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elitequant_cpp

C/C++ 11 High frequency quantitative trading platform. It follows modern design patterns such as event-driven, server/client architect, dependency injection and loosely-coupled robust distributed system. It is self-contained and can be used out of box. At the same time, it serves as server side for other EliteQuant projects.

elitequant_python

Python quantitative trading and investment platform; Python3 based multi-threading, concurrent high-frequency trading platform that provides consistent backtest and live trading solutions. It follows modern design patterns such as event-driven, server/client architect, and loosely-coupled robust distributed system. It follows the same structure and performance metrix as other EliteQuant product line, which makes it easier to share with traders using other languages.

ems-qart

A Qt application to flash EMS 64M cartridges

eng-practices

Google's Engineering Practices documentation

engine

Droidefense: Advance Android Malware Analysis Framework

engineering-best-practices

10up Engineering Best Practices

eod-quandl-cassandra-kafka

Load Quandl EOD Stocks EOD Data into Cassandra

epat

equityforchampions

Group project in machine learning. Analysis of 130k+ tweets using scikit-learn, NLTK, spaCy, vader, D3.js and Bokeh.

etos-backtester

Simple backtesting for sleepless cryptocurrency markets. for ETOS Ecosystem

event-driven-stock-prediction

My presentations for the event-driven stock prediction research group

event-embedding-stock-prediction

Model news data in short, medium and long term for stock price trend prediction

event-sourcing-in-practice

Slides for my talk "Event Sourcing in practice"

evolutionary-trading-strategies

This code illustrates the use of genetic programming to evolve financial trading strategies for a single equity stock. Individuals (strategies) are considered as functions of historical price data, outputting a position allocation. Strategy fitness evaluation is computed by simulating the strategy over historical financial data. Because financial investment requires a fundamental tradeoff between risk and return, strategies are evaluated on multi-objective fitness functions depending on profit and maximum drawdown of the strategy and ranging from very risk-prone to very risk-averse. The population of individual strategies is evolved using tournament selection, single-point crossover, and random mutation as evolutionary operators. Strategies with the best fitness at any stage in the evolutionary process are recorded in a ‘hall-of-fame’. At the end of the evolutionary process, strategies in the ‘hall-of-fame’ are evaluated over a set of test data and selected based on a train-test criterion which penalizes strategies that do not generalize well.

exact-php-client

PHP Client library for Exact Online

exactonline

Exact Online (accounting software) REST API Library in Python

example-code

Example code for the book Fluent Python

example-hftish

Example Order Book Imbalance Algorithm

example-scalping

A working example algorithm for scalping strategy trading multiple stocks concurrently using python asyncio

example-scripts

Example Machine Learning Scripts for Numerai's Tournament

examples

Example code from my blog posts

express

Sinatra inspired web development framework for node.js -- insanely fast, flexible, and simple

ezibpy

ezIBpy, a Pythonic Client for Interactive Brokers API

face-api

This is an example of face.com API made in the huevapi's meetup

facebook-messenger-bot-tutorial

facebook-messenger-bot-tutorial use Python Django

facedetect

人臉偵測 use Python (Python OpenCV)

faceit

A script to make it easy to swap faces in videos using the faceswap library, and YouTube videos.

faceswap

Non official project based on original /r/Deepfakes thread. Many thanks to him!

facets

Visualizations for machine learning datasets

fantasy-baseball-python-script

The purpose of this python script is to accept a .csv of fanduel's daily fantasy baseball players and output an optimized lineup.