Editing Algorithmic Trading Systems (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Algorithmic trading''' — Using computer programs to automatically execute trades based on pre-defined or learned strategies.
* '''High-frequency trading (HFT)''' — Trading strategies operating on millisecond to microsecond timescales; exploits latency advantages.
* '''Quantitative trading (quant)''' — Trading based on statistical and mathematical models; often uses ML to identify signals.
* '''Alpha''' — Excess return above a benchmark; the goal of active trading strategies.
* '''Factor model''' — A statistical model expressing asset returns as linear combinations of factors (momentum, value, quality); ML discovers new factors.
* '''Momentum''' — Assets that have risen recently tend to continue rising (short-term); a robust empirical factor.
* '''Mean reversion''' — Assets that have deviated from their mean tend to return to it; the basis of pairs trading.
* '''Order book''' — The record of all outstanding buy and sell orders for an asset; HFT exploits order book dynamics.
* '''Market microstructure''' — The mechanics of how trades occur: order types, bid-ask spread, market impact.
* '''Sharpe ratio''' — (Return - Risk-free rate) / Standard deviation; the key risk-adjusted return metric.
* '''Drawdown''' — Peak-to-trough decline in portfolio value; a key risk measure for trading strategies.
* '''Regime detection''' — Identifying different market states (bull, bear, volatile, ranging) to apply appropriate strategies.
* '''Reinforcement learning (trading)''' — Training agents to optimize trading decisions through interaction with market simulations.
* '''Alternative data''' — Non-traditional data sources: satellite imagery, credit card transactions, web scraping, social media sentiment.
* '''Slippage''' — The difference between expected and actual execution price; a key cost for any trading strategy.
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