105 lines
3.6 KiB
Python
105 lines
3.6 KiB
Python
"""TickFlow provider implementation."""
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from __future__ import annotations
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import logging
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from datetime import datetime
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import polars as pl
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from app.data_providers.base import AssetType, ProviderCapabilities
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from app.data_providers.normalizer import normalize_adj_factors, normalize_daily, normalize_instruments
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from app.tickflow.client import get_client
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logger = logging.getLogger(__name__)
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_EXCHANGES = ["SH", "SZ", "BJ"]
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class TickFlowProvider:
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name = "tickflow"
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capabilities = ProviderCapabilities(
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instruments=True,
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daily=True,
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adj_factor=True,
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minute=True,
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financial=True,
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)
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def get_instruments(self, asset_type: AssetType) -> pl.DataFrame:
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tf = get_client()
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instrument_type = "stock" if asset_type == "stock" else asset_type
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rows: list[dict] = []
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for ex in _EXCHANGES:
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try:
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items = tf.exchanges.get_instruments(ex, instrument_type=instrument_type)
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rows.extend([it for it in (items or []) if isinstance(it, dict)])
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except Exception as e: # noqa: BLE001
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logger.warning("TickFlow instruments %s/%s failed: %s", ex, instrument_type, e)
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return normalize_instruments(rows, asset_type=asset_type, source=self.name)
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def get_daily(
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self,
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symbols: list[str],
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start_time: datetime | None,
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end_time: datetime | None,
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asset_type: AssetType, # noqa: ARG002
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) -> pl.DataFrame:
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if not symbols:
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return pl.DataFrame()
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tf = get_client()
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kwargs = {
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"period": "1d",
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"adjust": "none",
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"count": 10000 if start_time and end_time else 250,
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"as_dataframe": True,
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"show_progress": False,
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}
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if start_time and end_time:
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from app.services.kline_sync import _datetime_to_ms
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kwargs["start_time"] = _datetime_to_ms(start_time)
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kwargs["end_time"] = _datetime_to_ms(end_time)
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raw = tf.klines.batch(symbols, **kwargs)
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frames: list[pl.DataFrame] = []
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if isinstance(raw, dict):
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for sym, sub in raw.items():
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normalized = normalize_daily(sub, default_symbol=sym, source=self.name)
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if not normalized.is_empty():
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frames.append(normalized)
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else:
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normalized = normalize_daily(raw, source=self.name)
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if not normalized.is_empty():
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frames.append(normalized)
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return pl.concat(frames, how="diagonal_relaxed") if frames else pl.DataFrame()
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def get_adj_factors(
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self,
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symbols: list[str],
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start_time: datetime | None,
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end_time: datetime | None,
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asset_type: AssetType, # noqa: ARG002
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) -> pl.DataFrame:
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if not symbols:
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return pl.DataFrame()
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tf = get_client()
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kwargs = {"as_dataframe": False}
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if start_time or end_time:
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from app.services.kline_sync import _datetime_to_ms
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if start_time:
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kwargs["start_time"] = _datetime_to_ms(start_time)
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if end_time:
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kwargs["end_time"] = _datetime_to_ms(end_time)
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raw = tf.klines.ex_factors(symbols, **kwargs)
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return normalize_adj_factors(raw, source=self.name)
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def get_minute(
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self,
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symbols: list[str],
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start_time: datetime | None,
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end_time: datetime | None,
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asset_type: AssetType, # noqa: ARG002
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freq: str = "1m", # noqa: ARG002
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) -> pl.DataFrame:
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# Existing minute sync remains in app.services.kline_sync for now.
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return pl.DataFrame()
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