38 skills found · Page 1 of 2
SuLab / WikidataIntegratorA Wikidata Python module integrating the MediaWiki API and the Wikidata SPARQL endpoint
macbre / NodemwMediaWiki API and WikiData client written in Node.js
LeMyst / WikibaseIntegratorA Python module to manipulate data on a Wikibase instance (like Wikidata) through the MediaWiki Wikibase API and the Wikibase SPARQL endpoint.
freearhey / WikidataA PHP client for working with Wikidata API.
Ironholds / WikidataRAn R package for the Wikidata API
lisongx / Wikidata GraphqlWikidata + GraphQL (Dream API for everything)
zzaebok / MCP WikidataA server implementation for Wikidata API using the Model Context Protocol (MCP).
klacointe / Wikidata ClientWikidata and Wikipedia API client.
jcreus / PywikidataPython API to WikiData
Navid2zp / Go WikidataWikidata API bindings in go.
wikimedia / WikidataQueryServiceRAn R package for the Wikidata Query Service API
ictchenbo / SmartETLSmartETL：一个简单、灵活、可配置、开箱即用的Python ETL框架，具有领域特色，拒绝重复造轮子！提供Wikidata / Wikipedia / GDELT等多种开源数据的处理流程； 支持txt/json/csv/excel等文件格式、MySQL/PostgreSQL/MongoDB/ClickHouse/ElasticSearch等数据库作为输入和输出； 提供大模型、Web API等多种处理算子
ginking / Archimedes 1Archimedes 1 is a bot based sentient based trader, heavily influenced on forked existing bots, with a few enhancements here or there, this was completed to understand how the bots worked to roll the forward in our own manner to our own complete ai based trading system (Archimedes 2:0) This bot watches [followed accounts] tweets and waits for them to mention any publicly traded companies. When they do, sentiment analysis is used determine whether the opinions are positive or negative toward those companies. The bot then automatically executes trades on the relevant stocks according to the expected market reaction. The code is written in Python and is meant to run on a Google Compute Engine instance. It uses the Twitter Streaming APIs (however new version) to get notified whenever tweets within remit are of interest. The entity detection and sentiment analysis is done using Google's Cloud Natural Language API and the Wikidata Query Service provides the company data. The TradeKing (ALLY) API does the stock trading (changed to ALLY). The main module defines a callback where incoming tweets are handled and starts streaming user's feed: def twitter_callback(tweet): companies = analysis.find_companies(tweet) if companies: trading.make_trades(companies) twitter.tweet(companies, tweet) if __name__ == "__main__": twitter.start_streaming(twitter_callback) The core algorithms are implemented in the analysis and trading modules. The former finds mentions of companies in the text of the tweet, figures out what their ticker symbol is, and assigns a sentiment score to them. The latter chooses a trading strategy, which is either buy now and sell at close or sell short now and buy to cover at close. The twitter module deals with streaming and tweeting out the summary. Follow these steps to run the code yourself: 1. Create VM instance Check out the quickstart to create a Cloud Platform project and a Linux VM instance with Compute Engine, then SSH into it for the steps below. The predefined machine type g1-small (1 vCPU, 1.7 GB memory) seems to work well. 2. Set up auth The authentication keys for the different APIs are read from shell environment variables. Each service has different steps to obtain them. Twitter Log in to your Twitter account and create a new application. Under the Keys and Access Tokens tab for your app you'll find the Consumer Key and Consumer Secret. Export both to environment variables: export TWITTER_CONSUMER_KEY="<YOUR_CONSUMER_KEY>" export TWITTER_CONSUMER_SECRET="<YOUR_CONSUMER_SECRET>" If you want the tweets to come from the same account that owns the application, simply use the Access Token and Access Token Secret on the same page. If you want to tweet from a different account, follow the steps to obtain an access token. Then export both to environment variables: export TWITTER_ACCESS_TOKEN="<YOUR_ACCESS_TOKEN>" export TWITTER_ACCESS_TOKEN_SECRET="<YOUR_ACCESS_TOKEN_SECRET>" Google Follow the Google Application Default Credentials instructions to create, download, and export a service account key. export GOOGLE_APPLICATION_CREDENTIALS="/path/to/credentials-file.json" You also need to enable the Cloud Natural Language API for your Google Cloud Platform project. TradeKing (ALLY) Log in to your TradeKing (ALLY account and create a new application. Behind the Details button for your application you'll find the Consumer Key, Consumer Secret, OAuth (Access) Token, and Oauth (Access) Token Secret. Export them all to environment variables: export TRADEKING_CONSUMER_KEY="<YOUR_CONSUMER_KEY>" export TRADEKING_CONSUMER_SECRET="<YOUR_CONSUMER_SECRET>" export TRADEKING_ACCESS_TOKEN="<YOUR_ACCESS_TOKEN>" export TRADEKING_ACCESS_TOKEN_SECRET="<YOUR_ACCESS_TOKEN_SECRET>" Also export your TradeKing (ALLY) account number, which you'll find under My Accounts: export TRADEKING_ACCOUNT_NUMBER="<YOUR_ACCOUNT_NUMBER>" 3. Install dependencies There are a few library dependencies, which you can install using pip: $ pip install -r requirements.txt 4. Run the tests Verify that everything is working as intended by running the tests with pytest using this command: $ export USE_REAL_MONEY=NO && pytest *.py --verbose 5. Run the benchmark The benchmark report shows how the current implementation of the analysis and trading algorithms would have performed against historical data. You can run it again to benchmark any changes you may have made: $ ./benchmark.py > benchmark.md 6. Start the bot Enable real orders that use your money: $ export USE_REAL_MONEY=YES Have the code start running in the background with this command: $ nohup ./main.py & License Archimedes (edits under Invacio) Max Braun Frame under Max Braun, licence under Apache V2 License. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
deeplook / IpymapsImplement an online/notebook maps application with customized features.
HongGiwon / Wikidata Relation ExtractorCode to extract KB triples from given contexts using Wikidata API
syvb / WikidataRust library for working with data from Wikidata.
saddamalsalfi / Ojs Quickstatements ExportAn OJS/PKP plugin to export issues and articles to Wikidata via QuickStatements, with optional auto-submit, DOI de-dup via Wikidata API, and i18n.
connorshea / WikidatumInteract with the Wikidata/Wikibase REST API from Ruby.
geohci / Wikipedia Topic Model ApiToolforge API to label Wikipedia articles with topics based on Wikidata properties
King-s-Knowledge-Graph-Lab / ProVeA tool for reference verification of Wikidata items by verbalizing claims and gathering HTML reference resources utilizing various language models. Please refer to the ProVe API through the link provided below.
