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Commit 657d8d97 authored by Dr.李's avatar Dr.李
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update examples

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notebooks/Example 10 - Quadratic Optimizer Comparison with CVXOPT.ipynb
View file @ 657d8d97
...@@ -89,7 +89,9 @@ ...@@ -89,7 +89,9 @@
"lbound = 0.\n", "lbound = 0.\n",
"ubound = 1. / n * 20\n", "ubound = 1. / n * 20\n",
"\n", "\n",
"objective = Minimize(risk_penlty * quad_form(w, sec_cov_values) - signal * w)\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",
"\n",
"objective = Minimize(risk_penlty * risk - signal * w)\n",
"constraints = [w >= lbound,\n", "constraints = [w >= lbound,\n",
" w <= ubound,\n", " w <= ubound,\n",
" sum_entries(w) == 1,]\n", " sum_entries(w) == 1,]\n",
...@@ -177,7 +179,17 @@ ...@@ -177,7 +179,17 @@
"cons_matrix = np.ones((1, n))\n", "cons_matrix = np.ones((1, n))\n",
"clb = np.ones(1)\n", "clb = np.ones(1)\n",
"cub = np.ones(1)\n", "cub = np.ones(1)\n",
"qpopt = QPOptimizer(signal, sec_cov_values, lbound, ubound, cons_matrix, clb, cub, 1.)\n", "qpopt = QPOptimizer(signal,\n",
" None,\n",
" lbound,\n",
" ubound,\n",
" cons_matrix,\n",
" clb,\n",
" cub,\n",
" 1.,\n",
" risk_cov_values[:n, :n] / 10000.,\n",
" risk_exposure_values[:n],\n",
" special_risk_values[:n] * special_risk_values[:n] / 10000.)\n",
"qpopt.feval()\n", "qpopt.feval()\n",
"qpopt.status()" "qpopt.status()"
] ]
...@@ -222,14 +234,16 @@ ...@@ -222,14 +234,16 @@
"\n", "\n",
" lbound = 0.\n", " lbound = 0.\n",
" ubound = 0.01\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",
"\n", "\n",
" objective = Minimize(risk_penlty * quad_form(w, sec_cov_values) - signal * w)\n", " objective = Minimize(risk_penlty * risk - signal * w)\n",
" constraints = [w >= lbound,\n", " constraints = [w >= lbound,\n",
" w <= ubound,\n", " w <= ubound,\n",
" sum_entries(w) == 1,]\n", " sum_entries(w) == 1,]\n",
"\n", "\n",
" prob = Problem(objective, constraints)\n", " prob = Problem(objective, constraints)\n",
" prob.solve(verbose=False, solver='CVXOPT', display=False)\n", " prob.solve(verbose=False)\n",
" return prob.value" " return prob.value"
] ]
}, },
...@@ -277,7 +291,10 @@ ...@@ -277,7 +291,10 @@
" cons_matrix = np.ones((1, n))\n", " cons_matrix = np.ones((1, n))\n",
" clb = np.ones(1)\n", " clb = np.ones(1)\n",
" cub = np.ones(1)\n", " cub = np.ones(1)\n",
" qpopt = QPOptimizer(signal, sec_cov_values, lbound, ubound, cons_matrix, clb, cub, 1.)\n", " qpopt = QPOptimizer(signal, None, lbound, ubound, cons_matrix, clb, cub, 1.,\n",
" risk_cov_values[:n, :n] / 10000.,\n",
" risk_exposure_values[:n],\n",
" special_risk_values[:n] * special_risk_values[:n] / 10000.)\n",
" return qpopt.feval()" " return qpopt.feval()"
] ]
}, },
......
notebooks/Example 7 - Portfolio Optimizer Performance.ipynb
View file @ 657d8d97
...@@ -261,29 +261,24 @@ ...@@ -261,29 +261,24 @@
" bm = np.zeros(n)\n", " bm = np.zeros(n)\n",
" lbound = -np.ones(n) * np.inf\n", " lbound = -np.ones(n) * np.inf\n",
" ubound = np.ones(n) * np.inf\n", " ubound = np.ones(n) * np.inf\n",
" risk_model = dict(cov=None, factor_cov=risk_cov/10000., factor_loading=risk_exposure, idsync=(special_risk**2)/10000.)\n",
"\n", "\n",
" status, y, x1 = mean_variance_builder(er,\n", " status, y, x1 = mean_variance_builder(er,\n",
" None,\n", " risk_model,\n",
" bm,\n", " bm,\n",
" lbound,\n", " lbound,\n",
" ubound,\n", " ubound,\n",
" None,\n", " None,\n",
" None,\n", " None,\n",
" lam=1,\n", " lam=1)\n",
" factor_cov=risk_cov / 10000.,\n",
" factor_loading=risk_exposure,\n",
" idsync=(special_risk ** 2.) / 10000)\n",
" elasped_time1 = timeit.timeit(\"\"\"mean_variance_builder(er,\n", " elasped_time1 = timeit.timeit(\"\"\"mean_variance_builder(er,\n",
" None,\n", " risk_model,\n",
" bm,\n", " bm,\n",
" lbound,\n", " lbound,\n",
" ubound,\n", " ubound,\n",
" None,\n", " None,\n",
" None,\n", " None,\n",
" lam=1,\n", " lam=1)\"\"\",\n",
" factor_cov=risk_cov / 10000.,\n",
" factor_loading=risk_exposure,\n",
" idsync=(special_risk ** 2.) / 10000)\"\"\",\n",
" number=number, globals=globals()) / number * 1000\n", " number=number, globals=globals()) / number * 1000\n",
" \n", " \n",
" w = cvxpy.Variable(n)\n", " w = cvxpy.Variable(n)\n",
...@@ -344,29 +339,23 @@ ...@@ -344,29 +339,23 @@
" bm = np.zeros(n)\n", " bm = np.zeros(n)\n",
" lbound = np.zeros(n)\n", " lbound = np.zeros(n)\n",
" ubound = np.ones(n) * 0.1\n", " ubound = np.ones(n) * 0.1\n",
" \n",
" risk_model = dict(cov=None, factor_cov=risk_cov/10000., factor_loading=risk_exposure, idsync=(special_risk**2)/10000.)\n",
"\n", "\n",
" status, y, x1 = mean_variance_builder(er,\n", " status, y, x1 = mean_variance_builder(er,\n",
" None,\n", " risk_model,\n",
" bm,\n", " bm,\n",
" lbound,\n", " lbound,\n",
" ubound,\n", " ubound,\n",
" None,\n", " None,\n",
" None,\n", " None)\n",
" lam=1,\n",
" factor_cov=risk_cov / 10000.,\n",
" factor_loading=risk_exposure,\n",
" idsync=(special_risk ** 2.) / 10000)\n",
" elasped_time1 = timeit.timeit(\"\"\"mean_variance_builder(er,\n", " elasped_time1 = timeit.timeit(\"\"\"mean_variance_builder(er,\n",
" None,\n", " risk_model,\n",
" bm,\n", " bm,\n",
" lbound,\n", " lbound,\n",
" ubound,\n", " ubound,\n",
" None,\n", " None,\n",
" None,\n", " None)\"\"\",\n",
" lam=1,\n",
" factor_cov=risk_cov / 10000.,\n",
" factor_loading=risk_exposure,\n",
" idsync=(special_risk ** 2.) / 10000)\"\"\",\n",
" number=number, globals=globals()) / number * 1000\n", " number=number, globals=globals()) / number * 1000\n",
" \n", " \n",
" w = cvxpy.Variable(n)\n", " w = cvxpy.Variable(n)\n",
...@@ -432,29 +421,23 @@ ...@@ -432,29 +421,23 @@
" \n", " \n",
" risk_constraints = np.ones((len(er), 1))\n", " risk_constraints = np.ones((len(er), 1))\n",
" risk_target = (np.array([1.]), np.array([1.]))\n", " risk_target = (np.array([1.]), np.array([1.]))\n",
" \n",
" risk_model = dict(cov=None, factor_cov=risk_cov/10000., factor_loading=risk_exposure, idsync=(special_risk**2)/10000.)\n",
"\n", "\n",
" status, y, x1 = mean_variance_builder(er,\n", " status, y, x1 = mean_variance_builder(er,\n",
" None,\n", " risk_model,\n",
" bm,\n", " bm,\n",
" lbound,\n", " lbound,\n",
" ubound,\n", " ubound,\n",
" risk_constraints,\n", " risk_constraints,\n",
" risk_target,\n", " risk_target)\n",
" lam=1,\n",
" factor_cov=risk_cov / 10000.,\n",
" factor_loading=risk_exposure,\n",
" idsync=(special_risk ** 2.) / 10000)\n",
" elasped_time1 = timeit.timeit(\"\"\"mean_variance_builder(er,\n", " elasped_time1 = timeit.timeit(\"\"\"mean_variance_builder(er,\n",
" None,\n", " risk_model,\n",
" bm,\n", " bm,\n",
" lbound,\n", " lbound,\n",
" ubound,\n", " ubound,\n",
" risk_constraints,\n", " risk_constraints,\n",
" risk_target,\n", " risk_target)\"\"\",\n",
" lam=1,\n",
" factor_cov=risk_cov / 10000.,\n",
" factor_loading=risk_exposure,\n",
" idsync=(special_risk ** 2.) / 10000)\"\"\",\n",
" number=number, globals=globals()) / number * 1000\n", " number=number, globals=globals()) / number * 1000\n",
" \n", " \n",
" w = cvxpy.Variable(n)\n", " w = cvxpy.Variable(n)\n",
...@@ -527,31 +510,24 @@ ...@@ -527,31 +510,24 @@
" \n", " \n",
" risk_constraints = np.ones((n, 1))\n", " risk_constraints = np.ones((n, 1))\n",
" risk_target = (np.array([bm.sum()]), np.array([bm.sum()]))\n", " risk_target = (np.array([bm.sum()]), np.array([bm.sum()]))\n",
" risk_model = dict(cov=None, factor_cov=risk_cov/10000., factor_loading=risk_exposure, idsync=(special_risk**2)/10000.)\n",
"\n", "\n",
" status, y, x1 = target_vol_builder(er,\n", " status, y, x1 = target_vol_builder(er,\n",
" None,\n", " risk_model,\n",
" bm,\n", " bm,\n",
" lbound,\n", " lbound,\n",
" ubound,\n", " ubound,\n",
" risk_constraints,\n", " risk_constraints,\n",
" risk_target,\n", " risk_target,\n",
" vol_low=0,\n", " vol_target=target_vol)\n",
" vol_high=target_vol,\n",
" factor_cov=risk_cov / 10000.,\n",
" factor_loading=risk_exposure,\n",
" idsync=(special_risk ** 2.) / 10000)\n",
" elasped_time1 = timeit.timeit(\"\"\"target_vol_builder(er,\n", " elasped_time1 = timeit.timeit(\"\"\"target_vol_builder(er,\n",
" None,\n", " risk_model,\n",
" bm,\n", " bm,\n",
" lbound,\n", " lbound,\n",
" ubound,\n", " ubound,\n",
" risk_constraints,\n", " risk_constraints,\n",
" risk_target,\n", " risk_target,\n",
" vol_low=0,\n", " vol_target=target_vol)\"\"\",\n",
" vol_high=target_vol,\n",
" factor_cov=risk_cov / 10000.,\n",
" factor_loading=risk_exposure,\n",
" idsync=(special_risk ** 2.) / 10000)\"\"\",\n",
" number=number, globals=globals()) / number * 1000\n", " number=number, globals=globals()) / number * 1000\n",
" \n", " \n",
" w = cvxpy.Variable(n)\n", " w = cvxpy.Variable(n)\n",
......
notebooks/Example 9 - Linear Optimizer Comparison with CVXOPT.ipynb
View file @ 657d8d97
...@@ -113,13 +113,6 @@ ...@@ -113,13 +113,6 @@
" print(\"{0:<8}{1:>12.2f}{2:>12.2f}{3:>12f}{4:>12f}{5:>12f}{6:>15}\".format(n, elapsed*1000, result[1], s.min(), s.max(), s.sum(), s[0] + s[1]))" " print(\"{0:<8}{1:>12.2f}{2:>12.2f}{3:>12f}{4:>12f}{5:>12f}{6:>15}\".format(n, elapsed*1000, result[1], s.min(), s.max(), s.sum(), s[0] + s[1]))"
] ]
}, },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
......
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