dienakdz
308 lines
12 KiB
Python
308 lines
12 KiB
Python
"""
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US stock data source
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Get data using yfinance and finnhub
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"""
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from typing import Dict, List, Any, Optional
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from datetime import datetime, timedelta
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import yfinance as yf
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from app.data_sources.base import BaseDataSource
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from app.utils.logger import get_logger
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from app.config import APIKeys, YFinanceConfig
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logger = get_logger(__name__)
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class USStockDataSource(BaseDataSource):
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"""US stock data source"""
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name = "USStock/yfinance"
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# yfinance time period mapping
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INTERVAL_MAP = {
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'1m': '1m',
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'5m': '5m',
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'15m': '15m',
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'30m': '30m',
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'1H': '1h',
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'4H': '4h',
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'1D': '1d',
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'1W': '1wk'
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}
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# The range of days to obtain data in different periods
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DAYS_MAP = {
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'1m': lambda limit: min(7, max(1, (limit // 390) + 2)),
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'5m': lambda limit: min(60, max(1, (limit // 78) + 2)),
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'15m': lambda limit: min(60, max(1, (limit // 26) + 2)),
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'30m': lambda limit: min(60, max(1, (limit // 13) + 2)),
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'1H': lambda limit: min(730, max(1, (limit // 24) + 2)),
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'4H': lambda limit: min(730, max(1, (limit // 6) + 2)),
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'1D': lambda limit: min(3650, limit + 1),
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'1W': lambda limit: min(3650, (limit * 7) + 7)
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}
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def __init__(self):
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# Initialize finnhub as an alternative
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self.finnhub_client = None
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try:
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import finnhub
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if APIKeys.is_configured('FINNHUB_API_KEY'):
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self.finnhub_client = finnhub.Client(api_key=APIKeys.FINNHUB_API_KEY)
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logger.info("Finnhub client initialized")
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except Exception as e:
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logger.warning(f"Finnhub init failed: {e}")
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def get_ticker(self, symbol: str) -> Dict[str, Any]:
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"""
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Get realtime quotes for U.S. stocks
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Use Finnhub first (more real-time), downgrade to yfinance fast_info
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Returns:
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dict: {
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'last': current price,
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'change': change amount,
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'changePercent': increase or decrease,
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'high': highest price,
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'low': lowest price,
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'open': opening price,
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'previousClose': yesterday's closing price
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}
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"""
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symbol = (symbol or '').strip().upper()
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# Prefer using Finnhub (live data)
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if self.finnhub_client:
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try:
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quote = self.finnhub_client.quote(symbol)
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if quote and quote.get('c'):
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return {
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'last': quote.get('c', 0), # current price
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'change': quote.get('d', 0), # Changes
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'changePercent': quote.get('dp', 0), # Increase or decrease
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'high': quote.get('h', 0), # Best in Japan
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'low': quote.get('l', 0), # Lowest within the day
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'open': quote.get('o', 0), # opening price
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'previousClose': quote.get('pc', 0) # Yesterday's closing price
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}
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except Exception as e:
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logger.warning(f"Finnhub quote failed for {symbol}: {e}")
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# Downgrade to use yfinance
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try:
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ticker = yf.Ticker(symbol)
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# Try fast_info (faster)
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try:
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fast_info = ticker.fast_info
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last_price = fast_info.get('lastPrice') or fast_info.get('last_price')
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prev_close = fast_info.get('previousClose') or fast_info.get('previous_close') or fast_info.get('regularMarketPreviousClose')
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if last_price:
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change = (last_price - prev_close) if prev_close else 0
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change_pct = (change / prev_close * 100) if prev_close else 0
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return {
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'last': float(last_price),
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'change': round(change, 4),
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'changePercent': round(change_pct, 2),
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'high': float(fast_info.get('dayHigh') or fast_info.get('day_high') or last_price),
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'low': float(fast_info.get('dayLow') or fast_info.get('day_low') or last_price),
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'open': float(fast_info.get('open') or fast_info.get('regularMarketOpen') or last_price),
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'previousClose': float(prev_close) if prev_close else 0
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}
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except Exception as e:
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logger.debug(f"yfinance fast_info failed for {symbol}: {e}")
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# Downgrade to use info (slower but more complete data)
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try:
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info = ticker.info
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last_price = info.get('regularMarketPrice') or info.get('currentPrice')
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prev_close = info.get('regularMarketPreviousClose') or info.get('previousClose')
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if last_price:
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change = (last_price - prev_close) if prev_close else 0
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change_pct = (change / prev_close * 100) if prev_close else 0
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return {
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'last': float(last_price),
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'change': round(change, 4),
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'changePercent': round(change_pct, 2),
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'high': float(info.get('regularMarketDayHigh') or info.get('dayHigh') or last_price),
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'low': float(info.get('regularMarketDayLow') or info.get('dayLow') or last_price),
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'open': float(info.get('regularMarketOpen') or info.get('open') or last_price),
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'previousClose': float(prev_close) if prev_close else 0
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}
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except Exception as e:
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logger.debug(f"yfinance info failed for {symbol}: {e}")
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# Last downgrade: use the most recent 1-minute K-line
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try:
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hist = ticker.history(period='1d', interval='1m')
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if hist is not None and not hist.empty:
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last_row = hist.iloc[-1]
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first_row = hist.iloc[0]
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last_price = float(last_row['Close'])
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open_price = float(first_row['Open'])
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return {
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'last': last_price,
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'change': round(last_price - open_price, 4),
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'changePercent': round((last_price - open_price) / open_price * 100, 2) if open_price else 0,
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'high': float(hist['High'].max()),
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'low': float(hist['Low'].min()),
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'open': open_price,
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'previousClose': open_price # approximate
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}
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except Exception as e:
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logger.debug(f"yfinance history fallback failed for {symbol}: {e}")
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except Exception as e:
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logger.error(f"Failed to get ticker for {symbol}: {e}")
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return {'last': 0, 'symbol': symbol}
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def get_kline(
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self,
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symbol: str,
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timeframe: str,
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limit: int,
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before_time: Optional[int] = None
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) -> List[Dict[str, Any]]:
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"""Get U.S. stock K-line data"""
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klines = []
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try:
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interval = self.INTERVAL_MAP.get(timeframe, '1d')
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days_func = self.DAYS_MAP.get(timeframe, lambda x: x + 1)
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days = days_func(limit)
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# Calculate date range
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if before_time:
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end_date = datetime.fromtimestamp(before_time)
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start_date = end_date - timedelta(days=days)
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else:
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end_date = datetime.now()
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start_date = end_date - timedelta(days=days)
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# logger.info(f"Use yfinance to get {symbol}, period: {interval}, date: {start_date.date()} ~ {end_date.date()}")
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# Try yfinance
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df = self._fetch_yfinance(symbol, interval, start_date, end_date)
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if df is None or df.empty:
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# try finnhub
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if self.finnhub_client and timeframe == '1D':
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klines = self._fetch_finnhub(symbol, start_date, end_date, limit)
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if klines:
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return klines
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else:
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klines = self._convert_dataframe(df, limit)
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# Filter and restrict
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klines = self.filter_and_limit(klines, limit, before_time)
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# Record results
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self.log_result(symbol, klines, timeframe)
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except Exception as e:
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logger.error(f"Failed to fetch US stock K-lines {symbol}: {str(e)}")
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import traceback
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logger.error(traceback.format_exc())
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return klines
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def _fetch_yfinance(self, symbol: str, interval: str, start_date: datetime, end_date: datetime):
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"""Use yfinance to get data"""
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try:
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ticker = yf.Ticker(symbol)
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# The end parameter of yfinance is not included (exclusive), so you need to add one day to include the end_date data of the current day.
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# For example: end="2026-01-12" actually only returns the data of 2026-01-11
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end_date_inclusive = end_date + timedelta(days=1)
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df = ticker.history(
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start=start_date.strftime('%Y-%m-%d'),
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end=end_date_inclusive.strftime('%Y-%m-%d'),
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interval=interval
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)
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# logger.info(f"yfinance returns {len(df) if df is not None and not df.empty else 0} pieces of data")
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return df
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except Exception as e:
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logger.warning(f"yfinance fetch failed: {e}")
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return None
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def _fetch_finnhub(
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self,
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symbol: str,
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start_date: datetime,
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end_date: datetime,
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limit: int
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) -> List[Dict[str, Any]]:
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"""Use finnhub to get daily data"""
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klines = []
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try:
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start_ts = int(start_date.timestamp())
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end_ts = int(end_date.timestamp())
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# logger.info(f"Use Finnhub to obtain {symbol} daily data")
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candles = self.finnhub_client.stock_candles(symbol, 'D', start_ts, end_ts)
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if candles and candles.get('s') == 'ok':
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for i in range(len(candles['t'])):
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klines.append(self.format_kline(
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timestamp=candles['t'][i],
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open_price=candles['o'][i],
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high=candles['h'][i],
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low=candles['l'][i],
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close=candles['c'][i],
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volume=candles['v'][i]
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))
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# logger.info(f"Finnhub returns {len(klines)} pieces of data")
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except Exception as e:
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logger.error(f"Finnhub fetch failed: {e}")
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return klines
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def _convert_dataframe(self, df, limit: int) -> List[Dict[str, Any]]:
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"""Convert DataFrame to K-line list"""
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klines = []
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df = df.tail(limit).reset_index()
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# Determine the time column name (the daily line is Date, the minute level is Datetime)
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time_col = None
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if 'Datetime' in df.columns:
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time_col = 'Datetime'
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elif 'Date' in df.columns:
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time_col = 'Date'
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elif 'index' in df.columns:
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time_col = 'index'
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if time_col is None:
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logger.warning(f"Unable to determine time column; available columns: {df.columns.tolist()}")
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return klines
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for _, row in df.iterrows():
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try:
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# Processing timestamps
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time_value = row[time_col]
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if hasattr(time_value, 'timestamp'):
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ts = int(time_value.timestamp())
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else:
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continue
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klines.append(self.format_kline(
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timestamp=ts,
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open_price=row['Open'],
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high=row['High'],
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low=row['Low'],
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close=row['Close'],
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volume=row['Volume']
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))
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except Exception as e:
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logger.debug(f"Failed to parse row data: {e}")
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continue
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return klines
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