# --- Required Imports and Dummy Functions for a Runnable Example ---
import time
import random

# Dummy database of positions. In production, this comes from db.py
POSITIONS_DB = [
    {
        "id": 1, "market_id": "market-1", "strategy": "theta_sniper",
        "question": "Will Pakistan win the next T20 against India?",
        "entry_price": 60.0, "shares": 100, "status": "open",
        "initial_value_pkr": 6000,
        "thesis": "Pakistan's bowling lineup is strong, and Indian batting has shown weakness."
    },
    {
        "id": 2, "market_id": "market-2", "strategy": "vol_harvester",
        "question": "Will the KSE-100 Index close above 80,000 points by end of month?",
        "entry_price": 45.0, "shares": 200, "status": "open",
        "initial_value_pkr": 9000,
        "thesis": "Positive economic indicators suggest a bullish trend for the short term."
    },
    {
        "id": 3, "market_id": "market-3", "strategy": "theta_sniper",
        "question": "Will oil prices be above $90 by Friday?",
        "entry_price": 70.0, "shares": 50, "status": "closed", # This should be ignored
        "initial_value_pkr": 3500,
        "thesis": "OPEC meeting is likely to result in production cuts."
    }
]

# Dummy market data fetcher. In production, this calls an API.
MARKET_PRICES = {
    "market-1": 52.0,  # Price has dropped -> potential stop loss
    "market-2": 51.0,  # Price has increased -> potential take profit
}

def fetch_market_by_id(market_id):
    """Simulates fetching market data."""
    if market_id in MARKET_PRICES:
        return {"id": market_id, "price": MARKET_PRICES[market_id]}
    return None

def get_market_price(market):
    """Simulates getting a clean price from market object."""
    return market['price']

def check_exit_triggers(pos, current_price):
    """
    Simulates checking for various exit triggers.
    In production, this is a complex function, possibly using ai/haiku.py for quick analysis.
    """
    triggers = []
    entry_price = pos['entry_price']
    profit_percent = ((current_price - entry_price) / entry_price) * 100

    # 1. Hard Stop-Loss
    HARD_STOP_LOSS_PERCENT = -15.0
    if profit_percent <= HARD_STOP_LOSS_PERCENT:
        triggers.append({
            "type": "hard_stop",
            "reason": f"Profit at {profit_percent:.1f}%, breached SL of {HARD_STOP_LOSS_PERCENT}%",
            "urgency": "critical"
        })

    # 2. AI-based trigger (simulated)
    # Let's pretend for the KSE market, some negative news came out
    if pos['market_id'] == 'market-2' and random.random() > 0.5:
         triggers.append({
            "type": "ai_alert",
            "reason": "Haiku detected negative sentiment in news about a new tax policy.",
            "urgency": "high"
        })

    return triggers

def sonnet_decide_exit(pos, current_price, trigger):
    """
    Simulates the advanced AI making a final decision.
    In production, this calls ai/sonnet.py via ai/gemini.py.
    """
    print(f'  [AI-SONNET] Analyzing "{pos["question"][:30]}..."')
    print(f'  [AI-SONNET] Entry: {pos["entry_price"]:.1f}, Current: {current_price:.1f}')
    print(f'  [AI-SONNET] Initial Thesis: {pos["thesis"]}')
    print(f'  [AI-SONNET] Trigger Reason: {trigger["reason"]}')
    # Simple logic for simulation: if the trigger is 'high' or 'critical', we decide to sell.
    if trigger['urgency'] in ('high', 'critical'):
        print(f'  [AI-SONNET] DECISION: Exit position. The new information invalidates our initial thesis.')
        return {"decision": "SELL", "confidence": 0.95}
    else:
        print(f'  [AI-SONNET] DECISION: Hold. The event is low impact.')
        return {"decision": "HOLD", "confidence": 0.80}

# --- The ACTUAL Lesson Code ---

def run_position_manager(positions):
    """Check all open positions for exit triggers."""
    print(f'\n[PM] Running Position Manager. Checking {len(positions)} total positions...')
    
    open_positions = [p for p in positions if p['status'] == 'open']
    print(f'[PM] Found {len(open_positions)} open positions to monitor.')
    
    for pos in open_positions:
        # Get fresh price
        print(f'  -> Checking Position ID {pos["id"]}: "{pos["question"][:40]}..."')
        market = fetch_market_by_id(pos['market_id'])
        # Automatic position monitoring ensures no trade is left unmanaged

Position managers are critical for 24/7 bot operations without human supervision.

---

## 📺 Recommended Videos & Resources
- **[Real-Time Position Monitoring](https://www.youtube.com/results?search_query=position+monitoring+system+trading+bot)** — Live tracking
  - Type: YouTube
  - Link description: Search "real-time position monitoring trading"
- **[Automated Alerts & Notifications](https://www.youtube.com/results?search_query=trading+bot+alerts+notifications+discord)** — Real-time notifications
  - Type: YouTube
  - Link description: Search "Discord/Slack alerts for trading bots"
- **[Heartbeat Monitoring](https://en.wikipedia.org/wiki/Heartbeat_(computing))** — System health checks
  - Type: Wikipedia
  - Link description: Learn monitoring patterns for robustness
- **[Trade Journal Systems](https://www.youtube.com/results?search_query=trade+journal+system+automated)** — Recording trades
  - Type: YouTube
  - Link description: Search "trade journaling automated systems"
- **[Health Monitoring for Always-On Systems](https://www.youtube.com/results?search_query=health+monitoring+always+on+systems)** — Keepalive checks
  - Type: YouTube
  - Link description: Search "health monitoring for always-on services"

---

## 🎯 Mini-Challenge
**5-Minute Practical Task:** Create a position monitor that: (1) Checks all open positions every 30 seconds, (2) Triggers take profit if P&L > 20%, (3) Triggers stop loss if P&L < -10%, (4) Logs all actions. Test with 5 sample positions and verify monitoring works correctly.

---

## 🖼️ Visual Reference

---