import datetime
from screener.data_fetcher import validate_date_range, fetch_financial_data, get_stocks_in_time_range
from screener.c_canslim import check_quarterly_earnings, check_return_on_equity, check_sales_growth
from screener.a_canslim import check_annual_eps_growth
from screener.l_canslim import check_industry_leadership  # ✅ NEW: Import L Score function
from screener.i_canslim import check_institutional_sponsorship  # ✅ NEW: Import I Score function
from screener.csv_appender import append_scores_to_csv
from screener.screeners import SCREENERS  # Import categories
from screener.user_input import get_user_screener_selection  # Import function

def main():
    # 1️⃣ Ask user for start and end date
    user_start_date = input("Enter start date (YYYY-MM-DD): ")
    user_end_date = input("Enter end date (YYYY-MM-DD): ")
    
    # 2️⃣ Validate and adjust date range if needed
    start_date, end_date = validate_date_range(user_start_date, user_end_date, required_quarters=4)

    # 3️⃣ Get selected screeners & customization preferences
    selected_screeners = get_user_screener_selection()  
    print(f"\n✅ Selected Screeners: {selected_screeners}\n")  # ✅ DEBUG LOG

    # 4️⃣ Get all stock symbols dynamically
    symbol_list = get_stocks_in_time_range(start_date, end_date)
    
    if not symbol_list:
        print("No stocks found within the given date range.")
        return

    print(f"Processing {len(symbol_list)} stocks within the given date range...\n")

    # 5️⃣ Process each stock symbol
    for symbol in symbol_list:
        data = fetch_financial_data(symbol, start_date, end_date)

        if not data:
            print(f"⚠️ Warning: No data returned for {symbol}. Assigning default score.\n")
            scores = {screener: 0.25 for category in selected_screeners for screener in selected_screeners[category]}
        else:
            scores = {}

            # 6️⃣ Compute scores dynamically based on user selection
            for category, screeners in selected_screeners.items():
                for screener, threshold in screeners.items():
                    if screener == "EPS_Score":
                        scores[screener] = check_quarterly_earnings(data.get("quarterly_eps", []))
                    elif screener == "Annual_EPS_Score":
                        scores[screener] = check_annual_eps_growth(data.get("annual_eps", []))
                    elif screener == "Sales_Score":
                        scores[screener] = check_sales_growth(data.get("sales_growth", []))
                    elif screener == "ROE_Score":
                        scores[screener] = check_return_on_equity(data.get("roe", []))
                    elif screener == "L_Score":
                        scores[screener] = check_industry_leadership(symbol)  # ✅ NEW: Industry Leadership Calculation
                        print(f"🟢 {symbol} - L_Score: {scores[screener]}")  # ✅ DEBUG LOG
                    elif screener == "I_Score":
                        scores[screener] = check_institutional_sponsorship(symbol)  # ✅ NEW: Institutional Sponsorship Check
                        print(f"🏢 {symbol} - I_Score: {scores[screener]}")  # ✅ DEBUG LOG
                    
                    # Apply user-defined threshold if applicable
                    if isinstance(threshold, (int, float)):
                        scores[screener] = scores[screener] >= threshold

        # 7️⃣ Calculate Total Score
        scores["Total_Score"] = sum(scores.values())  # ✅ NEW: Total Score Calculation

        # 8️⃣ Append results to CSV
        append_scores_to_csv(symbol, scores)

    print("✅ Scores saved in data/metrics/stock_scores.csv\n")

if __name__ == "__main__":
    main()