Algorithmic Trading
Algorithmic trading uses programmed rules to generate, route, or execute orders based on market data, portfolio rules, and risk controls.
Systematic trading pages covering backtesting, forward testing, quantitative rules, algorithmic execution, and model-driven signals.
Systematic testing and signal strategies use explicit rules, data, models, and execution processes instead of discretionary trade-by-trade judgment. The point is not that models remove risk. The point is that rules make the signal, assumptions, costs, and controls easier to test.
Use this section for pages such as Algorithmic Trading, Quantitative Trading, High-Frequency Trading, Backtesting, Forward Testing, and Mean Reversion.
| Topic | Practical question |
|---|---|
| Backtesting | Did the rule work historically after realistic costs and constraints? |
| Forward testing | Does the rule behave in live or paper conditions before capital is scaled? |
| Quantitative trading | What data, model, and portfolio rules define the signal? |
| Algorithmic trading | Which decisions or order instructions are automated? |
| High-frequency trading | Does speed, queue position, and market-data latency drive the edge? |
| Mean reversion | Is the strategy betting on a spread or price returning toward a reference level? |
A systematic strategy should be documented with the data source, model rule, timestamp convention, entry and exit logic, position-sizing rule, cost assumptions, risk limits, and post-trade review process. Without those items, the label is only a strategy description.
The common failure mode is overconfidence in clean historical results. Useful review asks whether the model includes survivorship bias, look-ahead bias, stale prices, market impact, liquidity, borrow constraints, taxes, outages, and the possibility that the signal stops working after more traders crowd into it.
SEC staff materials on algorithmic trading in U.S. capital markets and FINRA’s algorithmic trading guidance provide useful context on automated trading, supervision, testing, controls, and post-change review.
Choose a subsection first. Deeper term pages live inside each subsection, which keeps large topic hubs readable.
Algorithmic trading uses programmed rules to generate, route, or execute orders based on market data, portfolio rules, and risk controls.
Backtesting applies a trading or investment rule to historical data to evaluate hypothetical performance, risk, and implementation limits.
Forward testing runs a trading rule on current paper or limited live data to validate behavior, execution assumptions, and risk controls after a backtest.
High-frequency trading is a fast automated trading style that relies on market data, low-latency systems, and high message volumes.
A martingale strategy increases position size after losses in an attempt to recover with a later winning trade, creating rapidly escalating risk.
Mean reversion is the idea that a price, spread, return, or valuation measure may move back toward a reference level after an extreme deviation.
Quantitative trading uses data, statistics, models, and systematic rules to identify signals, size positions, and manage trading risk.
Simulation trading uses paper trades, demo accounts, or modeled fills to practice trading and test strategy workflows without committing full live capital.
A trading strategy is a documented rule set for entering, sizing, managing, exiting, testing, and reviewing trades under defined market conditions.
Virtual funds are simulated balances used in demo or paper trading accounts to practice order mechanics, risk controls, and strategy workflows.