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Commit fd8a9b2c authored by Dr.李's avatar Dr.李
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update example

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notebooks/Example 7 - Portfolio Optimizer Performance.ipynb
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...@@ -28,9 +28,6 @@ ...@@ -28,9 +28,6 @@
"import numpy as np\n", "import numpy as np\n",
"import pandas as pd\n", "import pandas as pd\n",
"import cvxpy\n", "import cvxpy\n",
"from cvxopt import solvers\n",
"from scipy.optimize import linprog\n",
"from scipy.optimize import minimize\n",
"from alphamind.api import *\n", "from alphamind.api import *\n",
"from alphamind.portfolio.linearbuilder import linear_builder\n", "from alphamind.portfolio.linearbuilder import linear_builder\n",
"from alphamind.portfolio.meanvariancebuilder import mean_variance_builder\n", "from alphamind.portfolio.meanvariancebuilder import mean_variance_builder\n",
...@@ -102,9 +99,8 @@ ...@@ -102,9 +99,8 @@
" A_eq = risk_constraints.T\n", " A_eq = risk_constraints.T\n",
" b_eq = np.array([1.])\n", " b_eq = np.array([1.])\n",
" \n", " \n",
" solvers.options['glpk'] = {'msg_lev': 'GLP_MSG_OFF'}\n",
" w = cvxpy.Variable(n)\n", " w = cvxpy.Variable(n)\n",
" curr_risk_exposure = risk_constraints.T @ w\n", " curr_risk_exposure = w * risk_constraints\n",
" \n", " \n",
" constraints = [w >= lbound,\n", " constraints = [w >= lbound,\n",
" w <= ubound,\n", " w <= ubound,\n",
...@@ -113,8 +109,8 @@ ...@@ -113,8 +109,8 @@
" objective = cvxpy.Minimize(-w.T * er)\n", " objective = cvxpy.Minimize(-w.T * er)\n",
" prob = cvxpy.Problem(objective, constraints)\n", " prob = cvxpy.Problem(objective, constraints)\n",
" \n", " \n",
" prob.solve()\n", " prob.solve(solver='ECOS')\n",
" elasped_time2 = timeit.timeit(\"prob.solve()\",\n", " elasped_time2 = timeit.timeit(\"prob.solve(solver='ECOS')\",\n",
" number=number, globals=globals()) / number * 1000\n", " number=number, globals=globals()) / number * 1000\n",
"\n", "\n",
" np.testing.assert_almost_equal(x1 @ er, np.array(w.value).flatten() @ er, 4)\n", " np.testing.assert_almost_equal(x1 @ er, np.array(w.value).flatten() @ er, 4)\n",
...@@ -133,6 +129,15 @@ ...@@ -133,6 +129,15 @@
"df" "df"
] ]
}, },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"prob.value"
]
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
...@@ -149,7 +154,7 @@ ...@@ -149,7 +154,7 @@
"source": [ "source": [
"from cvxpy import pnorm\n", "from cvxpy import pnorm\n",
"\n", "\n",
"df = pd.DataFrame(columns=u_names, index=['cvxpy', 'alphamind (simplex)', 'alphamind (interior)'])\n", "df = pd.DataFrame(columns=u_names, index=['cvxpy', 'alphamind (clp simplex)', 'alphamind (clp interior)', 'alphamind (ecos)'])\n",
"turn_over_target = 0.5\n", "turn_over_target = 0.5\n",
"number = 1\n", "number = 1\n",
"\n", "\n",
...@@ -196,10 +201,18 @@ ...@@ -196,10 +201,18 @@
" objective = cvxpy.Minimize(-w.T * er)\n", " objective = cvxpy.Minimize(-w.T * er)\n",
" prob = cvxpy.Problem(objective, constraints)\n", " prob = cvxpy.Problem(objective, constraints)\n",
" \n", " \n",
" prob.solve()\n", " prob.solve(solver='ECOS')\n",
" elasped_time2 = timeit.timeit(\"prob.solve()\",\n", " elasped_time2 = timeit.timeit(\"prob.solve(solver='ECOS')\",\n",
" number=number, globals=globals()) / number * 1000\n", " number=number, globals=globals()) / number * 1000\n",
" \n", " \n",
" status, y, x2 = linear_builder(er,\n",
" lbound,\n",
" ubound,\n",
" risk_constraints,\n",
" risk_target,\n",
" turn_over_target=turn_over_target,\n",
" current_position=current_position,\n",
" method='simplex')\n",
" elasped_time3 = timeit.timeit(\"\"\"linear_builder(er,\n", " elasped_time3 = timeit.timeit(\"\"\"linear_builder(er,\n",
" lbound,\n", " lbound,\n",
" ubound,\n", " ubound,\n",
...@@ -209,11 +222,31 @@ ...@@ -209,11 +222,31 @@
" current_position=current_position,\n", " current_position=current_position,\n",
" method='simplex')\"\"\", number=number, globals=globals()) / number * 1000\n", " method='simplex')\"\"\", number=number, globals=globals()) / number * 1000\n",
" \n", " \n",
" status, y, x3 = linear_builder(er,\n",
" lbound,\n",
" ubound,\n",
" risk_constraints,\n",
" risk_target,\n",
" turn_over_target=turn_over_target,\n",
" current_position=current_position,\n",
" method='ecos')\n",
" elasped_time4 = timeit.timeit(\"\"\"linear_builder(er,\n",
" lbound,\n",
" ubound,\n",
" risk_constraints,\n",
" risk_target,\n",
" turn_over_target=turn_over_target,\n",
" current_position=current_position,\n",
" method='ecos')\"\"\", number=number, globals=globals()) / number * 1000\n",
" \n",
" \n", " \n",
" np.testing.assert_almost_equal(x1 @ er, np.array(w.value).flatten() @ er, 4)\n", " np.testing.assert_almost_equal(x1 @ er, np.array(w.value).flatten() @ er, 4)\n",
" np.testing.assert_almost_equal(x2 @ er, np.array(w.value).flatten() @ er, 4)\n",
" np.testing.assert_almost_equal(x3 @ er, np.array(w.value).flatten() @ er, 4)\n",
"\n", "\n",
" df.loc['alphamind (interior)', u_name] = elasped_time1\n", " df.loc['alphamind (clp interior)', u_name] = elasped_time1\n",
" df.loc['alphamind (simplex)', u_name] = elasped_time3\n", " df.loc['alphamind (clp simplex)', u_name] = elasped_time3\n",
" df.loc['alphamind (ecos)', u_name] = elasped_time4\n",
" df.loc['cvxpy', u_name] = elasped_time2\n", " df.loc['cvxpy', u_name] = elasped_time2\n",
" alpha_logger.info(f\"{u_name} is finished\")" " alpha_logger.info(f\"{u_name} is finished\")"
] ]
...@@ -282,7 +315,7 @@ ...@@ -282,7 +315,7 @@
" 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",
" risk = sum_squares(mul_elemwise(special_risk / 100., w)) + quad_form((w.T * risk_exposure).T, risk_cov / 10000.)\n", " risk = sum_squares(multiply(special_risk / 100., w)) + quad_form((w.T * risk_exposure).T, risk_cov / 10000.)\n",
" objective = cvxpy.Minimize(-w.T * er + 0.5 * risk)\n", " objective = cvxpy.Minimize(-w.T * er + 0.5 * risk)\n",
" prob = cvxpy.Problem(objective)\n", " prob = cvxpy.Problem(objective)\n",
" prob.solve(solver='ECOS')\n", " prob.solve(solver='ECOS')\n",
...@@ -359,7 +392,7 @@ ...@@ -359,7 +392,7 @@
" 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",
" risk = sum_squares(mul_elemwise(special_risk / 100., w)) + quad_form((w.T * risk_exposure).T, risk_cov / 10000.)\n", " risk = sum_squares(multiply(special_risk / 100., w)) + quad_form((w.T * risk_exposure).T, risk_cov / 10000.)\n",
" objective = cvxpy.Minimize(-w.T * er + 0.5 * risk)\n", " objective = cvxpy.Minimize(-w.T * er + 0.5 * risk)\n",
" constraints = [w >= lbound,\n", " constraints = [w >= lbound,\n",
" w <= ubound]\n", " w <= ubound]\n",
...@@ -441,7 +474,7 @@ ...@@ -441,7 +474,7 @@
" 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",
" risk = sum_squares(mul_elemwise(special_risk / 100., w)) + quad_form((w.T * risk_exposure).T, risk_cov / 10000.)\n", " risk = sum_squares(multiply(special_risk / 100., w)) + quad_form((w.T * risk_exposure).T, risk_cov / 10000.)\n",
" objective = cvxpy.Minimize(-w.T * er + 0.5 * risk)\n", " objective = cvxpy.Minimize(-w.T * er + 0.5 * risk)\n",
" curr_risk_exposure = risk_constraints.T @ w\n", " curr_risk_exposure = risk_constraints.T @ w\n",
" constraints = [w >= lbound,\n", " constraints = [w >= lbound,\n",
...@@ -531,7 +564,7 @@ ...@@ -531,7 +564,7 @@
" 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",
" risk = sum_squares(mul_elemwise(special_risk / 100., w)) + quad_form((w.T * risk_exposure).T, risk_cov / 10000.)\n", " risk = sum_squares(multiply(special_risk / 100., w)) + quad_form((w.T * risk_exposure).T, risk_cov / 10000.)\n",
" objective = cvxpy.Minimize(-w.T * er)\n", " objective = cvxpy.Minimize(-w.T * er)\n",
" curr_risk_exposure = risk_constraints.T @ w\n", " curr_risk_exposure = risk_constraints.T @ w\n",
" constraints = [w >= lbound,\n", " constraints = [w >= lbound,\n",
......
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