Strategy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# オプション戦略"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import ivolat3\n",
    "from payoff import plot_payoff\n",
    "\n",
    "pd.options.display.max_rows = 999\n",
    "\n",
    "s_df = pd.read_pickle('data/s.pickle')\n",
    "op_pn = pd.read_pickle('data/op1.pickle')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2018年2月26日のデータ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "t0 = pd.to_datetime('2018-02-26 15:15')\n",
    "t1 = pd.to_datetime('2018-03-09 09:00')\n",
    "s0 = s_df.loc[t0, '1803']\n",
    "r = 0.001\n",
    "delta_t = t1 - t0\n",
    "t = (delta_t.days / 365) + (delta_t.seconds / 31536000)\n",
    "op_df = op_pn.loc['2018-02-26 15:15'].dropna().copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'先物価格:22214.1, 満期までの期間:0.02942351598173516'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'先物価格:{}, 満期までの期間:{}'.format(s0, t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>iv</th>\n",
       "      <th>Call/Put</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15750.0</th>\n",
       "      <td>1.83762</td>\n",
       "      <td>0.719635</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16000.0</th>\n",
       "      <td>1.91618</td>\n",
       "      <td>0.692107</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16250.0</th>\n",
       "      <td>1.98373</td>\n",
       "      <td>0.664316</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16500.0</th>\n",
       "      <td>2.73333</td>\n",
       "      <td>0.655723</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16750.0</th>\n",
       "      <td>2.855</td>\n",
       "      <td>0.628376</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           price        iv Call/Put\n",
       "k                                  \n",
       "15750.0  1.83762  0.719635        P\n",
       "16000.0  1.91618  0.692107        P\n",
       "16250.0  1.98373  0.664316        P\n",
       "16500.0  2.73333  0.655723        P\n",
       "16750.0    2.855  0.628376        P"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "op_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _get_greeks(fop):\n",
    "    right, qty_price = fop.split('x')\n",
    "    qty = float(qty_price.split('@')[0])\n",
    "    if right == 's':\n",
    "        return 'f', qty, 0, 0, 0\n",
    "    \n",
    "    k = float(right[:-1])\n",
    "    pc = right[-1]\n",
    "    sigma = op_df.loc[k, 'iv']\n",
    "    delta = qty * ivolat3.delta(s0, k, r, 0, t, sigma, pc)\n",
    "    gamma = qty * ivolat3.gamma(s0, k, r, 0, t, sigma)\n",
    "    vega = qty * ivolat3.vega(s0, k, r, 0, t, sigma) * 0.01\n",
    "    theta = qty * ivolat3.theta(s0, k, r, 0, t, sigma, pc) / 365\n",
    "    return right, delta, gamma, vega, theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_greeks(portfolio):\n",
    "    df = pd.DataFrame([_get_greeks(p) for p in portfolio],\n",
    "                     columns=['right', 'delta', 'gamma', 'vega', 'theta'])\n",
    "    df.set_index('right', inplace=True)\n",
    "    df.loc['sum'] = df.sum()\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2018年3月限のオプションの価格\n",
    "\n",
    "k: 権利行使価格  \n",
    "price: 仲値  \n",
    "iv: ボラティリティ  \n",
    "Call/Put: コールまたはプット"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>iv</th>\n",
       "      <th>Call/Put</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15750.0</th>\n",
       "      <td>1.83762</td>\n",
       "      <td>0.719635</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16000.0</th>\n",
       "      <td>1.91618</td>\n",
       "      <td>0.692107</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16250.0</th>\n",
       "      <td>1.98373</td>\n",
       "      <td>0.664316</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16500.0</th>\n",
       "      <td>2.73333</td>\n",
       "      <td>0.655723</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16750.0</th>\n",
       "      <td>2.855</td>\n",
       "      <td>0.628376</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17000.0</th>\n",
       "      <td>3.52886</td>\n",
       "      <td>0.61238</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17250.0</th>\n",
       "      <td>3.90949</td>\n",
       "      <td>0.588903</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17500.0</th>\n",
       "      <td>4.63427</td>\n",
       "      <td>0.569882</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17750.0</th>\n",
       "      <td>5.09237</td>\n",
       "      <td>0.545632</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18000.0</th>\n",
       "      <td>5.99268</td>\n",
       "      <td>0.525789</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18250.0</th>\n",
       "      <td>7.04284</td>\n",
       "      <td>0.505644</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18500.0</th>\n",
       "      <td>7.91892</td>\n",
       "      <td>0.482303</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18750.0</th>\n",
       "      <td>9.00889</td>\n",
       "      <td>0.459455</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19000.0</th>\n",
       "      <td>10.7982</td>\n",
       "      <td>0.439673</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19125.0</th>\n",
       "      <td>11.5</td>\n",
       "      <td>0.427816</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19250.0</th>\n",
       "      <td>12.8129</td>\n",
       "      <td>0.418812</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19375.0</th>\n",
       "      <td>14.124</td>\n",
       "      <td>0.408991</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19500.0</th>\n",
       "      <td>15.0888</td>\n",
       "      <td>0.396954</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19625.0</th>\n",
       "      <td>16.6809</td>\n",
       "      <td>0.387047</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19750.0</th>\n",
       "      <td>18.0312</td>\n",
       "      <td>0.375485</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19875.0</th>\n",
       "      <td>20.2134</td>\n",
       "      <td>0.366192</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000.0</th>\n",
       "      <td>22.1687</td>\n",
       "      <td>0.355246</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20125.0</th>\n",
       "      <td>24.2222</td>\n",
       "      <td>0.34384</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20250.0</th>\n",
       "      <td>27.0323</td>\n",
       "      <td>0.333662</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20375.0</th>\n",
       "      <td>30.0102</td>\n",
       "      <td>0.32289</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20500.0</th>\n",
       "      <td>33.7129</td>\n",
       "      <td>0.312693</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20625.0</th>\n",
       "      <td>37.0312</td>\n",
       "      <td>0.300618</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20750.0</th>\n",
       "      <td>42.0484</td>\n",
       "      <td>0.290603</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20875.0</th>\n",
       "      <td>47.5556</td>\n",
       "      <td>0.279982</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21000.0</th>\n",
       "      <td>54.4845</td>\n",
       "      <td>0.269986</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21125.0</th>\n",
       "      <td>61.7717</td>\n",
       "      <td>0.258792</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21250.0</th>\n",
       "      <td>71.8098</td>\n",
       "      <td>0.249176</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21375.0</th>\n",
       "      <td>83.8667</td>\n",
       "      <td>0.239566</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21500.0</th>\n",
       "      <td>100.897</td>\n",
       "      <td>0.232302</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21625.0</th>\n",
       "      <td>115.062</td>\n",
       "      <td>0.219551</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21750.0</th>\n",
       "      <td>139.474</td>\n",
       "      <td>0.212425</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21875.0</th>\n",
       "      <td>167.115</td>\n",
       "      <td>0.203922</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22000.0</th>\n",
       "      <td>201.898</td>\n",
       "      <td>0.196336</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22125.0</th>\n",
       "      <td>247.857</td>\n",
       "      <td>0.191515</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22250.0</th>\n",
       "      <td>261.136</td>\n",
       "      <td>0.183005</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22375.0</th>\n",
       "      <td>197.241</td>\n",
       "      <td>0.176868</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22500.0</th>\n",
       "      <td>144.965</td>\n",
       "      <td>0.17216</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22625.0</th>\n",
       "      <td>108.198</td>\n",
       "      <td>0.172209</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22750.0</th>\n",
       "      <td>76.4527</td>\n",
       "      <td>0.169926</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22875.0</th>\n",
       "      <td>54.9558</td>\n",
       "      <td>0.170694</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23000.0</th>\n",
       "      <td>37.9677</td>\n",
       "      <td>0.170424</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23125.0</th>\n",
       "      <td>26.275</td>\n",
       "      <td>0.171314</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23250.0</th>\n",
       "      <td>17.994</td>\n",
       "      <td>0.172478</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23375.0</th>\n",
       "      <td>12.9683</td>\n",
       "      <td>0.175904</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23500.0</th>\n",
       "      <td>8.99706</td>\n",
       "      <td>0.17816</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23625.0</th>\n",
       "      <td>6.87132</td>\n",
       "      <td>0.183405</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23750.0</th>\n",
       "      <td>4.98936</td>\n",
       "      <td>0.187001</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23875.0</th>\n",
       "      <td>3.83624</td>\n",
       "      <td>0.192046</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24000.0</th>\n",
       "      <td>2.86294</td>\n",
       "      <td>0.196121</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24125.0</th>\n",
       "      <td>2.05158</td>\n",
       "      <td>0.199089</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24250.0</th>\n",
       "      <td>1.82305</td>\n",
       "      <td>0.207065</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24375.0</th>\n",
       "      <td>1.70825</td>\n",
       "      <td>0.216083</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           price        iv Call/Put\n",
       "k                                  \n",
       "15750.0  1.83762  0.719635        P\n",
       "16000.0  1.91618  0.692107        P\n",
       "16250.0  1.98373  0.664316        P\n",
       "16500.0  2.73333  0.655723        P\n",
       "16750.0    2.855  0.628376        P\n",
       "17000.0  3.52886   0.61238        P\n",
       "17250.0  3.90949  0.588903        P\n",
       "17500.0  4.63427  0.569882        P\n",
       "17750.0  5.09237  0.545632        P\n",
       "18000.0  5.99268  0.525789        P\n",
       "18250.0  7.04284  0.505644        P\n",
       "18500.0  7.91892  0.482303        P\n",
       "18750.0  9.00889  0.459455        P\n",
       "19000.0  10.7982  0.439673        P\n",
       "19125.0     11.5  0.427816        P\n",
       "19250.0  12.8129  0.418812        P\n",
       "19375.0   14.124  0.408991        P\n",
       "19500.0  15.0888  0.396954        P\n",
       "19625.0  16.6809  0.387047        P\n",
       "19750.0  18.0312  0.375485        P\n",
       "19875.0  20.2134  0.366192        P\n",
       "20000.0  22.1687  0.355246        P\n",
       "20125.0  24.2222   0.34384        P\n",
       "20250.0  27.0323  0.333662        P\n",
       "20375.0  30.0102   0.32289        P\n",
       "20500.0  33.7129  0.312693        P\n",
       "20625.0  37.0312  0.300618        P\n",
       "20750.0  42.0484  0.290603        P\n",
       "20875.0  47.5556  0.279982        P\n",
       "21000.0  54.4845  0.269986        P\n",
       "21125.0  61.7717  0.258792        P\n",
       "21250.0  71.8098  0.249176        P\n",
       "21375.0  83.8667  0.239566        P\n",
       "21500.0  100.897  0.232302        P\n",
       "21625.0  115.062  0.219551        P\n",
       "21750.0  139.474  0.212425        P\n",
       "21875.0  167.115  0.203922        P\n",
       "22000.0  201.898  0.196336        P\n",
       "22125.0  247.857  0.191515        P\n",
       "22250.0  261.136  0.183005        C\n",
       "22375.0  197.241  0.176868        C\n",
       "22500.0  144.965   0.17216        C\n",
       "22625.0  108.198  0.172209        C\n",
       "22750.0  76.4527  0.169926        C\n",
       "22875.0  54.9558  0.170694        C\n",
       "23000.0  37.9677  0.170424        C\n",
       "23125.0   26.275  0.171314        C\n",
       "23250.0   17.994  0.172478        C\n",
       "23375.0  12.9683  0.175904        C\n",
       "23500.0  8.99706   0.17816        C\n",
       "23625.0  6.87132  0.183405        C\n",
       "23750.0  4.98936  0.187001        C\n",
       "23875.0  3.83624  0.192046        C\n",
       "24000.0  2.86294  0.196121        C\n",
       "24125.0  2.05158  0.199089        C\n",
       "24250.0  1.82305  0.207065        C\n",
       "24375.0  1.70825  0.216083        C"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "op_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 先物ロング/ショート\n",
    "\n",
    "* 相場の上下を予測\n",
    "* デルタだけをとりにいく戦略"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f77faa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['sx1@22214'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       delta  gamma  vega  theta\n",
       "right                           \n",
       "f        1.0    0.0   0.0    0.0\n",
       "sum      1.0    0.0   0.0    0.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['sx1@22214'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f77fa470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['sx-1@22214'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       delta  gamma  vega  theta\n",
       "right                           \n",
       "f       -1.0    0.0   0.0    0.0\n",
       "sum     -1.0    0.0   0.0    0.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['sx-1@22214'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ネイキッドロング\n",
    "\n",
    "* 単純にオプションを買う\n",
    "* 相場の方向性とボラティリティ上昇の両方を当てにいく\n",
    "* 損失限定でリスクは比較的小さいがコストはやや高い\n",
    "* 非常に難しい"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f56f39e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['22250cx1@261'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22250c</th>\n",
       "      <td>0.486102</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>15.192281</td>\n",
       "      <td>-12.972841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>0.486102</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>15.192281</td>\n",
       "      <td>-12.972841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "22250c  0.486102  0.000572  15.192281 -12.972841\n",
       "sum     0.486102  0.000572  15.192281 -12.972841"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['22250cx1@261'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f56f32b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['22250px1@297'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22250p</th>\n",
       "      <td>-0.513868</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>15.192281</td>\n",
       "      <td>-12.911884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>-0.513868</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>15.192281</td>\n",
       "      <td>-12.911884</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "22250p -0.513868  0.000572  15.192281 -12.911884\n",
       "sum    -0.513868  0.000572  15.192281 -12.911884"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['22250px1@297'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ネイキッドショート\n",
    "\n",
    "* 単純にオプションを売る\n",
    "* 相場の方向性とボラティリティ下落の両方を当てにいく\n",
    "* 損失無限大\n",
    "* 証拠金が高くなる\n",
    "* 勝率が高いがリスクが高い"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f55fc908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['22250cx-1@261'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22250c</th>\n",
       "      <td>-0.486102</td>\n",
       "      <td>-0.000572</td>\n",
       "      <td>-15.192281</td>\n",
       "      <td>12.972841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>-0.486102</td>\n",
       "      <td>-0.000572</td>\n",
       "      <td>-15.192281</td>\n",
       "      <td>12.972841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "22250c -0.486102 -0.000572 -15.192281  12.972841\n",
       "sum    -0.486102 -0.000572 -15.192281  12.972841"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['22250cx-1@261'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f55d15f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['22250px-1@297'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22250p</th>\n",
       "      <td>0.513868</td>\n",
       "      <td>-0.000572</td>\n",
       "      <td>-15.192281</td>\n",
       "      <td>12.911884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>0.513868</td>\n",
       "      <td>-0.000572</td>\n",
       "      <td>-15.192281</td>\n",
       "      <td>12.911884</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "22250p  0.513868 -0.000572 -15.192281  12.911884\n",
       "sum     0.513868 -0.000572 -15.192281  12.911884"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['22250px-1@297'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### プロティクティブコール\n",
    "\n",
    "* 先物ショートとコール買いを組み合わせた戦略\n",
    "* コール買いの保険がついた先物売り\n",
    "* プット買いに似ているが、プットのプレミアムが割高なときにコールで代用できる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f5589978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['sx-1@22214', '22250cx1@261'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22250c</th>\n",
       "      <td>0.486102</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>15.192281</td>\n",
       "      <td>-12.972841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>-0.513898</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>15.192281</td>\n",
       "      <td>-12.972841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "f      -1.000000  0.000000   0.000000   0.000000\n",
       "22250c  0.486102  0.000572  15.192281 -12.972841\n",
       "sum    -0.513898  0.000572  15.192281 -12.972841"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['sx-1@22214', '22250cx1@261'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### プロティクティブプット\n",
    "\n",
    "* 先物ロングとプット買いを組み合わせた戦略\n",
    "* プット買いの保険がついた先物売り\n",
    "* コール買いに似ているが、コールのプレミアムが割高なときにプットで代用できる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f556c518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['sx1@22214', '22250px1@297'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22250p</th>\n",
       "      <td>-0.513868</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>15.192281</td>\n",
       "      <td>-12.911884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>0.486132</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>15.192281</td>\n",
       "      <td>-12.911884</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "f       1.000000  0.000000   0.000000   0.000000\n",
       "22250p -0.513868  0.000572  15.192281 -12.911884\n",
       "sum     0.486132  0.000572  15.192281 -12.911884"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['sx1@22214', '22250px1@297'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### カバードコール(1:1)\n",
    "\n",
    "* 先物買いとコール売りを組み合わせた戦略\n",
    "* 先物買いの利益に上限を設ける代わりにオプションのプレミアムを受け取る\n",
    "* 先物買いの利益確定の手段として使われることもある"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f5555ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['sx1@22214', '22250cx-1@261'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22250c</th>\n",
       "      <td>-0.486102</td>\n",
       "      <td>-0.000572</td>\n",
       "      <td>-15.192281</td>\n",
       "      <td>12.972841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>0.513898</td>\n",
       "      <td>-0.000572</td>\n",
       "      <td>-15.192281</td>\n",
       "      <td>12.972841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "f       1.000000  0.000000   0.000000   0.000000\n",
       "22250c -0.486102 -0.000572 -15.192281  12.972841\n",
       "sum     0.513898 -0.000572 -15.192281  12.972841"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['sx1@22214', '22250cx-1@261'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### カバードプット(1:1)\n",
    "\n",
    "* 先物売りとプット売りを組み合わせた戦略\n",
    "* 先物売りの利益に上限を設ける代わりにオプションのプレミアムを受け取る\n",
    "* 先物売りの利益確定の手段として使われることもある"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f5646e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['sx-1@22214', '22250px-1@297'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22250p</th>\n",
       "      <td>0.513868</td>\n",
       "      <td>-0.000572</td>\n",
       "      <td>-15.192281</td>\n",
       "      <td>12.911884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>-0.486132</td>\n",
       "      <td>-0.000572</td>\n",
       "      <td>-15.192281</td>\n",
       "      <td>12.911884</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "f      -1.000000  0.000000   0.000000   0.000000\n",
       "22250p  0.513868 -0.000572 -15.192281  12.911884\n",
       "sum    -0.486132 -0.000572 -15.192281  12.911884"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['sx-1@22214', '22250px-1@297'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### カバードコール(ショートストラドル)\n",
    "\n",
    "* 先物買いとATMコール売りを複数組み合わせた戦略\n",
    "* 通常はデルタをニュートラルにする\n",
    "* 相場が動かないまたはボラティリティが低下することを狙った戦略\n",
    "* オプションのみで組成したショートストラドルよりはリスクが少ない"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f56f3240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['sx1@22214', '22250cx-2@261'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22250c</th>\n",
       "      <td>-0.972204</td>\n",
       "      <td>-0.001144</td>\n",
       "      <td>-30.384562</td>\n",
       "      <td>25.945682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>0.027796</td>\n",
       "      <td>-0.001144</td>\n",
       "      <td>-30.384562</td>\n",
       "      <td>25.945682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "f       1.000000  0.000000   0.000000   0.000000\n",
       "22250c -0.972204 -0.001144 -30.384562  25.945682\n",
       "sum     0.027796 -0.001144 -30.384562  25.945682"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['sx1@22214', '22250cx-2@261'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### カバードプット(ショートストラドル)\n",
    "\n",
    "* 先物売りとATMプット売りを複数組み合わせた戦略\n",
    "* 通常はデルタをニュートラルにする\n",
    "* 先物が動かないまたはボラティリティが低下することを狙った戦略\n",
    "* オプションのみで組成したショートストラドルよりはリスクが少ない"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f55d6908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['sx-1@22214', '22250px-2@297'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22250p</th>\n",
       "      <td>1.027737</td>\n",
       "      <td>-0.001144</td>\n",
       "      <td>-30.384562</td>\n",
       "      <td>25.823768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>0.027737</td>\n",
       "      <td>-0.001144</td>\n",
       "      <td>-30.384562</td>\n",
       "      <td>25.823768</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "f      -1.000000  0.000000   0.000000   0.000000\n",
       "22250p  1.027737 -0.001144 -30.384562  25.823768\n",
       "sum     0.027737 -0.001144 -30.384562  25.823768"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['sx-1@22214', '22250px-2@297'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ロングストラドル\n",
    "\n",
    "* ATMコールとATMプットを買う戦略\n",
    "* 先物が動くまたはボラティリティが上昇することを狙った戦略\n",
    "* ガンマを積極的にとりにいく、セータが敵となる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f5615748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['22250cx1@261', '22250px1@297'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22250c</th>\n",
       "      <td>0.486102</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>15.192281</td>\n",
       "      <td>-12.972841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22250p</th>\n",
       "      <td>-0.513868</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>15.192281</td>\n",
       "      <td>-12.911884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>-0.027766</td>\n",
       "      <td>0.001144</td>\n",
       "      <td>30.384562</td>\n",
       "      <td>-25.884725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "22250c  0.486102  0.000572  15.192281 -12.972841\n",
       "22250p -0.513868  0.000572  15.192281 -12.911884\n",
       "sum    -0.027766  0.001144  30.384562 -25.884725"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['22250cx1@261', '22250px1@297'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ショートストラドル\n",
    "\n",
    "* ATMコールとATMプットを売る戦略\n",
    "* 先物が動かないまたはボラティリティが低下することを狙った戦略\n",
    "* セータまたはボラティリティの低下をとりにいくが、ガンマが敵となる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f545cc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['22250cx-1@261', '22250px-1@297'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ロングストラングル\n",
    "\n",
    "* OTMコールとOTMプットを買う戦略\n",
    "* 先物が大きく動くまたはボラティリティが大きく上昇することを狙った戦略"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f54141d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['22625cx1@108', '21500px1@101'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22625c</th>\n",
       "      <td>0.272680</td>\n",
       "      <td>0.000506</td>\n",
       "      <td>12.661436</td>\n",
       "      <td>-10.167566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21500p</th>\n",
       "      <td>-0.200267</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>10.676542</td>\n",
       "      <td>-11.534445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>0.072414</td>\n",
       "      <td>0.000823</td>\n",
       "      <td>23.337978</td>\n",
       "      <td>-21.702011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "22625c  0.272680  0.000506  12.661436 -10.167566\n",
       "21500p -0.200267  0.000317  10.676542 -11.534445\n",
       "sum     0.072414  0.000823  23.337978 -21.702011"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['22625cx1@108', '21500px1@101'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ショートストラングル\n",
    "\n",
    "* OTMコールとOTMプットを売る戦略\n",
    "* 先物が動かないまたはボラティリティが低下することを狙った戦略\n",
    "* 勝率が高いが枚数を増やすとリスクが非常に高くなる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f5398e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['22625cx-1@108', '21500px-1@101'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22625c</th>\n",
       "      <td>-0.272680</td>\n",
       "      <td>-0.000506</td>\n",
       "      <td>-12.661436</td>\n",
       "      <td>10.167566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21500p</th>\n",
       "      <td>0.200267</td>\n",
       "      <td>-0.000317</td>\n",
       "      <td>-10.676542</td>\n",
       "      <td>11.534445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>-0.072414</td>\n",
       "      <td>-0.000823</td>\n",
       "      <td>-23.337978</td>\n",
       "      <td>21.702011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "22625c -0.272680 -0.000506 -12.661436  10.167566\n",
       "21500p  0.200267 -0.000317 -10.676542  11.534445\n",
       "sum    -0.072414 -0.000823 -23.337978  21.702011"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['22625cx-1@108', '21500px-1@101'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### デビットスプレッド\n",
    "\n",
    "* OTMを買い、その外側のOTMを売る戦略\n",
    "* ネイキッドロングの利益を限定した形だが、コストが安い"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f52d9f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['22250cx1@261', '22500cx-1@144'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22250c</th>\n",
       "      <td>0.486102</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>15.192281</td>\n",
       "      <td>-12.972841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22500c</th>\n",
       "      <td>-0.338229</td>\n",
       "      <td>-0.000557</td>\n",
       "      <td>-13.934029</td>\n",
       "      <td>11.188627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>0.147874</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>1.258252</td>\n",
       "      <td>-1.784214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "22250c  0.486102  0.000572  15.192281 -12.972841\n",
       "22500c -0.338229 -0.000557 -13.934029  11.188627\n",
       "sum     0.147874  0.000014   1.258252  -1.784214"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['22250cx1@261', '22500cx-1@144'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f52a0e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['21750px1@139', '21500px-1@101'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21750p</th>\n",
       "      <td>-0.274758</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>12.709052</td>\n",
       "      <td>-12.551943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21500p</th>\n",
       "      <td>0.200267</td>\n",
       "      <td>-0.000317</td>\n",
       "      <td>-10.676542</td>\n",
       "      <td>11.534445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>-0.074491</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>2.032510</td>\n",
       "      <td>-1.017498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "21750p -0.274758  0.000412  12.709052 -12.551943\n",
       "21500p  0.200267 -0.000317 -10.676542  11.534445\n",
       "sum    -0.074491  0.000096   2.032510  -1.017498"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['21750px1@139', '21500px-1@101'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### クレジットスプレッド\n",
    "\n",
    "* OTMを売り、その外側のOTMを買う戦略\n",
    "* ネイキッドショートの損失を限定した形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f527abe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['22250cx-1@261', '22500cx1@144'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22250c</th>\n",
       "      <td>-0.486102</td>\n",
       "      <td>-0.000572</td>\n",
       "      <td>-15.192281</td>\n",
       "      <td>12.972841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22500c</th>\n",
       "      <td>0.338229</td>\n",
       "      <td>0.000557</td>\n",
       "      <td>13.934029</td>\n",
       "      <td>-11.188627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>-0.147874</td>\n",
       "      <td>-0.000014</td>\n",
       "      <td>-1.258252</td>\n",
       "      <td>1.784214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "22250c -0.486102 -0.000572 -15.192281  12.972841\n",
       "22500c  0.338229  0.000557  13.934029 -11.188627\n",
       "sum    -0.147874 -0.000014  -1.258252   1.784214"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['22250cx-1@261', '22500cx1@144'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f525e128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['21750px-1@139', '21500px1@101'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21750p</th>\n",
       "      <td>0.274758</td>\n",
       "      <td>-0.000412</td>\n",
       "      <td>-12.709052</td>\n",
       "      <td>12.551943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21500p</th>\n",
       "      <td>-0.200267</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>10.676542</td>\n",
       "      <td>-11.534445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>0.074491</td>\n",
       "      <td>-0.000096</td>\n",
       "      <td>-2.032510</td>\n",
       "      <td>1.017498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "21750p  0.274758 -0.000412 -12.709052  12.551943\n",
       "21500p -0.200267  0.000317  10.676542 -11.534445\n",
       "sum     0.074491 -0.000096  -2.032510   1.017498"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['21750px-1@139', '21500px1@101'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ブルシンセティック(リスクリバーサルの売り)\n",
    "\n",
    "* OTMコールを買い、OTMプットを売る戦略\n",
    "* 先物ロングに似ているが、動きがなかった場合に中立となる\n",
    "* ボラティリティの歪み(skew)をとる場合に組成される"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f547c358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['22625cx1@108', '21500px-1@101'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22625c</th>\n",
       "      <td>0.272680</td>\n",
       "      <td>0.000506</td>\n",
       "      <td>12.661436</td>\n",
       "      <td>-10.167566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21500p</th>\n",
       "      <td>0.200267</td>\n",
       "      <td>-0.000317</td>\n",
       "      <td>-10.676542</td>\n",
       "      <td>11.534445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>0.472947</td>\n",
       "      <td>0.000190</td>\n",
       "      <td>1.984894</td>\n",
       "      <td>1.366879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "22625c  0.272680  0.000506  12.661436 -10.167566\n",
       "21500p  0.200267 -0.000317 -10.676542  11.534445\n",
       "sum     0.472947  0.000190   1.984894   1.366879"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['22625cx1@108', '21500px-1@101'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ベアシンセティック(リスクリバーサルの買い)\n",
    "\n",
    "* OTMコールを売り、OTMプットを買う戦略\n",
    "* 先物ショートに似ているが、動きがなかった場合に中立となる\n",
    "* ボラティリティの歪み(skew)をとる場合に組成される"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f56a10b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['22625cx-1@108', '21500px1@101'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22625c</th>\n",
       "      <td>-0.272680</td>\n",
       "      <td>-0.000506</td>\n",
       "      <td>-12.661436</td>\n",
       "      <td>10.167566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21500p</th>\n",
       "      <td>-0.200267</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>10.676542</td>\n",
       "      <td>-11.534445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>-0.472947</td>\n",
       "      <td>-0.000190</td>\n",
       "      <td>-1.984894</td>\n",
       "      <td>-1.366879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "22625c -0.272680 -0.000506 -12.661436  10.167566\n",
       "21500p -0.200267  0.000317  10.676542 -11.534445\n",
       "sum    -0.472947 -0.000190  -1.984894  -1.366879"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['22625cx-1@108', '21500px1@101'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### デルタヘッジ+ブルシンセティック(リスクリバーサルの売り)\n",
    "\n",
    "* 先物でデルタを調整したシンセティック"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f5755978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['22625cx1@108', '21500px-1@101', 'sx-0.47@22214'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22625c</th>\n",
       "      <td>0.272680</td>\n",
       "      <td>0.000506</td>\n",
       "      <td>12.661436</td>\n",
       "      <td>-10.167566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21500p</th>\n",
       "      <td>0.200267</td>\n",
       "      <td>-0.000317</td>\n",
       "      <td>-10.676542</td>\n",
       "      <td>11.534445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>-0.470000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>0.002947</td>\n",
       "      <td>0.000190</td>\n",
       "      <td>1.984894</td>\n",
       "      <td>1.366879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "22625c  0.272680  0.000506  12.661436 -10.167566\n",
       "21500p  0.200267 -0.000317 -10.676542  11.534445\n",
       "f      -0.470000  0.000000   0.000000   0.000000\n",
       "sum     0.002947  0.000190   1.984894   1.366879"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['22625cx1@108', '21500px-1@101', 'sx-0.47@22214'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### デルタヘッジ+ベアシンセティック(リスクリバーサルの買い)\n",
    "\n",
    "* 先物でデルタを調整したシンセティック"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f5581e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['22625cx-1@108', '21500px1@101', 'sx0.47@22214'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### レシオスプレッド\n",
    "\n",
    "* オプションを買いその外側を複数売る戦略\n",
    "* 先物が緩やかに動くことを狙って組成\n",
    "* 勝率は高いがリスクも高い"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f53396a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['22500cx1@145', '22625cx-2@108'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22500c</th>\n",
       "      <td>0.338229</td>\n",
       "      <td>0.000557</td>\n",
       "      <td>13.934029</td>\n",
       "      <td>-11.188627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22625c</th>\n",
       "      <td>-0.545361</td>\n",
       "      <td>-0.001013</td>\n",
       "      <td>-25.322872</td>\n",
       "      <td>20.335132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>-0.207132</td>\n",
       "      <td>-0.000455</td>\n",
       "      <td>-11.388843</td>\n",
       "      <td>9.146505</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "22500c  0.338229  0.000557  13.934029 -11.188627\n",
       "22625c -0.545361 -0.001013 -25.322872  20.335132\n",
       "sum    -0.207132 -0.000455 -11.388843   9.146505"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['22500cx1@145', '22625cx-2@108'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f5646710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['21875px1@167', '21750px-2@139'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21875p</th>\n",
       "      <td>-0.323431</td>\n",
       "      <td>0.000462</td>\n",
       "      <td>13.687263</td>\n",
       "      <td>-12.974453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21750p</th>\n",
       "      <td>0.549515</td>\n",
       "      <td>-0.000824</td>\n",
       "      <td>-25.418104</td>\n",
       "      <td>25.103885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>0.226084</td>\n",
       "      <td>-0.000362</td>\n",
       "      <td>-11.730841</td>\n",
       "      <td>12.129432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "21875p -0.323431  0.000462  13.687263 -12.974453\n",
       "21750p  0.549515 -0.000824 -25.418104  25.103885\n",
       "sum     0.226084 -0.000362 -11.730841  12.129432"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['21875px1@167', '21750px-2@139'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### バックスプレッド\n",
    "\n",
    "* オプションを売りその外側を複数買う戦略\n",
    "* ガンマとベガの上昇を効率よくとりにいける"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f56961d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['22500cx-1@145', '22625cx2@108'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22500c</th>\n",
       "      <td>-0.338229</td>\n",
       "      <td>-0.000557</td>\n",
       "      <td>-13.934029</td>\n",
       "      <td>11.188627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22625c</th>\n",
       "      <td>0.545361</td>\n",
       "      <td>0.001013</td>\n",
       "      <td>25.322872</td>\n",
       "      <td>-20.335132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>0.207132</td>\n",
       "      <td>0.000455</td>\n",
       "      <td>11.388843</td>\n",
       "      <td>-9.146505</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "22500c -0.338229 -0.000557 -13.934029  11.188627\n",
       "22625c  0.545361  0.001013  25.322872 -20.335132\n",
       "sum     0.207132  0.000455  11.388843  -9.146505"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['22500cx-1@145', '22625cx2@108'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f54aaa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['21875px-1@167', '21750px2@139'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21875p</th>\n",
       "      <td>0.323431</td>\n",
       "      <td>-0.000462</td>\n",
       "      <td>-13.687263</td>\n",
       "      <td>12.974453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21750p</th>\n",
       "      <td>-0.549515</td>\n",
       "      <td>0.000824</td>\n",
       "      <td>25.418104</td>\n",
       "      <td>-25.103885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>-0.226084</td>\n",
       "      <td>0.000362</td>\n",
       "      <td>11.730841</td>\n",
       "      <td>-12.129432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "21875p  0.323431 -0.000462 -13.687263  12.974453\n",
       "21750p -0.549515  0.000824  25.418104 -25.103885\n",
       "sum    -0.226084  0.000362  11.730841 -12.129432"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['21875px-1@167', '21750px2@139'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ロングバタフライ\n",
    "\n",
    "* オプションを1枚買いその外側を2枚売り、更にその外側を1枚買う戦略\n",
    "* 精算値がオプションを売った位置にくると最大の利益となる\n",
    "* コストが安い\n",
    "* グリークスがほぼニュートラルで非常にリスクが少ない"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f5431da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['22500cx1@145', '22625cx-2@108', '22750cx1@77'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22500c</th>\n",
       "      <td>0.338229</td>\n",
       "      <td>0.000557</td>\n",
       "      <td>13.934029</td>\n",
       "      <td>-11.188627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22625c</th>\n",
       "      <td>-0.545361</td>\n",
       "      <td>-0.001013</td>\n",
       "      <td>-25.322872</td>\n",
       "      <td>20.335132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22750c</th>\n",
       "      <td>0.211200</td>\n",
       "      <td>0.000447</td>\n",
       "      <td>11.018721</td>\n",
       "      <td>-8.729772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>0.004068</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>-0.370122</td>\n",
       "      <td>0.416733</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "22500c  0.338229  0.000557  13.934029 -11.188627\n",
       "22625c -0.545361 -0.001013 -25.322872  20.335132\n",
       "22750c  0.211200  0.000447  11.018721  -8.729772\n",
       "sum     0.004068 -0.000009  -0.370122   0.416733"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['22500cx1@145', '22625cx-2@108', '22750cx1@77'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ショートバタフライ\n",
    "\n",
    "* オプションを1枚売りその外側を2枚買い、更にその外側を1枚売る戦略\n",
    "* 精算値がオプションを買った外側に来ると利益となる\n",
    "* コストが安い\n",
    "* グリークスがほぼニュートラルで非常にリスクが少ない"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f53e4a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['22250cx-1@261', '22375cx2@197', '22500cx-1@144'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22250c</th>\n",
       "      <td>-0.486102</td>\n",
       "      <td>-0.000572</td>\n",
       "      <td>-15.192281</td>\n",
       "      <td>12.972841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22375c</th>\n",
       "      <td>0.824490</td>\n",
       "      <td>0.001155</td>\n",
       "      <td>29.664632</td>\n",
       "      <td>-24.476199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22500c</th>\n",
       "      <td>-0.338229</td>\n",
       "      <td>-0.000557</td>\n",
       "      <td>-13.934029</td>\n",
       "      <td>11.188627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>0.000159</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.538322</td>\n",
       "      <td>-0.314730</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "22250c -0.486102 -0.000572 -15.192281  12.972841\n",
       "22375c  0.824490  0.001155  29.664632 -24.476199\n",
       "22500c -0.338229 -0.000557 -13.934029  11.188627\n",
       "sum     0.000159  0.000026   0.538322  -0.314730"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['22250cx-1@261', '22375cx2@197', '22500cx-1@144'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ロングコンドル\n",
    "* オプションを1枚買いその外側を1枚売り、更にその外側を1枚売り、更にその外側を1枚買う戦略\n",
    "* 精算値がオプションを売った位置にくると最大の利益となる\n",
    "* ロングバタフライよりも勝率は高いが、リスクが少し高くなる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f5402278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['22250cx1@261', '22375cx-1@197', '22500cx-1@144', '22625cx1@108'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22250c</th>\n",
       "      <td>0.486102</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>15.192281</td>\n",
       "      <td>-12.972841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22375c</th>\n",
       "      <td>-0.412245</td>\n",
       "      <td>-0.000578</td>\n",
       "      <td>-14.832316</td>\n",
       "      <td>12.238099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22500c</th>\n",
       "      <td>-0.338229</td>\n",
       "      <td>-0.000557</td>\n",
       "      <td>-13.934029</td>\n",
       "      <td>11.188627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22625c</th>\n",
       "      <td>0.272680</td>\n",
       "      <td>0.000506</td>\n",
       "      <td>12.661436</td>\n",
       "      <td>-10.167566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>0.008309</td>\n",
       "      <td>-0.000057</td>\n",
       "      <td>-0.912628</td>\n",
       "      <td>0.286319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "22250c  0.486102  0.000572  15.192281 -12.972841\n",
       "22375c -0.412245 -0.000578 -14.832316  12.238099\n",
       "22500c -0.338229 -0.000557 -13.934029  11.188627\n",
       "22625c  0.272680  0.000506  12.661436 -10.167566\n",
       "sum     0.008309 -0.000057  -0.912628   0.286319"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['22250cx1@261', '22375cx-1@197', '22500cx-1@144', '22625cx1@108'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ショートコンドル\n",
    "* オプションを1枚売りその外側を1枚買い、更にその外側を1枚買い、更にその外側を1枚売る戦略\n",
    "* 精算値がオプションを売った外側にくると最大の利益となる\n",
    "* ショートバタフライよりも利益の上限が増えるが、リスクが少し高くなる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79f542bf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_payoff(['22250cx-1@261', '22375cx1@197', '22500cx1@144', '22625cx-1@108'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>delta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>vega</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22250c</th>\n",
       "      <td>-0.486102</td>\n",
       "      <td>-0.000572</td>\n",
       "      <td>-15.192281</td>\n",
       "      <td>12.972841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22375c</th>\n",
       "      <td>0.412245</td>\n",
       "      <td>0.000578</td>\n",
       "      <td>14.832316</td>\n",
       "      <td>-12.238099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22500c</th>\n",
       "      <td>0.338229</td>\n",
       "      <td>0.000557</td>\n",
       "      <td>13.934029</td>\n",
       "      <td>-11.188627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22625c</th>\n",
       "      <td>-0.272680</td>\n",
       "      <td>-0.000506</td>\n",
       "      <td>-12.661436</td>\n",
       "      <td>10.167566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>-0.008309</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.912628</td>\n",
       "      <td>-0.286319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           delta     gamma       vega      theta\n",
       "right                                           \n",
       "22250c -0.486102 -0.000572 -15.192281  12.972841\n",
       "22375c  0.412245  0.000578  14.832316 -12.238099\n",
       "22500c  0.338229  0.000557  13.934029 -11.188627\n",
       "22625c -0.272680 -0.000506 -12.661436  10.167566\n",
       "sum    -0.008309  0.000057   0.912628  -0.286319"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_greeks(['22250cx-1@261', '22375cx1@197', '22500cx1@144', '22625cx-1@108'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}