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Unverified Commit f1709d16 authored by iLampard's avatar iLampard Committed by GitHub
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Merge pull request #3 from alpha-miner/master 

merge update
parents 945f80e0 5a734318
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Showing with 576 additions and 1599 deletions
alphamind/data/dbmodel/models.py
View file @ f1709d16
...@@ -670,6 +670,12 @@ class Experimental(Base): ...@@ -670,6 +670,12 @@ class Experimental(Base):
ep_q = Column(Float(53)) ep_q = Column(Float(53))
ep_q_d_1w = Column(Float(53)) ep_q_d_1w = Column(Float(53))
ev = Column(Float(53)) ev = Column(Float(53))
liq = Column(Float(53))
pure_liq_0 = Column(Float(53))
pure_liq_1 = Column(Float(53))
pure_liq_2 = Column(Float(53))
pure_liq_3 = Column(Float(53))
pure_liq_4 = Column(Float(53))
class FactorMaster(Base): class FactorMaster(Base):
......
alphamind/data/engines/utilities.py
View file @ f1709d16
...@@ -18,10 +18,11 @@ from alphamind.data.dbmodel.models import Gogoal ...@@ -18,10 +18,11 @@ from alphamind.data.dbmodel.models import Gogoal
from alphamind.data.dbmodel.models import Experimental from alphamind.data.dbmodel.models import Experimental
from alphamind.data.dbmodel.models import LegacyFactor from alphamind.data.dbmodel.models import LegacyFactor
from alphamind.data.dbmodel.models import Tiny from alphamind.data.dbmodel.models import Tiny
from alphamind.data.dbmodel.models import RiskExposure
from alphamind.data.engines.industries import INDUSTRY_MAPPING from alphamind.data.engines.industries import INDUSTRY_MAPPING
factor_tables = [Uqer, Gogoal, Experimental, LegacyFactor, Tiny] factor_tables = [RiskExposure, Uqer, Gogoal, Experimental, LegacyFactor, Tiny]
def _map_risk_model_table(risk_model: str) -> tuple: def _map_risk_model_table(risk_model: str) -> tuple:
......
notebooks/Example 3 - Multi Weight Gap Comparison.ipynb
View file @ f1709d16
...@@ -50,13 +50,6 @@ ...@@ -50,13 +50,6 @@
"target_vol = 0.05\n", "target_vol = 0.05\n",
"risk_model = 'short'\n", "risk_model = 'short'\n",
"\n", "\n",
"if risk_model == 'day':\n",
" risk_model_name = 'd_srisk'\n",
"elif risk_model == 'short':\n",
" risk_model_name = 's_srisk'\n",
"else:\n",
" risk_model_name = 'l_srisk'\n",
"\n",
"executor = NaiveExecutor()\n", "executor = NaiveExecutor()\n",
"ref_dates = makeSchedule(start_date, end_date, freq, 'china.sse')\n", "ref_dates = makeSchedule(start_date, end_date, freq, 'china.sse')\n",
"engine = SqlEngine(data_source)" "engine = SqlEngine(data_source)"
...@@ -182,7 +175,7 @@ ...@@ -182,7 +175,7 @@
" \n", " \n",
" risk_exposure = res[all_styles].values\n", " risk_exposure = res[all_styles].values\n",
" risk_cov = risk_cov[all_styles].values\n", " risk_cov = risk_cov[all_styles].values\n",
" special_risk = res[risk_model_name].values\n", " special_risk = res.srisk.values\n",
" sec_cov = risk_exposure @ risk_cov @ risk_exposure.T / 10000 + np.diag(special_risk ** 2) / 10000\n", " sec_cov = risk_exposure @ risk_cov @ risk_exposure.T / 10000 + np.diag(special_risk ** 2) / 10000\n",
"\n", "\n",
" benchmark_w = res.weight.values\n", " benchmark_w = res.weight.values\n",
......
notebooks/Example 9 - Linear Optimizer Comparison with CVXOPT.ipynb
View file @ f1709d16
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -15,7 +15,7 @@ ...@@ -15,7 +15,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -24,7 +24,7 @@ ...@@ -24,7 +24,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -64,32 +64,9 @@ ...@@ -64,32 +64,9 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stdout",
"output_type": "stream",
"text": [
"Scale(n) time(ms) feval min(x) max(x) sum(x) x(0) + x(1)\n",
"200 39.72 -0.82 0.000000 0.010000 1.000000 0.015\n",
"400 25.20 -1.28 0.000000 0.010000 1.000000 0.015\n",
"600 29.23 -1.54 0.000000 0.010000 1.000000 0.015\n",
"800 32.27 -1.63 0.000000 0.010000 1.000000 0.015\n",
"1000 15.13 -1.72 0.000000 0.010000 1.000000 0.015\n",
"1200 16.79 -1.81 0.000000 0.010000 1.000000 0.015\n",
"1400 18.62 -1.90 0.000000 0.010000 1.000000 0.015\n",
"1600 20.90 -1.96 0.000000 0.010000 1.000000 0.015\n",
"1800 24.02 -2.03 0.000000 0.010000 1.000000 0.015\n",
"2000 27.05 -2.06 0.000000 0.010000 1.000000 0.015\n",
"2200 28.04 -2.07 0.000000 0.010000 1.000000 0.015\n",
"2400 30.25 -2.13 0.000000 0.010000 1.000000 0.015\n",
"2600 31.96 -2.14 0.000000 0.010000 1.000000 0.015\n",
"2800 34.44 -2.16 0.000000 0.010000 1.000000 0.015\n",
"3000 36.86 -2.19 0.000000 0.010000 1.000000 0.015\n"
]
}
],
"source": [ "source": [
"print(\"{0:<8}{1:>12}{2:>12}{3:>12}{4:>12}{5:>12}{6:>15}\".format('Scale(n)', 'time(ms)', 'feval', 'min(x)', 'max(x)', 'sum(x)', 'x(0) + x(1)'))\n", "print(\"{0:<8}{1:>12}{2:>12}{3:>12}{4:>12}{5:>12}{6:>15}\".format('Scale(n)', 'time(ms)', 'feval', 'min(x)', 'max(x)', 'sum(x)', 'x(0) + x(1)'))\n",
"\n", "\n",
...@@ -101,7 +78,7 @@ ...@@ -101,7 +78,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -124,32 +101,9 @@ ...@@ -124,32 +101,9 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stdout",
"output_type": "stream",
"text": [
"Scale(n) time(ms) feval min(x) max(x) sum(x) x(0) + x(1)\n",
"200 2.95 -0.82 0.000000 0.010000 1.000000 0.015\n",
"400 2.34 -1.28 0.000000 0.010000 1.000000 0.015\n",
"600 2.44 -1.54 0.000000 0.010000 1.000000 0.015\n",
"800 2.91 -1.63 0.000000 0.010000 1.000000 0.015\n",
"1000 7.58 -1.72 0.000000 0.010000 1.000000 0.015\n",
"1200 3.89 -1.81 0.000000 0.010000 1.000000 0.015\n",
"1400 4.22 -1.90 0.000000 0.010000 1.000000 0.015\n",
"1600 4.37 -1.96 0.000000 0.010000 1.000000 0.015\n",
"1800 4.81 -2.03 0.000000 0.010000 1.000000 0.015\n",
"2000 4.98 -2.06 0.000000 0.010000 1.000000 0.015\n",
"2200 5.31 -2.07 0.000000 0.010000 1.000000 0.015\n",
"2400 6.13 -2.13 0.000000 0.010000 1.000000 0.015\n",
"2600 6.12 -2.14 0.000000 0.010000 1.000000 0.015\n",
"2800 6.73 -2.16 0.000000 0.010000 1.000000 0.015\n",
"3000 7.39 -2.19 0.000000 0.010000 1.000000 0.015\n"
]
}
],
"source": [ "source": [
"print(\"{0:<8}{1:>12}{2:>12}{3:>12}{4:>12}{5:>12}{6:>15}\".format('Scale(n)', 'time(ms)', 'feval', 'min(x)', 'max(x)', 'sum(x)', 'x(0) + x(1)'))\n", "print(\"{0:<8}{1:>12}{2:>12}{3:>12}{4:>12}{5:>12}{6:>15}\".format('Scale(n)', 'time(ms)', 'feval', 'min(x)', 'max(x)', 'sum(x)', 'x(0) + x(1)'))\n",
"\n", "\n",
...@@ -162,9 +116,7 @@ ...@@ -162,9 +116,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [] "source": []
} }
...@@ -186,6 +138,35 @@ ...@@ -186,6 +138,35 @@
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.4" "version": "3.6.4"
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
],
"window_display": false
} }
}, },
"nbformat": 4, "nbformat": 4,
......
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