import logging
import time
import pandas as pd
import numpy as np
from datetime import datetime

from backend.agents.signal_agent import detect_signals
from backend.agents.regime_agent import detect_regime, filter_signals_by_regime
from backend.agents.risk_agent import evaluate_risk
from backend.agents.portfolio_agent import size_position
from backend.agents.alpha_scorer import compute_alpha_score
from backend.features.feature_store import compute_universe_features
from config import BACKTEST_PARAMS as BP
from config import RISK_PARAMS as RP

logger = logging.getLogger(__name__)

def run_portfolio_backtest(tickers: list[str], period: str = "2y", initial_capital: float = 10_00_000):
    """
    Run a walk-forward portfolio backtest.
    Simulates the daily scan pipeline over historical data.
    """
    start_time = time.time()
    logger.info(f"🚀 Starting Walk-Forward Backtest: {len(tickers)} tickers, period={period}")

    # 1. Fetch data and compute all features upfront
    # We get a list of dicts: {"ticker": str, "df": DataFrame, "latest": dict}
    # For backtesting, we'll use the 'df' which contains the full history
    feature_results = compute_universe_features(tickers, period=period)
    if not feature_results:
        return {"error": "No data found for backtest"}

    # Find common date range across all tickers
    all_dates = []
    for r in feature_results:
        all_dates.extend(r["df"].index.tolist())
    
    unique_dates = sorted(list(set(all_dates)))
    # Start from index 100 to ensure indicators (like SMA200) are warmed up
    test_dates = unique_dates[100:]
    
    logger.info(f"Backtesting over {len(test_dates)} trading days...")

    # Portfolio State
    capital = initial_capital
    portfolio_value = initial_capital
    active_trades = []  # List of trade dicts
    equity_curve = []
    trade_log = []

    # 2. Walk-forward loop (Day by Day)
    for i, current_date in enumerate(test_dates):
        # Update current portfolio value based on latest prices
        current_equity = capital
        still_active = []
        
        for trade in active_trades:
            ticker = trade["ticker"]
            # Get price at current_date for this ticker
            ticker_data = next((r["df"] for r in feature_results if r["ticker"] == ticker), None)
            if ticker_data is not None and current_date in ticker_data.index:
                current_price = ticker_data.loc[current_date, "Close"]
                high_price = ticker_data.loc[current_date, "High"]
                low_price = ticker_data.loc[current_date, "Low"]
                
                # Check Stop Loss / Targets
                if low_price <= trade["stop_loss"]:
                    # SL Hit
                    exit_price = trade["stop_loss"]
                    pnl = (exit_price - trade["entry_price"]) * trade["quantity"]
                    capital += (trade["quantity"] * exit_price)
                    trade_log.append({**trade, "exit_date": current_date, "exit_price": exit_price, "pnl": pnl, "status": "SL"})
                elif high_price >= trade["target_1"]:
                    # Target 1 Hit (Simplification: Exit 50% at T1, 50% at T2 or SL)
                    # For this V1, let's just exit full at Target 1
                    exit_price = trade["target_1"]
                    pnl = (exit_price - trade["entry_price"]) * trade["quantity"]
                    capital += (trade["quantity"] * exit_price)
                    trade_log.append({**trade, "exit_date": current_date, "exit_price": exit_price, "pnl": pnl, "status": "TP1"})
                else:
                    # Trade still open
                    current_equity += (trade["quantity"] * current_price)
                    still_active.append(trade)
            else:
                # No data for this day, assume trade continues
                still_active.append(trade)
        
        active_trades = still_active
        portfolio_value = current_equity
        equity_curve.append({"date": current_date, "equity": portfolio_value})

        # --- Daily Scan Simulation ---
        # 1. Detect signals for this day
        daily_signals = []
        for r in feature_results:
            ticker = r["ticker"]
            df_until_now = r["df"].loc[:current_date]
            if len(df_until_now) < 2 or current_date not in df_until_now.index:
                continue
                
            latest_row = df_until_now.iloc[-1].to_dict()
            # Wrap detect_signals to only see history
            signals = detect_signals(ticker, latest_row, df_until_now)
            daily_signals.extend(signals)

        if not daily_signals:
            continue

        # 2. Simple Regime (Backtest version)
        # Note: In a real backtest, we'd fetch index data for each date. 
        # For V1, we assume a neutral regime or use a simplified index check if available.
        regime = {"regime": "trend_up", "confidence": 0.8, "valid_signals": ["momentum_breakout", "golden_cross", "macd_bullish_cross", "volume_breakout"]}

        # 3. Filter & Score
        # (Filtering logic simplified for backtest)
        scored_signals = []
        for s in daily_signals:
            ticker = s["ticker"]
            r = next((res for res in feature_results if res["ticker"] == ticker), None)
            latest_features = r["df"].loc[:current_date].iloc[-1].to_dict()
            
            # Risk check
            risk = evaluate_risk(s, latest_features, active_trades)
            if risk["decision"] in ("approve", "reduce"):
                s["alpha_score"] = compute_alpha_score(s, latest_features)
                scored_signals.append(s)

        scored_signals.sort(key=lambda x: x["alpha_score"], reverse=True)

        # 4. Execute Top Signals (Limited by available capital)
        for s in scored_signals[:3]:  # Max 3 new trades per day
            if any(t["ticker"] == s["ticker"] for t in active_trades):
                continue # Already in this stock
                
            # Size position
            s = size_position(s, portfolio_value, active_trades)
            cost = s["quantity"] * s["entry_price"]
            
            if cost > 0 and capital >= cost:
                capital -= cost
                active_trades.append({**s, "entry_date": current_date})

    # 3. Performance Metrics
    duration = time.time() - start_time
    performance = _calculate_metrics(equity_curve, trade_log, initial_capital)
    
    logger.info(f"✅ Backtest Complete in {duration:.1f}s. Final Equity: {portfolio_value:,.0f}")
    
    return {
        "metrics": performance,
        "equity_curve": equity_curve,
        "trades": trade_log,
        "duration_seconds": duration
    }

def _calculate_metrics(equity_curve, trade_log, initial_capital):
    if not equity_curve:
        return {}
    
    df_equity = pd.DataFrame(equity_curve)
    final_equity = df_equity["equity"].iloc[-1]
    total_return_pct = (final_equity / initial_capital - 1) * 100
    
    # Drawdown
    df_equity["peak"] = df_equity["equity"].cummax()
    df_equity["drawdown"] = (df_equity["equity"] - df_equity["peak"]) / df_equity["peak"] * 100
    max_drawdown = df_equity["drawdown"].min()
    
    # Trade stats
    df_trades = pd.DataFrame(trade_log)
    win_rate = 0
    if not df_trades.empty:
        win_rate = (df_trades["pnl"] > 0).mean() * 100
        
    return {
        "total_return_pct": round(total_return_pct, 2),
        "max_drawdown_pct": round(max_drawdown, 2),
        "win_rate_pct": round(win_rate, 1),
        "total_trades": len(trade_log),
        "final_equity": round(final_equity, 2)
    }