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Diffstat (limited to 'python')
| -rw-r--r-- | python/notebooks/Dispersion.ipynb | 186 |
1 files changed, 0 insertions, 186 deletions
diff --git a/python/notebooks/Dispersion.ipynb b/python/notebooks/Dispersion.ipynb deleted file mode 100644 index 0d7e4cd3..00000000 --- a/python/notebooks/Dispersion.ipynb +++ /dev/null @@ -1,186 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import itertools\n", - "import datetime\n", - "import exploration.dispersion as disp\n", - "import matplotlib.pyplot as plt\n", - "import statsmodels.formula.api as smf\n", - "import analytics.tranche_data as tdata\n", - "\n", - "from analytics.basket_index import MarkitBasketIndex\n", - "from analytics import on_the_run\n", - "from statsmodels.graphics.regressionplots import plot_fit\n", - "from scipy.special import logit, expit\n", - "from pygam import LinearGAM, s, f, GAM\n", - "from utils.db import dbengine, dbconn" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "value_date = (datetime.datetime.today() - pd.offsets.BDay(1)).date()\n", - "start = (datetime.datetime.today() - pd.offsets.BDay(1) * 365 *4).date()\n", - "#end = (start + pd.offsets.BDay(1) * 365).date()\n", - "end = datetime.datetime.today()\n", - "gini_model, gini_results = {}, {}\n", - "conn = dbconn(\"serenitasdb\")\n", - "conn.autocommit = True\n", - "for index_type in ['HY', 'IG', 'EU', 'XO']:\n", - " risk = disp.get_tranche_data(dbconn(\"serenitasdb\"), index_type)\n", - " risk = risk[risk.index_duration > .5] #filter out the short duration ones\n", - " gini_results[index_type], gini_model[index_type] = disp.create_models_v2(conn, risk)\n", - " fitted = gini_model[index_type].fit()\n", - " w = 1/(expit(fitted.fittedvalues + fitted.resid) -expit(fitted.fittedvalues))**2\n", - " gini_results[index_type], gini_model[index_type] = disp.create_models_v2(conn, risk, w)\n", - "gini_model['HY'].fit().summary()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fieldlist = ['exp_percentage','dispersion','gini','tranche_loss_per','mispricing']\n", - "for index_type in ['HY', 'IG', 'EU', 'XO']:\n", - " gini_results[index_type][fieldlist].to_csv('/home/serenitas/edwin/' + index_type + '_results.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Run a particular gini scenario\n", - "scenario = gini_results['HY'].loc(axis=0)[value_date,'HY',33,:,'5yr',0]\n", - "scenario['gini'].iloc[0] = .7\n", - "scenario_disp = expit(gini_model['HY'].fit().predict(scenario))\n", - "mispricing = scenario['tranche_loss_per'] - scenario_disp\n", - "mispricing" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#plot the residuals\n", - "fitted = gini_model['HY'].fit()\n", - "plt.figure(figsize=(8,5))\n", - "p=plt.scatter(x=expit(fitted.fittedvalues),y=expit(fitted.fittedvalues + fitted.resid) -expit(fitted.fittedvalues),edgecolor='k')\n", - "xmin=min(expit(fitted.fittedvalues))\n", - "xmax = max(expit(fitted.fittedvalues))\n", - "plt.hlines(y=0,xmin=xmin*0.9,xmax=xmax*1.1,color='red',linestyle='--',lw=3)\n", - "plt.xlabel(\"Fitted values\",fontsize=15)\n", - "plt.ylabel(\"Residuals\",fontsize=15)\n", - "plt.title(\"Fitted vs. residuals plot\",fontsize=18)\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "value_date = (datetime.datetime.today() - pd.offsets.BDay(1)).date()\n", - "start = (datetime.datetime.today() - pd.offsets.BDay(1) * 365 *4).date()\n", - "#end = (start + pd.offsets.BDay(1) * 365).date()\n", - "end = datetime.datetime.today()\n", - "index_type = 'IG'\n", - "serenitasconn = dbconn(\"serenitasdb\")\n", - "serenitasconn.autocommit = True\n", - "risk = disp.get_tranche_data(serenitasconn, index_type)\n", - "train_data = risk[start: end]\n", - "gini_calc, gini_model = disp.create_models(serenitasconn, train_data)\n", - "gini_model.fit().summary()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#compare to realized delta-adjusted return\n", - "tranche_returns = tdata.get_tranche_quotes(index=index_type)\n", - "tranche_returns = tdata.tranche_returns(df=tranche_returns)\n", - "attach = 0\n", - "t = tranche_returns['delhedged_return'].reset_index(['index', 'tenor'], drop=True).xs(attach, level='attach')\n", - "temp={}\n", - "for i,g in t.groupby('series'):\n", - " temp[i] = (g.dropna()+1).cumprod()\n", - "t = pd.concat(temp).reset_index(0, drop=True)\n", - "t.unstack(level='series').plot()\n", - "tranche_returns.to_csv('/home/serenitas/edwin/Python/temp3.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "attach = 0\n", - "\n", - "returns = tranche_returns.xs(['HY', 29, '5yr', attach], level = ['index', 'series', 'tenor','attach'])['delhedged_return']\n", - "model = gini_calc.xs(['HY', 29, '5yr', attach], level = ['index', 'series', 'tenor','attach'])['mispricing']\n", - "returns = pd.merge(returns, model, left_index=True, right_index=True)\n", - "model_verification = smf.ols(\"delhedged_return ~ mispricing \", data=returns).fit()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tranche_returns.xs(29, level='series').unstack(level='attach').to_csv('/home/serenitas/edwin/Python/temp1.csv')" - ] - } - ], - "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": "3.8.1" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} |