alpaca-py

Alpaca-py provides access to three primary API suites:

• Broker API: Used for building full brokerage experiences, including account opening, funding, and trading for end users.
• Market Data API: Provides real-time and historical data for equities, crypto, and options.
• Trading API: Enables trading of stocks and crypto. Paper trading is available for free to all Alpaca users.

The LBR Anti Setup Trading Bot is a production-ready algorithmic trading system implementing Linda Raschke's LBR 3/10 Anti setup strategy. It targets the 'Magnificent 7' stocks (AAPL, MSFT, NVDA, GOOGL, AMZN, META, TSLA) using a serverless AWS Lambda architecture.

Core Workflow:

1. Trigger: Runs every weekday at 9:30 AM ET via AWS CloudWatch.
2. Market Check: Verifies market status via Alpaca's API.
3. Signal Scan: Filters stocks using a 6-rule technical indicator logic (MACD, ADX, EMA, ATR).
4. Execution: Performs equal-weighted rebalancing (selling excess, buying new setups).
5. Risk Management: Places ATR-based trailing stop orders.
6. Persistence: Logs portfolio state to DynamoDB.

The alpaca.data.requests module provides specialized request classes to fetch various types of cryptocurrency market data. Depending on your needs, you can request historical bars, real-time quotes, individual trades, or snapshots of the current market state.

Available request types include:

• CryptoBarsRequest: For fetching historical OHLCV (Open, High, Low, Close, Volume) bar data.
• CryptoQuoteRequest: For fetching historical quote data.
• CryptoTradesRequest: For fetching historical trade data.
• CryptoLatestQuoteRequest: For fetching the most recent quote.
• CryptoLatestTradeRequest: For fetching the most recent trade.
• CryptoSnapshotRequest: For fetching a comprehensive snapshot of the current market state (including latest trade, quote, and bars).

The bot identifies trading setups by filtering the Magnificent 7 stocks through six sequential technical rules:

1. Minimum bars: Requires $\ge 31$ bars of price history.
2. ADX filter: ADX must be $\le 32$ and not rising (rejecting strong trends).
3. Trend filter: Price must be above the 20-bar EMA (Close > EMA(20)).
4. Signal crossover: The MACD signal line must have crossed zero from below within the last 16 bars.
5. Pullback: MACD must be $\le$ (signal line + $0.5 \times$ ATR).
6. Hook: The MACD histogram must be rising.

Symbols passing all rules are selected for equal-weight allocation.

The bot manages risk using dynamic stops based on the Average True Range (ATR):

• Entry: Market order during regular hours (9:30 AM – 4:00 PM ET).
• Stop Loss: $\text{entry} - \text{ATR}(14) \times 1.5$
• Profit Target: $\text{entry} + \text{ATR}(14) \times 1.5$
• Trailing Stop Percentage: $\frac{\text{ATR} \times 1.5}{\text{entry price}}$

Alpaca-py uses pydantic for runtime data validation. All request models can be instantiated by passing in data in dictionary format (e.g., from a JSON payload), allowing you to handle parsing and validation automatically.

 @app.route('/post_json', methods=['POST'])
 def do_trade():
     # ...

     order_data_json = request.get_json()

     # validate data
     MarketOrderRequest(**order_data_json)

     # ...

Alpaca-py provides specialized client classes based on the API product and asset class you are targeting. You must instantiate the specific client required for your task:

Broker API

• BrokerClient

Trading API

• TradingClient

Market Data API

• StockHistoricalDataClient
• CryptoHistoricalDataClient
• OptionHistoricalDataClient
• StockDataStream
• CryptoDataStream
• OptionDataStream