"""
Common utilities for quantitative investment strategies.
"""
import pandas as pd
from db.common import DBManager


def get_sector_data():
    """Get sector data for all stocks."""
    db = DBManager()
    return db.get_sector_list()


def get_stock_data():
    """Get stock data for all available stocks."""
    db = DBManager()
    return db.get_ticker_list()


def get_financial_data():
    """Get financial statement data."""
    db = DBManager()
    return db.get_fs_list()


def get_expanded_financial_data():
    """Get expanded financial statement data."""
    db = DBManager()
    return db.get_expanded_fs_list()


def get_price_data(interval_months=60):
    """Get price data for the specified interval."""
    db = DBManager()
    return db.get_price_list(interval_months)


def get_value_metrics():
    """Get value metrics for all stocks."""
    db = DBManager()
    return db.get_value_list()


def calculate_zscore(series):
    """Calculate z-score for a pandas series."""
    return (series - series.mean()) / series.std()


def rank_by_metric(df, metric, ascending=True):
    """Rank stocks by a metric."""
    return df.sort_values(by=metric, ascending=ascending)


def rank_combined(df, metrics, weights=None, ascending=True):
    """Rank stocks by combined metrics.
    
    Args:
        df: DataFrame with stock data
        metrics: List of column names to use for ranking
        weights: Optional weights for each metric
        ascending: Direction for ranking (True = smaller is better)
    
    Returns:
        DataFrame with combined rank
    """
    if weights is None:
        weights = [1] * len(metrics)
    
    # Calculate rank for each metric
    ranks = pd.DataFrame(index=df.index)
    for i, metric in enumerate(metrics):
        ranks[f'rank_{metric}'] = df[metric].rank(ascending=ascending) * weights[i]
    
    # Calculate combined rank
    ranks['combined_rank'] = ranks.mean(axis=1)
    
    # Combine with original data
    result = pd.concat([df, ranks['combined_rank']], axis=1)
    
    return result.sort_values('combined_rank')