Saravanan666/pypi-org huggingface dataset:
Package: marketplace-handler
marketplace_handlerPackage to interact with marketplaces.Installationpippipinstallmarketplace_handlerpoetrypoetryaddmarketplace_handlerUsagefrommarketpalce_handlerimportWildberriesmarketplace=Wildberries(token_id=0,token_service_token="your_token",token_service_url="https://your_token_url",mapping_url="https://your_mapping_url",)marketplace.refresh_price("id",0)
Package: marketplace-scrapper
Get the details of an extension frommarketplace.visualstudio.com.Details are:Extension title:title: STRINGExtension publisher name:publisher_name: STRINGExtension main image/logo:default_image: URLNumber of installs:installs: INTEGERAll you need is the extension ID eg:muremwa.read-urlsThe extension ID is called the‘Unique Identifier’.InstallationYou can install using pip.pip install marketplace-scrapperUsageIn python code.Import the main function.importvscrapdetails=vscrap.get_extension_details('muremwa.read-urls')The main function returns a dict with the details as described above.{'title':'django-read-urls','publisher_name':'muremwa','default_image':'https://cdn./*','installs':1010}In command lineUse thescrap.extensionmodule and add one argument, the extension ID.python -m scrap.extension muremwa.read-urls
Package: marketplace-sdk
MarketPlace SDKPython Software Development Toolkit (SDK) to communicate with the Materials MarketPlace platform.InstallationTo install the package, execute:pip install marketplace-sdkUsageTheMarketPlace documentationcontainsa tutorialon how to configure and use this package.AuthorsCarl Simon Adorf (EPFL)-@csadorfPablo de Andres (Fraunhofer IWM)-@pablo-de-andresPranjali Singh (Fraunhofer IWM)-@singhpranjaliSee also the list ofcontributors.Contactsimon.adorf@epfl.chpablo.de.andres@iwm.fraunhofer.depranjali.singh@iwm.fraunhofer.deFor maintainersTo create a new release, clone the repository, install development dependencies withpip install -e '.[dev]', and then executebumpver update --[major|minor|patch].
This will:Create a tagged release with bumped version and push it to the repository.Trigger a GitHub actions workflow that creates a GitHub release and publishes it on PyPI.Additional notes:The project follows semantic versioning.Use the--dryoption to preview the release change.The release tag (e.g. a/b/rc) is determined from the last release.
Use the--tagoption to switch the release tag.MIT LicenseCopyright (c) 2021 Carl Simon Adorf (EPFL)All rights reserved.Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.AcknowledgementsThis work is supported by theMARVEL National Centre for Competency in Researchfunded by theSwiss National Science Foundation,
and the MarketPlace project funded byHorizon 2020under the H2020-NMBP-25-2017 call (Grant No. 760173).
Package: marketplace-standard-app-api
READMEAboutThis repository contains the specification of the Materials MarketPlace Standard App API.
The API is used as basis for the interaction between apps on the Materials MarketPlace platform.This repository contains the API version: 0.6.0.AuthorsSimon Adorf (simon.adorf@epfl.ch)MarketPlace consortium partnersSpecificationThe repository contains both a programmatic definition of the API (implemented viaPydanticandFastAPI) within themarketplace_standard_app_api.mainmodule as well as an equivalentOpenAPIrepresentation in theopenapi.jsonfile.
Programmatic checks are performed to ensure that both representations remain synchronized and conflict-free.
In case that the definition within the Python module and the OpenAPI JSON-file are conflicting with each other for whatever reason, the former must be considered authoritive.Tip: You can use themarketplace-standard-app-api showcommand to generate the OpenAPI file from the Python module (requires thecliextra).InstallationNote: Users should use theMarketPlace Python SDKfor app development.You can install this package from PyPI with:pip install marketplace-standard-app-apiTestsTests for this repository are implemented viapytest.
To run these tests, first install the test dependencies withpip install '.[tests]'and then run the tests with thepytestcommand.ContributingContributions in the form of issues, comments, and pull request are very welcome.To make code contributions, please fork this repository, and then create a pull request.
For development you will need to setup a Python environment (with a recent Python version), install the development requiremments, and the pre-commit hooks with:pip install pre-commit==2.17.0pre-commit installFor maintainersTo create a new release, clone the repository, install development dependencies withpip install -e '.[dev]', and then executebumpver update --[major|minor|patch].
This will:Create a tagged release with bumped version and push it to the repository.Trigger a GitHub actions workflow that creates a GitHub release and publishes it on PyPI.Additional notes:The project follows semantic versioning.Use the--dryoption to preview the release change.AcknowledgementsThis work is supported by the MarketPlace project funded byHorizon 2020under the H2020-NMBP-25-2017 call (Grant No. 760173).
Package: marketpsych
MarketPsych: Power of minds over marketsMarketPsych produces the global standard in financial sentiment and ESG data from the news and social media information flow. We serve global funds, corporations, banks and brokerages in over 25 countries. Our products are distributed exclusively, and in partnership, withRefinitiv, a global provider of financial market data and infrastructure.MarketPsych's proprietary NLP engine evaluates thousands of market moving themes, which are further aggregated into scores. The scores form time-series that are updated every 60 seconds. Each score represents the scale and direction of the media focus on complex themes, including sentiments, sustainability and price. They can be translated directly into spreadsheets or charts that can be monitored by traders, risk managers or analysts – or they can be plugged straight into your algorithms for low frequency or longer-term asset allocation or sector rotation decisions.For more details check out ourwebsite.FeaturesWide coverage:252 countries36,000+ global companies, sectors, and ETFs62 stock indexes and sovereign bonds44 currencies51 commodities300+ cryptocurrenciesLong history: 1998 to the presentMulti-language: sources in 12 languagesAI-based: machine learning NLP systemHighly granular: 100+ scoresReal-time: 60-second, hourly, and daily updatesSources: 6000+ news and social media outletsInstall MarketPsych supporting librarypippip install marketpsychModulesIf using Jupyter Notebook, be sure to have version >= 6.0. If using Jupyter Lab, be sure to have version >= 3.0.sftpThis library assists with loading your trialling data from SFTP directly into a Jupyter notebook.mpwidgetsThis library provides widgets through which you can set your data parameters inside a Jupyter notebook.
Package: marketrisk
#Exemplo de calculo do Value at Risk paramÃ©trico de um portfolio de uma carteira composta por:#Ativos: PETR4, VALE3 e B3SA3#Tempo: de 01/01/2020 atÃ© 01/01/2021#Para buscar os preÃ§os e gerar os retornos:import marketrisk as mrportfolio = mr.Stocks([‘PETR4’, ‘MOVI3’, ‘IBOV’], start=’2020-01-01’, end=’2021-02-20’)retornos = portfolio.get_returns()#Para calcular o risco:#O modulo risco recebe os retornos e pode receber os respectivos volumesrisco = mr.Risk(retornos, [1000, 1500, 800])#Calculo do var paramÃ©trico para 1 dia e 99% de confianÃ§a:var = risco.var(0.99, days=1)#Calculo do var paramÃ©trico EWMA para 1 dia e 99% de confianÃ§a:var_ewma = risco.var(0.99, days=1, ewma=1)
Package: markets
Log-Periodic Power Law Singularity (LPPLS) Model for bubble detectionIn financial markets, bubbles are the result of a faster-than-exponential growth
which becomes unsustainable over time, thus forcing a significant correction in
price level.LPPLS model comes as a combination of: mathematical and statistical physics of
phase transitions; behavorial finance, imitation and herding of traders that
creates positive feedback; economic theory of bubbles.Installationpip install marketsQuickstartimport markets
import pandas as pd
import matplotlib.pyplot as plt

# Load observations from an index of prices of cryptocurrencies.
time, price = markets.load_index_data()

# Init and fit the model (dates are converted to ordinal format).
model = markets.LPPLS()
time_ord = [pd.Timestamp.toordinal(t) for t in time]
model.fit(time_ord, price, persist = True)

# Compute price values estimated by model.
fitted = [model(t) for t in time_ord]

# Compare observations and fitted model.
fig, ax = plt.subplots(figsize = (14, 8))
ax.plot(time, price, color = 'black', linewidth = 1, label = 'price')
ax.plot(time, fitted, color = 'green', alpha = 0.5, label = 'LPPLS fit')
ax.grid(which = 'major', axis = 'both', linestyle='--')
ax.set_ylabel('Time (ordinal)')
ax.set_ylabel('Log-price')
ax.legend(loc = 'best')
plt.xticks(rotation = 70)
plt.savefig('fitted_model.png', dpi = 300)Estimation of confidence indicators# Compute the indicators.
time_new, price_new, pos_conf, neg_conf = model.estimate_indicators(time_ord, price)
time_new_dt = [pd.Timestamp.fromordinal(t) for t in time_new.astype('int32')]

fig, (ax_1, ax_2) = plt.subplots(figsize = (14, 8), nrows = 2, ncols = 1, sharex = True)

# Plot confidence for positive bubbles.
ax_11 = ax_1.twinx()
ax_1.plot(time_new_dt, price_new, color = 'black', linewidth = 1)
ax_1.grid(which = 'major', axis = 'both', linestyle = '--')
ax_1.set_ylabel('Log-price')
ax_11.plot(time_new_dt, pos_conf, color = 'red', alpha = 0.5, label = 'Pos. confidence')
ax_11.set_ylabel('Confidence indicator')
ax_11.legend(loc = 'best')

# Plot confidence for negative bubbles.
ax_22 = ax_2.twinx()
ax_2.plot(time_new_dt, price_new, color = 'black', linewidth = 1)
ax_2.grid(which = 'major', axis = 'both', linestyle = '--')
ax_2.set_ylabel('Log-price')
ax_22.plot(time_new_dt, neg_conf, color = 'green', alpha = 0.5, label = 'Neg. confidence')
ax_22.set_ylabel('Confidence indicator')
ax_22.legend(loc = 'best')

plt.xticks(rotation = 70)
plt.savefig('confidence.png', dpi = 300)ReferencesFor details about the model see for example:Sornette, Johansen & Bouchaud (1996),"Stock market crashes, precursors
and replicas", Journal de Physique I 6(1)Sornette, Demos, Zhang, Cauwels, Filimonov & Zhang (2015),"Real-time
prediction and post-mortem analysis of the shanghai 2015 stock market
bubble and crash", Swiss Finance Institute Research Paper (15-31)For implementation details and estimation of confidence indicators see for example:Jeremy (2020),"Prediction of financial bubbles and backtesting of a
trading strategy", Master Thesis at Imperial College London
Package: marketsight
Python bindings for the Dynata Reporting & Analytics (MarketSight) APITheMarketSight Client APIlibrary provides Python bindings for the
Dynata Reporting & Analytics API, providing a Pythonic interface for
interacting with the underlying platform’s RESTful APIs.ContentsInstallationDependenciesKey MarketSight API FeaturesHello, World and Basic Usage1. Initialize the Library2. Authorize Against the API3. Call the APIQuestions and IssuesLicenseInstallationTo installMarketSight API Client, just execute:$pipinstallmarketsightDependenciesValidator-Collection v.1.5.0or highersimplejson v.3.0or higherbravado v.10.6.0or higherdpath v.2.0.1or higherKey MarketSight API FeaturesAbility to manage your Account settings within the Dynata Reporting
& Analytics platform.Ability to manage data within the Dynata Reporting & Analytics platform.Ability to perform high-end statistical analyses on your data in the
Dynata Reporting & Analytics platform.Ability to produce interactive visualizations of your data and insights.Ability to produce and share interactive dashboards to deliver your insights
to downstream users.Ability to embed or otherwise integrate the MarketSight platform into your
applications.Hello, World and Basic Usage1. Initialize the Library# Import the MarketSight API Clientimportmarketsight# Call the "client" factory function.api=marketsight.client("https://application.marketsight.com/api/v1/swagger/public.json")2. Authorize Against the API# Initialize the MarketSight API Client.api=marketsight.client("https://application.marketsight.com/api/v1/swagger/public.json")# Connect your instance to the API and authorize as a partner.api.connect(client_id="MY CLIENT ID GOES HERE",client_secret="MY CLIENT SECRET GOES HERE")3. Call the APIExecute API calls to perform operations, for example:# Retrieve an Accountaccount=api.Accounts.retrieve(account_id="MY ACCOUNT ID GOES HERE")# Retrieve a Useruser=api.Users.retrieve(user_id="MY USER ID GOES HERE")# Retrieve a Dataset's Meta-datadataset=api.Datasets.retrieve(dataset_id="MY DATASET ID GOES HERE")Questions and IssuesYou can ask questions and report issues on the project’sGithub Issues PageLicenseMarketSight Coreis made available under anMIT License.
Package: marketstack
marketstack-pythonInofficial Python OpenAPI implementation of themarketstack APIbased on themarketstack OpenAPI spec.UsageConfiguration (recommended)Set up your API key and TLS support as ENV variables.MARKETSTACK_API_KEY="xyz"MARKETSTACK_TLS_SUPPORT="1"Create your clientfrommarketstack.clientimportClientimportosdefcreate_client()->tuple[Client,str]:access_key=os.getenv("MARKETSTACK_API_KEY")assertaccess_keyisnotNoneandlen(access_key)>0,"Environment variable MARKETSTACK_API_KEY is not defined"tls_support=os.getenv("MARKETSTACK_TLS_SUPPORT")protocol="https"iftls_support=="1"else"http"client=Client(base_url=f"{protocol}://api.marketstack.com/v1")returnclient,access_keyCall operationsfrommarketstack.api.eodimporteodclient,access_key=create_client()response=eod.sync(client=client,access_key=access_key,symbols="AAPL,AMZN",limit=10,)All endpoint features are implemented and tested. For examples see the repository'stests/directory.Multiple asynchronous callsimportasynciofromtypingimportListfrommarketstack.api.intradayimportintraday_latestfrommarketstack.api.exchangesimportexchangesfrommarketstack.modelsimportPagedResponseListmodelsIntervalPrice,PagedResponseListmodelsExchangeasyncdefload_async(symbols:List[str]):client,access_key=create_client()prices_call=intraday_latest.asyncio(access_key=access_key,client=client,symbols=",".join(symbols))exchanges_call=exchanges.asyncio(access_key=access_key,client=client)# Type hints for future resultsprices_response:PagedResponseListmodelsIntervalPriceexchanges_response:PagedResponseListmodelsExchangeprices_response,exchanges_response=awaitasyncio.gather(prices_call,exchanges_call)Error handlingfrommarketstack.modelsimportErrorCode,ErrorResponseifisinstance(response,ErrorResponse):assertresponse.error.code==ErrorCode.FUNCTION_ACCESS_RESTRICTEDMap to Pandas dataframeimportpandasaspddf=pd.DataFrame(map(lambdax:x.__dict__,response.data))Developers areaGenerate the clientInstall the OpenAPI client generatorpipinstallopenapi-python-clientRegenerate the client./regenerate.shRun the testsSetup your marketstack API key intests/test.envRun the tests viapytestPYTHONPATH="."pytest--cov=marketstacktests/Release updateUpdate version insetup.pyPackage librarypythonsetup.pysdistPublish librarytwineuploaddist/marketstack-[version].tar.gzReferenceshttps://github.com/MichaelKim0407/tutorial-pip-packagehttps://packaging.python.org/en/latest/guides/making-a-pypi-friendly-readme/
Package: market-vis
Market Visualization PrototypeGenerates a three-dimensional interactive surface of market data.Runs through a basic command line interface:marketvis [-h] [-v] mode [startDate] [endDate]positional arguments:mode           Running mode: live (online downloads) | test (offline testdata)startDate      Start date of the live data pull (YYYYMMDD)endDate        End date of the live data pull (YYYYMMDD)optional arguments:-h, --help     show this help message and exit-v, --verbose  Includes time printouts during runtimeTest Option`python marketvis.py test`Displays a visualization based on test data of S&P 500 stocks from Jan 1, 2012 to Jan 1, 2013.Live Option`python marketvis.py live 20150101 20160101`Pulls new data from the Google Finance API and creates a more up-to-date visualization.For details about getting the prototype up and running, check out this [tutorial](https://github.com/ryanpstauffer/market-vis/wiki/Tutorial)
Package: marketwatch
Marketwatch API Python LibraryMarketWatchDocumentationA Python libary to interact with the MarketWatch Stock Market Game
Based on code fromhttps://github.com/kevindong/MarketWatch_API/https://github.com/bwees/pymarketwatchFeatureLogging in and out of the siteGetting the current price of a stockGetting information about games on the siteBuying, selling, shorting, and covering stocks in a gameCreating, adding to, getting, and deleting watchlistsGetting, adding to, and deleting items from a portfolioGetting and cancelling pending ordersChecking if the game is downInstallationpipinstallmarketwatchpipinstallgit+https://github.com/antoinebou12/marketwatch.gitgitclonehttps://github.com/antoinebou12/marketwatch.gitUsageHere are some examples of how you can use the MarketWatch class:ImportFirst, import the MarketWatch class from the script:frommarketwatchimportMarketWatchLoginThen, create an instance of the MarketWatch class using your MarketWatch username and password:marketwatch=MarketWatch(username,password)Get Stock PriceTo get the current price of a stock:marketwatch.get_price("AAPL")Interact with Gameshttps://www.marketwatch.com/gamesTo get information about games on the site:marketwatch.get_games()Get Gamemarketwatch.get_game("game-name")Get Game Settingsmarketwatch.get_game_settings("game-name")Get Leaderboardmarketwatch.get_leaderboard("game-name")Get Portfoliomarketwatch.get_portfolio("game-name")Get Positionsmarketwatch.get_positions("game-name")Get Pending Ordersmarketwatch.get_pending_orders("game-name")Buy Stockmarketwatch.buy(game_id,"AAPL",100)Sell Stockmarketwatch.sell("game-name","AAPL",100)Create Watchlisthttps://www.marketwatch.com/watchlistTo create a watchlist:marketwatch.create_watchlist('My Watchlist')Add Stock to WatchlistTo add stocks to a watchlist:marketwatch.add_to_watchlist(watchlist_id,['AAPL','GOOG'])Get All WatchlistsTo get all watchlists:watchlists=marketwatch.get_watchlists()Delete WatchlistTo delete a watchlist:marketwatch.delete_watchlist(watchlist_id)Exampleimportosusername=os.environ.get("MARKETWATCH_USERNAME")password=os.environ.get("MARKETWATCH_PASSWORD")marketwatch=MarketWatch(username,password)print(f"Price:{marketwatch.get_price('AAPL')}\n")print(f"Games:{marketwatch.get_games()}\n")games1=marketwatch.get_games()[0]["name"].lower().replace(" ","-")print(f"Game:{marketwatch.get_game(games1)}\n")print(f"Game Settings:{marketwatch.get_game_settings(games1)}\n")print(f"Leaderboard:{marketwatch.get_leaderboard(games1)}\n")print(f"Porfolio:{marketwatch.get_portfolio(games1)}\n")print(f"Position:{marketwatch.get_positions(games1)}")print(f"Orders Pending:{marketwatch.get_pending_orders(games1)}")marketwatch.buy(games1,"AAPL",100)print(f"Position diff:{marketwatch.get_positions(games1)}")ContributingContributions are welcome. Please open an issue or submit a pull request.LicenseThis project is licensed under the MIT License.
Package: marketwatchdata
marketwatchdataoverviewmarketwathdataretrieve datas from marketwatch.com.documentation:文档installationgeneralpipinstallmarketwatchdata--upgradechinapipinstallmarketwatchdata-ihttp://mirrors.aliyun.com/pypi/simple/--trusted-host=mirrors.aliyun.com--upgradetestimportmarketwatchdataasmwprint(mw.__version__)usagedatacodeimportmarketwatchdataasmwdf=mw.ohlc('stock/us/xnas/aapl','p1d','p1y')print(df)outputopen     high      low   close       volume
date
2022-07-25  154.010  155.040  152.280  152.95   53623953.0
2022-07-26  152.265  153.085  150.800  151.60   55138688.0
2022-07-27  152.580  157.330  152.160  156.79   78620688.0
2022-07-28  156.980  157.640  154.410  157.35   81378727.0
2022-07-29  161.240  163.630  159.500  162.51  101786898.0
...             ...      ...      ...     ...          ...
2023-07-17  191.900  194.320  191.810  193.99   50520160.0
2023-07-18  193.350  194.330  192.415  193.73   48353770.0
2023-07-19  193.100  198.230  192.650  195.10   80507320.0
2023-07-20  195.090  196.470  192.495  193.13   59581199.0
2023-07-21  194.100  194.970  191.230  191.94   71951683.0

[250 rows x 5 columns]plotcodeimportmarketwatchdataasmwimportmplfinanceasmpf# please install mplfinance as follows: pip install mplfinancedf=mw.ohlc('stock/us/xnas/aapl','p1d','p1y')mpf.plot(df,type='candle',mav=(3,6,9),volume=true,show_nontrading=false)outputacknowledgementthanks for the data provided bymarketwatch;
Package: markey
MarkeyMarkup parser framework.Changelog0.8 - 2016-06-15Removeinterpolatekeyword (kosmos342)Add argument type information (louyihua)0.7 - 2015-08-28Fix release0.6 - 2015-08-28Fixed removing some trailing symbols (#1, thanks kosmos342!)0.5 - 2014-12-24Force parsing to be unicode everywhere0.4 - 2014-05-25Fixed Python 2/3 compatibility0.3 - 2014-05-25Rename markey.handlebars to markey.underscore. This actually is
what the module implements.0.2 - 2014-05-17Fix release builds.0.1 - 2014-05-17Initial release.
Package: markhtml
Markdown to HTMLMarkdown to HTML converter. Converts a Markdown file into an HTML file
formed of two columns:TOC (left-column)An auto generated Table of Contents based in the markdown headingsMarkdown content (right-column)InstallCreate virtualenv.python3 -m venv ~/.virtualenvs/markhtmlActivate venv.source ~/.virtualenvs/markhtml/bin/activateInstall requirements.pip install -r requirements.txtUsageTo convertexample.mdusingsrc/markhtml.py path-to-markdown-file.mdgeneratesexample.htmlsrc/markhtml.py examples/example.md
Generated file: examples/example.htmlNotesUsespython-markdownwithTOCextension.