import os
import sys
from dotenv import load_dotenv

sys.path.append(os.path.join(os.path.dirname(__file__), 'src'))

from fetchers.factory import FetcherFactory
from storage.file_io import DataStorage
from analysis.calculator import FinancialCalculator
from reporting.markdown_generator import MarkdownReporter
from reporting.html_generator import HtmlReporter

def run_analysis(market, symbol):
    load_dotenv()
    tushare_token = os.getenv('TUSHARE_TOKEN')
    av_key = os.getenv('ALPHA_VANTAGE_KEY')
    
    print(f"\nProcessing {market} stock: {symbol}...")
        # 1. Fetch Data
    try:
        fetcher = FetcherFactory.get_fetcher(market, tushare_token, av_key)
        
        print("Fetching Income Statement...")
        df_inc = fetcher.get_income_statement(symbol)
        print("Fetching Balance Sheet...")
        df_bal = fetcher.get_balance_sheet(symbol)
        print("Fetching Cash Flow...")
        df_cf = fetcher.get_cash_flow(symbol)
        print("Fetching Market Metrics...")
        metrics = fetcher.get_market_metrics(symbol)
        print(f"Metrics: {metrics}")
        
        # 1.1 Fetch Historical Metrics
        historical_metrics_df = None
        if not df_inc.empty:
            # Tushare dates are often YYYYMMDD. Align format.
            dates = df_inc['date'].tolist() if 'date' in df_inc.columns else []
            # Also combine from bal/cf if needed? usually inc covers reporting periods.
            if dates and hasattr(fetcher, 'get_historical_metrics'):
                print(f"Fetching Historical Market Metrics for {len(dates)} periods...")
                historical_metrics_df = fetcher.get_historical_metrics(symbol, dates)
        
        # 2. Save Data
        storage = DataStorage()
        storage.save_data(df_inc, market, symbol, "income_statement")
        storage.save_data(df_bal, market, symbol, "balance_sheet")
        storage.save_data(df_cf, market, symbol, "cash_flow")
        
        # 3. Calculate Indicators
        print("Calculating Financial Indicators...")
        calculator = FinancialCalculator()
        df_analysis = calculator.process_data(df_inc, df_bal, df_cf, metrics, market, historical_metrics_df)
        
        if df_analysis.empty:
            print("Not enough data to calculate indicators.")
            return

        # 4. Generate Reports
        print("Generating Reports...")
        md_reporter = MarkdownReporter()
        html_reporter = HtmlReporter()

        md_content = md_reporter.generate_report(df_analysis, market, symbol, metrics)
        html_content = html_reporter.generate_report(df_analysis, market, symbol, metrics)

        output_dir = os.path.join("data", market, symbol)
        md_path = os.path.join(output_dir, "report.md")
        html_path = os.path.join(output_dir, "report.html")

        with open(md_path, "w", encoding='utf-8') as f:
            f.write(md_content)

        with open(html_path, "w", encoding='utf-8') as f:
            f.write(html_content)

        print(f"\nAnalysis completed! Reports saved to:")
        print(f"  - Markdown: {md_path}")
        print(f"  - HTML: {html_path}")
        print("-" * 50)
        
    except Exception as e:
        print(f"Error occurred: {str(e)}")
        import traceback
        traceback.print_exc()

if __name__ == "__main__":
    if len(sys.argv) > 2:
        market = sys.argv[1]
        symbol = sys.argv[2]
        run_analysis(market, symbol)
    else:
        print("Usage: python main.py <MARKET> <SYMBOL>")
        # Default test cases if run without args (for manual testing)
        print("Running default test cases:")
        # Test CN
        run_analysis('CN', '600519.SH') 
        # Test US
        run_analysis('US', 'AAPL')