Quant

Quantitative trading is not just about building strategies. Picking the wrong market kills more quant projects than picking the wrong signal. A mean reversion strategy that works on US equities might blow up on crypto because the volatility is 3x higher. A trend following system profitable on commodity futures might sit idle for a year on EUR/USD because major currency pairs barely trend. The market itself determines what strategies survive: its rules, data quality, leverage structure, and trading hours all matter. This article breaks down the five major markets from a quant trader’s perspective. ...

CTA (Commodity Trading Advisor) is a broad category of quantitative strategies that trade futures. Despite the name suggesting commodities only, CTA strategies cover equity index futures, treasury futures, and FX futures as well. The space splits into two camps: trend following (go long when prices rise, short when they fall) and statistical arbitrage (find mispricings between related instruments and bet on convergence). This article covers both, with Python code for each. ...

The core logic of pairs trading is straightforward: find two stocks whose prices “should move together,” and when they diverge from their historical relationship, bet on convergence. Go long the relatively cheap one, short the relatively expensive one, close when the spread reverts. You are not betting on market direction. You are betting on the relative pricing between two assets returning to normal. ...

The same strategy at 10% position size and 50% position size can mean the difference between steady compounding and a blown account. Stock selection, timing, and factor design answer “what to buy.” Position sizing answers “how much.” The Kelly Criterion is the mathematical optimum for that question. ...

Adjust parameters, watch Greeks change in real time. Based on the Black-Scholes model, this calculator covers first-order Greeks (Delta, Gamma, Theta, Vega, Rho) and second-order Greeks (Vanna, Charm, Volga). Drag any slider and both the numbers and the curve update instantly. For formula derivations, see the Option Greeks Guide. For second-order Greeks, see Vanna, Charm, Volga Explained. For trading applications, see Greeks in Practice. ...

This is the final article in the Alpha 101 series. The liquidity and composite group contains 23 factors with the most complex formulas of all five categories: the deepest nesting, the highest usage of decay_linear and IndNeutralize, and the most elaborate conditional logic. But complexity doesn’t mean obscurity. At their core, these factors tell the same story: follow the smart money. The overview established the classification framework. The previous three articles covered price-volume divergence, momentum and reversal, and volatility and intraday structure. After breaking down 4 classic factors here, we’ll discuss how to move from “single-factor research” to “factor portfolio construction.” ...

Alpha 101 contains 11 volatility factors and 12 intraday structure factors. They share the same raw inputs (open, high, low, close, vwap) and a similar economic intuition: extracting signals from price “shape” rather than price “direction.” The overview introduced the framework. This article picks 5 classic factors for detailed breakdowns. ...

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. ...