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Commit a029d6b9 authored by Dr.李's avatar Dr.李
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update example

parent 2843e07c
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Showing with 138 additions and 98 deletions
notebooks/optimizer check.ipynb
View file @ a029d6b9
......@@ -108,7 +108,7 @@
"source": [
"%%time\n",
"ne_factor_values = neutralize(risk_exposure_values, factor_values)\n",
"signal = ne_factor_values @ weights"
"signal_full = ne_factor_values @ weights"
]
},
{
......@@ -128,7 +128,7 @@
"source": [
"%%time\n",
"risk_cov_values = risk_cov_data.loc[risk_factor_columns, risk_factor_columns].astype(float)\n",
"sec_cov_values = risk_exposure_values @ risk_cov_values @ risk_exposure_values.T + np.diag(specific_risk.SRISK.values)"
"sec_cov_values_full = risk_exposure_values @ risk_cov_values @ risk_exposure_values.T + np.diag(specific_risk.SRISK.values)"
]
},
{
......@@ -137,7 +137,10 @@
"metadata": {},
"outputs": [],
"source": [
"sec_cov_values"
"n = 500\n",
"\n",
"sec_cov_values = sec_cov_values_full[:n, :n]\n",
"signal = signal_full[:n]"
]
},
{
......@@ -158,7 +161,7 @@
"w = Variable(n)\n",
"\n",
"lbound = 0.\n",
"ubound = 0.01\n",
"ubound = 1. / n * 20\n",
"\n",
"objective = Minimize(risk_penlty * quad_form(w, sec_cov_values) - signal * w)\n",
"constraints = [w >= lbound,\n",
......@@ -220,7 +223,7 @@
"h = np.zeros(2*n)\n",
"for i in range(n):\n",
" G[i, i] = 1.\n",
" h[i] = 0.01\n",
" h[i] = 1. / n * 20\n",
" G[i+n, i] = -1.\n",
" h[i+n] = 0.\n",
" \n",
......@@ -236,6 +239,62 @@
"sol = solvers.qp(P, q, G, h, A, b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Performace Timing\n",
"-------------------------"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import datetime as dt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def time_function(py_callable, n):\n",
" start = dt.datetime.now()\n",
" py_callable(n)\n",
" return (dt.datetime.now() - start).total_seconds()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def cvxpy(n):\n",
" w = Variable(n)\n",
"\n",
" lbound = 0.\n",
" ubound = 1. / n * 20\n",
"\n",
" objective = Minimize(risk_penlty * quad_form(w, sec_cov_values) - signal * w)\n",
" constraints = [w >= lbound,\n",
" w <= ubound,\n",
" sum_entries(w) == 1,]\n",
"\n",
" prob = Problem(objective, constraints)\n",
" prob.solve(verbose=False, solver='CVXOPT', display=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
......@@ -244,7 +303,78 @@
},
"outputs": [],
"source": [
"sec_cov_values.tofile('sec_cov_values.dat')"
"def cvxopt(n):\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",
" solvers.options['show_progress'] = False\n",
" sol = solvers.qp(P, q, G, h, A, b)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"s = time_function(cvxopt, n)\n",
"print(s)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"n_steps = list(range(200, 2001, 200))\n",
"cvxpy_times = [None] * len(n_steps)\n",
"cvxopt_times = [None] * len(n_steps)\n",
"\n",
"for i, n in enumerate(n_steps):\n",
" print(\"Scale for {0}\".format(n))\n",
" sec_cov_values = sec_cov_values_full[:n, :n]\n",
" signal = signal_full[:n]\n",
" cvxpy_times[i] = time_function(cvxpy, n) * 1000\n",
" cvxopt_times[i] = time_function(cvxopt, n) * 1000"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame({'cvxpy': cvxpy_times,\n",
" 'cvxopt': cvxopt_times},\n",
" index=n_steps)\n",
"df.index.name = 'Problem Scale (n)'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ipopt_times = [63.4241, 157.568, 208.774, 436.105, 522.81, 744.118, 1133.87, 1685.59, 1409.36, 3364.33]"
]
},
{
......@@ -255,7 +385,7 @@
},
"outputs": [],
"source": [
"signal.tofile('signal.dat')"
"df['ipopt'] = ipopt_times"
]
},
{
......@@ -270,7 +400,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
......
notebooks/optimizer check2.ipynb deleted 100644 → 0
View file @ 2843e07c
{
"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
}
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