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-rw-r--r--python/notebooks/Realized Vol.ipynb25
-rw-r--r--python/notebooks/path.py5
-rw-r--r--python/notebooks/tranches numbers.ipynb488
3 files changed, 436 insertions, 82 deletions
diff --git a/python/notebooks/Realized Vol.ipynb b/python/notebooks/Realized Vol.ipynb
index a67654c5..4fa97ede 100644
--- a/python/notebooks/Realized Vol.ipynb
+++ b/python/notebooks/Realized Vol.ipynb
@@ -6,18 +6,9 @@
"metadata": {},
"outputs": [],
"source": [
- "import warnings\n",
- "warnings.simplefilter('ignore')\n",
- "\n",
- "import os\n",
- "import sys\n",
- "sys.path.append(os.path.join(os.environ['CODE_DIR'], 'python', 'exploration'))\n",
- "sys.path.append(os.path.join(os.environ['CODE_DIR'], 'python'))\n",
- "\n",
- "%matplotlib inline\n",
- "import seaborn\n",
- "seaborn.mpl.rcParams['figure.figsize'] = (10.0, 6.0)\n",
- "seaborn.mpl.rcParams['savefig.dpi'] = 90"
+ "from option_trades import realized_vol\n",
+ "from matplotlib import pyplot as plt\n",
+ "plt.style.use('ggplot')"
]
},
{
@@ -26,7 +17,8 @@
"metadata": {},
"outputs": [],
"source": [
- "import option_trades as ot"
+ "ig28, model_ig = realized_vol(\"IG\", 28, \"5yr\", years=3)\n",
+ "hy27, model_hy = realized_vol(\"HY\", 27, \"5yr\", years=3)"
]
},
{
@@ -35,8 +27,7 @@
"metadata": {},
"outputs": [],
"source": [
- "HY28Vol = ot.realized_vol(\"HY\", 28,\"5yr\", years = 3)\n",
- "IG28Vol = ot.realized_vol(\"IG\", 28,\"5yr\", years = 3)"
+ "ig28.plot()"
]
},
{
@@ -45,7 +36,7 @@
"metadata": {},
"outputs": [],
"source": [
- "HY28Vol[0].plot()"
+ "hy27.plot()"
]
},
{
@@ -54,7 +45,7 @@
"metadata": {},
"outputs": [],
"source": [
- "IG28Vol[0].plot()"
+ "model_ig.summary()"
]
},
{
diff --git a/python/notebooks/path.py b/python/notebooks/path.py
new file mode 100644
index 00000000..9d22534b
--- /dev/null
+++ b/python/notebooks/path.py
@@ -0,0 +1,5 @@
+import sys
+import os
+sys.path.append(os.path.join(os.environ['CODE_DIR'], 'python'))
+sys.path.append(os.path.join(os.environ['CODE_DIR'], 'python',
+ 'exploration'))
diff --git a/python/notebooks/tranches numbers.ipynb b/python/notebooks/tranches numbers.ipynb
index 71cb44f8..cb45f6e2 100644
--- a/python/notebooks/tranches numbers.ipynb
+++ b/python/notebooks/tranches numbers.ipynb
@@ -3,34 +3,50 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "%matplotlib\n",
- "import seaborn\n",
- "seaborn.mpl.rcParams['figure.figsize'] = (12.0, 8.0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
+ "metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "hidden": true
+ },
+ "report_default": {}
+ }
+ }
+ }
+ },
"outputs": [],
"source": [
"import pandas as pd\n",
"from ipywidgets import interact\n",
- "from collections import OrderedDict"
+ "from collections import OrderedDict\n",
+ "from matplotlib import pyplot as plt\n",
+ "plt.interactive(False)\n",
+ "plt.style.use('ggplot')"
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "hidden": true
+ },
+ "report_default": {}
+ }
+ }
+ }
+ },
"outputs": [],
"source": [
- "SQL_CON = \"postgresql://serenitas_user@debian/serenitasdb\"\n",
- "runs = (pd.\n",
- " read_sql(\"SELECT DISTINCT index, series, tenor from risk_numbers ORDER BY index, series\", SQL_CON).\n",
+ "from db import dbengine\n",
+ "runs = (pd.read_sql_query(\"SELECT DISTINCT index, series, tenor from risk_numbers ORDER BY index, series\",\n",
+ " dbengine('serenitasdb')).\n",
" itertuples(index=False, name='run'))\n",
"runs = OrderedDict([(\"%s %s %s\" % (r.index, r.series, r.tenor), (r.index, r.series, r.tenor)) for r in runs])"
]
@@ -38,7 +54,19 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "hidden": true
+ },
+ "report_default": {}
+ }
+ }
+ }
+ },
"outputs": [],
"source": [
"df = pd.read_sql(\"SELECT * FROM risk_numbers\", SQL_CON, index_col=['date', 'index', 'series', 'tenor'])"
@@ -47,37 +75,91 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "col": 0,
+ "height": 57,
+ "hidden": false,
+ "row": 0,
+ "width": 10
+ },
+ "report_default": {}
+ }
+ }
+ }
+ },
"outputs": [],
"source": [
"risk_numbers = ['skew', 'Dealer Deltas', 'Model Deltas', 'Forward Deltas', 'gammas', 'durations', 'thetas']\n",
- "@interact(index=runs, what=risk_numbers)\n",
+ "\n",
"def corrplot(index, what):\n",
+ " plt.close('all')\n",
" selection = df.xs(index, level=[1,2,3], drop_level=True)\n",
" cols = selection.attach.iloc[0]\n",
" cols = [\"{}-{}\".format(a,d) for a, d in zip(cols[:-2], cols[1:-1])]\n",
" selection = selection[what].apply(pd.Series)\n",
" selection.drop(selection.columns[-1], axis=1, inplace=True)\n",
" selection.columns = cols\n",
- " selection.plot()"
+ " selection.plot()\n",
+ " plt.show()\n",
+ " \n",
+ "interact(corrplot, index=runs, what=risk_numbers)"
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "hidden": true
+ },
+ "report_default": {}
+ }
+ }
+ }
+ },
"outputs": [],
"source": [
"def get_tranche_quotes(index, series, tenor):\n",
" df = pd.read_sql(\"SELECT * FROM tranche_quotes WHERE index=%s and series=%s and tenor =%s\",\n",
- " SQL_CON, params=(index, series, tenor), index_col='quotedate')\n",
+ " SQL_CON, params=(index, series, tenor), index_col=['quotedate'],\n",
+ " parse_dates=['quotedate'])\n",
" df.sort_index(inplace=True)\n",
" return df\n",
+ "\n",
"def get_index_quotes(index, series, tenor):\n",
" df = pd.read_sql(\"SELECT * FROM index_quotes WHERE index=%s and series=%s and tenor=%s\",\n",
" SQL_CON, params = (index, series, tenor), index_col=['date'], parse_dates=['date'])\n",
" df.sort_index(inplace=True)\n",
- " return df\n",
+ " return df\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "hidden": true
+ },
+ "report_default": {}
+ }
+ }
+ }
+ },
+ "outputs": [],
+ "source": [
"ig25_tranches = get_tranche_quotes('IG', 25, '5yr')\n",
"ig25 = get_index_quotes('IG', 25, '5yr')\n",
"ig25_tranches = ig25_tranches[['attach', 'trancheupfrontmid','indexrefspread','tranchedelta']]"
@@ -86,7 +168,19 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "hidden": true
+ },
+ "report_default": {}
+ }
+ }
+ }
+ },
"outputs": [],
"source": [
"ig25_tranches = ig25_tranches.groupby([pd.TimeGrouper('D', level=0), 'attach']).last().dropna()\n",
@@ -98,7 +192,23 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "col": 0,
+ "height": 7,
+ "hidden": false,
+ "row": 57,
+ "width": 9
+ },
+ "report_default": {}
+ }
+ }
+ }
+ },
"outputs": [],
"source": [
"ig25_data = ig25_data.assign(tranche_adjusted =\n",
@@ -111,7 +221,19 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "hidden": true
+ },
+ "report_default": {}
+ }
+ }
+ }
+ },
"outputs": [],
"source": [
"#compute the accrued\n",
@@ -121,7 +243,19 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "hidden": true
+ },
+ "report_default": {}
+ }
+ }
+ }
+ },
"outputs": [],
"source": [
"#compute the daily pnl\n",
@@ -136,16 +270,49 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "col": 0,
+ "height": 11,
+ "hidden": false,
+ "row": 64,
+ "width": 4
+ },
+ "report_default": {}
+ }
+ }
+ }
+ },
"outputs": [],
"source": [
- "ig25_data.xs(3, level='attach').plot(x='index_pnl', y='tranche_pnl', kind='scatter')"
+ "ig25_data.xs(3, level='attach').plot(x='index_pnl', y='tranche_pnl', kind='scatter')\n",
+ "plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "col": 4,
+ "height": 19,
+ "hidden": false,
+ "row": 64,
+ "width": 4
+ },
+ "report_default": {}
+ }
+ }
+ }
+ },
"outputs": [],
"source": [
"import statsmodels.api as sm\n",
@@ -159,7 +326,19 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "hidden": true
+ },
+ "report_default": {}
+ }
+ }
+ }
+ },
"outputs": [],
"source": [
"def rolling_ols(df, formula, window=20):\n",
@@ -175,19 +354,51 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "col": 8,
+ "height": 11,
+ "hidden": false,
+ "row": 64,
+ "width": 4
+ },
+ "report_default": {}
+ }
+ }
+ }
+ },
"outputs": [],
"source": [
- "rols = rolling_ols(ig25_data, 'tranche_pnl~0+index_pnl+np.square(index_pnl)')\n",
- "rols.columns = ['delta','gamma']\n",
- "test = rols.join(ig25_data)[['delta','tranchedelta']]\n",
+ "rols = rolling_ols(ig25_data.xs(15, level='attach'), 'tranche_pnl~0+index_pnl+np.square(index_pnl)')\n",
+ "rols.columns = ['delta', 'gamma']\n",
+ "test = rols.join(ig25_data.xs(15, level='attach'))[['delta', 'tranchedelta']]\n",
"test.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "col": 0,
+ "height": 4,
+ "hidden": false,
+ "row": 75,
+ "width": 4
+ },
+ "report_default": {}
+ }
+ }
+ }
+ },
"outputs": [],
"source": [
"ig25_data.closespread.apply(np.log).diff().std()*np.sqrt(250)"
@@ -196,7 +407,23 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "col": 8,
+ "height": 11,
+ "hidden": false,
+ "row": 75,
+ "width": 4
+ },
+ "report_default": {}
+ }
+ }
+ }
+ },
"outputs": [],
"source": [
"ig25 = get_index_quotes('IG', 25, '5yr')\n",
@@ -206,18 +433,49 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "col": 0,
+ "height": 11,
+ "hidden": false,
+ "row": 79,
+ "width": 4
+ },
+ "report_default": {}
+ }
+ }
+ }
+ },
"outputs": [],
"source": [
"ig25 = get_index_quotes('IG', 25, '5yr')\n",
- "returns = ig25.closespread.pct_change().dropna()\n",
- "returns.plot()"
+ "returns = ig25.closespread.pct_change().dropna()"
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "col": 4,
+ "height": 32,
+ "hidden": false,
+ "row": 86,
+ "width": 5
+ },
+ "report_default": {}
+ }
+ }
+ }
+ },
"outputs": [],
"source": [
"from arch import arch_model\n",
@@ -229,7 +487,23 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "col": 0,
+ "height": 12,
+ "hidden": false,
+ "row": 90,
+ "width": 4
+ },
+ "report_default": {}
+ }
+ }
+ }
+ },
"outputs": [],
"source": [
"res.plot(annualize='D')"
@@ -238,18 +512,51 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "col": 0,
+ "height": 11,
+ "hidden": false,
+ "row": 102,
+ "width": 4
+ },
+ "report_default": {}
+ }
+ }
+ }
+ },
"outputs": [],
"source": [
"import math\n",
"annualized_vol = res.conditional_volatility * math.sqrt(252)\n",
- "annualized_vol.plot()"
+ "annualized_vol.plot()\n",
+ "plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "col": 0,
+ "height": 9,
+ "hidden": false,
+ "row": 113,
+ "width": 4
+ },
+ "report_default": {}
+ }
+ }
+ }
+ },
"outputs": [],
"source": [
"returns[returns.abs()>0.04].groupby(pd.TimeGrouper('M')).count()"
@@ -258,7 +565,23 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "col": 4,
+ "height": 15,
+ "hidden": false,
+ "row": 118,
+ "width": 4
+ },
+ "report_default": {}
+ }
+ }
+ }
+ },
"outputs": [],
"source": [
"returns['2016-03']"
@@ -267,7 +590,23 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "col": 0,
+ "height": 7,
+ "hidden": false,
+ "row": 133,
+ "width": 10
+ },
+ "report_default": {}
+ }
+ }
+ }
+ },
"outputs": [],
"source": [
"ig25.head()"
@@ -276,30 +615,49 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "col": 8,
+ "height": 7,
+ "hidden": false,
+ "row": 118,
+ "width": 4
+ },
+ "report_default": {}
+ }
+ }
+ }
+ },
"outputs": [],
"source": [
"ig25.index"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "pd.read_sql?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
+ "extensions": {
+ "jupyter_dashboards": {
+ "activeView": "grid_default",
+ "version": 1,
+ "views": {
+ "grid_default": {
+ "cellMargin": 10,
+ "defaultCellHeight": 20,
+ "maxColumns": 12,
+ "name": "grid",
+ "type": "grid"
+ },
+ "report_default": {
+ "name": "report",
+ "type": "report"
+ }
+ }
+ }
+ },
"kernelspec": {
"display_name": "Python 3",
"language": "python",
generated by cgit v1.2.3-70-g09d2 (git 2.52.0) at 2025-12-22 05:37:36 +0000