QNT What Is Quantitative Trading?

Trading went through three big shifts. Before the 1970s — Discretionary. Traders used gut instinct, chart patterns, and whatever information they could get. Decisions were subjective and inconsistent. 1970s–2000s — Quantitative Methods. Black-Scholes (1973), CAPM, and the first algorithmic execution strategies brought math into trading. Statistical arbitrage started at Morgan Stanley in the 1980s under Nunzio Tartaglia's group. 2000s–Now — ML and Big Data. Modern systems combine machine learning with massive datasets to find patterns no human could spot. Renaissance Technologies, Two Sigma, and DE Shaw manage hundreds of billions this way. Today's institutional systems typically use multi-layer ML pipelines: one layer finds candidate signals, another filters for timing, and a third manages exits. This separation of concerns — borrowed from software engineering — lets each piece be optimized for its specific job.

Edge is a statistical advantage. Over enough trades, it produces positive returns. That's it. The word to focus on is statistical. Any single trade can lose. What matters is the distribution across hundreds or thousands of trades. People get confused here. Edge doesn't mean winning every trade. It means the math favors you over time: • A 59% win rate means roughly 4 out of every 7 trades are profitable • Positive expected R means each unit of risk produces net positive return • Smart risk management amplifies this — cut losses quickly, let winners run • Adapting to market conditions keeps the edge from degrading The core idea is simple: a small edge, applied consistently over many trades with proper risk management, produces real returns. That's just the Law of Large Numbers applied to financial markets. Reference: Thorp, E.O. (2006). "The Kelly Criterion in Blackjack, Sports Betting, and the Stock Market."

There are three pieces that have to work together. Take away any one and the system breaks. 1. Signal Generation — When to trade. Use data and ML to find moments where the probability of a profitable trade crosses a meaningful threshold. Modern systems process dozens of features per bar — price action, momentum, volatility, microstructure data — to generate candidate signals. 2. Execution Management — How to trade. Get in at the right price with the right size, manage the trade through exit. This includes timing filters (which reject poorly-timed signals) and dynamic exit management based on what the market is doing right now. 3. Risk Management — How much to risk. Make sure no single loss — or streak of losses — can blow your account. Position sizing rules, drawdown-triggered scaling, and strict loss limits handle this. Here's the thing people miss: a great signal with bad risk management leads to ruin. Great risk management with no signal leads to slow bleeding. Both have to work together. Execution bridges the gap — it turns good signals into well-managed trades.