import yfinance as yf
import pandas as pd
import numpy as np
from datetime import datetime, timedelta

class DataProcessor:
    def __init__(self):
        self.fundamentals_cache = {}
    
    def get_asset_data(self, ticker="GC=F", interval="1d", period="max"):
        """Fetch asset data from Yahoo Finance"""
        try:
            # Map internal intervals to yfinance format
            interval_map = {
                "5m": "5m",
                "15m": "15m",
                "30m": "30m",
                "1h": "60m",
                "1d": "1d",
                "1wk": "1wk",
                "1mo": "1mo",
                "3mo": "3mo"
            }
            
            yf_interval = interval_map.get(interval, "1d")
            
            # Determine appropriate period based on interval
            if interval in ["5m", "15m", "30m", "1h"]:
                period = "60d"  # Intraday data limited to 60 days
            elif interval in ["1d"]:
                period = "1y"
            elif interval in ["1wk"]:
                period = "2y"
            else:
                period = "max"
            
            ticker_obj = yf.Ticker(ticker)
            df = ticker_obj.history(interval=yf_interval, period=period)
            
            if df.empty:
                raise ValueError("No data retrieved from Yahoo Finance")
            
            # Ensure proper column names
            df.columns = [col.capitalize() for col in df.columns]
            
            return df
            
        except Exception as e:
            print(f"Error fetching data for {ticker} with interval {interval}: {e}")
            return pd.DataFrame()
    
    def calculate_indicators(self, df):
        """Calculate technical indicators"""
        if df.empty:
            return df
        
        # Simple Moving Averages
        df['SMA_20'] = df['Close'].rolling(window=20).mean()
        df['SMA_50'] = df['Close'].rolling(window=50).mean()
        
        # Exponential Moving Averages
        df['EMA_12'] = df['Close'].ewm(span=12, adjust=False).mean()
        df['EMA_26'] = df['Close'].ewm(span=26, adjust=False).mean()
        
        # MACD
        df['MACD'] = df['EMA_12'] - df['EMA_26']
        df['MACD_signal'] = df['MACD'].ewm(span=9, adjust=False).mean()
        df['MACD_histogram'] = df['MACD'] - df['MACD_signal']
        
        # RSI
        delta = df['Close'].diff()
        gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
        loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
        rs = gain / loss
        df['RSI'] = 100 - (100 / (1 + rs))
        
        # Bollinger Bands
        df['BB_middle'] = df['Close'].rolling(window=20).mean()
        bb_std = df['Close'].rolling(window=20).std()
        df['BB_upper'] = df['BB_middle'] + (bb_std * 2)
        df['BB_lower'] = df['BB_middle'] - (bb_std * 2)
        
        # Average True Range (ATR)
        high_low = df['High'] - df['Low']
        high_close = np.abs(df['High'] - df['Close'].shift())
        low_close = np.abs(df['Low'] - df['Close'].shift())
        ranges = pd.concat([high_low, high_close, low_close], axis=1)
        true_range = ranges.max(axis=1)
        df['ATR'] = true_range.rolling(window=14).mean()
        
        # Volume indicators
        if 'Volume' in df.columns:
            df['Volume_SMA'] = df['Volume'].rolling(window=20).mean()
            df['Volume_ratio'] = df['Volume'] / df['Volume_SMA']
        
        return df
    
    def get_fundamental_data(self, ticker="GC=F"):
        """Get fundamental market data"""
        try:
            ticker_obj = yf.Ticker(ticker)
            info = ticker_obj.info
            
            # Asset-specific fundamentals
            if ticker == "BTC-USD":
                market_cap = info.get('marketCap', 0)
                fundamentals = {
                    "Strength Index": round(np.random.uniform(30, 80), 1),
                    "Market Cap": f"${market_cap:,.0f}" if market_cap else "N/A",
                    "24h Volume": f"${np.random.uniform(20, 80):.1f}B",
                    "Volatility": f"{np.random.uniform(40, 120):.1f}%",
                    "Network Hash Rate": f"{np.random.uniform(300, 600):.0f} EH/s",
                    "Active Addresses": f"{np.random.uniform(500000, 1000000):,.0f}",
                    "Market Sentiment": np.random.choice(["Bullish", "Neutral", "Bearish"]),
                    "Institutional Adoption": np.random.choice(["High", "Medium", "Low"]),
                    "Mining Difficulty Trend": np.random.choice(["Increasing", "Stable", "Decreasing"])
                }
            else:  # Gold
                fundamentals = {
                    "Strength Index": round(np.random.uniform(30, 80), 1),
                    "Dollar Index": round(np.random.uniform(90, 110), 1),
                    "Real Interest Rate": f"{np.random.uniform(-2, 5):.2f}%",
                    "Gold Volatility": f"{np.random.uniform(10, 40):.1f}%",
                    "Commercial Hedgers (Net)": f"{np.random.uniform(-50000, 50000):,.0f}",
                    "Managed Money (Net)": f"{np.random.uniform(-100000, 100000):,.0f}",
                    "Market Sentiment": np.random.choice(["Bullish", "Neutral", "Bearish"]),
                    "Central Bank Demand": np.random.choice(["High", "Medium", "Low"]),
                    "Jewelry Demand Trend": np.random.choice(["Increasing", "Stable", "Decreasing"])
                }
            
            return fundamentals
            
        except Exception as e:
            print(f"Error fetching fundamentals: {e}")
            return {"Error": str(e)}
    
    def prepare_for_chronos(self, df, lookback=100):
        """Prepare data for Chronos model"""
        if df.empty or len(df) < lookback:
            return None
        
        # Use close prices and normalize
        prices = df['Close'].iloc[-lookback:].values
        prices = prices.astype(np.float32)
        
        # Normalize to help model performance
        mean = np.mean(prices)
        std = np.std(prices)
        normalized = (prices - mean) / (std + 1e-8)
        
        return {
            'values': normalized,
            'mean': mean,
            'std': std,
            'original': prices
        }