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If a crypto-trading AI says “buy” but the bot does nothing, the useful question is not whether the signal looks right in hindsight. It is what the system knew at that moment, what action the signal proposed, why the trading policy changed or blocked it, and what a precisely defined alternative policy might have done under realistic execution assumptions.
A counterfactual replay or paper-trading result is an estimate—not a recovered version of an unobserved live trade and not proof that the signal is profitable. Make the decision record, alternative policy, execution assumptions, and evaluation stage explicit, and you can audit where the signal-to-trade chain diverged without confusing simulation with live evidence.
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Why can a bot ignore an AI signal?
A model signal is only one part of a trading system. Between “buy” and an executed position, a system may construct a portfolio, check available capital and risk limits, decide order size and timing, and submit an order subject to venue conditions. The signal may be followed, modified, delayed, or rejected at any of those stages.
Fengrui Hua and coauthors’ 2026 survey of agentic quantitative trading describes five distinct workflow stages: factor mining, signal discovery, portfolio construction, order execution, and risk management. The survey says current systems remain concentrated on signal discovery, and cautions that forecasting or model capability does not reliably translate into live trading performance under market and reliability controls. That is why an audit should record the whole decision path, not just the model output and final account balance.
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What should an ignored-signal audit preserve?
Build a point-in-time record for every evaluation opportunity. This is a recommended experiment design: the cited work supports evaluating the full workflow, but does not prescribe or validate one logging schema for every trading system.
- Decision context: timestamp, instrument, venue, point-in-time inputs or a reproducible reference to them, and the portfolio state available to the system.
- Model proposal: model and configuration version, signal, intended action, and any confidence or score the model actually produces.
- Policy and constraints: applicable position, sizing, capital, and risk constraints, together with the policy version that assessed the signal.
- System response: whether the signal was followed, modified, delayed, or rejected, plus the recorded reason and the stage that made the decision.
- Execution record: orders submitted, cancellations or rejections, fills, and observed execution costs when available.
Keep the original decision record immutable. A later replay should be able to use the information available at the time, rather than revised data, a newer model output, or an outcome-informed explanation.
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How do you define the counterfactual fairly?
Write down the alternative policy before examining its result. For example: “Follow the model signal, subject to the same portfolio, sizing, risk, and execution rules as the actual system.” This tests the effect of changing signal handling while holding the other rules as constant as possible.
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- Which signal types trigger an action and how the action is sized.
- Which risk, portfolio, and venue constraints still apply.
- When an order is submitted and what order behavior the policy assumes.
- How fees, slippage, fill probability, liquidity limits, and market impact are handled.
- What happens when required inputs or execution records are missing.
Use the same decision-time information for the actual and alternative policies. The actual outcome may be observed; the alternative outcome is replayed or simulated unless it was separately executed. Label that distinction wherever results are presented.
How should you compare replay, paper trading, and live results?
Keep the stages separate. They answer different questions and provide different levels of temporal and execution realism. Xingtong Yu and coauthors’ September 28, 2026 arXiv preprint proposes a three-stage evaluation—historical backtesting, prospective exchange-based paper trading, and real-money live trading—to measure the gap between backtests and realization. It is a benchmark proposal and motivation, not evidence that a strategy earns money.
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| Stage | What it can examine | What to disclose | What it cannot establish by itself |
|---|---|---|---|
| Historical replay | Whether a specified policy can be reproduced on historical decision opportunities using point-in-time information. | Sample boundaries, data timing, policy and model versions, constraints, and assumptions for fills, costs, and venue behavior. | That the policy generalizes to unseen markets or would have received the simulated fills in live conditions. |
| Prospective exchange-based paper trading | How the policy behaves during a future period not used to design the replay, under the paper-trading venue’s simulated conditions. | Evaluation dates, exchange or paper environment, order assumptions, missing observations, and any differences from live execution. | That simulated fills, costs, or market impact match real-money execution. |
| Live trading | Observed behavior and execution under the deployed system and its real venue and risk controls. | Deployment and policy version, capital and risk limits, venue, observed costs, and any operational interruptions. | That results will persist in other periods, at other sizes, or under a changed strategy or venue. |
Latency, slippage, liquidity constraints, and market impact are among the frictions named in Yu and coauthors’ preprint as reasons simulated performance may differ from real-market results. A paper environment can improve temporal realism over a historical replay, but its execution remains simulated; a live result adds execution evidence, not certainty about future performance.
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Which outcomes should you report?
Follow the chain from information to net outcome instead of treating signal accuracy as a proxy for trading performance. Hua and coauthors’ survey separates the workflow stages, while Linsen Zhu and Mengqing Cai’s September 4, 2026 review of AI in equity and crypto markets states: “Technical capability, however, is not evidence of investment profitability.” The review synthesizes literature available through August 31, 2026; it is not a quantitative estimate for every system.
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- Signal behavior: report the signal and action distribution, including how often signals were followed, modified, delayed, or rejected, where the records permit.
- Feasibility: show whether the proposed positions fit the portfolio and risk constraints and whether an order could plausibly be executed under the stated assumptions.
- Execution and costs: report observed costs for live trades and clearly labeled estimated costs for replay or paper results, including sensitivity to alternative fill and slippage assumptions.
- Net, risk-aware performance: report the outcome after costs with the risk measures and evaluation period identified. Do not present gross returns or prediction scores as if they were net trading results.
- Reliability and auditability: disclose missing decision records, system interruptions, rejected opportunities, and whether each result can be traced back to its decision-time inputs and policy version.
There is no single counterfactual estimator established by the cited sources as best for this exact problem. Choose a method that matches the data and question, state its assumptions, and avoid implying that a replay recovers the unknowable live fill for an order that was never placed.
How can you tell whether a backtest is misleading?
Historical performance can reflect choices made after seeing the data rather than a policy that would have worked prospectively. Zhu and Cai’s review identifies temporal contamination, repeated selection, survivorship, weak benchmarks, implementation costs, venue mechanics, and capacity assumptions as threats to translating predictions into risk-adjusted net returns. Those are the review authors’ synthesis of the literature, not a measured penalty that applies uniformly to every strategy.
- Future-data leakage: verify that each feature and decision uses only information genuinely available at that timestamp.
- Repeated selection: record model and parameter searches, and preserve a prospective period that was not repeatedly used to choose the winning variant.
- Survivorship and benchmark choice: explain which instruments and periods are included and why the comparison baseline is appropriate.
- Missing frictions: test plausible costs and venue mechanics rather than assuming every intended order fills at a convenient historical price.
- Capacity assumptions: state whether the strategy’s assumed size is plausible for the instrument and market conditions represented.
- Offline-policy overfitting: treat historical fit as insufficient evidence of generalization. H. Yuan and coauthors’ peer-reviewed AAAI proceedings paper, published March 14, 2026, warns that offline reinforcement-learning policies may “memorize” buying and selling actions while neglecting the non-stationary nature of markets. Its empirical domain is stock portfolio optimization, so it is relevant as a general warning about offline-policy generalization, not direct evidence about live crypto results.
How should you show uncertainty and failure?
Report the conditions that could change the conclusion, not just the most favorable run. State the sample dates and market regimes represented; identify missing records and excluded opportunities; and show how the counterfactual changes under reasonable execution assumptions. If the result depends on a narrow choice of fill probability, cost estimate, or parameter set, make that dependence visible.
Keep replay, prospective paper, and live results in separate result sets rather than combining them into a single performance number. A profitable-looking retrospective alternative can result from execution assumptions or repeated selection, and is not evidence that the bot could have captured that return in real trading.
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