A fixed-rule bot and a machine-learning trading system can look identical in a broker account: both may open and close positions automatically. The difference lies in how the decision rule is constructed, validated and maintained.
Rule-based systems
A rule-based program uses explicit conditions such as “when the moving average crosses a threshold, check available margin and send an order.” A developer can inspect each condition and reproduce an order from the same data. The system can still be unprofitable, and a rigid rule may fail when market behavior changes.
Machine-learning systems
A trained statistical model may learn relationships from historical features such as returns, volatility or order-book data. Predictions can change when the model is retrained, which creates additional questions about training data, leakage, drift and validation. High training accuracy is not proof of future performance.
- Rule-based: verify code logic, input assumptions and broker execution.
- Machine-learning: add out-of-sample testing and model-change records.
- Both: include spread, slippage, financing and stop behavior in evaluation.
Two systems, same risk mathematics
Suppose either bot trades a notional $10,000 position with leverage. A 3% unfavorable move corresponds to about $300 before costs, irrespective of whether the signal came from a neural network or a three-line rule. The model label does not change account exposure.
How we classify products
Our comparison pages will display separate fields for signal method, automatic execution, broker connectivity, strategy testing and user controls. If a vendor uses “AI” without identifying a real component, we mark the claim as unverified instead of inventing technical sophistication.
Forex position size & risk calculator
Change assumptions yourself. This free calculator uses transparent arithmetic; it does not predict a strategy’s future results.
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