loo on features
This commit is contained in:
Simon Moisy
parent
082a2835b6
commit
a877f14e65
362
.gitignore
vendored
362
.gitignore
vendored
@ -1,181 +1,181 @@
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# ---> Python
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/data/*.db
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/credentials/*.json
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*.csv
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*.png
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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/data/*.npy
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# UV
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# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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#uv.lock
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/latest/usage/project/#working-with-version-control
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.pdm.toml
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.pdm-python
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.pdm-build/
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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||||
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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An introduction to trading cycles.pdf
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An introduction to trading cycles.txt
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README.md
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.vscode/launch.json
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data/btcusd_1-day_data.csv
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data/btcusd_1-min_data.csv
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# ---> Python
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/data/*.db
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/credentials/*.json
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||||
*.csv
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*.png
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||||
# Byte-compiled / optimized / DLL files
|
||||
__pycache__/
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||||
*.py[cod]
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||||
*$py.class
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||||
/data/*.npy
|
||||
|
||||
# C extensions
|
||||
*.so
|
||||
|
||||
# Distribution / packaging
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||||
.Python
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||||
build/
|
||||
develop-eggs/
|
||||
dist/
|
||||
downloads/
|
||||
eggs/
|
||||
.eggs/
|
||||
lib/
|
||||
lib64/
|
||||
parts/
|
||||
sdist/
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var/
|
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wheels/
|
||||
share/python-wheels/
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||||
*.egg-info/
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.installed.cfg
|
||||
*.egg
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||||
MANIFEST
|
||||
|
||||
# PyInstaller
|
||||
# Usually these files are written by a python script from a template
|
||||
# before PyInstaller builds the exe, so as to inject date/other infos into it.
|
||||
*.manifest
|
||||
*.spec
|
||||
|
||||
# Installer logs
|
||||
pip-log.txt
|
||||
pip-delete-this-directory.txt
|
||||
|
||||
# Unit test / coverage reports
|
||||
htmlcov/
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||||
.tox/
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||||
.nox/
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.coverage
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||||
.coverage.*
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||||
.cache
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||||
nosetests.xml
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||||
coverage.xml
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||||
*.cover
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*.py,cover
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.hypothesis/
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||||
.pytest_cache/
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||||
cover/
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||||
|
||||
# Translations
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||||
*.mo
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||||
*.pot
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||||
|
||||
# Django stuff:
|
||||
*.log
|
||||
local_settings.py
|
||||
db.sqlite3
|
||||
db.sqlite3-journal
|
||||
|
||||
# Flask stuff:
|
||||
instance/
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.webassets-cache
|
||||
|
||||
# Scrapy stuff:
|
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.scrapy
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||||
|
||||
# Sphinx documentation
|
||||
docs/_build/
|
||||
|
||||
# PyBuilder
|
||||
.pybuilder/
|
||||
target/
|
||||
|
||||
# Jupyter Notebook
|
||||
.ipynb_checkpoints
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||||
|
||||
# IPython
|
||||
profile_default/
|
||||
ipython_config.py
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||||
|
||||
# pyenv
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||||
# For a library or package, you might want to ignore these files since the code is
|
||||
# intended to run in multiple environments; otherwise, check them in:
|
||||
# .python-version
|
||||
|
||||
# pipenv
|
||||
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
|
||||
# However, in case of collaboration, if having platform-specific dependencies or dependencies
|
||||
# having no cross-platform support, pipenv may install dependencies that don't work, or not
|
||||
# install all needed dependencies.
|
||||
#Pipfile.lock
|
||||
|
||||
# UV
|
||||
# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
|
||||
# This is especially recommended for binary packages to ensure reproducibility, and is more
|
||||
# commonly ignored for libraries.
|
||||
#uv.lock
|
||||
|
||||
# poetry
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||||
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
|
||||
# This is especially recommended for binary packages to ensure reproducibility, and is more
|
||||
# commonly ignored for libraries.
|
||||
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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||||
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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||||
#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
|
||||
# https://pdm.fming.dev/latest/usage/project/#working-with-version-control
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.pdm.toml
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.pdm-python
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.pdm-build/
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||||
|
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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|
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# SageMath parsed files
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||||
*.sage.py
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||||
|
||||
# Environments
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.env
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||||
.venv
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||||
env/
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venv/
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ENV/
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env.bak/
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||||
venv.bak/
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||||
|
||||
# Spyder project settings
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||||
.spyderproject
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||||
.spyproject
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# Rope project settings
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.ropeproject
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||||
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||||
# mkdocs documentation
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/site
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||||
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||||
# mypy
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||||
.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
|
||||
.pyre/
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||||
|
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# pytype static type analyzer
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||||
.pytype/
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||||
|
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# Cython debug symbols
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cython_debug/
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||||
|
||||
# PyCharm
|
||||
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
|
||||
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
|
||||
# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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|
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An introduction to trading cycles.pdf
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An introduction to trading cycles.txt
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README.md
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.vscode/launch.json
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data/btcusd_1-day_data.csv
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data/btcusd_1-min_data.csv
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@ -1,39 +1,39 @@
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import xgboost as xgb
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import numpy as np
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class CustomXGBoostGPU:
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def __init__(self, X_train, X_test, y_train, y_test):
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self.X_train = X_train.astype(np.float32)
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self.X_test = X_test.astype(np.float32)
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self.y_train = y_train.astype(np.float32)
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self.y_test = y_test.astype(np.float32)
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self.model = None
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self.params = None # Will be set during training
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def train(self, **xgb_params):
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params = {
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'tree_method': 'hist',
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'device': 'cuda',
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'objective': 'reg:squarederror',
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'eval_metric': 'rmse',
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'verbosity': 1,
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}
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params.update(xgb_params)
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self.params = params # Store params for later access
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dtrain = xgb.DMatrix(self.X_train, label=self.y_train)
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dtest = xgb.DMatrix(self.X_test, label=self.y_test)
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evals = [(dtrain, 'train'), (dtest, 'eval')]
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self.model = xgb.train(params, dtrain, num_boost_round=100, evals=evals, early_stopping_rounds=10)
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return self.model
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def predict(self, X):
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if self.model is None:
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raise ValueError('Model not trained yet.')
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dmatrix = xgb.DMatrix(X.astype(np.float32))
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return self.model.predict(dmatrix)
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def save_model(self, file_path):
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"""Save the trained XGBoost model to the specified file path."""
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if self.model is None:
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raise ValueError('Model not trained yet.')
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self.model.save_model(file_path)
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import xgboost as xgb
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import numpy as np
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class CustomXGBoostGPU:
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def __init__(self, X_train, X_test, y_train, y_test):
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self.X_train = X_train.astype(np.float32)
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self.X_test = X_test.astype(np.float32)
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self.y_train = y_train.astype(np.float32)
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self.y_test = y_test.astype(np.float32)
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self.model = None
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self.params = None # Will be set during training
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def train(self, **xgb_params):
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params = {
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'tree_method': 'hist',
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'device': 'cuda',
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'objective': 'reg:squarederror',
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'eval_metric': 'rmse',
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'verbosity': 1,
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}
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params.update(xgb_params)
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self.params = params # Store params for later access
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dtrain = xgb.DMatrix(self.X_train, label=self.y_train)
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dtest = xgb.DMatrix(self.X_test, label=self.y_test)
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evals = [(dtrain, 'train'), (dtest, 'eval')]
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self.model = xgb.train(params, dtrain, num_boost_round=100, evals=evals, early_stopping_rounds=10)
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return self.model
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def predict(self, X):
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if self.model is None:
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raise ValueError('Model not trained yet.')
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dmatrix = xgb.DMatrix(X.astype(np.float32))
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return self.model.predict(dmatrix)
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def save_model(self, file_path):
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"""Save the trained XGBoost model to the specified file path."""
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if self.model is None:
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raise ValueError('Model not trained yet.')
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self.model.save_model(file_path)
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1610
xgboost/main.py
1610
xgboost/main.py
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