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.
Pairs trading exploits the principle of relative value. Like in physics, we sometimes do not care only about absolute values — we care about deviations from a reference state. In this post, we'll use Python to construct a spread strategy between two stocks.
Stock prices — and even their cumulative returns — often show long-term upward or downward trends that can mask underlying behavior. In this post, we’ll use Python to calculate the yearly cumulative returns of a stock, understand what this metric really means, and then detrend the return series to reveal its true fluctuations and relative performance over time.
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.
Beyond just seasonal market patterns, learn to trade them using options can give you more flexibility and defined risk.
End of year seasonality of the S&P500 ETF (SPY) is one of the most persistent pattern among the seasonal effects study. In this post, I'll take a systematic look at this pattern looking at the monthly profile.
Markets may look chaotic, but under the intrinsic noise and unpredictability, some patterns quietly repeat. These seasonal effects — recurring behaviors tied to the calendar — can create subtle yet measurable edges for quantitative traders.
When you’re running a backtest, every line of code depends on a silent assumption: "The data actually reflects what happened in the market". But that assumption can break down — not because of your model, but because of something much smaller and easier to overlook: price adjustments.