Wouldn’t it be great to make entry decisions based on your probability of success? You can use this straightforward decision recipe to evaluate opportunity probabilities for any position type inside a scanner automation.

The recipe uses the Black-Scholes pricing model and pulls live market data to evaluate if the position’s probability of profit is more than or less than a defined value.

For example, in this tutorial, the put credit spread’s short option has a 0.30 delta and a 78% chance of profit. The bot is intelligent enough to factor in the premium received, break-even point, and current market conditions that affect extrinsic value, such as implied volatility.

If your probability of success threshold is met, the automation will continue down the “Yes” path, and you can add an open position action. The bot even knows to reference the position used in the decision recipe to save you the trouble of re-entering all the information.

Before you add the automation to your portfolio, it may be proactive to test the bot. Testing the automation gives you immediate feedback for the decision recipe based on current market conditions and allows you to tweak variable fields until you get pricing for your desired probability of success.