Automation alone isn't an algorithm
A lot of tools marketed as "algo trading" are really just automation — a script that clicks buy or sell based on a single indicator crossing a line. That's automation, not a trading system. A genuine algorithmic strategy is a complete, testable set of rules covering entries, exits, position sizing, and risk limits, all defined before a single live trade is placed.
The four components of a real system
Entry logic: a specific, repeatable condition for opening a position — not "the AI decided." Exit logic: equally specific rules for closing a position, whether at a profit target, a stop-loss, or a time-based exit. Position sizing: how much capital is allocated per trade relative to account equity, so no single loss can be catastrophic. Risk limits: hard ceilings on drawdown and exposure that are enforced in code, not left to discretion in a stressful moment.
Why backtesting and live verification both matter
A backtest shows how a rule set would have performed on historical data — useful, but incomplete on its own, since markets change and past data can be overfit. Live, verifiable performance across real accounts and multiple market regimes is what actually demonstrates a system holds up outside of a spreadsheet. Both pieces together — not one or the other — are what separate a durable system from a curve-fitted one.
Questions worth asking before trusting any automated system
- Can the exact entry and exit rules be explained in plain language?
- Is there a published, verifiable live track record — not just backtested figures?
- Are drawdown limits enforced automatically, or dependent on someone intervening?
- Does the system execute through your own regulated brokerage account, or does it require moving funds elsewhere?
Automated trading systems do not eliminate risk and may amplify losses if poorly designed. Understanding the mechanics behind a system is the first step before evaluating whether it fits your portfolio.