418 lines
14 KiB
Python
418 lines
14 KiB
Python
"""
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MND-IA Main Orchestrator
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=========================
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主调度程序 - 协调所有 Agent Skills 完成每日投资决策流程
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执行流程：
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1. Ingest (数据摄取): Analyst 读取新闻, Quant 读取行情
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2. Cognition (认知更新): Strategist 更新 WorldBook
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3. Decision (决策生成): PM 计算 Trust Index 并生成订单
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4. Execution (执行输出): 输出交易指令
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5. Audit (复盘审计): Auditor 评估昨日预测准确性
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"""
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import sys
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from pathlib import Path
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from datetime import datetime
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import json
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import argparse
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# 导入核心模块
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from core.world_book import WorldBook
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# 导入 Agent Skills
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from skills.analyst import NewsAnalyzer
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from skills.quant import QuantAnalyzer, get_all_etf_codes_from_asset_map
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from skills.strategist import MacroStrategist
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from skills.pm import PortfolioManager, save_orders_to_file
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from skills.auditor import PerformanceAuditor
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from skills.crawler import XinwenLianboCrawler
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class MNDAgent:
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"""MND-IA 主智能体"""
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def __init__(
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self,
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data_dir: str = "data",
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total_capital: float = 1000000.0,
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llm_client=None,
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use_llm: bool = True
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):
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"""
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Args:
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data_dir: 数据目录
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total_capital: 总资金（元）
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llm_client: LLM 客户端（可选，用于 Analyst 增强）
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use_llm: 是否使用 LLM 进行智能分析（默认 True）
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"""
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self.data_dir = Path(data_dir)
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self.data_dir.mkdir(exist_ok=True)
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self.use_llm = use_llm
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# 初始化核心
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print("=" * 60)
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print("🚀 MND-IA 系统启动")
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print(f" 模式: {'LLM 智能分析' if use_llm else '规则引擎（降级模式）'}")
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print("=" * 60)
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self.world_book = WorldBook(data_dir=data_dir)
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# 初始化 Agent Skills（传递 use_llm 参数）
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self.analyst = NewsAnalyzer(llm_client=llm_client, use_llm=use_llm)
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self.quant = QuantAnalyzer()
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self.strategist = MacroStrategist(self.world_book, use_llm=use_llm)
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self.pm = PortfolioManager(self.world_book, total_capital=total_capital, use_llm=use_llm)
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self.auditor = PerformanceAuditor(data_dir=data_dir)
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self.crawler = XinwenLianboCrawler()
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print("✅ 所有 Agent Skills 初始化完成\n")
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def daily_batch_workflow(
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self,
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news_list: list = None,
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etf_codes: list = None,
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use_crawler: bool = True
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) -> dict:
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"""
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每日批处理流程（08:30 AM 执行）
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Args:
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news_list: 新闻列表（如为 None 则使用爬虫或测试数据）
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etf_codes: ETF 代码列表（如为 None 则使用全部）
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use_crawler: 是否使用爬虫抓取新闻联播（默认 True）
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Returns:
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执行结果汇总
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"""
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print(f"📅 开始执行每日批处理流程")
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print(f"⏰ 执行时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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print("=" * 60)
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# ========== Phase 1: 数据摄取 (Ingest) ==========
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print("\n【Phase 1: 数据摄取】")
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print("-" * 60)
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# 1.1 获取新闻数据
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if news_list is None:
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if use_crawler:
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print("🕷️ 使用爬虫抓取新闻联播...")
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news_list = self.crawler.crawl_sync()
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if not news_list:
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print("⚠️ 爬虫未获取到数据，使用测试数据")
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news_list = self._get_test_news()
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else:
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print("⚠️ 未提供新闻数据，使用测试数据")
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news_list = self._get_test_news()
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print(f"📰 新闻分析: {len(news_list)} 条")
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news_analysis = self.analyst.batch_analyze(news_list)
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narrative_json = self.analyst.generate_narrative_json(news_analysis)
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# 1.2 获取行情数据
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if etf_codes is None:
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etf_codes = get_all_etf_codes_from_asset_map()
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print(f"📊 行情获取: {len(etf_codes)} 个 ETF")
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market_data = self.quant.batch_analyze(etf_codes)
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market_report = self.quant.generate_market_report(market_data)
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# ========== Phase 2: 认知更新 (Cognition) ==========
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print("\n【Phase 2: 认知更新】")
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print("-" * 60)
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# 2.1 处理叙事 JSON
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self.strategist.process_narrative_json(narrative_json)
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# 2.2 检测宏观周期变化
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cycle_change = self.strategist.detect_macro_cycle_change(
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narrative_json,
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market_report
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)
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if cycle_change:
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self.strategist.apply_macro_cycle_change(cycle_change)
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# 2.3 每日维护
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maintenance_report = self.strategist.daily_maintenance()
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# 2.4 生成 WorldBook 快照
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wb_snapshot = self.strategist.generate_world_book_snapshot()
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# ========== Phase 3: 决策生成 (Decision) ==========
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print("\n【Phase 3: 决策生成】")
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print("-" * 60)
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# 3.1 计算 Trust Index
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trust_results = self.pm.batch_calculate_trust_index(market_data)
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# 3.2 生成交易订单
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raw_orders = self.pm.generate_trade_orders(trust_results, market_data)
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# 3.3 风控检查
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final_orders = self.pm.apply_risk_control(raw_orders)
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# 3.4 生成投资组合报告
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portfolio_report = self.pm.generate_portfolio_report(
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trust_results,
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final_orders
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)
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# ========== Phase 4: 执行输出 (Execution) ==========
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print("\n【Phase 4: 执行输出】")
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print("-" * 60)
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if final_orders:
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# 保存订单到文件
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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save_orders_to_file(final_orders, f"orders_{timestamp}.json")
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print(f"📋 生成 {len(final_orders)} 条交易指令:")
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for order in final_orders[:5]: # 显示前5条
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print(f" {order['action'].upper()} {order['code']}: "
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f"{order['shares']}股 @ ¥{order['price']}")
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else:
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print("⚠️ 无符合条件的交易机会")
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# 保存 WorldBook
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self.world_book.save()
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# ========== Phase 5: 结果汇总 ==========
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execution_result = {
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"timestamp": datetime.now().isoformat(),
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"workflow": "daily_batch",
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"phases": {
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"ingest": {
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"news_count": len(news_list),
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"etf_count": len(etf_codes),
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"narratives_identified": len(narrative_json['narratives'])
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},
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"cognition": {
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"active_narratives": len(self.world_book.active_narratives),
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"macro_cycle": self.world_book.macro_cycle.to_dict(),
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"cycle_changed": cycle_change is not None
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},
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"decision": {
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"trust_results": len(trust_results),
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"orders_generated": len(final_orders),
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"capital_utilization": portfolio_report['risk_control']['capital_utilization']
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}
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},
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"top_opportunities": portfolio_report['top_opportunities'][:3],
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"orders": final_orders
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}
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# 保存执行记录
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self._save_execution_log(execution_result)
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print("\n" + "=" * 60)
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print("✅ 每日批处理流程完成")
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print("=" * 60)
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return execution_result
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def post_market_audit(
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self,
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yesterday_predictions: list = None
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) -> dict:
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"""
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盘后复盘流程（16:00 PM 执行）
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Args:
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yesterday_predictions: 昨日的预测记录
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Returns:
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审计报告
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"""
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print("\n📊 开始盘后复盘审计")
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print("=" * 60)
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if yesterday_predictions is None:
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print("⚠️ 无昨日预测数据，跳过审计")
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return {"status": "skipped", "reason": "no_predictions"}
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# 获取实际表现数据（这里需要从行情数据计算）
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actual_performances = self._fetch_actual_performances(yesterday_predictions)
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# 执行审计
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audit_summary = self.auditor.batch_audit(
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yesterday_predictions,
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actual_performances
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)
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# 生成报告
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report = self.auditor.generate_correction_report(audit_summary)
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# 保存报告
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report_path = self.data_dir / f"audit_report_{datetime.now().strftime('%Y%m%d')}.md"
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with open(report_path, 'w', encoding='utf-8') as f:
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f.write(report)
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print(f"\n📄 审计报告已保存: {report_path}")
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print(f"准确率: {audit_summary['performance']['accuracy']:.1f}%")
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return audit_summary
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def intraday_sentinel(
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self,
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breaking_news: dict
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) -> dict:
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"""
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盘中哨兵流程（09:30 - 15:00）
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当检测到 Level A 重大新闻时触发
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Args:
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breaking_news: 快讯内容
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Returns:
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应急响应结果
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"""
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print("\n🚨 盘中哨兵触发")
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print("=" * 60)
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# 分析快讯
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analysis = self.analyst.analyze_news(
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breaking_news.get('text', ''),
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breaking_news.get('title', '')
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)
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# 如果不是 Level A，忽略
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if analysis['level'] != 'A':
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print("⚠️ 非 A 级新闻，不触发应急响应")
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return {"triggered": False, "reason": "not_level_a"}
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print(f"⚠️ 检测到 Level A 重大新闻: {analysis['title']}")
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print(f" 涉及板块: {', '.join(analysis['sectors'])}")
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# 立即更新 WorldBook
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narrative_json = self.analyst.generate_narrative_json([analysis])
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self.strategist.process_narrative_json(narrative_json)
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# 检查宏观周期变化
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cycle_change = self.strategist.detect_macro_cycle_change(narrative_json)
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if cycle_change:
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self.strategist.apply_macro_cycle_change(cycle_change)
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# 重新评估持仓（如果有）
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# 这里简化处理，实际应该重新计算 Trust Index
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self.world_book.save()
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return {
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"triggered": True,
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"news_level": "A",
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"sectors_affected": analysis['sectors'],
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"macro_cycle_changed": cycle_change is not None,
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"timestamp": datetime.now().isoformat()
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}
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def _get_test_news(self) -> list:
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"""获取测试新闻数据"""
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return [
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{
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"title": "工信部发布低空经济指导意见 2027年规模达1万亿",
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"text": "工业和信息化部发布《低空经济高质量发展指导意见》，推动 eVTOL 产业化。"
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},
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{
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"title": "AI算力需求持续旺盛 多家公司业绩预增50%",
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"text": "智算中心建设加速，算力租赁业务快速增长。"
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}
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]
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def _fetch_actual_performances(
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self,
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predictions: list
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) -> dict:
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"""获取实际表现数据（简化版）"""
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# 实际应该从 Quant 模块获取真实数据
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# 这里返回模拟数据
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performances = {}
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for pred in predictions:
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code = pred['code']
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performances[code] = {
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"code": code,
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"price_change": 0, # 需要实际计算
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"volume_change": 1.0,
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"date": datetime.now().strftime("%Y-%m-%d")
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}
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return performances
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def _save_execution_log(self, result: dict) -> None:
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"""保存执行日志"""
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log_path = self.data_dir / "execution_logs.json"
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logs = []
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if log_path.exists():
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with open(log_path, 'r', encoding='utf-8') as f:
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logs = json.load(f)
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logs.append(result)
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# 只保留最近30天
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logs = logs[-30:]
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with open(log_path, 'w', encoding='utf-8') as f:
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json.dump(logs, f, ensure_ascii=False, indent=2)
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def main():
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"""命令行入口"""
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parser = argparse.ArgumentParser(description="MND-IA 投资智能体")
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parser.add_argument(
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'--mode',
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choices=['daily', 'audit', 'test'],
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default='test',
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help='运行模式: daily(每日批处理), audit(盘后审计), test(测试)'
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)
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parser.add_argument(
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'--capital',
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type=float,
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default=1000000.0,
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help='总资金（元）'
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)
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parser.add_argument(
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'--no-crawler',
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action='store_true',
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help='不使用爬虫，使用测试数据'
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)
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parser.add_argument(
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'--no-llm',
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action='store_true',
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help='不使用 LLM，使用规则引擎（降级模式）'
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)
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args = parser.parse_args()
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# 创建 Agent 实例
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agent = MNDAgent(
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total_capital=args.capital,
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use_llm=not args.no_llm
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)
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if args.mode == 'daily':
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# 每日批处理
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result = agent.daily_batch_workflow(use_crawler=not args.no_crawler)
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print(f"\n执行结果:")
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print(json.dumps(result, ensure_ascii=False, indent=2))
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elif args.mode == 'audit':
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# 盘后审计（需要提供昨日数据）
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print("盘后审计模式需要提供昨日预测数据")
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else:
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# 测试模式
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print("\n🧪 测试模式：执行完整流程演示\n")
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result = agent.daily_batch_workflow(use_crawler=not args.no_crawler)
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print("\n" + "=" * 60)
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print("📈 Top 投资机会:")
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print("=" * 60)
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for opp in result['top_opportunities']:
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print(f" {opp['code']}: Trust Index {opp['trust_index']:.1f} - {opp['verdict']}")
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if __name__ == "__main__":
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main()
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