update example

notebooks/optimizer check.ipynb

@@ -15,7 +15,7 @@
     "from cvxopt import *\n",
     "from alphamind.data.neutralize import neutralize\n",
     "\n",
-    "engine = sa.create_engine('mysql+pymysql://sa:We051253524522@rm-bp1psdz5615icqc0yo.mysql.rds.aliyuncs.com/uqer?charset=utf8')"
+    "engine = sa.create_engine('mysql+mysqldb://sa:We051253524522@rm-bp1psdz5615icqc0yo.mysql.rds.aliyuncs.com/uqer?charset=utf8')"
    ]
   },
   {
@@ -30,8 +30,7 @@
     "factor_list = [\"DebtEquityRatio\",\"EBITToTOR\"]\n",
     "ref_date = '2017-05-23'\n",
     "weights = np.array([0.3,0.7])\n",
-    "risk_penlty = 0.5\n",
-    "n = len(codes_list)"
+    "risk_penlty = 0.5"
    ]
   },
   {
@@ -61,7 +60,15 @@
    "outputs": [],
    "source": [
     "%%time\n",
+    "\n",
+    "risk_exposure_data = pd.read_sql(\"select * from risk_exposure where Date = '{0}'\".format(ref_date), engine).sort_values('Code')\n",
+    "risk_exposure_data.drop(['Date', 'exchangeCD', 'secShortName'], axis=1, inplace=True)\n",
+    "risk_exposure_data.dropna(inplace=True)\n",
+    "codes_list = risk_exposure_data.Code.astype(str).tolist()\n",
+    "codes = ','.join(codes_list)\n",
+    "\n",
     "factor_data = pd.read_sql(\"select Code, {1} from factors where Date = '{0}' and Code in ({2})\".format(ref_date, factors, codes), engine).sort_values('Code')\n",
+    "\n",
     "risk_exposure_data = pd.read_sql(\"select * from risk_exposure where Date = '{0}' and Code in ({1})\".format(ref_date, codes), engine).sort_values('Code')\n",
     "risk_exposure_data.drop(['Date', 'exchangeCD', 'secShortName'], axis=1, inplace=True)\n",
     "risk_cov_data = pd.read_sql(\"select * from risk_cov_day where Date = '{0}'\".format(ref_date), engine).sort_values('FactorID')\n",
@@ -70,6 +77,17 @@
     "specific_risk = pd.read_sql(\"select Code, SRISK from specific_risk_day where Date = '{0}' and Code in ({1})\".format(ref_date, codes), engine).sort_values('Code')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "n = len(codes_list)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -93,6 +111,15 @@
     "signal = ne_factor_values @ weights"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "risk_exposure_values.shape"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -206,13 +233,44 @@
     "A = matrix(A)\n",
     "b = matrix(b)\n",
     "\n",
-    "sol = solvers.qp(P, q, G, h, A, b, solver=None)"
+    "sol = solvers.qp(P, q, G, h, A, b)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "sec_cov_values.tofile('sec_cov_values.dat')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "signal.tofile('signal.dat')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python3"
   },

notebooks/optimizer check2.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from cvxopt import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "sec_cov_values = np.fromfile('sec_cov_values.dat')\n",
+    "signal = np.fromfile('signal.dat')\n",
+    "sec_cov_values = sec_cov_values.reshape((len(signal), -1))\n",
+    "n = len(sec_cov_values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "P = matrix(sec_cov_values)\n",
+    "q = -matrix(signal)\n",
+    "\n",
+    "G = np.zeros((2*n, n))\n",
+    "h = np.zeros(2*n)\n",
+    "for i in range(n):\n",
+    "    G[i, i] = 1.\n",
+    "    h[i] = 0.01\n",
+    "    G[i+n, i] = -1.\n",
+    "    h[i+n] = 0.\n",
+    "    \n",
+    "G = matrix(G)\n",
+    "h = matrix(h)\n",
+    "    \n",
+    "A = np.ones((1, n))\n",
+    "b = np.ones(1)\n",
+    "\n",
+    "A = matrix(A)\n",
+    "b = matrix(b)\n",
+    "\n",
+    "sol = solvers.qp(P, q, G, h, A, b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:py2]",
+   "language": "python",
+   "name": "conda-env-py2-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}