diff options
Diffstat (limited to 'python/notebooks')
| -rw-r--r-- | python/notebooks/Dispersion.ipynb | 80 |
1 files changed, 77 insertions, 3 deletions
diff --git a/python/notebooks/Dispersion.ipynb b/python/notebooks/Dispersion.ipynb index 84701ba8..e8e07f4a 100644 --- a/python/notebooks/Dispersion.ipynb +++ b/python/notebooks/Dispersion.ipynb @@ -8,10 +8,12 @@ "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", @@ -50,10 +52,10 @@ "outputs": [], "source": [ "#Get Gini factor\n", - "date_range = pd.bdate_range(end=value_date, freq='1B',periods=52*4)\n", + "date_range = pd.bdate_range(end=value_date, freq='5B',periods=52*4)\n", "risk = disp.get_tranche_data(index_type, serenitas_engine)\n", "risk = risk[risk.index.get_level_values(0).isin(date_range)]\n", - "gini_model, gini_calc = disp.create_models(risk, use_gini=True, use_log=False)" + "gini_model, gini_calc = disp.create_models(risk, use_gini=True, use_log=True)" ] }, { @@ -62,6 +64,7 @@ "metadata": {}, "outputs": [], "source": [ + "#Plot Gini if (use gini=True, use_log=False)\n", "to_plot_gini = gini_calc.xs(0, level='attach').groupby(['date', 'series']).nth(-1)\n", "to_plot_gini['dispersion'].unstack().plot()" ] @@ -72,6 +75,7 @@ "metadata": {}, "outputs": [], "source": [ + "#Today's mispricing\n", "today = gini_calc.xs([value_date,33], level=['date','series'])\n", "today[['exp_percentage', 'predict_N', 'predict_preN', 'mispricing']]" ] @@ -82,7 +86,21 @@ "metadata": {}, "outputs": [], "source": [ - "to_plot = gini_calc.xs(0, level='attach')['mispricing']\n", + "#plot mispricing of a tranche through time \n", + "attach = 0\n", + "to_plot = gini_calc.xs(attach, level='attach')['mispricing']\n", + "to_plot.reset_index(['index','tenor'], drop=True).unstack().plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#plot mispricing of series through time \n", + "series = 33\n", + "to_plot = gini_calc.xs(series, level='series')['mispricing']\n", "to_plot.reset_index(['index','tenor'], drop=True).unstack().plot()" ] }, @@ -101,6 +119,53 @@ "metadata": {}, "outputs": [], "source": [ + "gini_calc.to_csv('/home/serenitas/edwin/Python/' + index_type+ '_tranche_model.csv')" + ] + }, + { + "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['deladj_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()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#compare models\n", + "a = [True, False]\n", + "for years in [1,2,3,4,5,6]:\n", + " date_range = pd.bdate_range(end=value_date, freq='5B',periods=52*years)\n", + " risk = disp.get_tranche_data(index_type, serenitas_engine)\n", + " risk = risk[risk.index.get_level_values(0).isin(date_range)]\n", + " for x, y in list(itertools.product(a,a)):\n", + " gini_model, gini_calc = disp.create_models(risk, use_gini=x, use_log=y)\n", + " for i, m in gini_model.items():\n", + " print (years, x, y, i, m.rsquared)\n", + " today = gini_calc.xs([value_date,33], level=['date','series'])\n", + " print (today[['exp_percentage', 'predict_N', 'predict_preN', 'mispricing']])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "#Run a particular gini scenario\n", "scenario = gini_calc.loc(axis=0)[value_date,33,'HY','5yr',0]\n", "scenario['dispersion'] = .6\n", @@ -118,6 +183,15 @@ "metadata": {}, "outputs": [], "source": [ + "gini_calc.loc(axis=0)[:,33,'HY','5yr',0]['mispricing']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "#Let's use a GAM model instead?\n", "#only use the 5yr point for modeling\n", "equity = gini_calc.loc(axis=0)[:,:,[25,27,29,31,33],'5yr',0]\n", |