import os
import numpy as np
from datetime import datetime, timedelta
import pandas as pd
from db.db_connection import create_client
from indicators.sunny_bands import SunnyBands
from trading.position_calculator import PositionCalculator
from screener.user_input import get_interval_choice, get_date_range
from utils.data_utils import get_stock_data, validate_signal_date, print_signal, save_signals_to_csv



def check_entry_signal(df: pd.DataFrame) -> list:
    """
    Check for entry signals based on Sunny Bands strategy throughout the date range
    
    Args:
        df (pd.DataFrame): DataFrame with price data
        
    Returns:
        list: List of tuples (signal, date, signal_data) for each signal found
    """
    if len(df) < 2:  # Need at least 2 bars for comparison
        return []
        
    sunny = SunnyBands()
    results = sunny.calculate(df)
    
    if len(results) < 2:
        return []
    
    signals = []
    
    # Start from index 1 to compare with previous
    for i in range(1, len(df)):
        current = df.iloc[i]
        current_bands = results.iloc[i]
        
        # Check for bullish signal
        if current_bands['bullish_signal']:
            signal_data = {
                'price': current['close'],
                'upper_band': current_bands['upper_band'],
                'lower_band': current_bands['lower_band'],
                'dma': current_bands['dma']
            }
            signals.append((True, current['date'], signal_data))
    
    return signals

def get_valid_tickers(min_price: float, max_price: float, min_volume: int, interval: str) -> list:
    """Get tickers that meet the price and volume criteria"""
    # Get the most recent trading day
    today = datetime.now().date()
    yesterday = today - timedelta(days=1)
    
    try:
        with create_client() as client:
            # First get valid tickers from daily data
            daily_query = f"""
                SELECT DISTINCT ticker
                FROM stock_db.stock_prices_daily
                WHERE date = '{yesterday}'
                AND close BETWEEN {min_price} AND {max_price}
                AND volume >= {min_volume}
                ORDER BY ticker ASC
            """
            
            result = client.query(daily_query)
        tickers = [row[0] for row in result.result_rows]
        
        print(f"\nFound {len(tickers)} stocks matching price and volume criteria")
        
        if interval != "daily":
            # Now verify these tickers have intraday data
            # Convert to Unix timestamp in nanoseconds
            start_ts = int(datetime.combine(yesterday, datetime.strptime("09:30", "%H:%M").time()).timestamp() * 1000000000)
            end_ts = int(datetime.combine(yesterday, datetime.strptime("16:00", "%H:%M").time()).timestamp() * 1000000000)
            
            intraday_query = f"""
                SELECT DISTINCT ticker
                FROM stock_db.stock_prices
                WHERE ticker IN ({','.join([f"'{t}'" for t in tickers])})
                AND window_start BETWEEN {start_ts} AND {end_ts}
                GROUP BY ticker
                HAVING count() >= 50  -- Ensure we have enough data points for the indicator
            """
            
            result = client.query(intraday_query)
            tickers = [row[0] for row in result.result_rows]
            print(f"Of those, {len(tickers)} have recent intraday data")
            
        return tickers
        
    except Exception as e:
        print(f"Error fetching tickers: {str(e)}")
        return []

def view_stock_details(ticker: str, interval: str, start_date: datetime, end_date: datetime) -> None:
    """Display detailed data for a single stock"""
    print(f"\n📊 Detailed Analysis for {ticker}")
    print(f"Interval: {interval}")
    print(f"Date Range: {start_date.date()} to {end_date.date()}")
    
    try:
        with create_client() as client:
            # Construct query
            today = datetime.now().date()
        start_date = today - timedelta(days=60)
        
        if interval == "daily":
            table = "stock_prices_daily"
            date_col = "date"
            query = f"""
                SELECT 
                    {date_col} as date,
                    open,
                    high,
                    low,
                    close,
                    volume
                FROM stock_db.{table}
                WHERE ticker = '{ticker}'
                AND {date_col} BETWEEN '{start_date}' AND '{today}'
                ORDER BY date ASC
            """
        else:
            table = "stock_prices"
            date_col = "window_start"
            minutes_map = {
                "5min": 5,
                "15min": 15,
                "30min": 30,
                "1hour": 60
            }
            minutes = minutes_map[interval]
                
            query = f"""
                SELECT 
                    toDateTime(intDiv({date_col}/1000000000, {minutes}*60) * ({minutes}*60)) as interval_start,
                    min(open) as open,
                    max(high) as high,
                    min(low) as low,
                    argMax(close, {date_col}) as close,
                    sum(volume) as volume
                FROM stock_db.{table}
                WHERE ticker = '{ticker}'
                AND {date_col} BETWEEN {int(start_date.timestamp() * 1e9)} AND {int(today.timestamp() * 1e9)}
                GROUP BY interval_start
                ORDER BY interval_start ASC
            """
        
        # Execute query and get results
        result = client.query(query)
        
        if not result.result_rows:
            print("\nNo data found for this stock")
            return
            
        # Convert to DataFrame
        df = pd.DataFrame(
            result.result_rows,
            columns=['date', 'open', 'high', 'low', 'close', 'volume']
        )
        
        # Print raw query results
        print("\nRaw Query Results:")
        print(f"Number of rows returned: {len(result.result_rows)}")
        print("\nFirst 5 rows of raw data:")
        for i, row in enumerate(result.result_rows[:5]):
            print(f"Row {i + 1}: {row}")
        
        print("\nLast 5 rows of raw data:")
        for i, row in enumerate(result.result_rows[-5:]):
            print(f"Row {len(result.result_rows) - 4 + i}: {row}")
        
        # Calculate SunnyBands
        sunny = SunnyBands()
        results = sunny.calculate(df)
        
        # Get last values
        last_price = df.iloc[-1]
        last_bands = results.iloc[-1]
        
        print("\nLatest Values:")
        print(f"Date: {last_price['date']}")
        print(f"Open: ${last_price['open']:.2f}")
        print(f"High: ${last_price['high']:.2f}")
        print(f"Low: ${last_price['low']:.2f}")
        print(f"Close: ${last_price['close']:.2f}")
        print(f"Volume: {last_price['volume']:,}")
        print(f"\nSunnyBands Indicators:")
        print(f"DMA: ${last_bands['dma']:.2f}")
        print(f"Upper Band: ${last_bands['upper_band']:.2f}")
        print(f"Lower Band: ${last_bands['lower_band']:.2f}")
        print(f"Bullish Signal: {'Yes' if last_bands['bullish_signal'] else 'No'}")
        print(f"Bearish Signal: {'Yes' if last_bands['bearish_signal'] else 'No'}")
        
    except Exception as e:
        print(f"Error analyzing {ticker}: {str(e)}")

def run_sunny_scanner(min_price: float, max_price: float, min_volume: int, portfolio_size: float = None) -> None:
    print(f"\nScanning for stocks ${min_price:.2f}-${max_price:.2f} with min volume {min_volume:,}")
    
    interval = get_interval_choice()
    
    # Get date range from user input
    start_date, end_date = get_date_range()
    
    # First get qualified stocks from database
    # Convert dates to Unix timestamp in nanoseconds
    end_ts = int(end_date.timestamp() * 1000000000)
    start_ts = int(start_date.timestamp() * 1000000000)
    
    try:
        with create_client() as client:
            # Query to get stocks meeting criteria with their latest data
            query = f"""
                WITH filtered_data AS (
                    SELECT 
                        ticker,
                        window_start,
                        close,
                        volume,
                        toDate(toDateTime(window_start/1000000000)) as trade_date
                    FROM stock_db.stock_prices
                    WHERE window_start BETWEEN {start_ts} AND {end_ts}
                    AND toDateTime(window_start/1000000000) <= now()
                ),
                daily_data AS (
                    SELECT
                        ticker,
                        trade_date,
                        argMax(close, window_start) as daily_close,
                        sum(volume) as daily_volume
                    FROM filtered_data
                    GROUP BY ticker, trade_date
                    HAVING daily_close BETWEEN {min_price} AND {max_price}
                    AND daily_volume >= {min_volume}
                ),
                latest_data AS (
                    SELECT 
                        ticker,
                        argMax(daily_close, trade_date) as last_close,
                        sum(daily_volume) as total_volume,
                        max(trade_date) as last_update
                    FROM daily_data
                    GROUP BY ticker
                )
                SELECT 
                    ticker,
                    last_close,
                    total_volume,
                    last_update
                FROM latest_data
                ORDER BY ticker
            """
            
            result = client.query(query)
        qualified_stocks = [(row[0], row[1], row[2], row[3]) for row in result.result_rows]
        
        qualified_stocks = [(row[0], row[1], row[2], row[3]) for row in result.result_rows]
        
        if not qualified_stocks:
            print("No stocks found matching criteria.")
            return
            
        print(f"\nFound {len(qualified_stocks)} stocks matching criteria")
        
        # Initialize indicators
        sunny = SunnyBands()
        calculator = None
        if portfolio_size and portfolio_size > 0:
            calculator = PositionCalculator(
                account_size=portfolio_size,
                risk_percentage=1.0,
                stop_loss_percentage=7.0  # Explicit 7% stop loss
            )
        
        bullish_signals = []
        bearish_signals = []
        
        # Process each qualified stock
        for ticker, current_price, current_volume, last_update in qualified_stocks:
            try:
                # Get historical data based on interval
                df = get_stock_data(ticker, start_date, end_date, interval)
                
                if df.empty or len(df) < 50:  # Need at least 50 bars for the indicator
                    continue
                    
                # Check for signals throughout the date range
                signals = check_entry_signal(df)
                for signal, signal_date, signal_data in signals:
                    if calculator:
                        try:
                            position = calculator.calculate_position_size(
                                entry_price=signal_data['price'],
                                target_price=signal_data['upper_band']
                            )
                            if position['shares'] > 0:
                                entry_data = {
                                    'ticker': ticker,
                                    'entry_price': signal_data['price'],
                                    'target_price': signal_data['upper_band'],
                                    'signal_date': signal_date,
                                    'volume': current_volume,
                                    'last_update': datetime.fromtimestamp(last_update/1000000000),
                                    'shares': position['shares'],
                                    'position_size': position['position_value'],
                                    'stop_loss': signal_data['price'] * 0.93,  # 7% stop loss
                                    'risk_amount': position['potential_loss'],
                                    'profit_amount': position['potential_profit'],
                                    'risk_reward_ratio': position['risk_reward_ratio']
                                }
                                bullish_signals.append(entry_data)
                                print_signal(entry_data, "🟢")
                                
                        except ValueError as e:
                            print(f"Skipping {ticker} position: {str(e)}")
                            continue
                    
            except Exception as e:
                print(f"Error processing {ticker}: {str(e)}")
                continue
        
        save_signals_to_csv(bullish_signals, 'sunny')
        
    except Exception as e:
        print(f"Error during scan: {str(e)}")