update constraints settings

alphamind/analysis/factoranalysis.py

@@ -74,7 +74,7 @@ def er_portfolio_analysis(er: np.ndarray,
             lbound = kwargs['lbound'].copy()
             del kwargs['lbound']
         else:
-            lbound = 0.
+            lbound = np.maximum(0., benchmark - 0.01)
 
         if 'ubound' in kwargs:
             ubound = kwargs['ubound'].copy()

notebooks/turn_over_control_strategy.ipynb

@@ -2,8 +2,10 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -25,23 +27,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "#factors = ['RVOL', 'EPS', 'DROEAfterNonRecurring', 'DivP', 'CFinc1', 'BDTO']\n",
     "#factor_weights = np.array([0.05, 0.3, 0.35, 0.075, 0.15, 0.05])\n",
     "\n",
-    "factors = ['VAL', 'RVOL', 'ROEDiluted', 'GREV', 'EPS', 'CHV', 'CFinc1', 'BDTO']\n",
-    "factor_weights = np.array([0.034129344,\n",
-    "                    0.015881607,\n",
-    "                    0.048765746,\n",
-    "                    0.042747382,\n",
-    "                    -0.015900173,\n",
-    "                    0.019044573,\n",
-    "                    -0.001792638,\n",
-    "                    0.014277867,\n",
-    "                    ])\n",
+    "factors = alpha_factors = {\n",
+    "    'eps': LAST('eps_q'),\n",
+    "    'roe': LAST('roe_q'),\n",
+    "    'bdto': LAST('BDTO'),\n",
+    "    'cfinc1': LAST('CFinc1'),\n",
+    "    'chv': LAST('CHV'),\n",
+    "    'rvol': LAST('RVOL'),\n",
+    "    'val': LAST('VAL'),\n",
+    "    'grev': LAST('GREV'),\n",
+    "    'droeafternonorecurring': LAST('DROEAfterNonRecurring')\n",
+    "}\n",
+    "\n",
+    "factor_weights = np.array([])\n",
     "\n",
     "engine = SqlEngine()\n",
     "universe = Universe('custom', ['zz500'])\n",
@@ -63,13 +68,15 @@
     "else:\n",
     "    raise ValueError(\"Unrecognized freq: {0}\".format(freq))\n",
     "    \n",
-    "dates = makeSchedule('2012-01-01', '2017-09-19', tenor=freq, calendar='china.sse', dateGenerationRule=DateGeneration.Backward)"
+    "dates = makeSchedule('2012-01-01', '2017-11-02', tenor=freq, calendar='china.sse', dateGenerationRule=DateGeneration.Backward)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "all_data = engine.fetch_data_range(universe, factors, dates=dates, benchmark=905)\n",
@@ -88,7 +95,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "rets = []\n",
@@ -149,7 +158,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "ret_df = pd.DataFrame({'returns': rets, 'turn_over': turn_overs}, index=dates)\n",
@@ -162,7 +173,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "ret_df[['returns', 'tc_cost']].cumsum().plot(figsize=(12, 6), title='Fixed frequency rebalanced: {0}'.format(freq), secondary_y='tc_cost')"
@@ -218,7 +231,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "rets = []\n",
@@ -285,7 +300,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "ret_df = pd.DataFrame({'returns': rets, 'turn_over': turn_overs}, index=dates)\n",
@@ -298,7 +315,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "ret_df[['returns', 'tc_cost']].cumsum().plot(figsize=(12, 6),\n",
@@ -333,7 +352,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.6.3"
   }
  },
  "nbformat": 4,