Pairs_trading_backtesting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aae6fdec",
   "metadata": {},
   "outputs": [],
   "source": [
    "def account(data, cap, fee):\n",
    "    data1 = data.copy()\n",
    "    data1['cash'] = cap\n",
    "    data1['fee'] = np.where(data1['buyorsell'] != 0, fee, 0)\n",
    "    data1['open_position'] = 0.0\n",
    "    data1['traded_nominal_val'] = 0.0\n",
    "    data1['unrealised_P&L'] = 0.0\n",
    "    data1['cashflow'] = 0.0\n",
    "    data1['cash_p&l'] = 0.0\n",
    "    data1['total_assets'] = data1['cash'] + data1['traded_nominal_val']\n",
    "    data1['net_assets'] = 0.0\n",
    "    \n",
    "    for i in range(1, len(data1)):\n",
    "        \n",
    "        if data1['buyorsell'][i] == 1:\n",
    "            \n",
    "            if data['pos'][i] == 1: # buy to open a long position\n",
    "                \n",
    "                data1['open_position'][i:] = data1['pos'][i] * ((data1['cash'][i] - data1['fee'][i]) // data1['spread_cap_req'][i])\n",
    "\n",
    "                data1['traded_nominal_val'][i:] = data1['open_position'][i] * data1['spot_spread'][i]\n",
    "                \n",
    "                data1['cashflow'][i] = (-data1['traded_nominal_val'][i] - data1['fee'][i])\n",
    "\n",
    "                data1['cash'][i:] = data1['cash'][i-1] + data1['cashflow'][i]\n",
    "\n",
    "                data1['total_assets'][i:] = data1['cash'][i] + data1['traded_nominal_val'][i]\n",
    "                \n",
    "            elif data['pos'][i] == 0: # buy to close a short position\n",
    "                \n",
    "                data1['open_position'][i:] = data1['pos'][i]\n",
    "\n",
    "                data1['traded_nominal_val'][i:] = data1['open_position'][i] * data1['spot_spread'][i]\n",
    "\n",
    "                data1['cashflow'][i] = (((data1['open_position'][i-1] * data1['spot_spread'][i] ) - data1['fee'][i]) - data1['traded_nominal_val'][i-1])\n",
    "\n",
    "                data1['cash'][i:] = data1['cash'][i-1] + data1['cashflow'][i]\n",
    "\n",
    "                data1['total_assets'][i:] = data1['cash'][i] + data1['traded_nominal_val'][i]\n",
    "\n",
    "        elif data1['buyorsell'][i] == -1:\n",
    "            \n",
    "            if data['pos'][i] == -1: # sell to open a short position\n",
    "                \n",
    "                data1['open_position'][i:] = data1['pos'][i] * ((data1['cash'][i] - data1['fee'][i]) // data1['spread_cap_req'][i])\n",
    "\n",
    "                data1['traded_nominal_val'][i:] = data1['open_position'][i] * data1['spot_spread'][i]\n",
    "\n",
    "                data1['cashflow'][i] = ( - data1['fee'][i])\n",
    "\n",
    "                data1['cash'][i:] = data1['cash'][i-1] + data1['cashflow'][i]\n",
    "\n",
    "                data1['total_assets'][i:] = data1['cash'][i]\n",
    "                \n",
    "                \n",
    "            elif data['pos'][i] == 0: # sell to close a long position\n",
    "                \n",
    "                data1['open_position'][i:] = data1['pos'][i]\n",
    "                \n",
    "                data1['traded_nominal_val'][i:] = data1['open_position'][i] * data1['spot_spread'][i]\n",
    "                \n",
    "                data1['cashflow'][i] = ((data1['open_position'][i-1] * data1['spot_spread'][i]) - data1['fee'][i])\n",
    "                \n",
    "                data1['cash'][i:] = data1['cash'][i-1] + data1['cashflow'][i] - data1['traded_nominal_val'][i]\n",
    "                \n",
    "                data1['total_assets'][i:] = data1['cash'][i] + data1['traded_nominal_val'][i]                                                        \n",
    "    \n",
    "    \n",
    "    data1['cash_p&l'] = data1['total_assets'].diff()\n",
    "    data1['unrealised_P&L'] = (data1['open_position'] * data1['spot_spread']) - data1['traded_nominal_val']  \n",
    "    data1['net_assets'] = data1['total_assets'] + data1['unrealised_P&L']\n",
    "    data1['strat_acc_return_pct_chg'] = (data1['net_assets']).pct_change()\n",
    "    data1['strat_acc_return_nplog_net_assets'] = (data1['net_assets'] / data1['net_assets'].shift(1)).apply(np.log)\n",
    "    data1['cumsum_strat_acc'] = data1['strat_acc_return_nplog_net_assets'].cumsum().apply(np.exp)\n",
    "    data1['max_performance_acc'] = data1['cumsum_strat_acc'].cummax()\n",
    "    data1['drawdown_acc'] = data1['max_performance_acc'] - data1['cumsum_strat_acc']\n",
    "\n",
    "    \n",
    "    return data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "04c91f74",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.tsa.arima.model as stats\n",
    "\n",
    "from pandas.plotting import scatter_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b30a31b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')\n",
    "mpl.rcParams['axes.facecolor'] = 'white'\n",
    "mpl.rcParams['grid.color'] = 'grey'\n",
    "mpl.rcParams['grid.alpha'] = 0.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "20e9f483",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('once')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8435a6d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import adfuller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fb28324a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Li/opt/anaconda3/lib/python3.9/site-packages/pandas_datareader/compat/__init__.py:11: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  PANDAS_VERSION = LooseVersion(pd.__version__)\n"
     ]
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "import pandas_datareader.data as pdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7862b77d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract(tickers, s, e):\n",
    "    def data(t):\n",
    "        return pdr.get_data_yahoo(t, s, e)[['Close']]\n",
    "    \n",
    "    stock_data = map(data, tickers)\n",
    "    data_con = pd.concat(stock_data, axis = 1)\n",
    "    data_con.columns = tickers\n",
    "    return data_con"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1346487c",
   "metadata": {},
   "outputs": [],
   "source": [
    "stocks = ['ING', 'MS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d3b14913",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Li/opt/anaconda3/lib/python3.9/site-packages/pandas_datareader/data.py:80: ResourceWarning: unclosed <ssl.SSLSocket fd=79, family=AddressFamily.AF_INET6, type=SocketKind.SOCK_STREAM, proto=0, laddr=('2404:e801:2001:3973:492a:d11b:85d6:57de', 49867, 0, 0), raddr=('2406:2000:98:800::e6', 443, 0, 0)>\n",
      "  return YahooDailyReader(*args, **kwargs).read()\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
      "/Users/Li/opt/anaconda3/lib/python3.9/site-packages/pandas_datareader/data.py:80: ResourceWarning: unclosed <ssl.SSLSocket fd=79, family=AddressFamily.AF_INET6, type=SocketKind.SOCK_STREAM, proto=0, laddr=('2404:e801:2001:3973:492a:d11b:85d6:57de', 49868, 0, 0), raddr=('2406:2000:98:800::e6', 443, 0, 0)>\n",
      "  return YahooDailyReader(*args, **kwargs).read()\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n"
     ]
    }
   ],
   "source": [
    "df = extract(stocks, '2020-1-1', '2022-1-1')\n",
    "df.columns = stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a7db020e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ING    0.0\n",
       "MS     0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.isnull() == True].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e172f339",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ING</th>\n",
       "      <th>MS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>12.05</td>\n",
       "      <td>51.119999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>12.43</td>\n",
       "      <td>52.040001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>12.20</td>\n",
       "      <td>51.200001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>12.19</td>\n",
       "      <td>51.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>12.21</td>\n",
       "      <td>50.919998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-27</th>\n",
       "      <td>13.91</td>\n",
       "      <td>100.400002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-28</th>\n",
       "      <td>13.86</td>\n",
       "      <td>99.970001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-29</th>\n",
       "      <td>13.89</td>\n",
       "      <td>98.730003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>13.88</td>\n",
       "      <td>98.800003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>13.92</td>\n",
       "      <td>98.160004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              ING          MS\n",
       "Date                         \n",
       "2019-12-31  12.05   51.119999\n",
       "2020-01-02  12.43   52.040001\n",
       "2020-01-03  12.20   51.200001\n",
       "2020-01-06  12.19   51.020000\n",
       "2020-01-07  12.21   50.919998\n",
       "...           ...         ...\n",
       "2021-12-27  13.91  100.400002\n",
       "2021-12-28  13.86   99.970001\n",
       "2021-12-29  13.89   98.730003\n",
       "2021-12-30  13.88   98.800003\n",
       "2021-12-31  13.92   98.160004\n",
       "\n",
       "[506 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4d5b5110",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = sm.OLS(df[stocks[0]], sm.add_constant(df[stocks[1]])).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "633d794c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const    1.719641\n",
       "MS       0.125158\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a78e6db6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "43b8a8af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>ING</td>       <th>  R-squared:         </th> <td>   0.859</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.859</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   3068.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 04 Nov 2022</td> <th>  Prob (F-statistic):</th> <td>1.80e-216</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:15:57</td>     <th>  Log-Likelihood:    </th> <td> -788.70</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   1581.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   504</td>      <th>  BIC:               </th> <td>   1590.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    1.7196</td> <td>    0.166</td> <td>   10.374</td> <td> 0.000</td> <td>    1.394</td> <td>    2.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MS</th>    <td>    0.1252</td> <td>    0.002</td> <td>   55.392</td> <td> 0.000</td> <td>    0.121</td> <td>    0.130</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>130.351</td> <th>  Durbin-Watson:     </th> <td>   0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 267.136</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.401</td>  <th>  Prob(JB):          </th> <td>9.82e-59</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.194</td>  <th>  Cond. No.          </th> <td>    237.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    ING   R-squared:                       0.859\n",
       "Model:                            OLS   Adj. R-squared:                  0.859\n",
       "Method:                 Least Squares   F-statistic:                     3068.\n",
       "Date:                Fri, 04 Nov 2022   Prob (F-statistic):          1.80e-216\n",
       "Time:                        17:15:57   Log-Likelihood:                -788.70\n",
       "No. Observations:                 506   AIC:                             1581.\n",
       "Df Residuals:                     504   BIC:                             1590.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          1.7196      0.166     10.374      0.000       1.394       2.045\n",
       "MS             0.1252      0.002     55.392      0.000       0.121       0.130\n",
       "==============================================================================\n",
       "Omnibus:                      130.351   Durbin-Watson:                   0.027\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              267.136\n",
       "Skew:                           1.401   Prob(JB):                     9.82e-59\n",
       "Kurtosis:                       5.194   Cond. No.                         237.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3b0215e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2019-12-31    3.932296\n",
       "2020-01-02    4.197151\n",
       "2020-01-03    4.072283\n",
       "2020-01-06    4.084811\n",
       "2020-01-07    4.117327\n",
       "                ...   \n",
       "2021-12-27   -0.375478\n",
       "2021-12-28   -0.371660\n",
       "2021-12-29   -0.186464\n",
       "2021-12-30   -0.205226\n",
       "2021-12-31   -0.085125\n",
       "Length: 506, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.resid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "db417ae2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-3.4648243475647793,\n",
       " 0.008937792493588096,\n",
       " 0,\n",
       " 505,\n",
       " {'1%': -3.44336554668393,\n",
       "  '5%': -2.867280279529963,\n",
       "  '10%': -2.5698273512400744},\n",
       " -231.30698289392933)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adfuller(model.resid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5a1f7985",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Li/opt/anaconda3/lib/python3.9/site-packages/pandas_datareader/data.py:80: ResourceWarning: unclosed <ssl.SSLSocket fd=79, family=AddressFamily.AF_INET6, type=SocketKind.SOCK_STREAM, proto=0, laddr=('2404:e801:2001:3973:492a:d11b:85d6:57de', 49869, 0, 0), raddr=('2406:2000:98:800::e6', 443, 0, 0)>\n",
      "  return YahooDailyReader(*args, **kwargs).read()\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
      "/Users/Li/opt/anaconda3/lib/python3.9/site-packages/pandas_datareader/data.py:80: ResourceWarning: unclosed <ssl.SSLSocket fd=79, family=AddressFamily.AF_INET6, type=SocketKind.SOCK_STREAM, proto=0, laddr=('2404:e801:2001:3973:492a:d11b:85d6:57de', 49870, 0, 0), raddr=('2406:2000:98:800::e6', 443, 0, 0)>\n",
      "  return YahooDailyReader(*args, **kwargs).read()\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ING</th>\n",
       "      <th>MS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>12.05</td>\n",
       "      <td>51.119999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>12.43</td>\n",
       "      <td>52.040001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>12.20</td>\n",
       "      <td>51.200001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>12.19</td>\n",
       "      <td>51.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>12.21</td>\n",
       "      <td>50.919998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-28</th>\n",
       "      <td>13.86</td>\n",
       "      <td>99.970001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-29</th>\n",
       "      <td>13.89</td>\n",
       "      <td>98.730003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>13.88</td>\n",
       "      <td>98.800003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>13.92</td>\n",
       "      <td>98.160004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>14.08</td>\n",
       "      <td>100.190002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>507 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              ING          MS\n",
       "Date                         \n",
       "2019-12-31  12.05   51.119999\n",
       "2020-01-02  12.43   52.040001\n",
       "2020-01-03  12.20   51.200001\n",
       "2020-01-06  12.19   51.020000\n",
       "2020-01-07  12.21   50.919998\n",
       "...           ...         ...\n",
       "2021-12-28  13.86   99.970001\n",
       "2021-12-29  13.89   98.730003\n",
       "2021-12-30  13.88   98.800003\n",
       "2021-12-31  13.92   98.160004\n",
       "2022-01-03  14.08  100.190002\n",
       "\n",
       "[507 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_testing = extract(stocks, '2020-1-1', '2022-1-3')\n",
    "df_testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "75b1a249",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_testing['spot_spread'] = df_testing[stocks[0]] - model.params[1] * df_testing[stocks[1]]\n",
    "df_testing['positive_2std'] = model.params[0] + (2 * 0.166)\n",
    "df_testing['negative_2std'] = model.params[0] - (2 * 0.166)\n",
    "df_testing['s_spread_diff'] = df_testing['spot_spread'] - model.params[0]\n",
    "df_testing['pos'] = np.where(df_testing['spot_spread'] > df_testing['positive_2std'], -1, np.nan)\n",
    "df_testing['pos'] = np.where(df_testing['spot_spread'] < df_testing['negative_2std'], 1, df_testing['pos'])\n",
    "df_testing['pos'] = np.where((df_testing['s_spread_diff'] * df_testing['s_spread_diff'].shift(1)) < 0, 0, df_testing['pos'])\n",
    "df_testing['pos'] = df_testing['pos'].ffill().fillna(0)\n",
    "df_testing['buyorsell'] = df_testing['pos'].diff()\n",
    "df_testing['spread_cap_req'] = df_testing[stocks[0]] + model.params[1] * df_testing[stocks[1]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8913481a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0.0    478\n",
       " 1.0     15\n",
       "-1.0     13\n",
       "Name: buyorsell, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_testing['buyorsell'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "204d37dc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9b6fea46",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2020-03-13    1.456585\n",
       "2020-03-16    0.938758\n",
       "2020-03-30    1.109504\n",
       "2020-03-31    0.894637\n",
       "2020-06-11    1.341720\n",
       "2020-08-21    1.314440\n",
       "2020-09-21    1.077372\n",
       "2020-10-13    0.937044\n",
       "2020-12-10    1.704836\n",
       "2020-12-21    0.650551\n",
       "2021-03-11    1.420618\n",
       "2021-07-02    1.681733\n",
       "2021-07-12    1.321620\n",
       "2021-11-26    1.503303\n",
       "2021-12-07    1.361331\n",
       "Name: spot_spread, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_testing[df_testing['buyorsell'] == 1]['spot_spread']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9064555d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fd2b9578130>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Backtesting on training set\n",
    "\n",
    "df_testing['spot_spread'].plot(figsize = (16,10), title = 'Spot Spread', )\n",
    "plt.axhline(y = (1.719641 + (2 * 0.166)), color = 'green', linestyle = '--')\n",
    "\n",
    "plt.axhline(y = 1.719641, color = 'grey', linestyle = '--')\n",
    "\n",
    "plt.axhline(y = (1.719641 - (2 * 0.166)), color = 'green', linestyle = '--')\n",
    "\n",
    "plt.scatter(x = df_testing[df_testing['buyorsell'] == 1]['spot_spread'].index,\n",
    "            y = df_testing[df_testing['buyorsell'] == 1]['spot_spread'], marker = '^', color = 'green')\n",
    "\n",
    "plt.scatter(x = df_testing[df_testing['buyorsell'] == -1]['spot_spread'].index,\n",
    "            y = df_testing[df_testing['buyorsell'] == -1]['spot_spread'], marker = 'v', color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "683d8228",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ING</th>\n",
       "      <th>MS</th>\n",
       "      <th>spot_spread</th>\n",
       "      <th>positive_2std</th>\n",
       "      <th>negative_2std</th>\n",
       "      <th>s_spread_diff</th>\n",
       "      <th>pos</th>\n",
       "      <th>buyorsell</th>\n",
       "      <th>spread_cap_req</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-04</th>\n",
       "      <td>9.27</td>\n",
       "      <td>68.330002</td>\n",
       "      <td>0.717972</td>\n",
       "      <td>2.051641</td>\n",
       "      <td>1.387641</td>\n",
       "      <td>-1.001669</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.822029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-05</th>\n",
       "      <td>9.44</td>\n",
       "      <td>69.489998</td>\n",
       "      <td>0.742789</td>\n",
       "      <td>2.051641</td>\n",
       "      <td>1.387641</td>\n",
       "      <td>-0.976852</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.137211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-06</th>\n",
       "      <td>10.16</td>\n",
       "      <td>73.680000</td>\n",
       "      <td>0.938378</td>\n",
       "      <td>2.051641</td>\n",
       "      <td>1.387641</td>\n",
       "      <td>-0.781263</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.381622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-07</th>\n",
       "      <td>10.16</td>\n",
       "      <td>74.940002</td>\n",
       "      <td>0.780679</td>\n",
       "      <td>2.051641</td>\n",
       "      <td>1.387641</td>\n",
       "      <td>-0.938962</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.539321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-08</th>\n",
       "      <td>10.16</td>\n",
       "      <td>75.250000</td>\n",
       "      <td>0.741880</td>\n",
       "      <td>2.051641</td>\n",
       "      <td>1.387641</td>\n",
       "      <td>-0.977761</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.578120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-27</th>\n",
       "      <td>13.91</td>\n",
       "      <td>100.400002</td>\n",
       "      <td>1.344163</td>\n",
       "      <td>2.051641</td>\n",
       "      <td>1.387641</td>\n",
       "      <td>-0.375478</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.475837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-28</th>\n",
       "      <td>13.86</td>\n",
       "      <td>99.970001</td>\n",
       "      <td>1.347980</td>\n",
       "      <td>2.051641</td>\n",
       "      <td>1.387641</td>\n",
       "      <td>-0.371660</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.372019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-29</th>\n",
       "      <td>13.89</td>\n",
       "      <td>98.730003</td>\n",
       "      <td>1.533176</td>\n",
       "      <td>2.051641</td>\n",
       "      <td>1.387641</td>\n",
       "      <td>-0.186464</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.246824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>13.88</td>\n",
       "      <td>98.800003</td>\n",
       "      <td>1.514415</td>\n",
       "      <td>2.051641</td>\n",
       "      <td>1.387641</td>\n",
       "      <td>-0.205226</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.245585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>13.92</td>\n",
       "      <td>98.160004</td>\n",
       "      <td>1.634516</td>\n",
       "      <td>2.051641</td>\n",
       "      <td>1.387641</td>\n",
       "      <td>-0.085125</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.205484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              ING          MS  spot_spread  positive_2std  negative_2std  \\\n",
       "Date                                                                       \n",
       "2021-01-04   9.27   68.330002     0.717972       2.051641       1.387641   \n",
       "2021-01-05   9.44   69.489998     0.742789       2.051641       1.387641   \n",
       "2021-01-06  10.16   73.680000     0.938378       2.051641       1.387641   \n",
       "2021-01-07  10.16   74.940002     0.780679       2.051641       1.387641   \n",
       "2021-01-08  10.16   75.250000     0.741880       2.051641       1.387641   \n",
       "...           ...         ...          ...            ...            ...   \n",
       "2021-12-27  13.91  100.400002     1.344163       2.051641       1.387641   \n",
       "2021-12-28  13.86   99.970001     1.347980       2.051641       1.387641   \n",
       "2021-12-29  13.89   98.730003     1.533176       2.051641       1.387641   \n",
       "2021-12-30  13.88   98.800003     1.514415       2.051641       1.387641   \n",
       "2021-12-31  13.92   98.160004     1.634516       2.051641       1.387641   \n",
       "\n",
       "            s_spread_diff  pos  buyorsell  spread_cap_req  \n",
       "Date                                                       \n",
       "2021-01-04      -1.001669  1.0        0.0       17.822029  \n",
       "2021-01-05      -0.976852  1.0        0.0       18.137211  \n",
       "2021-01-06      -0.781263  1.0        0.0       19.381622  \n",
       "2021-01-07      -0.938962  1.0        0.0       19.539321  \n",
       "2021-01-08      -0.977761  1.0        0.0       19.578120  \n",
       "...                   ...  ...        ...             ...  \n",
       "2021-12-27      -0.375478  1.0        0.0       26.475837  \n",
       "2021-12-28      -0.371660  1.0        0.0       26.372019  \n",
       "2021-12-29      -0.186464  1.0        0.0       26.246824  \n",
       "2021-12-30      -0.205226  1.0        0.0       26.245585  \n",
       "2021-12-31      -0.085125  1.0        0.0       26.205484  \n",
       "\n",
       "[252 rows x 9 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_testing.loc['2021']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1828a18f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/vt/xp_pd8c10sz7qxkl9nb47lrw0000gp/T/ipykernel_1710/536343103.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['open_position'][i:] = data1['pos'][i]\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>ING</th>\n",
       "      <th>MS</th>\n",
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       "      <th>Date</th>\n",
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       "      <th>2019-12-31</th>\n",
       "      <td>12.05</td>\n",
       "      <td>51.119999</td>\n",
       "      <td>5.651937</td>\n",
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       "      <th>2020-01-02</th>\n",
       "      <td>12.43</td>\n",
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       "      <th>2020-01-03</th>\n",
       "      <td>12.20</td>\n",
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       "      <td>5.791924</td>\n",
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       "      <th>2020-01-06</th>\n",
       "      <td>12.19</td>\n",
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       "      <td>1.986331</td>\n",
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       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>13.92</td>\n",
       "      <td>98.160004</td>\n",
       "      <td>1.634516</td>\n",
       "      <td>2.051641</td>\n",
       "      <td>1.387641</td>\n",
       "      <td>-0.085125</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>26.205484</td>\n",
       "      <td>187562.712951</td>\n",
       "      <td>...</td>\n",
       "      <td>9951.333031</td>\n",
       "      <td>1996.979566</td>\n",
       "      <td>0.0</td>\n",
       "      <td>197514.045982</td>\n",
       "      <td>199511.025548</td>\n",
       "      <td>0.004420</td>\n",
       "      <td>0.004410</td>\n",
       "      <td>1.995110</td>\n",
       "      <td>1.995110</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>14.08</td>\n",
       "      <td>100.190002</td>\n",
       "      <td>1.540446</td>\n",
       "      <td>2.051641</td>\n",
       "      <td>1.387641</td>\n",
       "      <td>-0.179195</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.619554</td>\n",
       "      <td>187562.712951</td>\n",
       "      <td>...</td>\n",
       "      <td>9951.333031</td>\n",
       "      <td>1309.326350</td>\n",
       "      <td>0.0</td>\n",
       "      <td>197514.045982</td>\n",
       "      <td>198823.372332</td>\n",
       "      <td>-0.003447</td>\n",
       "      <td>-0.003453</td>\n",
       "      <td>1.988234</td>\n",
       "      <td>1.995110</td>\n",
       "      <td>0.006877</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>507 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              ING          MS  spot_spread  positive_2std  negative_2std  \\\n",
       "Date                                                                       \n",
       "2019-12-31  12.05   51.119999     5.651937       2.051641       1.387641   \n",
       "2020-01-02  12.43   52.040001     5.916792       2.051641       1.387641   \n",
       "2020-01-03  12.20   51.200001     5.791924       2.051641       1.387641   \n",
       "2020-01-06  12.19   51.020000     5.804452       2.051641       1.387641   \n",
       "2020-01-07  12.21   50.919998     5.836968       2.051641       1.387641   \n",
       "...           ...         ...          ...            ...            ...   \n",
       "2021-12-28  13.86   99.970001     1.347980       2.051641       1.387641   \n",
       "2021-12-29  13.89   98.730003     1.533176       2.051641       1.387641   \n",
       "2021-12-30  13.88   98.800003     1.514415       2.051641       1.387641   \n",
       "2021-12-31  13.92   98.160004     1.634516       2.051641       1.387641   \n",
       "2022-01-03  14.08  100.190002     1.540446       2.051641       1.387641   \n",
       "\n",
       "            s_spread_diff  pos  buyorsell  spread_cap_req           cash  ...  \\\n",
       "Date                                                                      ...   \n",
       "2019-12-31       3.932296 -1.0        NaN       18.448064  100000.000000  ...   \n",
       "2020-01-02       4.197151 -1.0        0.0       18.943209  100000.000000  ...   \n",
       "2020-01-03       4.072283 -1.0        0.0       18.608076  100000.000000  ...   \n",
       "2020-01-06       4.084811 -1.0        0.0       18.575547  100000.000000  ...   \n",
       "2020-01-07       4.117327 -1.0        0.0       18.583032  100000.000000  ...   \n",
       "...                   ...  ...        ...             ...            ...  ...   \n",
       "2021-12-28      -0.371660  1.0        0.0       26.372019  187562.712951  ...   \n",
       "2021-12-29      -0.186464  1.0        0.0       26.246824  187562.712951  ...   \n",
       "2021-12-30      -0.205226  1.0        0.0       26.245585  187562.712951  ...   \n",
       "2021-12-31      -0.085125  1.0        0.0       26.205484  187562.712951  ...   \n",
       "2022-01-03      -0.179195  1.0        0.0       26.619554  187562.712951  ...   \n",
       "\n",
       "            traded_nominal_val  unrealised_P&L  cashflow   total_assets  \\\n",
       "Date                                                                      \n",
       "2019-12-31            0.000000        0.000000       0.0  100000.000000   \n",
       "2020-01-02            0.000000        0.000000       0.0  100000.000000   \n",
       "2020-01-03            0.000000        0.000000       0.0  100000.000000   \n",
       "2020-01-06            0.000000        0.000000       0.0  100000.000000   \n",
       "2020-01-07            0.000000        0.000000       0.0  100000.000000   \n",
       "...                        ...             ...       ...            ...   \n",
       "2021-12-28         9951.333031      -97.595811       0.0  197514.045982   \n",
       "2021-12-29         9951.333031     1256.187051       0.0  197514.045982   \n",
       "2021-12-30         9951.333031     1119.042443       0.0  197514.045982   \n",
       "2021-12-31         9951.333031     1996.979566       0.0  197514.045982   \n",
       "2022-01-03         9951.333031     1309.326350       0.0  197514.045982   \n",
       "\n",
       "               net_assets  strat_acc_return_pct_chg  \\\n",
       "Date                                                  \n",
       "2019-12-31  100000.000000                       NaN   \n",
       "2020-01-02  100000.000000                  0.000000   \n",
       "2020-01-03  100000.000000                  0.000000   \n",
       "2020-01-06  100000.000000                  0.000000   \n",
       "2020-01-07  100000.000000                  0.000000   \n",
       "...                   ...                       ...   \n",
       "2021-12-28  197416.450171                  0.000141   \n",
       "2021-12-29  198770.233033                  0.006857   \n",
       "2021-12-30  198633.088425                 -0.000690   \n",
       "2021-12-31  199511.025548                  0.004420   \n",
       "2022-01-03  198823.372332                 -0.003447   \n",
       "\n",
       "            strat_acc_return_nplog_net_assets  cumsum_strat_acc  \\\n",
       "Date                                                              \n",
       "2019-12-31                                NaN               NaN   \n",
       "2020-01-02                           0.000000          1.000000   \n",
       "2020-01-03                           0.000000          1.000000   \n",
       "2020-01-06                           0.000000          1.000000   \n",
       "2020-01-07                           0.000000          1.000000   \n",
       "...                                       ...               ...   \n",
       "2021-12-28                           0.000141          1.974165   \n",
       "2021-12-29                           0.006834          1.987702   \n",
       "2021-12-30                          -0.000690          1.986331   \n",
       "2021-12-31                           0.004410          1.995110   \n",
       "2022-01-03                          -0.003453          1.988234   \n",
       "\n",
       "            max_performance_acc  drawdown_acc  \n",
       "Date                                           \n",
       "2019-12-31                  NaN           NaN  \n",
       "2020-01-02             1.000000      0.000000  \n",
       "2020-01-03             1.000000      0.000000  \n",
       "2020-01-06             1.000000      0.000000  \n",
       "2020-01-07             1.000000      0.000000  \n",
       "...                         ...           ...  \n",
       "2021-12-28             1.988985      0.014821  \n",
       "2021-12-29             1.988985      0.001283  \n",
       "2021-12-30             1.988985      0.002654  \n",
       "2021-12-31             1.995110      0.000000  \n",
       "2022-01-03             1.995110      0.006877  \n",
       "\n",
       "[507 rows x 22 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_acc_testing = account(df_testing, 100000, 10)\n",
    "df_acc_testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cd502ac1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/vt/xp_pd8c10sz7qxkl9nb47lrw0000gp/T/ipykernel_1710/1796526765.py:1: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.\n",
      "  df_acc_testing['2021-12'].tail()\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ING</th>\n",
       "      <th>MS</th>\n",
       "      <th>spot_spread</th>\n",
       "      <th>positive_2std</th>\n",
       "      <th>negative_2std</th>\n",
       "      <th>s_spread_diff</th>\n",
       "      <th>pos</th>\n",
       "      <th>buyorsell</th>\n",
       "      <th>spread_cap_req</th>\n",
       "      <th>cash</th>\n",
       "      <th>...</th>\n",
       "      <th>traded_nominal_val</th>\n",
       "      <th>unrealised_P&amp;L</th>\n",
       "      <th>cashflow</th>\n",
       "      <th>total_assets</th>\n",
       "      <th>net_assets</th>\n",
       "      <th>strat_acc_return_pct_chg</th>\n",
       "      <th>strat_acc_return_nplog_net_assets</th>\n",
       "      <th>cumsum_strat_acc</th>\n",
       "      <th>max_performance_acc</th>\n",
       "      <th>drawdown_acc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-12-27</th>\n",
       "      <td>13.91</td>\n",
       "      <td>100.400002</td>\n",
       "      <td>1.344163</td>\n",
       "      <td>2.051641</td>\n",
       "      <td>1.387641</td>\n",
       "      <td>-0.375478</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.475837</td>\n",
       "      <td>187562.712951</td>\n",
       "      <td>...</td>\n",
       "      <td>9951.333031</td>\n",
       "      <td>-125.503013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>197514.045982</td>\n",
       "      <td>197388.542969</td>\n",
       "      <td>-0.000839</td>\n",
       "      <td>-0.000839</td>\n",
       "      <td>1.973885</td>\n",
       "      <td>1.988985</td>\n",
       "      <td>0.015100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-28</th>\n",
       "      <td>13.86</td>\n",
       "      <td>99.970001</td>\n",
       "      <td>1.347980</td>\n",
       "      <td>2.051641</td>\n",
       "      <td>1.387641</td>\n",
       "      <td>-0.371660</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.372019</td>\n",
       "      <td>187562.712951</td>\n",
       "      <td>...</td>\n",
       "      <td>9951.333031</td>\n",
       "      <td>-97.595811</td>\n",
       "      <td>0.0</td>\n",
       "      <td>197514.045982</td>\n",
       "      <td>197416.450171</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>1.974165</td>\n",
       "      <td>1.988985</td>\n",
       "      <td>0.014821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-29</th>\n",
       "      <td>13.89</td>\n",
       "      <td>98.730003</td>\n",
       "      <td>1.533176</td>\n",
       "      <td>2.051641</td>\n",
       "      <td>1.387641</td>\n",
       "      <td>-0.186464</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.246824</td>\n",
       "      <td>187562.712951</td>\n",
       "      <td>...</td>\n",
       "      <td>9951.333031</td>\n",
       "      <td>1256.187051</td>\n",
       "      <td>0.0</td>\n",
       "      <td>197514.045982</td>\n",
       "      <td>198770.233033</td>\n",
       "      <td>0.006857</td>\n",
       "      <td>0.006834</td>\n",
       "      <td>1.987702</td>\n",
       "      <td>1.988985</td>\n",
       "      <td>0.001283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>13.88</td>\n",
       "      <td>98.800003</td>\n",
       "      <td>1.514415</td>\n",
       "      <td>2.051641</td>\n",
       "      <td>1.387641</td>\n",
       "      <td>-0.205226</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.245585</td>\n",
       "      <td>187562.712951</td>\n",
       "      <td>...</td>\n",
       "      <td>9951.333031</td>\n",
       "      <td>1119.042443</td>\n",
       "      <td>0.0</td>\n",
       "      <td>197514.045982</td>\n",
       "      <td>198633.088425</td>\n",
       "      <td>-0.000690</td>\n",
       "      <td>-0.000690</td>\n",
       "      <td>1.986331</td>\n",
       "      <td>1.988985</td>\n",
       "      <td>0.002654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>13.92</td>\n",
       "      <td>98.160004</td>\n",
       "      <td>1.634516</td>\n",
       "      <td>2.051641</td>\n",
       "      <td>1.387641</td>\n",
       "      <td>-0.085125</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.205484</td>\n",
       "      <td>187562.712951</td>\n",
       "      <td>...</td>\n",
       "      <td>9951.333031</td>\n",
       "      <td>1996.979566</td>\n",
       "      <td>0.0</td>\n",
       "      <td>197514.045982</td>\n",
       "      <td>199511.025548</td>\n",
       "      <td>0.004420</td>\n",
       "      <td>0.004410</td>\n",
       "      <td>1.995110</td>\n",
       "      <td>1.995110</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              ING          MS  spot_spread  positive_2std  negative_2std  \\\n",
       "Date                                                                       \n",
       "2021-12-27  13.91  100.400002     1.344163       2.051641       1.387641   \n",
       "2021-12-28  13.86   99.970001     1.347980       2.051641       1.387641   \n",
       "2021-12-29  13.89   98.730003     1.533176       2.051641       1.387641   \n",
       "2021-12-30  13.88   98.800003     1.514415       2.051641       1.387641   \n",
       "2021-12-31  13.92   98.160004     1.634516       2.051641       1.387641   \n",
       "\n",
       "            s_spread_diff  pos  buyorsell  spread_cap_req           cash  ...  \\\n",
       "Date                                                                      ...   \n",
       "2021-12-27      -0.375478  1.0        0.0       26.475837  187562.712951  ...   \n",
       "2021-12-28      -0.371660  1.0        0.0       26.372019  187562.712951  ...   \n",
       "2021-12-29      -0.186464  1.0        0.0       26.246824  187562.712951  ...   \n",
       "2021-12-30      -0.205226  1.0        0.0       26.245585  187562.712951  ...   \n",
       "2021-12-31      -0.085125  1.0        0.0       26.205484  187562.712951  ...   \n",
       "\n",
       "            traded_nominal_val  unrealised_P&L  cashflow   total_assets  \\\n",
       "Date                                                                      \n",
       "2021-12-27         9951.333031     -125.503013       0.0  197514.045982   \n",
       "2021-12-28         9951.333031      -97.595811       0.0  197514.045982   \n",
       "2021-12-29         9951.333031     1256.187051       0.0  197514.045982   \n",
       "2021-12-30         9951.333031     1119.042443       0.0  197514.045982   \n",
       "2021-12-31         9951.333031     1996.979566       0.0  197514.045982   \n",
       "\n",
       "               net_assets  strat_acc_return_pct_chg  \\\n",
       "Date                                                  \n",
       "2021-12-27  197388.542969                 -0.000839   \n",
       "2021-12-28  197416.450171                  0.000141   \n",
       "2021-12-29  198770.233033                  0.006857   \n",
       "2021-12-30  198633.088425                 -0.000690   \n",
       "2021-12-31  199511.025548                  0.004420   \n",
       "\n",
       "            strat_acc_return_nplog_net_assets  cumsum_strat_acc  \\\n",
       "Date                                                              \n",
       "2021-12-27                          -0.000839          1.973885   \n",
       "2021-12-28                           0.000141          1.974165   \n",
       "2021-12-29                           0.006834          1.987702   \n",
       "2021-12-30                          -0.000690          1.986331   \n",
       "2021-12-31                           0.004410          1.995110   \n",
       "\n",
       "            max_performance_acc  drawdown_acc  \n",
       "Date                                           \n",
       "2021-12-27             1.988985      0.015100  \n",
       "2021-12-28             1.988985      0.014821  \n",
       "2021-12-29             1.988985      0.001283  \n",
       "2021-12-30             1.988985      0.002654  \n",
       "2021-12-31             1.995110      0.000000  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_acc_testing['2021-12'].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ab650ec7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8927677348230357"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(252) * (np.exp(df_acc_testing['strat_acc_return_nplog_net_assets'].mean()) - 1) / (np.exp(df_acc_testing['strat_acc_return_nplog_net_assets'].std()) - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e07320d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0624987971120845"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_acc_testing['strat_acc_return_pct_chg'].mean() / df_acc_testing['strat_acc_return_pct_chg'].std()) * np.sqrt(252)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1536d32e",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.exp(df_acc_testing['strat_acc_return_nplog_net_assets'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5b4bf38f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23377794591417733"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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   ]
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