feat: Sprint 2 - 概率语义 + 轻量快照 + 数据库约束

P0-05: confidence → subjective_confidence 改名(15 文件)
       LLM 主观置信度与概率分离
P0-02: predictions 增加 cutoff_at + input_hash(轻量快照)
       MatchContext 暴露 match_dt
       单/多 Agent 路径均记录快照元数据
P2-05: 数据库 CHECK 约束
       - pred_home_goals >= 0
       - pred_away_goals >= 0
       - subjective_confidence 0~1
       - pred_1x2 IN (1,X,2)
       - mode IN (single,multi)
P2-06: limit 分页约束(ge=1, le=200)
P2-01: 新增 /health/ready 就绪检查

tests/test_agents.py

@@ -59,14 +59,14 @@ class TestReportParsing:
             "data_sufficiency": "high",
             "analysis": "主队交锋占优",
             "home_edge": 0.6,
-            "confidence": 0.8,
+            "subjective_confidence": 0.8,
             "key_evidence": ["近5次交锋主队4胜", "主场交锋3连胜"],
         }
         resp = LLMResponse(content="{}", parsed=parsed, prompt_tokens=100, completion_tokens=50, latency_ms=500)
         r = _parse_report("h2h", parsed, resp, "gpt-4o-mini")
         assert r.status == "ok"
         assert r.home_edge == 0.6
-        assert r.confidence == 0.8
+        assert r.subjective_confidence == 0.8
         assert len(r.key_evidence) == 2
         assert r.data_sufficiency == "high"
 
@@ -78,7 +78,7 @@ class TestReportParsing:
             "data_sufficiency": "medium",
             "analysis": "主队火力更强",
             "home_edge": 0.4,
-            "confidence": 0.7,
+            "subjective_confidence": 0.7,
             "key_evidence": ["场均xG 2.1"],
             "exp_home_goals": 2.1,
             "exp_away_goals": 1.2,
@@ -97,7 +97,7 @@ class TestReportParsing:
         parsed = {
             "data_sufficiency": "bogus",   # 非法 → medium
             "home_edge": "very strong",    # 非法 → None (宽容降级)
-            "confidence": None,
+            "subjective_confidence": None,
             "key_evidence": "单字符串",      # → [str]
         }
         resp = LLMResponse(content="{}", parsed=parsed)
@@ -109,7 +109,7 @@ class TestReportParsing:
         assert r.key_evidence == ["单字符串"]
 
         # 验证范围约束: confidence > 1 会被截断或拒绝
-        parsed2 = {"confidence": 1.5, "home_edge": 2.0}
+        parsed2 = {"subjective_confidence": 1.5, "home_edge": 2.0}
         r2 = _parse_report("form", parsed2, resp, "m")
         # Pydantic 会拒绝越界值 → parse_error
         assert r2.status == "parse_error"
@@ -175,7 +175,7 @@ class TestRunAgent:
                 assert "A 2-1 B" in user
                 return LLMResponse(
                     content="{}",
-                    parsed={"data_sufficiency": "high", "analysis": "ok", "home_edge": 0.5, "confidence": 0.9},
+                    parsed={"data_sufficiency": "high", "analysis": "ok", "home_edge": 0.5, "subjective_confidence": 0.9},
                     prompt_tokens=10, completion_tokens=5, latency_ms=100,
                 )
 
@@ -200,7 +200,7 @@ class TestOrchestratorAggregation:
         import json
 
         reports = [
-            AgentReport(agent="h2h", status="ok", home_edge=0.5, confidence=0.8, analysis="a"),
+            AgentReport(agent="h2h", status="ok", home_edge=0.5, subjective_confidence=0.8, analysis="a"),
             AgentReport(agent="injuries", status="no_data", data_sufficiency="none"),
         ]
         text = _reports_to_json(reports)

tests/test_core.py

@@ -96,7 +96,7 @@ class TestProviderMock:
                 def raise_for_status(self): pass
                 def json(self):
                     return {
-                        "choices": [{"message": {"content": '{"pred_home_goals": 1.5, "pred_away_goals": 1.0, "1x2": "1", "confidence": 0.7, "reasoning": "test"}'}}],
+                        "choices": [{"message": {"content": '{"pred_home_goals": 1.5, "pred_away_goals": 1.0, "1x2": "1", "subjective_confidence": 0.7, "reasoning": "test"}'}}],
                         "usage": {"prompt_tokens": 100, "completion_tokens": 50},
                     }
             return FakeResp()