update example

notebooks/Example 1 - Factor IC analysis.ipynb

@@ -34,7 +34,7 @@
     "industry_lower = 1.0\n",
     "industry_upper = 1.0\n",
     "method = 'risk_neutral'\n",
-    "neutralize_risk = ['SIZE'] + industry_styles\n",
+    "neutralize_risk = industry_styles\n",
     "industry_name = 'sw_adj'\n",
     "industry_level = 1\n",
     "benchmark_total_lower = 0.8\n",
@@ -62,7 +62,7 @@
     "\"\"\"\n",
     "\n",
     "industry_names = industry_list(industry_name, industry_level)\n",
-    "constraint_risk = ['SIZE', 'SIZENL', 'BETA']\n",
+    "constraint_risk = ['SIZE', 'SIZENL', 'BETA'] + industry_names\n",
     "total_risk_names = constraint_risk + ['benchmark', 'total']\n",
     "\n",
     "b_type = []\n",
@@ -160,7 +160,7 @@
     "        factor_values = factor_processing(total_data[alpha_name].values,\n",
     "                                          pre_process=[winsorize_normal, standardize],\n",
     "                                          risk_factors=risk_exp,\n",
-    "                                          post_process=[winsorize_normal, standardize, rank])\n",
+    "                                          post_process=[winsorize_normal, standardize])\n",
     "\n",
     "        # const linear model\n",
     "        er = const_model.predict(pd.DataFrame(data={alpha_name[0]: factor_values.flatten()}))\n",
@@ -224,8 +224,18 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wall time: 8.86 s\n"
+     ]
+    }
+   ],
    "source": [
+    "%%time\n",
+    "\n",
     "df = engine.fetch_factor_coverage(start_date='2011-01-01',\n",
     "                                  end_date='2018-02-12',\n",
     "                                  universe=universe_name[0])\n",
@@ -236,17 +246,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Wall time: 1h 25min 5s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "\n",
@@ -274,7 +276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -287,26 +289,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "with pd.ExcelWriter(f'{universe_name[0]}_{benchmark_code}.xlsx', engine='xlsxwriter') as writer:\n",
     "    factor_df.to_excel(writer, sheet_name='ret')\n",
-    "    factor_res.to_excel(writer, sheet_name='ic')\n",
-    "    factor_df.to_excel(writer, sheet_name='ret_stat')\n",
+    "    ic_df.to_excel(writer, sheet_name='ic')\n",
+    "    factor_res.to_excel(writer, sheet_name='ret_stat')\n",
     "    ic_res.to_excel(writer, sheet_name='ic_stat')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "client.close()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,