adase-api 0.0.1

ADA Sentiment Explorer Python API

ADA Sentiment Explorer API to pandas

Introduction

Alpha Data Analytics ("ADA") is a data analytics company, flagman product is Alternative Data Sentiment Explorer (“ADASE”), build on a sentiment monitoring technology that turns mentions from 2000+ news sources and social platforms into real-time machine-readable indicators. It is designed to provide an unbiased understanding of people opinions as a major driving force of capital markets, political processes, demand prediction or marketing.
ADA vision is to democratise advanced AI-system supporting business decisions, that benefit data proficient people and small- or medium- quantitative companies.

ADASE supports keyword and topic engines, details below

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

import os
os.environ['ADA_API_USERNAME'] = "[e-mail]"
os.environ['ADA_API_PASSWORD'] = "p@ssw0rd"

adase_api.query.Explorer class has more configurations described in the docstring

from adase_api import query

q = "(+Bitcoin -Luna) OR (+ETH), (+crypto)"
df = query.Explorer.get(q, process_count=1, 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

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

from adase_api import query

q = "inflation rates, OPEC cartel"
df = query.Explorer.get(q, process_count=1, engine='topic', start_date='2022-01-01')
df.unstack(2).tail(10)

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

• 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

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