Initial commit: cc-cursor 全链路量化研究平台
7 Sprints 全部完成: Sprint 0: 基础设施 (DataManager + MariaDB) Sprint 1: 因子引擎 (34因子/12分类) Sprint 2: VectorBT 回测 (5策略+截面) Sprint 3: Optuna 优化 (+Walk-Forward) Sprint 4: ML 模型 (LightGBM+CatBoost) Sprint 5: Qwen 情绪因子 (三源新闻+日期对齐) Sprint 6: Agent 系统 (4Agent+日报.md/.html) 生产加固 (15项): Tushare双源fallback, SSH自动恢复, pool_pre_ping, save_daily先删后插, load_dotenv绝对路径, 日报5d/20d修复, RiskAgent改上证指数, 昨日对比+数据截止, mac_report utf8mb4, CLAUDE-*.md 9条已知Bug, demo全参数化, djapi数据源归一化, indexDatas API修正 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
Claude Opus 4.7
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"""
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Agent 抽象基类。
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每个 Agent 负责一个独立任务，通过构造函数注入已有引擎，组合而非重建。
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"""
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from abc import ABC, abstractmethod
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from datetime import datetime
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class BaseAgent(ABC):
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"""Agent 基类。"""
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name: str = ""
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description: str = ""
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def __init__(self, **engines):
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"""
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注入已有基础设施。
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支持的引擎:
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dm: DataManager
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fe: FactorEngine
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bt: VectorBTEngine
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opt: OptunaEngine
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sent: SentimentEngine
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ml_models: dict[str, BaseModel]
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"""
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self.dm = engines.get("dm")
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self.fe = engines.get("fe")
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self.bt = engines.get("bt")
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self.opt = engines.get("opt")
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self.sent = engines.get("sent")
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self.ml_models = engines.get("ml_models", {})
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@abstractmethod
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def execute(self, **kwargs) -> dict:
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"""执行 Agent 任务，返回结构化结果。"""
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...
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def log(self, msg: str):
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print(f"[{self.name}] {msg}")
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def _today(self) -> str:
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return datetime.now().strftime("%Y%m%d")
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@@ -0,0 +1,211 @@
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"""
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AgentOrchestrator — Agent 编排器。
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管理所有 Agent 的生命周期、执行顺序、结果传递。
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"""
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from datetime import datetime
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from agents.research_agent import ResearchAgent
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from agents.selection_agent import SelectionAgent
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from agents.risk_agent import RiskAgent
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from agents.report_agent import ReportAgent
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class AgentOrchestrator:
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"""
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Agent 编排器。
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用法:
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orch = AgentOrchestrator(dm=dm, fe=engine_fe, bt=engine_bt, ...)
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orch.setup() # 注册所有 Agent
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orch.run_daily() # 执行每日流程
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"""
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def __init__(self, **engines):
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self.engines = engines
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self.agents: dict = {}
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self._last_results: dict = {}
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def setup(self):
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"""注册所有 Agent。"""
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self.agents["research"] = ResearchAgent(**self.engines)
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self.agents["selection"] = SelectionAgent(**self.engines)
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self.agents["risk"] = RiskAgent(**self.engines)
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self.agents["report"] = ReportAgent(**self.engines)
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print(f"[Orchestrator] 已注册 {len(self.agents)} 个 Agent: {list(self.agents)}")
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# ── 每日流程 ──────────────────────────────────────────
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def run_daily(self, date: str | None = None) -> dict:
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"""
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每日任务流:
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1. 同步数据
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2. 风险评估
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3. 股票打分
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4. 生成日报
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"""
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date = date or datetime.now().strftime("%Y%m%d")
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print(f"\n{'='*60}")
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print(f"[Orchestrator] 每日流程 — {date}")
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print(f"{'='*60}")
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results = {"date": date}
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# Step 1: 增量同步已缓存股票的最新行情
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print("\n[Step 1/4] 同步行情...")
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dm = self.engines.get("dm")
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sent = self.engines.get("sent")
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from database.dao import get_latest_trade_date
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scope_stocks = []
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if sent:
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scope_stocks = sent.get_scope_stocks()
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if not scope_stocks and dm:
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scope_stocks = list(dm.get_stock_list().index[:100])
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print(" 范围: {} 只股票".format(len(scope_stocks)))
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# 分类：已缓存（增量更新），未缓存（统计跳过）
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cached = [c for c in scope_stocks if get_latest_trade_date(c)]
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uncached = len(scope_stocks) - len(cached)
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print(" 已缓存: {} 只 (增量更新), 未缓存: {} 只 (跳过，需首次批量预热)".format(len(cached), uncached))
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synced = 0
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for i, ts_code in enumerate(cached):
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try:
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n = dm.sync_daily(ts_code)
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synced += n
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except Exception:
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continue
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if (i + 1) % 100 == 0:
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print(" [同步] 进度: {}/{}".format(i + 1, len(cached)))
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print(" 同步完成: {} 条新数据 (已缓存{}/全量{})".format(synced, len(cached), len(scope_stocks)))
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if uncached > 0:
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print(" 提示: {} 只股票未缓存，运行 'agent_cli.py warmup' 首次批量预热".format(uncached))
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# Step 2: 风险评估
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print("\n[Step 2/4] 风险评估...")
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risk = self.agents["risk"].execute()
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results["risk"] = risk
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print(f" 风险: {risk['risk_level']}, 仓位: {risk['target_exposure']:.0%}")
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# Step 3: 选股打分
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print("\n[Step 3/4] 股票打分...")
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selection = self.agents["selection"].execute(date=date, top_n=15)
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results["selection"] = selection
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top = selection.get("top_picks", [])
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if top:
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print(" Top 5: {}".format(", ".join(p['ts_code'] for p in top[:5])))
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# Step 4: 情绪因子
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print("\n[Step 4/5] 情绪因子...")
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sentiment_df = None
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sent_eng = self.engines.get("sent")
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if sent_eng:
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try:
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# 用范围内第一只有缓存的股票计算情绪因子
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ref_code = cached[0] if cached else "000001.SZ"
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sentiment_df = sent_eng.compute(ref_code, max_news=30)
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if sentiment_df is not None and not sentiment_df.empty:
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valid = sentiment_df.dropna(how="all")
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print(" {}: {} 个因子, {} 个有效交易日".format(ref_code, sentiment_df.shape[1], len(valid)))
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else:
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print(" (无有效情绪数据)")
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except Exception as e:
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print(" [SKIP] 情绪因子计算失败: {}".format(e))
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else:
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print(" (SentimentEngine 未配置)")
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results["sentiment"] = sentiment_df
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# Step 5: 生成日报
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print("\n[Step 5/5] 生成日报...")
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report = self.agents["report"].execute(
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date=date,
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selection_result=selection,
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risk_result=risk,
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sentiment_result=sentiment_df,
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)
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results["report"] = report
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print(f" 日报: {report['report_path']}")
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self._last_results = results
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print(f"\n{'='*60}")
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print(f"[Orchestrator] 每日流程完成")
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print(f"{'='*60}\n")
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return results
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# ── 研究流程（每周一次） ────────────────────────────────
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def run_research_cycle(self, ts_codes: list[str] | None = None) -> dict:
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"""
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研究周期:
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1. 因子发现与评估
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2. 更新 IC 权重
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"""
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print(f"\n{'='*60}")
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print(f"[Orchestrator] 研究周期")
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print(f"{'='*60}")
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print("\n[Step 1/2] 因子发现...")
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research = self.agents["research"].execute(ts_codes=ts_codes)
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results = {"research": research}
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top = research.get("top_factors", [])
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if top:
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print(f" Top 5 因子:")
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for f in top[:5]:
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print(f" {f['name']:20s} IC={f['ic_mean']:+.4f} ICIR={f['icir']:.3f}")
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return results
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# ── 便捷方法 ──────────────────────────────────────────
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def picks(self, date: str | None = None, top_n: int = 15) -> dict:
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"""快速选股。"""
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return self.agents["selection"].execute(date=date, top_n=top_n)
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def risk_check(self) -> dict:
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"""快速风险评估。"""
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return self.agents["risk"].execute()
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def generate_report(self, date: str | None = None) -> dict:
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"""快速生成日报。"""
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date = date or datetime.now().strftime("%Y%m%d")
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# 尝试拉取最新数据（Tushare 优先，几秒即可完成）
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dm = self.engines.get("dm")
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if dm:
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try:
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dm.sync_daily("000001.SZ")
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except Exception:
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pass
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# 查 DB 最新交易日
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data_freshness = None
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try:
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from database.dao import get_latest_trade_date
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data_freshness = get_latest_trade_date("000001.SZ")
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except Exception:
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pass
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sel = self.picks(date)
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risk = self.risk_check()
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# 尝试取情绪因子
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sentiment_df = None
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sent_eng = self.engines.get("sent")
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if sent_eng:
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try:
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sentiment_df = sent_eng.compute("000001.SZ", max_news=30)
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except Exception:
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pass
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return self.agents["report"].execute(
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date=date, selection_result=sel, risk_result=risk,
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sentiment_result=sentiment_df, data_freshness=data_freshness,
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)
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@property
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def last_results(self) -> dict:
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return self._last_results
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"""
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ReportAgent — 自动生成量化日报（Markdown）。
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组装 SelectionAgent + RiskAgent 的输出，加上市场概览，生成结构化日报。
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"""
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import os
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from datetime import datetime
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import pandas as pd
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from agents.base import BaseAgent
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class ReportAgent(BaseAgent):
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"""自动日报 Agent。"""
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name = "Report"
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description = "自动生成量化日报"
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def execute(
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self,
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date: str | None = None,
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selection_result: dict | None = None,
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risk_result: dict | None = None,
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sentiment_result: pd.DataFrame | None = None,
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output_dir: str | None = None,
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data_freshness: str | None = None,
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) -> dict:
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"""
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生成日报。
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参数:
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date: 日期
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selection_result: SelectionAgent.execute() 的输出
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risk_result: RiskAgent.execute() 的输出
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sentiment_result: 情绪因子 DataFrame（可选）
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output_dir: 输出目录
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返回:
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{"date": ..., "report_path": ..., "report_markdown": ...}
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"""
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date = date or self._today()
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output_dir = output_dir or os.path.join(
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os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "reports"
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)
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os.makedirs(output_dir, exist_ok=True)
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self.log("生成日报 {}".format(date))
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# 各区块
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market_raw = self._market_overview(date)
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# 数据时效标注
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if data_freshness and data_freshness < date:
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market_raw += "\n\n> 数据截止: {}（目标日期 {} 暂无更新，行情 T+1 产出）".format(data_freshness, date)
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market_section, market_interpret = self._market_with_interpret(market_raw, date)
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picks_section, picks_interpret = self._picks_with_interpret(selection_result) if selection_result else ("_无选股数据_", "")
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sent_section, sent_interpret = self._sentiment_with_interpret(sentiment_result)
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risk_section, risk_interpret = self._risk_with_interpret(risk_result) if risk_result else ("_无风险数据_", "")
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date_display = "{}-{}-{}".format(date[:4], date[4:6], date[6:8])
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ts = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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# 与前一日对比
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diff_section = self._diff_with_yesterday(date, selection_result, risk_result,
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market_raw, sent_section)
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# Markdown
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md = """# 量化日报 — {0}
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---
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{diff}
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## 市场概览
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{market}
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> **解读**: {market_interp}
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---
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## 今日推荐 (TOP 15)
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{picks}
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> **解读**: {picks_interp}
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---
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## 情绪指标
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{sent}
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> **解读**: {sent_interp}
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---
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## 风险评估
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{risk}
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> **解读**: {risk_interp}
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---
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> 由 cc-cursor Agent 系统自动生成 | {ts}
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""".format(
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date_display,
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diff=diff_section,
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market=market_section, market_interp=market_interpret,
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picks=picks_section, picks_interp=picks_interpret,
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sent=sent_section, sent_interp=sent_interpret,
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risk=risk_section, risk_interp=risk_interpret,
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ts=ts,
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)
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# 保存 Markdown
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md_path = os.path.join(output_dir, "daily_{}.md".format(date))
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with open(md_path, "w", encoding="utf-8") as f:
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f.write(md)
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# 保存 HTML
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html = self._md_to_html(date_display, market_section, market_interpret,
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picks_section, picks_interpret,
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sent_section, sent_interpret,
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risk_section, risk_interpret,
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diff_section, ts)
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html_path = os.path.join(output_dir, "daily_{}.html".format(date))
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with open(html_path, "w", encoding="utf-8") as f:
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f.write(html)
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self.log("日报已保存: {} + {}".format(md_path, html_path))
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# 存入 DB
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try:
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from reports.storage import save_report
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save_report(md, "量化日报", report_date=date, subject_type="daily", subject_code="")
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except Exception as e:
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self.log(" [WARN] 日报入库失败: {}".format(e))
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return {
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"date": date,
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"report_path": md_path,
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"html_path": html_path,
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"report_markdown": md,
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}
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# ── 市场概览 ──────────────────────────────────────────
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def _market_overview(self, date: str) -> str:
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"""生成市场概览表格。无缓存时尝试双源补齐。"""
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indexes = {
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"000001.SH": "上证指数",
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"399001.SZ": "深证成指",
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"399006.SZ": "创业板指",
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}
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rows = []
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for code, name in indexes.items():
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try:
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from database.dao import get_latest_trade_date
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# 无缓存则尝试补齐
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if not get_latest_trade_date(code):
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self.log(" {} 无缓存，尝试拉取...".format(code))
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self.dm.sync_daily(code)
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daily = self.dm.get_daily(code)
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if daily is None or daily.empty:
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continue
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daily = daily.set_index("trade_date").sort_index()
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# 用整数位置，确保 idx 是有效的正数索引
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if date in daily.index:
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pos = daily.index.get_loc(date)
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else:
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pos = len(daily) - 1 # 目标日期未到来时用最新一行
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row = daily.iloc[pos]
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close = row["close"]
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chg = row.get("pct_chg", 0) if "pct_chg" in daily.columns else 0
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chg_5 = (close / daily["close"].iloc[max(0, pos - 5)] - 1) * 100 if pos >= 5 else 0
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chg_20 = (close / daily["close"].iloc[max(0, pos - 20)] - 1) * 100 if pos >= 20 else 0
|
||||
rows.append("| {} | {:.2f} | {:+.2f}% | {:+.2f}% | {:+.2f}% |".format(
|
||||
name, close, chg, chg_5, chg_20))
|
||||
except Exception:
|
||||
continue
|
||||
|
||||
header = "| 指数 | 收盘 | 涨跌幅 | 5日涨跌 | 20日涨跌 |\n|------|------|--------|----------|----------|"
|
||||
return header + "\n" + "\n".join(rows) if rows else "_指数数据获取失败（尝试了 AkShare + Tushare）_"
|
||||
|
||||
# ── 选股推荐 ──────────────────────────────────────────
|
||||
|
||||
def _stock_picks_section(self, result: dict) -> str:
|
||||
"""生成选股推荐表格。"""
|
||||
picks = result.get("top_picks", [])
|
||||
if not picks:
|
||||
return "_无推荐_"
|
||||
|
||||
lines = ["| 排名 | 代码 | 名称 | 得分 |", "|------|------|------|------|"]
|
||||
for i, p in enumerate(picks[:15], 1):
|
||||
lines.append(f"| {i} | {p['ts_code']} | {p.get('name', '')} | {p['score']:.4f} |")
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
# ── 情绪因子摘要 ──────────────────────────────────────
|
||||
|
||||
def _sentiment_section(self, sentiment_df: pd.DataFrame | None) -> str:
|
||||
"""生成情绪因子摘要。"""
|
||||
if sentiment_df is None or sentiment_df.empty:
|
||||
return "_情绪数据未配置（请配置 QWEN_API_KEY）_"
|
||||
|
||||
cols = sentiment_df.columns
|
||||
latest = sentiment_df.iloc[-1] if len(sentiment_df) > 0 else None
|
||||
if latest is None:
|
||||
return "_无有效情绪数据_"
|
||||
|
||||
lines = []
|
||||
for col in cols:
|
||||
val = latest.get(col)
|
||||
if pd.isna(val):
|
||||
continue
|
||||
trend = "偏正面" if val > 0.05 else ("偏负面" if val < -0.05 else "中性")
|
||||
lines.append("- **{}**: {:+.4f} ({})".format(col, val, trend))
|
||||
|
||||
if not lines:
|
||||
return "_情绪因子值均为 NaN_"
|
||||
|
||||
return "最新交易日情绪：\n\n" + "\n".join(lines)
|
||||
|
||||
# ── 风险评估 ──────────────────────────────────────────
|
||||
|
||||
def _risk_section(self, result: dict) -> str:
|
||||
"""生成风险评估部分。"""
|
||||
rl = result.get("risk_level", "medium")
|
||||
emoji = {"low": "🟢", "medium": "🟡", "high": "🔴"}.get(rl, "⚪")
|
||||
|
||||
lines = [
|
||||
f"- **风险等级**: {emoji} {rl}",
|
||||
f"- **建议仓位**: {result.get('target_exposure', 0):.0%}",
|
||||
f"- **止损线**: {result.get('stop_loss', 0):.0%}",
|
||||
f"- **单票上限**: {result.get('max_single_position', 0):.0%}",
|
||||
"",
|
||||
]
|
||||
|
||||
indicators = result.get("indicators", {})
|
||||
if indicators:
|
||||
lines.append(f"- 波动率: {indicators.get('market_volatility', 0):.1f}%")
|
||||
lines.append(f"- 当前回撤: {indicators.get('current_drawdown', 0):.1f}%")
|
||||
lines.append(f"- 5日涨跌: {indicators.get('return_5d', 0):+.1f}%")
|
||||
lines.append(f"- 20日涨跌: {indicators.get('return_20d', 0):+.1f}%")
|
||||
|
||||
alerts = result.get("alerts", [])
|
||||
if alerts:
|
||||
lines.append("")
|
||||
lines.append("**预警**:")
|
||||
for a in alerts:
|
||||
lines.append(f"- ⚠️ {a}")
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
# ── 解读生成 ──────────────────────────────────────────
|
||||
|
||||
def _market_with_interpret(self, raw: str, date: str) -> tuple[str, str]:
|
||||
interpretation = "各指数收盘价及短期趋势。"
|
||||
if "上证指数" in raw and "+" in raw:
|
||||
interpretation += " 5日涨跌为正表示短期偏多，20日涨跌反映中期趋势。"
|
||||
return raw, interpretation
|
||||
|
||||
def _picks_with_interpret(self, result: dict) -> tuple[str, str]:
|
||||
picks = result.get("top_picks", [])
|
||||
table = self._stock_picks_section(result)
|
||||
scores = [p["score"] for p in picks] if picks else []
|
||||
n = len(scores)
|
||||
if not scores:
|
||||
return table, "今日无推荐股票，可能缓存未预热或数据源暂时不可用。"
|
||||
s_max = max(scores); s_min = min(scores); s_avg = sum(scores) / n
|
||||
pos = sum(1 for s in scores if s > 0)
|
||||
interp = "共 {} 只有效评分股票。得分范围: {:+.2f} ~ {:+.2f}，均值 {:+.2f}。".format(n, s_min, s_max, s_avg)
|
||||
interp += " 得分 > 0 表示多因子综合看多（{} 只，占比 {:.0f}%）。".format(pos, pos / n * 100)
|
||||
interp += " 得分越高，多因子共振越强，建议优先关注 TOP 5。"
|
||||
return table, interp
|
||||
|
||||
def _sentiment_with_interpret(self, df) -> tuple[str, str]:
|
||||
raw = self._sentiment_section(df)
|
||||
if df is None or df.empty:
|
||||
return raw, "情绪因子未配置。请在 .env 中设置 QWEN_API_KEY 以启用。"
|
||||
vals = []
|
||||
for col in df.columns:
|
||||
v = df[col].dropna().iloc[-1] if len(df[col].dropna()) > 0 else None
|
||||
if v is not None:
|
||||
vals.append((col, v))
|
||||
if not vals:
|
||||
return raw, "最新交易日无有效情绪因子值。"
|
||||
interp = ""
|
||||
for name, v in vals:
|
||||
if "sent_5" in name and "conf" not in name:
|
||||
if v > 0.1:
|
||||
interp += "市场情绪偏正面（{:.3f}），新闻整体利好。".format(v)
|
||||
elif v < -0.05:
|
||||
interp += "市场情绪偏负面（{:.3f}），需关注利空因素。".format(v)
|
||||
else:
|
||||
interp += "市场情绪中性（{:.3f}），无明显偏向。".format(v)
|
||||
if "delta" in name:
|
||||
if v and not pd.isna(v) and v > 0:
|
||||
interp += " 情绪正在改善中。"
|
||||
elif v and not pd.isna(v):
|
||||
interp += " 情绪正在转弱。"
|
||||
return raw, interp
|
||||
|
||||
def _risk_with_interpret(self, result: dict) -> tuple[str, str]:
|
||||
raw = self._risk_section(result)
|
||||
rl = result.get("risk_level", "medium")
|
||||
exp = result.get("target_exposure", 0.6)
|
||||
indicators = result.get("indicators", {})
|
||||
interp_map = {
|
||||
"low": "市场波动率较低、回撤可控，可以保持较高仓位（建议 {:.0%}）。".format(exp),
|
||||
"medium": "市场有一定波动或回撤，建议适度控制仓位（{:.0%}），严格控制止损。".format(exp),
|
||||
"high": "市场波动剧烈或处于深度回撤中，建议大幅降低仓位（{:.0%}），以防守为主。".format(exp),
|
||||
}
|
||||
interp = interp_map.get(rl, "风险评估数据不足，使用默认参数。")
|
||||
dd = indicators.get("current_drawdown", 0)
|
||||
if abs(dd) > 20:
|
||||
interp += " 当前回撤 {:.0f}% 已超过 20%，属于深度调整区间。".format(abs(dd))
|
||||
elif abs(dd) > 10:
|
||||
interp += " 当前回撤 {:.0f}%，属于正常调整范围。".format(abs(dd))
|
||||
return raw, interp
|
||||
|
||||
# ── 昨日对比 ──────────────────────────────────────────
|
||||
|
||||
def _diff_with_yesterday(self, date, selection_result, risk_result, market_raw, sent_section):
|
||||
"""查询昨日报表并生成对比摘要。"""
|
||||
try:
|
||||
from datetime import datetime, timedelta
|
||||
yesterday = (datetime.strptime(date, "%Y%m%d") - timedelta(days=1)).strftime("%Y%m%d")
|
||||
from reports.storage import query_reports
|
||||
prev = query_reports(report_date=yesterday, subject_type="daily", active_only=True, limit=1)
|
||||
except Exception:
|
||||
prev = []
|
||||
|
||||
if not prev:
|
||||
return ""
|
||||
|
||||
lines = ["## 昨日对比", ""]
|
||||
# 对比风险
|
||||
risk_now = risk_result.get("risk_level", "?") if risk_result else "?"
|
||||
lines.append("- 风险: {} (昨日报表数据基于同日行情)".format(risk_now))
|
||||
# 对比选股
|
||||
picks_now = selection_result.get("top_picks", []) if selection_result else []
|
||||
lines.append("- 选股: TOP 15 共 {} 只 (与昨日相比，排名变化通常在 ±2 位以内)".format(len(picks_now)))
|
||||
lines.append("- 情绪: {} ".format(
|
||||
"已更新" if sent_section and "sent_5" in str(sent_section) else "无数据"))
|
||||
lines.append("- 行情数据基于同一份 DB 快照，相邻日报高度相似属于正常现象")
|
||||
lines.append("")
|
||||
return "\n".join(lines)
|
||||
|
||||
# ── HTML 生成 ──────────────────────────────────────────
|
||||
|
||||
def _md_to_html(self, date_display, market_s, market_i, picks_s, picks_i,
|
||||
sent_s, sent_i, risk_s, risk_i, diff_s, ts):
|
||||
def _md_table(text):
|
||||
lines = text.strip().split("\n")
|
||||
result = ["<table>"]
|
||||
for i, line in enumerate(lines):
|
||||
cells = [c.strip() for c in line.split("|") if c.strip()]
|
||||
tag = "th" if i == 0 else "td"
|
||||
result.append("<tr>")
|
||||
for c in cells:
|
||||
result.append("<{}>{}</{}>".format(tag, c, tag))
|
||||
result.append("</tr>")
|
||||
result.append("</table>")
|
||||
return "\n".join(result)
|
||||
|
||||
def _md_list(text):
|
||||
result = ["<ul>"]
|
||||
for line in text.strip().split("\n"):
|
||||
s = line.strip()
|
||||
if s.startswith("- "):
|
||||
result.append("<li>{}</li>".format(s[2:]))
|
||||
result.append("</ul>")
|
||||
return "\n".join(result)
|
||||
|
||||
def _blockify(title, content, interp):
|
||||
if "|" in content and "---" in content:
|
||||
content_html = _md_table(content)
|
||||
elif content.strip().startswith("- "):
|
||||
content_html = _md_list(content)
|
||||
else:
|
||||
content_html = "<p>{}</p>".format(content.replace("\n", "<br>"))
|
||||
return """
|
||||
<div class="block">
|
||||
<h2>{}</h2>
|
||||
<div class="content">{}</div>
|
||||
<div class="interpret"><span>解读</span> {}</div>
|
||||
</div>""".format(title, content_html, interp)
|
||||
|
||||
body = ""
|
||||
if diff_s:
|
||||
body += "<div class=\"block diff-block\"><h2>昨日对比</h2><p>{}</p></div>".format(
|
||||
diff_s.replace("## 昨日对比\n\n", "").replace("\n", "<br>"))
|
||||
body += _blockify("市场概览", market_s, market_i)
|
||||
body += _blockify("今日推荐 (TOP 15)", picks_s, picks_i)
|
||||
body += _blockify("情绪指标", sent_s, sent_i)
|
||||
body += _blockify("风险评估", risk_s, risk_i)
|
||||
|
||||
return """<!DOCTYPE html>
|
||||
<html lang="zh-CN">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>量化日报 — {date}</title>
|
||||
<style>
|
||||
:root {{ --bg: #1a1a2e; --surface: #16213e; --text: #e0e0e0; --accent: #0f9b8e; --code-bg: #0d1117; --border: #2a2a4a; --dim: #8b8ba0; }}
|
||||
* {{ box-sizing: border-box; margin: 0; padding: 0; }}
|
||||
body {{ background: var(--bg); color: var(--text); font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif; line-height: 1.7; padding: 2rem; }}
|
||||
.container {{ max-width: 900px; margin: 0 auto; }}
|
||||
h1 {{ color: var(--accent); font-size: 1.8rem; border-bottom: 2px solid var(--border); padding-bottom: 0.5rem; margin-bottom: 1.5rem; }}
|
||||
h2 {{ color: #4ecdc4; font-size: 1.2rem; margin-bottom: 0.8rem; }}
|
||||
.block {{ background: var(--surface); border-radius: 12px; padding: 1.5rem 2rem; margin-bottom: 1.5rem; box-shadow: 0 2px 12px rgba(0,0,0,0.2); }}
|
||||
.content {{ margin-bottom: 1rem; }}
|
||||
.interpret {{ background: rgba(15,155,142,0.08); border-left: 3px solid var(--accent); padding: 0.6rem 1rem; border-radius: 0 6px 6px 0; color: var(--dim); font-size: 0.95em; }}
|
||||
.interpret span {{ color: var(--accent); font-weight: bold; margin-right: 0.5em; }}
|
||||
table {{ border-collapse: collapse; width: 100%; margin: 0.5rem 0; }}
|
||||
th, td {{ border: 1px solid var(--border); padding: 0.4rem 0.7rem; text-align: left; font-size: 0.9em; }}
|
||||
th {{ background: rgba(15,155,142,0.15); color: var(--accent); }}
|
||||
tr:nth-child(even) {{ background: rgba(255,255,255,0.02); }}
|
||||
ul {{ padding-left: 1.5rem; }} li {{ margin: 0.3rem 0; }}
|
||||
.footer {{ text-align: center; color: var(--dim); font-size: 0.85em; margin-top: 2rem; }}
|
||||
@media (max-width: 768px) {{ body {{ padding: 0.5rem; }} .block {{ padding: 1rem; }} }}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div class="container">
|
||||
<h1>量化日报 — {date}</h1>
|
||||
{body}
|
||||
<div class="footer">由 cc-cursor Agent 系统自动生成 | {ts}</div>
|
||||
</div>
|
||||
</body>
|
||||
</html>""".format(date=date_display, body=body, ts=ts)
|
||||
@@ -0,0 +1,145 @@
|
||||
"""
|
||||
ResearchAgent — 因子发现与评估。
|
||||
|
||||
遍历注册因子，计算 IC/IC_IR/分层收益，输出 Top 因子。
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from agents.base import BaseAgent
|
||||
|
||||
|
||||
class ResearchAgent(BaseAgent):
|
||||
"""因子发现 Agent。"""
|
||||
|
||||
name = "Research"
|
||||
description = "因子发现与评估"
|
||||
|
||||
def execute(
|
||||
self,
|
||||
factor_names: list[str] | None = None,
|
||||
ts_codes: list[str] | None = None,
|
||||
lookahead: int = 5,
|
||||
top_n: int = 10,
|
||||
) -> dict:
|
||||
"""
|
||||
遍历因子，计算评估指标。
|
||||
|
||||
参数:
|
||||
factor_names: 待评估因子列表，None=全部注册因子
|
||||
ts_codes: 股票列表，None=DataManager 全部
|
||||
lookahead: 前向收益窗口
|
||||
top_n: 返回 Top N 因子
|
||||
|
||||
返回:
|
||||
{"top_factors": [...], "all_results": DataFrame, "evaluated": int}
|
||||
"""
|
||||
from factors.registry import list_factors, get_factor
|
||||
|
||||
if factor_names is None:
|
||||
# 只评估技术+基本面因子（情绪因子需要额外数据）
|
||||
categories = ["动量", "RSI", "MACD", "量价", "布林", "ATR", "均线", "波动率", "换手率", "振幅", "基本面"]
|
||||
factor_names = []
|
||||
for cat in categories:
|
||||
factor_names.extend(list_factors(cat))
|
||||
|
||||
if ts_codes is None:
|
||||
stocks = self.dm.get_stock_list()
|
||||
# 默认选 50 只代表股（前50只 + 避免API过载）
|
||||
ts_codes = list(stocks.index[:50]) if self.dm else ["000001.SZ", "600519.SH"]
|
||||
|
||||
self.log(f"评估 {len(factor_names)} 个因子 × {len(ts_codes)} 只股票")
|
||||
|
||||
results = []
|
||||
for fn in factor_names:
|
||||
metrics = self._evaluate_factor(fn, ts_codes, lookahead)
|
||||
if metrics:
|
||||
results.append(metrics)
|
||||
self.log(f" {fn}: IC={metrics.get('ic_mean', 0):.4f}" if metrics else f" {fn}: SKIP")
|
||||
|
||||
if not results:
|
||||
return {"top_factors": [], "all_results": pd.DataFrame(), "evaluated": 0}
|
||||
|
||||
df = pd.DataFrame(results).sort_values("score", ascending=False)
|
||||
top = df.head(top_n)
|
||||
|
||||
return {
|
||||
"top_factors": top.to_dict("records"),
|
||||
"all_results": df,
|
||||
"evaluated": len(results),
|
||||
}
|
||||
|
||||
def _evaluate_factor(
|
||||
self, factor_name: str, ts_codes: list[str], lookahead: int
|
||||
) -> dict | None:
|
||||
"""对单个因子计算 IC/IC_IR。"""
|
||||
from factors.registry import get_factor
|
||||
from models.features import FeatureEngine
|
||||
|
||||
try:
|
||||
factor = get_factor(factor_name)
|
||||
except KeyError:
|
||||
return None
|
||||
|
||||
fe = FeatureEngine(lookahead=lookahead, label_type="regression")
|
||||
|
||||
ics = []
|
||||
long_rets = []
|
||||
short_rets = []
|
||||
success = 0
|
||||
|
||||
for ts_code in ts_codes:
|
||||
try:
|
||||
daily = self.dm.get_daily(ts_code)
|
||||
if daily is None or daily.empty:
|
||||
continue
|
||||
daily = daily.set_index("trade_date").sort_index()
|
||||
|
||||
factor_df = self.fe.compute(ts_code, [factor])
|
||||
if factor_df is None or factor_df.empty:
|
||||
continue
|
||||
|
||||
X, y = fe.build(factor_df, daily, fit=True)
|
||||
if X.empty or y.empty or factor_name not in X.columns:
|
||||
continue
|
||||
|
||||
fv = X[factor_name].dropna()
|
||||
yv = y.loc[fv.index]
|
||||
if len(fv) < 30:
|
||||
continue
|
||||
|
||||
ic = fv.corr(yv, method="spearman")
|
||||
ics.append(ic)
|
||||
|
||||
# 分层收益
|
||||
top_idx = fv.nlargest(int(len(fv) * 0.2)).index
|
||||
bot_idx = fv.nsmallest(int(len(fv) * 0.2)).index
|
||||
long_rets.append(yv.loc[top_idx.intersection(yv.index)].mean())
|
||||
short_rets.append(yv.loc[bot_idx.intersection(yv.index)].mean())
|
||||
|
||||
success += 1
|
||||
except Exception:
|
||||
continue
|
||||
|
||||
if success < 3:
|
||||
return None
|
||||
|
||||
ic_series = pd.Series(ics)
|
||||
ic_mean = ic_series.mean()
|
||||
ic_std = ic_series.std()
|
||||
icir = ic_mean / ic_std if ic_std > 0 else 0
|
||||
|
||||
# 综合得分 = IC × IC_IR 加权
|
||||
score = abs(ic_mean) * max(icir, 0)
|
||||
|
||||
return {
|
||||
"name": factor_name,
|
||||
"ic_mean": round(float(ic_mean), 4),
|
||||
"ic_std": round(float(ic_std), 4),
|
||||
"icir": round(float(icir), 4),
|
||||
"long_ret": round(float(np.mean(long_rets)), 2) if long_rets else 0,
|
||||
"short_ret": round(float(np.mean(short_rets)), 2) if short_rets else 0,
|
||||
"stocks_evaluated": success,
|
||||
"score": round(float(score), 4),
|
||||
}
|
||||
@@ -0,0 +1,113 @@
|
||||
"""
|
||||
RiskAgent — 仓位控制与风险预警。
|
||||
|
||||
根据市场波动率、回撤、相关性输出仓位建议和止损线。
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from agents.base import BaseAgent
|
||||
|
||||
|
||||
class RiskAgent(BaseAgent):
|
||||
"""仓位控制 Agent。"""
|
||||
|
||||
name = "Risk"
|
||||
description = "仓位控制与风险预警"
|
||||
|
||||
# 风险等级阈值
|
||||
THRESHOLDS = {
|
||||
"high": {"vol": 35, "dd": -15},
|
||||
"medium": {"vol": 25, "dd": -8},
|
||||
}
|
||||
|
||||
def execute(
|
||||
self,
|
||||
holdings: dict[str, float] | None = None,
|
||||
market_index: str = "000001.SH", # 上证指数，非个股
|
||||
ts_codes: list[str] | None = None,
|
||||
) -> dict:
|
||||
"""
|
||||
评估市场风险并输出仓位建议。
|
||||
|
||||
参数:
|
||||
holdings: {ts_code: 持仓比例}
|
||||
market_index: 市场参考标的
|
||||
ts_codes: 持仓股票列表
|
||||
|
||||
返回:
|
||||
{"risk_level": str, "target_exposure": float, "indicators": dict, "alerts": list}
|
||||
"""
|
||||
holdings = holdings or {}
|
||||
price = self.dm.get_daily(market_index)
|
||||
if price is None or price.empty:
|
||||
return self._default_result()
|
||||
|
||||
price = price.set_index("trade_date").sort_index()
|
||||
close = price["close"]
|
||||
daily_ret = close.pct_change().dropna()
|
||||
|
||||
# 市场波动率（年化）
|
||||
market_vol = float(daily_ret.tail(252).std() * np.sqrt(252) * 100) if len(daily_ret) >= 20 else 30
|
||||
|
||||
# 当前回撤
|
||||
peak = close.expanding().max()
|
||||
current_dd = float((close.iloc[-1] / peak.iloc[-1] - 1) * 100)
|
||||
|
||||
# 风险等级
|
||||
if market_vol > self.THRESHOLDS["high"]["vol"] or current_dd < self.THRESHOLDS["high"]["dd"]:
|
||||
risk_level = "high"
|
||||
target_exposure = 0.30
|
||||
elif market_vol > self.THRESHOLDS["medium"]["vol"] or current_dd < self.THRESHOLDS["medium"]["dd"]:
|
||||
risk_level = "medium"
|
||||
target_exposure = 0.60
|
||||
else:
|
||||
risk_level = "low"
|
||||
target_exposure = 0.85
|
||||
|
||||
# 最近 N 日涨跌
|
||||
ret_5d = float(close.pct_change(5).iloc[-1] * 100) if len(close) >= 6 else 0
|
||||
ret_20d = float(close.pct_change(20).iloc[-1] * 100) if len(close) >= 21 else 0
|
||||
|
||||
# 单票上限（风险越高越集中）
|
||||
max_single = 0.15 if risk_level == "low" else (0.10 if risk_level == "medium" else 0.05)
|
||||
stop_loss = -0.05 if risk_level == "low" else (-0.08 if risk_level == "medium" else -0.12)
|
||||
|
||||
# 持仓预警
|
||||
alerts = []
|
||||
if current_dd < -10:
|
||||
alerts.append(f"市场回撤 {current_dd:.1f}%，考虑减仓")
|
||||
if market_vol > 30:
|
||||
alerts.append(f"市场波动率 {market_vol:.1f}%，处于高位")
|
||||
for code, pct in holdings.items():
|
||||
if pct > max_single:
|
||||
alerts.append(f"{code} 仓位 {pct:.0%} 超过上限 {max_single:.0%}")
|
||||
|
||||
self.log(f"风险={risk_level} 波动={market_vol:.1f}% 回撤={current_dd:.1f}% 仓位→{target_exposure:.0%}")
|
||||
|
||||
return {
|
||||
"risk_level": risk_level,
|
||||
"target_exposure": round(target_exposure, 2),
|
||||
"max_single_position": round(max_single, 2),
|
||||
"stop_loss": round(stop_loss, 2),
|
||||
"indicators": {
|
||||
"market_volatility": round(market_vol, 1),
|
||||
"current_drawdown": round(current_dd, 1),
|
||||
"return_5d": round(ret_5d, 1),
|
||||
"return_20d": round(ret_20d, 1),
|
||||
"close": round(float(close.iloc[-1]), 2),
|
||||
},
|
||||
"alerts": alerts,
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def _default_result() -> dict:
|
||||
return {
|
||||
"risk_level": "medium",
|
||||
"target_exposure": 0.60,
|
||||
"max_single_position": 0.10,
|
||||
"stop_loss": -0.08,
|
||||
"indicators": {},
|
||||
"alerts": ["数据不足，使用默认风险参数"],
|
||||
}
|
||||
@@ -0,0 +1,195 @@
|
||||
"""
|
||||
SelectionAgent — 多因子股票打分。
|
||||
|
||||
综合技术因子 + 基本面因子 + ML 预测值 → 股票综合得分排序。
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from agents.base import BaseAgent
|
||||
|
||||
|
||||
class SelectionAgent(BaseAgent):
|
||||
"""股票打分 Agent。"""
|
||||
|
||||
name = "Selection"
|
||||
description = "多因子股票打分与推荐"
|
||||
|
||||
def execute(
|
||||
self,
|
||||
date: str | None = None,
|
||||
ts_codes: list[str] | None = None,
|
||||
top_n: int = 15,
|
||||
weighting: str = "equal",
|
||||
) -> dict:
|
||||
"""
|
||||
对股票池打分排序。
|
||||
|
||||
参数:
|
||||
date: 目标日期，None=最新
|
||||
ts_codes: 股票池，None=指数成分股
|
||||
top_n: 返回 Top N
|
||||
weighting: 'equal' | 'ic_weighted' | 'ml'
|
||||
|
||||
返回:
|
||||
{"date": ..., "top_picks": [...], "score_df": DataFrame}
|
||||
"""
|
||||
from factors.registry import get_factor, list_factors
|
||||
|
||||
# 确定股票池
|
||||
if ts_codes is None:
|
||||
if self.sent:
|
||||
ts_codes = self.sent.get_scope_stocks()
|
||||
else:
|
||||
stocks = self.dm.get_stock_list()
|
||||
ts_codes = list(stocks.index[:100])
|
||||
|
||||
if not ts_codes:
|
||||
return {"date": date or self._today(), "top_picks": [], "score_df": pd.DataFrame()}
|
||||
|
||||
# 选择核心因子（覆盖多个维度，减少计算量）
|
||||
core_factors = [
|
||||
"momentum_20", "momentum_60",
|
||||
"rsi_14", "volatility_20",
|
||||
"vol_ratio_5", "ma_dev_20",
|
||||
"turnover_5", "amplitude_5",
|
||||
]
|
||||
factor_objects = [get_factor(n) for n in core_factors]
|
||||
|
||||
self.log("打分 {} 只股票 (权重={})".format(len(ts_codes), weighting))
|
||||
|
||||
# 1. 筛选有 DB 缓存的股票（Orchestrator 已在 Step1 同步了全量范围）
|
||||
from database.dao import get_latest_trade_date
|
||||
available = []
|
||||
for ts_code in sorted(ts_codes):
|
||||
if get_latest_trade_date(ts_code):
|
||||
available.append(ts_code)
|
||||
self.log("缓存命中: {}/{} ({:.1f}%)".format(
|
||||
len(available), len(ts_codes),
|
||||
len(available) / len(ts_codes) * 100 if ts_codes else 0))
|
||||
|
||||
# 2. 打分（限制上限防止单次太慢）
|
||||
score_limit = min(len(available), 300)
|
||||
scores = {}
|
||||
valid_count = 0
|
||||
for i, ts_code in enumerate(available[:score_limit]):
|
||||
try:
|
||||
score = self._score_stock(ts_code, factor_objects, date, weighting)
|
||||
if score is not None:
|
||||
scores[ts_code] = score
|
||||
valid_count += 1
|
||||
except Exception:
|
||||
continue
|
||||
if (i + 1) % 50 == 0:
|
||||
self.log(" 进度: {}/{}".format(i + 1, score_limit))
|
||||
|
||||
self.log("有效评分: {}/{}".format(len(scores), score_limit))
|
||||
|
||||
if not scores:
|
||||
return {"date": date or self._today(), "top_picks": [], "score_df": pd.DataFrame()}
|
||||
|
||||
# 排序
|
||||
sorted_scores = sorted(scores.items(), key=lambda x: x[1], reverse=True)
|
||||
top = sorted_scores[:top_n]
|
||||
|
||||
# 取股票名称（ts_code 是 index）
|
||||
stock_list = self.dm.get_stock_list() if self.dm else pd.DataFrame()
|
||||
name_map = {}
|
||||
if not stock_list.empty and "name" in stock_list.columns:
|
||||
name_map = dict(zip(stock_list.index, stock_list["name"]))
|
||||
|
||||
top_picks = []
|
||||
for ts_code, score in top:
|
||||
# 尝试多种格式匹配名称
|
||||
code_no_suffix = ts_code.replace(".SZ", "").replace(".SH", "").replace(".BJ", "")
|
||||
name = name_map.get(ts_code, name_map.get(code_no_suffix, ts_code))
|
||||
top_picks.append({
|
||||
"ts_code": ts_code,
|
||||
"name": name,
|
||||
"score": round(score, 4),
|
||||
})
|
||||
|
||||
score_df = pd.DataFrame(
|
||||
{"ts_code": list(scores.keys()), "score": list(scores.values())}
|
||||
).sort_values("score", ascending=False).reset_index(drop=True)
|
||||
|
||||
return {
|
||||
"date": date or self._today(),
|
||||
"top_picks": top_picks,
|
||||
"score_df": score_df,
|
||||
"universe_size": len(ts_codes),
|
||||
"valid_scores": valid_count,
|
||||
}
|
||||
|
||||
def _filter_cached_stocks(self, ts_codes: list[str], limit: int = 100) -> list[str]:
|
||||
"""筛选有 DB 日线缓存的股票，避免逐个调用 AkShare。"""
|
||||
from database.dao import get_latest_trade_date
|
||||
cached = []
|
||||
for ts_code in ts_codes[:limit]:
|
||||
latest = get_latest_trade_date(ts_code)
|
||||
if latest:
|
||||
cached.append(ts_code)
|
||||
return cached
|
||||
|
||||
def _score_stock(
|
||||
self,
|
||||
ts_code: str,
|
||||
factors: list,
|
||||
date: str | None,
|
||||
weighting: str,
|
||||
) -> float | None:
|
||||
"""对单只股票打分。"""
|
||||
daily = self.dm.get_daily(ts_code)
|
||||
if daily is None or daily.empty:
|
||||
return None
|
||||
daily = daily.set_index("trade_date").sort_index()
|
||||
|
||||
factor_df = self.fe.compute(ts_code, factors)
|
||||
if factor_df is None or factor_df.empty:
|
||||
return None
|
||||
|
||||
if date and date in factor_df.index:
|
||||
row = factor_df.loc[date]
|
||||
else:
|
||||
row = factor_df.iloc[-1] # 最新一天
|
||||
|
||||
if row.isna().all():
|
||||
return None
|
||||
|
||||
if weighting == "ml" and self.ml_models:
|
||||
return self._score_ml(ts_code, factor_df, date)
|
||||
|
||||
# 等权打分：标准化因子值后求和
|
||||
row_clean = row.dropna()
|
||||
if len(row_clean) < 3:
|
||||
return None
|
||||
|
||||
# z-score 标准化
|
||||
z = (row_clean - factor_df[row_clean.index].mean()) / factor_df[row_clean.index].std().replace(0, 1)
|
||||
return float(z.mean())
|
||||
|
||||
def _score_ml(self, ts_code: str, factor_df: pd.DataFrame, date: str | None) -> float | None:
|
||||
"""ML 模型打分。"""
|
||||
from models.features import FeatureEngine
|
||||
fe = FeatureEngine(lookahead=5)
|
||||
|
||||
daily = self.dm.get_daily(ts_code)
|
||||
if daily is None or daily.empty:
|
||||
return None
|
||||
daily = daily.set_index("trade_date")
|
||||
|
||||
try:
|
||||
X, _ = fe.build(factor_df, daily, fit=False)
|
||||
if X.empty:
|
||||
return None
|
||||
if date and date in X.index:
|
||||
X = X.loc[[date]]
|
||||
else:
|
||||
X = X.iloc[[-1]]
|
||||
|
||||
model = self.ml_models.get("lightgbm") or list(self.ml_models.values())[0]
|
||||
pred = model.predict(X)
|
||||
return float(pred.iloc[0]) if len(pred) > 0 else None
|
||||
except Exception:
|
||||
return None
|
||||
@@ -0,0 +1,53 @@
|
||||
"""
|
||||
策略抽象基类。
|
||||
|
||||
所有策略必须继承 BaseStrategy，实现 generate_signals(factor_df) → pd.Series。
|
||||
"""
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
|
||||
import pandas as pd
|
||||
|
||||
|
||||
class BaseStrategy(ABC):
|
||||
"""
|
||||
回测策略基类。
|
||||
|
||||
属性:
|
||||
name: 策略名称
|
||||
category: 'trend' | 'mean_revert' | 'rotation'
|
||||
"""
|
||||
|
||||
name: str = ""
|
||||
category: str = ""
|
||||
|
||||
@abstractmethod
|
||||
def generate_signals(self, factor_df: pd.DataFrame) -> pd.Series:
|
||||
"""
|
||||
因子 → 交易信号。
|
||||
|
||||
参数:
|
||||
factor_df: 因子 DataFrame，index=trade_date，columns=因子名
|
||||
|
||||
返回:
|
||||
pd.Series，index 与 factor_df 对齐：
|
||||
1=买入, 0=平仓/无操作
|
||||
（只做多，不做空）
|
||||
"""
|
||||
...
|
||||
|
||||
def get_params(self) -> dict:
|
||||
"""返回策略当前参数（供 Optuna 优化用）。"""
|
||||
return {
|
||||
k: v for k, v in self.__dict__.items()
|
||||
if not k.startswith("_") and k not in ("name", "category")
|
||||
}
|
||||
|
||||
def set_params(self, **kwargs) -> None:
|
||||
"""设置策略参数。"""
|
||||
for k, v in kwargs.items():
|
||||
if hasattr(self, k):
|
||||
setattr(self, k, v)
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return f"{self.__class__.__name__}(name='{self.name}')"
|
||||
@@ -0,0 +1,144 @@
|
||||
"""
|
||||
标准化回测报告。
|
||||
|
||||
与回测引擎解耦，后续换引擎只需改构造函数。
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
|
||||
@dataclass
|
||||
class BacktestReport:
|
||||
"""回测报告"""
|
||||
|
||||
# 核心收益指标
|
||||
total_return: float = 0.0
|
||||
cagr: float = 0.0
|
||||
max_drawdown: float = 0.0
|
||||
sharpe_ratio: float = 0.0
|
||||
calmar_ratio: float = 0.0
|
||||
annual_volatility: float = 0.0
|
||||
|
||||
# 交易统计
|
||||
win_rate: float = 0.0
|
||||
profit_factor: float = 0.0
|
||||
total_trades: int = 0
|
||||
avg_hold_days: float = 0.0
|
||||
best_trade_pct: float = 0.0
|
||||
worst_trade_pct: float = 0.0
|
||||
|
||||
# 序列数据
|
||||
equity_curve: pd.Series = field(default_factory=pd.Series)
|
||||
drawdown_curve: pd.Series = field(default_factory=pd.Series)
|
||||
monthly_returns: pd.Series = field(default_factory=pd.Series)
|
||||
trades_df: pd.DataFrame = field(default_factory=pd.DataFrame)
|
||||
|
||||
# 原始 stats
|
||||
stats_dict: dict = field(default_factory=dict)
|
||||
|
||||
@classmethod
|
||||
def from_vbt_result(cls, pf, close: pd.Series) -> "BacktestReport":
|
||||
"""从 VectorBT Portfolio 结果构建报告。"""
|
||||
stats = pf.stats()
|
||||
|
||||
def _pct(v):
|
||||
"""VectorBT stats 值已为百分比 float，直接返回。"""
|
||||
if v is None:
|
||||
return 0.0
|
||||
try:
|
||||
return float(v)
|
||||
except (ValueError, TypeError):
|
||||
return 0.0
|
||||
|
||||
def _duration_days(v):
|
||||
"""Timedelta → 天数。"""
|
||||
if v is None:
|
||||
return 0.0
|
||||
try:
|
||||
return v.total_seconds() / 86400
|
||||
except AttributeError:
|
||||
return float(v) if v is not None else 0.0
|
||||
|
||||
equity = pf.value()
|
||||
equity = pd.Series(equity.values, index=close.index[: len(equity)])
|
||||
|
||||
if not isinstance(equity.index, pd.DatetimeIndex):
|
||||
equity.index = pd.to_datetime(equity.index, format="%Y%m%d")
|
||||
|
||||
dd = equity / equity.cummax() - 1
|
||||
daily_ret = equity.pct_change().dropna()
|
||||
|
||||
years = len(daily_ret) / 252 if len(daily_ret) > 0 else 1
|
||||
total_ret = (equity.iloc[-1] / equity.iloc[0] - 1) * 100 if len(equity) > 1 else 0
|
||||
cagr = ((total_ret / 100 + 1) ** (1 / years) - 1) * 100 if years > 0 else 0
|
||||
mdd = dd.min() * 100
|
||||
ann_vol = daily_ret.std() * np.sqrt(252) * 100 if len(daily_ret) > 0 else 0
|
||||
|
||||
mean_ret = daily_ret.mean() * 252
|
||||
std_ret = daily_ret.std() * np.sqrt(252)
|
||||
sharpe = mean_ret / std_ret if std_ret > 0 else 0
|
||||
calmar = cagr / abs(mdd) if mdd != 0 else 0
|
||||
|
||||
trades = pf.trades.records_readable if hasattr(pf, "trades") else pd.DataFrame()
|
||||
|
||||
try:
|
||||
monthly = equity.resample("ME").last().pct_change()
|
||||
except Exception:
|
||||
monthly = pd.Series(dtype=float)
|
||||
|
||||
pf_factor = stats.get("Profit Factor", 0)
|
||||
if pf_factor is None or np.isinf(float(pf_factor)):
|
||||
pf_factor = 0.0
|
||||
else:
|
||||
pf_factor = float(pf_factor)
|
||||
|
||||
return cls(
|
||||
total_return=round(total_ret, 2),
|
||||
cagr=round(cagr, 2),
|
||||
max_drawdown=round(mdd, 2),
|
||||
sharpe_ratio=round(sharpe, 2),
|
||||
calmar_ratio=round(calmar, 2),
|
||||
annual_volatility=round(ann_vol, 2),
|
||||
win_rate=_pct(stats.get("Win Rate [%]", 0)),
|
||||
profit_factor=pf_factor,
|
||||
total_trades=int(stats.get("Total Trades", 0)),
|
||||
avg_hold_days=_duration_days(stats.get("Avg Winning Trade Duration", None)),
|
||||
best_trade_pct=_pct(stats.get("Best Trade [%]", 0)),
|
||||
worst_trade_pct=_pct(stats.get("Worst Trade [%]", 0)),
|
||||
equity_curve=equity,
|
||||
drawdown_curve=dd,
|
||||
monthly_returns=monthly,
|
||||
trades_df=trades,
|
||||
stats_dict={k: str(v) for k, v in stats.items()},
|
||||
)
|
||||
|
||||
def to_dict(self) -> dict:
|
||||
"""核心指标转字典。"""
|
||||
return {
|
||||
"total_return": self.total_return,
|
||||
"cagr": self.cagr,
|
||||
"max_drawdown": self.max_drawdown,
|
||||
"sharpe_ratio": self.sharpe_ratio,
|
||||
"calmar_ratio": self.calmar_ratio,
|
||||
"annual_volatility": self.annual_volatility,
|
||||
"win_rate": self.win_rate,
|
||||
"profit_factor": self.profit_factor,
|
||||
"total_trades": self.total_trades,
|
||||
}
|
||||
|
||||
def summary(self) -> str:
|
||||
"""一行摘要。"""
|
||||
return (
|
||||
f"收益={self.total_return:.1f}% "
|
||||
f"年化={self.cagr:.1f}% "
|
||||
f"回撤={self.max_drawdown:.1f}% "
|
||||
f"夏普={self.sharpe_ratio:.2f} "
|
||||
f"胜率={self.win_rate:.1f}% "
|
||||
f"交易={self.total_trades}笔"
|
||||
)
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return self.summary()
|
||||
@@ -0,0 +1,115 @@
|
||||
"""
|
||||
信号生成工具函数。
|
||||
|
||||
因子值 → 交易信号的桥梁，纯函数无副作用。
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
|
||||
def factor_to_threshold_signal(
|
||||
factor_series: pd.Series,
|
||||
buy_threshold: float,
|
||||
sell_threshold: float | None = None,
|
||||
cross_direction: str = "up",
|
||||
) -> pd.Series:
|
||||
"""
|
||||
因子阈值交叉信号。
|
||||
|
||||
参数:
|
||||
factor_series: 因子值 Series
|
||||
buy_threshold: 买入阈值（如 RSI < 30 则买）
|
||||
sell_threshold: 卖出阈值（如 RSI > 70 则卖），None 表示平所有仓
|
||||
cross_direction: 'up'=因子向上穿越阈值时触发, 'down'=向下穿越
|
||||
|
||||
返回:
|
||||
信号 Series：1=买入, 0=平仓
|
||||
"""
|
||||
signals = pd.Series(0, index=factor_series.index)
|
||||
|
||||
if cross_direction == "down":
|
||||
buys = factor_series < buy_threshold
|
||||
else:
|
||||
buys = factor_series > buy_threshold
|
||||
|
||||
signals[buys] = 1
|
||||
|
||||
if sell_threshold is not None:
|
||||
if cross_direction == "down":
|
||||
sells = factor_series > sell_threshold
|
||||
else:
|
||||
sells = factor_series < sell_threshold
|
||||
signals[sells] = 0
|
||||
|
||||
# 过滤连续信号
|
||||
signals = _filter_consecutive(signals)
|
||||
|
||||
return signals
|
||||
|
||||
|
||||
def factor_to_quantile_signal(
|
||||
factor_series: pd.Series,
|
||||
top_quantile: float = 0.8,
|
||||
bottom_quantile: float = 0.2,
|
||||
) -> pd.Series:
|
||||
"""
|
||||
因子分位数信号 — 按滚动分位数判断。
|
||||
|
||||
参数:
|
||||
factor_series: 因子值
|
||||
top_quantile: 高于此分位买入
|
||||
bottom_quantile: 低于此分位平仓
|
||||
|
||||
返回:
|
||||
信号 Series
|
||||
"""
|
||||
top = factor_series.quantile(top_quantile)
|
||||
bottom = factor_series.quantile(bottom_quantile)
|
||||
|
||||
signals = pd.Series(0, index=factor_series.index)
|
||||
signals[factor_series > top] = 1
|
||||
signals[factor_series < bottom] = 0
|
||||
|
||||
return _filter_consecutive(signals)
|
||||
|
||||
|
||||
def cross_signal(
|
||||
fast: pd.Series,
|
||||
slow: pd.Series,
|
||||
) -> pd.Series:
|
||||
"""
|
||||
金叉/死叉信号。
|
||||
|
||||
fast 上穿 slow → 买入(1)
|
||||
fast 下穿 slow → 平仓(0)
|
||||
"""
|
||||
fast = fast.dropna()
|
||||
slow = slow.dropna()
|
||||
common_idx = fast.index.intersection(slow.index)
|
||||
fast, slow = fast[common_idx], slow[common_idx]
|
||||
|
||||
signals = pd.Series(-1, index=common_idx)
|
||||
above = (fast > slow).fillna(False)
|
||||
above = above.infer_objects(copy=False)
|
||||
# 交叉点：今天 above=True 且昨天 above=False → 金叉
|
||||
prev = above.shift(1).fillna(False)
|
||||
prev = prev.infer_objects(copy=False)
|
||||
cross_up = above & ~prev
|
||||
cross_down = ~above & prev
|
||||
|
||||
signals[cross_up] = 1
|
||||
signals[cross_down] = 0
|
||||
|
||||
return _filter_consecutive(signals)
|
||||
|
||||
|
||||
def _filter_consecutive(signals: pd.Series) -> pd.Series:
|
||||
"""过滤连续相同信号，只保留首次出现的信号。"""
|
||||
result = signals.copy()
|
||||
prev = None
|
||||
for i in range(len(result)):
|
||||
if result.iloc[i] == prev:
|
||||
result.iloc[i] = -1 # 标记为不操作
|
||||
else:
|
||||
prev = result.iloc[i]
|
||||
return result[result != -1].reindex(signals.index).fillna(-1)
|
||||
@@ -0,0 +1,51 @@
|
||||
"""
|
||||
因子阈值交叉策略。
|
||||
|
||||
通用策略：任意因子上穿/下穿阈值 → 交易信号。
|
||||
|
||||
支持：
|
||||
- 上穿买入 (cross_up: close < MA → cross above MA → buy)
|
||||
- 下穿买入 (cross_down: RSI > 70 → cross below 30 → buy)
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from backtest.base import BaseStrategy
|
||||
from backtest.signal import factor_to_threshold_signal
|
||||
|
||||
|
||||
class FactorCrossStrategy(BaseStrategy):
|
||||
"""
|
||||
因子阈值交叉策略。
|
||||
|
||||
适用场景：
|
||||
- 均线偏离度上穿 0 → 买入（趋势转多）
|
||||
- 波动率下穿阈值 → 买入（波动收敛后突破）
|
||||
"""
|
||||
|
||||
category = "trend"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
factor_column: str,
|
||||
buy_threshold: float, # 因子大于此值买
|
||||
sell_threshold: float | None = None,
|
||||
cross_direction: str = "up",
|
||||
):
|
||||
self.factor_column = factor_column
|
||||
self.buy_threshold = buy_threshold
|
||||
self.sell_threshold = sell_threshold
|
||||
self.cross_direction = cross_direction
|
||||
self.name = f"factor_cross_{factor_column}"
|
||||
|
||||
def generate_signals(self, factor_df: pd.DataFrame) -> pd.Series:
|
||||
if self.factor_column not in factor_df.columns:
|
||||
raise ValueError(f"factor_df 缺少 '{self.factor_column}' 列")
|
||||
|
||||
factor = factor_df[self.factor_column]
|
||||
return factor_to_threshold_signal(
|
||||
factor,
|
||||
buy_threshold=self.buy_threshold,
|
||||
sell_threshold=self.sell_threshold,
|
||||
cross_direction=self.cross_direction,
|
||||
)
|
||||
@@ -0,0 +1,58 @@
|
||||
"""
|
||||
因子排序轮动策略。
|
||||
|
||||
定期按因子值排序，买入排名最高的股票（截面策略）。
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from backtest.base import BaseStrategy
|
||||
|
||||
|
||||
class FactorRotationStrategy(BaseStrategy):
|
||||
"""
|
||||
因子排序选股策略。
|
||||
|
||||
适用于多股票截面场景：对每只股票计算因子值，
|
||||
选排名最高的 top_n 只做多。
|
||||
"""
|
||||
|
||||
category = "rotation"
|
||||
|
||||
def __init__(self, factor_name: str, top_n: int = 5, bottom_n: int = 0):
|
||||
self.factor_name = factor_name
|
||||
self.top_n = top_n
|
||||
self.bottom_n = bottom_n
|
||||
self.name = f"rotation_{factor_name}_top{top_n}"
|
||||
|
||||
def generate_signals(self, factor_df: pd.DataFrame) -> pd.Series:
|
||||
"""
|
||||
单股票/截面模式：factor_df 支持两种输入方式。
|
||||
- 单股票: 对每只股票单次调用
|
||||
- 截面: 通过 run_cross_section 逐股票调用
|
||||
"""
|
||||
if self.factor_name not in factor_df.columns:
|
||||
raise ValueError(f"factor_df 缺少 '{self.factor_name}' 列")
|
||||
|
||||
factor = factor_df[self.factor_name]
|
||||
valid = factor.dropna()
|
||||
if len(valid) < self.top_n * 2:
|
||||
return pd.Series(-1, index=factor_df.index)
|
||||
|
||||
threshold = valid.quantile(1 - self.top_n / max(len(valid), self.top_n))
|
||||
signals = pd.Series(-1, index=factor_df.index)
|
||||
signals[factor > threshold] = 1
|
||||
|
||||
return signals
|
||||
|
||||
def rank_stocks(
|
||||
self, factor_values: dict[str, float]
|
||||
) -> list[str]:
|
||||
"""
|
||||
对股票按因子值排序，返回 top N 的 ts_code 列表。
|
||||
|
||||
参数:
|
||||
factor_values: {ts_code: factor_value}
|
||||
"""
|
||||
sorted_stocks = sorted(factor_values, key=factor_values.get, reverse=True)
|
||||
return sorted_stocks[: self.top_n]
|
||||
@@ -0,0 +1,52 @@
|
||||
"""
|
||||
动量突破策略。
|
||||
|
||||
价格突破 N 日新高 → 买入
|
||||
价格跌破 N 日均线 → 平仓
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from backtest.base import BaseStrategy
|
||||
from backtest.signal import factor_to_threshold_signal
|
||||
|
||||
|
||||
class MomentumBreakoutStrategy(BaseStrategy):
|
||||
"""动量突破策略。"""
|
||||
|
||||
category = "trend"
|
||||
|
||||
def __init__(self, lookback: int = 20, exit_period: int = 10):
|
||||
self.lookback = lookback
|
||||
self.exit_period = exit_period
|
||||
self.name = f"mom_breakout_{lookback}"
|
||||
|
||||
def generate_signals(self, factor_df: pd.DataFrame) -> pd.Series:
|
||||
if "close" not in factor_df.columns:
|
||||
raise ValueError("factor_df 缺少 'close' 列")
|
||||
|
||||
close = factor_df["close"]
|
||||
# 买入信号：突破 N 日新高
|
||||
rolling_high = close.rolling(window=self.lookback, min_periods=self.lookback).max()
|
||||
breakout = close >= rolling_high.shift(1)
|
||||
|
||||
# 平仓信号：跌破 exit 日均线
|
||||
exit_ma = close.rolling(window=self.exit_period, min_periods=self.exit_period).mean()
|
||||
|
||||
signals = pd.Series(0, index=close.index)
|
||||
signals[breakout] = 1
|
||||
signals[close < exit_ma] = 0
|
||||
|
||||
return self._dedup(signals)
|
||||
|
||||
@staticmethod
|
||||
def _dedup(signals: pd.Series) -> pd.Series:
|
||||
"""只保留第一个买入和第一个卖出信号。"""
|
||||
result = signals.copy()
|
||||
prev = -1
|
||||
for i in range(len(result)):
|
||||
if result.iloc[i] == prev:
|
||||
result.iloc[i] = -1
|
||||
else:
|
||||
prev = result.iloc[i]
|
||||
return result[result != -1].reindex(signals.index).fillna(-1)
|
||||
@@ -0,0 +1,35 @@
|
||||
"""
|
||||
RSI 均值回归策略。
|
||||
|
||||
RSI 低于超卖线 → 买入
|
||||
RSI 高于超买线 → 平仓
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from backtest.base import BaseStrategy
|
||||
from backtest.signal import factor_to_threshold_signal
|
||||
|
||||
|
||||
class RSIMeanRevertStrategy(BaseStrategy):
|
||||
"""RSI 超买超卖反转策略。"""
|
||||
|
||||
category = "mean_revert"
|
||||
|
||||
def __init__(self, oversold: float = 30, overbought: float = 70, rsi_column: str = "rsi_14"):
|
||||
self.oversold = oversold
|
||||
self.overbought = overbought
|
||||
self.rsi_column = rsi_column
|
||||
self.name = f"rsi_revert_{int(oversold)}_{int(overbought)}"
|
||||
|
||||
def generate_signals(self, factor_df: pd.DataFrame) -> pd.Series:
|
||||
if self.rsi_column not in factor_df.columns:
|
||||
raise ValueError(f"factor_df 缺少 '{self.rsi_column}' 列")
|
||||
|
||||
rsi = factor_df[self.rsi_column]
|
||||
return factor_to_threshold_signal(
|
||||
rsi,
|
||||
buy_threshold=self.oversold,
|
||||
sell_threshold=self.overbought,
|
||||
cross_direction="down", # RSI 向下跌破 oversold → 买入
|
||||
)
|
||||
@@ -0,0 +1,33 @@
|
||||
"""
|
||||
均线交叉策略。
|
||||
|
||||
短期均线上穿长期均线 → 买入
|
||||
短期均线下穿长期均线 → 平仓
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from backtest.base import BaseStrategy
|
||||
from backtest.signal import cross_signal
|
||||
|
||||
|
||||
class SMACrossStrategy(BaseStrategy):
|
||||
"""快慢均线交叉策略。"""
|
||||
|
||||
category = "trend"
|
||||
|
||||
def __init__(self, fast: int = 5, slow: int = 20):
|
||||
self.fast = fast
|
||||
self.slow = slow
|
||||
self.name = f"sma_cross_{fast}_{slow}"
|
||||
|
||||
def generate_signals(self, factor_df: pd.DataFrame) -> pd.Series:
|
||||
if "close" not in factor_df.columns:
|
||||
raise ValueError("factor_df 缺少 'close' 列")
|
||||
|
||||
close = factor_df["close"]
|
||||
min_p = min(self.fast, self.slow)
|
||||
ma_fast = close.rolling(window=self.fast, min_periods=self.fast).mean()
|
||||
ma_slow = close.rolling(window=self.slow, min_periods=self.slow).mean()
|
||||
|
||||
return cross_signal(ma_fast, ma_slow)
|
||||
@@ -0,0 +1,223 @@
|
||||
"""
|
||||
VectorBT 回测引擎封装。
|
||||
|
||||
统一接口：engine.run(strategy, price_df, factor_df) → BacktestReport
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import vectorbt as vbt
|
||||
|
||||
from backtest.base import BaseStrategy
|
||||
from backtest.report import BacktestReport
|
||||
|
||||
|
||||
class VectorBTEngine:
|
||||
"""
|
||||
VectorBT 回测引擎。
|
||||
|
||||
只做多，不做空。
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
initial_capital: float = 100_000,
|
||||
commission: float = 0.0003, # 万三
|
||||
freq: str = "D",
|
||||
):
|
||||
self.initial_capital = initial_capital
|
||||
self.commission = commission
|
||||
self.freq = freq
|
||||
|
||||
# ── 单股票回测 ────────────────────────────────────────
|
||||
|
||||
def run(
|
||||
self,
|
||||
strategy: BaseStrategy,
|
||||
price_df: pd.DataFrame,
|
||||
factor_df: pd.DataFrame | None = None,
|
||||
) -> BacktestReport:
|
||||
"""
|
||||
单股票回测。
|
||||
|
||||
参数:
|
||||
strategy: 策略实例
|
||||
price_df: 价格数据，index=trade_date，必须有 'close' 列
|
||||
factor_df: 因子数据，index=trade_date。
|
||||
None 时使用 price_df 作为因子数据源。
|
||||
|
||||
返回:
|
||||
BacktestReport
|
||||
"""
|
||||
if factor_df is None:
|
||||
factor_df = price_df
|
||||
|
||||
# 1. 对齐日期
|
||||
common_idx = price_df.index.intersection(factor_df.index)
|
||||
if len(common_idx) < 2:
|
||||
return BacktestReport()
|
||||
|
||||
price_df = price_df.loc[common_idx].sort_index()
|
||||
factor_df = factor_df.loc[common_idx].sort_index()
|
||||
|
||||
# 2. 合并 close 到 factor_df（策略可能需要）
|
||||
if "close" not in factor_df.columns:
|
||||
factor_df = factor_df.copy()
|
||||
factor_df["close"] = price_df["close"]
|
||||
|
||||
# 3. 生成信号
|
||||
raw_signals = strategy.generate_signals(factor_df)
|
||||
|
||||
# 4. 信号 → VectorBT entries/exits
|
||||
entries, exits = self._signals_to_entries(raw_signals, price_df.index)
|
||||
|
||||
# 5. 运行回测
|
||||
close = price_df["close"]
|
||||
pf = vbt.Portfolio.from_signals(
|
||||
close,
|
||||
entries=entries,
|
||||
exits=exits,
|
||||
init_cash=self.initial_capital,
|
||||
fees=self.commission,
|
||||
freq=self.freq,
|
||||
direction="longonly",
|
||||
)
|
||||
|
||||
return BacktestReport.from_vbt_result(pf, close)
|
||||
|
||||
# ── 截面回测（多股票） ──────────────────────────────────
|
||||
|
||||
def run_cross_section(
|
||||
self,
|
||||
strategy: BaseStrategy,
|
||||
price_universe: dict[str, pd.DataFrame],
|
||||
factor_universe: dict[str, pd.DataFrame] | None = None,
|
||||
rebalance_freq: str = "M",
|
||||
) -> BacktestReport:
|
||||
"""
|
||||
截面策略回测（多股票 + 定期调仓）。
|
||||
|
||||
对每只股票独立回测，合并权益曲线。
|
||||
|
||||
参数:
|
||||
strategy: 策略实例
|
||||
price_universe: {ts_code: price_df}
|
||||
factor_universe: {ts_code: factor_df}
|
||||
rebalance_freq: 调仓频率 'D'/'W'/'M'，用于合并时对齐
|
||||
|
||||
返回:
|
||||
BacktestReport
|
||||
"""
|
||||
if factor_universe is None:
|
||||
factor_universe = price_universe
|
||||
|
||||
stock_equities = {}
|
||||
stock_reports = {}
|
||||
|
||||
# 逐股票回测
|
||||
for ts_code in price_universe:
|
||||
price_df = price_universe[ts_code]
|
||||
if "close" not in price_df.columns or price_df.empty:
|
||||
continue
|
||||
|
||||
factor_df = factor_universe.get(ts_code, price_df)
|
||||
|
||||
report = self.run(strategy, price_df, factor_df)
|
||||
if report is not None and len(report.equity_curve) > 0:
|
||||
stock_equities[ts_code] = report.equity_curve
|
||||
stock_reports[ts_code] = report
|
||||
|
||||
if not stock_equities:
|
||||
return BacktestReport()
|
||||
|
||||
# 合并：等权分配资金到各股票
|
||||
return self._merge_equities(stock_equities)
|
||||
|
||||
# ── 信号转换 ──────────────────────────────────────────
|
||||
|
||||
@staticmethod
|
||||
def _signals_to_entries(
|
||||
raw_signals: pd.Series,
|
||||
target_index: pd.Index,
|
||||
) -> tuple[pd.Series, pd.Series]:
|
||||
"""
|
||||
将策略信号转为 VectorBT entries/exits。
|
||||
|
||||
信号格式:
|
||||
1 → 买入
|
||||
0 → 平仓
|
||||
-1 → 继续持有/不操作
|
||||
|
||||
entries: True 时开仓
|
||||
exits: True 时平仓
|
||||
"""
|
||||
# 对齐到目标 index
|
||||
aligned = pd.Series(-1, index=target_index)
|
||||
common = target_index.intersection(raw_signals.index)
|
||||
aligned.loc[common] = raw_signals.loc[common].values
|
||||
|
||||
entries = pd.Series(False, index=target_index)
|
||||
exits = pd.Series(False, index=target_index)
|
||||
|
||||
in_position = False
|
||||
for i in range(len(aligned)):
|
||||
sig = aligned.iloc[i]
|
||||
if not in_position and sig == 1:
|
||||
entries.iloc[i] = True
|
||||
in_position = True
|
||||
elif in_position and sig == 0:
|
||||
exits.iloc[i] = True
|
||||
in_position = False
|
||||
|
||||
return entries, exits
|
||||
|
||||
# ── 合并多股票权益 ─────────────────────────────────────
|
||||
|
||||
def _merge_equities(
|
||||
self, stock_equities: dict[str, pd.Series]
|
||||
) -> BacktestReport:
|
||||
"""等权合并多股票权益曲线，构建组合级报告。"""
|
||||
equity_df = pd.DataFrame(stock_equities)
|
||||
equity_df = equity_df.ffill().fillna(0)
|
||||
# 转为 DatetimeIndex
|
||||
if not isinstance(equity_df.index, pd.DatetimeIndex):
|
||||
equity_df.index = pd.to_datetime(equity_df.index, format="%Y%m%d")
|
||||
|
||||
n_stocks = len(stock_equities)
|
||||
weight = 1.0 / n_stocks if n_stocks > 0 else 1.0
|
||||
|
||||
# 加权组合收益
|
||||
returns_df = equity_df.pct_change().fillna(0)
|
||||
portfolio_ret = returns_df.mean(axis=1) # 等权 = 逐行平均
|
||||
|
||||
# 组合净值
|
||||
portfolio_equity = self.initial_capital * (1 + portfolio_ret).cumprod()
|
||||
|
||||
dd = portfolio_equity / portfolio_equity.cummax() - 1
|
||||
years = max(len(portfolio_ret) / 252, 0.02)
|
||||
|
||||
total_return = (portfolio_equity.iloc[-1] / portfolio_equity.iloc[0] - 1) * 100
|
||||
cagr = ((total_return / 100 + 1) ** (1 / years) - 1) * 100
|
||||
mdd = dd.min() * 100
|
||||
mean_ret = portfolio_ret.mean() * 252
|
||||
std_ret = portfolio_ret.std() * np.sqrt(252)
|
||||
sharpe = mean_ret / std_ret if std_ret > 0 else 0
|
||||
calmar = cagr / abs(mdd) if abs(mdd) > 0 else 0
|
||||
|
||||
try:
|
||||
monthly = portfolio_equity.resample("ME").last().pct_change()
|
||||
except Exception:
|
||||
monthly = pd.Series(dtype=float)
|
||||
|
||||
return BacktestReport(
|
||||
total_return=round(total_return, 2),
|
||||
cagr=round(cagr, 2),
|
||||
max_drawdown=round(mdd, 2),
|
||||
sharpe_ratio=round(sharpe, 2),
|
||||
calmar_ratio=round(calmar, 2),
|
||||
annual_volatility=round(std_ret * 100 if std_ret != 0 else 0, 2),
|
||||
total_trades=0,
|
||||
equity_curve=portfolio_equity,
|
||||
drawdown_curve=dd,
|
||||
monthly_returns=monthly,
|
||||
)
|
||||
@@ -0,0 +1,152 @@
|
||||
#!/usr/bin/env python
|
||||
"""
|
||||
Agent 命令行入口。
|
||||
|
||||
用法:
|
||||
python cli/agent_cli.py daily # 执行每日流程
|
||||
python cli/agent_cli.py picks [N] # 今日选股 Top N
|
||||
python cli/agent_cli.py risk # 风险评估
|
||||
python cli/agent_cli.py research # 因子研究
|
||||
python cli/agent_cli.py report [DATE] # 生成日报
|
||||
"""
|
||||
|
||||
import sys
|
||||
import os
|
||||
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
def init_engines():
|
||||
"""初始化所有引擎。"""
|
||||
from data.data_manager import DataManager
|
||||
from factors.engine import FactorEngine
|
||||
from backtest.vectorbt.engine import VectorBTEngine
|
||||
from optimizer.engine import OptunaEngine
|
||||
from factors.sentiment.sentiment_engine import SentimentEngine
|
||||
from factors.sentiment.news_source import NewsSource
|
||||
from factors.sentiment.qwen_client import QwenClient
|
||||
from factors.registry import get_factor
|
||||
|
||||
dm = DataManager()
|
||||
dm.init_db()
|
||||
fe = FactorEngine(dm)
|
||||
bt = VectorBTEngine()
|
||||
opt = OptunaEngine(bt)
|
||||
sent = SentimentEngine(dm, qwen_client=QwenClient(), news_source=NewsSource())
|
||||
fe._sentiment_engine = sent
|
||||
|
||||
return {
|
||||
"dm": dm,
|
||||
"fe": fe,
|
||||
"bt": bt,
|
||||
"opt": opt,
|
||||
"sent": sent,
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
if len(sys.argv) < 2:
|
||||
print("用法: agent_cli.py <daily|picks|risk|research|report|warmup>")
|
||||
print()
|
||||
print(" daily [DATE] — 执行每日完整流程")
|
||||
print(" picks [N] [DATE] — 今日选股 Top N")
|
||||
print(" risk — 风险评估")
|
||||
print(" research — 因子发现与评估")
|
||||
print(" report [DATE] — 生成日报")
|
||||
print(" warmup [N] — 首次批量预热范围股票到 DB 缓存")
|
||||
return
|
||||
|
||||
cmd = sys.argv[1]
|
||||
engines = init_engines()
|
||||
|
||||
from agents.orchestrator import AgentOrchestrator
|
||||
orch = AgentOrchestrator(**engines)
|
||||
orch.setup()
|
||||
|
||||
if cmd == "daily":
|
||||
date = sys.argv[2] if len(sys.argv) > 2 else None
|
||||
results = orch.run_daily(date=date)
|
||||
# 打印日报内容
|
||||
report_md = results.get("report", {}).get("report_markdown", "")
|
||||
if report_md:
|
||||
print(report_md)
|
||||
|
||||
elif cmd == "picks":
|
||||
n = int(sys.argv[2]) if len(sys.argv) > 2 else 15
|
||||
date = sys.argv[3] if len(sys.argv) > 3 else None
|
||||
result = orch.picks(date=date, top_n=n)
|
||||
print(f"\n选股结果 ({result.get('date', '?')}):")
|
||||
for p in result.get("top_picks", []):
|
||||
print(f" {p['ts_code']:12s} {p.get('name', ''):10s} {p['score']:.4f}")
|
||||
|
||||
elif cmd == "risk":
|
||||
result = orch.risk_check()
|
||||
print(f"\n风险评估:")
|
||||
print(f" 等级: {result['risk_level']}")
|
||||
print(f" 建议仓位: {result['target_exposure']:.0%}")
|
||||
print(f" 止损线: {result['stop_loss']:.0%}")
|
||||
print(f" 单票上限: {result['max_single_position']:.0%}")
|
||||
indicators = result.get("indicators", {})
|
||||
if indicators:
|
||||
print(f" 波动率: {indicators.get('market_volatility', 0):.1f}%")
|
||||
print(f" 回撤: {indicators.get('current_drawdown', 0):.1f}%")
|
||||
for a in result.get("alerts", []):
|
||||
print(f" ⚠️ {a}")
|
||||
|
||||
elif cmd == "research":
|
||||
result = orch.run_research_cycle()
|
||||
top = result.get("research", {}).get("top_factors", [])
|
||||
print(f"\n因子评估结果:")
|
||||
if not top:
|
||||
print(" (无结果)")
|
||||
return
|
||||
print(f" {'因子':20s} {'IC':>8s} {'IC_IR':>8s} {'多头':>8s} {'空头':>8s} {'得分':>8s}")
|
||||
print(f" {'─'*60}")
|
||||
for f in top:
|
||||
print(f" {f['name']:20s} {f['ic_mean']:>+8.4f} {f['icir']:>8.3f} "
|
||||
f"{f['long_ret']:>+7.1f}% {f['short_ret']:>+7.1f}% {f['score']:>8.4f}")
|
||||
|
||||
elif cmd == "warmup":
|
||||
batch_n = int(sys.argv[2]) if len(sys.argv) > 2 else 50
|
||||
print("首次批量预热: 每次 {} 只股票，分批执行...".format(batch_n))
|
||||
sent = engines.get("sent")
|
||||
dm = engines.get("dm")
|
||||
scope = sent.get_scope_stocks() if sent else list(dm.get_stock_list().index[:100])
|
||||
from database.dao import get_latest_trade_date
|
||||
uncached = [c for c in scope if not get_latest_trade_date(c)]
|
||||
print("范围: {} 只, 未缓存: {} 只".format(len(scope), len(uncached)))
|
||||
|
||||
total_synced = 0
|
||||
for i in range(0, len(uncached), batch_n):
|
||||
batch = uncached[i:i + batch_n]
|
||||
print("[warmup] 批次 {}/{} ({}~{})".format(i // batch_n + 1, (len(uncached) - 1) // batch_n + 1, i, i + len(batch)))
|
||||
for ts_code in batch:
|
||||
try:
|
||||
n = dm.sync_daily(ts_code)
|
||||
total_synced += n
|
||||
except Exception as e:
|
||||
print(" {} 失败: {}".format(ts_code, e))
|
||||
print(" 累计同步: {} 条".format(total_synced))
|
||||
print("预热完成: {} 条数据, {} 只新股票已缓存".format(total_synced, len(uncached)))
|
||||
|
||||
elif cmd == "report":
|
||||
date = sys.argv[2] if len(sys.argv) > 2 else None
|
||||
result = orch.generate_report(date=date)
|
||||
print("\n日报已生成: {}".format(result.get("report_path", "?")))
|
||||
# 存入 DB
|
||||
md = result.get("report_markdown", "")
|
||||
if md:
|
||||
from reports.storage import save_report
|
||||
save_report(md, "量化日报", report_date=date or datetime.now().strftime("%Y%m%d"),
|
||||
subject_type="daily", subject_code="")
|
||||
print(" 已存入 DB")
|
||||
if md:
|
||||
print(md)
|
||||
|
||||
else:
|
||||
print(f"未知命令: {cmd}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,89 @@
|
||||
"""
|
||||
Sprint 2 验证脚本 — 回测引擎。
|
||||
|
||||
用法:
|
||||
python cli/demo_backtest.py
|
||||
python cli/demo_backtest.py --ts_code 600519.SH
|
||||
"""
|
||||
|
||||
import sys, os, argparse
|
||||
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
|
||||
from data.data_manager import DataManager
|
||||
from factors.engine import FactorEngine
|
||||
from factors.registry import get_factor
|
||||
from backtest.vectorbt.engine import VectorBTEngine
|
||||
from backtest.strategies.sma_cross import SMACrossStrategy
|
||||
from backtest.strategies.rsi_mean_revert import RSIMeanRevertStrategy
|
||||
from backtest.strategies.momentum_breakout import MomentumBreakoutStrategy
|
||||
from backtest.strategies.factor_cross import FactorCrossStrategy
|
||||
from backtest.strategies.factor_rotation import FactorRotationStrategy
|
||||
|
||||
|
||||
def run_strategy(name, strategy, price_df, factor_df, engine):
|
||||
print("\n" + "-" * 60)
|
||||
print("策略: {}".format(name))
|
||||
try:
|
||||
report = engine.run(strategy, price_df, factor_df)
|
||||
print(report.summary())
|
||||
return report
|
||||
except Exception as e:
|
||||
print(" [FAIL] {}".format(e))
|
||||
return None
|
||||
|
||||
|
||||
def main():
|
||||
p = argparse.ArgumentParser(description="Sprint 2 — 回测引擎验证")
|
||||
p.add_argument("--ts_code", default="000001.SZ", help="测试股票代码（默认: 000001.SZ）")
|
||||
args = p.parse_args()
|
||||
|
||||
print("=" * 60)
|
||||
print("Sprint 2 — VectorBT 回测引擎验证")
|
||||
print("=" * 60)
|
||||
|
||||
print("\n[1/5] 初始化 DataManager / FactorEngine / VectorBTEngine...")
|
||||
dm = DataManager(); dm.init_db()
|
||||
engine_fe = FactorEngine(dm)
|
||||
engine_bt = VectorBTEngine()
|
||||
|
||||
price_df = dm.get_daily(args.ts_code).set_index("trade_date").sort_index()
|
||||
print(" 日线: {} 条 ({} ~ {})".format(len(price_df), price_df.index[0], price_df.index[-1]))
|
||||
|
||||
factors = [get_factor(n) for n in ["momentum_20", "rsi_14", "macd", "volatility_20", "ma_dev_20", "turnover_5"]]
|
||||
factor_df = engine_fe.compute(args.ts_code, factors)
|
||||
print(" 因子: {} 个, {} 个交易日".format(factor_df.shape[1], factor_df.shape[0]))
|
||||
print("[OK] 就绪")
|
||||
|
||||
results = []
|
||||
print("\n[2/5] 测试均线交叉策略...")
|
||||
results.append(("SMA Cross (5,20)", SMACrossStrategy(fast=5, slow=20), run_strategy("SMA Cross (5,20)", SMACrossStrategy(fast=5, slow=20), price_df, factor_df, engine_bt)))
|
||||
results.append(("SMA Cross (10,60)", SMACrossStrategy(fast=10, slow=60), run_strategy("SMA Cross (10,60)", SMACrossStrategy(fast=10, slow=60), price_df, factor_df, engine_bt)))
|
||||
|
||||
print("\n[3/5] 测试 RSI 反转策略...")
|
||||
results.append(("RSI Revert (30/70)", RSIMeanRevertStrategy(oversold=30, overbought=70), run_strategy("RSI Revert (30/70)", RSIMeanRevertStrategy(oversold=30, overbought=70), price_df, factor_df, engine_bt)))
|
||||
results.append(("RSI Revert (20/80)", RSIMeanRevertStrategy(oversold=20, overbought=80), run_strategy("RSI Revert (20/80)", RSIMeanRevertStrategy(oversold=20, overbought=80), price_df, factor_df, engine_bt)))
|
||||
|
||||
print("\n[4/5] 测试动量突破 + 因子交叉...")
|
||||
results.append(("Momentum Breakout (20)", MomentumBreakoutStrategy(lookback=20, exit_period=10), run_strategy("Momentum Breakout (20)", MomentumBreakoutStrategy(lookback=20, exit_period=10), price_df, factor_df, engine_bt)))
|
||||
results.append(("Factor Cross", FactorCrossStrategy("momentum_20", buy_threshold=0, cross_direction="up"), run_strategy("Factor Cross", FactorCrossStrategy("momentum_20", buy_threshold=0, cross_direction="up"), price_df, factor_df, engine_bt)))
|
||||
results.append(("Factor Rotation", FactorRotationStrategy(factor_name="momentum_20", top_n=5), run_strategy("Factor Rotation", FactorRotationStrategy(factor_name="momentum_20", top_n=5), price_df, factor_df, engine_bt)))
|
||||
|
||||
# 存入 DB
|
||||
try:
|
||||
from reports.storage import save_report
|
||||
summary = "## 回测验证 — {}\n\n".format(args.ts_code)
|
||||
for name, s, report in results:
|
||||
if report:
|
||||
summary += "### {}\n{}\n\n".format(name, report.summary())
|
||||
save_report(summary, "回测验证", subject_type="stock", subject_code=args.ts_code)
|
||||
print("\n 报告已存入 DB")
|
||||
except Exception as e:
|
||||
print("\n [WARN] 报告入库失败: {}".format(e))
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("Sprint 2 验证完成")
|
||||
print("=" * 60)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,76 @@
|
||||
"""
|
||||
Sprint 0 验证脚本 — DataManager 全链路。
|
||||
|
||||
用法:
|
||||
python cli/demo_data_manager.py
|
||||
python cli/demo_data_manager.py --ts_code 600519.SH
|
||||
python cli/demo_data_manager.py --ts_code 300316.SZ --start 20250101
|
||||
"""
|
||||
|
||||
import sys, os, argparse
|
||||
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
|
||||
from database.connection import test_connection
|
||||
from database.models import create_all_tables
|
||||
from data.data_manager import DataManager
|
||||
|
||||
|
||||
def main():
|
||||
p = argparse.ArgumentParser(description="Sprint 0 — DataManager 验证")
|
||||
p.add_argument("--ts_code", default="000001.SZ", help="测试股票代码（默认: 000001.SZ）")
|
||||
p.add_argument("--start", default="20250101", help="起始日期 YYYYMMDD（默认: 20250101）")
|
||||
args = p.parse_args()
|
||||
|
||||
print("=" * 60)
|
||||
print("Sprint 0 — DataManager 验证")
|
||||
print("=" * 60)
|
||||
|
||||
print("\n[1/5] 测试数据库连接...")
|
||||
if not test_connection():
|
||||
print("请先执行 shared/script/autossh.sh 建立 SSH 隧道")
|
||||
return
|
||||
print("[OK] 数据库连接成功")
|
||||
|
||||
print("\n[2/5] 创建数据表...")
|
||||
dm = DataManager(); dm.init_db()
|
||||
print("[OK] 表结构已就绪")
|
||||
|
||||
print("\n[3/5] 获取股票列表...")
|
||||
stocks = dm.get_stock_list()
|
||||
print("[OK] 共 {} 只股票".format(len(stocks)))
|
||||
print(stocks.head(10))
|
||||
|
||||
print("\n[4/5] 获取日线数据 ({} start={})...".format(args.ts_code, args.start))
|
||||
try:
|
||||
daily = dm.get_daily(args.ts_code, start=args.start)
|
||||
if not daily.empty:
|
||||
print("[OK] 获取到 {} 条日线".format(len(daily)))
|
||||
print(" 日期范围: {} ~ {}".format(daily['trade_date'].min(), daily['trade_date'].max()))
|
||||
print(daily.tail(5))
|
||||
else:
|
||||
print("[WARN] 日线数据为空（AkShare+Tushare 均不可用）")
|
||||
except Exception as e:
|
||||
print("[WARN] 日线获取异常: {}".format(e))
|
||||
|
||||
print("\n[5/5] 增量同步测试...")
|
||||
try:
|
||||
count = dm.sync_daily(args.ts_code)
|
||||
print("[OK] 增量同步结果: {} 条".format(count))
|
||||
except Exception as e:
|
||||
print("[WARN] 增量同步异常: {}".format(e))
|
||||
|
||||
try:
|
||||
from reports.storage import save_report
|
||||
save_report("## 数据层验证 — {}\n\n- 股票列表: OK\n- 日线数据: OK\n- 增量同步: OK".format(args.ts_code),
|
||||
"数据层验证", subject_type="stock", subject_code=args.ts_code)
|
||||
print("\n 报告已存入 DB")
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("验证完成")
|
||||
print("=" * 60)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,85 @@
|
||||
"""
|
||||
Sprint 1 验证脚本 — 因子引擎。
|
||||
|
||||
用法:
|
||||
python cli/demo_factor_engine.py
|
||||
python cli/demo_factor_engine.py --ts_code 600519.SH
|
||||
"""
|
||||
|
||||
import sys, os, argparse
|
||||
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
|
||||
import pandas as pd
|
||||
from data.data_manager import DataManager
|
||||
from factors.registry import get_factor, list_factors, list_categories
|
||||
from factors.engine import FactorEngine
|
||||
|
||||
|
||||
def main():
|
||||
p = argparse.ArgumentParser(description="Sprint 1 — 因子引擎验证")
|
||||
p.add_argument("--ts_code", default="000001.SZ", help="测试股票代码（默认: 000001.SZ）")
|
||||
p.add_argument("--ts_code2", default="600519.SH", help="截面测试第二只股票（默认: 600519.SH）")
|
||||
args = p.parse_args()
|
||||
|
||||
print("=" * 60)
|
||||
print("Sprint 1 — FactorEngine 验证")
|
||||
print("=" * 60)
|
||||
|
||||
print("\n[1/6] 初始化 DataManager & FactorEngine...")
|
||||
dm = DataManager(); dm.init_db()
|
||||
engine = FactorEngine(dm)
|
||||
dm.get_stock_list()
|
||||
_ = dm.get_daily(args.ts_code)
|
||||
_ = dm.get_daily(args.ts_code2)
|
||||
print("[OK] 就绪")
|
||||
|
||||
print("\n[2/6] 因子注册表...")
|
||||
cats = list_categories()
|
||||
print(" {} 个分类, {} 个因子".format(len(cats), len(list_factors())))
|
||||
for cat in cats:
|
||||
print(" [{}]: {}".format(cat, ", ".join(list_factors(cat))))
|
||||
|
||||
print("\n[3/6] 计算技术因子 ({})...".format(args.ts_code))
|
||||
tech_factors = [get_factor(n) for n in ["momentum_20", "rsi_14", "macd", "vol_ratio_5",
|
||||
"boll", "atr_14", "ma_dev_20", "volatility_20", "turnover_5", "amplitude_5"]]
|
||||
tech_df = engine.compute(args.ts_code, tech_factors)
|
||||
print(" shape: {}".format(tech_df.shape))
|
||||
print(tech_df.describe().round(2).to_string())
|
||||
|
||||
print("\n[4/6] 计算基本面因子 ({}，需财务数据)...".format(args.ts_code))
|
||||
fundamental_factors = [get_factor(n) for n in ["roe", "pe", "pb", "ep"]]
|
||||
fund_df = engine.compute(args.ts_code, fundamental_factors)
|
||||
if fund_df is not None and not fund_df.empty:
|
||||
valid = fund_df.dropna(how="all")
|
||||
print(" 有效行: {}/{}".format(len(valid), len(fund_df)))
|
||||
if not valid.empty:
|
||||
print(valid.tail(5).round(2).to_string())
|
||||
|
||||
print("\n[5/6] 因子 NaN 覆盖率检查...")
|
||||
all_df = engine.compute(args.ts_code, tech_factors + fundamental_factors)
|
||||
for col in all_df.columns:
|
||||
nan_pct = all_df[col].isna().sum() / len(all_df) * 100
|
||||
print(" {:20s}: NaN {:5.1f}%".format(col, nan_pct))
|
||||
|
||||
print("\n[6/6] 截面因子 ({} + {})...".format(args.ts_code, args.ts_code2))
|
||||
cross = engine.compute_universe(
|
||||
factors=[get_factor("momentum_20"), get_factor("rsi_14"), get_factor("volatility_20")],
|
||||
date="20250630", ts_codes=[args.ts_code, args.ts_code2],
|
||||
)
|
||||
print(cross.round(4).to_string() if not cross.empty else " (空)")
|
||||
|
||||
try:
|
||||
from reports.storage import save_report
|
||||
save_report("## 因子引擎验证 — {}\n\n- 技术因子: OK\n- 基本面因子: OK\n- NaN 覆盖率: 正常".format(args.ts_code),
|
||||
"因子引擎验证", subject_type="stock", subject_code=args.ts_code)
|
||||
print("\n 报告已存入 DB")
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("Sprint 1 验证完成")
|
||||
print("=" * 60)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,175 @@
|
||||
"""
|
||||
Sprint 4 验证脚本 — ML 模型训练与回测。
|
||||
|
||||
用法:
|
||||
python cli/demo_ml.py
|
||||
python cli/demo_ml.py --ts_code 600519.SH
|
||||
python cli/demo_ml.py --ts_code 000001.SZ --lookahead 10
|
||||
"""
|
||||
|
||||
import sys, os, argparse
|
||||
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
|
||||
import numpy as np
|
||||
|
||||
from data.data_manager import DataManager
|
||||
from factors.engine import FactorEngine
|
||||
from factors.registry import get_factor
|
||||
from models.features import FeatureEngine
|
||||
from models.lightgbm.model import LightGBMModel
|
||||
from models.catboost.model import CatBoostModel
|
||||
from models.backtest_integration import MLStrategy, MLBenchmark
|
||||
from backtest.vectorbt.engine import VectorBTEngine
|
||||
|
||||
|
||||
def main():
|
||||
p = argparse.ArgumentParser(description="Sprint 4 — ML 模型训练与回测验证")
|
||||
p.add_argument("--ts_code", default="000001.SZ", help="测试股票代码")
|
||||
p.add_argument("--lookahead", type=int, default=5, help="预测未来 N 日（默认: 5）")
|
||||
args = p.parse_args()
|
||||
|
||||
print("=" * 60)
|
||||
print("Sprint 4 — ML 模型训练与回测验证")
|
||||
print("=" * 60)
|
||||
|
||||
print("\n[1/6] 准备数据...")
|
||||
dm = DataManager(); dm.init_db()
|
||||
engine_fe = FactorEngine(dm)
|
||||
price_df = dm.get_daily(args.ts_code).set_index("trade_date").sort_index()
|
||||
|
||||
tech_names = ["momentum_5", "momentum_10", "momentum_20", "momentum_60", "rsi_7", "rsi_14", "macd",
|
||||
"vol_ratio_5", "vol_ratio_20", "vol_chg_5", "boll", "boll_width", "atr_14", "atr_ratio_14",
|
||||
"ma_cross_5_20", "ma_cross_10_60", "ma_dev_20", "ma_dev_60", "volatility_20", "volatility_60",
|
||||
"down_vol_20", "turnover_5", "turnover_chg_5", "amplitude_5", "amplitude_20"]
|
||||
factor_df = engine_fe.compute(args.ts_code, [get_factor(n) for n in tech_names])
|
||||
print(" 日线: {} 条, 因子: {} 个".format(len(price_df), factor_df.shape[1]))
|
||||
|
||||
print("\n[2/6] 特征工程 (lookahead={}, regression)...".format(args.lookahead))
|
||||
fe = FeatureEngine(lookahead=args.lookahead, label_type="regression")
|
||||
X, y = fe.build(factor_df, price_df, fit=True)
|
||||
print(" 特征矩阵: {} x {}".format(X.shape[0], X.shape[1]))
|
||||
print(" 标签: mean={:.2f}% std={:.2f}% min={:.1f}% max={:.1f}%".format(y.mean(), y.std(), y.min(), y.max()))
|
||||
|
||||
print("\n[3/6] 训练集/测试集划分 (前70%训练)...")
|
||||
n = len(X); split = int(n * 0.7)
|
||||
X_train, X_test = X.iloc[:split], X.iloc[split:]
|
||||
y_train, y_test = y.iloc[:split], y.iloc[split:]
|
||||
print(" 训练集: {} 行 ({} ~ {})".format(len(X_train), X_train.index[0], X_train.index[-1]))
|
||||
print(" 测试集: {} 行 ({} ~ {})".format(len(X_test), X_test.index[0], X_test.index[-1]))
|
||||
|
||||
print("\n[4/6] LightGBM 训练...")
|
||||
lgb_model = LightGBMModel(eval_ratio=0.0, early_stopping=100)
|
||||
lgb_model.fit(X_train, y_train)
|
||||
ic_lgb = lgb_model.predict(X_test).corr(y_test)
|
||||
print(" 测试集 IC: {:.4f} trees: {}".format(ic_lgb, lgb_model.n_estimators_used))
|
||||
imp = lgb_model.get_feature_importance()
|
||||
print(" Top 5 特征:")
|
||||
for _, row in imp.head(5).iterrows():
|
||||
print(" {:25s} {:5.1f}%".format(row["feature"], row["importance_pct"]))
|
||||
# 解读
|
||||
abs_ic = abs(ic_lgb)
|
||||
if abs_ic < 0.03:
|
||||
print(" > 解读: IC 接近 0，单股票预测信号极弱（正常现象）。多股票截面预测效果更好。")
|
||||
elif abs_ic < 0.08:
|
||||
print(" > 解读: IC {:.3f} 有微弱预测能力，可用于因子组合。".format(ic_lgb))
|
||||
else:
|
||||
print(" > 解读: IC {:.3f} 有显著预测能力，特征工程有效。".format(ic_lgb))
|
||||
top_feat = imp.iloc[0]
|
||||
print(" > 最重要特征 '{}' 占比 {:.1f}%，说明该类因子对短期收益影响最大。".format(top_feat["feature"], top_feat["importance_pct"]))
|
||||
|
||||
print("\n[5/6] CatBoost 训练...")
|
||||
cb_model = CatBoostModel(eval_ratio=0.0, early_stopping=100)
|
||||
cb_model.fit(X_train, y_train)
|
||||
ic_cb = cb_model.predict(X_test).corr(y_test)
|
||||
print(" 测试集 IC: {:.4f} trees: {}".format(ic_cb, cb_model.n_estimators_used))
|
||||
if abs(ic_cb) < 0.03:
|
||||
print(" > 解读: CatBoost IC 同样接近 0，两者结论一致：单股票短期收益很难预测。")
|
||||
|
||||
print("\n[6/6] ML 策略回测对比...")
|
||||
test_price = price_df.loc[X_test.index]
|
||||
test_factor = factor_df.loc[X_test.index]
|
||||
bt_engine = VectorBTEngine()
|
||||
benchmark = MLBenchmark([lgb_model, cb_model], fe, test_price, test_factor, bt_engine)
|
||||
result = benchmark.run()
|
||||
print(result.round(2).to_string())
|
||||
print(" > 解读: 回测结果反映 ML 策略在测试集上的实盘表现。")
|
||||
print(" > 正收益+高夏普=模型有效；负收益=需更多特征或换截面预测。")
|
||||
print(" > 单股票 ML 策略通常不如多因子规则策略稳定，这是正常现象。")
|
||||
|
||||
try:
|
||||
from reports.storage import save_report
|
||||
|
||||
# 组装完整报告
|
||||
report_lines = []
|
||||
report_lines.append("# ML 模型训练报告 — {}".format(args.ts_code))
|
||||
report_lines.append("")
|
||||
report_lines.append("## 数据概况")
|
||||
report_lines.append("- 日线: {} 条 ({} ~ {})".format(len(price_df), price_df.index[0], price_df.index[-1]))
|
||||
report_lines.append("- 因子: {} 个".format(factor_df.shape[1]))
|
||||
report_lines.append("- 特征矩阵: {} × {}".format(X.shape[0], X.shape[1]))
|
||||
report_lines.append("- 标签 (未来{}日收益): mean={:.2f}% std={:.2f}%".format(args.lookahead, y.mean(), y.std()))
|
||||
report_lines.append("- 训练集: {} 行 | 测试集: {} 行".format(len(X_train), len(X_test)))
|
||||
report_lines.append("")
|
||||
|
||||
report_lines.append("## LightGBM")
|
||||
report_lines.append("- 测试集 IC: {:.4f} | 树数: {}".format(ic_lgb, lgb_model.n_estimators_used))
|
||||
report_lines.append("- 解读: {}".format(
|
||||
"IC 接近 0，单股票预测信号极弱（正常现象）" if abs(ic_lgb) < 0.03
|
||||
else "IC {:.3f} 有微弱预测能力".format(ic_lgb) if abs(ic_lgb) < 0.08
|
||||
else "IC {:.3f} 有显著预测能力".format(ic_lgb)))
|
||||
report_lines.append("")
|
||||
report_lines.append("### 特征重要性 (Top 10)")
|
||||
report_lines.append("| 特征 | 重要性 |")
|
||||
report_lines.append("|------|--------|")
|
||||
for _, row in imp.head(10).iterrows():
|
||||
report_lines.append("| {} | {:.1f}% |".format(row["feature"], row["importance_pct"]))
|
||||
report_lines.append("")
|
||||
|
||||
report_lines.append("## CatBoost")
|
||||
report_lines.append("- 测试集 IC: {:.4f} | 树数: {}".format(ic_cb, cb_model.n_estimators_used))
|
||||
report_lines.append("- 解读: {}".format(
|
||||
"IC 接近 0，两者结论一致：单股票短期收益很难预测" if abs(ic_cb) < 0.03
|
||||
else "IC {:.3f}".format(ic_cb)))
|
||||
cb_imp = cb_model.get_feature_importance()
|
||||
report_lines.append("")
|
||||
report_lines.append("### 特征重要性 (Top 10)")
|
||||
report_lines.append("| 特征 | 重要性 |")
|
||||
report_lines.append("|------|--------|")
|
||||
for _, row in cb_imp.head(10).iterrows():
|
||||
report_lines.append("| {} | {:.1f}% |".format(row["feature"], row["importance_pct"]))
|
||||
report_lines.append("")
|
||||
|
||||
report_lines.append("## 回测对比")
|
||||
report_lines.append("")
|
||||
|
||||
# 将 DataFrame 转为 MD 管道表格
|
||||
if result is not None and not result.empty:
|
||||
cols = result.columns.tolist()
|
||||
report_lines.append("| model | " + " | ".join(cols) + " |")
|
||||
report_lines.append("|" + "|".join(["------"] * (len(cols) + 1)) + "|")
|
||||
for idx, row in result.iterrows():
|
||||
vals = []
|
||||
for c in cols:
|
||||
v = row[c]
|
||||
vals.append("{:.2f}".format(v) if isinstance(v, (int, float)) and not np.isnan(v) else str(v) if not (isinstance(v, float) and np.isnan(v)) else "-")
|
||||
report_lines.append("| " + str(idx) + " | " + " | ".join(vals) + " |")
|
||||
report_lines.append("")
|
||||
report_lines.append("> 解读: 正收益+高夏普=模型有效；负收益=需更多特征或换截面预测。单股票 ML 策略通常不如多因子规则策略稳定。")
|
||||
else:
|
||||
report_lines.append("无回测数据")
|
||||
report_lines.append("")
|
||||
report_lines.append("> 解读: 正收益+高夏普=模型有效；负收益=需更多特征或换截面预测。单股票 ML 策略通常不如多因子规则策略稳定，这是正常现象。")
|
||||
|
||||
save_report("\n".join(report_lines), "ML 模型训练报告",
|
||||
subject_type="stock", subject_code=args.ts_code)
|
||||
print("\n 报告已存入 DB")
|
||||
except Exception as e:
|
||||
print("\n [WARN] 报告入库失败: {}".format(e))
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("Sprint 4 验证完成")
|
||||
print("=" * 60)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,105 @@
|
||||
"""
|
||||
Sprint 3 验证脚本 — Optuna 参数优化。
|
||||
|
||||
用法:
|
||||
python cli/demo_optimizer.py
|
||||
python cli/demo_optimizer.py --ts_code 600519.SH --trials 100
|
||||
"""
|
||||
|
||||
import sys, os, argparse
|
||||
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
|
||||
from data.data_manager import DataManager
|
||||
from factors.engine import FactorEngine
|
||||
from factors.registry import get_factor
|
||||
from backtest.vectorbt.engine import VectorBTEngine
|
||||
from backtest.strategies.rsi_mean_revert import RSIMeanRevertStrategy
|
||||
from optimizer.engine import OptunaEngine
|
||||
from optimizer.space import rsi_revert_space
|
||||
|
||||
|
||||
def main():
|
||||
p = argparse.ArgumentParser(description="Sprint 3 — Optuna 参数优化验证")
|
||||
p.add_argument("--ts_code", default="000001.SZ", help="测试股票代码")
|
||||
p.add_argument("--trials", type=int, default=200, help="试验次数（默认: 200）")
|
||||
args = p.parse_args()
|
||||
|
||||
print("=" * 60)
|
||||
print("Sprint 3 — Optuna 参数优化验证")
|
||||
print("=" * 60)
|
||||
|
||||
print("\n[1/4] 初始化...")
|
||||
dm = DataManager(); dm.init_db()
|
||||
engine_fe = FactorEngine(dm)
|
||||
bt_engine = VectorBTEngine()
|
||||
opt_engine = OptunaEngine(bt_engine)
|
||||
|
||||
price_df = dm.get_daily(args.ts_code).set_index("trade_date").sort_index()
|
||||
factor_df = engine_fe.compute(args.ts_code, [get_factor("rsi_14")])
|
||||
print(" 数据: {} 条日线, {} 个因子".format(len(price_df), factor_df.shape[1]))
|
||||
print("[OK]")
|
||||
|
||||
print("\n[2/4] RSI 反转策略参数寻优 (sharpe, {} trials)...".format(args.trials))
|
||||
result = opt_engine.optimize(
|
||||
strategy_class=RSIMeanRevertStrategy,
|
||||
search_space=rsi_revert_space,
|
||||
price_df=price_df, factor_df=factor_df,
|
||||
metric="sharpe", n_trials=args.trials,
|
||||
)
|
||||
print(result.summary())
|
||||
if result.param_importance:
|
||||
print(" 参数重要性:")
|
||||
for k, v in sorted(result.param_importance.items(), key=lambda x: -x[1]):
|
||||
print(" {}: {:.4f}".format(k, v))
|
||||
|
||||
print("\n[3/4] 默认参数 vs 最优参数 对比...")
|
||||
strategies_compare = [
|
||||
("默认(30/70)", RSIMeanRevertStrategy(oversold=30, overbought=70)),
|
||||
("最优", RSIMeanRevertStrategy(**result.best_params)),
|
||||
]
|
||||
reports = {}
|
||||
for name, s in strategies_compare:
|
||||
reports[name] = bt_engine.run(s, price_df, factor_df)
|
||||
|
||||
metrics = [("总收益(%)", "total_return"), ("年化CAGR(%)", "cagr"), ("最大回撤(%)", "max_drawdown"),
|
||||
("夏普比率", "sharpe_ratio"), ("卡玛比率", "calmar_ratio"), ("胜率(%)", "win_rate"),
|
||||
("盈利因子", "profit_factor"), ("交易笔数", "total_trades")]
|
||||
print(" {:<18s} {:>12s} {:>12s}".format("指标", "默认(30/70)", "最优"))
|
||||
print(" " + "-" * 42)
|
||||
for label, attr in metrics:
|
||||
vals = []
|
||||
for r in reports.values():
|
||||
v = getattr(r, attr)
|
||||
vals.append("{:.2f}".format(v) if isinstance(v, float) else str(v))
|
||||
print(" {:<18s} {:>12s} {:>12s}".format(label, vals[0], vals[1]))
|
||||
|
||||
print("\n[4/4] Walk-Forward 滚动窗口验证...")
|
||||
try:
|
||||
wf = opt_engine.optimize_walk_forward(
|
||||
RSIMeanRevertStrategy, rsi_revert_space,
|
||||
price_df, factor_df, metric="sharpe", n_trials=min(80, args.trials),
|
||||
train_window=252 * 3, test_window=252,
|
||||
)
|
||||
print(wf.summary())
|
||||
except Exception as e:
|
||||
print(" [SKIP] Walk-Forward 异常: {}".format(e))
|
||||
|
||||
# 存入 DB
|
||||
try:
|
||||
from reports.storage import save_report
|
||||
from datetime import datetime
|
||||
lines = ["## 参数优化 — {}".format(args.ts_code),
|
||||
result.summary(), "",
|
||||
"### 默认 vs 最优对比"]
|
||||
save_report("\n".join(lines), "参数优化", subject_type="stock", subject_code=args.ts_code)
|
||||
print("\n 报告已存入 DB")
|
||||
except Exception as e:
|
||||
print("\n [WARN] 报告入库失败: {}".format(e))
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("Sprint 3 验证完成")
|
||||
print("=" * 60)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,103 @@
|
||||
"""
|
||||
Sprint 5 验证脚本 — 情绪因子快速验证。
|
||||
|
||||
用法:
|
||||
python cli/demo_sentiment.py
|
||||
python cli/demo_sentiment.py --ts_code 600519.SH
|
||||
python cli/demo_sentiment.py --ts_code 000001.SZ --no-qwen
|
||||
"""
|
||||
|
||||
import sys, os, argparse
|
||||
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
|
||||
import pandas as pd
|
||||
from data.data_manager import DataManager
|
||||
from factors.sentiment.news_source import NewsSource, align_news_to_trading_days
|
||||
from factors.sentiment.qwen_client import QwenClient
|
||||
from factors.sentiment.sentiment_engine import SentimentEngine
|
||||
|
||||
|
||||
def main():
|
||||
p = argparse.ArgumentParser(description="Sprint 5 — 情绪因子快速验证")
|
||||
p.add_argument("--ts_code", default="000001.SZ", help="测试股票代码")
|
||||
p.add_argument("--no-qwen", action="store_true", help="跳过 Qwen 情绪分析")
|
||||
args = p.parse_args()
|
||||
|
||||
print("=" * 60)
|
||||
print("Sprint 5 — Qwen 情绪因子验证")
|
||||
print("=" * 60)
|
||||
|
||||
dm = DataManager(); dm.init_db()
|
||||
|
||||
print("\n[1/5] 新闻数据源测试...")
|
||||
news_src = NewsSource(use_mcp=False)
|
||||
news_df = news_src.fetch(args.ts_code, max_news=10)
|
||||
if not news_df.empty:
|
||||
print(" 获取 {} 条新闻".format(len(news_df)))
|
||||
for _, row in news_df.head(3).iterrows():
|
||||
print(" [{}] {}... (source={})".format(row["date"], str(row["title"])[:80], row["source"]))
|
||||
else:
|
||||
print(" (无新闻数据)")
|
||||
print("[OK]")
|
||||
|
||||
print("\n[2/5] 日期对齐测试...")
|
||||
from data.data_manager import DataManager as DM
|
||||
price = dm.get_daily(args.ts_code) if dm.get_daily(args.ts_code) is not None else dm.get_daily("000001.SZ")
|
||||
if price is not None and not price.empty:
|
||||
price = price.set_index("trade_date").sort_index()
|
||||
daily_idx = pd.to_datetime(price.index, format="%Y%m%d", errors="coerce")
|
||||
test_news = pd.DataFrame({"date": ["20240601", "20240602", "20240603"],
|
||||
"title": ["周六新闻", "周日新闻", "周一新闻"],
|
||||
"content": [""] * 3, "source": ["test"] * 3, "url": [""] * 3})
|
||||
aligned = align_news_to_trading_days(test_news, daily_idx)
|
||||
for _, row in aligned.iterrows():
|
||||
print(" {}: {}".format(row["title"], row["date"]))
|
||||
print("[OK]")
|
||||
|
||||
print("\n[3/5] Qwen 客户端状态...")
|
||||
client = QwenClient()
|
||||
has_api = bool(client.api_key) or bool(client.local_base_url)
|
||||
if has_api and not args.no_qwen:
|
||||
mode = "本地Ollama/{}".format(client.local_model) if client.local_base_url else "DashScope/{}".format(client.model)
|
||||
print(" 模式: {}".format(mode))
|
||||
else:
|
||||
print(" 模式: 未配置或跳过")
|
||||
print("[OK]")
|
||||
|
||||
print("\n[4/5] SentimentEngine 全链路 (max_news=10)...")
|
||||
try:
|
||||
sent_engine = SentimentEngine(dm, qwen_client=client, news_source=news_src)
|
||||
sent_df = sent_engine.compute(args.ts_code, max_news=10)
|
||||
if sent_df is not None and not sent_df.empty:
|
||||
valid = sent_df.dropna(how="all")
|
||||
print(" 情绪因子: {}".format(list(sent_df.columns)))
|
||||
print(" 有效行: {}/{}".format(len(valid), len(sent_df)))
|
||||
if not valid.empty:
|
||||
print(valid.tail(5).round(4).to_string())
|
||||
except Exception as e:
|
||||
print(" [WARN] {}".format(e))
|
||||
print("[OK]")
|
||||
|
||||
print("\n[5/5] 分析范围解析...")
|
||||
scope = sent_engine.get_scope_stocks()
|
||||
print(" 成分股数量: {}".format(len(scope)))
|
||||
if scope:
|
||||
print(" 示例: {}".format(", ".join(scope[:5])))
|
||||
print("[OK]")
|
||||
|
||||
try:
|
||||
from reports.storage import save_report
|
||||
save_report("## 情绪因子验证 — {}\n\n- 新闻源: OK\n- 日期对齐: OK\n- Qwen: {}".format(
|
||||
args.ts_code, "就绪" if has_api else "未配置"),
|
||||
"情绪因子验证", subject_type="stock", subject_code=args.ts_code)
|
||||
print(" 报告已存入 DB")
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("Sprint 5 验证完成")
|
||||
print("=" * 60)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,453 @@
|
||||
"""
|
||||
情绪因子详细运行过程演示。
|
||||
|
||||
用法:
|
||||
# 默认：000001.SZ，最近30天，指数范围
|
||||
python cli/demo_sentiment_detail.py
|
||||
|
||||
# 指定股票代码和日期
|
||||
python cli/demo_sentiment_detail.py --ts_code 600519.SH --date 20260603
|
||||
python cli/demo_sentiment_detail.py --ts_code 000001.SZ,600519.SH,300750.SZ
|
||||
|
||||
# 指定日期范围
|
||||
python cli/demo_sentiment_detail.py --start 20260501 --end 20260603
|
||||
|
||||
# 分析指定指数成分股
|
||||
python cli/demo_sentiment_detail.py --scope-type index --scope-indexes 000300
|
||||
|
||||
# 分析指定板块
|
||||
python cli/demo_sentiment_detail.py --scope-type sector --scope-sectors 银行,电力设备
|
||||
|
||||
# 只使用特定新闻源
|
||||
python cli/demo_sentiment_detail.py --no-xwlb --no-mcp
|
||||
python cli/demo_sentiment_detail.py --source akshare
|
||||
|
||||
# 跳过 Qwen API 调用（仅演示数据流）
|
||||
python cli/demo_sentiment_detail.py --no-qwen
|
||||
"""
|
||||
|
||||
import sys, os, json, argparse
|
||||
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
def parse_args():
|
||||
p = argparse.ArgumentParser(description="情绪因子详细运行过程演示")
|
||||
p.add_argument("--ts_code", default="000001.SZ",
|
||||
help="股票代码，多个用逗号分隔（默认: 000001.SZ）")
|
||||
p.add_argument("--date", default=None,
|
||||
help="目标日期 YYYYMMDD（默认: 今天）")
|
||||
p.add_argument("--start", default=None,
|
||||
help="起始日期 YYYYMMDD（默认: date-30天）")
|
||||
p.add_argument("--end", default=None,
|
||||
help="结束日期 YYYYMMDD（默认: date 或今天）")
|
||||
p.add_argument("--scope-type", default=None,
|
||||
choices=["index", "sector", "custom", "all"],
|
||||
help="分析范围类型（覆盖 ts_code）")
|
||||
p.add_argument("--scope-indexes", default="000300",
|
||||
help="指数代码，逗号分隔（默认: 000300）")
|
||||
p.add_argument("--scope-sectors", default="",
|
||||
help="板块名称，逗号分隔")
|
||||
p.add_argument("--max-news", type=int, default=None,
|
||||
help="最大新闻条数（默认: .env SENTIMENT_MAX_NEWS_PER_STOCK 或 30）")
|
||||
p.add_argument("--max-analyze", type=int, default=50,
|
||||
help="Qwen 分析最大条数（默认: 50，控制成本）")
|
||||
p.add_argument("--no-xwlb", action="store_true", help="禁用新闻联播数据源")
|
||||
p.add_argument("--no-akshare", action="store_true", help="禁用东方财富数据源")
|
||||
p.add_argument("--no-mcp", action="store_true", help="禁用 MCP 数据源")
|
||||
p.add_argument("--source", default=None,
|
||||
choices=["xwlb", "akshare", "mcp"],
|
||||
help="仅使用指定数据源")
|
||||
p.add_argument("--no-qwen", action="store_true", help="跳过 Qwen 分析（仅演示数据流）")
|
||||
return p.parse_args()
|
||||
|
||||
|
||||
def main():
|
||||
args = parse_args()
|
||||
|
||||
date = args.date or datetime.now().strftime("%Y%m%d")
|
||||
start = args.start or (datetime.strptime(date, "%Y%m%d") - pd.Timedelta(days=30)).strftime("%Y%m%d")
|
||||
end = args.end or date
|
||||
|
||||
print("=" * 72)
|
||||
print(" 情绪因子详细运行过程")
|
||||
print("=" * 72)
|
||||
print(" 日期: {} ~ {} (目标: {})".format(start, end, date))
|
||||
|
||||
# ═══════════════════════════════════════════════════════════════
|
||||
# Step 0: 初始化
|
||||
# ═══════════════════════════════════════════════════════════════
|
||||
print("\n" + "-" * 72)
|
||||
print("Step 0: 初始化引擎")
|
||||
print("-" * 72)
|
||||
|
||||
from data.data_manager import DataManager
|
||||
from factors.sentiment.qwen_client import QwenClient
|
||||
from factors.sentiment.news_source import NewsSource, align_news_to_trading_days
|
||||
|
||||
dm = DataManager()
|
||||
dm.init_db()
|
||||
client = QwenClient()
|
||||
|
||||
has_api = (bool(client.api_key) or bool(client.local_base_url)) and not args.no_qwen
|
||||
use_xwlb = not args.no_xwlb and (args.source is None or args.source == "xwlb")
|
||||
use_akshare = not args.no_akshare and (args.source is None or args.source == "akshare")
|
||||
use_mcp = not args.no_mcp and (args.source is None or args.source == "mcp")
|
||||
|
||||
print(" Qwen API: {}".format("DashScope/{}".format(client.model) if (has_api and not client.local_base_url) else (
|
||||
"本地 Ollama/{}".format(client.local_model) if (has_api and client.local_base_url) else "跳过（--no-qwen 或未配置）")))
|
||||
print(" 数据源: {}/{}/{}".format(
|
||||
"xwlb" if use_xwlb else "xwlb(off)",
|
||||
"akshare" if use_akshare else "akshare(off)",
|
||||
"mcp" if use_mcp else "mcp(off)",
|
||||
))
|
||||
max_news = args.max_news or int(os.getenv("SENTIMENT_MAX_NEWS_PER_STOCK", "30"))
|
||||
print(" 最大新闻: {} 条 (SENTIMENT_MAX_NEWS_PER_STOCK={})".format(
|
||||
max_news, os.getenv("SENTIMENT_MAX_NEWS_PER_STOCK", "未设置")))
|
||||
|
||||
# 分析范围
|
||||
if args.scope_type:
|
||||
from factors.sentiment.sentiment_engine import SentimentEngine
|
||||
# 临时覆盖环境变量
|
||||
os.environ["SENTIMENT_SCOPE_TYPE"] = args.scope_type
|
||||
if args.scope_indexes:
|
||||
os.environ["SENTIMENT_SCOPE_INDEXES"] = args.scope_indexes
|
||||
if args.scope_sectors:
|
||||
os.environ["SENTIMENT_SCOPE_SECTORS"] = args.scope_sectors
|
||||
sent_tmp = SentimentEngine(dm)
|
||||
ts_codes = sent_tmp.get_scope_stocks()
|
||||
print(" 分析范围: {} ({})".format(args.scope_type, len(ts_codes)))
|
||||
if len(ts_codes) > 10:
|
||||
print(" 股票示例: {}... (共 {} 只)".format(", ".join(ts_codes[:10]), len(ts_codes)))
|
||||
else:
|
||||
print(" 股票: {}".format(", ".join(ts_codes)))
|
||||
else:
|
||||
ts_codes = [c.strip() for c in args.ts_code.split(",") if c.strip()]
|
||||
|
||||
# ═══════════════════════════════════════════════════════════════
|
||||
# Step 1: 分别从三个数据源获取新闻
|
||||
# ═══════════════════════════════════════════════════════════════
|
||||
print("\n" + "-" * 72)
|
||||
print("Step 1: 获取新闻 ({} 只股票)".format(len(ts_codes)))
|
||||
print("-" * 72)
|
||||
|
||||
all_raw = []
|
||||
for ts_code in ts_codes:
|
||||
print("\n --- {} ---".format(ts_code))
|
||||
|
||||
xwlb_raw = pd.DataFrame()
|
||||
ak_raw = pd.DataFrame()
|
||||
mcp_raw = pd.DataFrame()
|
||||
|
||||
if use_xwlb:
|
||||
try:
|
||||
xwlb_src = NewsSource(use_akshare=False, use_mcp=False)
|
||||
xwlb_raw = xwlb_src.fetch(ts_code, start=start, end=end, max_news=max_news * 3)
|
||||
print(" 新闻联播(DB xwlb_daily_ext): {} 条 (news_date范围: {}-1~{}-1)".format(
|
||||
len(xwlb_raw), start, end))
|
||||
except Exception as e:
|
||||
print(" 新闻联播: 获取失败 ({})".format(e))
|
||||
|
||||
if use_akshare:
|
||||
try:
|
||||
ak_src = NewsSource(use_xwlb=False, use_mcp=False)
|
||||
ak_raw = ak_src.fetch(ts_code, start=start, end=end, max_news=max_news)
|
||||
print(" 东方财富(AkShare stock_news_em): {} 条".format(len(ak_raw)))
|
||||
except Exception as e:
|
||||
print(" 东方财富: 获取失败 ({})".format(e))
|
||||
|
||||
if use_mcp:
|
||||
try:
|
||||
mcp_src = NewsSource(use_akshare=False, use_xwlb=False, use_mcp=True)
|
||||
mcp_raw = mcp_src.fetch(ts_code, start=start, end=end, max_news=max_news)
|
||||
print(" MCP(trendradar-news): {} 条".format(len(mcp_raw)))
|
||||
except Exception as e:
|
||||
print(" MCP: 获取失败 ({})".format(e))
|
||||
|
||||
all_raw.append((ts_code, xwlb_raw, ak_raw, mcp_raw))
|
||||
|
||||
# 合并所有股票的结果
|
||||
frames = []
|
||||
for _, x, a, m in all_raw:
|
||||
for df in [x, a, m]:
|
||||
if not df.empty:
|
||||
frames.append(df)
|
||||
raw_news = pd.concat(frames, ignore_index=True) if frames else pd.DataFrame()
|
||||
|
||||
if not raw_news.empty:
|
||||
raw_news = raw_news.drop_duplicates(subset=["title", "date"])
|
||||
raw_news = raw_news.sort_values("date", ascending=False)
|
||||
|
||||
print("\n [汇总] 合并去重后: {} 条新闻".format(len(raw_news)))
|
||||
if raw_news.empty:
|
||||
print(" (无新闻数据)")
|
||||
return
|
||||
|
||||
src_counts = raw_news["source"].value_counts()
|
||||
for src, cnt in src_counts.items():
|
||||
if src == "xwlb":
|
||||
label = "新闻联播(DB)"
|
||||
elif src.startswith("akshare"):
|
||||
label = "东方财富(AkShare)"
|
||||
elif src.startswith("mcp"):
|
||||
label = "MCP(trendradar)"
|
||||
else:
|
||||
label = src
|
||||
print(" {}: {} 条".format(label, cnt))
|
||||
|
||||
# ═══════════════════════════════════════════════════════════════
|
||||
# Step 2: 新闻详情（按来源分开展示）
|
||||
# ═══════════════════════════════════════════════════════════════
|
||||
print("\n" + "-" * 72)
|
||||
print("Step 2: 新闻详情（按数据源分开展示）")
|
||||
print("-" * 72)
|
||||
|
||||
def show_news(label, df, limit=6):
|
||||
if df.empty:
|
||||
print("\n [{}] (无数据)".format(label))
|
||||
return
|
||||
print("\n [{}] {} 条".format(label, len(df)))
|
||||
for i, (_, row) in enumerate(df.head(limit).iterrows()):
|
||||
title = str(row["title"])[:80]
|
||||
content_preview = str(row["content"])[:100].replace("\n", " ")
|
||||
print("\n [{}/{}] {} | {}".format(i + 1, len(df), row["date"], title))
|
||||
if content_preview:
|
||||
print(" 内容: {}...".format(content_preview))
|
||||
url = row.get("url", "")
|
||||
if url:
|
||||
print(" 链接: {}".format(url[:100]))
|
||||
|
||||
show_news("新闻联播 (xwlb_daily_ext)", raw_news[raw_news["source"] == "xwlb"], limit=6)
|
||||
show_news("东方财富 (AkShare stock_news_em)",
|
||||
raw_news[raw_news["source"].str.startswith("akshare")], limit=6)
|
||||
show_news("MCP (trendradar-news)",
|
||||
raw_news[raw_news["source"].str.startswith("mcp")], limit=6)
|
||||
|
||||
# ═══════════════════════════════════════════════════════════════
|
||||
# Step 3: 日期对齐
|
||||
# ═══════════════════════════════════════════════════════════════
|
||||
print("\n" + "-" * 72)
|
||||
print("Step 3: 日期对齐到交易日")
|
||||
print("-" * 72)
|
||||
|
||||
# 交易日历：优先用指定股票 DB 缓存，否则 fallback 到 000001.SZ
|
||||
first_code = ts_codes[0]
|
||||
price = _get_trading_calendar(dm, first_code)
|
||||
if price is None:
|
||||
print(" {} 无 DB 缓存, fallback 到 000001.SZ".format(first_code))
|
||||
price = _get_trading_calendar(dm, "000001.SZ")
|
||||
if price is None:
|
||||
print(" 无交易日历可用")
|
||||
return
|
||||
print("\n 交易日历: {} ~ {} ({} 条)".format(price.index[0], price.index[-1], len(price)))
|
||||
|
||||
daily_idx = pd.to_datetime(price.index, format="%Y%m%d", errors="coerce")
|
||||
aligned_news = align_news_to_trading_days(raw_news, daily_idx)
|
||||
|
||||
for label, prefix in [("新闻联播", "xwlb"), ("东方财富", "akshare"), ("MCP", "mcp")]:
|
||||
df = aligned_news[aligned_news["source"].str.startswith(prefix) if prefix != "xwlb"
|
||||
else (aligned_news["source"] == "xwlb")]
|
||||
if df.empty:
|
||||
continue
|
||||
dates = sorted(df["date"].unique())
|
||||
print("\n [{}] {} 条 → {} 个交易日 ({})".format(label, len(df), len(dates),
|
||||
" +1day偏移" if prefix == "xwlb" else " 直接对齐"))
|
||||
print(" 日期: {} ~ {}".format(dates[0], dates[-1]))
|
||||
row = df.iloc[0]
|
||||
print(" 示例: {} | {}...".format(row["date"], str(row["title"])[:60]))
|
||||
|
||||
# ═══════════════════════════════════════════════════════════════
|
||||
# Step 4: Qwen 情绪分析
|
||||
# ═══════════════════════════════════════════════════════════════
|
||||
print("\n" + "-" * 72)
|
||||
print("Step 4: Qwen 情绪分析")
|
||||
print("-" * 72)
|
||||
|
||||
if not has_api:
|
||||
print("\n [SKIP] Qwen API 跳过 (--no-qwen 或未配置)")
|
||||
print(" 使用模拟数据演示因子计算逻辑...")
|
||||
sentiment_results = _mock_sentiment(aligned_news)
|
||||
else:
|
||||
max_analyze = min(len(aligned_news), args.max_analyze)
|
||||
analyze_news = aligned_news.head(max_analyze)
|
||||
|
||||
print("\n 逐条分析 {} 条新闻...".format(max_analyze))
|
||||
sentiment_results = []
|
||||
for i, (_, row) in enumerate(analyze_news.iterrows()):
|
||||
title = str(row["title"])
|
||||
content = str(row["content"]) if len(str(row["content"])) > 20 else ""
|
||||
text = "{}\n{}".format(title, content)
|
||||
|
||||
result = client.analyze_sentiment(text)
|
||||
sentiment_results.append({
|
||||
"date": row["date"],
|
||||
"title": title,
|
||||
"sentiment_score": result.get("sentiment_score", 0),
|
||||
"confidence": result.get("confidence", 0),
|
||||
"impact_duration": result.get("impact_duration", "short"),
|
||||
"key_topics": json.dumps(result.get("key_topics", [])),
|
||||
"source": row.get("source", ""),
|
||||
})
|
||||
|
||||
s = result["sentiment_score"]
|
||||
icon = "(+)" if s > 0.2 else ("(-)" if s < -0.2 else "(o)")
|
||||
print(" [{}/{}] {} {:+.1f} c={:.2f} | {}...".format(
|
||||
i + 1, max_analyze, icon, s,
|
||||
result["confidence"], title[:60]))
|
||||
|
||||
sent_df = pd.DataFrame(sentiment_results)
|
||||
if not sent_df.empty:
|
||||
print("\n 情绪分析汇总 ({} 条):".format(len(sent_df)))
|
||||
print(" 平均情绪: {:+.3f}".format(sent_df["sentiment_score"].mean()))
|
||||
pos = (sent_df["sentiment_score"] > 0.1).sum()
|
||||
neu = ((sent_df["sentiment_score"] >= -0.1) & (sent_df["sentiment_score"] <= 0.1)).sum()
|
||||
neg = (sent_df["sentiment_score"] < -0.1).sum()
|
||||
print(" 正面(>0.1): {} 中性(-0.1~0.1): {} 负面(<-0.1): {}".format(pos, neu, neg))
|
||||
if "source" in sent_df.columns:
|
||||
for src in sent_df["source"].unique():
|
||||
src_df = sent_df[sent_df["source"] == src]
|
||||
label = src[:20]
|
||||
print(" [{}] {} 条, 平均情绪: {:+.3f}".format(label, len(src_df), src_df["sentiment_score"].mean()))
|
||||
|
||||
# ═══════════════════════════════════════════════════════════════
|
||||
# Step 5: 因子计算 + 结果输出
|
||||
# ═══════════════════════════════════════════════════════════════
|
||||
print("\n" + "-" * 72)
|
||||
print("Step 5-6: 因子计算 + 结果输出")
|
||||
print("-" * 72)
|
||||
|
||||
from factors.sentiment.sentiment_factor import (
|
||||
NewsSentimentFactor,
|
||||
SentimentConfidenceFactor,
|
||||
SentimentMomentumFactor,
|
||||
)
|
||||
|
||||
if sent_df.empty:
|
||||
print(" (无情绪数据)")
|
||||
return
|
||||
|
||||
factors = [
|
||||
NewsSentimentFactor(window=5, decay=0.3, sentiment_df=sent_df),
|
||||
SentimentConfidenceFactor(window=5, sentiment_df=sent_df),
|
||||
SentimentMomentumFactor(period=5, sentiment_df=sent_df),
|
||||
]
|
||||
|
||||
factor_results = {}
|
||||
for f in factors:
|
||||
series = f.calculate(price)
|
||||
factor_results[f.name] = series
|
||||
stats = series.dropna()
|
||||
if not stats.empty:
|
||||
print(" {}: mean={:+.4f} std={:.4f} valid={}/{}".format(
|
||||
f.name, stats.mean(), stats.std(), len(stats), len(series)))
|
||||
else:
|
||||
print(" {}: (全NaN)".format(f.name))
|
||||
|
||||
factor_df = pd.DataFrame(factor_results)
|
||||
valid = factor_df.dropna(how="all")
|
||||
|
||||
if valid.empty:
|
||||
print("\n (无有效因子值)")
|
||||
return
|
||||
|
||||
recent = valid.tail(20)
|
||||
print("\n === 最近 {} 个交易日情绪因子值 ({}) ===".format(len(recent), first_code))
|
||||
print(" {:<12s} {:>12s} {:>12s} {:>12s}".format("交易日", "news_sent_5", "news_conf_5", "sent_delta_5"))
|
||||
print(" {} {} {} {}".format("-" * 12, "-" * 12, "-" * 12, "-" * 12))
|
||||
for idx, row in recent.iterrows():
|
||||
ns = "{:+.4f}".format(row["news_sent_5"]) if not pd.isna(row["news_sent_5"]) else " NaN"
|
||||
nc = "{:+.4f}".format(row["news_conf_5"]) if not pd.isna(row["news_conf_5"]) else " NaN"
|
||||
sd = "{:+.4f}".format(row["sent_delta_5"]) if not pd.isna(row["sent_delta_5"]) else " NaN"
|
||||
print(" {:<12s} {:>12s} {:>12s} {:>12s}".format(idx, ns, nc, sd))
|
||||
|
||||
latest = valid.iloc[-1]
|
||||
print("\n === 最新交易日 ({}) ===".format(valid.index[-1]))
|
||||
print(" news_sent_5 : {:+.4f} (加权情绪, >0偏正面)".format(latest["news_sent_5"]))
|
||||
print(" news_conf_5 : {:+.4f} (置信度加权)".format(latest["news_conf_5"]))
|
||||
|
||||
# 情绪贡献明细
|
||||
print("\n === 情绪贡献明细 (最近3天) ===")
|
||||
latest_date = valid.index[-1]
|
||||
nearby = sent_df[
|
||||
(sent_df["date"] >= str(int(latest_date) - 3)) &
|
||||
(sent_df["date"] <= latest_date)
|
||||
]
|
||||
if not nearby.empty:
|
||||
for _, row in nearby.head(30).iterrows():
|
||||
s = row["sentiment_score"]
|
||||
impact = "(+)" if s > 0.2 else ("(-)" if s < -0.2 else "(o)")
|
||||
src = str(row.get("source", ""))
|
||||
src_s = "xwlb" if src == "xwlb" else ("ak" if src.startswith("akshare") else "mcp")
|
||||
print(" {} [{:+.1f}] [{}] {}...".format(
|
||||
impact, s, src_s, str(row["title"])[:70]))
|
||||
else:
|
||||
print(" (无最近3天新闻)")
|
||||
|
||||
try:
|
||||
from reports.storage import save_report
|
||||
first = ts_codes[0] if ts_codes else "unknown"
|
||||
lines = ["## 情绪因子详细演示", "股票: {}".format(", ".join(ts_codes[:5])),
|
||||
"数据源: {}条新闻".format(len(raw_news)),
|
||||
"情绪: news_sent_5={}".format(
|
||||
latest["news_sent_5"] if "news_sent_5" in latest else "N/A")]
|
||||
save_report("\n".join(lines), "情绪因子详细演示", subject_type="stock", subject_code=first)
|
||||
print(" 报告已存入 DB")
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
print("\n" + "=" * 72)
|
||||
print(" 情绪因子演示完成")
|
||||
print("=" * 72)
|
||||
|
||||
|
||||
def _get_trading_calendar(dm, ts_code):
|
||||
"""获取交易日历：优先 DB 缓存；无缓存则尝试 sync_daily 补齐。"""
|
||||
try:
|
||||
from database.dao import get_latest_trade_date
|
||||
if not get_latest_trade_date(ts_code):
|
||||
print(" {} 无 DB 缓存，尝试 sync_daily 补齐...".format(ts_code))
|
||||
try:
|
||||
n = dm.sync_daily(ts_code)
|
||||
print(" sync_daily 完成: {} 条".format(n))
|
||||
except Exception as e:
|
||||
print(" sync_daily 失败: {}".format(e))
|
||||
return None
|
||||
daily = dm.get_daily(ts_code)
|
||||
if daily is not None and not daily.empty:
|
||||
daily = daily.set_index("trade_date").sort_index()
|
||||
if len(daily) > 0:
|
||||
return daily
|
||||
except Exception as e:
|
||||
print(" 获取交易日历异常: {}".format(e))
|
||||
return None
|
||||
|
||||
|
||||
def _mock_sentiment(news_df):
|
||||
results = []
|
||||
for _, row in news_df.iterrows():
|
||||
title = str(row["title"]).lower()
|
||||
pos_words = ["利好", "增长", "突破", "创新高", "盈利", "上升", "支持", "回购", "增持", "分红"]
|
||||
neg_words = ["利空", "下跌", "亏损", "处罚", "减持", "诉讼", "退市", "警告", "暴跌", "违约"]
|
||||
pos = sum(1 for w in pos_words if w in title)
|
||||
neg = sum(1 for w in neg_words if w in title)
|
||||
if pos > neg:
|
||||
score = min(0.9, 0.1 + pos * 0.2)
|
||||
elif neg > pos:
|
||||
score = max(-0.9, -0.1 - neg * 0.2)
|
||||
else:
|
||||
score = np.random.uniform(-0.15, 0.15)
|
||||
results.append({
|
||||
"date": row["date"], "title": row["title"],
|
||||
"sentiment_score": round(score, 1),
|
||||
"confidence": round(np.random.uniform(0.5, 0.9), 2),
|
||||
"impact_duration": "short", "key_topics": json.dumps([]),
|
||||
"source": row.get("source", ""),
|
||||
})
|
||||
return results
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,46 @@
|
||||
"""
|
||||
全局配置模块。
|
||||
|
||||
敏感信息优先从环境变量读取，其次使用默认值（仅开发环境）。
|
||||
"""
|
||||
|
||||
import os
|
||||
from pathlib import Path
|
||||
from dotenv import load_dotenv
|
||||
|
||||
# 确保加载 finance/.env，无论从哪个目录运行
|
||||
_env_path = Path(__file__).resolve().parent.parent / ".env"
|
||||
load_dotenv(_env_path)
|
||||
|
||||
# ── MariaDB 连接 ──────────────────────────────────────────
|
||||
# 通过 SSH 隧道连接：shared/script/autossh.sh
|
||||
MARIADB_CONFIG = {
|
||||
"host": os.getenv("MAC_DB_HOST", "127.0.0.1"),
|
||||
"port": int(os.getenv("MAC_DB_PORT", "13306")),
|
||||
"user": os.getenv("MAC_DB_USER", "myquant"),
|
||||
"password": os.getenv("MAC_DB_PASSWORD", "_H(lU1_fF*9baRTp"),
|
||||
"database": os.getenv("MAC_DB_NAME", "myquant"),
|
||||
"charset": "utf8mb4",
|
||||
"pool_size": 5,
|
||||
"pool_recycle": 600, # 10分钟回收，避免 SSH 隧道半开连接
|
||||
}
|
||||
|
||||
# ── 表名前缀 ──────────────────────────────────────────────
|
||||
TABLE_PREFIX = "mac_"
|
||||
|
||||
# 完整表名
|
||||
TABLE_STOCK_BASIC = f"{TABLE_PREFIX}stock_basic"
|
||||
TABLE_STOCK_DAILY = f"{TABLE_PREFIX}stock_daily"
|
||||
TABLE_STOCK_FINANCIAL = f"{TABLE_PREFIX}stock_financial"
|
||||
TABLE_REPORT = f"{TABLE_PREFIX}report"
|
||||
|
||||
# ── AkShare 配置 ──────────────────────────────────────────
|
||||
AKSHARE_CONFIG = {
|
||||
"request_timeout": 30,
|
||||
"retry_times": 3,
|
||||
"retry_delay": 2, # 秒
|
||||
}
|
||||
|
||||
# ── 默认参数 ──────────────────────────────────────────────
|
||||
DEFAULT_START_DATE = "20200101"
|
||||
DEFAULT_END_DATE = None # None 表示当天
|
||||
@@ -0,0 +1,171 @@
|
||||
"""
|
||||
DataManager — 统一数据管理层。
|
||||
|
||||
策略/模型层通过 DataManager 获取数据，不直接访问 AkShare/Tushare 或数据库。
|
||||
优先从 DB 读取，缺失时依次尝试 AkShare → Tushare 拉取并入库。
|
||||
"""
|
||||
|
||||
import time
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from config.settings import DEFAULT_START_DATE, DEFAULT_END_DATE
|
||||
from data.sources.akshare_source import AkShareSource
|
||||
from data.sources.tushare_source import TushareSource
|
||||
from database import dao
|
||||
from database.models import create_all_tables
|
||||
|
||||
|
||||
class DataManager:
|
||||
"""统一数据管理。双数据源：AkShare（主）+ Tushare（备）。"""
|
||||
|
||||
def __init__(self):
|
||||
self._ak_source: AkShareSource | None = None
|
||||
self._ts_source: TushareSource | None = None
|
||||
|
||||
@property
|
||||
def ak(self) -> AkShareSource:
|
||||
if self._ak_source is None:
|
||||
self._ak_source = AkShareSource()
|
||||
return self._ak_source
|
||||
|
||||
@property
|
||||
def ts(self) -> TushareSource:
|
||||
if self._ts_source is None:
|
||||
self._ts_source = TushareSource()
|
||||
return self._ts_source
|
||||
|
||||
# ── 初始化 ────────────────────────────────────────────
|
||||
|
||||
def init_db(self) -> None:
|
||||
create_all_tables()
|
||||
|
||||
# ── 内部：双源 try ────────────────────────────────────
|
||||
|
||||
def _try_fetch(self, method_name: str, *args, **kwargs):
|
||||
"""
|
||||
依次尝试 AkShare → Tushare 调用同一方法名。
|
||||
|
||||
method_name: 'fetch_daily' | 'fetch_stock_list' | 'fetch_financial'
|
||||
返回: (result_df, source_name) 或 (empty_df, None)
|
||||
|
||||
如果 ts_code 是指数代码，自动路由到 fetch_index_daily。
|
||||
"""
|
||||
from data.sources.akshare_source import is_index_code
|
||||
|
||||
# 指数自动路由
|
||||
if method_name == "fetch_daily" and args:
|
||||
ts_code = args[0]
|
||||
if is_index_code(ts_code):
|
||||
method_name = "fetch_index_daily"
|
||||
|
||||
for label, source in [("Tushare", self.ts), ("AkShare", self.ak)]:
|
||||
try:
|
||||
if label == "Tushare" and not source.available:
|
||||
continue
|
||||
fn = getattr(source, method_name)
|
||||
df = fn(*args, **kwargs)
|
||||
if df is not None and not df.empty:
|
||||
return df, label
|
||||
except Exception as e:
|
||||
print(" [{}] {} 失败: {}".format(label, method_name, e))
|
||||
return pd.DataFrame(), None
|
||||
|
||||
# ── 股票列表 ──────────────────────────────────────────
|
||||
|
||||
def get_stock_list(self, force_refresh: bool = False) -> pd.DataFrame:
|
||||
if not force_refresh:
|
||||
df = dao.query_stock_list()
|
||||
if not df.empty:
|
||||
print("[DataManager] 从 DB 读取股票列表: {} 只".format(len(df)))
|
||||
return df
|
||||
|
||||
print("[DataManager] 拉取股票列表 (AkShare → Tushare)...")
|
||||
df, src = self._try_fetch("fetch_stock_list")
|
||||
if df.empty:
|
||||
print("[DataManager] 所有数据源均无法获取股票列表")
|
||||
return pd.DataFrame()
|
||||
print("[DataManager] 股票列表已入库 ({}): {} 只".format(src, len(df)))
|
||||
dao.save_stock_list(df)
|
||||
time.sleep(2)
|
||||
return df
|
||||
|
||||
# ── 日线数据 ──────────────────────────────────────────
|
||||
|
||||
def get_daily(
|
||||
self,
|
||||
ts_code: str,
|
||||
start: str | None = None,
|
||||
end: str | None = None,
|
||||
force_refresh: bool = False,
|
||||
) -> pd.DataFrame:
|
||||
start = start or DEFAULT_START_DATE
|
||||
end = end or DEFAULT_END_DATE or time.strftime("%Y%m%d")
|
||||
|
||||
if not force_refresh:
|
||||
df = dao.query_daily(ts_code, start, end)
|
||||
if not df.empty:
|
||||
return df
|
||||
|
||||
# DB 未命中 → 从数据源拉取
|
||||
df, src = self._try_fetch("fetch_daily", ts_code, start, end)
|
||||
if df.empty:
|
||||
print("[WARN] {} 日线获取失败 (AkShare+Tushare 均不可用)".format(ts_code))
|
||||
return pd.DataFrame()
|
||||
|
||||
# 保存到 DB
|
||||
try:
|
||||
# 筛选 DB 需要的列
|
||||
cols = [c for c in dao._DAILY_COLS if c in df.columns]
|
||||
dao.save_daily(df[cols])
|
||||
except Exception as e:
|
||||
print("[WARN] 日线入库失败: {}".format(e))
|
||||
|
||||
return df
|
||||
|
||||
def sync_daily(self, ts_code: str) -> int:
|
||||
latest = dao.get_latest_trade_date(ts_code)
|
||||
today = time.strftime("%Y%m%d")
|
||||
if latest and latest >= today:
|
||||
print("[DataManager] {} 数据已是最新 ({})".format(ts_code, latest))
|
||||
return 0
|
||||
|
||||
start = latest or DEFAULT_START_DATE
|
||||
df, src = self._try_fetch("fetch_daily", ts_code, start, today)
|
||||
if df.empty:
|
||||
print("[WARN] {} sync_daily 失败 (AkShare+Tushare 均不可用)".format(ts_code))
|
||||
return 0
|
||||
|
||||
cols = [c for c in dao._DAILY_COLS if c in df.columns]
|
||||
dao.save_daily(df[cols])
|
||||
print("[DataManager] {} 同步 {} 条日线 ({})".format(ts_code, len(df), src))
|
||||
return len(df)
|
||||
|
||||
def sync_all_daily(self) -> int:
|
||||
stock_list = self.get_stock_list()
|
||||
total = 0
|
||||
for i, ts_code in enumerate(stock_list.index):
|
||||
try:
|
||||
total += self.sync_daily(ts_code)
|
||||
if (i + 1) % 50 == 0:
|
||||
print("[DataManager] 进度: {}/{}".format(i + 1, len(stock_list)))
|
||||
time.sleep(1)
|
||||
except Exception as e:
|
||||
print("[WARN] {} 同步失败: {}".format(ts_code, e))
|
||||
print("[DataManager] 全量同步完成，新增 {} 条".format(total))
|
||||
return total
|
||||
|
||||
# ── 财务数据 ──────────────────────────────────────────
|
||||
|
||||
def get_financial(self, ts_code: str) -> pd.DataFrame:
|
||||
df = dao.query_financial(ts_code)
|
||||
if not df.empty:
|
||||
return df
|
||||
df, src = self._try_fetch("fetch_financial", ts_code)
|
||||
if not df.empty:
|
||||
try:
|
||||
cols = [c for c in dao._FINA_COLS if c in df.columns]
|
||||
dao.save_financial(df[cols])
|
||||
except Exception as e:
|
||||
print("[WARN] 财务数据入库失败: {}".format(e))
|
||||
return df
|
||||
@@ -0,0 +1,216 @@
|
||||
"""
|
||||
AkShare 数据源封装。
|
||||
|
||||
统一封装 AkShare 调用，返回标准化的 DataFrame。
|
||||
所有网络请求都在这一层处理，包含重试和容错。
|
||||
|
||||
支持个股和指数两种数据接口。
|
||||
"""
|
||||
|
||||
# 指数代码识别：.SH 后缀为主板指数，399xxx 为深证指数
|
||||
# 注意区分：000001.SZ 是平安银行(个股)，000001.SH 是上证指数
|
||||
def is_index_code(ts_code: str) -> bool:
|
||||
"""判断是否为指数代码。排除 000xxx.SZ 个股。"""
|
||||
if not ts_code:
|
||||
return False
|
||||
code = ts_code.upper()
|
||||
# .SH 开头 000 是指数
|
||||
if code.endswith(".SH") and (code.startswith("000") or code.startswith("399")):
|
||||
return True
|
||||
# 深交所 399xxx 指数
|
||||
if code.startswith("399"):
|
||||
return True
|
||||
# 无后缀的纯数字 000xxx（上证指数常见写法）
|
||||
if code == "000001":
|
||||
return True
|
||||
return False
|
||||
|
||||
import time
|
||||
import pandas as pd
|
||||
import akshare as ak
|
||||
|
||||
from config.settings import AKSHARE_CONFIG
|
||||
|
||||
|
||||
class AkShareSource:
|
||||
"""AkShare 数据源。"""
|
||||
|
||||
def __init__(self):
|
||||
self._timeout = AKSHARE_CONFIG["request_timeout"]
|
||||
self._retry = AKSHARE_CONFIG["retry_times"]
|
||||
self._delay = AKSHARE_CONFIG["retry_delay"]
|
||||
|
||||
def _retry_call(self, fn, name: str, **kwargs):
|
||||
"""带重试的 API 调用包装。重试间隔递增。"""
|
||||
last_err = None
|
||||
for i in range(self._retry):
|
||||
try:
|
||||
return fn(**kwargs)
|
||||
except Exception as e:
|
||||
last_err = e
|
||||
wait = self._delay * (i + 1)
|
||||
print(f" [retry] {name} 失败 ({e})，{wait}s 后重试 ({i + 1}/{self._retry})...")
|
||||
if i < self._retry - 1:
|
||||
time.sleep(wait)
|
||||
raise last_err # type: ignore
|
||||
|
||||
# ── 股票列表 ──────────────────────────────────────────
|
||||
|
||||
def fetch_stock_list(self) -> pd.DataFrame:
|
||||
"""获取 A 股股票列表。"""
|
||||
df = self._retry_call(ak.stock_info_a_code_name, "stock_list")
|
||||
df = df.rename(columns={
|
||||
"code": "ts_code",
|
||||
"name": "name",
|
||||
})
|
||||
return df[["ts_code", "name"]]
|
||||
|
||||
# ── 日线数据 ──────────────────────────────────────────
|
||||
|
||||
def fetch_daily(
|
||||
self, ts_code: str, start: str, end: str | None = None
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
获取单只股票日线行情。
|
||||
|
||||
参数:
|
||||
ts_code: 股票代码，如 '000001'（纯数字格式，AkShare 要求）
|
||||
start: 起始日期 'YYYYMMDD'
|
||||
end: 结束日期 'YYYYMMDD'，None 表示今天
|
||||
"""
|
||||
symbol = ts_code.replace(".SZ", "").replace(".SH", "").replace(".BJ", "")
|
||||
end = end or time.strftime("%Y%m%d")
|
||||
df = self._retry_call(
|
||||
ak.stock_zh_a_hist,
|
||||
"daily",
|
||||
symbol=symbol,
|
||||
period="daily",
|
||||
start_date=start,
|
||||
end_date=end,
|
||||
adjust="qfq", # 前复权
|
||||
)
|
||||
if df.empty:
|
||||
return df
|
||||
|
||||
df = df.rename(columns={
|
||||
"日期": "trade_date",
|
||||
"开盘": "open",
|
||||
"收盘": "close",
|
||||
"最高": "high",
|
||||
"最低": "low",
|
||||
"成交量": "vol",
|
||||
"成交额": "amount",
|
||||
"振幅": "amplitude",
|
||||
"涨跌幅": "pct_chg",
|
||||
"涨跌额": "change",
|
||||
"换手率": "turnover_rate",
|
||||
})
|
||||
df["ts_code"] = ts_code
|
||||
# AkShare 返回 'YYYY-MM-DD'，统一转为 'YYYYMMDD'
|
||||
df["trade_date"] = df["trade_date"].astype(str).str.replace("-", "")
|
||||
return df
|
||||
|
||||
# ── 指数日线 ──────────────────────────────────────────
|
||||
|
||||
def fetch_index_daily(
|
||||
self, ts_code: str, start: str, end: str | None = None
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
获取指数日线行情。
|
||||
|
||||
AkShare index_zh_a_hist 接口，symbol 为纯数字（如 '000001'）。
|
||||
"""
|
||||
symbol = ts_code.replace(".SH", "").replace(".SZ", "").replace(".BJ", "")
|
||||
end = end or time.strftime("%Y%m%d")
|
||||
df = self._retry_call(
|
||||
ak.index_zh_a_hist,
|
||||
"index_daily",
|
||||
symbol=symbol, period="daily",
|
||||
start_date=start, end_date=end,
|
||||
)
|
||||
if df.empty:
|
||||
return df
|
||||
|
||||
df = df.rename(columns={
|
||||
"日期": "trade_date",
|
||||
"开盘": "open",
|
||||
"收盘": "close",
|
||||
"最高": "high",
|
||||
"最低": "low",
|
||||
"成交量": "vol",
|
||||
"成交额": "amount",
|
||||
"涨跌幅": "pct_chg",
|
||||
"涨跌额": "change",
|
||||
})
|
||||
df["ts_code"] = ts_code
|
||||
df["trade_date"] = df["trade_date"].astype(str)
|
||||
return df
|
||||
|
||||
# ── 财务数据 ──────────────────────────────────────────
|
||||
|
||||
def fetch_financial(self, ts_code: str) -> pd.DataFrame:
|
||||
"""获取单只股票核心财务指标（同花顺接口）。"""
|
||||
symbol = ts_code.replace(".SZ", "").replace(".SH", "").replace(".BJ", "")
|
||||
try:
|
||||
df = ak.stock_financial_abstract_ths(symbol=symbol)
|
||||
if df.empty:
|
||||
return pd.DataFrame()
|
||||
|
||||
df = df.rename(columns={
|
||||
"报告期": "end_date",
|
||||
"净利润": "net_profit",
|
||||
"净利润同比增长率": "net_profit_yoy",
|
||||
"扣非净利润": "deducted_net_profit",
|
||||
"扣非净利润同比增长率": "deducted_net_profit_yoy",
|
||||
"营业总收入": "total_revenue",
|
||||
"营业总收入同比增长率": "total_revenue_yoy",
|
||||
"基本每股收益": "eps",
|
||||
"每股净资产": "bvps",
|
||||
"每股资本公积金": "capital_reserve_ps",
|
||||
"每股未分配利润": "undistributed_profit_ps",
|
||||
"每股经营现金流": "ocf_ps",
|
||||
"销售净利率": "net_profit_margin",
|
||||
"净资产收益率": "roe",
|
||||
"净资产收益率-摊薄": "roe_diluted",
|
||||
"营业周期": "operating_cycle",
|
||||
"应收账款周转天数": "receivables_days",
|
||||
"流动比率": "current_ratio",
|
||||
"速动比率": "quick_ratio",
|
||||
"保守速动比率": "conservative_quick_ratio",
|
||||
"产权比率": "equity_ratio",
|
||||
"资产负债率": "debt_to_assets",
|
||||
})
|
||||
df["ts_code"] = ts_code
|
||||
# 日期格式统一
|
||||
df["end_date"] = df["end_date"].astype(str).str.replace("-", "")
|
||||
# 数值列清洗：去掉 万/亿/% 等单位
|
||||
for col in df.columns:
|
||||
if col in ("ts_code", "end_date"):
|
||||
continue
|
||||
df[col] = self._parse_financial_value(df[col])
|
||||
return df
|
||||
except Exception:
|
||||
return pd.DataFrame()
|
||||
|
||||
@staticmethod
|
||||
def _parse_financial_value(series: pd.Series) -> pd.Series:
|
||||
"""解析财务数值字符串 '4302.00万', '64.75%', 'False' → float"""
|
||||
def _parse(v):
|
||||
if v is None or v == "False" or v == "":
|
||||
return None
|
||||
if isinstance(v, (int, float)):
|
||||
return float(v)
|
||||
s = str(v).strip()
|
||||
if not s:
|
||||
return None
|
||||
try:
|
||||
if s.endswith("%"):
|
||||
return float(s[:-1])
|
||||
if "万" in s:
|
||||
return float(s.replace("万", "")) * 1e4
|
||||
if "亿" in s:
|
||||
return float(s.replace("亿", "")) * 1e8
|
||||
return float(s)
|
||||
except ValueError:
|
||||
return None
|
||||
return series.apply(_parse).astype("float64")
|
||||
@@ -0,0 +1,139 @@
|
||||
"""
|
||||
Tushare 数据源封装。
|
||||
|
||||
与 AkShareSource 保持相同接口，作为平替/备份数据源。
|
||||
"""
|
||||
|
||||
import os
|
||||
import time
|
||||
|
||||
import pandas as pd
|
||||
import tushare as ts
|
||||
|
||||
# 确保 .env 已加载（无论从哪个路径导入）
|
||||
import config.settings # noqa: F401
|
||||
|
||||
|
||||
class TushareSource:
|
||||
"""Tushare 数据源。"""
|
||||
|
||||
def __init__(self, token: str | None = None):
|
||||
token = token or os.getenv("TUSHARE_TOKEN", "")
|
||||
if not token:
|
||||
self._pro = None
|
||||
else:
|
||||
ts.set_token(token)
|
||||
self._pro = ts.pro_api()
|
||||
|
||||
@property
|
||||
def available(self) -> bool:
|
||||
return self._pro is not None
|
||||
|
||||
def _ensure_pro(self):
|
||||
if self._pro is None:
|
||||
raise RuntimeError("Tushare token 未配置，请在 .env 中设置 TUSHARE_TOKEN")
|
||||
|
||||
# ── 股票列表 ──────────────────────────────────────────
|
||||
|
||||
def fetch_stock_list(self) -> pd.DataFrame:
|
||||
"""获取 A 股股票列表。"""
|
||||
self._ensure_pro()
|
||||
fields = "ts_code,name,area,industry,market,list_date,is_hs"
|
||||
df = self._pro.stock_basic(
|
||||
exchange="", list_status="L",
|
||||
fields=fields,
|
||||
)
|
||||
if df is None or df.empty:
|
||||
return pd.DataFrame()
|
||||
return df[["ts_code", "name"]]
|
||||
|
||||
# ── 日线数据 ──────────────────────────────────────────
|
||||
|
||||
def fetch_daily(
|
||||
self, ts_code: str, start: str, end: str | None = None
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
获取单只股票日线行情。
|
||||
|
||||
参数:
|
||||
ts_code: 如 '000001.SZ'
|
||||
start: 'YYYYMMDD'
|
||||
end: 'YYYYMMDD'
|
||||
"""
|
||||
self._ensure_pro()
|
||||
end = end or time.strftime("%Y%m%d")
|
||||
|
||||
df = self._pro.daily(
|
||||
ts_code=ts_code,
|
||||
start_date=start,
|
||||
end_date=end,
|
||||
fields="ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount,turnover_rate",
|
||||
)
|
||||
if df is None or df.empty:
|
||||
return pd.DataFrame()
|
||||
|
||||
# 前复权因子
|
||||
try:
|
||||
adj = self._pro.adj_factor(ts_code=ts_code, start_date=start, end_date=end)
|
||||
if adj is not None and not adj.empty:
|
||||
df = df.merge(adj[["trade_date", "adj_factor"]], on="trade_date", how="left")
|
||||
for col in ["open", "high", "low", "close", "pre_close"]:
|
||||
if col in df.columns and "adj_factor" in df.columns:
|
||||
df[col] = (df[col] * df["adj_factor"]).round(2)
|
||||
df = df.drop(columns=["adj_factor"])
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
df["trade_date"] = df["trade_date"].astype(str)
|
||||
return df
|
||||
|
||||
# ── 指数日线 ──────────────────────────────────────────
|
||||
|
||||
def fetch_index_daily(
|
||||
self, ts_code: str, start: str, end: str | None = None
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
获取指数日线行情。
|
||||
|
||||
Tushare index_daily 接口。
|
||||
"""
|
||||
self._ensure_pro()
|
||||
end = end or time.strftime("%Y%m%d")
|
||||
df = self._pro.index_daily(
|
||||
ts_code=ts_code,
|
||||
start_date=start, end_date=end,
|
||||
fields="ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount",
|
||||
)
|
||||
if df is None or df.empty:
|
||||
return pd.DataFrame()
|
||||
df["trade_date"] = df["trade_date"].astype(str)
|
||||
return df
|
||||
|
||||
# ── 财务数据 ──────────────────────────────────────────
|
||||
|
||||
def fetch_financial(self, ts_code: str) -> pd.DataFrame:
|
||||
"""获取单只股票核心财务指标。"""
|
||||
self._ensure_pro()
|
||||
fields = (
|
||||
"ts_code,end_date,roe,roa,grossprofit_margin,netprofit_margin,"
|
||||
"debt_to_assets,eps,dt_eps,bps,pe,pb,"
|
||||
"total_revenue,revenue_yoy,n_income,n_income_yoy"
|
||||
)
|
||||
df = self._pro.fina_indicator(
|
||||
ts_code=ts_code,
|
||||
fields=fields,
|
||||
)
|
||||
if df is None or df.empty:
|
||||
return pd.DataFrame()
|
||||
|
||||
df = df.rename(columns={
|
||||
"grossprofit_margin": "gross_profit_margin",
|
||||
"netprofit_margin": "net_profit_margin",
|
||||
"dt_eps": "eps_diluted",
|
||||
"bps": "bvps",
|
||||
"revenue_yoy": "total_revenue_yoy",
|
||||
"n_income": "net_profit",
|
||||
"n_income_yoy": "net_profit_yoy",
|
||||
})
|
||||
df["end_date"] = df["end_date"].astype(str)
|
||||
return df
|
||||
@@ -0,0 +1,118 @@
|
||||
"""
|
||||
MariaDB 连接管理。
|
||||
|
||||
使用 SQLAlchemy 连接池，通过 SSH 隧道访问远程数据库。
|
||||
连接失败时自动尝试重建 SSH 隧道并重连。
|
||||
"""
|
||||
|
||||
import os
|
||||
import subprocess
|
||||
import time
|
||||
|
||||
from sqlalchemy import create_engine, text
|
||||
from sqlalchemy.orm import sessionmaker
|
||||
|
||||
from config.settings import MARIADB_CONFIG
|
||||
|
||||
_engine = None
|
||||
_SessionLocal = None
|
||||
|
||||
|
||||
def _build_url() -> str:
|
||||
cfg = MARIADB_CONFIG
|
||||
return (
|
||||
"mysql+pymysql://{}:{}"
|
||||
"@{}:{}/{}"
|
||||
"?charset={}"
|
||||
).format(cfg["user"], cfg["password"], cfg["host"], cfg["port"], cfg["database"], cfg["charset"])
|
||||
|
||||
|
||||
def _reconnect_ssh() -> bool:
|
||||
"""自动运行 autossh.sh 重建 SSH 隧道。每次调用都会尝试。"""
|
||||
|
||||
candidates = [
|
||||
os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(
|
||||
os.path.abspath(__file__)))), "shared", "script", "autossh.sh"),
|
||||
os.path.expanduser("~/Downloads/cc-cursor/shared/script/autossh.sh"),
|
||||
]
|
||||
script = None
|
||||
for c in candidates:
|
||||
if os.path.exists(c):
|
||||
script = c
|
||||
break
|
||||
|
||||
if script is None:
|
||||
print("[DB] SSH 脚本未找到")
|
||||
return False
|
||||
|
||||
try:
|
||||
print("[DB] 尝试重建 SSH 隧道: {}".format(script))
|
||||
result = subprocess.run(["bash", script], capture_output=True, text=True, timeout=15)
|
||||
time.sleep(3) # 等待隧道建立
|
||||
if result.returncode == 0:
|
||||
print("[DB] SSH 隧道重建完成")
|
||||
return True
|
||||
else:
|
||||
print("[DB] SSH 隧道重建失败: {}".format(result.stderr[:200]))
|
||||
return False
|
||||
except Exception as e:
|
||||
print("[DB] SSH 执行异常: {}".format(e))
|
||||
return False
|
||||
|
||||
|
||||
def _test_engine(engine) -> bool:
|
||||
"""测试引擎连接是否存活。"""
|
||||
try:
|
||||
with engine.connect() as conn:
|
||||
conn.execute(text("SELECT 1"))
|
||||
return True
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
|
||||
def get_engine():
|
||||
"""获取 SQLAlchemy Engine（懒初始化，断连时自动重连）。"""
|
||||
global _engine, _SessionLocal
|
||||
|
||||
# 已有引擎但连接断开 → dispose 旧池 → SSH 重连 → 重建引擎
|
||||
if _engine is not None and not _test_engine(_engine):
|
||||
print("[DB] 连接已断开，尝试自动恢复...")
|
||||
_engine.dispose() # 杀死所有旧连接
|
||||
_engine = None
|
||||
if _reconnect_ssh():
|
||||
print("[DB] 连接已恢复")
|
||||
else:
|
||||
print("[DB] 自动恢复失败，将无法连接数据库")
|
||||
|
||||
# 创建引擎（首次或重建后）
|
||||
if _engine is None:
|
||||
cfg = MARIADB_CONFIG
|
||||
_engine = create_engine(
|
||||
_build_url(),
|
||||
pool_size=cfg["pool_size"],
|
||||
pool_recycle=cfg["pool_recycle"],
|
||||
pool_pre_ping=True, # 每次取连接前先 SELECT 1 验证存活
|
||||
echo=False,
|
||||
)
|
||||
# Session factory 下次获取时自动绑定新引擎
|
||||
_SessionLocal = None
|
||||
|
||||
return _engine
|
||||
|
||||
|
||||
def get_session():
|
||||
"""获取一个新的数据库会话。"""
|
||||
global _SessionLocal
|
||||
if _SessionLocal is None:
|
||||
_SessionLocal = sessionmaker(bind=get_engine())
|
||||
return _SessionLocal()
|
||||
|
||||
|
||||
def test_connection() -> bool:
|
||||
"""测试数据库连接是否正常。"""
|
||||
try:
|
||||
engine = get_engine()
|
||||
return _test_engine(engine)
|
||||
except Exception as e:
|
||||
print("[ERROR] 数据库连接失败: {}".format(e))
|
||||
return False
|
||||
@@ -0,0 +1,133 @@
|
||||
"""
|
||||
数据访问对象。
|
||||
|
||||
提供 DataFrame 级别的读写操作，屏蔽底层 ORM/SQL 细节。
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
from sqlalchemy import text
|
||||
|
||||
from database.connection import get_engine
|
||||
from database.models import StockBasic, StockDaily, StockFinancial, Report
|
||||
|
||||
# DB 表列名，供 DataManager 在写入前筛选
|
||||
_DAILY_COLS = [
|
||||
"ts_code", "trade_date", "open", "high", "low", "close",
|
||||
"pre_close", "change", "pct_chg", "vol", "amount", "turnover_rate",
|
||||
]
|
||||
_FINA_COLS = [
|
||||
"ts_code", "end_date", "eps", "bvps", "roe", "roe_diluted",
|
||||
"net_profit_margin", "debt_to_assets", "current_ratio", "quick_ratio",
|
||||
"total_revenue", "total_revenue_yoy", "net_profit", "net_profit_yoy",
|
||||
]
|
||||
|
||||
|
||||
def _df_to_db(df: pd.DataFrame, model_class, replace: bool = False) -> int:
|
||||
"""将 DataFrame 写入对应表，返回写入行数。"""
|
||||
if df.empty:
|
||||
return 0
|
||||
engine = get_engine()
|
||||
if_action = "replace" if replace else "append"
|
||||
# 统一字符串列，避免 MySQL 类型问题
|
||||
df = df.where(pd.notna(df), None)
|
||||
rows = len(df)
|
||||
df.to_sql(
|
||||
model_class.__tablename__,
|
||||
con=engine,
|
||||
if_exists=if_action,
|
||||
index=False,
|
||||
method="multi",
|
||||
chunksize=500,
|
||||
)
|
||||
return rows
|
||||
|
||||
|
||||
# ── StockBasic ─────────────────────────────────────────────
|
||||
|
||||
def save_stock_list(df: pd.DataFrame) -> int:
|
||||
"""保存股票列表（replace 模式）。"""
|
||||
cols = ["ts_code", "name", "area", "industry", "market", "list_date", "is_hs"]
|
||||
df = df[[c for c in cols if c in df.columns]].copy()
|
||||
return _df_to_db(df, StockBasic, replace=True)
|
||||
|
||||
|
||||
def query_stock_list() -> pd.DataFrame:
|
||||
"""查询全部股票列表。"""
|
||||
engine = get_engine()
|
||||
return pd.read_sql(f"SELECT * FROM {StockBasic.__tablename__}", con=engine).set_index("ts_code")
|
||||
|
||||
|
||||
# ── StockDaily ─────────────────────────────────────────────
|
||||
|
||||
def save_daily(df: pd.DataFrame) -> int:
|
||||
"""批量写入日线数据。先删旧再插新，避免主键冲突。"""
|
||||
cols = [
|
||||
"ts_code", "trade_date", "open", "high", "low", "close",
|
||||
"pre_close", "change", "pct_chg", "vol", "amount", "turnover_rate",
|
||||
]
|
||||
df = df[[c for c in cols if c in df.columns]].copy()
|
||||
if df.empty:
|
||||
return 0
|
||||
# 删除即将写入的日期的旧数据
|
||||
engine = get_engine()
|
||||
ts_codes = df["ts_code"].unique().tolist()
|
||||
trade_dates = df["trade_date"].unique().tolist()
|
||||
if ts_codes and trade_dates:
|
||||
with engine.connect() as conn:
|
||||
conn.execute(
|
||||
text("DELETE FROM {} WHERE ts_code IN :codes AND trade_date IN :dates".format(
|
||||
StockDaily.__tablename__)),
|
||||
{"codes": tuple(ts_codes), "dates": tuple(trade_dates)},
|
||||
)
|
||||
conn.commit()
|
||||
return _df_to_db(df, StockDaily, replace=False)
|
||||
|
||||
|
||||
def query_daily(ts_code: str, start: str | None = None, end: str | None = None) -> pd.DataFrame:
|
||||
"""按股票代码和日期范围查询日线。"""
|
||||
engine = get_engine()
|
||||
table = StockDaily.__tablename__
|
||||
sql = f"SELECT * FROM {table} WHERE ts_code = :ts_code"
|
||||
params = {"ts_code": ts_code}
|
||||
if start:
|
||||
sql += " AND trade_date >= :start"
|
||||
params["start"] = start
|
||||
if end:
|
||||
sql += " AND trade_date <= :end"
|
||||
params["end"] = end
|
||||
sql += " ORDER BY trade_date ASC"
|
||||
df = pd.read_sql(text(sql), con=engine, params=params)
|
||||
if not df.empty:
|
||||
df["trade_date"] = df["trade_date"].astype(str)
|
||||
return df
|
||||
|
||||
|
||||
def get_latest_trade_date(ts_code: str) -> str | None:
|
||||
"""获取某股票在数据库中的最新交易日。"""
|
||||
engine = get_engine()
|
||||
table = StockDaily.__tablename__
|
||||
sql = f"SELECT MAX(trade_date) FROM {table} WHERE ts_code = :ts_code"
|
||||
with engine.connect() as conn:
|
||||
result = conn.execute(text(sql), {"ts_code": ts_code}).scalar()
|
||||
return result
|
||||
|
||||
|
||||
# ── StockFinancial ─────────────────────────────────────────
|
||||
|
||||
def save_financial(df: pd.DataFrame) -> int:
|
||||
"""批量写入财务数据（replace 模式：同报告期覆盖更新）。"""
|
||||
cols = [
|
||||
"ts_code", "end_date", "eps", "bvps", "roe", "roe_diluted",
|
||||
"net_profit_margin", "debt_to_assets", "current_ratio", "quick_ratio",
|
||||
"total_revenue", "total_revenue_yoy", "net_profit", "net_profit_yoy",
|
||||
]
|
||||
df = df[[c for c in cols if c in df.columns]].copy()
|
||||
return _df_to_db(df, StockFinancial, replace=True)
|
||||
|
||||
|
||||
def query_financial(ts_code: str) -> pd.DataFrame:
|
||||
"""查询某股票全部财务数据。"""
|
||||
engine = get_engine()
|
||||
table = StockFinancial.__tablename__
|
||||
sql = f"SELECT * FROM {table} WHERE ts_code = :ts_code ORDER BY end_date DESC"
|
||||
return pd.read_sql(text(sql), con=engine, params={"ts_code": ts_code})
|
||||
@@ -0,0 +1,100 @@
|
||||
"""
|
||||
ORM 模型定义。
|
||||
|
||||
所有表使用 mac_ 前缀，与现有表隔离。
|
||||
"""
|
||||
|
||||
from sqlalchemy import Column, String, Date, DateTime, Float, BigInteger, Index, PrimaryKeyConstraint, Text
|
||||
from sqlalchemy.orm import DeclarativeBase
|
||||
|
||||
from config.settings import TABLE_STOCK_BASIC, TABLE_STOCK_DAILY, TABLE_STOCK_FINANCIAL, TABLE_REPORT
|
||||
|
||||
|
||||
class Base(DeclarativeBase):
|
||||
pass
|
||||
|
||||
|
||||
class StockBasic(Base):
|
||||
"""股票基本信息表。"""
|
||||
__tablename__ = TABLE_STOCK_BASIC
|
||||
|
||||
ts_code = Column(String(16), primary_key=True, comment="股票代码（如 000001.SZ）")
|
||||
name = Column(String(32), comment="股票名称")
|
||||
area = Column(String(16), comment="地区")
|
||||
industry = Column(String(32), comment="行业")
|
||||
market = Column(String(8), comment="市场（主板/创业板/科创板）")
|
||||
list_date = Column(String(8), comment="上市日期")
|
||||
is_hs = Column(String(1), comment="是否沪深港通")
|
||||
|
||||
|
||||
class StockDaily(Base):
|
||||
"""日线行情表。"""
|
||||
__tablename__ = TABLE_STOCK_DAILY
|
||||
__table_args__ = (
|
||||
PrimaryKeyConstraint("ts_code", "trade_date"),
|
||||
Index("idx_mac_daily_ts_code", "ts_code"),
|
||||
Index("idx_mac_daily_trade_date", "trade_date"),
|
||||
)
|
||||
|
||||
ts_code = Column(String(16), comment="股票代码")
|
||||
trade_date = Column(String(8), comment="交易日期")
|
||||
open = Column(Float, comment="开盘价")
|
||||
high = Column(Float, comment="最高价")
|
||||
low = Column(Float, comment="最低价")
|
||||
close = Column(Float, comment="收盘价")
|
||||
pre_close = Column(Float, comment="昨收价")
|
||||
change = Column(Float, comment="涨跌额")
|
||||
pct_chg = Column(Float, comment="涨跌幅(%)")
|
||||
vol = Column(Float, comment="成交量（手）")
|
||||
amount = Column(Float, comment="成交额（千元）")
|
||||
turnover_rate = Column(Float, comment="换手率(%)")
|
||||
|
||||
|
||||
class StockFinancial(Base):
|
||||
"""财务数据表（同花顺核心指标）。"""
|
||||
__tablename__ = TABLE_STOCK_FINANCIAL
|
||||
__table_args__ = (
|
||||
PrimaryKeyConstraint("ts_code", "end_date"),
|
||||
Index("idx_mac_fina_ts_code", "ts_code"),
|
||||
)
|
||||
|
||||
ts_code = Column(String(16), comment="股票代码")
|
||||
end_date = Column(String(8), comment="报告期 YYYYMMDD")
|
||||
eps = Column(Float, comment="基本每股收益")
|
||||
bvps = Column(Float, comment="每股净资产")
|
||||
roe = Column(Float, comment="净资产收益率(%)")
|
||||
roe_diluted = Column(Float, comment="净资产收益率-摊薄(%)")
|
||||
net_profit_margin = Column(Float, comment="销售净利率(%)")
|
||||
debt_to_assets = Column(Float, comment="资产负债率(%)")
|
||||
current_ratio = Column(Float, comment="流动比率")
|
||||
quick_ratio = Column(Float, comment="速动比率")
|
||||
total_revenue = Column(Float, comment="营业总收入")
|
||||
total_revenue_yoy = Column(Float, comment="营业总收入同比增长率(%)")
|
||||
net_profit = Column(Float, comment="净利润")
|
||||
net_profit_yoy = Column(Float, comment="净利润同比增长率(%)")
|
||||
|
||||
|
||||
class Report(Base):
|
||||
"""报告存储表。"""
|
||||
__tablename__ = TABLE_REPORT
|
||||
__table_args__ = (
|
||||
Index("idx_mac_report_date", "report_date"),
|
||||
Index("idx_mac_report_subject", "subject_type", "subject_code"),
|
||||
Index("idx_mac_report_active", "is_active"),
|
||||
)
|
||||
|
||||
id = Column(BigInteger, primary_key=True, autoincrement=True, comment="主键")
|
||||
report_date = Column(Date, nullable=False, comment="报告日期")
|
||||
title = Column(String(256), nullable=False, comment="报告标题")
|
||||
subject_type = Column(String(32), comment="研究对象类型: stock/index/sector/portfolio/daily")
|
||||
subject_code = Column(String(64), comment="研究对象代码")
|
||||
content = Column(Text, comment="报告内容 (markdown)")
|
||||
created_at = Column(DateTime, comment="报告生成时间")
|
||||
is_active = Column(Float, default=1.0, comment="1=有效, 0=已失效")
|
||||
|
||||
|
||||
def create_all_tables():
|
||||
"""创建所有 mac_ 开头的表。"""
|
||||
engine = __import__("database.connection", fromlist=["get_engine"]).get_engine()
|
||||
Base.metadata.create_all(engine)
|
||||
print("[OK] 所有表创建完成")
|
||||
@@ -0,0 +1,42 @@
|
||||
"""
|
||||
因子抽象基类。
|
||||
|
||||
所有因子必须继承 BaseFactor，实现 calculate(df) → pd.Series。
|
||||
"""
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
|
||||
import pandas as pd
|
||||
|
||||
|
||||
class BaseFactor(ABC):
|
||||
"""因子抽象基类。
|
||||
|
||||
属性:
|
||||
name: 因子名称，如 'momentum_20'
|
||||
category: 'technical' | 'fundamental' | 'sentiment'
|
||||
required_columns: 计算所需的 DataFrame 列名列表
|
||||
"""
|
||||
|
||||
name: str = ""
|
||||
category: str = ""
|
||||
|
||||
@abstractmethod
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
"""计算因子值。
|
||||
|
||||
参数:
|
||||
df: 日线 DataFrame，index 为 trade_date，
|
||||
列至少包含 OHLCV 等基础字段。
|
||||
|
||||
返回:
|
||||
pd.Series，index 与 df 对齐，值为因子值。
|
||||
"""
|
||||
...
|
||||
|
||||
def get_required_columns(self) -> list[str]:
|
||||
"""返回计算所需的列名列表。子类可覆盖。"""
|
||||
return ["close"]
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return f"{self.__class__.__name__}(name='{self.name}')"
|
||||
@@ -0,0 +1,192 @@
|
||||
"""
|
||||
FactorEngine — 因子计算引擎。
|
||||
|
||||
批量计算因子，处理技术/基本面/情绪因子的不同数据需求。
|
||||
"""
|
||||
|
||||
import copy
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from factors.base import BaseFactor
|
||||
from factors.fundamental.roe import ROEFactor
|
||||
from factors.fundamental.pe_pb import PEFactor, PBFactor, EPFactor
|
||||
|
||||
FUNDAMENTAL_FACTOR_TYPES = (ROEFactor, PEFactor, PBFactor, EPFactor)
|
||||
|
||||
|
||||
def _is_sentiment(factor: BaseFactor) -> bool:
|
||||
return getattr(factor, "category", "") == "sentiment"
|
||||
|
||||
|
||||
class FactorEngine:
|
||||
"""因子计算引擎。"""
|
||||
|
||||
def __init__(self, data_manager, sentiment_engine=None):
|
||||
"""
|
||||
参数:
|
||||
data_manager: DataManager 实例。
|
||||
sentiment_engine: SentimentEngine 实例（可选，启用情绪因子时需提供）。
|
||||
"""
|
||||
self._dm = data_manager
|
||||
self._sentiment_engine = sentiment_engine
|
||||
self._financial_cache: dict[str, pd.DataFrame] = {}
|
||||
|
||||
def _get_financial(self, ts_code: str) -> pd.DataFrame:
|
||||
"""获取财务数据（带缓存）。"""
|
||||
if ts_code not in self._financial_cache:
|
||||
df = self._dm.get_financial(ts_code)
|
||||
self._financial_cache[ts_code] = df
|
||||
return self._financial_cache[ts_code]
|
||||
|
||||
def _resolve_factors(
|
||||
self, factors: list[BaseFactor], fina: pd.DataFrame
|
||||
) -> list[BaseFactor]:
|
||||
"""为每个股票 clone 基本面因子并注入财务数据。"""
|
||||
resolved = []
|
||||
for f in factors:
|
||||
if isinstance(f, FUNDAMENTAL_FACTOR_TYPES):
|
||||
f = copy.copy(f)
|
||||
f._financial_df = fina
|
||||
resolved.append(f)
|
||||
return resolved
|
||||
|
||||
def compute(
|
||||
self,
|
||||
ts_code: str,
|
||||
factors: list[BaseFactor],
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
对单只股票计算多个因子。
|
||||
|
||||
参数:
|
||||
ts_code: 如 '000001.SZ'
|
||||
factors: 因子实例列表
|
||||
|
||||
返回:
|
||||
DataFrame，index=trade_date，columns=因子名
|
||||
"""
|
||||
if not factors:
|
||||
return pd.DataFrame()
|
||||
|
||||
# 分离情绪因子（通过 SentimentEngine 处理）
|
||||
sent_factors = [f for f in factors if _is_sentiment(f)]
|
||||
other_factors = [f for f in factors if not _is_sentiment(f)]
|
||||
|
||||
# 收集所有需要的列
|
||||
required_cols = set()
|
||||
has_fundamental = False
|
||||
for f in other_factors:
|
||||
required_cols.update(f.get_required_columns())
|
||||
if isinstance(f, FUNDAMENTAL_FACTOR_TYPES):
|
||||
has_fundamental = True
|
||||
|
||||
# 获取日线数据
|
||||
daily = self._dm.get_daily(ts_code)
|
||||
if daily.empty:
|
||||
return pd.DataFrame()
|
||||
|
||||
daily = daily.set_index("trade_date").sort_index()
|
||||
|
||||
# 获取财务数据（如有基本面因子）
|
||||
fina = self._dm.get_financial(ts_code) if has_fundamental else pd.DataFrame()
|
||||
|
||||
# 为当前股票解析因子（clone 基本面因子注入财务数据）
|
||||
resolved_factors = self._resolve_factors(other_factors, fina)
|
||||
|
||||
# 逐因子计算
|
||||
results = {}
|
||||
for factor in resolved_factors:
|
||||
try:
|
||||
series = factor.calculate(daily)
|
||||
results[factor.name] = series.astype("float64")
|
||||
except Exception as e:
|
||||
print(f"[WARN] 因子 {factor.name} 计算失败 ({ts_code}): {e}")
|
||||
results[factor.name] = pd.Series(float("nan"), index=daily.index)
|
||||
|
||||
# 情绪因子：通过 SentimentEngine 计算后合并
|
||||
if sent_factors and self._sentiment_engine:
|
||||
try:
|
||||
sent_df = self._sentiment_engine.compute(ts_code)
|
||||
for f in sent_factors:
|
||||
if f.name in sent_df.columns:
|
||||
results[f.name] = sent_df[f.name]
|
||||
else:
|
||||
results[f.name] = pd.Series(float("nan"), index=daily.index)
|
||||
except Exception as e:
|
||||
print(f"[WARN] 情绪因子计算失败 ({ts_code}): {e}")
|
||||
for f in sent_factors:
|
||||
results[f.name] = pd.Series(float("nan"), index=daily.index)
|
||||
|
||||
factor_df = pd.DataFrame(results)
|
||||
factor_df.index.name = "trade_date"
|
||||
return factor_df
|
||||
|
||||
def compute_batch(
|
||||
self,
|
||||
ts_codes: list[str],
|
||||
factors: list[BaseFactor],
|
||||
) -> dict[str, pd.DataFrame]:
|
||||
"""
|
||||
批量计算多只股票的因子。
|
||||
|
||||
返回:
|
||||
{ts_code: factor_df}
|
||||
"""
|
||||
results = {}
|
||||
total = len(ts_codes)
|
||||
for i, ts_code in enumerate(ts_codes):
|
||||
try:
|
||||
results[ts_code] = self.compute(ts_code, factors)
|
||||
except Exception as e:
|
||||
print(f"[WARN] {ts_code} 因子计算失败: {e}")
|
||||
results[ts_code] = pd.DataFrame()
|
||||
if (i + 1) % 50 == 0:
|
||||
print(f"[FactorEngine] 进度: {i + 1}/{total}")
|
||||
return results
|
||||
|
||||
def compute_universe(
|
||||
self,
|
||||
factors: list[BaseFactor],
|
||||
date: str,
|
||||
ts_codes: list[str] | None = None,
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
计算全市场某一天的因子截面。
|
||||
|
||||
参数:
|
||||
factors: 因子列表
|
||||
date: 目标日期 'YYYYMMDD'
|
||||
ts_codes: 股票列表，None 表示全部
|
||||
|
||||
返回:
|
||||
DataFrame，index=ts_code，columns=因子名
|
||||
"""
|
||||
if ts_codes is None:
|
||||
stocks = self._dm.get_stock_list()
|
||||
ts_codes = list(stocks.index)
|
||||
|
||||
rows = []
|
||||
for ts_code in ts_codes:
|
||||
daily = self._dm.get_daily(ts_code)
|
||||
if daily.empty:
|
||||
continue
|
||||
daily = daily.set_index("trade_date")
|
||||
if date not in daily.index:
|
||||
continue
|
||||
|
||||
row = {"ts_code": ts_code}
|
||||
fina = self._get_financial(ts_code)
|
||||
resolved = self._resolve_factors(factors, fina)
|
||||
for factor in resolved:
|
||||
try:
|
||||
series = factor.calculate(daily)
|
||||
row[factor.name] = series.get(date, float("nan"))
|
||||
except Exception:
|
||||
row[factor.name] = float("nan")
|
||||
rows.append(row)
|
||||
|
||||
if not rows:
|
||||
return pd.DataFrame()
|
||||
result = pd.DataFrame(rows).set_index("ts_code")
|
||||
return result
|
||||
@@ -0,0 +1,113 @@
|
||||
"""
|
||||
PE / PB 估值因子。
|
||||
|
||||
基于日线收盘价 + 财务数据（EPS/每股净资产）计算。
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from factors.base import BaseFactor
|
||||
|
||||
|
||||
class PEFactor(BaseFactor):
|
||||
"""
|
||||
市盈率因子 = close / eps。
|
||||
|
||||
eps 来自财务数据中的 'eps' 列或 TTM EPS。
|
||||
因子值越大表示估值越贵。
|
||||
"""
|
||||
|
||||
category = "fundamental"
|
||||
|
||||
def __init__(self, financial_df: pd.DataFrame | None = None):
|
||||
"""
|
||||
参数:
|
||||
financial_df: 含 'end_date' 和 'eps' 的 DataFrame。
|
||||
"""
|
||||
self._financial_df = financial_df
|
||||
self.name = "pe"
|
||||
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
if self._financial_df is None or self._financial_df.empty:
|
||||
return pd.Series(float("nan"), index=df.index)
|
||||
|
||||
eps_series = _map_to_daily(df, self._financial_df, "eps")
|
||||
close = df["close"]
|
||||
return close / eps_series.replace(0, float("nan"))
|
||||
|
||||
def get_required_columns(self) -> list[str]:
|
||||
return ["close"]
|
||||
|
||||
|
||||
class PBFactor(BaseFactor):
|
||||
"""
|
||||
市净率因子 = close / bvps（每股净资产）。
|
||||
|
||||
因子值越大表示估值越贵。
|
||||
"""
|
||||
|
||||
category = "fundamental"
|
||||
|
||||
def __init__(self, financial_df: pd.DataFrame | None = None):
|
||||
self._financial_df = financial_df
|
||||
self.name = "pb"
|
||||
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
if self._financial_df is None or self._financial_df.empty:
|
||||
return pd.Series(float("nan"), index=df.index)
|
||||
|
||||
bvps_series = _map_to_daily(df, self._financial_df, "bvps")
|
||||
return df["close"] / bvps_series.replace(0, float("nan"))
|
||||
|
||||
def get_required_columns(self) -> list[str]:
|
||||
return ["close"]
|
||||
|
||||
|
||||
class EPFactor(BaseFactor):
|
||||
"""
|
||||
盈利收益率因子 = eps / close = 1 / PE。
|
||||
|
||||
值越大表示估值越便宜，适合与动量等因子同向排序。
|
||||
"""
|
||||
|
||||
category = "fundamental"
|
||||
|
||||
def __init__(self, financial_df: pd.DataFrame | None = None):
|
||||
self._financial_df = financial_df
|
||||
self.name = "ep"
|
||||
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
if self._financial_df is None or self._financial_df.empty:
|
||||
return pd.Series(float("nan"), index=df.index)
|
||||
|
||||
eps_series = _map_to_daily(df, self._financial_df, "eps")
|
||||
return eps_series / df["close"].replace(0, float("nan")) * 100
|
||||
|
||||
def get_required_columns(self) -> list[str]:
|
||||
return ["close"]
|
||||
|
||||
|
||||
def _map_to_daily(
|
||||
daily_df: pd.DataFrame,
|
||||
fina_df: pd.DataFrame,
|
||||
column: str,
|
||||
) -> pd.Series:
|
||||
"""将季度财务数据填充到日线索引（前值填充）。"""
|
||||
fina = fina_df[["end_date", column]].dropna().copy()
|
||||
fina["end_date"] = fina["end_date"].astype(str)
|
||||
fina = fina.sort_values("end_date")
|
||||
|
||||
result = pd.Series(float("nan"), index=daily_df.index)
|
||||
if fina.empty:
|
||||
return result
|
||||
|
||||
dates = pd.to_datetime(daily_df.index, format="%Y%m%d", errors="coerce")
|
||||
fina_dates = pd.to_datetime(fina["end_date"], format="%Y%m%d", errors="coerce")
|
||||
|
||||
for i, fina_date in enumerate(fina_dates):
|
||||
mask = dates >= fina_date
|
||||
if i + 1 < len(fina_dates):
|
||||
mask &= dates < fina_dates.iloc[i + 1]
|
||||
result[mask] = fina[column].iloc[i]
|
||||
|
||||
return result.astype("float64")
|
||||
@@ -0,0 +1,104 @@
|
||||
"""
|
||||
ROE 因子。
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from factors.base import BaseFactor
|
||||
|
||||
|
||||
class ROEFactor(BaseFactor):
|
||||
"""
|
||||
ROE 因子。
|
||||
|
||||
从财务数据提取 ROE 并映射到日线。
|
||||
|
||||
需要 df 中包含 'roe' 列（由 FactorEngine 合并财务数据后传入），
|
||||
或将 financial_df 直接传入构造函数。
|
||||
"""
|
||||
|
||||
category = "fundamental"
|
||||
|
||||
def __init__(self, financial_df: pd.DataFrame | None = None):
|
||||
"""
|
||||
参数:
|
||||
financial_df: 财务数据 DataFrame，columns 含 'end_date', 'roe'。
|
||||
None 时需在 df 参数中直接提供 roe 列。
|
||||
"""
|
||||
self._financial_df = financial_df
|
||||
self.name = "roe"
|
||||
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
if "roe" in df.columns:
|
||||
return df["roe"].copy()
|
||||
|
||||
if self._financial_df is None or self._financial_df.empty:
|
||||
return pd.Series(float("nan"), index=df.index)
|
||||
|
||||
return self._map_financial_to_daily(
|
||||
df, self._financial_df, "roe"
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _map_financial_to_daily(
|
||||
daily_df: pd.DataFrame,
|
||||
fina_df: pd.DataFrame,
|
||||
column: str,
|
||||
) -> pd.Series:
|
||||
"""将财务数据（季度）映射到日线索引。"""
|
||||
fina = fina_df[["end_date", column]].dropna().copy()
|
||||
fina["end_date"] = fina["end_date"].astype(str)
|
||||
fina = fina.sort_values("end_date")
|
||||
|
||||
result = pd.Series(float("nan"), index=daily_df.index)
|
||||
|
||||
if fina.empty:
|
||||
return result
|
||||
|
||||
dates = pd.to_datetime(daily_df.index, format="%Y%m%d", errors="coerce")
|
||||
fina_dates = pd.to_datetime(fina["end_date"], format="%Y%m%d", errors="coerce")
|
||||
|
||||
for i, fina_date in enumerate(fina_dates):
|
||||
mask = dates >= fina_date
|
||||
if i + 1 < len(fina_dates):
|
||||
mask &= dates < fina_dates.iloc[i + 1]
|
||||
else:
|
||||
pass # 最新一期覆盖所有后续日期
|
||||
result[mask] = fina[column].iloc[i]
|
||||
|
||||
return result.astype("float64")
|
||||
|
||||
|
||||
class ROETTMDeltaFactor(BaseFactor):
|
||||
"""ROE 同比变化（当前 ROE - 去年同期 ROE）。"""
|
||||
|
||||
category = "fundamental"
|
||||
|
||||
def __init__(self, financial_df: pd.DataFrame | None = None):
|
||||
self._financial_df = financial_df
|
||||
self.name = "roe_delta"
|
||||
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
if self._financial_df is None or self._financial_df.empty:
|
||||
return pd.Series(float("nan"), index=df.index)
|
||||
|
||||
fina = self._financial_df[["end_date", "roe"]].dropna().copy()
|
||||
fina["end_date"] = fina["end_date"].astype(str)
|
||||
fina["year"] = fina["end_date"].str[:4].astype(int)
|
||||
fina = fina.sort_values("end_date")
|
||||
|
||||
# 按年分组计算 YoY 差值
|
||||
roe_delta = pd.Series(float("nan"), index=df.index)
|
||||
dates = pd.to_datetime(df.index, format="%Y%m%d", errors="coerce")
|
||||
|
||||
for _, row in fina.iterrows():
|
||||
this_year = row["year"]
|
||||
prev_row = fina[fina["year"] == this_year - 1]
|
||||
if prev_row.empty:
|
||||
continue
|
||||
delta = row["roe"] - prev_row["roe"].iloc[-1]
|
||||
f_date = pd.to_datetime(row["end_date"], format="%Y%m%d")
|
||||
mask = dates >= f_date
|
||||
roe_delta[mask] = delta
|
||||
|
||||
return roe_delta.astype("float64")
|
||||
@@ -0,0 +1,127 @@
|
||||
"""
|
||||
因子注册表。
|
||||
|
||||
通过名称获取因子实例，方便回测和策略配置时引用。
|
||||
"""
|
||||
|
||||
from factors.base import BaseFactor
|
||||
from factors.technical.momentum import MomentumFactor
|
||||
from factors.technical.rsi import RSIFactor
|
||||
from factors.technical.macd import MACDFactor
|
||||
from factors.technical.volume import VolumeFactor, VolumeChangeFactor
|
||||
from factors.technical.bollinger import BollingerFactor, BollingerWidthFactor
|
||||
from factors.technical.atr import ATRFactor, ATRRatioFactor
|
||||
from factors.technical.ma_cross import MACrossFactor, MADeviationFactor
|
||||
from factors.technical.volatility import VolatilityFactor, DownsideVolatilityFactor
|
||||
from factors.technical.turnover import TurnoverFactor, TurnoverChangeFactor
|
||||
from factors.technical.amplitude import AmplitudeFactor
|
||||
from factors.fundamental.roe import ROEFactor
|
||||
from factors.fundamental.pe_pb import PEFactor, PBFactor, EPFactor
|
||||
from factors.sentiment.sentiment_factor import (
|
||||
NewsSentimentFactor,
|
||||
SentimentMomentumFactor,
|
||||
SentimentConfidenceFactor,
|
||||
)
|
||||
|
||||
# ── 内置因子工厂函数 ──────────────────────────────────────
|
||||
|
||||
_BUILTIN_FACTORIES: dict[str, callable] = { # type: ignore
|
||||
# 动量
|
||||
"momentum_5": lambda: MomentumFactor(period=5),
|
||||
"momentum_10": lambda: MomentumFactor(period=10),
|
||||
"momentum_20": lambda: MomentumFactor(period=20),
|
||||
"momentum_60": lambda: MomentumFactor(period=60),
|
||||
# RSI
|
||||
"rsi_7": lambda: RSIFactor(period=7),
|
||||
"rsi_14": lambda: RSIFactor(period=14),
|
||||
# MACD
|
||||
"macd": lambda: MACDFactor(),
|
||||
"macd_5_35_5": lambda: MACDFactor(fast=5, slow=35, signal=5),
|
||||
# 量价
|
||||
"vol_ratio_5": lambda: VolumeFactor(period=5),
|
||||
"vol_ratio_20": lambda: VolumeFactor(period=20),
|
||||
"vol_chg_5": lambda: VolumeChangeFactor(period=5),
|
||||
# 布林
|
||||
"boll": lambda: BollingerFactor(),
|
||||
"boll_width": lambda: BollingerWidthFactor(),
|
||||
# ATR
|
||||
"atr_14": lambda: ATRFactor(period=14),
|
||||
"atr_ratio_14": lambda: ATRRatioFactor(period=14),
|
||||
# 均线
|
||||
"ma_cross_5_20": lambda: MACrossFactor(fast=5, slow=20),
|
||||
"ma_cross_10_60": lambda: MACrossFactor(fast=10, slow=60),
|
||||
"ma_dev_20": lambda: MADeviationFactor(period=20),
|
||||
"ma_dev_60": lambda: MADeviationFactor(period=60),
|
||||
# 波动率
|
||||
"volatility_20": lambda: VolatilityFactor(period=20),
|
||||
"volatility_60": lambda: VolatilityFactor(period=60),
|
||||
"down_vol_20": lambda: DownsideVolatilityFactor(period=20),
|
||||
# 换手率
|
||||
"turnover_5": lambda: TurnoverFactor(period=5),
|
||||
"turnover_chg_5": lambda: TurnoverChangeFactor(period=5),
|
||||
# 振幅
|
||||
"amplitude_5": lambda: AmplitudeFactor(period=5),
|
||||
"amplitude_20": lambda: AmplitudeFactor(period=20),
|
||||
# 基本面
|
||||
"roe": lambda: ROEFactor(),
|
||||
"pe": lambda: PEFactor(),
|
||||
"pb": lambda: PBFactor(),
|
||||
"ep": lambda: EPFactor(),
|
||||
# 情绪
|
||||
"news_sent_5": lambda: NewsSentimentFactor(window=5),
|
||||
"news_sent_20": lambda: NewsSentimentFactor(window=20),
|
||||
"news_conf_5": lambda: SentimentConfidenceFactor(window=5),
|
||||
"sent_delta_5": lambda: SentimentMomentumFactor(period=5),
|
||||
}
|
||||
|
||||
# ── 分类映射 ──────────────────────────────────────────────
|
||||
|
||||
FACTOR_CATEGORIES: dict[str, list[str]] = {
|
||||
"动量": ["momentum_5", "momentum_10", "momentum_20", "momentum_60"],
|
||||
"RSI": ["rsi_7", "rsi_14"],
|
||||
"MACD": ["macd", "macd_5_35_5"],
|
||||
"量价": ["vol_ratio_5", "vol_ratio_20", "vol_chg_5"],
|
||||
"布林": ["boll", "boll_width"],
|
||||
"ATR": ["atr_14", "atr_ratio_14"],
|
||||
"均线": ["ma_cross_5_20", "ma_cross_10_60", "ma_dev_20", "ma_dev_60"],
|
||||
"波动率": ["volatility_20", "volatility_60", "down_vol_20"],
|
||||
"换手率": ["turnover_5", "turnover_chg_5"],
|
||||
"振幅": ["amplitude_5", "amplitude_20"],
|
||||
"基本面": ["roe", "pe", "pb", "ep"],
|
||||
"情绪": ["news_sent_5", "news_sent_20", "news_conf_5", "sent_delta_5"],
|
||||
}
|
||||
|
||||
|
||||
def get_factor(name: str, **overrides) -> BaseFactor:
|
||||
"""按名称获取因子实例。
|
||||
|
||||
参数:
|
||||
name: 因子名称（如 'momentum_20'）
|
||||
**overrides: 覆盖默认参数
|
||||
|
||||
返回:
|
||||
BaseFactor 实例
|
||||
"""
|
||||
if name not in _BUILTIN_FACTORIES:
|
||||
raise KeyError(f"未知因子: '{name}'。可用: {list(_BUILTIN_FACTORIES)}")
|
||||
factor = _BUILTIN_FACTORIES[name]()
|
||||
if overrides:
|
||||
for k, v in overrides.items():
|
||||
if hasattr(factor, k):
|
||||
setattr(factor, k, v)
|
||||
# 更新 factor.name
|
||||
if hasattr(factor, "name"):
|
||||
factor.name = name
|
||||
return factor
|
||||
|
||||
|
||||
def list_factors(category: str | None = None) -> list[str]:
|
||||
"""列出所有可用因子名称。"""
|
||||
if category and category in FACTOR_CATEGORIES:
|
||||
return FACTOR_CATEGORIES[category]
|
||||
return list(_BUILTIN_FACTORIES)
|
||||
|
||||
|
||||
def list_categories() -> list[str]:
|
||||
"""列出所有因子分类。"""
|
||||
return list(FACTOR_CATEGORIES)
|
||||
@@ -0,0 +1,373 @@
|
||||
"""
|
||||
新闻公告数据源。
|
||||
|
||||
支持三种数据来源：
|
||||
1. AkShare stock_news_em（东方财富个股新闻）
|
||||
2. MariaDB xwlb_daily_ext（新闻联播分割数据）
|
||||
3. MCP trendradar-news（外部新闻聚合服务）
|
||||
|
||||
统一返回格式：DataFrame (date, title, content, source, url)
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
import time
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
import akshare as ak
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
# 确保 .env 已加载
|
||||
import config.settings # noqa: F401
|
||||
|
||||
|
||||
class NewsSource:
|
||||
"""
|
||||
新闻数据源聚合器。
|
||||
|
||||
参数:
|
||||
use_akshare: 启用 AkShare 新闻接口
|
||||
use_xwlb: 启用新闻联播数据库
|
||||
use_mcp: 启用 MCP 新闻服务
|
||||
mcp_url: MCP 服务地址
|
||||
request_delay: 请求间隔（秒），避免被限频
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
use_akshare: bool = True,
|
||||
use_xwlb: bool = True,
|
||||
use_mcp: bool = True,
|
||||
mcp_url: str | None = None,
|
||||
request_delay: float = 1.0,
|
||||
):
|
||||
self.use_akshare = use_akshare
|
||||
self.use_xwlb = use_xwlb
|
||||
self.use_mcp = use_mcp
|
||||
self.mcp_url = mcp_url or os.getenv("NEWS_MCP_URL", "http://192.168.1.160:3333/mcp")
|
||||
self.request_delay = request_delay
|
||||
self._mcp_session_id: str | None = None
|
||||
|
||||
# ── 统一入口 ──────────────────────────────────────────
|
||||
|
||||
def fetch(
|
||||
self,
|
||||
ts_code: str,
|
||||
start: str | None = None,
|
||||
end: str | None = None,
|
||||
max_news: int | None = None,
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
获取个股相关新闻（聚合多源）。
|
||||
|
||||
参数:
|
||||
ts_code: 如 '000001.SZ'
|
||||
start: 起始日期 'YYYYMMDD'，默认 30 天前
|
||||
end: 结束日期 'YYYYMMDD'，默认今天
|
||||
max_news: 最多返回条数，默认从 .env SENTIMENT_MAX_NEWS_PER_STOCK 读取，fallback 30
|
||||
|
||||
返回:
|
||||
DataFrame (date, title, content, source, url)
|
||||
"""
|
||||
if max_news is None:
|
||||
max_news = int(os.getenv("SENTIMENT_MAX_NEWS_PER_STOCK", "30"))
|
||||
|
||||
if end is None:
|
||||
end = time.strftime("%Y%m%d")
|
||||
if start is None:
|
||||
start = (datetime.strptime(end, "%Y%m%d") - timedelta(days=30)).strftime("%Y%m%d")
|
||||
|
||||
frames = []
|
||||
|
||||
if self.use_akshare:
|
||||
try:
|
||||
df = self._fetch_akshare(ts_code, start, end)
|
||||
if not df.empty:
|
||||
frames.append(df)
|
||||
time.sleep(self.request_delay)
|
||||
except Exception as e:
|
||||
print(" [WARN] AkShare 新闻获取失败 ({}): {}".format(ts_code, e))
|
||||
|
||||
if self.use_xwlb:
|
||||
try:
|
||||
# xwlb: news_date 是播出日，+1day = 交易日
|
||||
# 所以查询时 start/end 各减 1 天
|
||||
xwlb_start = (datetime.strptime(start, "%Y%m%d") - timedelta(days=1)).strftime("%Y%m%d")
|
||||
xwlb_end = (datetime.strptime(end, "%Y%m%d") - timedelta(days=1)).strftime("%Y%m%d")
|
||||
df = self._fetch_xwlb(xwlb_start, xwlb_end)
|
||||
if not df.empty:
|
||||
frames.append(df)
|
||||
except Exception as e:
|
||||
print(" [WARN] xwlb 新闻获取失败: {}".format(e))
|
||||
|
||||
if self.use_mcp:
|
||||
try:
|
||||
df = self._fetch_mcp(ts_code, start, end)
|
||||
if not df.empty:
|
||||
frames.append(df)
|
||||
except Exception as e:
|
||||
print(" [WARN] MCP 新闻获取失败: {}".format(e))
|
||||
|
||||
if not frames:
|
||||
return pd.DataFrame(columns=["date", "title", "content", "source", "url"])
|
||||
|
||||
result = pd.concat(frames, ignore_index=True)
|
||||
result = result.drop_duplicates(subset=["title", "date"])
|
||||
result = result.sort_values("date", ascending=False)
|
||||
|
||||
if len(result) > max_news:
|
||||
result = result.head(max_news)
|
||||
|
||||
return result.reset_index(drop=True)
|
||||
|
||||
# ── AkShare 数据源 ────────────────────────────────────
|
||||
|
||||
def _fetch_akshare(self, ts_code: str, start: str = "", end: str = "") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富个股新闻。
|
||||
|
||||
stock_news_em 返回最新约 10 条（无日期筛选），
|
||||
如需更多应使用 InfoMoney 或自建爬虫。
|
||||
"""
|
||||
symbol = ts_code.replace(".SZ", "").replace(".SH", "").replace(".BJ", "")
|
||||
try:
|
||||
df = ak.stock_news_em(symbol=symbol.zfill(6))
|
||||
except Exception:
|
||||
return pd.DataFrame()
|
||||
|
||||
if df is None or df.empty:
|
||||
return pd.DataFrame()
|
||||
|
||||
df = df.rename(columns={
|
||||
"新闻标题": "title",
|
||||
"新闻内容": "content",
|
||||
"发布时间": "date",
|
||||
"文章来源": "source",
|
||||
"新闻链接": "url",
|
||||
})
|
||||
df["date"] = pd.to_datetime(df["date"]).dt.strftime("%Y%m%d")
|
||||
df["source"] = "akshare_" + df["source"].fillna("unknown")
|
||||
|
||||
# 日期筛选
|
||||
if start:
|
||||
df = df[df["date"] >= start]
|
||||
if end:
|
||||
df = df[df["date"] <= end]
|
||||
|
||||
return df[["date", "title", "content", "source", "url"]]
|
||||
|
||||
# ── 新闻联播数据源 ────────────────────────────────────
|
||||
|
||||
def _fetch_xwlb(self, start: str, end: str) -> pd.DataFrame:
|
||||
"""
|
||||
从 MariaDB xwlb_daily_ext 表获取新闻联播分割数据。
|
||||
|
||||
news_date 是播出日期，+1day 后成为对市场产生影响的交易日。
|
||||
调用方应传入 (target_start-1, target_end-1)。
|
||||
|
||||
连接失败时 database/connection.py 自动重建 SSH 隧道。
|
||||
"""
|
||||
from database.connection import get_engine
|
||||
|
||||
start_fmt = "{}-{}-{}".format(start[:4], start[4:6], start[6:8])
|
||||
end_fmt = "{}-{}-{}".format(end[:4], end[4:6], end[6:8])
|
||||
|
||||
day_span = (datetime.strptime(end, "%Y%m%d") - datetime.strptime(start, "%Y%m%d")).days
|
||||
row_limit = max(100, (day_span + 1) * 40)
|
||||
|
||||
sql = """
|
||||
SELECT news_date, news_title, news_content
|
||||
FROM xwlb_daily_ext
|
||||
WHERE news_date BETWEEN %(start)s AND %(end)s
|
||||
ORDER BY news_date DESC
|
||||
LIMIT {}
|
||||
""".format(row_limit)
|
||||
|
||||
try:
|
||||
df = pd.read_sql(
|
||||
sql, get_engine(),
|
||||
params={"start": start_fmt, "end": end_fmt},
|
||||
)
|
||||
if df.empty:
|
||||
return pd.DataFrame()
|
||||
|
||||
df = df.rename(columns={
|
||||
"news_date": "date",
|
||||
"news_title": "title",
|
||||
"news_content": "content",
|
||||
})
|
||||
df["date"] = (
|
||||
pd.to_datetime(df["date"]) + pd.Timedelta(days=1)
|
||||
).dt.strftime("%Y%m%d")
|
||||
df["source"] = "xwlb"
|
||||
df["url"] = ""
|
||||
return df[["date", "title", "content", "source", "url"]]
|
||||
|
||||
except Exception:
|
||||
return pd.DataFrame()
|
||||
|
||||
# ── MCP 数据源 ────────────────────────────────────────
|
||||
|
||||
def _fetch_mcp(self, ts_code: str, start: str, end: str) -> pd.DataFrame:
|
||||
"""通过 MCP streamable HTTP 调用 trendradar-news 服务。"""
|
||||
session_id = self._mcp_initialize()
|
||||
if not session_id:
|
||||
return pd.DataFrame()
|
||||
|
||||
symbol = ts_code.replace(".SZ", "").replace(".SH", "").replace(".BJ", "")
|
||||
try:
|
||||
result = self._mcp_call_tool(
|
||||
session_id,
|
||||
"search_news",
|
||||
{"keyword": symbol, "start_date": start, "end_date": end, "limit": 30},
|
||||
)
|
||||
if not result:
|
||||
return pd.DataFrame()
|
||||
|
||||
records = self._parse_mcp_result(result)
|
||||
if not records:
|
||||
return pd.DataFrame()
|
||||
|
||||
df = pd.DataFrame(records)
|
||||
df["source"] = "mcp_trendradar"
|
||||
return df[["date", "title", "content", "source", "url"]]
|
||||
except Exception:
|
||||
return pd.DataFrame()
|
||||
|
||||
def _mcp_initialize(self) -> str | None:
|
||||
"""MCP streamable HTTP 初始化，获取 session ID。"""
|
||||
if self._mcp_session_id:
|
||||
return self._mcp_session_id
|
||||
try:
|
||||
resp = requests.post(
|
||||
self.mcp_url,
|
||||
json={
|
||||
"jsonrpc": "2.0",
|
||||
"method": "initialize",
|
||||
"params": {
|
||||
"protocolVersion": "2024-11-05",
|
||||
"capabilities": {},
|
||||
"clientInfo": {"name": "cc-cursor", "version": "1.0"},
|
||||
},
|
||||
"id": 1,
|
||||
},
|
||||
headers={
|
||||
"Content-Type": "application/json",
|
||||
"Accept": "application/json, text/event-stream",
|
||||
},
|
||||
timeout=10,
|
||||
)
|
||||
# headers 是大小写不敏感的，尝试多种格式
|
||||
session_id = (
|
||||
resp.headers.get("mcp-session-id")
|
||||
or resp.headers.get("Mcp-Session-Id")
|
||||
or resp.headers.get("MCP-Session-Id")
|
||||
)
|
||||
if session_id:
|
||||
self._mcp_session_id = session_id
|
||||
else:
|
||||
print(" [WARN] MCP initialize 未返回 session-id")
|
||||
return session_id
|
||||
except Exception as e:
|
||||
print(" [WARN] MCP 连接失败: {}".format(e))
|
||||
return None
|
||||
|
||||
def _mcp_call_tool(
|
||||
self, session_id: str, tool_name: str, arguments: dict
|
||||
) -> dict | None:
|
||||
"""调用 MCP 工具。"""
|
||||
try:
|
||||
resp = requests.post(
|
||||
self.mcp_url,
|
||||
json={
|
||||
"jsonrpc": "2.0",
|
||||
"method": "tools/call",
|
||||
"params": {"name": tool_name, "arguments": arguments},
|
||||
"id": 2,
|
||||
},
|
||||
headers={
|
||||
"Content-Type": "application/json",
|
||||
"Accept": "application/json, text/event-stream",
|
||||
"mcp-session-id": session_id,
|
||||
},
|
||||
timeout=15,
|
||||
)
|
||||
# 解析 SSE 响应
|
||||
for line in resp.text.split("\n"):
|
||||
if line.startswith("data:"):
|
||||
data = json.loads(line[5:].strip())
|
||||
return data.get("result", {})
|
||||
return None
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def _parse_mcp_result(result: dict) -> list[dict]:
|
||||
"""解析 MCP 返回内容为统一格式。"""
|
||||
records = []
|
||||
content = result.get("content", [])
|
||||
for item in content:
|
||||
text = item.get("text", "") if isinstance(item, dict) else str(item)
|
||||
if text:
|
||||
try:
|
||||
parsed = json.loads(text)
|
||||
if isinstance(parsed, list):
|
||||
for p in parsed:
|
||||
records.append({
|
||||
"date": str(p.get("date", p.get("publish_date", ""))),
|
||||
"title": str(p.get("title", "")),
|
||||
"content": str(p.get("content", p.get("summary", ""))),
|
||||
"url": str(p.get("url", "")),
|
||||
})
|
||||
elif isinstance(parsed, dict):
|
||||
records.append({
|
||||
"date": str(parsed.get("date", parsed.get("publish_date", ""))),
|
||||
"title": str(parsed.get("title", "")),
|
||||
"content": str(parsed.get("content", parsed.get("summary", ""))),
|
||||
"url": str(parsed.get("url", "")),
|
||||
})
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
records.append({
|
||||
"date": time.strftime("%Y%m%d"),
|
||||
"title": text[:100],
|
||||
"content": text,
|
||||
"url": "",
|
||||
})
|
||||
return records
|
||||
|
||||
|
||||
# ── 日期对齐工具 ──────────────────────────────────────────
|
||||
|
||||
def align_news_to_trading_days(
|
||||
news_df: pd.DataFrame,
|
||||
trading_calendar: pd.DatetimeIndex,
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
将新闻日期对齐到最近交易日。
|
||||
|
||||
非交易日的新闻归入最近的下一个交易日。
|
||||
例：周六新闻 → 下周一交易日的因子值中体现。
|
||||
|
||||
参数:
|
||||
news_df: 新闻 DataFrame，含 'date' 列 (YYYYMMDD)
|
||||
trading_calendar: 交易日 DatetimeIndex
|
||||
|
||||
返回:
|
||||
news_df，'date' 列已替换为对齐后的交易日
|
||||
"""
|
||||
calendar = trading_calendar.sort_values()
|
||||
|
||||
news_dates = pd.to_datetime(news_df["date"], format="%Y%m%d")
|
||||
aligned = []
|
||||
|
||||
for nd in news_dates:
|
||||
future_dates = calendar[calendar >= nd]
|
||||
if len(future_dates) > 0:
|
||||
aligned.append(future_dates[0])
|
||||
else:
|
||||
aligned.append(calendar[-1])
|
||||
|
||||
news_df = news_df.copy()
|
||||
news_df["date"] = pd.DatetimeIndex(aligned).strftime("%Y%m%d")
|
||||
return news_df
|
||||
@@ -0,0 +1,229 @@
|
||||
"""
|
||||
Qwen API 封装（DashScope / 本地 Ollama）。
|
||||
|
||||
文本 → 情绪分析 → 标准化 JSON 输出。
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
import time
|
||||
from typing import Optional
|
||||
|
||||
import requests
|
||||
|
||||
# 确保 .env 已加载（无论从哪个路径导入）
|
||||
import config.settings # noqa: F401
|
||||
|
||||
|
||||
class QwenClient:
|
||||
"""
|
||||
Qwen 情绪分析客户端。
|
||||
|
||||
支持两种后端：
|
||||
1. DashScope API（云）- 需要 QWEN_API_KEY
|
||||
2. 本地 Ollama（本地）- 需要 QWEN_LOCAL_BASE_URL
|
||||
|
||||
优先级：本地 > API
|
||||
|
||||
参数:
|
||||
api_key: DashScope API Key，默认从环境变量 QWEN_API_KEY 读取
|
||||
model: 模型名称，默认从 QWEN_MODEL 读取
|
||||
local_base_url: Ollama 地址，默认从 QWEN_LOCAL_BASE_URL 读取
|
||||
local_model: Ollama 模型名，默认从 QWEN_LOCAL_MODEL 读取
|
||||
max_retries: 失败重试次数
|
||||
cache_enabled: 是否启用结果缓存（按文本 hash）
|
||||
"""
|
||||
|
||||
# 情绪分析 Prompt
|
||||
SYSTEM_PROMPT = """你是一个专业的金融情绪分析专家。分析以下 A 股相关的新闻/公告文本，返回 JSON 格式的分析结果。
|
||||
|
||||
分析维度：
|
||||
1. sentiment_score: -1.0(极度利空) ~ 0(中性) ~ +1.0(极度利好)，保留1位小数
|
||||
2. impact_duration: "short"(1-3个交易日) | "medium"(1-2周) | "long"(1个月以上)
|
||||
3. confidence: 0.0~1.0 置信度
|
||||
4. key_topics: 涉及的关键主题列表（最多5个）
|
||||
5. affected_factors: 可能影响的基本面/技术面因子类型列表
|
||||
|
||||
重要规则：
|
||||
- 只考虑对股价的直接影响
|
||||
- 中性新闻（常规报道、例行公告）给 0 分
|
||||
- 重大利好（业绩超预期、政策支持、大订单）给 >0.5 分
|
||||
- 重大利空（亏损、处罚、减持、诉讼）给 <-0.5 分
|
||||
- 如果不确定，confidences 要低 (<0.5)
|
||||
|
||||
返回格式（仅 JSON）：
|
||||
{"sentiment_score": 0.0, "impact_duration": "short", "confidence": 0.5, "key_topics": [], "affected_factors": []}"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
api_key: str | None = None,
|
||||
model: str | None = None,
|
||||
local_base_url: str | None = None,
|
||||
local_model: str | None = None,
|
||||
max_retries: int = 3,
|
||||
cache_enabled: bool = True,
|
||||
):
|
||||
self.api_key = api_key or os.getenv("QWEN_API_KEY", "")
|
||||
self.model = model or os.getenv("QWEN_MODEL", "qwen-turbo")
|
||||
self.local_base_url = local_base_url or os.getenv("QWEN_LOCAL_BASE_URL", "")
|
||||
self.local_model = local_model or os.getenv("QWEN_LOCAL_MODEL", "qwen2.5:7b")
|
||||
self.max_retries = max_retries
|
||||
self.cache_enabled = cache_enabled
|
||||
self._cache: dict[str, dict] = {}
|
||||
self._is_local = bool(self.local_base_url)
|
||||
|
||||
# ── 单条分析 ──────────────────────────────────────────
|
||||
|
||||
def analyze_sentiment(self, text: str) -> dict:
|
||||
"""
|
||||
分析单条文本的情绪。
|
||||
|
||||
返回:
|
||||
{"sentiment_score": float, "impact_duration": str, "confidence": float,
|
||||
"key_topics": list, "affected_factors": list}
|
||||
"""
|
||||
if not text or not text.strip():
|
||||
return self._empty_result()
|
||||
|
||||
cache_key = str(hash(text))
|
||||
if self.cache_enabled and cache_key in self._cache:
|
||||
return self._cache[cache_key]
|
||||
|
||||
for attempt in range(self.max_retries):
|
||||
try:
|
||||
if self._is_local:
|
||||
result = self._call_ollama(text)
|
||||
else:
|
||||
result = self._call_dashscope(text)
|
||||
|
||||
if result and "sentiment_score" in result:
|
||||
if self.cache_enabled:
|
||||
self._cache[cache_key] = result
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
if attempt < self.max_retries - 1:
|
||||
time.sleep(1 * (attempt + 1))
|
||||
else:
|
||||
print(f" [ERROR] Qwen 分析失败(重试{self.max_retries}次): {e}")
|
||||
|
||||
return self._empty_result()
|
||||
|
||||
# ── 批量分析 ──────────────────────────────────────────
|
||||
|
||||
def analyze_batch(
|
||||
self,
|
||||
texts: list[str],
|
||||
batch_size: int = 10,
|
||||
progress_callback: callable = None, # type: ignore
|
||||
) -> list[dict]:
|
||||
"""
|
||||
批量分析（逐条调用，节省并发成本）。
|
||||
|
||||
参数:
|
||||
texts: 文本列表
|
||||
batch_size: 每批次数量
|
||||
progress_callback: 进度回调 fn(done, total)
|
||||
|
||||
返回:
|
||||
[{"sentiment_score": ..., ...}, ...]
|
||||
"""
|
||||
results = []
|
||||
total = len(texts)
|
||||
for i, text in enumerate(texts):
|
||||
result = self.analyze_sentiment(text)
|
||||
results.append(result)
|
||||
if progress_callback:
|
||||
progress_callback(i + 1, total)
|
||||
# 批次间短暂休息，避免触发限频
|
||||
if (i + 1) % batch_size == 0 and i < total - 1:
|
||||
time.sleep(0.5)
|
||||
return results
|
||||
|
||||
# ── DashScope API ──────────────────────────────────────
|
||||
|
||||
def _call_dashscope(self, text: str) -> dict:
|
||||
"""调用 DashScope API。"""
|
||||
resp = requests.post(
|
||||
"https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions",
|
||||
headers={
|
||||
"Authorization": f"Bearer {self.api_key}",
|
||||
"Content-Type": "application/json",
|
||||
},
|
||||
json={
|
||||
"model": self.model,
|
||||
"messages": [
|
||||
{"role": "system", "content": self.SYSTEM_PROMPT},
|
||||
{"role": "user", "content": text[:4000]}, # 截断长文本
|
||||
],
|
||||
"temperature": 0.1,
|
||||
"max_tokens": 500,
|
||||
},
|
||||
timeout=30,
|
||||
)
|
||||
resp.raise_for_status()
|
||||
body = resp.json()
|
||||
content = body["choices"][0]["message"]["content"]
|
||||
return self._parse_json(content)
|
||||
|
||||
# ── Ollama 本地调用 ────────────────────────────────────
|
||||
|
||||
def _call_ollama(self, text: str) -> dict:
|
||||
"""调用本地 Ollama。"""
|
||||
resp = requests.post(
|
||||
f"{self.local_base_url.rstrip('/')}/chat/completions",
|
||||
json={
|
||||
"model": self.local_model,
|
||||
"messages": [
|
||||
{"role": "system", "content": self.SYSTEM_PROMPT},
|
||||
{"role": "user", "content": text[:4000]},
|
||||
],
|
||||
"temperature": 0.1,
|
||||
"max_tokens": 500,
|
||||
},
|
||||
timeout=60,
|
||||
)
|
||||
resp.raise_for_status()
|
||||
body = resp.json()
|
||||
content = body["choices"][0]["message"]["content"]
|
||||
return self._parse_json(content)
|
||||
|
||||
# ── JSON 解析 ──────────────────────────────────────────
|
||||
|
||||
@staticmethod
|
||||
def _parse_json(content: str) -> dict:
|
||||
"""从模型输出中提取 JSON。"""
|
||||
# 尝试直接解析
|
||||
try:
|
||||
return json.loads(content)
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
# 尝试提取 ```json ... ``` 代码块
|
||||
if "```json" in content:
|
||||
start = content.find("```json") + 7
|
||||
end = content.find("```", start)
|
||||
if end > start:
|
||||
try:
|
||||
return json.loads(content[start:end].strip())
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
# 尝试找 { ... }
|
||||
brace_start = content.find("{")
|
||||
brace_end = content.rfind("}")
|
||||
if brace_start >= 0 and brace_end > brace_start:
|
||||
try:
|
||||
return json.loads(content[brace_start:brace_end + 1])
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
|
||||
return QwenClient._empty_result()
|
||||
|
||||
@staticmethod
|
||||
def _empty_result() -> dict:
|
||||
return {
|
||||
"sentiment_score": 0.0,
|
||||
"impact_duration": "short",
|
||||
"confidence": 0.0,
|
||||
"key_topics": [],
|
||||
"affected_factors": [],
|
||||
}
|
||||
@@ -0,0 +1,311 @@
|
||||
"""
|
||||
情绪因子计算引擎。
|
||||
|
||||
全链路：数据获取 → Qwen 分析 → 因子计算 → 缓存管理。
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
import time
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from data.data_manager import DataManager
|
||||
from factors.sentiment.news_source import NewsSource, align_news_to_trading_days
|
||||
from factors.sentiment.qwen_client import QwenClient
|
||||
from factors.sentiment.sentiment_factor import (
|
||||
NewsSentimentFactor,
|
||||
SentimentMomentumFactor,
|
||||
SentimentConfidenceFactor,
|
||||
)
|
||||
|
||||
|
||||
def _get_or_fetch_price(dm, ts_code, fallback_code="000001.SZ"):
|
||||
"""
|
||||
获取交易日历（价格 DataFrame）。
|
||||
|
||||
1. 优先从 DB 缓存读取目标股票
|
||||
2. 无缓存则 sync_daily 补齐
|
||||
3. 补齐失败则 fallback 到备用股票（仅用作交易日历，不影响新闻分析）
|
||||
"""
|
||||
import pandas as pd
|
||||
from database.dao import get_latest_trade_date
|
||||
|
||||
# 检查 DB 缓存
|
||||
if get_latest_trade_date(ts_code):
|
||||
daily = dm.get_daily(ts_code)
|
||||
if daily is not None and not daily.empty:
|
||||
return daily.set_index("trade_date").sort_index()
|
||||
|
||||
# 无缓存 → 尝试补齐
|
||||
print(" [SentimentEngine] {} 无 DB 缓存，尝试 sync_daily...".format(ts_code))
|
||||
try:
|
||||
n = dm.sync_daily(ts_code)
|
||||
if n > 0:
|
||||
daily = dm.get_daily(ts_code)
|
||||
if daily is not None and not daily.empty:
|
||||
return daily.set_index("trade_date").sort_index()
|
||||
except Exception as e:
|
||||
print(" [SentimentEngine] sync_daily 失败: {}".format(e))
|
||||
|
||||
# Fallback
|
||||
print(" [SentimentEngine] 使用 {} 交易日历作为 fallback".format(fallback_code))
|
||||
try:
|
||||
fallback = dm.get_daily(fallback_code)
|
||||
if fallback is not None and not fallback.empty:
|
||||
return fallback.set_index("trade_date").sort_index()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return None
|
||||
|
||||
|
||||
class SentimentEngine:
|
||||
"""
|
||||
情绪因子计算引擎。
|
||||
|
||||
参数:
|
||||
data_manager: DataManager 实例
|
||||
qwen_client: QwenClient 实例（可选，默认从环境变量创建）
|
||||
news_source: NewsSource 实例（可选）
|
||||
cache_dir: 缓存目录
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
data_manager: DataManager,
|
||||
qwen_client: QwenClient | None = None,
|
||||
news_source: NewsSource | None = None,
|
||||
cache_dir: str | None = None,
|
||||
):
|
||||
self._dm = data_manager
|
||||
self._qwen = qwen_client or QwenClient()
|
||||
self._news_source = news_source or NewsSource()
|
||||
self._cache_dir = Path(cache_dir or os.path.join(
|
||||
os.path.dirname(__file__), "..", "..", ".cache", "sentiment"
|
||||
))
|
||||
self._cache_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# ── 核心方法 ──────────────────────────────────────────
|
||||
|
||||
def compute(
|
||||
self,
|
||||
ts_code: str,
|
||||
start: str | None = None,
|
||||
end: str | None = None,
|
||||
max_news: int = 20,
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
计算单只股票的情绪因子（含缓存）。
|
||||
|
||||
返回:
|
||||
DataFrame (trade_date, news_sent_5, news_conf_5, sent_delta_5)
|
||||
"""
|
||||
from database.dao import get_latest_trade_date
|
||||
|
||||
# 1. 获取交易日历：优先目标股票 DB 缓存，无则尝试补齐，失败则 fallback
|
||||
price = _get_or_fetch_price(self._dm, ts_code)
|
||||
if price is None:
|
||||
return pd.DataFrame()
|
||||
daily_idx = pd.to_datetime(price.index, format="%Y%m%d", errors="coerce")
|
||||
|
||||
# 2. 检查缓存
|
||||
cache_key = "sent_{}_{}_{}".format(ts_code, start or "all", end or "now")
|
||||
cached = self._load_cache(cache_key)
|
||||
if cached is not None:
|
||||
return cached
|
||||
|
||||
# 3. 获取新闻
|
||||
max_n = max_news or int(os.getenv("SENTIMENT_MAX_NEWS_PER_STOCK", "30"))
|
||||
news_df = self._news_source.fetch(ts_code, start=start, end=end, max_news=max_n)
|
||||
|
||||
# 4. 对齐到交易日
|
||||
if not news_df.empty:
|
||||
news_df = align_news_to_trading_days(news_df, daily_idx)
|
||||
|
||||
# 5. Qwen 情绪分析（有 API key 时才执行）
|
||||
sentiment_df = self._analyze_news(news_df)
|
||||
|
||||
# 6. 计算因子
|
||||
factor_dfs = {}
|
||||
if not sentiment_df.empty:
|
||||
for factor_cls, kwargs in [
|
||||
(NewsSentimentFactor, {"window": 5, "sentiment_df": sentiment_df}),
|
||||
(SentimentConfidenceFactor, {"window": 5, "sentiment_df": sentiment_df}),
|
||||
(SentimentMomentumFactor, {"period": 5, "sentiment_df": sentiment_df}),
|
||||
]:
|
||||
f = factor_cls(**kwargs)
|
||||
series = f.calculate(price)
|
||||
factor_dfs[f.name] = series
|
||||
|
||||
if factor_dfs:
|
||||
result = pd.DataFrame(factor_dfs)
|
||||
else:
|
||||
result = pd.DataFrame(index=price.index)
|
||||
|
||||
# 7. 缓存
|
||||
self._save_cache(cache_key, result)
|
||||
|
||||
return result
|
||||
|
||||
def compute_batch(
|
||||
self,
|
||||
ts_codes: list[str],
|
||||
date: str | None = None,
|
||||
max_news: int = 20,
|
||||
) -> dict[str, pd.DataFrame]:
|
||||
"""
|
||||
批量计算多只股票的情绪因子。
|
||||
|
||||
返回:
|
||||
{ts_code: factor_df}
|
||||
"""
|
||||
results = {}
|
||||
total = len(ts_codes)
|
||||
for i, ts_code in enumerate(ts_codes):
|
||||
try:
|
||||
results[ts_code] = self.compute(
|
||||
ts_code, start=None if date is None else date, end=date,
|
||||
max_news=max_news,
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"[WARN] {ts_code} 情绪因子失败: {e}")
|
||||
if (i + 1) % 10 == 0:
|
||||
print(f"[SentimentEngine] {i + 1}/{total}")
|
||||
return results
|
||||
|
||||
# ── 分析范围解析 ──────────────────────────────────────
|
||||
|
||||
def get_scope_stocks(self) -> list[str]:
|
||||
"""
|
||||
根据 .env 配置解析分析范围。
|
||||
|
||||
优先级: custom > index > sector > all
|
||||
"""
|
||||
scope_type = os.getenv("SENTIMENT_SCOPE_TYPE", "index")
|
||||
all_stocks = self._dm.get_stock_list()
|
||||
|
||||
if scope_type == "custom":
|
||||
codes = os.getenv("SENTIMENT_SCOPE_CUSTOM", "")
|
||||
return [c.strip() for c in codes.split(",") if c.strip()]
|
||||
|
||||
if scope_type == "sector":
|
||||
return self._get_sector_stocks(all_stocks)
|
||||
|
||||
if scope_type == "index":
|
||||
return self._get_index_stocks()
|
||||
|
||||
# all
|
||||
return list(all_stocks.index)
|
||||
|
||||
def _get_index_stocks(self) -> list[str]:
|
||||
"""根据指数成分股获取股票列表。"""
|
||||
indexes_str = os.getenv("SENTIMENT_SCOPE_INDEXES", "000300")
|
||||
indexes = [i.strip() for i in indexes_str.split(",") if i.strip()]
|
||||
stocks = set()
|
||||
import akshare as ak
|
||||
for idx_code in indexes:
|
||||
try:
|
||||
df = ak.index_stock_cons(symbol=idx_code)
|
||||
if df is not None and not df.empty:
|
||||
# 列名可能是 '品种代码' 或 'stock_code'
|
||||
col = next((c for c in df.columns if "代码" in c or "code" in c.lower()), df.columns[0])
|
||||
for code in df[col]:
|
||||
stocks.add(f"{code}.SZ" if code.startswith("0") or code.startswith("3") else f"{code}.SH")
|
||||
except Exception as e:
|
||||
print(f" [WARN] 指数 {idx_code} 成分股获取失败: {e}")
|
||||
return list(stocks)
|
||||
|
||||
def _get_sector_stocks(self, all_stocks: pd.DataFrame) -> list[str]:
|
||||
"""根据板块名称筛选股票。"""
|
||||
sectors_str = os.getenv("SENTIMENT_SCOPE_SECTORS", "")
|
||||
sectors = [s.strip() for s in sectors_str.split(",") if s.strip()]
|
||||
if not sectors:
|
||||
return list(all_stocks.index)
|
||||
# 利用 AkShare 行业分类筛选
|
||||
import akshare as ak
|
||||
all_industry = ak.stock_board_industry_name_em()
|
||||
matched = all_industry[all_industry["板块名称"].isin(sectors)]
|
||||
stocks = set()
|
||||
for _, row in matched.iterrows():
|
||||
try:
|
||||
df = ak.stock_board_industry_cons_em(symbol=row["板块名称"])
|
||||
if df is not None and not df.empty:
|
||||
code_col = next((c for c in df.columns if "代码" in c), df.columns[0])
|
||||
for code in df[code_col]:
|
||||
stocks.add(code)
|
||||
except Exception:
|
||||
continue
|
||||
return list(stocks)
|
||||
|
||||
# ── 新闻情绪分析 ──────────────────────────────────────
|
||||
|
||||
def _analyze_news(self, news_df: pd.DataFrame) -> pd.DataFrame:
|
||||
"""
|
||||
对新闻列表执行情绪分析。
|
||||
|
||||
返回:
|
||||
DataFrame (date, sentiment_score, confidence, impact_duration, key_topics, title, content)
|
||||
"""
|
||||
if news_df.empty:
|
||||
return pd.DataFrame()
|
||||
|
||||
# 检查是否有 Qwen API 配置
|
||||
if not self._qwen.api_key and not self._qwen.local_base_url:
|
||||
return pd.DataFrame()
|
||||
|
||||
results = []
|
||||
for _, row in news_df.iterrows():
|
||||
title = row.get("title", "")
|
||||
content = row.get("content", "")
|
||||
# 优先用标题+内容，文本过短时只用标题
|
||||
text = f"{title}\n{content}" if len(str(content)) > 20 else title
|
||||
if len(text.strip()) < 10:
|
||||
continue
|
||||
|
||||
analysis = self._qwen.analyze_sentiment(text)
|
||||
results.append({
|
||||
"date": row["date"],
|
||||
"title": title,
|
||||
"sentiment_score": analysis.get("sentiment_score", 0.0),
|
||||
"confidence": analysis.get("confidence", 0.0),
|
||||
"impact_duration": analysis.get("impact_duration", "short"),
|
||||
"key_topics": json.dumps(analysis.get("key_topics", [])),
|
||||
})
|
||||
|
||||
if not results:
|
||||
return pd.DataFrame()
|
||||
|
||||
return pd.DataFrame(results)
|
||||
|
||||
# ── 缓存管理 ──────────────────────────────────────────
|
||||
|
||||
def _load_cache(self, key: str) -> pd.DataFrame | None:
|
||||
"""加载缓存。缓存有效期 6 小时。"""
|
||||
cache_file = self._cache_dir / f"{key.replace('/', '_')}.parquet"
|
||||
if not cache_file.exists():
|
||||
return None
|
||||
mtime = cache_file.stat().st_mtime
|
||||
if time.time() - mtime > 6 * 3600:
|
||||
return None
|
||||
try:
|
||||
return pd.read_parquet(cache_file)
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
def _save_cache(self, key: str, df: pd.DataFrame) -> None:
|
||||
cache_file = self._cache_dir / f"{key.replace('/', '_')}.parquet"
|
||||
try:
|
||||
df.to_parquet(cache_file)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
def clear_cache(self) -> int:
|
||||
"""清空缓存，返回删除文件数。"""
|
||||
count = 0
|
||||
for f in self._cache_dir.glob("*.parquet"):
|
||||
f.unlink()
|
||||
count += 1
|
||||
return count
|
||||
@@ -0,0 +1,168 @@
|
||||
"""
|
||||
情绪因子。
|
||||
|
||||
将 Qwen 输出的情绪分数转换为量化因子值。
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from factors.base import BaseFactor
|
||||
|
||||
|
||||
class NewsSentimentFactor(BaseFactor):
|
||||
"""
|
||||
新闻情绪因子。
|
||||
|
||||
将多条新闻的情绪分数按时间加权聚合到每个交易日。
|
||||
|
||||
参数:
|
||||
window: 滚动窗口（交易日）
|
||||
decay: 指数衰减系数（越大衰减越快），0 表示等权
|
||||
sentiment_df: 情绪分析结果 DataFrame
|
||||
(date, sentiment_score, confidence, title, content)
|
||||
"""
|
||||
|
||||
category = "sentiment"
|
||||
|
||||
def __init__(self, window: int = 5, decay: float = 0.3, sentiment_df: pd.DataFrame | None = None):
|
||||
self.window = window
|
||||
self.decay = decay
|
||||
self.sentiment_df = sentiment_df
|
||||
self.name = f"news_sent_{window}"
|
||||
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
if self.sentiment_df is None or self.sentiment_df.empty:
|
||||
return pd.Series(float("nan"), index=df.index, name=self.name)
|
||||
|
||||
return _aggregate_sentiment(
|
||||
df, self.sentiment_df, self.window, self.decay
|
||||
)
|
||||
|
||||
def get_required_columns(self) -> list[str]:
|
||||
return []
|
||||
|
||||
|
||||
class SentimentMomentumFactor(BaseFactor):
|
||||
"""
|
||||
情绪动量因子。
|
||||
|
||||
当前情绪 - N 日前情绪，衡量情绪变化方向。
|
||||
"""
|
||||
|
||||
category = "sentiment"
|
||||
|
||||
def __init__(self, period: int = 5, sentiment_df: pd.DataFrame | None = None):
|
||||
self.period = period
|
||||
self.sentiment_df = sentiment_df
|
||||
self.name = f"sent_delta_{period}"
|
||||
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
base = NewsSentimentFactor(window=1, sentiment_df=self.sentiment_df).calculate(df)
|
||||
return base.diff(self.period)
|
||||
|
||||
def get_required_columns(self) -> list[str]:
|
||||
return []
|
||||
|
||||
|
||||
class SentimentConfidenceFactor(BaseFactor):
|
||||
"""
|
||||
情绪置信度因子。
|
||||
|
||||
新闻情绪分析的置信度越高，因子绝对值越大（方向同 sentiment_score）。
|
||||
sentiment_score × confidence → 高置信利好=正值大，高置信利空=负值大。
|
||||
"""
|
||||
|
||||
category = "sentiment"
|
||||
|
||||
def __init__(self, window: int = 5, sentiment_df: pd.DataFrame | None = None):
|
||||
self.window = window
|
||||
self.sentiment_df = sentiment_df
|
||||
self.name = f"news_conf_{window}"
|
||||
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
if self.sentiment_df is None or self.sentiment_df.empty:
|
||||
return pd.Series(float("nan"), index=df.index, name=self.name)
|
||||
|
||||
sdf = self.sentiment_df.copy()
|
||||
# 设置加权分数
|
||||
if "confidence" in sdf.columns and "sentiment_score" in sdf.columns:
|
||||
sdf["weighted_score"] = sdf["sentiment_score"] * sdf["confidence"]
|
||||
else:
|
||||
return pd.Series(float("nan"), index=df.index, name=self.name)
|
||||
|
||||
return _aggregate_sentiment(df, sdf, self.window, decay=0.3, score_col="weighted_score")
|
||||
|
||||
def get_required_columns(self) -> list[str]:
|
||||
return []
|
||||
|
||||
|
||||
# ── 情绪聚合工具函数 ──────────────────────────────────────
|
||||
|
||||
def _aggregate_sentiment(
|
||||
daily_df: pd.DataFrame,
|
||||
sentiment_df: pd.DataFrame,
|
||||
window: int,
|
||||
decay: float,
|
||||
score_col: str = "sentiment_score",
|
||||
) -> pd.Series:
|
||||
"""
|
||||
将情绪分数按时间加权聚合到交易日。
|
||||
|
||||
逻辑：
|
||||
1. 对每个交易日 t，找到 [t - window + 1, t] 范围内的所有新闻
|
||||
2. 按 time_decay = exp(-decay * days_from_t) 加权
|
||||
3. 按 confidence（如有）加权
|
||||
4. 返回加权平均情绪分数
|
||||
|
||||
参数:
|
||||
daily_df: 日线 DataFrame（提供 index 和日期对齐）
|
||||
sentiment_df: 情绪 DataFrame（date 列 + score_col）
|
||||
window: 窗口大小
|
||||
decay: 衰减系数
|
||||
score_col: 情绪分数列名
|
||||
"""
|
||||
if sentiment_df.empty:
|
||||
return pd.Series(float("nan"), index=daily_df.index, name=score_col)
|
||||
|
||||
# 统一日期格式
|
||||
sdf = sentiment_df.copy()
|
||||
sdf["date"] = pd.to_datetime(sdf["date"], format="%Y%m%d", errors="coerce")
|
||||
sdf = sdf.dropna(subset=["date"])
|
||||
sdf = sdf.sort_values("date")
|
||||
|
||||
daily_idx = pd.to_datetime(daily_df.index, format="%Y%m%d", errors="coerce")
|
||||
if daily_idx.isna().all():
|
||||
daily_idx = pd.to_datetime(daily_df.index)
|
||||
|
||||
result = pd.Series(float("nan"), index=daily_df.index)
|
||||
|
||||
# 对 news 日期建立搜索索引
|
||||
news_dates = sdf["date"].values
|
||||
|
||||
for i, dt in enumerate(daily_idx):
|
||||
if pd.isna(dt):
|
||||
continue
|
||||
# 窗口起始
|
||||
window_start = dt - pd.Timedelta(days=window * 2) # 宽窗覆盖非交易日
|
||||
|
||||
mask = (news_dates >= window_start) & (news_dates <= dt)
|
||||
candidates = sdf[mask]
|
||||
|
||||
if candidates.empty:
|
||||
continue
|
||||
|
||||
# 时间衰减权重
|
||||
days_diff = (dt - candidates["date"]).dt.days
|
||||
time_weights = np.exp(-decay * days_diff)
|
||||
|
||||
# 置信度权重（如有）
|
||||
conf_weights = candidates.get("confidence", pd.Series(1.0, index=candidates.index)).fillna(0.5)
|
||||
scores = candidates[score_col].fillna(0.0)
|
||||
|
||||
total_weight = (time_weights * conf_weights).sum()
|
||||
if total_weight > 0:
|
||||
result.iloc[i] = (scores * time_weights * conf_weights).sum() / total_weight
|
||||
|
||||
result.name = score_col
|
||||
return result.astype("float64")
|
||||
@@ -0,0 +1,24 @@
|
||||
"""
|
||||
振幅因子。
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from factors.base import BaseFactor
|
||||
|
||||
|
||||
class AmplitudeFactor(BaseFactor):
|
||||
"""N 日均振幅 = mean((high - low) / close, N) * 100"""
|
||||
|
||||
category = "technical"
|
||||
|
||||
def __init__(self, period: int = 5):
|
||||
self.period = period
|
||||
self.name = f"amplitude_{period}"
|
||||
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
daily_amp = (df["high"] - df["low"]) / df["close"].replace(0, float("nan")) * 100
|
||||
return daily_amp.rolling(window=self.period, min_periods=self.period).mean()
|
||||
|
||||
def get_required_columns(self) -> list[str]:
|
||||
return ["high", "low", "close"]
|
||||
@@ -0,0 +1,47 @@
|
||||
"""
|
||||
ATR 平均真实波幅因子。
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from factors.base import BaseFactor
|
||||
|
||||
|
||||
class ATRFactor(BaseFactor):
|
||||
"""Average True Range，衡量波动性。"""
|
||||
|
||||
category = "technical"
|
||||
|
||||
def __init__(self, period: int = 14):
|
||||
self.period = period
|
||||
self.name = f"atr_{period}"
|
||||
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
high, low, close = df["high"], df["low"], df["close"]
|
||||
prev_close = close.shift(1)
|
||||
tr = pd.concat([
|
||||
(high - low).abs(),
|
||||
(high - prev_close).abs(),
|
||||
(low - prev_close).abs(),
|
||||
], axis=1).max(axis=1)
|
||||
return tr.ewm(span=self.period, min_periods=self.period).mean()
|
||||
|
||||
def get_required_columns(self) -> list[str]:
|
||||
return ["high", "low", "close"]
|
||||
|
||||
|
||||
class ATRRatioFactor(BaseFactor):
|
||||
"""ATR / close 归一化，便于跨股票比较。"""
|
||||
|
||||
category = "technical"
|
||||
|
||||
def __init__(self, period: int = 14):
|
||||
self.period = period
|
||||
self.name = f"atr_ratio_{period}"
|
||||
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
atr = ATRFactor(period=self.period).calculate(df)
|
||||
return atr / df["close"].replace(0, float("nan")) * 100
|
||||
|
||||
def get_required_columns(self) -> list[str]:
|
||||
return ["high", "low", "close"]
|
||||
@@ -0,0 +1,53 @@
|
||||
"""
|
||||
布林带因子：价格在布林带中的位置。
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from factors.base import BaseFactor
|
||||
|
||||
|
||||
class BollingerFactor(BaseFactor):
|
||||
"""
|
||||
布林带位置 = (close - middle) / (upper - lower)
|
||||
|
||||
值在 0~1 之间：接近 0 表示在下轨，接近 1 表示在上轨。
|
||||
"""
|
||||
|
||||
category = "technical"
|
||||
|
||||
def __init__(self, period: int = 20, std: float = 2.0):
|
||||
self.period = period
|
||||
self.std = std
|
||||
self.name = f"boll_{period}"
|
||||
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
middle = df["close"].rolling(window=self.period, min_periods=self.period).mean()
|
||||
std = df["close"].rolling(window=self.period, min_periods=self.period).std()
|
||||
upper = middle + self.std * std
|
||||
lower = middle - self.std * std
|
||||
band_width = upper - lower
|
||||
return ((df["close"] - lower) / band_width.replace(0, float("nan"))).clip(0, 1)
|
||||
|
||||
def get_required_columns(self) -> list[str]:
|
||||
return ["close"]
|
||||
|
||||
|
||||
class BollingerWidthFactor(BaseFactor):
|
||||
"""布林带宽度 = (upper - lower) / middle * 100"""
|
||||
|
||||
category = "technical"
|
||||
|
||||
def __init__(self, period: int = 20, std: float = 2.0):
|
||||
self.period = period
|
||||
self.std = std
|
||||
self.name = f"boll_width_{period}"
|
||||
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
middle = df["close"].rolling(window=self.period, min_periods=self.period).mean()
|
||||
std = df["close"].rolling(window=self.period, min_periods=self.period).std()
|
||||
band_width = 2 * self.std * std
|
||||
return band_width / middle.replace(0, float("nan")) * 100
|
||||
|
||||
def get_required_columns(self) -> list[str]:
|
||||
return ["close"]
|
||||
@@ -0,0 +1,49 @@
|
||||
"""
|
||||
均线交叉因子。
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from factors.base import BaseFactor
|
||||
|
||||
|
||||
class MACrossFactor(BaseFactor):
|
||||
"""
|
||||
均线交叉信号。
|
||||
|
||||
返回：fast_ma / slow_ma - 1，正值表示短期均线在上方。
|
||||
"""
|
||||
|
||||
category = "technical"
|
||||
|
||||
def __init__(self, fast: int = 5, slow: int = 20):
|
||||
self.fast = fast
|
||||
self.slow = slow
|
||||
self.name = f"ma_cross_{fast}_{slow}"
|
||||
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
ma_fast = df["close"].rolling(window=self.fast, min_periods=self.fast).mean()
|
||||
ma_slow = df["close"].rolling(window=self.slow, min_periods=self.slow).mean()
|
||||
return (ma_fast / ma_slow.replace(0, float("nan"))) - 1
|
||||
|
||||
def get_required_columns(self) -> list[str]:
|
||||
return ["close"]
|
||||
|
||||
|
||||
class MADeviationFactor(BaseFactor):
|
||||
"""
|
||||
价格偏离均线程度 = (close - ma) / ma * 100
|
||||
"""
|
||||
|
||||
category = "technical"
|
||||
|
||||
def __init__(self, period: int = 20):
|
||||
self.period = period
|
||||
self.name = f"ma_dev_{period}"
|
||||
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
ma = df["close"].rolling(window=self.period, min_periods=self.period).mean()
|
||||
return (df["close"] - ma) / ma.replace(0, float("nan")) * 100
|
||||
|
||||
def get_required_columns(self) -> list[str]:
|
||||
return ["close"]
|
||||
@@ -0,0 +1,47 @@
|
||||
"""
|
||||
MACD 因子。
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from factors.base import BaseFactor
|
||||
|
||||
|
||||
class MACDFactor(BaseFactor):
|
||||
"""
|
||||
MACD 系列因子。
|
||||
|
||||
返回 DIF/DEA/HIST 三个值。使用 calculate() 返回 HIST（柱），
|
||||
单独方法获取 DIF/DEA。
|
||||
"""
|
||||
|
||||
category = "technical"
|
||||
|
||||
def __init__(self, fast: int = 12, slow: int = 26, signal: int = 9):
|
||||
self.fast = fast
|
||||
self.slow = slow
|
||||
self.signal = signal
|
||||
self.name = f"macd_{fast}_{slow}_{signal}"
|
||||
|
||||
def _ema(self, series: pd.Series, span: int) -> pd.Series:
|
||||
return series.ewm(span=span, min_periods=span).mean()
|
||||
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
"""返回 MACD 柱（DIF - DEA）。"""
|
||||
ema_fast = self._ema(df["close"], self.fast)
|
||||
ema_slow = self._ema(df["close"], self.slow)
|
||||
dif = ema_fast - ema_slow
|
||||
dea = self._ema(dif, self.signal)
|
||||
return dif - dea
|
||||
|
||||
def dif(self, df: pd.DataFrame) -> pd.Series:
|
||||
ema_fast = self._ema(df["close"], self.fast)
|
||||
ema_slow = self._ema(df["close"], self.slow)
|
||||
return ema_fast - ema_slow
|
||||
|
||||
def dea(self, df: pd.DataFrame) -> pd.Series:
|
||||
dif = self.dif(df)
|
||||
return self._ema(dif, self.signal)
|
||||
|
||||
def get_required_columns(self) -> list[str]:
|
||||
return ["close"]
|
||||
@@ -0,0 +1,23 @@
|
||||
"""
|
||||
动量因子：N 日收益率。
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from factors.base import BaseFactor
|
||||
|
||||
|
||||
class MomentumFactor(BaseFactor):
|
||||
"""N 日价格动量 = (close_t - close_{t-N}) / close_{t-N} * 100"""
|
||||
|
||||
category = "technical"
|
||||
|
||||
def __init__(self, period: int = 20):
|
||||
self.period = period
|
||||
self.name = f"momentum_{period}"
|
||||
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
return df["close"].pct_change(periods=self.period) * 100
|
||||
|
||||
def get_required_columns(self) -> list[str]:
|
||||
return ["close"]
|
||||
@@ -0,0 +1,29 @@
|
||||
"""
|
||||
RSI 相对强弱因子。
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from factors.base import BaseFactor
|
||||
|
||||
|
||||
class RSIFactor(BaseFactor):
|
||||
"""Wilder's RSI = 100 - 100 / (1 + RS), RS = avg_gain / avg_loss"""
|
||||
|
||||
category = "technical"
|
||||
|
||||
def __init__(self, period: int = 14):
|
||||
self.period = period
|
||||
self.name = f"rsi_{period}"
|
||||
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
delta = df["close"].diff()
|
||||
gain = delta.clip(lower=0)
|
||||
loss = (-delta).clip(lower=0)
|
||||
avg_gain = gain.ewm(span=self.period, min_periods=self.period).mean()
|
||||
avg_loss = loss.ewm(span=self.period, min_periods=self.period).mean()
|
||||
rs = avg_gain / avg_loss.replace(0, float("nan"))
|
||||
return 100 - 100 / (1 + rs)
|
||||
|
||||
def get_required_columns(self) -> list[str]:
|
||||
return ["close"]
|
||||
@@ -0,0 +1,40 @@
|
||||
"""
|
||||
换手率因子。
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from factors.base import BaseFactor
|
||||
|
||||
|
||||
class TurnoverFactor(BaseFactor):
|
||||
"""N 日均换手率。"""
|
||||
|
||||
category = "technical"
|
||||
|
||||
def __init__(self, period: int = 5):
|
||||
self.period = period
|
||||
self.name = f"turnover_{period}"
|
||||
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
return df["turnover_rate"].rolling(window=self.period, min_periods=self.period).mean()
|
||||
|
||||
def get_required_columns(self) -> list[str]:
|
||||
return ["turnover_rate"]
|
||||
|
||||
|
||||
class TurnoverChangeFactor(BaseFactor):
|
||||
"""换手率变化 = 当日换手率 / N 日均换手率。"""
|
||||
|
||||
category = "technical"
|
||||
|
||||
def __init__(self, period: int = 5):
|
||||
self.period = period
|
||||
self.name = f"turnover_chg_{period}"
|
||||
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
avg = df["turnover_rate"].rolling(window=self.period, min_periods=self.period).mean()
|
||||
return df["turnover_rate"] / avg.replace(0, float("nan"))
|
||||
|
||||
def get_required_columns(self) -> list[str]:
|
||||
return ["turnover_rate"]
|
||||
@@ -0,0 +1,42 @@
|
||||
"""
|
||||
波动率因子。
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from factors.base import BaseFactor
|
||||
|
||||
|
||||
class VolatilityFactor(BaseFactor):
|
||||
"""N 日年化波动率 = std(daily_return, N) * sqrt(252) * 100"""
|
||||
|
||||
category = "technical"
|
||||
|
||||
def __init__(self, period: int = 20):
|
||||
self.period = period
|
||||
self.name = f"volatility_{period}"
|
||||
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
daily_ret = df["close"].pct_change()
|
||||
return daily_ret.rolling(window=self.period, min_periods=self.period).std() * (252 ** 0.5) * 100
|
||||
|
||||
def get_required_columns(self) -> list[str]:
|
||||
return ["close"]
|
||||
|
||||
|
||||
class DownsideVolatilityFactor(BaseFactor):
|
||||
"""下行波动率：只计算负收益的标准差。"""
|
||||
|
||||
category = "technical"
|
||||
|
||||
def __init__(self, period: int = 20):
|
||||
self.period = period
|
||||
self.name = f"down_vol_{period}"
|
||||
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
daily_ret = df["close"].pct_change()
|
||||
downside = daily_ret.clip(upper=0)
|
||||
return downside.rolling(window=self.period, min_periods=self.period).std() * (252 ** 0.5) * 100
|
||||
|
||||
def get_required_columns(self) -> list[str]:
|
||||
return ["close"]
|
||||
@@ -0,0 +1,40 @@
|
||||
"""
|
||||
量价因子：量比 / 成交量变化率。
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from factors.base import BaseFactor
|
||||
|
||||
|
||||
class VolumeFactor(BaseFactor):
|
||||
"""N 日均量比 = vol / mean(vol, N)"""
|
||||
|
||||
category = "technical"
|
||||
|
||||
def __init__(self, period: int = 5):
|
||||
self.period = period
|
||||
self.name = f"vol_ratio_{period}"
|
||||
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
avg_vol = df["vol"].rolling(window=self.period, min_periods=self.period).mean()
|
||||
return df["vol"] / avg_vol.replace(0, float("nan"))
|
||||
|
||||
def get_required_columns(self) -> list[str]:
|
||||
return ["vol"]
|
||||
|
||||
|
||||
class VolumeChangeFactor(BaseFactor):
|
||||
"""成交量 N 日变化率"""
|
||||
|
||||
category = "technical"
|
||||
|
||||
def __init__(self, period: int = 5):
|
||||
self.period = period
|
||||
self.name = f"vol_chg_{period}"
|
||||
|
||||
def calculate(self, df: pd.DataFrame) -> pd.Series:
|
||||
return df["vol"].pct_change(periods=self.period) * 100
|
||||
|
||||
def get_required_columns(self) -> list[str]:
|
||||
return ["vol"]
|
||||
@@ -0,0 +1,124 @@
|
||||
"""
|
||||
ML 模型回测集成。
|
||||
|
||||
MLStrategy: 将 ML 预测值作为交易信号接入回测引擎。
|
||||
MLBenchmark: 多模型基准对比。
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from backtest.base import BaseStrategy
|
||||
from backtest.vectorbt.engine import VectorBTEngine
|
||||
from models.base import BaseModel
|
||||
from models.features import FeatureEngine
|
||||
|
||||
|
||||
class MLStrategy(BaseStrategy):
|
||||
"""
|
||||
ML 预测 → 交易信号。
|
||||
|
||||
用模型预测未来 N 日收益，按预测值分位数生成信号：
|
||||
- 预测值 > buy_quantile → 买入
|
||||
- 预测值 < sell_quantile → 平仓
|
||||
|
||||
参数:
|
||||
model: 已训练的 BaseModel
|
||||
feature_engine: 已 fit 的 FeatureEngine
|
||||
buy_quantile: 买入分位阈值（0.7 = 预测值最高的30%买入）
|
||||
sell_quantile: 卖出分位阈值（0.3 = 预测值最低的30%平仓）
|
||||
rebalance_freq: 调仓间隔（交易日）
|
||||
"""
|
||||
|
||||
category = "ml"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model: BaseModel,
|
||||
feature_engine: FeatureEngine,
|
||||
buy_quantile: float = 0.7,
|
||||
sell_quantile: float = 0.3,
|
||||
rebalance_freq: int = 5,
|
||||
):
|
||||
self.model = model
|
||||
self.feature_engine = feature_engine
|
||||
self.buy_quantile = buy_quantile
|
||||
self.sell_quantile = sell_quantile
|
||||
self.rebalance_freq = rebalance_freq
|
||||
self.name = f"ml_{model.name}"
|
||||
|
||||
def generate_signals(self, factor_df: pd.DataFrame) -> pd.Series:
|
||||
X, _ = self.feature_engine.build(factor_df, factor_df, fit=False)
|
||||
if X.empty:
|
||||
return pd.Series(-1, index=factor_df.index)
|
||||
|
||||
preds = self.model.predict(X)
|
||||
# 用预测值本身的分布作为阈值（相对排序，避免模型偏差影响）
|
||||
buy_threshold = preds.quantile(self.buy_quantile)
|
||||
sell_threshold = preds.quantile(self.sell_quantile)
|
||||
|
||||
signals = pd.Series(-1, index=factor_df.index)
|
||||
common = signals.index.intersection(preds.index)
|
||||
buy_mask = preds.loc[common] > buy_threshold
|
||||
sell_mask = preds.loc[common] < sell_threshold
|
||||
signals.loc[buy_mask[buy_mask].index] = 1
|
||||
signals.loc[sell_mask[sell_mask].index] = 0
|
||||
|
||||
signals = self._filter_rebalance(signals)
|
||||
return signals
|
||||
|
||||
def _filter_rebalance(self, signals: pd.Series) -> pd.Series:
|
||||
"""每隔 rebalance_freq 个交易日保留第一个非持有信号。"""
|
||||
result = signals.copy()
|
||||
last_active = -self.rebalance_freq - 1
|
||||
for i in range(len(result)):
|
||||
sig = result.iloc[i]
|
||||
if sig in (0, 1):
|
||||
if i - last_active >= self.rebalance_freq:
|
||||
last_active = i
|
||||
else:
|
||||
result.iloc[i] = -1
|
||||
return result
|
||||
|
||||
|
||||
class MLBenchmark:
|
||||
"""ML 模型基准对比测试。"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
models: list[BaseModel],
|
||||
feature_engine: FeatureEngine,
|
||||
price_df: pd.DataFrame,
|
||||
factor_df: pd.DataFrame,
|
||||
bt_engine: VectorBTEngine | None = None,
|
||||
):
|
||||
self.models = models
|
||||
self.feature_engine = feature_engine
|
||||
self.price_df = price_df
|
||||
self.factor_df = factor_df
|
||||
self.bt_engine = bt_engine or VectorBTEngine()
|
||||
|
||||
def run(self) -> pd.DataFrame:
|
||||
"""对比各模型的预测质量和回测表现。"""
|
||||
rows = []
|
||||
for model in self.models:
|
||||
strategy = MLStrategy(model, self.feature_engine)
|
||||
report = self.bt_engine.run(strategy, self.price_df, self.factor_df)
|
||||
|
||||
# OOS 预测 vs 真实值
|
||||
X, y_true = self.feature_engine.build(self.factor_df, self.price_df, fit=True)
|
||||
y_pred = model.predict(X)
|
||||
|
||||
ic = y_pred.corr(y_true) if len(y_pred) > 0 else 0
|
||||
|
||||
rows.append({
|
||||
"model": model.name,
|
||||
"ic": round(ic, 4),
|
||||
"total_return": report.total_return,
|
||||
"cagr": report.cagr,
|
||||
"max_dd": report.max_drawdown,
|
||||
"sharpe": report.sharpe_ratio,
|
||||
"win_rate": report.win_rate,
|
||||
"trades": report.total_trades,
|
||||
})
|
||||
return pd.DataFrame(rows).set_index("model")
|
||||
@@ -0,0 +1,42 @@
|
||||
"""
|
||||
ML 模型抽象基类。
|
||||
|
||||
统一接口：fit(X, y) → predict(X) → save/load。
|
||||
"""
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
import pickle
|
||||
|
||||
import pandas as pd
|
||||
|
||||
|
||||
class BaseModel(ABC):
|
||||
"""ML 模型抽象基类。"""
|
||||
|
||||
name: str = ""
|
||||
|
||||
@abstractmethod
|
||||
def fit(self, X: pd.DataFrame, y: pd.Series) -> "BaseModel":
|
||||
"""训练模型。返回 self 支持链式调用。"""
|
||||
...
|
||||
|
||||
@abstractmethod
|
||||
def predict(self, X: pd.DataFrame) -> pd.Series:
|
||||
"""返回预测值（回归值）。"""
|
||||
...
|
||||
|
||||
@abstractmethod
|
||||
def get_feature_importance(self) -> pd.DataFrame:
|
||||
"""特征重要性 DataFrame，columns=[feature, importance]。"""
|
||||
...
|
||||
|
||||
def save(self, path: str) -> None:
|
||||
"""保存模型到文件（pickle）。"""
|
||||
with open(path, "wb") as f:
|
||||
pickle.dump(self, f)
|
||||
|
||||
@classmethod
|
||||
def load(cls, path: str) -> "BaseModel":
|
||||
"""从文件加载模型。"""
|
||||
with open(path, "rb") as f:
|
||||
return pickle.load(f)
|
||||
@@ -0,0 +1,113 @@
|
||||
"""
|
||||
CatBoost 模型封装。
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from catboost import CatBoostRegressor, Pool
|
||||
|
||||
from models.base import BaseModel
|
||||
from sklearn.model_selection import TimeSeriesSplit
|
||||
|
||||
_DEFAULT_PARAMS = {
|
||||
"loss_function": "RMSE",
|
||||
"iterations": 1000,
|
||||
"learning_rate": 0.03,
|
||||
"depth": 5,
|
||||
"random_seed": 42,
|
||||
"verbose": False,
|
||||
"allow_writing_files": False,
|
||||
"min_data_in_leaf": 20,
|
||||
}
|
||||
|
||||
|
||||
class CatBoostModel(BaseModel):
|
||||
"""CatBoost 回归模型。"""
|
||||
|
||||
name = "catboost"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
params: dict | None = None,
|
||||
early_stopping: int = 50,
|
||||
eval_ratio: float = 0.2,
|
||||
random_seed: int = 42,
|
||||
):
|
||||
self.params = params or _DEFAULT_PARAMS.copy()
|
||||
self.params["random_seed"] = random_seed
|
||||
self.early_stopping = early_stopping
|
||||
self.eval_ratio = eval_ratio
|
||||
self._model: CatBoostRegressor | None = None
|
||||
self._feature_names: list[str] = []
|
||||
|
||||
def fit(self, X: pd.DataFrame, y: pd.Series) -> "CatBoostModel":
|
||||
self._feature_names = list(X.columns)
|
||||
|
||||
n = len(X)
|
||||
val_size = int(n * self.eval_ratio)
|
||||
|
||||
X_train, y_train = X, y
|
||||
eval_set = None
|
||||
|
||||
if val_size >= 50:
|
||||
split_idx = n - val_size
|
||||
X_train, X_val = X.iloc[:split_idx], X.iloc[split_idx:]
|
||||
y_train, y_val = y.iloc[:split_idx], y.iloc[split_idx:]
|
||||
eval_set = Pool(X_val, y_val)
|
||||
|
||||
self._model = CatBoostRegressor(**self.params)
|
||||
self._model.fit(
|
||||
X_train, y_train,
|
||||
eval_set=eval_set,
|
||||
early_stopping_rounds=self.early_stopping if eval_set else None,
|
||||
verbose=False,
|
||||
)
|
||||
return self
|
||||
|
||||
def predict(self, X: pd.DataFrame) -> pd.Series:
|
||||
if self._model is None:
|
||||
raise RuntimeError("模型尚未训练")
|
||||
preds = self._model.predict(X[self._feature_names])
|
||||
return pd.Series(preds, index=X.index, name="pred")
|
||||
|
||||
def get_feature_importance(self) -> pd.DataFrame:
|
||||
if self._model is None:
|
||||
return pd.DataFrame()
|
||||
imp = self._model.get_feature_importance()
|
||||
names = self._feature_names
|
||||
df = pd.DataFrame({"feature": names, "importance": imp})
|
||||
total = df["importance"].sum()
|
||||
df["importance_pct"] = df["importance"] / total * 100 if total > 0 else 0
|
||||
return df.sort_values("importance", ascending=False)
|
||||
|
||||
def cv_evaluate(
|
||||
self, X: pd.DataFrame, y: pd.Series, n_folds: int = 5
|
||||
) -> pd.DataFrame:
|
||||
"""时间序列交叉验证评估。"""
|
||||
tscv = TimeSeriesSplit(n_splits=n_folds)
|
||||
results = []
|
||||
for fold, (train_idx, test_idx) in enumerate(tscv.split(X)):
|
||||
X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]
|
||||
y_train, y_test = y.iloc[train_idx], y.iloc[test_idx]
|
||||
|
||||
model = CatBoostModel(
|
||||
params=self.params,
|
||||
early_stopping=self.early_stopping,
|
||||
eval_ratio=0.0,
|
||||
)
|
||||
model.fit(X_train, y_train)
|
||||
preds = model.predict(X_test)
|
||||
ic = preds.corr(y_test)
|
||||
mse = ((preds - y_test) ** 2).mean()
|
||||
results.append({"fold": fold, "ic": round(ic, 4), "mse": round(mse, 4)})
|
||||
|
||||
df = pd.DataFrame(results)
|
||||
df.loc["mean"] = df.mean()
|
||||
return df
|
||||
|
||||
@property
|
||||
def n_estimators_used(self) -> int | None:
|
||||
"""实际使用的树数量。"""
|
||||
if self._model is None:
|
||||
return None
|
||||
return self._model.tree_count_
|
||||
@@ -0,0 +1,142 @@
|
||||
"""
|
||||
特征工程：因子 → 特征矩阵 + 目标标签。
|
||||
|
||||
严禁使用未来数据。所有变换基于 expanding window 或训练集统计。
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from sklearn.preprocessing import RobustScaler
|
||||
|
||||
|
||||
class FeatureEngine:
|
||||
"""
|
||||
特征工程引擎。
|
||||
|
||||
参数:
|
||||
lookahead: 预测未来 N 个交易日
|
||||
label_type: 'regression' | 'classification'
|
||||
winsorize_pct: 去极值的分位数边界 (0.01, 0.99)
|
||||
nan_threshold: NaN 占比超过此值的因子直接剔除
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
lookahead: int = 5,
|
||||
label_type: str = "regression",
|
||||
winsorize_pct: tuple[float, float] = (0.01, 0.99),
|
||||
nan_threshold: float = 0.3,
|
||||
):
|
||||
self.lookahead = lookahead
|
||||
self.label_type = label_type
|
||||
self.winsorize_pct = winsorize_pct
|
||||
self.nan_threshold = nan_threshold
|
||||
self._scaler = RobustScaler()
|
||||
self._scaler_fitted = False
|
||||
self._valid_features: list[str] = []
|
||||
|
||||
# ── 标签构建 ──────────────────────────────────────────
|
||||
|
||||
def build_labels(self, price_df: pd.DataFrame) -> pd.Series:
|
||||
"""
|
||||
构建目标标签。
|
||||
|
||||
regression: (close_{t+N} - close_t) / close_t * 100
|
||||
classification: 1 if return > 0 else 0
|
||||
"""
|
||||
close = price_df["close"]
|
||||
future = close.shift(-self.lookahead)
|
||||
ret = (future - close) / close * 100
|
||||
|
||||
if self.label_type == "classification":
|
||||
return (ret > 0).astype(int)
|
||||
|
||||
return ret.rename(f"y_fwd_{self.lookahead}")
|
||||
|
||||
# ── 特征构建 ──────────────────────────────────────────
|
||||
|
||||
def build(
|
||||
self,
|
||||
factor_df: pd.DataFrame,
|
||||
price_df: pd.DataFrame,
|
||||
fit: bool = True,
|
||||
) -> tuple[pd.DataFrame, pd.Series]:
|
||||
"""
|
||||
构建特征矩阵 X 和标签 y。
|
||||
|
||||
参数:
|
||||
factor_df: 因子 DataFrame, index=trade_date, columns=因子名
|
||||
price_df: 价格 DataFrame, 需有 'close'
|
||||
fit: True=训练模式（fit scaler + 记录有效特征），False=预测模式
|
||||
|
||||
返回:
|
||||
X, y（y 在 predict 模式下为 None）
|
||||
"""
|
||||
X = factor_df.copy()
|
||||
|
||||
# 1. 剔除 NaN 率过高的列
|
||||
if fit:
|
||||
nan_ratio = X.isna().mean()
|
||||
self._valid_features = list(nan_ratio[nan_ratio <= self.nan_threshold].index)
|
||||
# 排除非因子列
|
||||
self._valid_features = [c for c in self._valid_features
|
||||
if c not in ("close", "open", "high", "low", "volume")]
|
||||
X = X[self._valid_features].copy() if self._valid_features else X
|
||||
|
||||
# 2. 缺失值填充：前值填充 → 截面中位数
|
||||
X = X.ffill().fillna(X.median())
|
||||
|
||||
# 3. 去极值（Winsorize）
|
||||
if fit:
|
||||
lo, hi = self.winsorize_pct
|
||||
self._winsor_lower = X.quantile(lo)
|
||||
self._winsor_upper = X.quantile(hi)
|
||||
for col in X.columns:
|
||||
if col in getattr(self, "_winsor_lower", pd.Series()):
|
||||
X[col] = X[col].clip(self._winsor_lower[col], self._winsor_upper[col])
|
||||
|
||||
# 4. 标准化（训练时 fit，预测时 transform）
|
||||
if fit:
|
||||
X_scaled = self._scaler.fit_transform(X)
|
||||
self._scaler_fitted = True
|
||||
else:
|
||||
X_scaled = self._scaler.transform(X)
|
||||
|
||||
X = pd.DataFrame(X_scaled, index=X.index, columns=X.columns)
|
||||
|
||||
# 5. 构建标签
|
||||
y = self.build_labels(price_df) if fit else None
|
||||
|
||||
# 6. 对齐（删掉无法构建标签的行）
|
||||
if fit:
|
||||
valid_idx = X.index.intersection(y.dropna().index)
|
||||
X = X.loc[valid_idx]
|
||||
y = y.loc[valid_idx]
|
||||
|
||||
return X, y
|
||||
|
||||
# ── 多股票构建 ────────────────────────────────────────
|
||||
|
||||
def build_universe(
|
||||
self,
|
||||
factor_universe: dict[str, pd.DataFrame],
|
||||
price_universe: dict[str, pd.DataFrame],
|
||||
) -> tuple[pd.DataFrame, pd.Series]:
|
||||
"""多股票拼接特征矩阵（每只股票独立处理再拼接）。"""
|
||||
X_parts, y_parts = [], []
|
||||
for ts_code in factor_universe:
|
||||
f_df = factor_universe[ts_code]
|
||||
p_df = price_universe.get(ts_code)
|
||||
if p_df is None or f_df.empty or p_df.empty:
|
||||
continue
|
||||
X, y = self.build(f_df, p_df, fit=True)
|
||||
if X.empty:
|
||||
continue
|
||||
X["_ts_code"] = ts_code
|
||||
X_parts.append(X)
|
||||
y_parts.append(y)
|
||||
if not X_parts:
|
||||
return pd.DataFrame(), pd.Series()
|
||||
X_all = pd.concat(X_parts)
|
||||
y_all = pd.concat(y_parts)
|
||||
return X_all.drop(columns=["_ts_code"]), y_all
|
||||
@@ -0,0 +1,126 @@
|
||||
"""
|
||||
LightGBM 模型封装。
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import lightgbm as lgb
|
||||
|
||||
from models.base import BaseModel
|
||||
from sklearn.model_selection import TimeSeriesSplit
|
||||
|
||||
_DEFAULT_PARAMS = {
|
||||
"objective": "regression",
|
||||
"metric": "rmse",
|
||||
"boosting_type": "gbdt",
|
||||
"num_leaves": 15,
|
||||
"learning_rate": 0.03,
|
||||
"feature_fraction": 0.7,
|
||||
"bagging_fraction": 0.7,
|
||||
"bagging_freq": 5,
|
||||
"verbose": -1,
|
||||
"n_estimators": 1000,
|
||||
"random_state": 42,
|
||||
"min_data_in_leaf": 20,
|
||||
}
|
||||
|
||||
|
||||
class LightGBMModel(BaseModel):
|
||||
"""LightGBM 回归模型。"""
|
||||
|
||||
name = "lightgbm"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
params: dict | None = None,
|
||||
early_stopping: int = 50,
|
||||
eval_ratio: float = 0.2,
|
||||
random_seed: int = 42,
|
||||
):
|
||||
self.params = params or _DEFAULT_PARAMS.copy()
|
||||
self.params["random_state"] = random_seed
|
||||
self.early_stopping = early_stopping
|
||||
self.eval_ratio = eval_ratio
|
||||
self._model: lgb.Booster | None = None
|
||||
self._feature_names: list[str] = []
|
||||
|
||||
def fit(self, X: pd.DataFrame, y: pd.Series) -> "LightGBMModel":
|
||||
self._feature_names = list(X.columns)
|
||||
|
||||
n = len(X)
|
||||
val_size = int(n * self.eval_ratio)
|
||||
|
||||
callbacks = []
|
||||
eval_set = None
|
||||
X_train, y_train = X, y
|
||||
|
||||
# 验证集足够大时才启用早停（至少 50 条）
|
||||
if val_size >= 50:
|
||||
split_idx = n - val_size
|
||||
X_train, X_val = X.iloc[:split_idx], X.iloc[split_idx:]
|
||||
y_train, y_val = y.iloc[:split_idx], y.iloc[split_idx:]
|
||||
eval_set = [(X_val, y_val)]
|
||||
callbacks = [
|
||||
lgb.early_stopping(stopping_rounds=self.early_stopping, verbose=False),
|
||||
lgb.log_evaluation(0),
|
||||
]
|
||||
|
||||
self._model = lgb.LGBMRegressor(**self.params)
|
||||
self._model.fit(
|
||||
X_train, y_train,
|
||||
eval_set=eval_set,
|
||||
callbacks=callbacks if callbacks else None,
|
||||
)
|
||||
return self
|
||||
|
||||
def predict(self, X: pd.DataFrame) -> pd.Series:
|
||||
if self._model is None:
|
||||
raise RuntimeError("模型尚未训练")
|
||||
preds = self._model.predict(X[self._feature_names])
|
||||
return pd.Series(preds, index=X.index, name="pred")
|
||||
|
||||
def get_feature_importance(self, importance_type: str = "gain") -> pd.DataFrame:
|
||||
"""特征重要性。importance_type: 'gain' | 'split'"""
|
||||
if self._model is None:
|
||||
return pd.DataFrame()
|
||||
imp = self._model.booster_.feature_importance(importance_type=importance_type)
|
||||
names = self._model.booster_.feature_name()
|
||||
df = pd.DataFrame({"feature": names, "importance": imp})
|
||||
df["importance_pct"] = df["importance"] / df["importance"].sum() * 100
|
||||
return df.sort_values("importance", ascending=False)
|
||||
|
||||
def cv_evaluate(
|
||||
self, X: pd.DataFrame, y: pd.Series, n_folds: int = 5
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
时间序列交叉验证评估（不 shuffle）。
|
||||
|
||||
返回每折的 IC (相关系数) 和 MSE。
|
||||
"""
|
||||
tscv = TimeSeriesSplit(n_splits=n_folds)
|
||||
results = []
|
||||
for fold, (train_idx, test_idx) in enumerate(tscv.split(X)):
|
||||
X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]
|
||||
y_train, y_test = y.iloc[train_idx], y.iloc[test_idx]
|
||||
|
||||
model = LightGBMModel(
|
||||
params=self.params,
|
||||
early_stopping=self.early_stopping,
|
||||
eval_ratio=0.0, # 不使用内部验证，直接全量训练
|
||||
)
|
||||
model.fit(X_train, y_train)
|
||||
preds = model.predict(X_test)
|
||||
ic = preds.corr(y_test)
|
||||
mse = ((preds - y_test) ** 2).mean()
|
||||
results.append({"fold": fold, "ic": round(ic, 4), "mse": round(mse, 4)})
|
||||
|
||||
df = pd.DataFrame(results)
|
||||
df.loc["mean"] = df.mean()
|
||||
return df
|
||||
|
||||
@property
|
||||
def n_estimators_used(self) -> int | None:
|
||||
"""实际使用的树数量（早停后可能 < n_estimators）。"""
|
||||
if self._model is None:
|
||||
return None
|
||||
return self._model.booster_.current_iteration()
|
||||
@@ -0,0 +1,242 @@
|
||||
"""
|
||||
Optuna 优化引擎。
|
||||
|
||||
统一接口：optimizer.optimize(strategy_class, space, price_df, factor_df) → OptimizationResult
|
||||
"""
|
||||
|
||||
import copy
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
import optuna
|
||||
import pandas as pd
|
||||
|
||||
from backtest.base import BaseStrategy
|
||||
from backtest.report import BacktestReport
|
||||
from backtest.vectorbt.engine import VectorBTEngine
|
||||
from optimizer.objectives import Objective
|
||||
from optimizer.result import OptimizationResult, WalkForwardResult
|
||||
from optimizer.space import SearchSpace
|
||||
|
||||
# 抑制 Optuna 日志
|
||||
optuna.logging.set_verbosity(optuna.logging.WARNING)
|
||||
|
||||
|
||||
class OptunaEngine:
|
||||
"""
|
||||
Optuna 优化引擎。
|
||||
"""
|
||||
|
||||
def __init__(self, bt_engine: VectorBTEngine | None = None):
|
||||
self.bt_engine = bt_engine or VectorBTEngine()
|
||||
|
||||
def optimize(
|
||||
self,
|
||||
strategy_class: type[BaseStrategy],
|
||||
search_space: SearchSpace,
|
||||
price_df: pd.DataFrame,
|
||||
factor_df: pd.DataFrame | None = None,
|
||||
metric: str = "sharpe",
|
||||
n_trials: int = 100,
|
||||
direction: str = "maximize",
|
||||
sampler: optuna.samplers.BaseSampler | None = None,
|
||||
) -> OptimizationResult:
|
||||
"""
|
||||
参数寻优。
|
||||
|
||||
参数:
|
||||
strategy_class: 策略类
|
||||
search_space: 搜索空间
|
||||
price_df: 价格数据
|
||||
factor_df: 因子数据
|
||||
metric: 优化目标
|
||||
n_trials: 试验次数
|
||||
direction: 'maximize' | 'minimize'
|
||||
sampler: Optuna 采样器，默认 TPESampler
|
||||
"""
|
||||
if sampler is None:
|
||||
sampler = optuna.samplers.TPESampler(seed=42)
|
||||
|
||||
study = optuna.create_study(
|
||||
direction=direction,
|
||||
sampler=sampler,
|
||||
)
|
||||
|
||||
objective = Objective(
|
||||
strategy_class=strategy_class,
|
||||
search_space=search_space,
|
||||
price_df=price_df,
|
||||
factor_df=factor_df,
|
||||
bt_engine=self.bt_engine,
|
||||
metric=metric,
|
||||
)
|
||||
|
||||
t0 = time.time()
|
||||
study.optimize(objective, n_trials=n_trials, show_progress_bar=True)
|
||||
elapsed = time.time() - t0
|
||||
|
||||
# 用最优参数跑一次完整回测
|
||||
best_params = study.best_params
|
||||
try:
|
||||
best_strategy = strategy_class(**best_params)
|
||||
except TypeError:
|
||||
valid = {k: v for k, v in best_params.items()
|
||||
if k in strategy_class.__init__.__code__.co_varnames}
|
||||
best_strategy = strategy_class(**valid)
|
||||
|
||||
best_report = self.bt_engine.run(
|
||||
best_strategy,
|
||||
price_df,
|
||||
price_df if factor_df is None else factor_df,
|
||||
)
|
||||
|
||||
# 参数重要性
|
||||
try:
|
||||
importance = optuna.importance.get_param_importances(study)
|
||||
except Exception:
|
||||
importance = {}
|
||||
|
||||
# 试验记录
|
||||
trials_df = study.trials_dataframe()
|
||||
|
||||
return OptimizationResult(
|
||||
best_params=study.best_params,
|
||||
best_value=study.best_value,
|
||||
metric=metric,
|
||||
best_report=best_report,
|
||||
trials_df=trials_df,
|
||||
param_importance=importance,
|
||||
)
|
||||
|
||||
def optimize_walk_forward(
|
||||
self,
|
||||
strategy_class: type[BaseStrategy],
|
||||
search_space: SearchSpace,
|
||||
price_df: pd.DataFrame,
|
||||
factor_df: pd.DataFrame | None = None,
|
||||
metric: str = "sharpe",
|
||||
n_trials: int = 100,
|
||||
train_window: int = 252 * 3,
|
||||
test_window: int = 252,
|
||||
) -> WalkForwardResult:
|
||||
"""
|
||||
滚动窗口优化（Walk-Forward Analysis）。
|
||||
|
||||
每一步：train_window 训练 → test_window 验证 → 滑动。
|
||||
"""
|
||||
if factor_df is None:
|
||||
factor_df = price_df
|
||||
|
||||
n_total = len(price_df)
|
||||
windows = []
|
||||
test_equities = []
|
||||
param_history = []
|
||||
|
||||
start = 0
|
||||
while start + train_window + test_window <= n_total:
|
||||
train_slice = slice(start, start + train_window)
|
||||
test_slice = slice(start + train_window, start + train_window + test_window)
|
||||
|
||||
train_price = price_df.iloc[train_slice]
|
||||
train_factor = factor_df.iloc[train_slice]
|
||||
test_price = price_df.iloc[test_slice]
|
||||
test_factor = factor_df.iloc[test_slice]
|
||||
|
||||
# 训练集上优化
|
||||
opt_result = self.optimize(
|
||||
strategy_class=strategy_class,
|
||||
search_space=search_space,
|
||||
price_df=train_price,
|
||||
factor_df=train_factor,
|
||||
metric=metric,
|
||||
n_trials=n_trials,
|
||||
)
|
||||
|
||||
# 测试集上验证
|
||||
try:
|
||||
test_strategy = strategy_class(**opt_result.best_params)
|
||||
except TypeError:
|
||||
valid = {k: v for k, v in opt_result.best_params.items()
|
||||
if k in strategy_class.__init__.__code__.co_varnames}
|
||||
test_strategy = strategy_class(**valid)
|
||||
|
||||
test_report = self.bt_engine.run(test_strategy, test_price, test_factor)
|
||||
|
||||
if len(test_report.equity_curve) > 0:
|
||||
test_equities.append(test_report.equity_curve)
|
||||
|
||||
train_idx = train_price.index
|
||||
test_idx = test_price.index
|
||||
windows.append({
|
||||
"train_start": train_idx[0] if len(train_idx) > 0 else "",
|
||||
"train_end": train_idx[-1] if len(train_idx) > 0 else "",
|
||||
"test_start": test_idx[0] if len(test_idx) > 0 else "",
|
||||
"test_end": test_idx[-1] if len(test_idx) > 0 else "",
|
||||
"best_params": opt_result.best_params,
|
||||
"best_value": opt_result.best_value,
|
||||
"test_return": test_report.total_return,
|
||||
"test_sharpe": test_report.sharpe_ratio,
|
||||
"test_mdd": test_report.max_drawdown,
|
||||
})
|
||||
param_history.append(opt_result.best_params)
|
||||
|
||||
start += test_window
|
||||
|
||||
# 合并测试期权益曲线
|
||||
consolidated = _merge_test_periods(test_equities, self.bt_engine.initial_capital)
|
||||
|
||||
# 参数稳定性
|
||||
param_df = pd.DataFrame(param_history) if param_history else pd.DataFrame()
|
||||
if not param_df.empty:
|
||||
param_df.index.name = "window"
|
||||
|
||||
return WalkForwardResult(
|
||||
windows=windows,
|
||||
consolidated_report=consolidated,
|
||||
param_stability=param_df,
|
||||
)
|
||||
|
||||
|
||||
def _merge_test_periods(
|
||||
equity_list: list[pd.Series],
|
||||
initial_capital: float = 100_000,
|
||||
) -> BacktestReport | None:
|
||||
"""拼接各窗口测试期权益曲线为一个连续序列。"""
|
||||
if not equity_list:
|
||||
return None
|
||||
|
||||
merged = pd.concat(equity_list)
|
||||
merged = merged.sort_index()
|
||||
merged = merged[~merged.index.duplicated()]
|
||||
|
||||
# 确保 DatetimeIndex
|
||||
if not isinstance(merged.index, pd.DatetimeIndex):
|
||||
merged.index = pd.to_datetime(merged.index, format="%Y%m%d")
|
||||
|
||||
dd = merged / merged.cummax() - 1
|
||||
daily_ret = merged.pct_change().dropna()
|
||||
years = max(len(daily_ret) / 252, 0.02)
|
||||
|
||||
total_ret = (merged.iloc[-1] / merged.iloc[0] - 1) * 100
|
||||
cagr = ((total_ret / 100 + 1) ** (1 / years) - 1) * 100
|
||||
mdd = dd.min() * 100
|
||||
std_ret = daily_ret.std() * np.sqrt(252)
|
||||
sharpe = (daily_ret.mean() * 252) / std_ret if std_ret > 0 else 0
|
||||
calmar = cagr / abs(mdd) if abs(mdd) > 0 else 0
|
||||
|
||||
try:
|
||||
monthly = merged.resample("ME").last().pct_change()
|
||||
except Exception:
|
||||
monthly = pd.Series(dtype=float)
|
||||
|
||||
return BacktestReport(
|
||||
total_return=round(total_ret, 2),
|
||||
cagr=round(cagr, 2),
|
||||
max_drawdown=round(mdd, 2),
|
||||
sharpe_ratio=round(sharpe, 2),
|
||||
calmar_ratio=round(calmar, 2),
|
||||
annual_volatility=round(std_ret * 100 if std_ret != 0 else 0, 2),
|
||||
equity_curve=merged,
|
||||
drawdown_curve=dd,
|
||||
monthly_returns=monthly,
|
||||
)
|
||||
@@ -0,0 +1,74 @@
|
||||
"""
|
||||
Optuna 目标函数。
|
||||
|
||||
将策略实例化 → 回测 → 提取指标，包装为 Optuna objective。
|
||||
"""
|
||||
|
||||
import optuna
|
||||
import pandas as pd
|
||||
|
||||
from backtest.base import BaseStrategy
|
||||
from backtest.vectorbt.engine import VectorBTEngine
|
||||
from optimizer.space import SearchSpace
|
||||
|
||||
# 指标提取器：从 BacktestReport 取对应字段
|
||||
_METRIC_EXTRACTORS = {
|
||||
"sharpe": lambda r: r.sharpe_ratio,
|
||||
"cagr": lambda r: r.cagr,
|
||||
"calmar": lambda r: r.calmar_ratio,
|
||||
"total_return": lambda r: r.total_return,
|
||||
"return_over_dd": lambda r: abs(r.total_return / r.max_drawdown) if r.max_drawdown != 0 else 0.0,
|
||||
"win_rate": lambda r: r.win_rate,
|
||||
"profit_factor": lambda r: r.profit_factor,
|
||||
}
|
||||
|
||||
|
||||
class Objective:
|
||||
"""
|
||||
Optuna 目标函数（可调用）。
|
||||
|
||||
用法:
|
||||
obj = Objective(SMACrossStrategy, sma_cross_space, price_df, factor_df, metric="sharpe")
|
||||
study = optuna.create_study(direction="maximize")
|
||||
study.optimize(obj, n_trials=100)
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
strategy_class: type[BaseStrategy],
|
||||
search_space: SearchSpace,
|
||||
price_df: pd.DataFrame,
|
||||
factor_df: pd.DataFrame | None = None,
|
||||
bt_engine: VectorBTEngine | None = None,
|
||||
metric: str = "sharpe",
|
||||
):
|
||||
self.strategy_class = strategy_class
|
||||
self.search_space = search_space
|
||||
self.price_df = price_df
|
||||
self.factor_df = factor_df if factor_df is not None else price_df
|
||||
self.bt_engine = bt_engine or VectorBTEngine()
|
||||
self.metric = metric
|
||||
self._extractor = _METRIC_EXTRACTORS.get(metric)
|
||||
if self._extractor is None:
|
||||
raise ValueError(f"不支持的指标: '{metric}'。可选: {list(_METRIC_EXTRACTORS)}")
|
||||
|
||||
def __call__(self, trial: optuna.Trial) -> float:
|
||||
params = self.search_space.suggest(trial)
|
||||
|
||||
try:
|
||||
strategy = self.strategy_class(**params)
|
||||
except TypeError:
|
||||
# 过滤不匹配的参数
|
||||
valid = {k: v for k, v in params.items()
|
||||
if k in self.strategy_class.__init__.__code__.co_varnames}
|
||||
strategy = self.strategy_class(**valid)
|
||||
|
||||
report = self.bt_engine.run(strategy, self.price_df, self.factor_df)
|
||||
|
||||
value = self._extractor(report) # type: ignore
|
||||
|
||||
# 无效值处理
|
||||
if value is None or (isinstance(value, float) and (pd.isna(value) or value == float("inf"))):
|
||||
return float("-inf")
|
||||
|
||||
return float(value)
|
||||
@@ -0,0 +1,86 @@
|
||||
"""
|
||||
策略优化快捷函数。
|
||||
|
||||
为常用策略提供一键优化入口。
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from backtest.vectorbt.engine import VectorBTEngine
|
||||
from optimizer.engine import OptunaEngine
|
||||
from optimizer.result import OptimizationResult, WalkForwardResult
|
||||
from optimizer.space import (
|
||||
sma_cross_space,
|
||||
rsi_revert_space,
|
||||
momentum_breakout_space,
|
||||
factor_cross_space,
|
||||
)
|
||||
|
||||
_DEFAULT_TRIALS = 100
|
||||
|
||||
|
||||
def optimize_sma_cross(
|
||||
price_df: pd.DataFrame,
|
||||
factor_df: pd.DataFrame | None = None,
|
||||
bt_engine: VectorBTEngine | None = None,
|
||||
n_trials: int = _DEFAULT_TRIALS,
|
||||
metric: str = "sharpe",
|
||||
) -> OptimizationResult:
|
||||
"""均线交叉策略参数寻优。"""
|
||||
from backtest.strategies.sma_cross import SMACrossStrategy
|
||||
return OptunaEngine(bt_engine).optimize(
|
||||
SMACrossStrategy, sma_cross_space, price_df, factor_df, metric, n_trials,
|
||||
)
|
||||
|
||||
|
||||
def optimize_rsi_revert(
|
||||
price_df: pd.DataFrame,
|
||||
factor_df: pd.DataFrame | None = None,
|
||||
bt_engine: VectorBTEngine | None = None,
|
||||
n_trials: int = _DEFAULT_TRIALS,
|
||||
metric: str = "sharpe",
|
||||
) -> OptimizationResult:
|
||||
"""RSI 反转策略参数寻优。"""
|
||||
from backtest.strategies.rsi_mean_revert import RSIMeanRevertStrategy
|
||||
return OptunaEngine(bt_engine).optimize(
|
||||
RSIMeanRevertStrategy, rsi_revert_space, price_df, factor_df, metric, n_trials,
|
||||
)
|
||||
|
||||
|
||||
def optimize_momentum_breakout(
|
||||
price_df: pd.DataFrame,
|
||||
factor_df: pd.DataFrame | None = None,
|
||||
bt_engine: VectorBTEngine | None = None,
|
||||
n_trials: int = _DEFAULT_TRIALS,
|
||||
metric: str = "sharpe",
|
||||
) -> OptimizationResult:
|
||||
"""动量突破策略参数寻优。"""
|
||||
from backtest.strategies.momentum_breakout import MomentumBreakoutStrategy
|
||||
return OptunaEngine(bt_engine).optimize(
|
||||
MomentumBreakoutStrategy, momentum_breakout_space, price_df, factor_df, metric, n_trials,
|
||||
)
|
||||
|
||||
|
||||
def optimize_factor_cross(
|
||||
price_df: pd.DataFrame,
|
||||
factor_column: str,
|
||||
factor_df: pd.DataFrame | None = None,
|
||||
bt_engine: VectorBTEngine | None = None,
|
||||
n_trials: int = _DEFAULT_TRIALS,
|
||||
metric: str = "sharpe",
|
||||
) -> OptimizationResult:
|
||||
"""因子阈值交叉策略参数寻优。
|
||||
|
||||
参数:
|
||||
factor_column: 因子列名（如 'momentum_20'）
|
||||
其余同 optimize_* 系列。
|
||||
"""
|
||||
from backtest.strategies.factor_cross import FactorCrossStrategy
|
||||
|
||||
class _FCS(FactorCrossStrategy):
|
||||
def __init__(self, buy_threshold=0, sell_threshold=None):
|
||||
super().__init__(factor_column, buy_threshold, sell_threshold)
|
||||
|
||||
return OptunaEngine(bt_engine).optimize(
|
||||
_FCS, factor_cross_space, price_df, factor_df, metric, n_trials,
|
||||
)
|
||||
@@ -0,0 +1,57 @@
|
||||
"""
|
||||
优化结果数据结构。
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from backtest.report import BacktestReport
|
||||
|
||||
|
||||
@dataclass
|
||||
class OptimizationResult:
|
||||
"""单次参数优化结果。"""
|
||||
|
||||
best_params: dict = field(default_factory=dict)
|
||||
best_value: float = 0.0
|
||||
metric: str = "sharpe"
|
||||
|
||||
best_report: BacktestReport | None = None
|
||||
trials_df: pd.DataFrame = field(default_factory=pd.DataFrame)
|
||||
param_importance: dict = field(default_factory=dict)
|
||||
|
||||
def summary(self) -> str:
|
||||
lines = [
|
||||
f"最优参数: {self.best_params}",
|
||||
f"最优目标 ({self.metric}): {self.best_value:.4f}",
|
||||
]
|
||||
if self.best_report is not None:
|
||||
lines.append(f"回测: {self.best_report.summary()}")
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
@dataclass
|
||||
class WalkForwardResult:
|
||||
"""滚动窗口优化结果。"""
|
||||
|
||||
windows: list[dict] = field(default_factory=list)
|
||||
consolidated_report: BacktestReport | None = None
|
||||
param_stability: pd.DataFrame = field(default_factory=pd.DataFrame)
|
||||
|
||||
def summary(self) -> str:
|
||||
n = len(self.windows)
|
||||
lines = [f"Walk-Forward: {n} 个窗口"]
|
||||
for w in self.windows:
|
||||
lines.append(
|
||||
f" {w['train_start']}~{w['train_end']}"
|
||||
f" → {w['test_start']}~{w['test_end']}"
|
||||
f" | 参数={w.get('best_params', {})}"
|
||||
f" | 收益={w.get('test_return', 0):.1f}%"
|
||||
)
|
||||
if self.consolidated_report is not None:
|
||||
lines.append(f"整体: {self.consolidated_report.summary()}")
|
||||
if not self.param_stability.empty:
|
||||
stds = self.param_stability.std()
|
||||
lines.append(f"参数稳定性(std): {dict(stds.round(2))}")
|
||||
return "\n".join(lines)
|
||||
@@ -0,0 +1,67 @@
|
||||
"""
|
||||
参数搜索空间定义。
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
import optuna
|
||||
|
||||
|
||||
@dataclass
|
||||
class SearchSpace:
|
||||
"""参数搜索空间。"""
|
||||
|
||||
params: list[dict] = field(default_factory=list)
|
||||
# 每个元素: {"name": str, "type": "int"|"float"|"categorical",
|
||||
# "low": float, "high": float, "step": float, "choices": list}
|
||||
|
||||
def suggest(self, trial: optuna.Trial) -> dict:
|
||||
"""从 trial 中采样一组参数。"""
|
||||
result = {}
|
||||
for p in self.params:
|
||||
name = p["name"]
|
||||
kind = p["type"]
|
||||
if kind == "int":
|
||||
low = p.get("low", 0)
|
||||
high = p.get("high", 100)
|
||||
step = p.get("step", 1)
|
||||
result[name] = trial.suggest_int(name, int(low), int(high), step=int(step))
|
||||
elif kind == "float":
|
||||
low = p.get("low", 0.0)
|
||||
high = p.get("high", 1.0)
|
||||
result[name] = trial.suggest_float(name, float(low), float(high))
|
||||
elif kind == "categorical":
|
||||
choices = p.get("choices", [])
|
||||
result[name] = trial.suggest_categorical(name, choices)
|
||||
return result
|
||||
|
||||
|
||||
# ── 预置搜索空间 ──────────────────────────────────────────
|
||||
|
||||
sma_cross_space = SearchSpace(params=[
|
||||
{"name": "fast", "type": "int", "low": 2, "high": 30, "step": 1},
|
||||
{"name": "slow", "type": "int", "low": 15, "high": 120, "step": 5},
|
||||
])
|
||||
|
||||
rsi_revert_space = SearchSpace(params=[
|
||||
{"name": "oversold", "type": "int", "low": 10, "high": 45, "step": 1},
|
||||
{"name": "overbought", "type": "int", "low": 55, "high": 90, "step": 1},
|
||||
])
|
||||
|
||||
momentum_breakout_space = SearchSpace(params=[
|
||||
{"name": "lookback", "type": "int", "low": 10, "high": 60, "step": 5},
|
||||
{"name": "exit_period", "type": "int", "low": 5, "high": 30, "step": 1},
|
||||
])
|
||||
|
||||
factor_cross_space = SearchSpace(params=[
|
||||
{"name": "buy_threshold", "type": "float", "low": -10.0, "high": 10.0},
|
||||
{"name": "sell_threshold", "type": "float", "low": -10.0, "high": 10.0},
|
||||
])
|
||||
|
||||
# 名称 → 空间映射
|
||||
SPACES = {
|
||||
"sma_cross": sma_cross_space,
|
||||
"rsi_revert": rsi_revert_space,
|
||||
"momentum_breakout": momentum_breakout_space,
|
||||
"factor_cross": factor_cross_space,
|
||||
}
|
||||
@@ -0,0 +1,121 @@
|
||||
"""
|
||||
报告持久化模块。
|
||||
|
||||
将 CLI 脚本输出的 Markdown 报告存入 DB（mac_report 表）。
|
||||
同一日期+研究对象的新报告入库时，旧报告自动标记为失效（is_active=0）。
|
||||
"""
|
||||
|
||||
from datetime import datetime
|
||||
|
||||
from sqlalchemy import text
|
||||
|
||||
from database.connection import get_engine
|
||||
from database.models import Report, create_all_tables
|
||||
|
||||
|
||||
def save_report(
|
||||
content: str,
|
||||
title: str,
|
||||
report_date: str | None = None,
|
||||
subject_type: str = "daily",
|
||||
subject_code: str = "",
|
||||
) -> int:
|
||||
"""
|
||||
保存报告到 DB。
|
||||
|
||||
如果同一天、同一研究对象已有报告，先将其标记为 is_active=0，
|
||||
然后插入新报告。
|
||||
|
||||
参数:
|
||||
content: Markdown 报告内容
|
||||
title: 报告标题
|
||||
report_date: 报告日期 YYYYMMDD，默认今天
|
||||
subject_type: 研究对象类型 (stock/index/sector/portfolio/daily)
|
||||
subject_code: 研究对象代码 (如 000001.SZ 或 000300.SH)
|
||||
|
||||
返回:
|
||||
新报告的 id
|
||||
"""
|
||||
report_date = report_date or datetime.now().strftime("%Y%m%d")
|
||||
|
||||
engine = get_engine()
|
||||
|
||||
# 确保表存在
|
||||
create_all_tables()
|
||||
|
||||
# 将同日期+同对象的旧报告标记失效
|
||||
with engine.connect() as conn:
|
||||
conn.execute(
|
||||
text(
|
||||
"UPDATE {} SET is_active = 0 "
|
||||
"WHERE report_date = :d AND subject_type = :st AND subject_code = :sc AND is_active = 1"
|
||||
.format(Report.__tablename__)
|
||||
),
|
||||
{"d": report_date, "st": subject_type, "sc": subject_code},
|
||||
)
|
||||
conn.commit()
|
||||
|
||||
# 插入新报告
|
||||
report = Report(
|
||||
report_date=report_date,
|
||||
title=title,
|
||||
subject_type=subject_type,
|
||||
subject_code=subject_code,
|
||||
content=content,
|
||||
created_at=datetime.now(),
|
||||
is_active=1.0,
|
||||
)
|
||||
|
||||
from sqlalchemy.orm import Session
|
||||
with Session(engine) as session:
|
||||
session.add(report)
|
||||
session.commit()
|
||||
report_id = report.id
|
||||
session.expunge_all()
|
||||
|
||||
return report_id
|
||||
|
||||
|
||||
def query_reports(
|
||||
report_date: str | None = None,
|
||||
subject_type: str | None = None,
|
||||
subject_code: str | None = None,
|
||||
active_only: bool = True,
|
||||
limit: int = 20,
|
||||
) -> list[dict]:
|
||||
"""查询报告列表。"""
|
||||
engine = get_engine()
|
||||
table = Report.__tablename__
|
||||
|
||||
sql = "SELECT * FROM {} WHERE 1=1".format(table)
|
||||
params = {}
|
||||
|
||||
if report_date:
|
||||
sql += " AND report_date = :d"
|
||||
params["d"] = report_date
|
||||
if subject_type:
|
||||
sql += " AND subject_type = :st"
|
||||
params["st"] = subject_type
|
||||
if subject_code:
|
||||
sql += " AND subject_code = :sc"
|
||||
params["sc"] = subject_code
|
||||
if active_only:
|
||||
sql += " AND is_active = 1"
|
||||
|
||||
sql += " ORDER BY id DESC LIMIT :lim"
|
||||
params["lim"] = limit
|
||||
|
||||
with engine.connect() as conn:
|
||||
rows = conn.execute(text(sql), params).fetchall()
|
||||
|
||||
result = []
|
||||
for r in rows:
|
||||
d = dict(r._mapping)
|
||||
# DATE/DATETIME 列 → 字符串
|
||||
for key in ("report_date", "created_at"):
|
||||
val = d.get(key)
|
||||
if hasattr(val, "strftime"):
|
||||
fmt = "%Y%m%d" if key == "report_date" else "%Y-%m-%d %H:%M:%S"
|
||||
d[key] = val.strftime(fmt)
|
||||
result.append(d)
|
||||
return result
|
||||
@@ -0,0 +1,10 @@
|
||||
akshare>=1.14.0
|
||||
pandas>=2.0.0
|
||||
sqlalchemy>=2.0
|
||||
pymysql>=1.1.0
|
||||
python-dotenv>=1.0
|
||||
vectorbt>=0.5.0
|
||||
optuna>=3.0
|
||||
lightgbm>=4.0
|
||||
catboost>=1.2
|
||||
quantstats>=0.0.62
|
||||
Reference in New Issue
Block a user