FEATURE: update examples

notebooks/1. debug examples.py

+"""
+Created on 2020-11-21
+
+@author: cheng.li
+"""
+
+import os
+import datetime as dt
+import numpy as np
+import pandas as pd
+from PyFin.api import *
+from alphamind.api import *
+
+start_date = '2020-01-01'
+end_date = '2020-02-21'
+
+freq = '10b'
+horizon = map_freq(freq)
+neutralized_risk = risk_styles + industry_styles
+universe = Universe('hs300')
+data_source = "mysql+mysqldb://reader:Reader#2020@121.37.138.1:13317/vision?charset=utf8"
+offset = 1
+method = 'ls'
+industry_name = 'sw'
+industry_level = 1
+
+risk_model = 'short'
+executor = NaiveExecutor()
+ref_dates = makeSchedule(start_date, end_date, freq, 'china.sse')
+engine = SqlEngine(data_source)
+
+alpha_factors = {
+    'f01': LAST('EMA5D'),
+    'f02': LAST('EMV6D')
+    }
+
+weights = dict(f01=1.0,
+               f02=1.0,
+              )
+
+alpha_model = ConstLinearModel(features=alpha_factors, weights=weights)
+
+
+def predict_worker(params):
+    data_meta = DataMeta(freq=freq,
+                         universe=universe,
+                         batch=1,
+                         neutralized_risk=neutralized_risk,
+                         risk_model='short',
+                         pre_process=[winsorize_normal, standardize],
+                         post_process=[standardize],
+                         warm_start=0,
+                         data_source=data_source)
+    ref_date, model = params
+    er, _ = predict_by_model(ref_date, model, data_meta)
+    return er
+
+
+predicts = [predict_worker((d.strftime('%Y-%m-%d'), alpha_model)) for d in ref_dates]
+
+
+

notebooks/Example 10 - Quadratic Optimizer Comparison with CVXOPT.ipynb

@@ -87,7 +87,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Wall time: 1.98 ms\n"
+      "Wall time: 3.22 ms\n"
      ]
     }
    ],
@@ -134,17 +134,17 @@
       "          warm start: on, polish: on, time_limit: off\n",
       "\n",
       "iter   objective    pri res    dua res    rho        time\n",
-      "   1  -7.8878e+03   4.61e+00   6.68e+04   1.00e-01   1.10e-03s\n",
-      " 125  -2.4830e+02   3.58e-07   2.76e-05   5.82e-01   3.80e-03s\n",
+      "   1  -7.8878e+03   4.61e+00   6.68e+04   1.00e-01   1.28e-03s\n",
+      " 125  -2.4830e+02   3.58e-07   2.76e-05   5.82e-01   5.35e-03s\n",
       "\n",
       "status:               solved\n",
       "solution polish:      unsuccessful\n",
       "number of iterations: 125\n",
       "optimal objective:    -248.2989\n",
-      "run time:             4.54e-03s\n",
+      "run time:             7.43e-03s\n",
       "optimal rho estimate: 1.87e+00\n",
       "\n",
-      "Wall time: 48 ms\n"
+      "Wall time: 43 ms\n"
      ]
     },
     {
@@ -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",
       "\n",
       "OPTIMAL (within feastol=5.3e-13, reltol=1.3e-09, abstol=3.2e-07).\n",
-      "Runtime: 0.013942 seconds.\n",
+      "Runtime: 0.013621 seconds.\n",
       "\n",
-      "Wall time: 51.3 ms\n"
+      "Wall time: 52 ms\n"
      ]
     },
     {
@@ -277,7 +277,7 @@
       "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",
       "Optimal solution found.\n",
-      "Wall time: 108 ms\n"
+      "Wall time: 80.5 ms\n"
      ]
     }
    ],
@@ -315,7 +315,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Wall time: 24 ms\n"
+      "Wall time: 20.6 ms\n"
      ]
     },
     {
@@ -465,9 +465,9 @@
      "output_type": "stream",
      "text": [
       "Scale(n)       cvxpy      cvxopt       ipopt\n",
-      "100           0.0527      0.0660      0.0142\n",
-      "200           0.0710      0.3040      0.0601\n",
-      "300           0.1614      0.0690      0.0155\n"
+      "100           0.0520      0.0440      0.0070\n",
+      "200           0.0413      0.0520      0.0120\n",
+      "300           0.0420      0.0630      0.0170\n"
      ]
     }
    ],