Option Greeks Strategies Backtesting in Python. File 7 - Straddle .ipynb

{
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  {
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   "metadata": {},
   "outputs": [],
   "source": [
    "from nsepy import get_history\n",
    "from datetime import datetime\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Nifty</th>\n",
       "      <th>Type</th>\n",
       "      <th>Expiry</th>\n",
       "      <th>9000</th>\n",
       "      <th>9100</th>\n",
       "      <th>9200</th>\n",
       "      <th>9300</th>\n",
       "      <th>9400</th>\n",
       "      <th>9500</th>\n",
       "      <th>...</th>\n",
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       "      <th>12100</th>\n",
       "      <th>12200</th>\n",
       "      <th>12300</th>\n",
       "      <th>12400</th>\n",
       "      <th>12500</th>\n",
       "      <th>12600</th>\n",
       "      <th>12700</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>10681.90</td>\n",
       "      <td>PE</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1311.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>10748.50</td>\n",
       "      <td>CE</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1630.20</td>\n",
       "      <td>1521.15</td>\n",
       "      <td>1319.35</td>\n",
       "      <td>1333.55</td>\n",
       "      <td>1246.45</td>\n",
       "      <td>...</td>\n",
       "      <td>0.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
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       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>10748.50</td>\n",
       "      <td>PE</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.30</td>\n",
       "      <td>...</td>\n",
       "      <td>1249.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>10783.65</td>\n",
       "      <td>CE</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1689.20</td>\n",
       "      <td>1576.45</td>\n",
       "      <td>1465.35</td>\n",
       "      <td>1364.65</td>\n",
       "      <td>1280.05</td>\n",
       "      <td>...</td>\n",
       "      <td>0.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>10783.65</td>\n",
       "      <td>PE</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>...</td>\n",
       "      <td>1213.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
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       "</table>\n",
       "<p>5 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date     Nifty Type      Expiry  9000     9100     9200     9300  \\\n",
       "977  2018-12-24  10681.90   PE  2018-12-27   NaN     0.25     0.80     0.90   \n",
       "978  2018-12-26  10748.50   CE  2018-12-27   NaN  1630.20  1521.15  1319.35   \n",
       "979  2018-12-26  10748.50   PE  2018-12-27   NaN     0.25     0.25     0.30   \n",
       "980  2018-12-27  10783.65   CE  2018-12-27   NaN  1689.20  1576.45  1465.35   \n",
       "981  2018-12-27  10783.65   PE  2018-12-27   NaN     0.05     0.05     0.05   \n",
       "\n",
       "        9400     9500  ...    12000  12100  12200  12300  12400  12500  12600  \\\n",
       "977     0.95     1.00  ...  1311.80    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "978  1333.55  1246.45  ...     0.20    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "979     0.35     0.30  ...  1249.35    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "980  1364.65  1280.05  ...     0.05    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "981     0.05     0.05  ...  1213.75    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "     12700  month  day  \n",
       "977    NaN     12   24  \n",
       "978    NaN     12   26  \n",
       "979    NaN     12   26  \n",
       "980    NaN     12   27  \n",
       "981    NaN     12   27  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nifty = pd.read_csv('Optionpivot.csv')\n",
    "nifty['month'] = pd.to_datetime(nifty['Date']).dt.strftime('%m')\n",
    "nifty['day'] = pd.to_datetime(nifty['Date']).dt.strftime('%d')\n",
    "nifty.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Expiry  CE_ATM  PE_ATM  Straddle     P&L  Total_P&L\n",
      "04  2018-04-26    0.05  307.90    307.95   17.45      17.45\n",
      "05  2018-05-31    2.75   20.75     23.50 -257.55    -240.10\n",
      "06  2018-06-28  106.95    0.10    107.05 -172.85    -412.95\n",
      "07  2018-07-26    0.05  459.80    459.85  191.80    -221.15\n",
      "08  2018-08-30    0.05  270.60    270.65   -1.60    -222.75\n",
      "09  2018-09-27  621.60    0.05    621.65  336.40     113.65\n",
      "10  2018-10-25  975.35    0.05    975.40  629.00     742.65\n",
      "11  2018-11-29    0.05  450.40    450.45   43.10     785.75\n",
      "12  2018-12-27  107.55    0.10    107.65 -282.10     503.65\n"
     ]
    }
   ],
   "source": [
    "Table1 = nifty.groupby('month').first()\n",
    "Table1['M'] = pd.to_datetime(Table1['Date']).dt.strftime('%m')\n",
    "Table1['ATM_Strike'] = round(Table1['Nifty']/100)*100\n",
    "\n",
    "Table3 = pd.DataFrame({\"Date\": [\"0\"], \"Close\":0, \"Expiry\": [\"0\"], \"Type\": [\"0\"], \"month\":0, \"day\": [\"0\"]})\n",
    "Table5 = pd.DataFrame({\"Date\": [\"0\"], \"Close\":0, \"Expiry\": [\"0\"], \"Type\": [\"0\"], \"month\":0, \"day\": [\"0\"]})\n",
    "for x in range (0,9):\n",
    "    e=Table1.M[x]\n",
    "    d=Table1.Expiry[x]\n",
    "    a= int((Table1.ATM_Strike[x]))\n",
    "    f1 = str(a)\n",
    "    Table2 = pd.DataFrame({\"Date\": nifty[\"Date\"], \"Close\": nifty[\"Nifty\"], \"Expiry\": nifty[\"Expiry\"], \n",
    "                           \"Type\": nifty[\"Type\"], \"month\": nifty[\"month\"], \"day\": nifty[\"day\"], \"CE_ATM\": nifty[f1]})\n",
    "    Table6= Table2.where((Table2.month == e) & (Table2.Expiry == d) & (Table2.Type == 'CE'))\n",
    "    Table4= Table2.where((Table2.month == e) & (Table2.Expiry == d) & (Table2.Type == 'PE'))\n",
    "    Table6 = Table6.dropna()\n",
    "    Table4 = Table4.dropna()\n",
    "    Table6.set_index(\"Date\", inplace = True)\n",
    "    Table4.set_index(\"Date\", inplace = True)\n",
    "    Table4['PE_ATM'] = Table6['CE_ATM']\n",
    "    Table4['Straddle']=(Table4['CE_ATM']+Table4['PE_ATM'])\n",
    "    Table4['P&L']=Table4['Straddle'].diff().cumsum()\n",
    "    Table3 = Table3.append(Table4)\n",
    "    Table7 = Table4.groupby('month').last()\n",
    "    Table5 = Table5.append(Table7)\n",
    "    \n",
    "    \n",
    "Table5.drop([\"Date\", \"Close\", \"Type\", \"month\", \"day\"], axis = 1, inplace = True)\n",
    "Table5 = Table5.dropna()\n",
    "Table5['Total_P&L']=Table5['P&L'].cumsum()\n",
    "print (Table5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "Table8.to_csv(\"Strategy_computation.csv\", index=True, encoding='utf8')\n",
    "Table10.to_csv(\"Strategy_computation1.csv\", index=True, encoding='utf8')"
   ]
  }
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