A modern Python wrapper for the Alpaca Trading API, providing easy access to trading, market data, and account management functionality with full type safety and comprehensive testing.
- 🔐 Complete Alpaca API Coverage: Trading, market data, account management, and more
- 📊 Stock Market Analysis: Built-in screeners for gainers/losers, historical data analysis
- 🚀 Batch Operations: Efficient multi-symbol data fetching with automatic batching (200+ symbols)
- 🤖 ML-Powered Predictions: Stock price predictions using Facebook Prophet
- 📰 Financial News Integration: Real-time news from Yahoo Finance and Benzinga
- 📈 Technical Analysis: Stock recommendations and sentiment analysis
- 📉 Bid/Ask Quotes: Historical quote data with spread analysis
- 🔔 Auction Data: Opening and closing auction prices and volumes
- 🖼️ Company Logos: Retrieve company logos for display
- 🎯 Type Safety: Full type annotations with mypy strict mode
- 🧪 Battle-Tested: 350+ tests with comprehensive coverage
- ⚡ Modern Python: Python 3.10+ with latest best practices
- 📸 Market Snapshots: Get complete market snapshots with latest trade, quote, and bar data
- ⚙️ Account Configuration: Manage PDT settings, trade confirmations, and margin configurations
- 📋 Market Metadata: Access condition codes, exchange information, and trading metadata
- 🔄 Enhanced Orders: Replace orders, client order IDs, and advanced order management
- 🎯 Smart Feed Management: Automatic feed selection and fallback (SIP → IEX → OTC)
- 💾 Intelligent Caching: Built-in caching system with configurable TTLs for optimal performance
- 🏢 Corporate Actions: Track dividends, splits, mergers, and other corporate events
- 📊 Trade Data API: Access historical and real-time trade data with pagination
- 📉 Historical Quotes API: Bid/ask quotes with spread calculations
- 🔔 Auction Data API: Opening and closing auction prices and volumes
- 🖼️ Company Logos API: Retrieve company logo images
pip install py-alpaca-apiuv add py-alpaca-api# Clone the repository
git clone https://github.com/TexasCoding/py-alpaca-api.git
cd py-alpaca-api
# Install with development dependencies using uv
uv sync --all-extras --dev
# Or using pip
pip install -e ".[dev]"from py_alpaca_api import PyAlpacaAPI
# Initialize with your API credentials
api = PyAlpacaAPI(
api_key="YOUR_API_KEY",
api_secret="YOUR_SECRET_KEY",
api_paper=True # Use paper trading for testing
)
# Get account information
account = api.trading.account.get()
print(f"Account Balance: ${account.cash}")
print(f"Buying Power: ${account.buying_power}")# Place a market order
order = api.trading.orders.market(
symbol="AAPL",
qty=1,
side="buy"
)
print(f"Order placed: {order.id}")
# Place a limit order
limit_order = api.trading.orders.limit(
symbol="GOOGL",
qty=1,
side="buy",
limit_price=150.00
)
# Get all positions
positions = api.trading.positions.get_all()
for position in positions:
print(f"{position.symbol}: {position.qty} shares @ ${position.avg_entry_price}")
# Cancel all open orders
api.trading.orders.cancel_all()# Get historical stock data for a single symbol
history = api.stock.history.get(
symbol="TSLA",
start="2024-01-01",
end="2024-12-31"
)
# NEW: Get historical data for multiple symbols (batch operation)
symbols = ["AAPL", "GOOGL", "MSFT", "TSLA", "AMZN"]
multi_history = api.stock.history.get(
symbol=symbols, # Pass a list for batch operation
start="2024-01-01",
end="2024-12-31"
)
# Returns DataFrame with all symbols' data, automatically handles batching for 200+ symbols
# Get real-time quote for a single symbol
quote = api.stock.latest_quote.get("MSFT")
print(f"MSFT Price: ${quote.ask}")
# NEW: Get real-time quotes for multiple symbols (batch operation)
quotes = api.stock.latest_quote.get(["AAPL", "GOOGL", "MSFT"])
for quote in quotes:
print(f"{quote.symbol}: ${quote.ask}")
# Screen for top gainers
gainers = api.stock.screener.gainers(
price_greater_than=10.0,
change_greater_than=5.0,
volume_greater_than=1000000
)
print("Top Gainers:")
for stock in gainers.head(10).itertuples():
print(f"{stock.symbol}: +{stock.change}%")
# Screen for top losers
losers = api.stock.screener.losers(
price_greater_than=10.0,
change_less_than=-5.0,
volume_greater_than=1000000
)# Predict future stock prices using Prophet
predictions = api.stock.predictor.predict(
symbol="AAPL",
days_to_predict=30,
forecast_days_back=365
)
# Get prediction for specific date
future_price = predictions[predictions['ds'] == '2024-12-31']['yhat'].values[0]
print(f"Predicted AAPL price on 2024-12-31: ${future_price:.2f}")# Get latest financial news
news = api.trading.news.get(symbol="NVDA")
for article in news[:5]:
print(f"- {article['headline']}")
print(f" Sentiment: {article.get('sentiment', 'N/A')}")
# Get stock recommendations
recommendations = api.trading.recommendations.get_recommendations("META")
sentiment = api.trading.recommendations.get_sentiment("META")
print(f"META Sentiment: {sentiment}")# Get portfolio history
portfolio_history = api.trading.account.portfolio_history(
period="1M",
timeframe="1D"
)
# Calculate returns
returns = (
(portfolio_history['equity'].iloc[-1] - portfolio_history['equity'].iloc[0]) /
portfolio_history['equity'].iloc[0] * 100
)
print(f"Monthly Return: {returns:.2f}%")
# Get account activities
activities = api.trading.account.get_activities()
for activity in activities:
print(f"{activity.created_at}: {activity.activity_type} - {activity.symbol}")# Get historical quotes with bid/ask spreads
quotes = api.stock.quotes.get_historical_quotes(
"AAPL",
start="2024-01-01T09:30:00Z",
end="2024-01-01T16:00:00Z"
)
print(f"Average spread: ${quotes['spread'].mean():.4f}")
print(f"Spread percentage: {quotes['spread_pct'].mean():.4f}%")
# Get auction data (opening and closing auctions)
auctions = api.stock.auctions.get_auctions(
["AAPL", "MSFT"],
start="2024-01-01",
end="2024-01-31"
)
for symbol, df in auctions.items():
print(f"{symbol} average intraday return: {df['intraday_return'].mean():.2f}%")
# Get daily aggregated auction data
daily_auctions = api.stock.auctions.get_daily_auctions(
"AAPL",
start="2024-01-01",
end="2024-01-31"
)
print(f"Days with positive returns: {len(daily_auctions[daily_auctions['daily_return'] > 0])}")
# Get company logos
logo_url = api.stock.logos.get_logo_url("AAPL")
print(f"Apple logo URL: {logo_url}")
# Save logo to file
api.stock.logos.save_logo("MSFT", "msft_logo.png")
# Get logo as base64 for embedding
logo_base64 = api.stock.logos.get_logo_base64("GOOGL")
html = f'<img src="data:image/png;base64,{logo_base64}" alt="Google logo">'
# Get logos for multiple companies
logos = api.stock.logos.get_multiple_logos(
["AAPL", "MSFT", "GOOGL"],
placeholder=True # Use placeholder if logo not found
)# Create a watchlist
watchlist = api.trading.watchlists.create_watchlist(
name="Tech Stocks",
symbols=["AAPL", "GOOGL", "MSFT", "NVDA"]
)
# Add symbols to existing watchlist
api.trading.watchlists.add_assets_to_watchlist(
watchlist_id=watchlist.id,
symbols=["META", "AMZN"]
)
# Get all watchlists
watchlists = api.trading.watchlists.get_all_watchlists()# Get dividend announcements
dividends = api.trading.corporate_actions.get_announcements(
since="2024-01-01",
until="2024-03-31",
ca_types=["dividend"],
symbol="AAPL" # Optional: filter by symbol
)
for dividend in dividends:
print(f"{dividend.initiating_symbol}: ${dividend.cash_amount} on {dividend.payable_date}")
# Get stock splits
splits = api.trading.corporate_actions.get_announcements(
since="2024-01-01",
until="2024-03-31",
ca_types=["split"]
)
for split in splits:
print(f"{split.initiating_symbol}: {split.split_from}:{split.split_to} split")
# Get mergers and acquisitions
mergers = api.trading.corporate_actions.get_announcements(
since="2024-01-01",
until="2024-03-31",
ca_types=["merger"]
)
# Get specific announcement by ID
announcement = api.trading.corporate_actions.get_announcement_by_id("123456")
print(f"Corporate Action: {announcement.ca_type} for {announcement.initiating_symbol}")
# Get all types of corporate actions
all_actions = api.trading.corporate_actions.get_announcements(
since="2024-01-01",
until="2024-03-31",
ca_types=["dividend", "split", "merger", "spinoff"],
date_type="ex_dividend" # Filter by specific date type
)# Get historical trades for a symbol
trades_response = api.stock.trades.get_trades(
symbol="AAPL",
start="2024-01-15T09:30:00Z",
end="2024-01-15T10:00:00Z",
limit=100
)
for trade in trades_response.trades:
print(f"Trade: {trade.size} shares @ ${trade.price} on {trade.exchange}")
# Get latest trade for a symbol
latest_trade = api.stock.trades.get_latest_trade("MSFT")
print(f"Latest MSFT trade: ${latest_trade.price} x {latest_trade.size}")
# Get trades for multiple symbols
multi_trades = api.stock.trades.get_trades_multi(
symbols=["AAPL", "MSFT", "GOOGL"],
start="2024-01-15T09:30:00Z",
end="2024-01-15T10:00:00Z",
limit=10
)
for symbol, trades_data in multi_trades.items():
print(f"{symbol}: {len(trades_data.trades)} trades")
# Get all trades with automatic pagination
all_trades = api.stock.trades.get_all_trades(
symbol="SPY",
start="2024-01-15T09:30:00Z",
end="2024-01-15T09:35:00Z"
)
print(f"Total SPY trades: {len(all_trades)}")
# Use different data feeds (requires subscription)
sip_trades = api.stock.trades.get_trades(
symbol="AAPL",
start="2024-01-15T09:30:00Z",
end="2024-01-15T10:00:00Z",
feed="sip" # or "iex", "otc"
)# Get snapshot for a single symbol
snapshot = api.stock.snapshots.get_snapshot("AAPL")
print(f"Latest trade: ${snapshot.latest_trade.price}")
print(f"Latest quote: Bid ${snapshot.latest_quote.bid} / Ask ${snapshot.latest_quote.ask}")
print(f"Daily bar: Open ${snapshot.daily_bar.open} / Close ${snapshot.daily_bar.close}")
print(f"Previous daily: Open ${snapshot.prev_daily_bar.open} / Close ${snapshot.prev_daily_bar.close}")
# Get snapshots for multiple symbols (efficient batch operation)
symbols = ["AAPL", "GOOGL", "MSFT", "TSLA", "NVDA"]
snapshots = api.stock.snapshots.get_snapshots(symbols)
for symbol, snapshot in snapshots.items():
print(f"{symbol}: ${snapshot.latest_trade.price} ({snapshot.daily_bar.volume:,} volume)")
# Get snapshots with specific feed
snapshots = api.stock.snapshots.get_snapshots(
symbols=["SPY", "QQQ"],
feed="iex" # or "sip", "otc"
)# Get current account configuration
config = api.trading.account.get_configuration()
print(f"PDT Check: {config.pdt_check}")
print(f"Trade Confirm Email: {config.trade_confirm_email}")
print(f"Suspend Trade: {config.suspend_trade}")
print(f"No Shorting: {config.no_shorting}")
# Update account configuration
updated_config = api.trading.account.update_configuration(
trade_confirm_email=True,
suspend_trade=False,
pdt_check="both", # "both", "entry", or "exit"
no_shorting=False
)
print("Account configuration updated successfully")# Get condition codes for trades
condition_codes = api.stock.metadata.get_condition_codes(tape="A")
for code in condition_codes:
print(f"Code {code.code}: {code.description}")
# Get exchange codes
exchanges = api.stock.metadata.get_exchange_codes()
for exchange in exchanges:
print(f"{exchange.code}: {exchange.name} ({exchange.type})")
# Get all condition codes at once (cached for performance)
all_codes = api.stock.metadata.get_all_condition_codes()
print(f"Loaded {len(all_codes)} condition codes")
# Lookup specific codes
code_info = api.stock.metadata.lookup_condition_code("R")
print(f"Code R means: {code_info.description}")# Place order with client order ID for tracking
order = api.trading.orders.market(
symbol="AAPL",
qty=1,
side="buy",
client_order_id="my-app-order-123"
)
# Replace an existing order (modify price, quantity, etc.)
replaced_order = api.trading.orders.replace_order(
order_id=order.id,
qty=2, # Change quantity
limit_price=155.00 # Add/change limit price
)
# Get order by client order ID (useful for tracking)
orders = api.trading.orders.get_all(status="open")
my_order = next((o for o in orders if o.client_order_id == "my-app-order-123"), None)
# Advanced OCO/OTO orders
oco_order = api.trading.orders.limit(
symbol="TSLA",
qty=1,
side="buy",
limit_price=200.00,
order_class="oco", # One-Cancels-Other
take_profit={"limit_price": 250.00},
stop_loss={"stop_price": 180.00}
)# The library automatically manages feed selection based on your subscription
# No configuration needed - it automatically detects and falls back as needed
# Manual feed configuration (optional)
from py_alpaca_api.http.feed_manager import FeedManager, FeedConfig, FeedType
# Configure preferred feeds
feed_config = FeedConfig(
preferred_feed=FeedType.SIP, # Try SIP first
fallback_feeds=[FeedType.IEX], # Fall back to IEX if needed
auto_fallback=True # Automatically handle permission errors
)
# The feed manager automatically:
# - Detects your subscription level (Basic/Unlimited/Business)
# - Falls back to available feeds on permission errors
# - Caches failed feeds to avoid repeated attempts
# - Provides clear logging for debugging# Caching is built-in and automatic for improved performance
# Configure caching (optional - sensible defaults are provided)
from py_alpaca_api.cache import CacheManager, CacheConfig
# Custom cache configuration
cache_config = CacheConfig(
max_size=1000, # Maximum items in cache
default_ttl=300, # Default time-to-live in seconds
data_ttls={
"market_hours": 86400, # 1 day
"assets": 3600, # 1 hour
"quotes": 1, # 1 second
"positions": 10, # 10 seconds
}
)
# Cache manager automatically:
# - Caches frequently accessed data
# - Reduces API calls and improves response times
# - Manages memory efficiently with LRU eviction
# - Supports optional Redis backend for distributed caching
# Use the @cached decorator for custom caching
cache_manager = CacheManager(cache_config)
@cache_manager.cached("custom_data", ttl=600)
def expensive_calculation(symbol: str):
# This result will be cached for 10 minutes
return complex_analysis(symbol)# Stop-loss order
stop_loss = api.trading.orders.stop(
symbol="TSLA",
qty=1,
side="sell",
stop_price=180.00
)
# Trailing stop order
trailing_stop = api.trading.orders.trailing_stop(
symbol="NVDA",
qty=1,
side="sell",
trail_percent=5.0 # 5% trailing stop
)
# One-Cancels-Other (OCO) order
oco_order = api.trading.orders.market(
symbol="AAPL",
qty=1,
side="buy",
take_profit=200.00,
stop_loss=150.00
)# Check if market is open
clock = api.trading.market.clock()
print(f"Market is {'open' if clock.is_open else 'closed'}")
print(f"Next open: {clock.next_open}")
print(f"Next close: {clock.next_close}")
# Get market calendar
calendar = api.trading.market.calendar(
start_date="2024-01-01",
end_date="2024-12-31"
)The project includes comprehensive test coverage. Run tests using:
# Run all tests
./test.sh
# Run specific test file
./test.sh tests/test_trading/test_orders.py
# Run with coverage
uv run pytest --cov=py_alpaca_api --cov-report=html
# Run with markers
uv run pytest -m "not slow" # Skip slow tests# Install development dependencies
uv sync --all-extras --dev
# Install pre-commit hooks
pre-commit install
# Run code quality checks
make check
# Format code
make format
# Run type checking
make type-check
# Run linting
make lint- Ruff: Fast Python linter and formatter
- MyPy: Static type checker with strict mode
- Pre-commit: Git hooks for code quality
- Pytest: Testing framework with coverage
py-alpaca-api/
├── src/py_alpaca_api/
│ ├── __init__.py # Main API client
│ ├── exceptions.py # Custom exceptions
│ ├── trading/ # Trading operations
│ │ ├── account.py # Account management & configuration
│ │ ├── orders.py # Order management (enhanced)
│ │ ├── positions.py # Position tracking
│ │ ├── watchlists.py # Watchlist operations
│ │ ├── market.py # Market hours & calendar
│ │ ├── news.py # Financial news
│ │ ├── recommendations.py # Stock analysis
│ │ └── corporate_actions.py # Corporate events (v3.0.0)
│ ├── stock/ # Stock market data
│ │ ├── assets.py # Asset information
│ │ ├── history.py # Historical data (batch support)
│ │ ├── quotes.py # Historical quotes with bid/ask (v3.0.0)
│ │ ├── auctions.py # Opening/closing auctions (v3.0.0)
│ │ ├── logos.py # Company logos (v3.0.0)
│ │ ├── screener.py # Stock screening
│ │ ├── predictor.py # ML predictions
│ │ ├── latest_quote.py # Real-time quotes (batch support)
│ │ ├── trades.py # Trade data API (v3.0.0)
│ │ ├── snapshots.py # Market snapshots (v3.0.0)
│ │ └── metadata.py # Market metadata (v3.0.0)
│ ├── models/ # Data models
│ ├── cache/ # Caching system (v3.0.0)
│ │ ├── cache_manager.py # Cache management
│ │ └── cache_config.py # Cache configuration
│ └── http/ # HTTP client
│ ├── requests.py # Request handling
│ └── feed_manager.py # Feed management (v3.0.0)
├── tests/ # Test suite (300+ tests)
├── docs/ # Documentation
└── pyproject.toml # Project configuration
Full documentation is available at Read the Docs
- Trading API - Orders, positions, and account management
- Market Data API - Historical and real-time data
- Stock Analysis - Screeners, predictions, and sentiment
- Models - Data models and type definitions
We welcome contributions! Please see our Contributing Guidelines for details.
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
- Write tests for new features
- Follow the existing code style (enforced by ruff)
- Add type hints to all functions
- Update documentation as needed
- Ensure all tests pass before submitting PR
This project is licensed under the MIT License - see the LICENSE file for details.
- Alpaca Markets for providing the trading API
- Prophet for time series forecasting
- yfinance for market data
- Beautiful Soup for web scraping
- All contributors who have helped improve this project
- 📧 Issues: GitHub Issues
- 💬 Discussions: GitHub Discussions
- 📖 Wiki: GitHub Wiki
- ✅ Complete Alpaca Stock API coverage
- ✅ Market Snapshots API
- ✅ Account Configuration API
- ✅ Market Metadata API
- ✅ Enhanced Order Management
- ✅ Corporate Actions API
- ✅ Trade Data API
- ✅ Smart Feed Management System
- ✅ Intelligent Caching System
- ✅ Batch Operations for all data endpoints
- WebSocket support for real-time data streaming
- Live market data subscriptions
- Real-time order and trade updates
- Full async/await support
- Concurrent API operations
- Async context managers
- Options trading support
- Crypto trading integration
- Advanced portfolio analytics
- Backtesting framework
- Strategy automation tools
This software is for educational purposes only. Do not risk money which you are afraid to lose. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS.
Always start with paper trading to test your strategies before using real money.
Made with ❤️ by the py-alpaca-api team
