update examples

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

@@ -89,12 +89,12 @@
     "lbound = 0.\n",
     "ubound = 1. / n * 20\n",
     "\n",
-    "risk = sum_squares(mul_elemwise(special_risk_values[:n] / 100., w)) + quad_form((w.T * risk_exposure_values[:n]).T, risk_cov_values[:n, :n] / 10000.)\n",
+    "risk = sum_squares(multiply(special_risk_values[:n] / 100., w)) + quad_form((w.T * risk_exposure_values[:n]).T, risk_cov_values[:n, :n] / 10000.)\n",
     "\n",
     "objective = Minimize(risk_penlty * risk  - signal * w)\n",
     "constraints = [w >= lbound,\n",
     "               w <= ubound,\n",
-    "               sum_entries(w) == 1,]\n",
+    "               sum(w) == 1,]\n",
     "\n",
     "prob = Problem(objective, constraints)"
    ]
@@ -125,7 +125,7 @@
    "outputs": [],
    "source": [
     "%%time\n",
-    "prob.solve(verbose=True, solver='CVXOPT')"
+    "prob.solve(verbose=True, solver='ECOS')"
    ]
   },
   {
@@ -235,15 +235,15 @@
     "    lbound = 0.\n",
     "    ubound = 0.01\n",
     "    \n",
-    "    risk = sum_squares(mul_elemwise(special_risk_values[:n] / 100., w)) + quad_form((w.T * risk_exposure_values[:n]).T, risk_cov_values[:n, :n] / 10000.)\n",
+    "    risk = sum_squares(multiply(special_risk_values[:n] / 100., w)) + quad_form((w.T * risk_exposure_values[:n]).T, risk_cov_values[:n, :n] / 10000.)\n",
     "\n",
     "    objective = Minimize(risk_penlty * risk  - signal * w)\n",
     "    constraints = [w >= lbound,\n",
     "                   w <= ubound,\n",
-    "                   sum_entries(w) == 1,]\n",
+    "                   sum(w) == 1,]\n",
     "\n",
     "    prob = Problem(objective, constraints)\n",
-    "    prob.solve(verbose=False)\n",
+    "    prob.solve(verbose=False, solver='ECOS')\n",
     "    return prob.value"
    ]
   },
@@ -347,7 +347,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.6"
   },
   "varInspector": {
    "cols": {

notebooks/Example 12 - Machine Learning Model Prediction.ipynb

@@ -407,7 +407,6 @@
     "    result2 = pd.merge(target_pos2, dx_returns, on=['code'])\n",
     "    ret2 = result2.weight.values @ (np.exp(result2.dx.values) - 1.)\n",
     "    rets2.append(np.log(1. + ret2))\n",
-    "    ## perfect forcast\n",
     "    \n",
     "    alpha_logger.info('{0} is finished'.format(ref_date))"
    ]
@@ -451,7 +450,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.6"
   },
   "varInspector": {
    "cols": {

notebooks/Example 9 - Linear Optimizer Comparison with CVXOPT.ipynb

@@ -9,19 +9,9 @@
     "import datetime as dt\n",
     "import numpy as np\n",
     "import cvxpy\n",
-    "from cvxopt import solvers\n",
     "from alphamind.portfolio.linearbuilder import linear_builder"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "solvers.options['glpk'] = {'msg_lev': 'GLP_MSG_OFF'}"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -137,7 +127,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.6"
   },
   "varInspector": {
    "cols": {