pair_trading_OU_process.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8270cf54",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from functools import reduce\n",
    "from scipy.optimize import minimize\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "HINDUNILVR=pd.read_csv('HINDUNILVR.BO.csv').dropna() \n",
    "PGHH = pd.read_csv('PGHH.BO.csv').dropna()\n",
    "HAL= pd.read_csv('HAL.csv').dropna()\n",
    "COCHINSHIP = pd.read_csv('COCHINSHIP.NS.csv').dropna()\n",
    "SOLARINDS = pd.read_csv('SOLARINDS.NS.csv').dropna()\n",
    "ZENTEC = pd.read_csv('ZENTEC.NS.csv').dropna()\n",
    "\n",
    "# stock_3 = pd.read_csv('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a2032b20",
   "metadata": {},
   "outputs": [],
   "source": [
    "stocks = [HINDUNILVR,PGHH,HAL,COCHINSHIP,SOLARINDS,ZENTEC]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "905f5558",
   "metadata": {},
   "outputs": [],
   "source": [
    "HINDUNILVR['Date'] = pd.to_datetime(HINDUNILVR['Date'])\n",
    "PGHH['Date'] = pd.to_datetime(PGHH['Date'])\n",
    "HAL['Date'] = pd.to_datetime(HAL['Date'])\n",
    "COCHINSHIP['Date'] = pd.to_datetime(COCHINSHIP['Date'])\n",
    "SOLARINDS['Date'] = pd.to_datetime(SOLARINDS['Date'])\n",
    "ZENTEC['Date'] = pd.to_datetime(ZENTEC['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1617f4bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "common_dates = reduce(lambda left, right: pd.merge(left, right, on='Date'), [stock[['Date']] for stock in stocks])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2fda0572",
   "metadata": {},
   "outputs": [],
   "source": [
    "stocks_common = [stock[stock['Date'].isin(common_dates['Date'])].reset_index(drop=True) for stock in stocks]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1a60646d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-08-07</td>\n",
       "      <td>15400.049805</td>\n",
       "      <td>15666.250000</td>\n",
       "      <td>15400.049805</td>\n",
       "      <td>15506.049805</td>\n",
       "      <td>15267.430664</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>15619.400391</td>\n",
       "      <td>15750.000000</td>\n",
       "      <td>15582.799805</td>\n",
       "      <td>15658.599609</td>\n",
       "      <td>15417.632813</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-08-09</td>\n",
       "      <td>15679.849609</td>\n",
       "      <td>15720.700195</td>\n",
       "      <td>15470.000000</td>\n",
       "      <td>15484.549805</td>\n",
       "      <td>15246.261719</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-08-10</td>\n",
       "      <td>15484.000000</td>\n",
       "      <td>15637.750000</td>\n",
       "      <td>15484.000000</td>\n",
       "      <td>15607.000000</td>\n",
       "      <td>15366.827148</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-08-11</td>\n",
       "      <td>15507.049805</td>\n",
       "      <td>15580.000000</td>\n",
       "      <td>15237.599609</td>\n",
       "      <td>15286.900391</td>\n",
       "      <td>15051.653320</td>\n",
       "      <td>75.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>2024-07-31</td>\n",
       "      <td>17038.150391</td>\n",
       "      <td>17075.099609</td>\n",
       "      <td>16853.050781</td>\n",
       "      <td>16905.650391</td>\n",
       "      <td>16905.650391</td>\n",
       "      <td>6044.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>2024-08-01</td>\n",
       "      <td>16855.800781</td>\n",
       "      <td>16974.050781</td>\n",
       "      <td>16467.000000</td>\n",
       "      <td>16778.400391</td>\n",
       "      <td>16778.400391</td>\n",
       "      <td>862.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>2024-08-02</td>\n",
       "      <td>17076.949219</td>\n",
       "      <td>17337.449219</td>\n",
       "      <td>16700.000000</td>\n",
       "      <td>17200.849609</td>\n",
       "      <td>17200.849609</td>\n",
       "      <td>293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>2024-08-05</td>\n",
       "      <td>17199.949219</td>\n",
       "      <td>17199.949219</td>\n",
       "      <td>16424.000000</td>\n",
       "      <td>16686.199219</td>\n",
       "      <td>16686.199219</td>\n",
       "      <td>562.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>2024-08-06</td>\n",
       "      <td>16916.900391</td>\n",
       "      <td>16990.650391</td>\n",
       "      <td>16745.099609</td>\n",
       "      <td>16835.599609</td>\n",
       "      <td>16835.599609</td>\n",
       "      <td>139.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>235 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date          Open          High           Low         Close  \\\n",
       "0    2023-08-07  15400.049805  15666.250000  15400.049805  15506.049805   \n",
       "1    2023-08-08  15619.400391  15750.000000  15582.799805  15658.599609   \n",
       "2    2023-08-09  15679.849609  15720.700195  15470.000000  15484.549805   \n",
       "3    2023-08-10  15484.000000  15637.750000  15484.000000  15607.000000   \n",
       "4    2023-08-11  15507.049805  15580.000000  15237.599609  15286.900391   \n",
       "..          ...           ...           ...           ...           ...   \n",
       "230  2024-07-31  17038.150391  17075.099609  16853.050781  16905.650391   \n",
       "231  2024-08-01  16855.800781  16974.050781  16467.000000  16778.400391   \n",
       "232  2024-08-02  17076.949219  17337.449219  16700.000000  17200.849609   \n",
       "233  2024-08-05  17199.949219  17199.949219  16424.000000  16686.199219   \n",
       "234  2024-08-06  16916.900391  16990.650391  16745.099609  16835.599609   \n",
       "\n",
       "        Adj Close  Volume  \n",
       "0    15267.430664    69.0  \n",
       "1    15417.632813   153.0  \n",
       "2    15246.261719   120.0  \n",
       "3    15366.827148    28.0  \n",
       "4    15051.653320    75.0  \n",
       "..            ...     ...  \n",
       "230  16905.650391  6044.0  \n",
       "231  16778.400391   862.0  \n",
       "232  17200.849609   293.0  \n",
       "233  16686.199219   562.0  \n",
       "234  16835.599609   139.0  \n",
       "\n",
       "[235 rows x 7 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks_common[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3b709488",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_data = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1aa5a640",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_data['HINDUNILVR'] = stocks_common[0]['Open'].values\n",
    "corr_data['PGHH'] = stocks_common[1]['Open'].values\n",
    "corr_data['HAL'] = stocks_common[2]['Open'].values\n",
    "corr_data['COCHINSHIP'] = stocks_common[3]['Open'].values\n",
    "corr_data['SOLARINDS'] = stocks_common[4]['Open'].values\n",
    "corr_data['ZENTEC'] = stocks_common[5]['Open'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e9884e4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HINDUNILVR</th>\n",
       "      <th>PGHH</th>\n",
       "      <th>HAL</th>\n",
       "      <th>COCHINSHIP</th>\n",
       "      <th>SOLARINDS</th>\n",
       "      <th>ZENTEC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HINDUNILVR</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.425552</td>\n",
       "      <td>-0.251206</td>\n",
       "      <td>0.149375</td>\n",
       "      <td>-0.101327</td>\n",
       "      <td>0.163599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PGHH</th>\n",
       "      <td>0.425552</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.076093</td>\n",
       "      <td>-0.160817</td>\n",
       "      <td>-0.185459</td>\n",
       "      <td>-0.208633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HAL</th>\n",
       "      <td>-0.251206</td>\n",
       "      <td>0.076093</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.638361</td>\n",
       "      <td>-0.629278</td>\n",
       "      <td>-0.520629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COCHINSHIP</th>\n",
       "      <td>0.149375</td>\n",
       "      <td>-0.160817</td>\n",
       "      <td>-0.638361</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.914582</td>\n",
       "      <td>0.902243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOLARINDS</th>\n",
       "      <td>-0.101327</td>\n",
       "      <td>-0.185459</td>\n",
       "      <td>-0.629278</td>\n",
       "      <td>0.914582</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.822717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZENTEC</th>\n",
       "      <td>0.163599</td>\n",
       "      <td>-0.208633</td>\n",
       "      <td>-0.520629</td>\n",
       "      <td>0.902243</td>\n",
       "      <td>0.822717</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            HINDUNILVR      PGHH       HAL  COCHINSHIP  SOLARINDS    ZENTEC\n",
       "HINDUNILVR    1.000000  0.425552 -0.251206    0.149375  -0.101327  0.163599\n",
       "PGHH          0.425552  1.000000  0.076093   -0.160817  -0.185459 -0.208633\n",
       "HAL          -0.251206  0.076093  1.000000   -0.638361  -0.629278 -0.520629\n",
       "COCHINSHIP    0.149375 -0.160817 -0.638361    1.000000   0.914582  0.902243\n",
       "SOLARINDS    -0.101327 -0.185459 -0.629278    0.914582   1.000000  0.822717\n",
       "ZENTEC        0.163599 -0.208633 -0.520629    0.902243   0.822717  1.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb4c1558",
   "metadata": {},
   "source": [
    "- solarwind and coachinship is very correlated so we can chhose these for pair trading "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c4788489",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_data = corr_data['SOLARINDS'] - corr_data['COCHINSHIP']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8fa0d60",
   "metadata": {},
   "source": [
    "- we need to minimize the variance of spread \n",
    "- spread is given by $ price_{cochinship} - \\beta *price_{solarwind} $\n",
    "- we will use the optimizer libaray for this pupose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8ef971bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal Hedge Ratio (Beta): 0.3003793401536033\n"
     ]
    }
   ],
   "source": [
    "def spread_variance(beta, P1, P2):\n",
    "    spread = P1 - beta * P2\n",
    "    return np.var(spread)\n",
    "\n",
    "# Step 3: Minimize the variance of the spread\n",
    "result = minimize(spread_variance, x0=1, args=(corr_data['COCHINSHIP'], corr_data['SOLARINDS']), method='BFGS')\n",
    "\n",
    "# Optimal beta\n",
    "beta_optimal = result.x[0]\n",
    "\n",
    "print(\"Optimal Hedge Ratio (Beta):\", beta_optimal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5037f483",
   "metadata": {},
   "outputs": [],
   "source": [
    "spread = corr_data['COCHINSHIP'] - beta_optimal *  corr_data['SOLARINDS']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f78726c7",
   "metadata": {},
   "source": [
    "- estimating the parameter of the mu, theta and sigma, we will calculate the log liklihood of the data given OU process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ed374ff4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def OU_log_likelihood(params, spread):\n",
    "    mu, theta, sigma = params\n",
    "    dt = 1  # Assuming the data is daily\n",
    "    S_diff = np.diff(spread)\n",
    "    S_shifted = spread[:-1]\n",
    "    likelihood = -np.sum(np.log(sigma * np.sqrt(2 * np.pi * dt)) +\n",
    "                         ((S_diff - theta * (mu - S_shifted) * dt)**2) / (2 * sigma**2 * dt))\n",
    "    return -likelihood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "07eb8c4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\E080965\\AppData\\Local\\Temp\\ipykernel_10700\\883733649.py:6: RuntimeWarning: invalid value encountered in log\n",
      "  likelihood = -np.sum(np.log(sigma * np.sqrt(2 * np.pi * dt)) +\n"
     ]
    }
   ],
   "source": [
    "params_initial = [np.mean(spread), 0.5, np.std(spread)]\n",
    "result = minimize(OU_log_likelihood, params_initial, args=(spread,), method='L-BFGS-B')\n",
    "mu_ou, theta_ou, sigma_ou = result.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1ab30bb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated OU parameters: mu=-1148.3496484422458, theta=-0.6771845007682855, sigma=-4.937730001689374\n"
     ]
    }
   ],
   "source": [
    "print(f\"Estimated OU parameters: mu={mu_ou}, theta={theta_ou}, sigma={sigma_ou}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "0f422830",
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 1.5\n",
    "upper_bound = mu_ou + k * sigma_ou\n",
    "lower_bound = mu_ou - k * sigma_ou"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "5d3864fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1155.7562434447798"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper_bound"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "4f3e7653",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1140.9430534397118"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_bound"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "0a80d560",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1900.315179076396"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spread.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "7ea5e4d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-586.9257035105893"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spread.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "73dac66f",
   "metadata": {},
   "outputs": [],
   "source": [
    "positions = []\n",
    "for S in spread:\n",
    "    if S > upper_bound:\n",
    "        positions.append(-1)  # Short spread ==> \n",
    "    elif S < lower_bound:\n",
    "        positions.append(1)   # Long spread\n",
    "    else:\n",
    "        positions.append(0)   # Hold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "a34253a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert positions to a numpy array\n",
    "positions = np.array(positions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "e38cde28",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 5: Backtest the Strategy\n",
    "# Calculate the daily returns\n",
    "P1_returns = np.diff(corr_data['COCHINSHIP']) / corr_data['COCHINSHIP'][:-1]\n",
    "P2_returns = np.diff(corr_data['SOLARINDS']) / corr_data['SOLARINDS'][:-1]\n",
    "spread_returns = P1_returns - beta_optimal * P2_returns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "73514709",
   "metadata": {},
   "outputs": [],
   "source": [
    "strategy_returns = positions[:-1] * spread_returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "6774e57d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cumulative_returns = np.cumsum(strategy_returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "579a7708",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(cumulative_returns, label=\"Strategy Cumulative Returns\")\n",
    "plt.axhline(y=0, color='r', linestyle='--')\n",
    "plt.title(\"Pair Trading Strategy Cumulative Returns\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Cumulative Returns\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "2b6fe9ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sharpe Ratio: 1.072310125549421\n",
      "Max Drawdown: -0.6474032031754333\n"
     ]
    }
   ],
   "source": [
    "sharpe_ratio = np.mean(strategy_returns) / np.std(strategy_returns) * np.sqrt(252)  # Annualized Sharpe Ratio\n",
    "max_drawdown = np.min(cumulative_returns - np.maximum.accumulate(cumulative_returns))\n",
    "\n",
    "print(f\"Sharpe Ratio: {sharpe_ratio}\")\n",
    "print(f\"Max Drawdown: {max_drawdown}\")"
   ]
  }
 ],
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