update all the examples

notebooks/Example 1 - Factor IC analysis.ipynb

 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* 请在环境变量中设置`DB_URI`指向数据库"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -8,6 +15,7 @@
    "source": [
     "%matplotlib inline\n",
     "\n",
+    "import os\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "from matplotlib import pyplot as plt\n",
@@ -47,7 +55,7 @@
     "ref_dates = makeSchedule(start_date, end_date, frequency, 'china.sse')\n",
     "\n",
     "executor = NaiveExecutor()\n",
-    "data_source = 'postgres+psycopg2://postgres:A12345678!@10.63.6.220/alpha'\n",
+    "data_source = os.environ['DB_URI']\n",
     "engine = SqlEngine(data_source)"
    ]
   },
@@ -278,13 +286,6 @@
     "    ic_res.to_excel(writer, sheet_name='ic_stat')"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -309,7 +310,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.3"
   },
   "varInspector": {
    "cols": {

notebooks/Example 10 - Quadratic Optimizer Comparison with CVXOPT.ipynb

 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* 请在环境变量中设置`DB_URI`指向数据库"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
+    "import os\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "from cvxpy import *\n",
@@ -31,7 +39,7 @@
     "risk_penlty = 0.5\n",
     "ref_date = '2018-02-08'\n",
     "\n",
-    "engine = SqlEngine()\n",
+    "engine = SqlEngine(os.environ['DB_URI'])\n",
     "universe = Universe('custom', ['ashare_ex'])\n",
     "codes = engine.fetch_codes(ref_date, universe)\n",
     "\n",
@@ -331,7 +339,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.3"
   },
   "varInspector": {
    "cols": {

notebooks/Example 5 - Style Factor Analysis.ipynb

@@ -4,7 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> The methodolegy is similar to The Barra China Equity Model (CNE5)'s documentation"
+    "* 方法参考自 The Barra China Equity Model (CNE5)'s 文档\n",
+    "\n",
+    "* 请在环境变量中设置`DB_URI`指向数据库"
    ]
   },
   {
@@ -15,6 +17,7 @@
    "source": [
     "%matplotlib inline\n",
     "\n",
+    "import os\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "from matplotlib import pyplot as plt\n",
@@ -54,7 +57,7 @@
    "outputs": [],
    "source": [
     "def risk_factor_analysis(factor_name):\n",
-    "    data_source = 'postgres+psycopg2://postgres:A12345678!@10.63.6.220/alpha'\n",
+    "    data_source = os.environ['DB_URI']\n",
     "    engine = SqlEngine(data_source)\n",
     "    risk_names = list(set(risk_styles).difference({factor_name}))\n",
     "    industry_names = list(set(industry_styles).difference({factor_name}))\n",
@@ -115,13 +118,6 @@
     "df[['factor', 'abs t.']].groupby('factor').mean().sort_values('abs t.', ascending=False).head()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -146,7 +142,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.3"
   },
   "varInspector": {
    "cols": {