项目目录从 refer 迁移到 local

Co-Authored-By: Claude <noreply@anthropic.com>
2026-07-04 16:32:48 +08:00
parent 648a8b7f1c
commit aad34202f1
302 changed files with 0 additions and 0 deletions
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+"""TickFlow provider implementation."""
+from __future__ import annotations
+
+import logging
+from datetime import datetime
+
+import polars as pl
+
+from app.data_providers.base import AssetType, ProviderCapabilities
+from app.data_providers.normalizer import normalize_adj_factors, normalize_daily, normalize_instruments
+from app.tickflow.client import get_client
+
+logger = logging.getLogger(__name__)
+
+_EXCHANGES = ["SH", "SZ", "BJ"]
+
+
+class TickFlowProvider:
+    name = "tickflow"
+    capabilities = ProviderCapabilities(
+        instruments=True,
+        daily=True,
+        adj_factor=True,
+        minute=True,
+        realtime=True,
+        financial=True,
+    )
+
+    def get_instruments(self, asset_type: AssetType) -> pl.DataFrame:
+        tf = get_client()
+        instrument_type = "stock" if asset_type == "stock" else asset_type
+        rows: list[dict] = []
+        for ex in _EXCHANGES:
+            try:
+                items = tf.exchanges.get_instruments(ex, instrument_type=instrument_type)
+                rows.extend([it for it in (items or []) if isinstance(it, dict)])
+            except Exception as e:  # noqa: BLE001
+                logger.warning("TickFlow instruments %s/%s failed: %s", ex, instrument_type, e)
+        return normalize_instruments(rows, asset_type=asset_type, source=self.name)
+
+    def get_daily(
+        self,
+        symbols: list[str],
+        start_time: datetime | None,
+        end_time: datetime | None,
+        asset_type: AssetType,  # noqa: ARG002
+    ) -> pl.DataFrame:
+        if not symbols:
+            return pl.DataFrame()
+        tf = get_client()
+        kwargs = {
+            "period": "1d",
+            "adjust": "none",
+            "count": 10000 if start_time and end_time else 250,
+            "as_dataframe": True,
+            "show_progress": False,
+        }
+        if start_time and end_time:
+            from app.services.kline_sync import _datetime_to_ms
+            kwargs["start_time"] = _datetime_to_ms(start_time)
+            kwargs["end_time"] = _datetime_to_ms(end_time)
+        raw = tf.klines.batch(symbols, **kwargs)
+        frames: list[pl.DataFrame] = []
+        if isinstance(raw, dict):
+            for sym, sub in raw.items():
+                normalized = normalize_daily(sub, default_symbol=sym, source=self.name)
+                if not normalized.is_empty():
+                    frames.append(normalized)
+        else:
+            normalized = normalize_daily(raw, source=self.name)
+            if not normalized.is_empty():
+                frames.append(normalized)
+        return pl.concat(frames, how="diagonal_relaxed") if frames else pl.DataFrame()
+
+    def get_adj_factors(
+        self,
+        symbols: list[str],
+        start_time: datetime | None,
+        end_time: datetime | None,
+        asset_type: AssetType,  # noqa: ARG002
+    ) -> pl.DataFrame:
+        if not symbols:
+            return pl.DataFrame()
+        tf = get_client()
+        kwargs = {"as_dataframe": False}
+        if start_time or end_time:
+            from app.services.kline_sync import _datetime_to_ms
+            if start_time:
+                kwargs["start_time"] = _datetime_to_ms(start_time)
+            if end_time:
+                kwargs["end_time"] = _datetime_to_ms(end_time)
+        raw = tf.klines.ex_factors(symbols, **kwargs)
+        return normalize_adj_factors(raw, source=self.name)
+
+    def get_minute(
+        self,
+        symbols: list[str],
+        start_time: datetime | None,
+        end_time: datetime | None,
+        asset_type: AssetType,  # noqa: ARG002
+        freq: str = "1m",  # noqa: ARG002
+    ) -> pl.DataFrame:
+        # Existing minute sync remains in app.services.kline_sync for now.
+        return pl.DataFrame()
+
+    def get_realtime(
+        self,
+        universes: list[str] | None = None,
+        symbols: list[str] | None = None,
+    ) -> pl.DataFrame:
+        tf = get_client()
+        if universes and symbols:
+            raise ValueError("TickFlow realtime accepts either universes or symbols, not both")
+        if universes:
+            resp = tf.quotes.get_by_universes(universes=universes)
+        elif symbols:
+            resp = tf.quotes.get(symbols=symbols)
+        else:
+            return pl.DataFrame()
+        return pl.DataFrame(resp or [])