update example

notebooks/Example 3 - Multi Weight Gap Comparison.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,7 +45,7 @@
     "horizon = map_freq(freq)\n",
     "universe = Universe(\"custom\", ['zz800'])\n",
     "data_source = 'postgres+psycopg2://postgres:A12345678!@10.63.6.220/alpha'\n",
-    "benchmark_code = 905\n",
+    "benchmark_code = 300\n",
     "\n",
     "executor = NaiveExecutor()\n",
     "ref_dates = makeSchedule(start_date, end_date, freq, 'china.sse')\n",
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,39 +62,16 @@
     "Factor Model\n",
     "\"\"\"\n",
     "\n",
-    "# alpha_factors = {\n",
-    "#     'f01': LAST('ep_q'),\n",
-    "#     'f02': LAST('roe_q'),\n",
-    "#     'f03': LAST('market_confidence_75d'),\n",
-    "#     'f04': LAST('DivP'),\n",
-    "#     'f05': LAST('val_q'),\n",
-    "#     'f06': LAST('con_np_rolling'),\n",
-    "#     'f07': LAST('GREV'),\n",
-    "#     'f08': LAST('con_pe_rolling_order'),\n",
-    "#     'f09': LAST('con_pb_rolling_order')\n",
-    "# }\n",
-    "\n",
-    "# weights = dict(f01=1.,\n",
-    "#                f02=0.5,\n",
-    "#                f03=0.5,\n",
-    "#                f04=0.5,\n",
-    "#                f05=0.5,\n",
-    "#                f06=0.5,\n",
-    "#                f07=0.5,\n",
-    "#                f08=-0.5,\n",
-    "#                f09=-0.5)\n",
-    "\n",
-    "\n",
     "alpha_factors = {\n",
     "    'f01': LAST('ep_q'),\n",
     "    'f02': LAST('roe_q'),\n",
-    "    'f03': LAST('market_confidence_25d'),\n",
-    "    'f04': LAST('ILLIQUIDITY'),\n",
-    "    'f05': LAST('cfinc1_q'),\n",
-    "    'f06': LAST('CFO2EV'),\n",
-    "    'f07': LAST('IVR'),\n",
+    "    'f03': LAST('market_confidence_75d'),\n",
+    "    'f04': LAST('DivP'),\n",
+    "    'f05': LAST('val_q'),\n",
+    "    'f06': LAST('con_np_rolling'),\n",
+    "    'f07': LAST('GREV'),\n",
     "    'f08': LAST('con_pe_rolling_order'),\n",
-    "    'f09': LAST('con_pb_rolling_order'),\n",
+    "    'f09': LAST('con_pb_rolling_order')\n",
     "}\n",
     "\n",
     "weights = dict(f01=1.,\n",
@@ -107,6 +84,29 @@
     "               f08=-0.5,\n",
     "               f09=-0.5)\n",
     "\n",
+    "\n",
+    "# alpha_factors = {\n",
+    "#     'f01': LAST('ep_q'),\n",
+    "#     'f02': LAST('roe_q'),\n",
+    "#     'f03': LAST('market_confidence_25d'),\n",
+    "#     'f04': LAST('ILLIQUIDITY'),\n",
+    "#     'f05': LAST('cfinc1_q'),\n",
+    "#     'f06': LAST('CFO2EV'),\n",
+    "#     'f07': LAST('IVR'),\n",
+    "#     'f08': LAST('con_pe_rolling_order'),\n",
+    "#     'f09': LAST('con_pb_rolling_order'),\n",
+    "# }\n",
+    "\n",
+    "# weights = dict(f01=1.,\n",
+    "#                f02=0.5,\n",
+    "#                f03=0.5,\n",
+    "#                f04=0.5,\n",
+    "#                f05=0.5,\n",
+    "#                f06=0.5,\n",
+    "#                f07=0.5,\n",
+    "#                f08=-0.5,\n",
+    "#                f09=-0.5)\n",
+    "\n",
     "alpha_model = ConstLinearModel(features=alpha_factors, weights=weights)\n",
     "\n",
     "def predict_worker(params):\n",
@@ -126,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -149,14 +149,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Wall time: 6.91 s\n"
+      "Wall time: 6.83 s\n"
      ]
     }
    ],
@@ -177,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -198,11 +198,7 @@
     "        b_type.append(BoundaryType.RELATIVE)\n",
     "        l_val.append(benchmark_total_lower)\n",
     "        u_val.append(benchmark_total_upper)\n",
-    "    if name == 'total':\n",
-    "        b_type.append(BoundaryType.RELATIVE)\n",
-    "        l_val.append(1.0)\n",
-    "        u_val.append(1.0)\n",
-    "    elif name in {'SIZE', 'SIZENL', 'BETA'}:\n",
+    "    elif name in {'SIZE', 'SIZENL', 'BETA', 'total'}:\n",
     "        b_type.append(BoundaryType.ABSOLUTE)\n",
     "        l_val.append(0.0)\n",
     "        u_val.append(0.0)\n",
@@ -220,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -362,21 +358,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:39: FutureWarning: \n",
-      "Passing list-likes to .loc or [] with any missing label will raise\n",
-      "KeyError in the future, you can use .reindex() as an alternative.\n",
-      "\n",
-      "See the documentation here:\n",
-      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
-      "2018-03-02 18:18:22,979 - ALPHA_MIND - INFO - 0.005 finished\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "weight_gaps = [0.005, 0.010, 0.015, 0.020]\n",
     "\n",