The term "algorithmic trading" (also: algo trading) refers to the automated or semi-automated trading of financial instruments (e.g., equities, bonds, FX, derivatives) where predefined rules and quantitative models control the selection, timing, and execution of orders. The goal is to implement trading decisions systematically, reproducibly, and often at very high speed—for example to reduce costs, improve execution quality, or enforce specific risk and portfolio rules.
Strategy Definition & Rules Engine: Modeling trading rules (e.g., signal logic, entry/exit, position sizing) including parameterization and version control.
Market Data Connectivity: Integrating real-time and historical price data (Level 1/Level 2, tick data) including cleansing and normalization.
Signal Generation: Deriving trade signals from indicators, statistical models, or events (e.g., volatility, trend, news triggers).
Order & Execution Management (OMS/EMS): Creating, routing, and executing orders across venues/brokers including status tracking, partial fills, and cancellations.
Execution Algorithms: Supporting common execution logics (e.g., VWAP, TWAP, iceberg, smart order routing) to reduce market impact and transaction costs.
Risk Management & Limits: Pre-trade checks (e.g., max position size, exposure, loss limits), stop mechanisms, kill switch, and compliance rules.
Backtesting & Simulation: Historical simulation of strategies including assumptions for slippage, fees, latency, and liquidity.
Optimization & Parameter Tuning: Systematic parameter variation (e.g., walk-forward analysis) and robustness/overfitting assessment.
Monitoring & Alerting: Real-time monitoring of strategy performance, executions, latencies, and system health including notifications.
Reporting & Audit Trail: Traceable logging of signals, decisions, and order events for audit, compliance, and performance analysis.
Interfaces & Integration: Connectivity to broker APIs, trading platforms, portfolio management, data feeds, as well as permission and role models.
An asset manager uses a VWAP algorithm to execute a large equity order throughout the day in a market-neutral manner.
A trading system runs a trend-following strategy that automatically places buy/sell orders when moving averages cross.
A market-making algorithm continuously quotes bid/ask prices and dynamically adjusts spreads based on volatility and order book conditions.
An arbitrage approach detects price discrepancies across two venues and executes offsetting trades almost simultaneously.
A risk module automatically stops trading (kill switch) when a predefined daily loss limit is exceeded.