feat: macro_context/market数据全部DB优先，JSON回退
- 建 macro_context_log 表，macro_context_collector.py 双写 - strategy_lifecycle.py load_macro_context() 优先DB - strategy_tree.py detect_scenario() 优先DB - stale_push_wlin.py load_macro_line() 优先DB - xiaoguo_signal_consumer.py 大盘判断优先DB - stock_profile.py load_macro() 优先DB - system_audit.py 管道审计改查DB market_snapshots - JSON保留作fallback，确保过渡期不中断
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知微
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@@ -111,6 +111,28 @@ NON_BUY_SIGNALS = ["观望", "弱势持有", "深套持有"]
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def load_macro_line():
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"""加载大盘和市场的简要描述"""
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parts = []
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try:
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# 优先 DB
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import sqlite3
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db = sqlite3.connect("/home/hmo/MoFin/data/mofin.db")
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row = db.execute(
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"SELECT structure FROM macro_context_log "
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"WHERE has_valid_data=1 ORDER BY created_at DESC LIMIT 1"
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).fetchone()
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db.close()
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if row and row[0]:
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m = json.loads(row[0])
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else:
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raise ValueError("no db data")
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overall = m.get("overall", "neutral")
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desc = m.get("description", "")
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if "bearish" in overall:
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parts.append("大盘偏弱")
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elif overall == "bullish":
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parts.append("大盘偏强")
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elif desc:
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parts.append(f"大盘{desc}")
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except Exception:
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try:
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with open(MACRO_CTX) as f:
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m = json.load(f).get("structure", {})
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@@ -74,8 +74,30 @@ def quick_assess(quote):
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score = 0
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reasons = []
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# 大盘环境（简化：交易日9:30-15:00且在涨）
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# 引用 macro_context.json 中的大盘方向
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# 大盘环境，从DB读（回退JSON）
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try:
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import sqlite3
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conn = sqlite3.connect(str(DB_PATH))
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row = conn.execute(
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"SELECT indices FROM macro_context_log WHERE has_valid_data=1 ORDER BY created_at DESC LIMIT 1"
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).fetchone()
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conn.close()
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if row and row[0]:
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mc = json.loads(row[0])
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else:
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raise ValueError
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sh = 0
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for k, v in mc.items():
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if "上证" in k:
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sh = v.get("change_pct", 0)
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break
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if sh > 0.5:
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score += 1
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reasons.append(f"大盘+{sh:.1f}%偏强")
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elif sh < -0.5:
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score -= 1
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reasons.append(f"大盘{sh:.1f}%偏弱")
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except Exception:
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try:
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mc = json.loads((DATA / "macro_context.json").read_text())
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sh = mc.get("shanghai", {}).get("change_pct", 0)
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+15
-1
@@ -119,7 +119,21 @@ def load_mtf_cache() -> dict:
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def load_macro() -> dict:
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"""加载宏观上下文"""
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"""加载宏观上下文，优先DB"""
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try:
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import sqlite3
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conn = sqlite3.connect(os.path.join(DATA_DIR, "mofin.db"))
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row = conn.execute(
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"SELECT indices, structure, key_sectors FROM macro_context_log "
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"WHERE has_valid_data=1 ORDER BY created_at DESC LIMIT 1"
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).fetchone()
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conn.close()
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if row:
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return {"indices": json.loads(row[0] or "{}"),
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"structure": json.loads(row[1] or "{}"),
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"key_sectors": json.loads(row[2] or "[]")}
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except:
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pass
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try:
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with open(MACRO_PATH) as f:
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return json.load(f)
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+20
-3
@@ -286,12 +286,31 @@ def compute_sector_adjustment(code, market_ctx, stock_sector_map):
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def load_macro_context():
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"""读取宏观上下文，返回 (bias, desc)，bias 取 0.8/1.0/1.1 分别对应 bearish/neutral/bullish"""
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"""读取宏观上下文，返回 (bias, desc)，优先 DB，回退 JSON"""
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try:
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import sqlite3
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from pathlib import Path
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conn = sqlite3.connect(str(Path(__file__).parent.parent / "data" / "mofin.db"))
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row = conn.execute(
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"SELECT indices, structure FROM macro_context_log "
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"WHERE has_valid_data=1 ORDER BY created_at DESC LIMIT 1"
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).fetchone()
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conn.close()
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if row:
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indices = json.loads(row[0]) if row[0] else {}
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structure = json.loads(row[1]) if row[1] else {}
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overall = structure.get("overall", "neutral")
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desc = structure.get("description", "")
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else:
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raise ValueError("no db data")
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except Exception:
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try:
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with open(MACRO_CONTEXT_PATH) as f:
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ctx = json.load(f)
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overall = ctx.get("structure", {}).get("overall", "neutral")
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desc = ctx.get("structure", {}).get("description", "")
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except Exception:
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return 1.0, "宏观未加载"
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if "bearish" in overall:
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return 0.8, f"宏观{desc}"
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elif overall == "bullish":
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@@ -300,8 +319,6 @@ def load_macro_context():
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return 1.1, f"宏观{desc}"
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else:
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return 1.0, f"宏观{desc}"
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except Exception:
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return 1.0, "宏观未加载"
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def batch_fetch_prices(codes):
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+19
-4
@@ -69,16 +69,31 @@ def detect_scenario():
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scenario_id = "weak_consolidation" # 默认
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confidence = 0.5
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try:
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# 优先 DB
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import sqlite3
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from pathlib import Path
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db = sqlite3.connect(str(Path(__file__).parent / "data" / "mofin.db"))
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mrow = db.execute(
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"SELECT indices, structure, sector_mood FROM macro_context_log "
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"WHERE has_valid_data=1 ORDER BY created_at DESC LIMIT 1"
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).fetchone()
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db.close()
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if mrow:
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structure = json.loads(mrow[1]) if mrow[1] else {}
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overall = structure.get("overall", "").lower()
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mood = (mrow[2] or "").lower() if len(mrow) > 2 else ""
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else:
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raise ValueError("no db data")
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except Exception:
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try:
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macro = json.load(open(MACRO_PATH))
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market = json.load(open(MARKET_PATH))
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except Exception:
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return {"id": scenario_id, "label": "默认-弱势震荡", "confidence": 0.3, "portfolio_action": "观望"}
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mood = market.get("mood", "").lower()
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# Try to get trend from macro_context
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structure = macro.get("structure", {})
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overall = structure.get("overall", "").lower()
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except Exception:
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return {"id": "weak_consolidation", "label": "默认-弱势震荡", "confidence": 0.3, "portfolio_action": "观望"}
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trend_desc = structure.get("description", "").lower()
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# Check for sharp decline
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+11
-8
@@ -128,16 +128,19 @@ def audit_portfolio(conn):
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# ── 6. 数据管道审计 ──
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def audit_pipeline():
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# 检查market.json是否今天更新
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# 检查DB市场数据是否今天更新
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try:
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mkt = json.loads((DATA_DIR / "market.json").read_text())
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mkt_ts = mkt.get("timestamp", "")
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if mkt_ts[:10] == datetime.now().strftime("%Y-%m-%d"):
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log_ok("数据管道", f"市场数据今天更新({mkt_ts})")
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conn = sqlite3.connect(str(DATA_DIR / "mofin.db"))
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row = conn.execute(
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"SELECT created_at FROM market_snapshots ORDER BY created_at DESC LIMIT 1"
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).fetchone()
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conn.close()
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if row and row[0][:10] == datetime.now().strftime("%Y-%m-%d"):
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log_ok("数据管道", f"市场数据今天更新({row[0]})")
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else:
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log_issue("数据管道", "HIGH", f"市场数据未更新, 最后{mkt_ts}")
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except:
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log_issue("数据管道", "HIGH", "market.json缺失")
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log_issue("数据管道", "HIGH", f"市场数据未更新(DB), 最后{row[0] if row else '无数据'}")
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except Exception as e:
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log_issue("数据管道", "HIGH", f"DB检查失败: {e}")
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# ── 7. 系统服务 ──
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