Market Making
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.
Essential concepts for quant trading, research, and interviews — explained clearly.
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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 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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