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Commit e97a8be4 authored by Dr.李's avatar Dr.李
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FIX: update all the examples

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Showing with 1281 additions and 840 deletions
alphamind/portfolio/meanvariancebuilder.py
View file @ e97a8be4
...@@ -69,10 +69,10 @@ def mean_variance_builder(er: np.ndarray, ...@@ -69,10 +69,10 @@ def mean_variance_builder(er: np.ndarray,
risk_exposure = risk_model['factor_loading'] risk_exposure = risk_model['factor_loading']
if cov is None: if cov is None:
risk = cvxpy.sum_squares(cvxpy.multiply(cvxpy.sqrt(special_risk), w)) \ risk = cvxpy.sum_squares(cvxpy.multiply(cvxpy.sqrt(special_risk), w)) \
+ cvxpy.quad_form((w.T * risk_exposure).T, risk_cov) + cvxpy.quad_form((w.T @ risk_exposure).T, risk_cov)
else: else:
risk = cvxpy.quad_form(w, cov) risk = cvxpy.quad_form(w, cov)
objective = cvxpy.Minimize(-w.T * er + 0.5 * lam * risk) objective = cvxpy.Minimize(-w.T @ er + 0.5 * lam * risk)
prob = cvxpy.Problem(objective) prob = cvxpy.Problem(objective)
prob.solve(solver='ECOS', feastol=1e-9, abstol=1e-9, reltol=1e-9) prob.solve(solver='ECOS', feastol=1e-9, abstol=1e-9, reltol=1e-9)
......
notebooks/Example 10 - Quadratic Optimizer Comparison with CVXOPT.ipynb
View file @ e97a8be4
...@@ -134,17 +134,17 @@ ...@@ -134,17 +134,17 @@
" warm start: on, polish: on, time_limit: off\n", " warm start: on, polish: on, time_limit: off\n",
"\n", "\n",
"iter objective pri res dua res rho time\n", "iter objective pri res dua res rho time\n",
" 1 -7.8878e+03 4.61e+00 6.68e+04 1.00e-01 1.40e-03s\n", " 1 -7.8878e+03 4.61e+00 6.68e+04 1.00e-01 1.82e-03s\n",
" 125 -2.4830e+02 3.58e-07 2.76e-05 5.82e-01 4.57e-03s\n", " 125 -2.4830e+02 3.58e-07 2.76e-05 5.82e-01 5.80e-03s\n",
"\n", "\n",
"status: solved\n", "status: solved\n",
"solution polish: unsuccessful\n", "solution polish: unsuccessful\n",
"number of iterations: 125\n", "number of iterations: 125\n",
"optimal objective: -248.2989\n", "optimal objective: -248.2989\n",
"run time: 5.42e-03s\n", "run time: 6.87e-03s\n",
"optimal rho estimate: 1.87e+00\n", "optimal rho estimate: 1.87e+00\n",
"\n", "\n",
"Wall time: 49 ms\n" "Wall time: 38 ms\n"
] ]
}, },
{ {
...@@ -211,9 +211,9 @@ ...@@ -211,9 +211,9 @@
"12 -2.483e+02 -2.483e+02 +3e-07 5e-13 2e-15 1e-10 8e-10 0.9775 1e-04 1 1 1 | 0 0\n", "12 -2.483e+02 -2.483e+02 +3e-07 5e-13 2e-15 1e-10 8e-10 0.9775 1e-04 1 1 1 | 0 0\n",
"\n", "\n",
"OPTIMAL (within feastol=5.3e-13, reltol=1.3e-09, abstol=3.2e-07).\n", "OPTIMAL (within feastol=5.3e-13, reltol=1.3e-09, abstol=3.2e-07).\n",
"Runtime: 0.015751 seconds.\n", "Runtime: 0.014540 seconds.\n",
"\n", "\n",
"Wall time: 59 ms\n" "Wall time: 49 ms\n"
] ]
}, },
{ {
...@@ -277,7 +277,7 @@ ...@@ -277,7 +277,7 @@
"12: -2.4829e+02 -2.4831e+02 1e-02 2e-16 4e-16\n", "12: -2.4829e+02 -2.4831e+02 1e-02 2e-16 4e-16\n",
"13: -2.4830e+02 -2.4830e+02 1e-04 2e-16 1e-15\n", "13: -2.4830e+02 -2.4830e+02 1e-04 2e-16 1e-15\n",
"Optimal solution found.\n", "Optimal solution found.\n",
"Wall time: 69 ms\n" "Wall time: 74 ms\n"
] ]
} }
], ],
...@@ -315,7 +315,7 @@ ...@@ -315,7 +315,7 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Wall time: 26 ms\n" "Wall time: 15 ms\n"
] ]
}, },
{ {
...@@ -465,9 +465,9 @@ ...@@ -465,9 +465,9 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Scale(n) cvxpy cvxopt ipopt\n", "Scale(n) cvxpy cvxopt ipopt\n",
"100 0.0320 0.0240 0.0060\n", "100 0.0320 0.0270 0.0090\n",
"200 0.0360 0.0420 0.0120\n", "200 0.0370 0.0500 0.0160\n",
"300 0.0440 0.0630 0.0180\n" "300 0.0430 0.0630 0.0160\n"
] ]
} }
], ],
......
notebooks/Example 11 - Long Short Strategy Model.ipynb
View file @ e97a8be4
...@@ -15,7 +15,7 @@ ...@@ -15,7 +15,7 @@
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"datetime.datetime(2020, 11, 22, 1, 6, 28, 118072)" "datetime.datetime(2020, 11, 23, 0, 38, 28, 821914)"
] ]
}, },
"execution_count": 1, "execution_count": 1,
...@@ -117,7 +117,7 @@ ...@@ -117,7 +117,7 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Wall time: 5.93 s\n" "Wall time: 6.09 s\n"
] ]
} }
], ],
...@@ -135,14 +135,14 @@ ...@@ -135,14 +135,14 @@
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"2020-11-22 01:06:34,553 - ALPHA_MIND - INFO - 2020-01-02 full re-balance: 300\n", "2020-11-23 00:38:35,439 - ALPHA_MIND - INFO - 2020-01-02 full re-balance: 300\n",
"2020-11-22 01:06:35,020 - ALPHA_MIND - INFO - 2020-01-02 is finished\n", "2020-11-23 00:38:35,874 - ALPHA_MIND - INFO - 2020-01-02 is finished\n",
"2020-11-22 01:06:35,023 - ALPHA_MIND - INFO - 2020-01-16 full re-balance: 300\n", "2020-11-23 00:38:35,877 - ALPHA_MIND - INFO - 2020-01-16 full re-balance: 300\n",
"2020-11-22 01:06:35,230 - ALPHA_MIND - INFO - 2020-01-16 is finished\n", "2020-11-23 00:38:36,076 - ALPHA_MIND - INFO - 2020-01-16 is finished\n",
"2020-11-22 01:06:35,233 - ALPHA_MIND - INFO - 2020-02-07 full re-balance: 300\n", "2020-11-23 00:38:36,080 - ALPHA_MIND - INFO - 2020-02-07 full re-balance: 300\n",
"2020-11-22 01:06:35,428 - ALPHA_MIND - INFO - 2020-02-07 is finished\n", "2020-11-23 00:38:36,276 - ALPHA_MIND - INFO - 2020-02-07 is finished\n",
"2020-11-22 01:06:35,432 - ALPHA_MIND - INFO - 2020-02-21 full re-balance: 300\n", "2020-11-23 00:38:36,280 - ALPHA_MIND - INFO - 2020-02-21 full re-balance: 300\n",
"2020-11-22 01:06:35,711 - ALPHA_MIND - INFO - 2020-02-21 is finished\n" "2020-11-23 00:38:36,486 - ALPHA_MIND - INFO - 2020-02-21 is finished\n"
] ]
} }
], ],
......
notebooks/Example 12 - Machine Learning Model Prediction.ipynb
View file @ e97a8be4
...@@ -11,7 +11,7 @@ ...@@ -11,7 +11,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -35,7 +35,7 @@ ...@@ -35,7 +35,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -57,16 +57,16 @@ ...@@ -57,16 +57,16 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"我们使用当期的`[ROE, EPS, ETOP]`因子，来尝试预测未来大概一个月以后的收益。\n", "我们使用当期的`[\"EMA5D\", \"EMV6D\"]`因子，来尝试预测未来大概一个月以后的收益。\n",
"\n", "\n",
"* 训练的股票池为`zz800`;；\n", "* 训练的股票池为`hs300`;；\n",
"* 因子都经过中性化以及标准化等预处理；\n", "* 因子都经过中性化以及标准化等预处理；\n",
"* 对于线性模型，我们以20个工作日为一个时间间隔，用过去8期的数据作为训练用特征。" "* 对于线性模型，我们以10个工作日为一个时间间隔，用过去8期的数据作为训练用特征。"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -100,7 +100,7 @@ ...@@ -100,7 +100,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -111,7 +111,7 @@ ...@@ -111,7 +111,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -144,7 +144,7 @@ ...@@ -144,7 +144,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 7,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -157,7 +157,7 @@ ...@@ -157,7 +157,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -167,7 +167,7 @@ ...@@ -167,7 +167,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -203,7 +203,7 @@ ...@@ -203,7 +203,7 @@
} }
], ],
"source": [ "source": [
"print(\"\\nConst. Testing IC: {0:.4f}\".format(const_composer.ic(ref_date=ref_date)[0]))\n", "print(\"Const. Testing IC: {0:.4f}\".format(const_composer.ic(ref_date=ref_date)[0]))\n",
"print(\"Regression Testing IC: {0:.4f}\".format(regression_composer.ic(ref_date=ref_date)[0]))" "print(\"Regression Testing IC: {0:.4f}\".format(regression_composer.ic(ref_date=ref_date)[0]))"
] ]
}, },
...@@ -227,7 +227,7 @@ ...@@ -227,7 +227,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 10,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -324,7 +324,7 @@ ...@@ -324,7 +324,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 11,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -373,7 +373,7 @@ ...@@ -373,7 +373,7 @@
"std 0.140552 0.166313" "std 0.140552 0.166313"
] ]
}, },
"execution_count": 10, "execution_count": 11,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -399,7 +399,7 @@ ...@@ -399,7 +399,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -416,7 +416,7 @@ ...@@ -416,7 +416,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 13,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -489,7 +489,7 @@ ...@@ -489,7 +489,7 @@
"16 2020-01-02 2010000012 -0.017889" "16 2020-01-02 2010000012 -0.017889"
] ]
}, },
"execution_count": 12, "execution_count": 13,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -500,21 +500,21 @@ ...@@ -500,21 +500,21 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 17, "execution_count": 14,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"2020-11-22 01:31:22,196 - ALPHA_MIND - INFO - 2020-01-02 full re-balance: 300\n", "2020-11-23 00:39:52,840 - ALPHA_MIND - INFO - 2020-01-02 full re-balance: 300\n",
"2020-11-22 01:31:22,210 - ALPHA_MIND - INFO - 2020-01-02 is finished\n", "2020-11-23 00:39:52,869 - ALPHA_MIND - INFO - 2020-01-02 is finished\n",
"2020-11-22 01:31:22,220 - ALPHA_MIND - INFO - 2020-01-16 full re-balance: 300\n", "2020-11-23 00:39:52,878 - ALPHA_MIND - INFO - 2020-01-16 full re-balance: 300\n",
"2020-11-22 01:31:22,239 - ALPHA_MIND - INFO - 2020-01-16 is finished\n", "2020-11-23 00:39:52,888 - ALPHA_MIND - INFO - 2020-01-16 is finished\n",
"2020-11-22 01:31:22,252 - ALPHA_MIND - INFO - 2020-02-07 full re-balance: 300\n", "2020-11-23 00:39:52,897 - ALPHA_MIND - INFO - 2020-02-07 full re-balance: 300\n",
"2020-11-22 01:31:22,269 - ALPHA_MIND - INFO - 2020-02-07 is finished\n", "2020-11-23 00:39:52,909 - ALPHA_MIND - INFO - 2020-02-07 is finished\n",
"2020-11-22 01:31:22,280 - ALPHA_MIND - INFO - 2020-02-21 full re-balance: 300\n", "2020-11-23 00:39:52,917 - ALPHA_MIND - INFO - 2020-02-21 full re-balance: 300\n",
"2020-11-22 01:31:22,297 - ALPHA_MIND - INFO - 2020-02-21 is finished\n" "2020-11-23 00:39:52,931 - ALPHA_MIND - INFO - 2020-02-21 is finished\n"
] ]
} }
], ],
...@@ -575,7 +575,7 @@ ...@@ -575,7 +575,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 18, "execution_count": 15,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -584,7 +584,7 @@ ...@@ -584,7 +584,7 @@
"<AxesSubplot:title={'center':'Fixed freq rebalanced: 10b'}>" "<AxesSubplot:title={'center':'Fixed freq rebalanced: 10b'}>"
] ]
}, },
"execution_count": 18, "execution_count": 15,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
}, },
......
notebooks/Example 2 - Strategy Analysis.ipynb
View file @ e97a8be4
...@@ -131,19 +131,19 @@ ...@@ -131,19 +131,19 @@
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"2020-11-22 01:37:30,583 - ALPHA_MIND - INFO - alpha factor data loading finished ...\n", "2020-11-23 00:40:30,181 - ALPHA_MIND - INFO - alpha factor data loading finished ...\n",
"2020-11-22 01:37:30,745 - ALPHA_MIND - INFO - industry data loading finished ...\n", "2020-11-23 00:40:30,383 - ALPHA_MIND - INFO - industry data loading finished ...\n",
"2020-11-22 01:37:30,878 - ALPHA_MIND - INFO - benchmark data loading finished ...\n", "2020-11-23 00:40:30,576 - ALPHA_MIND - INFO - benchmark data loading finished ...\n",
"2020-11-22 01:37:31,240 - ALPHA_MIND - INFO - risk_model data loading finished ...\n", "2020-11-23 00:40:30,944 - ALPHA_MIND - INFO - risk_model data loading finished ...\n",
"2020-11-22 01:37:31,909 - ALPHA_MIND - INFO - returns data loading finished ...\n", "2020-11-23 00:40:31,694 - ALPHA_MIND - INFO - returns data loading finished ...\n",
"2020-11-22 01:37:32,006 - ALPHA_MIND - INFO - starting backting ...\n", "2020-11-23 00:40:31,795 - ALPHA_MIND - INFO - starting backting ...\n",
"2020-11-22 01:37:32,013 - ALPHA_MIND - INFO - alpha models training finished ...\n", "2020-11-23 00:40:31,803 - ALPHA_MIND - INFO - alpha models training finished ...\n",
"2020-11-22 01:37:32,018 - ALPHA_MIND - INFO - 2020-01-02 00:00:00 re-balance: 300 codes\n", "2020-11-23 00:40:31,811 - ALPHA_MIND - INFO - 2020-01-02 00:00:00 re-balance: 300 codes\n",
"2020-11-22 01:37:32,037 - ALPHA_MIND - INFO - 2020-01-16 00:00:00 re-balance: 300 codes\n", "2020-11-23 00:40:31,828 - ALPHA_MIND - INFO - 2020-01-16 00:00:00 re-balance: 300 codes\n",
"D:\\ProgramData\\Anaconda3\\envs\\alpha-mind\\lib\\site-packages\\cvxpy\\problems\\problem.py:1061: UserWarning: Solution may be inaccurate. Try another solver, adjusting the solver settings, or solve with verbose=True for more information.\n", "D:\\ProgramData\\Anaconda3\\envs\\alpha-mind\\lib\\site-packages\\cvxpy\\problems\\problem.py:1061: UserWarning: Solution may be inaccurate. Try another solver, adjusting the solver settings, or solve with verbose=True for more information.\n",
" \"Solution may be inaccurate. Try another solver, \"\n", " \"Solution may be inaccurate. Try another solver, \"\n",
"2020-11-22 01:37:32,130 - ALPHA_MIND - INFO - 2020-02-07 00:00:00 re-balance: 300 codes\n", "2020-11-23 00:40:31,910 - ALPHA_MIND - INFO - 2020-02-07 00:00:00 re-balance: 300 codes\n",
"2020-11-22 01:37:32,218 - ALPHA_MIND - INFO - 2020-02-21 00:00:00 re-balance: 300 codes\n" "2020-11-23 00:40:31,986 - ALPHA_MIND - INFO - 2020-02-21 00:00:00 re-balance: 300 codes\n"
] ]
} }
], ],
......
notebooks/Example 5 - Style Factor Analysis.ipynb
View file @ e97a8be4
...@@ -11,7 +11,7 @@ ...@@ -11,7 +11,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 1,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -30,7 +30,7 @@ ...@@ -30,7 +30,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -51,7 +51,7 @@ ...@@ -51,7 +51,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -77,7 +77,7 @@ ...@@ -77,7 +77,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -86,44 +86,44 @@ ...@@ -86,44 +86,44 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"2020-11-21 15:10:18,028 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n", "2020-11-23 00:41:53,439 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2020-11-21 15:10:18,038 - ALPHA_MIND - INFO - 2020-01-02 00:00:00 is finished with 300 stocks for BETA\n", "2020-11-23 00:41:53,445 - ALPHA_MIND - INFO - 2020-01-02 00:00:00 is finished with 300 stocks for BETA\n",
"2020-11-21 15:10:18,041 - ALPHA_MIND - INFO - 2020-01-02 00:00:00 risk_exposure: 3.25829145513029e-31\n", "2020-11-23 00:41:53,447 - ALPHA_MIND - INFO - 2020-01-02 00:00:00 risk_exposure: 4.943553232055517e-31\n",
"2020-11-21 15:10:18,510 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n", "2020-11-23 00:41:54,467 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2020-11-21 15:10:18,513 - ALPHA_MIND - INFO - 2020-01-16 00:00:00 is finished with 300 stocks for BETA\n", "2020-11-23 00:41:54,470 - ALPHA_MIND - INFO - 2020-01-16 00:00:00 is finished with 300 stocks for BETA\n",
"2020-11-21 15:10:18,516 - ALPHA_MIND - INFO - 2020-01-16 00:00:00 risk_exposure: 4.602789610027951e-31\n", "2020-11-23 00:41:54,473 - ALPHA_MIND - INFO - 2020-01-16 00:00:00 risk_exposure: 8.396812257213363e-31\n",
"2020-11-21 15:10:19,244 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n", "2020-11-23 00:41:55,338 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2020-11-21 15:10:19,248 - ALPHA_MIND - INFO - 2020-02-07 00:00:00 is finished with 300 stocks for BETA\n", "2020-11-23 00:41:55,340 - ALPHA_MIND - INFO - 2020-02-07 00:00:00 is finished with 300 stocks for BETA\n",
"2020-11-21 15:10:19,252 - ALPHA_MIND - INFO - 2020-02-07 00:00:00 risk_exposure: 7.380374200927195e-31\n", "2020-11-23 00:41:55,343 - ALPHA_MIND - INFO - 2020-02-07 00:00:00 risk_exposure: 5.682726866635588e-31\n",
"2020-11-21 15:10:20,041 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n", "2020-11-23 00:41:56,058 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2020-11-21 15:10:20,046 - ALPHA_MIND - INFO - 2020-02-21 00:00:00 is finished with 300 stocks for BETA\n", "2020-11-23 00:41:56,061 - ALPHA_MIND - INFO - 2020-02-21 00:00:00 is finished with 300 stocks for BETA\n",
"2020-11-21 15:10:20,052 - ALPHA_MIND - INFO - 2020-02-21 00:00:00 risk_exposure: 1.425987166389731e-31\n", "2020-11-23 00:41:56,063 - ALPHA_MIND - INFO - 2020-02-21 00:00:00 risk_exposure: 9.795355575973521e-32\n",
"2020-11-21 15:10:21,151 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n", "2020-11-23 00:41:57,012 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2020-11-21 15:10:21,159 - ALPHA_MIND - INFO - 2020-01-02 00:00:00 is finished with 300 stocks for SIZE\n", "2020-11-23 00:41:57,014 - ALPHA_MIND - INFO - 2020-01-02 00:00:00 is finished with 300 stocks for SIZE\n",
"2020-11-21 15:10:21,169 - ALPHA_MIND - INFO - 2020-01-02 00:00:00 risk_exposure: 1.626600676017078e-31\n", "2020-11-23 00:41:57,016 - ALPHA_MIND - INFO - 2020-01-02 00:00:00 risk_exposure: 1.484415398332342e-31\n",
"2020-11-21 15:10:21,952 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n", "2020-11-23 00:41:57,794 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2020-11-21 15:10:21,961 - ALPHA_MIND - INFO - 2020-01-16 00:00:00 is finished with 300 stocks for SIZE\n", "2020-11-23 00:41:57,796 - ALPHA_MIND - INFO - 2020-01-16 00:00:00 is finished with 300 stocks for SIZE\n",
"2020-11-21 15:10:21,970 - ALPHA_MIND - INFO - 2020-01-16 00:00:00 risk_exposure: 7.114752841729456e-31\n", "2020-11-23 00:41:57,799 - ALPHA_MIND - INFO - 2020-01-16 00:00:00 risk_exposure: 4.379171870810272e-31\n",
"2020-11-21 15:10:22,823 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n", "2020-11-23 00:41:58,810 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2020-11-21 15:10:22,831 - ALPHA_MIND - INFO - 2020-02-07 00:00:00 is finished with 300 stocks for SIZE\n", "2020-11-23 00:41:58,813 - ALPHA_MIND - INFO - 2020-02-07 00:00:00 is finished with 300 stocks for SIZE\n",
"2020-11-21 15:10:22,839 - ALPHA_MIND - INFO - 2020-02-07 00:00:00 risk_exposure: 2.894999049336361e-31\n", "2020-11-23 00:41:58,815 - ALPHA_MIND - INFO - 2020-02-07 00:00:00 risk_exposure: 2.2500853516126014e-31\n",
"2020-11-21 15:10:23,345 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n", "2020-11-23 00:42:00,437 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2020-11-21 15:10:23,348 - ALPHA_MIND - INFO - 2020-02-21 00:00:00 is finished with 300 stocks for SIZE\n", "2020-11-23 00:42:00,439 - ALPHA_MIND - INFO - 2020-02-21 00:00:00 is finished with 300 stocks for SIZE\n",
"2020-11-21 15:10:23,350 - ALPHA_MIND - INFO - 2020-02-21 00:00:00 risk_exposure: 3.207077036087234e-31\n" "2020-11-23 00:42:00,442 - ALPHA_MIND - INFO - 2020-02-21 00:00:00 risk_exposure: 1.4975497993984135e-31\n"
] ]
}, },
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Wall time: 6.91 s\n" "Wall time: 8.23 s\n"
] ]
} }
], ],
...@@ -134,7 +134,7 @@ ...@@ -134,7 +134,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -147,7 +147,7 @@ ...@@ -147,7 +147,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 7,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -198,7 +198,7 @@ ...@@ -198,7 +198,7 @@
"SIZE 0.941047" "SIZE 0.941047"
] ]
}, },
"execution_count": 16, "execution_count": 7,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
......
notebooks/Example 9 - Linear Optimizer Comparison with CVXOPT.ipynb
View file @ e97a8be4
...@@ -14,7 +14,7 @@ ...@@ -14,7 +14,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -54,7 +54,7 @@ ...@@ -54,7 +54,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -62,21 +62,21 @@ ...@@ -62,21 +62,21 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Scale(n) time(ms) feval min(x) max(x) sum(x) x(0) + x(1)\n", "Scale(n) time(ms) feval min(x) max(x) sum(x) x(0) + x(1)\n",
"200 23.15 -0.82 -0.000000 0.010000 1.0000000.014999999999355636\n", "200 16.00 -0.82 -0.000000 0.010000 1.0000000.014999999999355636\n",
"400 29.00 -1.28 -0.000000 0.010000 1.0000000.014999999999977868\n", "400 14.00 -1.28 -0.000000 0.010000 1.0000000.014999999999977868\n",
"600 31.04 -1.54 -0.000000 0.010000 1.0000000.014999999999630973\n", "600 15.00 -1.54 -0.000000 0.010000 1.0000000.014999999999630973\n",
"800 37.00 -1.63 -0.000000 0.010000 1.0000000.014999999999937863\n", "800 16.00 -1.63 -0.000000 0.010000 1.0000000.014999999999937863\n",
"1000 42.99 -1.72 -0.000000 0.010000 1.0000000.014999999999985369\n", "1000 20.00 -1.72 -0.000000 0.010000 1.0000000.014999999999985369\n",
"1200 83.97 -1.81 -0.000000 0.010000 1.0000000.014999999999661145\n", "1200 24.00 -1.81 -0.000000 0.010000 1.0000000.014999999999661145\n",
"1400 121.77 -1.90 -0.000000 0.010000 1.0000000.014999999999617875\n", "1400 27.00 -1.90 -0.000000 0.010000 1.0000000.014999999999617875\n",
"1600 125.93 -1.96 -0.000000 0.010000 1.0000000.01499999999998295\n", "1600 36.00 -1.96 -0.000000 0.010000 1.0000000.01499999999998295\n",
"1800 75.05 -2.03 -0.000000 0.010000 1.0000000.014999999999785373\n", "1800 35.03 -2.03 -0.000000 0.010000 1.0000000.014999999999785373\n",
"2000 45.95 -2.06 -0.000000 0.010000 1.0000000.014999999999994327\n", "2000 43.00 -2.06 -0.000000 0.010000 1.0000000.014999999999994327\n",
"2200 68.05 -2.07 -0.000000 0.010000 1.0000000.014999999999979582\n", "2200 36.97 -2.07 -0.000000 0.010000 1.0000000.014999999999979582\n",
"2400 144.36 -2.13 -0.000000 0.010000 1.0000000.014999999999836155\n", "2400 45.00 -2.13 -0.000000 0.010000 1.0000000.014999999999836155\n",
"2600 140.00 -2.14 -0.000000 0.010000 1.0000000.01499999999985058\n", "2600 49.00 -2.14 -0.000000 0.010000 1.0000000.01499999999985058\n",
"2800 145.48 -2.16 -0.000000 0.010000 1.0000000.014999999999853686\n", "2800 59.00 -2.16 -0.000000 0.010000 1.0000000.014999999999853686\n",
"3000 125.97 -2.19 -0.000000 0.010000 1.0000000.014999999999981861\n" "3000 61.00 -2.19 -0.000000 0.010000 1.0000000.014999999999981861\n"
] ]
} }
], ],
...@@ -91,7 +91,7 @@ ...@@ -91,7 +91,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -114,7 +114,7 @@ ...@@ -114,7 +114,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -122,21 +122,21 @@ ...@@ -122,21 +122,21 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Scale(n) time(ms) feval min(x) max(x) sum(x) x(0) + x(1)\n", "Scale(n) time(ms) feval min(x) max(x) sum(x) x(0) + x(1)\n",
"200 4.00 -0.82 0.000000 0.010000 1.0000000.015000000005429394\n", "200 2.00 -0.82 0.000000 0.010000 1.0000000.015000000005429394\n",
"400 5.38 -1.28 0.000000 0.010000 1.0000000.015000000000751215\n", "400 3.00 -1.28 0.000000 0.010000 1.0000000.015000000000751215\n",
"600 5.02 -1.54 0.000000 0.010000 1.0000000.01500000000851949\n", "600 3.00 -1.54 0.000000 0.010000 1.0000000.01500000000851949\n",
"800 7.89 -1.63 0.000000 0.010000 1.0000000.015000000002481837\n", "800 5.00 -1.63 0.000000 0.010000 1.0000000.015000000002481837\n",
"1000 7.97 -1.72 0.000000 0.010000 1.0000000.015000000001100414\n", "1000 9.00 -1.72 0.000000 0.010000 1.0000000.015000000001100414\n",
"1200 14.04 -1.81 0.000000 0.010000 1.0000000.01500000000548405\n", "1200 7.00 -1.81 0.000000 0.010000 1.0000000.01500000000548405\n",
"1400 13.85 -1.90 0.000000 0.010000 1.0000000.015000000001956426\n", "1400 7.03 -1.90 0.000000 0.010000 1.0000000.015000000001956426\n",
"1600 20.96 -1.96 0.000000 0.010000 1.0000000.015000000000082848\n", "1600 7.97 -1.96 0.000000 0.010000 1.0000000.015000000000082848\n",
"1800 26.95 -2.03 0.000000 0.010000 1.0000000.01500000000204834\n", "1800 8.00 -2.03 0.000000 0.010000 1.0000000.01500000000204834\n",
"2000 25.59 -2.06 0.000000 0.010000 1.0000000.0150000000008303\n", "2000 10.03 -2.06 0.000000 0.010000 1.0000000.0150000000008303\n",
"2200 27.25 -2.07 0.000000 0.010000 1.0000000.01500000000729576\n", "2200 15.97 -2.07 0.000000 0.010000 1.0000000.01500000000729576\n",
"2400 13.10 -2.13 0.000000 0.010000 1.0000000.015000000004022507\n", "2400 16.03 -2.13 0.000000 0.010000 1.0000000.015000000004022507\n",
"2600 19.00 -2.14 0.000000 0.010000 1.0000000.015000000001118521\n", "2600 13.97 -2.14 0.000000 0.010000 1.0000000.015000000001118521\n",
"2800 30.60 -2.16 0.000000 0.010000 1.0000000.01500000000064263\n", "2800 17.02 -2.16 0.000000 0.010000 1.0000000.01500000000064263\n",
"3000 25.00 -2.19 0.000000 0.010000 1.0000000.015000000003030482\n" "3000 17.98 -2.19 0.000000 0.010000 1.0000000.015000000003030482\n"
] ]
} }
], ],
......
notebooks/Quick Start 1 - Factor Preprocess.ipynb
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notebooks/Quick Start 3 - Portfolio Builder.ipynb
View file @ e97a8be4
...@@ -31,24 +31,10 @@ ...@@ -31,24 +31,10 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Optimization status - optimal\n", "Optimization status - optimal\n",
"Optimal expect return - 0.750529042208698\n", "Optimal expect return - -0.5259220412385935\n",
"Optimial portfolio weights - [0.1 0.2 0.05 0.4 0.25]\n", "Optimial portfolio weights - [0.11111111 0.38333333 0.22222222 0.11111111 0.17222222]\n",
"Initial portfolio weights - [0.1 0.2 0. 0.4 0.3]\n", "Initial portfolio weights - [0.11111111 0.33333333 0.22222222 0.11111111 0.22222222]\n",
"Turn over amount - 0.10000000000050759\n" "Turn over amount - 0.10000000000058906\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\ProgramData\\Anaconda3\\envs\\alpha-mind\\lib\\site-packages\\cvxpy\\expressions\\expression.py:550: UserWarning: \n",
"This use of ``*`` has resulted in matrix multiplication.\n",
"Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1.\n",
" Use ``*`` for matrix-scalar and vector-scalar multiplication.\n",
" Use ``@`` for matrix-matrix and matrix-vector multiplication.\n",
" Use ``multiply`` for elementwise multiplication.\n",
"\n",
" warnings.warn(__STAR_MATMUL_WARNING__, UserWarning)\n"
] ]
} }
], ],
...@@ -195,16 +181,7 @@ ...@@ -195,16 +181,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\ProgramData\\Anaconda3\\envs\\alpha-mind\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
" return f(*args, **kwds)\n"
]
}
],
"source": [ "source": [
"import numpy as np\n", "import numpy as np\n",
"import pandas as pd\n", "import pandas as pd\n",
......
notebooks/Quick Start 5 - Alpha Factor Quantile Analysis.ipynb
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notebooks/Quick Start 7 - Single Factor IC Analysis.ipynb
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notebooks/Quick Start 9 - Back Testing Stock Screening.ipynb
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requirements.txt
View file @ e97a8be4
...@@ -15,4 +15,5 @@ scikit-learn ...@@ -15,4 +15,5 @@ scikit-learn
scipy scipy
simpleutils simpleutils
sqlalchemy sqlalchemy
statsmodels
xgboost xgboost
\ No newline at end of file
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