from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
import ccxt
import os
import pandas as pd
from dotenv import load_dotenv

load_dotenv()

app = FastAPI()

# CORS (optional)
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

exchange = ccxt.gateio({
    'apiKey': os.getenv("GATE_API_KEY"),
    'secret': os.getenv("GATE_API_SECRET"),
    'enableRateLimit': True,
    'options': {'defaultType': 'swap'}
})

@app.get("/api/data")
def get_data():
    try:
        markets = exchange.load_markets()
        usdt_pairs = [s for s in markets if "/USDT" in s and markets[s].get("type") == "swap"]
        results = []

        for symbol in usdt_pairs:
            try:
                ticker = exchange.fetch_ticker(symbol)
                price = ticker['last']
                volume = 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
                    bid_depth = sum(b[1] for b in orderbook['bids'][:5])
                    ask_depth = sum(a[1] for a in orderbook['asks'][:5])
                    depth = bid_depth + ask_depth
                    ohlcv = exchange.fetch_ohlcv(symbol, '1h', limit=24)
                    closes = [x[4] for x in ohlcv]
                    volatility = (pd.Series(closes).std() / pd.Series(closes).mean()) * 100

                    score = (
                        max(0, 100 - (spread_pct * 20)) +
                        min(100, volume / 200000 * 100) +
                        min(100, depth / 100) +
                        max(0, 100 - (volatility * 10))
                    ) / 4

                    results.append({
                        "symbol": symbol,
                        "price": price,
                        "spread_pct": spread_pct,
                        "volume_24h": volume,
                        "depth": depth,
                        "volatility": volatility,
                        "mm_score": round(score, 2)
                    })
            except:
                continue

        top_symbols = sorted(results, key=lambda x: x['mm_score'], reverse=True)[:10]
        return {"top_symbols": top_symbols}

    except Exception as e:
        return {"error": str(e)}