update factor analysis

alphamind/analysis/factoranalysis.py

@@ -7,8 +7,11 @@ Created on 2017-5-25
 
 from typing import Optional
 from typing import List
+from typing import Tuple
 import numpy as np
 import pandas as pd
+from alphamind.data.standardize import standardize
+from alphamind.data.winsorize import winsorize_normal
 from alphamind.data.neutralize import neutralize
 from alphamind.portfolio.longshortbulder import long_short_build
 from alphamind.portfolio.rankbuilder import rank_build
@@ -56,6 +59,7 @@ class FDataPack(object):
 
     def __init__(self,
                  raw_factor: np.ndarray,
+                 d1returns,
                  factor_name: str=None,
                  codes: List=None,
                  groups: Optional[np.ndarray]=None,
@@ -64,19 +68,52 @@ class FDataPack(object):
                  risk_names: List[str]=None):
 
         self.raw_factor = raw_factor
+        self.d1returns = d1returns.flatten()
         if factor_name:
             self.factor_name = factor_name
         else:
             self.factor_name = 'factor'
         self.codes = codes
-        self.groups = groups
-        self.benchmark = benchmark
+        self.groups = groups.flatten()
+        self.benchmark = benchmark.flatten()
         self.risk_exp = risk_exp
         self.risk_names = risk_names
 
     def benchmark_risk_exp(self) -> np.ndarray:
         return self.risk_exp @ self.benchmark
 
+    def settle(self, weights: np.ndarray) -> pd.DataFrame:
+        weights = weights.flatten()
+
+        if self.benchmark is not None:
+            net_pos = weights - self.benchmark
+        else:
+            net_pos = weights
+
+        ret_arr = net_pos * self.d1returns
+
+        if self.groups is not None:
+            ret_agg = pd.Series(ret_arr).groupby(self.groups).sum()
+            ret_agg.loc['total'] = ret_agg.sum()
+        else:
+            ret_agg = pd.Series(ret_arr.sum(), index=['total'])
+
+        ret_agg.index.name = 'industry'
+        ret_agg.name = 'er'
+
+        pos_table = pd.DataFrame(net_pos, columns=[self.factor_name])
+        pos_table['ret'] = self.d1returns
+
+        if self.groups is not None:
+            ic_table = pos_table.groupby(self.groups).corr()['ret'].loc[(slice(None), self.factor_name)]
+            ic_table.loc['total'] = pos_table.corr().iloc[0, 1]
+        else:
+            ic_table = pd.Series(pos_table.corr().iloc[0, 1], index=['total'])
+
+        return pd.DataFrame({'er': ret_agg.values,
+                             'ic': ic_table.values},
+                            index=ret_agg.index)
+
     def factor_processing(self, pre_process) -> np.ndarray:
 
         if self.risk_exp is None:
@@ -89,7 +126,7 @@ class FDataPack(object):
 
     def to_df(self) -> pd.DataFrame:
         cols = [self.factor_name]
-        to_concat = [self.raw_factor]
+        to_concat = [self.raw_factor.reshape((-1, 1))]
 
         if self.groups is not None:
             cols.append('groups')
@@ -97,7 +134,7 @@ class FDataPack(object):
 
         if self.benchmark is not None:
             cols.append('benchmark')
-            to_concat.append(self.benchmark)
+            to_concat.append(self.benchmark.reshape(-1, 1))
 
         if self.risk_exp is not None:
             cols.extend(self.risk_names)
@@ -108,14 +145,61 @@ class FDataPack(object):
                             index=self.codes)
 
 
+def factor_analysis(factor_values,
+                    industry,
+                    d1returns,
+                    detail_analysis=True,
+                    benchmark: Optional[np.ndarray]=None,
+                    risk_exp: Optional[np.ndarray]=None) -> Tuple[np.ndarray, Optional[pd.DataFrame]]:
+
+    data_pack = FDataPack(raw_factor=factor_values,
+                          d1returns=d1returns,
+                          groups=industry,
+                          benchmark=benchmark,
+                          risk_exp=risk_exp)
+
+    processed_data = data_pack.factor_processing([winsorize_normal, standardize])
+
+    if benchmark is not None and risk_exp is not None:
+        # using linear programming portfolio builder
+        benchmark = benchmark.flatten()
+        lbound = 0.
+        ubound = 0.01 + benchmark
+
+        risk_lbound = benchmark @ risk_exp
+        risk_ubound = benchmark @ risk_exp
+
+        weights = build_portfolio(processed_data,
+                                  builder='linear',
+                                  risk_exposure=risk_exp,
+                                  lbound=lbound,
+                                  ubound=ubound,
+                                  risk_target=(risk_lbound, risk_ubound),
+                                  solver='GLPK')
+
+    else:
+        # using rank builder
+        weights = build_portfolio(processed_data,
+                                  builder='rank',
+                                  use_rank=100) / 100.
+
+    if detail_analysis:
+        analysis = data_pack.settle(weights)
+    else:
+        analysis = None
+    return weights, analysis
+
+
 if __name__ == '__main__':
     raw_factor = np.random.randn(1000, 1)
+    d1returns = np.random.randn(1000, 1)
     groups = np.random.randint(30, size=1000)
     benchmark = np.random.randn(1000, 1)
     risk_exp = np.random.randn(1000, 3)
     codes = list(range(1, 1001))
 
     data_pack = FDataPack(raw_factor,
+                          d1returns,
                           'cfinc1',
                           codes=codes,
                           groups=groups,
@@ -123,7 +207,8 @@ if __name__ == '__main__':
                           risk_exp=risk_exp,
                           risk_names=['market', 'size', 'growth'])
 
-    print(data_pack.to_df())
+    weights = np.random.randn(1000)
+    print(data_pack.settle(weights))
 
 
 

alphamind/tests/analysis/test_factoranalysis.py

@@ -12,6 +12,7 @@ from alphamind.data.winsorize import winsorize_normal
 from alphamind.data.standardize import standardize
 from alphamind.data.neutralize import neutralize
 from alphamind.analysis.factoranalysis import factor_processing
+from alphamind.analysis.factoranalysis import factor_analysis
 
 
 class TestFactorAnalysis(unittest.TestCase):
@@ -19,6 +20,7 @@ class TestFactorAnalysis(unittest.TestCase):
     def setUp(self):
         self.raw_factor = np.random.randn(1000, 1)
         self.risk_factor = np.random.randn(1000, 3)
+        self.d1returns = np.random.randn(1000, 1)
 
     def test_factor_processing(self):
         new_factor = factor_processing(self.raw_factor)
@@ -36,6 +38,20 @@ class TestFactorAnalysis(unittest.TestCase):
         np.testing.assert_array_almost_equal(new_factor, neutralize(self.risk_factor,
                                                                     winsorize_normal(standardize(self.raw_factor))))
 
+    def test_factor_analysis(self):
+        benchmark = np.random.randint(50, size=1000)
+        benchmark = benchmark / benchmark.sum()
+        industry = np.random.randint(30, size=1000)
+        weight, analysis_table = factor_analysis(self.raw_factor,
+                                                 d1returns=self.d1returns,
+                                                 industry=industry,
+                                                 benchmark=benchmark,
+                                                 risk_exp=self.risk_factor)
+
+        self.assertEqual(analysis_table['er'].sum() / analysis_table['er'][-1], 2.0)
+        np.testing.assert_array_almost_equal(weight @ self.risk_factor, benchmark @ self.risk_factor)
+        self.assertTrue((weight @ self.d1returns)[0] > (benchmark @ self.d1returns)[0])
+
 
 if __name__ == '__main__':
-    unittest.main()
+    unittest.main()
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