ADA Sentiment Explorer Python API
Project description
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.
ADASE supports keyword
and topic
engines, as explained below
To install
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
orETH
but notLuna
for the first query, andcrypto
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'] = "myaccount@email.com"
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.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
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.load_frame(q, 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 mediasocial
includes social platforms and blogscorp
covers corporate communication as company newsrooms and regulatory filingsavg
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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