from db.db_connection import create_client

def check_institutional_sponsorship(symbol):
    """
    Determines if a stock has strong institutional sponsorship.

    Criteria:
    - Should have at least several institutional sponsors
    - Look for increasing institutional ownership
    - Focus on higher-quality institutions
    - Avoid stocks with too much institutional ownership (>80-90%)

    Args:
        symbol (str): Stock ticker symbol.

    Returns:
        float: 1 (Pass), 0 (Fail), 0.25 (Insufficient Data)
    """
    client = create_client()

    # Get current institutional ownership data
    query = """
        SELECT 
            COUNT(DISTINCT holder_name) as num_institutions,
            SUM(percentage_held) as total_ownership,
            MAX(date_reported) as latest_date
        FROM stock_db.yf_institutional_holders
        WHERE ticker = '{}'
        GROUP BY ticker
    """.format(symbol)

    result = client.query(query)

    if not result.result_rows:
        return 0.25  # Insufficient data

    num_institutions, total_ownership, _ = result.result_rows[0]

    # Get ownership trend (comparing with 3 months ago)
    trend_query = """
        WITH current AS (
            SELECT SUM(percentage_held) as current_ownership,
                   MAX(date_reported) as latest_date
            FROM stock_db.yf_institutional_holders
            WHERE ticker = '{}'
        ),
        previous AS (
            SELECT SUM(percentage_held) as previous_ownership
            FROM stock_db.yf_institutional_holders
            WHERE ticker = '{}'
            AND date_reported <= (SELECT latest_date - INTERVAL 3 MONTH FROM current)
        )
        SELECT current_ownership, previous_ownership
        FROM current, previous
    """.format(symbol, symbol)

    trend_result = client.query(trend_query)

    # Evaluate criteria
    if num_institutions is None or total_ownership is None:
        return 0.25

    # Convert total_ownership to percentage (0-100 scale)
    total_ownership = float(total_ownership)

    # Criteria thresholds
    MIN_INSTITUTIONS = 10
    MIN_OWNERSHIP = 20  # percentage
    MAX_OWNERSHIP = 85  # percentage

    # Basic institutional presence check
    has_sufficient_institutions = num_institutions >= MIN_INSTITUTIONS
    has_healthy_ownership = MIN_OWNERSHIP <= total_ownership <= MAX_OWNERSHIP

    # Check ownership trend
    ownership_increasing = False
    if trend_result.result_rows:
        current_ownership, previous_ownership = trend_result.result_rows[0]
        if current_ownership and previous_ownership:
            ownership_increasing = current_ownership > previous_ownership

    # Final evaluation
    if has_sufficient_institutions and has_healthy_ownership and ownership_increasing:
        return 1
    elif not has_sufficient_institutions or total_ownership < MIN_OWNERSHIP:
        return 0
    else:
        return 0.25  # Borderline cases