What is algorithmic trading?
Algorithmic trading uses software to make or execute trading decisions according to explicit rules. An algorithm can decide when to enter or exit a position, how large the position should be, how quickly to trade, where to send an order, or some combination of those jobs.
The defining feature is that part of the trading process is encoded as a repeatable procedure. The procedure can be simple or complex, and it can operate in microseconds, once a day, or only a few times per year.
High-frequency trading is therefore one form of algorithmic trading, not a synonym for it.
For the broader end-to-end guide covering system architecture, strategy families, backtesting, execution, monitoring, and live deployment, see Algorithmic Trading: How Systematic Trading Strategies Work.
Signal algorithms and execution algorithms
The phrase algorithmic trading covers at least two distinct jobs.
A signal algorithm tries to decide what exposure to hold. It can turn prices, fundamentals, economic data, statistical relationships, or other inputs into a forecast, ranking, or trading condition.
An execution algorithm tries to complete an already-decided order efficiently. It may split a large order into smaller pieces, choose venues, or target an execution benchmark such as VWAP.
A signal is therefore not the same thing as an order. A strategy can identify an attractive position without yet deciding how large that position should be, what risk limits apply, or how the trade should be executed.
Automation does not create an edge
Software can make a trading process faster, more repeatable, and easier to monitor. It does not make the underlying idea profitable.
A weak signal implemented perfectly is still a weak signal. A promising historical result can also disappear after realistic transaction costs, financing, borrow, slippage, market impact, portfolio constraints, or changing market conditions are included.
That distinction is why algorithmic trading should be evaluated as both a research problem and an engineering problem.
The main parts of a production system
A complete system usually separates several responsibilities:
- data and state: the information the strategy is allowed to know at each decision time;
- signal generation: rules or models that convert inputs into a forecast, ranking, or condition;
- portfolio and risk rules: position sizing, leverage, concentration, liquidity, and exposure limits;
- order generation and execution: how target positions become executable orders; and
- monitoring and reconciliation: checking data freshness, orders, fills, broker positions, and failure states.
This separation matters because a good research signal can still fail in production if execution is unrealistic or operational controls are weak.
Algorithmic trading and backtesting
Historical simulation is commonly used to test whether a systematic strategy would have produced sensible historical behavior under stated assumptions.
A backtest can expose drawdowns, turnover, parameter sensitivity, and dependence on one favorable market regime. It can also be badly misleading if it uses future information, excludes securities that later disappeared, understates trading costs, or selects a winning rule after testing many alternatives.
See Backtesting: What Historical Simulations Can and Cannot Prove for the deeper methodology discussion.
What changes in live trading?
Live trading introduces operational uncertainty that a historical simulation can hide. Data can arrive late, orders can be rejected, broker connectivity can fail, a security can become hard to borrow, and actual fills can differ from modeled fills.
FINRA's algorithmic-trading guidance emphasizes testing, system validation, monitoring, controls, and the ability to disable malfunctioning systems. In practice, a production strategy needs to reconcile what the model thinks it owns with what the broker actually reports and make stale or inconsistent state visible quickly.
Related Grizzly Bulls research
- Algorithmic Trading: How Systematic Trading Strategies Work
- Backtesting: what historical simulations can and cannot prove
- Building an Algorithmic Trading Backtester with Node.js
- Overfitting in Algorithmic Trading: How Good Backtests Go Bad
- Grizzly Bulls algorithmic trading models and market-timing signals
Sources and further reading
- FINRA: Algorithmic Trading
- FINRA Regulatory Notice 15-09: Effective Supervision and Control Practices for Algorithmic Trading
- SEC: Responses to Frequently Asked Questions Concerning Risk Management Controls for Brokers or Dealers with Market Access
- CFA Institute: Backtesting & Simulation
Research Tools
These tools turn the concept into something you can inspect, calculate, or apply. Inputs are illustrative unless a module explicitly cites live or historical data.
Trading-cost drag calculator
Estimate how commissions, spread, slippage, and other per-dollar trading frictions can consume gross strategy returns.
This is a bookkeeping approximation, not an execution simulator. Real costs depend on order type, liquidity, market impact, borrow, financing, taxes, and how turnover is measured.
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