update examples

notebooks/Example 1 - Factor IC analysis.ipynb

@@ -50,8 +50,8 @@
     "horizon = map_freq(frequency)\n",
     "weight_gap = 0.01\n",
     "benchmark_code = 905\n",
-    "universe_name = ['zz800']\n",
-    "universe = Universe('custom', universe_name)\n",
+    "universe_name = 'zz800'\n",
+    "universe = Universe(universe_name)\n",
     "ref_dates = makeSchedule(start_date, end_date, frequency, 'china.sse')\n",
     "\n",
     "executor = NaiveExecutor()\n",
@@ -279,13 +279,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "with pd.ExcelWriter(f'{universe_name[0]}_{benchmark_code}.xlsx', engine='xlsxwriter') as writer:\n",
+    "with pd.ExcelWriter(f'{universe_name}_{benchmark_code}.xlsx', engine='xlsxwriter') as writer:\n",
     "    factor_df.to_excel(writer, sheet_name='ret')\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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -310,7 +317,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.5"
   },
   "varInspector": {
    "cols": {

notebooks/Example 2 - Strategy Analysis.ipynb

@@ -42,7 +42,7 @@
     "batch = 1\n",
     "horizon = map_freq(freq)\n",
     "weights_bandwidth = 0.01\n",
-    "universe = Universe(\"custom\", ['zz800'])\n",
+    "universe = Universe('zz800')\n",
     "data_source = os.environ['DB_URI']\n",
     "benchmark_code = 905\n",
     "method = 'risk_neutral'"
@@ -83,7 +83,7 @@
     "# Constraintes settings\n",
     "\n",
     "industry_names = industry_list(industry_name, industry_level)\n",
-    "constraint_risk = ['SIZE', 'SIZENL', 'BETA']  + industry_names\n",
+    "constraint_risk = ['SIZE', 'SIZENL', 'BETA']\n",
     "total_risk_names = constraint_risk + ['benchmark', 'total']\n",
     "all_styles = risk_styles + industry_styles + macro_styles\n",
     "\n",
@@ -171,7 +171,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.5"
   },
   "varInspector": {
    "cols": {

notebooks/Example 3 - Multi Weight Gap Comparison.ipynb

@@ -40,10 +40,10 @@
     "\"\"\"\n",
     "\n",
     "benchmark_code = 905\n",
-    "universe = Universe(\"zz800_cyb\", ['zz800', 'cyb'])\n",
+    "universe = Universe('zz800') + Universe('cyb')\n",
     "\n",
     "def create_scenario(weights_bandwidth=0.02, target_vol=0.01, method='risk_neutral'):\n",
-    "    start_date = '2010-01-01'\n",
+    "    start_date = '2017-01-01'\n",
     "    end_date = '2018-04-27'\n",
     "    freq = '10b'\n",
     "    neutralized_risk = None\n",
@@ -69,7 +69,7 @@
     "                         data_source=os.environ['DB_URI'])\n",
     "\n",
     "    industry_names = industry_list('sw', 1)\n",
-    "    constraint_risk = ['SIZE', 'SIZENL', 'BETA']  + industry_names\n",
+    "    constraint_risk = ['SIZE', 'SIZENL', 'BETA']\n",
     "    total_risk_names = constraint_risk + ['benchmark', 'total']\n",
     "    all_styles = risk_styles + industry_styles + macro_styles\n",
     "\n",
@@ -152,7 +152,7 @@
    "source": [
     "weight_gaps = [0.005, 0.010, 0.015, 0.020]\n",
     "\n",
-    "with pd.ExcelWriter(f'{universe.name}_{benchmark_code}_gap.xlsx', engine='xlsxwriter') as writer:\n",
+    "with pd.ExcelWriter(f'zz800_cyb_{benchmark_code}_gap.xlsx', engine='xlsxwriter') as writer:\n",
     "    for i, weight_gap in enumerate(weight_gaps):\n",
     "        ret_df = create_scenario(weight_gap, target_vol=0.01, method='risk_neutral')\n",
     "        res_df = create_report(ret_df, 25)\n",
@@ -168,7 +168,7 @@
    "source": [
     "target_vols = [0.015, 0.030, 0.045, 0.060]\n",
     "\n",
-    "with pd.ExcelWriter(f'{universe.name}_{benchmark_code}_tv.xlsx', engine='xlsxwriter') as writer:\n",
+    "with pd.ExcelWriter(f'zz800_cyb_{benchmark_code}_tv.xlsx', engine='xlsxwriter') as writer:\n",
     "    for i, target_vol in enumerate(target_vols):\n",
     "        ret_df = create_scenario(0.01, target_vol=target_vol, method='tv')\n",
     "        res_df = create_report(ret_df, 25)\n",

notebooks/Example 4 - Single Factor Analysis.ipynb

@@ -48,7 +48,7 @@
     "benchmark_total_upper = 1.0\n",
     "batch = 1\n",
     "horizon = map_freq(freq)\n",
-    "universe = Universe(\"custom\", ['zz800'])\n",
+    "universe = Universe('zz800')\n",
     "data_source = os.environ['DB_URI']\n",
     "benchmark_code = 905\n",
     "weight_gap = 0.01\n",
@@ -113,8 +113,7 @@
     "Shared Data\n",
     "\"\"\"\n",
     "\n",
-    "industry_names = industry_list(industry_name, industry_level)\n",
-    "constraint_risk = ['SIZE', 'SIZENL', 'BETA'] + industry_names\n",
+    "constraint_risk = ['SIZE', 'SIZENL', 'BETA']\n",
     "total_risk_names = constraint_risk + ['benchmark', 'total']\n",
     "\n",
     "b_type = []\n",
@@ -262,7 +261,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.5"
   },
   "varInspector": {
    "cols": {

notebooks/Example 5 - Style Factor Analysis.ipynb

@@ -44,7 +44,7 @@
     "category = 'sw'\n",
     "level = 1\n",
     "freq = '20b'\n",
-    "universe = Universe('custom', ['ashare_ex'])\n",
+    "universe = Universe('ashare_ex')\n",
     "\n",
     "horizon = map_freq(freq)\n",
     "ref_dates = makeSchedule(start_date, end_date, freq, 'china.sse')"
@@ -118,6 +118,13 @@
     "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,
@@ -142,7 +149,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.5"
   },
   "varInspector": {
    "cols": {

notebooks/Example 6 - Target Volatility Builder.ipynb

@@ -42,7 +42,7 @@
    "source": [
     "ref_date = '2018-01-08'\n",
     "engine = SqlEngine(os.environ['DB_URI'])\n",
-    "universe = Universe('custom', ['zz800'])"
+    "universe = Universe('zz800')"
    ]
   },
   {
@@ -148,7 +148,7 @@
     "risk_model = 'short'\n",
     "batch = 0\n",
     "horizon = map_freq(freq)\n",
-    "universe = Universe(\"custom\", ['zz800'])\n",
+    "universe = Universe('zz800')\n",
     "data_source = os.environ['DB_URI']\n",
     "benchmark_code = 906\n",
     "target_vol = 0.05\n",
@@ -190,7 +190,7 @@
     "Constraintes settings\n",
     "\"\"\"\n",
     "\n",
-    "constraint_risk = ['SIZE', 'SIZENL', 'BETA']  + industry_names\n",
+    "constraint_risk = ['SIZE', 'SIZENL', 'BETA']\n",
     "total_risk_names = constraint_risk + ['benchmark', 'total']\n",
     "\n",
     "b_type = []\n",
@@ -284,7 +284,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.5"
   },
   "varInspector": {
    "cols": {