Fetch Market Data

Fetch market data

Steps

1. Validate the ticker list — strip whitespace, uppercase, dedupe, drop empties.
2. Choose the data source in this preference order:
   • yfinance if no broker keys configured (free, anonymous)
   • alpaca-py if ALPACA_KEY + ALPACA_SECRET set (better data quality, included with Alpaca account)
   • polygon-api-client if POLYGON_API_KEY set (real-time)
3. Fetch in one batch call when the library supports it (yfinance does via yf.download(tickers=[...])). Don't per-ticker loop in serial.
4. Record provenance in session memory: {ticker, source, fetched_at, period, interval}.
5. On rate-limit error: backoff exponentially (cap 60s), retry up to 3 times; if still failing, surface the error per-ticker — don't fail the whole run.
6. On missing ticker: report it; don't fabricate data.

Output shape

{
  "AAPL": DataFrame(index=DatetimeIndex, columns=["Open", "High", "Low", "Close", "Volume"]),
  "MSFT": DataFrame(...),
  ...
}

Failure modes to avoid

• Per-ticker serial fetch in a loop (slow + multiplies rate-limit risk).
• Silent failure on missing tickers.
• Caching forever — quote data is stale within minutes during market hours.
• Hard-coded API keys.
• Calling broker WebSocket subscriptions during a one-shot analysis (paid streaming for a single read).