import streamlit as st
import ccxt
import pandas as pd
import numpy as np
import time
from datetime import datetime
import os
from dotenv import load_dotenv
from rich.console import Console

# Set page config
st.set_page_config(
    page_title="Gate.io Market Making Dashboard",
    page_icon="📊",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Custom CSS for enhanced neumorphism
st.markdown("""
<style>
    .stApp {
        background: linear-gradient(145deg, #1a1a1a, #2d2d2d);
        color: #ffffff;
    }
    .stButton>button {
        background: linear-gradient(145deg, #ff6b00, #ff8533);
        border: none;
        border-radius: 10px;
        box-shadow: 5px 5px 10px rgba(0,0,0,0.3),
                   -5px -5px 10px rgba(255,255,255,0.1);
        color: white;
        padding: 10px 20px;
        transition: all 0.3s ease;
    }
    .stButton>button:hover {
        transform: translateY(-2px);
        box-shadow: 6px 6px 12px rgba(0,0,0,0.4),
                   -6px -6px 12px rgba(255,255,255,0.15);
    }
    .stSelectbox>div>div>select {
        background: linear-gradient(145deg, #2d2d2d, #1a1a1a);
        color: white;
        border-radius: 10px;
        box-shadow: inset 3px 3px 6px rgba(0,0,0,0.3),
                   inset -3px -3px 6px rgba(255,255,255,0.1);
    }
    .stSlider>div>div>div {
        background: linear-gradient(145deg, #ff6b00, #ff8533);
    }
    .stMarkdown {
        color: #ffffff;
    }
    .stDataFrame {
        background: linear-gradient(145deg, #2d2d2d, #1a1a1a);
        border-radius: 15px;
        padding: 20px;
        box-shadow: 5px 5px 10px rgba(0,0,0,0.3),
                   -5px -5px 10px rgba(255,255,255,0.1);
    }
    .error-message {
        color: #ff4444;
        background: rgba(255,68,68,0.1);
        padding: 10px;
        border-radius: 10px;
        margin: 10px 0;
    }
</style>
""", unsafe_allow_html=True)

# Load environment variables
load_dotenv()

# Initialize Gate.io exchange
try:
    exchange = ccxt.gateio({
        'apiKey': st.secrets["GATE_API_KEY"],
        'secret': st.secrets["GATE_API_SECRET"],
        'enableRateLimit': True,
        'options': {
            'defaultType': 'swap',
            'defaultSettle': 'usdt',
        },
        'urls': {
            'api': {
                'rest': st.secrets["GATE_API_BASE"]
            }
        }
    })
    
    # Test API connection
    exchange.load_markets()
    st.success("✅ Successfully connected to Gate.io API")
except Exception as e:
    st.error(f"❌ Failed to connect to Gate.io API: {str(e)}")
    st.stop()

st.title("Gate.io Futures Live Data")

# Position Control Parameters
col1, col2, col3 = st.columns(3)

with col1:
    min_spread = st.slider("Minimum Spread %", 0.3, 5.0, 0.5, 0.1)
    min_volume = st.slider("Minimum 24h Volume (USDT)", 100000, 1000000, 200000, 10000)
    
with col2:
    max_positions = st.slider("Max Active Positions", 1, 10, 3, 1)
    dca_multiplier = st.slider("DCA Size Multiplier", 1.0, 5.0, 2.0, 0.5)
    
with col3:
    hedge_ratio = st.slider("Hedge Ratio", 0.5, 2.0, 1.0, 0.1)
    leverage = st.slider("Leverage", 1, 10, 3, 1)

def fetch_market_data():
    try:
        markets = exchange.load_markets()
        usdt_pairs = [symbol for symbol in markets.keys() 
                     if "/USDT" in symbol and markets[symbol].get("type") == "swap"]
        
        data = []
        for symbol in usdt_pairs:
            try:
                ticker = exchange.fetch_ticker(symbol)
                price = ticker['last']
                volume_24h = ticker['quoteVolume']
                
                orderbook = exchange.fetch_order_book(symbol)
                if orderbook['asks'] and orderbook['bids']:
                    spread = orderbook['asks'][0][0] - orderbook['bids'][0][0]
                    spread_pct = (spread / price) * 100
                    
                    # Calculate liquidity metrics
                    bid_depth = sum(bid[1] for bid in orderbook['bids'][:5])
                    ask_depth = sum(ask[1] for ask in orderbook['asks'][:5])
                    total_depth = bid_depth + ask_depth
                    
                    # Calculate volatility
                    ohlcv = exchange.fetch_ohlcv(symbol, timeframe='1h', limit=24)
                    if len(ohlcv) > 0:
                        prices = [x[4] for x in ohlcv]  # Close prices
                        volatility = np.std(prices) / np.mean(prices) * 100
                    else:
                        volatility = 0
                    
                    # Calculate market making score (0-100)
                    spread_score = max(0, 100 - (spread_pct * 20))  # Lower spread = higher score
                    volume_score = min(100, volume_24h / min_volume * 100)  # Higher volume = higher score
                    depth_score = min(100, total_depth / 100)  # Higher depth = higher score
                    volatility_score = max(0, 100 - (volatility * 10))  # Lower volatility = higher score
                    
                    mm_score = (spread_score + volume_score + depth_score + volatility_score) / 4
                    
                    if volume_24h >= min_volume and spread_pct >= min_spread:
                        data.append({
                            'Symbol': symbol,
                            'Price': price,
                            'Spread %': spread_pct,
                            '24h Volume (USDT)': volume_24h,
                            'Total Depth': total_depth,
                            'Volatility %': volatility,
                            'MM Score': mm_score
                        })
            except Exception as e:
                st.error(f"Error fetching data for {symbol}: {str(e)}")
                continue
        
        if not data:
            st.warning("No market data available matching the criteria")
            return None
            
        df = pd.DataFrame(data)
        df = df.dropna()
        return df
        
    except Exception as e:
        st.error(f"Error fetching market data: {str(e)}")
        return None

# Create containers for tables
spread_container = st.empty()
position_container = st.empty()

while True:
    try:
        df = fetch_market_data()
        
        if df is not None and not df.empty:
            # Sort by market making score (highest first)
            df_sorted = df.sort_values(by='MM Score', ascending=False)
            
            # Update spread data
            with spread_container.container():
                st.markdown("### 📈 Best Market Making Opportunities")
                st.dataframe(
                    df_sorted[['Symbol', 'Price', 'Spread %', '24h Volume (USDT)', 'MM Score']].head(33),
                    use_container_width=True
                )
            
            # Update position data
            with position_container.container():
                st.markdown("### 💰 Active Positions")
                st.dataframe(
                    df_sorted[['Symbol', 'Price', 'Spread %', 'Total Depth', 'Volatility %']].head(max_positions),
                    use_container_width=True
                )
        
        time.sleep(3)  # Update every 3 seconds
        
    except Exception as e:
        st.error(f"Error in main loop: {str(e)}")
        time.sleep(3)
        continue