# Polymarket Oracle -> strategies/config.py (example file)

# Pehle, humein kuch dummy functions chahiye taake code run ho sake
def fetch_active_markets(limit=100):
    """Dummy function to simulate fetching markets from Polymarket API."""
    print(f"--- Fetching {limit} dummy markets ---")
    return [
        {'question': 'Will Babar Azam score a 50 in the next T20?', 'volume24hr': 65000, 'clob_price': 0.60},
        {'question': 'Will Pakistan win the next PSL season?', 'volume24hr': 120000, 'clob_price': 0.35},
        {'question': 'Will interest rates in Pakistan decrease by end of year?', 'volume24hr': 25000, 'clob_price': 0.90},
        {'question': 'Will Bitcoin cross $75,000 this month?', 'volume24hr': 80000, 'clob_price': 0.75},
    ]

def get_market_price(market):
    """Dummy function to get a market's price."""
    # In a real bot, this would be a complex calculation from the order book.
    # For now, we'll use a simplified price from the dummy data.
    return market.get('clob_price', 0) * 100 # Price is in cents (0-100)

# Yeh hai humara main strategies ka blueprint
STRATEGIES = {
    'flash_momentum': {
        'description': 'Targets high-volume (>$50k) markets priced 20-80%',
        'edge': 'Breaking news not yet priced in',
        'risk': 'Fast-moving markets can reverse quickly',
        'code_snippet': '''
def run_flash_momentum():
    markets = fetch_active_markets(200)
    # Filter: >$50k volume, 20-80% price range
    flash = [m for m in markets 
             if float(m.get('volume24hr', 0)) > 50000
             and 20 <= get_market_price(m) <= 80]
    # Gemini: identify breaking catalysts
    # Haiku: strict gatekeeper (confidence >= 0.75)
    # Sonnet: final BUY/SKIP
'''
    },
    'news_sniper': {
        'description': 'RSS feeds + Google News per market',
        'edge': 'Information from multiple news sources aggregated by AI',
        'risk': 'News can be priced in faster than bot processes'
    },
    'whale_tracker': {
        'description': 'Follows high-P&L Polymarket wallets',
        'edge': 'Smart money tends to be right',
        'risk': 'Wallet addresses can change, lag in detection'
    }
}

print("--- ADVANCED STRATEGIES OVERVIEW ---")
for name, s in STRATEGIES.items():
    print(f'\nSTRATEGY: {name.upper()}')
    print(f'  Description: {s["description"]}')
    print(f'  Edge: {s["edge"]}')
    print(f'  Risk: {s["risk"]}')

import time

# --- Dummy functions from before ---
def fetch_active_markets(limit=100):
    print(f"\n[{time.strftime('%H:%M:%S')}] Fetching {limit} dummy markets...")
    return [
        {'id': 1, 'question': 'Will Babar Azam score a 50 in the next T20?', 'volume24hr': 65000, 'clob_price': 0.60},
        {'id': 2, 'question': 'Will Pakistan win the next PSL season?', 'volume24hr': 120000, 'clob_price': 0.35},
        {'id': 3, 'question': 'Will interest rates in Pakistan decrease by end of year?', 'volume24hr': 25000, 'clob_price': 0.90},
        {'id': 4, 'question': 'Will Bitcoin cross $75,000 this month?', 'volume24hr': 80000, 'clob_price': 0.75},
        {'id': 5, 'question': 'Will a new AI model be announced by Google this week?', 'volume24hr': 51000, 'clob_price': 0.25},
    ]

def get_market_price(market):
    return market.get('clob_price', 0) * 100

# --- AI Simulation ---
# In the real bot, these are complex API calls to ai/gemini.py, etc.
def analyze_with_ai_models(market):
    print(f"  > AI analyzing: '{market['question']}'")
    # Simulate AI analysis
    if "Babar Azam" in market['question']:
        print("  > Gemini: Found news 'Babar hits 4 boundaries in first over'. Catalyst is strong.")
        print("  > Haiku: Confidence Score: 0.85 (>= 0.75). Approved.")
        print("  > Sonnet: Risk/Reward favorable. Decision: BUY")
        return "BUY"
    elif "AI model" in market['question']:
        print("  > Gemini: Found a rumor on Twitter, no official source. Catalyst is weak.")
        print("  > Haiku: Confidence Score: 0.50 (< 0.75). Rejected.")
        return "SKIP"
    else:
        print("  > Gemini: No significant recent news found.")
        return "SKIP"

def run_flash_momentum():
    print("--- Running Flash Momentum Strategy ---")
    markets = fetch_active_markets(200)
    
    # List comprehension to filter markets. Yeh Python ka ek bohot powerful feature hai.
    # Asaan lafzon mein: "Ek nayi list 'flash_markets'