Metadata-Version: 2.1
Name: pyhrp
Version: 0.0.8
Summary: Python for Hierarchical Risk Parity
Home-page: https://github.com/tschm/hrp
Author: Thomas Schmelzer
Author-email: thomas.schmelzer@gmail.com
License: UNKNOWN
Project-URL: Source Code, https://github.com/tschm/hrp
Description: # pyhrp
        
        A recursive implementation of the Hierarchical Risk Parity (hrp) approach by Marcos Lopez de Prado.
        We take heavily advantage of the scipy.cluster.hierarchy package. 
        
        Here's a simple example
        
        ```python
        import pandas as pd
        from pyhrp.hrp import dist, linkage, tree, _hrp
        
        prices = pd.read_csv("test/resources/stock_prices.csv", index_col=0, parse_dates=True)
        
        returns = prices.pct_change().dropna(axis=0, how="all")
        cov, cor = returns.cov(), returns.corr()
        links = linkage(dist(cor.values), method='ward')
        node = tree(links)
        
        rootcluster = _hrp(node, cov)
        
        ax = dendrogram(links, orientation="left")
        ax.get_figure().savefig("dendrogram.png")
        ```
        For your convenience you can bypass the construction of the covariance and correlation matrix, the links and the node, e.g. the root of the tree (dendrogram).
        ```python
        import pandas as pd
        from pyhrp.hrp import hrp
        
        prices = pd.read_csv("test/resources/stock_prices.csv", index_col=0, parse_dates=True)
        root = hrp(prices=prices)
        ```
        You may expect a weight series here but instead the `hrp` function returns a `Cluster` object. The `Cluster` simplifies all further post-analysis.
        ```python
        print(cluster.weights)
        print(cluster.variance)
        # You can drill into the graph by going downstream
        print(cluster.left)
        print(cluster.right)
        ```
        
        ## Installation:
        ```
        pip install pyhpr
        ```
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
