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The Official Python SDK for Alpaca APIs

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Alpaca-py provides an interface for interacting with the API products Alpaca offers. These API products are provided as various REST, WebSocket and SSE endpoints that allow you to do everything from streaming market data to creating your own investment apps.

Learn more about the API products Alpaca offers at


Alpaca-py has a supplementary documentation site which contains references for all clients, methods and models found in this codebase. The documentation also contains examples to get started with alpaca-py.

You can find the documentation site here:


Alpaca-py is supported on Python 3.7+. You can install Alpaca-py using pip.

Run the following command in your terminal.

  pip install alpaca-py


If you already have Alpaca-py installed, and would like to use the latest version available...

Run the following command in your terminal:

  pip install alpaca-py --upgrade

What’s New?

If you’ve used the previous python SDK alpaca-trade-api, there are a few key differences to be aware of.

Broker API

Alpaca-py lets you use Broker API to start building your investment apps! Learn more at the Broker page.

OOP Design

Alpaca-py uses a more OOP approach to submitting requests compared to the previous SDK. To submit a request, you will most likely need to create a request object containing the desired request data. Generally, there is a unique request model for each method.

Some examples of request models corresponding to methods:

  • GetOrdersRequest for TradingClient.get_orders()
  • CryptoLatestOrderbookRequest for CryptoHistoricalDataClient.get_crypto_latest_orderbook()

Request Models Usage Example

To get historical bar data for crypto, you will need to provide a CryptoBarsRequest object.

from import CryptoHistoricalDataClient
from import CryptoBarsRequest
from import TimeFrame

# no keys required for crypto data
client = CryptoHistoricalDataClient()

request_params = CryptoBarsRequest(
                        symbol_or_symbols=["BTC/USD", "ETH/USD"],

bars = client.get_crypto_bars(request_params)

Data Validation

Alpaca-py uses pydantic to validate data models at run-time. This means if you are receiving request data via JSON from a client. You can handle parsing and validation through Alpaca’s request models. All request models can be instantiated by passing in data in dictionary format.

Here is a rough example of what is possible.

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

     order_data_json = request.get_json()

     # validate data

     # ...

Many Clients

Alpaca-py has a lot of client classes. There is a client for each API and even asset class specific clients (StockHistoricalDataClient, CryptoDataStream). This requires you to pick and choose clients based on your needs.

Broker API: BrokerClient

Trading API: TradingClient

Market Data API: StockHistoricalDataClient, CryptoHistoricalDataClient, CryptoDataStream, StockDataStream

API Keys

Trading and Market Data API

In order to use Alpaca’s services you’ll need to sign up for an Alpaca account and retrieve your API keys. Signing up is completely free and takes only a few minutes. Sandbox environments are available to test out the API. To use the sandbox environment, you will need to provide sandbox/paper keys. API keys are passed into Alpaca-py through either TradingClient, StockHistoricalDataClient, CryptoHistoricalDataClient, StockDataStream, or CryptoDataStream.

Broker API

To use the Broker API, you will need to sign up for a broker account and retrieve your Broker API keys. The API keys can be found on the dashboard once you’ve logged in. Alpaca also provides a sandbox environment to test out Broker API. To use the sandbox mode, provide your sandbox keys. Once you have your keys, you can pass them into BrokerClient to get started.


Alpaca’s APIs allow you to do everything from building algorithmic trading strategies to building a full brokerage experience for your own end users. Here are some things you can do with Alpaca-py.

To view full descriptions and examples view the documentation page.

Market Data API: Access live and historical market data for 5000+ stocks and 20+ crypto.

Trading API: Trade stock and crypto with lightning fast execution speeds.

Broker API & Connect: Build investment apps - from robo-advisors to brokerages.

Broker API Example

Listing All Accounts

The BrokerClient.list_accounts method allows you to list all the brokerage accounts under your management. The method takes an optional parameter search_parameters which requires a ListAccountsRequest object. This parameter allows you to filter the list of accounts returned.

from import BrokerClient
from import ListAccountsRequest
from import AccountEntities

broker_client = BrokerClient('api-key', 'secret-key')

# search for accounts created after January 30th 2022.
# Response should contain Contact and Identity fields for each account.
filter = ListAccountsRequest(
                    created_after=datetime.datetime.strptime("2022-01-30", "%Y-%m-%d"),
                    entities=[AccountEntities.CONTACT, AccountEntities.IDENTITY]

accounts = broker_client.list_accounts(search_parameters=filter)

Trading API Example

Submitting an Order

To create an order on Alpaca-py you must use an OrderRequest object. There are different OrderRequest objects based on the type of order you want to make. For market orders, there is MarketOrderRequest, limit orders have LimitOrderRequest, stop orders StopOrderRequest, and trailing stop orders have TrailingStopOrderRequest. Each order type have their own required parameters for a successful order.

from import TradingClient
from import MarketOrderRequest
from import OrderSide, TimeInForce

trading_client = TradingClient('api-key', 'secret-key')

# preparing order data
market_order_data = MarketOrderRequest(

# Market order
market_order = trading_client.submit_order(

Market Data API Example

Querying Historical Bar Data

You can request bar data via the HistoricalDataClients. In this example, we query daily bar data for “BTC/USD” and “ETH/USD” since July 1st 2022. You can convert the response to a multi-index pandas dataframe using the .df property.

from import CryptoHistoricalDataClient
from import CryptoBarsRequest
from import TimeFrame
from datetime import datetime

# no keys required for crypto data
client = CryptoHistoricalDataClient()

request_params = CryptoBarsRequest(
                        symbol_or_symbols=["BTC/USD", "ETH/USD"],
                        start=datetime.strptime("2022-07-01", '%Y-%m-%d')

bars = client.get_crypto_bars(request_params)

# convert to dataframe

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