Essential concepts for quant trading, research, and interviews — explained clearly.
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Concepts
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21 concepts
Quantitative finance applies mathematical models, statistical methods, and computational tools to financial markets. It powers everything from derivatives pricing to algorithmic trading.
A quant trader uses mathematical models and algorithms to identify and execute trading opportunities in financial markets, combining quantitative skills with real-time decision-making.
The three main quant career paths — researcher, trader, and developer — require different skills and offer different day-to-day experiences. Understanding the differences is crucial for career planning.
A comprehensive guide to the top quantitative trading firms and hedge funds in 2026, covering culture, compensation, hiring processes, and what makes each firm unique.
A comprehensive, actionable guide to preparing for quantitative finance interviews — from understanding the process to building a 4-8 week study plan that covers math, coding, and behavioral prep.
Market making is the practice of continuously quoting buy and sell prices for a financial instrument, profiting from the bid-ask spread while providing liquidity to other market participants.
Proprietary trading (prop trading) is when a firm trades financial instruments with its own capital rather than managing client money, allowing it to keep all profits from successful strategies.
The bid-ask spread is the difference between the highest price a buyer is willing to pay (bid) and the lowest price a seller is willing to accept (ask), representing the cost of immediacy in financial markets.
Backtesting is the process of testing a trading strategy against historical market data to assess how it would have performed, helping quants evaluate strategies before deploying real capital.
The Sharpe ratio measures risk-adjusted return by dividing a portfolio's excess return over the risk-free rate by its standard deviation, making it the gold standard for comparing strategy performance.
Expected value is the probability-weighted average of all possible outcomes of a random variable, forming the mathematical foundation for every rational trading and betting decision.
Bayes' theorem provides a mathematical framework for updating the probability of a hypothesis as new evidence becomes available, making it central to both quant interviews and trading decision-making.
Alpha represents the excess return a portfolio generates above its benchmark (a measure of skill), while beta measures the portfolio's sensitivity to market movements (systematic risk exposure).
The Central Limit Theorem states that the sum (or average) of a large number of independent random variables tends toward a normal distribution, regardless of the original distribution shape.
Conditional probability is the probability of an event occurring given that another event has already occurred, forming the basis for Bayesian reasoning and many quant interview questions.
The normal (Gaussian) distribution is the bell-shaped probability distribution that appears throughout statistics, finance, and natural science, characterized by its mean and standard deviation.
The Law of Large Numbers states that as the number of trials increases, the sample average converges to the expected value — the mathematical justification for why systematic trading works.
Maximum drawdown measures the largest peak-to-trough decline in portfolio value, representing the worst-case loss a strategy has experienced and a key metric for evaluating downside risk.
Random walk theory suggests that stock price changes are independent and identically distributed, meaning past prices cannot predict future movements — a foundational concept in financial economics.
The Efficient Market Hypothesis (EMH) states that asset prices fully reflect all available information, making it impossible to consistently achieve excess returns through trading — a theory that quant firms both challenge and exploit.
Algorithmic trading uses computer programs to execute trading strategies automatically based on predefined rules, enabling faster execution, reduced costs, and the ability to process vast amounts of data.
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