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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import exploration.option_trades as rvol\n",
"import datetime\n",
"import pandas as pd\n",
"\n",
"from analytics import on_the_run\n",
"from scipy.interpolate import interp1d\n",
"from matplotlib import pyplot as plt\n",
"from ipywidgets import widgets"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b7808ffade83485bba8f589bedb10f68",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Index:', options=('IG', 'HY'), value='IG')"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"w = widgets.Dropdown(\n",
" options=['IG', 'HY'],\n",
" value='IG',\n",
" description='Index:',\n",
" disabled=False,\n",
")\n",
"w"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"index = w.value\n",
"start_date=datetime.date(2014, 6, 11)\n",
"onTR, model = rvol.realized_vol(index, tenor=\"5yr\", years=3)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4dfbcdf09a294dc781ad1a5b4c382c96",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fe3eb2ad390>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"onTR.plot()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Constant Mean - GARCH Model Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>spread_return</td> <th> R-squared: </th> <td> -0.001</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Mean Model:</th> <td>Constant Mean</td> <th> Adj. R-squared: </th> <td> -0.001</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Vol Model:</th> <td>GARCH</td> <th> Log-Likelihood: </th> <td> 94.1921</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Distribution:</th> <td>Normal</td> <th> AIC: </th> <td> -180.384</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Maximum Likelihood</td> <th> BIC: </th> <td> -161.947</td>\n",
"</tr>\n",
"<tr>\n",
" <th></th> <td></td> <th> No. Observations: </th> <td>742</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Wed, Sep 11 2019</td> <th> Df Residuals: </th> <td>738</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>10:44:17</td> <th> Df Model: </th> <td>4</td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Mean Model</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>95.0% Conf. Int.</th> \n",
"</tr>\n",
"<tr>\n",
" <th>mu</th> <td> -0.0178</td> <td>7.408e-03</td> <td> -2.400</td> <td>1.641e-02</td> <td>[-3.230e-02,-3.258e-03]</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Volatility Model</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>95.0% Conf. Int.</th> \n",
"</tr>\n",
"<tr>\n",
" <th>omega</th> <td>3.6040e-03</td> <td>1.414e-03</td> <td> 2.548</td> <td>1.082e-02</td> <td>[8.322e-04,6.376e-03]</td>\n",
"</tr>\n",
"<tr>\n",
" <th>alpha[1]</th> <td> 0.1348</td> <td>4.345e-02</td> <td> 3.103</td> <td>1.914e-03</td> <td>[4.968e-02, 0.220]</td> \n",
"</tr>\n",
"<tr>\n",
" <th>beta[1]</th> <td> 0.7960</td> <td>5.441e-02</td> <td> 14.631</td> <td>1.774e-48</td> <td>[ 0.689, 0.903]</td> \n",
"</tr>\n",
"</table><br/><br/>Covariance estimator: robust"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Constant Mean - GARCH Model Results \n",
"==============================================================================\n",
"Dep. Variable: spread_return R-squared: -0.001\n",
"Mean Model: Constant Mean Adj. R-squared: -0.001\n",
"Vol Model: GARCH Log-Likelihood: 94.1921\n",
"Distribution: Normal AIC: -180.384\n",
"Method: Maximum Likelihood BIC: -161.947\n",
" No. Observations: 742\n",
"Date: Wed, Sep 11 2019 Df Residuals: 738\n",
"Time: 10:44:17 Df Model: 4\n",
" Mean Model \n",
"==============================================================================\n",
" coef std err t P>|t| 95.0% Conf. Int.\n",
"------------------------------------------------------------------------------\n",
"mu -0.0178 7.408e-03 -2.400 1.641e-02 [-3.230e-02,-3.258e-03]\n",
" Volatility Model \n",
"============================================================================\n",
" coef std err t P>|t| 95.0% Conf. Int.\n",
"----------------------------------------------------------------------------\n",
"omega 3.6040e-03 1.414e-03 2.548 1.082e-02 [8.322e-04,6.376e-03]\n",
"alpha[1] 0.1348 4.345e-02 3.103 1.914e-03 [4.968e-02, 0.220]\n",
"beta[1] 0.7960 5.441e-02 14.631 1.774e-48 [ 0.689, 0.903]\n",
"============================================================================\n",
"\n",
"Covariance estimator: robust\n",
"\"\"\""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'df' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m-----------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-bc681e92175e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#compute lo and hi percentiles of atm volatility daily change (vol of vol)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mrvol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvol_var\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/projects/code/python/exploration/option_trades.py\u001b[0m in \u001b[0;36mvol_var\u001b[0;34m(percentile, index, start_date)\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mwe\u001b[0m \u001b[0mshould\u001b[0m \u001b[0mgroup\u001b[0m \u001b[0mit\u001b[0m \u001b[0mby\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 112\u001b[0m \"\"\"\n\u001b[0;32m--> 113\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0matm_vol\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart_date\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 114\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrolling_vol\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mterm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/projects/code/python/exploration/option_trades.py\u001b[0m in \u001b[0;36matm_vol\u001b[0;34m(index, date, series, moneyness)\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 88\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0matm_vol\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmoneyness\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0matm_vol_calc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmoneyness\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrolling_vol\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'atm_vol'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mterm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
]
}
],
"source": [
"#compute lo and hi percentiles of atm volatility daily change (vol of vol)\n",
"rvol.vol_var()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = rvol.atm_vol(index, start_date, moneyness = .2)\n",
"df['steepness'] = df.otm_vol - df.atm_vol\n",
"df1 = df.reset_index()\n",
"df1['date'] = df1.quotedate.dt.date\n",
"df1 = df1.groupby(['date','expiry']).last()\n",
"#Need to do: the vol looks jumpy, is it because of quote source issue? yes, need to first try to get the same quote source..."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"r = []\n",
"time = [1/12, 2/12, 3/12, 4/12, 5/12]\n",
"for t in time:\n",
" for date, g in df1.groupby(level='date'):\n",
" f = interp1d(g['T'].values, g['steepness'].values, fill_value='extrapolate')\n",
" r.append((date, t, f(t)))\n",
"steepness = pd.DataFrame(r, columns=['date', 'T', 'steepness'])\n",
"steepness = steepness.set_index(['date','T']).unstack().astype('float')\n",
"steepness.columns = steepness.columns.droplevel()\n",
"steepness.ewm(span = 3).mean().plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"(steepness.iloc[-1] - steepness.mean()) / steepness.std()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Need to do: look at steepness not on moneyness but on delta range (60 delta vs 20 delta)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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|
