alpaca-backtrader-api

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

Note this module supports Python version 3.5 and above, with active development and testing on Python 3.7 through 3.10, due to
the underlying library alpaca-trade-api.

Install

$ pip3 install alpaca-backtrader-api

Example

These examples only work if you have a funded brokerage account or another means of accessing Polygon data.

you can find example strategies in the samples folder.

remember to add you credentials.

you can toggle between backtesting and paper trading by changing ALPACA_PAPER

a strategy looks like this:

In order to call Alpaca's trade API, you need to obtain API key pairs.
Replace <key_id> and <secret_key> with what you get from the web console.

import alpaca_backtrader_api
import backtrader as bt
from datetime import datetime

ALPACA_API_KEY = <key_id>
ALPACA_SECRET_KEY = <secret_key>
ALPACA_PAPER = True


class SmaCross(bt.SignalStrategy):
  def __init__(self):
    sma1, sma2 = bt.ind.SMA(period=10), bt.ind.SMA(period=30)
    crossover = bt.ind.CrossOver(sma1, sma2)
    self.signal_add(bt.SIGNAL_LONG, crossover)


cerebro = bt.Cerebro()
cerebro.addstrategy(SmaCross)

store = alpaca_backtrader_api.AlpacaStore(
    key_id=ALPACA_API_KEY,
    secret_key=ALPACA_SECRET_KEY,
    paper=ALPACA_PAPER
)

if not ALPACA_PAPER:
  broker = store.getbroker()  # or just alpaca_backtrader_api.AlpacaBroker()
  cerebro.setbroker(broker)

DataFactory = store.getdata  # or use alpaca_backtrader_api.AlpacaData
data0 = DataFactory(dataname='AAPL', historical=True, fromdate=datetime(
    2015, 1, 1), timeframe=bt.TimeFrame.Days)
cerebro.adddata(data0)

print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
cerebro.run()
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
cerebro.plot()

Testing

Unit Tests

To run the tests, use the following command:

python setup.py test

Or directly with pytest:

pytest

Integration Tests

Integration tests require Alpaca API credentials. You can run these tests using:

ALPACA_API_KEY=<your-key> ALPACA_SECRET_KEY=<your-secret> ./run_integration_tests.sh

See the tests README for more information.

Development

This project supports Python 3.5 and above, with active development and testing on Python 3.7 through 3.10.

Setting up a development environment

The easiest way to set up a development environment is by using the included setup script:

# Clone the repository
git clone https://github.com/alpacahq/alpaca-backtrader-api.git
cd alpaca-backtrader-api

# Run the setup script to create a virtual environment with Python 3.10
./setup_dev_env.sh

# Activate the virtual environment
source venv/bin/activate

Alternatively, you can manually set up your environment:

# Create and activate a virtual environment
python3.10 -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements/requirements.txt
pip install -r requirements/requirements_test.txt

# Install the package in development mode
pip install -e .

API Document

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

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 set them in the AlpacaStore constructor,
using 'key_id' and 'secret_key'.

Paper/Live mode

The 'paper' parameter is default to False, which allows live trading.
If you set it to True, then you are in the paper trading mode.

Running Multiple Strategies/Datas

There's a way to execute an algorithm with multiple datas or/and execute more than one algorithm.

The websocket connection is limited to 1 connection per account. Alpaca backtrader opens a websocket connection for each data you define.

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_WS to be the address of the proxy agent. (e.g: DATA_PROXY_WS=ws://192.168.99.100:8765)
• execute your algorithm. it will connect to the servers through the proxy agent allowing you to execute multiple datas/strategies

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 pull request is always
welcomed.