# Required libraries - yeh Python mein built-in aati hain
import csv
import random
from datetime import datetime, timedelta

# ==============================================================================
# NOTE: Yeh neeche walay functions humne pichle lessons (1.1, 1.2, 1.3) mein
#       banaye thay. Yahan hum inko sirf mock kar rahe hain taake yeh script
#       standalone chal sakay. Asal bot mein yeh real API calls hongi.
# ==============================================================================

def fetch_active_markets(limit=100):
    """
    MOCK FUNCTION: Simulates fetching active markets from Polymarket.
    Asal mein, yeh Polymarket API ko call karega.
    """
    print(f"Fetching {limit} markets from Polymarket (simulation)...")
    # Kuch sample markets banatay hain, bilkul real data jaisi
    sample_markets = [
        {'id': '0x001', 'question': 'Will Pakistan win the next T20 match against India?', 'volume24hr': 25000, 'creation_date': datetime.now() - timedelta(days=2)},
        {'id': '0x002', 'question': 'Will the KSE-100 index close above 75,000 points by the end of the week?', 'volume24hr': 150000, 'creation_date': datetime.now() - timedelta(days=1)},
        {'id': '0x003', 'question': 'Will the price of Bitcoin be over $70,000 on July 30, 2024?', 'volume24hr': 500000, 'creation_date': datetime.now() - timedelta(days=10)},
        {'id': '0x004', 'question': 'Will the next government complete its 5-year term?', 'volume24hr': 5000, 'creation_date': datetime.now() - timedelta(days=30)},
        {'id': '0x005', 'question': 'Will Babar Azam score a 50 in the next PSL match?', 'volume24hr': 8000, 'creation_date': datetime.now() - timedelta(hours=12)},
        {'id': '0x006', 'question': 'A market with very low liquidity and almost no chance', 'volume24hr': 10, 'creation_date': datetime.now() - timedelta(days=5)},
    ]
    # Thori randomness daal detay hain
    return random.sample(sample_markets, len(sample_markets))

def score_market(market):
    """
    MOCK FUNCTION: Simulates scoring a market based on our strategy.
    Asal bot mein, yahan AI models (Gemini/Haiku) aur custom logic chalegi.
    """
    score = 0
    reasons = []
    tradeable = True

    # Rule 1: Volume check - Kam se kam $1000 volume ho
    if market.get('volume24hr', 0) < 1000:
        tradeable = False
        reasons.append("Low Volume")
    else:
        score += 30  # Acha volume hai, score barhao
        reasons.append("Good Volume")

    # Rule 2: Market age - Bohat purani market na ho
    if market['creation_date'] < datetime.now() - timedelta(days=15):
        score -= 20 # Purani market, less interesting
        reasons.append("Old Market")
    else:
        score += 20 # Fresh market hai
    
    # Rule 3: Keyword check (simple example)
    keywords = ['Pakistan', 'KSE-100', 'Bitcoin', 'PSL']
    if any(keyword.lower() in market['question'].lower() for keyword in keywords):
        score += 40
        reasons.append("Relevant Keywords")

    # Final random adjustment to simulate complex scoring
    score += random.randint(-10, 10)
    
    return {
        'score': max(0, min(100, score)), # Score ko 0-100 ke beech rakho
        'reasons': reasons,
        'tradeable': tradeable
    }

# ==============================================================================
# AAJ KA ASLI CODE - The Dashboard Builder
# ==============================================================================

def build_dashboard():
    """
    Yeh function saaray tukron ko jorta hai: Fetch -> Score -> Sort -> Save
    """
    print("Dashboard generation started...")
    # Step 1: Data fetch karo
    markets = fetch_active_markets(100)
    
    # Step 2: Har market ko score karo aur ek nayi list mein daalo
    scored = []
    for m in markets:
        result = score_market(m)
        # Sirf "tradeable" markets ko consider karo
        if result['tradeable']:
            scored.append({
                'question': m['question'][:80], # Question ko thora chota karlo
                'score': result['score'],
                'volume': m.get('volume24hr', 0),
                'reasons': ' | '.join(result['reasons']) # Reasons ko ek string banalo
            })
    
    # Step 3: Score ke hisaab se sort karo, sabse upar sabse high score
    scored.sort(key=lambda x: x['score'], reverse=True)
    
    # Step 4: Top 20 results ko CSV file mein save kardo
    filename = f"market_dashboard_{datetime.now().strftime('%Y-%m-%d')}.csv"
    with open(filename, 'w', newline='', encoding='utf-8') as f:
        # Fieldnames define kartay hain CSV ke columns
        fieldnames = ['score', 'question', 'volume', 'reasons']
        writer = csv.DictWriter(f, fieldnames=fieldnames)
        
        writer.writeheader() # Column headings likho
        writer.writerows(scored[:20]) # Top 20 rows likh do
    
    print(f"Dashboard ready! {len(scored)} tradeable markets found. Results saved to {filename}")
    return scored

# Script ko run karne ke liye
if __name__ == "__main__":
    top_markets = build_dashboard()
    print("\n--- Top 5 Opportunities Today ---")
    for i, market in enumerate(top_markets[:5]):
        print(f"{i+1}. Score: {market['score']} | Vol: ${market['volume']} | {market['question']}")

📊 Market Dashboard Pipeline
┌────────────────────────────────┐
│ Step 1: Fetch Markets           │
│ (100 from Polymarket)           │
└────────────┬───────────────────┘
             │
             ▼
┌────────────────────────────────┐
│ Step 2: Score Each Market       │
│ (Volume, Age, Keywords)         │
└────────────┬───────────────────┘
             │
             ▼
┌────────────────────────────────┐
│ Step 3: Filter Tradeable        │
│ (Only score >= threshold)       │
└────────────┬───────────────────┘
             │
             ▼
┌────────────────────────────────┐
│ Step 4: Sort by Score           │
│ (Highest score first)           │
└────────────┬───────────────────┘
             │
             ▼
┌────────────────────────────────┐
│ Step 5: Save to CSV             │
│ (market_dashboard_YYYY-MM-DD)   │
└────────────────────────────────┘

markets = fetch_active_markets(100)