forked from QuantConnect/lean-cli
-
Notifications
You must be signed in to change notification settings - Fork 0
/
research.py
174 lines (144 loc) · 8.12 KB
/
research.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean CLI v1.0. Copyright 2021 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pathlib import Path
from typing import Optional
from click import command, argument, option, Choice
from lean.click import LeanCommand, PathParameter
from lean.constants import DEFAULT_RESEARCH_IMAGE, LEAN_ROOT_PATH
from lean.container import container
from lean.models.data_providers import QuantConnectDataProvider, all_data_providers
from lean.components.util.name_extraction import convert_to_class_name
def _check_docker_output(chunk: str, port: int) -> None:
"""Checks the output of the Docker container and opens the browser if Jupyter Lab has started.
:param chunk: the output chunk
:param port: the port Jupyter Lab will be running on
"""
from webbrowser import open
if "is running at:" in chunk:
open(f"http://localhost:{port}/")
@command(cls=LeanCommand, requires_lean_config=True, requires_docker=True)
@argument("project", type=PathParameter(exists=True, file_okay=False, dir_okay=True))
@option("--port", type=int, default=8888, help="The port to run Jupyter Lab on (defaults to 8888)")
@option("--data-provider",
type=Choice([dp.get_name() for dp in all_data_providers], case_sensitive=False),
help="Update the Lean configuration file to retrieve data from the given provider")
@option("--download-data",
is_flag=True,
default=False,
help=f"Update the Lean configuration file to download data from the QuantConnect API, alias for --data-provider {QuantConnectDataProvider.get_name()}")
@option("--data-purchase-limit",
type=int,
help="The maximum amount of QCC to spend on downloading data during the research session when using QuantConnect as data provider")
@option("--detach", "-d",
is_flag=True,
default=False,
help="Run Jupyter Lab in a detached Docker container and return immediately")
@option("--no-open",
is_flag=True,
default=False,
help="Don't open the Jupyter Lab environment in the browser after starting it")
@option("--image", type=str, help=f"The LEAN research image to use (defaults to {DEFAULT_RESEARCH_IMAGE})")
@option("--update",
is_flag=True,
default=False,
help="Pull the LEAN research image before starting the research environment")
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>`.
"""
from docker.types import Mount
from docker.errors import APIError
project_manager = container.project_manager
algorithm_file = project_manager.find_algorithm_file(project)
algorithm_name = convert_to_class_name(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_ROOT_PATH
lean_config["research-object-store-name"] = algorithm_name
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=f"{LEAN_ROOT_PATH}/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)
# 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": f"{LEAN_ROOT_PATH}/Notebooks",
"mode": "rw"
}
# Allow notebooks to be embedded in iframes
run_options["commands"].append("mkdir -p ~/.jupyter")
run_options["commands"].append(
'echo "c.ServerApp.disable_check_xsrf = True\nc.ServerApp.tornado_settings = {\'headers\': {\'Content-Security-Policy\': \'frame-ancestors self *\'}}" > ~/.jupyter/jupyter_server_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))
logger = container.logger
if str(research_image) != DEFAULT_RESEARCH_IMAGE:
logger.warn(f'A custom research image: "{research_image}" is being used!')
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
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")