import scipy.cluster.hierarchy as sch
import numpy as np
from .data_fetcher import fetch_data

def hierarchical_risk_parity(tickers, start_date, end_date):
    """Implement Hierarchical Risk Parity for asset allocation."""
    returns = fetch_data(tickers, start_date, end_date).pct_change(fill_method=None).dropna()
    corr_matrix = returns.corr()
    dist_matrix = np.sqrt(0.5 * (1 - corr_matrix))
    linkage_matrix = sch.linkage(dist_matrix, 'ward')
    cluster_order = sch.dendrogram(linkage_matrix, no_plot=True)['ivl']
    return cluster_order  # Implement risk parity allocation based on clustering
