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توضیحات

ADA Sentiment Explorer Python API

ویژگی	مقدار
سیستم عامل	OS Independent
نام فایل	adase-api-0.1.0
نام	adase-api
نسخه کتابخانه	0.1.0
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Alpha Data Analytics PSA
ایمیل نویسنده	tech@adalytica.io
آدرس صفحه اصلی	https://github.com/meely-ai/adase-api
آدرس اینترنتی	https://pypi.org/project/adase-api/
مجوز	-

![logo](ADA_logo.png) ## ADA Sentiment Explorer API ### Introduction Alpha Data Analytics ("ADA") is a data analytics company, core product is ADA Sentiment Explorer (“ADASE”), build on an opinion monitoring technology that intelligently reads news sources and social platforms into machine-readable indicators. It is designed to provide unbiased visibility of people's opinions as a driving force of capital markets, political processes, demand prediction or marketing ADA's vision is to democratise advanced AI-system supporting decisions, that benefit data proficient people and small- or medium- quantitative institutions.<br><br> ADASE supports `keyword` and `topic` engines, as explained below ### To install ```commandline pip install adase-api ``` ## Keyword search engine ### Query syntax - Each condition is placed inside of round brackets `()`, where - `+` indicates a search term must be found - and `-` excludes it - Multiple conditions can be combined with logical operators - `OR` - `AND` - Also you can separate by comma "," multiple requests for a parallel processing as below: - `"(+Bitcoin -Luna) OR (+ETH), (+crypto)"` - Will return matches to data that hit `Bitcoin` or `ETH` but not `Luna` for the first query, and `crypto` for the second - Amount of sub-queries is not limited and is executed in parallel #### To use API you need to provide API credentials as environment variables ```python import os os.environ['ADA_API_USERNAME'] = "myaccount@email.com" os.environ['ADA_API_PASSWORD'] = "p@ssw0rd" ``` `adase_api.query.Explorer` class has more configurations described in the docstring ```python from adase_api import query q = "(+Bitcoin -Luna) OR (+ETH), (+crypto)" df = query.load_frame(q, engine='keyword', start_date='2022-01-01', end_date='2022-05-29') df.unstack(2).tail() ``` Returns coverage, hits, score and score_coverage to a pandas dataframe ```text query (+Bitcoin -Luna) OR (+ETH) (+crypto) coverage hits score coverage hits score date_time source 2022-05-27 11:00:00 all 0.026520 36.676056 0.218439 0.055207 76.487535 0.267412 2022-05-27 12:00:00 all 0.026497 36.668539 0.216516 0.055200 76.518006 0.267331 2022-05-27 13:00:00 all 0.026443 36.616246 0.215001 0.055238 76.554017 0.266730 2022-05-27 14:00:00 all 0.026442 36.605042 0.213506 0.055187 76.481994 0.266553 2022-05-27 15:00:00 all 0.026452 36.647059 0.212794 0.055199 76.512465 0.265416 ``` Since data is weekly seasonal, a 7-day rolling average is applied by default ## Topic embedding search engine ### Topic syntax - In contrast with keyword based search, topic syntax allows to query data in a fuzzy way. It works the best when 2-5 words describe some wider concepts, examples: - "NASDAQ technology index" - "Airline travel demand" - "Energy disruptions in Europe" - Such queries will include related concept - for "NASDAQ technology index" it might also consider terms as "Dow Jones", "FAANG", "FTSE" etc. - exact structure depends mostly on how topics co-occur together - intuition behind is that NASDAQ is US tech stock index, but if data contains strong signals from FTSE, a British blue chip index, or Dow Jones, less tech heavy index, this will also have an impact on query of interest - to reflect changing world situation, underlying models are constantly re-trained making sure relations are up-to-date ```python from adase_api import query q = "inflation rates, OPEC cartel" df = query.load_frame(q, engine='topic', start_date='2022-01-01') df.unstack(2).tail(10) ``` ```text query inflation rates OPEC cartel coverage hits score coverage hits score date_time source 2022-05-26 07:00:00 media 0.002947 6.220238 -0.059335 0.001945 5.619048 -0.034639 social 0.008054 50.779762 0.023118 0.003774 29.595238 0.022136 2022-05-26 08:00:00 avg 0.004778 24.073413 0.002614 0.002553 15.003968 0.007849 corp 0.000297 0.565476 0.054003 0.000384 0.761905 0.050364 media 0.002935 6.172619 -0.060830 0.001940 5.595238 -0.034008 social 0.008023 50.416667 0.024123 0.003775 29.482143 0.020868 2022-05-26 09:00:00 avg 0.004770 23.942460 0.004983 0.002540 14.908730 0.009729 corp 0.000297 0.565476 0.054003 0.000384 0.761905 0.050364 media 0.002950 6.125000 -0.057586 0.001922 5.523810 -0.028692 social 0.007991 50.202381 0.025980 0.003767 29.363095 0.019497 ``` it's visible data feed comes detailed per source type: - `media` indicates newspapers, TV, radio and other mass media - `social` includes social platforms and blogs - `corp` covers corporate communication as company newsrooms and regulatory filings - `avg` is a weighted average of all ### In case you don't have yet the credentials, you can [sign up for free](https://adalytica.io/signup) - Data available since January 1, 2006 - Easy way to explore or backtest - In a trial version data lags 24-hours - Probably something else? Hopefully this data could inspire for some innovative solutions to your problem You can follow us on [LinkedIn](https://www.linkedin.com/company/alpha-data-analytics/)

نیازمندی

مقدار	نام
>=1.18.5	numpy
==1.1.5	pandas
==2.23.0	requests
>=3.5.2	asgiref
>=3.7	aiohttp
==1.2	setuptools-git

نحوه نصب

نصب پکیج whl adase-api-0.1.0:

pip install adase-api-0.1.0.whl

نصب پکیج tar.gz adase-api-0.1.0:

pip install adase-api-0.1.0.tar.gz