Fast answer
AI crypto trading signal checks compare model inputs, trade logic, risk controls, delivery timing, and full result history.
Before trusting an AI signal, record data sources, market timeframe, model owner, prompt or rule summary, entry logic, stop placement, take-profit logic, position size, fees, slippage, and every losing signal.
An AI label does not make a signal predictive, audited, or safe.
AI signal checks
What to inspect in AI crypto trading signals.
Input data
List price data, order-flow data, social data, news data, on-chain data, and any missing-source warnings.
Decision logic
Ask whether the signal is rules-based, model-scored, prompt-generated, human-approved, or fully automated.
Risk controls
Require stop loss, invalidation level, position size, leverage cap, fee estimate, and no guaranteed-return language.
Result history
Preserve wins, losses, skipped calls, edited posts, timestamps, and open-position outcomes.
Source context
CFTC warns that AI-created algorithms and trade signal strategies can be used to promise unrealistic or guaranteed crypto returns.
CSR treats AI signal pages as evidence reviews, not prediction pages. The output should be traceable, risk-limited, and reviewable across both wins and losses.
Review standard
A reviewable AI signal explains what the model knew and what it did not know.
For CSR evidence review, AI crypto signal records should include data sources, model or prompt owner, decision summary, timestamps, risk rules, fees, slippage, open trades, losing trades, edited posts, and model-change notes.