Metadata-Version: 2.1
Name: PyFolioC
Version: 0.1
Summary: Portfolio Optimization Package
Home-page: https://github.com/NailKhelifa/PyFolioC
Author: Naïl Khelifa
Author-email: nail.khelifa@ensae.fr
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Requires-Dist: numpy (>=1.24.3)
Requires-Dist: pandas (>=2.0.3)
Requires-Dist: scipy (>=1.11.1)
Requires-Dist: matplotlib (>=3.7.2)
Requires-Dist: seaborn (>=0.12.2)
Requires-Dist: PyPortfolioOpt (>=1.5.4)

# PyFolioC


The PyFolioCC class is designed to build an optimal portfolio in the sense of Markowitz using general graph clustering 
techniques. The idea is to provide a historical return database of an asset universe (historical_data), a lookback window 
(lookback_window) for portfolio construction, a number of clusters (number_clusters), a clustering method (clustering_method), 
and an evaluation window (evaluation_window). From there, the objective is to construct a portfolio based on historical return 
data over the period corresponding to lookback_window by creating a sub-portfolio composed of a specified number of synthetic 
assets (ETFs) using the clustering method specified in clustering_method. The performance (Sharpe ratio and cumulative PnL) of
the constructed portfolio is then evaluated over the evaluation_window.