Strategy post-trade analysis V2 / 策略复盘 V2¶

Boundary / 功能边界¶

The existing authenticated review and history endpoints return an additive schema_version: 2 report. No execution-engine, order, deployment, or strategy parameter changes are performed. Existing history remains readable. AI failures retain the deterministic evidence report and the existing billing refund path.

新版仅重构复盘报告，不改变执行引擎、订单、部署或策略参数。旧历史报告仍可读取。 AI 失败时保留证据报告，并沿用现有计费退款流程。

Evidence / 证据口径¶

evidence.facts: deterministic ledger statistics. Profit factor and average win/loss ratio are separate; undefined values are null, not invented zeroes.
Commission add-back is a same-window accounting identity, not a hypothetical execution backtest. Missing currency conversion or mixed quote currencies block this decomposition. Other ledger statistics still require reconciliation.
Close rows are fills, not necessarily complete trades. FIFO holding time includes pre-window entries, separates symbols and directions, and excludes unmatched closes. Five signal bars are a descriptive bucket, not a recommended holding rule.
Selected evidence contains the worst ten closes and latest twenty fills, with duplicates removed. It is not a random or representative sample.
Current source versions are read with the user's ownership scope. Static AST inspection never executes strategy code. String literals are redacted and only numeric/boolean parameters are included. Raw logs and exchange configuration are not sent to the AI. Current source is not proof of historical version continuity.
Missing market paths, funding, execution costs, and historical version linkage are displayed as evidence gaps rather than silently inferred.

核心原则：区分窗口账面盈亏、完整交易与平仓成交、利润因子与平均盈亏比。手续费加回不是零成本回测；短持仓亏损也不能证明延长持仓有效。当前代码版本仅用于提出假设，不冒充整个复盘窗口的历史版本。

Suggestions / 改进建议¶

The service exposes four setting-oriented evidence.checks: costs, frequency, exits and position risk. AI may annotate only these check codes with an interpretation, one prioritized adjustment, and a guardrail. The browser presents each as a direct problem-and-optimization answer, without a downloadable plan or experiment-status badge. Unsupported numeric thresholds are prohibited by the prompt; missing evidence calls for a specific measurement, not a fabricated optimal parameter. Suggestions do not create strategy copies, launch backtests, or deploy changes. AI prose is untrusted and rendered as plain text.

新版围绕费用、交易频次、止盈止损、仓位杠杆四项诊断，直接回答“存在什么问题”和“怎么优化”，附证据与执行边界。移除下载计划及实验状态展示；不创建策略副本、不运行回测、不修改实盘参数。缺少依据时明确需要补齐的测量数据，不编造最佳参数，也不将建议冒充已验证的改善结果。

Three signed, zero-anchored charts show commission accounting, exit-reason net P&L, and FIFO-matched holding-duration net P&L. Cohorts use the full evidence population rather than selected example fills. Count and P&L coverage remain visible; unknown amounts are omitted, not plotted as zero. Holding buckets are descriptive durations (up to 5 minutes, 5–30 minutes, 30–120 minutes, over 2 hours), not recommended holding limits. These views overlap and must not be summed as independent causes of loss. Historical reports without cohort data omit those charts until a new report is generated.

三类图表分别展示费用拆分、退出原因分组盈亏、已配对持仓时长分组盈亏。使用完整分组而非抽样成交，展示金额覆盖笔数；未知金额不按零处理。分组仅用于定位问题，不能叠加为独立亏损原因，也不能直接推导最优持仓时间。旧报告缺少分组数据时不展示对应图表，需重新生成报告才能补齐。

Exit cohorts are mutually exclusive close-fill classifications based on explicit reason codes, with unrecognized or absent reasons left unknown. Their net P&L already includes allocated fees; it must not be added to fees as a separate causal contribution. pnl_count discloses incomplete P&L coverage. Current raw settings are not interpreted as effective historical thresholds. Configured leverage is not effective leverage; largest close loss / initial capital is not risk at entry. Calendar-day fill rates include inactive days and are not independent signal counts. Missing data is an insufficient-evidence state, never a passing score.

退出分组用于定位亏损，不证明止损太紧或止盈太早，也不能与手续费相加进行因果归因。缺少止损参数不等于没有保护；当前配置杠杆不等于历史实际敞口。日均值包含窗口内未运行日期；分批成交不等于完整交易。强平成交优先展示风险检查。

Validation / 验证¶

Backend: run the focused test_strategy_review_evidence.py, test_strategy_review_execution_mode.py, and test_strategy_review_billing.py tests from backend_api_python using the project environment.

Frontend: node --test tests/unit/strategyReview.test.mjs. node tests/preview-strategy-review.mjs starts an isolated loopback-only preview on port 5197, with fake APIs and fixture data, without live trading or AI calls. Preview URL: /tests/fixtures/strategy-review.html; optional query parameters theme=light and lang=en-US exercise responsive/localized rendering.

测试预览全部使用样例数据，不连接交易账户，也不调用付费 AI。更新正式环境仍需按原有流程部署后端与前端构建；旧报告不会自动重算。