History data optimization

Here’s a detailed explanation for Dharmesh on the best practice for fetching historical data for multiple symbols in QuantConnect, using your code as an example:

Best Practice for Fetching History for Multiple Symbols

Before Optimization: Per-symbol Requests (Not Optimal)

for index, row in self.breakout_symbols:
    symbol_str = row['Symbol']
    symbol = Symbol.create(symbol_str, SecurityType.EQUITY, Market.USA)
    breakout_date = row['Date']
    initial_price = row['close_price']
    history =  self.history(symbol, 1, Resolution.DAILY)
    if history.empty:
        continue
    current_price = history['close'].iloc[0]
    # logic for using current_price

• Problem: This approach requests history one symbol at a time, causing many separate and sequential data fetches.
• Downside: Slow, inefficient, and increases compute and resource usage—especially with large symbol lists.

After Optimization: Batch (Vectorized) Request (Optimal, Recommended)

symbols_to_check = []
symbol_index_map = {}
for idx, row in self.breakout_symbols:
    symbol = Symbol.create(row['Symbol'], SecurityType.EQUITY, Market.USA)
    symbol_index_map[symbol] = idx
    symbols_to_check.append(symbol)

history = self.history(symbols_to_check, 1, Resolution.DAILY) if symbols_to_check else pd.DataFrame()

for symbol, idx in symbol_index_map.items():
    row = self.breakout_stocks.loc[idx]
    breakout_date = row['Date']
    initial_price = row['close_price']
    sym_hist = history.xs(symbol, level=0) if not history.empty else pd.DataFrame()
    if sym_hist.empty:
        continue
    current_price = sym_hist['close'].iloc[0]
    # logic for using current_price

• Advantage: You make one efficient batch request for all symbols at once. This is much faster and is the best practice in QuantConnect.
• Data Structure: The returned history is a multi-indexed pandas DataFrame (by symbol/date).
• How to Extract: Pull each symbol’s data using .xs(symbol, level=0) or .loc[symbol], only proceeding if not empty.
• Scalability: Works well for dozens to thousands of symbols. Uses QC’s back-end efficiently.
• Reliability: If some symbols have no history, you can easily handle that case in your loop (as above).

Why the Batch Approach is Best

• Performance: One single batched request is always faster than hundreds of small ones.
• Simplicity: All results in one DataFrame—less repetitive code.
• Resource Limits: QuantConnect may restrict/slow frequent calling, so batching is safer for robust algorithms.
• Scalable: Professional quant workflows (and QC docs) always recommend this for large universes.

Best Practice Code Structure

1. Collect all symbols you want data for.
2. Call self.History(symbols, ...) once, producing a MultiIndex pandas DataFrame.
3. Loop through your symbol list, extracting the slice of data for each; check if empty to handle missing data.
4. Proceed with your logic (use prices, check conditions, etc).

Example Extraction:

if not history.empty and symbol in history.index.get_level_values(0):
    sym_hist = history.loc[symbol]
    # Further processing
else:
    # Handle missing data

Summary:
The batch-history method demonstrated in your 'After Optimization' version is the correct and recommended best practice in QuantConnect. It is faster, more reliable, and more scalable for professional algorithmic trading research or production code.

Let me know if you’d like a further example or clarification!

Sources: Key Concepts - QuantConnect.com, Getting Started - QuantConnect.com, Using Data - QuantConnect.com, History Algorithm, Gaussian Naive Bayes Model