add pre-commit (#50)

.github/workflows/pre-commit.yml

@@ -0,0 +1,13 @@
+name: pre-commit
+
+on:
+  pull_request:
+  push:
+
+jobs:
+  pre-commit:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v3
+    - uses: actions/setup-python@v3
+    - uses: pre-commit/action@v3.0.0

.pre-commit-config.yaml

@@ -0,0 +1,19 @@
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v2.3.0
+    hooks:
+    -   id: check-toml
+    -   id: end-of-file-fixer
+    -   id: trailing-whitespace
+    -   id: fix-encoding-pragma
+
+-   repo: https://github.com/psf/black
+    rev: 23.1.0
+    hooks:
+    -   id: black
+
+-   repo: https://github.com/asottile/reorder_python_imports
+    rev: v3.9.0
+    hooks:
+    -   id: reorder-python-imports
+        args: [--py37-plus, --add-import, 'from __future__ import annotations']

README.md

@@ -46,13 +46,13 @@ n = returns.shape[1]
 # Define half-life pairs for K=3 experts, (halflife_vola, halflife_cov)
 halflife_pairs = [(10, 21), (21, 63), (63, 125)]
 
-# Define the covariance combinator 
+# Define the covariance combinator
 combinator = from_ewmas(returns,
                         halflife_pairs,
                         min_periods_vola=n,  # min periods for volatility estimation
                         min_periods_cov=3 * n)  # min periods for correlation estimation (must be at least n)
 
-# Solve combination problem and loop through combination results to get predictors   
+# Solve combination problem and loop through combination results to get predictors
 covariance_predictors = {}
 for predictor in combinator.solve(window=10):  # lookback window in convex optimization problem
     # From predictor we can access predictor.time, predictor.mean (=0 here), predictor.covariance, and predictor.weights
@@ -83,7 +83,7 @@ expert2 = {time: ewma63.loc[time] for time in ewma63.index.get_level_values(0).u
 # Create expert dictionary
 experts = {1: expert1, 2: expert2}
 
-# Define the covariance combinator 
+# Define the covariance combinator
 combinator = from_sigmas(sigmas=experts, returns=returns)
 
 # Solve combination problem and loop through combination results to get predictors
@@ -117,5 +117,3 @@ environment. Executing
 constructs a dedicated
 [Kernel](https://docs.jupyter.org/en/latest/projects/kernels.html) for the
 project.
-
-

book/_config.yml

@@ -23,8 +23,8 @@ sphinx:
   config:
     html_js_files:
     - https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js
-    
-    
+
+
 # Information about where the book exists on the web
 repository:
   url: https://github.com/cvxgrp/cov_pred_finance

book/docs/api.md

@@ -6,4 +6,4 @@
 
 ## Dependencies
 
-{{ '[Dependencies]({url}/artifacts/build/show.txt)'.format(url=book_url) }}
+{{ '[Dependencies]({url}/artifacts/build/show.txt)'.format(url=book_url) }}

book/docs/index.md

@@ -41,13 +41,13 @@ n = returns.shape[1]
 # Define half-life pairs for K=3 experts, (halflife_vola, halflife_cov)
 halflife_pairs = [(10, 21), (21, 63), (63, 125)]
 
-# Define the covariance combinator 
+# Define the covariance combinator
 combinator = from_ewmas(returns,
                         halflife_pairs,
                         min_periods_vola=n,  # min periods for volatility estimation
                         min_periods_cov=3 * n)  # min periods for correlation estimation (must be at least n)
 
-# Solve combination problem and loop through combination results to get predictors   
+# Solve combination problem and loop through combination results to get predictors
 covariance_predictors = {}
 for predictor in combinator.solve(window=10):  # lookback window in convex optimization problem
     # From predictor we can access predictor.time, predictor.mean (=0 here), predictor.covariance, and predictor.weights
@@ -78,7 +78,7 @@ expert2 = {time: ewma63.loc[time] for time in ewma63.index.get_level_values(0).u
 # Create expert dictionary
 experts = {1: expert1, 2: expert2}
 
-# Define the covariance combinator 
+# Define the covariance combinator
 combinator = from_sigmas(sigmas=experts, returns=returns)
 
 # Solve combination problem and loop through combination results to get predictors

book/docs/reports.md

@@ -6,4 +6,4 @@
 
 ## Test report
 
-{{ '[Report]({url}/artifacts/tests/html-report/report.html)'.format(url=book_url) }}
+{{ '[Report]({url}/artifacts/tests/html-report/report.html)'.format(url=book_url) }}

cvx/covariance/combination.py

@@ -1,14 +1,19 @@
+# -*- coding: utf-8 -*-
+from __future__ import annotations
+
+import warnings
 from collections import namedtuple
+
 import cvxpy as cvx
 import numpy as np
 import pandas as pd
-import warnings
 
 from cvx.covariance.ewma import iterated_ewma
 
 # Mute specific warning
 warnings.filterwarnings("ignore", message="Solution may be inaccurate.*")
 
+
 def _map_nested_dicts(ob, func):
     """
     Recursively applies a function to a nested dictionary
@@ -100,12 +105,7 @@ def weights(self):
 
 
 def from_ewmas(
-    returns,
-    pairs,
-    min_periods_vola=20,
-    min_periods_cov=20,
-    clip_at=None,
-    mean=False
+    returns, pairs, min_periods_vola=20, min_periods_cov=20, clip_at=None, mean=False
 ):
     """
     Estimate a series of covariance matrices using the iterated EWMA method
@@ -144,6 +144,7 @@ def from_ewmas(
     # combination of covariance matrix valued time series
     return _CovarianceCombination(sigmas=sigmas, returns=returns, means=means)
 
+
 def from_sigmas(sigmas, returns, means=None):
     return _CovarianceCombination(sigmas=sigmas, returns=returns, means=means)
 
@@ -198,7 +199,6 @@ def means(self):
     def returns(self):
         return self.__returns
 
-
     @property
     def K(self):
         """
@@ -282,4 +282,3 @@ def _solve(self, time, problem, **kwargs):
             index=self.assets, columns=self.assets, data=np.linalg.inv(L @ L.T)
         )
         return Result(time=time, mean=mean, covariance=sigma, weights=weights)
-

cvx/covariance/ewma.py

@@ -1,9 +1,12 @@
+# -*- coding: utf-8 -*-
+from __future__ import annotations
+
 from collections import namedtuple
+from typing import Union
 
 import numpy as np
 import pandas as pd
 from pandas._typing import TimedeltaConvertibleTypes
-from typing import Union
 
 IEWMA = namedtuple("IEWMA", ["time", "mean", "covariance", "volatility"])
 

cvx/covariance/regularization.py

@@ -1,7 +1,10 @@
+# -*- coding: utf-8 -*-
+from __future__ import annotations
+
 from collections import namedtuple
 
-import pandas as pd
 import numpy as np
+import pandas as pd
 import scipy as sc
 
 LowRank = namedtuple("LowRank", ["Loading", "Cov", "D", "Approximation"])

experiments/data/lt_reversal.csv

@@ -24335,4 +24335,4 @@
 20230223,  -0.07
 20230224,   0.24
 20230227,  -0.11
-20230228,  -0.05
+20230228,  -0.05

experiments/data/momentum.CSV

@@ -1,4 +1,4 @@
-,Mom   
+,Mom
 19261103,   0.56
 19261104,  -0.50
 19261105,   1.17
@@ -25335,4 +25335,4 @@
 20230223,   0.06
 20230224,   1.21
 20230227,  -0.24
-20230228,  -0.41
+20230228,  -0.41

experiments/data/st_reversal.csv

@@ -25565,4 +25565,4 @@
 20230223,  -0.47
 20230224,  -0.10
 20230227,  -0.09
-20230228,   0.17
+20230228,   0.17

experiments/utils/experiment_utils.py

@@ -1,3 +1,6 @@
+# -*- coding: utf-8 -*-
+from __future__ import annotations
+
 import numpy as np
 import pandas as pd