PyPI 官网下载 | pylivetrader-0.0.31.tar.gz

[](https://badge.fury.io/py/pylivetrader) [](https://circleci.com/gh/alpacahq/alpaca-trade-api-python) # pylivetrader pylivetrader is a simple python live trading framework with zipline interface. The main purpose is to run algorithms developed in the Quantopian platform in live trading via broker API. In order to convert your algorithm for pylivetrader, please read the [migration document](./migration.md). ## Simple Usage Here is the example dual moving average algorithm (by [quantopian/zipline](https://github.com/quantopian/zipline/blob/master/zipline/examples/dual_moving_average.py)). We provide mostly the same API interfaces with zipline. ```py from pylivetrader.api import order_target, symbol def initialize(context): context.i = 0 context.asset = symbol('AAPL') def handle_data(context, data): # Compute averages # data.history() has to be called with the same params # from above and returns a pandas dataframe. short_mavg = data.history(context.asset, 'price', bar_count=100, frequency="1m").mean() long_mavg = data.history(context.asset, 'price', bar_count=300, frequency="1m").mean() # Trading logic if short_mavg > long_mavg: # order_target orders as many shares as needed to # achieve the desired number of shares. order_target(context.asset, 100) elif short_mavg < long_mavg: order_target(context.asset, 0) ``` You can run your algorithm from the CLI tool named `pylivetrader`, simply like below. Then your algorithm starts running with broker API. You don't need the data bundle file in advance unlike zipline does. ```sh $ pylivetrader run -f algo.py --backend-config config.yaml ``` Config file is just simple yaml or json format. ``` $ cat config.yaml key_id: BROKER_API_KEY secret: BROKER_SECRET ``` ### Usage with redis If you are running pylivetrader in an environment with an ephemeral file store and need your context to persist across restarts, you can use the redis storage engine. This is useful if you launch in a place like heroku. To use this, you must install the redis-py library. ```sh $ pip install redis ``` After that everything is the same as above, except the `run` command looks like the following: ```sh $ pylivetrader run -f algo.py --backend-config config.yaml --storage-engine redis ``` Assuming you have redis running, this will now serialize your context object to and from redis. ## Installation Install with pip. **pylivetrader currently supports Python 3.5, 3.6 and 3.7+** ``` $ pip install pylivetrader ``` Additionally, pylivetrader works well with [pipeline-live](https://github.com/alpacahq/pipeline-live). ## Command Reference ### run `pylivetrader run` starts live trading using your algorithm script. It starts by calling the `initialize()` function if any, and waits until the market opens. It calls the `before_trading_start` function if it is 8:45 ET (45 minutes before the session starts) or if it starts after that. Once the session starts, it calls the `handle_data()` function every minute until the session ends, or any functions that are registered by `schedule_function` API. The options are as follows. - `-f` or `--file`: the file path to the algorithm source - `-b` or `--backend`: the name of backend to use - `--backend-config`: the yaml file for backend parameters - `--storage-engine`: the storage engine to use for persisting the context. ('file' or 'redis') - `-s` or `--statefile`: the file path to the persisted state file (look for the State Management section below) - `-r` or `--retry`: the algorithm runner continues execution in the event a general exception is raised - `-l` or `--log-level`: the minimum level of log which will be written ('DEBUG', 'INFO', 'WARNING', 'ERROR', or 'CRITICAL') ### shell `pylivetrader shell` goes into the IPython interactive shell mode as if you are in the algorithm script namespace. It means, you can call Algorithm API such as `symbol()` and `data.history()` so you can check the behavior of each operation. At the start of shell, nothing has been called, so you may want to initialize the context by `initialize(context)` which would execute your `initialize()` function. ``` $ pylivetrader shell algo.py ``` The options are the same as `run`. ## State Management One of the things you need to understand in live trading is that things can happen and you may need to restart the script or the program dies in the middle of process due to some external errors. There are couple of things to know in advance. First, pylivetrader saves the property fields to the disk that you add to the `context` object. It is stored in the pickle format and will be restored on the next startup. Second, because the context properties are restored, you may need to take care of the extra steps. Often an algorithm is written under the assumption that `initialize()` is called only once and `before_start_trading()` is called once every morning. If you are to restart the program in the middle of day, these functions are called again, with the restored context object. Therefore, you might need to check if the fields are from the other session or in the same session to make sure you don't override the indermediate states in the day. ## Supported Brokers ### Alpaca Configuration by environment variables. ``` $ export APCA_API_KEY_ID={your api key id} $ export APCA_API_SECRET_KEY={your api secret key} $ export APCA_API_BASE_URL={https://api.alpaca.markets/ or https://paper-api.alpaca.markets} $ pylivetrader run -f algo.py ``` Configuration by config file. Either yaml or json. ``` $ cat config.yaml key_id: {your api key id} secret: {your api secret key} base_url: {https://api.alpaca.markets/ or https://paper-api.alpaca.markets} $ pylivetrader run -f algo.py --backend-config config.yaml ``` ## Docker If you are already familiar with Docker, it is a good idea to try our [docker image `alpacamarkets/pylivetrader`](https://hub.docker.com/r/alpacamarkets/pylivetrader/). This has installed pylivetrader so you can start right away without worrying about your python environment. See more details in the `dockerfiles` directory. If your algorithm file is called `algo.py`, this could be it. ```sh docker run -v $PWD:/work -w /work alpacamarkets/pylivetrader pylivetrader run -f algo.py ``` Make sure you set up environment variables for the backend (use `-e KEY=VAL` for docker command). ## Smoke Test pylivetrader provides a facility for smoke testing. This helps catch issues such as typos, program errors and simple oversights. The following is an example of smoke testing. ```py import algo from pylivetrader.testing.smoke import harness def before_run(context, backend): '''This hook is called before algorithm starts.''' # Populate existing position backend.set_position( 'A', 10, 200, ) # modify some fields of context after `initialize(context)` is called _init = context._initialize def wrapper(ctx): _init(ctx) ctx.age[ctx.symbol('A')] = 3 ctx.age[ctx.symbol('B')] = 2 context._initialize = wrapper def test_algo(): pipeline = harness.DefaultPipelineHooker() # run the algorithm under the simulation environment harness.run_smoke(algo, before_run_hook=before_run, pipeline_hook=pipeline, ) if __name__ == '__main__': import logging logging.basicConfig(level=logging.DEBUG) test_algo() ``` This exercises the algorithm code by harnessing synthetic backend and price data. The `pylivetrader.testing.smoke` package provides the backend and simulator clock classes so that it simulates a market day from open to close. By default, the backend creates a universe with 50 stocks ('A' .. 'AX'). For each symbol, you can query synthetic historical price, and orders are managed within this simu