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ai-platform

An open-source platform for automating tasks using machine learning models

aiohttp-dashboard

Dashboard panels for aiohttp applications

algobot

A C++ stock market algorithmic trading bot

algorithmic-trading

Python scripts for testing algorithmic trading strategies

alpha_vantage

A python wrapper for Alpha Vantage API for financial data.

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.

automate-daily-development-tasks-with-bash

This accompanies the Egghead.io Course "Automate daily development tasks with bash". It's also a nice overview of common bash things that come up in front-end development.

automated-stock-prediction

Model using Long Short Term Memory to predict stock prices. Input is OHLC data which the program will automatically procure from Yahoo! Finance using Python web scrapers. The input will be ticker, date range of OHLC data, and number of days for prediction.

automated_stock_update

A personalized Python process that sends an email to you daily at a specified time giving updates on stocks you would like to follow.

chatterbot

ChatterBot is a machine learning, conversational dialog engine for creating chat bots

coreauth

.NET Core OAUTH service

dapper-sample

Dapper Example in an F# console application for dotnet core

dapper.graphql

A .NET Core library designed to integrate the Dapper and graphql-dotnet projects with ease-of-use in mind and performance as the primary concern.

demo.dotnetcore2.oauth2

dom-event-utils

Very small library that makes working with the DOM event emitters API (and others) a little easier to work with.

dotnetcore-microservices-poc

Very simplified insurance sales system made in a microservices architecture using .NET Core

dotnetopenauth

A C# implementation of the OpenID, OAuth protocols

easy_cheap_flights_checker

Use Python with Skyscanner API to search cheap flights.

easyapply-linkedin

RPA tool for applying to LinkedIn jobs

electronic-ticketing-system

This is a console based application. This application automates the task of buying tickets in a movie theater.I created this application in my first year while i learning the basics of c++ programming language.

elevate

HackMIT project - Messenger Bot for Trading Stocks

facerecognizer

freetube

An Open Source YouTube app for privacy

hair-style-recommendation-system

CA Assignment Project

halvatlennot

search cheap flights with robot framework, python and selenium - halvat lennot

harpstocks

A python based library to give automated stock buy and sell suggestions and portfolio optimization for your portfolio using Statistics and Machine Learning

http2-katana

HTTP 2.0 with Katana

idealautomate

A collection of C# WPF applications for Automating redundant tasks

identityserver4

OpenID Connect and OAuth 2.0 Framework for ASP.NET Core

intelligentbot-ibot-

I developed "Intelligent Bot [iBot]" which is a programmed application that performs an automated task in a conversational format using supervised learning [ML Paradigm] and Natural Language Processing [NLP]. It was programmed using C# and .NET libraries and designed using Microsoft Visual Studio, Bot Builder SDK, Bot Connector, Bot Emulator. NLP was done using LUIS to make the bot more interactive and natural along with computer vision like face detection, caption detection, emotion detection.