Essential concepts for quant trading, research, and interviews — explained clearly.
40
Concepts
7
Categories
40 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.
Probability questions are the cornerstone of quant trading interviews. This guide covers common question types, worked examples with solutions, and a study strategy to prepare effectively.
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.
Brain teasers and logic puzzles are a staple of quant trading interviews. This guide covers why firms use them, common categories, worked examples with solutions, and practical solving strategies.
The Kelly criterion is a mathematical formula that determines the optimal fraction of capital to risk on a bet or trade, maximizing long-term geometric growth while managing the risk of ruin.
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.
High-frequency trading uses ultra-fast technology and algorithms to execute large numbers of trades in fractions of a second, profiting from tiny price discrepancies and market microstructure.
Statistical arbitrage (stat arb) uses quantitative models to identify and exploit temporary pricing inefficiencies between related securities, typically holding diversified portfolios of long and short positions.
Pairs trading is a market-neutral strategy that simultaneously goes long one security and short a correlated one, profiting when the price spread between them reverts to its historical mean.
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.
Mean reversion is the tendency of asset prices, returns, or other financial metrics to move back toward their long-term average after deviating significantly, forming the basis for many systematic trading strategies.
The Options Greeks (delta, gamma, theta, vega, rho) measure the sensitivity of an option's price to changes in underlying price, time, volatility, and interest rates.
The Black-Scholes model is a mathematical framework for pricing European-style options, providing closed-form formulas that revolutionized derivatives markets when introduced in 1973.
Implied volatility is the market's forecast of future price volatility, derived by reverse-engineering the Black-Scholes model from observed option prices.
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.
Value at Risk (VaR) estimates the maximum expected loss of a portfolio over a specified time period at a given confidence level, serving as a standard risk measure across the financial industry.
Monte Carlo simulation uses repeated random sampling to model the probability of different outcomes in complex systems, making it essential for derivatives pricing, risk analysis, and strategy evaluation.
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 volatility smile is the observed pattern where implied volatility varies across option strike prices, forming a U-shaped curve that contradicts the constant-volatility assumption of Black-Scholes.
Risk-neutral pricing is a framework that prices derivatives by assuming all investors are risk-neutral, allowing expected payoffs to be discounted at the risk-free rate regardless of actual risk preferences.
Put-call parity is a fundamental relationship linking the prices of European call and put options with the same strike price and expiration, ensuring no-arbitrage pricing in options markets.
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.
A Markov chain is a stochastic process where the probability of transitioning to the next state depends only on the current state, not on the history — the 'memoryless' property.
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.
Stochastic calculus extends classical calculus to handle random processes, providing the mathematical foundation for derivatives pricing models like Black-Scholes and modern quantitative finance.
Brownian motion (Wiener process) is the continuous-time random process that models the random component of asset price movements and is the foundation of the Black-Scholes model and stochastic calculus.
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.
Factor investing is a systematic investment approach that targets specific characteristics (factors) — such as value, momentum, size, and quality — believed to drive returns across asset classes.
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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