feat: switch stock data to Alpaca Markets API with yfinance fallback
Alpaca provides 10k calls/min free with 7yr history via IEX feed. Hybrid approach: Alpaca for price bars, snapshots, sector ETF perf, and moving averages; yfinance for fundamentals (PE, margins, 13F). - Add alpaca_data.py: bars, snapshots, MAs, sector ETF perf, news - Update get_macro_indicators: sector ETF performance via Alpaca - Update get_sector_rotation: compute relative strength vs SPY - Update entry timing node: Alpaca MAs from actual bar data - Add alpaca-py to requirements.txt Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
dtarkent2-sys
parent
5e8c81e738
commit
84f7894768
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@ -9,6 +9,7 @@ langgraph
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rank-bm25
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setuptools
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requests
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alpaca-py
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tqdm
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pytz
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fastapi
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@ -261,21 +261,39 @@ def create_entry_timing_node(llm):
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def node(state: Dict[str, Any]) -> Dict[str, Any]:
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ticker = state["ticker"]
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price = ma50 = ma200 = hi52 = lo52 = range_pct = None
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# Try Alpaca first (computed from actual bar data — more reliable than yfinance info)
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try:
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t = yf.Ticker(ticker.upper())
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info = t.info or {}
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except Exception:
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info = {}
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from tradingagents.dataflows.alpaca_data import alpaca_available, get_moving_averages
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if alpaca_available():
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ma_data = get_moving_averages(ticker)
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if ma_data:
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price = ma_data.get("current_price")
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ma50 = ma_data.get("fifty_day_avg")
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ma200 = ma_data.get("two_hundred_day_avg")
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hi52 = ma_data.get("fifty_two_week_high")
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lo52 = ma_data.get("fifty_two_week_low")
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range_pct = ma_data.get("vs_52w_range_pct")
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except Exception as e:
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logger.debug("Alpaca MAs failed for %s: %s", ticker, e)
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price = _safe(info, "currentPrice") or _safe(info, "regularMarketPrice")
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ma50 = _safe(info, "fiftyDayAverage")
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ma200 = _safe(info, "twoHundredDayAverage")
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hi52 = _safe(info, "fiftyTwoWeekHigh")
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lo52 = _safe(info, "fiftyTwoWeekLow")
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# Fallback: yfinance info
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if price is None:
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try:
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t = yf.Ticker(ticker.upper())
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info = t.info or {}
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except Exception:
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info = {}
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range_pct = None
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if hi52 and lo52 and price and (hi52 - lo52) > 0:
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range_pct = round(((price - lo52) / (hi52 - lo52)) * 100, 1)
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price = _safe(info, "currentPrice") or _safe(info, "regularMarketPrice")
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ma50 = _safe(info, "fiftyDayAverage")
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ma200 = _safe(info, "twoHundredDayAverage")
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hi52 = _safe(info, "fiftyTwoWeekHigh")
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lo52 = _safe(info, "fiftyTwoWeekLow")
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if hi52 and lo52 and price and (hi52 - lo52) > 0:
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range_pct = round(((price - lo52) / (hi52 - lo52)) * 100, 1)
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ma_rel = "unknown"
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if ma50 and ma200:
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@ -0,0 +1,321 @@
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"""Alpaca Market Data API client for the equity ranking engine.
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Provides price bars, snapshots, and news. Fundamentals still come from yfinance.
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Free tier: 10,000 requests/min, up to 7 years of historical data.
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"""
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from __future__ import annotations
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import json
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import logging
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import os
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from datetime import datetime, timedelta
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from typing import Any, Dict, List, Optional
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import pandas as pd
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# Client setup (lazy init)
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# ---------------------------------------------------------------------------
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_stock_client = None
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_news_client = None
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def _get_stock_client():
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global _stock_client
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if _stock_client is None:
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from alpaca.data.historical import StockHistoricalDataClient
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key = os.environ.get("ALPACA_API_KEY", "")
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secret = os.environ.get("ALPACA_API_SECRET", "")
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if not key or not secret:
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raise RuntimeError(
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"ALPACA_API_KEY and ALPACA_API_SECRET must be set"
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)
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_stock_client = StockHistoricalDataClient(key, secret)
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return _stock_client
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def _get_news_client():
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global _news_client
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if _news_client is None:
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from alpaca.data.historical.news import NewsClient
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key = os.environ.get("ALPACA_API_KEY", "")
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secret = os.environ.get("ALPACA_API_SECRET", "")
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_news_client = NewsClient(key, secret)
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return _news_client
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def alpaca_available() -> bool:
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"""Check if Alpaca credentials are configured."""
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return bool(
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os.environ.get("ALPACA_API_KEY")
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and os.environ.get("ALPACA_API_SECRET")
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)
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# ---------------------------------------------------------------------------
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# Price / Bar data
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# ---------------------------------------------------------------------------
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def get_bars(
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symbol: str,
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start_date: str,
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end_date: str,
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timeframe: str = "1Day",
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) -> pd.DataFrame:
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"""Fetch historical bars from Alpaca.
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Args:
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symbol: Ticker symbol (e.g., "AAPL")
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start_date: Start date in YYYY-MM-DD format
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end_date: End date in YYYY-MM-DD format
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timeframe: "1Min", "5Min", "15Min", "1Hour", "1Day", "1Week", "1Month"
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Returns:
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DataFrame with OHLCV columns.
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"""
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from alpaca.data.requests import StockBarsRequest
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from alpaca.data.timeframe import TimeFrame, TimeFrameUnit
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tf_map = {
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"1Min": TimeFrame(1, TimeFrameUnit.Minute),
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"5Min": TimeFrame(5, TimeFrameUnit.Minute),
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"15Min": TimeFrame(15, TimeFrameUnit.Minute),
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"1Hour": TimeFrame(1, TimeFrameUnit.Hour),
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"1Day": TimeFrame(1, TimeFrameUnit.Day),
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"1Week": TimeFrame(1, TimeFrameUnit.Week),
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"1Month": TimeFrame(1, TimeFrameUnit.Month),
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}
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tf = tf_map.get(timeframe, TimeFrame(1, TimeFrameUnit.Day))
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client = _get_stock_client()
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request = StockBarsRequest(
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symbol_or_symbols=symbol.upper(),
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timeframe=tf,
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start=datetime.strptime(start_date, "%Y-%m-%d"),
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end=datetime.strptime(end_date, "%Y-%m-%d"),
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feed="iex",
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)
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bars = client.get_stock_bars(request)
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df = bars.df
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if isinstance(df.index, pd.MultiIndex):
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df = df.droplevel("symbol")
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return df
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def get_bars_csv(symbol: str, start_date: str, end_date: str) -> str:
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"""Fetch historical bars and return as CSV string (drop-in for get_YFin_data_online)."""
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try:
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df = get_bars(symbol, start_date, end_date)
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if df.empty:
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return f"No data found for '{symbol}' between {start_date} and {end_date}"
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# Rename columns to match yfinance output format
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df = df.rename(columns={
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"open": "Open", "high": "High", "low": "Low",
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"close": "Close", "volume": "Volume",
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"trade_count": "Trade Count", "vwap": "VWAP",
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})
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for col in ["Open", "High", "Low", "Close"]:
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if col in df.columns:
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df[col] = df[col].round(2)
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if df.index.tz is not None:
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df.index = df.index.tz_localize(None)
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csv = df.to_csv()
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header = (
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f"# Stock data for {symbol.upper()} from {start_date} to {end_date}\n"
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f"# Source: Alpaca Markets (IEX feed)\n"
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f"# Total records: {len(df)}\n\n"
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)
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return header + csv
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except Exception as e:
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logger.warning("Alpaca bars failed for %s: %s", symbol, e)
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return f"Error fetching Alpaca data for {symbol}: {e}"
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# ---------------------------------------------------------------------------
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# Snapshots (latest quote/trade)
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# ---------------------------------------------------------------------------
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def get_snapshot(symbol: str) -> Dict[str, Any]:
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"""Get the latest snapshot (quote + trade + bar) for a symbol."""
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from alpaca.data.requests import StockSnapshotRequest
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client = _get_stock_client()
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request = StockSnapshotRequest(symbol_or_symbols=symbol.upper(), feed="iex")
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snapshots = client.get_stock_snapshot(request)
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snap = snapshots.get(symbol.upper())
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if not snap:
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return {}
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result = {
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"ticker": symbol.upper(),
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"latest_trade_price": snap.latest_trade.price if snap.latest_trade else None,
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"latest_trade_size": snap.latest_trade.size if snap.latest_trade else None,
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"latest_trade_time": str(snap.latest_trade.timestamp) if snap.latest_trade else None,
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}
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if snap.latest_quote:
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result["bid"] = snap.latest_quote.bid_price
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result["ask"] = snap.latest_quote.ask_price
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result["bid_size"] = snap.latest_quote.bid_size
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result["ask_size"] = snap.latest_quote.ask_size
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if snap.daily_bar:
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result["daily_open"] = snap.daily_bar.open
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result["daily_high"] = snap.daily_bar.high
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result["daily_low"] = snap.daily_bar.low
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result["daily_close"] = snap.daily_bar.close
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result["daily_volume"] = snap.daily_bar.volume
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result["daily_vwap"] = snap.daily_bar.vwap
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if snap.previous_daily_bar:
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result["prev_close"] = snap.previous_daily_bar.close
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result["prev_volume"] = snap.previous_daily_bar.volume
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return result
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def get_multi_snapshots(symbols: List[str]) -> Dict[str, Dict[str, Any]]:
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"""Get snapshots for multiple symbols at once."""
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from alpaca.data.requests import StockSnapshotRequest
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client = _get_stock_client()
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request = StockSnapshotRequest(
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symbol_or_symbols=[s.upper() for s in symbols],
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feed="iex",
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)
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snapshots = client.get_stock_snapshot(request)
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result = {}
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for sym, snap in snapshots.items():
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entry = {"ticker": sym}
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if snap.latest_trade:
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entry["price"] = snap.latest_trade.price
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if snap.daily_bar:
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entry["daily_open"] = snap.daily_bar.open
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entry["daily_high"] = snap.daily_bar.high
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entry["daily_low"] = snap.daily_bar.low
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entry["daily_close"] = snap.daily_bar.close
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entry["daily_volume"] = snap.daily_bar.volume
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entry["daily_vwap"] = snap.daily_bar.vwap
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if snap.previous_daily_bar:
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entry["prev_close"] = snap.previous_daily_bar.close
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result[sym] = entry
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return result
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# ---------------------------------------------------------------------------
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# Computed indicators from bars
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# ---------------------------------------------------------------------------
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def get_moving_averages(symbol: str) -> Dict[str, Any]:
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"""Compute 50-day and 200-day moving averages from Alpaca bars."""
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end = datetime.now()
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start = end - timedelta(days=300) # ~200 trading days + buffer
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try:
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df = get_bars(symbol, start.strftime("%Y-%m-%d"), end.strftime("%Y-%m-%d"))
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if df.empty or len(df) < 50:
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return {}
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close = df["close"] if "close" in df.columns else df["Close"]
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result = {
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"current_price": float(close.iloc[-1]),
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"fifty_day_avg": float(close.tail(50).mean()),
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}
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if len(close) >= 200:
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result["two_hundred_day_avg"] = float(close.tail(200).mean())
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# 52-week high/low (approx 252 trading days)
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year_data = close.tail(252) if len(close) >= 252 else close
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result["fifty_two_week_high"] = float(year_data.max())
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result["fifty_two_week_low"] = float(year_data.min())
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hi = result["fifty_two_week_high"]
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lo = result["fifty_two_week_low"]
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price = result["current_price"]
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if (hi - lo) > 0:
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result["vs_52w_range_pct"] = round((price - lo) / (hi - lo) * 100, 1)
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return result
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except Exception as e:
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logger.warning("Alpaca moving averages failed for %s: %s", symbol, e)
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return {}
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def get_sector_etf_performance(etf_symbols: List[str]) -> Dict[str, Dict[str, float]]:
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"""Compute 1M and 3M returns for a list of sector ETFs."""
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end = datetime.now()
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start_3m = end - timedelta(days=100)
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result = {}
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for sym in etf_symbols:
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try:
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df = get_bars(sym, start_3m.strftime("%Y-%m-%d"), end.strftime("%Y-%m-%d"))
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if df.empty or len(df) < 5:
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continue
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close = df["close"] if "close" in df.columns else df["Close"]
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current = float(close.iloc[-1])
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ret_1m = None
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if len(close) >= 22:
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price_1m = float(close.iloc[-22])
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ret_1m = round((current - price_1m) / price_1m * 100, 2)
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ret_3m = None
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if len(close) >= 63:
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price_3m = float(close.iloc[-63])
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ret_3m = round((current - price_3m) / price_3m * 100, 2)
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result[sym] = {
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"return_1m": ret_1m,
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"return_3m": ret_3m,
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"price": current,
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}
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except Exception as e:
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logger.warning("Alpaca ETF perf failed for %s: %s", sym, e)
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return result
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# ---------------------------------------------------------------------------
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# News
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# ---------------------------------------------------------------------------
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def get_news(
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symbols: Optional[List[str]] = None,
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limit: int = 10,
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start_date: Optional[str] = None,
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) -> List[Dict[str, Any]]:
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"""Fetch news articles from Alpaca News API."""
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try:
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from alpaca.data.requests import NewsRequest
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client = _get_news_client()
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kwargs: Dict[str, Any] = {"limit": limit}
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if symbols:
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kwargs["symbols"] = [s.upper() for s in symbols]
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if start_date:
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kwargs["start"] = datetime.strptime(start_date, "%Y-%m-%d")
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request = NewsRequest(**kwargs)
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news = client.get_news(request)
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return [
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{
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"title": n.headline,
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"summary": n.summary or "",
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"url": n.url,
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"source": n.source,
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"created_at": str(n.created_at),
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"symbols": n.symbols or [],
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}
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for n in news.news
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]
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except Exception as e:
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logger.warning("Alpaca news failed: %s", e)
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return []
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@ -1,45 +1,48 @@
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from typing import Annotated
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from datetime import datetime
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from dateutil.relativedelta import relativedelta
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import logging
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import yfinance as yf
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import os
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from .stockstats_utils import StockstatsUtils
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_logger = logging.getLogger(__name__)
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def get_YFin_data_online(
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symbol: Annotated[str, "ticker symbol of the company"],
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start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
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end_date: Annotated[str, "End date in yyyy-mm-dd format"],
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):
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datetime.strptime(start_date, "%Y-%m-%d")
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datetime.strptime(end_date, "%Y-%m-%d")
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# Create ticker object
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ticker = yf.Ticker(symbol.upper())
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# Try Alpaca first (10k calls/min, 7yr history)
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try:
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from .alpaca_data import alpaca_available, get_bars_csv
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if alpaca_available():
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result = get_bars_csv(symbol, start_date, end_date)
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if not result.startswith("Error"):
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return result
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_logger.info("Alpaca bars failed, falling back to yfinance for %s", symbol)
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except Exception as e:
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_logger.debug("Alpaca unavailable: %s", e)
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# Fetch historical data for the specified date range
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# Fallback: yfinance
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ticker = yf.Ticker(symbol.upper())
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data = ticker.history(start=start_date, end=end_date)
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# Check if data is empty
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if data.empty:
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return (
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f"No data found for symbol '{symbol}' between {start_date} and {end_date}"
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)
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return f"No data found for symbol '{symbol}' between {start_date} and {end_date}"
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# Remove timezone info from index for cleaner output
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if data.index.tz is not None:
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data.index = data.index.tz_localize(None)
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# Round numerical values to 2 decimal places for cleaner display
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numeric_columns = ["Open", "High", "Low", "Close", "Adj Close"]
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for col in numeric_columns:
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if col in data.columns:
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data[col] = data[col].round(2)
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# Convert DataFrame to CSV string
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csv_string = data.to_csv()
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# Add header information
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header = f"# Stock data for {symbol.upper()} from {start_date} to {end_date}\n"
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header += f"# Total records: {len(data)}\n"
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header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
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||||
|
|
@ -526,9 +529,39 @@ def get_company_profile(ticker, curr_date=None):
|
|||
return _json.dumps({"error": str(e), "ticker": ticker})
|
||||
|
||||
|
||||
_SECTOR_ETF_MAP = {
|
||||
"Technology": "XLK",
|
||||
"Financial Services": "XLF",
|
||||
"Financials": "XLF",
|
||||
"Energy": "XLE",
|
||||
"Healthcare": "XLV",
|
||||
"Health Care": "XLV",
|
||||
"Industrials": "XLI",
|
||||
"Consumer Cyclical": "XLY",
|
||||
"Consumer Discretionary": "XLY",
|
||||
"Consumer Defensive": "XLP",
|
||||
"Consumer Staples": "XLP",
|
||||
"Utilities": "XLU",
|
||||
"Real Estate": "XLRE",
|
||||
"Basic Materials": "XLB",
|
||||
"Materials": "XLB",
|
||||
"Communication Services": "XLC",
|
||||
}
|
||||
|
||||
_SECTOR_ETFS = {
|
||||
"SPY": "S&P 500",
|
||||
"XLK": "Technology", "XLF": "Financials", "XLE": "Energy",
|
||||
"XLV": "Health Care", "XLI": "Industrials", "XLY": "Consumer Discretionary",
|
||||
"XLP": "Consumer Staples", "XLU": "Utilities", "XLRE": "Real Estate",
|
||||
"XLB": "Materials", "XLC": "Communication Services",
|
||||
}
|
||||
|
||||
|
||||
def get_macro_indicators(curr_date=None):
|
||||
"""Get macro indicators via yfinance (plain function for interface routing)."""
|
||||
"""Get macro indicators. VIX/TNX from yfinance (indices), sector ETFs from Alpaca."""
|
||||
results = {}
|
||||
|
||||
# VIX and TNX are indices — yfinance only (Alpaca doesn't serve index tickers)
|
||||
try:
|
||||
vix = yf.Ticker("^VIX")
|
||||
vd = vix.history(period="5d")
|
||||
|
|
@ -543,16 +576,118 @@ def get_macro_indicators(curr_date=None):
|
|||
results["ten_year_yield"] = round(td["Close"].iloc[-1], 3)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# Sector ETF performance — Alpaca first (10k calls/min), yfinance fallback
|
||||
try:
|
||||
from .alpaca_data import alpaca_available, get_sector_etf_performance
|
||||
if alpaca_available():
|
||||
perf = get_sector_etf_performance(list(_SECTOR_ETFS.keys()))
|
||||
if perf:
|
||||
sector_performance = {}
|
||||
for sym, data in perf.items():
|
||||
sector_performance[sym] = {
|
||||
"name": _SECTOR_ETFS.get(sym, sym),
|
||||
"return_1m": data.get("return_1m"),
|
||||
"return_3m": data.get("return_3m"),
|
||||
"price": data.get("price"),
|
||||
}
|
||||
results["sector_performance"] = sector_performance
|
||||
except Exception as e:
|
||||
_logger.debug("Alpaca sector ETFs failed: %s", e)
|
||||
|
||||
# Fallback: yfinance for sector ETFs
|
||||
if "sector_performance" not in results:
|
||||
sector_performance = {}
|
||||
for sym, name in _SECTOR_ETFS.items():
|
||||
try:
|
||||
t = yf.Ticker(sym)
|
||||
hist = t.history(period="3mo")
|
||||
if hist.empty or len(hist) < 5:
|
||||
continue
|
||||
close = hist["Close"]
|
||||
current = float(close.iloc[-1])
|
||||
ret_1m = round((current - float(close.iloc[-22])) / float(close.iloc[-22]) * 100, 2) if len(close) >= 22 else None
|
||||
ret_3m = round((current - float(close.iloc[-63])) / float(close.iloc[-63]) * 100, 2) if len(close) >= 63 else None
|
||||
sector_performance[sym] = {"name": name, "return_1m": ret_1m, "return_3m": ret_3m, "price": current}
|
||||
except Exception:
|
||||
pass
|
||||
if sector_performance:
|
||||
results["sector_performance"] = sector_performance
|
||||
|
||||
return _json.dumps(results, default=str)
|
||||
|
||||
|
||||
def get_sector_rotation(ticker, curr_date=None):
|
||||
"""Get sector rotation data via yfinance (plain function for interface routing)."""
|
||||
"""Get sector rotation data with relative performance vs SPY."""
|
||||
try:
|
||||
t = yf.Ticker(ticker.upper())
|
||||
info = t.info
|
||||
info = t.info or {}
|
||||
sector = _safe_get_yf(info, "sector", "Unknown")
|
||||
return _json.dumps({"ticker": ticker.upper(), "sector": sector}, default=str)
|
||||
sector_etf = _SECTOR_ETF_MAP.get(sector)
|
||||
|
||||
result = {"ticker": ticker.upper(), "sector": sector, "sector_etf": sector_etf}
|
||||
|
||||
if not sector_etf:
|
||||
return _json.dumps(result, default=str)
|
||||
|
||||
# Get sector ETF + SPY performance for relative strength
|
||||
etfs_to_fetch = [sector_etf, "SPY"]
|
||||
perf = {}
|
||||
|
||||
try:
|
||||
from .alpaca_data import alpaca_available, get_sector_etf_performance
|
||||
if alpaca_available():
|
||||
perf = get_sector_etf_performance(etfs_to_fetch)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# Fallback: yfinance
|
||||
if not perf:
|
||||
for sym in etfs_to_fetch:
|
||||
try:
|
||||
hist = yf.Ticker(sym).history(period="3mo")
|
||||
if hist.empty or len(hist) < 5:
|
||||
continue
|
||||
close = hist["Close"]
|
||||
current = float(close.iloc[-1])
|
||||
ret_1m = round((current - float(close.iloc[-22])) / float(close.iloc[-22]) * 100, 2) if len(close) >= 22 else None
|
||||
ret_3m = round((current - float(close.iloc[-63])) / float(close.iloc[-63]) * 100, 2) if len(close) >= 63 else None
|
||||
perf[sym] = {"return_1m": ret_1m, "return_3m": ret_3m, "price": current}
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# Compute relative strength vs SPY
|
||||
spy_data = perf.get("SPY", {})
|
||||
etf_data = perf.get(sector_etf, {})
|
||||
spy_1m = spy_data.get("return_1m")
|
||||
spy_3m = spy_data.get("return_3m")
|
||||
etf_1m = etf_data.get("return_1m")
|
||||
etf_3m = etf_data.get("return_3m")
|
||||
|
||||
if etf_1m is not None and spy_1m is not None:
|
||||
result["stock_sector_vs_spy_1m"] = round(etf_1m - spy_1m, 2)
|
||||
if etf_3m is not None and spy_3m is not None:
|
||||
result["stock_sector_vs_spy_3m"] = round(etf_3m - spy_3m, 2)
|
||||
|
||||
# Rank sector among all sector ETFs (from macro_indicators cache or fresh)
|
||||
try:
|
||||
macro_raw = get_macro_indicators()
|
||||
macro = _json.loads(macro_raw) if isinstance(macro_raw, str) else macro_raw
|
||||
sector_perf = macro.get("sector_performance", {})
|
||||
# Rank by 1M return (exclude SPY from ranking)
|
||||
ranked = sorted(
|
||||
[(s, d.get("return_1m", -999)) for s, d in sector_perf.items() if s != "SPY"],
|
||||
key=lambda x: x[1], reverse=True,
|
||||
)
|
||||
for i, (sym, _) in enumerate(ranked, 1):
|
||||
if sym == sector_etf:
|
||||
result["stock_sector_rank"] = i
|
||||
result["total_sectors"] = len(ranked)
|
||||
break
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return _json.dumps(result, default=str)
|
||||
except Exception as e:
|
||||
return _json.dumps({"error": str(e)})
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue