def live(project: Path, environment: str, output: Optional[Path],
image: Optional[str], update: bool) -> None:
"""Start live trading a project locally using Docker.
\b
If PROJECT is a directory, the algorithm in the main.py or Main.cs file inside it will be executed.
If PROJECT is a file, the algorithm in the specified file will be executed.
\b
ENVIRONMENT must be the name of an environment in the Lean configuration file with live-mode set to true.
By default the official LEAN engine image is used.
You can override this using the --image option.
Alternatively you can set the default engine image for all commands using `lean config set engine-image <image>`.
"""
project_manager = container.project_manager()
algorithm_file = project_manager.find_algorithm_file(Path(project))
if output is None:
output = algorithm_file.parent / "live" / datetime.now().strftime(
"%Y-%m-%d_%H-%M-%S")
lean_config_manager = container.lean_config_manager()
lean_config = lean_config_manager.get_complete_lean_config(
environment, algorithm_file, None)
if "environments" not in lean_config or environment not in lean_config[
"environments"]:
lean_config_path = lean_config_manager.get_lean_config_path()
raise MoreInfoError(
f"{lean_config_path} does not contain an environment named '{environment}'",
"https://www.quantconnect.com/docs/v2/lean-cli/tutorials/live-trading/local-live-trading"
)
if not lean_config["environments"][environment]["live-mode"]:
raise MoreInfoError(
f"The '{environment}' is not a live trading environment (live-mode is set to false)",
"https://www.quantconnect.com/docs/v2/lean-cli/tutorials/live-trading/local-live-trading"
)
_raise_for_missing_properties(lean_config, environment,
lean_config_manager.get_lean_config_path())
_start_iqconnect_if_necessary(lean_config, environment)
cli_config_manager = container.cli_config_manager()
engine_image = cli_config_manager.get_engine_image(image)
docker_manager = container.docker_manager()
if update or not docker_manager.supports_dotnet_5(engine_image):
docker_manager.pull_image(engine_image)
lean_runner = container.lean_runner()
lean_runner.run_lean(environment, algorithm_file, output, engine_image,
None)
if str(engine_image) == DEFAULT_ENGINE_IMAGE and not update:
update_manager = container.update_manager()
update_manager.warn_if_docker_image_outdated(engine_image)
def backtest(project: Path, output: Optional[Path], debug: Optional[str],
image: Optional[str], update: bool) -> None:
"""Backtest a project locally using Docker.
\b
If PROJECT is a directory, the algorithm in the main.py or Main.cs file inside it will be executed.
If PROJECT is a file, the algorithm in the specified file will be executed.
\b
Go to the following url to learn how to debug backtests locally using the Lean CLI:
https://www.quantconnect.com/docs/v2/lean-cli/tutorials/backtesting/debugging-local-backtests
By default the official LEAN engine image is used.
You can override this using the --image option.
Alternatively you can set the default engine image for all commands using `lean config set engine-image <image>`.
"""
project_manager = container.project_manager()
algorithm_file = project_manager.find_algorithm_file(Path(project))
if output is None:
output = algorithm_file.parent / "backtests" / datetime.now().strftime(
"%Y-%m-%d_%H-%M-%S")
debugging_method = None
if debug == "pycharm":
debugging_method = DebuggingMethod.PyCharm
_migrate_dotnet_5_python_pycharm(algorithm_file.parent)
elif debug == "ptvsd":
debugging_method = DebuggingMethod.PTVSD
_migrate_dotnet_5_python_vscode(algorithm_file.parent)
elif debug == "vsdbg":
debugging_method = DebuggingMethod.VSDBG
_migrate_dotnet_5_csharp_vscode(algorithm_file.parent)
elif debug == "rider":
debugging_method = DebuggingMethod.Rider
_migrate_dotnet_5_csharp_rider(algorithm_file.parent)
cli_config_manager = container.cli_config_manager()
engine_image = cli_config_manager.get_engine_image(image)
docker_manager = container.docker_manager()
if update or not docker_manager.supports_dotnet_5(engine_image):
docker_manager.pull_image(engine_image)
lean_runner = container.lean_runner()
lean_runner.run_lean("backtesting", algorithm_file, output, engine_image,
debugging_method)
if str(engine_image) == DEFAULT_ENGINE_IMAGE and not update:
update_manager = container.update_manager()
update_manager.warn_if_docker_image_outdated(engine_image)
def backtest(project: Path, output: Optional[Path], update: bool,
version: Optional[int], debug: Optional[str]) -> None:
"""Backtest a project locally using Docker.
\b
If PROJECT is a directory, the algorithm in the main.py or Main.cs file inside it will be executed.
If PROJECT is a file, the algorithm in the specified file will be executed.
\b
Go to the following url to learn how to debug backtests using the Lean CLI:
https://github.com/QuantConnect/lean-cli#debugging-backtests
"""
project_manager = container.project_manager()
algorithm_file = project_manager.find_algorithm_file(Path(project))
if output is None:
output = algorithm_file.parent / "backtests" / datetime.now().strftime(
"%Y-%m-%d_%H-%M-%S")
lean_runner = container.lean_runner()
if update:
lean_runner.force_update()
# Detect the debugging method to use based on the editor and project language
debugging_method = None
if debug == "pycharm":
debugging_method = "PyCharm"
if debug == "vs":
debugging_method = "VisualStudio"
if debug == "vscode":
debugging_method = "PTVSD" if algorithm_file.name.endswith(
".py") else "VisualStudio"
lean_runner.run_lean("backtesting", algorithm_file, output, version,
debugging_method)
def optimize(project: Path, output: Optional[Path],
optimizer_config: Optional[Path], image: Optional[str],
update: bool) -> None:
"""Optimize a project's parameters locally using Docker.
\b
If PROJECT is a directory, the algorithm in the main.py or Main.cs file inside it will be executed.
If PROJECT is a file, the algorithm in the specified file will be executed.
\b
The --optimizer-config option can be used to specify the configuration to run the optimizer with.
When using the option it should point to a file like this (the algorithm-* properties should be omitted):
https://github.com/QuantConnect/Lean/blob/master/Optimizer.Launcher/config.json
When --optimizer-config is not set, an interactive prompt will be shown to configure the optimizer.
By default the official LEAN engine image is used.
You can override this using the --image option.
Alternatively you can set the default engine image for all commands using `lean config set engine-image <image>`.
"""
project_manager = container.project_manager()
algorithm_file = project_manager.find_algorithm_file(project)
if output is None:
output = algorithm_file.parent / "optimizations" / datetime.now(
).strftime("%Y-%m-%d_%H-%M-%S")
if optimizer_config is None:
project_config_manager = container.project_config_manager()
project_config = project_config_manager.get_project_config(
algorithm_file.parent)
project_parameters = [
QCParameter(key=k, value=v)
for k, v in project_config.get("parameters", {}).items()
]
if len(project_parameters) == 0:
raise MoreInfoError(
"The given project has no parameters to optimize",
"https://www.lean.io/docs/lean-cli/tutorials/optimization/project-parameters"
)
optimizer_config_manager = container.optimizer_config_manager()
optimization_strategy = optimizer_config_manager.configure_strategy(
cloud=False)
optimization_target = optimizer_config_manager.configure_target()
optimization_parameters = optimizer_config_manager.configure_parameters(
project_parameters, cloud=False)
optimization_constraints = optimizer_config_manager.configure_constraints(
)
config = {
"optimization-strategy":
optimization_strategy,
"optimization-strategy-settings": {
"$type":
"QuantConnect.Optimizer.Strategies.StepBaseOptimizationStrategySettings, QuantConnect.Optimizer",
"default-segment-amount": 10
},
"optimization-criterion": {
"target": optimization_target.target,
"extremum": optimization_target.extremum.value
},
"parameters":
[parameter.dict() for parameter in optimization_parameters],
"constraints": [
constraint.dict(by_alias=True)
for constraint in optimization_constraints
]
}
else:
config = json5.loads(optimizer_config.read_text(encoding="utf-8"))
# Remove keys which are configured in the Lean config
for key in [
"algorithm-type-name", "algorithm-language",
"algorithm-location"
]:
config.pop(key, None)
config["optimizer-close-automatically"] = True
config["results-destination-folder"] = "/Results"
config_path = output / "optimizer-config.json"
config_path.parent.mkdir(parents=True, exist_ok=True)
with config_path.open("w+", encoding="utf-8") as file:
file.write(json.dumps(config, indent=4) + "\n")
cli_config_manager = container.cli_config_manager()
engine_image = cli_config_manager.get_engine_image(image)
lean_config_manager = container.lean_config_manager()
lean_config = lean_config_manager.get_complete_lean_config(
"backtesting", algorithm_file, None, None)
lean_runner = container.lean_runner()
run_options = lean_runner.get_basic_docker_config(lean_config,
algorithm_file, output,
None)
run_options["working_dir"] = "/Lean/Optimizer.Launcher/bin/Debug"
run_options["commands"].append(
"dotnet QuantConnect.Optimizer.Launcher.dll")
run_options["mounts"].append(
Mount(target="/Lean/Optimizer.Launcher/bin/Debug/config.json",
source=str(config_path),
type="bind",
read_only=True))
docker_manager = container.docker_manager()
if update or not docker_manager.supports_dotnet_5(engine_image):
docker_manager.pull_image(engine_image)
success = docker_manager.run_image(engine_image, **run_options)
cli_root_dir = container.lean_config_manager().get_cli_root_directory()
relative_project_dir = project.relative_to(cli_root_dir)
relative_output_dir = output.relative_to(cli_root_dir)
if success:
logger = container.logger()
optimizer_logs = (output / "log.txt").read_text(encoding="utf-8")
groups = re.findall(r"ParameterSet: \(([^)]+)\) backtestId '([^']+)'",
optimizer_logs)
if len(groups) > 0:
optimal_parameters, optimal_id = groups[0]
optimal_results = json.loads(
(output / optimal_id /
f"{optimal_id}.json").read_text(encoding="utf-8"))
optimal_backtest = QCBacktest(
backtestId=optimal_id,
projectId=1,
status="",
name=optimal_id,
created=datetime.now(),
completed=True,
progress=1.0,
runtimeStatistics=optimal_results["RuntimeStatistics"],
statistics=optimal_results["Statistics"])
logger.info(
f"Optimal parameters: {optimal_parameters.replace(':', ': ').replace(',', ', ')}"
)
logger.info(f"Optimal backtest results:")
logger.info(optimal_backtest.get_statistics_table())
logger.info(
f"Successfully optimized '{relative_project_dir}' and stored the output in '{relative_output_dir}'"
)
else:
raise RuntimeError(
f"Something went wrong while running the optimization, the output is stored in '{relative_output_dir}'"
)
if str(engine_image) == DEFAULT_ENGINE_IMAGE and not update:
update_manager = container.update_manager()
update_manager.warn_if_docker_image_outdated(engine_image)
def research(project: Path, port: int, data_provider: Optional[str],
download_data: bool, data_purchase_limit: Optional[int],
detach: bool, no_open: bool, image: Optional[str],
update: bool) -> None:
"""Run a Jupyter Lab environment locally using Docker.
By default the official LEAN research image is used.
You can override this using the --image option.
Alternatively you can set the default research image using `lean config set research-image <image>`.
"""
project_manager = container.project_manager()
algorithm_file = project_manager.find_algorithm_file(project)
lean_config_manager = container.lean_config_manager()
lean_config = lean_config_manager.get_complete_lean_config(
"backtesting", algorithm_file, None)
lean_config["composer-dll-directory"] = "/Lean/Launcher/bin/Debug"
if download_data:
data_provider = QuantConnectDataProvider.get_name()
if data_provider is not None:
data_provider = next(dp for dp in all_data_providers
if dp.get_name() == data_provider)
data_provider.build(lean_config, container.logger()).configure(
lean_config, "backtesting")
lean_config_manager.configure_data_purchase_limit(lean_config,
data_purchase_limit)
lean_runner = container.lean_runner()
temp_manager = container.temp_manager()
run_options = lean_runner.get_basic_docker_config(
lean_config, algorithm_file, temp_manager.create_temporary_directory(),
None, False, detach)
# Mount the config in the notebooks directory as well
local_config_path = next(m["Source"] for m in run_options["mounts"]
if m["Target"].endswith("config.json"))
run_options["mounts"].append(
Mount(target="/Lean/Launcher/bin/Debug/Notebooks/config.json",
source=str(local_config_path),
type="bind",
read_only=True))
# Jupyter Lab runs on port 8888, we expose it to the local port specified by the user
run_options["ports"]["8888"] = str(port)
# Open the browser as soon as Jupyter Lab has started
if detach or not no_open:
run_options["on_output"] = lambda chunk: _check_docker_output(
chunk, port)
# Give container an identifiable name when running it from the GUI
if container.module_manager().is_module_installed(GUI_PRODUCT_INSTALL_ID):
project_id = container.project_config_manager().get_local_id(
algorithm_file.parent)
run_options["name"] = f"lean_cli_gui_research_{project_id}"
# Make Ctrl+C stop Jupyter Lab immediately
run_options["stop_signal"] = "SIGKILL"
# Mount the project to the notebooks directory
run_options["volumes"][str(project)] = {
"bind": "/Lean/Launcher/bin/Debug/Notebooks",
"mode": "rw"
}
# Add references to all DLLs in QuantConnect.csx so custom C# libraries can be imported with using statements
run_options["commands"].append(" && ".join([
'find . -maxdepth 1 -iname "*.dll" | xargs -I _ echo \'#r "_"\' | cat - QuantConnect.csx > NewQuantConnect.csx',
"mv NewQuantConnect.csx QuantConnect.csx"
]))
# Allow notebooks to be embedded in iframes
run_options["commands"].append("mkdir -p ~/.jupyter")
run_options["commands"].append(
'echo "c.NotebookApp.disable_check_xsrf = True\nc.NotebookApp.tornado_settings = {\'headers\': {\'Content-Security-Policy\': \'frame-ancestors self *\'}}" > ~/.jupyter/jupyter_notebook_config.py'
)
# Hide headers in notebooks
run_options["commands"].append(
"mkdir -p ~/.ipython/profile_default/static/custom")
run_options["commands"].append(
'echo "#header-container { display: none !important; }" > ~/.ipython/profile_default/static/custom/custom.css'
)
# Run the script that starts Jupyter Lab when all set up has been done
run_options["commands"].append("./start.sh")
project_config_manager = container.project_config_manager()
cli_config_manager = container.cli_config_manager()
project_config = project_config_manager.get_project_config(
algorithm_file.parent)
research_image = cli_config_manager.get_research_image(
image or project_config.get("research-image", None))
container.update_manager().pull_docker_image_if_necessary(
research_image, update)
try:
container.docker_manager().run_image(research_image, **run_options)
except APIError as error:
msg = error.explanation
if isinstance(msg, str) and any(m in msg.lower() for m in [
"port is already allocated", "ports are not available"
"an attempt was made to access a socket in a way forbidden by its access permissions"
]):
raise RuntimeError(
f"Port {port} is already in use, please specify a different port using --port <number>"
)
raise error
if detach:
temp_manager.delete_temporary_directories_when_done = False
logger = container.logger()
relative_project_dir = algorithm_file.parent.relative_to(
lean_config_manager.get_cli_root_directory())
logger.info(
f"Successfully started Jupyter Lab environment for '{relative_project_dir}' in the '{run_options['name']}' container"
)
logger.info(
"You can use Docker's own commands to manage the detached container"
)
def backtest(project: Path, output: Optional[Path], detach: bool,
debug: Optional[str], data_provider: Optional[str],
download_data: bool, data_purchase_limit: Optional[int],
release: bool, image: Optional[str], update: bool) -> None:
"""Backtest a project locally using Docker.
\b
If PROJECT is a directory, the algorithm in the main.py or Main.cs file inside it will be executed.
If PROJECT is a file, the algorithm in the specified file will be executed.
\b
Go to the following url to learn how to debug backtests locally using the Lean CLI:
https://www.lean.io/docs/lean-cli/tutorials/backtesting/debugging
By default the official LEAN engine image is used.
You can override this using the --image option.
Alternatively you can set the default engine image for all commands using `lean config set engine-image <image>`.
"""
project_manager = container.project_manager()
algorithm_file = project_manager.find_algorithm_file(Path(project))
lean_config_manager = container.lean_config_manager()
if output is None:
output = algorithm_file.parent / "backtests" / datetime.now().strftime(
"%Y-%m-%d_%H-%M-%S")
debugging_method = None
if debug == "pycharm":
debugging_method = DebuggingMethod.PyCharm
_migrate_python_pycharm(algorithm_file.parent)
elif debug == "ptvsd":
debugging_method = DebuggingMethod.PTVSD
_migrate_python_vscode(algorithm_file.parent)
elif debug == "vsdbg":
debugging_method = DebuggingMethod.VSDBG
_migrate_csharp_vscode(algorithm_file.parent)
elif debug == "rider":
debugging_method = DebuggingMethod.Rider
_migrate_csharp_rider(algorithm_file.parent)
if debugging_method is not None and detach:
raise RuntimeError(
"Running a debugging session in a detached container is not supported"
)
if algorithm_file.name.endswith(".cs"):
_migrate_csharp_csproj(algorithm_file.parent)
lean_config = lean_config_manager.get_complete_lean_config(
"backtesting", algorithm_file, debugging_method)
if download_data:
data_provider = QuantConnectDataProvider.get_name()
if data_provider is not None:
data_provider = next(dp for dp in all_data_providers
if dp.get_name() == data_provider)
data_provider.build(lean_config, container.logger()).configure(
lean_config, "backtesting")
lean_config_manager.configure_data_purchase_limit(lean_config,
data_purchase_limit)
cli_config_manager = container.cli_config_manager()
project_config_manager = container.project_config_manager()
project_config = project_config_manager.get_project_config(
algorithm_file.parent)
engine_image = cli_config_manager.get_engine_image(
image or project_config.get("engine-image", None))
container.update_manager().pull_docker_image_if_necessary(
engine_image, update)
if not output.exists():
output.mkdir(parents=True)
output_config_manager = container.output_config_manager()
lean_config["algorithm-id"] = str(
output_config_manager.get_backtest_id(output))
lean_runner = container.lean_runner()
lean_runner.run_lean(lean_config, "backtesting", algorithm_file, output,
engine_image, debugging_method, release, detach)
def backtest(project: Path, output: Optional[Path], debug: Optional[str],
download_data: bool, data_purchase_limit: Optional[int],
image: Optional[str], update: bool) -> None:
"""Backtest a project locally using Docker.
\b
If PROJECT is a directory, the algorithm in the main.py or Main.cs file inside it will be executed.
If PROJECT is a file, the algorithm in the specified file will be executed.
\b
Go to the following url to learn how to debug backtests locally using the Lean CLI:
https://www.lean.io/docs/lean-cli/tutorials/backtesting/debugging-local-backtests
By default the official LEAN engine image is used.
You can override this using the --image option.
Alternatively you can set the default engine image for all commands using `lean config set engine-image <image>`.
"""
project_manager = container.project_manager()
algorithm_file = project_manager.find_algorithm_file(Path(project))
lean_config_manager = container.lean_config_manager()
if output is None:
output = algorithm_file.parent / "backtests" / datetime.now().strftime(
"%Y-%m-%d_%H-%M-%S")
debugging_method = None
if debug == "pycharm":
debugging_method = DebuggingMethod.PyCharm
_migrate_dotnet_5_python_pycharm(algorithm_file.parent)
elif debug == "ptvsd":
debugging_method = DebuggingMethod.PTVSD
_migrate_dotnet_5_python_vscode(algorithm_file.parent)
elif debug == "vsdbg":
debugging_method = DebuggingMethod.VSDBG
_migrate_dotnet_5_csharp_vscode(algorithm_file.parent)
elif debug == "rider":
debugging_method = DebuggingMethod.Rider
_migrate_dotnet_5_csharp_rider(algorithm_file.parent)
if download_data:
organization = _select_organization()
lean_config_manager.set_property("job-organization-id",
organization.id)
lean_config_manager.set_property(
"data-provider",
"QuantConnect.Lean.Engine.DataFeeds.ApiDataProvider")
lean_config_manager.set_property(
"map-file-provider",
"QuantConnect.Data.Auxiliary.LocalZipMapFileProvider")
lean_config_manager.set_property(
"factor-file-provider",
"QuantConnect.Data.Auxiliary.LocalZipFactorFileProvider")
if data_purchase_limit is not None:
config = lean_config_manager.get_lean_config()
if config.get(
"data-provider",
None) != "QuantConnect.Lean.Engine.DataFeeds.ApiDataProvider":
container.logger().warn(
"--data-purchase-limit is ignored because the data provider is not set to download from the API, use --download-data to set that up"
)
data_purchase_limit = None
cli_config_manager = container.cli_config_manager()
engine_image = cli_config_manager.get_engine_image(image)
docker_manager = container.docker_manager()
if update or not docker_manager.supports_dotnet_5(engine_image):
docker_manager.pull_image(engine_image)
lean_config = lean_config_manager.get_complete_lean_config(
"backtesting", algorithm_file, debugging_method, data_purchase_limit)
lean_runner = container.lean_runner()
lean_runner.run_lean(lean_config, "backtesting", algorithm_file, output,
engine_image, debugging_method)
if str(engine_image) == DEFAULT_ENGINE_IMAGE and not update:
update_manager = container.update_manager()
update_manager.warn_if_docker_image_outdated(engine_image)
def research(project: Path, port: int, image: Optional[str], update: bool) -> None:
"""Run a Jupyter Lab environment locally using Docker.
By default the official LEAN research image is used.
You can override this using the --image option.
Alternatively you can set the default research image using `lean config set research-image <image>`.
"""
cli_config_manager = container.cli_config_manager()
project_config_manager = container.project_config_manager()
project_config = project_config_manager.get_project_config(project)
# Copy the config to a temporary config file before we add some research-specific configuration to it
config_path = container.temp_manager().create_temporary_directory() / "config.json"
project_config.file = config_path
project_config.set("composer-dll-directory", "/Lean/Launcher/bin/Debug")
project_config.set("messaging-handler", "QuantConnect.Messaging.Messaging")
project_config.set("job-queue-handler", "QuantConnect.Queues.JobQueue")
project_config.set("api-handler", "QuantConnect.Api.Api")
project_config.set("job-user-id", cli_config_manager.user_id.get_value("1"))
project_config.set("api-access-token", cli_config_manager.api_token.get_value("default"))
lean_config_manager = container.lean_config_manager()
data_dir = lean_config_manager.get_data_directory()
run_options: Dict[str, Any] = {
"commands": [],
"environment": {},
"mounts": [
Mount(target="/Lean/Launcher/bin/Debug/Notebooks/config.json",
source=str(config_path),
type="bind",
read_only=True)
],
"volumes": {
str(data_dir): {
"bind": "/Lean/Launcher/Data",
"mode": "rw"
}
},
"ports": {
"8888": str(port)
},
"on_output": lambda chunk: _check_docker_output(chunk, port)
}
lean_runner = container.lean_runner()
if project_config.get("algorithm-language", "Python") == "Python":
lean_runner.set_up_python_options(project, "/Lean/Launcher/bin/Debug/Notebooks", run_options)
else:
lean_runner.set_up_csharp_options(project, run_options)
run_options["volumes"][str(project)] = {
"bind": "/Lean/Launcher/bin/Debug/Notebooks",
"mode": "rw"
}
# Add references to all DLLs in QuantConnect.csx so custom C# libraries can be imported with using statements
run_options["commands"].append(" && ".join([
'find . -maxdepth 1 -iname "*.dll" | xargs -I _ echo \'#r "_"\' | cat - QuantConnect.csx > NewQuantConnect.csx',
"mv NewQuantConnect.csx QuantConnect.csx"
]))
# Run the script that starts Jupyter Lab when all set up has been done
run_options["commands"].append("./start.sh")
cli_config_manager = container.cli_config_manager()
research_image = cli_config_manager.get_research_image(image)
docker_manager = container.docker_manager()
if update or not docker_manager.supports_dotnet_5(research_image):
docker_manager.pull_image(research_image)
if str(research_image) == DEFAULT_RESEARCH_IMAGE and not update:
update_manager = container.update_manager()
update_manager.warn_if_docker_image_outdated(research_image)
try:
docker_manager.run_image(research_image, **run_options)
except APIError as error:
msg = error.explanation
if isinstance(msg, str) and "port is already allocated" in msg:
raise RuntimeError(f"Port {port} is already in use, please specify a different port using --port <number>")
raise error
def optimize(project: Path, output: Optional[Path], detach: bool,
optimizer_config: Optional[Path], strategy: Optional[str],
target: Optional[str], target_direction: Optional[str],
parameter: List[Tuple[str, float, float,
float]], constraint: List[str],
release: bool, image: Optional[str], update: bool) -> None:
"""Optimize a project's parameters locally using Docker.
\b
If PROJECT is a directory, the algorithm in the main.py or Main.cs file inside it will be executed.
If PROJECT is a file, the algorithm in the specified file will be executed.
By default an interactive wizard is shown letting you configure the optimizer.
If --optimizer-config or --strategy is given the command runs in non-interactive mode.
In this mode the CLI does not prompt for input.
\b
The --optimizer-config option can be used to specify the configuration to run the optimizer with.
When using the option it should point to a file like this (the algorithm-* properties should be omitted):
https://github.com/QuantConnect/Lean/blob/master/Optimizer.Launcher/config.json
If --strategy is given the optimizer configuration is read from the given options.
In this case --strategy, --target, --target-direction and --parameter become required.
\b
In non-interactive mode the --parameter option can be provided multiple times to configure multiple parameters:
- --parameter <name> <min value> <max value> <step size>
- --parameter my-first-parameter 1 10 0.5 --parameter my-second-parameter 20 30 5
\b
In non-interactive mode the --constraint option can be provided multiple times to configure multiple constraints:
- --constraint "<statistic> <operator> <value>"
- --constraint "Sharpe Ratio >= 0.5" --constraint "Drawdown < 0.25"
By default the official LEAN engine image is used.
You can override this using the --image option.
Alternatively you can set the default engine image for all commands using `lean config set engine-image <image>`.
"""
project_manager = container.project_manager()
algorithm_file = project_manager.find_algorithm_file(project)
if output is None:
output = algorithm_file.parent / "optimizations" / datetime.now(
).strftime("%Y-%m-%d_%H-%M-%S")
optimizer_config_manager = container.optimizer_config_manager()
config = None
if optimizer_config is not None and strategy is not None:
raise RuntimeError(
"--optimizer-config and --strategy are mutually exclusive")
if optimizer_config is not None:
config = json5.loads(optimizer_config.read_text(encoding="utf-8"))
# Remove keys which are configured in the Lean config
for key in [
"algorithm-type-name", "algorithm-language",
"algorithm-location"
]:
config.pop(key, None)
elif strategy is not None:
ensure_options(["strategy", "target", "target_direction", "parameter"])
optimization_strategy = f"QuantConnect.Optimizer.Strategies.{strategy.replace(' ', '')}OptimizationStrategy"
optimization_target = OptimizationTarget(
target=optimizer_config_manager.parse_target(target),
extremum=target_direction)
optimization_parameters = optimizer_config_manager.parse_parameters(
parameter)
optimization_constraints = optimizer_config_manager.parse_constraints(
constraint)
else:
project_config_manager = container.project_config_manager()
project_config = project_config_manager.get_project_config(
algorithm_file.parent)
project_parameters = [
QCParameter(key=k, value=v)
for k, v in project_config.get("parameters", {}).items()
]
if len(project_parameters) == 0:
raise MoreInfoError(
"The given project has no parameters to optimize",
"https://www.lean.io/docs/lean-cli/optimization/parameters")
optimization_strategy = optimizer_config_manager.configure_strategy(
cloud=False)
optimization_target = optimizer_config_manager.configure_target()
optimization_parameters = optimizer_config_manager.configure_parameters(
project_parameters, cloud=False)
optimization_constraints = optimizer_config_manager.configure_constraints(
)
if config is None:
# noinspection PyUnboundLocalVariable
config = {
"optimization-strategy":
optimization_strategy,
"optimization-strategy-settings": {
"$type":
"QuantConnect.Optimizer.Strategies.StepBaseOptimizationStrategySettings, QuantConnect.Optimizer",
"default-segment-amount": 10
},
"optimization-criterion": {
"target": optimization_target.target,
"extremum": optimization_target.extremum.value
},
"parameters":
[parameter.dict() for parameter in optimization_parameters],
"constraints": [
constraint.dict(by_alias=True)
for constraint in optimization_constraints
]
}
config["optimizer-close-automatically"] = True
config["results-destination-folder"] = "/Results"
config_path = output / "optimizer-config.json"
config_path.parent.mkdir(parents=True, exist_ok=True)
with config_path.open("w+", encoding="utf-8") as file:
file.write(json.dumps(config, indent=4) + "\n")
project_config_manager = container.project_config_manager()
cli_config_manager = container.cli_config_manager()
project_config = project_config_manager.get_project_config(
algorithm_file.parent)
engine_image = cli_config_manager.get_engine_image(
image or project_config.get("engine-image", None))
lean_config_manager = container.lean_config_manager()
lean_config = lean_config_manager.get_complete_lean_config(
"backtesting", algorithm_file, None)
if not output.exists():
output.mkdir(parents=True)
output_config_manager = container.output_config_manager()
lean_config["algorithm-id"] = str(
output_config_manager.get_optimization_id(output))
lean_config["messaging-handler"] = "QuantConnect.Messaging.Messaging"
lean_runner = container.lean_runner()
run_options = lean_runner.get_basic_docker_config(lean_config,
algorithm_file, output,
None, release, detach)
run_options["working_dir"] = "/Lean/Optimizer.Launcher/bin/Debug"
run_options["commands"].append(
"dotnet QuantConnect.Optimizer.Launcher.dll")
run_options["mounts"].append(
Mount(target="/Lean/Optimizer.Launcher/bin/Debug/config.json",
source=str(config_path),
type="bind",
read_only=True))
container.update_manager().pull_docker_image_if_necessary(
engine_image, update)
project_manager.copy_code(algorithm_file.parent, output / "code")
success = container.docker_manager().run_image(engine_image, **run_options)
logger = container.logger()
cli_root_dir = container.lean_config_manager().get_cli_root_directory()
relative_project_dir = project.relative_to(cli_root_dir)
relative_output_dir = output.relative_to(cli_root_dir)
if detach:
temp_manager = container.temp_manager()
temp_manager.delete_temporary_directories_when_done = False
logger.info(
f"Successfully started optimization for '{relative_project_dir}' in the '{run_options['name']}' container"
)
logger.info(f"The output will be stored in '{relative_output_dir}'")
logger.info(
"You can use Docker's own commands to manage the detached container"
)
elif success:
optimizer_logs = (output / "log.txt").read_text(encoding="utf-8")
groups = re.findall(r"ParameterSet: \(([^)]+)\) backtestId '([^']+)'",
optimizer_logs)
if len(groups) > 0:
optimal_parameters, optimal_id = groups[0]
optimal_results = json.loads(
(output / optimal_id /
f"{optimal_id}.json").read_text(encoding="utf-8"))
optimal_backtest = QCBacktest(
backtestId=optimal_id,
projectId=1,
status="",
name=optimal_id,
created=datetime.now(),
completed=True,
progress=1.0,
runtimeStatistics=optimal_results["RuntimeStatistics"],
statistics=optimal_results["Statistics"])
logger.info(
f"Optimal parameters: {optimal_parameters.replace(':', ': ').replace(',', ', ')}"
)
logger.info(f"Optimal backtest results:")
logger.info(optimal_backtest.get_statistics_table())
logger.info(
f"Successfully optimized '{relative_project_dir}' and stored the output in '{relative_output_dir}'"
)
else:
raise RuntimeError(
f"Something went wrong while running the optimization, the output is stored in '{relative_output_dir}'"
)