All noteable changes to this project will be documented in this file.
- Add a get method for rolling cov estimator (
da29453)
- Add example notebook of alpha sizing rules (
220ba02)
- Support dask engine (
972973f)
- Use validity in rolling fit (
087939b)
- Update numpy backend engine notebook (
5037603)
- Convert y into numpy engine array (
e92fc34)
- Support setting the backend engine with environment variable (
a40ee5c)
- Support pytorch in engine (
1d9eb07)
- Use engine linalg in WLS (
98ea2ed)
- Fix syntax issue (
cc8b9f5) - Fix lint (
89a55c7) - Add engine (
7cd84d7) - Add numpy backend engine example (
a81a2eb)
- Revert forcing numpy array conversion (
6676fef)
- Ensure to_numpy converts with the numpy engine array (
cd014ce)
- Remove the type check to support multiple engines (
dd0d8a8)
- Update covariance page (
6c10a7c) - Update README (
bcbea6e) - Add statistical approach example doc (
d8b2e44) - Fix the missing import (
37e1dc4)
- Add covariance shrinkage (
fa59413)
- Insufficient returns in fitting rolling risk model (
96ca77b)
- Add covariance estimator (
4b5d5d4) - Allow passing dict of covariances in accuracy functions (
3f2452d)
- Remove unused imports in example notebook (
d415101) - Update example notebook (
cd71b0f) - Correct typo (
e711966) - Update notebook (
f9a72a9) - Update README and notebook example (
cc53ae3) - Update crypto statistical risk model notebook (
c66f934)
- Add API to download crypto sample data (
6041ada) - Support writing and reading from directory (
2553366) - Allow setting validity in fitting and transforming rolling risk model (
32a1f6a) - Support asymptotic principal components (APCA) (
7169189)
- Align parameter n_components type in apca with scikit-learn (
3394dbf)
- Update example notebook (
b102c02) - Add apca section (
7f41707) - Change the weights to market cap sqrt (
20574e4)
- Support WLS in PCA statistical risk model (
71fc3e2)
- Correct example configuration (
00e136d)
- Introduce cov half life (
92d96dc) - Add cov halflife into the accuracy functions (
cd20be4) - Support exponential weighted factor covariance (
ffac512) - Support exponential weighted least square (
db4ae53)
- Ewm in cov (
f34ddbf)
- Add value at risk accuracy (
7034a8e)
- Another bug created from the previous bugfix (
d48381a) - Bug in verifying ndarry existence (
f42bd2d) - Typo in parameter (
f5c5dab) - Correct the forecast return computation (
6603ce0)
- Add rolling risk model description (
e20bb28) - Update README (
a8fd716) - Correct typo (
d15e684) - Update bias and VaR documentation page (
e9c7542) - Add bias and var placeholder pages (
55d79b4) - Correct factor risk model title (
239c41f) - Add factor risk model doc (
b35ca51) - Add risk model descrption (
40af5a6) - Update README (
5877781) - Correct caller name (
62c7df9)
- Add equal weight portfolio pipeline (
a2a7db6) - Add bias statistics (
efe3f46) - Add standardized returns in bias check (
128c3f2) - Get covariance and correlation from risk model (
02b606d) - Support transforming rolling factor risk model (
77810b0) - Support exporting rolling factor risk model (
5da7006) - Support factor risk model transformer (
e26b61c) - Support factor risk model transformer (
770ba6a) - Add factor covariance attribute in risk model (
0b15a40) - Add WLS regressors (
2b8d60b) - Change risk model input format to indexed on date / time rather than instruments (
267d9a4) - Add parameter to show progress in rolling PCA (
35fc9e3) - Add statistical risk model Rolling PCA (
603344c) - Add the first statistical model PCA (
4da36f7)
- Incorrect mapping of factor exposures (
4bc0028)