提交

backend/app/schemas.py

@@ -710,3 +710,150 @@ class IndexWeightsResponse(BaseModel):
     total: int                       # 成分股总数（返回 items 为按权重降序的子集）
     items: list[IndexWeightItemOut] = []
 
+
+# ---------- 买卖点实验室（/signals：手动标注共同点分析 + 条件扫描） ----------
+class SignalPlanCreate(BaseModel):
+    ts_code: str = Field(min_length=6, max_length=12)
+    name: str = Field(default="", max_length=64)   # 空则后端默认「{code} 买点方案」
+
+
+class SignalPlanUpdate(BaseModel):
+    name: str | None = Field(default=None, min_length=1, max_length=64)
+    note: str | None = None
+    conditions: list[IndicatorCondition] | None = None   # None=不改；[] = 清空
+
+
+class SignalPointIn(BaseModel):
+    kind: Literal["buy", "sell"]
+    trade_date: date
+    source: Literal["manual", "scan"] = "manual"
+
+
+class SignalPointOut(BaseModel):
+    kind: str                        # buy | sell
+    trade_date: date
+    source: str                      # manual | scan
+
+    model_config = {"from_attributes": True}
+
+
+class SignalPlanOut(BaseModel):
+    id: int
+    ts_code: str
+    name: str
+    note: str | None = None
+    conditions: list[IndicatorCondition] = Field(default_factory=list)
+    points: list[SignalPointOut] = Field(default_factory=list)
+    created_at: datetime
+    updated_at: datetime
+
+
+class SignalPlanListResponse(BaseModel):
+    items: list[SignalPlanOut]
+
+
+class SignalsAnalyzeRequest(BaseModel):
+    ts_code: str = Field(min_length=6, max_length=12)
+    buy_dates: list[date] = Field(min_length=1, max_length=200)
+
+
+class FeatureFindingOut(BaseModel):
+    """共同特征发现：特征在买点日的命中率 vs 全期基线比例（纯算法统计）。"""
+    key: str
+    label: str
+    samples_hit: int                 # 命中特征的买点数
+    samples: int                     # 有效买点数
+    hit_rate: float                  # 买点命中率 %（0-100）
+    base_rate: float                 # 全期基线比例 %（0-100）
+    lift: float                      # 命中率 / 基线
+    mappable: bool                   # 是否可转成 IndicatorCondition（进扫描/回测）
+    condition: IndicatorCondition | None = None
+
+
+class ValueSummaryOut(BaseModel):
+    """连续值摘要：买点日中位数 vs 全期中位数（直觉对照用）。"""
+    key: str
+    label: str
+    at_points: float | None
+    baseline: float | None
+
+
+class SignalsAnalyzeResponse(BaseModel):
+    ts_code: str
+    samples: int                     # 有效买点数（落在该股交易日上）
+    total_bars: int
+    findings: list[FeatureFindingOut]
+    summaries: list[ValueSummaryOut]
+
+
+class WindowStatOut(BaseModel):
+    window: int                      # 持有 N 交易日
+    samples: int
+    mean: float                      # 平均收益 %
+    median: float
+    win_rate: float                  # 收益>0 占比 %
+    p10: float
+    p90: float
+
+
+class EvalPointOut(BaseModel):
+    """单个买点的评估：未来 N 交易日收益矩阵（复权校正）。"""
+    date: date                       # 买点（信号）日
+    entry_date: date | None = None   # 实际入场日（默认信号次日）
+    entry_price: float | None = None
+    rets: dict[str, float | None] = Field(default_factory=dict)      # "5" -> 收益%（越界/缺数据 None）
+    max_gain: dict[str, float | None] = Field(default_factory=dict)  # 窗口内最大涨幅 %
+    max_dd: dict[str, float | None] = Field(default_factory=dict)    # 窗口内最大回撤 %
+
+
+class EvalPairOut(BaseModel):
+    """手动配对（买点 -> 其后最近卖点）的一笔评估。"""
+    buy_date: date
+    sell_date: date
+    entry_date: date | None = None
+    entry_price: float | None = None
+    exit_price: float | None = None
+    ret_pct: float | None = None     # 复权校正收益 %
+
+
+class PairStatsOut(BaseModel):
+    samples: int
+    mean: float
+    median: float
+    win_rate: float
+    max: float
+    min: float
+
+
+class SignalsEvaluateResult(BaseModel):
+    samples: int
+    points: list[EvalPointOut] = Field(default_factory=list)
+    stats: list[WindowStatOut] = Field(default_factory=list)
+    pairs: list[EvalPairOut] = Field(default_factory=list)
+    pair_stats: PairStatsOut | None = None
+
+
+class SignalsScanRequest(BaseModel):
+    ts_code: str = Field(min_length=6, max_length=12)
+    conditions: list[IndicatorCondition] = Field(min_length=1, max_length=10)
+    windows: list[int] = Field(default_factory=lambda: [1, 3, 5, 10, 20, 60])
+    start: date | None = None
+    end: date | None = None
+
+
+class SignalsScanResponse(BaseModel):
+    ts_code: str
+    dates: list[date]                # 命中信号日（升序，已丢弃指标预热区）
+    total: int
+    evaluate: SignalsEvaluateResult
+
+
+class SignalsEvaluateRequest(BaseModel):
+    ts_code: str = Field(min_length=6, max_length=12)
+    buy_dates: list[date] = Field(min_length=1, max_length=500)
+    sell_dates: list[date] = Field(default_factory=list, max_length=500)
+    windows: list[int] = Field(default_factory=lambda: [1, 3, 5, 10, 20, 60])
+    # 进出场时机默认与 /api/backtest/event 对齐：信号次日入场、到期收盘卖出
+    entry_timing: Literal["next_open", "next_close"] = "next_open"
+    exit_timing: Literal["close", "open"] = "close"
+