The Put-Call parity is a mathematical law that connects every call and put option in the market. Break it, and the arbitrageurs eat you alive. Understand it, and you understand how options pricing actually works.

Selling a cash-secured put is one of the cleanest income strategies in options trading, but it has its own risks, and it must be done right. In this post, we break down the three pillars that make it work: proper cash backing, quality stock selection, and a neutral-to-bullish market outlook. With a concrete trade example and Python code to scan for candidates.

In my previous series of blog posts on (Pin Risk and Max Pain), we explored how massive open interest at specific strikes creates gravitational forces that pull stock prices toward those levels near expiration. We saw how dealer gamma hedging — the mechanical necessity of market makers rebalancing billions in exposure — creates observable pinning behavior every Friday. This post is about implementation — the practical rules, decision trees, and position management strategies you need to trade with pin risk rather than against it.

Max Pain tells us where the equilibrium point sits. But what's the actual mechanism that pulls the price toward it? Why do stocks repeatedly close within pennies of high open interest strikes, especially near expiration? The answer is pin risk — the gravitational force created by dealer gamma hedging.

It's Friday late evening, and I am trading options. The market closes in a few hours, and I am monitoring my positions before the options expiration. I am watching SPY trade at $601.50, and I notice something strange: massive open interest sits at the $600 strike. Over the next hour, despite no obvious news, SPY drifts down to close at $600.08. On Monday, I see the same pattern repeat — the stock closed within pennies of a high open interest strike. Is this a coincidence or is there an explaination about this behaviour?

Before any option trade, I spend a lot of time analyzing the market, identifying a perfect setup, calculating my edge, and then I enter the trade. My thesis plays out exactly as expected. The stock moves in my favor, and I feel right...But I lose money. How? Liquidity.

I often find that a simple physics analogy makes trading concepts easier to grasp, particularly when explaining the difference between what’s moving and what’s already there, the flux and the state. The flux measures flow through a surface, like current through a wire. The state describes what exists at a point in time, like charge accumulated in a capacitor. Options markets have a similar duality: volume measures the flow of contracts trading today, while open interest describes the total number of contracts that are still open and exist in the market.

Before earnings, product launches, or Fed announcements, traders face a critical question: How much will this stock actually move? The expected move answers this by quantifying the price range a stock is likely to stay within over a given time period — typically until options expiration. Unlike historical volatility, which looks backward, the expected move is forward-looking and derived from implied volatility priced into the options market.

Understanding options data is essential if you want to move from theory to actual, structured trading strategies. Whether you’re tracking implied volatility, evaluating premiums, or building spreads, the first step is always the same: fetching the option chain.