Quant

Momentum and reversal sound like opposites: momentum says “what’s rising keeps rising,” reversal says “what’s risen too much will fall.” Alpha 101 has 23 factors in this category, and their sophistication lies in not blindly picking a side. Instead, they use different conditions to judge whether the current regime favors momentum or mean-reversion. The overview introduced the broad framework. This article picks 5 classic factors and breaks down the conditional logic embedded in their formulas. ...

Price-volume divergence is the largest category in Alpha 101, with 32 factors. As covered in the overview, the core thesis fits in one sentence: price and volume should move together, and when they don’t, there’s a trading opportunity. This article picks 5 classic factors from the group, breaks down each formula layer by layer, and examines how WorldQuant turned the ancient technical analysis concept of “volume confirms price” into computable signals. ...

In 2016, Zura Kakushadze from WorldQuant published a six-page paper titled “101 Formulaic Alphas.” The paper did something unusual: it listed the exact formulas for 101 quantitative factors, 80 of which were running in WorldQuant’s production environment. These factors have holding periods of 0.6 to 6.4 days and an average pairwise correlation of just 15.9%. The paper gives formulas but zero explanation. Why does Alpha#3 look the way it does? What market phenomenon is it trying to capture? Not a word. That gap is what this series fills: we classify all 101 Alpha 101 factors by their economic logic, break down the formulas, and figure out what each factor is actually betting on. ...

Options trading has two profit engines: directional bets and volatility bets. Most retail traders only use the first one, buying calls to bet up and puts to bet down. But the other half of an option’s value comes from volatility trading: you do not need to know which way the underlying moves, only whether the magnitude of the move will exceed or fall short of market expectations. This article covers six core volatility trading strategies, split into long vol and short vol groups. Each strategy comes with a payoff diagram, use cases, and concrete numbers, building toward a practical decision framework: check IV Rank, pick a strategy. ...

The first two articles covered first-order Greeks and second-order Greeks, building up the math, the intuition, and the code. This article does one thing: use those numbers to make trading decisions. What Greeks profile does each strategy carry? How does Delta hedging actually work in discrete time? When does Gamma Scalping have positive expected value? How do market makers use Greeks for risk management? Numbers and code throughout. This is the final article in the Option Greeks series. ...

The previous article covered the five first-order Greeks: Delta, Gamma, Theta, Vega, and Rho. They handle hedging and risk management well, with one assumption: the first-order Greeks themselves are stable. They are not. The underlying moves, and Delta changes (that is Gamma). Volatility shifts, and Delta changes again, but first-order Greeks have no name for this effect. Second-order Greeks fill that gap: they quantify the instability of first-order Greeks themselves. This is part two of the Option Greeks series. It covers the three most important second-order Greeks (Vanna, Charm, Volga), plus their role in volatility surface modeling. Part three covers trading applications. ...

Options pricing is not about predicting direction. It is about quantifying multi-dimensional risk. The underlying price moves, time passes, volatility shifts, interest rates change, all simultaneously affecting an option’s value. Option Greeks extract the sensitivity of the option price to each of these variables, turning abstract risk into numbers you can hedge against. This is part one of a three-part series on option Greeks, covering the five first-order Greeks (Delta, Gamma, Theta, Vega, Rho) with their mathematical formulas, intuitive explanations, and Python implementations. Parts two and three will cover second-order Greeks and trading applications, respectively. ...

TA-Lib is the most widely used technical analysis library in quantitative trading. 158 functions covering technical indicators and candlestick pattern recognition, all backed by a C implementation. If you do any kind of systematic trading in Python, you’ll run into it sooner or later. This tutorial covers everything from installation to a working strategy: the two API styles and when to use each, parameter selection tips for core indicators, candlestick pattern recognition, Pandas/Polars integration, and how TA-Lib compares to pandas-ta. If you’ve already set up yfinance for data, TA-Lib is the next step in turning that data into trading signals. ...

The backtest shows 80% annualized returns with a 5% max drawdown. Three months after going live, the strategy is down 30%. This scenario plays out repeatedly in quant trading, and the root cause is almost always the same: the backtest itself was wrong. Not a code bug — systematic flaws in the assumptions, data, and statistical methods that made the strategy look profitable when it wasn’t. This article breaks down the most common backtesting pitfalls into four categories: data pitfalls, statistical pitfalls, execution pitfalls, and psychological pitfalls. Each comes with a wrong-vs-right Python code comparison, followed by a self-check checklist. ...

One of the core tasks in quantitative investing is mining alpha factors — finding signals that predict asset returns. The traditional approach relies on researchers manually constructing factor expressions, or using automated search methods like Genetic Programming (GP) to brute-force combinations in the operator space. The former depends on human experience and intuition — low efficiency but high interpretability. The latter is efficient but produces deeply nested operator expressions that are nearly impossible for researchers to interpret. AlphaGPT (paper) brings large language models into the factor mining pipeline, using an LLM as the factor “generator.” The follow-up work, AlphaGPT 2.0 (paper), further introduces a human-in-the-loop closed cycle. ...