+00, -1.09165431e+00, -8.89049103e-01,\n",
              "        -9.43456309e-01,  5.33227211e-02, -1.60260336e+00, -2.58009003e+00,\n",
              "        -1.46648821e+00, -7.51087474e-01, -9.35969202e-01, -2.19804573e-01,\n",
              "        -3.53122420e-01, -2.14559185e+00,  1.53127948e+00,  1.80967017e+00,\n",
              "         1.34711299e+00,  2.23005271e+00,  2.31658636e+00,  2.35843594e+00,\n",
              "         1.77299016e+00,  1.66099739e+00,  1.70204144e+00,  3.42417154e+00,\n",
              "         3.39838960e+00,  3.98810614e+00,  3.13521798e+00,  3.18123585e+00,\n",
              "         2.89350689e+00,  2.76530975e+00,  3.05114279e+00,  2.58602511e+00,\n",
              "         2.83597615e+00,  3.26929153e+00,  4.26175167e+00,  3.81796614e+00]))"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b = data[stock1][window+33]/data[stock2][window+33]\n",
        "a = np.average(data[stock1][window+33-window:window+33] - b*data[stock2][window+33-window:window+33])\n",
        "fair_value = a + b * data[stock2][window+33-window:window+33] # input stock1\n",
        "diff = np.array(data[stock1][window+33-window:window+33] - fair_value)\n",
        "diff_diff = diff[1:] - diff[:-1]\n",
        "reg = sm.OLS(diff_diff, diff[:-1])\n",
        "res = reg.fit()\n",
        "t_ss = res.params[0]/res.bse[0]\n",
        "###\n",
        "res0 = spop.minimize(unit_root, b, method='Nelder-Mead')\n",
        "t_opt = res0.fun\n",
        "b_opt = float(res0.x)\n",
        "a_opt = np.average(data[stock1][window+33-window:window+33] - b_opt*data[stock2][window+33-window:window+33])\n",
        "\n",
        "ADF = adfuller(data[stock1][window+33-window:window+33]/data[stock2][window+33-window:window+33])\n",
        "\n",
        "fair_value_opt = a_opt + b_opt*data[stock2][window+33]\n",
        "\n",
        "DF_threshold, ADF[0] ,t_opt ,t_ss\n",
        "#t_opt < DF_threshold, ADF[0] ,t_opt ,t_ss,data[stock2][window+33-window:window+33],'\\n',fair_value_opt,fair_value,a_opt,a,b_opt,b,b*data[stock1][window+33-window:window+33]#,'\\n',diff_diff,diff"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xu42W-5-70Uk",
        "outputId": "a76c1288-e225-43e7-b332-862ccf7d3bf4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-2.5, -2.167378182469805, -1.7290219876186756, -3.151773356003226)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b = data[stock1][window+33]/data[stock2][window+33]\n",
        "a = np.average(data[stock1][window+33-window:window+33] - b*data[stock2][window+33-window:window+33])\n",
        "fair_value = a + b * data[stock2][window+33-window:window+33] # input stock1\n",
        "diff = np.array(fair_value - data[stock1][window+33-window:window+33])\n",
        "diff_diff = diff[1:] - diff[:-1]\n",
        "reg = sm.OLS(diff_diff, diff[:-1])\n",
        "res = reg.fit()\n",
        "t_ss = res.params[0]/res.bse[0]\n",
        "###\n",
        "res0 = spop.minimize(unit_root, b, method='Nelder-Mead')\n",
        "t_opt = res0.fun\n",
        "b_opt = float(res0.x)\n",
        "a_opt = np.average(data[stock1][window+33-window:window+33] - b_opt*data[stock2][window+33-window:window+33])\n",
        "\n",
        "ADF = adfuller(data[stock1][window+33-window:window+33]/data[stock2][window+33-window:window+33])\n",
        "\n",
        "fair_value_opt = a_opt + b_opt*data[stock2][window+33]\n",
        "\n",
        "ADF[0] < DF_threshold ,t_opt < DF_threshold, DF_threshold, ADF[0] ,t_opt ,t_ss\n",
        "#t_opt < DF_threshold, ADF[0] ,t_opt ,t_ss,data[stock2][window+33-window:window+33],'\\n',fair_value_opt,fair_value,a_opt,a,b_opt,b,b*data[stock1][window+33-window:window+33]#,'\\n',diff_diff,diff"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MR-kgn2z-0ue",
        "outputId": "06ca9063-f0a2-4e52-9d4a-ac12e3a3e480"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(False,\n",
              " False,\n",
              " -2.5,\n",
              " -2.167378182469805,\n",
              " -1.7290219876186756,\n",
              " -3.151773356003226)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b = data[stock2][window+34]/data[stock1][window+34]\n",
        "a = np.average(data[stock2][window+34-window:window+34] - b*data[stock1][window+34-window:window+34])\n",
        "fair_value = a + b * data[stock1][window+34-window:window+34] # input stock1\n",
        "diff = np.array(fair_value - data[stock2][window+34-window:window+34])\n",
        "diff_diff = diff[1:] - diff[:-1]\n",
        "reg = sm.OLS(diff_diff, diff[:-1])\n",
        "res = reg.fit()\n",
        "t_ss = res.params[0]/res.bse[0]\n",
        "###\n",
        "res0 = spop.minimize(unit_root, b, method='Nelder-Mead')\n",
        "t_opt = res0.fun\n",
        "b_opt = float(res0.x)\n",
        "a_opt = np.average(data[stock2][window+34-window:window+34] - b_opt*data[stock1][window+34-window:window+34])\n",
        "\n",
        "\n",
        "\n",
        "fair_value_opt = a_opt + b_opt*data[stock1][window+34]\n",
        "\n",
        "np.sign(fair_value - data[stock2][window+34]),t_opt < DF_threshold, t_opt ,t_ss,data[stock2][window+34-window:window+34],'\\n',diff_diff,diff,'\\n',fair_value_opt,fair_value,a_opt,a,b_opt,b,b*data[stock1][window+34-window:window+34]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kqI4-cBhDbe1",
        "outputId": "204551cb-c550-45ea-cadd-e54b7a2bfac1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Date\n",
              " 2020-02-20   -1.0\n",
              " 2020-02-21   -1.0\n",
              " 2020-02-24   -1.0\n",
              " 2020-02-25   -1.0\n",
              " 2020-02-26   -1.0\n",
              "              ... \n",
              " 2021-02-11    1.0\n",
              " 2021-02-12    1.0\n",
              " 2021-02-16    1.0\n",
              " 2021-02-17    1.0\n",
              " 2021-02-18    1.0\n",
              " Name: JPM, Length: 252, dtype: float64,\n",
              " True,\n",
              " -2.511288184472454,\n",
              " -3.071827659803333,\n",
              " Date\n",
              " 2020-02-20    78.220001\n",
              " 2020-02-21    76.440002\n",
              " 2020-02-24    72.529999\n",
              " 2020-02-25    69.430000\n",
              " 2020-02-26    68.180000\n",
              "                 ...    \n",
              " 2021-02-11    63.200001\n",
              " 2021-02-12    63.630001\n",
              " 2021-02-16    64.120003\n",
              " 2021-02-17    64.760002\n",
              " 2021-02-18    63.480000\n",
              " Name: C, Length: 252, dtype: float64,\n",
              " '\\n',\n",
              " array([ 1.03340429,  2.28794862,  0.47804143,  1.41887055,  1.38802009,\n",
              "        -1.34754385, -1.72579835,  0.51353318, -1.05568239,  1.28693055,\n",
              "        -0.4075092 ,  3.40399313, -0.98366581,  2.68354878,  4.01480523,\n",
              "        -0.73182809,  2.93959051,  3.33975598, -0.56622345, -2.58339433,\n",
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              "         0.2023764 , -1.22579835,  0.39439034, -1.38349495,  0.60961686,\n",
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              "         1.03679158,  0.41346595,  0.41680185,  0.09095   , -0.57992163,\n",
              "        -1.72798484,  0.4489675 ,  0.14210307, -0.04550369,  0.58892331,\n",
              "        -1.24806428, -1.62358221,  2.42895445,  0.11678021, -0.9667253 ,\n",
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              "         1.03101564, -0.46775154, -0.39894309,  1.46229616,  0.40113896,\n",
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              "        -0.95223743,  0.05004342, -0.18347839, -1.33802685, -0.57327561,\n",
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              "        -1.55115846,  0.28557455, -0.1766704 , -1.01217821, -1.8966336 ,\n",
              "         1.25447654, -0.36994216, -0.03231123,  0.07553728, -0.74439862,\n",
              "         0.72780385, -0.18353238,  1.13452821, -0.00904187, -0.03442193,\n",
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              "         1.49775395]),\n",
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              "         3.61346339e+00,  3.42998501e+00,  2.09195816e+00,  1.51868255e+00,\n",
              "         1.54872334e+00,  1.66442610e+00,  2.09100770e+00,  1.38312810e+00,\n",
              "         1.64760617e+00,  9.64477135e-02,  3.82022267e-01,  2.05351872e-01,\n",
              "        -8.06826340e-01, -2.70345994e+00, -1.44898341e+00, -1.81892556e+00,\n",
              "        -1.85123679e+00, -1.77569951e+00, -2.52009813e+00, -1.79229428e+00,\n",
              "        -1.97582666e+00, -8.41298450e-01, -8.50340323e-01, -8.84762256e-01,\n",
              "        -1.08035627e+00, -1.00700427e+00, -8.01465925e-01, -8.41542622e-01,\n",
              "         1.45179931e-01, -1.51258985e+00, -2.44063204e+00, -1.29082687e+00,\n",
              "        -5.74168996e-01, -7.42038483e-01, -7.68836036e-03, -1.39916712e-01,\n",
              "        -1.92551427e+00,  1.73015461e+00,  2.00351700e+00,  1.52361242e+00,\n",
              "         2.39699847e+00,  2.47481641e+00,  2.50267071e+00,  1.91269955e+00,\n",
              "         1.76953162e+00,  1.82943161e+00,  3.53949388e+00,  3.52167332e+00,\n",
              "         4.14482778e+00,  3.30476165e+00,  3.37684263e+00,  3.08685092e+00,\n",
              "         2.97675552e+00,  3.25789554e+00,  2.79344831e+00,  3.04013099e+00,\n",
              "         3.49003956e+00,  4.51099308e+00,  4.07097884e+00,  5.56873279e+00]),\n",
              " '\\n',\n",
              " 66.3081284406465,\n",
              " Date\n",
              " 2020-02-20    65.449101\n",
              " 2020-02-21    64.702507\n",
              " 2020-02-24    63.080452\n",
              " 2020-02-25    60.458495\n",
              " 2020-02-26    60.627365\n",
              "                 ...    \n",
              " 2021-02-11    66.240132\n",
              " 2021-02-12    67.120041\n",
              " 2021-02-16    68.630996\n",
              " 2021-02-17    68.830981\n",
              " 2021-02-18    69.048732\n",
              " Name: JPM, Length: 252, dtype: float64,\n",
              " 14.095254409579606,\n",
              " 4.348627583896919,\n",
              " 0.3527420113767763,\n",
              " 0.4443993844116867,\n",
              " Date\n",
              " 2020-02-20    61.100474\n",
              " 2020-02-21    60.353879\n",
              " 2020-02-24    58.731824\n",
              " 2020-02-25    56.109867\n",
              " 2020-02-26    56.278738\n",
              "                 ...    \n",
              " 2021-02-11    61.891504\n",
              " 2021-02-12    62.771413\n",
              " 2021-02-16    64.282368\n",
              " 2021-02-17    64.482353\n",
              " 2021-02-18    64.700105\n",
              " Name: JPM, Length: 252, dtype: float64)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## C/JPM"
      ],
      "metadata": {
        "id": "KOyQaANnqBGb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#moving through the sample # Dynamic equalibrium\n",
        "for t in range(window, len(data)):\n",
        "    #defining the unit root function: stock2 = a + b*stock1\n",
        "    def unit_root(b):\n",
        "        a = np.average(data[stock2][t-window:t] - b*data[stock1][t-window:t])\n",
        "        fair_value = a + b*data[stock1][t-window:t]\n",
        "        diff = np.array(fair_value - data[stock2][t-window:t])\n",
        "        diff_diff = diff[1:] - diff[:-1]\n",
        "        reg = sm.OLS(diff_diff, diff[:-1])\n",
        "        res = reg.fit()\n",
        "        return res.params[0]/res.bse[0]\n",
        "    #optimising the cointegration equation parameters\n",
        "    res1 = spop.minimize(unit_root, data[stock2][t]/data[stock1][t], method='Nelder-Mead') # b = C/JPM <1\n",
        "    t_opt = res1.fun #\n",
        "    b_opt = float(res1.x)\n",
        "    a_opt = np.average(data[stock2][t-window:t] - b_opt*data[stock1][t-window:t])\n",
        "    #simulating trading\n",
        "    fair_value = a_opt + b_opt*data[stock1][t]\n",
        "    if t == window:\n",
        "        old_signal = 0\n",
        "    if t_opt > DF_threshold: # non-stationary, can't rely on noisy cointegration relationship\n",
        "        signal = 0\n",
        "        gross_return = 0\n",
        "    else:\n",
        "        signal = np.sign(fair_value - data[stock2][t])\n",
        "        gross_return = signal*returns[stock2][t] - signal*returns[stock1][t]\n",
        "    fees = fee*abs(signal - old_signal) # from long(+1) to short(-1) need to pay double fee\n",
        "    net_return = gross_return - fees\n",
        "    gross_returns = np.append(gross_returns, gross_return)\n",
        "    net_returns = np.append(net_returns, net_return)\n",
        "    t_s = np.append(t_s, t_opt)\n",
        "    #interface: reporting daily positions and realised returns\n",
        "    print('day '+str(data.index[t]))\n",
        "    print('')\n",
        "    if signal == 0:\n",
        "        print('no trading')\n",
        "    elif  signal == 1:\n",
        "        print('long position on '+stock2+' and short position on '+stock1)\n",
        "    else:\n",
        "        print('long position on '+stock1+' and short position on '+stock2)\n",
        "    print('gross daily return: '+str(round(gross_return*100,2))+'%')\n",
        "    print('net daily return: '+str(round(net_return*100,2))+'%')\n",
        "    print('cumulative net return so far: '+str(round(np.prod(1+net_returns)*100-100,2))+'%')\n",
        "    print('')\n",
        "    old_signal = signal\n",
        "\n"
      ],
      "metadata": {
        "id": "8ksLobfZbDlO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "faf64388-e814-4b0a-9fa9-a3de09dc35d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "day 2020-12-30 00:00:00\n",
            "\n",
            "no trading\n",
            "gross daily return: 0%\n",
            "net daily return: 0.0%\n",
            "cumulative net return so far: 0.0%\n",
            "\n",
            "day 2020-12-31 00:00:00\n",
            "\n",
            "no trading\n",
            "gross daily return: 0%\n",
            "net daily return: 0.0%\n",
            "cumulative net return so far: 0.0%\n",
            "\n",
            "day 2021-01-04 00:00:00\n",
            "\n",
            "no trading\n",
            "gross daily return: 0%\n",
            "net daily return: 0.0%\n",
            "cumulative net return so far: 0.0%\n",
            "\n",
            "day 2021-01-05 00:00:00\n",
            "\n",
            "no trading\n",
            "gross daily return: 0%\n",
            "net daily return: 0.0%\n",
            "cumulative net return so far: 0.0%\n",
            "\n",
            "day 2021-01-06 00:00:00\n",
            "\n",
            "no trading\n",
            "gross daily return: 0%\n",
            "net daily return: 0.0%\n",
            "cumulative net return so far: 0.0%\n",
            "\n",
            "day 2021-01-07 00:00:00\n",
            "\n",
            "no trading\n",
            "gross daily return: 0%\n",
            "net daily return: 0.0%\n",
            "cumulative net return so far: 0.0%\n",
            "\n",
            "day 2021-01-08 00:00:00\n",
            "\n",
            "no trading\n",
            "gross daily return: 0%\n",
            "net daily return: 0.0%\n",
            "cumulative net return so far: 0.0%\n",
            "\n",
            "day 2021-01-11 00:00:00\n",
            "\n",
            "no trading\n",
            "gross daily return: 0%\n",
            "net daily return: 0.0%\n",
            "cumulative net return so far: 0.0%\n",
            "\n",
            "day 2021-01-12 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: -0.19%\n",
            "net daily return: -0.29%\n",
            "cumulative net return so far: -0.29%\n",
            "\n",
            "day 2021-01-13 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: -2.63%\n",
            "net daily return: -2.63%\n",
            "cumulative net return so far: -2.92%\n",
            "\n",
            "day 2021-01-14 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: 5.13%\n",
            "net daily return: 5.13%\n",
            "cumulative net return so far: 2.07%\n",
            "\n",
            "day 2021-01-15 00:00:00\n",
            "\n",
            "long position on C and short position on JPM\n",
            "gross daily return: -0.41%\n",
            "net daily return: -0.61%\n",
            "cumulative net return so far: 1.45%\n",
            "\n",
            "day 2021-01-19 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: -0.81%\n",
            "net daily return: -1.01%\n",
            "cumulative net return so far: 0.42%\n",
            "\n",
            "day 2021-01-20 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: 1.34%\n",
            "net daily return: 1.34%\n",
            "cumulative net return so far: 1.77%\n",
            "\n",
            "day 2021-01-21 00:00:00\n",
            "\n",
            "long position on C and short position on JPM\n",
            "gross daily return: -0.1%\n",
            "net daily return: -0.3%\n",
            "cumulative net return so far: 1.47%\n",
            "\n",
            "day 2021-01-22 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: 0.01%\n",
            "net daily return: -0.19%\n",
            "cumulative net return so far: 1.27%\n",
            "\n",
            "day 2021-01-25 00:00:00\n",
            "\n",
            "long position on C and short position on JPM\n",
            "gross daily return: 0.99%\n",
            "net daily return: 0.79%\n",
            "cumulative net return so far: 2.07%\n",
            "\n",
            "day 2021-01-26 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: -0.35%\n",
            "net daily return: -0.55%\n",
            "cumulative net return so far: 1.51%\n",
            "\n",
            "day 2021-01-27 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: 0.18%\n",
            "net daily return: 0.18%\n",
            "cumulative net return so far: 1.69%\n",
            "\n",
            "day 2021-01-28 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: 2.79%\n",
            "net daily return: 2.79%\n",
            "cumulative net return so far: 4.52%\n",
            "\n",
            "day 2021-01-29 00:00:00\n",
            "\n",
            "long position on C and short position on JPM\n",
            "gross daily return: 0.02%\n",
            "net daily return: -0.18%\n",
            "cumulative net return so far: 4.34%\n",
            "\n",
            "day 2021-02-01 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: 1.11%\n",
            "net daily return: 0.91%\n",
            "cumulative net return so far: 5.29%\n",
            "\n",
            "day 2021-02-02 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: -1.41%\n",
            "net daily return: -1.41%\n",
            "cumulative net return so far: 3.81%\n",
            "\n",
            "day 2021-02-03 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: 0.16%\n",
            "net daily return: 0.16%\n",
            "cumulative net return so far: 3.97%\n",
            "\n",
            "day 2021-02-04 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: -0.47%\n",
            "net daily return: -0.47%\n",
            "cumulative net return so far: 3.48%\n",
            "\n",
            "day 2021-02-05 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: -0.14%\n",
            "net daily return: -0.14%\n",
            "cumulative net return so far: 3.33%\n",
            "\n",
            "day 2021-02-08 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: 0.43%\n",
            "net daily return: 0.43%\n",
            "cumulative net return so far: 3.78%\n",
            "\n",
            "day 2021-02-09 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: -0.73%\n",
            "net daily return: -0.73%\n",
            "cumulative net return so far: 3.02%\n",
            "\n",
            "day 2021-02-10 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: 0.38%\n",
            "net daily return: 0.38%\n",
            "cumulative net return so far: 3.41%\n",
            "\n",
            "day 2021-02-11 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: 0.74%\n",
            "net daily return: 0.74%\n",
            "cumulative net return so far: 4.18%\n",
            "\n",
            "day 2021-02-12 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: 1.64%\n",
            "net daily return: 1.64%\n",
            "cumulative net return so far: 5.88%\n",
            "\n",
            "day 2021-02-16 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: -0.69%\n",
            "net daily return: -0.69%\n",
            "cumulative net return so far: 5.15%\n",
            "\n",
            "day 2021-02-17 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: 2.31%\n",
            "net daily return: 2.31%\n",
            "cumulative net return so far: 7.59%\n",
            "\n",
            "day 2021-02-18 00:00:00\n",
            "\n",
            "long position on C and short position on JPM\n",
            "gross daily return: 1.95%\n",
            "net daily return: 1.75%\n",
            "cumulative net return so far: 9.48%\n",
            "\n",
            "day 2021-02-19 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: 1.33%\n",
            "net daily return: 1.13%\n",
            "cumulative net return so far: 10.72%\n",
            "\n",
            "day 2021-02-22 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: -1.06%\n",
            "net daily return: -1.06%\n",
            "cumulative net return so far: 9.54%\n",
            "\n",
            "day 2021-02-23 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: -1.03%\n",
            "net daily return: -1.03%\n",
            "cumulative net return so far: 8.42%\n",
            "\n",
            "day 2021-02-24 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: 0.38%\n",
            "net daily return: 0.38%\n",
            "cumulative net return so far: 8.83%\n",
            "\n",
            "day 2021-02-25 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: -0.38%\n",
            "net daily return: -0.38%\n",
            "cumulative net return so far: 8.41%\n",
            "\n",
            "day 2021-02-26 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: -3.29%\n",
            "net daily return: -3.29%\n",
            "cumulative net return so far: 4.84%\n",
            "\n",
            "day 2021-03-01 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: 0.58%\n",
            "net daily return: 0.58%\n",
            "cumulative net return so far: 5.45%\n",
            "\n",
            "day 2021-03-02 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: -0.2%\n",
            "net daily return: -0.2%\n",
            "cumulative net return so far: 5.24%\n",
            "\n",
            "day 2021-03-03 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: -0.73%\n",
            "net daily return: -0.73%\n",
            "cumulative net return so far: 4.47%\n",
            "\n",
            "day 2021-03-04 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: -0.37%\n",
            "net daily return: -0.37%\n",
            "cumulative net return so far: 4.09%\n",
            "\n",
            "day 2021-03-05 00:00:00\n",
            "\n",
            "long position on JPM and short position on C\n",
            "gross daily return: 0.0%\n",
            "net daily return: 0.0%\n",
            "cumulative net return so far: 4.09%\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t_s"
      ],
      "metadata": {
        "id": "3By-HQ0_tNs_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "outputId": "9c1fea02-f0c1-4243-90f6-3da8bcad8049"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fb7b82863e0>]"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WBIFq2A8cDUi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "outputId": "62edb9b2-f088-4dfd-ad22-0862f2ad96b3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fb7b7c59fc0>]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#plotting equity curves\n",
        "plt.plot(np.append(1,np.cumprod(1+gross_returns)))\n",
        "plt.plot(np.append(1,np.cumprod(1+net_returns)))"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ii1747I3BgRg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# KPSS"
      ],
      "metadata": {
        "id": "H44uuy0_v6wU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## $Stock_2 = a + b* Stock_1$"
      ],
      "metadata": {
        "id": "R1v6sYTUQTli"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Trail"
      ],
      "metadata": {
        "id": "AfO20Jrv92SB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "spread = raw_data['CVX']-raw_data['XOM']\n",
        "plt.plot(raw_data['CVX']-raw_data['XOM'], label = 'Spread (CVX - XOM)')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "CGbXdLU1aDmK",
        "outputId": "902742e9-cba1-4076-abde-20cb99703ece"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f8249e51c60>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(output['net'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "V3zbbQ_wi6Ug",
        "outputId": "515f074d-5276-44f1-863a-f76b80e09123"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f82499dece0>]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plt.plot(raw_data[stock2],label='CVX')\n",
        "#plt.plot(raw_data[stock1],label='XOM')\n",
        "plt.plot(np.log(raw_data[stock2]),label='ln(CVX)')\n",
        "plt.plot(np.log(raw_data[stock1]),label='ln(XOM)')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "outputId": "b8eb19ae-0d80-4386-a694-d6c99ecc107b",
        "id": "tza_Mu09XyuB"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f0fa47fa0b0>"
            ]
          },
          "metadata": {},
          "execution_count": 141
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(raw_data)\n",
        "        signal = np.sign(raw_data[tickers[1]][t] - (a_opt + b_opt*raw_data[tickers[0]][t]))\n",
        "        rets0 = np.array(raw_data[tickers[0]][t-window:t-1])/np.array(raw_data[tickers[0]][t-window+1:t]) - 1\n",
        "        rets1 = np.array(raw_data[tickers[1]][t-window:t-1])/np.array(raw_data[tickers[1]][t-window+1:t]) - 1\n",
        "        rets_mkt = np.array(raw_data['market'][t-window:t-1])/np.array(raw_data['market'][t-window+1:t]) - 1\n",
        "        reg = sm.OLS(rets0, sm.add_constant(rets_mkt))\n",
        "        res = reg.fit()\n",
        "        beta0 = res.params[1]\n",
        "        reg = sm.OLS(rets1, sm.add_constant(rets_mkt))\n",
        "        res = reg.fit()\n",
        "        beta1 = res.params[1]\n",
        "        position0 = beta1*signal\n",
        "        position1 = -beta0*signal\n",
        "        gross = position0*(raw_data[tickers[0]][t+1]/raw_data[tickers[0]][t] - 1) + position1*(raw_data[tickers[1]][t+1]/raw_data[tickers[1]][t] - 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "H-BlsL_NPov8",
        "outputId": "757faa42-2fd0-4327-c01d-a2725eae171b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f712dcab550>,\n",
              " <matplotlib.lines.Line2D at 0x7f712fe79b40>,\n",
              " <matplotlib.lines.Line2D at 0x7f712fe79ba0>]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#raw_data[raw_data.index == traded_dates]\n",
        "raw_data\n",
        "data\n",
        "#np.average(np.array(data[stock1][1:]) - data[stock1][-1]/data[stock1][-2]*np.array(data[stock1][:-1]))\n",
        "#data[stock1].diff(periods=1).dropna()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        },
        "id": "VfyQ8s7UKxl0",
        "outputId": "c6a38358-b3c5-4a60-8553-585fae417424"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  XOM         CVX       market\n",
              "Date                                          \n",
              "2019-10-01  68.949997  116.010002  2940.250000\n",
              "2019-10-02  67.150002  112.290001  2887.610107\n",
              "2019-10-03  67.980003  113.150002  2910.629883\n",
              "2019-10-04  68.970001  113.849998  2952.010010\n",
              "2019-10-07  68.019997  113.260002  2938.790039\n",
              "2019-10-08  66.699997  111.709999  2893.060059\n",
              "2019-10-09  67.440002  113.139999  2919.399902\n",
              "2019-10-10  68.250000  114.589996  2938.129883\n",
              "2019-10-11  68.980003  116.150002  2970.270020\n",
              "2019-10-14  69.180000  116.180000  2966.149902\n",
              "2019-10-15  69.419998  116.309998  2995.679932\n",
              "2019-10-16  68.230003  115.110001  2989.689941\n",
              "2019-10-17  68.139999  115.349998  2997.949951\n",
              "2019-10-18  67.610001  114.739998  2986.199951\n",
              "2019-10-21  68.739998  116.610001  3006.719971\n",
              "2019-10-22  69.089996  117.800003  2995.989990\n",
              "2019-10-23  69.750000  117.980003  3004.520020\n",
              "2019-10-24  69.089996  117.580002  3010.290039\n",
              "2019-10-25  69.250000  118.669998  3022.550049\n",
              "2019-10-28  68.639999  118.480003  3039.419922\n",
              "2019-10-29  68.440002  118.129997  3036.889893"
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              "      <th>XOM</th>\n",
              "      <th>CVX</th>\n",
              "      <th>market</th>\n",
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              "    <tr>\n",
              "      <th>Date</th>\n",
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              "      <th>2019-10-01</th>\n",
              "      <td>68.949997</td>\n",
              "      <td>116.010002</td>\n",
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              "      <th>2019-10-02</th>\n",
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              "      <th>2019-10-03</th>\n",
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              "      <td>113.150002</td>\n",
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              "      <th>2019-10-04</th>\n",
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              "      <td>113.849998</td>\n",
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              "      <th>2019-10-09</th>\n",
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              "      <th>2019-10-11</th>\n",
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              "      <td>68.739998</td>\n",
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Rolling window length: 252"
      ],
      "metadata": {
        "id": "KMg1AQdZzkq4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#specifying rolling window length\n",
        "window = 252"
      ],
      "metadata": {
        "id": "FNS7iN2wzlt2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#moving in a loop through the sample\n",
        "for t in range(window, len(raw_data)-1):\n",
        "    old_signal = signal\n",
        "    old_position0 = position0\n",
        "    old_position1 = position1\n",
        "    #specifying the subsample\n",
        "    data = raw_data[t-window:t]\n",
        "    #stock 2 = a + b*stock 1\n",
        "    #OLS parameters as starting values\n",
        "    reg = sm.OLS(np.array(data[tickers[1]]), sm.add_constant(np.array(data[tickers[0]])))\n",
        "    res = reg.fit()\n",
        "    a0 = res.params[0]\n",
        "    b0 = res.params[1]\n",
        "\n",
        "    if unbiased == 1:\n",
        "        #defining the KPSS function (unbiased one-parameter forecast)\n",
        "        def KPSS(b):\n",
        "            a = np.average(data[tickers[1]] - b*data[tickers[0]])\n",
        "            resid = np.array(data[tickers[1]] - (a + b*data[tickers[0]]))\n",
        "            cum_resid = np.cumsum(resid)\n",
        "            st_error = (np.sum(resid**2)/(len(resid)-2))**(1/2)\n",
        "            KPSS = np.sum(cum_resid**2)/(len(resid)**2*st_error**2)\n",
        "            return KPSS\n",
        "        #minimising the KPSS function (maximising the stationarity)\n",
        "        res = spop.minimize(KPSS, b0, method='Nelder-Mead')\n",
        "        KPSS_opt = res.fun\n",
        "        #retrieving optimal parameters\n",
        "        b_opt = float(res.x)\n",
        "        a_opt = np.average(data[tickers[1]] - b_opt*data[tickers[0]])\n",
        "\n",
        "    else:\n",
        "        #defining the KPSS function (two-parameter)\n",
        "        def KPSS2(kpss_params):\n",
        "            a = kpss_params[0]\n",
        "            b = kpss_params[1]\n",
        "            resid = np.array(data[tickers[1]] - (a + b*data[tickers[0]]))\n",
        "            cum_resid = np.cumsum(resid)\n",
        "            st_error = (np.sum(resid**2)/(len(resid)-2))**(1/2)\n",
        "            KPSS = np.sum(cum_resid**2)/(len(resid)**2*st_error**2)\n",
        "            return KPSS\n",
        "        #minimising the KPSS function (maximising the stationarity)\n",
        "        res = spop.minimize(KPSS2, [a0, b0], method='Nelder-Mead')\n",
        "        #retrieving optimal parameters\n",
        "        KPSS_opt = res.fun\n",
        "        a_opt = res.x[0]\n",
        "        b_opt = res.x[1]\n",
        "\n",
        "    #simulate trading\n",
        "    #first check whether stop-loss is violated\n",
        "    if current_return < stop_loss:\n",
        "        signal = 0\n",
        "        print('stop-loss triggered')\n",
        "    #if we are already in position, check whether the equilibrium is restored, continue in position if not\n",
        "    elif np.sign(raw_data[tickers[1]][t] - (a_opt + b_opt*raw_data[tickers[0]][t])) == old_signal:\n",
        "        singal = old_signal\n",
        "    else:\n",
        "        #only trade if the pair is cointegrated\n",
        "        if KPSS_opt > KPSS_max:\n",
        "            signal = 0\n",
        "        #only trade if there are large enough profit opportunities (optimal entry)\n",
        "        elif abs(raw_data[tickers[1]][t]/(a_opt + b_opt*raw_data[tickers[0]][t])-1) < entry:\n",
        "            signal = 0\n",
        "        else:\n",
        "            signal = np.sign(raw_data[tickers[1]][t] - (a_opt + b_opt*raw_data[tickers[0]][t])) # stocks diverge enough (>entry) to decide long/short the pair (stock2 to stock1)\n",
        "    #calculate strategy returns with beta loading\n",
        "    if beta_loading == 1:\n",
        "        rets0 = np.array(raw_data[tickers[0]][t-window:t-1])/np.array(raw_data[tickers[0]][t-window+1:t]) - 1\n",
        "        rets1 = np.array(raw_data[tickers[1]][t-window:t-1])/np.array(raw_data[tickers[1]][t-window+1:t]) - 1\n",
        "        rets_mkt = np.array(raw_data['market'][t-window:t-1])/np.array(raw_data['market'][t-window+1:t]) - 1\n",
        "        reg = sm.OLS(rets0, sm.add_constant(rets_mkt))\n",
        "        res = reg.fit()\n",
        "        beta0 = res.params[1]\n",
        "        print('beta_stock1:',beta0)\n",
        "        reg = sm.OLS(rets1, sm.add_constant(rets_mkt))\n",
        "        res = reg.fit()\n",
        "        beta1 = res.params[1]\n",
        "        print('beta_stock2:',beta1)\n",
        "        position0 = beta1*signal\n",
        "        position1 = -beta0*signal\n",
        "    #calculate strategy returns without beta loading\n",
        "    else:\n",
        "        position0 = signal\n",
        "        position1 = -signal\n",
        "    print('position_stk1:',position0)\n",
        "    print('times：',(raw_data[tickers[0]][t+1]/raw_data[tickers[0]][t] - 1))\n",
        "    print('position_stk2:',position1)\n",
        "    print('times：',(raw_data[tickers[1]][t+1]/raw_data[tickers[1]][t] - 1))\n",
        "    #calculating returns\n",
        "    gross = position0*(raw_data[tickers[0]][t+1]/raw_data[tickers[0]][t] - 1) + position1*(raw_data[tickers[1]][t+1]/raw_data[tickers[1]][t] - 1)\n",
        "    net = gross - fee*(abs(position0 - old_position0) + abs(position1 - old_position1))\n",
        "    market = raw_data['market'][t+1]/raw_data['market'][t] - 1\n",
        "    if signal == old_signal:\n",
        "        current_return = (1+current_return)*(1+gross)-1\n",
        "    else:\n",
        "        current_return = gross\n",
        "    #populating arrays\n",
        "    KPSS_stats = np.append(KPSS_stats, KPSS_opt)\n",
        "    signals = np.append(signals, signal)\n",
        "    gross_returns = np.append(gross_returns, gross)\n",
        "    net_returns = np.append(net_returns, net)\n",
        "    market_returns = np.append(market_returns, market)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t1kLd00-z6YY",
        "outputId": "145f2321-4eb2-43d8-e382-b7fee0c19c74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "beta_stock1: 1.089061872007094\n",
            "beta_stock2: 1.3700768368887384\n",
            "position_stk1: -1.3700768368887384\n",
            "times： 0.0014586655057879927\n",
            "position_stk2: 1.089061872007094\n",
            "times： 0.0013907993323909462\n",
            "beta_stock1: 1.089223481904165\n",
            "beta_stock2: 1.3706261245612312\n",
            "position_stk1: -1.3706261245612312\n",
            "times： -0.03495487034474731\n",
            "position_stk2: 1.089223481904165\n",
            "times： -0.021944469875759598\n",
            "beta_stock1: 1.0884271737531925\n",
            "beta_stock2: 1.3693547616627324\n",
            "position_stk1: -1.3693547616627324\n",
            "times： -0.004527664384027741\n",
            "position_stk2: 1.0884271737531925\n",
            "times： 0.010934454593617193\n",
            "beta_stock1: 1.0863715433734773\n",
            "beta_stock2: 1.3682614083210844\n",
            "position_stk1: -1.3682614083210844\n",
            "times： 0.02304433434746289\n",
            "position_stk2: 1.0863715433734773\n",
            "times： 0.02121076633026875\n",
            "beta_stock1: 1.0852194034562825\n",
            "beta_stock2: 1.3671317313746936\n",
            "position_stk1: -1.3671317313746936\n",
            "times： -0.01037351130429065\n",
            "position_stk2: 1.0852194034562825\n",
            "times： -0.005501979549863645\n",
            "beta_stock1: 1.0860343232169174\n",
            "beta_stock2: 1.3672279548603383\n",
            "position_stk1: -1.3672279548603383\n",
            "times： 0.003294417860506549\n",
            "position_stk2: 1.0860343232169174\n",
            "times： 0.020470202836361917\n",
            "beta_stock1: 1.085120061142506\n",
            "beta_stock2: 1.3666232752218668\n",
            "position_stk1: -1.3666232752218668\n",
            "times： 0.05253726332934927\n",
            "position_stk2: 1.085120061142506\n",
            "times： 0.019517517826393993\n",
            "beta_stock1: 1.083122331591102\n",
            "beta_stock2: 1.3663330644522327\n",
            "position_stk1: -1.3663330644522327\n",
            "times： -0.014747494833227126\n",
            "position_stk2: 1.083122331591102\n",
            "times： -0.016219106632603175\n",
            "beta_stock1: 1.085557700163668\n",
            "beta_stock2: 1.3667019028165497\n",
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            "times： -0.0031663962301919923\n",
            "position_stk2: 1.085557700163668\n",
            "times： 0.006891920759871217\n",
            "beta_stock1: 1.0838759438919836\n",
            "beta_stock2: 1.364768688915605\n",
            "position_stk1: -1.364768688915605\n",
            "times： -0.011839440796021394\n",
            "position_stk2: 1.0838759438919836\n",
            "times： -0.014897336981981146\n",
            "beta_stock1: 1.0817428657281751\n",
            "beta_stock2: 1.3632375982216365\n",
            "position_stk1: -1.3632375982216365\n",
            "times： -0.0020455783847801445\n",
            "position_stk2: 1.0817428657281751\n",
            "times： -0.0061308524289070965\n",
            "beta_stock1: 1.0820989028630141\n",
            "beta_stock2: 1.3641668533109073\n",
            "position_stk1: -1.3641668533109073\n",
            "times： 0.008784750326678914\n",
            "position_stk2: 1.0820989028630141\n",
            "times： 0.00767656218526791\n",
            "beta_stock1: 1.0813364116649142\n",
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            "beta_stock2: 1.0205588235695875\n",
            "position_stk1: 0.0\n",
            "times： -0.013523260230452805\n",
            "position_stk2: -0.0\n",
            "times： -0.013095126256757017\n",
            "beta_stock1: 0.9896739845624283\n",
            "beta_stock2: 1.0311700864169469\n",
            "position_stk1: 0.0\n",
            "times： 0.019374911186790023\n",
            "position_stk2: -0.0\n",
            "times： 0.014606029120712627\n",
            "beta_stock1: 0.9923565834674267\n",
            "beta_stock2: 1.0331513426398855\n",
            "position_stk1: 0.0\n",
            "times： -0.010937791022855303\n",
            "position_stk2: -0.0\n",
            "times： -0.0025344687910271446\n",
            "beta_stock1: 0.9916691329820597\n",
            "beta_stock2: 1.034679300061008\n",
            "position_stk1: 0.0\n",
            "times： -0.011602599554366488\n",
            "position_stk2: -0.0\n",
            "times： -0.016465115932834062\n",
            "beta_stock1: 0.9870763006087042\n",
            "beta_stock2: 1.0313076303809987\n",
            "position_stk1: 0.0\n",
            "times： -0.013756419547006793\n",
            "position_stk2: -0.0\n",
            "times： -0.010953783149373453\n",
            "beta_stock1: 0.9940325027845031\n",
            "beta_stock2: 1.0378287447470793\n",
            "position_stk1: 0.0\n",
            "times： 0.024363053043304772\n",
            "position_stk2: -0.0\n",
            "times： 0.02089645825024844\n",
            "beta_stock1: 1.007952182657296\n",
            "beta_stock2: 1.0469051861800855\n",
            "position_stk1: 0.0\n",
            "times： -0.0038126886744495048\n",
            "position_stk2: -0.0\n",
            "times： -0.0022515732551888767\n",
            "beta_stock1: 1.071431877364394\n",
            "beta_stock2: 1.0978037203092559\n",
            "position_stk1: 0.0\n",
            "times： -0.0058319610179228265\n",
            "position_stk2: -0.0\n",
            "times： -0.0044107120176231085\n",
            "beta_stock1: 1.0752314323695038\n",
            "beta_stock2: 1.1002828688076887\n",
            "position_stk1: 0.0\n",
            "times： -0.009165902969624717\n",
            "position_stk2: -0.0\n",
            "times： -0.00690292793939995\n",
            "beta_stock1: 1.0863888259792776\n",
            "beta_stock2: 1.0922969800075444\n",
            "position_stk1: 0.0\n",
            "times： 0.0007400724531354541\n",
            "position_stk2: -0.0\n",
            "times： -0.004046056563107747\n",
            "beta_stock1: 1.1133140700353032\n",
            "beta_stock2: 1.100154145541293\n",
            "position_stk1: 0.0\n",
            "times： -0.002033658902592883\n",
            "position_stk2: -0.0\n",
            "times： 0.0007291634877522046\n",
            "beta_stock1: 1.1059424091755572\n",
            "beta_stock2: 1.0960060681758927\n",
            "position_stk1: 0.0\n",
            "times： 0.025750266792070597\n",
            "position_stk2: -0.0\n",
            "times： 0.01977726170411631\n",
            "beta_stock1: 1.1028344499860834\n",
            "beta_stock2: 1.0913029940065566\n",
            "position_stk1: 0.0\n",
            "times： -0.014267601879529024\n",
            "position_stk2: -0.0\n",
            "times： -0.018066798513899585\n",
            "beta_stock1: 1.11644451934416\n",
            "beta_stock2: 1.103332201732731\n",
            "position_stk1: 0.0\n",
            "times： 0.033711914183991665\n",
            "position_stk2: -0.0\n",
            "times： 0.02120583139545129\n",
            "beta_stock1: 1.1229464821086141\n",
            "beta_stock2: 1.1112067585535577\n",
            "position_stk1: 0.0\n",
            "times： -0.010634500063690933\n",
            "position_stk2: -0.0\n",
            "times： -0.009364802411957363\n",
            "beta_stock1: 1.1451489711757339\n",
            "beta_stock2: 1.125654499486779\n",
            "position_stk1: 0.0\n",
            "times： -0.011823716428169484\n",
            "position_stk2: -0.0\n",
            "times： -0.005754187837544111\n",
            "beta_stock1: 1.1510530384781335\n",
            "beta_stock2: 1.1331128536042017\n",
            "position_stk1: 0.0\n",
            "times： -0.026649768396837525\n",
            "position_stk2: -0.0\n",
            "times： -0.020463035481808944\n",
            "beta_stock1: 1.1496030720583854\n",
            "beta_stock2: 1.1341859048340357\n",
            "position_stk1: 0.0\n",
            "times： -0.0009312579314478819\n",
            "position_stk2: -0.0\n",
            "times： 0.0007385492269018634\n",
            "beta_stock1: 1.1518526685689061\n",
            "beta_stock2: 1.1274833926027286\n",
            "position_stk1: 0.0\n",
            "times： 0.02926919673170625\n",
            "position_stk2: -0.0\n",
            "times： 0.02941487570280321\n",
            "beta_stock1: 1.1699683521562152\n",
            "beta_stock2: 1.1376284759463489\n",
            "position_stk1: 0.0\n",
            "times： 0.0338707259118205\n",
            "position_stk2: -0.0\n",
            "times： 0.02478490561319968\n",
            "beta_stock1: 1.1709183684075715\n",
            "beta_stock2: 1.11627560127011\n",
            "position_stk1: 0.0\n",
            "times： 0.008934798619010031\n",
            "position_stk2: -0.0\n",
            "times： 0.005396771224295938\n",
            "beta_stock1: 1.1820197336141007\n",
            "beta_stock2: 1.1231007727160274\n",
            "position_stk1: 0.0\n",
            "times： 0.02969263899881791\n",
            "position_stk2: -0.0\n",
            "times： 0.023558675770673165\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#building the output dataframe\n",
        "output252 = pd.DataFrame()\n",
        "output252['KPSS'] = KPSS_stats\n",
        "output252['signal'] = signals\n",
        "output252['gross'] = gross_returns\n",
        "output252['net'] = net_returns\n",
        "output252['market'] = market_returns"
      ],
      "metadata": {
        "id": "VI9EEFQAz-xY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(output252['market'],label='market')\n",
        "plt.plot(output252['net'],label='net')\n",
        "plt.plot(output252['KPSS'],label='KPSS')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "qwO9xLfv0DGX",
        "outputId": "8c362a8e-6307-4f21-ecac-1579f6d01f1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f8249478550>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#visualising the results\n",
        "plt.plot(np.append(1,np.cumprod(1+gross_returns)))\n",
        "plt.plot(np.append(1,np.cumprod(1+net_returns)))\n",
        "plt.plot(np.append(1,np.cumprod(1+market_returns)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "nfFPXiEr0uN8",
        "outputId": "ab975c9e-ee34-4b85-a85b-9d3c5fa5e187"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f82493791b0>]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Ref："
      ],
      "metadata": {
        "id": "-kPj9QxOkxJg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'position0 -> position_stk1'\n",
        "'position1 -> position_stk2'\n",
        "\n",
        "'rets0 -> rets_stk1'\n",
        "'rets1 -> rets_stk2'"
      ],
      "metadata": {
        "id": "B5NUFOEFhfta"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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        "output0.set_index(raw_data[21:-1].index)"
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              "                KPSS  signal     gross       net    market\n",
              "Date                                                      \n",
              "2019-10-29  0.076902     1.0  0.004463  0.004263  0.003253\n",
              "2019-10-30  0.055737     1.0 -0.000324 -0.000324 -0.003023\n",
              "2019-10-31  0.095836     1.0  0.029440  0.029440  0.009662\n",
              "2019-11-01  0.124835    -1.0  0.016382  0.015982  0.003704\n",
              "2019-11-04  0.067337    -1.0 -0.016769 -0.016769 -0.001186\n",
              "...              ...     ...       ...       ...       ...\n",
              "2021-09-20  0.150360     0.0  0.000000  0.000000 -0.000812\n",
              "2021-09-21  0.066362    -1.0  0.000146 -0.000054  0.009520\n",
              "2021-09-22  0.117177    -1.0 -0.009086 -0.009086  0.012135\n",
              "2021-09-23  0.104602    -1.0 -0.003538 -0.003538  0.001461\n",
              "2021-09-24  0.087983     1.0  0.006134  0.005734 -0.002776\n",
              "\n",
              "[481 rows x 5 columns]"
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              "      <th>KPSS</th>\n",
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              "      <th>2019-10-29</th>\n",
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              "      <th>2021-09-20</th>\n",
              "      <td>0.150360</td>\n",
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              "      <th>2021-09-21</th>\n",
              "      <td>0.066362</td>\n",
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              "      <td>-0.000054</td>\n",
              "      <td>0.009520</td>\n",
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              "      <th>2021-09-22</th>\n",
              "      <td>0.117177</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.009086</td>\n",
              "      <td>-0.009086</td>\n",
              "      <td>0.012135</td>\n",
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              "      <th>2021-09-23</th>\n",
              "      <td>0.104602</td>\n",
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              "      <td>-0.003538</td>\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
            "beta_stock1: 1.119844314731032\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
            "beta_stock1: 1.1713808036999303\n",
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            "===========\n",
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            "===========\n",
            "stop-loss triggered\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
            "stop-loss triggered\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
            "beta_stock1: 2.063638356985714\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
            "stop-loss triggered\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
            "stop-loss triggered\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
            "stop-loss triggered\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "===========\n",
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            "beta_stock2: 1.4144187032596611\n",
            "position_stk1: 1.4144187032596611\n",
            "times_ret1： -0.006792943560004594\n",
            "position_stk2: -1.7557712188275274\n",
            "times_ret2： -0.0018826279771989585\n",
            "===========\n",
            "beta_stock1: 1.608761429793728\n",
            "beta_stock2: 1.2547512547082642\n",
            "position_stk1: 1.2547512547082642\n",
            "times_ret1： -0.021058350516978686\n",
            "position_stk2: -1.608761429793728\n",
            "times_ret2： -0.04000800431239149\n",
            "===========\n",
            "beta_stock1: 1.7482518851920492\n",
            "beta_stock2: 1.4886788073936015\n",
            "position_stk1: -1.4886788073936015\n",
            "times_ret1： -0.03052031377165343\n",
            "position_stk2: 1.7482518851920492\n",
            "times_ret2： -0.02492240029753856\n",
            "===========\n",
            "beta_stock1: 1.4354529898195523\n",
            "beta_stock2: 1.7412139203902073\n",
            "position_stk1: -1.7412139203902073\n",
            "times_ret1： 0.00018968587743795062\n",
            "position_stk2: 1.4354529898195523\n",
            "times_ret2： 0.0001060784402731052\n",
            "===========\n",
            "beta_stock1: 1.4198368092272178\n",
            "beta_stock2: 1.7346369936199384\n",
            "position_stk1: -1.7346369936199384\n",
            "times_ret1： 0.04114520477596617\n",
            "position_stk2: 1.4198368092272178\n",
            "times_ret2： 0.02576882530790625\n",
            "===========\n",
            "beta_stock1: 1.534833061261044\n",
            "beta_stock2: 1.8442917265756662\n",
            "position_stk1: -1.8442917265756662\n",
            "times_ret1： 0.008195242472954778\n",
            "position_stk2: 1.534833061261044\n",
            "times_ret2： 0.01147516739828025\n",
            "===========\n",
            "beta_stock1: 1.9108771374463878\n",
            "beta_stock2: 1.9669186448590168\n",
            "position_stk1: -1.9669186448590168\n",
            "times_ret1： 0.0018063307987463695\n",
            "position_stk2: 1.9108771374463878\n",
            "times_ret2： 0.006847974483011887\n",
            "===========\n",
            "beta_stock1: 1.947993775366427\n",
            "beta_stock2: 2.087533120219191\n",
            "position_stk1: -2.087533120219191\n",
            "times_ret1： -0.013523260230452805\n",
            "position_stk2: 1.947993775366427\n",
            "times_ret2： -0.013095126256757017\n",
            "===========\n",
            "beta_stock1: 1.9799051379398456\n",
            "beta_stock2: 2.1244465886670967\n",
            "position_stk1: -2.1244465886670967\n",
            "times_ret1： 0.019374911186790023\n",
            "position_stk2: 1.9799051379398456\n",
            "times_ret2： 0.014606029120712627\n",
            "===========\n",
            "beta_stock1: 1.8913458620707624\n",
            "beta_stock2: 2.0323145157389764\n",
            "position_stk1: -2.0323145157389764\n",
            "times_ret1： -0.010937791022855303\n",
            "position_stk2: 1.8913458620707624\n",
            "times_ret2： -0.0025344687910271446\n",
            "===========\n",
            "beta_stock1: 1.889222221231448\n",
            "beta_stock2: 2.13156716626758\n",
            "position_stk1: 0.0\n",
            "times_ret1： -0.011602599554366488\n",
            "position_stk2: -0.0\n",
            "times_ret2： -0.016465115932834062\n",
            "===========\n",
            "beta_stock1: 1.8633823166085373\n",
            "beta_stock2: 2.1365580549519834\n",
            "position_stk1: 0.0\n",
            "times_ret1： -0.013756419547006793\n",
            "position_stk2: -0.0\n",
            "times_ret2： -0.010953783149373453\n",
            "===========\n",
            "beta_stock1: 1.8727421266058304\n",
            "beta_stock2: 2.2173895621476545\n",
            "position_stk1: 0.0\n",
            "times_ret1： 0.024363053043304772\n",
            "position_stk2: -0.0\n",
            "times_ret2： 0.02089645825024844\n",
            "===========\n",
            "beta_stock1: 1.7698229192821706\n",
            "beta_stock2: 2.1414555802013173\n",
            "position_stk1: 0.0\n",
            "times_ret1： -0.0038126886744495048\n",
            "position_stk2: -0.0\n",
            "times_ret2： -0.0022515732551888767\n",
            "===========\n",
            "beta_stock1: 1.845620593507264\n",
            "beta_stock2: 2.194121805396671\n",
            "position_stk1: 0.0\n",
            "times_ret1： -0.0058319610179228265\n",
            "position_stk2: -0.0\n",
            "times_ret2： -0.0044107120176231085\n",
            "===========\n",
            "beta_stock1: 1.8265244119812487\n",
            "beta_stock2: 2.174811039160211\n",
            "position_stk1: 0.0\n",
            "times_ret1： -0.009165902969624717\n",
            "position_stk2: -0.0\n",
            "times_ret2： -0.00690292793939995\n",
            "===========\n",
            "beta_stock1: 1.7589198700376052\n",
            "beta_stock2: 2.062869015572594\n",
            "position_stk1: 0.0\n",
            "times_ret1： 0.0007400724531354541\n",
            "position_stk2: -0.0\n",
            "times_ret2： -0.004046056563107747\n",
            "===========\n",
            "beta_stock1: 1.7655552413749223\n",
            "beta_stock2: 2.0593656531268616\n",
            "position_stk1: 0.0\n",
            "times_ret1： -0.002033658902592883\n",
            "position_stk2: -0.0\n",
            "times_ret2： 0.0007291634877522046\n",
            "===========\n",
            "beta_stock1: 1.6119011833552515\n",
            "beta_stock2: 1.9416962538903808\n",
            "position_stk1: 0.0\n",
            "times_ret1： 0.025750266792070597\n",
            "position_stk2: -0.0\n",
            "times_ret2： 0.01977726170411631\n",
            "===========\n",
            "beta_stock1: 1.5430434095288736\n",
            "beta_stock2: 1.6978328996665608\n",
            "position_stk1: 0.0\n",
            "times_ret1： -0.014267601879529024\n",
            "position_stk2: -0.0\n",
            "times_ret2： -0.018066798513899585\n",
            "===========\n",
            "beta_stock1: 1.6709916473544637\n",
            "beta_stock2: 1.7919718462004313\n",
            "position_stk1: 0.0\n",
            "times_ret1： 0.033711914183991665\n",
            "position_stk2: -0.0\n",
            "times_ret2： 0.02120583139545129\n",
            "===========\n",
            "beta_stock1: 1.7953987881351712\n",
            "beta_stock2: 1.9135555495379992\n",
            "position_stk1: -1.9135555495379992\n",
            "times_ret1： -0.010634500063690933\n",
            "position_stk2: 1.7953987881351712\n",
            "times_ret2： -0.009364802411957363\n",
            "===========\n",
            "beta_stock1: 2.117678403872862\n",
            "beta_stock2: 2.164541706876636\n",
            "position_stk1: 0.0\n",
            "times_ret1： -0.011823716428169484\n",
            "position_stk2: -0.0\n",
            "times_ret2： -0.005754187837544111\n",
            "===========\n",
            "beta_stock1: 2.194026988115661\n",
            "beta_stock2: 1.7928850084508574\n",
            "position_stk1: 0.0\n",
            "times_ret1： -0.026649768396837525\n",
            "position_stk2: -0.0\n",
            "times_ret2： -0.020463035481808944\n",
            "===========\n",
            "beta_stock1: 2.1036107236735173\n",
            "beta_stock2: 1.6396414061402564\n",
            "position_stk1: 0.0\n",
            "times_ret1： -0.0009312579314478819\n",
            "position_stk2: -0.0\n",
            "times_ret2： 0.0007385492269018634\n",
            "===========\n",
            "beta_stock1: 2.1595257438269178\n",
            "beta_stock2: 1.6602823746439628\n",
            "position_stk1: -1.6602823746439628\n",
            "times_ret1： 0.02926919673170625\n",
            "position_stk2: 2.1595257438269178\n",
            "times_ret2： 0.02941487570280321\n",
            "===========\n",
            "beta_stock1: 1.8955735769415694\n",
            "beta_stock2: 1.5280808451548495\n",
            "position_stk1: -1.5280808451548495\n",
            "times_ret1： 0.0338707259118205\n",
            "position_stk2: 1.8955735769415694\n",
            "times_ret2： 0.02478490561319968\n",
            "===========\n",
            "beta_stock1: 2.0025642772523513\n",
            "beta_stock2: 1.673213134543773\n",
            "position_stk1: -1.673213134543773\n",
            "times_ret1： 0.008934798619010031\n",
            "position_stk2: 2.0025642772523513\n",
            "times_ret2： 0.005396771224295938\n",
            "===========\n",
            "beta_stock1: 2.1064800213337787\n",
            "beta_stock2: 1.7026086614714857\n",
            "position_stk1: 1.7026086614714857\n",
            "times_ret1： 0.02969263899881791\n",
            "position_stk2: -2.1064800213337787\n",
            "times_ret2： 0.023558675770673165\n",
            "===========\n"
          ]
        }
      ],
      "source": [
        "#moving in a loop through the sample\n",
        "for t in range(window, len(raw_data)-1): ###\n",
        "    old_signal = signal\n",
        "    old_position0 = position0\n",
        "    old_position1 = position1\n",
        "\n",
        "    #specifying the subsample\n",
        "    data = raw_data[t-window:t]\n",
        "    #stock 2 = a + b*stock 1\n",
        "    ####\n",
        "    #OLS parameters as starting values\n",
        "    reg = sm.OLS(np.array(data[tickers[1]]), sm.add_constant(np.array(data[tickers[0]])))\n",
        "    res = reg.fit()\n",
        "    a0 = res.params[0]\n",
        "    b0 = res.params[1] ###\n",
        "    ####\n",
        "\n",
        "    if unbiased == 1:\n",
        "        #defining the KPSS function (unbiased one-parameter forecast)\n",
        "        def KPSS(b):\n",
        "            a = np.average(data[tickers[1]] - b*data[tickers[0]])\n",
        "            resid = np.array(data[tickers[1]] - (a + b*data[tickers[0]]))\n",
        "            cum_resid = np.cumsum(resid)\n",
        "            st_error = (np.sum(resid**2)/(len(resid)-2))**(1/2)\n",
        "            KPSS = np.sum(cum_resid**2)/(len(resid)**2*st_error**2)\n",
        "            return KPSS\n",
        "        #minimising the KPSS function (maximising the stationarity)\n",
        "        res = spop.minimize(KPSS, b0, method='Nelder-Mead')\n",
        "        KPSS_opt = res.fun\n",
        "        #retrieving optimal parameters\n",
        "        b_opt = float(res.x)\n",
        "        a_opt = np.average(data[tickers[1]] - b_opt*data[tickers[0]])\n",
        "\n",
        "    else:\n",
        "        #defining the KPSS function (two-parameter)\n",
        "        def KPSS2(kpss_params):\n",
        "            a = kpss_params[0]\n",
        "            b = kpss_params[1]\n",
        "            resid = np.array(data[tickers[1]] - (a + b*data[tickers[0]]))\n",
        "            cum_resid = np.cumsum(resid)\n",
        "            st_error = (np.sum(resid**2)/(len(resid)-2))**(1/2)\n",
        "            KPSS = np.sum(cum_resid**2)/(len(resid)**2*st_error**2)\n",
        "            return KPSS\n",
        "        #minimising the KPSS function (maximising the stationarity)\n",
        "        res = spop.minimize(KPSS2, [a0, b0], method='Nelder-Mead')\n",
        "        #retrieving optimal parameters\n",
        "        KPSS_opt = res.fun\n",
        "        a_opt = res.x[0]\n",
        "        b_opt = res.x[1]\n",
        "\n",
        "    ##DF unit test # Detrending by Differencing\n",
        "\n",
        "    print('optimal slope:',b_opt)\n",
        "    #simulate trading\n",
        "    #first check whether stop-loss is violated\n",
        "    if current_return < stop_loss:\n",
        "        signal = 0\n",
        "        print('stop-loss triggered')\n",
        "    #if we are already in position, check whether the equilibrium is restored, continue in position if not\n",
        "    elif np.sign(raw_data[tickers[1]][t] - (a_opt + b_opt*raw_data[tickers[0]][t])) == old_signal:\n",
        "        singal = old_signal\n",
        "    else:\n",
        "        #only trade if the pair is cointegrated\n",
        "        if KPSS_opt > KPSS_max:\n",
        "            signal = 0\n",
        "        #only trade if there are large enough profit opportunities (optimal entry)\n",
        "        elif abs(raw_data[tickers[1]][t]/(a_opt + b_opt*raw_data[tickers[0]][t])-1) < entry:\n",
        "            signal = 0\n",
        "        else:\n",
        "            signal = np.sign(raw_data[tickers[1]][t] - (a_opt + b_opt*raw_data[tickers[0]][t])) # stocks diverge enough (>entry) to decide long/short the pair (stock2 to stock1)\n",
        "    #calculate strategy returns with beta loading\n",
        "    if beta_loading == 1:\n",
        "        rets0 = np.array(raw_data[tickers[0]][t-window:t-1])/np.array(raw_data[tickers[0]][t-window+1:t]) - 1\n",
        "        rets1 = np.array(raw_data[tickers[1]][t-window:t-1])/np.array(raw_data[tickers[1]][t-window+1:t]) - 1\n",
        "        rets_mkt = np.array(raw_data['market'][t-window:t-1])/np.array(raw_data['market'][t-window+1:t]) - 1\n",
        "        reg = sm.OLS(rets0, sm.add_constant(rets_mkt))\n",
        "        res = reg.fit()\n",
        "        beta0 = res.params[1]\n",
        "        print('beta_stock1:',beta0)\n",
        "        reg = sm.OLS(rets1, sm.add_constant(rets_mkt))\n",
        "        res = reg.fit()\n",
        "        beta1 = res.params[1]\n",
        "        print('beta_stock2:',beta1)\n",
        "        position0 = beta1*signal\n",
        "        position1 = -beta0*signal\n",
        "    #calculate strategy returns without beta loading\n",
        "    else:\n",
        "        position0 = signal\n",
        "        position1 = -signal\n",
        "    print('position_stk1:',position0)\n",
        "    print('times_ret1：',(raw_data[tickers[0]][t+1]/raw_data[tickers[0]][t] - 1))\n",
        "    print('position_stk2:',position1)\n",
        "    print('times_ret2：',(raw_data[tickers[1]][t+1]/raw_data[tickers[1]][t] - 1))\n",
        "    print(\"===========\")\n",
        "    #calculating returns\n",
        "    gross = position0*(raw_data[tickers[0]][t+1]/raw_data[tickers[0]][t] - 1) + position1*(raw_data[tickers[1]][t+1]/raw_data[tickers[1]][t] - 1)\n",
        "    net = gross - fee*(abs(position0 - old_position0) + abs(position1 - old_position1))\n",
        "    market = raw_data['market'][t+1]/raw_data['market'][t] - 1\n",
        "    if signal == old_signal:\n",
        "        current_return = (1+current_return)*(1+gross)-1\n",
        "    else:\n",
        "        current_return = gross\n",
        "    #populating arrays\n",
        "    KPSS_stats = np.append(KPSS_stats, KPSS_opt)\n",
        "    signals = np.append(signals, signal)\n",
        "    gross_returns = np.append(gross_returns, gross)\n",
        "    net_returns = np.append(net_returns, net)\n",
        "    market_returns = np.append(market_returns, market)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Convergen Entry"
      ],
      "metadata": {
        "id": "TIv-oKhrDdAE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "raw_data.corr()"
      ],
      "metadata": {
        "id": "BWrrCKQ7Eb9w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#moving in a loop through the sample\n",
        "for t in range(window, len(raw_data)-1): ###\n",
        "    old_signal = signal\n",
        "    old_position0 = position0\n",
        "    old_position1 = position1\n",
        "\n",
        "    #specifying the subsample\n",
        "    data = raw_data[t-window:t]\n",
        "\n",
        "    #stock 2 = a + b* stock 1\n",
        "    #OLS parameters as starting values\n",
        "    reg = sm.OLS(np.array(data[stock2]), sm.add_constant(np.array(data[stock1])))\n",
        "    res = reg.fit()\n",
        "    a0 = res.params[0]\n",
        "    b0 = res.params[1] ###\n",
        "    ####\n",
        "\n",
        "    if unbiased == 1:\n",
        "        #defining the KPSS function (unbiased one-parameter forecast)\n",
        "        def KPSS(b):\n",
        "            a = np.average(data[stock2] - b*data[stock1])\n",
        "            resid = np.array(data[stock2] - (a + b*data[stock1]))\n",
        "            cum_resid = np.cumsum(resid)\n",
        "            st_error = (np.sum(resid**2)/(len(resid)-2))**(1/2)\n",
        "            KPSS = np.sum(cum_resid**2)/(len(resid)**2*st_error**2)\n",
        "            return KPSS\n",
        "        #minimising the KPSS function (maximising the stationarity)\n",
        "        res = spop.minimize(KPSS, b0, method='Nelder-Mead')\n",
        "        KPSS_opt = res.fun\n",
        "        #retrieving optimal parameters\n",
        "        b_opt = float(res.x)\n",
        "        a_opt = np.average(data[stock2] - b_opt*data[stock1])\n",
        "\n",
        "    else:\n",
        "        #defining the KPSS function (two-parameter)\n",
        "        def KPSS2(kpss_params):\n",
        "            a = kpss_params[0]\n",
        "            b = kpss_params[1]\n",
        "            resid = np.array(data[stock2] - (a + b*data[stock1]))\n",
        "            cum_resid = np.cumsum(resid)\n",
        "            st_error = (np.sum(resid**2)/(len(resid)-2))**(1/2)\n",
        "            KPSS = np.sum(cum_resid**2)/(len(resid)**2*st_error**2)\n",
        "            return KPSS\n",
        "        #minimising the KPSS function (maximising the stationarity)\n",
        "        res = spop.minimize(KPSS2, [a0, b0], method='Nelder-Mead')\n",
        "        #retrieving optimal parameters\n",
        "        KPSS_opt = res.fun\n",
        "        a_opt = res.x[0]\n",
        "        b_opt = res.x[1]\n",
        "\n",
        "\n",
        "    print('optimal slope:',b_opt)\n",
        "    #simulate trading\n",
        "    #first check whether stop-loss is violated\n",
        "    if current_return < stop_loss:\n",
        "        signal = 0\n",
        "        print('stop-loss triggered')\n",
        "    #if we are already in position, check whether the equilibrium is restored, continue in position if not\n",
        "    elif np.sign(raw_data[stock2][t] - (a_opt + b_opt*raw_data[stock1][t])) == old_signal:\n",
        "        singal = old_signal\n",
        "    else:\n",
        "        #only trade if the pair is cointegrated\n",
        "        'if KPSS_opt < KPSS_max:'\n",
        "        if KPSS_opt > KPSS_max:\n",
        "            signal = 0\n",
        "        #only trade if there are large enough profit opportunities (optimal entry)\n",
        "        'elif abs(raw_data[stock2][t]/(a_opt + b_opt*raw_data[stock1][t])-1) > entry:'\n",
        "        elif abs(raw_data[stock2][t]/(a_opt + b_opt*raw_data[stock1][t])-1) < entry:\n",
        "            signal = 0\n",
        "        #comparing % ($stock2 to dynamic equilibrium -1) diverge  large enough (>entry) and stock1 diverge relatively\n",
        "\n",
        "        else:\n",
        "            signal = np.sign(raw_data[stock2][t] - (a_opt + b_opt*raw_data[stock1][t]))\n",
        "        #signal to decide long/short the pair (stock2 / stock1)\n",
        "\n",
        "    #calculate strategy returns with beta loading\n",
        "    if beta_loading == 1:\n",
        "        rets_stk1 = np.array(raw_data[stock1][t-window:t-1])/np.array(raw_data[stock1][t-window+1:t]) - 1\n",
        "        rets_stk2 = np.array(raw_data[stock2][t-window:t-1])/np.array(raw_data[stock2][t-window+1:t]) - 1\n",
        "        rets_mkt = np.array(raw_data['market'][t-window:t-1])/np.array(raw_data['market'][t-window+1:t]) - 1\n",
        "        reg = sm.OLS(rets_stk1, sm.add_constant(rets_mkt))\n",
        "        res = reg.fit()\n",
        "        beta_stk1 = res.params[1]\n",
        "        print('beta_stock1:',beta_stk1)\n",
        "        reg = sm.OLS(rets_stk2, sm.add_constant(rets_mkt))\n",
        "        res = reg.fit()\n",
        "        beta_stk2 = res.params[1]\n",
        "        print('beta_stock2:',beta_stk2)\n",
        "        position0 = beta_stk2*signal\n",
        "        position1 = -beta_stk1*signal\n",
        "    #calculate strategy returns without beta loading\n",
        "    else:\n",
        "        position0 = signal\n",
        "        position1 = -signal\n",
        "    print(f'position_{stock1}:',position0)\n",
        "    print('times_ret1：',(raw_data[stock1][t+1]/raw_data[stock1][t] - 1))\n",
        "    print(f'position_{stock2}:',position1)\n",
        "    print('times_ret2：',(raw_data[stock2][t+1]/raw_data[stock2][t] - 1))\n",
        "    print(\"===========\")\n",
        "    #calculating returns\n",
        "    gross = position0*(raw_data[stock1][t+1]/raw_data[stock1][t] - 1) + position1*(raw_data[stock2][t+1]/raw_data[stock2][t] - 1)\n",
        "    net = gross - fee*(abs(position0 - old_position0) + abs(position1 - old_position1))\n",
        "    market = raw_data['market'][t+1]/raw_data['market'][t] - 1\n",
        "    if signal == old_signal:\n",
        "        current_return = (1+current_return)*(1+gross)-1\n",
        "    else:\n",
        "        current_return = gross\n",
        "    #populating arrays\n",
        "    #DF_stats = np.append(DF_stats, DF_opt)\n",
        "    KPSS_stats = np.append(KPSS_stats, KPSS_opt)\n",
        "    signals = np.append(signals, signal)\n",
        "    gross_returns = np.append(gross_returns, gross)\n",
        "    net_returns = np.append(net_returns, net)\n",
        "    market_returns = np.append(market_returns, market)\n",
        "\n",
        "    #interface: reporting daily positions and realised returns\n",
        "    print('day '+str(raw_data.index[t]))\n",
        "    print('')\n",
        "    print('old_signal:',old_position0, '→signal:',position0)\n",
        "    if position0 == old_position0: #signal == 0: #position0 == 0:\n",
        "        print('no trading')\n",
        "    elif  position0 > old_position0: #signal == 1: # position0 > 0:\n",
        "        print(f'buy {stock1}:',(position0 - old_position0),'*',raw_data[stock1][t],f' and sell {stock2}:',abs(position1 - old_position1),'*',raw_data[stock2][t])\n",
        "        print('long position on '+stock1+' and short position on '+stock2)\n",
        "        traded_date = raw_data.index[t]\n",
        "    else:\n",
        "        print(f'buy {stock2}:',(position1 - old_position1),'*',raw_data[stock1][t],f' and sell {stock1}:',abs(position0 - old_position0),'*',raw_data[stock1][t])\n",
        "        print('long position on '+stock2+' and short position on '+stock1)\n",
        "        traded_date = raw_data.index[t]\n",
        "\n",
        "    traded_dates = np.append(traded_dates, traded_date)\n",
        "    print('gross daily return: '+str(round(gross*100,2))+'%')\n",
        "    print('net daily return: '+str(round(net*100,2))+'%')\n",
        "    print('cumulative net return so far: '+str(round(np.prod(1+net)*100-100,2))+'%')\n",
        "    print('—————————next day—————————')"
      ],
      "metadata": {
        "id": "5ERhAsUsDcIP"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}