feat(research): expand factor research and quick trade depth

README.md

@@ -158,6 +158,12 @@ and deployment remain under the operator's control.
 
 ### Strategy evolution and signal-only virtual accounts
 
+- **Factor Lab** loads one point-in-time universe panel once and benchmarks up
+  to 40 registered technical, fundamental, TA-Lib, or Qlib factors. It uses a
+  time-ordered holdout, out-of-sample IC retention, Benjamini-Hochberg
+  false-discovery control, cost-aware scoring, factor-life-cycle labels, and
+  cross-factor IC correlation. The same workflow is available from the web UI,
+  Human API, Agent Gateway, and MCP.
 - **Strategy evolution** searches declared tunable parameters with random,
   grid, or TPE optimization, using bar-count walk-forward validation and a final
   blind holdout. Jobs run asynchronously with per-strategy history, automatic

README_CN.md

@@ -146,6 +146,10 @@ QuantDinger 是面向独立交易者、Python 策略开发者和小型团队的*
 
 ### 策略进化与仅通知虚拟账户
 
+- **因子实验室**：一次加载同一个时点化标的池面板，批量评估最多 40 个技术、基本面、
+  TA-Lib 或 Qlib 因子；使用按时间切分的样本外验证、IC 保持率、Benjamini-Hochberg
+  错误发现率控制、成本感知评分、因子生命周期分类和跨因子 IC 相关矩阵。Web 界面、
+  Human API、Agent Gateway 与 MCP 均可调用同一套流程。
 - **策略进化**：对策略声明的可调参数执行随机、网格或 TPE 搜索，按实际 K 线数量进行
   walk-forward 验证，并保留最终盲测区间。研究任务异步运行且按策略保存历史，支持自动剪枝、
   综合评分、PBO、Deflated Sharpe、区块自助法蒙特卡洛和交易成本压力测试。结果用于比较参数

backend_api_python/app/routes/agent_v1/research.py

@@ -1,6 +1,7 @@
 """Research workspace endpoints: universes, factors, and watchlists."""
 from __future__ import annotations
 
+from datetime import datetime
 from flask import request
 
 from app.services.factors import (
@@ -16,12 +17,56 @@
     remove_watchlist_item,
 )
 from app.services.universe import UniverseError, UniverseService
-from app.utils.agent_auth import SCOPE_R, SCOPE_W, agent_required, current_user_id
+from app.services.strategy_v2 import StrategyV2BacktestService
+from app.utils.agent_auth import (
+    SCOPE_B,
+    SCOPE_R,
+    SCOPE_W,
+    agent_required,
+    current_token,
+    current_user_id,
+    instrument_allowed,
+    market_allowed,
+    with_idempotency,
+)
+from app.utils.agent_jobs import _job_receipt, submit_job
 
 from . import agent_v1_bp
 from ._helpers import clip_int, envelope, error, get_json_or_400
 
 _universes = UniverseService()
+_factor_benchmark = StrategyV2BacktestService(universe_service=_universes)
+
+
+def _run_factor_benchmark(payload: dict, on_progress=None):
+    if on_progress:
+        on_progress({"phase": "loading_universe", "percent": 5})
+    start_date = datetime.strptime(str(payload["startDate"]), "%Y-%m-%d")
+    end_date = datetime.strptime(str(payload["endDate"]), "%Y-%m-%d").replace(
+        hour=23, minute=59, second=59
+    )
+    result = _factor_benchmark.benchmark_universe_factors(
+        user_id=int(payload["__user_id"]),
+        universe_id=int(payload["universeId"]),
+        frequency=str(payload.get("frequency") or "1d"),
+        start_date=start_date,
+        end_date=end_date,
+        factors=list(payload.get("factors") or []),
+        groups=int(payload.get("groups") or 5),
+        holding_period=int(payload.get("holdingPeriod") or 5),
+        commission=float(payload.get("commission") or 0.0005),
+        slippage=float(payload.get("slippage") or 0.0005),
+        neutralize_industry=bool(payload.get("neutralizeIndustry", False)),
+        train_fraction=float(payload.get("trainFraction") or 0.6),
+        false_discovery_rate=float(payload.get("falseDiscoveryRate") or 0.1),
+        minimum_oos_observations=int(payload.get("minimumOosObservations") or 5),
+        minimum_abs_oos_ic=float(payload.get("minimumAbsOosIc") or 0.02),
+        minimum_coverage=float(payload.get("minimumCoverage") or 0.5),
+    )
+    result.pop("factorResults", None)
+    if on_progress:
+        on_progress({"phase": "validated", "percent": 100})
+    return result
 
 
 @agent_v1_bp.route("/research/universes", methods=["GET"])
@@ -78,6 +123,72 @@ def get_research_factor(factor_id: str):
         return error(404, exc.code, http=404)
 
 
+@agent_v1_bp.route("/research/factor-benchmarks", methods=["POST"])
+@agent_required(SCOPE_B)
+def create_factor_benchmark():
+    body, err = get_json_or_400()
+    if err:
+        return err
+    allowed_fields = {
+        "universeId", "frequency", "startDate", "endDate", "factors",
+        "groups", "holdingPeriod", "commission", "slippage",
+        "neutralizeIndustry", "trainFraction", "falseDiscoveryRate",
+        "minimumOosObservations", "minimumAbsOosIc", "minimumCoverage",
+    }
+    unsupported = sorted(set(body) - allowed_fields)
+    if unsupported:
+        return error(400, f"Unsupported factor benchmark fields: {', '.join(unsupported)}")
+    try:
+        universe_id = int(body.get("universeId") or 0)
+        datetime.strptime(str(body.get("startDate") or ""), "%Y-%m-%d")
+        datetime.strptime(str(body.get("endDate") or ""), "%Y-%m-%d")
+    except (TypeError, ValueError):
+        return error(400, "universeId and YYYY-MM-DD startDate/endDate are required")
+    factors = body.get("factors")
+    if not isinstance(factors, list) or len(factors) < 2:
+        return error(400, "factors must contain at least two factor definitions")
+    try:
+        universe = _universes.get_universe(current_user_id(), universe_id)
+        members = _universes.candidate_members(
+            current_user_id(),
+            universe_id,
+            start=datetime.strptime(str(body["startDate"]), "%Y-%m-%d").date(),
+            end=datetime.strptime(str(body["endDate"]), "%Y-%m-%d").date(),
+        )
+    except UniverseError as exc:
+        return error(exc.status_code, exc.code, http=exc.status_code)
+    market = str(universe.get("market") or "")
+    if market and not market_allowed(market):
+        return error(403, f"Market not allowed: {market}", http=403)
+    for member in members:
+        member_market = str(member.get("market") or market)
+        symbol = str(member.get("symbol") or "")
+        if member_market and not market_allowed(member_market):
+            return error(403, f"Market not allowed: {member_market}", http=403)
+        if symbol and not instrument_allowed(symbol):
+            return error(403, f"Instrument not allowed: {symbol}", http=403)
+
+    with with_idempotency("factor_benchmark") as existing:
+        if existing:
+            return envelope(
+                _job_receipt(existing, duplicate=True),
+                message="idempotent replay",
+            )
+    payload = dict(body)
+    payload["__user_id"] = current_user_id()
+    job = submit_job(
+        user_id=current_user_id(),
+        agent_token_id=int(current_token().get("id") or 0),
+        kind="factor_benchmark",
+        request_payload=payload,
+        runner=_run_factor_benchmark,
+        idempotency_key=request.headers.get("Idempotency-Key"),
+    )
+    if job["status"] == "failed":
+        return error(503, "AGENT_JOB_DISPATCH_FAILED", details=job, http=503)
+    return envelope(job, message="queued", status=202)
+
+
 @agent_v1_bp.route("/research/watchlist", methods=["GET"])
 @agent_required(SCOPE_R)
 def get_research_watchlist():