Historical Data

The history module provides access to historical price data, bars, and latest market data.

Overview

The history module allows you to:

Retrieve historical price bars (OHLCV data)
Get the latest bars for symbols
Access multi-timeframe data (1Min, 5Min, 15Min, 1Hour, 1Day, etc.)
Batch retrieve data for multiple symbols

Usage

Historical Bars

from py_alpaca_api import PyAlpacaAPI
import pendulum

api = PyAlpacaAPI(
    api_key="your_api_key",
    api_secret="your_secret_key"
)

# Get daily bars for the last 30 days
bars = api.stock.history.get_bars(
    symbol="AAPL",
    start=pendulum.now().subtract(days=30),
    end=pendulum.now(),
    timeframe="1Day"
)

# Returns a DataFrame with columns:
# - open: Opening price
# - high: High price
# - low: Low price
# - close: Closing price
# - volume: Trading volume
# - trade_count: Number of trades
# - vwap: Volume-weighted average price

Latest Bars

# Get the latest bar for a single symbol
latest = api.stock.history.get_latest_bars("AAPL")

print(f"Latest close: ${latest.iloc[0]['close']}")
print(f"Volume: {latest.iloc[0]['volume']:,}")

# Get latest bars for multiple symbols
symbols = ["AAPL", "MSFT", "GOOGL"]
latest_bars = api.stock.history.get_latest_bars(symbols)

# Returns a dictionary mapping symbols to DataFrames
for symbol, df in latest_bars.items():
    print(f"{symbol}: ${df.iloc[0]['close']}")

Intraday Bars

# Get 5-minute bars for today
bars = api.stock.history.get_bars(
    symbol="AAPL",
    start=pendulum.today("America/New_York").add(hours=9, minutes=30),
    end=pendulum.now("America/New_York"),
    timeframe="5Min"
)

# Analyze price movements
print(f"Bars retrieved: {len(bars)}")
print(f"Day's range: ${bars['low'].min():.2f} - ${bars['high'].max():.2f}")
print(f"Total volume: {bars['volume'].sum():,}")

Methods

get_bars() / get_stock_data()

Retrieve historical bar data for a symbol.

Parameters:

symbol: Stock symbol
start: Start date/time (pendulum datetime)
end: End date/time (pendulum datetime)
timeframe: Bar timeframe (“1Min”, “5Min”, “15Min”, “30Min”, “1Hour”, “1Day”, “1Week”, “1Month”)
adjustment: Price adjustment for splits (“raw”, “split”, “dividend”, “all”)
feed: Data feed to use (“iex”, “sip”, “otc”)
limit: Maximum number of bars to return
page_limit: Number of bars per page
asof: As-of date for corporate actions

get_latest_bars()

Get the most recent bar for one or more symbols.

Parameters:

symbols: Single symbol string or list of symbols
feed: Data feed to use (“iex”, “sip”, “otc”)
currency: Currency for the bars (default: “USD”)

get_bars_multi()

Get bars for multiple symbols in a single request.

Parameters:

symbols: List of stock symbols
start: Start date/time
end: End date/time
timeframe: Bar timeframe
adjustment: Price adjustment
feed: Data feed

Timeframes

Available timeframes for bar data:

Timeframe	Description
1Min	One minute bars
5Min	Five minute bars
15Min	Fifteen minute bars
30Min	Thirty minute bars
1Hour	Hourly bars
1Day	Daily bars
1Week	Weekly bars
1Month	Monthly bars

Price Adjustments

Control how historical prices are adjusted:

# Raw prices (no adjustments)
bars_raw = api.stock.history.get_bars(
    symbol="AAPL",
    start=start_date,
    end=end_date,
    timeframe="1Day",
    adjustment="raw"
)

# Split-adjusted only
bars_split = api.stock.history.get_bars(
    symbol="AAPL",
    start=start_date,
    end=end_date,
    timeframe="1Day",
    adjustment="split"
)

# All adjustments (splits and dividends)
bars_adjusted = api.stock.history.get_bars(
    symbol="AAPL",
    start=start_date,
    end=end_date,
    timeframe="1Day",
    adjustment="all"
)

Technical Analysis

# Calculate moving averages
bars = api.stock.history.get_bars(
    symbol="AAPL",
    start=pendulum.now().subtract(days=100),
    end=pendulum.now(),
    timeframe="1Day"
)

# Simple moving averages
bars['SMA_20'] = bars['close'].rolling(window=20).mean()
bars['SMA_50'] = bars['close'].rolling(window=50).mean()

# Bollinger Bands
bars['BB_middle'] = bars['close'].rolling(window=20).mean()
bars['BB_std'] = bars['close'].rolling(window=20).std()
bars['BB_upper'] = bars['BB_middle'] + (bars['BB_std'] * 2)
bars['BB_lower'] = bars['BB_middle'] - (bars['BB_std'] * 2)

# RSI calculation
def calculate_rsi(data, window=14):
    delta = data.diff()
    gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()
    rs = gain / loss
    return 100 - (100 / (1 + rs))

bars['RSI'] = calculate_rsi(bars['close'])

Volume Analysis

# Analyze volume patterns
bars = api.stock.history.get_bars(
    symbol="AAPL",
    start=pendulum.now().subtract(days=30),
    end=pendulum.now(),
    timeframe="1Day"
)

# Volume indicators
bars['Volume_MA'] = bars['volume'].rolling(window=20).mean()
bars['Volume_Ratio'] = bars['volume'] / bars['Volume_MA']

# Find high volume days
high_volume_days = bars[bars['Volume_Ratio'] > 1.5]
print(f"Days with 50% above average volume: {len(high_volume_days)}")

# VWAP analysis
bars['VWAP_Diff'] = bars['close'] - bars['vwap']
bars['VWAP_Pct'] = (bars['VWAP_Diff'] / bars['vwap']) * 100

Batch Operations

# Get data for multiple symbols efficiently
symbols = ["AAPL", "MSFT", "GOOGL", "AMZN", "META"]

# Latest bars for all symbols
latest = api.stock.history.get_latest_bars(symbols)

# Historical data for multiple symbols
all_data = {}
for symbol in symbols:
    all_data[symbol] = api.stock.history.get_bars(
        symbol=symbol,
        start=pendulum.now().subtract(days=7),
        end=pendulum.now(),
        timeframe="1Hour"
    )

# Analyze correlations
closes = pd.DataFrame({
    symbol: data['close']
    for symbol, data in all_data.items()
})
correlation_matrix = closes.corr()

Error Handling

from py_alpaca_api.exceptions import APIRequestError

try:
    bars = api.stock.history.get_bars(
        symbol="INVALID",
        start=pendulum.now().subtract(days=30),
        end=pendulum.now(),
        timeframe="1Day"
    )
except APIRequestError as e:
    print(f"API error: {e}")
except Exception as e:
    print(f"Error retrieving data: {e}")

Performance Tips

Use appropriate timeframes: Don’t request minute bars for multi-year periods
Implement caching: The library includes built-in caching support
Batch requests: Use get_latest_bars() with multiple symbols instead of looping
Limit data range: Request only the data you need
Use pagination: For large datasets, the library handles pagination automatically