Every trader fears the bear market. But how many have actually studied one? I ran a full cycle analysis on SPY, from 1996 to 2026, nearly 30 years and 7,650 trading days. I classified every significant drawdown into bear markets, corrections, and small corrections. Then I measured the duration, the depth, and the recovery time for each one.
The previous post on spread trading kept things simple on purpose. One regression, one slope, one number to rule the whole strategy. Clean and clear. But that static hedge ratio? It's silently absorbing regime drift and calling it a signal. Time to fix it.
On April 1st, SeasonHunter flagged a seasonal pattern on Hasbro (HAS) with a 77.8% win rate across 45 years of data. I opened a Bull Put Spread with less than $500 of margin. Seventeen days later, I closed it with $162 in profit. That's a 32% return on deployed capital, while the stock itself moved only 5.3%. In this article I walk through the complete trade lifecycle: the SeasonHunter signal, the spread structure, the exit rule I use when premium decays faster than time passes, and why capital efficiency is the real argument for options over equity in seasonal strategies.
How 56 years of historical data reveal McDonald's most predictable pattern, and why March 22 to May 6 might be the best seasonal window.
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.
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.