"""Repository 层(§7.4)。 数据分层: - DuckDB 视图: 冷查询(统计、元数据、用户自定义SQL) - Polars 缓存: 热路径(enriched 最新日 ~5500行 + instruments ~5500行) - Polars scan_parquet: 分钟K/历史日K (predicate pushdown) 缓存生命周期: - startup 时不加载(数据可能为空) - pipeline 完成后调用 refresh_cache() - 服务层通过 get_enriched_latest() / get_instruments() 获取缓存 """ from __future__ import annotations import logging import threading from datetime import date from pathlib import Path import duckdb import polars as pl from app.config import settings logger = logging.getLogger(__name__) class DataStore: """唯一的存储入口 — 进程启动时创建。""" def __init__(self, data_dir: Path | None = None) -> None: self.data_dir = Path(data_dir or settings.data_dir) self.data_dir.mkdir(parents=True, exist_ok=True) # 关键子目录(§7.2) for sub in ( "kline_daily", "kline_daily_enriched", "kline_index_daily", "kline_index_enriched", "kline_minute", "adj_factor", "financials", "instruments", "instruments_index", "instruments_ext", "kline_ext", "pools", "backtest_results", "screener_results", "ai_cache", "user_data", "depth5", ): (self.data_dir / sub).mkdir(parents=True, exist_ok=True) # 财务数据子目录 for sub in ("metrics", "income", "balance_sheet", "cash_flow"): (self.data_dir / "financials" / sub).mkdir(parents=True, exist_ok=True) # DuckDB 内存模式 — 不建 .db 文件(§7.1) self.db = duckdb.connect(database=":memory:") self._register_views() def _register_views(self) -> None: """把 Parquet 目录挂载为 DuckDB 视图(§7.3)。""" d = self.data_dir.as_posix() statements = [ f"""CREATE OR REPLACE VIEW kline_daily AS SELECT * FROM read_parquet('{d}/kline_daily/**/*.parquet', union_by_name=true)""", f"""CREATE OR REPLACE VIEW kline_enriched AS SELECT * FROM read_parquet('{d}/kline_daily_enriched/**/*.parquet', union_by_name=true)""", f"""CREATE OR REPLACE VIEW kline_index_daily AS SELECT * FROM read_parquet('{d}/kline_index_daily/**/*.parquet', union_by_name=true)""", f"""CREATE OR REPLACE VIEW kline_index_enriched AS SELECT * FROM read_parquet('{d}/kline_index_enriched/**/*.parquet', union_by_name=true)""", f"""CREATE OR REPLACE VIEW kline_minute AS SELECT * FROM read_parquet('{d}/kline_minute/**/*.parquet', union_by_name=true)""", f"""CREATE OR REPLACE VIEW adj_factor AS SELECT * FROM read_parquet('{d}/adj_factor/**/*.parquet', union_by_name=true)""", f"""CREATE OR REPLACE VIEW instruments AS SELECT * FROM read_parquet('{d}/instruments/**/*.parquet', union_by_name=true)""", f"""CREATE OR REPLACE VIEW instruments_index AS SELECT * FROM read_parquet('{d}/instruments_index/**/*.parquet', union_by_name=true)""", f"""CREATE OR REPLACE VIEW instruments_ext AS SELECT * FROM read_parquet('{d}/instruments_ext/**/*.parquet', union_by_name=true)""", f"""CREATE OR REPLACE VIEW kline_ext AS SELECT * FROM read_parquet('{d}/kline_ext/**/*.parquet', union_by_name=true)""", # 财务数据视图 f"""CREATE OR REPLACE VIEW financials_metrics AS SELECT * FROM read_parquet('{d}/financials/metrics/*.parquet', union_by_name=true)""", f"""CREATE OR REPLACE VIEW financials_income AS SELECT * FROM read_parquet('{d}/financials/income/*.parquet', union_by_name=true)""", f"""CREATE OR REPLACE VIEW financials_balance_sheet AS SELECT * FROM read_parquet('{d}/financials/balance_sheet/*.parquet', union_by_name=true)""", f"""CREATE OR REPLACE VIEW financials_cash_flow AS SELECT * FROM read_parquet('{d}/financials/cash_flow/*.parquet', union_by_name=true)""", # 五档盘口 sealed 真假涨停(独立旁路存储,不进 enriched) f"""CREATE OR REPLACE VIEW depth5 AS SELECT * FROM read_parquet('{d}/depth5/**/*.parquet', union_by_name=true)""", ] for sql in statements: try: self.db.execute(sql) except duckdb.IOException: logger.debug("view registration skipped (no parquet yet): %s", sql[:60]) class KlineRepository: """日 K / 分钟 K 的读写入口。""" def __init__(self, store: DataStore) -> None: self.store = store self.db = store.db self._lock = threading.Lock() # ---- Polars 缓存 ---- self._enriched_cache: pl.DataFrame | None = None # 最新一天 (~5500行) self._enriched_cache_date: date | None = None self._live_agg_cache: pl.DataFrame | None = None # 预计算聚合表 (~5500行) self._live_agg_cache_date: date | None = None self._instruments_cache: pl.DataFrame | None = None # 完整 enriched 历史 (含所有指标, 供 filter_history 策略使用) self._enriched_history_cache: pl.DataFrame | None = None # ~100万行 self._enriched_history_start: date | None = None self._index_instruments_cache: pl.DataFrame | None = None # parquet glob 路径 self._enriched_glob = str(store.data_dir / "kline_daily_enriched" / "**" / "*.parquet") self._index_enriched_glob = str(store.data_dir / "kline_index_enriched" / "**" / "*.parquet") self._minute_glob = str(store.data_dir / "kline_minute" / "**" / "*.parquet") self._inst_glob = str(store.data_dir / "instruments" / "**" / "*.parquet") self._index_inst_glob = str(store.data_dir / "instruments_index" / "**" / "*.parquet") def execute_all(self, sql: str, params: list | None = None) -> list[tuple]: """线程安全的 SELECT → fetchall。DuckDB 单 connection 非线程安全，所有读路径须走此方法。""" with self._lock: return self.db.execute(sql, params or []).fetchall() def execute_one(self, sql: str, params: list | None = None) -> tuple | None: """线程安全的 SELECT → fetchone。""" with self._lock: return self.db.execute(sql, params or []).fetchone() # ================================================================ # Polars 缓存管理 # ================================================================ def refresh_cache(self) -> None: """刷新 Polars 缓存。在 pipeline 完成后、服务启动时调用。""" self._refresh_instruments() self._refresh_index_instruments() self._refresh_enriched() def clear_cache(self) -> None: """清空所有 Polars 内存缓存。 与 refresh_cache 的区别: refresh_cache 在磁盘无数据时会提前 return, 导致内存里的旧缓存残留 (clear 数据后看板仍显示旧数据的根因)。 本方法无条件清空, 供清除数据/重置场景调用。 """ self._enriched_cache = None self._enriched_cache_date = None self._enriched_history_cache = None self._enriched_history_start = None self._live_agg_cache = None self._live_agg_cache_date = None self._instruments_cache = None self._index_instruments_cache = None def _refresh_enriched(self) -> None: """从 parquet 加载 enriched 最新日到内存 + 构建聚合表。 enriched parquet 仅存 14 列基础数据。启动时读入历史数据并即时计算完整指标， 将结果缓存在内存中供各服务使用。 优化: 扩大历史读取范围, 同时缓存完整历史 (含指标), 供 filter_history 策略直接复用。 """ try: latest = self._latest_enriched_date_duckdb() if not latest: # 磁盘已无数据: 必须清空内存缓存, 否则旧数据会残留 # (清数据后看板仍显示旧数据的根因) self.clear_cache() return # Step 1: 直接读最新日期的分区文件 (仅 14 列) enriched_dir = self.store.data_dir / "kline_daily_enriched" ds = latest.isoformat() if hasattr(latest, "isoformat") else str(latest) target_parquet = enriched_dir / f"date={ds}" / "part.parquet" if not target_parquet.exists(): return df_latest = pl.read_parquet(target_parquet) if df_latest.is_empty(): return # Step 2: 读近 300 天 14 列数据 → compute → filter(latest) → 缓存 # 300 日历天 ≈ 210 交易日, 覆盖 filter_history 最大 lookback(90) + warmup(60) try: from datetime import timedelta from app.indicators.pipeline import compute_indicators, compute_signals, compute_limit_signals start_full = latest - timedelta(days=300) read_cols = [c for c in ["symbol", "date", "open", "high", "low", "close", "volume", "amount", "raw_close", "raw_high", "raw_low"] if c in df_latest.columns] lf = ( pl.scan_parquet(self._enriched_glob) .filter(pl.col("date") >= start_full) .sort(["symbol", "date"]) ) df_hist = lf.select(read_cols).collect() if not df_hist.is_empty(): instruments = self._instruments_cache if self._instruments_cache is not None else pl.DataFrame() df_full = compute_indicators(df_hist) df_full = compute_signals(df_full) if instruments is not None and not instruments.is_empty(): df_full = compute_limit_signals(df_full, instruments) # JOIN instruments 到完整历史 (filter_history/basic_filter 需要 name/股本等列) if instruments is not None and not instruments.is_empty(): inst_cols = [c for c in ["name", "total_shares", "float_shares"] if c in instruments.columns and c not in df_full.columns] if inst_cols: df_full = df_full.join( instruments.select(["symbol", *inst_cols]).unique(subset=["symbol"]), on="symbol", how="left", ) # 缓存完整历史 (含指标+必要基础信息) 供 filter_history/backtest 直接复用 self._enriched_history_cache = df_full self._enriched_history_start = df_full["date"].min() logger.info("enriched 历史缓存: %d rows, %s ~ %s", len(df_full), self._enriched_history_start, latest) # 只取最新一天作为 enriched_cache df_today = df_full.filter(pl.col("date") == latest) if not df_today.is_empty(): self._enriched_cache = df_today self._enriched_cache_date = latest # 构建盘中递推基准: 若最新分区是今天的实时盘中数据, # 递推状态必须停在上一交易日, 不能把今天作为“昨日”。 self._build_live_agg(self._live_agg_baseline_date(latest)) logger.info("enriched 缓存已计算: %d 只, 日期 %s (即时计算)", len(df_today), latest) return except Exception as e: # noqa: BLE001 logger.warning("enriched 即时计算失败, 使用原始 14 列缓存: %s", e) # 降级: 直接使用 14 列数据 + 构建 live_agg self._enriched_cache = df_latest self._enriched_cache_date = latest self._build_live_agg(self._live_agg_baseline_date(latest)) logger.info("enriched 缓存已加载: %d 只, 日期 %s", len(df_latest), latest) except Exception as e: # noqa: BLE001 logger.warning("enriched 缓存刷新失败: %s", e) def _build_live_agg(self, latest: date) -> None: """从 OHLCV 即时计算递推状态 + 窗口聚合, 构建盘中实时聚合表。 优化: 优先使用 _enriched_history_cache (启动时已计算), 避免重复 compute_indicators。 """ from datetime import timedelta from app.indicators.pipeline import _ema_alpha start_60d = latest - timedelta(days=90) # 日历90天 ≈ 60个交易日 # 优先使用已有的历史缓存 (避免重复 scan_parquet + compute_indicators) if self._enriched_history_cache is not None and not self._enriched_history_cache.is_empty(): hist_all = self._enriched_history_cache if "date" in hist_all.columns and hist_all["date"].min() <= start_60d: # 从历史缓存中提取所需列 (历史缓存已有指标列) base_cols = ["symbol", "date", "open", "high", "low", "close", "volume", "raw_close", "raw_high", "raw_low"] needed = [c for c in base_cols if c in hist_all.columns] df_hist = hist_all.filter( (pl.col("date") >= start_60d) & (pl.col("date") <= latest) ).select(needed).sort(["symbol", "date"]) # 用历史缓存的指标列提取最新日状态 (无需再次 compute_indicators) state_source = hist_all.filter(pl.col("date") == latest) state_cols = [ "symbol", "ema5", "ema10", "ema20", "ema30", "ema60", "macd_dea", "kdj_k", "kdj_d", "atr_14", "close", "high", "low", "annual_vol_20d", ] existing_state = [c for c in state_cols if c in state_source.columns] agg_a = state_source.select(existing_state) else: df_hist = pl.DataFrame() agg_a = pl.DataFrame() else: # 降级: 读 parquet + compute_indicators df_hist, agg_a = self._build_live_agg_from_parquet(latest, start_60d) if df_hist.is_empty(): self._live_agg_cache = pl.DataFrame() self._live_agg_cache_date = None return if agg_a.is_empty(): self._live_agg_cache = pl.DataFrame() self._live_agg_cache_date = None return # 单独计算 _ema12 / _ema26 (compute_indicators 内部会 drop 掉) df_ema = df_hist.sort(["symbol", "date"]).with_columns([ pl.col("close").ewm_mean(alpha=_ema_alpha(12), adjust=False).over("symbol").alias("_ema12"), pl.col("close").ewm_mean(alpha=_ema_alpha(26), adjust=False).over("symbol").alias("_ema26"), ]).filter(pl.col("date") == latest).select("symbol", "_ema12", "_ema26") agg_a = agg_a.join(df_ema, on="symbol", how="inner") # 单独计算 RSI 状态列 (compute_indicators 内部会 drop 掉) df_rsi_base = df_hist.sort(["symbol", "date"]).with_columns( pl.col("close").diff().over("symbol").alias("_daily_delta") ) gain = pl.when(pl.col("_daily_delta") > 0).then(pl.col("_daily_delta")).otherwise(0.0) loss = pl.when(pl.col("_daily_delta") < 0).then(-pl.col("_daily_delta")).otherwise(0.0) rsi_exprs = [] for n in (6, 14, 24): a = 1.0 / n rsi_exprs.append(gain.ewm_mean(alpha=a, adjust=False).over("symbol").alias(f"_rsi_avg_gain_{n}")) rsi_exprs.append(loss.ewm_mean(alpha=a, adjust=False).over("symbol").alias(f"_rsi_avg_loss_{n}")) df_rsi = ( df_rsi_base .with_columns(rsi_exprs) .filter(pl.col("date") == latest) .select("symbol", *[f"_rsi_avg_gain_{n}" for n in (6, 14, 24)], *[f"_rsi_avg_loss_{n}" for n in (6, 14, 24)]) ) agg_a = agg_a.join(df_rsi, on="symbol", how="inner") # 前复权因子: adj_factor = close(复权) / raw_close(原始) if "raw_close" in df_hist.columns: adj_factor_df = ( df_hist.filter(pl.col("date") == latest) .select("symbol", (pl.col("close") / pl.col("raw_close")).alias("_adj_factor")) ) agg_a = agg_a.join(adj_factor_df, on="symbol", how="left") if "_adj_factor" in agg_a.columns: agg_a = agg_a.with_columns(pl.col("_adj_factor").fill_null(1.0)) # annual_vol_20d 递推状态: 最近 19 天日收益率的部分和 / 平方和 df_daily_pct = ( df_hist.sort(["symbol", "date"]) .with_columns( pl.col("close").pct_change().over("symbol").alias("_daily_pct") ) ) df_vol = df_daily_pct.group_by("symbol").agg([ pl.col("_daily_pct").tail(19).sum().alias("_vol_19d_pct_sum"), (pl.col("_daily_pct") ** 2).tail(19).sum().alias("_vol_19d_pct_sq_sum"), ]) agg_a = agg_a.join(df_vol, on="symbol", how="left") # 昨日连板数: 从 enriched parquet 取 (用于增量计算同向 +1) lf = pl.scan_parquet(self._enriched_glob).filter(pl.col("date") == latest) consec_cols = [c for c in ["symbol", "consecutive_limit_ups", "consecutive_limit_downs"] if c in lf.collect_schema().names()] if len(consec_cols) == 3: consec_df = lf.select(consec_cols).collect() if not consec_df.is_empty(): consec = consec_df.select( "symbol", pl.col("consecutive_limit_ups").alias("_prev_consec_up"), pl.col("consecutive_limit_downs").alias("_prev_consec_down"), ) agg_a = agg_a.join(consec, on="symbol", how="left") # B类: 按 symbol 分组聚合 — 窗口统计 agg_b = ( df_hist.sort(["symbol", "date"]) .group_by("symbol") .agg([ pl.col("close").tail(4).sum().alias("_ma5_partial_sum"), pl.col("close").tail(9).sum().alias("_ma10_partial_sum"), pl.col("close").tail(19).sum().alias("_ma20_partial_sum"), pl.col("close").tail(29).sum().alias("_ma30_partial_sum"), pl.col("close").tail(59).sum().alias("_ma60_partial_sum"), pl.col("close").tail(19).sum().alias("_boll_partial_sum"), (pl.col("close").tail(19) ** 2).sum().alias("_boll_partial_sq_sum"), pl.col("high").tail(59).max().alias("_high_59d"), pl.col("low").tail(59).min().alias("_low_59d"), pl.col("close").tail(5).first().alias("_close_5d_ago"), pl.col("close").tail(10).first().alias("_close_10d_ago"), pl.col("close").tail(20).first().alias("_close_20d_ago"), pl.col("close").tail(30).first().alias("_close_30d_ago"), pl.col("close").tail(60).first().alias("_close_60d_ago"), pl.col("volume").tail(4).sum().alias("_vol_ma5_partial_sum"), pl.col("volume").tail(9).sum().alias("_vol_ma10_partial_sum"), pl.col("low").tail(8).min().alias("_kdj_8d_low"), pl.col("high").tail(8).max().alias("_kdj_8d_high"), pl.col("close").tail(59).len().alias("_window_len"), ]) ) self._live_agg_cache = agg_a.join(agg_b, on="symbol", how="inner") self._live_agg_cache_date = latest def _live_agg_baseline_date(self, latest: date) -> date: """盘中递推基准日期。当天实时分区存在时使用上一可用交易日。""" if latest != date.today(): return latest try: row = self.execute_one( "SELECT max(date) FROM kline_enriched WHERE date < ?", [latest], ) if row and row[0]: d = row[0] return d if isinstance(d, date) else date.fromisoformat(str(d)) except Exception: # noqa: BLE001 pass return latest def _build_live_agg_from_parquet(self, latest: date, start_60d: date) -> tuple[pl.DataFrame, pl.DataFrame]: """降级路径: 从 parquet 读取数据并计算指标 (当 _enriched_history_cache 不可用时)。""" from app.indicators.pipeline import compute_indicators lf = ( pl.scan_parquet(self._enriched_glob) .filter(pl.col("date") >= start_60d) .filter(pl.col("date") <= latest) .sort(["symbol", "date"]) ) read_cols = [c for c in ["symbol", "date", "open", "high", "low", "close", "volume", "raw_close", "raw_high", "raw_low"] if c in lf.collect_schema().names()] df_hist = lf.select(read_cols).collect() if df_hist.is_empty(): return df_hist, pl.DataFrame() df_with_indicators = compute_indicators(df_hist) state_cols = [ "symbol", "ema5", "ema10", "ema20", "ema30", "ema60", "macd_dea", "kdj_k", "kdj_d", "atr_14", "close", "high", "low", "annual_vol_20d", ] existing_state = [c for c in state_cols if c in df_with_indicators.columns] agg_a = df_with_indicators.filter(pl.col("date") == latest).select(existing_state) return df_hist, agg_a def _refresh_instruments(self) -> None: """加载 instruments 到内存。""" try: df = pl.scan_parquet(self._inst_glob).collect() if not df.is_empty(): self._instruments_cache = df logger.info("instruments 缓存已加载: %d 只", len(df)) except Exception as e: # noqa: BLE001 logger.warning("instruments 缓存刷新失败: %s", e) def _refresh_index_instruments(self) -> None: """加载指数 instruments 到内存。""" try: df = pl.scan_parquet(self._index_inst_glob).collect() if not df.is_empty(): self._index_instruments_cache = df logger.info("index instruments 缓存已加载: %d 只", len(df)) except Exception as e: # noqa: BLE001 logger.debug("index instruments 缓存刷新跳过: %s", e) def get_enriched_latest(self) -> tuple[pl.DataFrame, date | None]: """返回缓存的 enriched 最新日 DataFrame + 日期。如无缓存则懒加载。""" if self._enriched_cache is None: self._refresh_enriched() if self._enriched_cache is None: return pl.DataFrame(), self._enriched_cache_date return self._enriched_cache, self._enriched_cache_date def get_enriched_history(self, target_date: date, lookback_days: int) -> pl.DataFrame | None: """返回预计算的 enriched 历史数据 (仅 lookback 范围, 不含 warmup)。 warmup 部分在 _refresh_enriched 计算指标时已使用, 策略只需要最终的 lookback 窗口。 返回 ~33万行 (90日历天) 而非 ~107万行, filter_history 策略的 group_by 快 20x+。 """ cache = self._enriched_history_cache if cache is None or cache.is_empty(): return None if "date" not in cache.columns: return None cache_max = cache["date"].max() cache_min = cache["date"].min() from datetime import timedelta # 验证缓存覆盖完整范围 (含 warmup) warmup_start = target_date - timedelta(days=(lookback_days + 60) * 2) if cache_min > warmup_start or cache_max < target_date: return None # 只返回 lookback 范围 (日历天数 ≈ 2/3 交易日, 足够覆盖) lookback_start = target_date - timedelta(days=lookback_days) return cache.filter((pl.col("date") >= lookback_start) & (pl.col("date") <= target_date)) def get_enriched_range( self, start: date, end: date, symbols: list[str] | None = None, columns: list[str] | None = None, ) -> pl.DataFrame | None: """从预计算 enriched 历史缓存返回完整区间；缓存不覆盖时返回 None。""" if self._enriched_history_cache is None: self._refresh_enriched() cache = self._enriched_history_cache if cache is None or cache.is_empty() or "date" not in cache.columns: return None cache_min = cache["date"].min() cache_max = cache["date"].max() if cache_min > start or cache_max < end: return None df = cache.filter((pl.col("date") >= start) & (pl.col("date") <= end)) if symbols is not None: df = df.filter(pl.col("symbol").is_in(symbols)) if columns and not df.is_empty(): existing = [c for c in columns if c in df.columns] if "symbol" not in existing and "symbol" in df.columns: existing.insert(0, "symbol") if "date" not in existing and "date" in df.columns: existing.insert(1, "date") df = df.select(existing) return df.sort(["symbol", "date"]) def get_live_agg(self) -> pl.DataFrame: """返回盘中实时指标预计算聚合表。如无缓存则懒加载。""" if self._live_agg_cache is None: self._refresh_enriched() if self._live_agg_cache is None: return pl.DataFrame() return self._live_agg_cache def get_instruments(self) -> pl.DataFrame: """返回缓存的 instruments DataFrame。如无缓存则懒加载。""" if self._instruments_cache is None: self._refresh_instruments() if self._instruments_cache is None: return pl.DataFrame() return self._instruments_cache def get_index_instruments(self) -> pl.DataFrame: """返回缓存的指数 instruments DataFrame。如无缓存则懒加载。""" if self._index_instruments_cache is None: self._refresh_index_instruments() if self._index_instruments_cache is None: return pl.DataFrame() return self._index_instruments_cache def get_index_symbol_set(self) -> set[str]: """返回已缓存指数 symbol 集合。""" df = self.get_index_instruments() if df.is_empty() or "symbol" not in df.columns: return set() return set(df["symbol"].cast(pl.Utf8).to_list()) def enriched_latest_date(self) -> date | None: """返回缓存中的 enriched 最新日期。""" return self._enriched_cache_date # ================================================================ # 热路径: Polars 查询 (Chart / Screener / Signals / Intraday) # ================================================================ def get_daily( self, symbol: str, start: date, end: date, columns: list[str] | None = None, ) -> pl.DataFrame: """单股日K查询 — 从14列parquet读取后即时计算指标。""" from datetime import timedelta # 扩展范围用于指标预热 (MA60 需要 ~60 交易日 ≈ 120 日历日) warmup_start = start - timedelta(days=150) # 扫描14列 parquet df = self._scan_daily_symbol(symbol, warmup_start, end, None) if not df.is_empty(): df = self._compute_enriched_range(df) # 尝试用缓存数据覆盖最新日 (盘中更准确) cached, cache_date = self.get_enriched_latest() if not df.is_empty() and cached is not None and not cached.is_empty() and cache_date: if start <= cache_date <= end: cached_part = self._filter_cached(cached, symbol, None) if not cached_part.is_empty(): df = df.filter(pl.col("date") != cache_date) common_cols = [c for c in df.columns if c in cached_part.columns] df = pl.concat([df.select(common_cols), cached_part.select(common_cols)]) # 裁剪到请求范围 if not df.is_empty(): df = df.filter((pl.col("date") >= start) & (pl.col("date") <= end)) if columns and not df.is_empty(): existing = [c for c in columns if c in df.columns] df = df.select(existing) return df def get_daily_batch( self, symbols: list[str], start: date, end: date, columns: list[str] | None = None, ) -> pl.DataFrame: """批量日K查询。""" cached, cache_date = self.get_enriched_latest() if cached is not None and not cached.is_empty() and cache_date: if start >= cache_date: return self._filter_cached_batch(cached, symbols, columns) # 回退 scan_parquet return self._scan_daily_batch(symbols, start, end, columns) def get_index_daily( self, symbol: str, start: date, end: date, columns: list[str] | None = None, ) -> pl.DataFrame: """指数日K查询 — 从独立指数 enriched parquet 读取后即时计算通用指标。""" from datetime import timedelta warmup_start = start - timedelta(days=150) df = self._scan_index_daily_symbol(symbol, warmup_start, end, None) if not df.is_empty(): df = self._compute_index_enriched_range(df) df = df.filter((pl.col("date") >= start) & (pl.col("date") <= end)) if columns and not df.is_empty(): existing = [c for c in columns if c in df.columns] df = df.select(existing) return df def get_minute( self, symbol: str, trade_date: date, ) -> pl.DataFrame: """分钟K查询 — Polars scan_parquet + predicate pushdown。""" try: return pl.scan_parquet(self._minute_glob).filter( (pl.col("symbol") == symbol) & (pl.col("datetime").dt.date() == trade_date) ).sort("datetime").collect() except Exception as e: # noqa: BLE001 logger.warning("分钟K查询失败: %s", e) return pl.DataFrame() # ================================================================ # Polars 查询内部方法 # ================================================================ def _compute_enriched_range(self, df: pl.DataFrame) -> pl.DataFrame: """对14列enriched数据即时计算完整指标+信号。输入应含足够预热行数。""" from app.indicators.pipeline import compute_indicators, compute_signals, compute_limit_signals, filter_halt_days if df.is_empty() or df.height < 2: return df # 兜底过滤历史脏数据中的停牌日 (close 可能被填充为前收盘价) df = filter_halt_days(df) if df.is_empty() or df.height < 2: return df try: df = compute_indicators(df) df = compute_signals(df) instruments = self.get_instruments() df = compute_limit_signals(df, instruments) except Exception as e: # noqa: BLE001 logger.warning("on-demand compute failed: %s", e) return df def _compute_index_enriched_range(self, df: pl.DataFrame) -> pl.DataFrame: """指数只计算通用技术指标和通用信号，跳过涨跌停/股本/市值逻辑。""" from app.indicators.pipeline import compute_indicators, compute_signals if df.is_empty() or df.height < 2: return df try: df = compute_indicators(df) df = compute_signals(df) except Exception as e: # noqa: BLE001 logger.warning("index on-demand compute failed: %s", e) return df def _filter_cached(self, cached: pl.DataFrame, symbol: str, columns: list[str] | None) -> pl.DataFrame: df = cached.filter(pl.col("symbol") == symbol) if columns and not df.is_empty(): existing = [c for c in columns if c in df.columns] df = df.select(existing) return df def _filter_cached_batch(self, cached: pl.DataFrame, symbols: list[str], columns: list[str] | None) -> pl.DataFrame: df = cached.filter(pl.col("symbol").is_in(symbols)) if columns and not df.is_empty(): existing = [c for c in columns if c in df.columns] df = df.select(existing) return df.sort(["symbol", "date"]) def _scan_daily_symbol(self, symbol: str, start: date, end: date, columns: list[str] | None) -> pl.DataFrame: try: lf = pl.scan_parquet(self._enriched_glob, cast_options=pl.ScanCastOptions(integer_cast="allow-float")).filter( (pl.col("symbol") == symbol) & (pl.col("date") >= start) & (pl.col("date") <= end) ).sort("date") if columns: schema_names = lf.collect_schema().names() existing = [c for c in columns if c in schema_names] lf = lf.select(existing) return lf.collect() except Exception as e: # noqa: BLE001 logger.warning("日K查询失败: %s", e) return pl.DataFrame() def _scan_daily_batch(self, symbols: list[str], start: date, end: date, columns: list[str] | None) -> pl.DataFrame: try: lf = pl.scan_parquet(self._enriched_glob, cast_options=pl.ScanCastOptions(integer_cast="allow-float")).filter( (pl.col("symbol").is_in(symbols)) & (pl.col("date") >= start) & (pl.col("date") <= end) ).sort(["symbol", "date"]) if columns: schema_names = lf.collect_schema().names() existing = [c for c in columns if c in schema_names] lf = lf.select(existing) return lf.collect() except Exception as e: # noqa: BLE001 logger.warning("日K批量查询失败: %s", e) return pl.DataFrame() def _scan_index_daily_symbol(self, symbol: str, start: date, end: date, columns: list[str] | None) -> pl.DataFrame: try: lf = pl.scan_parquet(self._index_enriched_glob, cast_options=pl.ScanCastOptions(integer_cast="allow-float")).filter( (pl.col("symbol") == symbol) & (pl.col("date") >= start) & (pl.col("date") <= end) ).sort("date") if columns: schema_names = lf.collect_schema().names() existing = [c for c in columns if c in schema_names] lf = lf.select(existing) return lf.collect() except Exception as e: # noqa: BLE001 logger.warning("指数日K查询失败: %s", e) return pl.DataFrame() def _merge_cached_and_scan( self, cached: pl.DataFrame, cache_date: date, symbol: str, start: date, end: date, columns: list[str] | None, ) -> pl.DataFrame: """合并缓存部分 + scan 历史部分。 历史部分用 strict < cache_date, 避免与缓存重复。 两部分 schema 可能不一致 (增量 vs 全量), concat 前对齐列。 """ hist = self._scan_daily_symbol(symbol, start, cache_date, columns) cached_part = self._filter_cached(cached, symbol, columns) if hist.is_empty(): return cached_part if cached_part.is_empty(): return hist # 去重: 历史部分可能包含 cache_date, 去掉后再合并 hist = hist.filter(pl.col("date") < cache_date) # 对齐列: 取交集, 统一类型 common_cols = [c for c in hist.columns if c in cached_part.columns] hist = hist.select(common_cols) cached_part = cached_part.select(common_cols) # 统一类型: 历史可能是 Float64, 缓存可能是 Int64, 统一为 cast for c in common_cols: if hist[c].dtype != cached_part[c].dtype: # 统一到更宽的类型 target = hist[c].dtype if hist.height > cached_part.height else cached_part[c].dtype hist = hist.with_columns(pl.col(c).cast(target)) cached_part = cached_part.with_columns(pl.col(c).cast(target)) return pl.concat([hist, cached_part]) # ================================================================ # DuckDB 查询 (冷路径: 统计/元数据/自定义SQL) # ================================================================ def latest_minute_date(self, symbol: str) -> date | None: try: with self._lock: row = self.db.execute( "SELECT max(CAST(datetime AS DATE)) FROM kline_minute WHERE symbol = ?", [symbol], ).fetchone() if row and row[0]: return row[0] if isinstance(row[0], date) else date.fromisoformat(str(row[0])) except duckdb.CatalogException: pass return None def earliest_daily_date(self) -> date | None: """本地日K数据的最早日期。""" try: with self._lock: res = self.db.execute( "SELECT min(date) FROM kline_daily", ).fetchone() if res and res[0]: d = res[0] return d if isinstance(d, date) else date.fromisoformat(str(d)) except Exception: return None return None def earliest_minute_date(self) -> date | None: """本地分钟K数据的最早日期。""" try: with self._lock: res = self.db.execute( "SELECT min(CAST(datetime AS DATE)) FROM kline_minute", ).fetchone() if res and res[0]: d = res[0] return d if isinstance(d, date) else date.fromisoformat(str(d)) except Exception: return None return None def latest_daily_date(self) -> date | None: """本地日K数据的最新日期。""" try: with self._lock: res = self.db.execute( "SELECT max(date) FROM kline_daily", ).fetchone() if res and res[0]: d = res[0] return d if isinstance(d, date) else date.fromisoformat(str(d)) except Exception: return None return None def _latest_enriched_date_duckdb(self) -> date | None: try: with self._lock: res = self.db.execute( "SELECT max(date) FROM kline_enriched", ).fetchone() if res and res[0]: d = res[0] return d if isinstance(d, date) else date.fromisoformat(str(d)) except Exception: # noqa: BLE001 return None return None # ================================================================ # 写入 (Pipeline / Sync) # ================================================================ def append_daily(self, df: pl.DataFrame) -> None: """按日分区写入日K数据 (merge-upsert)。""" if df.is_empty(): return self._write_daily_partition(df, "kline_daily") def append_enriched(self, df: pl.DataFrame) -> None: """按日分区写入 enriched 数据 (merge-upsert)。磁盘仅写入 14 列存储列。""" if df.is_empty(): return from app.indicators.pipeline import ENRICHED_STORAGE_COLS storage_cols = [c for c in ENRICHED_STORAGE_COLS if c in df.columns] df_storage = df.select(storage_cols) self._write_daily_partition(df_storage, "kline_daily_enriched") def append_index_daily(self, df: pl.DataFrame) -> None: """按日分区写入指数日K数据 (merge-upsert)。""" if df.is_empty(): return self._write_daily_partition(df, "kline_index_daily") def append_index_enriched(self, df: pl.DataFrame) -> None: """按日分区写入指数 enriched 数据。磁盘仅写入通用基础行情窄表。""" if df.is_empty(): return from app.indicators.pipeline import ENRICHED_STORAGE_COLS storage_cols = [c for c in ENRICHED_STORAGE_COLS if c in df.columns] df_storage = df.select(storage_cols) self._write_daily_partition(df_storage, "kline_index_enriched") def save_index_instruments(self, df: pl.DataFrame) -> None: """保存指数标的维表。""" if df.is_empty() or "symbol" not in df.columns: return out = self.store.data_dir / "instruments_index" / "instruments_index.parquet" out.parent.mkdir(parents=True, exist_ok=True) df.unique(subset=["symbol"], keep="last").sort("symbol").write_parquet(out) self._index_instruments_cache = None self._refresh_index_instruments() def refresh_index_views(self) -> None: """刷新指数相关 DuckDB 视图。""" d = self.store.data_dir.as_posix() statements = [ f"""CREATE OR REPLACE VIEW kline_index_daily AS SELECT * FROM read_parquet('{d}/kline_index_daily/**/*.parquet', union_by_name=true)""", f"""CREATE OR REPLACE VIEW kline_index_enriched AS SELECT * FROM read_parquet('{d}/kline_index_enriched/**/*.parquet', union_by_name=true)""", f"""CREATE OR REPLACE VIEW instruments_index AS SELECT * FROM read_parquet('{d}/instruments_index/**/*.parquet', union_by_name=true)""", ] for sql in statements: try: with self._lock: self.db.execute(sql) except Exception as e: # noqa: BLE001 logger.debug("index view refresh skipped: %s", e) def _write_daily_partition(self, df: pl.DataFrame, table: str) -> None: """按 date 分区写入 parquet，每个日期一个文件，支持 merge-upsert。""" base = self.store.data_dir / table for date_df in df.partition_by("date"): dt = date_df["date"][0] ds = dt.isoformat() if hasattr(dt, "isoformat") else str(dt) out = base / f"date={ds}" / "part.parquet" out.parent.mkdir(parents=True, exist_ok=True) if out.exists(): existing = pl.read_parquet(out) date_df = pl.concat([existing, date_df], how="diagonal_relaxed").unique( subset=["symbol", "date"], keep="last" ) date_df = date_df.sort(["symbol", "date"]) date_df.write_parquet(out) def flush_live_daily(self, df: pl.DataFrame) -> None: """覆写当天 kline_daily 分区 (实时行情落盘, 非merge)。""" if df.is_empty() or "date" not in df.columns: return base = self.store.data_dir / "kline_daily" dt = df["date"][0] ds = dt.isoformat() if hasattr(dt, "isoformat") else str(dt) out = base / f"date={ds}" / "part.parquet" out.parent.mkdir(parents=True, exist_ok=True) df.sort(["symbol", "date"]).write_parquet(out) def flush_live_enriched(self, df: pl.DataFrame) -> None: """覆写当天 kline_daily_enriched 分区 (实时 enriched 落盘, 非merge)。 内存缓存保留完整指标列供各服务使用，磁盘仅写入 14 列存储列。 """ if df.is_empty() or "date" not in df.columns: return # 内存缓存: 保留完整 66 列 self._enriched_cache = df.sort(["symbol"]) dt = df["date"][0] self._enriched_cache_date = dt # 磁盘写入: 仅 14 列存储列 from app.indicators.pipeline import ENRICHED_STORAGE_COLS storage_cols = [c for c in ENRICHED_STORAGE_COLS if c in df.columns] df_storage = df.select(storage_cols).sort(["symbol"]) base = self.store.data_dir / "kline_daily_enriched" ds = dt.isoformat() if hasattr(dt, "isoformat") else str(dt) out = base / f"date={ds}" / "part.parquet" out.parent.mkdir(parents=True, exist_ok=True) df_storage.write_parquet(out)