1851 lines
83 KiB
Python
1851 lines
83 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.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.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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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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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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metrics_container = st.sidebar.container()
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decisions_container = st.sidebar.container()
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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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_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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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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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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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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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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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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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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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 = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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}
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response = requests.get(url, headers=headers, timeout=timeout)
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response.raise_for_status()
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payload = response.json()
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models = [
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str(item.get("id")).strip()
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for item in payload.get("data", [])
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if item.get("id")
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]
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return sorted(set(models)), None
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except RequestException as exc: # noqa: BLE001
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return [], f"HTTP 错误：{exc}"
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except Exception as exc: # noqa: BLE001
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return [], f"解析失败：{exc}"
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def _load_stock_options(limit: int = 500) -> list[str]:
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try:
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with db_session(read_only=True) as conn:
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rows = conn.execute(
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"SELECT ts_code, name FROM stock_basic WHERE list_status = 'L' ORDER BY ts_code"
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).fetchall()
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except Exception:
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LOGGER.exception("加载股票列表失败", extra=LOG_EXTRA)
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return []
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options: list[str] = []
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for row in rows[:limit]:
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code = row["ts_code"]
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name = row["name"] or ""
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label = f"{code} | {name}" if name else code
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options.append(label)
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LOGGER.info("加载股票选项完成，数量=%s", len(options), extra=LOG_EXTRA)
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return options
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def _parse_ts_code(selection: str) -> str:
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return selection.split(' | ')[0].strip().upper()
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def _load_daily_frame(ts_code: str, start: date, end: date) -> pd.DataFrame:
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LOGGER.info(
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"加载行情数据：ts_code=%s start=%s end=%s",
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ts_code,
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start,
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end,
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extra=LOG_EXTRA,
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)
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start_str = start.strftime('%Y%m%d')
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end_str = end.strftime('%Y%m%d')
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range_query = (
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"SELECT trade_date, open, high, low, close, vol, amount "
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"FROM daily WHERE ts_code = ? AND trade_date BETWEEN ? AND ? ORDER BY trade_date"
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)
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fallback_query = (
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"SELECT trade_date, open, high, low, close, vol, amount "
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"FROM daily WHERE ts_code = ? ORDER BY trade_date DESC LIMIT 200"
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)
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with db_session(read_only=True) as conn:
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df = pd.read_sql_query(range_query, conn, params=(ts_code, start_str, end_str))
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if df.empty:
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df = pd.read_sql_query(fallback_query, conn, params=(ts_code,))
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if df.empty:
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LOGGER.warning(
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"行情数据为空：ts_code=%s start=%s end=%s",
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ts_code,
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start,
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end,
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extra=LOG_EXTRA,
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)
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return df
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df = df.sort_values('trade_date')
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df['trade_date'] = pd.to_datetime(df['trade_date'])
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df.set_index('trade_date', inplace=True)
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LOGGER.info("行情数据加载完成：条数=%s", len(df), extra=LOG_EXTRA)
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return df
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def _get_latest_trade_date() -> Optional[date]:
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try:
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with db_session(read_only=True) as conn:
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row = conn.execute(
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"SELECT trade_date FROM daily ORDER BY trade_date DESC LIMIT 1"
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).fetchone()
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except Exception: # noqa: BLE001
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LOGGER.exception("查询最新交易日失败", extra=LOG_EXTRA)
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return None
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if not row:
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return None
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raw_value = row["trade_date"]
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if not raw_value:
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return None
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try:
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return datetime.strptime(str(raw_value), "%Y%m%d").date()
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except ValueError:
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try:
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return datetime.fromisoformat(str(raw_value)).date()
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except ValueError:
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LOGGER.warning("无法解析交易日：%s", raw_value, extra=LOG_EXTRA)
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return None
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def _default_backtest_range(window_days: int = 60) -> tuple[date, date]:
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latest = _get_latest_trade_date() or date.today()
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start = latest - timedelta(days=window_days)
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if start > latest:
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start = latest
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return start, latest
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def render_today_plan() -> None:
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LOGGER.info("渲染今日计划页面", extra=LOG_EXTRA)
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st.header("今日计划")
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latest_trade_date = _get_latest_trade_date()
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if latest_trade_date:
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st.caption(f"最新交易日：{latest_trade_date.isoformat()}（统计数据请见左侧系统监控）")
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else:
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st.caption("统计与决策概览现已移至左侧“系统监控”侧栏。")
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try:
|
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with db_session(read_only=True) as conn:
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date_rows = conn.execute(
|
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"""
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SELECT DISTINCT trade_date
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FROM agent_utils
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ORDER BY trade_date DESC
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LIMIT 30
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"""
|
||
).fetchall()
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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
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||
|
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trade_dates = [row["trade_date"] for row in date_rows]
|
||
if not trade_dates:
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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
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|
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trade_date = st.selectbox("交易日", trade_dates, index=0)
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||
|
||
with db_session(read_only=True) as conn:
|
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code_rows = conn.execute(
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"""
|
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SELECT DISTINCT ts_code
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||
FROM agent_utils
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||
WHERE trade_date = ?
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||
ORDER BY ts_code
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||
""",
|
||
(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
|
||
|
||
ts_code = st.selectbox("标的", symbols, index=0)
|
||
|
||
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 = ?
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ORDER BY CASE WHEN agent = 'global' THEN 1 ELSE 0 END, agent
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||
""",
|
||
(trade_date, ts_code),
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||
).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):
|
||
if global_info.get("scope_values"):
|
||
st.write("最新字段：")
|
||
st.json(global_info["scope_values"])
|
||
if global_info.get("close_series"):
|
||
st.write("收盘价时间序列 (最近窗口)：")
|
||
st.json(global_info["close_series"])
|
||
if global_info.get("turnover_series"):
|
||
st.write("换手率时间序列 (最近窗口)：")
|
||
st.json(global_info["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:
|
||
dept_df = pd.DataFrame(dept_records)
|
||
st.dataframe(dept_df, width='stretch', hide_index=True)
|
||
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:
|
||
agent_df = pd.DataFrame(agent_records)
|
||
st.dataframe(agent_df, width='stretch', hide_index=True)
|
||
else:
|
||
st.info("暂无基础代理评分。")
|
||
|
||
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。")
|
||
|
||
|
||
def render_backtest() -> None:
|
||
LOGGER.info("渲染回测页面", extra=LOG_EXTRA)
|
||
st.header("回测与复盘")
|
||
st.write("在此运行回测、展示净值曲线与代理贡献。")
|
||
|
||
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。")
|
||
|
||
|
||
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)
|
||
token = st.text_input("TuShare Token", value=cfg.tushare_token or "", type="password")
|
||
|
||
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
|
||
LOGGER.info("TuShare Token 更新，是否为空=%s", cfg.tushare_token is None, 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}"
|
||
default_model_key = f"provider_default_model_{selected_provider}"
|
||
mode_key = f"provider_mode_{selected_provider}"
|
||
temp_key = f"provider_temp_{selected_provider}"
|
||
timeout_key = f"provider_timeout_{selected_provider}"
|
||
prompt_key = f"provider_prompt_{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")
|
||
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。")
|
||
|
||
model_choice_key = f"{default_model_key}_choice"
|
||
if provider_cfg.models:
|
||
options = provider_cfg.models + ["自定义"]
|
||
default_choice = provider_cfg.default_model if provider_cfg.default_model in provider_cfg.models else "自定义"
|
||
model_choice = st.selectbox("默认模型", options, index=options.index(default_choice), key=model_choice_key)
|
||
if model_choice == "自定义":
|
||
default_model_val = st.text_input("自定义默认模型", value=provider_cfg.default_model or "", key=default_model_key).strip() or None
|
||
else:
|
||
default_model_val = model_choice
|
||
else:
|
||
default_model_val = st.text_input("默认模型", value=provider_cfg.default_model or "", key=default_model_key).strip() or None
|
||
mode_val = st.selectbox("调用模式", ["openai", "ollama"], index=0 if provider_cfg.mode == "openai" else 1, key=mode_key)
|
||
temp_val = st.slider("默认温度", min_value=0.0, max_value=2.0, value=float(provider_cfg.default_temperature), step=0.05, key=temp_key)
|
||
timeout_val = st.number_input("默认超时(秒)", min_value=5, max_value=300, value=int(provider_cfg.default_timeout or 30), step=5, key=timeout_key)
|
||
prompt_template_val = st.text_area("默认 Prompt 模板（可选，使用 {prompt} 占位）", value=provider_cfg.prompt_template or "", key=prompt_key, height=120)
|
||
enabled_val = st.checkbox("启用", value=provider_cfg.enabled, key=enabled_key)
|
||
|
||
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
|
||
if provider_cfg.models and default_model_val in provider_cfg.models:
|
||
provider_cfg.default_model = default_model_val
|
||
else:
|
||
provider_cfg.default_model = default_model_val
|
||
provider_cfg.default_temperature = float(temp_val)
|
||
provider_cfg.default_timeout = float(timeout_val)
|
||
provider_cfg.prompt_template = prompt_template_val.strip()
|
||
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 ""
|
||
st.session_state[default_model_key] = provider_cfg.default_model 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}")
|
||
|
||
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()
|
||
days = int(st.number_input("检查窗口（天数）", min_value=30, max_value=1095, 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("执行开机检查"):
|
||
LOGGER.info("点击执行开机检查按钮", 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("开机检查进度：%s -> %.2f", message, value, extra=LOG_EXTRA)
|
||
|
||
with st.spinner("正在执行开机检查..."):
|
||
try:
|
||
report = run_boot_check(
|
||
days=days,
|
||
progress_hook=hook,
|
||
force_refresh=force_refresh,
|
||
)
|
||
LOGGER.info("开机检查成功", extra=LOG_EXTRA)
|
||
st.success("开机检查完成，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("开机检查失败", extra=LOG_EXTRA)
|
||
st.error(f"开机检查失败：{exc}")
|
||
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
|
||
|
||
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, use_container_width=True)
|
||
|
||
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, use_container_width=True)
|
||
|
||
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, use_container_width=True)
|
||
|
||
df_reset["月份"] = df_reset["交易日"].dt.to_period("M").astype(str)
|
||
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, use_container_width=True)
|
||
|
||
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 main() -> None:
|
||
LOGGER.info("初始化 Streamlit UI", extra=LOG_EXTRA)
|
||
st.set_page_config(page_title="多智能体投资助理", layout="wide")
|
||
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_backtest()
|
||
with tabs[2]:
|
||
render_settings()
|
||
with tabs[3]:
|
||
render_tests()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|