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diff --git a/python/notebooks/bespokes/zero_recovery_EUXO.ipynb b/python/notebooks/bespokes/zero_recovery_EUXO.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from analytics.tranche_basket import DualCorrTranche, TrancheBasket\n",
+    "from pandas.tseries.offsets import BDay\n",
+    "\n",
+    "import scipy.interpolate as intp\n",
+    "import numpy as np\n",
+    "import datetime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "index = 'EU'\n",
+    "series = 30\n",
+    "value_date=datetime.date.today() - BDay(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "idx = TrancheBasket(index, series, \"5yr\", value_date=value_date)\n",
+    "idx.tweak()\n",
+    "idx.build_skew()\n",
+    "a,b, bond_prices =idx.tranche_pvs()\n",
+    "bond_prices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "idx.tranche_spreads()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a,b, bond_prices = idx.tranche_pvs(zero_recovery=True)\n",
+    "bond_prices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "idx.tranche_spreads(zero_recovery=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#map rho to the new expected loss\n",
+    "idx_zero_loss = idx.expected_loss()/(1-idx.recovery_rates.mean())\n",
+    "exp_loss = np.zeros(1)\n",
+    "for k in idx.K[1:]:\n",
+    "    exp_loss = np.append(exp_loss, idx.expected_loss_trunc(k))\n",
+    "moneyness = np.divide(idx.K, exp_loss)\n",
+    "zero_moneyness = np.divide(idx.K, exp_loss/(1-idx.recovery_rates.mean()))\n",
+    "extrapolate = intp.interp1d(moneyness[1:4], idx.rho[1:4], fill_value='extrapolate')\n",
+    "idx.rho[1:4] = extrapolate(zero_moneyness[1:4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a,b, bond_prices = idx.tranche_pvs(zero_recovery=True)\n",
+    "bond_prices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "idx.tranche_spreads(zero_recovery=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#top down - to price SS\n",
+    "idx.build_skew(skew_type='topdown')\n",
+    "extrapolate = intp.interp1d(moneyness[1:4], idx.rho[1:4], fill_value='extrapolate')\n",
+    "idx.rho[1:4] = extrapolate(zero_moneyness[1:4])\n",
+    "idx.tranche_spreads(zero_recovery=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}