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.
In quantitative trading, like in physics, every model starts with data — and often, that means historical prices. But not all price data is created equal. One of the most overlooked — and misunderstood — aspects of market data is the distinction between adjusted and unadjusted stock prices. It might seem like a small technical detail, but it’s often the difference between a strategy that looks profitable —and one that truly is making money. In this post, I’ll unpack why that distinction matters.
When I first started backtesting strategies, I thought one long backtest was enough. It wasn’t. Markets change, conditions shift, and what worked in one year might fail in the next. That realization led me to walk-forward analysis — a systematic way to simulate how a strategy learns and adapts over time. In this post, I’ll show you how to generate the date sequences that make this method possible using Python.
Like many traders, I started by scraping Wikipedia for S&P 500 tickers. It worked… until it didn’t. In this post, I’ll share a cleaner, more professional and repeatable approach: using iShares ETF data and Python to build a reliable, extensible list of stocks to start your initial analysis.