2804 lines
124 KiB
Python
2804 lines
124 KiB
Python
"""Streamlit UI scaffold for the investment assistant."""
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from __future__ import annotations
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import sys
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from dataclasses import asdict
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from datetime import date, datetime, timedelta
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from pathlib import Path
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from typing import Dict, List, Optional
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ROOT = Path(__file__).resolve().parents[2]
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if str(ROOT) not in sys.path:
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sys.path.insert(0, str(ROOT))
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import json
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from datetime import datetime
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import uuid
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import pandas as pd
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import plotly.express as px
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import plotly.graph_objects as go
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import requests
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from requests.exceptions import RequestException
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import streamlit as st
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from app.agents.base import AgentContext
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from app.agents.game import Decision
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from app.backtest.engine import BtConfig, run_backtest
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from app.ui.portfolio_config import render_portfolio_config
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from app.backtest.decision_env import DecisionEnv, ParameterSpec
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from app.data.schema import initialize_database
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from app.ingest.checker import run_boot_check
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from app.ingest.tushare import FetchJob, run_ingestion
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from app.llm.client import llm_config_snapshot, run_llm
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from app.llm.metrics import (
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recent_decisions as llm_recent_decisions,
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register_listener as register_llm_metrics_listener,
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reset as reset_llm_metrics,
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snapshot as snapshot_llm_metrics,
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)
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from app.utils import alerts
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from app.utils.config import (
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ALLOWED_LLM_STRATEGIES,
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DEFAULT_LLM_BASE_URLS,
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DEFAULT_LLM_MODEL_OPTIONS,
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DEFAULT_LLM_MODELS,
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DepartmentSettings,
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LLMEndpoint,
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LLMProvider,
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get_config,
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save_config,
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)
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from app.utils.db import db_session
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from app.utils.logging import get_logger
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from app.utils.portfolio import (
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get_latest_snapshot,
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list_investment_pool,
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list_positions,
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list_recent_trades,
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)
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from app.agents.registry import default_agents
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from app.utils.tuning import log_tuning_result
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from app.backtest.engine import BacktestEngine, PortfolioState
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LOGGER = get_logger(__name__)
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LOG_EXTRA = {"stage": "ui"}
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_DECISION_ENV_SINGLE_RESULT_KEY = "decision_env_single_result"
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_DECISION_ENV_BATCH_RESULTS_KEY = "decision_env_batch_results"
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_DASHBOARD_CONTAINERS: Optional[tuple[object, object]] = None
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_DASHBOARD_ELEMENTS: Optional[Dict[str, object]] = None
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_SIDEBAR_LISTENER_ATTACHED = False
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# ADD: simple in-memory cache for provider model discovery
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_MODEL_CACHE: Dict[str, Dict[str, object]] = {}
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_CACHE_TTL_SECONDS = 300
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_WARNINGS_CONTAINER = None
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_WARNINGS_PLACEHOLDER = None
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# ADD: query param helpers
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def _get_query_params() -> Dict[str, List[str]]:
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try:
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return dict(st.query_params)
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except Exception:
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return {}
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def _set_query_params(**kwargs: object) -> None:
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try:
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payload = {k: v for k, v in kwargs.items() if v is not None}
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if payload:
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st.query_params.update(payload)
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except Exception:
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pass
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def _sidebar_metrics_listener(metrics: Dict[str, object]) -> None:
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try:
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_update_dashboard_sidebar(metrics)
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except Exception: # noqa: BLE001
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LOGGER.debug("侧边栏监听器刷新失败", exc_info=True, extra=LOG_EXTRA)
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def render_global_dashboard() -> None:
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"""Render a persistent sidebar with realtime LLM stats and recent decisions."""
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global _DASHBOARD_CONTAINERS
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global _DASHBOARD_ELEMENTS
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global _SIDEBAR_LISTENER_ATTACHED
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global _WARNINGS_CONTAINER
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global _WARNINGS_PLACEHOLDER
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# ADD: warning badge on top
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warnings = alerts.get_warnings()
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badge = f" ({len(warnings)})" if warnings else ""
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st.sidebar.header(f"系统监控{badge}")
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metrics_container = st.sidebar.container()
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decisions_container = st.sidebar.container()
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_WARNINGS_CONTAINER = st.sidebar.container()
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_WARNINGS_PLACEHOLDER = st.sidebar.empty()
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_DASHBOARD_CONTAINERS = (metrics_container, decisions_container)
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_DASHBOARD_ELEMENTS = _ensure_dashboard_elements(metrics_container, decisions_container)
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if not _SIDEBAR_LISTENER_ATTACHED:
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register_llm_metrics_listener(_sidebar_metrics_listener)
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_SIDEBAR_LISTENER_ATTACHED = True
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_update_dashboard_sidebar()
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def _update_dashboard_sidebar(
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metrics: Optional[Dict[str, object]] = None,
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) -> None:
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global _DASHBOARD_CONTAINERS
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global _DASHBOARD_ELEMENTS
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global _WARNINGS_CONTAINER
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global _WARNINGS_PLACEHOLDER
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containers = _DASHBOARD_CONTAINERS
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if not containers:
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return
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metrics_container, decisions_container = containers
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elements = _DASHBOARD_ELEMENTS
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if elements is None:
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elements = _ensure_dashboard_elements(metrics_container, decisions_container)
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_DASHBOARD_ELEMENTS = elements
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||
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if metrics is None:
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metrics = snapshot_llm_metrics()
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elements["metrics_calls"].metric("LLM 调用", metrics.get("total_calls", 0))
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elements["metrics_prompt"].metric("Prompt Tokens", metrics.get("total_prompt_tokens", 0))
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elements["metrics_completion"].metric(
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"Completion Tokens", metrics.get("total_completion_tokens", 0)
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)
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provider_calls = metrics.get("provider_calls", {})
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model_calls = metrics.get("model_calls", {})
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provider_placeholder = elements["provider_distribution"]
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provider_placeholder.empty()
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if provider_calls:
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provider_placeholder.json(provider_calls)
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else:
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provider_placeholder.info("暂无 Provider 分布数据。")
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model_placeholder = elements["model_distribution"]
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model_placeholder.empty()
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if model_calls:
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model_placeholder.json(model_calls)
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else:
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model_placeholder.info("暂无模型分布数据。")
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decisions = metrics.get("recent_decisions") or llm_recent_decisions(10)
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if decisions:
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lines = []
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for record in reversed(decisions[-10:]):
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ts_code = record.get("ts_code")
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trade_date = record.get("trade_date")
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action = record.get("action")
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confidence = record.get("confidence", 0.0)
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summary = record.get("summary")
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line = f"**{trade_date} {ts_code}** → {action} (置信度 {confidence:.2f})"
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if summary:
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line += f"\n<small>{summary}</small>"
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lines.append(line)
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decisions_placeholder = elements["decisions_list"]
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decisions_placeholder.empty()
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decisions_placeholder.markdown("\n\n".join(lines), unsafe_allow_html=True)
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else:
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decisions_placeholder = elements["decisions_list"]
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decisions_placeholder.empty()
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decisions_placeholder.info("暂无决策记录。执行回测或实时评估后可在此查看。")
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# Render warnings section in-place (clear then write)
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if _WARNINGS_PLACEHOLDER is not None:
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_WARNINGS_PLACEHOLDER.empty()
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with _WARNINGS_PLACEHOLDER.container():
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st.subheader("数据告警")
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warn_list = alerts.get_warnings()
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if warn_list:
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lines = []
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for warning in warn_list[-10:]:
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detail = warning.get("detail")
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appendix = f" {detail}" if detail else ""
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lines.append(
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f"- **{warning['source']}** {warning['message']}{appendix}\n<small>{warning['timestamp']}</small>"
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)
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st.markdown("\n".join(lines), unsafe_allow_html=True)
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btn_cols = st.columns([1,1])
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if btn_cols[0].button("清除数据告警", key="clear_data_alerts_sibling"):
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alerts.clear_warnings()
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_update_dashboard_sidebar()
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try:
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st.download_button(
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"导出告警(JSON)",
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data=json.dumps(warn_list, ensure_ascii=False, indent=2),
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file_name="data_warnings.json",
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mime="application/json",
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key="dl_warnings_json_sibling",
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)
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except Exception:
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pass
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else:
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st.info("暂无数据告警。")
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def _ensure_dashboard_elements(metrics_container, decisions_container) -> Dict[str, object]:
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metrics_container.header("系统监控")
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col_a, col_b, col_c = metrics_container.columns(3)
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metrics_calls = col_a.empty()
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metrics_prompt = col_b.empty()
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metrics_completion = col_c.empty()
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distribution_expander = metrics_container.expander("调用分布", expanded=False)
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provider_distribution = distribution_expander.empty()
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model_distribution = distribution_expander.empty()
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decisions_container.subheader("最新决策")
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decisions_list = decisions_container.empty()
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elements = {
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"metrics_calls": metrics_calls,
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"metrics_prompt": metrics_prompt,
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||
"metrics_completion": metrics_completion,
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||
"provider_distribution": provider_distribution,
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||
"model_distribution": model_distribution,
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"decisions_list": decisions_list,
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}
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return elements
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||
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||
def _discover_provider_models(provider: LLMProvider, base_override: str = "", api_override: Optional[str] = None) -> tuple[list[str], Optional[str]]:
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"""Attempt to query provider API and return available model ids."""
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||
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base_url = (base_override or provider.base_url or DEFAULT_LLM_BASE_URLS.get(provider.key, "")).strip()
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if not base_url:
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return [], "请先填写 Base URL"
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||
timeout = float(provider.default_timeout or 30.0)
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||
mode = provider.mode or ("ollama" if provider.key == "ollama" else "openai")
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||
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# ADD: simple cache by provider+base URL
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||
cache_key = f"{provider.key}|{base_url}"
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||
now = datetime.now()
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||
cached = _MODEL_CACHE.get(cache_key)
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if cached:
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ts = cached.get("ts")
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if isinstance(ts, float) and (now.timestamp() - ts) < _CACHE_TTL_SECONDS:
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models = list(cached.get("models") or [])
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return models, None
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||
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try:
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||
if mode == "ollama":
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||
url = base_url.rstrip('/') + "/api/tags"
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||
response = requests.get(url, timeout=timeout)
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response.raise_for_status()
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||
data = response.json()
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||
models = []
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for item in data.get("models", []) or data.get("data", []):
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||
name = item.get("name") or item.get("model") or item.get("tag")
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if name:
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models.append(str(name).strip())
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_MODEL_CACHE[cache_key] = {"ts": now.timestamp(), "models": sorted(set(models))}
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return sorted(set(models)), None
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api_key = (api_override or provider.api_key or "").strip()
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||
if not api_key:
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||
return [], "缺少 API Key"
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||
url = base_url.rstrip('/') + "/v1/models"
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||
headers = {
|
||
"Authorization": f"Bearer {api_key}",
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||
"Content-Type": "application/json",
|
||
}
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||
response = requests.get(url, headers=headers, timeout=timeout)
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||
response.raise_for_status()
|
||
payload = response.json()
|
||
models = [
|
||
str(item.get("id")).strip()
|
||
for item in payload.get("data", [])
|
||
if item.get("id")
|
||
]
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||
_MODEL_CACHE[cache_key] = {"ts": now.timestamp(), "models": sorted(set(models))}
|
||
return sorted(set(models)), None
|
||
except RequestException as exc: # noqa: BLE001
|
||
return [], f"HTTP 错误：{exc}"
|
||
except Exception as exc: # noqa: BLE001
|
||
return [], f"解析失败：{exc}"
|
||
|
||
def _load_stock_options(limit: int = 500) -> list[str]:
|
||
try:
|
||
with db_session(read_only=True) as conn:
|
||
rows = conn.execute(
|
||
"SELECT ts_code, name FROM stock_basic WHERE list_status = 'L' ORDER BY ts_code"
|
||
).fetchall()
|
||
except Exception:
|
||
LOGGER.exception("加载股票列表失败", extra=LOG_EXTRA)
|
||
return []
|
||
options: list[str] = []
|
||
for row in rows[:limit]:
|
||
code = row["ts_code"]
|
||
name = row["name"] or ""
|
||
label = f"{code} | {name}" if name else code
|
||
options.append(label)
|
||
LOGGER.info("加载股票选项完成，数量=%s", len(options), extra=LOG_EXTRA)
|
||
return options
|
||
|
||
|
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def _parse_ts_code(selection: str) -> str:
|
||
return selection.split(' | ')[0].strip().upper()
|
||
|
||
|
||
def _load_daily_frame(ts_code: str, start: date, end: date) -> pd.DataFrame:
|
||
LOGGER.info(
|
||
"加载行情数据：ts_code=%s start=%s end=%s",
|
||
ts_code,
|
||
start,
|
||
end,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
start_str = start.strftime('%Y%m%d')
|
||
end_str = end.strftime('%Y%m%d')
|
||
range_query = (
|
||
"SELECT trade_date, open, high, low, close, vol, amount "
|
||
"FROM daily WHERE ts_code = ? AND trade_date BETWEEN ? AND ? ORDER BY trade_date"
|
||
)
|
||
fallback_query = (
|
||
"SELECT trade_date, open, high, low, close, vol, amount "
|
||
"FROM daily WHERE ts_code = ? ORDER BY trade_date DESC LIMIT 200"
|
||
)
|
||
with db_session(read_only=True) as conn:
|
||
df = pd.read_sql_query(range_query, conn, params=(ts_code, start_str, end_str))
|
||
if df.empty:
|
||
df = pd.read_sql_query(fallback_query, conn, params=(ts_code,))
|
||
if df.empty:
|
||
LOGGER.warning(
|
||
"行情数据为空：ts_code=%s start=%s end=%s",
|
||
ts_code,
|
||
start,
|
||
end,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
return df
|
||
df = df.sort_values('trade_date')
|
||
df['trade_date'] = pd.to_datetime(df['trade_date'])
|
||
df.set_index('trade_date', inplace=True)
|
||
LOGGER.info("行情数据加载完成：条数=%s", len(df), extra=LOG_EXTRA)
|
||
return df
|
||
|
||
|
||
def _get_latest_trade_date() -> Optional[date]:
|
||
try:
|
||
with db_session(read_only=True) as conn:
|
||
row = conn.execute(
|
||
"SELECT trade_date FROM daily ORDER BY trade_date DESC LIMIT 1"
|
||
).fetchone()
|
||
except Exception: # noqa: BLE001
|
||
LOGGER.exception("查询最新交易日失败", extra=LOG_EXTRA)
|
||
return None
|
||
if not row:
|
||
return None
|
||
raw_value = row["trade_date"]
|
||
if not raw_value:
|
||
return None
|
||
try:
|
||
return datetime.strptime(str(raw_value), "%Y%m%d").date()
|
||
except ValueError:
|
||
try:
|
||
return datetime.fromisoformat(str(raw_value)).date()
|
||
except ValueError:
|
||
LOGGER.warning("无法解析交易日：%s", raw_value, extra=LOG_EXTRA)
|
||
return None
|
||
|
||
|
||
def _default_backtest_range(window_days: int = 60) -> tuple[date, date]:
|
||
latest = _get_latest_trade_date() or date.today()
|
||
start = latest - timedelta(days=window_days)
|
||
if start > latest:
|
||
start = latest
|
||
return start, latest
|
||
|
||
|
||
def render_today_plan() -> None:
|
||
LOGGER.info("渲染今日计划页面", extra=LOG_EXTRA)
|
||
st.header("今日计划")
|
||
latest_trade_date = _get_latest_trade_date()
|
||
if latest_trade_date:
|
||
st.caption(f"最新交易日：{latest_trade_date.isoformat()}（统计数据请见左侧系统监控）")
|
||
else:
|
||
st.caption("统计与决策概览现已移至左侧'系统监控'侧栏。")
|
||
try:
|
||
with db_session(read_only=True) as conn:
|
||
date_rows = conn.execute(
|
||
"""
|
||
SELECT DISTINCT trade_date
|
||
FROM agent_utils
|
||
ORDER BY trade_date DESC
|
||
LIMIT 30
|
||
"""
|
||
).fetchall()
|
||
except Exception: # noqa: BLE001
|
||
LOGGER.exception("加载 agent_utils 失败", extra=LOG_EXTRA)
|
||
st.warning("暂未写入部门/代理决策，请先运行回测或策略评估流程。")
|
||
return
|
||
|
||
trade_dates = [row["trade_date"] for row in date_rows]
|
||
if not trade_dates:
|
||
st.info("暂无决策记录，完成一次回测后即可在此查看部门意见与投票结果。")
|
||
return
|
||
|
||
# ADD: read default selection from URL
|
||
q = _get_query_params()
|
||
default_trade_date = q.get("date", [trade_dates[0]])[0]
|
||
try:
|
||
default_idx = trade_dates.index(default_trade_date)
|
||
except ValueError:
|
||
default_idx = 0
|
||
trade_date = st.selectbox("交易日", trade_dates, index=default_idx)
|
||
|
||
with db_session(read_only=True) as conn:
|
||
code_rows = conn.execute(
|
||
"""
|
||
SELECT DISTINCT ts_code
|
||
FROM agent_utils
|
||
WHERE trade_date = ?
|
||
ORDER BY ts_code
|
||
""",
|
||
(trade_date,),
|
||
).fetchall()
|
||
symbols = [row["ts_code"] for row in code_rows]
|
||
if not symbols:
|
||
st.info("所选交易日暂无 agent_utils 记录。")
|
||
return
|
||
|
||
default_ts = q.get("code", [symbols[0]])[0]
|
||
try:
|
||
default_ts_idx = symbols.index(default_ts)
|
||
except ValueError:
|
||
default_ts_idx = 0
|
||
ts_code = st.selectbox("标的", symbols, index=default_ts_idx)
|
||
# ADD: batch selection for re-evaluation
|
||
batch_symbols = st.multiselect("批量重评估（可多选）", symbols, default=[])
|
||
|
||
# 一键重评估所有标的按钮
|
||
if st.button("一键重评估所有标的", type="primary", width='stretch'):
|
||
with st.spinner("正在对所有标的进行重评估，请稍候..."):
|
||
try:
|
||
# 解析交易日
|
||
trade_date_obj = None
|
||
try:
|
||
trade_date_obj = date.fromisoformat(str(trade_date))
|
||
except Exception:
|
||
try:
|
||
trade_date_obj = datetime.strptime(str(trade_date), "%Y%m%d").date()
|
||
except Exception:
|
||
pass
|
||
if trade_date_obj is None:
|
||
raise ValueError(f"无法解析交易日：{trade_date}")
|
||
|
||
progress = st.progress(0.0)
|
||
changes_all = []
|
||
success_count = 0
|
||
error_count = 0
|
||
|
||
# 遍历所有标的
|
||
for idx, code in enumerate(symbols, start=1):
|
||
try:
|
||
# 保存重评估前的状态
|
||
with db_session(read_only=True) as conn:
|
||
before_rows = conn.execute(
|
||
"SELECT agent, action FROM agent_utils WHERE trade_date = ? AND ts_code = ?",
|
||
(trade_date, code),
|
||
).fetchall()
|
||
before_map = {row["agent"]: row["action"] for row in before_rows}
|
||
|
||
# 执行重评估
|
||
cfg = BtConfig(
|
||
id="reeval_ui_all",
|
||
name="UI All Re-eval",
|
||
start_date=trade_date_obj,
|
||
end_date=trade_date_obj,
|
||
universe=[code],
|
||
params={},
|
||
)
|
||
engine = BacktestEngine(cfg)
|
||
state = PortfolioState()
|
||
_ = engine.simulate_day(trade_date_obj, state)
|
||
|
||
# 检查变化
|
||
with db_session(read_only=True) as conn:
|
||
after_rows = conn.execute(
|
||
"SELECT agent, action FROM agent_utils WHERE trade_date = ? AND ts_code = ?",
|
||
(trade_date, code),
|
||
).fetchall()
|
||
for row in after_rows:
|
||
agent = row["agent"]
|
||
new_action = row["action"]
|
||
old_action = before_map.get(agent)
|
||
if new_action != old_action:
|
||
changes_all.append({"代码": code, "代理": agent, "原动作": old_action, "新动作": new_action})
|
||
success_count += 1
|
||
except Exception as e:
|
||
LOGGER.exception(f"重评估 {code} 失败", extra=LOG_EXTRA)
|
||
error_count += 1
|
||
|
||
# 更新进度
|
||
progress.progress(idx / len(symbols))
|
||
|
||
# 显示结果
|
||
if error_count > 0:
|
||
st.error(f"一键重评估完成：成功 {success_count} 个，失败 {error_count} 个")
|
||
else:
|
||
st.success(f"一键重评估完成：所有 {success_count} 个标的重评估成功")
|
||
|
||
# 显示变更记录
|
||
if changes_all:
|
||
st.write("检测到以下动作变更：")
|
||
st.dataframe(pd.DataFrame(changes_all), hide_index=True, width='stretch')
|
||
|
||
# 刷新页面数据
|
||
st.rerun()
|
||
except Exception as exc:
|
||
LOGGER.exception("一键重评估失败", extra=LOG_EXTRA)
|
||
st.error(f"一键重评估执行过程中发生错误：{exc}")
|
||
|
||
# sync URL params
|
||
_set_query_params(date=str(trade_date), code=str(ts_code))
|
||
|
||
with db_session(read_only=True) as conn:
|
||
rows = conn.execute(
|
||
"""
|
||
SELECT agent, action, utils, feasible, weight
|
||
FROM agent_utils
|
||
WHERE trade_date = ? AND ts_code = ?
|
||
ORDER BY CASE WHEN agent = 'global' THEN 1 ELSE 0 END, agent
|
||
""",
|
||
(trade_date, ts_code),
|
||
).fetchall()
|
||
|
||
if not rows:
|
||
st.info("未查询到详细决策记录，稍后再试。")
|
||
return
|
||
|
||
try:
|
||
feasible_actions = json.loads(rows[0]["feasible"] or "[]")
|
||
except (KeyError, TypeError, json.JSONDecodeError):
|
||
feasible_actions = []
|
||
|
||
global_info = None
|
||
dept_records: List[Dict[str, object]] = []
|
||
dept_details: Dict[str, Dict[str, object]] = {}
|
||
agent_records: List[Dict[str, object]] = []
|
||
|
||
for item in rows:
|
||
agent_name = item["agent"]
|
||
action = item["action"]
|
||
weight = float(item["weight"] or 0.0)
|
||
try:
|
||
utils = json.loads(item["utils"] or "{}")
|
||
except json.JSONDecodeError:
|
||
utils = {}
|
||
|
||
if agent_name == "global":
|
||
global_info = {
|
||
"action": action,
|
||
"confidence": float(utils.get("_confidence", 0.0)),
|
||
"target_weight": float(utils.get("_target_weight", 0.0)),
|
||
"department_votes": utils.get("_department_votes", {}),
|
||
"requires_review": bool(utils.get("_requires_review", False)),
|
||
"scope_values": utils.get("_scope_values", {}),
|
||
"close_series": utils.get("_close_series", []),
|
||
"turnover_series": utils.get("_turnover_series", []),
|
||
"department_supplements": utils.get("_department_supplements", {}),
|
||
"department_dialogue": utils.get("_department_dialogue", {}),
|
||
"department_telemetry": utils.get("_department_telemetry", {}),
|
||
}
|
||
continue
|
||
|
||
if agent_name.startswith("dept_"):
|
||
code = agent_name.split("dept_", 1)[-1]
|
||
signals = utils.get("_signals", [])
|
||
risks = utils.get("_risks", [])
|
||
supplements = utils.get("_supplements", [])
|
||
dialogue = utils.get("_dialogue", [])
|
||
telemetry = utils.get("_telemetry", {})
|
||
dept_records.append(
|
||
{
|
||
"部门": code,
|
||
"行动": action,
|
||
"信心": float(utils.get("_confidence", 0.0)),
|
||
"权重": weight,
|
||
"摘要": utils.get("_summary", ""),
|
||
"核心信号": "；".join(signals) if isinstance(signals, list) else signals,
|
||
"风险提示": "；".join(risks) if isinstance(risks, list) else risks,
|
||
"补充次数": len(supplements) if isinstance(supplements, list) else 0,
|
||
}
|
||
)
|
||
dept_details[code] = {
|
||
"supplements": supplements if isinstance(supplements, list) else [],
|
||
"dialogue": dialogue if isinstance(dialogue, list) else [],
|
||
"summary": utils.get("_summary", ""),
|
||
"signals": signals,
|
||
"risks": risks,
|
||
"telemetry": telemetry if isinstance(telemetry, dict) else {},
|
||
}
|
||
else:
|
||
score_map = {
|
||
key: float(val)
|
||
for key, val in utils.items()
|
||
if not str(key).startswith("_")
|
||
}
|
||
agent_records.append(
|
||
{
|
||
"代理": agent_name,
|
||
"建议动作": action,
|
||
"权重": weight,
|
||
"SELL": score_map.get("SELL", 0.0),
|
||
"HOLD": score_map.get("HOLD", 0.0),
|
||
"BUY_S": score_map.get("BUY_S", 0.0),
|
||
"BUY_M": score_map.get("BUY_M", 0.0),
|
||
"BUY_L": score_map.get("BUY_L", 0.0),
|
||
}
|
||
)
|
||
|
||
if feasible_actions:
|
||
st.caption(f"可行操作集合：{', '.join(feasible_actions)}")
|
||
|
||
st.subheader("全局策略")
|
||
if global_info:
|
||
col1, col2, col3 = st.columns(3)
|
||
col1.metric("最终行动", global_info["action"])
|
||
col2.metric("信心", f"{global_info['confidence']:.2f}")
|
||
col3.metric("目标权重", f"{global_info['target_weight']:+.2%}")
|
||
if global_info["department_votes"]:
|
||
st.json(global_info["department_votes"])
|
||
if global_info["requires_review"]:
|
||
st.warning("部门分歧较大，已标记为需人工复核。")
|
||
with st.expander("基础上下文数据", expanded=False):
|
||
# ADD: export buttons
|
||
scope = global_info.get("scope_values") or {}
|
||
close_series = global_info.get("close_series") or []
|
||
turnover_series = global_info.get("turnover_series") or []
|
||
st.write("最新字段：")
|
||
if scope:
|
||
st.json(scope)
|
||
st.download_button(
|
||
"下载字段(JSON)",
|
||
data=json.dumps(scope, ensure_ascii=False, indent=2),
|
||
file_name=f"{ts_code}_{trade_date}_scope.json",
|
||
mime="application/json",
|
||
key="dl_scope_json",
|
||
)
|
||
if close_series:
|
||
st.write("收盘价时间序列 (最近窗口)：")
|
||
st.json(close_series)
|
||
try:
|
||
import io, csv
|
||
buf = io.StringIO()
|
||
writer = csv.writer(buf)
|
||
writer.writerow(["trade_date", "close"])
|
||
for dt, val in close_series:
|
||
writer.writerow([dt, val])
|
||
st.download_button(
|
||
"下载收盘价(CSV)",
|
||
data=buf.getvalue(),
|
||
file_name=f"{ts_code}_{trade_date}_close_series.csv",
|
||
mime="text/csv",
|
||
key="dl_close_csv",
|
||
)
|
||
except Exception:
|
||
pass
|
||
if turnover_series:
|
||
st.write("换手率时间序列 (最近窗口)：")
|
||
st.json(turnover_series)
|
||
dept_sup = global_info.get("department_supplements") or {}
|
||
dept_dialogue = global_info.get("department_dialogue") or {}
|
||
dept_telemetry = global_info.get("department_telemetry") or {}
|
||
if dept_sup or dept_dialogue:
|
||
with st.expander("部门补数与对话记录", expanded=False):
|
||
if dept_sup:
|
||
st.write("补充数据：")
|
||
st.json(dept_sup)
|
||
if dept_dialogue:
|
||
st.write("对话片段：")
|
||
st.json(dept_dialogue)
|
||
if dept_telemetry:
|
||
with st.expander("部门 LLM 元数据", expanded=False):
|
||
st.json(dept_telemetry)
|
||
else:
|
||
st.info("暂未写入全局策略摘要。")
|
||
|
||
st.subheader("部门意见")
|
||
if dept_records:
|
||
# ADD: keyword filter for department summaries
|
||
keyword = st.text_input("筛选摘要/信号关键词", value="")
|
||
filtered = dept_records
|
||
if keyword.strip():
|
||
kw = keyword.strip()
|
||
filtered = [
|
||
item for item in dept_records
|
||
if kw in str(item.get("摘要", "")) or kw in str(item.get("核心信号", ""))
|
||
]
|
||
# ADD: confidence filter and sort
|
||
min_conf = st.slider("最低信心过滤", 0.0, 1.0, 0.0, 0.05)
|
||
sort_col = st.selectbox("排序列", ["信心", "权重"], index=0)
|
||
filtered = [row for row in filtered if float(row.get("信心", 0.0)) >= min_conf]
|
||
filtered = sorted(filtered, key=lambda r: float(r.get(sort_col, 0.0)), reverse=True)
|
||
dept_df = pd.DataFrame(filtered)
|
||
st.dataframe(dept_df, width='stretch', hide_index=True)
|
||
try:
|
||
st.download_button(
|
||
"下载部门意见(CSV)",
|
||
data=dept_df.to_csv(index=False),
|
||
file_name=f"{trade_date}_{ts_code}_departments.csv",
|
||
mime="text/csv",
|
||
key="dl_dept_csv",
|
||
)
|
||
except Exception:
|
||
pass
|
||
for code, details in dept_details.items():
|
||
with st.expander(f"{code} 补充详情", expanded=False):
|
||
supplements = details.get("supplements", [])
|
||
dialogue = details.get("dialogue", [])
|
||
if supplements:
|
||
st.write("补充数据：")
|
||
st.json(supplements)
|
||
else:
|
||
st.caption("无补充数据请求。")
|
||
if dialogue:
|
||
st.write("对话记录：")
|
||
for idx, line in enumerate(dialogue, start=1):
|
||
st.markdown(f"**回合 {idx}:** {line}")
|
||
else:
|
||
st.caption("无额外对话。")
|
||
telemetry = details.get("telemetry") or {}
|
||
if telemetry:
|
||
st.write("LLM 元数据：")
|
||
st.json(telemetry)
|
||
else:
|
||
st.info("暂无部门记录。")
|
||
|
||
st.subheader("代理评分")
|
||
if agent_records:
|
||
# ADD: sorting and CSV export for agents
|
||
sort_agent_by = st.selectbox(
|
||
"代理排序",
|
||
["权重", "SELL", "HOLD", "BUY_S", "BUY_M", "BUY_L"],
|
||
index=1,
|
||
)
|
||
agent_df = pd.DataFrame(agent_records)
|
||
if sort_agent_by in agent_df.columns:
|
||
agent_df = agent_df.sort_values(sort_agent_by, ascending=False)
|
||
st.dataframe(agent_df, width='stretch', hide_index=True)
|
||
try:
|
||
st.download_button(
|
||
"下载代理评分(CSV)",
|
||
data=agent_df.to_csv(index=False),
|
||
file_name=f"{trade_date}_{ts_code}_agents.csv",
|
||
mime="text/csv",
|
||
key="dl_agent_csv",
|
||
)
|
||
except Exception:
|
||
pass
|
||
else:
|
||
st.info("暂无基础代理评分。")
|
||
|
||
# 添加相关新闻展示部分
|
||
st.divider()
|
||
st.subheader("相关新闻")
|
||
# 获取与当前标的相关的最新新闻
|
||
try:
|
||
with db_session(read_only=True) as conn:
|
||
# 解析当前trade_date为datetime对象
|
||
try:
|
||
trade_date_obj = date.fromisoformat(str(trade_date))
|
||
except:
|
||
try:
|
||
trade_date_obj = datetime.strptime(str(trade_date), "%Y%m%d").date()
|
||
except:
|
||
# 如果解析失败，使用当前日期向前推7天
|
||
trade_date_obj = date.today() - timedelta(days=7)
|
||
|
||
# 查询近7天内与当前标的相关的新闻，按发布时间降序排列
|
||
news_query = """
|
||
SELECT id, title, source, pub_time, sentiment, heat, entities
|
||
FROM news
|
||
WHERE ts_code = ? AND pub_time >= ?
|
||
ORDER BY pub_time DESC
|
||
LIMIT 10
|
||
"""
|
||
# 计算7天前的日期字符串
|
||
seven_days_ago = (trade_date_obj - timedelta(days=7)).strftime("%Y-%m-%d")
|
||
news_rows = conn.execute(news_query, (ts_code, seven_days_ago)).fetchall()
|
||
|
||
if news_rows:
|
||
news_data = []
|
||
for row in news_rows:
|
||
# 解析entities字段获取更多信息
|
||
entities_info = {}
|
||
try:
|
||
if row["entities"]:
|
||
entities_info = json.loads(row["entities"])
|
||
except (json.JSONDecodeError, TypeError):
|
||
pass
|
||
|
||
# 准备新闻数据
|
||
news_item = {
|
||
"标题": row["title"],
|
||
"来源": row["source"],
|
||
"发布时间": row["pub_time"],
|
||
"情感指数": f"{row['sentiment']:.2f}" if row["sentiment"] is not None else "-",
|
||
"热度评分": f"{row['heat']:.2f}" if row["heat"] is not None else "-"
|
||
}
|
||
|
||
# 如果有行业信息，添加到展示数据中
|
||
industries = entities_info.get("industries", [])
|
||
if industries:
|
||
news_item["相关行业"] = "、".join(industries[:3]) # 只显示前3个行业
|
||
|
||
news_data.append(news_item)
|
||
|
||
# 显示新闻表格
|
||
news_df = pd.DataFrame(news_data)
|
||
# 确保所有列都是字符串类型，避免PyArrow序列化错误
|
||
for col in news_df.columns:
|
||
news_df[col] = news_df[col].astype(str)
|
||
st.dataframe(news_df, width='stretch', hide_index=True)
|
||
|
||
# 添加新闻详情展开视图
|
||
st.write("详细新闻内容：")
|
||
for idx, row in enumerate(news_rows):
|
||
with st.expander(f"{idx+1}. {row['title']}", expanded=False):
|
||
st.write(f"**来源：** {row['source']}")
|
||
st.write(f"**发布时间：** {row['pub_time']}")
|
||
|
||
# 解析entities获取更多详细信息
|
||
entities_info = {}
|
||
try:
|
||
if row["entities"]:
|
||
entities_info = json.loads(row["entities"])
|
||
except (json.JSONDecodeError, TypeError):
|
||
pass
|
||
|
||
# 显示情感和热度信息
|
||
sentiment_display = f"{row['sentiment']:.2f}" if row["sentiment"] is not None else "-"
|
||
heat_display = f"{row['heat']:.2f}" if row["heat"] is not None else "-"
|
||
st.write(f"**情感指数：** {sentiment_display} | **热度评分：** {heat_display}")
|
||
|
||
# 显示行业信息
|
||
industries = entities_info.get("industries", [])
|
||
if industries:
|
||
st.write(f"**相关行业：** {'、'.join(industries)}")
|
||
|
||
# 显示重要关键词
|
||
important_keywords = entities_info.get("important_keywords", [])
|
||
if important_keywords:
|
||
st.write(f"**重要关键词：** {'、'.join(important_keywords)}")
|
||
|
||
# 显示URL链接（如果有）
|
||
url = entities_info.get("source_url", "")
|
||
if url:
|
||
st.markdown(f"[查看原文]({url})", unsafe_allow_html=True)
|
||
else:
|
||
st.info(f"近7天内暂无关于 {ts_code} 的新闻。")
|
||
except Exception as e:
|
||
LOGGER.exception("获取新闻数据失败", extra=LOG_EXTRA)
|
||
st.error(f"获取新闻数据时发生错误：{e}")
|
||
|
||
st.divider()
|
||
st.subheader("投资池与仓位概览")
|
||
|
||
snapshot = get_latest_snapshot()
|
||
if snapshot:
|
||
col_a, col_b, col_c = st.columns(3)
|
||
if snapshot.total_value is not None:
|
||
col_a.metric("组合净值", f"{snapshot.total_value:,.2f}")
|
||
if snapshot.cash is not None:
|
||
col_b.metric("现金余额", f"{snapshot.cash:,.2f}")
|
||
if snapshot.invested_value is not None:
|
||
col_c.metric("持仓市值", f"{snapshot.invested_value:,.2f}")
|
||
detail_cols = st.columns(4)
|
||
if snapshot.unrealized_pnl is not None:
|
||
detail_cols[0].metric("浮盈", f"{snapshot.unrealized_pnl:,.2f}")
|
||
if snapshot.realized_pnl is not None:
|
||
detail_cols[1].metric("已实现盈亏", f"{snapshot.realized_pnl:,.2f}")
|
||
if snapshot.net_flow is not None:
|
||
detail_cols[2].metric("净流入", f"{snapshot.net_flow:,.2f}")
|
||
if snapshot.exposure is not None:
|
||
detail_cols[3].metric("风险敞口", f"{snapshot.exposure:.2%}")
|
||
if snapshot.notes:
|
||
st.caption(f"备注：{snapshot.notes}")
|
||
else:
|
||
st.info("暂无组合快照，请在执行回测或实盘同步后写入 portfolio_snapshots。")
|
||
|
||
candidates = list_investment_pool(trade_date=trade_date)
|
||
if candidates:
|
||
candidate_df = pd.DataFrame(
|
||
[
|
||
{
|
||
"交易日": item.trade_date,
|
||
"代码": item.ts_code,
|
||
"评分": item.score,
|
||
"状态": item.status,
|
||
"标签": "、".join(item.tags) if item.tags else "-",
|
||
"理由": item.rationale or "",
|
||
}
|
||
for item in candidates
|
||
]
|
||
)
|
||
st.write("候选投资池：")
|
||
st.dataframe(candidate_df, width='stretch', hide_index=True)
|
||
else:
|
||
st.caption("候选投资池暂无数据。")
|
||
|
||
positions = list_positions(active_only=False)
|
||
if positions:
|
||
position_df = pd.DataFrame(
|
||
[
|
||
{
|
||
"ID": pos.id,
|
||
"代码": pos.ts_code,
|
||
"开仓日": pos.opened_date,
|
||
"平仓日": pos.closed_date or "-",
|
||
"状态": pos.status,
|
||
"数量": pos.quantity,
|
||
"成本": pos.cost_price,
|
||
"现价": pos.market_price,
|
||
"市值": pos.market_value,
|
||
"浮盈": pos.unrealized_pnl,
|
||
"已实现": pos.realized_pnl,
|
||
"目标权重": pos.target_weight,
|
||
}
|
||
for pos in positions
|
||
]
|
||
)
|
||
st.write("组合持仓：")
|
||
st.dataframe(position_df, width='stretch', hide_index=True)
|
||
else:
|
||
st.caption("组合持仓暂无记录。")
|
||
|
||
trades = list_recent_trades(limit=20)
|
||
if trades:
|
||
trades_df = pd.DataFrame(trades)
|
||
st.write("近期成交：")
|
||
st.dataframe(trades_df, width='stretch', hide_index=True)
|
||
else:
|
||
st.caption("近期成交暂无记录。")
|
||
|
||
st.caption("数据来源：agent_utils、investment_pool、portfolio_positions、portfolio_trades、portfolio_snapshots。")
|
||
|
||
st.divider()
|
||
st.subheader("策略重评估")
|
||
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。")
|
||
cols_re = st.columns([1,1])
|
||
if cols_re[0].button("对该标的重评估", key="reevaluate_current_symbol"):
|
||
with st.spinner("正在重评估..."):
|
||
try:
|
||
trade_date_obj = None
|
||
try:
|
||
trade_date_obj = date.fromisoformat(str(trade_date))
|
||
except Exception:
|
||
try:
|
||
trade_date_obj = datetime.strptime(str(trade_date), "%Y%m%d").date()
|
||
except Exception:
|
||
pass
|
||
if trade_date_obj is None:
|
||
raise ValueError(f"无法解析交易日：{trade_date}")
|
||
# snapshot before
|
||
with db_session(read_only=True) as conn:
|
||
before_rows = conn.execute(
|
||
"""
|
||
SELECT agent, action, utils FROM agent_utils
|
||
WHERE trade_date = ? AND ts_code = ?
|
||
""",
|
||
(trade_date, ts_code),
|
||
).fetchall()
|
||
before_map = {row["agent"]: (row["action"], row["utils"]) for row in before_rows}
|
||
cfg = BtConfig(
|
||
id="reeval_ui",
|
||
name="UI Re-evaluation",
|
||
start_date=trade_date_obj,
|
||
end_date=trade_date_obj,
|
||
universe=[ts_code],
|
||
params={},
|
||
)
|
||
engine = BacktestEngine(cfg)
|
||
state = PortfolioState()
|
||
_ = engine.simulate_day(trade_date_obj, state)
|
||
# compare after
|
||
with db_session(read_only=True) as conn:
|
||
after_rows = conn.execute(
|
||
"""
|
||
SELECT agent, action, utils FROM agent_utils
|
||
WHERE trade_date = ? AND ts_code = ?
|
||
""",
|
||
(trade_date, ts_code),
|
||
).fetchall()
|
||
changes = []
|
||
for row in after_rows:
|
||
agent = row["agent"]
|
||
new_action = row["action"]
|
||
old_action, _old_utils = before_map.get(agent, (None, None))
|
||
if new_action != old_action:
|
||
changes.append({"代理": agent, "原动作": old_action, "新动作": new_action})
|
||
if changes:
|
||
st.success("重评估完成，检测到动作变更：")
|
||
st.dataframe(pd.DataFrame(changes), hide_index=True, width='stretch')
|
||
else:
|
||
st.success("重评估完成，无动作变更。")
|
||
st.rerun()
|
||
except Exception as exc: # noqa: BLE001
|
||
LOGGER.exception("重评估失败", extra=LOG_EXTRA)
|
||
st.error(f"重评估失败：{exc}")
|
||
if cols_re[1].button("批量重评估（所选）", key="reevaluate_batch", disabled=not batch_symbols):
|
||
with st.spinner("批量重评估执行中..."):
|
||
try:
|
||
trade_date_obj = None
|
||
try:
|
||
trade_date_obj = date.fromisoformat(str(trade_date))
|
||
except Exception:
|
||
try:
|
||
trade_date_obj = datetime.strptime(str(trade_date), "%Y%m%d").date()
|
||
except Exception:
|
||
pass
|
||
if trade_date_obj is None:
|
||
raise ValueError(f"无法解析交易日：{trade_date}")
|
||
progress = st.progress(0.0)
|
||
changes_all: List[Dict[str, object]] = []
|
||
for idx, code in enumerate(batch_symbols, start=1):
|
||
with db_session(read_only=True) as conn:
|
||
before_rows = conn.execute(
|
||
"SELECT agent, action FROM agent_utils WHERE trade_date = ? AND ts_code = ?",
|
||
(trade_date, code),
|
||
).fetchall()
|
||
before_map = {row["agent"]: row["action"] for row in before_rows}
|
||
cfg = BtConfig(
|
||
id="reeval_ui_batch",
|
||
name="UI Batch Re-eval",
|
||
start_date=trade_date_obj,
|
||
end_date=trade_date_obj,
|
||
universe=[code],
|
||
params={},
|
||
)
|
||
engine = BacktestEngine(cfg)
|
||
state = PortfolioState()
|
||
_ = engine.simulate_day(trade_date_obj, state)
|
||
with db_session(read_only=True) as conn:
|
||
after_rows = conn.execute(
|
||
"SELECT agent, action FROM agent_utils WHERE trade_date = ? AND ts_code = ?",
|
||
(trade_date, code),
|
||
).fetchall()
|
||
for row in after_rows:
|
||
agent = row["agent"]
|
||
new_action = row["action"]
|
||
old_action = before_map.get(agent)
|
||
if new_action != old_action:
|
||
changes_all.append({"代码": code, "代理": agent, "原动作": old_action, "新动作": new_action})
|
||
progress.progress(idx / max(1, len(batch_symbols)))
|
||
st.success("批量重评估完成。")
|
||
if changes_all:
|
||
st.dataframe(pd.DataFrame(changes_all), hide_index=True, width='stretch')
|
||
st.rerun()
|
||
except Exception as exc: # noqa: BLE001
|
||
LOGGER.exception("批量重评估失败", extra=LOG_EXTRA)
|
||
st.error(f"批量重评估失败：{exc}")
|
||
|
||
|
||
def render_log_viewer() -> None:
|
||
"""渲染日志钻取与历史对比视图页面。"""
|
||
LOGGER.info("渲染日志视图页面", extra=LOG_EXTRA)
|
||
st.header("日志钻取与历史对比")
|
||
st.write("查看系统运行日志，支持时间范围筛选、关键词搜索和历史对比功能。")
|
||
|
||
# 日志时间范围选择
|
||
col1, col2 = st.columns(2)
|
||
with col1:
|
||
start_date = st.date_input("开始日期", value=date.today() - timedelta(days=7))
|
||
with col2:
|
||
end_date = st.date_input("结束日期", value=date.today())
|
||
|
||
# 日志级别筛选
|
||
log_levels = ["ALL", "DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]
|
||
selected_level = st.selectbox("日志级别", log_levels, index=1)
|
||
|
||
# 关键词搜索
|
||
search_query = st.text_input("搜索关键词")
|
||
|
||
# 阶段筛选
|
||
with db_session(read_only=True) as conn:
|
||
stages = [row["stage"] for row in conn.execute("SELECT DISTINCT stage FROM run_log").fetchall()]
|
||
stages = [s for s in stages if s] # 过滤空值
|
||
stages.insert(0, "ALL")
|
||
selected_stage = st.selectbox("执行阶段", stages)
|
||
|
||
# 查询日志
|
||
with st.spinner("加载日志数据中..."):
|
||
try:
|
||
with db_session(read_only=True) as conn:
|
||
query_parts = ["SELECT ts, stage, level, msg FROM run_log WHERE 1=1"]
|
||
params = []
|
||
|
||
# 添加日期过滤
|
||
start_ts = f"{start_date.isoformat()}T00:00:00Z"
|
||
end_ts = f"{end_date.isoformat()}T23:59:59Z"
|
||
query_parts.append("AND ts BETWEEN ? AND ?")
|
||
params.extend([start_ts, end_ts])
|
||
|
||
# 添加级别过滤
|
||
if selected_level != "ALL":
|
||
query_parts.append("AND level = ?")
|
||
params.append(selected_level)
|
||
|
||
# 添加关键词过滤
|
||
if search_query:
|
||
query_parts.append("AND msg LIKE ?")
|
||
params.append(f"%{search_query}%")
|
||
|
||
# 添加阶段过滤
|
||
if selected_stage != "ALL":
|
||
query_parts.append("AND stage = ?")
|
||
params.append(selected_stage)
|
||
|
||
# 添加排序
|
||
query_parts.append("ORDER BY ts DESC")
|
||
|
||
# 执行查询
|
||
query = " ".join(query_parts)
|
||
rows = conn.execute(query, params).fetchall()
|
||
|
||
# 转换为DataFrame
|
||
if rows:
|
||
# 将sqlite3.Row对象转换为字典列表
|
||
rows_dict = [{key: row[key] for key in row.keys()} for row in rows]
|
||
log_df = pd.DataFrame(rows_dict)
|
||
# 格式化时间戳并确保数据类型一致
|
||
log_df["ts"] = pd.to_datetime(log_df["ts"]).dt.strftime("%Y-%m-%d %H:%M:%S")
|
||
# 确保所有列都是字符串类型，避免PyArrow序列化错误
|
||
for col in log_df.columns:
|
||
log_df[col] = log_df[col].astype(str)
|
||
else:
|
||
log_df = pd.DataFrame(columns=["ts", "stage", "level", "msg"])
|
||
|
||
# 显示日志表格
|
||
st.dataframe(
|
||
log_df,
|
||
hide_index=True,
|
||
width="stretch",
|
||
column_config={
|
||
"ts": st.column_config.TextColumn("时间"),
|
||
"stage": st.column_config.TextColumn("执行阶段"),
|
||
"level": st.column_config.TextColumn("日志级别"),
|
||
"msg": st.column_config.TextColumn("日志消息", width="large")
|
||
}
|
||
)
|
||
|
||
# 下载功能
|
||
if not log_df.empty:
|
||
csv_data = log_df.to_csv(index=False).encode('utf-8')
|
||
st.download_button(
|
||
label="下载日志CSV",
|
||
data=csv_data,
|
||
file_name=f"logs_{start_date}_{end_date}.csv",
|
||
mime="text/csv",
|
||
key="download_logs"
|
||
)
|
||
|
||
# JSON下载
|
||
json_data = log_df.to_json(orient='records', force_ascii=False, indent=2)
|
||
st.download_button(
|
||
label="下载日志JSON",
|
||
data=json_data,
|
||
file_name=f"logs_{start_date}_{end_date}.json",
|
||
mime="application/json",
|
||
key="download_logs_json"
|
||
)
|
||
except Exception as e:
|
||
LOGGER.exception("加载日志失败", extra=LOG_EXTRA)
|
||
st.error(f"加载日志数据失败：{e}")
|
||
|
||
# 历史对比功能
|
||
st.subheader("历史对比")
|
||
st.write("选择两个时间点的日志进行对比分析。")
|
||
|
||
# 第一个时间点选择
|
||
col3, col4 = st.columns(2)
|
||
with col3:
|
||
compare_date1 = st.date_input("对比日期1", value=date.today() - timedelta(days=1))
|
||
with col4:
|
||
compare_date2 = st.date_input("对比日期2", value=date.today())
|
||
|
||
if st.button("执行对比", type="secondary"):
|
||
with st.spinner("执行日志对比分析中..."):
|
||
try:
|
||
with db_session(read_only=True) as conn:
|
||
# 获取两个日期的日志统计
|
||
query_date1 = f"{compare_date1.isoformat()}T00:00:00Z"
|
||
query_date2 = f"{compare_date1.isoformat()}T23:59:59Z"
|
||
logs1 = conn.execute(
|
||
"SELECT level, COUNT(*) as count FROM run_log WHERE ts BETWEEN ? AND ? GROUP BY level",
|
||
(query_date1, query_date2)
|
||
).fetchall()
|
||
|
||
query_date3 = f"{compare_date2.isoformat()}T00:00:00Z"
|
||
query_date4 = f"{compare_date2.isoformat()}T23:59:59Z"
|
||
logs2 = conn.execute(
|
||
"SELECT level, COUNT(*) as count FROM run_log WHERE ts BETWEEN ? AND ? GROUP BY level",
|
||
(query_date3, query_date4)
|
||
).fetchall()
|
||
|
||
# 转换为DataFrame并可视化
|
||
df1 = pd.DataFrame(logs1, columns=["level", "count"])
|
||
df1["date"] = compare_date1.strftime("%Y-%m-%d")
|
||
df2 = pd.DataFrame(logs2, columns=["level", "count"])
|
||
df2["date"] = compare_date2.strftime("%Y-%m-%d")
|
||
|
||
# 确保所有列的数据类型一致，避免PyArrow序列化错误
|
||
for df in [df1, df2]:
|
||
for col in df.columns:
|
||
if col != "level": # level列保持字符串类型
|
||
df[col] = df[col].astype(object)
|
||
|
||
compare_df = pd.concat([df1, df2])
|
||
|
||
# 绘制对比图表
|
||
fig = px.bar(
|
||
compare_df,
|
||
x="level",
|
||
y="count",
|
||
color="date",
|
||
barmode="group",
|
||
title=f"日志级别分布对比 ({compare_date1} vs {compare_date2})"
|
||
)
|
||
st.plotly_chart(fig, width='stretch')
|
||
|
||
# 显示详细对比表格
|
||
st.write("日志统计对比：")
|
||
# 使用不含连字符的日期格式作为列名后缀，避免Arrow类型转换错误
|
||
date1_str = compare_date1.strftime("%Y%m%d")
|
||
date2_str = compare_date2.strftime("%Y%m%d")
|
||
merged_df = df1.merge(df2, on="level", suffixes=(f"_{date1_str}", f"_{date2_str}"), how="outer").fillna(0)
|
||
st.dataframe(merged_df, hide_index=True, width="stretch")
|
||
except Exception as e:
|
||
LOGGER.exception("日志对比失败", extra=LOG_EXTRA)
|
||
st.error(f"日志对比分析失败：{e}")
|
||
|
||
cfg = get_config()
|
||
default_start, default_end = _default_backtest_range(window_days=60)
|
||
LOGGER.debug(
|
||
"回测默认参数：start=%s end=%s universe=%s target=%s stop=%s hold_days=%s",
|
||
default_start,
|
||
default_end,
|
||
"000001.SZ",
|
||
0.035,
|
||
-0.015,
|
||
10,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
|
||
col1, col2 = st.columns(2)
|
||
start_date = col1.date_input("开始日期", value=default_start)
|
||
end_date = col2.date_input("结束日期", value=default_end)
|
||
universe_text = st.text_input("股票列表（逗号分隔）", value="000001.SZ")
|
||
target = st.number_input("目标收益（例：0.035 表示 3.5%）", value=0.035, step=0.005, format="%.3f")
|
||
stop = st.number_input("止损收益（例：-0.015 表示 -1.5%）", value=-0.015, step=0.005, format="%.3f")
|
||
hold_days = st.number_input("持有期（交易日）", value=10, step=1)
|
||
LOGGER.debug(
|
||
"当前回测表单输入：start=%s end=%s universe_text=%s target=%.3f stop=%.3f hold_days=%s",
|
||
start_date,
|
||
end_date,
|
||
universe_text,
|
||
target,
|
||
stop,
|
||
hold_days,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
|
||
if st.button("运行回测"):
|
||
LOGGER.info("用户点击运行回测按钮", extra=LOG_EXTRA)
|
||
decision_log_container = st.container()
|
||
status_box = st.status("准备执行回测...", expanded=True)
|
||
llm_stats_placeholder = st.empty()
|
||
decision_entries: List[str] = []
|
||
|
||
def _decision_callback(ts_code: str, trade_dt: date, ctx: AgentContext, decision: Decision) -> None:
|
||
ts_label = trade_dt.isoformat()
|
||
summary = ""
|
||
for dept_decision in decision.department_decisions.values():
|
||
if getattr(dept_decision, "summary", ""):
|
||
summary = str(dept_decision.summary)
|
||
break
|
||
entry_lines = [
|
||
f"**{ts_label} {ts_code}** → {decision.action.value} (信心 {decision.confidence:.2f})",
|
||
]
|
||
if summary:
|
||
entry_lines.append(f"摘要：{summary}")
|
||
dep_highlights = []
|
||
for dept_code, dept_decision in decision.department_decisions.items():
|
||
dep_highlights.append(
|
||
f"{dept_code}:{dept_decision.action.value}({dept_decision.confidence:.2f})"
|
||
)
|
||
if dep_highlights:
|
||
entry_lines.append("部门意见：" + "；".join(dep_highlights))
|
||
decision_entries.append(" \n".join(entry_lines))
|
||
decision_log_container.markdown("\n\n".join(decision_entries[-200:]))
|
||
status_box.write(f"{ts_label} {ts_code} → {decision.action.value} (信心 {decision.confidence:.2f})")
|
||
stats = snapshot_llm_metrics()
|
||
llm_stats_placeholder.json(
|
||
{
|
||
"LLM 调用次数": stats.get("total_calls", 0),
|
||
"Prompt Tokens": stats.get("total_prompt_tokens", 0),
|
||
"Completion Tokens": stats.get("total_completion_tokens", 0),
|
||
"按 Provider": stats.get("provider_calls", {}),
|
||
"按模型": stats.get("model_calls", {}),
|
||
}
|
||
)
|
||
_update_dashboard_sidebar(stats)
|
||
|
||
reset_llm_metrics()
|
||
status_box.update(label="执行回测中...", state="running")
|
||
try:
|
||
universe = [code.strip() for code in universe_text.split(',') if code.strip()]
|
||
LOGGER.info(
|
||
"回测参数：start=%s end=%s universe=%s target=%s stop=%s hold_days=%s",
|
||
start_date,
|
||
end_date,
|
||
universe,
|
||
target,
|
||
stop,
|
||
hold_days,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
cfg = BtConfig(
|
||
id="streamlit_demo",
|
||
name="Streamlit Demo Strategy",
|
||
start_date=start_date,
|
||
end_date=end_date,
|
||
universe=universe,
|
||
params={
|
||
"target": target,
|
||
"stop": stop,
|
||
"hold_days": int(hold_days),
|
||
},
|
||
)
|
||
result = run_backtest(cfg, decision_callback=_decision_callback)
|
||
LOGGER.info(
|
||
"回测完成：nav_records=%s trades=%s",
|
||
len(result.nav_series),
|
||
len(result.trades),
|
||
extra=LOG_EXTRA,
|
||
)
|
||
status_box.update(label="回测执行完成", state="complete")
|
||
st.success("回测执行完成，详见下方结果与统计。")
|
||
metrics = snapshot_llm_metrics()
|
||
llm_stats_placeholder.json(
|
||
{
|
||
"LLM 调用次数": metrics.get("total_calls", 0),
|
||
"Prompt Tokens": metrics.get("total_prompt_tokens", 0),
|
||
"Completion Tokens": metrics.get("total_completion_tokens", 0),
|
||
"按 Provider": metrics.get("provider_calls", {}),
|
||
"按模型": metrics.get("model_calls", {}),
|
||
}
|
||
)
|
||
_update_dashboard_sidebar(metrics)
|
||
st.json({"nav_records": result.nav_series, "trades": result.trades})
|
||
except Exception as exc: # noqa: BLE001
|
||
LOGGER.exception("回测执行失败", extra=LOG_EXTRA)
|
||
status_box.update(label="回测执行失败", state="error")
|
||
st.error(f"回测执行失败：{exc}")
|
||
|
||
with st.expander("离线调参实验 (DecisionEnv)", expanded=False):
|
||
st.caption(
|
||
"使用 DecisionEnv 对代理权重做离线调参。请选择需要优化的代理并设定权重范围，"
|
||
"系统会运行一次回测并返回收益、回撤等指标。若 LLM 网络不可用，将返回失败标记。"
|
||
)
|
||
|
||
disable_departments = st.checkbox(
|
||
"禁用部门 LLM（仅规则代理，适合离线快速评估）",
|
||
value=True,
|
||
help="关闭部门调用后不依赖外部 LLM 网络，仅根据规则代理权重模拟。",
|
||
)
|
||
|
||
default_experiment_id = f"streamlit_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
|
||
experiment_id = st.text_input(
|
||
"实验 ID",
|
||
value=default_experiment_id,
|
||
help="用于在 tuning_results 表中区分不同实验。",
|
||
)
|
||
strategy_label = st.text_input(
|
||
"策略说明",
|
||
value="DecisionEnv",
|
||
help="可选：为本次调参记录一个策略名称或备注。",
|
||
)
|
||
|
||
agent_objects = default_agents()
|
||
agent_names = [agent.name for agent in agent_objects]
|
||
if not agent_names:
|
||
st.info("暂无可调整的代理。")
|
||
else:
|
||
selected_agents = st.multiselect(
|
||
"选择调参的代理权重",
|
||
agent_names,
|
||
default=agent_names[:2],
|
||
key="decision_env_agents",
|
||
)
|
||
|
||
specs: List[ParameterSpec] = []
|
||
action_values: List[float] = []
|
||
range_valid = True
|
||
for idx, agent_name in enumerate(selected_agents):
|
||
col_min, col_max, col_action = st.columns([1, 1, 2])
|
||
min_key = f"decision_env_min_{agent_name}"
|
||
max_key = f"decision_env_max_{agent_name}"
|
||
action_key = f"decision_env_action_{agent_name}"
|
||
default_min = 0.0
|
||
default_max = 1.0
|
||
min_val = col_min.number_input(
|
||
f"{agent_name} 最小权重",
|
||
min_value=0.0,
|
||
max_value=1.0,
|
||
value=default_min,
|
||
step=0.05,
|
||
key=min_key,
|
||
)
|
||
max_val = col_max.number_input(
|
||
f"{agent_name} 最大权重",
|
||
min_value=0.0,
|
||
max_value=1.0,
|
||
value=default_max,
|
||
step=0.05,
|
||
key=max_key,
|
||
)
|
||
if max_val <= min_val:
|
||
range_valid = False
|
||
action_val = col_action.slider(
|
||
f"{agent_name} 动作 (0-1)",
|
||
min_value=0.0,
|
||
max_value=1.0,
|
||
value=0.5,
|
||
step=0.01,
|
||
key=action_key,
|
||
)
|
||
specs.append(
|
||
ParameterSpec(
|
||
name=f"weight_{agent_name}",
|
||
target=f"agent_weights.{agent_name}",
|
||
minimum=min_val,
|
||
maximum=max_val,
|
||
)
|
||
)
|
||
action_values.append(action_val)
|
||
|
||
run_decision_env = st.button("执行单次调参", key="run_decision_env_button")
|
||
just_finished_single = False
|
||
if run_decision_env:
|
||
if not selected_agents:
|
||
st.warning("请至少选择一个代理进行调参。")
|
||
elif not range_valid:
|
||
st.error("请确保所有代理的最大权重大于最小权重。")
|
||
else:
|
||
LOGGER.info(
|
||
"离线调参（单次）按钮点击，已选择代理=%s 动作=%s disable_departments=%s",
|
||
selected_agents,
|
||
action_values,
|
||
disable_departments,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
baseline_weights = cfg.agent_weights.as_dict()
|
||
for agent in agent_objects:
|
||
baseline_weights.setdefault(agent.name, 1.0)
|
||
|
||
universe_env = [code.strip() for code in universe_text.split(',') if code.strip()]
|
||
if not universe_env:
|
||
st.error("请先指定至少一个股票代码。")
|
||
else:
|
||
bt_cfg_env = BtConfig(
|
||
id="decision_env_streamlit",
|
||
name="DecisionEnv Streamlit",
|
||
start_date=start_date,
|
||
end_date=end_date,
|
||
universe=universe_env,
|
||
params={
|
||
"target": target,
|
||
"stop": stop,
|
||
"hold_days": int(hold_days),
|
||
},
|
||
method=cfg.decision_method,
|
||
)
|
||
env = DecisionEnv(
|
||
bt_config=bt_cfg_env,
|
||
parameter_specs=specs,
|
||
baseline_weights=baseline_weights,
|
||
disable_departments=disable_departments,
|
||
)
|
||
env.reset()
|
||
LOGGER.debug(
|
||
"离线调参（单次）启动 DecisionEnv：cfg=%s 参数维度=%s",
|
||
bt_cfg_env,
|
||
len(specs),
|
||
extra=LOG_EXTRA,
|
||
)
|
||
with st.spinner("正在执行离线调参……"):
|
||
try:
|
||
observation, reward, done, info = env.step(action_values)
|
||
LOGGER.info(
|
||
"离线调参（单次）完成，obs=%s reward=%.4f done=%s",
|
||
observation,
|
||
reward,
|
||
done,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
except Exception as exc: # noqa: BLE001
|
||
LOGGER.exception("DecisionEnv 调用失败", extra=LOG_EXTRA)
|
||
st.error(f"离线调参失败：{exc}")
|
||
st.session_state.pop(_DECISION_ENV_SINGLE_RESULT_KEY, None)
|
||
else:
|
||
if observation.get("failure"):
|
||
st.error("调参失败：回测执行未完成，可能是 LLM 网络不可用或参数异常。")
|
||
st.json(observation)
|
||
st.session_state.pop(_DECISION_ENV_SINGLE_RESULT_KEY, None)
|
||
else:
|
||
resolved_experiment_id = experiment_id or str(uuid.uuid4())
|
||
resolved_strategy = strategy_label or "DecisionEnv"
|
||
action_payload = {
|
||
name: value
|
||
for name, value in zip(selected_agents, action_values)
|
||
}
|
||
metrics_payload = dict(observation)
|
||
metrics_payload["reward"] = reward
|
||
log_success = False
|
||
try:
|
||
log_tuning_result(
|
||
experiment_id=resolved_experiment_id,
|
||
strategy=resolved_strategy,
|
||
action=action_payload,
|
||
reward=reward,
|
||
metrics=metrics_payload,
|
||
weights=info.get("weights", {}),
|
||
)
|
||
except Exception: # noqa: BLE001
|
||
LOGGER.exception("记录调参结果失败", extra=LOG_EXTRA)
|
||
else:
|
||
log_success = True
|
||
LOGGER.info(
|
||
"离线调参（单次）日志写入成功：experiment=%s strategy=%s",
|
||
resolved_experiment_id,
|
||
resolved_strategy,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
st.session_state[_DECISION_ENV_SINGLE_RESULT_KEY] = {
|
||
"observation": dict(observation),
|
||
"reward": float(reward),
|
||
"weights": info.get("weights", {}),
|
||
"nav_series": info.get("nav_series"),
|
||
"trades": info.get("trades"),
|
||
"selected_agents": list(selected_agents),
|
||
"action_values": list(action_values),
|
||
"experiment_id": resolved_experiment_id,
|
||
"strategy_label": resolved_strategy,
|
||
"logged": log_success,
|
||
}
|
||
just_finished_single = True
|
||
single_result = st.session_state.get(_DECISION_ENV_SINGLE_RESULT_KEY)
|
||
if single_result:
|
||
if just_finished_single:
|
||
st.success("离线调参完成")
|
||
else:
|
||
st.success("离线调参结果（最近一次运行）")
|
||
st.caption(
|
||
f"实验 ID：{single_result.get('experiment_id', '-') } | 策略：{single_result.get('strategy_label', 'DecisionEnv')}"
|
||
)
|
||
observation = single_result.get("observation", {})
|
||
reward = float(single_result.get("reward", 0.0))
|
||
col_metrics = st.columns(4)
|
||
col_metrics[0].metric("总收益", f"{observation.get('total_return', 0.0):+.2%}")
|
||
col_metrics[1].metric("最大回撤", f"{observation.get('max_drawdown', 0.0):+.2%}")
|
||
col_metrics[2].metric("波动率", f"{observation.get('volatility', 0.0):+.2%}")
|
||
col_metrics[3].metric("奖励", f"{reward:+.4f}")
|
||
|
||
weights_dict = single_result.get("weights") or {}
|
||
if weights_dict:
|
||
st.write("调参后权重：")
|
||
st.json(weights_dict)
|
||
if st.button("保存这些权重为默认配置", key="save_decision_env_weights_single"):
|
||
try:
|
||
cfg.agent_weights.update_from_dict(weights_dict)
|
||
save_config(cfg)
|
||
except Exception as exc: # noqa: BLE001
|
||
LOGGER.exception("保存权重失败", extra={**LOG_EXTRA, "error": str(exc)})
|
||
st.error(f"写入配置失败：{exc}")
|
||
else:
|
||
st.success("代理权重已写入 config.json")
|
||
|
||
if single_result.get("logged"):
|
||
st.caption("调参结果已写入 tuning_results 表。")
|
||
|
||
nav_series = single_result.get("nav_series") or []
|
||
if nav_series:
|
||
try:
|
||
nav_df = pd.DataFrame(nav_series)
|
||
if {"trade_date", "nav"}.issubset(nav_df.columns):
|
||
nav_df = nav_df.sort_values("trade_date")
|
||
nav_df["trade_date"] = pd.to_datetime(nav_df["trade_date"])
|
||
st.line_chart(nav_df.set_index("trade_date")["nav"], height=220)
|
||
except Exception: # noqa: BLE001
|
||
LOGGER.debug("导航曲线绘制失败", extra=LOG_EXTRA)
|
||
|
||
trades = single_result.get("trades") or []
|
||
if trades:
|
||
st.write("成交记录：")
|
||
st.dataframe(pd.DataFrame(trades), hide_index=True, width='stretch')
|
||
|
||
if st.button("清除单次调参结果", key="clear_decision_env_single"):
|
||
st.session_state.pop(_DECISION_ENV_SINGLE_RESULT_KEY, None)
|
||
st.success("已清除单次调参结果缓存。")
|
||
|
||
st.divider()
|
||
st.caption("批量调参：在下方输入多组动作，每行表示一组 0-1 之间的值，用逗号分隔。")
|
||
default_grid = "\n".join(
|
||
[
|
||
",".join(["0.2" for _ in specs]),
|
||
",".join(["0.5" for _ in specs]),
|
||
",".join(["0.8" for _ in specs]),
|
||
]
|
||
) if specs else ""
|
||
action_grid_raw = st.text_area(
|
||
"动作列表",
|
||
value=default_grid,
|
||
height=120,
|
||
key="decision_env_batch_actions",
|
||
)
|
||
run_batch = st.button("批量执行调参", key="run_decision_env_batch")
|
||
batch_just_ran = False
|
||
if run_batch:
|
||
if not selected_agents:
|
||
st.warning("请先选择调参代理。")
|
||
elif not range_valid:
|
||
st.error("请确保所有代理的最大权重大于最小权重。")
|
||
else:
|
||
LOGGER.info(
|
||
"离线调参（批量）按钮点击，已选择代理=%s disable_departments=%s",
|
||
selected_agents,
|
||
disable_departments,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
lines = [line.strip() for line in action_grid_raw.splitlines() if line.strip()]
|
||
if not lines:
|
||
st.warning("请在文本框中输入至少一组动作。")
|
||
else:
|
||
LOGGER.debug(
|
||
"离线调参（批量）原始输入=%s",
|
||
lines,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
parsed_actions: List[List[float]] = []
|
||
for line in lines:
|
||
try:
|
||
values = [float(val.strip()) for val in line.split(',') if val.strip()]
|
||
except ValueError:
|
||
st.error(f"无法解析动作行：{line}")
|
||
parsed_actions = []
|
||
break
|
||
if len(values) != len(specs):
|
||
st.error(f"动作维度不匹配（期望 {len(specs)} 个值）：{line}")
|
||
parsed_actions = []
|
||
break
|
||
parsed_actions.append(values)
|
||
if parsed_actions:
|
||
LOGGER.info(
|
||
"离线调参（批量）解析动作成功，数量=%s",
|
||
len(parsed_actions),
|
||
extra=LOG_EXTRA,
|
||
)
|
||
baseline_weights = cfg.agent_weights.as_dict()
|
||
for agent in agent_objects:
|
||
baseline_weights.setdefault(agent.name, 1.0)
|
||
|
||
universe_env = [code.strip() for code in universe_text.split(',') if code.strip()]
|
||
if not universe_env:
|
||
st.error("请先指定至少一个股票代码。")
|
||
else:
|
||
bt_cfg_env = BtConfig(
|
||
id="decision_env_streamlit_batch",
|
||
name="DecisionEnv Batch",
|
||
start_date=start_date,
|
||
end_date=end_date,
|
||
universe=universe_env,
|
||
params={
|
||
"target": target,
|
||
"stop": stop,
|
||
"hold_days": int(hold_days),
|
||
},
|
||
method=cfg.decision_method,
|
||
)
|
||
env = DecisionEnv(
|
||
bt_config=bt_cfg_env,
|
||
parameter_specs=specs,
|
||
baseline_weights=baseline_weights,
|
||
disable_departments=disable_departments,
|
||
)
|
||
results: List[Dict[str, object]] = []
|
||
resolved_experiment_id = experiment_id or str(uuid.uuid4())
|
||
resolved_strategy = strategy_label or "DecisionEnv"
|
||
LOGGER.debug(
|
||
"离线调参（批量）启动 DecisionEnv：cfg=%s 动作组=%s",
|
||
bt_cfg_env,
|
||
len(parsed_actions),
|
||
extra=LOG_EXTRA,
|
||
)
|
||
with st.spinner("正在批量执行调参……"):
|
||
for idx, action_vals in enumerate(parsed_actions, start=1):
|
||
env.reset()
|
||
try:
|
||
observation, reward, done, info = env.step(action_vals)
|
||
except Exception as exc: # noqa: BLE001
|
||
LOGGER.exception("批量调参失败", extra=LOG_EXTRA)
|
||
results.append(
|
||
{
|
||
"序号": idx,
|
||
"动作": action_vals,
|
||
"状态": "error",
|
||
"错误": str(exc),
|
||
}
|
||
)
|
||
continue
|
||
if observation.get("failure"):
|
||
results.append(
|
||
{
|
||
"序号": idx,
|
||
"动作": action_vals,
|
||
"状态": "failure",
|
||
"奖励": -1.0,
|
||
}
|
||
)
|
||
else:
|
||
LOGGER.info(
|
||
"离线调参（批量）第 %s 组完成，reward=%.4f obs=%s",
|
||
idx,
|
||
reward,
|
||
observation,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
action_payload = {
|
||
name: value
|
||
for name, value in zip(selected_agents, action_vals)
|
||
}
|
||
metrics_payload = dict(observation)
|
||
metrics_payload["reward"] = reward
|
||
weights_payload = info.get("weights", {})
|
||
try:
|
||
log_tuning_result(
|
||
experiment_id=resolved_experiment_id,
|
||
strategy=resolved_strategy,
|
||
action=action_payload,
|
||
reward=reward,
|
||
metrics=metrics_payload,
|
||
weights=weights_payload,
|
||
)
|
||
except Exception: # noqa: BLE001
|
||
LOGGER.exception("记录调参结果失败", extra=LOG_EXTRA)
|
||
results.append(
|
||
{
|
||
"序号": idx,
|
||
"动作": action_vals,
|
||
"状态": "ok",
|
||
"总收益": observation.get("total_return", 0.0),
|
||
"最大回撤": observation.get("max_drawdown", 0.0),
|
||
"波动率": observation.get("volatility", 0.0),
|
||
"奖励": reward,
|
||
"权重": weights_payload,
|
||
}
|
||
)
|
||
st.session_state[_DECISION_ENV_BATCH_RESULTS_KEY] = {
|
||
"results": results,
|
||
"selectable": [
|
||
row
|
||
for row in results
|
||
if row.get("状态") == "ok" and row.get("权重")
|
||
],
|
||
"experiment_id": resolved_experiment_id,
|
||
"strategy_label": resolved_strategy,
|
||
}
|
||
batch_just_ran = True
|
||
LOGGER.info(
|
||
"离线调参（批量）执行结束，总结果条数=%s",
|
||
len(results),
|
||
extra=LOG_EXTRA,
|
||
)
|
||
batch_state = st.session_state.get(_DECISION_ENV_BATCH_RESULTS_KEY)
|
||
if batch_state:
|
||
results = batch_state.get("results") or []
|
||
if results:
|
||
if batch_just_ran:
|
||
st.success("批量调参完成")
|
||
else:
|
||
st.success("批量调参结果（最近一次运行）")
|
||
st.caption(
|
||
f"实验 ID：{batch_state.get('experiment_id', '-') } | 策略：{batch_state.get('strategy_label', 'DecisionEnv')}"
|
||
)
|
||
results_df = pd.DataFrame(results)
|
||
st.write("批量调参结果：")
|
||
st.dataframe(results_df, hide_index=True, width='stretch')
|
||
selectable = batch_state.get("selectable") or []
|
||
if selectable:
|
||
option_labels = [
|
||
f"序号 {row['序号']} | 奖励 {row.get('奖励', 0.0):+.4f}"
|
||
for row in selectable
|
||
]
|
||
selected_label = st.selectbox(
|
||
"选择要保存的记录",
|
||
option_labels,
|
||
key="decision_env_batch_select",
|
||
)
|
||
selected_row = None
|
||
for label, row in zip(option_labels, selectable):
|
||
if label == selected_label:
|
||
selected_row = row
|
||
break
|
||
if selected_row and st.button(
|
||
"保存所选权重为默认配置",
|
||
key="save_decision_env_weights_batch",
|
||
):
|
||
try:
|
||
cfg.agent_weights.update_from_dict(selected_row.get("权重", {}))
|
||
save_config(cfg)
|
||
except Exception as exc: # noqa: BLE001
|
||
LOGGER.exception("批量保存权重失败", extra={**LOG_EXTRA, "error": str(exc)})
|
||
st.error(f"写入配置失败：{exc}")
|
||
else:
|
||
st.success(
|
||
f"已将序号 {selected_row['序号']} 的权重写入 config.json"
|
||
)
|
||
else:
|
||
st.caption("暂无成功的结果可供保存。")
|
||
else:
|
||
st.caption("批量调参在最近一次执行中未产生结果。")
|
||
if st.button("清除批量调参结果", key="clear_decision_env_batch"):
|
||
st.session_state.pop(_DECISION_ENV_BATCH_RESULTS_KEY, None)
|
||
st.session_state.pop("decision_env_batch_select", None)
|
||
st.success("已清除批量调参结果缓存。")
|
||
|
||
# ADD: Comparison view for multiple backtest configurations
|
||
with st.expander("回测结果对比", expanded=False):
|
||
st.caption("从历史回测配置中选择多个进行净值曲线与指标对比。")
|
||
normalize_to_one = st.checkbox("归一化到 1 起点", value=True)
|
||
use_log_y = st.checkbox("对数坐标", value=False)
|
||
metric_options = ["总收益", "最大回撤", "交易数", "平均换手", "风险事件"]
|
||
selected_metrics = st.multiselect("显示指标列", metric_options, default=metric_options)
|
||
try:
|
||
with db_session(read_only=True) as conn:
|
||
cfg_rows = conn.execute(
|
||
"SELECT id, name FROM bt_config ORDER BY rowid DESC LIMIT 50"
|
||
).fetchall()
|
||
except Exception: # noqa: BLE001
|
||
LOGGER.exception("读取 bt_config 失败", extra=LOG_EXTRA)
|
||
cfg_rows = []
|
||
cfg_options = [f"{row['id']} | {row['name']}" for row in cfg_rows]
|
||
selected_labels = st.multiselect("选择配置", cfg_options, default=cfg_options[:2])
|
||
selected_ids = [label.split(" | ")[0].strip() for label in selected_labels]
|
||
nav_df = pd.DataFrame()
|
||
rpt_df = pd.DataFrame()
|
||
if selected_ids:
|
||
try:
|
||
with db_session(read_only=True) as conn:
|
||
nav_df = pd.read_sql_query(
|
||
"SELECT cfg_id, trade_date, nav FROM bt_nav WHERE cfg_id IN (%s)" % (",".join(["?"]*len(selected_ids))),
|
||
conn,
|
||
params=tuple(selected_ids),
|
||
)
|
||
rpt_df = pd.read_sql_query(
|
||
"SELECT cfg_id, summary FROM bt_report WHERE cfg_id IN (%s)" % (",".join(["?"]*len(selected_ids))),
|
||
conn,
|
||
params=tuple(selected_ids),
|
||
)
|
||
except Exception: # noqa: BLE001
|
||
LOGGER.exception("读取回测结果失败", extra=LOG_EXTRA)
|
||
st.error("读取回测结果失败")
|
||
nav_df = pd.DataFrame()
|
||
rpt_df = pd.DataFrame()
|
||
if not nav_df.empty:
|
||
try:
|
||
nav_df["trade_date"] = pd.to_datetime(nav_df["trade_date"], errors="coerce")
|
||
# ADD: date window filter
|
||
overall_min = pd.to_datetime(nav_df["trade_date"].min()).date()
|
||
overall_max = pd.to_datetime(nav_df["trade_date"].max()).date()
|
||
col_d1, col_d2 = st.columns(2)
|
||
start_filter = col_d1.date_input("起始日期", value=overall_min)
|
||
end_filter = col_d2.date_input("结束日期", value=overall_max)
|
||
if start_filter > end_filter:
|
||
start_filter, end_filter = end_filter, start_filter
|
||
mask = (nav_df["trade_date"].dt.date >= start_filter) & (nav_df["trade_date"].dt.date <= end_filter)
|
||
nav_df = nav_df.loc[mask]
|
||
pivot = nav_df.pivot_table(index="trade_date", columns="cfg_id", values="nav")
|
||
if normalize_to_one:
|
||
pivot = pivot.apply(lambda s: s / s.dropna().iloc[0] if s.dropna().size else s)
|
||
import plotly.graph_objects as go
|
||
fig = go.Figure()
|
||
for col in pivot.columns:
|
||
fig.add_trace(go.Scatter(x=pivot.index, y=pivot[col], mode="lines", name=str(col)))
|
||
fig.update_layout(height=300, margin=dict(l=10, r=10, t=30, b=10))
|
||
if use_log_y:
|
||
fig.update_yaxes(type="log")
|
||
st.plotly_chart(fig, width='stretch')
|
||
# ADD: export pivot
|
||
try:
|
||
csv_buf = pivot.reset_index()
|
||
csv_buf.columns = ["trade_date"] + [str(c) for c in pivot.columns]
|
||
st.download_button(
|
||
"下载曲线(CSV)",
|
||
data=csv_buf.to_csv(index=False),
|
||
file_name="bt_nav_compare.csv",
|
||
mime="text/csv",
|
||
key="dl_nav_compare",
|
||
)
|
||
except Exception:
|
||
pass
|
||
except Exception: # noqa: BLE001
|
||
LOGGER.debug("绘制对比曲线失败", extra=LOG_EXTRA)
|
||
if not rpt_df.empty:
|
||
try:
|
||
metrics_rows: List[Dict[str, object]] = []
|
||
for _, row in rpt_df.iterrows():
|
||
cfg_id = row["cfg_id"]
|
||
try:
|
||
summary = json.loads(row["summary"]) if isinstance(row["summary"], str) else (row["summary"] or {})
|
||
except json.JSONDecodeError:
|
||
summary = {}
|
||
record = {
|
||
"cfg_id": cfg_id,
|
||
"总收益": summary.get("total_return"),
|
||
"最大回撤": summary.get("max_drawdown"),
|
||
"交易数": summary.get("trade_count"),
|
||
"平均换手": summary.get("avg_turnover"),
|
||
"风险事件": summary.get("risk_events"),
|
||
}
|
||
metrics_rows.append({k: v for k, v in record.items() if (k == "cfg_id" or k in selected_metrics)})
|
||
if metrics_rows:
|
||
dfm = pd.DataFrame(metrics_rows)
|
||
st.dataframe(dfm, hide_index=True, width='stretch')
|
||
try:
|
||
st.download_button(
|
||
"下载指标(CSV)",
|
||
data=dfm.to_csv(index=False),
|
||
file_name="bt_metrics_compare.csv",
|
||
mime="text/csv",
|
||
key="dl_metrics_compare",
|
||
)
|
||
except Exception:
|
||
pass
|
||
except Exception: # noqa: BLE001
|
||
LOGGER.debug("渲染指标表失败", extra=LOG_EXTRA)
|
||
else:
|
||
st.info("请选择至少一个配置进行对比。")
|
||
|
||
|
||
def render_settings() -> None:
|
||
LOGGER.info("渲染设置页面", extra=LOG_EXTRA)
|
||
st.header("数据与设置")
|
||
cfg = get_config()
|
||
LOGGER.debug("当前 TuShare Token 是否已配置=%s", bool(cfg.tushare_token), extra=LOG_EXTRA)
|
||
|
||
# 基础配置
|
||
col1, col2 = st.columns([2, 1])
|
||
with col1:
|
||
token = st.text_input("TuShare Token", value=cfg.tushare_token or "", type="password")
|
||
with col2:
|
||
auto_update = st.checkbox(
|
||
"自动更新数据",
|
||
value=cfg.auto_update_data,
|
||
help="勾选后，每次启动程序将自动执行Tushare和RSS数据拉取"
|
||
)
|
||
|
||
if st.button("保存设置"):
|
||
LOGGER.info("保存设置按钮被点击", extra=LOG_EXTRA)
|
||
cfg.tushare_token = token.strip() or None
|
||
cfg.auto_update_data = auto_update
|
||
LOGGER.info("TuShare Token 更新，是否为空=%s", cfg.tushare_token is None, extra=LOG_EXTRA)
|
||
LOGGER.info("自动更新数据设置=%s", cfg.auto_update_data, extra=LOG_EXTRA)
|
||
save_config()
|
||
st.success("设置已保存，仅在当前会话生效。")
|
||
|
||
st.write("新闻源开关与数据库备份将在此配置。")
|
||
|
||
st.divider()
|
||
st.subheader("LLM 设置")
|
||
providers = cfg.llm_providers
|
||
provider_keys = sorted(providers.keys())
|
||
st.caption("先在 Provider 中维护基础连接（URL、Key、模型），再为全局与各部门设置个性化参数。")
|
||
|
||
# Provider management -------------------------------------------------
|
||
provider_select_col, provider_manage_col = st.columns([3, 1])
|
||
if provider_keys:
|
||
try:
|
||
default_provider = cfg.llm.primary.provider or provider_keys[0]
|
||
provider_index = provider_keys.index(default_provider)
|
||
except ValueError:
|
||
provider_index = 0
|
||
selected_provider = provider_select_col.selectbox(
|
||
"选择 Provider",
|
||
provider_keys,
|
||
index=provider_index,
|
||
key="llm_provider_select",
|
||
)
|
||
else:
|
||
selected_provider = None
|
||
provider_select_col.info("尚未配置 Provider，请先创建。")
|
||
|
||
new_provider_name = provider_manage_col.text_input("新增 Provider", key="new_provider_name")
|
||
if provider_manage_col.button("创建 Provider", key="create_provider_btn"):
|
||
key = (new_provider_name or "").strip().lower()
|
||
if not key:
|
||
st.warning("请输入有效的 Provider 名称。")
|
||
elif key in providers:
|
||
st.warning(f"Provider {key} 已存在。")
|
||
else:
|
||
providers[key] = LLMProvider(key=key)
|
||
cfg.llm_providers = providers
|
||
save_config()
|
||
st.success(f"已创建 Provider {key}。")
|
||
st.rerun()
|
||
|
||
if selected_provider:
|
||
provider_cfg = providers.get(selected_provider, LLMProvider(key=selected_provider))
|
||
title_key = f"provider_title_{selected_provider}"
|
||
base_key = f"provider_base_{selected_provider}"
|
||
api_key_key = f"provider_api_{selected_provider}"
|
||
mode_key = f"provider_mode_{selected_provider}"
|
||
enabled_key = f"provider_enabled_{selected_provider}"
|
||
|
||
title_val = st.text_input("备注名称", value=provider_cfg.title or "", key=title_key)
|
||
base_val = st.text_input("Base URL", value=provider_cfg.base_url or "", key=base_key, help="调用地址，例如：https://api.openai.com")
|
||
api_val = st.text_input("API Key", value=provider_cfg.api_key or "", key=api_key_key, type="password")
|
||
|
||
enabled_val = st.checkbox("启用", value=provider_cfg.enabled, key=enabled_key)
|
||
mode_val = st.selectbox("模式", options=["openai", "ollama"], index=0 if provider_cfg.mode == "openai" else 1, key=mode_key)
|
||
st.markdown("可用模型：")
|
||
if provider_cfg.models:
|
||
st.code("\n".join(provider_cfg.models), language="text")
|
||
else:
|
||
st.info("尚未获取模型列表，可点击下方按钮自动拉取。")
|
||
# ADD: show cache last updated if available
|
||
try:
|
||
cache_key = f"{selected_provider}|{(base_val or '').strip()}"
|
||
entry = _MODEL_CACHE.get(cache_key)
|
||
if entry and isinstance(entry.get("ts"), float):
|
||
ts = datetime.fromtimestamp(entry["ts"]).strftime("%Y-%m-%d %H:%M:%S")
|
||
st.caption(f"最近拉取时间：{ts}")
|
||
except Exception:
|
||
pass
|
||
|
||
fetch_key = f"fetch_models_{selected_provider}"
|
||
if st.button("获取模型列表", key=fetch_key):
|
||
with st.spinner("正在获取模型列表..."):
|
||
models, error = _discover_provider_models(provider_cfg, base_val, api_val)
|
||
if error:
|
||
st.error(error)
|
||
else:
|
||
provider_cfg.models = models
|
||
if models and (not provider_cfg.default_model or provider_cfg.default_model not in models):
|
||
provider_cfg.default_model = models[0]
|
||
providers[selected_provider] = provider_cfg
|
||
cfg.llm_providers = providers
|
||
cfg.sync_runtime_llm()
|
||
save_config()
|
||
st.success(f"共获取 {len(models)} 个模型。")
|
||
st.rerun()
|
||
|
||
if st.button("保存 Provider", key=f"save_provider_{selected_provider}"):
|
||
provider_cfg.title = title_val.strip()
|
||
provider_cfg.base_url = base_val.strip()
|
||
provider_cfg.api_key = api_val.strip() or None
|
||
provider_cfg.enabled = enabled_val
|
||
provider_cfg.mode = mode_val
|
||
providers[selected_provider] = provider_cfg
|
||
cfg.llm_providers = providers
|
||
cfg.sync_runtime_llm()
|
||
save_config()
|
||
st.success("Provider 已保存。")
|
||
st.session_state[title_key] = provider_cfg.title or ""
|
||
|
||
provider_in_use = (cfg.llm.primary.provider == selected_provider) or any(
|
||
ep.provider == selected_provider for ep in cfg.llm.ensemble
|
||
)
|
||
if not provider_in_use:
|
||
for dept in cfg.departments.values():
|
||
if dept.llm.primary.provider == selected_provider or any(ep.provider == selected_provider for ep in dept.llm.ensemble):
|
||
provider_in_use = True
|
||
break
|
||
if st.button(
|
||
"删除 Provider",
|
||
key=f"delete_provider_{selected_provider}",
|
||
disabled=provider_in_use or len(providers) <= 1,
|
||
):
|
||
providers.pop(selected_provider, None)
|
||
cfg.llm_providers = providers
|
||
cfg.sync_runtime_llm()
|
||
save_config()
|
||
st.success("Provider 已删除。")
|
||
st.rerun()
|
||
|
||
st.markdown("##### 全局推理配置")
|
||
if not provider_keys:
|
||
st.warning("请先配置至少一个 Provider。")
|
||
else:
|
||
global_cfg = cfg.llm
|
||
primary = global_cfg.primary
|
||
try:
|
||
provider_index = provider_keys.index(primary.provider or provider_keys[0])
|
||
except ValueError:
|
||
provider_index = 0
|
||
selected_global_provider = st.selectbox(
|
||
"主模型 Provider",
|
||
provider_keys,
|
||
index=provider_index,
|
||
key="global_provider_select",
|
||
)
|
||
provider_cfg = providers.get(selected_global_provider)
|
||
available_models = provider_cfg.models if provider_cfg else []
|
||
default_model = primary.model or (provider_cfg.default_model if provider_cfg else None)
|
||
if available_models:
|
||
options = available_models + ["自定义"]
|
||
try:
|
||
model_index = available_models.index(default_model)
|
||
model_choice = st.selectbox("主模型", options, index=model_index, key="global_model_choice")
|
||
except ValueError:
|
||
model_choice = st.selectbox("主模型", options, index=len(options) - 1, key="global_model_choice")
|
||
if model_choice == "自定义":
|
||
model_val = st.text_input("自定义模型", value=default_model or "", key="global_model_custom").strip()
|
||
else:
|
||
model_val = model_choice
|
||
else:
|
||
model_val = st.text_input("主模型", value=default_model or "", key="global_model_custom").strip()
|
||
|
||
temp_default = primary.temperature if primary.temperature is not None else (provider_cfg.default_temperature if provider_cfg else 0.2)
|
||
temp_val = st.slider("主模型温度", min_value=0.0, max_value=2.0, value=float(temp_default), step=0.05, key="global_temp")
|
||
timeout_default = primary.timeout if primary.timeout is not None else (provider_cfg.default_timeout if provider_cfg else 30.0)
|
||
timeout_val = st.number_input("主模型超时(秒)", min_value=5, max_value=300, value=int(timeout_default), step=5, key="global_timeout")
|
||
prompt_template_val = st.text_area(
|
||
"主模型 Prompt 模板（可选）",
|
||
value=primary.prompt_template or provider_cfg.prompt_template if provider_cfg else "",
|
||
height=120,
|
||
key="global_prompt_template",
|
||
)
|
||
|
||
strategy_val = st.selectbox("推理策略", sorted(ALLOWED_LLM_STRATEGIES), index=sorted(ALLOWED_LLM_STRATEGIES).index(global_cfg.strategy) if global_cfg.strategy in ALLOWED_LLM_STRATEGIES else 0, key="global_strategy")
|
||
show_ensemble = strategy_val != "single"
|
||
majority_threshold_val = st.number_input(
|
||
"多数投票门槛",
|
||
min_value=1,
|
||
max_value=10,
|
||
value=int(global_cfg.majority_threshold),
|
||
step=1,
|
||
key="global_majority",
|
||
disabled=not show_ensemble,
|
||
)
|
||
if not show_ensemble:
|
||
majority_threshold_val = 1
|
||
|
||
ensemble_rows: List[Dict[str, str]] = []
|
||
if show_ensemble:
|
||
ensemble_rows = [
|
||
{
|
||
"provider": ep.provider,
|
||
"model": ep.model or "",
|
||
"temperature": "" if ep.temperature is None else f"{ep.temperature:.3f}",
|
||
"timeout": "" if ep.timeout is None else str(int(ep.timeout)),
|
||
"prompt_template": ep.prompt_template or "",
|
||
}
|
||
for ep in global_cfg.ensemble
|
||
] or [{"provider": primary.provider or selected_global_provider, "model": "", "temperature": "", "timeout": "", "prompt_template": ""}]
|
||
|
||
ensemble_editor = st.data_editor(
|
||
ensemble_rows,
|
||
num_rows="dynamic",
|
||
key="global_ensemble_editor",
|
||
width='stretch',
|
||
hide_index=True,
|
||
column_config={
|
||
"provider": st.column_config.SelectboxColumn("Provider", options=provider_keys),
|
||
"model": st.column_config.TextColumn("模型"),
|
||
"temperature": st.column_config.TextColumn("温度"),
|
||
"timeout": st.column_config.TextColumn("超时(秒)"),
|
||
"prompt_template": st.column_config.TextColumn("Prompt 模板"),
|
||
},
|
||
)
|
||
if hasattr(ensemble_editor, "to_dict"):
|
||
ensemble_rows = ensemble_editor.to_dict("records")
|
||
else:
|
||
ensemble_rows = ensemble_editor
|
||
else:
|
||
st.info("当前策略为单模型，未启用协作模型。")
|
||
|
||
if st.button("保存全局配置", key="save_global_llm"):
|
||
primary.provider = selected_global_provider
|
||
primary.model = model_val or None
|
||
primary.temperature = float(temp_val)
|
||
primary.timeout = float(timeout_val)
|
||
primary.prompt_template = prompt_template_val.strip() or None
|
||
primary.base_url = None
|
||
primary.api_key = None
|
||
|
||
new_ensemble: List[LLMEndpoint] = []
|
||
if show_ensemble:
|
||
for row in ensemble_rows:
|
||
provider_val = (row.get("provider") or "").strip().lower()
|
||
if not provider_val:
|
||
continue
|
||
model_raw = (row.get("model") or "").strip() or None
|
||
temp_raw = (row.get("temperature") or "").strip()
|
||
timeout_raw = (row.get("timeout") or "").strip()
|
||
prompt_raw = (row.get("prompt_template") or "").strip()
|
||
new_ensemble.append(
|
||
LLMEndpoint(
|
||
provider=provider_val,
|
||
model=model_raw,
|
||
temperature=float(temp_raw) if temp_raw else None,
|
||
timeout=float(timeout_raw) if timeout_raw else None,
|
||
prompt_template=prompt_raw or None,
|
||
)
|
||
)
|
||
cfg.llm.ensemble = new_ensemble
|
||
cfg.llm.strategy = strategy_val
|
||
cfg.llm.majority_threshold = int(majority_threshold_val)
|
||
cfg.sync_runtime_llm()
|
||
save_config()
|
||
st.success("全局 LLM 配置已保存。")
|
||
st.json(llm_config_snapshot())
|
||
|
||
# Department configuration -------------------------------------------
|
||
st.markdown("##### 部门配置")
|
||
dept_settings = cfg.departments or {}
|
||
dept_rows = [
|
||
{
|
||
"code": code,
|
||
"title": dept.title,
|
||
"description": dept.description,
|
||
"weight": float(dept.weight),
|
||
"strategy": dept.llm.strategy,
|
||
"majority_threshold": dept.llm.majority_threshold,
|
||
"provider": dept.llm.primary.provider or (provider_keys[0] if provider_keys else ""),
|
||
"model": dept.llm.primary.model or "",
|
||
"temperature": "" if dept.llm.primary.temperature is None else f"{dept.llm.primary.temperature:.3f}",
|
||
"timeout": "" if dept.llm.primary.timeout is None else str(int(dept.llm.primary.timeout)),
|
||
"prompt_template": dept.llm.primary.prompt_template or "",
|
||
}
|
||
for code, dept in sorted(dept_settings.items())
|
||
]
|
||
|
||
if not dept_rows:
|
||
st.info("当前未配置部门，可在 config.json 中添加。")
|
||
dept_rows = []
|
||
|
||
dept_editor = st.data_editor(
|
||
dept_rows,
|
||
num_rows="fixed",
|
||
key="department_editor",
|
||
width='stretch',
|
||
hide_index=True,
|
||
column_config={
|
||
"code": st.column_config.TextColumn("编码", disabled=True),
|
||
"title": st.column_config.TextColumn("名称"),
|
||
"description": st.column_config.TextColumn("说明"),
|
||
"weight": st.column_config.NumberColumn("权重", min_value=0.0, max_value=10.0, step=0.1),
|
||
"strategy": st.column_config.SelectboxColumn("策略", options=sorted(ALLOWED_LLM_STRATEGIES)),
|
||
"majority_threshold": st.column_config.NumberColumn("投票阈值", min_value=1, max_value=10, step=1),
|
||
"provider": st.column_config.SelectboxColumn("Provider", options=provider_keys or [""]),
|
||
"model": st.column_config.TextColumn("模型"),
|
||
"temperature": st.column_config.TextColumn("温度"),
|
||
"timeout": st.column_config.TextColumn("超时(秒)"),
|
||
"prompt_template": st.column_config.TextColumn("Prompt 模板"),
|
||
},
|
||
)
|
||
|
||
if hasattr(dept_editor, "to_dict"):
|
||
dept_rows = dept_editor.to_dict("records")
|
||
else:
|
||
dept_rows = dept_editor
|
||
|
||
col_reset, col_save = st.columns([1, 1])
|
||
|
||
if col_save.button("保存部门配置"):
|
||
updated_departments: Dict[str, DepartmentSettings] = {}
|
||
for row in dept_rows:
|
||
code = row.get("code")
|
||
if not code:
|
||
continue
|
||
existing = dept_settings.get(code) or DepartmentSettings(code=code, title=code)
|
||
existing.title = row.get("title") or existing.title
|
||
existing.description = row.get("description") or ""
|
||
try:
|
||
existing.weight = max(0.0, float(row.get("weight", existing.weight)))
|
||
except (TypeError, ValueError):
|
||
pass
|
||
|
||
strategy_val = (row.get("strategy") or existing.llm.strategy).lower()
|
||
if strategy_val in ALLOWED_LLM_STRATEGIES:
|
||
existing.llm.strategy = strategy_val
|
||
if existing.llm.strategy == "single":
|
||
existing.llm.majority_threshold = 1
|
||
existing.llm.ensemble = []
|
||
else:
|
||
majority_raw = row.get("majority_threshold")
|
||
try:
|
||
majority_val = int(majority_raw)
|
||
if majority_val > 0:
|
||
existing.llm.majority_threshold = majority_val
|
||
except (TypeError, ValueError):
|
||
pass
|
||
|
||
provider_val = (row.get("provider") or existing.llm.primary.provider or (provider_keys[0] if provider_keys else "ollama")).strip().lower()
|
||
model_val = (row.get("model") or "").strip() or None
|
||
temp_raw = (row.get("temperature") or "").strip()
|
||
timeout_raw = (row.get("timeout") or "").strip()
|
||
prompt_raw = (row.get("prompt_template") or "").strip()
|
||
|
||
endpoint = existing.llm.primary or LLMEndpoint()
|
||
endpoint.provider = provider_val
|
||
endpoint.model = model_val
|
||
endpoint.temperature = float(temp_raw) if temp_raw else None
|
||
endpoint.timeout = float(timeout_raw) if timeout_raw else None
|
||
endpoint.prompt_template = prompt_raw or None
|
||
endpoint.base_url = None
|
||
endpoint.api_key = None
|
||
existing.llm.primary = endpoint
|
||
if existing.llm.strategy != "single":
|
||
existing.llm.ensemble = []
|
||
|
||
updated_departments[code] = existing
|
||
|
||
if updated_departments:
|
||
cfg.departments = updated_departments
|
||
cfg.sync_runtime_llm()
|
||
save_config()
|
||
st.success("部门配置已更新。")
|
||
else:
|
||
st.warning("未能解析部门配置输入。")
|
||
|
||
if col_reset.button("恢复默认部门"):
|
||
from app.utils.config import _default_departments
|
||
|
||
cfg.departments = _default_departments()
|
||
cfg.sync_runtime_llm()
|
||
save_config()
|
||
st.success("已恢复默认部门配置。")
|
||
st.rerun()
|
||
|
||
st.caption("部门配置存储为独立 LLM 参数，执行时会自动套用对应 Provider 的连接信息。")
|
||
|
||
|
||
def render_tests() -> None:
|
||
LOGGER.info("渲染自检页面", extra=LOG_EXTRA)
|
||
st.header("自检测试")
|
||
st.write("用于快速检查数据库与数据拉取是否正常工作。")
|
||
|
||
if st.button("测试数据库初始化"):
|
||
LOGGER.info("点击测试数据库初始化按钮", extra=LOG_EXTRA)
|
||
with st.spinner("正在检查数据库..."):
|
||
result = initialize_database()
|
||
if result.skipped:
|
||
LOGGER.info("数据库已存在，无需初始化", extra=LOG_EXTRA)
|
||
st.success("数据库已存在，检查通过。")
|
||
else:
|
||
LOGGER.info("数据库初始化完成，执行语句数=%s", result.executed, extra=LOG_EXTRA)
|
||
st.success(f"数据库初始化完成，共执行 {result.executed} 条语句。")
|
||
|
||
st.divider()
|
||
|
||
if st.button("测试 TuShare 拉取（示例 2024-01-01 至 2024-01-03）"):
|
||
LOGGER.info("点击示例 TuShare 拉取按钮", extra=LOG_EXTRA)
|
||
with st.spinner("正在调用 TuShare 接口..."):
|
||
try:
|
||
run_ingestion(
|
||
FetchJob(
|
||
name="streamlit_self_test",
|
||
start=date(2024, 1, 1),
|
||
end=date(2024, 1, 3),
|
||
ts_codes=("000001.SZ",),
|
||
),
|
||
include_limits=False,
|
||
)
|
||
LOGGER.info("示例 TuShare 拉取成功", extra=LOG_EXTRA)
|
||
st.success("TuShare 示例拉取完成，数据已写入数据库。")
|
||
except Exception as exc: # noqa: BLE001
|
||
LOGGER.exception("示例 TuShare 拉取失败", extra=LOG_EXTRA)
|
||
st.error(f"拉取失败：{exc}")
|
||
alerts.add_warning("TuShare", "示例拉取失败", str(exc))
|
||
_update_dashboard_sidebar()
|
||
|
||
st.info("注意：TuShare 拉取依赖网络与 Token，若环境未配置将出现错误提示。")
|
||
|
||
st.divider()
|
||
|
||
st.subheader("RSS 数据测试")
|
||
st.write("用于验证 RSS 配置是否能够正常抓取新闻并写入数据库。")
|
||
rss_url = st.text_input(
|
||
"测试 RSS 地址",
|
||
value="https://rsshub.app/cls/depth/1000",
|
||
help="留空则使用默认配置的全部 RSS 来源。",
|
||
).strip()
|
||
rss_hours = int(
|
||
st.number_input(
|
||
"回溯窗口（小时）",
|
||
min_value=1,
|
||
max_value=168,
|
||
value=24,
|
||
step=6,
|
||
help="仅抓取最近指定小时内的新闻。",
|
||
)
|
||
)
|
||
rss_limit = int(
|
||
st.number_input(
|
||
"单源抓取条数",
|
||
min_value=1,
|
||
max_value=200,
|
||
value=50,
|
||
step=10,
|
||
)
|
||
)
|
||
if st.button("运行 RSS 测试"):
|
||
from app.ingest import rss as rss_ingest
|
||
|
||
LOGGER.info(
|
||
"点击 RSS 测试按钮 rss_url=%s hours=%s limit=%s",
|
||
rss_url,
|
||
rss_hours,
|
||
rss_limit,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
with st.spinner("正在抓取 RSS 新闻..."):
|
||
try:
|
||
if rss_url:
|
||
items = rss_ingest.fetch_rss_feed(
|
||
rss_url,
|
||
hours_back=rss_hours,
|
||
max_items=rss_limit,
|
||
)
|
||
count = rss_ingest.save_news_items(items)
|
||
else:
|
||
count = rss_ingest.ingest_configured_rss(
|
||
hours_back=rss_hours,
|
||
max_items_per_feed=rss_limit,
|
||
)
|
||
st.success(f"RSS 测试完成，新增 {count} 条新闻记录。")
|
||
except Exception as exc: # noqa: BLE001
|
||
LOGGER.exception("RSS 测试失败", extra=LOG_EXTRA)
|
||
st.error(f"RSS 测试失败：{exc}")
|
||
alerts.add_warning("RSS", "RSS 测试执行失败", str(exc))
|
||
_update_dashboard_sidebar()
|
||
|
||
st.divider()
|
||
days = int(
|
||
st.number_input(
|
||
"检查窗口（天数）",
|
||
min_value=30,
|
||
max_value=10950,
|
||
value=365,
|
||
step=30,
|
||
)
|
||
)
|
||
LOGGER.debug("检查窗口天数=%s", days, extra=LOG_EXTRA)
|
||
cfg = get_config()
|
||
force_refresh = st.checkbox(
|
||
"强制刷新数据（关闭增量跳过）",
|
||
value=cfg.force_refresh,
|
||
help="勾选后将重新拉取所选区间全部数据",
|
||
)
|
||
if force_refresh != cfg.force_refresh:
|
||
cfg.force_refresh = force_refresh
|
||
LOGGER.info("更新 force_refresh=%s", force_refresh, extra=LOG_EXTRA)
|
||
save_config()
|
||
|
||
if st.button("执行手动数据同步"):
|
||
LOGGER.info("点击执行手动数据同步按钮", extra=LOG_EXTRA)
|
||
progress_bar = st.progress(0.0)
|
||
status_placeholder = st.empty()
|
||
log_placeholder = st.empty()
|
||
messages: list[str] = []
|
||
|
||
def hook(message: str, value: float) -> None:
|
||
progress_bar.progress(min(max(value, 0.0), 1.0))
|
||
status_placeholder.write(message)
|
||
messages.append(message)
|
||
LOGGER.debug("手动数据同步进度：%s -> %.2f", message, value, extra=LOG_EXTRA)
|
||
|
||
with st.spinner("正在执行手动数据同步..."):
|
||
try:
|
||
report = run_boot_check(
|
||
days=days,
|
||
progress_hook=hook,
|
||
force_refresh=force_refresh,
|
||
)
|
||
LOGGER.info("手动数据同步成功", extra=LOG_EXTRA)
|
||
st.success("手动数据同步完成，以下为数据覆盖摘要。")
|
||
st.json(report.to_dict())
|
||
if messages:
|
||
log_placeholder.markdown("\n".join(f"- {msg}" for msg in messages))
|
||
except Exception as exc: # noqa: BLE001
|
||
LOGGER.exception("手动数据同步失败", extra=LOG_EXTRA)
|
||
st.error(f"手动数据同步失败：{exc}")
|
||
alerts.add_warning("数据同步", "手动数据同步失败", str(exc))
|
||
_update_dashboard_sidebar()
|
||
if messages:
|
||
log_placeholder.markdown("\n".join(f"- {msg}" for msg in messages))
|
||
finally:
|
||
progress_bar.progress(1.0)
|
||
|
||
st.divider()
|
||
st.subheader("股票行情可视化")
|
||
options = _load_stock_options()
|
||
default_code = options[0] if options else "000001.SZ"
|
||
|
||
if options:
|
||
selection = st.selectbox("选择股票", options, index=0)
|
||
ts_code = _parse_ts_code(selection)
|
||
LOGGER.debug("选择股票：%s", ts_code, extra=LOG_EXTRA)
|
||
else:
|
||
ts_code = st.text_input("输入股票代码（如 000001.SZ）", value=default_code).strip().upper()
|
||
LOGGER.debug("输入股票：%s", ts_code, extra=LOG_EXTRA)
|
||
|
||
viz_col1, viz_col2 = st.columns(2)
|
||
default_start = date.today() - timedelta(days=180)
|
||
start_date = viz_col1.date_input("开始日期", value=default_start, key="viz_start")
|
||
end_date = viz_col2.date_input("结束日期", value=date.today(), key="viz_end")
|
||
LOGGER.debug("行情可视化日期范围：%s-%s", start_date, end_date, extra=LOG_EXTRA)
|
||
|
||
if start_date > end_date:
|
||
LOGGER.warning("无效日期范围：%s>%s", start_date, end_date, extra=LOG_EXTRA)
|
||
st.error("开始日期不能晚于结束日期")
|
||
return
|
||
|
||
with st.spinner("正在加载行情数据..."):
|
||
try:
|
||
df = _load_daily_frame(ts_code, start_date, end_date)
|
||
except Exception as exc: # noqa: BLE001
|
||
LOGGER.exception("加载行情数据失败", extra=LOG_EXTRA)
|
||
st.error(f"读取数据失败：{exc}")
|
||
return
|
||
|
||
if df.empty:
|
||
LOGGER.warning("指定区间无行情数据：%s %s-%s", ts_code, start_date, end_date, extra=LOG_EXTRA)
|
||
st.warning("未查询到该区间的交易数据，请确认数据库已拉取对应日线。")
|
||
return
|
||
|
||
price_df = df[["close"]].rename(columns={"close": "收盘价"})
|
||
volume_df = df[["vol"]].rename(columns={"vol": "成交量(手)"})
|
||
|
||
if price_df.shape[0] > 180:
|
||
sampled = price_df.resample('3D').last().dropna()
|
||
else:
|
||
sampled = price_df
|
||
|
||
if volume_df.shape[0] > 180:
|
||
volume_sampled = volume_df.resample('3D').mean().dropna()
|
||
else:
|
||
volume_sampled = volume_df
|
||
|
||
first_close = sampled.iloc[0, 0]
|
||
last_close = sampled.iloc[-1, 0]
|
||
delta_abs = last_close - first_close
|
||
delta_pct = (delta_abs / first_close * 100) if first_close else 0.0
|
||
|
||
metric_col1, metric_col2, metric_col3 = st.columns(3)
|
||
metric_col1.metric("最新收盘价", f"{last_close:.2f}", delta=f"{delta_abs:+.2f}")
|
||
metric_col2.metric("区间涨跌幅", f"{delta_pct:+.2f}%")
|
||
metric_col3.metric("平均成交量", f"{volume_sampled['成交量(手)'].mean():.0f}")
|
||
|
||
df_reset = df.reset_index().rename(columns={
|
||
"trade_date": "交易日",
|
||
"open": "开盘价",
|
||
"high": "最高价",
|
||
"low": "最低价",
|
||
"close": "收盘价",
|
||
"vol": "成交量(手)",
|
||
"amount": "成交额(千元)",
|
||
})
|
||
df_reset["成交额(千元)"] = df_reset["成交额(千元)"] / 1000
|
||
|
||
# 确保所有列的数据类型正确，避免PyArrow序列化错误
|
||
numeric_columns = ["开盘价", "最高价", "最低价", "收盘价", "成交量(手)", "成交额(千元)"]
|
||
for col in numeric_columns:
|
||
if col in df_reset.columns:
|
||
df_reset[col] = pd.to_numeric(df_reset[col], errors='coerce')
|
||
|
||
# 确保日期列是datetime类型
|
||
df_reset["交易日"] = pd.to_datetime(df_reset["交易日"])
|
||
|
||
candle_fig = go.Figure(
|
||
data=[
|
||
go.Candlestick(
|
||
x=df_reset["交易日"],
|
||
open=df_reset["开盘价"],
|
||
high=df_reset["最高价"],
|
||
low=df_reset["最低价"],
|
||
close=df_reset["收盘价"],
|
||
name="K线",
|
||
)
|
||
]
|
||
)
|
||
candle_fig.update_layout(height=420, margin=dict(l=10, r=10, t=40, b=10))
|
||
st.plotly_chart(candle_fig, width='stretch')
|
||
|
||
vol_fig = px.bar(
|
||
df_reset,
|
||
x="交易日",
|
||
y="成交量(手)",
|
||
labels={"成交量(手)": "成交量(手)"},
|
||
title="成交量",
|
||
)
|
||
vol_fig.update_layout(height=280, margin=dict(l=10, r=10, t=40, b=10))
|
||
st.plotly_chart(vol_fig, width='stretch')
|
||
|
||
amt_fig = px.bar(
|
||
df_reset,
|
||
x="交易日",
|
||
y="成交额(千元)",
|
||
labels={"成交额(千元)": "成交额(千元)"},
|
||
title="成交额",
|
||
)
|
||
amt_fig.update_layout(height=280, margin=dict(l=10, r=10, t=40, b=10))
|
||
st.plotly_chart(amt_fig, width='stretch')
|
||
|
||
df_reset["月份"] = df_reset["交易日"].dt.to_period("M").astype(str)
|
||
# 确保收盘价列是数值类型
|
||
df_reset["收盘价"] = pd.to_numeric(df_reset["收盘价"], errors='coerce')
|
||
box_fig = px.box(
|
||
df_reset,
|
||
x="月份",
|
||
y="收盘价",
|
||
points="outliers",
|
||
title="月度收盘价分布",
|
||
)
|
||
box_fig.update_layout(height=320, margin=dict(l=10, r=10, t=40, b=10))
|
||
st.plotly_chart(box_fig, width='stretch')
|
||
|
||
st.caption("提示：成交量单位为手，成交额以千元显示。箱线图按月展示收盘价分布。")
|
||
st.dataframe(df_reset.tail(20), width='stretch')
|
||
LOGGER.info("行情可视化完成，展示行数=%s", len(df_reset), extra=LOG_EXTRA)
|
||
|
||
st.divider()
|
||
st.subheader("LLM 接口测试")
|
||
st.json(llm_config_snapshot())
|
||
llm_prompt = st.text_area("测试 Prompt", value="请概述今天的市场重点。", height=160)
|
||
system_prompt = st.text_area(
|
||
"System Prompt (可选)",
|
||
value="你是一名量化策略研究助手，用简洁中文回答。",
|
||
height=100,
|
||
)
|
||
if st.button("执行 LLM 测试"):
|
||
with st.spinner("正在调用 LLM..."):
|
||
try:
|
||
response = run_llm(llm_prompt, system=system_prompt or None)
|
||
except Exception as exc: # noqa: BLE001
|
||
LOGGER.exception("LLM 测试失败", extra=LOG_EXTRA)
|
||
st.error(f"LLM 调用失败：{exc}")
|
||
else:
|
||
LOGGER.info("LLM 测试成功", extra=LOG_EXTRA)
|
||
st.success("LLM 调用成功，以下为返回内容：")
|
||
st.write(response)
|
||
|
||
|
||
def render_data_settings() -> None:
|
||
"""渲染数据源配置界面."""
|
||
st.subheader("Tushare 数据源")
|
||
cfg = get_config()
|
||
|
||
col1, col2 = st.columns(2)
|
||
with col1:
|
||
tushare_token = st.text_input(
|
||
"Tushare Token",
|
||
value=cfg.tushare_token or "",
|
||
type="password",
|
||
help="从 tushare.pro 获取的 API token"
|
||
)
|
||
|
||
with col2:
|
||
auto_update = st.checkbox(
|
||
"启动时自动更新数据",
|
||
value=cfg.auto_update_data,
|
||
help="启动应用时自动检查并更新数据"
|
||
)
|
||
|
||
update_interval = st.slider(
|
||
"数据更新间隔(天)",
|
||
min_value=1,
|
||
max_value=30,
|
||
value=cfg.data_update_interval,
|
||
help="自动更新时检查的数据时间范围"
|
||
)
|
||
|
||
if st.button("保存数据源配置"):
|
||
cfg.tushare_token = tushare_token
|
||
cfg.auto_update_data = auto_update
|
||
cfg.data_update_interval = update_interval
|
||
save_config(cfg)
|
||
st.success("数据源配置已更新！")
|
||
|
||
st.divider()
|
||
st.subheader("数据更新记录")
|
||
|
||
with db_session() as session:
|
||
df = pd.read_sql_query(
|
||
"""
|
||
SELECT job_type, status, created_at, updated_at, error_msg
|
||
FROM fetch_jobs
|
||
ORDER BY created_at DESC
|
||
LIMIT 50
|
||
""",
|
||
session
|
||
)
|
||
|
||
if not df.empty:
|
||
df["duration"] = (df["updated_at"] - df["created_at"]).dt.total_seconds().round(2)
|
||
df = df.drop(columns=["updated_at"])
|
||
df = df.rename(columns={
|
||
"job_type": "数据类型",
|
||
"status": "状态",
|
||
"created_at": "开始时间",
|
||
"error_msg": "错误信息",
|
||
"duration": "耗时(秒)"
|
||
})
|
||
st.dataframe(df, width='stretch')
|
||
else:
|
||
st.info("暂无数据更新记录")
|
||
|
||
|
||
def main() -> None:
|
||
LOGGER.info("初始化 Streamlit UI", extra=LOG_EXTRA)
|
||
st.set_page_config(page_title="多智能体个人投资助理", layout="wide")
|
||
|
||
# 确保数据库表已创建
|
||
from app.data.schema import initialize_database
|
||
initialize_database()
|
||
|
||
# 检查是否需要自动更新数据
|
||
cfg = get_config()
|
||
if cfg.auto_update_data:
|
||
LOGGER.info("检测到自动更新数据选项已启用，开始执行数据拉取", extra=LOG_EXTRA)
|
||
try:
|
||
# 初始化数据库
|
||
from app.data.schema import initialize_database
|
||
initialize_database()
|
||
|
||
# 执行开机检查（包含数据拉取）
|
||
from app.ingest.checker import run_boot_check
|
||
with st.spinner("正在自动更新数据..."):
|
||
def progress_hook(message: str, progress: float) -> None:
|
||
st.write(f"📊 {message} ({progress:.1%})")
|
||
|
||
report = run_boot_check(
|
||
days=30, # 最近30天
|
||
auto_fetch=True,
|
||
progress_hook=progress_hook,
|
||
force_refresh=False
|
||
)
|
||
|
||
# 执行RSS新闻拉取
|
||
from app.ingest.rss import ingest_configured_rss
|
||
rss_count = ingest_configured_rss(hours_back=24, max_items_per_feed=50)
|
||
|
||
LOGGER.info("自动数据更新完成：日线数据覆盖%s-%s，RSS新闻%s条",
|
||
report.start, report.end, rss_count, extra=LOG_EXTRA)
|
||
st.success(f"✅ 自动数据更新完成：获取RSS新闻 {rss_count} 条")
|
||
|
||
except Exception as exc:
|
||
LOGGER.exception("自动数据更新失败", extra=LOG_EXTRA)
|
||
st.error(f"❌ 自动数据更新失败：{exc}")
|
||
|
||
render_global_dashboard()
|
||
tabs = st.tabs(["今日计划", "回测与复盘", "数据与设置", "自检测试"])
|
||
LOGGER.debug("Tabs 初始化完成：%s", ["今日计划", "回测与复盘", "数据与设置", "自检测试"], extra=LOG_EXTRA)
|
||
with tabs[0]:
|
||
render_today_plan()
|
||
with tabs[1]:
|
||
render_log_viewer()
|
||
with tabs[2]:
|
||
st.header("系统设置")
|
||
settings_tabs = st.tabs(["基本配置", "投资组合", "数据源"])
|
||
|
||
with settings_tabs[0]:
|
||
render_settings()
|
||
|
||
with settings_tabs[1]:
|
||
from app.ui.portfolio_config import render_portfolio_config
|
||
render_portfolio_config()
|
||
|
||
with settings_tabs[2]:
|
||
render_data_settings()
|
||
with tabs[3]:
|
||
render_tests()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|