import requests
import json

# Note: This is a conceptual URL. Real implementation uses GQL.
# Hamare 'scanner.py' mein aisi hi logic hai.
API_URL = "https://api.polymarket.com/markets" # Fictional endpoint for this example

def get_active_markets():
    """Polymarket se active markets ka data uthao."""
    try:
        # Headers zaroori hain to mimic a real browser
        headers = {
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
        }
        response = requests.get(API_URL, headers=headers)
        response.raise_for_status()  # Agar koi error ho (like 404, 500) to exception raise karega
        markets = response.json()
        print(f"Total {len(markets)} markets fetched.")
        return markets
    except requests.exceptions.RequestException as e:
        print(f"API se data fetch karne mein masla hua: {e}")
        return None

# Example usage (isko run karne ke liye aapko real API/data source chahiye hoga)
# For now, let's create a dummy market list.
dummy_markets = [
    {
        "id": "market-1",
        "question": "Will Pakistan win the next T20 against India?",
        "volume24hr": "55000.50",
        "liquidity": "150000.75",
        "outcomePrices": "[\"0.65\", \"0.35\"]" 
    },
    {
        "id": "market-2",
        "question": "Will a new political party be registered in Sindh by August?",
        "volume24hr": "1250.20",
        "liquidity": "3000.00",
        "outcomePrices": "[\"0.10\", \"0.90\"]"
    }
]

# Let's check the volume of the first dummy market
pak_vs_ind_market = dummy_markets[0]
volume = float(pak_vs_ind_market.get('volume24hr', 0))
print(f"Pakistan vs India market ka 24hr volume: ${volume:,.2f}")

sindh_politics_market = dummy_markets[1]
volume = float(sindh_politics_market.get('volume24hr', 0))
print(f"Sindh politics market ka 24hr volume: ${volume:,.2f}")

def score_market(market):
    """Market ko score karo — trade karna chahiye ya nahi?"""
    import json
    
    # Step 1: Data Safai - API se data string mein aa sakta hai
    prices = market.get('outcomePrices', '[0,0]')
    if isinstance(prices, str): 
        prices = json.loads(prices)
        
    yes_price = float(prices[0]) * 100 # Price ko cents mein convert karna
    volume = float(market.get('volume24hr', 0))
    liquidity = float(market.get('liquidity', 0))
    
    score = 0
    reasons = [] # Hum reasons bhi track karte hain for logging/debugging
    
    # Step 2: Volume Check - Kya is market se nikalna aasan hoga?
    # Minimum $3000/day for liquid exits. Isse kam mein risk hai.
    if volume >= 3000:
        score += 30
        reasons.append(f'Good volume: ${volume:,.0f}')
    else:
        # Score nahi milega, aur hum note kar lenge ke volume kam hai
        reasons.append(f'LOW volume: ${volume:,.0f} — EXIT mushkil hoga')
    
    # Step 3: Price Sweet Spot - Theta strategy ke liye best range
    # 60-95% is where theta works best. High probability, but still has premium.
    if 60 <= yes_price <= 95:
        score += 40 # Sab se zyada weightage isko diya hai
        reasons.append(f'Price sweet spot: {yes_price:.1f}c')
    elif yes_price > 95:
        reasons.append(f'Too expensive: {yes_price:.1f}c — 95c risk for 5c reward. Not worth it.')
    elif yes_price < 60:
         reasons.append(f'Too risky: {yes_price:.1f}c — Probability hamare favor mein nahi hai.')
    
    # Step 4: Liquidity Check - Market kitni "gehri" hai?
    # Kam se kam $10,000 ki liquidity honi chahiye taake hamari trade price ko zyada na hilaye.
    if liquidity >= 10000:
        score += 30
        reasons.append(f'Deep liquidity: ${liquidity:,.0f}')
    else:
        reasons.append(f'Thin liquidity: ${liquidity:,.0f} — Slippage ka khatra hai.')
    
    # Final Decision: Agar score 70 se upar hai, tabhi tradeable hai
    return {'score': score, 'reasons': reasons, 'tradeable': score >= 70}

# Chalo isko dummy data pe test karte hain
print("--- Testing Market 1 (Cricket) ---")
result1 = score_market(dummy_markets[0])
print(json.dumps(result1, indent=2))

print("\n--- Testing Market 2 (Politics) ---")
result2 = score_market(dummy_markets[1])
print(json.dumps(result2, indent=2))

📊 Market Selection Decision Tree
┌──────────────────────────────┐
│ Polymarket Feed (100s of     │
│ active markets)              │
└──────────────┬───────────────┘
               │
               ▼
    ┌──────────────────────┐
    │ Score Market Filter  │
    │ (Volume, Liquidity,  │
    │  Price Range)        │
    └──────────┬───────────┘
               │
        ┌──────┴──────┐
        │             │
        ▼             ▼
  ┌─────────┐   ┌──────────┐
  │ Score   │   │ Score    │
  │ >= 70   │   │ < 70     │
  │ TRADE   │   │ SKIP     │
  └─────────┘   └──────────┘