feat: expose local self-correlation tools through MCP

.scratch/mcp-self-correlation/issues/01-tools.md

@@ -0,0 +1,11 @@
+# 补充 MCP 自相关工具
+
+Type: task
+Status: ready-for-agent
+
+实现 spec.md 中的工具及验证。
+
+## Comments
+
+- 已实现工具注册、输入约束、共享业务调用、权限与队列唤醒，并补充能力说明和接入文档。
+- 官方 MCP ClientSession 经 HTTP 完成发起、进度及结果读取；11 项 MCP 测试通过，包括缓存补取、活动任务复用、审计、只读权限及异常输入。

.scratch/mcp-self-correlation/spec.md

@@ -0,0 +1,7 @@
+# MCP 本地自相关
+
+新增 check_self_correlation（research:refresh）和 get_self_correlation（research:read），并扩展 get_refresh_job 查询 self_correlation 任务。复用网页 Business 和 Runner，不新建算法，不调用平台提交检查。
+
+发起输入为 1–100 个已导入 Alpha ID，去重排序后复用活动任务；缺失目标整批拒绝并返回 affected_items。返回 job_id 后后台补取缺失 PnL 并计算，提交事务后唤醒队列。读取明确区分无缓存、可用及待重算，不隐式请求平台。沿用 MCP 审计、错误契约和权限过滤，无迁移或新增权限。
+
+验收：官方 SDK 工具发现及调用往返、只读权限拒绝发起、参数与缺失目标校验、异步唤醒、去重、缺失 PnL 落库、结果与 stale 读取、任务审计。更新接入文档与能力声明。

backend/app/mcp_api/server.py

@@ -25,7 +25,9 @@ TOOLS = {
     "search_catalog": (c.CatalogSearch, "catalog", "research:read", "分页查询指定范围的数据集或字段元数据；无缓存不等于无数据，不隐式刷新。"),
     "get_research_metadata": (c.Metadata, "metadata", "research:read", "读取范围、设置快照、算子定义或字段可用性；未知不认定通过。"),
     "refresh_research_data": (c.Refresh, "refresh", "research:refresh", "显式刷新目录、算子、设置、字段可用性或 PnL；不会创建模拟。任务返回 job_id。"),
-    "get_refresh_job": (c.JobReference, "refresh_job", "research:read", "查询研究刷新任务的状态和产物引用。"),
+    "get_refresh_job": (c.JobReference, "refresh_job", "research:read", "查询研究刷新或本地自相关任务的状态、进度与分页错误。"),
+    "check_self_correlation": (c.SelfCorrelationCheck, "check_self_correlation", "research:refresh", "对 1–100 个已导入 Alpha 发起本地自相关检查，返回 job_id。与本地同地区已提交 Alpha 比较，排除自身；优先用缓存，缺失 PnL 自动补取。需先同步已提交 Alpha；不调用平台提交检查。用 get_refresh_job 查进度、get_self_correlation 读结果。"),
+    "get_self_correlation": (c.SelfCorrelationReference, "self_correlation", "research:read", "读取指定 Alpha 最新的本地自相关缓存，包括最大相关系数、样本覆盖与 stale 状态；无缓存不自动检查，结果不等同于平台提交资格。"),
     "search_backtests": (c.History, "history", "research:read", "分页查历史候选与固定设置；candidates 按完整输入精确匹配，不推断数学等价。"),
     "submit_backtests": (c.Submit, "submit", "backtests:execute", "执行用户已授权的固定批次，自动留痕并立即返回运行 ID。每项必须完整设置；重复默认拒绝，rerun 明确重跑。不需要研究资产。"),
     "get_backtest": (c.RunReference, "run", "research:read", "读取真实运行进度、提交数量和可选增量事件；受理不等于成功。"),
@@ -64,7 +66,7 @@ class MCPResearchServer:
             inputSchema=schema.model_json_schema(), annotations=types.ToolAnnotations(
                 readOnlyHint=scope == "research:read", destructiveHint=method == "control",
                 idempotentHint=method in {"submit", "control"} or scope == "research:read",
-                openWorldHint=method in {"refresh", "submit", "metadata"}))
+                openWorldHint=method in {"refresh", "submit", "metadata", "check_self_correlation"}))
             for name, (schema, method, scope, description) in TOOLS.items()
             if scope in principal.scopes and "research:read" in principal.scopes])
 

backend/app/research_access/contracts.py

@@ -11,6 +11,7 @@ from ..schemas import Contract
 
 Identifier = Annotated[str, Field(min_length=1, max_length=100)]
 RunId = Annotated[str, Field(min_length=1, max_length=36)]
+AlphaId = Annotated[str, Field(min_length=1, max_length=100, pattern=r"^[A-Za-z0-9_-]+$")]
 
 
 class Empty(Contract):
@@ -124,6 +125,14 @@ class JobReference(Page):
     job_id: RunId
 
 
+class SelfCorrelationCheck(Contract):
+    alpha_ids: list[AlphaId] = Field(min_length=1, max_length=100)
+
+
+class SelfCorrelationReference(Contract):
+    alpha_id: AlphaId
+
+
 class History(Page):
     source: str | None = Field(default=None, max_length=100)
     reference: str | None = Field(default=None, max_length=200)

backend/app/research_access/service.py

@@ -16,6 +16,7 @@ from ..catalog.contracts import CatalogJobInput
 from ..catalog.platform import platform_options, validate_platform_scope
 from ..catalog.research_metadata import ResearchMetadata, availability_key
 from ..catalog.service import Catalog
+from ..correlation import MIN_SAMPLES, THRESHOLD, WINDOW_YEARS
 from ..models import Account, Alpha, BacktestItem, Job, JobItem, ResearchRequest, SimulationAttempt, now
 from ..research.serialization import encode_snapshot
 from ..research.workspace_contracts import FieldAvailabilityInput
@@ -48,7 +49,15 @@ class ResearchAccess:
                 "settings_schema": DirectCandidate.model_json_schema(),
                 "confirmation": "调用者须已获本批执行授权；直接提交后返回稳定运行 ID",
                 "duplicate_policies": ["reject", "rerun"], "permissions": sorted(self.principal.scopes),
-                "metadata_only": True, "actual_platform_allowance": None}
+                "metadata_only": True, "actual_platform_allowance": None,
+                "self_correlation": {
+                    "check_with": "check_self_correlation", "read_with": "get_self_correlation",
+                    "job_with": "get_refresh_job", "max_targets": 100, "source": "local",
+                    "reference_scope": "本地已同步的同地区已提交 Alpha，排除自身",
+                    "method": "累计 PnL 日变化的 Pearson 相关系数，取带符号最大值",
+                    "threshold": THRESHOLD, "min_samples": MIN_SAMPLES, "window_years": WINDOW_YEARS,
+                    "platform_check": False,
+                }}
 
     async def catalog(self, args):
         data = await Catalog(self.db).search(args.filters, args.dataset_id)
@@ -101,7 +110,7 @@ class ResearchAccess:
 
     async def refresh_job(self, args):
         job = await self.db.get(Job, args.job_id)
-        if not job or job.kind not in {"catalog_sync", "field_sync", "pnl_refresh"}:
+        if not job or job.kind not in {"catalog_sync", "field_sync", "pnl_refresh", "self_correlation"}:
             raise ResearchError("NOT_FOUND", "研究刷新任务不存在")
         result = await self.business.get_job_status(args.job_id)
         query = select(JobItem).where(JobItem.job_id == job.id, JobItem.error.is_not(None))
@@ -111,6 +120,27 @@ class ResearchAccess:
         return {**result, "job_id": job.id, "artifact_reference": job.payload,
                 "errors": page([{"alpha_id": e.alpha_id, "error": e.error} for e in errors], total, args.limit, args.offset)}
 
+    async def check_self_correlation(self, args):
+        """Queue local checks for synced IDs; caller commits before waking the runner.
+
+        The shared job service deduplicates active batches. Missing PnL is fetched
+        by the durable runner, so slow upstream reads do not hold the MCP call.
+        """
+        ids = sorted(set(args.alpha_ids))
+        existing = set(await self.db.scalars(select(Alpha.id).where(Alpha.id.in_(ids))))
+        if existing != set(ids):
+            raise ResearchError("NOT_FOUND", "部分 Alpha 尚未同步，请先导入", affected_items=sorted(set(ids)-existing))
+        job = await self.business.create_sync_job(JobInput(kind="self_correlation", alpha_ids=ids))
+        self.wake = "jobs"
+        return {"job_id": job["id"], "status": job["status"], "alpha_ids": ids,
+                "source": "local", "job_with": "get_refresh_job", "read_with": "get_self_correlation"}
+
+    async def self_correlation(self, args):
+        """Read the latest local result without fetching PnL or starting a check."""
+        data = await self.business.get_self_correlation(args.alpha_id)
+        status = "not_cached" if not data["cached"] else "stale" if data["result"]["stale"] else "available"
+        return {"alpha_id": args.alpha_id, "source": "local", "status": status, **data}
+
     async def history(self, args):
         return await self.evidence.history(args)
 

backend/tests/test_mcp.py

@@ -162,7 +162,7 @@ async def test_official_sdk_client_and_error_contract(mcp_app):
             async with ClientSession(streams[0], streams[1]) as client:
                 await client.initialize()
                 listed = await client.list_tools()
-                assert len(listed.tools) == 11
+                assert len(listed.tools) == 13
                 caps = await client.call_tool("get_research_capabilities", {})
                 assert caps.structured_content["max_candidates"] == 100
                 result = await client.call_tool("submit_backtests", submission())
@@ -278,3 +278,108 @@ async def test_refresh_job_error_pages_and_kind_isolation(mcp_app):
     assert result["errors"]["items"][0]["alpha_id"] == "1"
     error = await mcp_app.state.mcp.invoke(principal, "get_refresh_job", {"job_id": "auth"})
     assert error.structured_content["error"]["code"] == "NOT_FOUND"
+
+
+async def test_self_correlation_sdk_workflow_cache_and_staleness(mcp_app, monkeypatch):
+    import httpx2
+    from mcp import ClientSession
+    from mcp.client.streamable_http import streamable_http_client
+
+    from app.alphas import upsert_alpha
+    from app.models import Job, SelfCorrelation
+    from tests.conftest import alpha
+    from tests.test_alpha_management import points
+
+    data = points([1, -2, 4, 3] * 20)
+    async with mcp_app.state.sessions.begin() as db:
+        for raw in (alpha("target"), alpha("peer", status="ACTIVE"), alpha("pending")):
+            await upsert_alpha(db, raw)
+        db.add(Pnl(alpha_id="target", raw={}, points=data))
+    calls = []
+
+    async def pnl(alpha_id):
+        calls.append(alpha_id)
+        return {"records": [{"date": p["date"], "pnl": p["value"]} for p in data]}
+
+    monkeypatch.setattr(mcp_app.state.runner.client, "pnl", pnl)
+    principal, secret = await credentials(mcp_app, {"research:read", "research:refresh"})
+    async with httpx2.AsyncClient(transport=httpx2.ASGITransport(app=mcp_app),
+        headers={"Authorization": f"Bearer {secret}"}) as http:
+        async with streamable_http_client("http://testserver/api/v1/mcp/", http_client=http) as streams:
+            async with ClientSession(streams[0], streams[1]) as client:
+                await client.initialize()
+                listed = {t.name: t for t in (await client.list_tools()).tools}
+                assert listed["get_self_correlation"].annotations.read_only_hint
+                assert not listed["check_self_correlation"].annotations.read_only_hint
+                assert listed["check_self_correlation"].annotations.open_world_hint
+                before = await client.call_tool("get_self_correlation", {"alpha_id": "target"})
+                assert before.structured_content["status"] == "not_cached"
+                assert before.structured_content["result"] is None and calls == []
+                mcp_app.state.runner.wake.clear()
+                started = await client.call_tool("check_self_correlation", {"alpha_ids": ["target", "target"]})
+                assert not started.is_error, started
+                job_id = started.structured_content["job_id"]
+                assert mcp_app.state.runner.wake.is_set() and calls == []
+                assert started.structured_content["alpha_ids"] == ["target"]
+                again = await client.call_tool("check_self_correlation", {"alpha_ids": ["target"]})
+                assert again.structured_content["job_id"] == job_id
+                queued = await client.call_tool("get_refresh_job", {"job_id": job_id})
+                assert queued.structured_content["status"] == "queued"
+                await mcp_app.state.runner.execute(job_id)
+                done = await client.call_tool("get_refresh_job", {"job_id": job_id})
+                assert done.structured_content["status"] == "completed"
+                assert done.structured_content["processed"] == 1
+                result = await client.call_tool("get_self_correlation", {"alpha_id": "target"})
+                body = result.structured_content
+                assert body["source"] == "local" and body["status"] == "available"
+                assert body["result"]["max_correlation"] == pytest.approx(1)
+                assert body["result"]["compared_count"] == 1
+                assert body["result"]["matches"][0]["alpha_id"] == "peer"
+                assert calls == ["peer"]
+    async with mcp_app.state.sessions.begin() as db:
+        row = await db.get(SelfCorrelation, "target")
+        row.stale = True
+        audit = await db.scalar(select(MCPAudit).where(MCPAudit.tool == "check_self_correlation"))
+        assert audit.business_id == job_id and audit.result_code == "OK"
+        assert await db.scalar(select(func.count()).select_from(Job).where(Job.kind == "self_correlation")) == 1
+        assert await db.get(Pnl, "peer") is not None
+    stale = await invoke(mcp_app, principal, "get_self_correlation", {"alpha_id": "target"})
+    assert stale["status"] == "stale" and stale["result"]["stale"] and calls == ["peer"]
+
+
+async def test_self_correlation_read_only_scope_and_invalid_inputs(mcp_app):
+    from app.alphas import upsert_alpha
+    from app.models import Job
+    from tests.conftest import alpha
+
+    async with mcp_app.state.sessions.begin() as db:
+        await upsert_alpha(db, alpha("known"))
+    _, secret = await credentials(mcp_app, {"research:read"})
+    async with httpx.AsyncClient(transport=httpx.ASGITransport(app=mcp_app), base_url="http://testserver",
+        headers={"Authorization": f"Bearer {secret}", "Accept": "application/json, text/event-stream"}) as http:
+        listed = await http.post(ENDPOINT, json={"jsonrpc": "2.0", "id": 1, "method": "tools/list"})
+        names = {t["name"] for t in listed.json()["result"]["tools"]}
+        assert "get_self_correlation" in names and "check_self_correlation" not in names
+        for name, args, status in (
+            ("check_self_correlation", {"alpha_ids": ["known"]}, 403),
+            ("get_self_correlation", {"alpha_id": "known"}, 200),
+        ):
+            response = await http.post(ENDPOINT, json={"jsonrpc": "2.0", "id": 2, "method": "tools/call",
+                "params": {"name": name, "arguments": args}})
+            assert response.status_code == status
+    principal, _ = await credentials(mcp_app)
+    for args, code in (
+        ({"alpha_ids": []}, "INVALID_INPUT"),
+        ({"alpha_ids": ["known"] * 101}, "INVALID_INPUT"),
+        ({"alpha_ids": ["../secret"]}, "INVALID_INPUT"),
+        ({"alpha_ids": ["known"], "force": True}, "INVALID_INPUT"),
+        ({"alpha_ids": ["known", "missing"]}, "NOT_FOUND"),
+    ):
+        failure = await mcp_app.state.mcp.invoke(principal, "check_self_correlation", args)
+        assert failure.is_error and failure.structured_content["error"]["code"] == code
+        if code == "NOT_FOUND":
+            assert failure.structured_content["error"]["affected_items"] == ["missing"]
+    missing = await mcp_app.state.mcp.invoke(principal, "get_self_correlation", {"alpha_id": "missing"})
+    assert missing.is_error and missing.structured_content["error"]["code"] == "NOT_FOUND"
+    async with mcp_app.state.sessions() as db:
+        assert await db.scalar(select(func.count()).select_from(Job)) == 0

docs/mcp-research.md

@@ -1,6 +1,6 @@
 # MCP 研究接入
 
-MCP 让外部助手直接查询数据元信息、查回测历史、提交固定候选并读取结果。无需先建立特征、模板、变体或 QuantFlow。执行仍由网页共用的持久队列负责，回测页保存同一个运行。
+MCP 让外部助手直接查询数据元信息、查回测历史、提交固定候选、执行本地自相关检查并读取结果。无需先建立特征、模板、变体或 QuantFlow。执行仍由网页共用的持久队列负责，回测页保存同一个运行。
 
 ## 启用与令牌
 
@@ -33,8 +33,8 @@ python -m app.cli mcp-token-revoke TOKEN_ID
 
 | 权限 | 可调用能力 |
 | --- | --- |
-| research:read | 能力、数据目录、元数据、历史、运行、结果、证据、刷新任务查询 |
+| research:read | 能力、数据目录、元数据、历史、运行、结果、证据、刷新及自相关任务查询、自相关结果读取 |
-| research:refresh | 显式更新元数据及 PnL 缓存；同时要求 read |
+| research:refresh | 显式更新元数据及 PnL 缓存、发起本地自相关检查；同时要求 read |
 | backtests:execute | 直接提交固定候选；同时要求 read |
 | backtests:control | 暂停、继续、停止、恢复采集；同时要求 read |
 
@@ -50,7 +50,9 @@ python -m app.cli mcp-token-revoke TOKEN_ID
 | search_catalog | `{filters:{region,universe,delay,...},dataset_id?}`；省略 dataset_id 查数据集，提供则查字段 |
 | get_research_metadata | `{query:{kind,...}}`；kind 为 scopes/settings/operators/field_availability |
 | refresh_research_data | `{query:{kind,...}}`；kind 为 catalog/operators/settings/field_availability/pnl |
-| get_refresh_job | `{job_id,limit?,offset?}`；错误列表独立分页 |
+| get_refresh_job | `{job_id,limit?,offset?}`；查询刷新或自相关任务，错误列表独立分页 |
+| check_self_correlation | `{alpha_ids:[...]}`，1–100 个已导入 Alpha ID；异步返回 job_id |
+| get_self_correlation | `{alpha_id}`；只读最新本地结果，含缓存和 stale 状态 |
 | search_backtests | 来源、reference、status、带时区起止时间、scope、q、候选精确匹配及分页 |
 | submit_backtests | `{name,candidates,idempotency_key,duplicate_policy?,source?}` |
 | get_backtest | `{run_id,after?,event_limit?}`，after 为事件游标 |
@@ -62,6 +64,18 @@ metadata 的 operators 支持 q/category 和分页；settings 支持分页；fie
 
 列表默认 25 项、最多 100 项；返回 total、offset、limit、has_more。快照证据按顶层 key/value 分页，嵌套内容完整保留；PnL 按日期过滤和分页。元数据仅提供字段与算子资料，不提供原始财务时间序列。
 
+## 本地自相关检查
+
+目标 Alpha 必须已导入；比较基准为本地已同步的同地区已提交 Alpha，排除自身。建议先在网页全量同步已提交 Alpha。MCP 不隐式导入 Alpha 或同步基准列表。
+
+1. 调用 `check_self_correlation`，例如 `{"alpha_ids":["LL977PqL"]}`，获取 `job_id`。目标去重排序，相同目标集合的活动任务会复用；完成后再次调用会重新计算。
+2. 用 `get_refresh_job` 查询进度、失败原因与分页错误。受理后客户端断开不影响后台任务；取消及重试可在网页任务面板执行。
+3. 完成后调用 `get_self_correlation`，例如 `{"alpha_id":"LL977PqL"}`。返回 `source=local`，`status` 为 `not_cached`、`available` 或 `stale`；没有结果时 `cached=false`、`result=null`，读取不会隐式启动检查。
+
+检查优先使用已有 PnL 缓存，缺失时由后台自动补取并落库，等待遵循平台 Retry-After。计算使用累计 PnL 的日变化、目标最新数据日前四年的共同窗口、至少 30 个有效样本，取带符号最大的 Pearson 相关系数，阈值为 0.7。结果包含比较数、跳过数、最相关 Alpha、窗口、样本和缓存时间；最多列出前 10 个匹配及前 100 个跳过原因。缺少基准或有效样本会明确报告数据不足，不能当作通过。
+
+`result.stale=true` 表示缓存已待重算；新任务执行期间读取仍可能返回上一次结果，应同时查看任务状态和 `calculated_at`。这是本地研究规则，不调用 WorldQuant 的提交检查，也不代表平台提交资格。`get_research_capabilities.self_correlation` 提供工具名及当前规则。已有包含 `research:refresh` 的 Key 可直接发起检查；只读 Key 只能查看结果。
+
 ## 一轮研究示例
 
 先读取能力和设置快照，发现字段并检查历史。用户授权本批执行后，提交以下形态的固定输入；设置仅为结构示例，实际范围需依据平台选项选择：