add monitor mode for reevaluation without database writes
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sam
parent
ae1a49f79f
commit
d2a056d7c0
@ -93,9 +93,10 @@ class BacktestSession:
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class BacktestEngine:
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"""Runs the multi-agent game inside a daily event-driven loop."""
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def __init__(self, cfg: BtConfig) -> None:
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def __init__(self, cfg: BtConfig, *, persist_results: bool = True) -> None:
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self.cfg = cfg
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self.agents = default_agents()
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self.persist_results = bool(persist_results)
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app_cfg = get_config()
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weight_config = app_cfg.agent_weights.as_dict() if app_cfg.agent_weights else {}
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if weight_config:
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@ -572,6 +573,9 @@ class BacktestEngine:
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)
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)
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if not self.persist_results:
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return
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try:
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with db_session() as conn:
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conn.executemany(
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@ -3,6 +3,7 @@ from __future__ import annotations
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import json
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from collections import Counter
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from dataclasses import dataclass
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from datetime import date, datetime, timedelta
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from typing import Dict, List, Optional
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@ -111,15 +112,115 @@ def _fetch_agent_actions(trade_date: str, symbols: List[str]) -> Dict[str, Dict[
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return mapping
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@dataclass
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class ReevaluationOutcome:
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changes: List[Dict[str, object]]
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summary: List[Dict[str, object]]
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monitor_only: bool = False
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def _summarize_decisions(
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records: List[tuple[str, object, object]],
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engine: BacktestEngine,
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expected_symbols: List[str],
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) -> tuple[Dict[str, Dict[str, Optional[str]]], List[Dict[str, object]]]:
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agent_names = [agent.name for agent in getattr(engine, "agents", [])]
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after_map: Dict[str, Dict[str, Optional[str]]] = {}
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summary_rows: List[Dict[str, object]] = []
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seen_codes: set[str] = set()
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for ts_code, _context, decision in records:
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seen_codes.add(ts_code)
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agent_actions: Dict[str, Optional[str]] = {}
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final_action = getattr(decision, "action", None)
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agent_actions["global"] = getattr(final_action, "value", final_action)
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department_map = getattr(decision, "department_decisions", {}) or {}
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for dept_code, dept_decision in department_map.items():
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agent_actions[f"dept_{dept_code}"] = getattr(dept_decision.action, "value", dept_decision.action)
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utilities = getattr(decision, "utilities", {}) or {}
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for agent_name in agent_names:
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best_action: Optional[str] = None
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best_score: Optional[float] = None
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for action, agent_scores in utilities.items():
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if not isinstance(agent_scores, dict):
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continue
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score = agent_scores.get(agent_name)
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if score is None:
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continue
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if best_score is None or score > best_score:
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best_score = score
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best_action = getattr(action, "value", action)
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if best_action is not None:
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agent_actions[agent_name] = best_action
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after_map[ts_code] = agent_actions
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target_weight = getattr(decision, "target_weight", None)
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summary_rows.append(
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{
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"代码": ts_code,
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"最终动作": getattr(final_action, "value", final_action),
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"信心": float(getattr(decision, "confidence", 0.0) or 0.0),
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"目标权重": float(target_weight) if target_weight is not None else None,
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"需复核": bool(getattr(decision, "requires_review", False)),
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"备注": "",
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}
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)
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expected_unique = list(dict.fromkeys(expected_symbols))
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for code in expected_unique:
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if code not in seen_codes:
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summary_rows.append(
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{
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"代码": code,
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"最终动作": None,
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"信心": None,
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"目标权重": None,
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"需复核": None,
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"备注": "未返回决策（可能缺少数据或行情）",
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}
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)
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return after_map, summary_rows
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def _render_summary_table(rows: List[Dict[str, object]], *, title: str) -> None:
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if not rows:
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return
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df = pd.DataFrame(rows)
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if df.empty:
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return
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formatted = df.copy()
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if "信心" in formatted.columns:
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formatted["信心"] = formatted["信心"].apply(
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lambda x: f"{x:.2f}" if x is not None else "-"
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)
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if "目标权重" in formatted.columns:
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formatted["目标权重"] = formatted["目标权重"].apply(
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lambda x: f"{x:+.2%}" if x is not None else "-"
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)
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if "需复核" in formatted.columns:
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formatted["需复核"] = formatted["需复核"].apply(
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lambda x: "是" if x is True else ("否" if x is False else "-")
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)
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if "备注" in formatted.columns:
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formatted["备注"] = formatted["备注"].apply(lambda x: x if x else "")
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st.write(title)
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st.dataframe(formatted, hide_index=True, width="stretch")
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def _reevaluate_symbols(
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trade_date_obj: date,
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symbols: List[str],
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cfg_id: str,
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cfg_name: str,
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) -> List[Dict[str, object]]:
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*,
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monitor_only: bool = False,
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) -> ReevaluationOutcome:
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unique_symbols = list(dict.fromkeys(symbols))
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if not unique_symbols:
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return []
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return ReevaluationOutcome(changes=[], summary=[], monitor_only=monitor_only)
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trade_date_str = trade_date_obj.isoformat()
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before_map = _fetch_agent_actions(trade_date_str, unique_symbols)
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engine_params: Dict[str, object] = {}
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@ -155,17 +256,26 @@ def _reevaluate_symbols(
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universe=unique_symbols,
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params=engine_params,
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)
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engine = BacktestEngine(cfg)
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engine = BacktestEngine(cfg, persist_results=not monitor_only)
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state = PortfolioState(cash=engine.initial_cash)
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engine.simulate_day(trade_date_obj, state)
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after_map = _fetch_agent_actions(trade_date_str, unique_symbols)
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records = engine.simulate_day(trade_date_obj, state)
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after_map, summary_rows = _summarize_decisions(records, engine, unique_symbols)
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if monitor_only:
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for row in summary_rows:
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if not row.get("备注"):
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row["备注"] = "监控模式预览"
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symbol_order = list(
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dict.fromkeys(unique_symbols + list(before_map.keys()) + list(after_map.keys()))
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)
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changes: List[Dict[str, object]] = []
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for code in unique_symbols:
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for code in symbol_order:
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before_agents = before_map.get(code, {})
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after_agents = after_map.get(code, {})
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for agent, new_action in after_agents.items():
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agents = set(before_agents.keys()) | set(after_agents.keys())
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for agent in sorted(agents):
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old_action = before_agents.get(agent)
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if new_action != old_action:
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new_action = after_agents.get(agent)
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if old_action != new_action:
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changes.append(
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{
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"代码": code,
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@ -174,17 +284,7 @@ def _reevaluate_symbols(
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"新动作": new_action,
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}
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)
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for agent, old_action in before_agents.items():
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if agent not in after_agents:
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changes.append(
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{
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"代码": code,
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"代理": agent,
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"原动作": old_action,
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"新动作": None,
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}
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)
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return changes
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return ReevaluationOutcome(changes=changes, summary=summary_rows, monitor_only=monitor_only)
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def render_today_plan() -> None:
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@ -281,25 +381,39 @@ def render_today_plan() -> None:
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metrics_cols[0].metric("已产出决策数", len(agent_symbols))
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metrics_cols[1].metric("候选池标的", len(candidate_records))
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metrics_cols[2].metric("当前持仓", len(position_codes))
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st.caption("对当前交易日所有标的触发策略重评估。")
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monitor_only = st.checkbox(
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"仅监控模式（不写入数据库）",
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key="reeval_monitor_only",
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)
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if monitor_only:
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st.caption("监控模式：仅生成策略预览，不更新 agent_utils 或候选池记录。")
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else:
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st.caption("对当前交易日所有标的触发策略重评估。")
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if st.button("一键重评估全部", type="primary", width="stretch"):
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with st.spinner("正在对所有标的进行重评估，请稍候..."):
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try:
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trade_date_obj = _parse_trade_date(trade_date)
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progress = st.progress(0.0)
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progress.progress(0.3 if symbols else 1.0)
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changes_all = _reevaluate_symbols(
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outcome = _reevaluate_symbols(
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trade_date_obj,
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symbols,
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"reeval_ui_all",
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"UI All Re-eval",
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monitor_only=monitor_only,
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)
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progress.progress(1.0)
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st.success(f"一键重评估完成：共处理 {len(symbols)} 个标的")
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if changes_all:
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if outcome.changes:
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st.write("检测到以下动作变更：")
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st.dataframe(pd.DataFrame(changes_all), hide_index=True, width='stretch')
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st.rerun()
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st.dataframe(pd.DataFrame(outcome.changes), hide_index=True, width='stretch')
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if monitor_only:
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_render_summary_table(
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outcome.summary,
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title="最新策略摘要（监控模式预览）",
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)
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else:
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st.rerun()
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except Exception as exc: # noqa: BLE001
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LOGGER.exception("一键重评估失败", extra=LOG_EXTRA)
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st.error(f"一键重评估执行过程中发生错误：{exc}")
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@ -800,18 +914,24 @@ def _render_today_plan_reevaluation_controls(
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if add_divider:
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st.divider()
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st.subheader("策略重评估")
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st.caption("对当前选中的交易日与标的，立即触发一次策略评估并回写 agent_utils。")
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monitor_only = bool(st.session_state.get("reeval_monitor_only", False))
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if monitor_only:
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st.caption("监控模式已开启：生成策略预览但不会写入数据库。")
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else:
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st.caption("对当前选中的交易日与标的，立即触发一次策略评估并回写 agent_utils。")
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cols_re = st.columns([1, 1])
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if cols_re[0].button("对该标的重评估", key=f"reevaluate_{ts_code}"):
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with st.spinner("正在重评估..."):
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try:
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trade_date_obj = _parse_trade_date(trade_date)
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changes = _reevaluate_symbols(
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outcome = _reevaluate_symbols(
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trade_date_obj,
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[ts_code],
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"reeval_ui",
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"UI Re-evaluation",
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monitor_only=monitor_only,
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)
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changes = outcome.changes
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if changes:
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for change in changes:
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change.setdefault("代码", ts_code)
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@ -822,7 +942,13 @@ def _render_today_plan_reevaluation_controls(
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st.dataframe(df_changes, hide_index=True, width='stretch')
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else:
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st.success("重评估完成，无动作变更。")
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st.rerun()
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if monitor_only:
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_render_summary_table(
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outcome.summary,
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title="策略结果预览（监控模式）",
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)
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else:
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st.rerun()
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except Exception as exc: # noqa: BLE001
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LOGGER.exception("重评估失败", extra=LOG_EXTRA)
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st.error(f"重评估失败：{exc}")
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@ -837,17 +963,24 @@ def _render_today_plan_reevaluation_controls(
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trade_date_obj = _parse_trade_date(trade_date)
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progress = st.progress(0.0)
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progress.progress(0.3 if batch_symbols else 1.0)
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changes_all = _reevaluate_symbols(
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outcome = _reevaluate_symbols(
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trade_date_obj,
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batch_symbols,
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"reeval_ui_batch",
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"UI Batch Re-eval",
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monitor_only=monitor_only,
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)
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progress.progress(1.0)
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st.success(f"批量重评估完成：共处理 {len(batch_symbols)} 个标的")
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if changes_all:
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st.dataframe(pd.DataFrame(changes_all), hide_index=True, width='stretch')
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st.rerun()
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if outcome.changes:
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st.dataframe(pd.DataFrame(outcome.changes), hide_index=True, width='stretch')
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if monitor_only:
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_render_summary_table(
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outcome.summary,
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title="批量策略结果预览（监控模式）",
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)
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else:
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st.rerun()
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except Exception as exc: # noqa: BLE001
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LOGGER.exception("批量重评估失败", extra=LOG_EXTRA)
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st.error(f"批量重评估失败：{exc}")
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@ -39,10 +39,10 @@
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| 工作项 | 状态 | 说明 |
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| --- | --- | --- |
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| 一键重评估入口 | ✅ | 今日计划页提供批量/全量重评估入口，待收集反馈再做优化。 |
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| 回测实验对比 | 🔄 | 新增会话实验保存与曲线/指标对比，后续接入更多提示参数。 |
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| 回测实验对比 | ✅ | 会话实验保存、曲线/指标对比与日期过滤均已落地，后续按需扩充提示参数。 |
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| 实时指标面板 | ✅ | Streamlit 监控页已具备核心实时指标。 |
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| 异常日志钻取 | ⏳ | 待补充筛选、定位与历史对比能力。 |
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| 仅监控模式 | ⏳ | 支持“监控不干预”场景的一键复评策略。 |
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| 异常日志钻取 | ✅ | 日志视图支持时间/级别/阶段筛选、关键词搜索及双日期历史对比导出。 |
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| 仅监控模式 | ✅ | 今日计划页新增监控模式一键重评估，预览策略而不写入数据库。 |
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| 风险预警面板 | ✅ | 风险预警能力已落地，仍需持续扩充指标。 |
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## 风险控制与执行
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