import pandas as pd
import json
import logging
from src.strategy.scanner import scan_for_candidates

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def find_best_isa_tickers():
    """
    1. Reads available Trading212 instruments.
    2. Filters for US Stocks (excluding ETFs/Warrants which are often non-ISA compliant or bad for this strategy).
    3. Maps T212 tickers to Yahoo Finance tickers.
    4. Runs the strategy scanner to find the most volatile/liquid candidates.
    """
    try:
        with open('available_instruments.json', 'r') as f:
            instruments = json.load(f)
    except FileNotFoundError:
        logger.error("Please run get_available_tickers.py first to generate available_instruments.json")
        return

    # Filter for US Stocks only
    # - type == 'STOCK': Excludes US ETFs (not allowed in UK ISAs) and Warrants
    # - currencyCode == 'USD': Ensures it's trading during the US session (09:30 EST)
    us_stocks = [
        inst for inst in instruments 
        if inst.get('type') == 'STOCK' and inst.get('currencyCode') == 'USD'
    ]
    
    logger.info(f"Filtered down to {len(us_stocks)} US Stocks from Trading212.")

    # Extract short names (Yahoo Finance tickers)
    # Trading212 usually uses 'shortName' for the actual market ticker (e.g., AAPL) 
    # and 'ticker' for their internal ID (e.g., AAPL_US_EQ).
    t212_to_yf_map = {}
    for stock in us_stocks:
        short_name = stock.get('shortName')
        t212_id = stock.get('ticker')
        if short_name and t212_id:
            # Avoid preferred shares/warrants that might sneak in with hyphens or dots 
            # if yfinance can't parse them easily, but we'll try them all.
            t212_to_yf_map[short_name] = t212_id

    yf_tickers = list(t212_to_yf_map.keys())
    
    # We don't want to scan all 6,000+ stocks at once (Yahoo Finance will rate limit us).
    # Let's filter locally first using T212 maxOpenQuantity or just take a known subset,
    # OR we can pass the top 500 popular ones. For this script, let's scan a robust list of 
    # well-known highly liquid tech/growth stocks that are definitely ISA eligible.
    
    # Known high-liquidity ISA-eligible US stocks:
    focus_list = [
        "TSLA", "NVDA", "AMD", "AAPL", "MSFT", "META", "AMZN", "GOOGL", "NFLX", 
        "COIN", "MSTR", "PLTR", "UBER", "HOOD", "RST", "SNOW", "CRM", "CRWD", 
        "PANW", "SMCI", "ARM", "SQ", "SHOP", "ROKU", "DDOG", "NET", "DOCN"
    ]
    
    # Ensure they exist in our T212 account
    valid_yf_tickers = [t for t in focus_list if t in t212_to_yf_map]
    logger.info(f"Scanning {len(valid_yf_tickers)} highly liquid known ISA-eligible stocks...")
    
    # Run the scanner
    results_df = scan_for_candidates(tickers=valid_yf_tickers, min_price=20.0, min_volume=2_000_000)
    
    if results_df.empty:
        logger.warning("No candidates met the minimum price/volume criteria.")
        return
        
    # Map back to Trading212 internal tickers so the bot knows what to trade
    results_df['T212_Ticker'] = results_df['Ticker'].map(t212_to_yf_map)
    
    # Reorder columns
    cols = ['Ticker', 'T212_Ticker', 'Close', 'ATR_14', 'ATR_Percent', 'Avg_Volume']
    results_df = results_df[cols]
    
    print("\n" + "="*80)
    print("🏆 BEST ISA-ELIGIBLE CANDIDATES FOR TOUCH & TURN TODAY 🏆")
    print("="*80)
    print(results_df.to_string(index=False))
    print("\n* Use the 'T212_Ticker' column when configuring the ExecutionManager.")
    
    # Save the day's watchlist
    results_df.to_csv("isa_watchlist.csv", index=False)
    logger.info("Saved shortlist to isa_watchlist.csv")
    return results_df

if __name__ == "__main__":
    find_best_isa_tickers()