update example

notebooks/factor analysis.ipynb

@@ -40,7 +40,7 @@
     "benchmark = 'zz500'\n",
     "factor = 'ROEAfterNonRecurring' # 'DROEAfterNonRecurring'\n",
     "\n",
-    "conn = sqlalchemy.create_engine('mysql+mysqldb://root:we083826@localhost:3306/multifactor?charset=utf8')\n",
+    "conn = sqlalchemy.create_engine('mysql+pymysql://sa:we083826@10.63.6.176:3306/multifactor?charset=utf8')\n",
     "df = pd.read_sql('select factor_data.{0},  trade_data.Return as dailyReturn, {1}.*, 1 as Market '\n",
     "                 'from factor_data, trade_data, {1} '\n",
     "                 'where factor_data.Date = {1}.Date and factor_data.Code = {1}.Code '\n",
@@ -252,7 +252,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "total_data.pos.groupby(groups).apply(lambda x: np.abs(x).sum()).head()"
+    "total_data.pos.groupby(groups).apply(lambda x: x.sum()).head()"
    ]
   },
   {
@@ -484,7 +484,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import gc\n",
@@ -503,9 +505,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda root]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-root-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -517,9 +519,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 1
 }