"""
Pro Trading Signals - Finnhub Live Data Version
"""

import streamlit as st
import pandas as pd
import requests
from datetime import datetime
import time

FINNHUB_API_KEY = "demo"
try:
    import os
    FINNHUB_API_KEY = os.environ.get("FINNHUB_API_KEY", "demo")
except:
    pass

st.set_page_config(
    page_title="Pro Trading Signals",
    page_icon="📈",
    layout="centered",
    initial_sidebar_state="collapsed"
)

st.markdown("""
<style>
    .main-title { font-size: 2.5rem; font-weight: bold; color: #FFD700; text-align: center; }
    .signal-box { background: #1a1a1a; border-radius: 15px; padding: 25px; margin: 10px 0; }
    .prob-big { font-size: 4rem; font-weight: bold; text-align: center; }
    .buy-signal { color: #00E676; }
    .sell-signal { color: #FF5252; }
    .wait-signal { color: #FFD740; }
</style>
""", unsafe_allow_html=True)

st.markdown('<h1 class="main-title">📈 PRO TRADING SIGNALS</h1>', unsafe_allow_html=True)
st.markdown('<p style="text-align:center;color:#888;">Live Finnhub Data • Futures & Forex</p>', unsafe_allow_html=True)

SYMBOL_MAP = {
    "🥇 Gold (XAU/USD)": {"symbol": "XAUUSD", "type": "forex"},
    "🥈 Silver (XAG/USD)": {"symbol": "XAGUSD", "type": "forex"},
    "🛢️ Crude Oil (CL)": {"symbol": "CL", "type": "commodity"},
    "⛽ Natural Gas (NG)": {"symbol": "NG", "type": "commodity"},
    "🌽 Corn (ZC)": {"symbol": "ZC", "type": "commodity"},
    "🌾 Wheat (ZW)": {"symbol": "ZW", "type": "commodity"},
    "EUR/USD": {"symbol": "EURUSD", "type": "forex"},
    "GBP/USD": {"symbol": "GBPUSD", "type": "forex"},
    "USD/JPY": {"symbol": "USDJPY", "type": "forex"},
    "USD/CHF": {"symbol": "USDCHF", "type": "forex"},
    "AUD/USD": {"symbol": "AUDUSD", "type": "forex"},
    "USD/CAD": {"symbol": "USDCAD", "type": "forex"},
    "BTC/USD": {"symbol": "BTC-USD", "type": "crypto"},
    "ETH/USD": {"symbol": "ETH-USD", "type": "crypto"},
    "AAPL": {"symbol": "AAPL", "type": "stock"},
    "TSLA": {"symbol": "TSLA", "type": "stock"},
    "NVDA": {"symbol": "NVDA", "type": "stock"},
}

TIMEFRAMES = {
    "1 Min": {"resolution": "1", "days": 1},
    "5 Min": {"resolution": "5", "days": 5},
    "15 Min": {"resolution": "15", "days": 5},
    "30 Min": {"resolution": "30", "days": 10},
    "1 Hour": {"resolution": "60", "days": 30},
    "4 Hour": {"resolution": "240", "days": 60},
    "Daily": {"resolution": "D", "days": 180},
}


def get_finnhub_price(symbol: str) -> float:
    try:
        url = f"https://finnhub.io/api/v1/quote?symbol={symbol}&token={FINNHUB_API_KEY}"
        r = requests.get(url, timeout=5)
        data = r.json()
        if data.get('c'):
            return data['c']
    except:
        pass
    return None


def get_finnhub_candle(symbol: str, resolution: str, days: int) -> pd.DataFrame:
    try:
        to_time = int(datetime.now().timestamp())
        from_time = to_time - (days * 24 * 60 * 60)
        
        url = f"https://finnhub.io/api/v1/stock/candle?symbol={symbol}&resolution={resolution}&from={from_time}&to={to_time}&token={FINNHUB_API_KEY}"
        r = requests.get(url, timeout=10)
        data = r.json()
        
        if data.get('s') == 'ok':
            df = pd.DataFrame({
                'Open': data['o'],
                'High': data['h'],
                'Low': data['l'],
                'Close': data['c'],
                'Volume': data['v']
            }, index=pd.to_datetime(data['t'], unit='s'))
            return df
    except Exception as e:
        pass
    return pd.DataFrame()


col1, col2, col3 = st.columns([2, 2, 1])
with col1:
    selected_pair = st.selectbox("📊 Asset", list(SYMBOL_MAP.keys()), label_visibility="collapsed")
with col2:
    selected_tf = st.selectbox("⏱ Timeframe", list(TIMEFRAMES.keys()), label_visibility="collapsed")
with col3:
    st.write("")
    analyze_btn = st.button("⚡ ANALYZE", use_container_width=True)

if analyze_btn:
    with st.spinner("📡 Fetching Finnhub live data..."):
        try:
            start_time = time.time()
            asset = SYMBOL_MAP[selected_pair]
            symbol = asset["symbol"]
            tf = TIMEFRAMES[selected_tf]
            
            price = get_finnhub_price(symbol)
            
            df = get_finnhub_candle(symbol, tf["resolution"], tf["days"])
            
            if df.empty or len(df) < 20:
                st.error("❌ No data. Try different asset or check API key.")
            else:
                close = df['Close']
                
                df['sma9'] = close.rolling(9).mean()
                df['sma21'] = close.rolling(21).mean()
                df['sma50'] = close.rolling(50).mean()
                df['ema8'] = close.ewm(span=8, adjust=False).mean()
                df['ema21'] = close.ewm(span=21, adjust=False).mean()
                
                delta = close.diff()
                gain = delta.clip(lower=0)
                loss = (-delta).clip(lower=0)
                avg_gain = gain.ewm(alpha=1/14, adjust=False).mean()
                avg_loss = loss.ewm(alpha=1/14, adjust=False).mean()
                rs = avg_gain / loss.replace(0, 1e-12)
                df['rsi'] = 100 - (100 / (1 + rs))
                
                ema12 = close.ewm(span=12, adjust=False).mean()
                ema26 = close.ewm(span=26, adjust=False).mean()
                df['macd'] = ema12 - ema26
                df['macd_signal'] = df['macd'].ewm(span=9, adjust=False).mean()
                df['macd_hist'] = df['macd'] - df['macd_signal']
                
                high = df['High']
                low = df['Low']
                prev_close = close.shift(1)
                tr = pd.concat([high - low, (high - prev_close).abs(), (low - prev_close).abs()], axis=1).max(axis=1)
                df['atr'] = tr.ewm(alpha=1/14, adjust=False).mean()
                
                up_move = high.diff()
                down_move = -low.diff()
                plus_dm = ((up_move > down_move) & (up_move > 0)) * up_move
                minus_dm = ((down_move > up_move) & (down_move > 0)) * down_move
                tr_s = tr.ewm(alpha=1/14, adjust=False).mean()
                p_dm_s = plus_dm.ewm(alpha=1/14, adjust=False).mean()
                m_dm_s = minus_dm.ewm(alpha=1/14, adjust=False).mean()
                tr_safe = tr_s.replace(0, 1e-12)
                df['plus_di'] = 100 * (p_dm_s / tr_safe)
                df['minus_di'] = 100 * (m_dm_s / tr_safe)
                di_sum = (df['plus_di'] + df['minus_di']).replace(0, 1e-12)
                dx = 100 * (df['plus_di'] - df['minus_di']).abs() / di_sum
                df['adx'] = dx.ewm(alpha=1/14, adjust=False).mean()
                
                last = df.iloc[-1]
                price = float(last['Close']) if not price else price
                atr = float(last['atr'])
                
                score_buy = 0
                score_sell = 0
                reasons = []
                
                if price > last['sma50']:
                    score_buy += 1
                    reasons.append("✓ Price > SMA50")
                else:
                    score_sell += 1
                    reasons.append("✗ Price < SMA50")
                
                if last['sma21'] > last['sma50']:
                    score_buy += 1
                    reasons.append("✓ SMA21 > SMA50")
                else:
                    score_sell += 1
                    reasons.append("✗ SMA21 < SMA50")
                
                if last['ema8'] > last['ema21']:
                    score_buy += 1
                    reasons.append("✓ EMA8 > EMA21")
                else:
                    score_sell += 1
                    reasons.append("✗ EMA8 < EMA21")
                
                if last['macd'] > last['macd_signal']:
                    score_buy += 1
                    reasons.append("✓ MACD bullish")
                else:
                    score_sell += 1
                    reasons.append("✗ MACD bearish")
                
                if last['macd_hist'] > 0:
                    score_buy += 1
                    reasons.append("✓ MACD histogram +")
                else:
                    score_sell += 1
                    reasons.append("✗ MACD histogram -")
                
                rsi = last['rsi']
                if 50 < rsi < 70:
                    score_buy += 1
                    reasons.append(f"✓ RSI healthy ({rsi:.0f})")
                elif 30 < rsi < 50:
                    score_sell += 1
                    reasons.append(f"✗ RSI weak ({rsi:.0f})")
                elif rsi >= 70:
                    score_sell += 1
                    reasons.append(f"⚠ RSI overbought")
                elif rsi <= 30:
                    score_buy += 1
                    reasons.append(f"✓ RSI oversold")
                
                adx = last['adx']
                if adx > 20:
                    if last['plus_di'] > last['minus_di']:
                        score_buy += 2
                        reasons.append(f"✓ Strong uptrend (ADX:{adx:.0f})")
                    else:
                        score_sell += 2
                        reasons.append(f"✗ Strong downtrend (ADX:{adx:.0f})")
                else:
                    reasons.append(f"○ Ranging market")
                
                if score_buy >= 5:
                    prob = min(95, 55 + (score_buy * 7))
                    action = "🟢 BUY"
                    color_class = "buy-signal"
                    tp = round(price + (atr * 2), 2)
                    sl = round(price - (atr * 1), 2)
                elif score_sell >= 5:
                    prob = min(95, 55 + (score_sell * 7))
                    action = "🔴 SELL"
                    color_class = "sell-signal"
                    tp = round(price - (atr * 2), 2)
                    sl = round(price + (atr * 1), 2)
                else:
                    prob = max(40, 50 + (score_buy - score_sell) * 5)
                    action = "🟡 WAIT"
                    color_class = "wait-signal"
                    tp = sl = round(price, 2)
                
                fetch_time = round(time.time() - start_time, 2)
                
                st.markdown(f"""
                <div class="signal-box">
                    <h2 class="{color_class}" style="text-align:center;">{action}</h2>
                    <h1 class="prob-big {color_class}">{prob}%</h1>
                    <p style="text-align:center;color:#888;">WIN PROBABILITY • {fetch_time}s</p>
                    <hr style="border-color:#333;">
                    <div style="display:flex;justify-content:space-around;text-align:center;">
                        <div>
                            <p style="color:#888;">CURRENT PRICE</p>
                            <h3>${price:.2f}</h3>
                        </div>
                        <div>
                            <p style="color:#00E676;">TAKE PROFIT</p>
                            <h3>${tp:.2f}</h3>
                        </div>
                        <div>
                            <p style="color:#FF5252;">STOP LOSS</p>
                            <h3>${sl:.2f}</h3>
                        </div>
                    </div>
                    <hr style="border-color:#333;">
                    <h4 style="color:#FFD700;">📊 TECHNICAL ANALYSIS</h4>
                    <p style="font-family:monospace;color:#ccc;">{chr(10).join(reasons)}</p>
                    <p style="color:#888;text-align:center;">{selected_pair} • {selected_tf} • {datetime.now().strftime('%H:%M:%S')}</p>
                </div>
                """, unsafe_allow_html=True)
                
        except Exception as e:
            st.error(f"Error: {str(e)}")

st.markdown("---")
st.markdown('<p style="text-align:center;color:#555;font-size:12px;">Pro Trading Signals • Powered by Finnhub</p>', unsafe_allow_html=True)