Part 10: Algorithmic Risk Management & Position Adjustments

In systematic option trading, entry is only 20% of the game. The remaining 80% is risk management and adjustments. When you sell options, the market will eventually test your short strikes. A disciplined, automated exit or adjustment protocol is what separates professional systems from retail blowups.

Let's look at how to implement dynamic risk controls in Python.

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1. Portfolio-Level Stop Loss

A portfolio-level stop-loss is your absolute fail-safe. If the market experiences a gap up or down, and the aggregate loss across all open legs exceeds a predefined limit (e.g., 1.5% of your capital, or ₹2,000 for a ₹1.5L account), the bot must exit all legs immediately.

Here is the Python logic:

def monitor_portfolio_risk(bot, active_positions, daily_max_loss=2000):
    total_unrealized_pnl = 0
    
    for pos in active_positions:
        quote = bot.get_quotes(exchange="NFO", token=pos["token"])
        ltp = float(quote["lp"])
        
        # Calculate PnL (Option selling)
        leg_pnl = (pos["entry_price"] - ltp) * pos["quantity"]
        total_unrealized_pnl += leg_pnl
        
    if total_unrealized_pnl <= -daily_max_loss:
        print(f"Portfolio Stop-Loss Triggered: -₹{abs(total_unrealized_pnl)}")
        exit_all_positions(bot, active_positions)
        send_telegram_alert(f"🚨 *Portfolio Stop-Loss Triggered!*\nTotal Loss: `-₹{abs(total_unrealized_pnl)}`")

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2. Dynamic Adjustments: Rolling the Untested Side

If the market trends in one direction, one side of your Iron Condor will gain value (challenged side) while the other side loses value (untested side). To balance our delta and collect additional premium to offset losses:

• If Nifty rises: The Put spread loses value. We buy back the short Put spread for a cheap price (e.g., ₹2) and sell a new, higher Put spread closer to the current spot price.
• This collects more premium and moves our overall break-even point higher on the upside.

def roll_untested_side(bot, active_positions, index_spot_price):
    # If Spot moves closer to our Short Call strike:
    # 1. Buy back active Put Spread (untested side)
    # 2. Sell a higher Put Spread to collect more premium
    
    untested_put_legs = [pos for pos in active_positions if pos["option_type"] == "PE"]
    
    # Exit old PE legs
    for leg in untested_put_legs:
        bot.place_order(buy_or_sell="B", product_type="M", exchange="NFO", ...)
        
    # Calculate new PE strikes (closer to spot)
    new_short_pe_strike = round(index_spot_price / 50) * 50 - 150
    new_long_pe_strike = new_short_pe_strike - 100
    
    # Enter new PE spread...
    print(f"Rolled Put spread higher to strike {new_short_pe_strike}")

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3. Converting Iron Condors to Iron Flies

Under heavy market trends where rolling the untested side is insufficient, you can convert your Iron Condor into an Iron Butterfly (Iron Fly):

• Move the short Put strike up until it matches the short Call strike (converting the range into a single peak).
• This maximizes the net credit received and decreases the maximum potential loss on the challenged side, though it narrows your overall profit range.

By coding these rules into your script, your bot can execute defensive adjustments during the day without requiring you to look at your screens during office hours.

In the next part, we will look at how to build active income streams using your coding skills to fund your trading accounts.

Proceed to Part 11: Active Income Accelerator: Monetizing Developer Skills →