Alpaca API python client

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Project description

alpaca-trade-api-python

alpaca-trade-api-python is a python library for the Alpaca Commission Free Trading API. It allows rapid trading algo development easily, with support for both REST and streaming data interfaces. For details of each API behavior, please see the online API document.

Note that this package supports only python version 3.6 and above, due to the async/await and websockets module dependency.

Install

We support python>=3.6. If you want to work with python 3.6, please note that these package dropped support for python <3.7 for the following versions:

pandas >= 1.2.0
numpy >= 1.20.0
scipy >= 1.6.0

The solution - manually install these package before installing alpaca-trade-api. e.g:

pip install pandas==1.1.5 numpy==1.19.4 scipy==1.5.4

Installing using pip

$ pip3 install alpaca-trade-api

API Keys

To use this package you first need to obtain an API key. Go here to signup

Services

These services are provided by Alpaca:

Data:
- Historical
- Live Data Stream
Account/Porfolio Management

The free services are limited, please check the docs to see the differences between paid/free services.

Alpaca Environment Variables

The Alpaca SDK will check the environment for a number of variables that can be used rather than hard-coding these into your scripts.
Alternatively you could pass the credentials directly to the SDK instances.

Environment	default	Description
APCA_API_KEY_ID=<key_id>		Your API Key
APCA_API_SECRET_KEY=<secret_key>		Your API Secret Key
APCA_API_BASE_URL=url	https://api.alpaca.markets (for live)	Specify the URL for API calls, Default is live, you must specify https://paper-api.alpaca.markets to switch to paper endpoint!
APCA_API_DATA_URL=url	https://data.alpaca.markets	Endpoint for data API
APCA_RETRY_MAX=3	3	The number of subsequent API calls to retry on timeouts
APCA_RETRY_WAIT=3	3	seconds to wait between each retry attempt
APCA_RETRY_CODES=429,504	429,504	comma-separated HTTP status code for which retry is attempted
DATA_PROXY_WS		When using the alpaca-proxy-agent you need to set this environment variable as described

Working with Data

Historic Data

You could get one of these historic data types:

Bars
Quotes
Trades First thing to understand is the new data polling mechanism. You could query up to 10000 items, and the API is using a pagination mechanism to provide you with the data.
You now have 2 options:
Working with data as it is received with a generator. (meaning it's faster but you need to process each item alone)
Wait for the entire data to be received, and then work with it as a list or dataframe. We provide you with both options to choose from.

Bars

option 1: wait for the data

from alpaca_trade_api.rest import REST
api = REST()

api.get_bars("AAPL", TimeFrame.Hour, "2021-02-08", "2021-02-08", limit=10, adjustment='raw').df

                               open    high      low     close    volume
timestamp                                                               
2021-02-08 09:00:00+00:00  136.6800  137.15  136.450  136.9600     38707
2021-02-08 10:00:00+00:00  136.9600  137.08  136.800  136.8300     22334
2021-02-08 11:00:00+00:00  136.8300  136.97  136.710  136.9100     19546
2021-02-08 12:00:00+00:00  136.9000  136.97  136.050  136.2200    483167
2021-02-08 13:00:00+00:00  136.2200  136.25  136.010  136.1700    307755
2021-02-08 14:00:00+00:00  136.1525  136.35  134.920  135.6900  12159693
2021-02-08 15:00:00+00:00  135.6800  136.68  135.595  136.6100   8076122
2021-02-08 16:00:00+00:00  136.6800  136.91  135.040  136.2350   8484923
2021-02-08 17:00:00+00:00  136.2400  136.54  135.030  135.9745   4610247
2021-02-08 18:00:00+00:00  135.9799  136.30  135.800  135.9330   3375300

option 2: iterate over bars

def process_bar(bar):
    # process bar
    print(bar)

bar_iter = api.get_bars_iter("AAPL", TimeFrame.Hour, "2021-02-08", "2021-02-08", limit=10, adjustment='raw')
for bar in bar_iter:
    process_bar(bar)

Quotes

option 1: wait for the data

from alpaca_trade_api.rest import REST
api = REST()

api.get_quotes("AAPL", "2021-02-08", "2021-02-08", limit=10).df

                                    ask_exchange  ask_price  ask_size bid_exchange  bid_price  bid_size conditions
timestamp                                                                                                         
2021-02-08 09:02:07.697204555+00:00            Q     136.80         1            P     136.52         1        [R]
2021-02-08 09:02:07.706401536+00:00            Q     136.80         1            P     136.56         2        [R]
2021-02-08 09:02:07.837365238+00:00            P     136.81         1            P     136.56         2        [R]
2021-02-08 09:02:07.838885705+00:00            Q     136.79         1            P     136.56         2        [R]
2021-02-08 09:02:30.946732544+00:00            P     136.64         1            P     136.50         1        [R]
2021-02-08 09:02:32.558048512+00:00            P     136.64         1            P     136.37         1        [R]
2021-02-08 09:02:32.794415360+00:00            Q     136.69         1            P     136.37         1        [R]
2021-02-08 09:02:32.795173632+00:00            P     136.62         1            P     136.37         1        [R]
2021-02-08 09:02:33.969686528+00:00            Q     136.69         1            P     136.37         1        [R]
2021-02-08 09:02:34.069692672+00:00            P     136.55         1            P     136.37         1        [R]

option 2: iterate over quotes

def process_quote(quote):
    # process quote
    print(quote)

quote_iter = api.get_quotes_iter("AAPL", "2021-02-08", "2021-02-08", limit=10)
for quote in quote_iter:
    process_quote(quote)

Trades

option 1: wait for the data

from alpaca_trade_api.rest import REST
api = REST()

api.get_trades("AAPL", "2021-02-08", "2021-02-08", limit=10).df

                                    exchange   price  size conditions  id tape
timestamp                                                                     
2021-02-08 09:00:11.764828160+00:00        P  136.68     1  [@, T, I]  46    C
2021-02-08 09:00:13.885322240+00:00        P  136.75    35  [@, T, I]  49    C
2021-02-08 09:00:13.885322240+00:00        P  136.75    10  [@, T, I]  48    C
2021-02-08 09:00:13.885322240+00:00        P  136.68    28  [@, T, I]  47    C
2021-02-08 09:00:17.024569856+00:00        P  136.61    16  [@, T, I]  50    C
2021-02-08 09:00:17.810107904+00:00        P  136.66     1  [@, T, I]  51    C
2021-02-08 09:00:19.932405248+00:00        P  136.68    25  [@, T, I]  55    C
2021-02-08 09:00:19.932405248+00:00        P  136.75    18  [@, T, I]  56    C
2021-02-08 09:00:19.932405248+00:00        P  136.68    11  [@, T, I]  54    C
2021-02-08 09:00:19.932405248+00:00        P  136.67    10  [@, T, I]  53    C

option 2: iterate over trades

def process_trade(trade):
    # process trade
    print(trade)

trades_iter = api.get_trades_iter("AAPL", "2021-02-08", "2021-02-08", limit=10)
for trade in trades_iter:
    process_trade(trade)

Live Stream Data

There are 2 streams available as described here.
The free plan is using the iex stream, while the paid subscription is using the sip stream.
You could subscribe to bars, trades or quotes and accoutn updates as well.
Under the example folder you could find different code samples to achieve different goals. Let's see the basic example
We present a new Streamer class under alpaca_trade_api.stream for API V2.

async def trade_callback(t):
    print('trade', t)


async def quote_callback(q):
    print('quote', q)


# Initiate Class Instance
stream = Stream(<ALPACA_API_KEY>,
                <ALPACA_SECRET_KEY>,
                base_url=URL('https://paper-api.alpaca.markets'),
                data_feed='iex')  # <- replace to SIP if you have PRO subscription

# subscribing to event
stream.subscribe_trades(trade_callback, 'AAPL')
stream.subscribe_quotes(quote_callback, 'IBM')

stream.run()

Acount & Portfolio Management

The HTTP API document is located at https://docs.alpaca.markets/

API Version

API Version now defaults to 'v2', however, if you still have a 'v1' account, you may need to specify api_version='v1' to properly use the API until you migrate.

Authentication

The Alpaca API requires API key ID and secret key, which you can obtain from the web console after you sign in. You can pass key_id and secret_key to the initializers of REST or StreamConn as arguments, or set up environment variables as outlined below.

REST

The REST class is the entry point for the API request. The instance of this class provides all REST API calls such as account, orders, positions, and bars.

Each returned object is wrapped by a subclass of the Entity class (or a list of it). This helper class provides property access (the "dot notation") to the json object, backed by the original object stored in the _raw field. It also converts certain types to the appropriate python object.

import alpaca_trade_api as tradeapi

api = tradeapi.REST()
account = api.get_account()
account.status
=> 'ACTIVE'

The Entity class also converts the timestamp string field to a pandas.Timestamp object. Its _raw property returns the original raw primitive data unmarshaled from the response JSON text.

Please note that the API is throttled, currently 200 requests per minute, per account. If your client exceeds this number, a 429 Too many requests status will be returned and this library will retry according to the retry environment variables as configured.

If the retries are exceeded, or other API error is returned, alpaca_trade_api.rest.APIError is raised. You can access the following information through this object.

the API error code: .code property
the API error message: str(error)
the original request object: .request property
the original response objecgt: .response property
the HTTP status code: .status_code property

API REST Methods

Rest Method	End Point	Result
get_account()	`GET /account` and	`Account` entity.
get_order_by_client_order_id(client_order_id)	`GET /orders` with client_order_id	`Order` entity.
list_orders(status=None, limit=None, after=None, until=None, direction=None, nested=None)	`GET /orders`	list of `Order` entities. `after` and `until` need to be string format, which you can obtain by `pd.Timestamp().isoformat()`
submit_order(symbol, qty=None, side="buy", type="market", time_in_force="day", limit_price=None, stop_price=None, client_order_id=None, order_class=None, take_profit=None, stop_loss=None, trail_price=None, trail_percent=None, notional=None)	`POST /orders`	`Order` entity.
get_order(order_id)	`GET /orders/{order_id}`	`Order` entity.
cancel_order(order_id)	`DELETE /orders/{order_id}`
cancel_all_orders()	`DELETE /orders`
list_positions()	`GET /positions`	list of `Position` entities
get_position(symbol)	`GET /positions/{symbol}`	`Position` entity.
list_assets(status=None, asset_class=None)	`GET /assets`	list of `Asset` entities
get_asset(symbol)	`GET /assets/{symbol}`	`Asset` entity
get_barset(symbols, timeframe, limit, start=None, end=None, after=None, until=None)	`GET /bars/{timeframe}`	Barset with `limit` Bar objects for each of the the requested symbols. `timeframe` can be one of `minute`, `1Min`, `5Min`, `15Min`, `day` or `1D`. `minute` is an alias of `1Min`. Similarly, `day` is an alias of `1D`. `start`, `end`, `after`, and `until` need to be string format, which you can obtain with `pd.Timestamp().isoformat()` `after` cannot be used with `start` and `until` cannot be used with `end`.
get_aggs(symbol, timespan, multiplier, _from, to)	`GET /aggs/ticker/{symbol}/range/{multiplier}/{timespan}/{from}/{to}`	`Aggs` entity. `multiplier` is the size of the timespan multiplier. `timespan` is the size of the time window, can be one of `minute`, `hour`, `day`, `week`, `month`, `quarter` or `year`. `_from` and `to` must be in `YYYY-MM-DD` format, e.g. `2020-01-15`.
get_last_trade(symbol)	`GET /last/stocks/{symbol}`	`Trade` entity
get_last_quote(symbol)	`GET /last_quote/stocks/{symbol}`	`Quote` entity
get_clock()	`GET /clock`	`Clock` entity
get_calendar(start=None, end=None)	`GET /calendar`	`Calendar` entity
get_portfolio_history(date_start=None, date_end=None, period=None, timeframe=None, extended_hours=None)	`GET /account/portfolio/history`	PortfolioHistory entity. PortfolioHistory.df can be used to get the results as a dataframe

Rest Examples

Please see the examples/ folder for some example scripts that make use of this API

Using `submit_order()`

Below is an example of submitting a bracket order.

api.submit_order(
    symbol='SPY',
    side='buy',
    type='market',
    qty='100',
    time_in_force='day',
    order_class='bracket',
    take_profit=dict(
        limit_price='305.0',
    ),
    stop_loss=dict(
        stop_price='295.5',
        limit_price='295.5',
    )
)

For simple orders with type='market' and time_in_force='day', you can pass a fractional amount (qty) or a notional amount (but not both). For instace, if the current market price for SPY is $300, the following calls are equivalent:

api.submit_order(
    symbol='SPY',
    qty=1.5,  # fractional shares
    side='buy',
    type='market',
    time_in_force='day',
)

api.submit_order(
    symbol='SPY',
    notional=450,  # notional value of 1.5 shares of SPY at $300
    side='buy',
    type='market',
    time_in_force='day',
)

Using `get_barset()` (Deprecated. use `get_bars()` instead)

import pandas as pd
NY = 'America/New_York'
start=pd.Timestamp('2020-08-01', tz=NY).isoformat()
end=pd.Timestamp('2020-08-30', tz=NY).isoformat()
print(api.get_barset(['AAPL', 'GOOG'], 'day', start=start, end=end).df)

# Minute data example
start=pd.Timestamp('2020-08-28 9:30', tz=NY).isoformat()
end=pd.Timestamp('2020-08-28 16:00', tz=NY).isoformat()
print(api.get_barset(['AAPL', 'GOOG'], 'minute', start=start, end=end).df)

please note that if you are using limit, it is calculated from the end date. and if end date is not specified, "now" is used.
Take that under consideration when using start date with a limit.

Debugging

Websocket exceptions may occur during execution. It will usually happen during the consume() method, which basically is the websocket steady-state.
exceptions during the consume method may occur due to:

server disconnections
error while handling the response data

We handle the first issue by reconnecting the websocket every time there's a disconnection. The second issue, is usually a user's code issue. To help you find it, we added a flag to the StreamConn object called debug. It is set to False by default, but you can turn it on to get a more verbose logs when this exception happens. Turn it on like so StreamConn(debug=True)

Logging

You should define a logger in your app in order to make sure you get all the messages from the different components.
It will help you debug, and make sure you don't miss issues when they occur.
The simplest way to define a logger, if you have no experience with the python logger - will be something like this:

import logging
logging.basicConfig(format='%(asctime)s %(message)s', level=logging.INFO)

Websocket best practices

Under the examples folder you could find several examples to do the following:

Different subscriptions(channels) usage with the alpaca streams
pause / resume connection
change subscriptions/channels of existing connection
ws disconnections handler (make sure we reconnect when the internal mechanism fails)

Running Multiple Strategies

There's a way to execute more than one algorithm at once.
The websocket connection is limited to 1 connection per account.
For that exact purpose this project was created
The steps to execute this are:

Run the Alpaca Proxy Agent as described in the project's README
Define this env variable: DATA_PROXY_WS to be the address of the proxy agent. (e.g: DATA_PROXY_WS=ws://127.0.0.1:8765)
If you are using the Alpaca data stream, make sure you you initiate the StreamConn object with the container's url, like so: data_url='http://127.0.0.1:8765'
execute your algorithm. it will connect to the servers through the proxy agent allowing you to execute multiple strategies

Raw Data vs Entity Data

By default the data returned from the api or streamed via StreamConn is wrapped with an Entity object for ease of use.
Some users may prefer working with raw python objects (lists, dicts, ...).
You have 2 options to get the raw data:

Each Entity object as a _raw property that extract the raw data from the object.
If you only want to work with raw data, and avoid casting to Entity (which may take more time, casting back and forth)
you could pass raw_data argument to Rest() object or the StreamConn() object.

Support and Contribution

For technical issues particular to this module, please report the issue on this GitHub repository. Any API issues can be reported through Alpaca's customer support.

New features, as well as bug fixes, by sending a pull request is always welcomed.

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alpaca-trade-api 1.2.1

Navigation

Verified details

Maintainers

Unverified details

Project links

GitHub Statistics

Meta

Project description

alpaca-trade-api-python

Install

API Keys

Services

Alpaca Environment Variables

Working with Data

Historic Data

Bars

Quotes

Trades

Live Stream Data

Acount & Portfolio Management

API Version

Authentication

REST

API REST Methods

Rest Examples

Using submit_order()

Using get_barset() (Deprecated. use get_bars() instead)

Debugging

Logging

Websocket best practices

Running Multiple Strategies

Raw Data vs Entity Data

Support and Contribution

Project details

Verified details

Maintainers

Unverified details

Project links

GitHub Statistics

Meta

Release history Release notifications | RSS feed

Download files

Source Distribution

Built Distribution

Using `submit_order()`

Using `get_barset()` (Deprecated. use `get_bars()` instead)