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"""
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Alpaca bars → AlpacaPriceData conversion and DB storage
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"""
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import logging
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from datetime import datetime, timezone
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from typing import Dict, List, Optional
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select, and_, func
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from sqlalchemy.dialects.postgresql import insert as pg_insert
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from app.models.alpaca_price import AlpacaPriceData
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from app.services.alpaca_client import AlpacaClient, normalize_ticker
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logger = logging.getLogger(__name__)
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class AlpacaPriceService:
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"""Alpaca bars → AlpacaPriceData conversion and DB storage"""
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def __init__(self, client: Optional[AlpacaClient] = None):
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self.client = client or AlpacaClient()
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def is_available(self) -> bool:
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return self.client.is_configured()
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async def fetch_and_store_bars(
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self,
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db: AsyncSession,
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ticker: str,
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start_date: datetime,
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end_date: datetime,
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interval: str = "1d",
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) -> int:
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"""
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Fetch bars from Alpaca and upsert into AlpacaPriceData table.
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Returns:
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Number of newly inserted records.
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"""
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ticker = ticker.upper()
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start_str = start_date.strftime("%Y-%m-%d")
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end_str = end_date.strftime("%Y-%m-%d")
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bars = await self.client.get_bars(
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symbol=ticker,
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timeframe=interval,
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start=start_str,
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end=end_str,
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)
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if not bars:
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logger.warning(f"Alpaca returned 0 bars for {ticker}")
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return 0
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# Build rows for batch insert
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rows = []
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for bar in bars:
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bar_dt = _parse_bar_timestamp(bar["t"])
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rows.append({
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"ticker": ticker,
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"date": bar_dt,
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"interval": interval,
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"open": float(bar.get("o", 0)),
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"high": float(bar.get("h", 0)),
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"low": float(bar.get("l", 0)),
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"close": float(bar.get("c", 0)),
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"volume": float(bar.get("v", 0)),
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"vwap": float(bar["vw"]) if bar.get("vw") else None,
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"trade_count": int(bar["n"]) if bar.get("n") else None,
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"data_source": "ALPACA",
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})
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if not rows:
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return 0
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# Batch insert with chunking (14 params/row → CHUNK=2300, 2300×14=32,200 < 32,767)
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CHUNK = 2300
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inserted = 0
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for i in range(0, len(rows), CHUNK):
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stmt = pg_insert(AlpacaPriceData).values(rows[i : i + CHUNK])
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stmt = stmt.on_conflict_do_nothing(constraint='uq_alpaca_price_data')
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result = await db.execute(stmt)
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inserted += result.rowcount
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await db.commit()
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if inserted:
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logger.info(f"Alpaca: inserted {inserted} bars for {ticker}")
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return inserted
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async def get_or_fetch_multi_bars(
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self,
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db: AsyncSession,
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tickers: List[str],
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start_dt: datetime,
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end_dt: datetime,
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interval: str = "1d",
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force_refresh: bool = False,
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feed: Optional[str] = None,
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) -> Dict[str, List[AlpacaPriceData]]:
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"""
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DB-first multi-ticker daily bars.
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1. Check which tickers are missing data (max_date < end_dt) in DB.
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2. Fetch only missing tickers from Alpaca and upsert.
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3. Read all data from DB and return as Dict[ticker → rows].
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"""
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upper_tickers = [t.upper() for t in tickers]
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if not force_refresh:
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result = await db.execute(
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select(
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AlpacaPriceData.ticker,
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func.max(AlpacaPriceData.date).label("max_date"),
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)
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.where(
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and_(
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AlpacaPriceData.ticker.in_(upper_tickers),
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AlpacaPriceData.interval == interval,
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AlpacaPriceData.date >= start_dt,
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AlpacaPriceData.date <= end_dt,
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)
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)
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.group_by(AlpacaPriceData.ticker)
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)
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coverage = {row.ticker: row.max_date for row in result}
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need_fetch = [
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t for t in upper_tickers
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if coverage.get(t) is None or coverage[t].date() < end_dt.date()
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]
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else:
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need_fetch = upper_tickers
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if need_fetch:
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start_str = start_dt.strftime("%Y-%m-%d")
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end_str = end_dt.strftime("%Y-%m-%d")
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# reverse-map: normalized Alpaca symbol → original input ticker
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reverse_map = {normalize_ticker(t): t for t in need_fetch}
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raw = await self.client.get_multi_bars(
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symbols=need_fetch,
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timeframe=interval,
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start=start_str,
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end=end_str,
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feed=feed,
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)
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rows = []
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for alpaca_sym, bar_list in raw.items():
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original = reverse_map.get(alpaca_sym, alpaca_sym)
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for bar in bar_list:
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bar_dt = _parse_bar_timestamp(bar["t"])
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rows.append({
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"ticker": original,
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"date": bar_dt,
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"interval": interval,
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"open": float(bar.get("o", 0)),
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"high": float(bar.get("h", 0)),
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"low": float(bar.get("l", 0)),
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"close": float(bar.get("c", 0)),
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"volume": float(bar.get("v", 0)),
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"vwap": float(bar["vw"]) if bar.get("vw") else None,
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"trade_count": int(bar["n"]) if bar.get("n") else None,
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"data_source": "ALPACA",
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})
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if rows:
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# Chunk to stay under asyncpg's 32767-param limit (14 params/row → 2300/chunk)
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# 14 params: id/ticker/date/open/high/low/close/volume/vwap/trade_count/
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# interval/data_source/created_at/updated_at
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# 2300 × 14 = 32,200 < 32,767
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CHUNK = 2300
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for i in range(0, len(rows), CHUNK):
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stmt = pg_insert(AlpacaPriceData).values(rows[i : i + CHUNK])
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stmt = stmt.on_conflict_do_nothing(constraint="uq_alpaca_price_data")
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await db.execute(stmt)
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await db.commit()
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logger.info(
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f"Alpaca multi-bars: stored {len(rows)} rows for {len(need_fetch)} tickers"
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)
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# Read back from DB — filtered by interval to avoid mixing 1d/5m/etc.
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result = await db.execute(
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select(AlpacaPriceData)
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.where(
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and_(
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AlpacaPriceData.ticker.in_(upper_tickers),
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AlpacaPriceData.interval == interval,
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AlpacaPriceData.date >= start_dt,
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AlpacaPriceData.date <= end_dt,
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)
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)
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.order_by(AlpacaPriceData.ticker, AlpacaPriceData.date)
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)
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db_rows = result.scalars().all()
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data: Dict[str, List[AlpacaPriceData]] = {t: [] for t in upper_tickers}
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for row in db_rows:
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data[row.ticker].append(row)
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return data
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async def fetch_bars_raw(
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self,
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ticker: str,
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interval: str = "1d",
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start_date: Optional[datetime] = None,
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end_date: Optional[datetime] = None,
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feed: Optional[str] = None,
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) -> List[Dict]:
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"""
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Return raw bar dicts without touching the DB (useful for intraday / non-persistent use).
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"""
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start_str = start_date.strftime("%Y-%m-%d") if start_date else None
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end_str = end_date.strftime("%Y-%m-%d") if end_date else None
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bars = await self.client.get_bars(
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symbol=ticker.upper(),
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timeframe=interval,
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start=start_str,
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end=end_str,
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feed=feed,
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)
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return [
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{
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"timestamp": bar["t"],
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"open": bar.get("o"),
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"high": bar.get("h"),
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"low": bar.get("l"),
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"close": bar.get("c"),
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"volume": bar.get("v"),
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"vwap": bar.get("vw"),
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"trade_count": bar.get("n"),
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}
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for bar in bars
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]
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def _parse_bar_timestamp(ts_str: str) -> datetime:
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"""Parse Alpaca bar timestamp (RFC-3339) into a timezone-aware datetime."""
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# Alpaca returns e.g. "2024-01-02T05:00:00Z"
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dt = datetime.fromisoformat(ts_str.replace("Z", "+00:00"))
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if dt.tzinfo is None:
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dt = dt.replace(tzinfo=timezone.utc)
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return dt
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