修复标准报告亏损归因

.agents/skills/analyze-quant-robustness/scripts/quant_analysis/reporting.py

@@ -141,6 +141,52 @@ def render_standard_analysis_report(
         for item in _rows(analysis.get("evidence_rows"))
     ]
     attribution = _mapping(analysis.get("attribution"))
+    loss_events = _rows(attribution.get("loss_events"))
+    shown_losses = loss_events[:10]
+
+    def evidence_text(event: Mapping[str, Any], key: str) -> str:
+        item = _mapping(_mapping(event.get("evidence")).get(key))
+        return (
+            str(item.get("value"))
+            if item.get("status") == "available"
+            else "证据不足（来源未提供）"
+        )
+
+    loss_rows = [
+        [
+            str(event.get("security", "—")),
+            str(event.get("date", "—")),
+            str(event.get("security_daily_pnl", "—")),
+            "退出" if event.get("is_exit") else "持仓估值",
+            str(event.get("source_reason", event.get("reason_code", "—"))),
+        ]
+        for event in shown_losses
+    ]
+    loss_evidence_rows = [
+        [
+            str(event.get("security", "—")),
+            evidence_text(event, "entry"),
+            evidence_text(event, "common_stop_before"),
+            evidence_text(event, "previous_trading_day_signal"),
+            evidence_text(event, "fill_price"),
+            evidence_text(event, "stop_failure_loss"),
+        ]
+        for event in shown_losses
+    ]
+    reconciliation_rows = [
+        [
+            str(row.get("date", "—")),
+            str(row.get("daily_security_pnl_total", "证据不足（来源未提供）")),
+            str(row.get("portfolio_daily_pnl", "证据不足（来源未提供）")),
+            str(row.get("reconciliation_difference", "证据不足（来源未提供）")),
+            "已勾稽"
+            if row.get("status") == "reconciled"
+            else "证据不足（来源未提供）"
+            if row.get("status") == "evidence_insufficient"
+            else "不一致",
+        ]
+        for row in _rows(attribution.get("loss_reconciliation"))
+    ]
     robustness = _mapping(analysis.get("robustness"))
     lines = [
         "# 标准策略分析报告",
@@ -214,6 +260,24 @@ def render_standard_analysis_report(
         f"方法：{attribution.get('method', '—')}；"
         f"原因：{attribution.get('reason', '—')}。",
         "",
+        f"### 亏损事件（共 {len(loss_events)} 条，展示前 {min(10, len(loss_events))} 条）",
+        "",
+        *_table(["标的", "日期", "单标的盈亏", "事件", "来源原因"], loss_rows),
+        "",
+        "### 执行与止损证据",
+        "",
+        *_table(
+            ["标的", "入场证据", "止损线", "前一交易日信号", "实际成交价", "止损失败损失"],
+            loss_evidence_rows,
+        ),
+        "",
+        "### 日级勾稽",
+        "",
+        *_table(
+            ["日期", "单标的盈亏合计", "组合日盈亏", "差额", "状态"],
+            reconciliation_rows,
+        ),
+        "",
         "## 稳健性分析",
         "",
     ]

.agents/skills/analyze-quant-robustness/scripts/quant_analysis/unified_analysis.py

@@ -32,7 +32,7 @@
     "NASDAQ100_CNY_TOTAL_RETURN",
 )
 _FORMULA_VERSION = "standard-strategy-analysis/1"
-_SCRIPT_VERSION = "analyze-quant-robustness/1"
+_SCRIPT_VERSION = "analyze-quant-robustness/2"
 _SCRIPT_ENTRY = (
     ".agents/skills/analyze-quant-robustness/scripts/analyze_quant_robustness.py"
 )
@@ -314,13 +314,22 @@ def _immutable_json(path: Path, value: object) -> None:
 
 def _valuation_facts(scenario: ScenarioInput) -> pd.DataFrame:
     columns = [
+        "event_id",
         "date",
         "security",
         "reason_code",
         "source_reason",
         "security_daily_pnl",
+        "action",
+        "position_before",
+        "position_after",
+        "common_stop_before",
         "common_stop_after",
+        "fill_price",
         "stop_failure_loss",
+        "daily_security_pnl_total",
+        "portfolio_daily_pnl",
+        "reconciliation_difference",
     ]
     if scenario.events.empty:
         return pd.DataFrame(columns=columns)
@@ -352,19 +361,36 @@ def _valuation_facts(scenario: ScenarioInput) -> pd.DataFrame:
             raise UnifiedAnalysisError("valuation security_daily_pnl is invalid")
         records.append(
             {
+                "event_id": str(event["event_id"]),
                 "date": pd.Timestamp(event["date"]).normalize(),
                 "security": str(event["security"]),
                 "reason_code": str(event["reason_code"]),
                 "source_reason": str(
                     details.get("source_reason", event["reason_code"])
                 ),
                 "security_daily_pnl": security_pnl,
+                "action": details.get("action"),
+                "position_before": _safe_number(details.get("position_before")),
+                "position_after": _safe_number(details.get("position_after")),
+                "common_stop_before": _safe_number(
+                    details.get("common_stop_before")
+                ),
                 "common_stop_after": _safe_number(
                     details.get("common_stop_after")
                 ),
+                "fill_price": _safe_number(details.get("fill_price")),
                 "stop_failure_loss": _safe_number(
                     details.get("stop_failure_loss")
                 ),
+                "daily_security_pnl_total": _safe_number(
+                    details.get("daily_security_pnl_total")
+                ),
+                "portfolio_daily_pnl": _safe_number(
+                    details.get("portfolio_daily_pnl")
+                ),
+                "reconciliation_difference": _safe_number(
+                    details.get("reconciliation_difference")
+                ),
             }
         )
     facts = pd.DataFrame.from_records(records, columns=columns)
@@ -460,6 +486,86 @@ def _security_pnl_facts(
     return facts
 
 
+def _loss_attribution(facts: pd.DataFrame) -> tuple[list[dict[str, object]], list[dict[str, object]]]:
+    def evidence(value: object) -> dict[str, object]:
+        return (
+            {"status": "available", "value": value}
+            if value is not None and not pd.isna(value)
+            else {"status": "evidence_insufficient", "reason": "missing_at_source"}
+        )
+
+    losses: list[dict[str, object]] = []
+    for row in facts.loc[facts["security_daily_pnl"] < 0].sort_values(
+        ["security_daily_pnl", "date", "security"]
+    ).to_dict("records"):
+        position_before = row["position_before"]
+        position_after = row["position_after"]
+        losses.append(
+            {
+                "event_id": row["event_id"],
+                "security": row["security"],
+                "date": pd.Timestamp(row["date"]).date().isoformat(),
+                "security_daily_pnl": float(row["security_daily_pnl"]),
+                "reason_code": row["reason_code"],
+                "source_reason": row["source_reason"],
+                "is_exit": row["action"] == "full_exit"
+                or (
+                    position_before is not None
+                    and not pd.isna(position_before)
+                    and float(position_before) > 0
+                    and position_after is not None
+                    and not pd.isna(position_after)
+                    and float(position_after) == 0
+                ),
+                "evidence": {
+                    "entry": {
+                        "status": "evidence_insufficient",
+                        "reason": "missing_at_source",
+                    },
+                    "common_stop_before": evidence(row["common_stop_before"]),
+                    "previous_trading_day_signal": {
+                        "status": "evidence_insufficient",
+                        "reason": "missing_at_source",
+                    },
+                    "fill_price": evidence(row["fill_price"]),
+                    "stop_failure_loss": {
+                        "status": "evidence_insufficient",
+                        "reason": "missing_at_source",
+                    },
+                },
+            }
+        )
+
+    reconciliation: list[dict[str, object]] = []
+    for date, rows in facts.loc[
+        facts["date"].isin(facts.loc[facts["security_daily_pnl"] < 0, "date"])
+    ].groupby("date", sort=True):
+        source = rows.iloc[0]
+        total = source["daily_security_pnl_total"]
+        portfolio = source["portfolio_daily_pnl"]
+        difference = source["reconciliation_difference"]
+        if any(value is None or pd.isna(value) for value in (total, portfolio, difference)):
+            reconciliation.append(
+                {
+                    "date": pd.Timestamp(date).date().isoformat(),
+                    "status": "evidence_insufficient",
+                    "reason": "missing_at_source",
+                }
+            )
+            continue
+        reconciliation.append(
+            {
+                "date": pd.Timestamp(date).date().isoformat(),
+                "daily_security_pnl_total": float(total),
+                "portfolio_daily_pnl": float(portfolio),
+                "reconciliation_difference": float(difference),
+                "tolerance": 0.02,
+                "status": "reconciled" if abs(float(difference)) <= 0.02 else "mismatch",
+            }
+        )
+    return losses, reconciliation
+
+
 def _risk_metrics(scenario: ScenarioInput, positions: pd.DataFrame) -> dict[str, object]:
     balances = scenario.balances.copy()
     balances["invested_ratio"] = (
@@ -1225,6 +1331,9 @@ def run_standard_analysis(
 
     security_pnl = _security_pnl_facts(baseline, universe)
     attribution = _attribution(baseline, security_pnl)
+    loss_events, loss_reconciliation = _loss_attribution(security_pnl)
+    attribution["loss_events"] = loss_events
+    attribution["loss_reconciliation"] = loss_reconciliation
     attribution_row, attribution_evidence = (
         _unavailable_result(
             "baseline-attribution", "deep_attribution", str(attribution.get("reason", "missing_at_source"))

docs/comet/specs/standard-strategy-analysis-workflow/spec.md

@@ -51,6 +51,20 @@
 - **WHEN** 结果包缺少某项分析所需扩展
 - **THEN** 系统将该项标记为 `evidence_insufficient`（证据不足），不阻断无依赖的共同分析
 
+### Requirement: 来源原生亏损事件必须可审计
+
+系统 MUST（必须）按来源原生 `valuation.security_daily_pnl`（估值事件单标的日盈亏）从小到大保留全部亏损事件，并在 Markdown（标记文档）报告中展示损失最大的前十条及事件总数。系统 MUST（必须）标识来源明确声明的退出事件，按亏损日期展示来源单标的盈亏合计、组合日盈亏和差额，并以 `0.02` 为勾稽容差。入场、事件前止损线、前一交易日信号、实际成交价或止损失败损失缺少语义匹配的来源字段时，系统 MUST（必须）逐字段标记 `evidence_insufficient`（证据不足），不得从活跃持仓或订单重建完整交易。
+
+#### Scenario: 退出日没有活跃持仓行
+
+- **WHEN** 来源归因扩展记录一笔退出日负 `security_daily_pnl`，而共同持仓事实没有该退出日证券行
+- **THEN** 确定性 JSON（结构化数据）仍保留该亏损事件，Markdown 报告展示其标的、日期、盈亏、来源原因、退出标识和可用执行证据
+
+#### Scenario: 可选执行证据来源缺失
+
+- **WHEN** 亏损事件缺少入场、事件前止损线、前一交易日信号、实际成交价或语义匹配的止损失败损失
+- **THEN** 报告逐字段显示证据不足，不从共同持仓或订单事实推断
+
 ### Requirement: 成本执行压力必须复用标准场景契约
 
 佣金、单边滑点或额外交易日延迟压力 MUST（必须）作为分析计划中 `dimension=cost_execution`（成本执行）的普通场景声明。冻结场景配置 MUST（必须）是压力参数的唯一事实；分析不得维护第二份成本定义、从基线结果推导延迟成交或重新运行研究。

tests/quant_analysis/test_reporting.py

@@ -147,6 +147,70 @@ def test_standard_report_lists_package_identity_capabilities_and_evidence_gaps(
     assert (workspace / "standard-strategy-analysis-report.md").is_file()
 
 
+def test_standard_report_shows_top_ten_loss_events_exit_evidence_and_reconciliation() -> None:
+    analysis = _standard_analysis()
+    attribution = analysis["attribution"]
+    assert isinstance(attribution, dict)
+    attribution.update(
+        {
+            "status": "available",
+            "method": "source_native_security_daily_pnl",
+            "loss_events": [
+                {
+                    "event_id": f"loss-{index}",
+                    "security": f"ETF-{index}",
+                    "date": "2024-01-03",
+                    "security_daily_pnl": -float(20 - index),
+                    "reason_code": "protective_stop",
+                    "source_reason": "protective_stop",
+                    "is_exit": index == 0,
+                    "evidence": {
+                        "entry": {
+                            "status": "evidence_insufficient",
+                            "reason": "missing_at_source",
+                        },
+                        "common_stop_before": {"status": "available", "value": 9.5},
+                        "previous_trading_day_signal": {
+                            "status": "evidence_insufficient",
+                            "reason": "missing_at_source",
+                        },
+                        "fill_price": {"status": "available", "value": 9.0},
+                        "stop_failure_loss": {
+                            "status": "evidence_insufficient",
+                            "reason": "missing_at_source",
+                        },
+                    },
+                }
+                for index in range(11)
+            ],
+            "loss_reconciliation": [
+                {
+                    "date": "2024-01-03",
+                    "daily_security_pnl_total": 10.0,
+                    "portfolio_daily_pnl": 10.0,
+                    "reconciliation_difference": 0.0,
+                    "tolerance": 0.02,
+                    "status": "reconciled",
+                }
+            ],
+        }
+    )
+
+    report = render_standard_analysis_report(
+        analysis, build_standard_recommendation(analysis)
+    )
+
+    assert "亏损事件（共 11 条，展示前 10 条）" in report
+    assert "ETF-0" in report
+    assert "ETF-9" in report
+    assert "ETF-10" not in report
+    assert "退出" in report
+    assert "证据不足（来源未提供）" in report
+    assert "9.5" in report
+    assert "日级勾稽" in report
+    assert "已勾稽" in report
+
+
 def test_standard_delivery_rejects_workspace_outside_repository(
     tmp_path: Path,
 ) -> None: