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:
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= |
Your API Key | |
| APCA_API_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:
.codeproperty - the API error message:
str(error) - the original request object:
.requestproperty - the original response objecgt:
.responseproperty - the HTTP status code:
.status_codeproperty
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 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_WSto 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
_rawproperty 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 passraw_dataargument toRest()object or theStreamConn()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.
![[Question]: list_orders() Quantity](https://avatars.githubusercontent.com/u/12942731?v=4)
![ConnectionResetError: [Errno 54] Connection reset by peer](https://avatars.githubusercontent.com/u/46823027?v=4)
![[Bug]: Fails to install with Python 3.11 due to `aiohttp==3.8.1` dependency](https://avatars.githubusercontent.com/u/2100868?v=4)
![[Bug]: asyncio.run() cannot be called from a running event loop](https://avatars.githubusercontent.com/u/30324762?v=4)
![[Bug]:](https://avatars.githubusercontent.com/u/57646?v=4)
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