1 files changed, 70 insertions, 2 deletions
diff --git a/python/notebooks/tranches numbers.ipynb b/python/notebooks/tranches numbers.ipynb
index fc936b7a..d655f8ba 100644
--- a/python/notebooks/tranches numbers.ipynb
+++ b/python/notebooks/tranches numbers.ipynb
@@ -54,7 +54,7 @@
    "outputs": [],
    "source": [
     "sql_string = '''SELECT a.*, b.trancheupfrontmid, b.indexrefspread, b.tranchedelta, b.trancherunningmid\n",
-    "                FROM risk_numbers_new a \n",
+    "                FROM risk_numbers a \n",
     "                join tranche_quotes b on a.tranche_id = b.id\n",
     "                where a.index <> 'EU'\n",
     "                '''\n",
@@ -435,6 +435,74 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "#Back test tranche pair trades  \n",
+    "sql_str = \"select * from markit_tranche_quotes a left join \" \\\n",
+    "        \"(select index, series, version, basketid from index_version) b \" \\\n",
+    "        \"using (basketid) order by quotedate asc\"\n",
+    "index_columns=['index', 'series', 'version', 'tenor', 'attach', 'detach']\n",
+    "df = pd.read_sql_query(sql_str, engine, parse_dates=['quotedate'], index_col=index_columns)\n",
+    "df['day_frac'] = (df.groupby(index_columns)['quotedate'].\n",
+    "                transform(lambda s: s.\n",
+    "                          diff().astype('timedelta64[D]') / 360))\n",
+    "df['close_price'] = 1-df['upfront_mid']\n",
+    "df = df.set_index('quotedate', append=True)\n",
+    "df['price_return'] = df['close_price'].groupby(level=index_columns).diff()\n",
+    "df['price_return'] += df.day_frac * df.tranche_spread/10000\n",
+    "df = df.drop(['basketid', 'upfront_bid', 'upfront_ask', 'upfront_mid', 'index_price', 'day_frac', 'tranche_spread', 'close_price'], axis=1)\n",
+    "df = df.dropna()\n",
+    "#Focus on IG\n",
+    "ig_tranches = df.xs(('IG', '5yr', 23), level = ['index', 'tenor', 'series'])\n",
+    "ig_tranches = ig_tranches.reset_index(['version', 'detach'], drop=True)\n",
+    "ig_tranches = ig_tranches.unstack(level='attach')\n",
+    "ig_tranches.columns = ig_tranches.columns.droplevel()\n",
+    "\n",
+    "#carry strat = long 15-100 and short 7-15: 4.6 by 1, 50bps IA\n",
+    "carrystrat = (4.6 * ig_tranches[15] - ig_tranches[7])/.05\n",
+    "cum_return = (carrystrat+1).cumprod()\n",
+    "cum_return.plot()\n",
+    "#equity gamma strat = long 0-3 and short 7-15: 1 by 6, 12% IA\n",
+    "equitygammastrat = (1 * ig_tranches[0] - 6 * ig_tranches[7])/.12\n",
+    "cum_return = (equitygammastrat+1).cumprod()\n",
+    "cum_return.plot()\n",
+    "#mezz gamma strat = long 3-7 and short 7-15: 1 by 2.75, 2.5% IA\n",
+    "mezzgammastrat =(1 * ig_tranches[3] - 2.75 * ig_tranches[7])/.025\n",
+    "cum_return = (mezzgammastrat+1).cumprod()\n",
+    "cum_return.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r=[]\n",
+    "r.append(carrystrat.mean()/carrystrat.std() * math.sqrt(252))\n",
+    "r.append(equitygammastrat.mean()/equitygammastrat.std() * math.sqrt(252))\n",
+    "r.append(mezzgammastrat.mean()/mezzgammastrat.std() * math.sqrt(252))\n",
+    "r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": []
   }
  ],
@@ -473,7 +541,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.7.1"
   }
  },
  "nbformat": 4,