add functions
This commit is contained in:
parent
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# Created by https://www.toptal.com/developers/gitignore/api/python,visualstudiocode,linux,windows
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# Edit at https://www.toptal.com/developers/gitignore?templates=python,visualstudiocode,linux,windows
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### Linux ###
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*~
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# temporary files which can be created if a process still has a handle open of a deleted file
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.fuse_hidden*
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# KDE directory preferences
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.directory
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# Linux trash folder which might appear on any partition or disk
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.Trash-*
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# .nfs files are created when an open file is removed but is still being accessed
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.nfs*
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### Python ###
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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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# 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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parts/
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sdist/
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var/
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wheels/
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pip-wheel-metadata/
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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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pytestdebug.log
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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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doc/_build/
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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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.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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# poetry
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#poetry.lock
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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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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.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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pythonenv*
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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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# operating system-related files
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# file properties cache/storage on macOS
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*.DS_Store
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# thumbnail cache on Windows
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Thumbs.db
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# profiling data
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.prof
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### VisualStudioCode ###
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.vscode/*
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!.vscode/settings.json
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!.vscode/tasks.json
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!.vscode/launch.json
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!.vscode/extensions.json
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*.code-workspace
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### VisualStudioCode Patch ###
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# Ignore all local history of files
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.history
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.ionide
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### Windows ###
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# Windows thumbnail cache files
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Thumbs.db:encryptable
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ehthumbs.db
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ehthumbs_vista.db
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# Dump file
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*.stackdump
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# Folder config file
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[Dd]esktop.ini
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# Recycle Bin used on file shares
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$RECYCLE.BIN/
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# Windows Installer files
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*.cab
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*.msi
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*.msix
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*.msm
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*.msp
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# Windows shortcuts
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*.lnk
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# End of https://www.toptal.com/developers/gitignore/api/python,visualstudiocode,linux,windows
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@ -1,3 +1,4 @@
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from CEDA.economic.macro import (
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cn_gdp_quarter
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cn_gdp_quarter,
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cn_ig_monthly
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)
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@ -5,6 +5,8 @@ import demjson
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import requests
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from fake_useragent import UserAgent
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# TODO need add comments
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url = {
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"eastmoney": "http://datainterface.eastmoney.com/EM_DataCenter/JS.aspx"
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}
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@ -45,6 +47,37 @@ def cn_gdp_quarter():
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#df[(df['Date'] >= startdate) & (df['Date'] <= enddate)]
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return df
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def cn_ppi_monthly():
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"""
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ABS: absolute value (per 100 million CNY)
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YoY: year on year growth
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"""
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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tmp_url = url["eastmoney"]
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request_params = {
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"cb": "datatable9051497",
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"type": "GJZB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
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"p": "1",
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"ps": "200",
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"mkt": "22",
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"_": "1622047940401"
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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df = pd.DataFrame([item.split(",") for item in data_json["data"]])
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df.columns = [
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"Date",
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"Current_Month",
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"Current_Month_YoY",
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"Current_Month_Accum"
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]
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#df[(df['Date'] >= startdate) & (df['Date'] <= enddate)]
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return df
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def cn_cpi_monthly():
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"""
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Accum: Accumulation
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@ -184,42 +217,373 @@ def cn_hi_old_monthly(): # house index old version (2008-2010)
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]
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return df
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def cn_hi_mew_monthly(): # house index old version (2008-2010)
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def cn_ci_eei_monthly(): # Climate Index & Entrepreneur Expectation Index
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"""
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Man: manufacturing
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Non-Man: Non-manufacturing
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http://data.eastmoney.com/dataapi/cjsj/getnewhousechartdata?mkt=1&stat=1&city1=%E5%8C%97%E4%BA%AC&city2=%E9%95%BF%E6%98%A5
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"""
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tmp_url = url["eastmoney"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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tmp_url = url["eastmoney"]
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request_params = {
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"cb": "datatable6451982",
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"cb": "datatable7709842",
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"type": "GJZB",
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"sty": "XFJLB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
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"p": "1",
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"ps": "2000",
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"mkt": "19",
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"pageNo": "1",
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"pageNum": "1",
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"_": "1603023435552",
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"ps": "200",
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"mkt": "8",
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"_": "1622041485306"
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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data = pd.DataFrame([item.split(",") for item in data_json["data"]])
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df = pd.DataFrame([item.split(",") for item in data_json["data"]])
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df.columns = [
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"Date",
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"Housing_Prosperity_Index",
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"HPI_YoY",
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"Land_Development_Area_Index",
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"LDAI_YoY",
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"Sales_Price_Index",
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"SPI_YoY"
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"Climate_Index",
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"CI_YoY",
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"CI_MoM",
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"Entrepreneur_Expectation_Index",
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"EEI_YoY",
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"EEI_MoM"
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]
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return df
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def cn_ig_monthly(): # Industry Growth
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"""
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Man: manufacturing
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Non-Man: Non-manufacturing
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"""
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tmp_url = url["eastmoney"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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tmp_url = url["eastmoney"]
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request_params = {
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"cb": "datatable4577327",
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"type": "GJZB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
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"p": "1",
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"ps": "200",
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"mkt": "0",
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"_": "1622042259898"
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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df = pd.DataFrame([item.split(",") for item in data_json["data"]])
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df.columns = [
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"Date",
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"IG_YoY",
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"IG_Accum",
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]
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return df
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def cn_cgpi_monthly(): # Corporate Goods Price Index
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"""
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Man: manufacturing
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Non-Man: Non-manufacturing
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"""
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tmp_url = url["eastmoney"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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tmp_url = url["eastmoney"]
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request_params = {
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"cb": "datatable7184534",
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"type": "GJZB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
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"p": "1",
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"ps": "200",
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"mkt": "9",
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"_": "1622042652353"
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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df = pd.DataFrame([item.split(",") for item in data_json["data"]])
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df.columns = [
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"Date",
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"General_Index",
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"General_Index_YoY",
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"Total_Index_MoM",
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"Agricultural_Product",
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"Agricultural_Product_YoY",
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"Agricultural_PRoduct_MoM",
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"Mineral_Product",
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"Mineral_Product_YoY",
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"Mineral_Product_MoM",
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"Coal_Oil_Electricity",
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"Coal_Oil_Electricity_YoY",
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"Coal_Oil_Electricity_MoM"
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]
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return df
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def cn_cci_csi_cei_monthly(): # Consumer Confidence Index & Consumer Satisfaction Index & Consumer Expectation Index
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"""
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Man: manufacturing
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Non-Man: Non-manufacturing
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"""
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tmp_url = url["eastmoney"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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tmp_url = url["eastmoney"]
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request_params = {
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"cb": "datatable1243218",
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"type": "GJZB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
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"p": "1",
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"ps": "200",
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"mkt": "4",
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"_": "1622043704818"
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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df = pd.DataFrame([item.split(",") for item in data_json["data"]])
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df.columns = [
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"Date",
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"CCI",
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"CCI_YoY",
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"CCI_MoM",
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"CSI",
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"CSI_YoY",
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"CSI_MoM",
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"CEI",
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"CEI_YoY",
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"CEI_MoM"
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]
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return df
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def cn_trscg_monthly(): # Total Retail Sales of Consumer Goods
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"""
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Man: manufacturing
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Non-Man: Non-manufacturing
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"""
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tmp_url = url["eastmoney"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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tmp_url = url["eastmoney"]
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request_params = {
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"cb": "datatable3665821",
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"type": "GJZB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
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"p": "1",
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"ps": "200",
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"mkt": "5",
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"_": "1622044011316"
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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df = pd.DataFrame([item.split(",") for item in data_json["data"]])
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df.columns = [
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"Date",
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"Current_Month",
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"TRSCG_YoY",
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"TRSCG_MoM",
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"TRSCG_Accum",
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"TRSCG_Accum_YoY"
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]
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return df
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def cn_ms_monthly(): # monetary Supply
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"""
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Man: manufacturing
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Non-Man: Non-manufacturing
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"""
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tmp_url = url["eastmoney"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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tmp_url = url["eastmoney"]
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request_params = {
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"cb": "datatable3818891",
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"type": "GJZB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
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"p": "1",
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"ps": "200",
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"mkt": "11",
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"_": "1622044292103"
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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df = pd.DataFrame([item.split(",") for item in data_json["data"]])
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df.columns = [
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"Date",
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"M2",
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"M2_YoY",
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"M2_MoM",
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"M1",
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"M1_YoY",
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"M1_MoM",
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"M0",
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"M0_YoY",
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"M0_MoM"
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]
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return df
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def cn_ie_monthly(): # Import & Export
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"""
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"""
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tmp_url = url["eastmoney"]
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ua = UserAgent()
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request_header = {"User-Agent": ua.random}
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tmp_url = url["eastmoney"]
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request_params = {
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"cb": "datatable3818891",
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"type": "GJZB",
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"sty": "ZGZB",
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"js": "({data:[(x)],pages:(pc)})",
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"p": "1",
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"ps": "200",
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"mkt": "1",
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"_": "1622044292103"
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}
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r = requests.get(tmp_url, params = request_params, headers = request_header)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("{") : -1])
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df = pd.DataFrame([item.split(",") for item in data_json["data"]])
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df.columns = [
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"Date",
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"Current_Month_Export",
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"Current_Month_Export_YoY",
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"Current_Month_Export_MoM",
|
||||
"Current_Month_Import",
|
||||
"Current_Month_Import_YoY",
|
||||
"Current_Month_Import_MoM",
|
||||
"Accumulation_Export",
|
||||
"Accumulation_Export_YoY",
|
||||
"Accumulation_Import",
|
||||
"Accumulation_Import_YoY",
|
||||
]
|
||||
return df
|
||||
|
||||
|
||||
def cn_ie_monthly(): # Import & Export
|
||||
"""
|
||||
|
||||
"""
|
||||
tmp_url = url["eastmoney"]
|
||||
ua = UserAgent()
|
||||
request_header = {"User-Agent": ua.random}
|
||||
tmp_url = url["eastmoney"]
|
||||
request_params = {
|
||||
"cb": "datatable3818891",
|
||||
"type": "GJZB",
|
||||
"sty": "ZGZB",
|
||||
"js": "({data:[(x)],pages:(pc)})",
|
||||
"p": "1",
|
||||
"ps": "200",
|
||||
"mkt": "1",
|
||||
"_": "1622044292103"
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
data_text = r.text
|
||||
data_json = demjson.decode(data_text[data_text.find("{") : -1])
|
||||
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
|
||||
df.columns = [
|
||||
"Date",
|
||||
"Current_Month_Export",
|
||||
"Current_Month_Export_YoY",
|
||||
"Current_Month_Export_MoM",
|
||||
"Current_Month_Import",
|
||||
"Current_Month_Import_YoY",
|
||||
"Current_Month_Import_MoM",
|
||||
"Accumulation_Export",
|
||||
"Accumulation_Export_YoY",
|
||||
"Accumulation_Import",
|
||||
"Accumulation_Import_YoY",
|
||||
]
|
||||
return df
|
||||
|
||||
def cn_fgr_monthly(): # Forex and Gold Reserve
|
||||
"""
|
||||
|
||||
"""
|
||||
tmp_url = url["eastmoney"]
|
||||
ua = UserAgent()
|
||||
request_header = {"User-Agent": ua.random}
|
||||
tmp_url = url["eastmoney"]
|
||||
request_params = {
|
||||
"cb": "atatable6260802",
|
||||
"type": "GJZB",
|
||||
"sty": "ZGZB",
|
||||
"js": "({data:[(x)],pages:(pc)})",
|
||||
"p": "1",
|
||||
"ps": "200",
|
||||
"mkt": "16",
|
||||
"_": "1622044863548"
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
data_text = r.text
|
||||
data_json = demjson.decode(data_text[data_text.find("{") : -1])
|
||||
df = pd.DataFrame([item.split(",") for item in data_json["data"]])
|
||||
df.columns = [
|
||||
"Date",
|
||||
"Forex",
|
||||
"Forex_YoY",
|
||||
"Forex_MoM",
|
||||
"Gold",
|
||||
"Gold_YoY",
|
||||
"Gold_MoM"
|
||||
]
|
||||
return df
|
||||
|
||||
def cn_ctsf_monthly(): # Client Transaction Settlement Funds
|
||||
"""
|
||||
|
||||
"""
|
||||
tmp_url = "http://data.eastmoney.com/dataapi/cjsj/getbanktransferdata?"
|
||||
ua = UserAgent()
|
||||
request_header = {"User-Agent": ua.random}
|
||||
request_params = {
|
||||
"p": "1",
|
||||
"ps": "200"
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
data_text = r.text
|
||||
data_json = demjson.decode(data_text[data_text.find("["):-11])
|
||||
df = pd.DataFrame(data_json)
|
||||
return df
|
||||
|
||||
# TODO: needs help (missing two tables)
|
||||
def cn_sao_monthly(): # Stock Account Overview
|
||||
"""
|
||||
"""
|
||||
tmp_url = "http://dcfm.eastmoney.com/em_mutisvcexpandinterface/api/js/get?"
|
||||
ua = UserAgent()
|
||||
request_header = {"User-Agent": ua.random}
|
||||
request_params = {
|
||||
"callback": "jQuery1123014377091065513636_1622046865705",
|
||||
"type": "GPKHData",
|
||||
"st": "HdDate",
|
||||
"sr": "-1",
|
||||
"sty": "Chart",
|
||||
"token": "894050c76af8597a853f5b408b759f5d",
|
||||
"ps": "2000",
|
||||
"_": "1622046865706"
|
||||
}
|
||||
r = requests.get(tmp_url, params = request_params, headers = request_header)
|
||||
data_text = r.text
|
||||
data_json = demjson.decode(data_text[data_text.find("(")+1:-1])
|
||||
df = pd.DataFrame(data_json)
|
||||
df.columns = [
|
||||
"Date",
|
||||
"New_Investor",
|
||||
"Active_Investor",
|
||||
"SHIndexClose"
|
||||
]
|
||||
df.Date = pd.to_datetime(df.Date, format = "%Y年%m月")
|
||||
return df
|
||||
|
||||
|
||||
"""
|
||||
if __name__ == "__main__":
|
||||
"""
|
||||
Loading…
Reference in New Issue