inos-soft Goto Github PK

.codebits

:books: List of resources for Algorithms and Data Structures in Python & other CS topics @2017

.github.io

Nunataryuk codes and script for modeling Arctic landforms, hydrology and water flow

26-inos-software

abuse

Abuse (1995) by Crack dot Com

actors.unity

🚀Actors is a framework empowering developers to make better games faster on Unity.

ai

ai-1

Microsoft AI

ai-2

Repo for work for AI Survey

ale

Check syntax in Vim asynchronously and fix files, with Language Server Protocol (LSP) support

alfred-devdocs

Alfred workflow for devdocs.io

altspace-vr

Public sync with MicrosoftDocs/altspace-vr-pr

angular-cli-ghpages

🚀 Deploy your 🅰️Angular app to GitHub pages directly from the Angular CLI! Available on NPM.

angular-electron

Ultra-fast bootstrapping with Angular and Electron (Typescript + SASS + Hot Reload) :speedboat:

angular-rails-starter-kit

An opinionated starter template for crafting single-page web applications using Angular.js, Rails, Devise, Pundit, Grunt, SASS, Bootstrap, UI-Router, Font-Awesome and a few other carefully picked tools.

ansi-escapes

ANSI escape codes for manipulating the terminal

ansi-regex

Regular expression for matching ANSI escape codes

anti-anti-spider

越来越多的网站具有反爬虫特性，有的用图片隐藏关键数据，有的使用反人类的验证码，建立反反爬虫的代码仓库，通过与不同特性的网站做斗争（无恶意）提高技术。（欢迎提交难以采集的网站）（因工作原因，项目暂停）

anti-emulator

Android Anti-Emulator

antiav

Anti AntiVirus + Obfuscator - make any file undetectable.

antispy

AntiSpy is a free but powerful anti virus and rootkits toolkit.It offers you the ability with the highest privileges that can detect,analyze and restore various kernel modifications and hooks.With its assistance,you can easily spot and neutralize malwares hidden from normal detectors.

antivirus-python-

apollo-client

:rocket:  A fully-featured, production ready caching GraphQL client for every UI framework and GraphQL server.

arch

ARCH models in Python

archimedes-1

Archimedes 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.

archivebox

🗃 Open source self-hosted web archiving. Takes URLs/browser history/bookmarks/Pocket/Pinboard/etc., saves HTML, JS, PDFs, media, and more...

arduino-firebase-pi-serial-client

Client app to read data from Arduino and send to Fire-base Real Time database

arithmetic-operations

artbase-collective-access-profile

artbaseltallies

asasarep