import argparse
import json
import os
import time
from datetime import datetime, timezone
from pathlib import Path

from dotenv import load_dotenv
from loguru import logger

from data.weather import (
    fetch_weather_openmeteo,
    fetch_weather_apify,
    get_today_high,
    summarize_weather,
    TARGET_CITIES,
)
from data.polymarket import (
    init_account,
    search_weather_markets,
    get_market_odds,
    summarize_markets,
    get_portfolio,
    get_stats,
    is_demo_mode,
)
from agent.llm import run_agent_analysis
from trading.kelly import kelly_size
from trading.paper_trader import (
    place_order,
    get_balance,
    get_portfolio_summary,
    get_stats_summary,
    export_trades,
)

load_dotenv()

RESULTS_DIR = Path("./results")
RESULTS_DIR.mkdir(exist_ok=True)
LOG_FILE = RESULTS_DIR / "agent.log"

logger.add(LOG_FILE, rotation="10 MB", level="DEBUG")


# ── Demo market ──────────────────────────────────────────────────────────────

def _create_demo_market(weather_data: dict) -> list[dict]:
    """Create a simulated weather market for demo/testing when no real markets exist."""
    city = "New York"
    records = weather_data.get(city, [])
    today_high = records[0].get("high_f", 85) if records else 85
    threshold = int(today_high) + 5
    yes_price = 0.30

    slug = f"demo-nyc-high-above-{threshold}f"
    title = f"Will the high temperature in New York City exceed {threshold}°F today?"

    market = {
        "slug": slug,
        "title": title,
        "question": title,
        "yes_price": yes_price,
        "no_price": round(1 - yes_price, 3),
        "volume": 50000,
    }

    logger.info(f"🧪 Demo market created: [{slug}] {title}")
    logger.info(f"    YES: {yes_price} | Today's high: {today_high}°F → threshold: {threshold}°F")
    return [market]


# ── Main agent cycle ─────────────────────────────────────────────────────────

def run_cycle(use_apify: bool = False, demo_mode: bool = False) -> dict:
    """
    Execute one full agent cycle. Returns a summary dict.
    """
    cycle_start = datetime.now(timezone.utc)
    logger.info(f"\n{'='*60}")
    logger.info(f"Agent cycle started: {cycle_start.isoformat()}")
    logger.info(f"{'='*60}")

    # 1. Fetch weather data
    logger.info("Step 1: Fetching weather data...")
    try:
        if use_apify:
            weather_data = fetch_weather_apify()
        else:
            weather_data = fetch_weather_openmeteo()
    except Exception as e:
        logger.warning(f"Weather fetch error ({e}), using Open-Meteo fallback")
        weather_data = fetch_weather_openmeteo()

    weather_summary = summarize_weather(weather_data)
    logger.info(f"\n{weather_summary}")

    # 2. Fetch Polymarket markets
    logger.info("\nStep 2: Searching Polymarket weather markets...")
    markets = search_weather_markets()
    if not markets and demo_mode:
        logger.info("No real markets found — injecting demo market for testing")
        markets = _create_demo_market(weather_data)
    markets_summary = summarize_markets(markets)
    logger.info(f"\n{markets_summary}")

    # 3. Get current balance & portfolio
    logger.info("\nStep 3: Checking portfolio...")
    balance = get_balance()
    portfolio = get_portfolio_summary()
    portfolio_str = json.dumps(portfolio, indent=2) if portfolio else "No open positions"
    logger.info(f"Balance: ${balance:,.2f}")

    # 4. Run LLM agent analysis
    logger.info("\nStep 4: Running LLM agent analysis...")
    agent_result = run_agent_analysis(
        weather_summary=weather_summary,
        markets_summary=markets_summary,
        portfolio_summary=f"Portfolio:\n{portfolio_str}",
        balance=balance,
    )

    logger.info(f"Agent analysis: {agent_result.get('analysis', 'N/A')}")
    logger.info(f"Risk summary: {agent_result.get('risk_summary', 'N/A')}")

    # 5. Execute trades
    demo_active = is_demo_mode()
    logger.info("\nStep 5: Executing paper trades...")
    trade_results = []
    executed_count = 0

    for trade_signal in agent_result.get("trades", []):
        action = trade_signal.get("action", "skip")
        if action == "skip":
            logger.info(f"  SKIP: {trade_signal.get('market_slug', '?')}")
            continue

        market_slug  = trade_signal.get("market_slug", "")
        city         = trade_signal.get("city", "")
        model_prob   = float(trade_signal.get("model_prob", 0.5))
        market_title = trade_signal.get("market_title", "")

        # Get current odds from paper trader (or demo market)
        odds = get_market_odds(market_slug)
        if not odds or not odds.get("yes_price"):
            if demo_active:
                odds = {"yes_price": trade_signal.get("market_prob", 0.30)}
                logger.info(f"  Using demo odds for {market_slug}: YES={odds['yes_price']}")
            else:
                logger.warning(f"  No odds for {market_slug}, skipping")
                continue

        yes_price = float(odds["yes_price"])

        # Kelly sizing
        decision = kelly_size(
            model_prob=model_prob,
            yes_price=yes_price,
            bankroll=balance,
        )

        if not decision.should_trade:
            logger.info(f"  NO EDGE: {market_slug}")
            trade_results.append({
                "signal": trade_signal,
                "decision": {
                    "should_trade": False,
                    "side": decision.side,
                    "amount_usd": 0,
                    "edge_pct": decision.edge_pct,
                    "reasoning": decision.reasoning,
                },
                "result": None,
            })
            continue

        if demo_active:
            logger.info(f"  ⚠️  DEMO — would {decision.side.upper()} {market_slug} for ${decision.amount_usd:.2f} ({decision.reasoning})")
            trade_results.append({
                "signal": trade_signal,
                "decision": {
                    "should_trade": True,
                    "side": decision.side,
                    "amount_usd": decision.amount_usd,
                    "edge_pct": decision.edge_pct,
                    "reasoning": f"[DEMO] {decision.reasoning}",
                },
                "result": {"status": "demo_skipped", "reason": "Demo mode — no real trade"},
            })
        else:
            result = place_order(market_slug, decision, apply_hedge=True)
            trade_results.append({
                "signal": trade_signal,
                "decision": {
                    "should_trade": decision.should_trade,
                    "side": decision.side,
                    "amount_usd": decision.amount_usd,
                    "edge_pct": decision.edge_pct,
                    "reasoning": decision.reasoning,
                },
                "result": result,
            })

        executed_count += 1

    # 6. Save cycle results
    cycle_summary = {
        "timestamp":      cycle_start.isoformat(),
        "balance_before": balance,
        "balance_after":  get_balance(),
        "markets_found":  len(markets),
        "trades_signals": len(agent_result.get("trades", [])),
        "trades_executed": executed_count,
        "agent_analysis": agent_result.get("analysis"),
        "risk_summary":   agent_result.get("risk_summary"),
        "trade_results":  trade_results,
        "stats":          get_stats_summary(),
    }

    out_file = RESULTS_DIR / f"cycle_{cycle_start.strftime('%Y%m%d_%H%M%S')}.json"
    with open(out_file, "w") as f:
        json.dump(cycle_summary, f, indent=2, default=str)

    logger.success(f"\nCycle complete. Results saved to {out_file}")
    logger.info(
        f"Summary: {executed_count} trades executed | "
        f"Balance: ${cycle_summary['balance_after']:,.2f}"
    )

    return cycle_summary


# ── Entry point ───────────────────────────────────────────────────────────────

def main():
    parser = argparse.ArgumentParser(description="skyAlpha — Multi-Agent Weather Prediction & Polymarket Trading System")
    parser.add_argument("--loop",     action="store_true", help="Run continuously every 30 min")
    parser.add_argument("--ui",       action="store_true", help="Launch Gradio dashboard")
    parser.add_argument("--apify",    action="store_true", help="Use Apify for weather data")
    parser.add_argument("--interval", type=int, default=1800, help="Loop interval in seconds")
    parser.add_argument("--init",     action="store_true", help="Initialize paper account only")
    parser.add_argument("--demo",     action="store_true", help="Inject demo market for testing when no real markets exist")
    args = parser.parse_args()

    # Always init account on startup
    balance_start = float(os.getenv("STARTING_BALANCE", "10000"))
    init_account(balance=balance_start)

    if args.init:
        logger.info("Account initialized. Run without --init to start trading.")
        return

    if args.ui:
        from ui.dashboard import launch_ui
        launch_ui()
        return

    if args.loop:
        logger.info(f"Starting agent loop (interval: {args.interval}s)...")
        while True:
            try:
                run_cycle(use_apify=args.apify, demo_mode=args.demo)
            except KeyboardInterrupt:
                logger.info("Loop stopped by user")
                break
            except Exception as e:
                logger.error(f"Cycle error: {e}")

            logger.info(f"Sleeping {args.interval}s until next cycle...")
            time.sleep(args.interval)
    else:
        # Single run
        run_cycle(use_apify=args.apify, demo_mode=args.demo)


if __name__ == "__main__":
    main()